ambiser
/
face-api
mirror of https://github.com/vladmandic/face-api.git
9
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
face-api/dist/face-api.esm.js

4938 lines
1.2 MiB
Raw Blame History

/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
var E$=Object.defineProperty;var A$=(e=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof require!="undefined"?require:t)[n]}):e)(function(e){if(typeof require!="undefined")return require.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')});var nb=(e,t)=>{for(var n in t)E$(e,n,{get:t[n],enumerable:!0})};var ze={};nb(ze,{Abs:()=>Il,Acos:()=>Sl,Acosh:()=>Nl,AdadeltaOptimizer:()=>cf,AdagradOptimizer:()=>df,AdamOptimizer:()=>hf,AdamaxOptimizer:()=>mf,Add:()=>ds,AddN:()=>xi,All:()=>Tl,Any:()=>Cl,ArgMax:()=>vi,ArgMin:()=>lc,Asin:()=>_l,Asinh:()=>El,Atan:()=>Al,Atan2:()=>Fl,Atanh:()=>$l,AvgPool:()=>wi,AvgPool3D:()=>uc,AvgPool3DGrad:()=>nm,AvgPoolGrad:()=>tm,BackendWasm:()=>RE,BatchMatMul:()=>ki,BatchToSpaceND:()=>Dl,Bincount:()=>am,BroadcastArgs:()=>rm,BroadcastTo:()=>lI,Callback:()=>cN,CallbackList:()=>f2,Cast:()=>Ii,Ceil:()=>Si,ClipByValue:()=>hs,Complex:()=>sm,ComplexAbs:()=>pc,Concat:()=>Rl,Conv2D:()=>Ni,Conv2DBackpropFilter:()=>im,Conv2DBackpropInput:()=>Ti,Conv3D:()=>cc,Conv3DBackpropFilterV2:()=>om,Conv3DBackpropInputV2:()=>lm,Cos:()=>Ci,Cosh:()=>_i,CropAndResize:()=>Pl,Cumprod:()=>Ml,Cumsum:()=>Ei,CustomCallback:()=>y2,DataStorage:()=>Zh,DenseBincount:()=>um,DepthToSpace:()=>Ol,DepthwiseConv2dNative:()=>Ai,DepthwiseConv2dNativeBackpropFilter:()=>pm,DepthwiseConv2dNativeBackpropInput:()=>cm,Diag:()=>dm,Dilation2D:()=>dc,Dilation2DBackpropFilter:()=>Nh,Dilation2DBackpropInput:()=>Sh,ENV:()=>wx,EarlyStopping:()=>dN,Einsum:()=>hm,Elu:()=>Fi,EluGrad:()=>mm,Environment:()=>iI,Equal:()=>zl,Erf:()=>Ll,Exp:()=>Di,ExpandDims:()=>Bl,Expm1:()=>Wl,FFT:()=>fm,Fill:()=>hc,FlipLeftRight:()=>Vl,Floor:()=>Ri,FloorDiv:()=>Mi,FromPixels:()=>Th,FusedBatchNorm:()=>Pi,FusedConv2D:()=>ni,FusedDepthwiseConv2D:()=>ai,GPGPUContext:()=>xh,GatherNd:()=>Gl,GatherV2:()=>Ul,GraphModel:()=>PN,Greater:()=>Hl,GreaterEqual:()=>Oi,History:()=>g2,IFFT:()=>gm,Identity:()=>Li,Imag:()=>ym,InputSpec:()=>Bt,IsFinite:()=>jl,IsInf:()=>ql,IsNan:()=>Kl,KernelBackend:()=>oc,LRN:()=>gc,LRNGrad:()=>xm,LayerVariable:()=>l2,LayersModel:()=>Tr,LeakyRelu:()=>zi,Less:()=>Xl,LessEqual:()=>Yl,LinSpace:()=>bm,Log:()=>Bi,Log1p:()=>Jl,LogSoftmax:()=>uI,LogicalAnd:()=>Zl,LogicalNot:()=>mc,LogicalOr:()=>fc,MathBackendWebGL:()=>zf,Max:()=>Wi,MaxPool:()=>Ui,MaxPool3D:()=>yc,MaxPool3DGrad:()=>wm,MaxPoolGrad:()=>vm,MaxPoolWithArgmax:()=>km,Maximum:()=>Vi,Mean:()=>Gi,Min:()=>Hi,Minimum:()=>ji,MirrorPad:()=>qi,Mod:()=>Ql,MomentumOptimizer:()=>ff,Multinomial:()=>Im,Multiply:()=>Ki,Neg:()=>eu,NonMaxSuppressionV3:()=>nu,NonMaxSuppressionV4:()=>au,NonMaxSuppressionV5:()=>ru,NotEqual:()=>tu,OP_SCOPE_SUFFIX:()=>xI,OneHot:()=>Xi,OnesLike:()=>su,Optimizer:()=>Ar,OptimizerConstructors:()=>Hr,Pack:()=>iu,PadV2:()=>Yi,Pool:()=>NF,Pow:()=>Ji,Prelu:()=>Zi,Prod:()=>Qi,RMSPropOptimizer:()=>gf,RNN:()=>fr,Range:()=>bc,Rank:()=>vb,Real:()=>Sm,RealDiv:()=>$i,Reciprocal:()=>ou,Reduction:()=>kn,Relu:()=>eo,Relu6:()=>no,Reshape:()=>lu,ResizeBilinear:()=>to,ResizeBilinearGrad:()=>Tm,ResizeNearestNeighbor:()=>xc,ResizeNearestNeighborGrad:()=>Nm,Reverse:()=>ao,RotateWithOffset:()=>Su,Round:()=>ro,Rsqrt:()=>so,SGDOptimizer:()=>Bc,ScatterNd:()=>uu,Select:()=>pu,Selu:()=>cu,Sequential:()=>gl,Sigmoid:()=>oo,Sign:()=>mu,Sin:()=>io,Sinh:()=>hu,Slice:()=>du,Softmax:()=>po,Softplus:()=>fu,SpaceToBatchND:()=>gu,SparseFillEmptyRows:()=>vc,SparseReshape:()=>bu,SparseSegmentMean:()=>wc,SparseSegmentSum:()=>kc,SparseToDense:()=>Cm,SplitV:()=>yu,Sqrt:()=>lo,Square:()=>Ic,SquaredDifference:()=>co,Step:()=>fs,StridedSlice:()=>xu,StringNGrams:()=>_m,StringSplit:()=>Em,StringToHashBucketFast:()=>Am,Sub:()=>ho,Sum:()=>uo,SymbolicTensor:()=>Ua,Tan:()=>mo,Tanh:()=>fo,Tensor:()=>Ae,TensorBuffer:()=>jt,Tile:()=>ms,TopK:()=>vu,Transform:()=>wu,Transpose:()=>go,Unique:()=>$m,Unpack:()=>ku,UnsortedSegmentSum:()=>Sc,Variable:()=>ts,ZerosLike:()=>Iu,_FusedMatMul:()=>ti,abs:()=>zt,acos:()=>Ox,acosh:()=>Lx,add:()=>J,addN:()=>ZI,all:()=>Om,any:()=>Xp,argMax:()=>ii,argMin:()=>zx,asin:()=>Bx,asinh:()=>Wx,atan:()=>Vx,atan2:()=>Ux,atanh:()=>Gx,avgPool:()=>fa,avgPool3d:()=>jx,backend:()=>JI,backend_util:()=>_,basicLSTMCell:()=>yM,batchNorm:()=>Cr,batchNorm2d:()=>nS,batchNorm3d:()=>aS,batchNorm4d:()=>rS,batchToSpaceND:()=>$c,bincount:()=>qx,booleanMaskAsync:()=>I3,broadcastArgs:()=>sS,broadcastTo:()=>il,broadcast_util:()=>Nu,browser:()=>yo,buffer:()=>He,callbacks:()=>e6,cast:()=>oe,ceil:()=>Kx,clipByValue:()=>nn,clone:()=>Nr,complex:()=>ns,concat:()=>Qe,concat1d:()=>iS,concat2d:()=>oS,concat3d:()=>lS,concat4d:()=>uS,constraints:()=>c2,conv1d:()=>Lm,conv2d:()=>Rt,conv2dTranspose:()=>zm,conv3d:()=>Yx,conv3dTranspose:()=>cS,copyRegisteredKernels:()=>EF,cos:()=>Fc,cosh:()=>Bm,cosineWindow:()=>kv,cumprod:()=>Yp,cumsum:()=>Wm,customGrad:()=>ur,data:()=>ON,denseBincount:()=>dS,deprecationWarn:()=>Px,depthToSpace:()=>Jx,depthwiseConv2d:()=>bs,deregisterOp:()=>a6,device_util:()=>_c,diag:()=>KM,dilation2d:()=>Zx,disableDeprecationWarnings:()=>ER,dispose:()=>Re,disposeVariables:()=>AR,div:()=>fe,divNoNan:()=>Qx,dot:()=>hS,dropout:()=>RS,einsum:()=>mS,elu:()=>Tu,enableDebugMode:()=>_R,enableProdMode:()=>CR,enclosingPowerOfTwo:()=>MS,engine:()=>ar,env:()=>X,equal:()=>Qn,erf:()=>ev,exp:()=>gn,expandDims:()=>mn,expm1:()=>tv,eye:()=>nv,fft:()=>Lc,fill:()=>Cn,findBackend:()=>OR,findBackendFactory:()=>LR,floor:()=>Cu,floorDiv:()=>Pm,forceHalfFloat:()=>n_,fused:()=>rs,gather:()=>li,gatherND:()=>DS,gather_util:()=>Ax,getBackend:()=>MR,getGradient:()=>bb,getKernel:()=>Ch,getKernelsForBackend:()=>_h,getThreadsCount:()=>sue,gpgpu_util:()=>RC,grad:()=>IP,grads:()=>SP,greater:()=>Gn,greaterEqual:()=>xs,ifft:()=>hl,imag:()=>Vm,image:()=>Ln,inTopKAsync:()=>R3,initializers:()=>d2,input:()=>$2,io:()=>Qt,irfft:()=>nf,isFinite:()=>fS,isInf:()=>gS,isNaN:()=>av,keep:()=>en,kernel_impls:()=>mr,layers:()=>h2,leakyRelu:()=>Dc,less:()=>Um,lessEqual:()=>vs,linalg:()=>HS,linspace:()=>yS,loadGraphModel:()=>iH,loadLayersModel:()=>pU,localResponseNormalization:()=>rv,log:()=>ea,log1p:()=>Rc,logSigmoid:()=>xS,logSoftmax:()=>Hm,logSumExp:()=>ov,logicalAnd:()=>Ta,logicalNot:()=>Mc,logicalOr:()=>jm,logicalXor:()=>IS,losses:()=>gz,matMul:()=>De,math:()=>DI,max:()=>Sa,maxPool:()=>Pt,maxPool3d:()=>lv,maxPoolWithArgmax:()=>SS,maximum:()=>hr,mean:()=>Et,memory:()=>$h,meshgrid:()=>jP,metrics:()=>lN,min:()=>Jp,minimum:()=>_u,mirrorPad:()=>uv,mod:()=>pv,model:()=>lU,models:()=>uN,moments:()=>qm,movingAverage:()=>T3,mul:()=>z,multiRNNCell:()=>eO,multinomial:()=>NS,neg:()=>St,nextFrame:()=>Nv,norm:()=>sf,notEqual:()=>pi,oneHot:()=>cl,ones:()=>Jn,onesLike:()=>ta,op:()=>B,outerProduct:()=>sO,pad:()=>ga,pad1d:()=>lO,pad2d:()=>pO,pad3d:()=>dO,pad4d:()=>mO,pool:()=>TS,pow:()=>_r,prelu:()=>Oc,print:()=>EI,prod:()=>Km,profile:()=>$R,rand:()=>IO,randomGamma:()=>CO,randomNormal:()=>CS,randomUniform:()=>Eu,range:()=>dl,ready:()=>RR,real:()=>Zp,reciprocal:()=>hv,registerBackend:()=>Mm,registerCallbackConstructor:()=>cU,registerGradient:()=>pI,registerKernel:()=>Nc,registerOp:()=>n6,regularizers:()=>pN,relu:()=>Xe,relu6:()=>Xm,removeBackend:()=>PR,reshape:()=>W,reverse:()=>na,reverse1d:()=>PO,reverse2d:()=>LO,reverse3d:()=>BO,reverse4d:()=>VO,rfft:()=>zc,round:()=>Ym,rsqrt:()=>Jm,scalar:()=>we,scatterND:()=>FS,scatter_util:()=>$x,selu:()=>Zm,separableConv2d:()=>xo,sequential:()=>uU,serialization:()=>se,setBackend:()=>DR,setPlatform:()=>zR,setThreadsCount:()=>rue,setWasmPath:()=>nue,setWasmPaths:()=>aue,setWebGLContext:()=>rC,setdiff1dAsync:()=>_S,sigmoid:()=>ha,sign:()=>mv,signal:()=>fz,sin:()=>Qm,sinh:()=>ef,slice:()=>Ge,slice1d:()=>tf,slice2d:()=>fv,slice3d:()=>Au,slice4d:()=>Qp,slice_util:()=>qt,softmax:()=>Ja,softplus:()=>bo,spaceToBatchND:()=>Pc,sparse:()=>Fp,sparseToDense:()=>wv,spectral:()=>mz,split:()=>zn,sqrt:()=>ln,square:()=>lt,squaredDifference:()=>af,squeeze:()=>pr,stack:()=>Mt,step:()=>$u,stridedSlice:()=>gv,string:()=>dh,sub:()=>ce,sum:()=>be,sumOutType:()=>Dm,tan:()=>yv,tanh:()=>oi,tensor:()=>Zn,tensor1d:()=>qe,tensor2d:()=>Ha,tensor3d:()=>Rm,tensor4d:()=>Za,tensor5d:()=>m3,tensor6d:()=>f3,tensor_util:()=>Ga,test_util:()=>KI,tidy:()=>O,tile:()=>On,time:()=>FR,topk:()=>bv,train:()=>Us,transpose:()=>$e,truncatedNormal:()=>rf,unique:()=>Dh,unregisterGradient:()=>_F,unregisterKernel:()=>CF,unsortedSegmentSum:()=>xv,unstack:()=>mt,upcastType:()=>ma,util:()=>k,valueAndGrad:()=>NP,valueAndGrads:()=>TP,variable:()=>ES,variableGrads:()=>bS,version:()=>gue,version_converter:()=>oH,version_core:()=>TR,version_layers:()=>qv,version_wasm:()=>iue,version_webgl:()=>g9,webgl:()=>y9,webgl_util:()=>aC,where:()=>fn,whereAsync:()=>vv,zeros:()=>kt,zerosLike:()=>Ke});var $$=Object.create,gx=Object.defineProperty,F$=Object.getOwnPropertyDescriptor,D$=Object.getOwnPropertyNames,R$=Object.getPrototypeOf,M$=Object.prototype.hasOwnProperty,ft=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Me=(e,t)=>{for(var n in t)gx(e,n,{get:t[n],enumerable:!0})},P$=(e,t,n,a)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of D$(t))!M$.call(e,r)&&r!==n&&gx(e,r,{get:()=>t[r],enumerable:!(a=F$(t,r))||a.enumerable});return e},yi=(e,t,n)=>(n=e!=null?$$(R$(e)):{},P$(t||!e||!e.__esModule?gx(n,"default",{value:e,enumerable:!0}):n,e)),O$=ft((e,t)=>{t.exports=a;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(S){}function a(S,M,V){this.low=S|0,this.high=M|0,this.unsigned=!!V}a.prototype.__isLong__,Object.defineProperty(a.prototype,"__isLong__",{value:!0});function r(S){return(S&&S.__isLong__)===!0}a.isLong=r;var s={},i={};function o(S,M){var V,j,q;return M?(S>>>=0,(q=0<=S&&S<256)&&(j=i[S],j)?j:(V=u(S,(S|0)<0?-1:0,!0),q&&(i[S]=V),V)):(S|=0,(q=-128<=S&&S<128)&&(j=s[S],j)?j:(V=u(S,S<0?-1:0,!1),q&&(s[S]=V),V))}a.fromInt=o;function l(S,M){if(isNaN(S))return M?v:x;if(M){if(S<0)return v;if(S>=g)return $}else{if(S<=-y)return P;if(S+1>=y)return E}return S<0?l(-S,M).neg():u(S%f|0,S/f|0,M)}a.fromNumber=l;function u(S,M,V){return new a(S,M,V)}a.fromBits=u;var p=Math.pow;function d(S,M,V){if(S.length===0)throw Error("empty string");if(S==="NaN"||S==="Infinity"||S==="+Infinity"||S==="-Infinity")return x;if(typeof M=="number"?(V=M,M=!1):M=!!M,V=V||10,V<2||36<V)throw RangeError("radix");var j;if((j=S.indexOf("-"))>0)throw Error("interior hyphen");if(j===0)return d(S.substring(1),M,V).neg();for(var q=l(p(V,8)),K=x,Q=0;Q<S.length;Q+=8){var ee=Math.min(8,S.length-Q),re=parseInt(S.substring(Q,Q+ee),V);if(ee<8){var Z=l(p(V,ee));K=K.mul(Z).add(l(re))}else K=K.mul(q),K=K.add(l(re))}return K.unsigned=M,K}a.fromString=d;function c(S,M){return typeof S=="number"?l(S,M):typeof S=="string"?d(S,M):u(S.low,S.high,typeof M=="boolean"?M:S.unsigned)}a.fromValue=c;var h=1<<16,m=1<<24,f=h*h,g=f*f,y=g/2,b=o(m),x=o(0);a.ZERO=x;var v=o(0,!0);a.UZERO=v;var w=o(1);a.ONE=w;var T=o(1,!0);a.UONE=T;var C=o(-1);a.NEG_ONE=C;var E=u(-1,2147483647,!1);a.MAX_VALUE=E;var $=u(-1,-1,!0);a.MAX_UNSIGNED_VALUE=$;var P=u(0,-2147483648,!1);a.MIN_VALUE=P;var F=a.prototype;F.toInt=function(){return this.unsigned?this.low>>>0:this.low},F.toNumber=function(){return this.unsigned?(this.high>>>0)*f+(this.low>>>0):this.high*f+(this.low>>>0)},F.toString=function(S){if(S=S||10,S<2||36<S)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(P)){var M=l(S),V=this.div(M),j=V.mul(M).sub(this);return V.toString(S)+j.toInt().toString(S)}else return"-"+this.neg().toString(S);for(var q=l(p(S,6),this.unsigned),K=this,Q="";;){var ee=K.div(q),re=K.sub(ee.mul(q)).toInt()>>>0,Z=re.toString(S);if(K=ee,K.isZero())return Z+Q;for(;Z.length<6;)Z="0"+Z;Q=""+Z+Q}},F.getHighBits=function(){return this.high},F.getHighBitsUnsigned=function(){return this.high>>>0},F.getLowBits=function(){return this.low},F.getLowBitsUnsigned=function(){return this.low>>>0},F.getNumBitsAbs=function(){if(this.isNegative())return this.eq(P)?64:this.neg().getNumBitsAbs();for(var S=this.high!=0?this.high:this.low,M=31;M>0&&(S&1<<M)==0;M--);return this.high!=0?M+33:M+1},F.isZero=function(){return this.high===0&&this.low===0},F.eqz=F.isZero,F.isNegative=function(){return!this.unsigned&&this.high<0},F.isPositive=function(){return this.unsigned||this.high>=0},F.isOdd=function(){return(this.low&1)===1},F.isEven=function(){return(this.low&1)===0},F.equals=function(S){return r(S)||(S=c(S)),this.unsigned!==S.unsigned&&this.high>>>31===1&&S.high>>>31===1?!1:this.high===S.high&&this.low===S.low},F.eq=F.equals,F.notEquals=function(S){return!this.eq(S)},F.neq=F.notEquals,F.ne=F.notEquals,F.lessThan=function(S){return this.comp(S)<0},F.lt=F.lessThan,F.lessThanOrEqual=function(S){return this.comp(S)<=0},F.lte=F.lessThanOrEqual,F.le=F.lessThanOrEqual,F.greaterThan=function(S){return this.comp(S)>0},F.gt=F.greaterThan,F.greaterThanOrEqual=function(S){return this.comp(S)>=0},F.gte=F.greaterThanOrEqual,F.ge=F.greaterThanOrEqual,F.compare=function(S){if(r(S)||(S=c(S)),this.eq(S))return 0;var M=this.isNegative(),V=S.isNegative();return M&&!V?-1:!M&&V?1:this.unsigned?S.high>>>0>this.high>>>0||S.high===this.high&&S.low>>>0>this.low>>>0?-1:1:this.sub(S).isNegative()?-1:1},F.comp=F.compare,F.negate=function(){return!this.unsigned&&this.eq(P)?P:this.not().add(w)},F.neg=F.negate,F.add=function(S){r(S)||(S=c(S));var M=this.high>>>16,V=this.high&65535,j=this.low>>>16,q=this.low&65535,K=S.high>>>16,Q=S.high&65535,ee=S.low>>>16,re=S.low&65535,Z=0,ie=0,ae=0,le=0;return le+=q+re,ae+=le>>>16,le&=65535,ae+=j+ee,ie+=ae>>>16,ae&=65535,ie+=V+Q,Z+=ie>>>16,ie&=65535,Z+=M+K,Z&=65535,u(ae<<16|le,Z<<16|ie,this.unsigned)},F.subtract=function(S){return r(S)||(S=c(S)),this.add(S.neg())},F.sub=F.subtract,F.multiply=function(S){if(this.isZero())return x;if(r(S)||(S=c(S)),n){var M=n.mul(this.low,this.high,S.low,S.high);return u(M,n.get_high(),this.unsigned)}if(S.isZero())return x;if(this.eq(P))return S.isOdd()?P:x;if(S.eq(P))return this.isOdd()?P:x;if(this.isNegative())return S.isNegative()?this.neg().mul(S.neg()):this.neg().mul(S).neg();if(S.isNegative())return this.mul(S.neg()).neg();if(this.lt(b)&&S.lt(b))return l(this.toNumber()*S.toNumber(),this.unsigned);var V=this.high>>>16,j=this.high&65535,q=this.low>>>16,K=this.low&65535,Q=S.high>>>16,ee=S.high&65535,re=S.low>>>16,Z=S.low&65535,ie=0,ae=0,le=0,ue=0;return ue+=K*Z,le+=ue>>>16,ue&=65535,le+=q*Z,ae+=le>>>16,le&=65535,le+=K*re,ae+=le>>>16,le&=65535,ae+=j*Z,ie+=ae>>>16,ae&=65535,ae+=q*re,ie+=ae>>>16,ae&=65535,ae+=K*ee,ie+=ae>>>16,ae&=65535,ie+=V*Z+j*re+q*ee+K*Q,ie&=65535,u(le<<16|ue,ie<<16|ae,this.unsigned)},F.mul=F.multiply,F.divide=function(S){if(r(S)||(S=c(S)),S.isZero())throw Error("division by zero");if(n){if(!this.unsigned&&this.high===-2147483648&&S.low===-1&&S.high===-1)return this;var M=(this.unsigned?n.div_u:n.div_s)(this.low,this.high,S.low,S.high);return u(M,n.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?v:x;var V,j,q;if(this.unsigned){if(S.unsigned||(S=S.toUnsigned()),S.gt(this))return v;if(S.gt(this.shru(1)))return T;q=v}else{if(this.eq(P)){if(S.eq(w)||S.eq(C))return P;if(S.eq(P))return w;var K=this.shr(1);return V=K.div(S).shl(1),V.eq(x)?S.isNegative()?w:C:(j=this.sub(S.mul(V)),q=V.add(j.div(S)),q)}else if(S.eq(P))return this.unsigned?v:x;if(this.isNegative())return S.isNegative()?this.neg().div(S.neg()):this.neg().div(S).neg();if(S.isNegative())return this.div(S.neg()).neg();q=x}for(j=this;j.gte(S);){V=Math.max(1,Math.floor(j.toNumber()/S.toNumber()));for(var Q=Math.ceil(Math.log(V)/Math.LN2),ee=Q<=48?1:p(2,Q-48),re=l(V),Z=re.mul(S);Z.isNegative()||Z.gt(j);)V-=ee,re=l(V,this.unsigned),Z=re.mul(S);re.isZero()&&(re=w),q=q.add(re),j=j.sub(Z)}return q},F.div=F.divide,F.modulo=function(S){if(r(S)||(S=c(S)),n){var M=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,S.low,S.high);return u(M,n.get_high(),this.unsigned)}return this.sub(this.div(S).mul(S))},F.mod=F.modulo,F.rem=F.modulo,F.not=function(){return u(~this.low,~this.high,this.unsigned)},F.and=function(S){return r(S)||(S=c(S)),u(this.low&S.low,this.high&S.high,this.unsigned)},F.or=function(S){return r(S)||(S=c(S)),u(this.low|S.low,this.high|S.high,this.unsigned)},F.xor=function(S){return r(S)||(S=c(S)),u(this.low^S.low,this.high^S.high,this.unsigned)},F.shiftLeft=function(S){return r(S)&&(S=S.toInt()),(S&=63)===0?this:S<32?u(this.low<<S,this.high<<S|this.low>>>32-S,this.unsigned):u(0,this.low<<S-32,this.unsigned)},F.shl=F.shiftLeft,F.shiftRight=function(S){return r(S)&&(S=S.toInt()),(S&=63)===0?this:S<32?u(this.low>>>S|this.high<<32-S,this.high>>S,this.unsigned):u(this.high>>S-32,this.high>=0?0:-1,this.unsigned)},F.shr=F.shiftRight,F.shiftRightUnsigned=function(S){if(r(S)&&(S=S.toInt()),S&=63,S===0)return this;var M=this.high;if(S<32){var V=this.low;return u(V>>>S|M<<32-S,M>>>S,this.unsigned)}else return S===32?u(M,0,this.unsigned):u(M>>>S-32,0,this.unsigned)},F.shru=F.shiftRightUnsigned,F.shr_u=F.shiftRightUnsigned,F.toSigned=function(){return this.unsigned?u(this.low,this.high,!1):this},F.toUnsigned=function(){return this.unsigned?this:u(this.low,this.high,!0)},F.toBytes=function(S){return S?this.toBytesLE():this.toBytesBE()},F.toBytesLE=function(){var S=this.high,M=this.low;return[M&255,M>>>8&255,M>>>16&255,M>>>24,S&255,S>>>8&255,S>>>16&255,S>>>24]},F.toBytesBE=function(){var S=this.high,M=this.low;return[S>>>24,S>>>16&255,S>>>8&255,S&255,M>>>24,M>>>16&255,M>>>8&255,M&255]},a.fromBytes=function(S,M,V){return V?a.fromBytesLE(S,M):a.fromBytesBE(S,M)},a.fromBytesLE=function(S,M){return new a(S[0]|S[1]<<8|S[2]<<16|S[3]<<24,S[4]|S[5]<<8|S[6]<<16|S[7]<<24,M)},a.fromBytesBE=function(S,M){return new a(S[4]<<24|S[5]<<16|S[6]<<8|S[7],S[0]<<24|S[1]<<16|S[2]<<8|S[3],M)}}),L$=ft(()=>{}),z$=ft(()=>{}),B$=ft((e,t)=>{(function(n,a,r){function s(u){var p=this,d=l();p.next=function(){var c=2091639*p.s0+p.c*23283064365386963e-26;return p.s0=p.s1,p.s1=p.s2,p.s2=c-(p.c=c|0)},p.c=1,p.s0=d(" "),p.s1=d(" "),p.s2=d(" "),p.s0-=d(u),p.s0<0&&(p.s0+=1),p.s1-=d(u),p.s1<0&&(p.s1+=1),p.s2-=d(u),p.s2<0&&(p.s2+=1),d=null}function i(u,p){return p.c=u.c,p.s0=u.s0,p.s1=u.s1,p.s2=u.s2,p}function o(u,p){var d=new s(u),c=p&&p.state,h=d.next;return h.int32=function(){return d.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,c&&(typeof c=="object"&&i(c,d),h.state=function(){return i(d,{})}),h}function l(){var u=4022871197,p=function(d){d=d.toString();for(var c=0;c<d.length;c++){u+=d.charCodeAt(c);var h=.02519603282416938*u;u=h>>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return p}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),W$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var c=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^c^c>>>8},l===(l|0)?u.x=l:p+=l;for(var d=0;d<p.length+64;d++)u.x^=p.charCodeAt(d)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),V$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.next=function(){var c=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(c^c<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:p+=l;for(var d=0;d<p.length+64;d++)u.x^=p.charCodeAt(d)|0,d==p.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),U$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var d=u.x,c=u.i,h,m,f;return h=d[c],h^=h>>>7,m=h^h<<24,h=d[c+1&7],m^=h^h>>>10,h=d[c+3&7],m^=h^h>>>3,h=d[c+4&7],m^=h^h<<7,h=d[c+7&7],h=h^h<<13,m^=h^h<<9,d[c]=m,u.i=c+1&7,m};function p(d,c){var h,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,h=0;h<c.length;++h)f[h&7]=f[h&7]<<15^c.charCodeAt(h)+f[h+1&7]<<13;for(;f.length<8;)f.push(0);for(h=0;h<8&&f[h]===0;++h);for(h==8?m=f[7]=-1:m=f[h],d.x=f,d.i=0,h=256;h>0;--h)d.next()}p(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(d.x&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),G$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var d=u.w,c=u.X,h=u.i,m,f;return u.w=d=d+1640531527|0,f=c[h+34&127],m=c[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[h]=f^m,u.i=h,f+(d^d>>>16)|0};function p(d,c){var h,m,f,g,y,b=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,g=-32;g<x;++g)c&&(m^=c.charCodeAt((g+32)%c.length)),g===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,g>=0&&(y=y+1640531527|0,h=b[g&127]^=m+y,f=h==0?f+1:0);for(f>=128&&(b[(c&&c.length||0)&127]=-1),f=127,g=4*128;g>0;--g)m=b[f+34&127],h=b[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,b[f]=m^h;d.w=y,d.X=b,d.i=f}p(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(d.X&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),H$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.next=function(){var c=u.b,h=u.c,m=u.d,f=u.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,u.b=c=c<<20^c>>>12^h,u.c=h=h-m|0,u.d=m<<16^h>>>16^f,u.a=f-c|0},u.a=0,u.b=0,u.c=-1640531527,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):p+=l;for(var d=0;d<p.length+20;d++)u.b^=p.charCodeAt(d)|0,u.next()}function i(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),jk=ft(()=>{}),j$=ft((e,t)=>{(function(n,a){var r=this,s=256,i=6,o=52,l="random",u=a.pow(s,i),p=a.pow(2,o),d=p*2,c=s-1,h;function m(w,T,C){var E=[];T=T==!0?{entropy:!0}:T||{};var $=b(y(T.entropy?[w,v(n)]:w==null?x():w,3),E),P=new f(E),F=function(){for(var S=P.g(i),M=u,V=0;S<p;)S=(S+V)*s,M*=s,V=P.g(1);for(;S>=d;)S/=2,M/=2,V>>>=1;return(S+V)/M};return F.int32=function(){return P.g(4)|0},F.quick=function(){return P.g(4)/4294967296},F.double=F,b(v(P.S),n),(T.pass||C||function(S,M,V,j){return j&&(j.S&&g(j,P),S.state=function(){return g(P,{})}),V?(a[l]=S,M):S})(F,$,"global"in T?T.global:this==a,T.state)}a["seed"+l]=m;function f(w){var T,C=w.length,E=this,$=0,P=E.i=E.j=0,F=E.S=[];for(C||(w=[C++]);$<s;)F[$]=$++;for($=0;$<s;$++)F[$]=F[P=c&P+w[$%C]+(T=F[$])],F[P]=T;(E.g=function(S){for(var M,V=0,j=E.i,q=E.j,K=E.S;S--;)M=K[j=c&j+1],V=V*s+K[c&(K[j]=K[q=c&q+M])+(K[q]=M)];return E.i=j,E.j=q,V})(s)}function g(w,T){return T.i=w.i,T.j=w.j,T.S=w.S.slice(),T}function y(w,T){var C=[],E=typeof w,$;if(T&&E=="object")for($ in w)try{C.push(y(w[$],T-1))}catch(P){}return C.length?C:E=="string"?w:w+"\0"}function b(w,T){for(var C=w+"",E,$=0;$<C.length;)T[c&$]=c&(E^=T[c&$]*19)+C.charCodeAt($++);return v(T)}function x(){try{var w;return h&&(w=h.randomBytes)?w=w(s):(w=new Uint8Array(s),(r.crypto||r.msCrypto).getRandomValues(w)),v(w)}catch(E){var T=r.navigator,C=T&&T.plugins;return[+new Date,r,C,r.screen,v(n)]}}function v(w){return String.fromCharCode.apply(0,w)}if(b(a.random(),n),typeof t=="object"&&t.exports){t.exports=m;try{h=jk()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return m})})([],Math)}),qk=ft((e,t)=>{var n=B$(),a=W$(),r=V$(),s=U$(),i=G$(),o=H$(),l=j$();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),q$=ft((e,t)=>{(function(n,a,r){function s(u){var p=this,d=l();p.next=function(){var c=2091639*p.s0+p.c*23283064365386963e-26;return p.s0=p.s1,p.s1=p.s2,p.s2=c-(p.c=c|0)},p.c=1,p.s0=d(" "),p.s1=d(" "),p.s2=d(" "),p.s0-=d(u),p.s0<0&&(p.s0+=1),p.s1-=d(u),p.s1<0&&(p.s1+=1),p.s2-=d(u),p.s2<0&&(p.s2+=1),d=null}function i(u,p){return p.c=u.c,p.s0=u.s0,p.s1=u.s1,p.s2=u.s2,p}function o(u,p){var d=new s(u),c=p&&p.state,h=d.next;return h.int32=function(){return d.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,c&&(typeof c=="object"&&i(c,d),h.state=function(){return i(d,{})}),h}function l(){var u=4022871197,p=function(d){d=String(d);for(var c=0;c<d.length;c++){u+=d.charCodeAt(c);var h=.02519603282416938*u;u=h>>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return p}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),K$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var c=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^c^c>>>8},l===(l|0)?u.x=l:p+=l;for(var d=0;d<p.length+64;d++)u.x^=p.charCodeAt(d)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),X$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.next=function(){var c=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^(c^c<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:p+=l;for(var d=0;d<p.length+64;d++)u.x^=p.charCodeAt(d)|0,d==p.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(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 o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Y$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var d=u.x,c=u.i,h,m,f;return h=d[c],h^=h>>>7,m=h^h<<24,h=d[c+1&7],m^=h^h>>>10,h=d[c+3&7],m^=h^h>>>3,h=d[c+4&7],m^=h^h<<7,h=d[c+7&7],h=h^h<<13,m^=h^h<<9,d[c]=m,u.i=c+1&7,m};function p(d,c){var h,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,h=0;h<c.length;++h)f[h&7]=f[h&7]<<15^c.charCodeAt(h)+f[h+1&7]<<13;for(;f.length<8;)f.push(0);for(h=0;h<8&&f[h]===0;++h);for(h==8?m=f[7]=-1:m=f[h],d.x=f,d.i=0,h=256;h>0;--h)d.next()}p(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(d.x&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),J$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var d=u.w,c=u.X,h=u.i,m,f;return u.w=d=d+1640531527|0,f=c[h+34&127],m=c[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[h]=f^m,u.i=h,f+(d^d>>>16)|0};function p(d,c){var h,m,f,g,y,b=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,g=-32;g<x;++g)c&&(m^=c.charCodeAt((g+32)%c.length)),g===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,g>=0&&(y=y+1640531527|0,h=b[g&127]^=m+y,f=h==0?f+1:0);for(f>=128&&(b[(c&&c.length||0)&127]=-1),f=127,g=4*128;g>0;--g)m=b[f+34&127],h=b[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,b[f]=m^h;d.w=y,d.X=b,d.i=f}p(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(d.X&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Z$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.next=function(){var c=u.b,h=u.c,m=u.d,f=u.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,u.b=c=c<<20^c>>>12^h,u.c=h=h-m|0,u.d=m<<16^h>>>16^f,u.a=f-c|0},u.a=0,u.b=0,u.c=-1640531527,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):p+=l;for(var d=0;d<p.length+20;d++)u.b^=p.charCodeAt(d)|0,u.next()}function i(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Q$=ft((e,t)=>{(function(n,a,r){var s=256,i=6,o=52,l="random",u=r.pow(s,i),p=r.pow(2,o),d=p*2,c=s-1,h;function m(w,T,C){var E=[];T=T==!0?{entropy:!0}:T||{};var $=b(y(T.entropy?[w,v(a)]:w==null?x():w,3),E),P=new f(E),F=function(){for(var S=P.g(i),M=u,V=0;S<p;)S=(S+V)*s,M*=s,V=P.g(1);for(;S>=d;)S/=2,M/=2,V>>>=1;return(S+V)/M};return F.int32=function(){return P.g(4)|0},F.quick=function(){return P.g(4)/4294967296},F.double=F,b(v(P.S),a),(T.pass||C||function(S,M,V,j){return j&&(j.S&&g(j,P),S.state=function(){return g(P,{})}),V?(r[l]=S,M):S})(F,$,"global"in T?T.global:this==r,T.state)}function f(w){var T,C=w.length,E=this,$=0,P=E.i=E.j=0,F=E.S=[];for(C||(w=[C++]);$<s;)F[$]=$++;for($=0;$<s;$++)F[$]=F[P=c&P+w[$%C]+(T=F[$])],F[P]=T;(E.g=function(S){for(var M,V=0,j=E.i,q=E.j,K=E.S;S--;)M=K[j=c&j+1],V=V*s+K[c&(K[j]=K[q=c&q+M])+(K[q]=M)];return E.i=j,E.j=q,V})(s)}function g(w,T){return T.i=w.i,T.j=w.j,T.S=w.S.slice(),T}function y(w,T){var C=[],E=typeof w,$;if(T&&E=="object")for($ in w)try{C.push(y(w[$],T-1))}catch(P){}return C.length?C:E=="string"?w:w+"\0"}function b(w,T){for(var C=w+"",E,$=0;$<C.length;)T[c&$]=c&(E^=T[c&$]*19)+C.charCodeAt($++);return v(T)}function x(){try{var w;return h&&(w=h.randomBytes)?w=w(s):(w=new Uint8Array(s),(n.crypto||n.msCrypto).getRandomValues(w)),v(w)}catch(E){var T=n.navigator,C=T&&T.plugins;return[+new Date,n,C,n.screen,v(a)]}}function v(w){return String.fromCharCode.apply(0,w)}if(b(r.random(),a),typeof t=="object"&&t.exports){t.exports=m;try{h=jk()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return m}):r["seed"+l]=m})(typeof self!="undefined"?self:e,[],Math)}),Kk=ft((e,t)=>{var n=q$(),a=K$(),r=X$(),s=Y$(),i=J$(),o=Z$(),l=Q$();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),Xk=ft(()=>{}),yx=ft(()=>{}),wh=ft(()=>{}),eF=ft(()=>{}),tF=ft(()=>{}),nF=ft(()=>{}),aF=ft((e,t)=>{var n=(()=>{var a=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(a=a||__filename),function(r){r=r||{};function s(){return Te.buffer!=bn&&Ra(Te.buffer),xd}function i(){return Te.buffer!=bn&&Ra(Te.buffer),vd}function o(){return Te.buffer!=bn&&Ra(Te.buffer),mp}function l(){return Te.buffer!=bn&&Ra(Te.buffer),wd}function u(){return Te.buffer!=bn&&Ra(Te.buffer),kd}function p(){return Te.buffer!=bn&&Ra(Te.buffer),Id}function d(){return Te.buffer!=bn&&Ra(Te.buffer),Sd}var c=typeof r!="undefined"?r:{},h,m;c.ready=new Promise(function(N,D){h=N,m=D});var f;typeof process!="undefined"&&process.listeners&&(f={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var g=Object.assign({},c),y=[],b="./this.program",x=(N,D)=>{throw D},v=typeof window=="object",w=typeof importScripts=="function",T=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",C=c.ENVIRONMENT_IS_PTHREAD||!1,E="";function $(N){return c.locateFile?c.locateFile(N,E):E+N}var P,F,S,M;function V(N){N instanceof Sp||Z("exiting due to exception: "+N)}var j,q,K;if(T){w?E=wh().dirname(E)+"/":E=__dirname+"/",K=()=>{q||(j=yx(),q=wh())},P=function(D,U){return K(),D=q.normalize(D),j.readFileSync(D,U?void 0:"utf8")},S=D=>{var U=P(D,!0);return U.buffer||(U=new Uint8Array(U)),U},F=(D,U,Y)=>{K(),D=q.normalize(D),j.readFile(D,function(pe,he){pe?Y(pe):U(he.buffer)})},process.argv.length>1&&(b=process.argv[1].replace(/\\/g,"/")),y=process.argv.slice(2),process.on("uncaughtException",function(D){if(!(D instanceof Sp))throw D}),process.on("unhandledRejection",function(D){throw D}),x=(D,U)=>{if(Ms())throw process.exitCode=D,U;V(U),process.exit(D)},c.inspect=function(){return"[Emscripten Module object]"};let N;try{N=eF()}catch(D){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),D}global.Worker=N.Worker}else(v||w)&&(w?E=self.location.href:typeof document!="undefined"&&document.currentScript&&(E=document.currentScript.src),typeof a!="undefined"&&a&&(E=a),E.indexOf("blob:")!==0?E=E.substr(0,E.replace(/[?#].*/,"").lastIndexOf("/")+1):E="",T||(P=N=>{var D=new XMLHttpRequest;return D.open("GET",N,!1),D.send(null),D.responseText},w&&(S=N=>{var D=new XMLHttpRequest;return D.open("GET",N,!1),D.responseType="arraybuffer",D.send(null),new Uint8Array(D.response)}),F=(N,D,U)=>{var Y=new XMLHttpRequest;Y.open("GET",N,!0),Y.responseType="arraybuffer",Y.onload=()=>{if(Y.status==200||Y.status==0&&Y.response){D(Y.response);return}U()},Y.onerror=U,Y.send(null)}),M=N=>document.title=N);T&&typeof performance=="undefined"&&(global.performance=tF().performance);var Q=console.log.bind(console),ee=console.warn.bind(console);T&&(K(),Q=N=>j.writeSync(1,N+`
`),ee=N=>j.writeSync(2,N+`
`));var re=c.print||Q,Z=c.printErr||ee;Object.assign(c,g),g=null,c.arguments&&(y=c.arguments),c.thisProgram&&(b=c.thisProgram),c.quit&&(x=c.quit);var ie=4;function ae(N){ae.shown||(ae.shown={}),ae.shown[N]||(ae.shown[N]=1,Z(N))}function le(N,D){if(typeof WebAssembly.Function=="function"){for(var U={i:"i32",j:"i64",f:"f32",d:"f64"},Y={parameters:[],results:D[0]=="v"?[]:[U[D[0]]]},pe=1;pe<D.length;++pe)Y.parameters.push(U[D[pe]]);return new WebAssembly.Function(Y,N)}var he=[1,0,1,96],ve=D.slice(0,1),Ce=D.slice(1),_t={i:127,j:126,f:125,d:124};he.push(Ce.length);for(var pe=0;pe<Ce.length;++pe)he.push(_t[Ce[pe]]);ve=="v"?he.push(0):he=he.concat([1,_t[ve]]),he[1]=he.length-2;var La=new Uint8Array([0,97,115,109,1,0,0,0].concat(he,[2,7,1,1,101,1,102,0,0,7,5,1,1,102,0,0])),za=new WebAssembly.Module(La),Zd=new WebAssembly.Instance(za,{e:{f:N}}),Np=Zd.exports.f;return Np}var ue=[],ke;function ye(){if(ue.length)return ue.pop();try{oa.grow(1)}catch(N){throw N instanceof RangeError?"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.":N}return oa.length-1}function Ie(N,D){for(var U=N;U<N+D;U++){var Y=Uo(U);Y&&ke.set(Y,U)}}var Ee=0,Fe=N=>{Ee=N},Be=Atomics.load,je=Atomics.store,st=Atomics.compareExchange,et;c.wasmBinary&&(et=c.wasmBinary);var tt=c.noExitRuntime||!0;typeof WebAssembly!="object"&&Bo("no native wasm support detected");var Te,gt,ct=!1,yn;function Yt(N,D){N||Bo(D)}function Dn(N){var D=c["_"+N];return D}function Ut(N,D,U,Y,pe){var he={string:function(la){var Yo=0;if(la!=null&&la!==0){var y1=(la.length<<2)+1;Yo=Xo(y1),Ds(la,Yo,y1)}return Yo},array:function(la){var Yo=Xo(la.length);return xr(la,Yo),Yo}};function ve(la){return D==="string"?ia(la):D==="boolean"?Boolean(la):la}var Ce=Dn(N),_t=[],La=0;if(Y)for(var za=0;za<Y.length;za++){var Zd=he[U[za]];Zd?(La===0&&(La=eb()),_t[za]=Zd(Y[za])):_t[za]=Y[za]}var Np=Ce.apply(null,_t);function _$(la){return La!==0&&Kd(La),ve(la)}return Np=_$(Np),Np}function Jt(N,D,U,Y){U=U||[];var pe=U.every(function(ve){return ve==="number"}),he=D!=="string";return he&&pe&&!Y?Dn(N):function(){return Ut(N,D,U,arguments,Y)}}var Da=1;function Rn(N){var D=new TextDecoder(N);this.decode=U=>(U.buffer instanceof SharedArrayBuffer&&(U=new Uint8Array(U)),D.decode.call(D,U))}var Gt=typeof TextDecoder!="undefined"?new Rn("utf8"):void 0;function sa(N,D,U){for(var Y=D+U,pe=D;N[pe]&&!(pe>=Y);)++pe;if(pe-D>16&&N.subarray&&Gt)return Gt.decode(N.subarray(D,pe));for(var he="";D<pe;){var ve=N[D++];if(!(ve&128)){he+=String.fromCharCode(ve);continue}var Ce=N[D++]&63;if((ve&224)==192){he+=String.fromCharCode((ve&31)<<6|Ce);continue}var _t=N[D++]&63;if((ve&240)==224?ve=(ve&15)<<12|Ce<<6|_t:ve=(ve&7)<<18|Ce<<12|_t<<6|N[D++]&63,ve<65536)he+=String.fromCharCode(ve);else{var La=ve-65536;he+=String.fromCharCode(55296|La>>10,56320|La&1023)}}return he}function ia(N,D){return N?sa(i(),N,D):""}function Br(N,D,U,Y){if(!(Y>0))return 0;for(var pe=U,he=U+Y-1,ve=0;ve<N.length;++ve){var Ce=N.charCodeAt(ve);if(Ce>=55296&&Ce<=57343){var _t=N.charCodeAt(++ve);Ce=65536+((Ce&1023)<<10)|_t&1023}if(Ce<=127){if(U>=he)break;D[U++]=Ce}else if(Ce<=2047){if(U+1>=he)break;D[U++]=192|Ce>>6,D[U++]=128|Ce&63}else if(Ce<=65535){if(U+2>=he)break;D[U++]=224|Ce>>12,D[U++]=128|Ce>>6&63,D[U++]=128|Ce&63}else{if(U+3>=he)break;D[U++]=240|Ce>>18,D[U++]=128|Ce>>12&63,D[U++]=128|Ce>>6&63,D[U++]=128|Ce&63}}return D[U]=0,U-pe}function Ds(N,D,U){return Br(N,i(),D,U)}function bd(N){for(var D=0,U=0;U<N.length;++U){var Y=N.charCodeAt(U);Y>=55296&&Y<=57343&&(Y=65536+((Y&1023)<<10)|N.charCodeAt(++U)&1023),Y<=127?++D:Y<=2047?D+=2:Y<=65535?D+=3:D+=4}return D}var Wr=typeof TextDecoder!="undefined"?new Rn("utf-16le"):void 0;function xr(N,D){s().set(N,D)}function hp(N,D,U){for(var Y=0;Y<N.length;++Y)s()[D++>>0]=N.charCodeAt(Y);U||(s()[D>>0]=0)}function Lo(N,D){return N%D>0&&(N+=D-N%D),N}var bn,xd,vd,mp,wd,kd,J0,Id,Sd;C&&(bn=c.buffer);function Ra(N){bn=N,c.HEAP8=xd=new Int8Array(N),c.HEAP16=mp=new Int16Array(N),c.HEAP32=kd=new Int32Array(N),c.HEAPU8=vd=new Uint8Array(N),c.HEAPU16=wd=new Uint16Array(N),c.HEAPU32=J0=new Uint32Array(N),c.HEAPF32=Id=new Float32Array(N),c.HEAPF64=Sd=new Float64Array(N)}var Nd=c.INITIAL_MEMORY||16777216;if(C)Te=c.wasmMemory,bn=c.buffer;else if(c.wasmMemory)Te=c.wasmMemory;else if(Te=new WebAssembly.Memory({initial:Nd/65536,maximum:32768,shared:!0}),!(Te.buffer instanceof SharedArrayBuffer))throw Z("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"),T&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Te&&(bn=Te.buffer),Nd=bn.byteLength,Ra(bn);var oa,zo=[],Vr=[],Ng=[],Td=[],Rs=!1,Tg=!1,Cd=0;function Ms(){return tt||Cd>0}function xn(){if(c.preRun)for(typeof c.preRun=="function"&&(c.preRun=[c.preRun]);c.preRun.length;)Z0(c.preRun.shift());$d(zo)}function fp(){Rs=!0,!C&&$d(Vr)}function Cg(){C||(_e.terminateAllThreads(),Tg=!0)}function _g(){if(!C){if(c.postRun)for(typeof c.postRun=="function"&&(c.postRun=[c.postRun]);c.postRun.length;)gp(c.postRun.shift());$d(Td)}}function Z0(N){zo.unshift(N)}function Q0(N){Vr.unshift(N)}function gp(N){Td.unshift(N)}var Ur=0,_d=null,Ma=null;function yp(N){Ur++,c.monitorRunDependencies&&c.monitorRunDependencies(Ur)}function e1(N){if(Ur--,c.monitorRunDependencies&&c.monitorRunDependencies(Ur),Ur==0&&(_d!==null&&(clearInterval(_d),_d=null),Ma)){var D=Ma;Ma=null,D()}}c.preloadedImages={},c.preloadedAudios={};function Bo(N){C?postMessage({cmd:"onAbort",arg:N}):c.onAbort&&c.onAbort(N),N="Aborted("+N+")",Z(N),ct=!0,yn=1,N+=". Build with -s ASSERTIONS=1 for more info.";var D=new WebAssembly.RuntimeError(N);throw m(D),D}var Eg="data:application/octet-stream;base64,";function bp(N){return N.startsWith(Eg)}function Ed(N){return N.startsWith("file://")}var vn;vn="tfjs-backend-wasm-threaded-simd.wasm",bp(vn)||(vn=$(vn));function Ad(N){try{if(N==vn&&et)return new Uint8Array(et);if(S)return S(N);throw"both async and sync fetching of the wasm failed"}catch(D){Bo(D)}}function Wo(){if(!et&&(v||w)){if(typeof fetch=="function"&&!Ed(vn))return fetch(vn,{credentials:"same-origin"}).then(function(N){if(!N.ok)throw"failed to load wasm binary file at '"+vn+"'";return N.arrayBuffer()}).catch(function(){return Ad(vn)});if(F)return new Promise(function(N,D){F(vn,function(U){N(new Uint8Array(U))},D)})}return Promise.resolve().then(function(){return Ad(vn)})}function Ag(){var N={env:Ud,wasi_snapshot_preview1:Ud};function D(ve,Ce){var _t=ve.exports;if(c.asm=_t,Og(c.asm.emscripten_tls_init),oa=c.asm.__indirect_function_table,Q0(c.asm.__wasm_call_ctors),gt=Ce,!C){var La=_e.unusedWorkers.length;_e.unusedWorkers.forEach(function(za){_e.loadWasmModuleToWorker(za,function(){--La||e1("wasm-instantiate")})})}}C||yp("wasm-instantiate");function U(ve){D(ve.instance,ve.module)}function Y(ve){return Wo().then(function(Ce){return WebAssembly.instantiate(Ce,N)}).then(function(Ce){return Ce}).then(ve,function(Ce){Z("failed to asynchronously prepare wasm: "+Ce),Bo(Ce)})}function pe(){return!et&&typeof WebAssembly.instantiateStreaming=="function"&&!bp(vn)&&!Ed(vn)&&typeof fetch=="function"?fetch(vn,{credentials:"same-origin"}).then(function(ve){var Ce=WebAssembly.instantiateStreaming(ve,N);return Ce.then(U,function(_t){return Z("wasm streaming compile failed: "+_t),Z("falling back to ArrayBuffer instantiation"),Y(U)})}):Y(U)}if(c.instantiateWasm)try{var he=c.instantiateWasm(N,D);return he}catch(ve){return Z("Module.instantiateWasm callback failed with error: "+ve),!1}return pe().catch(m),{}}var t1,n1,$g={};function $d(N){for(;N.length>0;){var D=N.shift();if(typeof D=="function"){D(c);continue}var U=D.func;typeof U=="number"?D.arg===void 0?Uo(U)():Uo(U)(D.arg):U(D.arg===void 0?null:D.arg)}}function Vo(N){var D=eb(),U=N();return Kd(D),U}function PA(N){return N}function a1(N){var D=/\b_Z[\w\d_]+/g;return N.replace(D,function(U){var Y=U;return U===Y?U:Y+" ["+U+"]"})}function Fg(N){u()[N>>2]=0;var D=_e.pthreads[N];delete _e.pthreads[N],D.worker.terminate(),Qy(N),_e.runningWorkers.splice(_e.runningWorkers.indexOf(D.worker),1),D.worker.pthread=void 0}function Dg(N){var D=_e.pthreads[N];D.worker.postMessage({cmd:"cancel"})}function Fd(N){var D=_e.pthreads[N];if(D){u()[N>>2]=0;var U=D.worker;_e.returnWorkerToPool(U)}}function Dd(N){N$(N)}function Rg(N){if(N instanceof Sp||N=="unwind")return yn;x(1,N)}var _e={unusedWorkers:[],runningWorkers:[],tlsInitFunctions:[],init:function(){C?_e.initWorker():_e.initMainThread()},initMainThread:function(){for(var N=8,D=0;D<N;++D)_e.allocateUnusedWorker()},initWorker:function(){tt=!1},pthreads:{},setExitStatus:function(N){yn=N},terminateAllThreads:function(){for(var N in _e.pthreads){var D=_e.pthreads[N];D&&D.worker&&_e.returnWorkerToPool(D.worker)}for(var U=0;U<_e.unusedWorkers.length;++U){var Y=_e.unusedWorkers[U];Y.terminate()}_e.unusedWorkers=[]},returnWorkerToPool:function(N){_e.runWithoutMainThreadQueuedCalls(function(){delete _e.pthreads[N.pthread.threadInfoStruct],_e.unusedWorkers.push(N),_e.runningWorkers.splice(_e.runningWorkers.indexOf(N),1),Qy(N.pthread.threadInfoStruct),N.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(N){u()[g1>>2]=0;try{N()}finally{u()[g1>>2]=1}},receiveObjectTransfer:function(N){},threadInit:function(){for(var N in _e.tlsInitFunctions)_e.tlsInitFunctions[N]()},loadWasmModuleToWorker:function(N,D){N.onmessage=U=>{var Y=U.data,pe=Y.cmd;if(N.pthread&&(_e.currentProxiedOperationCallerThread=N.pthread.threadInfoStruct),Y.targetThread&&Y.targetThread!=qd()){var he=_e.pthreads[Y.targetThread];he?he.worker.postMessage(Y,Y.transferList):Z('Internal error! Worker sent a message "'+pe+'" to target pthread '+Y.targetThread+", but that thread no longer exists!"),_e.currentProxiedOperationCallerThread=void 0;return}pe==="processQueuedMainThreadWork"?c1():pe==="spawnThread"?Md(Y):pe==="cleanupThread"?Fd(Y.thread):pe==="killThread"?Fg(Y.thread):pe==="cancelThread"?Dg(Y.thread):pe==="loaded"?(N.loaded=!0,D&&D(N),N.runPthread&&(N.runPthread(),delete N.runPthread)):pe==="print"?re("Thread "+Y.threadId+": "+Y.text):pe==="printErr"?Z("Thread "+Y.threadId+": "+Y.text):pe==="alert"?alert("Thread "+Y.threadId+": "+Y.text):Y.target==="setimmediate"?N.postMessage(Y):pe==="onAbort"?c.onAbort&&c.onAbort(Y.arg):Z("worker sent an unknown command "+pe),_e.currentProxiedOperationCallerThread=void 0},N.onerror=U=>{var Y="worker sent an error!";throw Z(Y+" "+U.filename+":"+U.lineno+": "+U.message),U},T&&(N.on("message",function(U){N.onmessage({data:U})}),N.on("error",function(U){N.onerror(U)}),N.on("detachedExit",function(){})),N.postMessage({cmd:"load",urlOrBlob:c.mainScriptUrlOrBlob||a,wasmMemory:Te,wasmModule:gt})},allocateUnusedWorker:function(){var N=$("tfjs-backend-wasm-threaded-simd.worker.js");_e.unusedWorkers.push(new Worker(N))},getNewWorker:function(){return _e.unusedWorkers.length==0&&(_e.allocateUnusedWorker(),_e.loadWasmModuleToWorker(_e.unusedWorkers[0])),_e.unusedWorkers.pop()}};function Mg(){var N=qd(),D=u()[N+44>>2],U=u()[N+48>>2],Y=D-U;f1(D,Y),Kd(D)}c.establishStackSpace=Mg;function Rd(N){if(C)return Ls(1,0,N);try{Dd(N)}catch(D){Rg(D)}}var Ps=[];function Uo(N){var D=Ps[N];return D||(N>=Ps.length&&(Ps.length=N+1),Ps[N]=D=oa.get(N)),D}function Pg(N,D){return Uo(N)(D)}c.invokeEntryPoint=Pg;function r1(){var N=new Error;if(!N.stack){try{throw new Error}catch(D){N=D}if(!N.stack)return"(no stack trace available)"}return N.stack.toString()}function Og(N,D,U){_e.tlsInitFunctions.push(N)}function s1(N,D){oa.set(N,D),Ps[N]=D}var Os;T?Os=()=>{var N=process.hrtime();return N[0]*1e3+N[1]/1e6}:C?Os=()=>performance.now()-c.__performance_now_clock_drift:Os=()=>performance.now();var Lg=!0;function zg(N){return u()[p1()>>2]=N,N}function Bg(N,D){var U;if(N===0)U=Date.now();else if((N===1||N===4)&&Lg)U=Os();else return zg(28),-1;return u()[D>>2]=U/1e3|0,u()[D+4>>2]=U%1e3*1e3*1e3|0,0}function Wg(N,D){return Bg(N,D)}function Vg(N){d1(N,!w,1,!v),_e.threadInit()}function Ug(N){C?postMessage({cmd:"cleanupThread",thread:N}):Fd(N)}function Md(N){var D=_e.getNewWorker();if(!D)return 6;_e.runningWorkers.push(D);var U=_e.pthreads[N.pthread_ptr]={worker:D,threadInfoStruct:N.pthread_ptr};D.pthread=U;var Y={cmd:"run",start_routine:N.startRoutine,arg:N.arg,threadInfoStruct:N.pthread_ptr};return D.runPthread=()=>{Y.time=performance.now(),D.postMessage(Y,N.transferList)},D.loaded&&(D.runPthread(),delete D.runPthread),0}function Gg(N,D,U,Y){if(typeof SharedArrayBuffer=="undefined")return Z("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;var pe=[],he=0;if(C&&(pe.length===0||he))return h1(687865856,N,D,U,Y);if(he)return he;var ve={startRoutine:U,pthread_ptr:N,arg:Y,transferList:pe};return C?(ve.cmd="spawnThread",postMessage(ve,pe),0):Md(ve)}function Hg(){return 2097152}function jg(N,D){if(N==D)postMessage({cmd:"processQueuedMainThreadWork"});else if(C)postMessage({targetThread:N,cmd:"processThreadQueue"});else{var U=_e.pthreads[N],Y=U&&U.worker;if(!Y)return;Y.postMessage({cmd:"processThreadQueue"})}return 1}function qg(){Bo("")}function Kg(){T||w||ae("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread")}function Pd(){return 2147483648}function Xg(N,D,U){i().copyWithin(N,D,D+U)}function Yg(){return T?nF().cpus().length:navigator.hardwareConcurrency}function Ls(N,D){var U=arguments.length-2,Y=arguments;return Vo(function(){for(var pe=U,he=Xo(pe*8),ve=he>>3,Ce=0;Ce<U;Ce++){var _t=Y[2+Ce];d()[ve+Ce]=_t}return m1(N,pe,he,D)})}var xp=[];function Jg(N,D,U){xp.length=D;for(var Y=U>>3,pe=0;pe<D;pe++)xp[pe]=d()[Y+pe];var he=N<0,ve=he?$g[-N-1]:gy[N];return ve.apply(null,xp)}function Zg(N){try{return Te.grow(N-bn.byteLength+65535>>>16),Ra(Te.buffer),1}catch(D){}}function Qg(N){var D=i().length;if(N=N>>>0,N<=D)return!1;var U=Pd();if(N>U)return!1;for(var Y=1;Y<=4;Y*=2){var pe=D*(1+.2/Y);pe=Math.min(pe,N+100663296);var he=Math.min(U,Lo(Math.max(N,pe),65536)),ve=Zg(he);if(ve)return!0}return!1}var Ve={inEventHandler:0,removeAllEventListeners:function(){for(var N=Ve.eventHandlers.length-1;N>=0;--N)Ve._removeHandler(N);Ve.eventHandlers=[],Ve.deferredCalls=[]},registerRemoveEventListeners:function(){Ve.removeEventListenersRegistered||(Ng.push(Ve.removeAllEventListeners),Ve.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(N,D,U){function Y(ve,Ce){if(ve.length!=Ce.length)return!1;for(var _t in ve)if(ve[_t]!=Ce[_t])return!1;return!0}for(var pe in Ve.deferredCalls){var he=Ve.deferredCalls[pe];if(he.targetFunction==N&&Y(he.argsList,U))return}Ve.deferredCalls.push({targetFunction:N,precedence:D,argsList:U}),Ve.deferredCalls.sort(function(ve,Ce){return ve.precedence<Ce.precedence})},removeDeferredCalls:function(N){for(var D=0;D<Ve.deferredCalls.length;++D)Ve.deferredCalls[D].targetFunction==N&&(Ve.deferredCalls.splice(D,1),--D)},canPerformEventHandlerRequests:function(){return Ve.inEventHandler&&Ve.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Ve.canPerformEventHandlerRequests())for(var N=0;N<Ve.deferredCalls.length;++N){var D=Ve.deferredCalls[N];Ve.deferredCalls.splice(N,1),--N,D.targetFunction.apply(null,D.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(N,D){for(var U=0;U<Ve.eventHandlers.length;++U)Ve.eventHandlers[U].target==N&&(!D||D==Ve.eventHandlers[U].eventTypeString)&&Ve._removeHandler(U--)},_removeHandler:function(N){var D=Ve.eventHandlers[N];D.target.removeEventListener(D.eventTypeString,D.eventListenerFunc,D.useCapture),Ve.eventHandlers.splice(N,1)},registerOrRemoveHandler:function(N){var D=function(Y){++Ve.inEventHandler,Ve.currentEventHandler=N,Ve.runDeferredCalls(),N.handlerFunc(Y),Ve.runDeferredCalls(),--Ve.inEventHandler};if(N.callbackfunc)N.eventListenerFunc=D,N.target.addEventListener(N.eventTypeString,D,N.useCapture),Ve.eventHandlers.push(N),Ve.registerRemoveEventListeners();else for(var U=0;U<Ve.eventHandlers.length;++U)Ve.eventHandlers[U].target==N.target&&Ve.eventHandlers[U].eventTypeString==N.eventTypeString&&Ve._removeHandler(U--)},queueEventHandlerOnThread_iiii:function(N,D,U,Y,pe){Vo(function(){var he=Xo(12);u()[he>>2]=U,u()[he+4>>2]=Y,u()[he+8>>2]=pe,Zy(N,637534208,D,Y,he)})},getTargetThreadForEventCallback:function(N){switch(N){case 1:return 0;case 2:return _e.currentProxiedOperationCallerThread;default:return N}},getNodeNameForTarget:function(N){return N?N==window?"#window":N==screen?"#screen":N&&N.nodeName?N.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function ey(N){var D=bd(N)+1,U=Jy(D);return Ds(N,U,D),U}function ty(N,D,U,Y){Vo(function(){var pe=Xo(12),he=0;D&&(he=ey(D)),u()[pe>>2]=he,u()[pe+4>>2]=U,u()[pe+8>>2]=Y,Zy(N,657457152,0,he,pe)})}function ny(N,D,U,Y){D=D?ia(D):"",ty(N,D,U,Y)}function ay(N){return N>2?ia(N):N}var ry=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function sy(N){N=ay(N);var D=ry[N]||(typeof document!="undefined"?document.querySelector(N):void 0);return D}function vp(N){return sy(N)}function Od(N,D,U){var Y=vp(N);if(!Y)return-4;if(Y.canvasSharedPtr&&(u()[Y.canvasSharedPtr>>2]=D,u()[Y.canvasSharedPtr+4>>2]=U),Y.offscreenCanvas||!Y.controlTransferredOffscreen){Y.offscreenCanvas&&(Y=Y.offscreenCanvas);var pe=!1;if(Y.GLctxObject&&Y.GLctxObject.GLctx){var he=Y.GLctxObject.GLctx.getParameter(2978);pe=he[0]===0&&he[1]===0&&he[2]===Y.width&&he[3]===Y.height}Y.width=D,Y.height=U,pe&&Y.GLctxObject.GLctx.viewport(0,0,D,U)}else if(Y.canvasSharedPtr){var ve=u()[Y.canvasSharedPtr+8>>2];return ny(ve,N,D,U),1}else return-4;return 0}function Ld(N,D,U){return C?Ls(2,1,N,D,U):Od(N,D,U)}function iy(N,D,U){var Y=vp(N);return Y?Od(N,D,U):Ld(N,D,U)}function oy(){throw"unwind"}function ly(N){var D=N.getExtension("ANGLE_instanced_arrays");if(D)return N.vertexAttribDivisor=function(U,Y){D.vertexAttribDivisorANGLE(U,Y)},N.drawArraysInstanced=function(U,Y,pe,he){D.drawArraysInstancedANGLE(U,Y,pe,he)},N.drawElementsInstanced=function(U,Y,pe,he,ve){D.drawElementsInstancedANGLE(U,Y,pe,he,ve)},1}function uy(N){var D=N.getExtension("OES_vertex_array_object");if(D)return N.createVertexArray=function(){return D.createVertexArrayOES()},N.deleteVertexArray=function(U){D.deleteVertexArrayOES(U)},N.bindVertexArray=function(U){D.bindVertexArrayOES(U)},N.isVertexArray=function(U){return D.isVertexArrayOES(U)},1}function py(N){var D=N.getExtension("WEBGL_draw_buffers");if(D)return N.drawBuffers=function(U,Y){D.drawBuffersWEBGL(U,Y)},1}function cy(N){return!!(N.multiDrawWebgl=N.getExtension("WEBGL_multi_draw"))}var Ct={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},queries:[],stringCache:{},unpackAlignment:4,recordError:function(N){Ct.lastError||(Ct.lastError=N)},getNewId:function(N){for(var D=Ct.counter++,U=N.length;U<D;U++)N[U]=null;return D},getSource:function(N,D,U,Y){for(var pe="",he=0;he<D;++he){var ve=Y?u()[Y+he*4>>2]:-1;pe+=ia(u()[U+he*4>>2],ve<0?void 0:ve)}return pe},createContext:function(N,D){N.getContextSafariWebGL2Fixed||(N.getContextSafariWebGL2Fixed=N.getContext,N.getContext=function(pe,he){var ve=N.getContextSafariWebGL2Fixed(pe,he);return pe=="webgl"==ve instanceof WebGLRenderingContext?ve:null});var U=N.getContext("webgl",D);if(!U)return 0;var Y=Ct.registerContext(U,D);return Y},registerContext:function(N,D){var U=Jy(8);u()[U+4>>2]=qd();var Y={handle:U,attributes:D,version:D.majorVersion,GLctx:N};return N.canvas&&(N.canvas.GLctxObject=Y),Ct.contexts[U]=Y,(typeof D.enableExtensionsByDefault=="undefined"||D.enableExtensionsByDefault)&&Ct.initExtensions(Y),U},makeContextCurrent:function(N){return Ct.currentContext=Ct.contexts[N],c.ctx=Vd=Ct.currentContext&&Ct.currentContext.GLctx,!(N&&!Vd)},getContext:function(N){return Ct.contexts[N]},deleteContext:function(N){Ct.currentContext===Ct.contexts[N]&&(Ct.currentContext=null),typeof Ve=="object"&&Ve.removeAllHandlersOnTarget(Ct.contexts[N].GLctx.canvas),Ct.contexts[N]&&Ct.contexts[N].GLctx.canvas&&(Ct.contexts[N].GLctx.canvas.GLctxObject=void 0),u1(Ct.contexts[N].handle),Ct.contexts[N]=null},initExtensions:function(N){if(N||(N=Ct.currentContext),!N.initExtensionsDone){N.initExtensionsDone=!0;var D=N.GLctx;ly(D),uy(D),py(D),D.disjointTimerQueryExt=D.getExtension("EXT_disjoint_timer_query"),cy(D);var U=D.getSupportedExtensions()||[];U.forEach(function(Y){!Y.includes("lose_context")&&!Y.includes("debug")&&D.getExtension(Y)})}}},dy=["default","low-power","high-performance"];function hy(N,D){var U=D>>2,Y=u()[U+6],pe={alpha:!!u()[U+0],depth:!!u()[U+1],stencil:!!u()[U+2],antialias:!!u()[U+3],premultipliedAlpha:!!u()[U+4],preserveDrawingBuffer:!!u()[U+5],powerPreference:dy[Y],failIfMajorPerformanceCaveat:!!u()[U+7],majorVersion:u()[U+8],minorVersion:u()[U+9],enableExtensionsByDefault:u()[U+10],explicitSwapControl:u()[U+11],proxyContextToMainThread:u()[U+12],renderViaOffscreenBackBuffer:u()[U+13]},he=vp(N);if(!he||pe.explicitSwapControl)return 0;var ve=Ct.createContext(he,pe);return ve}function my(N,D){return hy(N,D)}var Go={mappings:{},buffers:[null,[],[]],printChar:function(N,D){var U=Go.buffers[N];D===0||D===10?((N===1?re:Z)(sa(U,0)),U.length=0):U.push(D)},varargs:void 0,get:function(){Go.varargs+=4;var N=u()[Go.varargs-4>>2];return N},getStr:function(N){var D=ia(N);return D},get64:function(N,D){return N}};function zd(N){return C?Ls(3,1,N):0}function Bd(N,D,U,Y,pe){if(C)return Ls(4,1,N,D,U,Y,pe)}function Wd(N,D,U,Y){if(C)return Ls(5,1,N,D,U,Y);for(var pe=0,he=0;he<U;he++){var ve=u()[D>>2],Ce=u()[D+4>>2];D+=8;for(var _t=0;_t<Ce;_t++)Go.printChar(N,i()[ve+_t]);pe+=Ce}return u()[Y>>2]=pe,0}function fy(N){Fe(N)}_e.init();var Vd,gy=[null,Rd,Ld,zd,Bd,Wd],i1=!1,Ud={__clock_gettime:Wg,__emscripten_init_main_thread_js:Vg,__emscripten_thread_cleanup:Ug,__pthread_create_js:Gg,_emscripten_default_pthread_stack_size:Hg,_emscripten_notify_thread_queue:jg,abort:qg,emscripten_check_blocking_allowed:Kg,emscripten_get_heap_max:Pd,emscripten_get_now:Os,emscripten_memcpy_big:Xg,emscripten_num_logical_cores:Yg,emscripten_receive_on_main_thread_js:Jg,emscripten_resize_heap:Qg,emscripten_set_canvas_element_size:iy,emscripten_unwind_to_js_event_loop:oy,emscripten_webgl_create_context:my,exit:Dd,fd_close:zd,fd_seek:Bd,fd_write:Wd,memory:Te||c.wasmMemory,setTempRet0:fy},o1=Ag(),yy=c.___wasm_call_ctors=function(){return(yy=c.___wasm_call_ctors=c.asm.__wasm_call_ctors).apply(null,arguments)},by=c._init=function(){return(by=c._init=c.asm.init).apply(null,arguments)},xy=c._init_with_threads_count=function(){return(xy=c._init_with_threads_count=c.asm.init_with_threads_count).apply(null,arguments)},vy=c._get_threads_count=function(){return(vy=c._get_threads_count=c.asm.get_threads_count).apply(null,arguments)},wy=c._register_tensor=function(){return(wy=c._register_tensor=c.asm.register_tensor).apply(null,arguments)},ky=c._dispose_data=function(){return(ky=c._dispose_data=c.asm.dispose_data).apply(null,arguments)},Iy=c._dispose=function(){return(Iy=c._dispose=c.asm.dispose).apply(null,arguments)},Sy=c._Abs=function(){return(Sy=c._Abs=c.asm.Abs).apply(null,arguments)},Ny=c._Add=function(){return(Ny=c._Add=c.asm.Add).apply(null,arguments)},Ty=c._AddN=function(){return(Ty=c._AddN=c.asm.AddN).apply(null,arguments)},Cy=c._All=function(){return(Cy=c._All=c.asm.All).apply(null,arguments)},_y=c._Any=function(){return(_y=c._Any=c.asm.Any).apply(null,arguments)},Ey=c._ArgMax=function(){return(Ey=c._ArgMax=c.asm.ArgMax).apply(null,arguments)},Ay=c._AvgPool=function(){return(Ay=c._AvgPool=c.asm.AvgPool).apply(null,arguments)},$y=c._BatchMatMul=function(){return($y=c._BatchMatMul=c.asm.BatchMatMul).apply(null,arguments)},Fy=c._Ceil=function(){return(Fy=c._Ceil=c.asm.Ceil).apply(null,arguments)},Dy=c._ClipByValue=function(){return(Dy=c._ClipByValue=c.asm.ClipByValue).apply(null,arguments)},Ry=c._Conv2D=function(){return(Ry=c._Conv2D=c.asm.Conv2D).apply(null,arguments)},My=c._Conv2DBackpropInput=function(){return(My=c._Conv2DBackpropInput=c.asm.Conv2DBackpropInput).apply(null,arguments)},Py=c._Cos=function(){return(Py=c._Cos=c.asm.Cos).apply(null,arguments)},Oy=c._Cosh=function(){return(Oy=c._Cosh=c.asm.Cosh).apply(null,arguments)},Ly=c._CropAndResize=function(){return(Ly=c._CropAndResize=c.asm.CropAndResize).apply(null,arguments)},zy=c._Cumprod=function(){return(zy=c._Cumprod=c.asm.Cumprod).apply(null,arguments)},By=c._Cumsum=function(){return(By=c._Cumsum=c.asm.Cumsum).apply(null,arguments)},Wy=c._DepthToSpace=function(){return(Wy=c._DepthToSpace=c.asm.DepthToSpace).apply(null,arguments)},Vy=c._DepthwiseConv2dNative=function(){return(Vy=c._DepthwiseConv2dNative=c.asm.DepthwiseConv2dNative).apply(null,arguments)},Uy=c._Elu=function(){return(Uy=c._Elu=c.asm.Elu).apply(null,arguments)},Gy=c._Equal=function(){return(Gy=c._Equal=c.asm.Equal).apply(null,arguments)},Hy=c._Exp=function(){return(Hy=c._Exp=c.asm.Exp).apply(null,arguments)},jy=c._FlipLeftRight=function(){return(jy=c._FlipLeftRight=c.asm.FlipLeftRight).apply(null,arguments)},Gd=c._Floor=function(){return(Gd=c._Floor=c.asm.Floor).apply(null,arguments)},Hd=c._FloorDiv=function(){return(Hd=c._FloorDiv=c.asm.FloorDiv).apply(null,arguments)},wp=c._FusedBatchNorm=function(){return(wp=c._FusedBatchNorm=c.asm.FusedBatchNorm).apply(null,arguments)},qy=c._FusedConv2D=function(){return(qy=c._FusedConv2D=c.asm.FusedConv2D).apply(null,arguments)},Ky=c._FusedDepthwiseConv2D=function(){return(Ky=c._FusedDepthwiseConv2D=c.asm.FusedDepthwiseConv2D).apply(null,arguments)},Ho=c._Gather=function(){return(Ho=c._Gather=c.asm.Gather).apply(null,arguments)},kp=c._GatherNd=function(){return(kp=c._GatherNd=c.asm.GatherNd).apply(null,arguments)},Ip=c._Greater=function(){return(Ip=c._Greater=c.asm.Greater).apply(null,arguments)},l1=c._GreaterEqual=function(){return(l1=c._GreaterEqual=c.asm.GreaterEqual).apply(null,arguments)},jo=c._LeakyRelu=function(){return(jo=c._LeakyRelu=c.asm.LeakyRelu).apply(null,arguments)},qo=c._Less=function(){return(qo=c._Less=c.asm.Less).apply(null,arguments)},Xy=c._LessEqual=function(){return(Xy=c._LessEqual=c.asm.LessEqual).apply(null,arguments)},G=c._Log=function(){return(G=c._Log=c.asm.Log).apply(null,arguments)},te=c._LogicalAnd=function(){return(te=c._LogicalAnd=c.asm.LogicalAnd).apply(null,arguments)},de=c._Max=function(){return(de=c._Max=c.asm.Max).apply(null,arguments)},Se=c._MaxPool=function(){return(Se=c._MaxPool=c.asm.MaxPool).apply(null,arguments)},Ze=c._Maximum=function(){return(Ze=c._Maximum=c.asm.Maximum).apply(null,arguments)},nt=c._Mean=function(){return(nt=c._Mean=c.asm.Mean).apply(null,arguments)},Ue=c._Min=function(){return(Ue=c._Min=c.asm.Min).apply(null,arguments)},We=c._Minimum=function(){return(We=c._Minimum=c.asm.Minimum).apply(null,arguments)},Lt=c._MirrorPad=function(){return(Lt=c._MirrorPad=c.asm.MirrorPad).apply(null,arguments)},Pa=c._Multiply=function(){return(Pa=c._Multiply=c.asm.Multiply).apply(null,arguments)},Oa=c._Neg=function(){return(Oa=c._Neg=c.asm.Neg).apply(null,arguments)},Ko=c._NonMaxSuppressionV3=function(){return(Ko=c._NonMaxSuppressionV3=c.asm.NonMaxSuppressionV3).apply(null,arguments)},zs=c._NonMaxSuppressionV4=function(){return(zs=c._NonMaxSuppressionV4=c.asm.NonMaxSuppressionV4).apply(null,arguments)},Yy=c._NonMaxSuppressionV5=function(){return(Yy=c._NonMaxSuppressionV5=c.asm.NonMaxSuppressionV5).apply(null,arguments)},Mn=c._NotEqual=function(){return(Mn=c._NotEqual=c.asm.NotEqual).apply(null,arguments)},Gr=c._OneHot=function(){return(Gr=c._OneHot=c.asm.OneHot).apply(null,arguments)},jd=c._PadV2=function(){return(jd=c._PadV2=c.asm.PadV2).apply(null,arguments)},OA=c._Pow=function(){return(OA=c._Pow=c.asm.Pow).apply(null,arguments)},LA=c._Prelu=function(){return(LA=c._Prelu=c.asm.Prelu).apply(null,arguments)},zA=c._Prod=function(){return(zA=c._Prod=c.asm.Prod).apply(null,arguments)},BA=c._RealDiv=function(){return(BA=c._RealDiv=c.asm.RealDiv).apply(null,arguments)},WA=c._Relu=function(){return(WA=c._Relu=c.asm.Relu).apply(null,arguments)},VA=c._Relu6=function(){return(VA=c._Relu6=c.asm.Relu6).apply(null,arguments)},UA=c._ResizeBilinear=function(){return(UA=c._ResizeBilinear=c.asm.ResizeBilinear).apply(null,arguments)},GA=c._Reverse=function(){return(GA=c._Reverse=c.asm.Reverse).apply(null,arguments)},HA=c._RotateWithOffset=function(){return(HA=c._RotateWithOffset=c.asm.RotateWithOffset).apply(null,arguments)},jA=c._Round=function(){return(jA=c._Round=c.asm.Round).apply(null,arguments)},qA=c._Rsqrt=function(){return(qA=c._Rsqrt=c.asm.Rsqrt).apply(null,arguments)},KA=c._ScatterNd=function(){return(KA=c._ScatterNd=c.asm.ScatterNd).apply(null,arguments)},XA=c._SelectV2=function(){return(XA=c._SelectV2=c.asm.SelectV2).apply(null,arguments)},YA=c._Sigmoid=function(){return(YA=c._Sigmoid=c.asm.Sigmoid).apply(null,arguments)},JA=c._Sin=function(){return(JA=c._Sin=c.asm.Sin).apply(null,arguments)},ZA=c._Softmax=function(){return(ZA=c._Softmax=c.asm.Softmax).apply(null,arguments)},QA=c._SparseFillEmptyRows=function(){return(QA=c._SparseFillEmptyRows=c.asm.SparseFillEmptyRows).apply(null,arguments)},e$=c._SparseReshape=function(){return(e$=c._SparseReshape=c.asm.SparseReshape).apply(null,arguments)},t$=c._SparseSegmentReduction=function(){return(t$=c._SparseSegmentReduction=c.asm.SparseSegmentReduction).apply(null,arguments)},n$=c._Sqrt=function(){return(n$=c._Sqrt=c.asm.Sqrt).apply(null,arguments)},a$=c._Square=function(){return(a$=c._Square=c.asm.Square).apply(null,arguments)},r$=c._SquaredDifference=function(){return(r$=c._SquaredDifference=c.asm.SquaredDifference).apply(null,arguments)},s$=c._Step=function(){return(s$=c._Step=c.asm.Step).apply(null,arguments)},i$=c._StridedSlice=function(){return(i$=c._StridedSlice=c.asm.StridedSlice).apply(null,arguments)},o$=c._Sub=function(){return(o$=c._Sub=c.asm.Sub).apply(null,arguments)},l$=c._Sum=function(){return(l$=c._Sum=c.asm.Sum).apply(null,arguments)},u$=c._Tan=function(){return(u$=c._Tan=c.asm.Tan).apply(null,arguments)},p$=c._Tanh=function(){return(p$=c._Tanh=c.asm.Tanh).apply(null,arguments)},c$=c._Tile=function(){return(c$=c._Tile=c.asm.Tile).apply(null,arguments)},d$=c._TopK=function(){return(d$=c._TopK=c.asm.TopK).apply(null,arguments)},h$=c._Transform=function(){return(h$=c._Transform=c.asm.Transform).apply(null,arguments)},m$=c._Transpose=function(){return(m$=c._Transpose=c.asm.Transpose).apply(null,arguments)},f$=c.__FusedMatMul=function(){return(f$=c.__FusedMatMul=c.asm._FusedMatMul).apply(null,arguments)},Jy=c._malloc=function(){return(Jy=c._malloc=c.asm.malloc).apply(null,arguments)},u1=c._free=function(){return(u1=c._free=c.asm.free).apply(null,arguments)},g$=c._emscripten_tls_init=function(){return(g$=c._emscripten_tls_init=c.asm.emscripten_tls_init).apply(null,arguments)},p1=c.___errno_location=function(){return(p1=c.___errno_location=c.asm.__errno_location).apply(null,arguments)},qd=c._pthread_self=function(){return(qd=c._pthread_self=c.asm.pthread_self).apply(null,arguments)},c1=c._emscripten_main_thread_process_queued_calls=function(){return(c1=c._emscripten_main_thread_process_queued_calls=c.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},y$=c.__emscripten_thread_crashed=function(){return(y$=c.__emscripten_thread_crashed=c.asm._emscripten_thread_crashed).apply(null,arguments)},d1=c.__emscripten_thread_init=function(){return(d1=c.__emscripten_thread_init=c.asm._emscripten_thread_init).apply(null,arguments)},b$=c._emscripten_current_thread_process_queued_calls=function(){return(b$=c._emscripten_current_thread_process_queued_calls=c.asm.emscripten_current_thread_process_queued_calls).apply(null,arguments)},x$=c._emscripten_main_browser_thread_id=function(){return(x$=c._emscripten_main_browser_thread_id=c.asm.emscripten_main_browser_thread_id).apply(null,arguments)},v$=c._emscripten_sync_run_in_main_thread_2=function(){return(v$=c._emscripten_sync_run_in_main_thread_2=c.asm.emscripten_sync_run_in_main_thread_2).apply(null,arguments)},h1=c._emscripten_sync_run_in_main_thread_4=function(){return(h1=c._emscripten_sync_run_in_main_thread_4=c.asm.emscripten_sync_run_in_main_thread_4).apply(null,arguments)},m1=c._emscripten_run_in_main_runtime_thread_js=function(){return(m1=c._emscripten_run_in_main_runtime_thread_js=c.asm.emscripten_run_in_main_runtime_thread_js).apply(null,arguments)},Zy=c._emscripten_dispatch_to_thread_=function(){return(Zy=c._emscripten_dispatch_to_thread_=c.asm.emscripten_dispatch_to_thread_).apply(null,arguments)},Qy=c.__emscripten_thread_free_data=function(){return(Qy=c.__emscripten_thread_free_data=c.asm._emscripten_thread_free_data).apply(null,arguments)},w$=c.__emscripten_thread_exit=function(){return(w$=c.__emscripten_thread_exit=c.asm._emscripten_thread_exit).apply(null,arguments)},k$=c._memalign=function(){return(k$=c._memalign=c.asm.memalign).apply(null,arguments)},f1=c._emscripten_stack_set_limits=function(){return(f1=c._emscripten_stack_set_limits=c.asm.emscripten_stack_set_limits).apply(null,arguments)},eb=c.stackSave=function(){return(eb=c.stackSave=c.asm.stackSave).apply(null,arguments)},Kd=c.stackRestore=function(){return(Kd=c.stackRestore=c.asm.stackRestore).apply(null,arguments)},Xo=c.stackAlloc=function(){return(Xo=c.stackAlloc=c.asm.stackAlloc).apply(null,arguments)},I$=c.dynCall_iijjiiii=function(){return(I$=c.dynCall_iijjiiii=c.asm.dynCall_iijjiiii).apply(null,arguments)},S$=c.dynCall_jiji=function(){return(S$=c.dynCall_jiji=c.asm.dynCall_jiji).apply(null,arguments)},g1=c.__emscripten_allow_main_runtime_queued_calls=21464;c.cwrap=Jt,c.keepRuntimeAlive=Ms,c.PThread=_e,c.PThread=_e,c.wasmMemory=Te,c.ExitStatus=Sp;var Xd;function Sp(N){this.name="ExitStatus",this.message="Program terminated with exit("+N+")",this.status=N}Ma=function N(){Xd||tb(),Xd||(Ma=N)};function tb(N){if(N=N||y,Ur>0)return;if(C){h(c),fp(),postMessage({cmd:"loaded"});return}if(xn(),Ur>0)return;function D(){Xd||(Xd=!0,c.calledRun=!0,!ct&&(fp(),h(c),c.onRuntimeInitialized&&c.onRuntimeInitialized(),_g()))}c.setStatus?(c.setStatus("Running..."),setTimeout(function(){setTimeout(function(){c.setStatus("")},1),D()},1)):D()}c.run=tb;function N$(N,D){if(yn=N,!D&&C)throw Rd(N),"unwind";Ms()||Cg(),T$(N)}function T$(N){yn=N,Ms()||(_e.terminateAllThreads(),c.onExit&&c.onExit(N),ct=!0),x(N,new Sp(N))}if(c.preInit)for(typeof c.preInit=="function"&&(c.preInit=[c.preInit]);c.preInit.length>0;)c.preInit.pop()();tb();var Yd;f&&(Yd={uncaughtException:process.listeners("uncaughtException").filter(function(N){return!f.uncaughtException.indexOf(N)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(N){return!f.unhandledRejection.indexOf(N)>-1})});var Jd;if(typeof WasmBackendModule!="undefined")Jd=WasmBackendModule;else if(typeof r!="undefined")Jd=r;else throw new Error("Could not find wasm module in post.js");if(Yd){var C$=Jd._dispose;Jd._dispose=function(){C$(),Yd.uncaughtException.forEach(function(N){process.removeListener("uncaughtException",N)}),Yd.unhandledRejection.forEach(function(N){process.removeListener("unhandledRejection",N)})}}return r.ready}})();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}),rF=ft((e,t)=>{var n=(()=>{var a=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(a=a||__filename),function(r){r=r||{};var s=typeof r!="undefined"?r:{},i,o;s.ready=new Promise(function(G,te){i=G,o=te});var l;typeof process!="undefined"&&process.listeners&&(l={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var u=Object.assign({},s),p=[],d="./this.program",c=(G,te)=>{throw te},h=typeof window=="object",m=typeof importScripts=="function",f=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g="";function y(G){return s.locateFile?s.locateFile(G,g):g+G}var b,x,v,w;function T(G){G instanceof kp||F("exiting due to exception: "+G)}var C,E,$;f?(m?g=wh().dirname(g)+"/":g=__dirname+"/",$=()=>{E||(C=yx(),E=wh())},b=function(G,te){return $(),G=E.normalize(G),C.readFileSync(G,te?void 0:"utf8")},v=G=>{var te=b(G,!0);return te.buffer||(te=new Uint8Array(te)),te},x=(G,te,de)=>{$(),G=E.normalize(G),C.readFile(G,function(Se,Ze){Se?de(Se):te(Ze.buffer)})},process.argv.length>1&&(d=process.argv[1].replace(/\\/g,"/")),p=process.argv.slice(2),process.on("uncaughtException",function(G){if(!(G instanceof kp))throw G}),process.on("unhandledRejection",function(G){throw G}),c=(G,te)=>{if(mp())throw process.exitCode=G,te;T(te),process.exit(G)},s.inspect=function(){return"[Emscripten Module object]"}):(h||m)&&(m?g=self.location.href:typeof document!="undefined"&&document.currentScript&&(g=document.currentScript.src),a&&(g=a),g.indexOf("blob:")!==0?g=g.substr(0,g.replace(/[?#].*/,"").lastIndexOf("/")+1):g="",b=G=>{var te=new XMLHttpRequest;return te.open("GET",G,!1),te.send(null),te.responseText},m&&(v=G=>{var te=new XMLHttpRequest;return te.open("GET",G,!1),te.responseType="arraybuffer",te.send(null),new Uint8Array(te.response)}),x=(G,te,de)=>{var Se=new XMLHttpRequest;Se.open("GET",G,!0),Se.responseType="arraybuffer",Se.onload=()=>{if(Se.status==200||Se.status==0&&Se.response){te(Se.response);return}de()},Se.onerror=de,Se.send(null)},w=G=>document.title=G);var P=s.print||console.log.bind(console),F=s.printErr||console.warn.bind(console);Object.assign(s,u),u=null,s.arguments&&(p=s.arguments),s.thisProgram&&(d=s.thisProgram),s.quit&&(c=s.quit);var S=4;function M(G){M.shown||(M.shown={}),M.shown[G]||(M.shown[G]=1,F(G))}function V(G,te){if(typeof WebAssembly.Function=="function"){for(var de={i:"i32",j:"i64",f:"f32",d:"f64"},Se={parameters:[],results:te[0]=="v"?[]:[de[te[0]]]},Ze=1;Ze<te.length;++Ze)Se.parameters.push(de[te[Ze]]);return new WebAssembly.Function(Se,G)}var nt=[1,0,1,96],Ue=te.slice(0,1),We=te.slice(1),Lt={i:127,j:126,f:125,d:124};nt.push(We.length);for(var Ze=0;Ze<We.length;++Ze)nt.push(Lt[We[Ze]]);Ue=="v"?nt.push(0):nt=nt.concat([1,Lt[Ue]]),nt[1]=nt.length-2;var Pa=new Uint8Array([0,97,115,109,1,0,0,0].concat(nt,[2,7,1,1,101,1,102,0,0,7,5,1,1,102,0,0])),Oa=new WebAssembly.Module(Pa),Ko=new WebAssembly.Instance(Oa,{e:{f:G}}),zs=Ko.exports.f;return zs}var j=[],q;function K(){if(j.length)return j.pop();try{Wr.grow(1)}catch(G){throw G instanceof RangeError?"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.":G}return Wr.length-1}function Q(G,te){for(var de=G;de<G+te;de++){var Se=yp(de);Se&&q.set(Se,de)}}var ee=0,re=G=>{ee=G},Z;s.wasmBinary&&(Z=s.wasmBinary);var ie=s.noExitRuntime||!0;typeof WebAssembly!="object"&&Rs("no native wasm support detected");var ae,le=!1,ue;function ke(G,te){G||Rs(te)}function ye(G){var te=s["_"+G];return te}function Ie(G,te,de,Se,Ze){var nt={string:function(Mn){var Gr=0;if(Mn!=null&&Mn!==0){var jd=(Mn.length<<2)+1;Gr=wp(jd),tt(Mn,Gr,jd)}return Gr},array:function(Mn){var Gr=wp(Mn.length);return ct(Mn,Gr),Gr}};function Ue(Mn){return te==="string"?st(Mn):te==="boolean"?Boolean(Mn):Mn}var We=ye(G),Lt=[],Pa=0;if(Se)for(var Oa=0;Oa<Se.length;Oa++){var Ko=nt[de[Oa]];Ko?(Pa===0&&(Pa=Gd()),Lt[Oa]=Ko(Se[Oa])):Lt[Oa]=Se[Oa]}var zs=We.apply(null,Lt);function Yy(Mn){return Pa!==0&&Hd(Pa),Ue(Mn)}return zs=Yy(zs),zs}function Ee(G,te,de,Se){de=de||[];var Ze=de.every(function(Ue){return Ue==="number"}),nt=te!=="string";return nt&&Ze&&!Se?ye(G):function(){return Ie(G,te,de,arguments,Se)}}var Fe=1,Be=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function je(G,te,de){for(var Se=te+de,Ze=te;G[Ze]&&!(Ze>=Se);)++Ze;if(Ze-te>16&&G.subarray&&Be)return Be.decode(G.subarray(te,Ze));for(var nt="";te<Ze;){var Ue=G[te++];if(!(Ue&128)){nt+=String.fromCharCode(Ue);continue}var We=G[te++]&63;if((Ue&224)==192){nt+=String.fromCharCode((Ue&31)<<6|We);continue}var Lt=G[te++]&63;if((Ue&240)==224?Ue=(Ue&15)<<12|We<<6|Lt:Ue=(Ue&7)<<18|We<<12|Lt<<6|G[te++]&63,Ue<65536)nt+=String.fromCharCode(Ue);else{var Pa=Ue-65536;nt+=String.fromCharCode(55296|Pa>>10,56320|Pa&1023)}}return nt}function st(G,te){return G?je(Jt,G,te):""}function et(G,te,de,Se){if(!(Se>0))return 0;for(var Ze=de,nt=de+Se-1,Ue=0;Ue<G.length;++Ue){var We=G.charCodeAt(Ue);if(We>=55296&&We<=57343){var Lt=G.charCodeAt(++Ue);We=65536+((We&1023)<<10)|Lt&1023}if(We<=127){if(de>=nt)break;te[de++]=We}else if(We<=2047){if(de+1>=nt)break;te[de++]=192|We>>6,te[de++]=128|We&63}else if(We<=65535){if(de+2>=nt)break;te[de++]=224|We>>12,te[de++]=128|We>>6&63,te[de++]=128|We&63}else{if(de+3>=nt)break;te[de++]=240|We>>18,te[de++]=128|We>>12&63,te[de++]=128|We>>6&63,te[de++]=128|We&63}}return te[de]=0,de-Ze}function tt(G,te,de){return et(G,Jt,te,de)}function Te(G){for(var te=0,de=0;de<G.length;++de){var Se=G.charCodeAt(de);Se>=55296&&Se<=57343&&(Se=65536+((Se&1023)<<10)|G.charCodeAt(++de)&1023),Se<=127?++te:Se<=2047?te+=2:Se<=65535?te+=3:te+=4}return te}var gt=typeof TextDecoder!="undefined"?new TextDecoder("utf-16le"):void 0;function ct(G,te){Ut.set(G,te)}function yn(G,te,de){for(var Se=0;Se<G.length;++Se)Ut[te++>>0]=G.charCodeAt(Se);de||(Ut[te>>0]=0)}function Yt(G,te){return G%te>0&&(G+=te-G%te),G}var Dn,Ut,Jt,Da,Rn,Gt,sa,ia,Br;function Ds(G){Dn=G,s.HEAP8=Ut=new Int8Array(G),s.HEAP16=Da=new Int16Array(G),s.HEAP32=Gt=new Int32Array(G),s.HEAPU8=Jt=new Uint8Array(G),s.HEAPU16=Rn=new Uint16Array(G),s.HEAPU32=sa=new Uint32Array(G),s.HEAPF32=ia=new Float32Array(G),s.HEAPF64=Br=new Float64Array(G)}var bd=s.INITIAL_MEMORY||16777216,Wr,xr=[],hp=[],Lo=[],bn=!1,xd=!1,vd=0;function mp(){return ie||vd>0}function wd(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Sd(s.preRun.shift());gp(xr)}function kd(){bn=!0,gp(hp)}function J0(){xd=!0}function Id(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)Nd(s.postRun.shift());gp(Lo)}function Sd(G){xr.unshift(G)}function Ra(G){hp.unshift(G)}function Nd(G){Lo.unshift(G)}var oa=0,zo=null,Vr=null;function Ng(G){oa++,s.monitorRunDependencies&&s.monitorRunDependencies(oa)}function Td(G){if(oa--,s.monitorRunDependencies&&s.monitorRunDependencies(oa),oa==0&&(zo!==null&&(clearInterval(zo),zo=null),Vr)){var te=Vr;Vr=null,te()}}s.preloadedImages={},s.preloadedAudios={};function Rs(G){s.onAbort&&s.onAbort(G),G="Aborted("+G+")",F(G),le=!0,ue=1,G+=". Build with -s ASSERTIONS=1 for more info.";var te=new WebAssembly.RuntimeError(G);throw o(te),te}var Tg="data:application/octet-stream;base64,";function Cd(G){return G.startsWith(Tg)}function Ms(G){return G.startsWith("file://")}var xn;xn="tfjs-backend-wasm.wasm",Cd(xn)||(xn=y(xn));function fp(G){try{if(G==xn&&Z)return new Uint8Array(Z);if(v)return v(G);throw"both async and sync fetching of the wasm failed"}catch(te){Rs(te)}}function Cg(){if(!Z&&(h||m)){if(typeof fetch=="function"&&!Ms(xn))return fetch(xn,{credentials:"same-origin"}).then(function(G){if(!G.ok)throw"failed to load wasm binary file at '"+xn+"'";return G.arrayBuffer()}).catch(function(){return fp(xn)});if(x)return new Promise(function(G,te){x(xn,function(de){G(new Uint8Array(de))},te)})}return Promise.resolve().then(function(){return fp(xn)})}function _g(){var G={env:Vo,wasi_snapshot_preview1:Vo};function te(Ue,We){var Lt=Ue.exports;s.asm=Lt,ae=s.asm.memory,Ds(ae.buffer),Wr=s.asm.__indirect_function_table,Ra(s.asm.__wasm_call_ctors),Td("wasm-instantiate")}Ng("wasm-instantiate");function de(Ue){te(Ue.instance)}function Se(Ue){return Cg().then(function(We){return WebAssembly.instantiate(We,G)}).then(function(We){return We}).then(Ue,function(We){F("failed to asynchronously prepare wasm: "+We),Rs(We)})}function Ze(){return!Z&&typeof WebAssembly.instantiateStreaming=="function"&&!Cd(xn)&&!Ms(xn)&&typeof fetch=="function"?fetch(xn,{credentials:"same-origin"}).then(function(Ue){var We=WebAssembly.instantiateStreaming(Ue,G);return We.then(de,function(Lt){return F("wasm streaming compile failed: "+Lt),F("falling back to ArrayBuffer instantiation"),Se(de)})}):Se(de)}if(s.instantiateWasm)try{var nt=s.instantiateWasm(G,te);return nt}catch(Ue){return F("Module.instantiateWasm callback failed with error: "+Ue),!1}return Ze().catch(o),{}}var Z0,Q0;function gp(G){for(;G.length>0;){var te=G.shift();if(typeof te=="function"){te(s);continue}var de=te.func;typeof de=="number"?te.arg===void 0?yp(de)():yp(de)(te.arg):de(te.arg===void 0?null:te.arg)}}function Ur(G){return G}function _d(G){var te=/\b_Z[\w\d_]+/g;return G.replace(te,function(de){var Se=de;return de===Se?de:Se+" ["+de+"]"})}var Ma=[];function yp(G){var te=Ma[G];return te||(G>=Ma.length&&(Ma.length=G+1),Ma[G]=te=Wr.get(G)),te}function e1(){var G=new Error;if(!G.stack){try{throw new Error}catch(te){G=te}if(!G.stack)return"(no stack trace available)"}return G.stack.toString()}function Bo(G,te){Wr.set(G,te),Ma[G]=te}function Eg(){Rs("")}function bp(){return 2147483648}function Ed(G,te,de){Jt.copyWithin(G,te,te+de)}function vn(G){try{return ae.grow(G-Dn.byteLength+65535>>>16),Ds(ae.buffer),1}catch(te){}}function Ad(G){var te=Jt.length;G=G>>>0;var de=bp();if(G>de)return!1;for(var Se=1;Se<=4;Se*=2){var Ze=te*(1+.2/Se);Ze=Math.min(Ze,G+100663296);var nt=Math.min(de,Yt(Math.max(G,Ze),65536)),Ue=vn(nt);if(Ue)return!0}return!1}var Wo={mappings:{},buffers:[null,[],[]],printChar:function(G,te){var de=Wo.buffers[G];te===0||te===10?((G===1?P:F)(je(de,0)),de.length=0):de.push(te)},varargs:void 0,get:function(){Wo.varargs+=4;var G=Gt[Wo.varargs-4>>2];return G},getStr:function(G){var te=st(G);return te},get64:function(G,te){return G}};function Ag(G){return 0}function t1(G,te,de,Se,Ze){}function n1(G,te,de,Se){for(var Ze=0,nt=0;nt<de;nt++){var Ue=Gt[te>>2],We=Gt[te+4>>2];te+=8;for(var Lt=0;Lt<We;Lt++)Wo.printChar(G,Jt[Ue+Lt]);Ze+=We}return Gt[Se>>2]=Ze,0}function $g(G){re(G)}var $d=!1,Vo={abort:Eg,emscripten_get_heap_max:bp,emscripten_memcpy_big:Ed,emscripten_resize_heap:Ad,fd_close:Ag,fd_seek:t1,fd_write:n1,setTempRet0:$g},PA=_g(),a1=s.___wasm_call_ctors=function(){return(a1=s.___wasm_call_ctors=s.asm.__wasm_call_ctors).apply(null,arguments)},Fg=s._init=function(){return(Fg=s._init=s.asm.init).apply(null,arguments)},Dg=s._init_with_threads_count=function(){return(Dg=s._init_with_threads_count=s.asm.init_with_threads_count).apply(null,arguments)},Fd=s._get_threads_count=function(){return(Fd=s._get_threads_count=s.asm.get_threads_count).apply(null,arguments)},Dd=s._register_tensor=function(){return(Dd=s._register_tensor=s.asm.register_tensor).apply(null,arguments)},Rg=s._dispose_data=function(){return(Rg=s._dispose_data=s.asm.dispose_data).apply(null,arguments)},_e=s._dispose=function(){return(_e=s._dispose=s.asm.dispose).apply(null,arguments)},Mg=s._Abs=function(){return(Mg=s._Abs=s.asm.Abs).apply(null,arguments)},Rd=s._Add=function(){return(Rd=s._Add=s.asm.Add).apply(null,arguments)},Ps=s._AddN=function(){return(Ps=s._AddN=s.asm.AddN).apply(null,arguments)},Uo=s._All=function(){return(Uo=s._All=s.asm.All).apply(null,arguments)},Pg=s._Any=function(){return(Pg=s._Any=s.asm.Any).apply(null,arguments)},r1=s._ArgMax=function(){return(r1=s._ArgMax=s.asm.ArgMax).apply(null,arguments)},Og=s._AvgPool=function(){return(Og=s._AvgPool=s.asm.AvgPool).apply(null,arguments)},s1=s._BatchMatMul=function(){return(s1=s._BatchMatMul=s.asm.BatchMatMul).apply(null,arguments)},Os=s._Ceil=function(){return(Os=s._Ceil=s.asm.Ceil).apply(null,arguments)},Lg=s._ClipByValue=function(){return(Lg=s._ClipByValue=s.asm.ClipByValue).apply(null,arguments)},zg=s._Conv2D=function(){return(zg=s._Conv2D=s.asm.Conv2D).apply(null,arguments)},Bg=s._Conv2DBackpropInput=function(){return(Bg=s._Conv2DBackpropInput=s.asm.Conv2DBackpropInput).apply(null,arguments)},Wg=s._Cos=function(){return(Wg=s._Cos=s.asm.Cos).apply(null,arguments)},Vg=s._Cosh=function(){return(Vg=s._Cosh=s.asm.Cosh).apply(null,arguments)},Ug=s._CropAndResize=function(){return(Ug=s._CropAndResize=s.asm.CropAndResize).apply(null,arguments)},Md=s._Cumprod=function(){return(Md=s._Cumprod=s.asm.Cumprod).apply(null,arguments)},Gg=s._Cumsum=function(){return(Gg=s._Cumsum=s.asm.Cumsum).apply(null,arguments)},Hg=s._DepthToSpace=function(){return(Hg=s._DepthToSpace=s.asm.DepthToSpace).apply(null,arguments)},jg=s._DepthwiseConv2dNative=function(){return(jg=s._DepthwiseConv2dNative=s.asm.DepthwiseConv2dNative).apply(null,arguments)},qg=s._Elu=function(){return(qg=s._Elu=s.asm.Elu).apply(null,arguments)},Kg=s._Equal=function(){return(Kg=s._Equal=s.asm.Equal).apply(null,arguments)},Pd=s._Exp=function(){return(Pd=s._Exp=s.asm.Exp).apply(null,arguments)},Xg=s._FlipLeftRight=function(){return(Xg=s._FlipLeftRight=s.asm.FlipLeftRight).apply(null,arguments)},Yg=s._Floor=function(){return(Yg=s._Floor=s.asm.Floor).apply(null,arguments)},Ls=s._FloorDiv=function(){return(Ls=s._FloorDiv=s.asm.FloorDiv).apply(null,arguments)},xp=s._FusedBatchNorm=function(){return(xp=s._FusedBatchNorm=s.asm.FusedBatchNorm).apply(null,arguments)},Jg=s._FusedConv2D=function(){return(Jg=s._FusedConv2D=s.asm.FusedConv2D).apply(null,arguments)},Zg=s._FusedDepthwiseConv2D=function(){return(Zg=s._FusedDepthwiseConv2D=s.asm.FusedDepthwiseConv2D).apply(null,arguments)},Qg=s._Gather=function(){return(Qg=s._Gather=s.asm.Gather).apply(null,arguments)},Ve=s._GatherNd=function(){return(Ve=s._GatherNd=s.asm.GatherNd).apply(null,arguments)},ey=s._Greater=function(){return(ey=s._Greater=s.asm.Greater).apply(null,arguments)},ty=s._GreaterEqual=function(){return(ty=s._GreaterEqual=s.asm.GreaterEqual).apply(null,arguments)},ny=s._LeakyRelu=function(){return(ny=s._LeakyRelu=s.asm.LeakyRelu).apply(null,arguments)},ay=s._Less=function(){return(ay=s._Less=s.asm.Less).apply(null,arguments)},ry=s._LessEqual=function(){return(ry=s._LessEqual=s.asm.LessEqual).apply(null,arguments)},sy=s._Log=function(){return(sy=s._Log=s.asm.Log).apply(null,arguments)},vp=s._LogicalAnd=function(){return(vp=s._LogicalAnd=s.asm.LogicalAnd).apply(null,arguments)},Od=s._Max=function(){return(Od=s._Max=s.asm.Max).apply(null,arguments)},Ld=s._MaxPool=function(){return(Ld=s._MaxPool=s.asm.MaxPool).apply(null,arguments)},iy=s._Maximum=function(){return(iy=s._Maximum=s.asm.Maximum).apply(null,arguments)},oy=s._Mean=function(){return(oy=s._Mean=s.asm.Mean).apply(null,arguments)},ly=s._Min=function(){return(ly=s._Min=s.asm.Min).apply(null,arguments)},uy=s._Minimum=function(){return(uy=s._Minimum=s.asm.Minimum).apply(null,arguments)},py=s._MirrorPad=function(){return(py=s._MirrorPad=s.asm.MirrorPad).apply(null,arguments)},cy=s._Multiply=function(){return(cy=s._Multiply=s.asm.Multiply).apply(null,arguments)},Ct=s._Neg=function(){return(Ct=s._Neg=s.asm.Neg).apply(null,arguments)},dy=s._NonMaxSuppressionV3=function(){return(dy=s._NonMaxSuppressionV3=s.asm.NonMaxSuppressionV3).apply(null,arguments)},hy=s._NonMaxSuppressionV4=function(){return(hy=s._NonMaxSuppressionV4=s.asm.NonMaxSuppressionV4).apply(null,arguments)},my=s._NonMaxSuppressionV5=function(){return(my=s._NonMaxSuppressionV5=s.asm.NonMaxSuppressionV5).apply(null,arguments)},Go=s._NotEqual=function(){return(Go=s._NotEqual=s.asm.NotEqual).apply(null,arguments)},zd=s._OneHot=function(){return(zd=s._OneHot=s.asm.OneHot).apply(null,arguments)},Bd=s._PadV2=function(){return(Bd=s._PadV2=s.asm.PadV2).apply(null,arguments)},Wd=s._Pow=function(){return(Wd=s._Pow=s.asm.Pow).apply(null,arguments)},fy=s._Prelu=function(){return(fy=s._Prelu=s.asm.Prelu).apply(null,arguments)},Vd=s._Prod=function(){return(Vd=s._Prod=s.asm.Prod).apply(null,arguments)},gy=s._RealDiv=function(){return(gy=s._RealDiv=s.asm.RealDiv).apply(null,arguments)},i1=s._Relu=function(){return(i1=s._Relu=s.asm.Relu).apply(null,arguments)},Ud=s._Relu6=function(){return(Ud=s._Relu6=s.asm.Relu6).apply(null,arguments)},o1=s._ResizeBilinear=function(){return(o1=s._ResizeBilinear=s.asm.ResizeBilinear).apply(null,arguments)},yy=s._Reverse=function(){return(yy=s._Reverse=s.asm.Reverse).apply(null,arguments)},by=s._RotateWithOffset=function(){return(by=s._RotateWithOffset=s.asm.RotateWithOffset).apply(null,arguments)},xy=s._Round=function(){return(xy=s._Round=s.asm.Round).apply(null,arguments)},vy=s._Rsqrt=function(){return(vy=s._Rsqrt=s.asm.Rsqrt).apply(null,arguments)},wy=s._ScatterNd=function(){return(wy=s._ScatterNd=s.asm.ScatterNd).apply(null,arguments)},ky=s._SelectV2=function(){return(ky=s._SelectV2=s.asm.SelectV2).apply(null,arguments)},Iy=s._Sigmoid=function(){return(Iy=s._Sigmoid=s.asm.Sigmoid).apply(null,arguments)},Sy=s._Sin=function(){return(Sy=s._Sin=s.asm.Sin).apply(null,arguments)},Ny=s._Softmax=function(){return(Ny=s._Softmax=s.asm.Softmax).apply(null,arguments)},Ty=s._SparseFillEmptyRows=function(){return(Ty=s._SparseFillEmptyRows=s.asm.SparseFillEmptyRows).apply(null,arguments)},Cy=s._SparseReshape=function(){return(Cy=s._SparseReshape=s.asm.SparseReshape).apply(null,arguments)},_y=s._SparseSegmentReduction=function(){return(_y=s._SparseSegmentReduction=s.asm.SparseSegmentReduction).apply(null,arguments)},Ey=s._Sqrt=function(){return(Ey=s._Sqrt=s.asm.Sqrt).apply(null,arguments)},Ay=s._Square=function(){return(Ay=s._Square=s.asm.Square).apply(null,arguments)},$y=s._SquaredDifference=function(){return($y=s._SquaredDifference=s.asm.SquaredDifference).apply(null,arguments)},Fy=s._Step=function(){return(Fy=s._Step=s.asm.Step).apply(null,arguments)},Dy=s._StridedSlice=function(){return(Dy=s._StridedSlice=s.asm.StridedSlice).apply(null,arguments)},Ry=s._Sub=function(){return(Ry=s._Sub=s.asm.Sub).apply(null,arguments)},My=s._Sum=function(){return(My=s._Sum=s.asm.Sum).apply(null,arguments)},Py=s._Tan=function(){return(Py=s._Tan=s.asm.Tan).apply(null,arguments)},Oy=s._Tanh=function(){return(Oy=s._Tanh=s.asm.Tanh).apply(null,arguments)},Ly=s._Tile=function(){return(Ly=s._Tile=s.asm.Tile).apply(null,arguments)},zy=s._TopK=function(){return(zy=s._TopK=s.asm.TopK).apply(null,arguments)},By=s._Transform=function(){return(By=s._Transform=s.asm.Transform).apply(null,arguments)},Wy=s._Transpose=function(){return(Wy=s._Transpose=s.asm.Transpose).apply(null,arguments)},Vy=s.__FusedMatMul=function(){return(Vy=s.__FusedMatMul=s.asm._FusedMatMul).apply(null,arguments)},Uy=s._malloc=function(){return(Uy=s._malloc=s.asm.malloc).apply(null,arguments)},Gy=s._free=function(){return(Gy=s._free=s.asm.free).apply(null,arguments)},Hy=s.___errno_location=function(){return(Hy=s.___errno_location=s.asm.__errno_location).apply(null,arguments)},jy=s._emscripten_main_thread_process_queued_calls=function(){return(jy=s._emscripten_main_thread_process_queued_calls=s.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},Gd=s.stackSave=function(){return(Gd=s.stackSave=s.asm.stackSave).apply(null,arguments)},Hd=s.stackRestore=function(){return(Hd=s.stackRestore=s.asm.stackRestore).apply(null,arguments)},wp=s.stackAlloc=function(){return(wp=s.stackAlloc=s.asm.stackAlloc).apply(null,arguments)},qy=s.dynCall_iijjiiii=function(){return(qy=s.dynCall_iijjiiii=s.asm.dynCall_iijjiiii).apply(null,arguments)},Ky=s.dynCall_jiji=function(){return(Ky=s.dynCall_jiji=s.asm.dynCall_jiji).apply(null,arguments)};s.cwrap=Ee;var Ho;function kp(G){this.name="ExitStatus",this.message="Program terminated with exit("+G+")",this.status=G}Vr=function G(){Ho||Ip(),Ho||(Vr=G)};function Ip(G){if(G=G||p,oa>0||(wd(),oa>0))return;function te(){Ho||(Ho=!0,s.calledRun=!0,!le&&(kd(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Id()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),te()},1)):te()}s.run=Ip;function l1(G){ue=G,mp()||(s.onExit&&s.onExit(G),le=!0),c(G,new kp(G))}if(s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();Ip();var jo;l&&(jo={uncaughtException:process.listeners("uncaughtException").filter(function(G){return!l.uncaughtException.indexOf(G)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(G){return!l.unhandledRejection.indexOf(G)>-1})});var qo;if(typeof r!="undefined")qo=r;else if(typeof WasmBackendModuleThreadedSimd!="undefined")qo=WasmBackendModuleThreadedSimd;else throw new Error("Could not find wasm module in post.js");if(jo){var Xy=qo._dispose;qo._dispose=function(){Xy(),jo.uncaughtException.forEach(function(G){process.removeListener("uncaughtException",G)}),jo.unhandledRejection.forEach(function(G){process.removeListener("unhandledRejection",G)})}}return 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)}),Zh=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}},oc=class{refCount(e){return ua("refCount")}incRef(e){return ua("incRef")}timerAvailable(){return!0}time(e){return ua("time")}read(e){return ua("read")}readSync(e){return ua("readSync")}readToGPU(e,t){return ua("readToGPU")}numDataIds(){return ua("numDataIds")}disposeData(e,t){return ua("disposeData")}write(e,t,n){return ua("write")}move(e,t,n,a,r){return ua("move")}memory(){return ua("memory")}floatPrecision(){return ua("floatPrecision")}epsilon(){return this.floatPrecision()===32?1e-7:1e-4}dispose(){return ua("dispose")}};function ua(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 Yk(e){let t=e.length,n=0;for(;t>0;)n=Math.random()*t|0,t--,kh(e,t,n)}function sF(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,a=0;for(;n>0;)a=Math.random()*n|0,n--,kh(e,n,a),kh(t,n,a)}function Gp(e,t,n){return Math.max(e,Math.min(t,n))}function iF(e){return e%2===0?e:e+1}function kh(e,t,n){let a=e[t];e[t]=e[n],e[n]=a}function oF(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function lF(e,t){let n=Math.random();return t*n+(1-n)*e}function uF(e,t){let n=0;for(let a=0;a<e.length;a++){let r=Number(e[a])-Number(t[a]);n+=r*r}return n}function R(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function Nn(e,t,n=""){R(cs(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function bi(e){R(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function ei(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||hn(e)&&!n)for(let a=0;a<e.length;++a)ei(e[a],t,n);else t.push(e);return t}function xt(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 pF(e){return e.length===0}function cs(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 ll(e){return e%1===0}function cF(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 dF(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function hF(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return Yk(t),t}function Bp(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function mF(e,t=a=>0,n){return new Promise((a,r)=>{let s=0,i=()=>{if(e()){a();return}s++;let o=t(s);if(n!=null&&s>=n){r();return}setTimeout(i,o)};i()})}function fF(e,t){let n=1,a=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)n*=e[s];else if(e[s]===-1){if(a!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${a} and dim ${s}`);a=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(a===-1){if(t>0&&t!==n)throw Error(`Size(${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} / ${n}`);let r=e.slice();return r[a]=t/n,r}function Ca(e,t){let n=t.length;return e=e==null?t.map((a,r)=>r):[].concat(e),R(e.every(a=>a>=-n&&a<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),R(e.every(a=>ll(a)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(a=>a<0?n+a:a)}function Jk(e,t){let n=[],a=[],r=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||r?null:Ca(t,e).sort(),i=0;for(let o=0;o<e.length;++o){if(s!=null){if(s[i]===o&&e[o]!==1)throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);(s[i]==null||s[i]>o)&&e[o]===1&&(n.push(e[o]),a.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),a.push(o))}return{newShape:n,keptDims:a}}function Zk(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 Qk(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 eI(e,t){for(let n=0;n<e.length;n++){let a=e[n];if(isNaN(a)||!isFinite(a))throw Error(`A tensor of type ${t} being uploaded contains ${a}.`)}}function tI(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function gF(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function hn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray}function yb(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function nI(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Kr(e){return typeof e=="string"||e instanceof String}function aI(e){return typeof e=="boolean"}function rI(e){return typeof e=="number"}function Qh(e){return Array.isArray(e)?Qh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray?"int32":rI(e)?"float32":Kr(e)?"string":aI(e)?"bool":"float32"}function es(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Ih(e,t){for(let n=t;n<e;++n)if(e%n===0)return n;return e}function kl(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let a=t-3;a>=0;--a)n[a]=n[a+1]*e[a+1];return n}function sI(e,t,n,a=!1){let r=new Array;if(t.length===1){let s=t[0]*(a?2:1);for(let i=0;i<s;i++)r[i]=n[e+i]}else{let s=t[0],i=t.slice(1),o=i.reduce((l,u)=>l*u)*(a?2:1);for(let l=0;l<s;l++)r[l]=sI(e+l*o,i,n,a)}return r}function al(e,t,n=!1){if(e.length===0)return t[0];let a=e.reduce((r,s)=>r*s)*(n?2:1);if(a===0)return[];if(a!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return sI(0,e,t,n)}function bx(e,t){let n=em(e,t);for(let a=0;a<n.length;a++)n[a]=1;return n}function em(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 yF(e,t){let n=e.reduce((a,r)=>a*r,1);if(t==null||t==="float32")return al(e,new Float32Array(n));if(t==="int32")return al(e,new Int32Array(n));if(t==="bool")return al(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function xx(e){e.forEach(t=>{R(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function bF(e,t,n){if(t===0)return 0;if(t===1)return e[0];let a=e[e.length-1];for(let r=0;r<e.length-1;++r)a+=n[r]*e[r];return a}function xF(e,t,n){if(t===0)return[];if(t===1)return[e];let a=new Array(t);for(let r=0;r<a.length-1;++r)a[r]=Math.floor(e/n[r]),e-=a[r]*n[r];return a[a.length-1]=e,a}function vx(e){return e&&e.then&&typeof e.then=="function"}var b1="tfjsflags",iI=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=vF,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&(X().getBool("IS_TEST")||X().getBool("PROD")||console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let a=this.urlFlags[e];X().getBool("IS_TEST")||X().getBool("PROD")||console.warn(`Setting feature override from URL ${e}: ${a}.`),this.set(e,a)}}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(vx(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);b1 in e&&e[b1].split(",").forEach(t=>{let[n,a]=t.split(":");this.urlFlags[n]=kF(n,a)})}};function vF(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...a)=>(wF(t,a[0],a[1]),a.join("="))),t}function wF(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function kF(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 ${e}.`)}function X(){return wx}var wx=null;function IF(e){wx=e}var ab;function oI(){if(ab==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");ab=e}return ab}function SF(){let e=oI();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function kx(e,t){let n=SF();if(n.has(e))return n.get(e);{let a=t();return n.set(e,a),n.get(e)}}var Il="Abs",Sl="Acos",Nl="Acosh",ds="Add",xi="AddN",Tl="All",Cl="Any",vi="ArgMax",lc="ArgMin",_l="Asin",El="Asinh",Al="Atan",$l="Atanh",Fl="Atan2",wi="AvgPool",tm="AvgPoolGrad",uc="AvgPool3D",nm="AvgPool3DGrad",ki="BatchMatMul",Dl="BatchToSpaceND",am="Bincount",lI="BroadcastTo",rm="BroadcastArgs",Ii="Cast",Si="Ceil",hs="ClipByValue",sm="Complex",pc="ComplexAbs",Rl="Concat",Ni="Conv2D",im="Conv2DBackpropFilter",Ti="Conv2DBackpropInput",cc="Conv3D",om="Conv3DBackpropFilterV2",lm="Conv3DBackpropInputV2",Ci="Cos",_i="Cosh",Ml="Cumprod",Ei="Cumsum",Pl="CropAndResize",um="DenseBincount",Ol="DepthToSpace",Ai="DepthwiseConv2dNative",pm="DepthwiseConv2dNativeBackpropFilter",cm="DepthwiseConv2dNativeBackpropInput",dm="Diag",dc="Dilation2D",Sh="Dilation2DBackpropInput",Nh="Dilation2DBackpropFilter",$i="RealDiv",hm="Einsum",Fi="Elu",mm="EluGrad",Ll="Erf",zl="Equal",Di="Exp",Bl="ExpandDims",Wl="Expm1",fm="FFT",hc="Fill",Vl="FlipLeftRight",Ri="Floor",Mi="FloorDiv",Pi="FusedBatchNorm",Ul="GatherV2",Gl="GatherNd",Hl="Greater",Oi="GreaterEqual",Li="Identity",gm="IFFT",ym="Imag",jl="IsFinite",ql="IsInf",Kl="IsNan",zi="LeakyRelu",Xl="Less",Yl="LessEqual",bm="LinSpace",Bi="Log",Jl="Log1p",Zl="LogicalAnd",mc="LogicalNot",fc="LogicalOr",uI="LogSoftmax",gc="LRN",xm="LRNGrad",Wi="Max",Vi="Maximum",Ui="MaxPool",vm="MaxPoolGrad",yc="MaxPool3D",wm="MaxPool3DGrad",km="MaxPoolWithArgmax",Gi="Mean",Hi="Min",ji="Minimum",qi="MirrorPad",Ql="Mod",Im="Multinomial",Ki="Multiply",eu="Neg",tu="NotEqual",nu="NonMaxSuppressionV3",au="NonMaxSuppressionV4",ru="NonMaxSuppressionV5",su="OnesLike",Xi="OneHot",iu="Pack",Yi="PadV2",NF="Pool",Ji="Pow",Zi="Prelu",Qi="Prod",bc="Range",Sm="Real",ou="Reciprocal",eo="Relu",lu="Reshape",xc="ResizeNearestNeighbor",Nm="ResizeNearestNeighborGrad",to="ResizeBilinear",Tm="ResizeBilinearGrad",no="Relu6",ao="Reverse",ro="Round",so="Rsqrt",uu="ScatterNd",pu="Select",cu="Selu",du="Slice",io="Sin",hu="Sinh",mu="Sign",oo="Sigmoid",fu="Softplus",lo="Sqrt",uo="Sum",gu="SpaceToBatchND",yu="SplitV",po="Softmax",vc="SparseFillEmptyRows",bu="SparseReshape",wc="SparseSegmentMean",kc="SparseSegmentSum",Cm="SparseToDense",co="SquaredDifference",Ic="Square",xu="StridedSlice",_m="StringNGrams",Em="StringSplit",Am="StringToHashBucketFast",ho="Sub",mo="Tan",fo="Tanh",ms="Tile",vu="TopK",wu="Transform",go="Transpose",$m="Unique",ku="Unpack",Sc="UnsortedSegmentSum",Iu="ZerosLike",fs="Step",Th="FromPixels",Su="RotateWithOffset",ti="_FusedMatMul",ni="FusedConv2D",ai="FusedDepthwiseConv2D";function qr(...e){X().getBool("IS_TEST")||X().getBool("PROD")||console.warn(...e)}function TF(...e){X().getBool("IS_TEST")||X().getBool("PROD")||console.log(...e)}var ul=kx("kernelRegistry",()=>new Map),Hp=kx("gradRegistry",()=>new Map);function Ch(e,t){let n=Ix(e,t);return ul.get(n)}function bb(e){return Hp.get(e)}function _h(e){let t=ul.entries(),n=[];for(;;){let{done:a,value:r}=t.next();if(a)break;let[s,i]=r,[o]=s.split("_");o===e&&n.push(i)}return n}function Nc(e){let{kernelName:t,backendName:n}=e,a=Ix(t,n);ul.has(a)&&qr(`The kernel '${t}' for backend '${n}' is already registered`),ul.set(a,e)}function pI(e){let{kernelName:t}=e;Hp.has(t)&&X().getBool("DEBUG")&&qr(`Overriding the gradient for '${t}'`),Hp.set(t,e)}function CF(e,t){let n=Ix(e,t);if(!ul.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);ul.delete(n)}function _F(e){if(!Hp.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Hp.delete(e)}function EF(e,t){_h(e).forEach(n=>{let a=Object.assign({},n,{backendName:t});Nc(a)})}function Ix(e,t){return`${t}_${e}`}var k={};Me(k,{arraysEqual:()=>cs,assert:()=>R,assertNonNegativeIntegerDimensions:()=>xx,assertNonNull:()=>bi,assertShapesMatch:()=>Nn,bytesFromStringArray:()=>nI,bytesPerElement:()=>yb,checkConversionForErrors:()=>eI,clamp:()=>Gp,computeStrides:()=>kl,createScalarValue:()=>MF,createShuffledIndices:()=>hF,decodeString:()=>Eh,distSquared:()=>uF,encodeString:()=>Cc,fetch:()=>OF,fingerPrint64:()=>RF,flatten:()=>ei,getArrayFromDType:()=>Qk,getTypedArrayFromDType:()=>Zk,hasEncodingLoss:()=>gF,hexToLong:()=>Tc,indexToLoc:()=>xF,inferDtype:()=>Qh,inferFromImplicitShape:()=>fF,isBoolean:()=>aI,isFunction:()=>es,isInt:()=>ll,isNumber:()=>rI,isPromise:()=>vx,isScalarShape:()=>pF,isString:()=>Kr,isTypedArray:()=>hn,isValidDtype:()=>tI,locToIndex:()=>bF,makeOnesTypedArray:()=>bx,makeZerosNestedTypedArray:()=>yF,makeZerosTypedArray:()=>em,nearestDivisor:()=>Ih,nearestLargerEven:()=>iF,now:()=>jp,parseAxisParam:()=>Ca,randUniform:()=>lF,repeatedTry:()=>mF,rightPad:()=>Bp,shuffle:()=>Yk,shuffleCombo:()=>sF,sizeFromShape:()=>xt,sizeToSquarishShape:()=>dF,squeezeShape:()=>Jk,sum:()=>oF,swap:()=>kh,tanh:()=>cF,toNestedArray:()=>al,toTypedArray:()=>Fm});var x1=yi(O$()),Gs=x1.default||x1;function Tc(e){return Gs.fromString(e,!0,16)}var cI=Tc("c3a5c85c97cb3127"),Vs=Tc("b492b66fbe98f273"),wn=Tc("9ae16a3b2f90404f");function xb(e){return e.xor(e.shru(47))}function dI(e,t,n){let a=e.slice(t,t+n);return Gs.fromBytes(Array.from(a),!0,!0)}function yt(e,t){return dI(e,t,8)}function v1(e,t){return dI(e,t,4)}function Zt(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function Jr(e,t,n=Tc("9ddfea08eb382d69")){let a=e.xor(t).mul(n);a=a.xor(a.shru(47));let r=t.xor(a).mul(n);return r=r.xor(r.shru(47)),r=r.mul(n),r}function AF(e,t,n,a,r,s){r=r.add(e),s=Zt(s.add(r).add(a),21);let i=r;return r=r.add(t),r=r.add(n),s=s.add(Zt(r,44)),[r.add(a),s.add(i)]}function Qd(e,t,n,a){return AF(yt(e,t),yt(e,t+8),yt(e,t+16),yt(e,t+24),n,a)}function $F(e,t=e.length){if(t>=8){let n=wn.add(t*2),a=yt(e,0).add(wn),r=yt(e,t-8),s=Zt(r,37).mul(n).add(a),i=Zt(a,25).add(r).mul(n);return Jr(s,i,n)}if(t>=4){let n=wn.add(t*2),a=v1(e,0);return Jr(a.shl(3).add(t),v1(e,t-4),n)}if(t>0){let n=e[0],a=e[t>>1],r=e[t-1],s=n+(a<<8),i=t+(r<<2);return xb(wn.mul(s).xor(cI.mul(i))).mul(wn)}return wn}function FF(e,t=e.length){let n=wn.add(t*2),a=yt(e,0).mul(Vs),r=yt(e,8),s=yt(e,t-8).mul(n),i=yt(e,t-16).mul(wn);return Jr(Zt(a.add(r),43).add(Zt(s,30)).add(i),a.add(Zt(r.add(wn),18)).add(s),n)}function DF(e,t=e.length){let n=wn.add(t*2),a=yt(e,0).mul(wn),r=yt(e,8),s=yt(e,t-8).mul(n),i=yt(e,t-16).mul(wn),o=Zt(a.add(r),43).add(Zt(s,30)).add(i),l=Jr(o,a.add(Zt(r.add(wn),18)).add(s),n),u=yt(e,16).mul(n),p=yt(e,24),d=o.add(yt(e,t-32)).mul(n),c=l.add(yt(e,t-24)).mul(n);return Jr(Zt(u.add(p),43).add(Zt(d,30)).add(c),u.add(Zt(p.add(a),18)).add(d),n)}function RF(e,t=e.length){let n=Gs.fromNumber(81,!0);if(t<=32)return t<=16?$F(e,t):FF(e,t);if(t<=64)return DF(e,t);let a=n,r=n.mul(Vs).add(113),s=xb(r.mul(wn).add(113)).mul(wn),i=[Gs.UZERO,Gs.UZERO],o=[Gs.UZERO,Gs.UZERO];a=a.mul(wn).add(yt(e,0));let l=0,u=(t-1>>6)*64,p=u+(t-1&63)-63;do a=Zt(a.add(r).add(i[0]).add(yt(e,l+8)),37).mul(Vs),r=Zt(r.add(i[1]).add(yt(e,l+48)),42).mul(Vs),a=a.xor(o[1]),r=r.add(i[0]).add(yt(e,l+40)),s=Zt(s.add(o[0]),33).mul(Vs),i=Qd(e,l,i[1].mul(Vs),a.add(o[0])),o=Qd(e,l+32,s.add(o[1]),r.add(yt(e,l+16))),[s,a]=[a,s],l+=64;while(l!==u);let d=Vs.add(s.and(255).shl(1));return l=p,o[0]=o[0].add(t-1&63),i[0]=i[0].add(o[0]),o[0]=o[0].add(i[0]),a=Zt(a.add(r).add(i[0]).add(yt(e,l+8)),37).mul(d),r=Zt(r.add(i[1]).add(yt(e,l+48)),42).mul(d),a=a.xor(o[1].mul(9)),r=r.add(i[0].mul(9).add(yt(e,l+40))),s=Zt(s.add(o[0]),33).mul(d),i=Qd(e,l,i[1].mul(d),a.add(o[0])),o=Qd(e,l+32,s.add(o[1]),r.add(yt(e,l+16))),[s,a]=[a,s],Jr(Jr(i[0],o[0],d).add(xb(r).mul(cI)).add(s),Jr(i[1],o[1],d).add(a),d)}function MF(e,t){return t==="string"?Cc(e):Fm([e],t)}function PF(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function Fm(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=ei(e)),X().getBool("DEBUG")&&eI(e,t),PF(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 a=0;a<n.length;++a)Math.round(e[a])!==0&&(n[a]=1);return n}else throw new Error(`Unknown data type ${t}`)}function jp(){return X().platform.now()}function OF(e,t){return X().platform.fetch(e,t)}function Cc(e,t="utf-8"){return t=t||"utf-8",X().platform.encode(e,t)}function Eh(e,t="utf-8"){return t=t||"utf-8",X().platform.decode(e,t)}var LF=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new BF)}profileKernel(e,t,n){let a,r=()=>{a=n()},s,i=jp();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(r);else{r();for(let o of a)o.dataSync();s=Promise.resolve({kernelMs:jp()-i})}if(X().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<a.length;o++){let l=a[o];l.data().then(u=>{zF(u,l.dtype,e)})}return{kernelName:e,outputs:a,inputs:t,timeMs:s.then(o=>o.kernelMs),extraInfo:s.then(o=>o.getExtraProfileInfo!=null?o.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:a,inputs:r,extraInfo:s}=e;n.forEach(i=>{Promise.all([i.data(),a,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],r,o[2])})})}};function zF(e,t,n){if(t!=="float32")return!1;for(let a=0;a<e.length;a++){let r=e[a];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}var BF=class{logKernelProfile(e,t,n,a,r,s){let i=typeof a=="number"?Bp(`${a}ms`,9):a.error,o=Bp(e,25),l=t.rank,u=t.size,p=Bp(t.shape.toString(),14),d="";for(let c in r){let h=r[c];if(h!=null){let m=h.shape||t.shape,f=m.length;d+=`${c}: ${f}D ${f>0?m:""} `}}console.log(`%c${o} %c${i} %c${l}D ${p} %c${u} %c${d} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function WF(e,t,n){let a={},r={};for(let l=0;l<t.length;l++)a[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],p=u.inputs;for(let d in p){let c=p[d],h=!1;for(let m=0;m<t.length;m++)if(a[c.id]){u.outputs.forEach(f=>a[f.id]=!0),h=!0,r[u.id]=!0;break}if(h)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let u=e[l],p=u.inputs;for(let d=0;d<u.outputs.length;d++)if(s[u.outputs[d].id]){for(let c in p)s[p[c].id]=!0,i[u.id]=!0;break}}let o=[];for(let l=0;l<e.length;l++){let u=e[l];if(r[u.id]&&i[u.id]){let p={};for(let c in u.inputs){let h=u.inputs[c];a[h.id]&&(p[c]=h)}let d=Object.assign({},u);d.inputs=p,d.outputs=u.outputs,o.push(d)}}return o}function VF(e,t,n,a){for(let r=t.length-1;r>=0;r--){let s=t[r],i=[];if(s.outputs.forEach(l=>{let u=e[l.id];u!=null?i.push(u):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let o=s.gradient(i);for(let l in s.inputs){if(!(l in o))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(o)}.`);let u=n(()=>o[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let p=s.inputs[l];if(!cs(u.shape,p.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${p.shape}'`);if(e[p.id]==null)e[p.id]=u;else{let d=e[p.id];e[p.id]=a(d,u),d.dispose()}}}}var w1=20,Tp=3,rb=7;function UF(e,t,n,a){let r=kl(t),s=GF(e,t,n,r),i=t.length,o=ph(e,t,n,r,s),l=["Tensor"];return a&&(l.push(` dtype: ${n}`),l.push(` rank: ${i}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(o.map(u=>" "+u).join(`
`)),l.join(`
`)}function GF(e,t,n,a){let r=xt(t),s=a[a.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?$p(e):e;if(o>1)for(let u=0;u<r/s;u++){let p=u*s;for(let d=0;d<s;d++)i[d]=Math.max(i[d],Ap(l[p+d],0,n).length)}return i}function Ap(e,t,n){let a;return Array.isArray(e)?a=`${parseFloat(e[0].toFixed(rb))} + ${parseFloat(e[1].toFixed(rb))}j`:Kr(e)?a=`'${e}'`:n==="bool"?a=hI(e):a=parseFloat(e.toFixed(rb)).toString(),Bp(a,t)}function hI(e){return e===0?"false":"true"}function ph(e,t,n,a,r,s=!0){let i=n==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(n==="complex64"){let f=$p(e);return[Ap(f[0],0,n)]}return n==="bool"?[hI(e[0])]:[e[0].toString()]}if(l===1){if(o>w1){let g=Tp*i,y=Array.from(e.slice(0,g)),b=Array.from(e.slice((o-Tp)*i,o*i));return n==="complex64"&&(y=$p(y),b=$p(b)),["["+y.map((x,v)=>Ap(x,r[v],n)).join(", ")+", ..., "+b.map((x,v)=>Ap(x,r[o-Tp+v],n)).join(", ")+"]"]}let f=n==="complex64"?$p(e):Array.from(e);return["["+f.map((g,y)=>Ap(g,r[y],n)).join(", ")+"]"]}let u=t.slice(1),p=a.slice(1),d=a[0]*i,c=[];if(o>w1){for(let f=0;f<Tp;f++){let g=f*d,y=g+d;c.push(...ph(e.slice(g,y),u,n,p,r,!1))}c.push("...");for(let f=o-Tp;f<o;f++){let g=f*d,y=g+d;c.push(...ph(e.slice(g,y),u,n,p,r,f===o-1))}}else for(let f=0;f<o;f++){let g=f*d,y=g+d;c.push(...ph(e.slice(g,y),u,n,p,r,f===o-1))}let h=l===2?",":"";c[0]="["+c[0]+h;for(let f=1;f<c.length-1;f++)c[f]=" "+c[f]+h;let m=`,
`;for(let f=2;f<l;f++)m+=`
`;return c[c.length-1]=" "+c[c.length-1]+"]"+(s?"":m),c}function $p(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var jt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=xt(e),n!=null){let a=n.length;R(a===this.size,()=>`Length of values '${a}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Qk(t,this.size),this.strides=kl(e)}set(e,...t){t.length===0&&(t=[0]),R(t.length===this.rank,()=>`The number of provided coordinates (${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 a of e){if(a<0||a>=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 a=0;a<e.length-1;++a)n+=this.strides[a]*e[a];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 Ba().makeTensor(this.values,this.shape,this.dtype)}},Ba=null,el=null,HF=null;function jF(e){Ba=e}function qF(e){el=e}function KF(e){HF=e}var Ae=class{constructor(e,t,n,a){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=xt(e),this.strides=kl(e),this.dataId=n,this.id=a,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return el.buffer(this.shape,this.dtype,e)}bufferSync(){return el.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return al(this.shape,e,this.dtype==="complex64")}arraySync(){return al(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Ba().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Eh(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}dataToGPU(e){return this.throwIfDisposed(),Ba().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=Ba().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Eh(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 Ba().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Ba().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return el.print(this,e)}clone(){return this.throwIfDisposed(),el.clone(this)}toString(e=!1){let t=this.dataSync();return UF(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),el.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ba().makeVariable(this,e,t,n)}};Object.defineProperty(Ae,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function ne(){return kx("Tensor",()=>Ae)}ne();var ts=class extends Ae{constructor(e,t,n,a){super(e.shape,e.dtype,e.dataId,a),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(!cs(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ba().disposeTensor(this),this.dataId=e.dataId,Ba().incRef(this,null)}dispose(){Ba().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(ts,Symbol.hasInstance,{value:e=>e instanceof Ae&&e.assign!=null&&e.assign instanceof Function});var Ga={};Me(Ga,{assertTypesMatch:()=>mI,getTensorsInContainer:()=>Sx,isTensorInList:()=>YF,makeTypesMatch:()=>$t});var vb;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(vb||(vb={}));var wb;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(wb||(wb={}));var kb;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(kb||(kb={}));var Ib;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Ib||(Ib={}));var Sb;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Sb||(Sb={}));var XF={float32:Ib,int32:wb,bool:kb,complex64:Sb};function ma(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return XF[e][t]}function Dm(e){return ma(e,"int32")}function $t(e,t){if(e.dtype===t.dtype)return[e,t];let n=ma(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function mI(e,t){R(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function YF(e,t){return t.some(n=>n.id===e.id)}function Sx(e){let t=[];return fI(e,t,new Set),t}function fI(e,t,n){if(e==null)return;if(e instanceof Ae){t.push(e);return}if(!JF(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),fI(s,t,n))}}function JF(e){return Array.isArray(e)||typeof e=="object"}function sb(e){return e.kernelName!=null}var k1=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},qp=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new k1}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(qr(`${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 LF(this.backendInstance),!0}setupRegisteredKernels(){_h(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){_h(e).forEach(t=>{t.disposeFunc!=null&&t.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 oc)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,qr(`Initialization of backend ${e} failed`),qr(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return qr(`Initialization of backend ${e} failed`),qr(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:a,asyncInit:r}=this.initializeBackend(n);if(r||a)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),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),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 a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return qp.nextTensorId++}nextVariableId(){return qp.nextVariableId++}clone(e){let t=L.runKernel(Li,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return L.runKernel(Ii,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,Ch(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 a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=sb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(sb(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Ch(h,this.backendName);R(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let x=b.map(v=>{if(v.rank!=null)return v;let{dataId:w,shape:T,dtype:C}=v;return this.makeTensorFromDataId(w,T,C)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:p}=e,d=sb(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,u,t,d,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=bb(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(R(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Kr(e[0])&&(r=e.map(o=>Cc(o)));let s=a.write(r,t,n),i=new Ae(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=nI(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Ae(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new ts(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*yb(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 ts||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*yb(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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=bb(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,p)=>{if(u==null){let d=n[p],c=em(d.size,d.dtype);return this.makeTensor(c,d.shape,d.dtype)}return u}),a(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}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=Sx(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=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===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(R(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));R(r instanceof Ae,()=>"The result y returned by f() must be a tensor.");let s=WF(this.state.activeTape,t,r);if(!a&&s.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 i={};i[r.id]=n==null?ZF(r.shape):n,VF(i,s,l=>this.tidy(l),QF);let o=t.map(l=>i[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:o}})}customGrad(e){return R(es(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{R(t.every(i=>i instanceof Ae),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};t.forEach((i,o)=>{a[o]=i});let r=(i,o)=>(n=e(...t,o),R(n.value instanceof Ae,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),R(es(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),u=Array.isArray(l)?l:[l];R(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(...)."),R(u.every(d=>d instanceof Ae),()=>"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 p={};return u.forEach((d,c)=>{p[c]=()=>d}),p};return this.runKernelFunc({forwardFunc:r,backwardsFunc:s,inputs:a})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=jp(),n=await this.backend.time(e);return n.wallMs=jp()-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 k1;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}};qp.nextTensorId=0;qp.nextVariableId=0;function ZF(e){let t=bx(xt(e),"float32");return L.makeTensor(t,e,"float32")}function gI(){let e=oI();if(e._tfengine==null){let t=new iI(e);e._tfengine=new qp(t)}return IF(e._tfengine.ENV),jF(()=>e._tfengine),e._tfengine}var L=gI();function QF(e,t){let n={a:e,b:t};return L.runKernel(ds,n)}var _c={};Me(_c,{isBrowser:()=>yI,isMobile:()=>nD,mockIsMobile:()=>tD});function eD(){return typeof navigator!="undefined"&&navigator!=null}var Nb;function tD(e){Nb=e}function nD(e){if(Nb!==void 0)return Nb;if(e||eD()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let n=e;return n.userAgentData&&n.userAgentData.mobile}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 yI(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Na=X();Na.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.")});Na.registerFlag("IS_BROWSER",()=>yI());Na.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Na.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Na.registerFlag("PROD",()=>!1);Na.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Na.getBool("DEBUG"));Na.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Na.registerFlag("IS_TEST",()=>!1);Na.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);Na.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);Na.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function lr(e,t){let n=e;if(hn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let a=[];for(;Array.isArray(n)||hn(n)&&t!=="string";)a.push(n.length),n=n[0];return Array.isArray(e)&&X().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&bI(e,a,[]),a}function bI(e,t,n){if(n=n||[],!Array.isArray(e)&&!hn(e)){R(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}R(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),R(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let a=t.slice(1);for(let r=0;r<e.length;++r)bI(e[r],a,n.concat(r))}function I1(e,t,n,a){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${a}' must be ${e} tensor, but got ${t} tensor`)}}function A(e,t,n,a="numeric"){if(e instanceof Ae)return I1(a,e.dtype,t,n),e;let r=Qh(e);if(r!=="string"&&["bool","int32","float32"].indexOf(a)>=0&&(r=a),I1(a,r,t,n),e==null||!hn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=lr(e,r);!hn(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?Fm(e,r):ei(e,[],!0);return L.makeTensor(i,s,r)}function Kp(e,t,n,a="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((r,s)=>A(r,`${t}[${s}]`,n,a))}var xI="__op";function B(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],a=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+xI;let r=(...s)=>{L.startScope(n);try{let i=a(...s);return vx(i)&&console.error("Cannot return a Promise inside of tidy."),L.endScope(i),i}catch(i){throw L.endScope(null),i}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function aD(e,t){let n=A(e,"real","complex"),a=A(t,"imag","complex");Nn(n.shape,a.shape,`real and imag shapes, ${n.shape} and ${a.shape}, must match in call to tf.complex().`);let r={real:n,imag:a};return L.runKernel(sm,r)}var ns=B({complex_:aD});function gs(e,t,n,a){if(a==null&&(a=Qh(e)),a==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!hn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){xx(t);let r=xt(t),s=xt(n);R(r===s,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==xt(t.slice(i)):!0;R(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!hn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=a!=="string"?Fm(e,a):ei(e,[],!0),L.makeTensor(e,t,a)}function Zn(e,t,n){let a=lr(e,n);return gs(e,t,a,n)}var Tb={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},Ah=4;async function rD(e,t){let n=[],a=[],r=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<r.length;++i){let o=r[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let u={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let p=new Promise(async d=>{let c=await l.bytes(),h=c.reduce((g,y)=>g+y.length,0)+Ah*c.length,m=new Uint8Array(h),f=0;for(let g=0;g<c.length;g++){let y=c[g],b=new Uint8Array(new Uint32Array([y.length]).buffer);m.set(b,f),f+=Ah,m.set(y,f),f+=y.length}d(m)});a.push(p)}else a.push(l.data());t!=null&&(u.group=t),n.push(u)}let s=await Promise.all(a);return{data:sD(s),specs:n}}function vI(e,t){let n={},a,r=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,u=xt(l),p;if("quantization"in s){let d=s.quantization;if(d.dtype==="uint8"||d.dtype==="uint16"){if(!("min"in d&&"scale"in d))throw new Error(`Weight ${s.name} with quantization ${d.dtype} doesn't have corresponding metadata min and scale.`)}else if(d.dtype==="float16"){if(o!=="float32")throw new Error(`Weight ${s.name} is quantized with ${d.dtype} which only supports weights of type float32 not ${o}.`)}else throw new Error(`Weight ${s.name} has unknown quantization dtype ${d.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let c=Tb[d.dtype],h=e.slice(r,r+u*c),m=d.dtype==="uint8"?new Uint8Array(h):new Uint16Array(h);if(o==="float32")if(d.dtype==="uint8"||d.dtype==="uint16"){p=new Float32Array(m.length);for(let f=0;f<m.length;f++){let g=m[f];p[f]=g*d.scale+d.min}}else if(d.dtype==="float16")a===void 0&&(a=cD()),p=a(m);else throw new Error(`Unsupported quantization type ${d.dtype} for weight type float32.`);else if(o==="int32"){if(d.dtype!=="uint8"&&d.dtype!=="uint16")throw new Error(`Unsupported quantization type ${d.dtype} for weight type int32.`);p=new Int32Array(m.length);for(let f=0;f<m.length;f++){let g=m[f];p[f]=Math.round(g*d.scale+d.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=u*c}else if(o==="string"){let d=xt(s.shape);p=[];for(let c=0;c<d;c++){let h=new Uint32Array(e.slice(r,r+Ah))[0];r+=Ah;let m=new Uint8Array(e.slice(r,r+h));p.push(m),r+=h}}else{let d=Tb[o],c=e.slice(r,r+u*d);if(o==="float32")p=new Float32Array(c);else if(o==="int32")p=new Int32Array(c);else if(o==="bool")p=new Uint8Array(c);else if(o==="complex64"){p=new Float32Array(c);let h=new Float32Array(p.length/2),m=new Float32Array(p.length/2);for(let y=0;y<h.length;y++)h[y]=p[y*2],m[y]=p[y*2+1];let f=Zn(h,l,"float32"),g=Zn(m,l,"float32");n[i]=ns(f,g),f.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=u*d}o!=="complex64"&&(n[i]=Zn(p,l,o))}return n}function sD(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(s=>{if(t+=s.byteLength,n.push(s.byteLength===s.buffer.byteLength?s:new s.constructor(s)),!(s instanceof Float32Array||s instanceof Int32Array||s instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`)});let a=new Uint8Array(t),r=0;return n.forEach(s=>{a.set(new Uint8Array(s.buffer),r),r+=s.byteLength}),a.buffer}var Nx=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function S1(e){return Nx?Buffer.byteLength(e):new Blob([e]).size}function iD(e){if(Nx)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let a=0,r=t.length;a<r;a++)n+=String.fromCharCode(t[a]);return btoa(n)}function oD(e){if(Nx){let a=Buffer.from(e,"base64");return a.buffer.slice(a.byteOffset,a.byteOffset+a.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let a=0;a<t.length;++a)n.set([t.charCodeAt(a)],a);return n.buffer}function Tx(e){if(e.length===1)return e[0];let t=0;e.forEach(r=>{t+=r.byteLength});let n=new Uint8Array(t),a=0;return e.forEach(r=>{n.set(new Uint8Array(r),a),a+=r.byteLength}),n.buffer}function N1(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 wI(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 Cx(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[a,r]=await t(e.weightsManifest);n.weightSpecs=a,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 Ec(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:S1(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:S1(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function lD(){let e=n=>{let a=n<<13,r=0;for(;(a&8388608)===0;)r-=8388608,a<<=1;return a&=-8388609,r+=947912704,a|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 uD(){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 pD(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function cD(){let e=lD(),t=uD(),n=pD();return a=>{let r=new ArrayBuffer(4*a.length),s=new Uint32Array(r);for(let i=0;i<a.length;i++){let o=a[i],l=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(r)}}var Dt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Dt.instance==null&&(Dt.instance=new Dt),Dt.instance}static registerSaveRouter(e){Dt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Dt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Dt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Dt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let a=[];return(t==="load"?Dt.getInstance().loadRouters:Dt.getInstance().saveRouters).forEach(r=>{let s=r(e,n);s!==null&&a.push(s)}),a}},dD=e=>Dt.registerSaveRouter(e),hD=e=>Dt.registerLoadRouter(e),mD=e=>Dt.getSaveHandlers(e),fD=(e,t)=>Dt.getLoadHandlers(e,t),Cb="tensorflowjs",_b=1,Ks="models_store",Xr="model_info_store";function kI(){if(!X().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 Eb(e){let t=e.result;t.createObjectStore(Ks,{keyPath:"modelPath"}),t.createObjectStore(Xr,{keyPath:"modelPath"})}var ri=class{constructor(e){if(this.indexedDB=kI(),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,a)=>{let r=this.indexedDB.open(Cb,_b);r.onupgradeneeded=()=>Eb(r),r.onsuccess=()=>{let s=r.result;if(t==null){let i=s.transaction(Ks,"readonly"),o=i.objectStore(Ks).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),a(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),a(o.error)),i.oncomplete=()=>s.close()}else{let i=Ec(t),o=s.transaction(Xr,"readwrite"),l=o.objectStore(Xr),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),p;u.onsuccess=()=>{p=s.transaction(Ks,"readwrite");let d=p.objectStore(Ks).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});d.onsuccess=()=>n({modelArtifactsInfo:i}),d.onerror=c=>{l=o.objectStore(Xr);let h=l.delete(this.modelPath);h.onsuccess=()=>(s.close(),a(d.error)),h.onerror=m=>(s.close(),a(d.error))}},u.onerror=d=>(s.close(),a(u.error)),o.oncomplete=()=>{p==null?s.close():p.oncomplete=()=>s.close()}}},r.onerror=s=>a(r.error)})}};ri.URL_SCHEME="indexeddb://";var II=e=>X().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ri.URL_SCHEME)?gD(e.slice(ri.URL_SCHEME.length)):null;Dt.registerSaveRouter(II);Dt.registerLoadRouter(II);function gD(e){return new ri(e)}function yD(e){return e.startsWith(ri.URL_SCHEME)?e.slice(ri.URL_SCHEME.length):e}var bD=class{constructor(){this.indexedDB=kI()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(Cb,_b);n.onupgradeneeded=()=>Eb(n),n.onsuccess=()=>{let a=n.result,r=a.transaction(Xr,"readonly"),s=r.objectStore(Xr).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(a.close(),t(s.error)),r.oncomplete=()=>a.close()},n.onerror=a=>t(n.error)})}async removeModel(e){return e=yD(e),new Promise((t,n)=>{let a=this.indexedDB.open(Cb,_b);a.onupgradeneeded=()=>Eb(a),a.onsuccess=()=>{let r=a.result,s=r.transaction(Xr,"readwrite"),i=s.objectStore(Xr),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=i.delete(e),p=()=>{l=r.transaction(Ks,"readwrite");let d=l.objectStore(Ks).delete(e);d.onsuccess=()=>t(o.result.modelArtifactsInfo),d.onerror=c=>n(o.error)};u.onsuccess=p,u.onerror=d=>(p(),r.close(),n(o.error))}},o.onerror=u=>(r.close(),n(o.error)),s.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},a.onerror=r=>n(a.error)})}},Ir="/",tl="tensorflowjs_models",SI="info",xD="model_topology",vD="weight_specs",wD="weight_data",kD="model_metadata";function NI(e){return{info:[tl,e,SI].join(Ir),topology:[tl,e,xD].join(Ir),weightSpecs:[tl,e,vD].join(Ir),weightData:[tl,e,wD].join(Ir),modelMetadata:[tl,e,kD].join(Ir)}}function TI(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function ID(e){let t=e.split(Ir);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ir)}function SD(e){return e.startsWith(si.URL_SCHEME)?e.slice(si.URL_SCHEME.length):e}var si=class{constructor(e){if(!X().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=NI(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),a=Ec(e);try{this.LS.setItem(this.keys.info,JSON.stringify(a)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,iD(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:a}}catch(r){throw TI(this.keys),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${a.modelTopologyBytes}, weightSpecsBytes=${a.weightSpecsBytes}, weightDataBytes=${a.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${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 '${this.modelPath}' is missing.`);t.modelTopology=n;let a=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(a==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=a;let r=this.LS.getItem(this.keys.modelMetadata);if(r!=null){let i=JSON.parse(r);t.format=i.format,t.generatedBy=i.generatedBy,t.convertedBy=i.convertedBy,i.signature!=null&&(t.signature=i.signature),i.userDefinedMetadata!=null&&(t.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(t.modelInitializer=i.modelInitializer),i.trainingConfig!=null&&(t.trainingConfig=i.trainingConfig)}let s=this.LS.getItem(this.keys.weightData);if(s==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=oD(s),t}};si.URL_SCHEME="localstorage://";var CI=e=>X().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(si.URL_SCHEME)?ND(e.slice(si.URL_SCHEME.length)):null;Dt.registerSaveRouter(CI);Dt.registerLoadRouter(CI);function ND(e){return new si(e)}var TD=class{constructor(){R(X().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),R(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=tl+Ir,n=Ir+SI;for(let a=0;a<this.LS.length;++a){let r=this.LS.key(a);if(r.startsWith(t)&&r.endsWith(n)){let s=ID(r);e[s]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=SD(e);let t=NI(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return TI(t),n}},rl="://",pa=class{constructor(){this.managers={}}static getInstance(){return pa.instance==null&&(pa.instance=new pa),pa.instance}static registerManager(e,t){R(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(rl)&&(e=e.slice(0,e.indexOf(rl))),R(e.length>0,()=>"scheme must not be an empty string.");let n=pa.getInstance();R(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 ch(e){if(e.indexOf(rl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${pa.getSchemes().join(",")}`);return{scheme:e.split(rl)[0],path:e.split(rl)[1]}}async function _I(e,t,n=!1){R(e!==t,()=>`Old path and new path are the same: '${e}'`);let a=Dt.getLoadHandlers(e);R(a.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),R(a.length<2,()=>`Copying failed because more than one (${a.length}) load handlers for source URL ${e}.`);let r=a[0],s=Dt.getSaveHandlers(t);R(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),R(s.length<2,()=>`Copying failed because more than one (${a.length}) save handlers for destination URL ${t}.`);let i=s[0],o=ch(e).scheme,l=ch(e).path,u=o===ch(e).scheme,p=await r.load();n&&u&&await pa.getManager(o).removeModel(l);let d=await i.save(p);return n&&!u&&await pa.getManager(o).removeModel(l),d.modelArtifactsInfo}async function CD(){let e=pa.getSchemes(),t={};for(let n of e){let a=await pa.getManager(n).listModels();for(let r in a){let s=n+rl+r;t[s]=a[r]}}return t}async function _D(e){let t=ch(e);return pa.getManager(t.scheme).removeModel(t.path)}async function ED(e,t){return _I(e,t,!1)}async function AD(e,t){return _I(e,t,!0)}var $D=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(X().get("IS_BROWSER")){X().setPlatform("browser",new $D);try{pa.registerManager(si.URL_SCHEME,new TD)}catch(e){}try{pa.registerManager(ri.URL_SCHEME,new bD)}catch(e){}}var FD={importFetch:()=>L$()},ib,DD=class{constructor(){this.util=z$(),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return X().global.fetch!=null?X().global.fetch(e,t):(ib==null&&(ib=FD.importFetch()),ib(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)}};X().get("IS_NODE")&&!X().get("IS_BROWSER")&&X().setPlatform("node",new DD);function He(e,t="float32",n){return t=t||"float32",xx(e),new jt(e,t,n)}function RD(e,t){let n=A(e,"x","cast");if(!tI(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let a={x:n},r={dtype:t};return L.runKernel(Ii,a,r)}var oe=B({cast_:RD});function MD(e){let t={x:A(e,"x","clone","string_or_numeric")};return L.runKernel(Li,t)}var Nr=B({clone_:MD});function EI(e,t=!1){console.log(e.toString(t))}gI();var PD={buffer:He,cast:oe,clone:Nr,print:EI};qF(PD);var Qt={};Me(Qt,{browserFiles:()=>UD,browserHTTPRequest:()=>KD,concatenateArrayBuffers:()=>Tx,copyModel:()=>ED,decodeWeights:()=>vI,encodeWeights:()=>rD,fromMemory:()=>YD,getLoadHandlers:()=>fD,getModelArtifactsForJSON:()=>Cx,getModelArtifactsInfoForJSON:()=>Ec,getSaveHandlers:()=>mD,http:()=>Ex,isHTTPScheme:()=>Ab,listModels:()=>CD,loadWeights:()=>GD,moveModel:()=>AD,registerLoadRouter:()=>hD,registerSaveRouter:()=>dD,removeModel:()=>_D,weightsLoaderFactory:()=>$I,withSaveHandler:()=>JD});var OD="model",LD=".json",zD=".weights.bin";function T1(e){return new Promise(t=>setTimeout(t)).then(e)}var pl=class{constructor(e){if(!X().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(pl.URL_SCHEME)&&(e=e.slice(pl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=OD),this.modelJsonFileName=e+LD,this.weightDataFileName=e+zD}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}],a=wI(e,n),r=window.URL.createObjectURL(new Blob([JSON.stringify(a)],{type:"application/json"})),s=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(s.download=this.modelJsonFileName,s.href=r,await T1(()=>s.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await T1(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ec(e)}}}};pl.URL_SCHEME="downloads://";var BD=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=a=>{let r=JSON.parse(a.target.result),s=r.modelTopology;if(s==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 ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:s});return}let i=Cx(r,o=>this.loadWeights(o));e(i)},n.onerror=a=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)})}loadWeights(e){let t=[],n=[];for(let s of e)t.push(...s.weights),n.push(...s.paths);let a=this.checkManifestAndWeightFiles(e),r=n.map(s=>this.loadWeightsFile(s,a[s]));return Promise.all(r).then(s=>[t,Tx(s)])}loadWeightsFile(e,t){return new Promise((n,a)=>{let r=new FileReader;r.onload=s=>{let i=s.target.result;n(i)},r.onerror=s=>a(`Failed to weights data from file of path '${e}'.`),r.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],n=this.weightsFiles.map(r=>N1(r.name)),a={};for(let r of e)r.paths.forEach(s=>{let i=N1(s);if(t.indexOf(i)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);if(t.push(i),n.indexOf(i)===-1)throw new Error(`Weight file with basename '${i}' is not provided.`);a[s]=this.weightsFiles[n.indexOf(i)]});if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return a}},WD=e=>X().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(pl.URL_SCHEME)?VD(e.slice(pl.URL_SCHEME.length)):null;Dt.registerSaveRouter(WD);function VD(e="model"){return new pl(e)}function UD(e){return new BD(e)}function C1(e,t,n,a){i(e),n=n==null?0:n,a=a==null?1:a,o(n,a);let r=0,s=l=>(l.then(u=>{let p=n+ ++r/e.length*(a-n);return t(p),u}),l);function i(l){R(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,u){R(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),R(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),R(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(s))}async function AI(e,t){t==null&&(t={});let n=t.fetchFunc==null?X().platform.fetch:t.fetchFunc,a=e.map(u=>n(u,t.requestInit,{isBinary:!0})),r=0,s=.5,i=(t.onProgress==null?await Promise.all(a):await C1(a,t.onProgress,r,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await C1(i,t.onProgress,o,l)}async function GD(e,t="",n,a){return $I(r=>AI(r,{requestInit:a}))(e,t,n)}function $I(e){return async(t,n="",a)=>{let r=t.map(()=>!1),s={},i=a!=null?a.map(()=>!1):[],o=[];if(t.forEach((h,m)=>{let f=0;h.weights.forEach(g=>{let y="quantization"in g?g.quantization.dtype:g.dtype,b=Tb[y]*xt(g.shape),x=()=>{r[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:g,groupOffset:f,sizeBytes:b})};a!=null?a.forEach((v,w)=>{v===g.name&&(x(),i[w]=!0)}):x(),o.push(g.name),f+=b})}),!i.every(h=>h)){let h=a.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}.
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=r.reduce((h,m,f)=>(m&&h.push(f),h),[]),u=[];l.forEach(h=>{t[h].paths.forEach(m=>{let f=n+(n.endsWith("/")?"":"/")+m;u.push(f)})});let p=await e(u),d={},c=0;return l.forEach(h=>{let m=t[h].paths.length,f=0;for(let x=0;x<m;x++)f+=p[c+x].byteLength;let g=new ArrayBuffer(f),y=new Uint8Array(g),b=0;for(let x=0;x<m;x++){let v=new Uint8Array(p[c+x]);y.set(v,b),b+=v.byteLength}s[h].forEach(x=>{let v=g.slice(x.groupOffset,x.groupOffset+x.sizeBytes),w=vI(v,[x.manifestEntry]);for(let T in w)d[T]=w[T]}),c+=m}),d}}var HD="application/octet-stream",jD="application/json",_x=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?(R(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=X().platform.fetch,R(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&R(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}],a=wI(e,n);t.body.append("model.json",new Blob([JSON.stringify(a)],{type:jD}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:HD}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Ec(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 s=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?s+=" 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.":s+=" Please make sure the server is serving valid JSON for this request.",new Error(s)}let n=t.modelTopology,a=t.weightsManifest;if(n==null&&a==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return Cx(t,r=>this.loadWeights(r))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,a]=qD(t),r=this.weightPathPrefix||n,s=[];for(let u of e)s.push(...u.weights);let i=[],o=[];for(let u of e)for(let p of u.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(p)):i.push(r+p+a);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await AI(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,Tx(l)]}};_x.URL_SCHEME_REGEX=/^https?:\/\//;function qD(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),a=e.substring(0,t),r=n>t?e.substring(n):"";return[a+"/",r]}function Ab(e){return e.match(_x.URL_SCHEME_REGEX)!=null}var FI=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(a=>Ab(a)):n=Ab(e),n)return Ex(e,t)}return null};Dt.registerSaveRouter(FI);Dt.registerLoadRouter(FI);function Ex(e,t){return new _x(e,t)}function KD(e,t){return Ex(e,t)}var ob=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},XD=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function YD(e,t,n,a){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new ob(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 ob({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 ob({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:a}))}function JD(e){return new XD(e)}var DI={};Me(DI,{confusionMatrix:()=>nR});function ZD(e,t,n=!1,a=!1){let r=A(e,"a","matMul"),s=A(t,"b","matMul");[r,s]=$t(r,s);let i={a:r,b:s},o={transposeA:n,transposeB:a};return L.runKernel(ki,i,o)}var De=B({matMul_:ZD});function QD(e,t,n=1,a=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let r={indices:A(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:a};return L.runKernel(Xi,r,s)}var cl=B({oneHot_:QD});function eR(e,t){let n=A(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),R(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{R(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let a={x:n},r={perm:t};return L.runKernel(go,a,r)}var $e=B({transpose_:eR});function tR(e,t,n){let a=A(e,"labels","confusionMatrix"),r=A(t,"predictions","confusionMatrix");R(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),R(a.rank===1,()=>`Expected the rank of labels to be 1, but got ${a.rank}`),R(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),R(a.shape[0]===r.shape[0],()=>`Mismatch in the number of examples: ${a.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`),R(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=cl(oe(a,"int32"),n),i=cl(oe(r,"int32"),n),o=$e(s),l=De(o,i);return oe(l,"int32")}var nR=B({confusionMatrix_:tR}),Nu={};Me(Nu,{assertAndGetBroadcastShape:()=>ht,getBroadcastDims:()=>RI,getReductionAxes:()=>Wt});function RI(e,t){let n=e.length,a=[];for(let r=0;r<n;r++){let s=n-1-r,i=e[s]||1;(t[t.length-1-r]||1)>1&&i===1&&a.unshift(s)}return a}function Wt(e,t){let n=[];for(let a=0;a<t.length;a++){let r=e[e.length-a-1],s=t.length-a-1,i=t[s];(r==null||r===1&&i>1)&&n.unshift(s)}return n}function ht(e,t){let n=[],a=Math.max(e.length,t.length);for(let r=0;r<a;r++){let s=e[e.length-r-1];s==null&&(s=1);let i=t[t.length-r-1];if(i==null&&(i=1),s===1)n.unshift(i);else if(i===1)n.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else n.unshift(s)}return n}var yo={};Me(yo,{fromPixels:()=>uR,fromPixelsAsync:()=>oR,toPixels:()=>lR});function Rm(e,t,n){if(bi(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let a=lr(e,n);if(a.length!==3&&a.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return gs(e,t,a,n)}var Bs;function MI(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,a=!1,r=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)a=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)r=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)s=!0;else if(e.getContext!=null)i=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)o=!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&&r&&e.readyState<2)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");if(Ch(Th,L.backendName)!=null){let c={pixels:e},h={numChannels:t};return L.runKernel(Th,c,h)}let[l,u]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],p;if(i)p=e.getContext("2d").getImageData(0,0,l,u).data;else if(a||n)p=e.data;else if(s||r||o){if(Bs==null)if(typeof document=="undefined")if(typeof OffscreenCanvas!="undefined"&&typeof OffscreenCanvasRenderingContext2D!="undefined")Bs=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else Bs=document.createElement("canvas").getContext("2d");Bs.canvas.width=l,Bs.canvas.height=u,Bs.drawImage(e,0,0,l,u),p=Bs.getImageData(0,0,l,u).data}let d;if(t===4)d=new Int32Array(p);else{let c=l*u;d=new Int32Array(c*t);for(let h=0;h<c;h++)for(let m=0;m<t;++m)d[h*t+m]=p[h*4+m]}return Rm(d,[u,l,t],"int32")}function aR(e){return e!=null&&e.data instanceof Uint8Array}function rR(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function sR(e){return e!=null&&e.width!==0&&e.height!==0}function iR(e){return rR()&&!(e instanceof ImageBitmap)&&sR(e)&&!aR(e)}async function oR(e,t=3){let n=null;if(X().getBool("WRAP_TO_IMAGEBITMAP")&&iR(e)){let a;try{a=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(r){a=null}a!=null&&a.width===e.width&&a.height===e.height?n=a:n=e}else n=e;return MI(n,t)}async function lR(e,t){let n=A(e,"img","toPixels");if(!(e instanceof Ae)){let u=n;n=oe(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[a,r]=n.shape.slice(0,2),s=n.rank===2?1:n.shape[2];if(s>4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let i=await n.data(),o=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(r*a*4);for(let u=0;u<a*r;++u){let p=[0,0,0,255];for(let c=0;c<s;c++){let h=i[u*s+c];if(n.dtype==="float32"){if(h<0||h>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${h}.`)}else if(n.dtype==="int32"&&(h<0||h>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${h}.`);s===1?(p[0]=h*o,p[1]=h*o,p[2]=h*o):p[c]=h*o}let d=u*4;l[d+0]=Math.round(p[0]),l[d+1]=Math.round(p[1]),l[d+2]=Math.round(p[2]),l[d+3]=Math.round(p[3])}if(t!=null){t.width=r,t.height=a;let u=t.getContext("2d"),p=new ImageData(l,r,a);u.putImageData(p,0,0)}return n!==e&&n.dispose(),l}var uR=B({fromPixels_:MI}),Ax={};Me(Ax,{prepareAndValidate:()=>PI});function PI(e,t){let n=e.shape.length,a=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(a<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${a}.`);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[a-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[a-1]} vs. ${n}`);if(xt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let r=t.shape,s=r[r.length-1],i=1;for(let d=0;d<r.length-1;++d)i*=r[d];let o=e.shape,l=r.slice();l.pop();let u=1;for(let d=s;d<n;++d)u*=o[d],l.push(o[d]);let p=[...kl(e.shape).map(d=>d/u),1].slice(0,s);return[l,i,u,p]}var $x={};Me($x,{calculateShapes:()=>OI,validateInput:()=>Dx,validateUpdateShape:()=>Fx});function Fx(e,t,n){let a=t.rank>1?t.shape[t.rank-1]:1,r=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${a}, and batchDim: ${r}.`;if(n.rank<r)throw new Error(s+` update.rank < ${r}. `);if(e.length<a+(n.rank-r))throw new Error(s+` Output shape length < ${a+(n.rank-r)}`);if(n.rank!==r+e.length-a)throw new Error(s+` update.rank != ${r+e.length-a}`);for(let i=0;i<r;++i)if(n.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<n.rank-r;++i)if(n.shape[i+r]!==e[i+a])throw new Error(s+` updates.shape[${i+r}] (${n.shape[i+r]}) != shape[${i+r}] (${e[i+r]})`)}function Dx(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}`)}Fx(n,t,e)}function OI(e,t,n){let a=t.shape.length,r=a>1?t.shape[a-1]:1,s=n.length,i=1;for(let d=r;d<s;++d)i*=n[d];let o=r<1?1:r,l=xt(t.shape)/o,u=[...kl(n.slice(0,r)),1],p=xt(n);return{sliceRank:r,numUpdates:l,sliceSize:i,strides:u,outputSize:p}}var qt={};Me(qt,{assertParamsValid:()=>cR,computeFlatOffset:()=>gR,computeOutShape:()=>hR,getNormalizedAxes:()=>mR,isSliceContinous:()=>fR,maskToAxes:()=>dR,parseSliceParams:()=>jI,sliceInfo:()=>yR,startForAxis:()=>GI,startIndicesWithElidedDims:()=>WI,stopForAxis:()=>HI,stopIndicesWithElidedDims:()=>VI,stridesForAxis:()=>UI,stridesWithElidedDims:()=>LI});var $b=-2,pR=-1;function cR(e,t,n){let a=e.shape.length;R(a===t.length,()=>`Error in slice${a}D: Length of begin ${t} must match the rank of the array (${a}).`),R(a===n.length,()=>`Error in slice${a}D: Length of size ${n} must match the rank of the array (${a}).`);for(let r=0;r<a;++r)R(t[r]+n[r]<=e.shape[r],()=>`Error in slice${a}D: begin[${r}] + size[${r}] (${t[r]+n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`)}function dR(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function hR(e,t,n){let a=[];for(let r=0;r<e.length;r++)a[r]=Math.ceil((t[r]-e[r])/n[r]);return a}function LI(e,t,n,a){let r=[...e];for(let s=r.length;s<a.length;s++)r.push(1);for(let s=0;s<n;s++)s===0?r[t]=1:(r.splice(t,0,1),r.pop());return r}function zI(e,t,n){return n<=e?n:n-(t-1)}function BI(e,t){let n=[];for(let a=0;a<e;a++)n.push(t+a);return n}function mR(e,t,n,a,r,s,i,o,l){let u=e.length,p=new Array(u),d=new Array(u),c=new Array(u);if(t.length&&n>0){let h=t[0],m=n+1;p=WI(i,h,m,a,e),d=VI(o,h,m,r,e),c=LI(s,h,m,e)}else for(let h=0;h<u;h++)p[h]=GI(i,a,s,e,h,l),d[h]=HI(o,r,s,e,h,l),c[h]=UI(s,h,l);return{begin:p,end:d,strides:c}}function WI(e,t,n,a,r){let s=[...r],i=BI(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=zI(t,n,o),u=a[l];e&1<<l&&(u=0),s[o]=u}return s}function VI(e,t,n,a,r){let s=[...r],i=BI(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=zI(t,n,o),u=a[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),s[o]=u}for(let o=0;o<s.length;o++){let l=r[o];s[o]<0&&(s[o]+=l),s[o]=Gp(0,s[o],r[o])}return s}function UI(e,t,n){let a=e[t];return(n&1<<t||a==null)&&(a=1),a}function GI(e,t,n,a,r,s){let i=t[r],o=n[r]||1;(e&1<<r||s&1<<r||i==null)&&(o>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),i=Gp(0,i,l-1),i}function HI(e,t,n,a,r,s){let i=t[r],o=n[r]||1;(e&1<<r||s&1<<r||i==null)&&(o>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),o>0?i=Gp(0,i,l):i=Gp(-1,i,l-1),i}function fR(e,t,n){let a=n.length;for(let r=0;r<n.length;r++)if(n[r]>1){a=r;break}for(let r=a+1;r<n.length;r++)if(t[r]>0||n[r]!==e[r])return!1;return!0}function gR(e,t){let n=e.length>0?e[e.length-1]:1;for(let a=0;a<e.length-1;a++)n+=e[a]*t[a];return n}function jI(e,t,n){let a,r=e.shape.length;typeof t=="number"?a=[t,...new Array(r-1).fill(0)]:t.length<r?a=t.concat(new Array(r-t.length).fill(0)):a=t.slice(),a.forEach(i=>{R(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(r).fill(-1):typeof n=="number"?s=[n,...new Array(r-1).fill(-1)]:n.length<r?s=n.concat(new Array(r-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(R(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-a[o])),[a,s]}function yR(e,t,n,a,r,s,i,o,l){let u;if(a==null?(u=new Array(t.length),u.fill(1)):u=a,i!=null&&(i&i-1)!==0)throw new Error("Multiple ellipses in slice is not allowed.");let p=!1,d={dims:u.length,numAddAxisAfterEllipsis:0,begin:t.slice(),end:n.slice(),strides:u.slice(),beginMask:r,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};for(let b=0;b<d.dims;b++)p&&(1<<b&o)!==0&&d.numAddAxisAfterEllipsis++,1<<b&i&&(p=!0);p||(d.ellipsisMask|=1<<d.dims,d.dims++);let c={dims:e.length,beginMask:0,endMask:0,beginValid:!1,endValid:!1};bR(d,c);let h=!0,m=!0,f=!0,g=[],y=[];for(let b=0;b<e.length;++b){if(c.strides[b]===0)throw Error(`strides[${b}] must be non-zero`);let x=!!(c.shrinkAxisMask&1<<b),v=e[b];if(v===-1){g.push(x?1:-1);continue}let w=[c.beginMask&1<<b,c.endMask&1<<b],T=[c.strides[b]>0?0:-1,c.strides[b]>0?v:v-1];if(x&&c.strides[b]<=0)throw Error("only stride 1 allowed on non-range indexing.");f=f&&c.strides[b]===1;let C=!!(c.beginMask&1<<b&&c.endMask&1<<b);if(c.beginValid&&c.endValid){if(x){let F=c.begin[b]<0?v+c.begin[b]:c.begin[b];if(c.begin[b]=F,c.end[b]=c.begin[b]+1,F<0||F>=v)throw Error(`slice index ${c.begin[b]} of dimension ${b} out of bounds.`)}else c.begin[b]=_1(c.begin[b],0,c.strides[b],v,w,T),c.end[b]=_1(c.end[b],1,c.strides[b],v,w,T);let P=c.strides[b]===1&&c.begin[b]===0&&c.end[b]===v;h=h&&P,m=m&&(b===0&&c.strides[b]===1||P)}else h=h&&c.strides[b]===1&&C,m=m&&(b===0&&c.strides[b]===1||C);let E,$=!1;if(c.beginValid&&c.endValid?(E=c.end[b]-c.begin[b],$=!0):x?(E=1,$=!0):C&&v>=0&&(c.strides[b]<0?E=-v:E=v,$=!0),$){let P;E===0||E<0!=c.strides[b]<0?P=0:P=Math.trunc(E/c.strides[b])+(E%c.strides[b]!==0?1:0),g.push(P)}else g.push(-1)}for(let b=0;b<c.finalShapeGatherIndices.length;++b){let x=c.finalShapeGatherIndices[b];x>=0?y.push(g[x]):x===$b&&y.push(1)}return{finalShapeSparse:y.filter((b,x)=>c.finalShapeGatherIndices[x]!==$b),finalShape:y,isIdentity:h,sliceDim0:m,isSimpleSlice:f,begin:c.begin,end:c.end,strides:c.strides}}function bR(e,t){t.beginMask=0,t.endMask=0,t.shrinkAxisMask=0;let n=0;t.beginValid=e.begin!=null,t.endValid=e.end!=null,t.begin=new Array(t.dims),t.end=new Array(t.dims),t.strides=new Array(t.dims),t.finalShapeGatherIndices=[],t.finalShapeGatherIndicesSparse=[],t.inputShapeGatherIndicesSparse=new Array(t.dims);for(let a=0;a<e.dims;a++)if(1<<a&e.ellipsisMask){let r=Math.min(t.dims-(e.dims-a)+1+e.numAddAxisAfterEllipsis,t.dims);for(;n<r;n++)t.begin[n]=0,t.end[n]=0,t.strides[n]=1,t.beginMask|=1<<n,t.endMask|=1<<n,t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(-1),t.inputShapeGatherIndicesSparse[n]=a}else if(1<<a&e.newAxisMask)t.finalShapeGatherIndices.push($b),t.finalShapeGatherIndicesSparse.push(-1);else{if(n===t.begin.length)throw Error(`Index out of range using input dim ${n}; input has only ${t.dims} dims, ${t.begin.length}.`);e.begin!=null&&(t.begin[n]=e.begin[a]),e.end!=null&&(t.end[n]=e.end[a]),t.strides[n]=e.strides[a],e.beginMask&1<<a&&(t.beginMask|=1<<n),e.endMask&1<<a&&(t.endMask|=1<<n),e.shrinkAxisMask&1<<a?(t.finalShapeGatherIndices.push(pR),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<n):(t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(a)),t.inputShapeGatherIndicesSparse[n]=a,n++}}function _1(e,t,n,a,r,s){if(r[t])return n>0?s[t]:s[t+1&1];{let i=e<0?a+e:e;return i<s[0]?s[0]:i>s[1]?s[1]:i}}var se={};Me(se,{Serializable:()=>qI,SerializationMap:()=>Hs,registerClass:()=>ys});var qI=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Hs=class{constructor(){this.classNameMap={}}static getMap(){return Hs.instance==null&&(Hs.instance=new Hs),Hs.instance}static register(e){Hs.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function ys(e){R(e.className!=null,()=>"Class being registered does not have the static className property defined."),R(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),R(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Hs.register(e)}var KI={};Me(KI,{TEST_EPSILON_FLOAT16:()=>XI,encodeStrings:()=>YI,expectArrayBuffersEqual:()=>NR,expectArraysClose:()=>vR,expectArraysEqual:()=>kR,expectNumbersClose:()=>IR,expectPromiseToFail:()=>wR,expectValuesInRange:()=>SR,testEpsilon:()=>Rx});var xR=.001,XI=.1;function vR(e,t,n){return n==null&&(n=Rx()),Fb(e,t,(a,r)=>Mx(a,r,n))}function Rx(){return L.backend.floatPrecision()===32?xR:XI}function Fb(e,t,n){let a=!0;if((hn(e)||hn(t))&&(a=!1),hn(e)&&hn(t)&&(a=!0),a){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=lr(e),o=lr(t);if(!cs(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let r=hn(e)?e:ei(e),s=hn(t)?t:ei(t);if(r.length!==s.length)throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${s.length}.
Actual: ${r}.
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=r[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
Actual: ${r}.
Expected: ${s}.`)}}function wR(e,t){e().then(()=>t.fail(),()=>t())}function kR(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Kr(e)||Kr(e[0])||Kr(t)||Kr(t[0])?Fb(e,n,(a,r)=>a==r):Fb(e,t,(a,r)=>Mx(a,r,0))}function IR(e,t,n){if(n==null&&(n=Rx()),!Mx(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Mx(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function SR(e,t,n){for(let a=0;a<e.length;a++)if(e[a]<t||e[a]>n)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function NR(e,t){let n=new Float32Array(e),a=new Float32Array(t);if(n.length!==a.length)throw new Error(`Expected ArrayBuffer to be of length ${a.length}, but it was ${n.length}`);for(let r=0;r<a.length;r++)if(n[r]!==a[r])throw new Error(`Expected ArrayBuffer value at ${r} to be ${a[r]} but got ${n[r]} instead`)}function YI(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?YI(n):e[t]=Cc(n)}return e}var TR="3.16.0";function CR(){X().set("PROD",!0)}function _R(){X().set("DEBUG",!0)}function ER(){X().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Px(e){X().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}KF(Px);function AR(){L.disposeVariables()}function ar(){return L}function $h(){return L.memory()}function $R(e){return L.profile(e)}function O(e,t){return L.tidy(e,t)}function Re(e){Sx(e).forEach(t=>t.dispose())}function en(e){return L.keep(e)}function FR(e){return L.time(e)}function DR(e){return L.setBackend(e)}function RR(){return L.ready()}function MR(){return L.backendName}function PR(e){L.removeBackend(e)}function OR(e){return L.findBackend(e)}function LR(e){return L.findBackendFactory(e)}function Mm(e,t,n=1){return L.registerBackend(e,t,n)}function JI(){return L.backend}function zR(e,t){X().setPlatform(e,t)}function BR(e,t){let n=A(e,"a","add"),a=A(t,"b","add");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(ds,r)}var J=B({add_:BR});function WR(e,t){let n=A(e,"a","floorDiv"),a=A(t,"b","floorDiv");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(Mi,r)}var Pm=B({floorDiv_:WR});function VR(e,t){let n=A(e,"a","div"),a=A(t,"b","div");if([n,a]=$t(n,a),n.dtype==="int32"&&a.dtype==="int32")return Pm(n,a);let r={a:n,b:a},s={};return L.runKernel($i,r,s)}var fe=B({div_:VR});function UR(e,t){let n=A(e,"a","mul"),a=A(t,"b","mul");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(Ki,r)}var z=B({mul_:UR});function GR(e){let t=A(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return L.runKernel(pc,n)}else{let n={x:t};return L.runKernel(Il,n)}}var zt=B({abs_:GR});function HR(e){let t={x:A(e,"x","acos")};return L.runKernel(Sl,t)}var Ox=B({acos_:HR});function jR(e){let t={x:A(e,"x","acosh")};return L.runKernel(Nl,t)}var Lx=B({acosh_:jR});function qR(e){R(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),R(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((r,s)=>A(r,`tensors${s}`,"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(!cs(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let a=t;return L.runKernel(xi,a)}var ZI=B({addN_:qR});function KR(e,t=null,n=!1){let a={x:A(e,"x","all","bool")},r={axis:t,keepDims:n};return L.runKernel(Tl,a,r)}var Om=B({all_:KR});function XR(e,t=null,n=!1){let a={x:A(e,"x","any","bool")},r={axis:t,keepDims:n};return L.runKernel(Cl,a,r)}var Xp=B({any_:XR});function YR(e,t=0){let n={x:A(e,"x","argMax")},a={axis:t};return L.runKernel(vi,n,a)}var ii=B({argMax_:YR});function JR(e,t=0){let n={x:A(e,"x","argMin")},a={axis:t};return L.runKernel(lc,n,a)}var zx=B({argMin_:JR});function ZR(e){let t={x:A(e,"x","asin")};return L.runKernel(_l,t)}var Bx=B({asin_:ZR});function QR(e){let t={x:A(e,"x","asinh")};return L.runKernel(El,t)}var Wx=B({asinh_:QR});function eM(e){let t={x:A(e,"x","atan")};return L.runKernel(Al,t)}var Vx=B({atan_:eM});function tM(e,t){let n=A(e,"a","atan2"),a=A(t,"b","atan2");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(Fl,r)}var Ux=B({atan2_:tM});function nM(e){let t={x:A(e,"x","atanh")};return L.runKernel($l,t)}var Gx=B({atanh_:nM});function aM(e,t,n,a,r="NHWC",s){let i=e[3],o=[...t,i],l=tS(r);return Ac(e,o,n,s,a,null,null,l)}function QI(e,t,n,a,r,s,i="channelsLast"){let[o,l]=Fh(t),u;if(i==="channelsLast")u=[o,l,e[3],e[3]];else if(i==="channelsFirst")u=[o,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return Ac(e,u,n,a,r,s,!1,i)}function rM(e,t,n,a,r,s,i="NDHWC"){let[o,l,u]=Db(t),p,d;if(i==="NDHWC")d="channelsLast",p=[o,l,u,e[4],e[4]];else if(i==="NCDHW")d="channelsFirst",p=[o,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return eS(e,p,n,a,r,!1,d,s)}function Ac(e,t,n,a,r,s,i=!1,o="channelsLast"){let[l,u,p,d]=[-1,-1,-1,-1];if(o==="channelsLast")[l,u,p,d]=e;else if(o==="channelsFirst")[l,d,u,p]=e;else throw new Error(`Unknown dataFormat ${o}`);let[c,h,,m]=t,[f,g]=Fh(n),[y,b]=Fh(a),x=sl(c,y),v=sl(h,b),{padInfo:w,outHeight:T,outWidth:C}=oM(r,u,p,f,g,x,v,s,o),E=i?m*d:m,$;return o==="channelsFirst"?$=[l,E,T,C]:o==="channelsLast"&&($=[l,T,C,E]),{batchSize:l,dataFormat:o,inHeight:u,inWidth:p,inChannels:d,outHeight:T,outWidth:C,outChannels:E,padInfo:w,strideHeight:f,strideWidth:g,filterHeight:c,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:y,dilationWidth:b,inShape:e,outShape:$,filterShape:t}}function eS(e,t,n,a,r,s=!1,i="channelsLast",o){let[l,u,p,d,c]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,u,p,d,c]=e;else if(i==="channelsFirst")[l,c,u,p,d]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,m,f,,g]=t,[y,b,x]=Db(n),[v,w,T]=Db(a),C=sl(h,v),E=sl(m,w),$=sl(f,T),{padInfo:P,outDepth:F,outHeight:S,outWidth:M}=lM(r,u,p,d,y,b,x,C,E,$,o),V=s?g*c:g,j;return i==="channelsFirst"?j=[l,V,F,S,M]:i==="channelsLast"&&(j=[l,F,S,M,V]),{batchSize:l,dataFormat:i,inDepth:u,inHeight:p,inWidth:d,inChannels:c,outDepth:F,outHeight:S,outWidth:M,outChannels:V,padInfo:P,strideDepth:y,strideHeight:b,strideWidth:x,filterDepth:h,filterHeight:m,filterWidth:f,effectiveFilterDepth:C,effectiveFilterHeight:E,effectiveFilterWidth:$,dilationDepth:v,dilationHeight:w,dilationWidth:T,inShape:e,outShape:j,filterShape:t}}function sM(e,t,n,a,r){a==null&&(a=Hx(e,t,n));let s=e[0],i=e[1],o=Js((s-t+2*a)/n+1,r),l=Js((i-t+2*a)/n+1,r);return[o,l]}function iM(e,t,n,a,r,s){r==null&&(r=Hx(e,t,a));let i=e[0],o=e[1],l=e[2],u=Js((i-t+2*r)/a+1,s),p=Js((o-t+2*r)/a+1,s),d=Js((l-t+2*r)/a+1,s);return[u,p,d,n]}function Hx(e,t,n,a=1){let r=sl(t,a);return Math.floor((e[0]*(n-1)-n+r)/2)}function Fh(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Db(e){return typeof e=="number"?[e,e,e]:e}function sl(e,t){return t<=1?e:e+(e-1)*(t-1)}function oM(e,t,n,a,r,s,i,o,l){let u,p,d;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let c=sM([t,n],s,a,e,o);p=c[0],d=c[1]}else if(e==="same"){p=Math.ceil(t/a),d=Math.ceil(n/r);let c=Math.max(0,(p-1)*a+s-t),h=Math.max(0,(d-1)*r+i-n),m=Math.floor(c/2),f=c-m,g=Math.floor(h/2),y=h-g;u={top:m,bottom:f,left:g,right:y,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},p=Math.ceil((t-s+1)/a),d=Math.ceil((n-i+1)/r);else if(typeof e=="object"){let c=l==="channelsLast"?e[1][0]:e[2][0],h=l==="channelsLast"?e[1][1]:e[2][1],m=l==="channelsLast"?e[2][0]:e[3][0],f=l==="channelsLast"?e[2][1]:e[3][1];u={top:c,bottom:h,left:m,right:f,type:c===0&&h===0&&m===0&&f===0?"VALID":"EXPLICIT"},p=Js((t-s+c+h)/a+1,o),d=Js((n-i+m+f)/r+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:p,outWidth:d}}function lM(e,t,n,a,r,s,i,o,l,u,p){let d,c,h,m;if(typeof e=="number"){d={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let f=iM([t,n,a,1],o,1,r,e,p);c=f[0],h=f[1],m=f[2]}else if(e==="same"){c=Math.ceil(t/r),h=Math.ceil(n/s),m=Math.ceil(a/i);let f=(c-1)*r+o-t,g=(h-1)*s+l-n,y=(m-1)*i+u-a,b=Math.floor(f/2),x=f-b,v=Math.floor(g/2),w=g-v,T=Math.floor(y/2),C=y-T;d={top:v,bottom:w,left:T,right:C,front:b,back:x,type:"SAME"}}else if(e==="valid")d={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},c=Math.ceil((t-o+1)/r),h=Math.ceil((n-l+1)/s),m=Math.ceil((a-u+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:d,outDepth:c,outHeight:h,outWidth:m}}function Js(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 as(e){let[t,n,a]=Fh(e);return t===1&&n===1&&a===1}function dr(e,t){return as(e)||as(t)}function tS(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function Tn(e,t,n){if(n!=null){if(typeof t=="string")throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);if(typeof t=="number")R(ll(t),()=>`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);else if(typeof t=="object")t.forEach(a=>{a.forEach(r=>{R(ll(r),()=>`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`)})});else throw Error(`Error in ${e}: Unknown padding parameter: ${t}`)}}function uM(e,t){let n={x:A(e,"x","reshape","string_or_numeric")},a={shape:t};return L.runKernel(lu,n,a)}var W=B({reshape_:uM});function pM(e,t,n,a,r){let s=A(e,"x","avgPool","float32"),i=1;R(dr(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=W(s,[1,s.shape[0],s.shape[1],s.shape[2]])),R(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),Tn("avgPool",a,r);let u={x:o},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r},d=L.runKernel(wi,u,p);return d=oe(d,s.dtype),l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var fa=B({avgPool_:pM});function cM(e,t,n,a,r,s="NDHWC"){let i=A(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=W(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),R(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),R(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Tn("avgPool3d",a,r);let u={x:o},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},d=L.runKernel(uc,u,p);return d=oe(d,o.dtype),l?W(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var jx=B({avgPool3d_:cM});function dM(e,t=0){R(e.length>=1,()=>"Pass at least one tensor to concat");let n=Kp(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${s.dtype}. `)}),n.length===1)return Nr(n[0]);let a=n,r={axis:t};return L.runKernel(Rl,a,r)}var Qe=B({concat_:dM});function hM(e){let t={x:A(e,"x","sigmoid","float32")};return L.runKernel(oo,t)}var ha=B({sigmoid_:hM});function mM(e,t,n){let a=A(e,"x","slice","string_or_numeric");if(a.rank===0)throw new Error("Slicing scalar is not possible");let r={x:a},s={begin:t,size:n};return L.runKernel(du,r,s)}var Ge=B({slice_:mM});function fM(e){let t={x:A(e,"x","tanh","float32")};return L.runKernel(fo,t)}var oi=B({tanh_:fM});function gM(e,t,n,a,r,s){let i=A(e,"forgetBias","basicLSTMCell"),o=A(t,"lstmKernel","basicLSTMCell"),l=A(n,"lstmBias","basicLSTMCell"),u=A(a,"data","basicLSTMCell"),p=A(r,"c","basicLSTMCell"),d=A(s,"h","basicLSTMCell"),c=Qe([u,d],1),h=De(c,o),m=J(h,l),f=m.shape[0],g=m.shape[1]/4,y=[f,g],b=Ge(m,[0,0],y),x=Ge(m,[0,g],y),v=Ge(m,[0,g*2],y),w=Ge(m,[0,g*3],y),T=J(z(ha(b),oi(x)),z(p,ha(J(i,v)))),C=z(oi(T),ha(w));return[T,C]}var yM=B({basicLSTMCell_:gM});function bM(e,t,n){let a=A(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);R(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),R(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),R(a.shape[0]%r===0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return L.runKernel(Dl,s,i)}var $c=B({batchToSpaceND_:bM});function xM(e){let t;return e.rank===0||e.rank===1?t=W(e,[1,1,1,e.size]):e.rank===2?t=W(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function vM(e,t,n,a,r,s){s==null&&(s=.001);let i=A(e,"x","batchNorm"),o=A(t,"mean","batchNorm"),l=A(n,"variance","batchNorm"),u;r!=null&&(u=A(r,"scale","batchNorm"));let p;a!=null&&(p=A(a,"offset","batchNorm")),R(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),R(p==null||o.rank===p.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),R(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let d={x:xM(i),scale:u,offset:p,mean:o,variance:l},c={varianceEpsilon:s},h=L.runKernel(Pi,d,c);return W(h,i.shape)}var Cr=B({batchNorm_:vM});function wM(e,t,n,a,r,s){let i=A(e,"x","batchNorm"),o=A(t,"mean","batchNorm"),l=A(n,"variance","batchNorm"),u;r!=null&&(u=A(r,"scale","batchNorm"));let p;return a!=null&&(p=A(a,"offset","batchNorm")),R(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),R(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),R(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&R(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),p!=null&&R(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),Cr(i,o,l,p,u,s)}var nS=B({batchNorm2d_:wM});function kM(e,t,n,a,r,s){let i=A(e,"x","batchNorm"),o=A(t,"mean","batchNorm"),l=A(n,"variance","batchNorm"),u;r!=null&&(u=A(r,"scale","batchNorm"));let p;return a!=null&&(p=A(a,"offset","batchNorm")),R(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),R(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),R(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&R(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&R(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),Cr(i,o,l,p,u,s)}var aS=B({batchNorm3d_:kM});function IM(e,t,n,a,r,s){let i=A(e,"x","batchNorm"),o=A(t,"mean","batchNorm"),l=A(n,"variance","batchNorm"),u;r!=null&&(u=A(r,"scale","batchNorm"));let p;return a!=null&&(p=A(a,"offset","batchNorm")),R(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),R(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),R(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&R(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&R(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),Cr(i,o,l,p,u,s)}var rS=B({batchNorm4d_:IM});function SM(e,t,n){let a=A(e,"x","bincount"),r=A(t,"weights","bincount");R(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),R(n>=0,()=>`size must be non-negative, but got ${n}.`),R(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return L.runKernel(am,s,i)}var qx=B({bincount_:SM});function NM(e,t){let n=A(e,"s0","broadcastArgs","int32"),a=A(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(a.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${a.rank}`);let r={s0:n,s1:a};return L.runKernel(rm,r)}var sS=B({broadcastArgs_:NM});function TM(e,t){let n=A(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=W(n,l)}let r=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return Nr(n);let i={x:n},o={reps:s};return L.runKernel(ms,i,o)}var il=B({broadcastTo_:TM});function CM(e){let t={x:A(e,"x","ceil","float32")};return L.runKernel(Si,t)}var Kx=B({ceil_:CM});function _M(e,t,n){let a=A(e,"x","clipByValue");R(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return L.runKernel(hs,r,s)}var nn=B({clipByValue_:_M});function EM(e){return Qe(e,0)}var iS=B({concat1d_:EM});function AM(e,t){return Qe(e,t)}var oS=B({concat2d_:AM});function $M(e,t){return Qe(e,t)}var lS=B({concat3d_:$M});function FM(e,t){return Qe(e,t)}var uS=B({concat4d_:FM});function DM(e,t,n,a,r="NHWC",s=[1,1],i){let o=A(e,"x","conv2d","float32"),l=A(t,"filter","conv2d","float32"),u=o,p=!1;o.rank===3&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1],o.shape[2]])),R(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),R(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Tn("conv2d",a,i);let d=r==="NHWC"?u.shape[3]:u.shape[1];R(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),R(dr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let c={x:u,filter:l},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=L.runKernel(Ni,c,h);return p?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Rt=B({conv2d_:DM});function RM(e,t,n,a,r="NWC",s=1,i){let o=A(e,"x","conv1d"),l=A(t,"filter","conv1d"),u=o,p=!1;o.rank===2&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1]])),R(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),R(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Tn("conv1d",a,i),R(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),R(dr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),R(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=W(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=Rt(c,d,[1,n],a,"NHWC",[1,s],i);return p?W(h,[h.shape[2],h.shape[3]]):W(h,[h.shape[0],h.shape[2],h.shape[3]])}var Lm=B({conv1d_:RM});function MM(e,t,n,a,r,s="NHWC",i){R(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),R(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),R(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),R(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let p=s==="NHWC"?o[3]:o[1],d=s==="NHWC"?l.shape[3]:l.shape[1];R(p===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${p}) must match input depth for filter ${n.shape[2]}.`),R(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),Tn("conv2dDerInput",r,i);let c={dy:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=L.runKernel(Ti,c,h);return u?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Xx=B({conv2DBackpropInput_:MM});function PM(e,t,n,a,r,s){let i=A(e,"x","conv2dTranspose"),o=A(t,"filter","conv2dTranspose");return Xx(n,i,o,a,r,"NHWC",s)}var zm=B({conv2dTranspose_:PM});function OM(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=A(e,"x","conv3d"),o=A(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=W(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),R(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),R(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),R(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),R(dr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),R(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let p={x:l,filter:o},d={strides:n,pad:a,dataFormat:r,dilations:s},c=L.runKernel(cc,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var Yx=B({conv3d_:OM});function LM(e,t,n,a,r){R(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=W(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],u=i.shape[4];R(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),R(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),R(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),R(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),R(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let p={dy:i,filter:n},d={pad:r,strides:a,inputShape:s},c=L.runKernel(lm,p,d);return o?W(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var pS=B({conv3DBackpropInput_:LM});function zM(e,t,n,a,r){let s=A(e,"x","conv3dTranspose"),i=A(t,"filter","conv3dTranspose");return pS(n,s,i,a,r)}var cS=B({conv3dTranspose_:zM});function BM(e){let t={x:A(e,"x","cos","float32")};return L.runKernel(Ci,t)}var Fc=B({cos_:BM});function WM(e){let t={x:A(e,"x","cosh","float32")};return L.runKernel(_i,t)}var Bm=B({cosh_:WM});function VM(e,t=0,n=!1,a=!1){let r={x:A(e,"x","cumprod")},s={axis:t,exclusive:n,reverse:a};return L.runKernel(Ml,r,s)}var Yp=B({cumprod_:VM});function UM(e,t=0,n=!1,a=!1){let r={x:A(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return L.runKernel(Ei,r,s)}var Wm=B({cumsum_:UM});function GM(e,t,n,a=!1){let r=A(e,"x","denseBincount"),s=A(t,"weights","denseBincount");R(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),R(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),R(n>=0,()=>`size must be non-negative, but got ${n}.`),R(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:n,binaryOutput:a};return L.runKernel(um,i,o)}var dS=B({denseBincount_:GM});function HM(e,t,n="NHWC"){let a=A(e,"x","depthToSpace","float32"),r=n==="NHWC"?a.shape[1]:a.shape[2],s=n==="NHWC"?a.shape[2]:a.shape[3],i=n==="NHWC"?a.shape[3]:a.shape[1];R(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),R(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${r} and ${t} for depthToSpace with input shape
${a.shape}`),R(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${s} and ${t} for depthToSpace with input shape
${a.shape}`),R(i%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${a.shape}`);let o={x:a},l={blockSize:t,dataFormat:n};return L.runKernel(Ol,o,l)}var Jx=B({depthToSpace_:HM});function jM(e,t,n,a,r="NHWC",s=[1,1],i){let o=A(e,"x","depthwiseConv2d","float32"),l=A(t,"filter","depthwiseConv2d","float32"),u=o,p=!1;o.rank===3&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1],o.shape[2]])),R(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),R(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),R(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),Tn("depthwiseConv2d",a,i);let d={x:u,filter:l},c={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=L.runKernel(Ai,d,c);return p?W(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var bs=B({depthwiseConv2d_:jM});function qM(e){let t={x:A(e,"x","diag")};return L.runKernel(dm,t)}var KM=B({diag_:qM});function XM(e,t,n,a,r=[1,1],s="NHWC"){let i=A(e,"x","dilation2d"),o=A(t,"filter","dilation2d");R(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),R(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),R(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,u=!1;i.rank===3&&(l=W(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let p={x:l,filter:o},d={strides:n,pad:a,dilations:r},c=L.runKernel(dc,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Zx=B({dilation2d_:XM});function YM(e,t){let n=A(e,"a","equal","string_or_numeric"),a=A(t,"b","equal","string_or_numeric");[n,a]=$t(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(zl,r)}var Qn=B({equal_:YM});function JM(e,t,n){let a=A(t,"a","where"),r=A(n,"b","where"),s=A(e,"condition","where","bool"),i=ht(ht(s.shape,a.shape),r.shape),o=il(s,i),l=il(a,i),u=il(r,i),p={condition:o,t:l,e:u};return L.runKernel(pu,p)}var fn=B({where_:JM});function ZM(e){let t={x:A(e,"x","zerosLike")};return L.runKernel(Iu,t)}var Ke=B({zerosLike_:ZM});function QM(e,t){let n=A(e,"a","div"),a=A(t,"b","div");[n,a]=$t(n,a);let r=fe(n,a),s=Ke(r),i=Qn(a,s);return fn(i,s,r)}var Qx=B({divNoNan_:QM});function eP(e,t){let n=A(e,"t1","dot"),a=A(t,"t2","dot");R((n.rank===1||n.rank===2)&&(a.rank===1||a.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${a.rank}.`);let r=n.rank===1?n.size:n.shape[1],s=a.rank===1?a.size:a.shape[0];if(R(r===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${s}.`),n.rank===1&&a.rank===1){let i=W(n,[1,-1]),o=W(a,[-1,1]),l=De(i,o);return W(l,[])}else if(n.rank===1&&a.rank===2){let i=W(n,[1,-1]),o=W(a,[a.shape[0],a.shape[1]]),l=De(i,o);return W(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=W(a,[-1,1]),o=De(n,i);return W(o,[o.size])}else{let i=W(a,[a.shape[0],a.shape[1]]);return De(n,i)}}var hS=B({dot_:eP});function tP(e,...t){let n=t.map((r,s)=>A(r,`tensors${s}`,"einsum")),a={equation:e};return L.runKernel(hm,n,a)}var mS=B({einsum_:tP});function nP(e){let t={x:A(e,"x","elu","float32")};return L.runKernel(Fi,t)}var Tu=B({elu_:nP});function aP(e){let t=A(e,"x","erf");R(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=oe(t,"float32"));let n={x:t};return L.runKernel(Ll,n)}var ev=B({erf_:aP});function rP(e){let t={x:A(e,"x","exp")};return L.runKernel(Di,t)}var gn=B({exp_:rP});function sP(e,t=0){let n=A(e,"x","expandDims","string_or_numeric");R(t<=n.rank,()=>"Axis must be <= rank of the tensor");let a={input:n},r={dim:t};return L.runKernel(Bl,a,r)}var mn=B({expandDims_:sP});function iP(e){let t={x:A(e,"x","expm1")};return L.runKernel(Wl,t)}var tv=B({expm1_:iP});function oP(e,t){let n=A(e,"x","tile","string_or_numeric");R(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let a={x:n},r={reps:t};return L.runKernel(ms,a,r)}var On=B({tile_:oP});function lP(e,t,n,a="float32"){t==null&&(t=e);let r=He([e,t],a),s=e<=t?e:t;for(let o=0;o<s;++o)r.set(1,o,o);let i=W(r.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return On(mn(i,0),[n[0],1,1]);if(n.length===2)return On(mn(mn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return On(mn(mn(mn(i,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var nv=B({eye_:lP});function Cn(e,t,n){let a={shape:e,value:t,dtype:n};return L.runKernel(hc,{},a)}function uP(e){let t={x:A(e,"x","floor","float32")};return L.runKernel(Ri,t)}var Cu=B({floor_:uP});function pP(e,t,n=0,a=0){let r=A(e,"x","gather"),s=A(t,"indices","gather","int32"),i={x:r,indices:s},o={axis:n,batchDims:a};return L.runKernel(Ul,i,o)}var li=B({gather_:pP});function cP(e,t){let n=A(e,"a","greater","string_or_numeric"),a=A(t,"b","greater","string_or_numeric");[n,a]=$t(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Hl,r)}var Gn=B({greater_:cP});function dP(e,t){let n=A(e,"a","greaterEqual","string_or_numeric"),a=A(t,"b","greaterEqual","string_or_numeric");[n,a]=$t(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Oi,r)}var xs=B({greaterEqual_:dP});function hP(e){let t={input:A(e,"input","imag")};return L.runKernel(ym,t)}var Vm=B({imag_:hP});function mP(e){let t={x:A(e,"x","isFinite")};return L.runKernel(jl,t)}var fS=B({isFinite_:mP});function fP(e){let t={x:A(e,"x","isInf")};return L.runKernel(ql,t)}var gS=B({isInf_:fP});function gP(e){let t={x:A(e,"x","isNaN")};return L.runKernel(Kl,t)}var av=B({isNaN_:gP});function yP(e,t=.2){let n={x:A(e,"x","leakyRelu")},a={alpha:t};return L.runKernel(zi,n,a)}var Dc=B({leakyRelu_:yP});function bP(e,t){let n=A(e,"a","less","string_or_numeric"),a=A(t,"b","less","string_or_numeric");[n,a]=$t(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Xl,r)}var Um=B({less_:bP});function xP(e,t){let n=A(e,"a","lessEqual","string_or_numeric"),a=A(t,"b","lessEqual","string_or_numeric");[n,a]=$t(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Yl,r)}var vs=B({lessEqual_:xP});function yS(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let a={start:e,stop:t,num:n};return L.runKernel(bm,{},a)}function vP(e,t=5,n=1,a=1,r=.5){let s=A(e,"x","localResponseNormalization");R(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${s.rank}.`),R(ll(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=W(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},u={depthRadius:t,bias:n,alpha:a,beta:r},p=L.runKernel(gc,l,u);return o?W(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var rv=B({localResponseNormalization_:vP});function wP(e){let t={x:A(e,"x","log","float32")};return L.runKernel(Bi,t)}var ea=B({log_:wP});function kP(e){let t={x:A(e,"x","log1p")};return L.runKernel(Jl,t)}var Rc=B({log1p_:kP});function IP(e){return R(es(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=A(t,"x","tf.grad","string_or_numeric"),r=n!=null?A(n,"dy","tf.grad"):null;return L.tidy(()=>{let{value:s,grads:i}=L.gradients(()=>e(a),[a],r);return r!=null&&Nn(s.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Gm(i),i[0]})}}function SP(e){return R(es(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{R(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=Kp(t,"args","tf.grads","string_or_numeric"),r=n!=null?A(n,"dy","tf.grads"):null;return L.tidy(()=>{let{value:s,grads:i}=L.gradients(()=>e(...a),a,r);return r!=null&&Nn(s.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Gm(i),i})}}function NP(e){return R(es(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{R(t instanceof Ae,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),R(n==null||n instanceof Ae,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:a,value:r}=L.gradients(()=>e(t),[t],n);return Gm(a),{grad:a[0],value:r}}}function TP(e){return R(es(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{R(Array.isArray(t)&&t.every(r=>r instanceof Ae),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),R(n==null||n instanceof Ae,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let a=L.gradients(()=>e(...t),t,n);return n!=null&&Nn(a.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Gm(a.grads),a}}function bS(e,t){R(es(e),()=>"The f passed in variableGrads(f) must be a function"),R(t==null||Array.isArray(t)&&t.every(u=>u instanceof ts),()=>"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 a=n?t.filter(u=>!u.trainable):null,r=t.length;t=t.filter(u=>u.trainable),R(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=L.gradients(e,t,null,s);R(o.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),R(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((u,p)=>{o[p]!=null&&(l[u.name]=o[p])}),a!=null&&a.forEach(u=>l[u.name]=null),{value:i,grads:l}}function ur(e){return L.customGrad(e)}function Gm(e){if(e.filter(t=>t==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 CP(e){let t={x:A(e,"x","neg")};return L.runKernel(eu,t)}var St=B({neg_:CP});function _P(e){let t={x:A(e,"x","softplus")};return L.runKernel(fu,t)}var bo=B({softplus_:_P});function EP(e){let t=A(e,"x","logSigmoid");return ur(n=>({value:St(bo(St(n))),gradFunc:a=>z(a,ha(St(n)))}))(t)}var xS=B({logSigmoid_:EP});function AP(e,t=null,n=!1){let a={x:A(e,"x","max")},r={reductionIndices:t,keepDims:n};return L.runKernel(Wi,a,r)}var Sa=B({max_:AP});function $P(e,t){let n=A(e,"a","sub"),a=A(t,"b","sub");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(ho,r)}var ce=B({sub_:$P});function FP(e,t=null,n=!1){let a=A(e,"x","sum");a.dtype==="bool"&&(a=oe(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return L.runKernel(uo,r,s)}var be=B({sum_:FP});function DP(e,t=-1){let n=A(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 ${t}`);return ur((a,r)=>{let s=Sa(a,t,!0),i=ce(a,s),o=ce(oe(i,"float32"),ea(be(gn(i),t,!0)));return r([o]),{value:o,gradFunc:(l,u)=>{let[p]=u,d=!0,c=gn(p);return ce(l,z(be(l,t,d),c))}}})(n)}var Hm=B({logSoftmax_:DP});function sv(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function vS(e,t,n){let a=e.length+t.length,r=[],s=0,i=0;for(let o=0;o<a;o++)n.indexOf(o)===-1?r.push(e[s++]):r.push(t[i++]);return r}function wS(e,t){let n=[],a=e.length;for(let s=0;s<a;s++)t.indexOf(s)===-1&&n.push(e[s]);let r=t.map(s=>e[s]);return[n,r]}function ui(e,t){let n=t.map(a=>1);return vS(e,n,t)}function RP(e,t,n){R(sv(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function kS(e,t){if(sv(e,t))return null;let n=[];for(let a=0;a<t;++a)e.indexOf(a)===-1&&n.push(a);return e.forEach(a=>n.push(a)),n}function iv(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function MP(e,t){let n=[];for(let a=t-e;a<t;++a)n.push(a);return n}function PP(e,t=null,n=!1){let a=A(e,"x","logSumExp"),r=Ca(t,a.shape),s=Sa(a,r,!0),i=ce(a,s),o=gn(i),l=be(o,r),u=ea(l),p=J(W(s,u.shape),u);if(n){let d=ui(p.shape,r);return W(p,d)}return p}var ov=B({logSumExp_:PP});function OP(e,t){let n=A(e,"a","logicalAnd","bool"),a=A(t,"b","logicalAnd","bool");ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Zl,r)}var Ta=B({logicalAnd_:OP});function LP(e){let t={x:A(e,"x","logicalNot","bool")};return L.runKernel(mc,t)}var Mc=B({logicalNot_:LP});function zP(e,t){let n=A(e,"a","logicalOr","bool"),a=A(t,"b","logicalOr","bool");ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(fc,r)}var jm=B({logicalOr_:zP});function BP(e,t){let n=A(e,"a","logicalXor","bool"),a=A(t,"b","logicalXor","bool");return ht(n.shape,a.shape),Ta(jm(e,t),Mc(Ta(e,t)))}var IS=B({logicalXor_:BP});function WP(e,t,n,a,r){let s=A(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=W(s,[1,s.shape[0],s.shape[1],s.shape[2]])),R(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),R(dr(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),Tn("maxPool",a,r);let u={x:o},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r},d=L.runKernel(Ui,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Pt=B({maxPool_:WP});function VP(e,t=[1,1,1],n,a,r,s="NDHWC"){let i=A(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=W(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),R(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),R(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Tn("maxPool3d",a,r);let u={x:o},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},d=L.runKernel(yc,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var lv=B({maxPool3d_:VP});function UP(e,t,n,a,r=!1){let s={x:A(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:a,includeBatchInIndex:r},o=L.runKernel(km,s,i);return{result:o[0],indexes:o[1]}}var SS=B({maxPoolWithArgmax_:UP});function GP(e,t){let n=A(e,"a","maximum"),a=A(t,"b","maximum");[n,a]=$t(n,a),n.dtype==="bool"&&(n=oe(n,"int32"),a=oe(a,"int32")),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Vi,r)}var hr=B({maximum_:GP});function HP(e,t=null,n=!1){let a={x:A(e,"x","mean")},r={axis:t,keepDims:n};return L.runKernel(Gi,a,r)}var Et=B({mean_:HP});function kt(e,t="float32"){if(t==="complex64"){let a=kt(e,"float32"),r=kt(e,"float32");return ns(a,r)}let n=em(xt(e),t);return L.makeTensor(n,e,t)}function Jn(e,t="float32"){if(t==="complex64"){let a=Jn(e,"float32"),r=kt(e,"float32");return ns(a,r)}let n=bx(xt(e),t);return L.makeTensor(n,e,t)}function jP(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let a=A(e,"x","meshgrid",e instanceof Ae?e.dtype:"float32");if(t===void 0)return[a];let r=A(t,"y","meshgrid",t instanceof Ae?t.dtype:"float32"),s=xt(a.shape),i=xt(r.shape);return n==="xy"?(a=W(a,[1,-1]),r=W(r,[-1,1]),[De(Jn([i,1],a.dtype),a),De(r,Jn([1,s],r.dtype))]):(a=W(a,[-1,1]),r=W(r,[1,-1]),[De(a,Jn([1,i],a.dtype)),De(Jn([s,1],r.dtype),r)])}function qP(e,t=null,n=!1){let a={x:A(e,"x","min")},r={axis:t,keepDims:n};return L.runKernel(Hi,a,r)}var Jp=B({min_:qP});function KP(e,t){let n=A(e,"a","minimum"),a=A(t,"b","minimum");[n,a]=$t(n,a),n.dtype==="bool"&&(n=oe(n,"int32"),a=oe(a,"int32")),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(ji,r)}var _u=B({minimum_:KP});function XP(e,t,n){R(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let a=A(e,"x","mirrorPad");if(a.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");R(t.length===a.rank,()=>`Padding doesn't match input. Must be ${a.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let o=0;o<a.rank;o++)R(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),R(t[o][0]>=0&&t[o][0]<=a.shape[o]-r&&t[o][1]>=0&&t[o][1]<=a.shape[o]-r,()=>`Padding in dimension ${o} cannot be greater than or equal to ${a.shape[o]-r} or less than 0 for input of shape ${a.shape}`);let s={paddings:t,mode:n},i={x:a};return L.runKernel(qi,i,s)}var uv=B({mirrorPad_:XP});function YP(e,t){let n=A(e,"a","mod"),a=A(t,"b","mod");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(Ql,r)}var pv=B({mod_:YP});function JP(e){let t=A(e,"x","square"),n={};return L.runKernel("Square",{x:t},n)}var lt=B({square_:JP});function ZP(e,t=null,n=!1){e=A(e,"x","moments");let a=Ca(t,e.shape),r=Et(e,a,n),s=r.shape;n||(s=ui(r.shape,a));let i=lt(ce(oe(e,"float32"),W(r,s))),o=Et(i,a,n);return{mean:r,variance:o}}var qm=B({moments_:ZP});function QP(e,t,n,a){let r=A(t,"data","multiRNNCell"),s=Kp(n,"c","multiRNNCell"),i=Kp(a,"h","multiRNNCell"),o=r,l=[];for(let d=0;d<e.length;d++){let c=e[d](o,s[d],i[d]);l.push(c[0]),l.push(c[1]),o=c[1]}let u=[],p=[];for(let d=0;d<l.length;d+=2)u.push(l[d]),p.push(l[d+1]);return[u,p]}var eO=B({multiRNNCell_:QP});function tO(e,t,n,a=!1){let r=A(e,"logits","multinomial"),s=r.size,i=r.rank;if(s<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);if(i>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);n=n||Math.random();let o={logits:i===1?W(r,[1,-1]):r},l={numSamples:t,seed:n,normalized:a},u=L.runKernel(Im,o,l);return i===1?W(u,[u.size]):u}var NS=B({multinomial_:tO});function nO(e,t){let n=A(e,"a","notEqual","string_or_numeric"),a=A(t,"b","notEqual","string_or_numeric");[n,a]=$t(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(tu,r)}var pi=B({notEqual_:nO});function aO(e){let t={x:A(e,"x","onesLike")};return L.runKernel(su,t)}var ta=B({onesLike_:aO});function rO(e,t){let n=A(e,"v1","outerProduct"),a=A(t,"v2","outerProduct");R(n.rank===1&&a.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${a.rank}.`);let r=W(n,[-1,1]),s=W(a,[1,-1]);return De(r,s)}var sO=B({outerProduct_:rO});function iO(e,t,n=0){let a=A(e,"x","pad");if(a.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let r={paddings:t,constantValue:n},s={x:a};return L.runKernel(Yi,s,r)}var ga=B({pad_:iO});function oO(e,t,n=0){return R(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),ga(e,[t],n)}var lO=B({pad1d_:oO});function uO(e,t,n=0){return R(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ga(e,t,n)}var pO=B({pad2d_:uO});function cO(e,t,n=0){return R(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."),ga(e,t,n)}var dO=B({pad3d_:cO});function hO(e,t,n=0){return R(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."),ga(e,t,n)}var mO=B({pad4d_:hO});function fO(e,t,n){let a=A(e,"x","spaceToBatchND");R(a.rank>=1+t.length,()=>`input rank ${a.rank} should be > than [blockShape] ${t.length}`),R(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),R(a.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]===0:i,!0),()=>`input spatial dimensions ${a.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let r={x:a},s={blockShape:t,paddings:n};return L.runKernel(gu,r,s)}var Pc=B({spaceToBatchND_:fO});function gO(e,t,n,a,r,s,i){r==null&&(r=[1,1]),s==null&&(s=1),a===0&&(a="valid");let o=A(e,"x","maxPool"),l=o,u=!1;o.rank===3&&(u=!0,l=W(o,[1,o.shape[0],o.shape[1],o.shape[2]])),R(dr(s,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${r}'`);let p=QI(l.shape,t,s,r,a),d=[p.dilationHeight,p.dilationWidth],c;a==="same"?c=bO([p.filterHeight,p.filterWidth],d):c=[[0,0],[0,0]];let h=d[0]===1&&d[1]===1,[m,f]=yO([p.inHeight,p.inWidth],d,c),g=h?a:"valid",y=h?l:Pc(l,d,m),b=(n==="avg"?()=>fa(y,t,s,g,i):()=>Pt(y,t,s,g,i))(),x=h?b:$c(b,d,f);return u?W(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function yO(e,t,n){let a=n.map(p=>p[0]),r=n.map(p=>p[1]),s=e.concat(a,r),i=t.map((p,d)=>(p-s[d]%p)%p),o=r.map((p,d)=>p+i[d]),l=t.map((p,d)=>[a[d],o[d]]),u=t.map((p,d)=>[0,i[d]]);return[l,u]}function bO(e,t){let n=e.map((s,i)=>s+(s-1)*(t[i]-1)).map(s=>s-1),a=n.map(s=>Math.floor(s/2)),r=n.map((s,i)=>s-a[i]);return n.map((s,i)=>[a[i],r[i]])}var TS=B({pool_:gO});function xO(e,t){let n=A(e,"base","pow"),a=A(t,"exp","pow");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(Ji,r)}var _r=B({pow_:xO});function vO(e,t){let n=A(e,"x","prelu"),a=A(t,"alpha","prelu"),r={x:n,alpha:a};return L.runKernel(Zi,r)}var Oc=B({prelu_:vO});function wO(e,t=null,n=!1){let a=A(e,"x","prod");a.dtype==="bool"&&(a=oe(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return L.runKernel(Qi,r,s)}var Km=B({prod_:wO});function kO(e,t,n){let a=xt(e),r=null;if(n==null||n==="float32")r=new Float32Array(a);else if(n==="int32")r=new Int32Array(a);else if(n==="bool")r=new Uint8Array(a);else throw new Error(`Unknown data type ${n}`);for(let s=0;s<a;s++)r[s]=t();return L.makeTensor(r,e,n)}var IO=B({rand_:kO}),cv=yi(qk()),dv=class{constructor(e,t,n,a,r){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=a,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=r||Math.random();this.random=cv.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let a=this.nextVal;return this.nextVal=NaN,a}let e,t,n=!1;for(;!n;){let a,r,s;do a=2*this.random()-1,r=2*this.random()-1,s=a*a+r*r;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*a*i,t=this.mean+this.stdDev*r*i,(!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}},SO=class{constructor(e,t,n,a){this.alpha=e,this.beta=1/t,this.dtype=n;let r=a||Math.random();this.randu=cv.alea(r.toString()),this.randn=new dv(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,a,r,s;for(;;){do a=this.randn.nextValue(),s=1+this.c*a;while(s<=0);if(s*=s*s,e=a*a,t=1-.331*e*e,n=.5*e+this.d*(1-s+Math.log(s)),r=this.randu(),r<t||Math.log(r)<n)break}return s=1/this.beta*this.d*s,this.alpha<1&&(s*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(s)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},NO=class{constructor(e=0,t=1,n,a){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,a==null&&(a=Math.random()),typeof a=="number"&&(a=a.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=cv.alea(a)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function TO(e,t,n=1,a="float32",r){if(n==null&&(n=1),a==null&&(a="float32"),a!=="float32"&&a!=="int32")throw new Error(`Unsupported data type ${a}`);let s=new SO(t,n,a,r),i=He(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var CO=B({randomGamma_:TO});function _O(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error(`Unsupported data type ${a}`);let s=new dv(t,n,a,!1,r),i=He(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var CS=B({randomNormal_:_O});function EO(e,t=0,n=1,a="float32",r){let s=He(e,a),i=new NO(t,n,null,r);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var Eu=B({randomUniform_:EO});function dl(e,t,n=1,a="float32"){if(n===0)throw new Error("Cannot have a step of zero");let r={start:e,stop:t,step:n,dtype:a};return L.runKernel(bc,{},r)}function AO(e){let t={input:A(e,"input","real")};return L.runKernel(Sm,t)}var Zp=B({real_:AO});function $O(e){let t={x:A(e,"x","reciprocal")};return L.runKernel(ou,t)}var hv=B({reciprocal_:$O});function FO(e){let t={x:A(e,"x","relu")};return L.runKernel(eo,t)}var Xe=B({relu_:FO});function DO(e){let t={x:A(e,"x","relu6")};return L.runKernel(no,t)}var Xm=B({relu6_:DO});function RO(e,t){let n={x:A(e,"x","reverse")},a={dims:t};return L.runKernel(ao,n,a)}var na=B({reverse_:RO});function MO(e){let t=A(e,"x","reverse");return R(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),na(t,0)}var PO=B({reverse1d_:MO});function OO(e,t){let n=A(e,"x","reverse");return R(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),na(n,t)}var LO=B({reverse2d_:OO});function zO(e,t){let n=A(e,"x","reverse");return R(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),na(n,t)}var BO=B({reverse3d_:zO});function WO(e,t){let n=A(e,"x","reverse");return R(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),na(n,t)}var VO=B({reverse4d_:WO});function UO(e){let t={x:A(e,"x","round")};return L.runKernel(ro,t)}var Ym=B({round_:UO});function GO(e){let t={x:A(e,"x","rsqrt","float32")};return L.runKernel(so,t)}var Jm=B({rsqrt_:GO});function we(e,t){if((hn(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"&&hn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return gs(e,[],[],t)}function HO(e){let t={x:A(e,"x","selu")};return L.runKernel(cu,t)}var Zm=B({selu_:HO});function jO(e,t,n,a,r,s=[1,1],i="NHWC"){let o=A(e,"x","separableConv2d"),l=A(t,"depthwiseFilter","separableConv2d"),u=A(n,"pointwiseFilter","separableConv2d"),p=o,d=!1;if(o.rank===3&&(d=!0,p=W(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");R(p.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${p.rank}.`),R(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),R(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),R(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),R(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let c=l.shape[2],h=l.shape[3];R(u.shape[2]===c*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${c*h}, but got ${u.shape[2]}.`);let m=bs(p,l,a,r,i,s),f=Rt(m,u,1,"valid",i);return d?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var xo=B({separableConv2d_:jO});async function qO(e,t){let n=A(e,"x","setdiff1d"),a=A(t,"y","setdiff1d");R(n.dtype===a.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${a.dtype}).`),R(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),R(a.rank===1,()=>`y should be 1D tensor, but got y (${a.shape}).`);let r=await n.data(),s=await a.data(),i=new Set(s),o=0;for(let p=0;p<r.length;p++)i.has(r[p])||o++;let l=new jt([o],n.dtype),u=new jt([o],"int32");for(let p=0,d=0;p<r.length;p++)i.has(r[p])||(l.values[d]=r[p],u.values[d]=p,d++);return[l.toTensor(),u.toTensor()]}var _S=qO;function KO(e){let t={x:A(e,"x","sign")};return L.runKernel(mu,t)}var mv=B({sign_:KO});function XO(e){let t={x:A(e,"x","sin","float32")};return L.runKernel(io,t)}var Qm=B({sin_:XO});function YO(e){let t={x:A(e,"x","sinh")};return L.runKernel(hu,t)}var ef=B({sinh_:YO});function JO(e,t,n){let a=A(e,"x","slice1d");return R(a.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${a.rank} tensor`),Ge(a,[t],[n])}var tf=B({slice1d_:JO});function ZO(e,t,n){let a=A(e,"x","slice2d");return R(a.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${a.rank} tensor`),Ge(a,t,n)}var fv=B({slice2d_:ZO});function QO(e,t,n){let a=A(e,"x","slice3d");return R(a.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${a.rank} tensor`),Ge(a,t,n)}var Au=B({slice3d_:QO});function e3(e,t,n){let a=A(e,"x","slice4d");return R(a.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${a.rank} tensor`),Ge(a,t,n)}var Qp=B({slice4d_:e3});function t3(e,t=-1){let n=A(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 a={logits:n},r={dim:t};return L.runKernel(po,a,r)}var Ja=B({softmax_:t3});function n3(e){R(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return L.runKernel(fm,t)}var Lc=B({fft_:n3});function a3(e){R(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return L.runKernel(gm,t)}var hl=B({ifft_:a3});function r3(e){let t=e.shape[e.shape.length-1],n=e.size/t,a;if(t<=2){let r=W(e,[n,t]);a=hl(r)}else{let r=[n,2*(t-1)],s=W(Zp(e),[n,t]),i=W(Vm(e),[n,t]),o=na(Ge(s,[0,1],[n,t-2]),1),l=z(na(Ge(i,[0,1],[n,t-2]),1),we(-1)),u=Qe([s,o],1),p=Qe([i,l],1),d=W(ns(u,p),[r[0],r[1]]);a=hl(d)}if(a=Zp(a),e.rank===3&&e.shape[0]!==0){let r=a,s=e.shape[0];a=W(a,[s,a.shape[0]/s,a.shape[1]]),r.dispose()}return a}var nf=B({irfft_:r3});function s3(e,t,n=0){let a={x:A(e,"x","split")},r={numOrSizeSplits:t,axis:n};return L.runKernel(yu,a,r)}var zn=B({split_:s3});function i3(e,t){R(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],a=e.size/n,r;if(t!=null&&t<n){let m=e.shape.map(g=>0),f=e.shape.map(g=>g);f[e.shape.length-1]=t,r=Ge(e,m,f),n=t}else if(t!=null&&t>n){let m=e.shape.map(f=>f);m[e.shape.length-1]=t-n,r=Qe([e,kt(m)],e.shape.length-1),n=t}else r=e;let s=Ke(r),i=W(ns(r,s),[a,n]),o=Lc(i),l=Math.floor(n/2)+1,u=Zp(o),p=Vm(o),d=zn(u,[l,n-l],u.shape.length-1),c=zn(p,[l,n-l],p.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,W(ns(d[0],c[0]),h)}var zc=B({rfft_:i3});function o3(e){let t={x:A(e,"x","sqrt","float32")};return L.runKernel(lo,t)}var ln=B({sqrt_:o3});function l3(e,t){let n=A(e,"a","squaredDifference"),a=A(t,"b","squaredDifference");[n,a]=$t(n,a),ht(n.shape,a.shape);let r={a:n,b:a},s={};return L.runKernel(co,r,s)}var af=B({squaredDifference_:l3});function u3(e,t){let n=A(e,"x","squeeze");return W(n,Jk(n.shape,t).newShape)}var pr=B({squeeze_:u3});function p3(e,t=0){let n=Kp(e,"tensors","stack","string_or_numeric");R(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&R(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let a=n,r={axis:t};return L.runKernel(iu,a,r)}var Mt=B({stack_:p3});function c3(e,t=0){let n={x:A(e,"x","step")},a={alpha:t};return L.runKernel(fs,n,a)}var $u=B({step_:c3});function d3(e,t,n,a,r=0,s=0,i=0,o=0,l=0){let u={x:A(e,"x","stridedSlice","string_or_numeric")},p={begin:t,end:n,strides:a,beginMask:r,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return L.runKernel(xu,u,p)}var gv=B({stridedSlice_:d3});function h3(e){let t={x:A(e,"x","tan","float32")};return L.runKernel(mo,t)}var yv=B({tan_:h3});function qe(e,t){bi(e);let n=lr(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return gs(e,null,n,t)}function Ha(e,t,n){if(bi(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let a=lr(e,n);if(a.length!==2&&a.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return gs(e,t,a,n)}function Za(e,t,n){if(bi(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let a=lr(e,n);if(a.length!==4&&a.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return gs(e,t,a,n)}function m3(e,t,n){if(bi(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let a=lr(e,n);if(a.length!==5&&a.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return gs(e,t,a,n)}function f3(e,t,n){if(bi(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let a=lr(e,n);if(a.length!==6&&a.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||a,gs(e,t,a,n)}function g3(e,t=1,n=!0){let a=A(e,"x","topk");if(a.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let r=a.shape[a.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 s={x:a},i={k:t,sorted:n},[o,l]=L.runKernel(vu,s,i);return{values:o,indices:l}}var bv=B({topk_:g3});function y3(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new dv(t,n,a,!0,r),i=He(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var rf=B({truncatedNormal_:y3});function b3(e,t=0){let n=A(e,"x","unique","string_or_numeric");R(n.rank>0,()=>"The input tensor must be at least 1D");let a={x:n},r={axis:t},[s,i]=L.runKernel($m,a,r);return{values:s,indices:i}}var Dh=B({unique_:b3});function x3(e,t,n){let a=A(e,"x","unsortedSegmentSum"),r=A(t,"segmentIds","unsortedSegmentSum","int32");R(ll(n),()=>"numSegments must be of dtype int");let s={x:a,segmentIds:r},i={numSegments:n};return L.runKernel(Sc,s,i)}var xv=B({unsortedSegmentSum_:x3});function v3(e,t=0){let n=A(e,"x","unstack","string_or_numeric");R(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let a={value:n},r={axis:t};return L.runKernel(ku,a,r)}var mt=B({unstack_:v3});function ES(e,t=!0,n,a){return L.makeVariable(e,t,n,a)}function AS(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let a=He(e,"int32"),r=He([n.length,e.length],"int32");for(let s=0;s<n.length;s++){let i=a.indexToLoc(n[s]),o=s*e.length;r.values.set(i,o)}return r.toTensor()}async function w3(e){let t=A(e,"condition","whereAsync","bool"),n=await t.data(),a=AS(t.shape,n);return e!==t&&t.dispose(),a}var vv=w3;async function k3(e,t,n){let a=A(e,"tensor","boolMask"),r=A(t,"mask","boolMask","bool"),s=n==null?0:n,i=r.rank,o=a.shape;R(i>0,()=>"mask cannot be scalar"),Nn(o.slice(s,s+i),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let f=s;f<s+i;f++)l*=o[f];let u=o.slice(0,s).concat([l],o.slice(s+i)),p=W(a,u),d=W(r,[-1]),c=await vv(d),h=pr(c,[1]),m=li(p,h,s);return e!==a&&a.dispose(),t!==r&&r.dispose(),h.dispose(),p.dispose(),d.dispose(),c.dispose(),m}var I3=k3;function S3(e,t="euclidean",n=null,a=!1){e=A(e,"x","norm");let r=$S(e,t,n),s=r.shape;if(a){let i=Ca(n,e.shape);s=ui(r.shape,i)}return W(r,s)}function $S(e,t,n=null){if(e.rank===0)return zt(e);if(e.rank!==1&&n===null)return $S(W(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return be(zt(e),n);if(t===1/0)return Sa(zt(e),n);if(t===-1/0)return Jp(zt(e),n);if(t==="euclidean"||t===2)return ln(be(_r(zt(e),we(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Sa(be(zt(e),n[0]),n[1]-1);if(t===1/0)return Sa(be(zt(e),n[1]),n[0]);if(t===-1/0)return Jp(be(zt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return ln(be(lt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var sf=B({norm_:S3});function N3(e,t,n,a,r=!0){let s=A(e,"v","movingAverage"),i=A(t,"x","movingAverage"),o=A(n,"decay","movingAverage");mI(s,i),R(cs(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=we(1),u=ce(l,o),p=z(ce(i,s),u);if(r){R(a!=null,()=>"When using zeroDebias: true, step is required.");let d=A(a,"step","movingAverage");p=fe(p,ce(l,_r(o,d)))}return J(s,p)}var T3=B({movingAverage_:N3});function C3(e,t,n){let a=A(e,"indices","scatterND","int32"),r=A(t,"updates","scatterND");Dx(r,a,n);let s={indices:a,updates:r},i={shape:n};return L.runKernel(uu,s,i)}var FS=B({scatterND_:C3});function _3(e,t,n,a){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,s=e.rank>1?e.shape[1]:1;if(n.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===r))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);if(t.dtype!==a.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function E3(e,t,n,a=0){let r=A(e,"sparseIndices","sparseToDense","int32"),s=A(t,"sparseValues","sparseToDense"),i=A(a,"defaultValue","sparseToDense",s.dtype);_3(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:n};return L.runKernel(Cm,o,l)}var wv=B({sparseToDense_:E3});function A3(e,t){let n=A(t,"indices","gatherND","int32"),a={params:A(e,"x","gatherND","string_or_numeric"),indices:n};return L.runKernel(Gl,a)}var DS=B({gatherND_:A3});function $3(e,t){if(t==null)return e.shape.slice();if(cs(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let a=0;a<e.shape.length;a++)t[a]==null&&e.shape[a]!=null?n.push(e.shape[a]):n.push(t[a]);return n}return t}function F3(e,t,n,a){let r=A(e,"x","dropout");if(R(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.`),R(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Ae?r.clone():r;let s=$3(r,n),i=1-t,o=fe(Cu(J(Eu(s,0,1,"float32",a),i)),i);return z(r,o)}var RS=B({dropout_:F3});function MS(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function kv(e,t,n){let a=1-e%2,r=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+a-1);r[s]=t-n*Math.cos(i)}return qe(r,"float32")}async function D3(e,t,n=1){let a=A(e,"predictions","inTopK"),r=A(t,"targets","inTopK");R(a.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${a.rank}`),R(a.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${a.rank} and targets rank ${r.rank}`),Nn(a.shape.slice(0,a.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=a.shape[a.shape.length-1];R(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await a.data(),o=await r.data(),[l,u]=[i.length/s,s],p=Zk("bool",l);for(let d=0;d<l;d++){let c=d*u,h=i.subarray(c,c+u),m=[];for(let f=0;f<h.length;f++)m.push({value:h[f],index:f});m.sort((f,g)=>g.value-f.value),p[d]=0;for(let f=0;f<n;f++)if(m[f].index===o[d]){p[d]=1;break}}return e!==a&&a.dispose(),t!==r&&r.dispose(),Zn(p,r.shape,"bool")}var R3=D3,rs={};Me(rs,{conv2d:()=>O3,depthwiseConv2d:()=>W3,matMul:()=>U3});function M3(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]])),R(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),R(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),R(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],p=s==="NHWC"?l.shape[3]:l.shape[1];R(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),R(p===n[3],()=>`Error in conv2dDerFilter: depth of dy (${p}) must match output depth for filter (${n[3]}).`),Tn("conv2dDerFilter",r,i);let d={x:o,dy:l},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return L.runKernel(im,d,c)}var Iv=B({conv2DBackpropFilter_:M3});function of(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return z(e,$u(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function lf(e,t){let n=t,a=Wt(e.shape,t.shape);return a.length>0&&(n=be(n,a)),W(n,e.shape)}function uf(e,t,n,a){if(t==="linear")return e;if(t==="relu")return Xe(e);if(t==="elu")return Tu(e);if(t==="relu6")return Xm(e);if(t==="prelu")return Oc(e,n);if(t==="leakyrelu")return Dc(e,a);if(t==="sigmoid")return ha(e);throw new Error(`Unknown fused activation ${t}.`)}var pf=(e,t)=>!(e>0)||t==="linear";function P3({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:p}){if(l=l||"linear",pf(L.state.gradientDepth,l)===!1){let w=Rt(e,t,n,a,r,s,i);return o!=null&&(w=J(w,o)),uf(w,l,u,p)}let d=A(e,"x","conv2d","float32"),c=A(t,"filter","conv2d","float32"),h=d,m=!1;d.rank===3&&(m=!0,h=W(d,[1,d.shape[0],d.shape[1],d.shape[2]])),R(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),R(c.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${c.rank}.`),Tn("fused conv2d",a,i),R(h.shape[3]===c.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${c.shape[2]}.`),R(dr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),R(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let f=Ac(h.shape,c.shape,n,s,a,i),g;o!=null&&(g=A(o,"bias","fused conv2d"),[g]=$t(g,d),ht(f.outShape,g.shape));let y;u!=null&&(y=A(u,"prelu weights","fused conv2d"));let b=(w,T)=>{let[C,E,$,P]=T,F=of(w,$,l);R(as(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let S=Xx(E.shape,F,C,n,a),M=Iv(E,F,C.shape,n,a),V=[S,M];if(P!=null){let j=lf(P,F);V.push(j)}return V},x={x:h,filter:c,bias:g,preluActivationWeights:y},v={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:p};return o==null?ur((w,T,C)=>{let E=L.runKernel(ni,x,v);return C([T,w,E]),m&&(E=W(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:b}})(h,c):ur((w,T,C,E)=>{let $=L.runKernel(ni,x,v);return E([T,w,$,C]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,c,g)}var O3=B({fusedConv2d_:P3});function L3(e,t,n,a,r,s=[1,1],i){let o=e;e.rank===3&&(o=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},p={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return L.runKernel(pm,u,p)}var PS=B({depthwiseConv2dNativeBackpropFilter_:L3});function z3(e,t,n,a,r,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},p={strides:a,pad:r,dimRoundingMode:i,dilations:s,inputShape:e},d=L.runKernel(cm,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var OS=B({depthwiseConv2dNativeBackpropInput_:z3});function B3({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:p}){if(pf(L.state.gradientDepth,l)===!1){let w=bs(e,t,n,a,r,s,i);return o!=null&&(w=J(w,o)),uf(w,l,u,p)}let d=A(e,"x","depthwiseConv2d","float32"),c=A(t,"filter","depthwiseConv2d","float32"),h=d,m=!1;d.rank===3&&(m=!0,h=W(d,[1,d.shape[0],d.shape[1],d.shape[2]])),R(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),R(c.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${c.rank}.`),R(h.shape[3]===c.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),s==null&&(s=[1,1]),R(dr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),Tn("fused depthwiseConv2d",a,i);let f=Ac(h.shape,c.shape,n,s,a,i,!0),g;o!=null&&(g=A(o,"bias","fused conv2d"),[g]=$t(g,d),ht(f.outShape,g.shape));let y;u!=null&&(y=A(u,"prelu weights","fused depthwiseConv2d"));let b=(w,T)=>{R(as(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[C,E,$,P]=T,F=of(w,$,l),S=OS(E.shape,F,C,n,a,s,i),M=PS(E,F,C.shape,n,a,s,i);if(P!=null){let V=lf(g,F);return[S,M,V]}return[S,M]},x={x:h,filter:c,bias:g,preluActivationWeights:y},v={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:p};return o==null?ur((w,T,C)=>{let E=L.runKernel(ai,x,v);return C([T,w,E]),m&&(E=W(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:b}})(h,c):ur((w,T,C,E)=>{let $=L.runKernel(ai,x,v);return E([T,w,$,C]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,c,g)}var W3=B({fusedDepthwiseConv2d_:B3});function V3({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(pf(L.state.gradientDepth,s)===!1){let P=De(e,t,n,a);return r!=null&&(P=J(P,r)),uf(P,s,i,o)}let l=A(e,"a","fused matMul"),u=A(t,"b","fused matMul");[l,u]=$t(l,u);let p=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=xt(m),y=xt(f);R(p===d,()=>`Error in fused matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=ht(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([c,h]),x=n?W(l,[g,p,c]):W(l,[g,c,p]),v=a?W(u,[y,h,d]):W(u,[y,d,h]),w;r!=null&&(w=A(r,"bias","fused matMul"),[w]=$t(w,l),ht(b,w.shape));let T;i!=null&&(T=A(i,"prelu weights","fused matMul"));let C=(P,F)=>{let[S,M,V,j]=F,q=of(W(P,V.shape),V,s),K,Q;if(!n&&!a?(K=De(q,M,!1,!0),Q=De(S,q,!0,!1)):!n&&a?(K=De(q,M,!1,!1),Q=De(q,S,!0,!1)):n&&!a?(K=De(M,q,!1,!0),Q=De(S,q,!1,!1)):(K=De(M,q,!0,!0),Q=De(q,S,!0,!0)),r!=null){let ee=lf(j,q);return[K,Q,ee]}else return[K,Q]},E={a:x,b:v,bias:w,preluActivationWeights:T},$={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?ur((P,F,S)=>{let M=L.runKernel(ti,E,$);return S([P,F,M]),{value:W(M,b),gradFunc:C}})(x,v):ur((P,F,S,M)=>{let V=L.runKernel(ti,E,$);return M([P,F,V,S]),{value:W(V,b),gradFunc:C}})(x,v,w)}var U3=B({fusedMatMul_:V3});function G3(e){return kv(e,.54,.46)}var H3=B({hammingWindow_:G3});function j3(e){return kv(e,.5,.5)}var LS=B({hannWindow_:j3});function q3(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Ge(e,s,t)),s+=n;if(a)for(;s<e.size;){let o=s+t-e.size,l=Qe([Ge(e,s,t-o),Cn([o],r)]);i.push(l),s+=n}return i.length===0?Ha([],[0,t]):W(Qe(i),[i.length,t])}var zS=B({frame_:q3});function K3(e,t,n,a,r=LS){a==null&&(a=MS(t));let s=zS(e,t,n),i=z(s,r(t));return zc(i,a)}var X3=B({stft_:K3});function Y3(e,t,n,a,r="bilinear",s=0){let i=A(e,"image","cropAndResize"),o=A(t,"boxes","cropAndResize","float32"),l=A(n,"boxInd","cropAndResize","int32"),u=o.shape[0];R(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),R(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),R(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),R(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${a.length}.`),R(a[0]>=1&&a[1]>=1,()=>`cropSize must be atleast [1,1], but was ${a}`),R(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let p={image:i,boxes:o,boxInd:l},d={method:r,extrapolationValue:s,cropSize:a};return L.runKernel(Pl,p,d)}var J3=B({cropAndResize_:Y3});function Z3(e){let t=A(e,"image","flipLeftRight","float32");R(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return L.runKernel(Vl,n,{})}var Q3=B({flipLeftRight_:Z3});function eL(e){let t=A(e,"image","grayscaleToRGB"),n=t.rank-1,a=t.shape[n];R(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),R(a===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${a}.`);let r=new Array(t.rank);return r.fill(1,0,n),r[n]=3,On(t,r)}var tL=B({grayscaleToRGB_:eL});function nL(e,t,n=0,a=.5){let r=A(e,"image","rotateWithOffset","float32");R(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return L.runKernel(Su,s,i)}var aL=B({rotateWithOffset_:nL});function Fu(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),R(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),R(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),R(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),R(t.rank===1,()=>"scores must be a 1D tensor"),R(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),R(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s}}function rL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=A(e,"boxes","nonMaxSuppression","float32"),i=A(t,"scores","nonMaxSuppression","float32"),o=Fu(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:a,scoreThreshold:r};return L.runKernel(nu,{boxes:s,scores:i},l)}var sL=B({nonMaxSuppression_:rL});function iL(e,t,n){let a=oL(e,t,n),r=a<0?-(a+1):a;e.splice(r,0,t)}function oL(e,t,n){return uL(e,t,n||lL)}function lL(e,t){return e>t?1:e<t?-1:0}function uL(e,t,n){let a=0,r=e.length,s=0,i=!1;for(;a<r;){s=a+(r-a>>>1);let o=n(t,e[s]);o>0?a=s+1:(r=s,i=!o)}return i?a:-a-1}function BS(e,t,n,a,r){return Sv(e,t,n,a,r,0)}function WS(e,t,n,a,r,s){return Sv(e,t,n,a,r,0,!1,s,!0)}function VS(e,t,n,a,r,s){return Sv(e,t,n,a,r,s,!0)}function Sv(e,t,n,a,r,s,i=!1,o=!1,l=!1){let u=[];for(let g=0;g<t.length;g++)t[g]>r&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(E1);let p=s>0?-.5/s:0,d=[],c=[];for(;d.length<n&&u.length>0;){let g=u.pop(),{score:y,boxIndex:b,suppressBeginIndex:x}=g;if(y<r)break;let v=!1;for(let w=d.length-1;w>=x;--w){let T=pL(e,b,d[w]);if(T>=a){v=!0;break}if(g.score=g.score*cL(a,p,T),g.score<=r)break}g.suppressBeginIndex=d.length,v||(g.score===y?(d.push(b),c.push(g.score)):g.score>r&&iL(u,g,E1))}let h=d.length,m=n-h;o&&m>0&&(d.push(...new Array(m).fill(0)),c.push(...new Array(m).fill(0)));let f={selectedIndices:d};return i&&(f.selectedScores=c),l&&(f.validOutputs=h),f}function pL(e,t,n){let a=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),s=Math.min(a[0],a[2]),i=Math.min(a[1],a[3]),o=Math.max(a[0],a[2]),l=Math.max(a[1],a[3]),u=Math.min(r[0],r[2]),p=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),c=Math.max(r[1],r[3]),h=(o-s)*(l-i),m=(d-u)*(c-p);if(h<=0||m<=0)return 0;let f=Math.max(s,u),g=Math.max(i,p),y=Math.min(o,d),b=Math.min(l,c),x=Math.max(y-f,0)*Math.max(b-g,0);return x/(h+m-x)}function cL(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function E1(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function dL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=A(e,"boxes","nonMaxSuppressionAsync"),i=A(t,"scores","nonMaxSuppressionAsync"),o=Fu(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],p=l[1],{selectedIndices:d}=BS(u,p,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),qe(d,"int32")}var hL=dL;function mL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=A(e,"boxes","nonMaxSuppression"),o=A(t,"scores","nonMaxSuppression"),l=Fu(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},p={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},d=L.runKernel(ru,u,p);return{selectedIndices:d[0],selectedScores:d[1]}}var fL=B({nonMaxSuppressionWithScore_:mL});async function gL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=A(e,"boxes","nonMaxSuppressionAsync"),o=A(t,"scores","nonMaxSuppressionAsync"),l=Fu(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),p=u[0],d=u[1],{selectedIndices:c,selectedScores:h}=VS(p,d,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:qe(c,"int32"),selectedScores:qe(h)}}var yL=gL;function bL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=A(e,"boxes","nonMaxSuppression"),o=A(t,"scores","nonMaxSuppression"),l=Fu(i,o,n,a,r,null),u=l.maxOutputSize,p=l.iouThreshold,d=l.scoreThreshold,c={boxes:i,scores:o},h={maxOutputSize:u,iouThreshold:p,scoreThreshold:d,padToMaxOutputSize:s},m=L.runKernel(au,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var xL=B({nonMaxSuppressionPadded_:bL});async function vL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=A(e,"boxes","nonMaxSuppressionAsync"),o=A(t,"scores","nonMaxSuppressionAsync"),l=Fu(i,o,n,a,r,null),u=l.maxOutputSize,p=l.iouThreshold,d=l.scoreThreshold,[c,h]=await Promise.all([i.data(),o.data()]),{selectedIndices:m,validOutputs:f}=WS(c,h,u,p,d,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:qe(m,"int32"),validOutputs:we(f,"int32")}}var wL=vL;function kL(e,t,n=!1,a=!1){let r=A(e,"images","resizeBilinear");R(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),R(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),R(a===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=W(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},u=L.runKernel(to,o,l);return i?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var US=B({resizeBilinear_:kL});function IL(e,t,n=!1,a=!1){let r=A(e,"images","resizeNearestNeighbor");R(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),R(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),R(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),R(a===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=W(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},u=L.runKernel(xc,o,l);return i?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var GS=B({resizeNearestNeighbor_:IL});function SL(e,t="binary",n=!1,a=.5){let r=A(e,"image","threshold"),s=.2989,i=.587,o=.114,l=r.shape[0]*r.shape[1],u=z(qe([a]),255),p,d,c,h;if(R(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),R(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]}.`),R(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),R(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[p,d,c]=zn(r,[1,1,1],-1);let f=z(p,s),g=z(d,i),y=z(c,o);h=J(J(f,g),y)}else h=e;if(t==="otsu"){let f=qx(oe(Ym(h),"int32"),Zn([]),256);u=NL(f,l)}let m=n?vs(h,u):Gn(h,u);return oe(z(m,255),"int32")}function NL(e,t){let n=qe([-1]),a=qe([0]),r=qe([0]),s,i,o,l,u,p;for(let d=0;d<e.size-1;d++){s=Ge(e,0,d+1),i=Ge(e,d+1),u=fe(be(s),t),p=fe(be(i),t);let c=be(z(s,dl(0,s.size)));o=fe(c,be(s));let h=Cn(i.shape,s.size),m=J(dl(0,i.size),h),f=z(i,m);l=fe(be(f),be(i));let g=ce(o,l),y=ce(o,l),b=z(u,p);r=z(z(b,g),y);let x=Gn(r,a);a=fn(x,r,a),n=fn(x,qe([d]),n)}return n}var TL=B({threshold_:SL});function CL(e,t,n="nearest",a="constant",r=0,s){let i=A(e,"image","transform","float32"),o=A(t,"transforms","transform","float32");R(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),R(o.rank===2&&(o.shape[0]===i.shape[0]||o.shape[0]===1)&&o.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),R(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},u={interpolation:n,fillMode:a,fillValue:r,outputShape:s};return L.runKernel(wu,l,u)}var _L=B({transform_:CL});function EL(e,t,n){R(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),R(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=A(e,"a","bandPart");R(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=W(dl(0,s,1,"int32"),[-1,1]),l=dl(0,i,1,"int32"),u=ce(o,l),p=Ta(vs(u,we(+t,"int32")),xs(u,we(-n,"int32"))),d=kt([s,i],a.dtype);return W(Mt(mt(W(a,[-1,s,i])).map(c=>fn(p,c,d))),r)}var AL=B({bandPart_:EL});function $L(e){let t;if(Array.isArray(e)){t=!1,R(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s<e.length;++s)R(e[s].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${r})`)}else t=!0,e=zn(e,e.shape[0],0).map(r=>pr(r,[0]));R(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],a=e;for(let r=0;r<e.length;++r)n.push(L.tidy(()=>{let s=a[r];if(r>0)for(let i=0;i<r;++i){let o=z(be(z(n[i],s)),n[i]);s=ce(s,o)}return fe(s,sf(s,"euclidean"))}));return t?Mt(n,0):n}var FL=B({gramSchmidt_:$L});function DL(e,t=!1){if(R(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return A1(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),a=mt(W(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(l=>{let[u,p]=A1(l,t);r.push(u),s.push(p)});let i=W(Mt(r,0),e.shape),o=W(Mt(s,0),e.shape);return[i,o]}}function A1(e,t=!1){return L.tidy(()=>{R(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],a=e.shape[1],r=nv(n),s=Nr(e),i=Ha([[1]],[1,1]),o=Nr(i),l=n>=a?a:n;for(let u=0;u<l;++u){let p=s,d=o,c=r;[o,s,r]=L.tidy(()=>{let h=Ge(s,[u,u],[n-u,1]),m=sf(h),f=Ge(s,[u,u],[1,1]),g=fn(Gn(f,0),Ha([[-1]]),Ha([[1]])),y=ce(f,z(g,m)),b=fe(h,y);b.shape[0]===1?o=Nr(i):o=Qe([i,Ge(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let x=St(fe(De(g,y),m)),v=Ge(s,[u,0],[n-u,a]),w=z(x,o),T=$e(o);if(u===0)s=ce(v,De(w,De(T,v)));else{let $=ce(v,De(w,De(T,v)));s=Qe([Ge(s,[0,0],[u,a]),$],0)}let C=$e(w),E=Ge(r,[0,u],[n,r.shape[1]-u]);if(u===0)r=ce(E,De(De(E,o),C));else{let $=ce(E,De(De(E,o),C));r=Qe([Ge(r,[0,0],[n,u]),$],1)}return[o,s,r]}),Re([p,d,c])}return!t&&n>a&&(r=Ge(r,[0,0],[n,a]),s=Ge(s,[0,0],[a,a])),[r,s]})}var RL=B({qr_:DL}),kn;(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"})(kn||(kn={}));function ML(e,t,n=kn.SUM_BY_NONZERO_WEIGHTS){let a=A(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=A(t,"weights","computeWeightedLoss"));let s=r==null?a:z(a,r);if(n===kn.NONE)return s;if(n===kn.SUM)return be(s);if(n===kn.MEAN){if(r==null)return Et(s);{let i=a.size/r.size,o=fe(be(s),be(r));return i>1?fe(o,we(i)):o}}if(n===kn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return fe(be(s),we(a.size));{let i=z(r,Jn(a.shape)),o=oe(be(pi(i,we(0))),"float32");return fe(be(s),o)}}throw Error(`Unknown reduction: ${n}`)}var Er=B({computeWeightedLoss_:ML});function PL(e,t,n,a=kn.SUM_BY_NONZERO_WEIGHTS){let r=A(e,"labels","absoluteDifference"),s=A(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=A(n,"weights","absoluteDifference")),Nn(r.shape,s.shape,"Error in absoluteDifference: ");let o=zt(ce(r,s));return Er(o,i,a)}var OL=B({absoluteDifference_:PL});function LL(e,t,n,a,r=kn.SUM_BY_NONZERO_WEIGHTS){let s=A(e,"labels","cosineDistance"),i=A(t,"predictions","cosineDistance"),o=null;a!=null&&(o=A(a,"weights","cosineDistance")),Nn(s.shape,i.shape,"Error in cosineDistance: ");let l=we(1),u=ce(l,be(z(s,i),n,!0));return Er(u,o,r)}var zL=B({cosineDistance_:LL});function BL(e,t,n,a=kn.SUM_BY_NONZERO_WEIGHTS){let r=A(e,"labels","hingeLoss"),s=A(t,"predictions","hingeLoss"),i=null;n!=null&&(i=A(n,"weights","hingeLoss")),Nn(r.shape,s.shape,"Error in hingeLoss: ");let o=we(1);r=ce(z(we(2),r),o);let l=Xe(ce(o,z(r,s)));return Er(l,i,a)}var WL=B({hingeLoss_:BL});function VL(e,t,n,a=1,r=kn.SUM_BY_NONZERO_WEIGHTS){let s=A(e,"labels","huberLoss"),i=A(t,"predictions","huberLoss"),o=null;n!=null&&(o=A(n,"weights","huberLoss")),Nn(s.shape,i.shape,"Error in huberLoss: ");let l=we(a),u=zt(ce(i,s)),p=_u(u,l),d=ce(u,p),c=J(z(we(.5),lt(p)),z(l,d));return Er(c,o,r)}var UL=B({huberLoss_:VL});function GL(e,t,n,a=1e-7,r=kn.SUM_BY_NONZERO_WEIGHTS){let s=A(e,"labels","logLoss"),i=A(t,"predictions","logLoss"),o=null;n!=null&&(o=A(n,"weights","logLoss")),Nn(s.shape,i.shape,"Error in logLoss: ");let l=we(1),u=we(a),p=St(z(s,ea(J(i,u)))),d=z(ce(l,s),ea(J(ce(l,i),u))),c=ce(p,d);return Er(c,o,r)}var HL=B({logLoss_:GL});function jL(e,t,n,a=kn.SUM_BY_NONZERO_WEIGHTS){let r=A(e,"labels","meanSquaredError"),s=A(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=A(n,"weights","meanSquaredError")),Nn(r.shape,s.shape,"Error in meanSquaredError: ");let o=af(r,s);return Er(o,i,a)}var qL=B({meanSquaredError_:jL});function KL(e,t){let n=A(e,"labels","sigmoidCrossEntropyWithLogits"),a=A(t,"logits","sigmoidCrossEntropyWithLogits");Nn(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Xe(a),s=z(a,n),i=Rc(gn(St(zt(a))));return J(ce(r,s),i)}function XL(e,t,n,a=0,r=kn.SUM_BY_NONZERO_WEIGHTS){let s=A(e,"multiClassLabels","sigmoidCrossEntropy"),i=A(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=A(n,"weights","sigmoidCrossEntropy")),Nn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let u=we(a),p=we(1),d=we(.5);s=J(z(s,ce(p,u)),z(d,u))}let l=KL(s,i);return Er(l,o,r)}var YL=B({sigmoidCrossEntropy_:XL});function JL(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 ur((a,r,s)=>{let i=ov(r,[n],!0),o=ce(oe(r,"float32"),i);s([a,o]);let l=St(z(o,a));return{value:be(l,[n]),gradFunc:(u,p)=>{let[d,c]=p,h=ui(u.shape,[n]);return[z(W(u,h),ce(oe(d,"float32"),gn(c))),z(W(u,h),ce(gn(c),oe(d,"float32")))]}}})(e,t)}function ZL(e,t,n,a=0,r=kn.SUM_BY_NONZERO_WEIGHTS){let s=A(e,"onehotLabels","softmaxCrossEntropy"),i=A(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=A(n,"weights","softmaxCrossEntropy")),Nn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let u=we(a),p=we(1),d=we(s.shape[1]);s=J(z(s,ce(p,u)),fe(u,d))}let l=JL(s,i);return Er(l,o,r)}var QL=B({softmaxCrossEntropy_:ZL});function ez(e,t,n,a){let r=A(e,"indices","sparseFillEmptyRows","int32"),s=A(t,"values","sparseFillEmptyRows"),i=A(n,"denseShape","sparseFillEmptyRows","int32"),o=A(a,"defaultValue","sparseFillEmptyRows",s.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
${r.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(o.rank!==0)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);let l={indices:r,values:s,denseShape:i,defaultValue:o},u=L.runKernel(vc,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var tz=B({sparseFillEmptyRows_:ez});function nz(e,t,n){let a=A(e,"inputIndices","sparseReshape","int32"),r=A(t,"inputShape","sparseReshape","int32"),s=A(n,"newShape","sparseReshape","int32");if(a.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
${a.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:a,inputShape:r,newShape:s},o=L.runKernel(bu,i);return{outputIndices:o[0],outputShape:o[1]}}var az=B({sparseReshape_:nz});function rz(e,t,n){let a=A(e,"data","sparseSegmentMean"),r=A(t,"indices","sparseSegmentMean","int32"),s=A(n,"segmentIds","sparseSegmentMean","int32");if(a.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(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
${s.shape}`);let i={data:a,indices:r,segmentIds:s};return L.runKernel(wc,i)}var sz=B({sparseSegmentMean_:rz});function iz(e,t,n){let a=A(e,"data","sparseSegmentSum"),r=A(t,"indices","sparseSegmentSum","int32"),s=A(n,"segmentIds","sparseSegmentSum","int32");if(a.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(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
${s.shape}`);let i={data:a,indices:r,segmentIds:s};return L.runKernel(kc,i)}var oz=B({sparseSegmentSum_:iz});function lz(e,t,n,a,r,s,i,o){let l=A(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=A(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let p={separator:n,nGramWidths:a,leftPad:r,rightPad:s,padWidth:i,preserveShortSequences:o},d={data:l,dataSplits:u},c=L.runKernel(_m,d,p);return{nGrams:c[0],nGramsSplits:c[1]}}var uz=B({stringNGrams_:lz});function pz(e,t,n=!0){let a=A(e,"input","stringSplit","string"),r=A(t,"delimiter","stringSplit","string");if(a.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${a.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let s={skipEmpty:n},i={input:a,delimiter:r},o=L.runKernel(Em,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var cz=B({stringSplit_:pz});function dz(e,t){let n=A(e,"input","stringToHashBucketFast","string"),a={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return L.runKernel(Am,r,a)}var hz=B({stringToHashBucketFast_:dz}),mz={fft:Lc,ifft:hl,rfft:zc,irfft:nf},fz={hammingWindow:H3,hannWindow:LS,frame:zS,stft:X3},Ln={flipLeftRight:Q3,grayscaleToRGB:tL,resizeNearestNeighbor:GS,resizeBilinear:US,rotateWithOffset:aL,cropAndResize:J3,nonMaxSuppression:sL,nonMaxSuppressionAsync:hL,nonMaxSuppressionWithScore:fL,nonMaxSuppressionWithScoreAsync:yL,nonMaxSuppressionPadded:xL,nonMaxSuppressionPaddedAsync:wL,threshold:TL,transform:_L},HS={bandPart:AL,gramSchmidt:FL,qr:RL},gz={absoluteDifference:OL,computeWeightedLoss:Er,cosineDistance:zL,hingeLoss:WL,huberLoss:UL,logLoss:HL,meanSquaredError:qL,sigmoidCrossEntropy:YL,softmaxCrossEntropy:QL},Fp={sparseFillEmptyRows:tz,sparseReshape:az,sparseSegmentMean:sz,sparseSegmentSum:oz},dh={stringNGrams:uz,stringSplit:cz,stringToHashBucketFast:hz},Ar=class extends qI{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Re(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return bS(e,t)}dispose(){this.iterations_!=null&&Re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:we(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ar,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var cf=class extends Ar{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:O(()=>Ke(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:O(()=>Ke(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;O(()=>{let l=J(z(i,this.rho),z(lt(s),1-this.rho)),u=z(fe(ln(J(o,this.epsilon)),ln(J(i,this.epsilon))),s),p=J(z(o,this.rho),z(lt(u),1-this.rho));i.assign(l),o.assign(p);let d=J(z(u,-this.learningRate),a);a.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};cf.className="Adadelta";ys(cf);var df=class extends Ar{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:O(()=>Cn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;O(()=>{let i=J(s,lt(r));s.assign(i);let o=J(z(fe(r,ln(J(i,L.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(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)}};df.className="Adagrad";ys(df);var hf=class extends Ar{constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=ce(1,this.accBeta1),a=ce(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:O(()=>Ke(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:O(()=>Ke(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,d=J(z(u,this.beta1),z(l,1-this.beta1)),c=J(z(p,this.beta2),z(lt(l),1-this.beta2)),h=fe(d,n),m=fe(c,a);u.assign(d),p.assign(c);let f=J(z(fe(h,J(ln(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),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&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(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),O(()=>{this.accBeta1.assign(_r(this.beta1,this.iterations_+1)),this.accBeta2.assign(_r(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.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)}};hf.className="Adam";ys(hf);var mf=class extends Ar{constructor(e,t,n,a=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=ce(1,this.accBeta1),a=fe(-this.learningRate,J(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ke(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ke(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,d=J(z(u,this.beta1),z(l,1-this.beta1)),c=z(p,this.beta2),h=zt(l),m=hr(c,h);u.assign(d),p.assign(m);let f=J(z(fe(a,n),fe(d,J(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(J(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(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)}};mf.className="Adamax";ys(mf);var Bc=class extends Ar{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=L.registeredVariables[t];O(()=>{let s=J(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=en(we(-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)}};Bc.className="SGD";ys(Bc);var ff=class extends Bc{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=we(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${t}/momentum`,variable:O(()=>Ke(a).variable(!1))});let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&O(()=>{let i,o=J(z(this.m,r),s);this.useNesterov?i=J(z(this.c,J(s,z(o,this.m))),a):i=J(z(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ff.className="Momentum";ys(ff);var gf=class extends Ar{constructor(e,t=.9,n=0,a=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=L.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:O(()=>Ke(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:O(()=>Ke(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:O(()=>Ke(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;O(()=>{let l=J(z(i,this.decay),z(lt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,p=J(z(u,this.decay),z(s,1-this.decay)),d=fe(z(s,this.learningRate),ln(ce(l,J(lt(p),this.epsilon)))),c=J(z(o,this.momentum),d);i.assign(l),u.assign(p),o.assign(c);let h=ce(a,c);a.assign(h)}else{let u=J(z(i,this.decay),z(lt(s),1-this.decay)),p=J(z(o,this.momentum),fe(z(s,this.learningRate),ln(J(u,this.epsilon))));i.assign(u),o.assign(p);let d=ce(a,p);a.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(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(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.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)}};gf.className="RMSProp";ys(gf);var Hr=class{static sgd(e){return new Bc(e)}static momentum(e,t,n=!1){return new ff(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new gf(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new hf(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new cf(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new mf(e,t,n,a,r)}static adagrad(e,t=.1){return new df(e,t)}},Us={sgd:Hr.sgd,momentum:Hr.momentum,adadelta:Hr.adadelta,adagrad:Hr.adagrad,rmsprop:Hr.rmsprop,adamax:Hr.adamax,adam:Hr.adam},yz=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Nv(){return new Promise(e=>yz(()=>e()))}var _={};Me(_,{ERF_A1:()=>_z,ERF_A2:()=>Ez,ERF_A3:()=>Az,ERF_A4:()=>$z,ERF_A5:()=>Fz,ERF_P:()=>Cz,PARALLELIZE_THRESHOLD:()=>Tv,SELU_SCALE:()=>qS,SELU_SCALEALPHA:()=>jS,applyActivation:()=>uf,assertAndGetBroadcastShape:()=>ht,assertAxesAreInnerMostDims:()=>RP,assertParamsConsistent:()=>bz,assignToTypedArray:()=>Lz,axesAreInnerMostDims:()=>sv,calculateShapes:()=>OI,checkEinsumDimSizes:()=>Gz,checkPadOnDimRoundingMode:()=>Tn,combineLocations:()=>vS,complexWithEvenIndex:()=>Mz,complexWithOddIndex:()=>Pz,computeConv2DInfo:()=>Ac,computeConv3DInfo:()=>eS,computeDefaultPad:()=>Hx,computeDilation2DInfo:()=>aM,computeOptimalWindowSize:()=>vz,computeOutAndReduceShapes:()=>wS,computeOutShape:()=>xz,computePool2DInfo:()=>QI,computePool3DInfo:()=>rM,convertConv2DDataFormat:()=>tS,decodeEinsumEquation:()=>Vz,eitherStridesOrDilationsAreOne:()=>dr,expandShapeToKeepDim:()=>ui,exponent:()=>Bz,exponents:()=>zz,fromStringArrayToUint8:()=>cB,fromUint8ToStringArray:()=>pB,getAxesPermutation:()=>kS,getBroadcastDims:()=>RI,getComplexWithIndex:()=>Oz,getEinsumComputePath:()=>Hz,getEinsumPermutation:()=>Uz,getFusedBiasGradient:()=>lf,getFusedDyActivation:()=>of,getImageCenter:()=>wz,getInnerMostAxes:()=>MP,getPermuted:()=>Iz,getReductionAxes:()=>Wt,getReshaped:()=>kz,getReshapedPermuted:()=>Sz,getSliceBeginCoords:()=>Nz,getSliceSize:()=>Tz,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>Xz,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>Yz,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>Jz,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>eB,getSparseReshapeInputOutputMismatchErrorMessage:()=>nB,getSparseReshapeInputOutputMultipleErrorMessage:()=>tB,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>Zz,getSparseReshapeNegativeOutputDimErrorMessage:()=>Qz,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>iB,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>aB,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>rB,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>sB,getUndoAxesPermutation:()=>iv,isIdentityPermutation:()=>jz,log:()=>TF,mergeRealAndImagArrays:()=>Dz,prepareAndValidate:()=>PI,prepareSplitSize:()=>Kz,segment_util:()=>KS,shouldFuse:()=>pf,slice_util:()=>qt,splitRealAndImagArrays:()=>Rz,tupleValuesAreOne:()=>as,upcastType:()=>ma,validateInput:()=>Dx,validateUpdateShape:()=>Fx,warn:()=>qr});function bz(e,t){let n=e[0].length;e.forEach((r,s)=>{R(r.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),R(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let a=e[0];e.forEach((r,s)=>{for(let i=0;i<n;i++)R(i===t||r[i]===a[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${a}) along the non-concatenated axis ${s}.`)})}function xz(e,t){let n=e[0].slice();for(let a=1;a<e.length;a++)n[t]+=e[a][t];return n}var Tv=30;function vz(e){return e<=Tv?e:Ih(e,Math.floor(Math.sqrt(e)))}function wz(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function kz(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(s+1))}return r}function Iz(e,t,n=!0){let a=[];if(n){a.push(t);for(let r=t+1;r<e;++r)r<=2*t?(a.push(r),a.push(r-(t+1))):a.push(r)}else{let r=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2===1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function Sz(e,t,n,a=!0){let r=[];a?r.push(e[0]/n):r.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?a?r.push(t[s-1]*e[s]):r.push(e[s]/t[s-1]):r.push(e[s]);return r}function Nz(e,t){let n=[0];for(let a=0;a<t;++a)n.push(e[a][0]);return n}function Tz(e,t,n){let a=e.slice(0,1);for(let r=0;r<n;++r)a.push(e[r+1]-t[r][0]-t[r][1]);return a}var jS=1.7580993408473768,qS=1.0507009873554805,Cz=.3275911,_z=.254829592,Ez=-.284496736,Az=1.421413741,$z=-1.453152027,Fz=1.061405429;function Dz(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 a=0;a<n.length;a+=2)n[a]=e[a/2],n[a+1]=t[a/2];return n}function Rz(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let a=0;a<e.length;a+=2)t[a/2]=e[a],n[a/2]=e[a+1];return{real:t,imag:n}}function Mz(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function Pz(e){let t=Math.floor(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function Oz(e,t){let n=e[t*2],a=e[t*2+1];return{real:n,imag:a}}function Lz(e,t,n,a){e[a*2]=t,e[a*2+1]=n}function zz(e,t){let n=new Float32Array(e/2),a=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let s=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(s),a[r]=Math.sin(s)}return{real:n,imag:a}}function Bz(e,t,n){let a=(n?2:-2)*Math.PI*(e/t),r=Math.cos(a),s=Math.sin(a);return{real:r,imag:s}}var lb="->",Wz=/->/g,$1=",",F1="...";function Vz(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(Wz,"").length)/lb.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${lb}").`);let[a,r]=e.split(lb);R(a.indexOf(F1)===-1,()=>`The ellipsis notation ("${F1}") is not supported yet.`);let s=a.split($1),i=s.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let o=[];for(let c=0;c<r.length;++c){let h=r[c];if(!s.some(m=>m.indexOf(h)!==-1))throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);o.indexOf(h)===-1&&o.push(h)}for(let c=0;c<a.length;++c){let h=a[c];o.indexOf(h)===-1&&h!==$1&&o.push(h)}let l=new Array(s.length);for(let c=0;c<i;++c){if(new Set(s[c].split("")).size!==s[c].length)throw new Error(`Found duplicate axes in input component ${s[c]}. Support for duplicate axes in input is not implemented yet.`);l[c]=[];for(let h=0;h<s[c].length;++h)l[c].push(o.indexOf(s[c][h]))}let u=o.length,p=r.length,d=[];for(let c=p;c<u;++c)d.push(c);return{allDims:o,summedDims:d,idDims:l}}function Uz(e,t){let n=new Array(e);n.fill(-1);for(let r=0;r<t.length;++r)n[t[r]]=r;let a=[];for(let r=0;r<e;++r)n[r]===-1&&a.push(r);return n=n.filter(r=>r!==-1),{permutationIndices:n,expandDims:a}}function Gz(e,t,n){let a=new Array(e);for(let r=0;r<n.length;++r){let s=n[r].shape;for(let i=0;i<t[r].length;++i)a[t[r][i]]===void 0?a[t[r][i]]=s[i]:R(a[t[r][i]]===s[i],()=>`Expected dimension ${a[t[r][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function Hz(e,t){let n=e,a=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let i=0;i<r;++i)a.push([]);let s=[];for(let i=0;i<n.length;++i){let o=n[i],l=qz(t,o);for(let u of l)s.indexOf(u)===-1&&(a[i].push(u),s.push(u))}return{path:n,steps:a}}function jz(e){return e.every((t,n)=>t===n)}function qz(e,t){let n=[];for(let a=0;a<e.length;++a)(e[a].length===0||e[a].indexOf(t)!==-1||t===-1)&&n.push(a);return n}function Kz(e,t,n=0){let a=[];if(typeof t=="number")R(e.shape[n]%t===0,()=>"Number of splits must evenly divide the axis."),a=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);R(r<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}R(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),a=t}return a}function Xz(e){return`Received SparseTensor with denseShape[0] = 0 but
indices.shape[0] = ${e}`}function Yz(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function Jz(e,t,n){return`indices(${e}, 0) is invalid: ${t} >= ${n}`}function Zz(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function Qz(e,t){return`size ${e} must be non-negative, not ${t}`}function eB(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function tB(e,t){let n=xt(e),a=xt(t);return`Input to reshape is a SparseTensor with ${n}
dense values, but the requested shape requires a multiple of ${a}. inputShape=${e} outputShape= ${t}`}function nB(e,t){let n=xt(e),a=xt(t);return`Input to reshape is a tensor with ${n} dense values, but the requested shape has ${a}. inputShape=${e} outputShape=${t}`}function aB(){return"segment ids must be >= 0"}function rB(){return"segment ids are not increasing"}function sB(e,t){return`Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function iB(e,t,n){return`Bad: indices[${e}] == ${t} out of range [0, ${n})`}var KS={};Me(KS,{collectGatherOpShapeInfo:()=>uB,computeOutShape:()=>lB,segOpComputeOptimalWindowSize:()=>oB});function oB(e,t){let n=!1,a;for(e<=Tv?(a=e,n=!0):a=Ih(e,Math.floor(Math.sqrt(e)));!n;)a>t||a===e?n=!0:a=Ih(e,a+1);return a}function lB(e,t,n){let a=[],r=e.length;for(let s=0;s<r;s++)s!==t?a.push(e[s]):a.push(n);return a}function uB(e,t,n,a){let r=t.shape.length,s=e.shape.length;if(a!==0&&(a<-r||a>r))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${a}`);if(a<0&&(a+=r),a>s)throw new Error(`batchDims (${a}) must be less than rank(x) (
${s}).`);if(n<a)throw new Error(`batchDims (${a}) must be less than or equal to axis (${n}).`);for(let d=0;d<a;++d)if(e.shape[d]!==t.shape[d])throw new Error(`x.shape[${d}]: ${e.shape[d]} should be equal to indices.shape[${d}]: ${t.shape[d]}.`);let i=e.shape[n],o=[],l=1,u=1,p=1;for(let d=0;d<a;++d)o.push(e.shape[d]),l*=e.shape[d];for(let d=a;d<n;d++)o.push(e.shape[d]),u*=e.shape[d];for(let d=a;d<r;d++)o.push(t.shape[d]);for(let d=n+1;d<s;d++)o.push(e.shape[d]),p*=e.shape[d];return{batchSize:l,sliceSize:p,outerSize:u,dimSize:i,outputShape:o}}function pB(e){try{return e.map(t=>Eh(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function cB(e){return e.map(t=>Cc(t))}var mr={};Me(mr,{nonMaxSuppressionV3Impl:()=>BS,nonMaxSuppressionV4Impl:()=>WS,nonMaxSuppressionV5Impl:()=>VS,whereImpl:()=>AS});var XS={kernelName:Il,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,$u(oe(n,"float32"),-1))}}},dB={kernelName:Sl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=lt(oe(n,"float32")),r=ln(ce(we(1),a));return St(fe(e,r))}}}},hB={kernelName:Nl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ln(ce(lt(oe(n,"float32")),1));return fe(e,a)}}}},mB={kernelName:ds,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ht(n.shape,a.shape);return{a:()=>{let s=e,i=Wt(n.shape,r);return i.length>0&&(s=be(s,i)),W(s,n.shape)},b:()=>{let s=e,i=Wt(a.shape,r);return i.length>0&&(s=be(s,i)),W(s,a.shape)}}}},fB={kernelName:xi,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((a,r)=>{n[r]=()=>e.clone()}),n}},gB={kernelName:vi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ke(n)}}},yB={kernelName:lc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ke(n)}}},bB={kernelName:_l,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ln(ce(we(1),lt(oe(n,"float32")))))}}},xB={kernelName:El,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ln(J(we(1),lt(oe(n,"float32"))));return fe(e,a)}}}},vB={kernelName:Fl,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ht(n.shape,a.shape);return{a:()=>{let s=J(lt(n),lt(a)),i=z(e,fe(a,s)),o=Wt(n.shape,r);return o.length>0&&(i=be(i,o)),W(i,n.shape)},b:()=>{let s=J(lt(n),lt(a)),i=St(z(e,fe(n,s))),o=Wt(a.shape,r);return o.length>0&&(i=be(i,o)),W(i,a.shape)}}}},wB={kernelName:Al,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,J(lt(oe(n,"float32")),1))}}},kB={kernelName:$l,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ce(we(1),lt(oe(n,"float32"))))}}};function IB(e,t,n,a,r,s){let i=A(e,"dy","avgPool3dGrad"),o=A(t,"input","avgPool3dGrad"),l=i,u=o,p=!1;o.rank===4&&(p=!0,l=W(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),u=W(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),R(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),R(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),Tn("avgPool3dGrad",r,s);let d={dy:l,input:u},c={filterSize:n,strides:a,pad:r,dimRoundingMode:s},h=L.runKernel(nm,d,c);return p?W(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var SB=B({avgPool3dGrad_:IB}),NB={kernelName:uc,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>SB(e,a,r,s,i,o)}}};function TB(e,t,n,a,r){let s=A(e,"dy","avgPoolGrad"),i=A(t,"input","avgPoolGrad");R(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,u=!1;i.rank===3&&(u=!0,o=W(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=W(s,[1,s.shape[0],s.shape[1],s.shape[2]])),R(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),R(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let p={dy:l,input:o},d={filterSize:n,strides:a,pad:r},c=L.runKernel(tm,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var CB=B({avgPoolGrad_:TB}),_B={kernelName:wi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i}=n;return{x:()=>CB(e,a,r,s,i)}}},EB={kernelName:ki,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[a,r]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>De(e,r,!1,!0),b:()=>De(a,e,!0,!1)}:!s&&i?{a:()=>De(e,r,!1,!1),b:()=>De(e,a,!0,!1)}:s&&!i?{a:()=>De(r,e,!1,!0),b:()=>De(a,e,!1,!1)}:{a:()=>De(r,e,!0,!0),b:()=>De(e,a,!0,!0)}}},AB={kernelName:Dl,gradFunc:(e,t,n)=>{let{blockShape:a,crops:r}=n;return{x:()=>Pc(e,a,r)}}},$B={kernelName:lI,gradFunc:(e,t,n)=>{let a=n,r=a.inputShape,s=a.shape,i=Array.from(s);for(let l=r.length-1;l>=0;l--)if(r[l]===s[l])i[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l<i.length;l++)i[l]>1&&o.push(l);return{x:()=>be(e,o,!0)}}},FB={kernelName:Ii,gradFunc:e=>({x:()=>e.clone()})},DB={kernelName:Si,gradFunc:e=>({x:()=>Ke(e)})},RB={kernelName:hs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{clipValueMin:r,clipValueMax:s}=n;return{x:()=>fn(Ta(xs(a,r),vs(a,s)),e,Ke(e))}}},MB={kernelName:pc,inputsToSave:["x"],gradFunc:XS.gradFunc},PB={kernelName:Rl,saveAllInputs:!0,gradFunc:(e,t,n)=>{let a=t.map(o=>o.shape),{axis:r}=n,s=Ca(r,t[0].shape)[0],i=a.map(o=>o[s]);return zn(e,i,s).map(o=>()=>o)}},OB={kernelName:Ni,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return R(as(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>Xx(a.shape,e,r,i,o,l),filter:()=>Iv(a,e,r.shape,i,o,l)}}},LB={kernelName:Ti,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>Rt(e,r,s,i,o,1,l),filter:()=>Iv(e,a,r.shape,s,i,o,l)}}};function zB(e,t,n,a,r){let s=e;e.rank===4&&(s=W(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=W(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),R(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),R(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),R(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),R(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),R(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:a,pad:r,filterShape:n};return L.runKernel(om,o,l)}var BB=B({conv3DBackpropFilter_:zB}),WB={kernelName:cc,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s}=n;R(as(a),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let[i,o]=t;return{x:()=>pS(i.shape,e,o,r,s),filter:()=>BB(i,e,o.shape,r,s)}}},VB={kernelName:Ci,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(St(Qm(oe(n,"float32"))),e)}}},UB={kernelName:_i,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(ef(oe(n,"float32")),e)}}},GB={kernelName:Ei,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r,exclusive:s,reverse:i}=n;return{x:()=>{let o=kS([r],a.rank),l=Wm(e,r,s,!i);return o!=null&&(l=$e(l,o)),l}}}},HB={kernelName:Ai,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s,dimRoundingMode:i}=n,o=a==null?[1,1]:a;R(as(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,u]=t;return R(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),R(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),R(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),R(dr(r,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${o}'.`),Tn("depthwiseConv2d",s,i),{x:()=>OS(l.shape,e,u,r,s,o,i),filter:()=>PS(l,e,u.shape,r,s,o,i)}}},jB={kernelName:dc,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,s={x:a,filter:r,dy:e},i={x:a,filter:r,dy:e};return{x:()=>L.runKernel(Sh,s,n),filter:()=>L.runKernel(Nh,i,n)}}},qB={kernelName:Fi,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,a={dy:e,y:n};return{x:()=>L.runKernel(mm,a)}}},KB={kernelName:Ll,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=z(gn(St(lt(n))),2/Math.sqrt(Math.PI));return{x:()=>z(e,a)}}},XB={kernelName:Di,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,n)}}},YB={kernelName:Bl,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>W(e,n.shape)}}},JB={kernelName:Wl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,gn(n))}}},ZB={kernelName:Ri,gradFunc:e=>({x:()=>Ke(e)})},QB={kernelName:Mi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ht(n.shape,a.shape);return{a:()=>{let s=fe(e,oe(a,"float32")),i=Wt(n.shape,r);return i.length>0?W(be(s,i),n.shape):s},b:()=>{let s=z(e,oe(n,"float32")),i=Wt(a.shape,r);i.length>0&&(s=W(be(s,i),a.shape));let o=lt(a);return St(fe(s,oe(o,"float32")))}}}},eW={kernelName:Pi,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:a}=n,[r,s,i,o]=t,l=o==null?we(1):o,u=Wt(s.shape,r.shape),p=[];if(s.rank===1){for(let f=0;f<r.shape.length-1;++f)p.push(r.shape[f]);p.push(1)}let d=ce(r,s),c=z(e,l),h=Jm(J(i,we(a))),m=z(z(z(h,h),h),we(-.5));return{x:()=>s.rank===1?W(z(z(e,On(W(h,[1,1,1,s.shape[0]]),p)),l),r.shape):W(z(z(e,h),l),r.shape),mean:()=>{let f=z(z(h,we(-1)),c);return s.rank===1&&(f=be(f,u)),W(f,s.shape)},variance:()=>{let f=z(z(m,d),c);return s.rank===1&&(f=be(f,u)),W(f,s.shape)},scale:()=>{let f=z(d,h),g=z(e,f);return s.rank===1&&(g=be(g,u)),W(g,s.shape)},offset:()=>{let f=e;return s.rank===1&&(f=be(f,u)),W(f,s.shape)}}}},tW={kernelName:Ul,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[a,r]=t,{axis:s}=n,i=Ca(s,a.shape)[0];return{x:()=>{let o=a.shape,l=r.size,u=o.slice(0,i),p=u.length,d=o.slice(s,o.length).slice(1),c=d.length,h=D1(0,p),m=D1(p+1,p+1+c),f=R1([u,[l],d]),g=W(e,f),y=W(r,[l]),b=R1([[p],h,m]),x=$e(g,b),v=xv(x,y,a.shape[i]),w=iv(b);return v=$e(v,w),v},indices:()=>r}}};function D1(e,t){let n=[];for(let a=e;a<t;++a)n.push(a);return n}function R1(e){let t=[];for(let n=0;n<e.length;++n)for(let a=0;a<e[n].length;++a)t.push(e[n][a]);return t}var nW={kernelName:Oi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>Ke(n),b:()=>Ke(a)}}},aW={kernelName:Li,gradFunc:e=>({x:()=>oe(e,"float32")})},rW={kernelName:jl,gradFunc:e=>({x:()=>Ke(e)})},sW={kernelName:ql,gradFunc:e=>({x:()=>Ke(e)})},iW={kernelName:Kl,gradFunc:e=>({x:()=>Ke(e)})},oW={kernelName:zi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{alpha:r}=n,s=Gn(a,0);return{x:()=>fn(s,e,z(e,r))}}},lW={kernelName:Jl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,J(n,1))}}},uW={kernelName:Bi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,oe(n,"float32"))}}},pW={kernelName:uI,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n;return{logits:()=>{let s=gn(a);return ce(e,z(be(e,r,!0),s))}}}};function cW(e,t,n,a=5,r=1,s=1,i=.5){let o={x:e,y:t,dy:n},l={depthRadius:a,bias:r,alpha:s,beta:i};return L.runKernel(xm,o,l)}var dW=B({localResponseNormalizationBackprop_:cW}),hW={kernelName:gc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>dW(a,r,e,s,i,o,l)}}};function YS(e,t,n,a){return t.rank<n.rank&&(t=W(t,ui(t.shape,a))),e.rank<n.rank&&(e=W(e,ui(e.shape,a))),{x:()=>z(e,oe(Qn(n,t),e.dtype))}}var M1={kernelName:Wi,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{reductionIndices:r}=a,s=t[0],i=t[1],o=Ca(r,s.shape),l=YS(e,i,s,o);return{x:()=>l.x()}}},mW={kernelName:Vi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>z(e,oe(xs(n,a),"float32")),b:()=>z(e,oe(Um(n,a),"float32"))}}};function fW(e,t,n,a,r,s,i){let o=A(e,"dy","maxPool3dGrad"),l=A(t,"input","maxPool3dGrad"),u=A(n,"output","maxPool3dGrad"),p=o,d=l,c=u,h=!1;l.rank===4&&(h=!0,p=W(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),d=W(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),c=W(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),R(p.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${p.rank}.`),R(d.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${d.rank}.`),R(c.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${c.rank}.`),Tn("maxPool3dGrad",s,i);let m={dy:p,input:d,output:c},f={filterSize:a,strides:r,pad:s,dimRoundingMode:i},g=L.runKernel(wm,m,f);return h?W(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var gW=B({maxPool3dGrad_:fW}),yW={kernelName:yc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>gW(e,a,r,s,i,o,l)}}};function bW(e,t,n,a,r,s,i){let o=A(e,"dy","maxPoolGrad"),l=A(t,"input","maxPoolGrad"),u=A(n,"output","maxPoolGrad");R(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),R(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),R(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),Tn("maxPoolGrad",s,i);let p={dy:o,input:l,output:u},d={filterSize:a,strides:r,pad:s,dimRoundingMode:i};return L.runKernel(vm,p,d)}var xW=B({maxPoolGrad_:bW}),vW={kernelName:Ui,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>xW(e,a,r,s,i,o)}}},wW={kernelName:Gi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=Ca(r,a.shape),i=wS(a.shape,s)[1],o=xt(i);return{x:()=>{let l=a.shape.slice();s.forEach(p=>{l[p]=1});let u=W(e,l);return fe(z(u,Jn(a.shape,"float32")),o)}}}},kW={kernelName:Hi,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{axis:r}=a,[s,i]=t,o=Ca(r,s.shape),l=YS(e,i,s,o);return{x:()=>l.x()}}},IW={kernelName:ji,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>z(e,oe(vs(n,a),"float32")),b:()=>z(e,oe(Gn(n,a),"float32"))}}},SW={kernelName:qi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Ge(e,s,a.shape)}}},NW={kernelName:Ql,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ht(n.shape,a.shape);return{a:()=>{let s=Wt(n.shape,r);return s.length>0?W(be(e,s),n.shape):e},b:()=>{let s=z(e,St(Cu(fe(n,a)))),i=Wt(a.shape,r);return i.length>0?W(be(s,i),a.shape):s}}}},TW={kernelName:Ki,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ht(n.shape,a.shape);return{a:()=>{let s=z(e,oe(a,"float32")),i=Wt(n.shape,r);return i.length>0?W(be(s,i),n.shape):s},b:()=>{let s=z(e,oe(n,"float32")),i=Wt(a.shape,r);return i.length>0?W(be(s,i),a.shape):s}}}},CW={kernelName:eu,gradFunc:e=>({x:()=>St(e)})},_W={kernelName:Xi,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>kt(n.shape,"float32")}}},EW={kernelName:su,gradFunc:e=>({x:()=>Ke(e)})},AW={kernelName:iu,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:a}=n;return mt(e,a).map(r=>()=>r)}},P1={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Ge(e,s,a.shape)}}},$W={kernelName:Ji,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,a,r]=t,s=n,i=a,o=ht(s.shape,i.shape);return{a:()=>{let l=oe(i,"float32"),u=z(e,z(l,_r(s,ce(l,we(1))))),p=Wt(s.shape,o);return p.length>0&&(u=be(u,p)),W(u,s.shape)},b:()=>{let l=Gn(s,0),u=fn(l,ea(s),Ke(s)),p=z(e,z(r,u)),d=Wt(i.shape,o);return d.length>0&&(p=be(p,d)),W(p,i.shape)}}}},FW={kernelName:Zi,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,a]=t,r=Gn(n,0);return{x:()=>fn(r,e,z(e,a)),alpha:()=>{let s=fn(r,Ke(e),z(e,n)),i=Wt(a.shape,e.shape);return i.length>0&&(s=be(s,i)),W(s,a.shape)}}}};function DW(e,t,n){let a=e.shape.slice();a[n]=1;let r=W(t,a),s=Yp(e,n,!0,!1),i=Yp(e,n,!0,!0),o=z(s,i);return z(r,o)}function RW(e,t,n){let a=e.shape.length,r=a-n.length,s=_.getAxesPermutation(n,a),i=e;s!=null&&(i=$e(e,s));let o=i.shape.slice(),l=o.splice(a-n.length,n.length).reduce((d,c)=>d*c,1);o.push(l);let u=i.reshape(o),p=DW(u,t,r);if(p=p.reshape(i.shape),s!=null){let d=_.getUndoAxesPermutation(s);p=$e(p,d)}return p}var MW={kernelName:Qi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=[];return r==null?s=a.shape.map((i,o)=>o):typeof r=="number"?s=[r]:s=r,{x:()=>RW(a,e,s)}}},PW={kernelName:$i,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ht(n.shape,a.shape);return{a:()=>{let s=fe(e,oe(a,"float32")),i=Wt(n.shape,r);return i.length>0?W(be(s,i),n.shape):s},b:()=>{let s=z(e,oe(n,"float32")),i=Wt(a.shape,r);i.length>0&&(s=W(be(s,i),a.shape));let o=lt(a);return St(fe(s,oe(o,"float32")))}}}},OW={kernelName:ou,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,St(lt(n)))}}},LW={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=z(vs(n,6),$u(n));return{x:()=>z(e,oe(a,"float32"))}}},zW={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,oe($u(n),"float32"))}}},BW={kernelName:lu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>W(e,n.shape)}}},WW={kernelName:to,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>L.runKernel(Tm,r,n)}}},VW={kernelName:xc,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>L.runKernel(Nm,r,n)}}},UW={kernelName:ao,gradFunc:(e,t,n)=>{let{dims:a}=n,r=Ca(a,e.shape);return{x:()=>na(e,r)}}},GW={kernelName:ro,gradFunc:e=>({x:()=>Ke(e)})},HW={kernelName:so,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>St(fe(e,z(_r(n,1.5),2)))}}},jW={kernelName:pu,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>oe(Ke(n),"float32"),t:()=>z(e,oe(n,e.dtype)),e:()=>z(e,oe(Mc(n),e.dtype))}}},qW={kernelName:cu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=Gn(n,we(0)),r=we(jS),s=we(qS),i=z(e,s),o=z(z(e,r),gn(oe(n,"float32")));return fn(a,i,o)}}}},KW={kernelName:oo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,z(n,ce(we(1),n)))}}},XW={kernelName:mu,gradFunc:e=>({x:()=>Ke(e)})},YW={kernelName:io,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(Fc(oe(n,"float32")),e)}}},JW={kernelName:hu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(Bm(oe(n,"float32")),e)}}},ZW={kernelName:du,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{begin:r,size:s}=n,i=a.shape,[o,l]=jI(a,r,s),u=[];for(let p=0;p<e.rank;p++)u.push([o[p],i[p]-o[p]-l[p]]);return{x:()=>ga(e,u)}}},QW={kernelName:po,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{dim:r}=n,s=!0,i=z(e,a);return{logits:()=>ce(i,z(be(i,[r],s),a))}}},e4={kernelName:fu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,ha(n))}}},O1={kernelName:gu,gradFunc:(e,t,n)=>{let{blockShape:a,paddings:r}=n;return{x:()=>$c(e,a,r)}}},L1={kernelName:yu,gradFunc:(e,t,n)=>{let{axis:a}=n;return{x:()=>Qe(e,a)}}},t4={kernelName:lo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,z(ln(oe(n,"float32")),2))}}},n4={kernelName:Ic,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,z(oe(n,"float32"),2))}}},a4={kernelName:co,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=we(2);return{a:()=>z(e,z(r,ce(n,a))),b:()=>z(e,z(r,ce(a,n)))}}},r4={kernelName:fs,gradFunc:e=>({x:()=>Ke(e)})},s4={kernelName:ho,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ht(n.shape,a.shape);return{a:()=>{let s=e,i=Wt(n.shape,r);return i.length>0&&(s=be(s,i)),W(s,n.shape)},b:()=>{let s=e,i=Wt(a.shape,r);return i.length>0&&(s=be(s,i)),W(St(s),a.shape)}}}},i4={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,r=a.shape.slice(),{axis:s}=n;Ca(s,a.shape).forEach(l=>{r[l]=1});let i=W(e,r),o=z(i,Jn(a.shape,"float32"));return{x:()=>o}}},o4={kernelName:mo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,lt(Fc(n)))}}},l4={kernelName:fo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(ce(we(1),lt(n)),e)}}},u4={kernelName:ms,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{reps:r}=n;return{x:()=>{let s=Ke(a);if(a.rank===1)for(let i=0;i<r[0];++i)s=J(s,Ge(e,[i*a.shape[0]],[a.shape[0]]));else if(a.rank===2)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)s=J(s,Ge(e,[i*a.shape[0],o*a.shape[1]],[a.shape[0],a.shape[1]]));else if(a.rank===3)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let l=0;l<r[2];++l)s=J(s,Ge(e,[i*a.shape[0],o*a.shape[1],l*a.shape[2]],[a.shape[0],a.shape[1],a.shape[2]]));else if(a.rank===4)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let l=0;l<r[2];++l)for(let u=0;u<r[3];++u)s=J(s,Ge(e,[i*a.shape[0],o*a.shape[1],l*a.shape[2],u*a.shape[3]],[a.shape[0],a.shape[1],a.shape[2],a.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${a.rank} tensors yet.`);return s}}}},p4={kernelName:go,gradFunc:(e,t,n)=>{let a=n,{perm:r}=a,s=iv(r);return{x:()=>$e(e,s)}}},c4={kernelName:ku,gradFunc:(e,t,n)=>{let a=n,{axis:r}=a;return{value:()=>Mt(e,r)}}},d4={kernelName:Sc,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>h4(e,n)}}};function h4(e,t){let n=hr(t,Ke(t)),a=li(e,n),r=xs(t,we(0,"int32")),s=a.rank-r.rank;for(let o=0;o<s;++o)r=mn(r,o+1);r=Ta(r,Jn(a.shape,"bool"));let i=Ke(a);return fn(r,a,i)}var m4={kernelName:Iu,gradFunc:e=>({x:()=>Ke(e)})},f4=[XS,dB,hB,mB,fB,gB,yB,bB,xB,vB,wB,kB,NB,_B,EB,AB,$B,FB,DB,RB,MB,PB,LB,OB,WB,VB,UB,GB,HB,jB,PW,qB,KB,XB,YB,JB,QB,ZB,eW,tW,nW,aW,rW,sW,iW,oW,lW,uW,pW,hW,M1,M1,mW,yW,vW,wW,kW,IW,SW,NW,TW,CW,_W,EW,AW,P1,P1,$W,FW,MW,OW,LW,zW,BW,WW,VW,UW,GW,HW,jW,qW,KW,XW,YW,JW,ZW,QW,e4,O1,O1,L1,L1,t4,a4,n4,r4,s4,i4,o4,l4,u4,p4,c4,d4,m4];for(let e of f4)pI(e);ne().prototype.abs=function(){return this.throwIfDisposed(),zt(this)};ne().prototype.acos=function(){return this.throwIfDisposed(),Ox(this)};ne().prototype.acosh=function(){return this.throwIfDisposed(),Lx(this)};ne().prototype.add=function(e){return this.throwIfDisposed(),J(this,e)};ne().prototype.all=function(e,t){return this.throwIfDisposed(),Om(this,e,t)};ne().prototype.any=function(e,t){return this.throwIfDisposed(),Xp(this,e,t)};ne().prototype.argMax=function(e){return this.throwIfDisposed(),ii(this,e)};ne().prototype.argMin=function(e){return this.throwIfDisposed(),zx(this,e)};ne().prototype.asScalar=function(){return this.throwIfDisposed(),R(this.size===1,()=>"The array must have only 1 element."),W(this,[])};ne().prototype.asType=function(e){return this.throwIfDisposed(),oe(this,e)};ne().prototype.as1D=function(){return this.throwIfDisposed(),W(this,[this.size])};ne().prototype.as2D=function(e,t){return this.throwIfDisposed(),W(this,[e,t])};ne().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),W(this,[e,t,n])};ne().prototype.as4D=function(e,t,n,a){return this.throwIfDisposed(),W(this,[e,t,n,a])};ne().prototype.as5D=function(e,t,n,a,r){return this.throwIfDisposed(),W(this,[e,t,n,a,r])};ne().prototype.asin=function(){return this.throwIfDisposed(),Bx(this)};ne().prototype.asinh=function(){return this.throwIfDisposed(),Wx(this)};ne().prototype.atan=function(){return this.throwIfDisposed(),Vx(this)};ne().prototype.atan2=function(e){return this.throwIfDisposed(),Ux(this,e)};ne().prototype.atanh=function(){return this.throwIfDisposed(),Gx(this)};ne().prototype.avgPool=function(e,t,n,a){return this.throwIfDisposed(),fa(this,e,t,n,a)};ne().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),$c(this,e,t)};ne().prototype.batchNorm=function(e,t,n,a,r){return this.throwIfDisposed(),Cr(this,e,t,n,a,r)};ne().prototype.broadcastTo=function(e){return this.throwIfDisposed(),il(this,e)};ne().prototype.cast=function(e){return this.throwIfDisposed(),oe(this,e)};ne().prototype.ceil=function(){return this.throwIfDisposed(),Kx(this)};ne().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),nn(this,e,t)};ne().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Ae&&(e=[e]),Qe([this,...e],t)};ne().prototype.conv1d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Lm(this,e,t,n,a,r,s)};ne().prototype.conv2dTranspose=function(e,t,n,a,r){return this.throwIfDisposed(),zm(this,e,t,n,a,r)};ne().prototype.conv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Rt(this,e,t,n,a,r,s)};ne().prototype.cos=function(){return this.throwIfDisposed(),Fc(this)};ne().prototype.cosh=function(){return this.throwIfDisposed(),Bm(this)};ne().prototype.cumprod=function(e,t,n){return this.throwIfDisposed(),Yp(this,e,t,n)};ne().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),Wm(this,e,t,n)};ne().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),Jx(this,e,t)};ne().prototype.depthwiseConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),bs(this,e,t,n,a,r,s)};ne().prototype.dilation2d=function(e,t,n,a,r){return this.throwIfDisposed(),Zx(this,e,t,n,a,r)};ne().prototype.divNoNan=function(e){return this.throwIfDisposed(),Qx(this,e)};ne().prototype.div=function(e){return this.throwIfDisposed(),fe(this,e)};ne().prototype.dot=function(e){return this.throwIfDisposed(),hS(this,e)};ne().prototype.elu=function(){return this.throwIfDisposed(),Tu(this)};ne().prototype.equal=function(e){return this.throwIfDisposed(),Qn(this,e)};ne().prototype.erf=function(){return this.throwIfDisposed(),ev(this)};ne().prototype.exp=function(){return this.throwIfDisposed(),gn(this)};ne().prototype.expandDims=function(e){return this.throwIfDisposed(),mn(this,e)};ne().prototype.expm1=function(){return this.throwIfDisposed(),tv(this)};ne().prototype.fft=function(){return this.throwIfDisposed(),Lc(this)};ne().prototype.flatten=function(){return this.throwIfDisposed(),W(this,[this.size])};ne().prototype.floor=function(){return this.throwIfDisposed(),Cu(this)};ne().prototype.floorDiv=function(e){return this.throwIfDisposed(),Pm(this,e)};ne().prototype.gather=function(e,t){return this.throwIfDisposed(),li(this,e,t)};ne().prototype.greaterEqual=function(e){return this.throwIfDisposed(),xs(this,e)};ne().prototype.greater=function(e){return this.throwIfDisposed(),Gn(this,e)};ne().prototype.ifft=function(){return this.throwIfDisposed(),hl(this)};ne().prototype.irfft=function(){return this.throwIfDisposed(),nf(this)};ne().prototype.isFinite=function(){return this.throwIfDisposed(),fS(this)};ne().prototype.isInf=function(){return this.throwIfDisposed(),gS(this)};ne().prototype.isNaN=function(){return this.throwIfDisposed(),av(this)};ne().prototype.leakyRelu=function(e){return this.throwIfDisposed(),Dc(this,e)};ne().prototype.lessEqual=function(e){return this.throwIfDisposed(),vs(this,e)};ne().prototype.less=function(e){return this.throwIfDisposed(),Um(this,e)};ne().prototype.localResponseNormalization=function(e,t,n,a){return this.throwIfDisposed(),rv(this,e,t,n,a)};ne().prototype.logSigmoid=function(){return this.throwIfDisposed(),xS(this)};ne().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Hm(this,e)};ne().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),ov(this,e,t)};ne().prototype.log=function(){return this.throwIfDisposed(),ea(this)};ne().prototype.log1p=function(){return this.throwIfDisposed(),Rc(this)};ne().prototype.logicalAnd=function(e){return this.throwIfDisposed(),Ta(this,e)};ne().prototype.logicalNot=function(){return this.throwIfDisposed(),Mc(this)};ne().prototype.logicalOr=function(e){return this.throwIfDisposed(),jm(this,e)};ne().prototype.logicalXor=function(e){return this.throwIfDisposed(),IS(this,e)};ne().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),De(this,e,t,n)};ne().prototype.maxPool=function(e,t,n,a){return this.throwIfDisposed(),Pt(this,e,t,n,a)};ne().prototype.max=function(e,t){return this.throwIfDisposed(),Sa(this,e,t)};ne().prototype.maximum=function(e){return this.throwIfDisposed(),hr(this,e)};ne().prototype.mean=function(e,t){return this.throwIfDisposed(),Et(this,e,t)};ne().prototype.min=function(e,t){return this.throwIfDisposed(),Jp(this,e,t)};ne().prototype.minimum=function(e){return this.throwIfDisposed(),_u(this,e)};ne().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),uv(this,e,t)};ne().prototype.mod=function(e){return this.throwIfDisposed(),pv(this,e)};ne().prototype.mul=function(e){return this.throwIfDisposed(),z(this,e)};ne().prototype.neg=function(){return this.throwIfDisposed(),St(this)};ne().prototype.norm=function(e,t,n){return this.throwIfDisposed(),sf(this,e,t,n)};ne().prototype.notEqual=function(e){return this.throwIfDisposed(),pi(this,e)};ne().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),cl(this,e,t,n)};ne().prototype.onesLike=function(){return this.throwIfDisposed(),ta(this)};ne().prototype.pad=function(e,t){return this.throwIfDisposed(),ga(this,e,t)};ne().prototype.pool=function(e,t,n,a,r,s){return this.throwIfDisposed(),TS(this,e,t,n,a,r,s)};ne().prototype.pow=function(e){return this.throwIfDisposed(),_r(this,e)};ne().prototype.prelu=function(e){return this.throwIfDisposed(),Oc(this,e)};ne().prototype.prod=function(e,t){return this.throwIfDisposed(),Km(this,e,t)};ne().prototype.reciprocal=function(){return this.throwIfDisposed(),hv(this)};ne().prototype.relu=function(){return this.throwIfDisposed(),Xe(this)};ne().prototype.relu6=function(){return this.throwIfDisposed(),Xm(this)};ne().prototype.reshapeAs=function(e){return this.throwIfDisposed(),W(this,e.shape)};ne().prototype.reshape=function(e){return this.throwIfDisposed(),W(this,e)};ne().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),US(this,e,t,n)};ne().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),GS(this,e,t,n)};ne().prototype.reverse=function(e){return this.throwIfDisposed(),na(this,e)};ne().prototype.rfft=function(){return this.throwIfDisposed(),zc(this)};ne().prototype.round=function(){return this.throwIfDisposed(),Ym(this)};ne().prototype.rsqrt=function(){return this.throwIfDisposed(),Jm(this)};ne().prototype.selu=function(){return this.throwIfDisposed(),Zm(this)};ne().prototype.separableConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),xo(this,e,t,n,a,r,s)};ne().prototype.sigmoid=function(){return this.throwIfDisposed(),ha(this)};ne().prototype.sign=function(){return this.throwIfDisposed(),mv(this)};ne().prototype.sin=function(){return this.throwIfDisposed(),Qm(this)};ne().prototype.sinh=function(){return this.throwIfDisposed(),ef(this)};ne().prototype.slice=function(e,t){return this.throwIfDisposed(),Ge(this,e,t)};ne().prototype.softmax=function(e){return this.throwIfDisposed(),Ja(this,e)};ne().prototype.softplus=function(){return this.throwIfDisposed(),bo(this)};ne().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Pc(this,e,t)};ne().prototype.split=function(e,t){return this.throwIfDisposed(),zn(this,e,t)};ne().prototype.sqrt=function(){return this.throwIfDisposed(),ln(this)};ne().prototype.square=function(){return this.throwIfDisposed(),lt(this)};ne().prototype.squaredDifference=function(e){return this.throwIfDisposed(),af(this,e)};ne().prototype.squeeze=function(e){return this.throwIfDisposed(),pr(this,e)};ne().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Ae?[this,e]:[this,...e];return Mt(n,t)};ne().prototype.step=function(e){return this.throwIfDisposed(),$u(this,e)};ne().prototype.stridedSlice=function(e,t,n,a,r,s,i,o){return this.throwIfDisposed(),gv(this,e,t,n,a,r,s,i,o)};ne().prototype.sub=function(e){return this.throwIfDisposed(),ce(this,e)};ne().prototype.sum=function(e,t){return this.throwIfDisposed(),be(this,e,t)};ne().prototype.tan=function(){return this.throwIfDisposed(),yv(this)};ne().prototype.tanh=function(){return this.throwIfDisposed(),oi(this)};ne().prototype.tile=function(e){return this.throwIfDisposed(),On(this,e)};ne().prototype.toBool=function(){return this.throwIfDisposed(),oe(this,"bool")};ne().prototype.toFloat=function(){return this.throwIfDisposed(),oe(this,"float32")};ne().prototype.toInt=function(){return this.throwIfDisposed(),oe(this,"int32")};ne().prototype.topk=function(e,t){return this.throwIfDisposed(),bv(this,e,t)};ne().prototype.transpose=function(e){return this.throwIfDisposed(),$e(this,e)};ne().prototype.unique=function(e){return this.throwIfDisposed(),Dh(this,e)};ne().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),xv(this,e,t)};ne().prototype.unstack=function(e){return this.throwIfDisposed(),mt(this,e)};ne().prototype.where=function(e,t){return this.throwIfDisposed(),fn(e,this,t)};ne().prototype.zerosLike=function(){return this.throwIfDisposed(),Ke(this)};var vr=class extends Error{constructor(e){super(e),Object.setPrototypeOf(this,vr.prototype)}},Va=class extends Error{constructor(e){super(e),Object.setPrototypeOf(this,Va.prototype)}},H=class extends Error{constructor(e){super(e),Object.setPrototypeOf(this,H.prototype)}},Pe=class extends Error{constructor(e){super(e),Object.setPrototypeOf(this,Pe.prototype)}},JS=class extends Error{constructor(e){super(e),Object.setPrototypeOf(this,JS.prototype)}},ZS=class{constructor(e){this.maxEntries=e||100,this.cache=new Map}get(e){let t;return this.cache.has(e)&&(t=this.cache.get(e),this.cache.delete(e),this.cache.set(e,t)),t}put(e,t){if(this.cache.has(e))this.cache.delete(e);else if(this.cache.size>=this.maxEntries){let n=this.cache.keys().next().value;this.cache.delete(n)}this.cache.set(e,t)}getMaxEntries(){return this.maxEntries}setMaxEntries(e){if(e<0)throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${e}.`);if(this.maxEntries>e)for(let t=0;t<this.maxEntries-e;t++){let n=this.cache.keys().next().value;this.cache.delete(n)}this.maxEntries=e}};function ci(e,t){if(Array.isArray(e)){let n=[];for(let a=0;a<t;a++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function rr(e,t){if(!e)throw new JS(t)}function z1(e,t){let n=0;for(let a of e)a===t&&n++;return n}function Pn(e){return e.length===1?e[0]:e}function bt(e){return Array.isArray(e)?e:[e]}function wr(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function js(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var va={};function Cv(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function Rb(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>Rb(t));else{let t=Object.keys(e);for(let n of t){let a=e[n];a!=null&&typeof a=="object"&&(!Array.isArray(a)&&a.type==="ndarray"&&typeof a.value=="number"?e[n]=a.value:Rb(a))}}}function Wc(e,t={},n={},a="object",r=!1){if(typeof e=="string"){let s=e,i;if(s in n)i=n[s];else if(s in va)i=va[s];else if(i=t[s],i==null)throw new H(`Unknown ${a}: ${e}. This may be due to one of the following reasons:
1. The ${a} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${a} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new H(`${a}: Improper config format: ${JSON.stringify(s)}.
'className' and 'config' must set.`);let i=s.className,o,l;if(i in n?[o,l]=n[i]:i in va?[o,l]=va.className:i in t&&([o,l]=t[i]),o==null)throw new H(`Unknown ${a}: ${i}. This may be due to one of the following reasons:
1. The ${a} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${a} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let h of Object.keys(va))u[h]=va[h];for(let h of Object.keys(n))u[h]=n[h];let p=s.config;p.customObjects=u;let d=Object.assign({},va);for(let h of Object.keys(n))va[h]=n[h];Rb(s.config);let c=l(o,s.config,n,r);return va=Object.assign({},d),c}else{let u=Object.assign({},va);for(let d of Object.keys(n))va[d]=n[d];let p=new o(s.config);return va=Object.assign({},u),p}}}function g4(e,t){return e<t?-1:e>t?1:0}function eh(e,t){return-1*g4(e,t)}function Zr(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function y4(e){if(e==null)throw new H(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function vo(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new H(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function _v(e,t,n=0,a=1/0){return rr(n>=0),rr(a>=n),Array.isArray(e)&&e.length>=n&&e.length<=a&&e.every(r=>typeof r===t)}function tn(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,a)=>tn(n,`element ${a+1} of ${t}`))):k.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${QS(e)}.`)}function QS(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>QS(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function b4(e,t,n){let a=n!=null?n():k.now(),r;return(...s)=>{let i=n!=null?n():k.now();return i-a<t||(a=i,r=e(...s)),r}}function e2(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}var x4=0;function t2(){return x4++}var th={};function yf(e=""){return e in th||(th[e]=0),th[e]+=1,e+th[e].toString()}var v4=["channelsFirst","channelsLast"],w4=["nearest","bilinear"],k4=["valid","same","causal"],I4=["max","avg"],S4=["sum","mul","concat","ave"],Jo=new Map;function Ot(e){vo(v4,"DataFormat",e)}function N4(e){vo(w4,"InterpolationFormat",e)}function ya(e){vo(k4,"PaddingMode",e)}function n2(e){vo(I4,"PoolMode",e)}var Wp=[],B1="/";function Zs(e,t){Wp.push(e);try{let n=t();return Wp.pop(),n}catch(n){throw Wp.pop(),n}}function T4(){return Wp.length===0?"":Wp.join(B1)+B1}function a2(e){if(!s2(e))throw new Error("Not a valid tensor name: '"+e+"'");return T4()+e}function r2(e){if(!s2(e))throw new Error("Not a valid tensor name: '"+e+"'");Jo.has(e)||Jo.set(e,0);let t=Jo.get(e);if(Jo.set(e,Jo.get(e)+1),t>0){let n=`${e}_${t}`;return Jo.set(n,1),n}else return e}var C4=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function s2(e){return!!e.match(C4)}function _4(e){return e===parseInt(e.toString(),10)}function Qr(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let a=1;for(let r=t;r<n;++r)a*=e[r];return a}function ml(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){let a=e[n];a<t&&(t=a)}return t}function ss(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){let a=e[n];a>t&&(t=a)}return t}function Ka(e,t){if(t<e)throw new H(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let a=e;a<t;++a)n.push(a);return n}var ub;function Ht(){return ub==null&&(ub=JI().epsilon()),ub}function Xa(){return"channelsLast"}function bf(e,t){return oe(e,t)}function Vc(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),W(e,n)}function E4(e,t){return O(()=>{if(e.shape.length!==2)throw new H(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Vc(e,1);return Mb(n,[1,t,1])})}function A4(e){let t=[Qr(e.shape)];return W(e,t)}function $4(e){if(e.rank<=1)throw new H(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Qr(e.shape,1)];return W(e,t)}function Qs(e,t,n){return O(()=>{switch(e.rank){case 1:return tf(e,t,n);case 2:return fv(e,[t,0],[n,e.shape[1]]);case 3:return Au(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Qp(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Ge(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Ge(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 H(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function pb(e,t,n){return O(()=>{switch(e.rank){case 1:return tf(e,t,n);case 2:return fv(e,[0,t],[e.shape[0],n]);case 3:return Au(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Qp(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new H(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function nh(e,t,n,a){return O(()=>{switch(e.rank){case 1:return tf(e,t,n);case 2:switch(a){case 1:return Qs(e,t,n);case 2:return pb(e,t,n);default:throw new H(`The axis is not within the rank of the tensor ${a}`)}case 3:switch(a){case 1:return Qs(e,t,n);case 2:return Au(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return pb(e,t,n);default:throw new H(`The axis is not within the rank of the tensor ${a}`)}case 4:switch(a){case 1:return Qs(e,t,n);case 2:return Qp(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Qp(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return pb(e,t,n);default:throw new H(`The axis is not within the rank of the tensor ${a}`)}default:throw new H(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Ev(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),Qe(e,t)}function W1(e,t){switch(e.rank){case 1:return iS([e,t]);case 2:return oS([e,t],0);case 3:return lS([e,t],0);case 4:return uS([e,t],0);default:throw new H(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function Mb(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new H(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return On(e,t)}function xf(e,t=0,n=1,a,r){return CS(e,t,n,a,r)}function or(e,t,n,a){if(e.rank<2||t.rank<2)throw new Pe(`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],s=t.shape.slice(-2)[0];if(r!==s)throw new Pe(`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 = ${t.shape}`)}if(e.rank===2&&t.rank===2)return rs.matMul({a:e,b:t,transposeA:!1,transposeB:!1,bias:a?Pb(e.rank,a,Xa()):null,activation:n});{let r=e.shape.slice(),s=r.pop();e=W(e,[-1,s]);let i=t.shape.slice(),o=i.pop(),l=i.pop(),u=[...i,o],p=Array.from({length:t.rank},(m,f)=>f===0?t.rank-2:f<=t.rank-2?f-1:f);t=W($e(t,p),[l,-1]);let d=[...r,...u],c=!1,h=!1;return W(rs.matMul({a:e,b:t,transposeA:c,transposeB:h,bias:a?Pb(e.rank,a,Xa()):null,activation:n}),d)}}function i2(e,t,n){return O(()=>(Array.isArray(t)?t=qe(t,"int32"):t=oe(t,"int32"),li(e,t,n)))}function Uc(e){return z(e,e)}function Pb(e,t,n){let a=t.shape;if(t.rank!==1&&t.rank!==e)throw new H(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return a.length===1?W(t,[1,a[0],1,1,1]):W(t,[1,a[3],a[0],a[1],a[2]]);if(n==="channelsLast")return a.length===1?W(t,[1,1,1,1,a[0]]):W(t,[1].concat(a))}else if(e===4){if(n==="channelsFirst")return a.length===1?W(t,[1,a[0],1,1]):W(t,[1,a[2],a[0],a[1]]);if(n==="channelsLast")return a.length===1?W(t,[1,1,1,a[0]]):W(t,[1].concat(a))}else if(e===3){if(n==="channelsFirst")return a.length===1?W(t,[1,a[0],1]):W(t,[1,a[1],a[0]]);if(n==="channelsLast")return a.length===1?W(t,[1,1,a[0]]):W(t,[1].concat(a))}else if(e<3)return t;throw new H(`Unsupported input rank by biasAdd: ${t.rank}`)}function Qa(e,t,n){return O(()=>(n==null&&(n=Xa()),Ot(n),J(e,Pb(e.rank,t,n))))}function F4(e,t=1){if(t!==1)throw new Pe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Tu(e)}function D4(e){return O(()=>fe(e,J(zt(e),1)))}function o2(e,t,n,a){return O(()=>RS(e,t,n,a))}function R4(e){return O(()=>{let t=J(.5,z(.2,e));return nn(t,0,1)})}function Gc(e,t,n=!1){return n?e():t()}var M4=["fanIn","fanOut","fanAvg"],P4=["normal","uniform","truncatedNormal"];function O4(e){vo(M4,"FanMode",e)}function L4(e){vo(P4,"Distribution",e)}var _a=class extends se.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Av=class extends _a{apply(e,t){return kt(e,t)}};Av.className="Zeros";se.registerClass(Av);var vf=class extends _a{apply(e,t){return Jn(e,t)}};vf.className="Ones";se.registerClass(vf);var $v=class extends _a{constructor(e){if(super(),typeof e!="object")throw new H(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new H(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return O(()=>z(we(this.value),Jn(e,t)))}getConfig(){return{value:this.value}}};$v.className="Constant";se.registerClass($v);var Fv=class extends _a{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 Eu(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};Fv.className="RandomUniform";se.registerClass(Fv);var Dv=class extends _a{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 Pe(`randomNormal does not support dType ${t}.`);return xf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Dv.className="RandomNormal";se.registerClass(Dv);var Rv=class extends _a{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 Pe(`truncatedNormal does not support dType ${t}.`);return rf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Rv.className="TruncatedNormal";se.registerClass(Rv);var Mv=class extends _a{constructor(e){super(),this.gain=e.gain!=null?e.gain:1}apply(e,t){return O(()=>{if(e.length!==2||e[0]!==e[1])throw new H("Identity matrix initializer can only be used for 2D square matrices.");return z(this.gain,nv(e[0]))})}getConfig(){return{gain:this.gain}}};Mv.className="Identity";se.registerClass(Mv);function z4(e,t="channelsLast"){let n,a;if(Ot(t),e.length===2)n=e[0],a=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=Qr(e,2);n=e[1]*r,a=e[0]*r}else if(t==="channelsLast"){let r=Qr(e,0,e.length-2);n=e[e.length-2]*r,a=e[e.length-1]*r}}else{let r=Qr(e);n=Math.sqrt(r),a=Math.sqrt(r)}return[n,a]}var Wn=class extends _a{constructor(e){if(super(),e.scale<0)throw new H(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,O4(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,L4(this.distribution),this.seed=e.seed}apply(e,t){let n=z4(e),a=n[0],r=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,a):this.mode==="fanOut"?s/=Math.max(1,r):s/=Math.max(1,(a+r)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Pe(`${this.getClassName()} does not support dType ${t}.`);return rf(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return Eu(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Wn.className="VarianceScaling";se.registerClass(Wn);var wf=class extends Wn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Wn.className}};wf.className="GlorotUniform";se.registerClass(wf);var kf=class extends Wn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Wn.className}};kf.className="GlorotNormal";se.registerClass(kf);var If=class extends Wn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Wn.className}};If.className="HeNormal";se.registerClass(If);var Sf=class extends Wn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Wn.className}};Sf.className="HeUniform";se.registerClass(Sf);var Nf=class extends Wn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Wn.className}};Nf.className="LeCunNormal";se.registerClass(Nf);var Tf=class extends Wn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Wn.className}};Tf.className="LeCunNormal";se.registerClass(Tf);var Pv=class extends _a{constructor(e){if(super(),this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Pe("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return O(()=>{if(e.length<2)throw new Pe("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,a=xf(n,0,1,"float32"),r=HS.gramSchmidt(a);return e[0]>e[1]&&(r=$e(r)),z(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Pv.className="Orthogonal";se.registerClass(Pv);var V1={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 U1(e,t={}){return Wc(e,se.SerializationMap.getMap().classNameMap,t,"initializer")}function At(e){return Cv(e)}function It(e){if(typeof e=="string"){let t=e in V1?V1[e]:e;if(t==="GlorotNormal")return new kf;if(t==="GlorotUniform")return new wf;if(t==="HeNormal")return new If;if(t==="HeUniform")return new Sf;if(t==="LeCunNormal")return new Nf;if(t==="LeCunUniform")return new Tf;{let n={};return n.className=t,n.config={},U1(n)}}else return e instanceof _a?e:U1(e)}function Ob(e){return Array.isArray(e)&&Array.isArray(e[0])}function Rh(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Le(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new H(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function it(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new H(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Mh(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((a,r)=>a*r);return t}var G1="Variable",l2=class{constructor(e,t="float32",n=G1,a=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=t2(),n=n==null?G1:n,this.originalName=a2(n),this.name=r2(this.originalName),this.trainable_=a,this.constraint=r,this.val=ES(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),B4(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 B4(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function Lb(e){return e.map(t=>t.read())}function Ov(e){e.forEach(t=>{t[0].write(t[1])})}var Bt=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||{}}},Ua=class{constructor(e,t,n,a,r,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=a,this.callArgs=r,this.outputTensorIndex=i,this.id=t2(),s!=null&&(this.originalName=a2(s),this.name=r2(this.originalName)),this.rank=t.length}},W4=0,Cf=class{constructor(e,t){this.callArgs=t,this.id=W4++,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}}},V4=0,Ye=class extends se.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=V4++,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=wr(n)+"_"+yf(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 a=e.dtype;a==null&&(a=e.inputDType),a==null&&(a="float32"),this.dtype=a}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 Va(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new H(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Pn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Pn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new vr(`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 vr(`Layer ${this.name} is not connected, no input to return.`);return Pn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new vr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new vr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Pn(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=bt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=bt(this.inputSpec);if(e.length!==t.length)throw new H(`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 a=e[n],r=t[n];if(r==null)continue;let s=a.rank;if(r.ndim!=null&&s!==r.ndim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${s}`);if(r.maxNDim!=null&&s>r.maxNDim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${s}`);if(r.minNDim!=null&&s<r.minNDim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${s}.`);if(r.dtype!=null&&a.dtype!==r.dtype)throw new H(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${a.dtype}.`);if(r.axes){let i=a.shape;for(let o in r.axes){let l=Number(o),u=r.axes[o],p=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(p)===-1)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],l=a.shape[i];if(o!=null&&l!=null&&o!==l)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${a.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=bt(e),a=!0;for(let s of n)if(!(s instanceof Ua)){a=!1;break}let r=!0;for(let s of n)if(s instanceof Ua){r=!1;break}if(a===r)throw new H("Arguments to apply() must be all SymbolicTensors or all Tensors");return Zs(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of bt(e))s.push(i.shape);this.build(Pn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=bt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Pn(o),this.activityRegularizer!=null)throw new Pe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=U4(e),i=this.computeOutputShape(s),o,l=G4(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,p)=>new Ua(l,u,this,bt(e),t,this.name,p)):o=new Ua(l,i,this,bt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Pe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,a)=>{n!=null&&e[a]!=null&&e[a]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 vr(`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 vr(`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 Va(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Mh(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Lb(e?this.trainableWeights:this.weights)}setWeights(e){O(()=>{let t=this.weights;if(t.length!==e.length)throw new H(`You called setWeights(weights) on layer "${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=[],a=Lb(t);for(let r=0;r<a.length;++r){let s=a[r],i=t[r],o=e[r];if(!k.arraysEqual(s.shape,o.shape))throw new H(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Ov(n)})}addWeight(e,t,n,a,r,s,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new H(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(a=o!=null?o():It("zeros"));let l=a.apply(t,n),u=new l2(l,n,e,s,i);return l.dispose(),r!=null&&this.addLoss(()=>r.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=bt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=bt(e);t=bt(t),n=bt(n),a=bt(a),r=Rh(r),s=Rh(s);let l=[],u=[],p=[];for(let d of o)l.push(d.sourceLayer),u.push(d.nodeIndex),p.push(d.tensorIndex);new Cf({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:p,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 U4(e){e=bt(e);let t=[];for(let n of e)t.push(n.shape);return Pn(t)}function G4(e){return"float32"}function u2(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let a=t.inboundNodes[n];if(a.inboundLayers.length===0)return a.inputTensors;{let r=[];for(let s=0;s<a.inboundLayers.length;s++){let i=a.inputTensors[s],o=a.inboundLayers[s],l=a.nodeIndices[s],u=u2(i,o,l);for(let p of u)r.indexOf(p)===-1&&r.push(p)}return r}}}var Du=class extends Ye{constructor(e){if(super({dtype:e.dtype,name:e.name!=null?e.name:yf("input").toString()}),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 H("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 H("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new H("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 a=new Ua(this.dtype,this.batchInputShape,this,[],{},this.name);a.nodeIndex=0,a.tensorIndex=0,new Cf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[a],outputTensors:[a],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new H(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};Du.className="InputLayer";se.registerClass(Du);function p2(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new H("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new Du({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}function H4(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return oe(t,e.dtype)}catch(n){throw new H(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Xs=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Xs)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]=H4(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new H(`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 Ua){if(this.id2Value[e.id]==null)throw new H(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new H(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Ua){if(this.id2Value[e.id]==null)throw new H(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new H(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Re(this.id2Mask)}},Ph=new ZS,Oh=new ZS;function j4(e){Ph!=null&&Ph.setMaxEntries(e),Oh!=null&&Oh.setMaxEntries(e)}function Dp(e,t,n,a){let r=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(m=>m.name),l=[],u=t.names();for(let m of o)u.indexOf(m)!==-1?l.push(t.getValue(m)):l.push(null);a!=null&&(a.maxNumTensors=-1/0,a.minNumTensors=1/0);let p=o.join(",")+"|"+t.names().join(","),d=Ph.get(p),c;if(d==null){let m=q4(i,t);d=m.sorted,c=m.recipientCounts,Ph.put(p,d),Oh.put(p,c)}c={},r||Object.assign(c,Oh.get(p));let h=new Xs(t);for(let m=0;m<d.length;++m){if(a!=null){let $=$h().numTensors;$>a.maxNumTensors&&(a.maxNumTensors=$),$<a.minNumTensors&&(a.minNumTensors=$)}let f=d[m],g=f.sourceLayer;if(g instanceof Du)continue;let y=[],b=[],x=[],v=!1;for(let $ of f.inputs){let P=h.getValue($),F=h.getMask($);y.push(P),b.push(F),F!=null&&(v=!0),r||(c[$.name]--,c[$.name]===0&&!t.hasKey($)&&o.indexOf($.name)===-1&&!P.isDisposed&&$.sourceLayer.stateful!==!0&&x.push(P))}v&&(n=n||{},n.mask=b[0]);let w=bt(g.apply(y,n)),T=null;g.supportsMasking&&(T=g.computeMask(y,b));let C=X4(f),E=Array.isArray(C)?C:[C];for(let $=0;$<E.length;++$){h.hasKey(E[$])||h.add(E[$],w[$],Array.isArray(T)?T[0]:T);let P=o.indexOf(E[$].name);P!==-1&&(l[P]=w[$])}r||Re(x)}return h.disposeMasks(),s?l:l[0]}function q4(e,t){k.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],a={};if(e.length===1){let r=H1(e[0],t);n=r.sorted,a=r.recipientMap}else{let r=new Set;for(let s of e){let{sorted:i,recipientMap:o}=H1(s,t);for(let l of i)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in o)a[l]==null&&(a[l]=new Set),o[l].forEach(u=>a[l].add(u))}}return{sorted:n,recipientCounts:K4(a)}}function K4(e){let t={};for(let n in e)t[n]=e[n].size;return t}function H1(e,t){let n=new Set,a=[],r={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),a.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)r[u.name]==null&&(r[u.name]=new Set),r[u.name].add(o.name),!n.has(u.name)&&s.push(u)}}return{sorted:a,recipientMap:r}}function X4(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let a=0;a<e.sourceLayer.inboundNodes.length;++a)for(let r of e.sourceLayer.inboundNodes[a].outputTensors)if(r.id===e.id){n=a;break}t=e.sourceLayer.getOutputAt(n)}return t}var Y4=X();Y4.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES",()=>100,j4);var c2={};Me(c2,{maxNorm:()=>J4,minMaxNorm:()=>eV,nonNeg:()=>Q4,unitNorm:()=>Z4});function Lv(e,t){return O(()=>ln(be(z(e,e),t,!0)))}var Hc=class extends se.Serializable{getConfig(){return{}}},zv=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 O(()=>{let t=Lv(e,this.axis),n=nn(t,0,this.maxValue);return z(e,fe(n,J(Ht(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};zv.className="MaxNorm";se.registerClass(zv);var Bv=class extends Hc{constructor(e){super(),this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return O(()=>fe(e,J(Ht(),Lv(e,this.axis))))}getConfig(){return{axis:this.axis}}};Bv.className="UnitNorm";se.registerClass(Bv);var Wv=class extends Hc{apply(e){return Xe(e)}};Wv.className="NonNeg";se.registerClass(Wv);var Vv=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 O(()=>{let t=Lv(e,this.axis),n=J(z(this.rate,nn(t,this.minValue,this.maxValue)),z(1-this.rate,t));return z(e,fe(n,J(Ht(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};Vv.className="MinMaxNorm";se.registerClass(Vv);var j1={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Kt(e){return Cv(e)}function q1(e,t={}){return Wc(e,se.SerializationMap.getMap().classNameMap,t,"constraint")}function Xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in j1?j1[e]:e,config:{}};return q1(t)}else return e instanceof Hc?e:q1(e)}function J4(e){return new zv(e)}function Z4(e){return new Bv(e)}function Q4(){return new Wv}function eV(e){return new Vv(e)}var d2={};Me(d2,{constant:()=>aV,glorotNormal:()=>pV,glorotUniform:()=>uV,heNormal:()=>cV,heUniform:()=>dV,identity:()=>oV,leCunNormal:()=>hV,leCunUniform:()=>mV,ones:()=>nV,orthogonal:()=>fV,randomNormal:()=>sV,randomUniform:()=>rV,truncatedNormal:()=>iV,varianceScaling:()=>lV,zeros:()=>tV});function tV(){return new Av}function nV(){return new vf}function aV(e){return new $v(e)}function rV(e){return new Fv(e)}function sV(e){return new Dv(e)}function iV(e){return new Rv(e)}function oV(e){return new Mv(e)}function lV(e){return new Wn(e)}function uV(e){return new wf(e)}function pV(e){return new kf(e)}function cV(e){return new If(e)}function dV(e){return new Sf(e)}function hV(e){return new Nf(e)}function mV(e){return new Tf(e)}function fV(e){return new Pv(e)}var h2={};Me(h2,{Layer:()=>Ye,RNN:()=>fr,RNNCell:()=>Yc,activation:()=>WU,add:()=>YU,alphaDropout:()=>DG,average:()=>JU,averagePooling1d:()=>Xw,averagePooling2d:()=>Yw,averagePooling3d:()=>Jw,avgPool1d:()=>iG,avgPool2d:()=>lG,avgPool3d:()=>pG,avgPooling1d:()=>oG,avgPooling2d:()=>uG,avgPooling3d:()=>cG,batchNormalization:()=>aG,bidirectional:()=>NG,concatenate:()=>ZU,conv1d:()=>FU,conv2d:()=>DU,conv2dTranspose:()=>RU,conv3d:()=>MU,conv3dTranspose:()=>PU,convLstm2d:()=>wG,convLstm2dCell:()=>kG,cropping2D:()=>LU,dense:()=>VU,depthwiseConv2d:()=>BU,dot:()=>nG,dropout:()=>UU,elu:()=>TU,embedding:()=>XU,flatten:()=>HU,gaussianDropout:()=>FG,gaussianNoise:()=>$G,globalAveragePooling1d:()=>dG,globalAveragePooling2d:()=>hG,globalMaxPool1d:()=>CG,globalMaxPool2d:()=>_G,globalMaxPooling1d:()=>rN,globalMaxPooling2d:()=>sN,gru:()=>fG,gruCell:()=>gG,input:()=>$2,inputLayer:()=>NU,layerNormalization:()=>rG,leakyReLU:()=>_U,lstm:()=>yG,lstmCell:()=>bG,masking:()=>RG,maxPool1d:()=>EG,maxPool2d:()=>AG,maxPooling1d:()=>iN,maxPooling2d:()=>oN,maxPooling3d:()=>mG,maximum:()=>QU,minimum:()=>eG,multiply:()=>tG,permute:()=>KU,prelu:()=>EU,reLU:()=>CU,repeatVector:()=>jU,reshape:()=>qU,rnn:()=>IG,separableConv2d:()=>OU,simpleRNN:()=>xG,simpleRNNCell:()=>vG,softmax:()=>AU,spatialDropout1d:()=>GU,stackedRNNCells:()=>SG,thresholdedReLU:()=>$U,timeDistributed:()=>TG,upSampling2d:()=>zU,zeroPadding2d:()=>sG});async function jr(e){if(e==null)return;let t=[],n=[],a=[];for(let r in e){let s=e[r];if(typeof s!="number"){let i=s;t.push(i.data()),n.push(r),a.push(i)}}if(t.length>0){let r=await Promise.all(t);for(let s=0;s<r.length;++s)e[n[s]]=r[s][0];Re(a)}}function m2(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var K1;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(K1||(K1={}));var gV=125,fl=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){}},f2=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)}},yV=class extends fl{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 a in t){let r=t[a];if(typeof r=="number")this.totals.hasOwnProperty(a)||(this.totals[a]=0),this.totals[a]=this.totals[a]+r*n;else{let s;a in this.totals?s=this.totals[a]:this.totals[a]=0;let i=O(()=>J(this.totals[a],z(r,n)));this.totals[a]=i,s!=null&&s.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:O(()=>{let a=z(fe(1,this.seen),this.totals[n]);t[n]=a,this.totals[n].dispose(),en(t[n])}))}},g2=class extends fl{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 s=this.history[r];for(let i=0;i<s.length;++i)if(typeof s[i]!="number"){let o=s[i];e.push(o.data()),t.push(r),n.push(i)}}let a=await Promise.all(e);for(let r=0;r<a.length;++r)this.history[t[r]][n[r]].dispose(),this.history[t[r]][n[r]]=a[r][0]}},y2=class extends fl{constructor(e,t){if(super(),this.currentEpoch=0,this.nowFunc=e.nowFunc,this.nextFrameFunc=e.nextFrameFunc||Nv,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=gV),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");k.isNumber(this.yieldEvery)&&(this.maybeWait=b4(this.maybeWait.bind(this),this.yieldEvery,this.nowFunc)),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 a=[];this.yield!=null&&(await jr(n),a.push(this.yield(e,t,n))),a.push(this.nextFrameFunc()),await Promise.all(a)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await jr(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await jr(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(this.nextFrameFunc()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await jr(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await jr(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(this.nextFrameFunc()):k.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await jr(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await jr(e),await this.trainEnd(e))}};function b2(e,t){return e==null&&(e={}),e instanceof fl?[e]:Array.isArray(e)&&e[0]instanceof fl?e:bt(e).map(n=>new y2(n,t))}var ka=class{constructor(){}static registerCallbackConstructor(e,t){k.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),ka.checkForDuplicate(t),ka.constructors[e]==null&&(ka.constructors[e]=[]),ka.constructors[e].push(t)}static checkForDuplicate(e){for(let t in ka.constructors)ka.constructors[+t].forEach(n=>{if(n===e)throw new H("Duplicate callback constructor.")})}static clear(){ka.constructors={}}static createCallbacks(e){let t=[];for(let n in ka.constructors){let a=+n;e>=a&&t.push(...ka.constructors[a])}return t.map(n=>new n)}};ka.constructors={};function x2(e,t,n,a,r,s,i,o,l){let u=new g2,p=[new yV,...ka.createCallbacks(t)];e!=null&&p.push(...e),p.push(u);let d=new f2(p);return d.setParams({epochs:n,initialEpoch:a,samples:r,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:d,history:u}}function ja(e,t={},n=!1){return Wc(e,se.SerializationMap.getMap().classNameMap,t,"layer",n)}function Lh(e,t){return O(()=>{e.dtype!=="float32"&&(e=oe(e,"float32"));let n=be(Uc(e),t,!0),a=Cn(n.shape,Ht()),r=ln(hr(n,a));return fe(e,r)})}function wo(e,t){return O(()=>Et(Uc(ce(t,e)),-1))}function _f(e,t){return O(()=>Et(zt(ce(t,e)),-1))}function Ru(e,t){return O(()=>{let n=ce(e,t),a=nn(zt(e),Ht(),Number.MAX_VALUE),r=zt(fe(n,a));return z(100,Et(r,-1))})}function bV(e,t){return O(()=>{let n=nn(t,Ht(),Number.MAX_VALUE),a=ea(J(1,n)),r=nn(e,Ht(),Number.MAX_VALUE),s=ea(J(1,r));return Et(Uc(ce(a,s)),-1)})}function xV(e,t){return O(()=>{let n=hr(0,ce(1,z(e,t)));return Et(Uc(n),-1)})}function vV(e,t){return O(()=>{let n=hr(0,ce(1,z(e,t)));return Et(n,-1)})}function wV(e,t){return O(()=>{let n=be(z(e,t),-1),a=Sa(z(ce(1,e),t),-1);return hr(0,J(1,ce(a,n)))})}function kV(e,t){return O(()=>{let n=Math.log(2),a=ce(t,e),r=ce(J(a,bo(z(-2,a))),n);return Et(r,-1)})}function ec(e,t,n=!1){return O(()=>{if(n)t=Ja(t);else{let a=be(t,t.shape.length-1,!0);t=fe(t,a)}return t=nn(t,Ht(),1-Ht()),St(be(z(oe(e,"float32"),ea(t)),t.shape.length-1))})}function zh(e,t,n=!1){return O(()=>{let a=oe(Cu(A4(e)),"int32");t=nn(t,Ht(),1-Ht());let r=t.shape,s=W(cl(a,r[r.length-1]),r);return ec(s,t,n)})}function IV(e,t){if(!k.arraysEqual(e.shape,t.shape))throw new H(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return O(()=>{let n=Xe(t),a=St(zt(t));return J(ce(n,z(t,e)),Rc(gn(a)))})}function Ef(e,t){return O(()=>{let n;return n=nn(t,Ht(),1-Ht()),n=ea(fe(n,ce(1,n))),Et(IV(e,n),-1)})}function SV(e,t){return O(()=>{let n=nn(e,Ht(),1),a=nn(t,Ht(),1);return be(z(e,ea(fe(n,a))),-1)})}function NV(e,t){return O(()=>{let n=ea(J(Ht(),t));return Et(ce(t,z(e,n)),-1)})}function Uv(e,t){return O(()=>{let n=Lh(e,-1),a=Lh(t,-1),r=z(n,a);return St(be(r,-1))})}var Bh={meanSquaredError:wo,meanAbsoluteError:_f,meanAbsolutePercentageError:Ru,meanSquaredLogarithmicError:bV,squaredHinge:xV,hinge:vV,categoricalHinge:wV,logcosh:kV,categoricalCrossentropy:ec,sparseCategoricalCrossentropy:zh,binaryCrossentropy:Ef,kullbackLeiblerDivergence:SV,poisson:NV,cosineProximity:Uv};function cb(e){if(typeof e=="string"){if(e in Bh)return Bh[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new H(t)}else return e}function Gv(e,t){return O(()=>{let n=z(.5,ta(t)),a=bf(Gn(t,n),e.dtype);return Et(Qn(e,a),-1)})}function Hv(e,t){return O(()=>bf(Qn(ii(e,-1),ii(t,-1)),"float32"))}function v2(e,t){return O(()=>oe(be(Ta(Qn(e,1),Qn(t,1))),"float32"))}function TV(e,t){return O(()=>oe(be(Ta(Qn(e,1),Qn(t,0))),"float32"))}function CV(e,t){return O(()=>oe(be(Ta(Qn(e,0),Qn(t,1))),"float32"))}function w2(e,t){return O(()=>{let n=v2(e,t),a=CV(e,t),r=J(n,a);return oe(fn(Gn(r,0),fe(n,r),0),"float32")})}function _V(e,t){return O(()=>{let n=v2(e,t),a=TV(e,t),r=J(n,a);return oe(fn(Gn(r,0),fe(n,r),0),"float32")})}function k2(e,t){return Ef(e,t)}function I2(e,t){return e.rank===t.rank&&(e=pr(e,[e.rank-1])),t=ii(t,-1),t.dtype!==e.dtype&&(t=oe(t,e.dtype)),oe(Qn(e,t),"float32")}var EV=wo,AV=wo,$V=_f,FV=_f,DV=Ru,RV=Ru,jv=ec,MV=Uv,S2=zh,Wh={binaryAccuracy:Gv,categoricalAccuracy:Hv,precision:w2,categoricalCrossentropy:jv,sparseCategoricalCrossentropy:S2,mse:EV,MSE:AV,mae:$V,MAE:FV,mape:DV,MAPE:RV,cosine:MV};function PV(e){if(typeof e=="string"&&e in Wh)return Wh[e];if(typeof e!="string"&&e!=null)return e;throw new H(`Unknown metric ${e}`)}function ah(e){if(rr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Bh))if(Bh[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(Wh))if(Wh[n]===e){t=n;break}return t!==void 0?t:e.name}}function OV(e){let t={Adagrad:()=>Us.adagrad(.01),Adadelta:()=>Us.adadelta(1,.95,Ht()),Adam:()=>Us.adam(.001,.9,.999,Ht()),Adamax:()=>Us.adamax(.002,.9,.999,Ht(),0),RMSProp:()=>Us.rmsprop(.001,.9,0,Ht()),SGD:()=>Us.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 H(`Unknown Optimizer ${e}`)}function X1(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!zb(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let a=JSON.stringify(e);a.length>1048576&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${a.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${1048576}.`)}}function zb(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"||!zb(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!zb(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function LV(e,t,n,a=console.log){let r=BV(e),s=["Layer (type)","Input Shape","Output shape","Param #"];r?(t=t||90,n=n||[.32,.61,.89,1]):(t=t||115,n=n||[.24,.48,.7,.8,1]),n[n.length-1]<=1&&(n=n.map(p=>Math.floor(t*p)));let i;if(!r){s.push("Receives inputs"),i=[];for(let p in e.nodesByDepth)i.push(...e.nodesByDepth[p])}a("_".repeat(t)),Vh(s,n,a),a("=".repeat(t));let o=e.layers;for(let p=0;p<o.length;++p)r?WV(o[p],n,a):VV(o[p],n,i,a),a((p===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=zV(e),u=Mh(e.nonTrainableWeights);a(`Total params: ${l+u}`),a(`Trainable params: ${l}`),a(`Non-trainable params: ${u}`),a("_".repeat(t))}function zV(e){let t;return e.collectedTrainableWeights!=null?t=Mh(e.collectedTrainableWeights):t=Mh(e.trainableWeights),t}function BV(e){let t=!0,n=[],a=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}a.push(...r)}if(t)for(let r of e.layers){let s=!1;for(let i of r.inboundNodes)if(a.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function Vh(e,t,n=console.log){let a="";for(let r=0;r<e.length;++r)r>0&&(a=a.slice(0,a.length-1)+" "),a+=e[r],a=a.slice(0,t[r]),a+=" ".repeat(t[r]-a.length);n(a)}function WV(e,t,n){let a,r;try{r=e.inboundNodes.map(l=>JSON.stringify(l.inputShapes)).join(",")}catch(l){r="multiple"}try{a=JSON.stringify(e.outputShape)}catch(l){a="multiple"}let s=e.name,i=e.getClassName(),o=[`${s} (${i})`,r,a,e.countParams().toString()];Vh(o,t,n)}function VV(e,t,n,a){let r,s;try{s=e.inboundNodes.map(d=>JSON.stringify(d.inputShapes)).join(",")}catch(d){s="multiple"}try{r=JSON.stringify(e.outputShape)}catch(d){r="multiple"}let i=[];for(let d of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(d)===-1))for(let c=0;c<d.inboundLayers.length;++c){let h=d.inboundLayers[c].name,m=d.nodeIndices[c],f=d.tensorIndices[c];i.push(`${h}[${m}][${f}]`)}let o=e.name,l=e.getClassName(),u=i.length===0?"":i[0],p=[`${o} (${l})`,s,r,e.countParams().toString(),u];Vh(p,t,a);for(let d=1;d<i.length;++d)Vh(["","","","",i[d]],t,a)}function N2(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function tc(e,t){if(e===null)return null;if(typeof e=="string")return js(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],a=e.length;for(let r=0;r<a;++r){let s=e[r];N2(t,r,s)?n.push(s):n.push(tc(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a];if(a==="name"&&typeof r=="string")n[a]=r;else{let s=js(a);n[s]=tc(r,s)}}return n}}function Bb(e,t){if(e==null)return null;if(typeof e=="string")return wr(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],a=e.length;for(let r=0;r<a;++r){let s=e[r];N2(t,r,s)?n.push(s):n.push(Bb(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a],s=wr(a);(a==="name"||a==="className")&&typeof r=="string"?n[s]=r:n[s]=Bb(r,a)}return n}}var qv="3.16.0",nr=class extends Ye{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=yf(y)}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],Zr(this.inputs).length!==this.inputs.length)throw new H(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Zr(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(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let b=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let b=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;rr(x===0,"input layer has >1 nodes"),rr(v===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof Du))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,b,x,v,w,T)=>{(v==null||w==null||T==null)&&(v=y.sourceLayer,w=y.nodeIndex,T=y.tensorIndex);let C=v.inboundNodes[w];if(x.indexOf(C)!==-1)throw new Va(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(nr.nodeKey(v,w)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(C)===-1&&x.push(C);let E=C.inboundLayers.length;for(let $=0;$<E;$++){let P=C.inputTensors[$],F=C.inboundLayers[$],S=C.nodeIndices[$],M=C.tensorIndices[$];o(P,b,x,F,S,M)}for(b.push(C);x.indexOf(C)>=0;)x.splice(x.indexOf(C),1);i.push(C)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let p=i.slice().reverse();for(let y of p){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,x),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let v=0;v<y.inboundLayers.length;v++){let w=y.inboundLayers[v],T=y.nodeIndices[v],C=w.inboundNodes[T],E=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,E),n[C.id]=C}}let d={};for(let y in t){let b=t[y];b in d||(d[b]=[]),d[b].push(n[y])}let c={};for(let y in a){let b=a[y];b in c||(c[b]=[]),c[b].push(r[y])}let h=Object.keys(c).map(y=>parseInt(y,10)).sort(eh);this.layers=[];for(let y of h){let b=c[y];b.sort((x,v)=>{let w=s[x.id],T=s[v.id];return w<T?-1:w>T?1:0});for(let x of b)x instanceof nr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(d).map(y=>parseInt(y,10)).sort(eh);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of d[y]){let x=b.outboundLayer;if(x!=null){for(let v of b.inputTensors)if(m.indexOf(v)===-1)throw new Va(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of b.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(x=>x===y).length;if(b!==1)throw new Va(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Cf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 H("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={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new H(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new H(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new H(`${s.length} of ${a} weights are not set: ${s}`)}Ov(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${qv}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Bb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return O(()=>{e=bt(e);let n=new Xs;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Dp(this.outputs,n,t)})}computeMask(e,t){return O(()=>{e=bt(e);let n;return t==null?n=ci(null,e.length):n=bt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Rh(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(eh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let p=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,x=n[b];p.push(x)}let d=u.computeOutputShape(Pn(p)),c=Rh(d),h=u.inboundNodes.indexOf(l);for(let m=0;m<c.length;m++){let f=`${u.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],p=`${o.name}_${l}_${u}`;s.push(p)}for(let i=0;i<s.length;i++){let o=s[i];rr(o in n),r.push(n[o])}return Pn(r)}runInternalGraph(e,t){t==null&&(t=ci(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],p=t[o];n[l.id]=[u,p]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(eh);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let p=u.outboundLayer,d=u.inputTensors,c=u.outputTensors,h=new Array;for(let m of d)m.id in n&&h.push(n[m.id]);if(h.length===d.length){let m={},f,g,y,b;if(u.callArgs!=null&&(m=u.callArgs),h.length===1){let[x,v]=h[0];m.mask==null&&(m.mask=v),y=bt(p.call(x,m)),b=bt(p.computeMask(x,v)),f=[x],g=[v]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),y=bt(p.call(f,m)),b=bt(p.computeMask(f,g));if(p.activityRegularizer)throw new Pe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let v=c[x],w=y[x],T=b[x];n[v.id]=[w,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){rr(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=n[o.id];i.push(l.shape),r.push(l),s.push(u)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof nr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=nr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new H(`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 H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return O(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=nr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let p=0;p<s.inboundNodes.length;p++){let d=s.inboundNodes[p],c=nr.nodeKey(s,p),h={};if(this.containerNodes.has(c)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(m){console.warn(`Layer ${s.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).`),h={}}if(d.inboundLayers.length>0){let m=[];for(let f=0;f<d.inboundLayers.length;f++){let g=d.inboundLayers[f],y=d.nodeIndices[f],b=d.tensorIndices[f],x=nr.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,b,h])}l.push(m)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,n.push(u)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=nr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let p=this.inputLayersTensorIndices[s];a.push([i.name,u,p])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=nr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let p=this.outputLayersTensorIndices[s];r.push([i.name,u,p])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let x of g){let v=x[0],w=x[1],T=x[2];if(b=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let C=r[v];if(C.inboundNodes.length<=w){i(f,g);return}let E=C.inboundNodes[w];y.push(E.outputTensors[T])}y.length>0&&f.apply(Pn(y),b)}function l(f){let g=f.name,y=ja(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let u=t.name,p=t.layers;for(let f of p)l(f);for(;!y4(s);)for(let f of p){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let d=[],c=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];rr(g in r);let x=r[g].inboundNodes[y].outputTensors;d.push(x[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];rr(g in r);let x=r[g].inboundNodes[y].outputTensors;c.push(x[b])}return new e({inputs:d,outputs:c,name:u})}get stateful(){if(this._stateful)throw new H("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(){O(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function UV(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===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!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function T2(e,t){return UV(e,t,"classWeight")}async function C2(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=O(()=>{if(e.shape.length===1)return Nr(e);if(e.shape.length===2){if(e.shape[1]>1)return ii(e,1);if(e.shape[1]===1)return W(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),s=Array.from(await r.data());Re(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),qe(i,"float32")}else return null}function GV(e,t){return z(e,t)}var HV=32;function _2(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=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 s=Y1("input",e.inputNames,n),i=Y1("output",e.outputNames,a),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)k.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function Y1(e,t,n){if(n instanceof Ae)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function jV(e){if(e.length===3)throw new Pe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function qV(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.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,s,i;if(r)if(J1(n.validationData))k.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 g=jV(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let p=b2(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=x2(p,d,n.epochs,null,null,KV(t,n),null,r,u);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await c.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());!a||y<n.batchesPerEpoch;){let x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:v,ys:w}=_2(e,x.value),T={};T.batch=b,T.size=v[0].shape[0],await c.onBatchBegin(b,T);let C=[];if(n.classWeight!=null){let P=T2(n.classWeight,e.outputNames);for(let F=0;F<P.length;++F)C.push(await C2(w[F],null,P[F]))}let E=v.concat(w).concat(C),$=o(E);Re(E);for(let P=0;P<l.length;++P){let F=l[P],S=$[P];T[F]=S,en(S)}await c.onBatchEnd(b,T),m2(T),b++,y++}if(a?y>=n.batchesPerEpoch:x.done){if(r){let v;J1(n.validationData)?v=bt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):v=bt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?HV:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${e.metricsNames[w]}`]=v[w]}break}if(e.stopTraining_)break}if(await c.onEpochEnd(m,g),m++,e.stopTraining_)break}return await c.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function KV(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function J1(e){return typeof e.iterator=="function"}function XV(e){return typeof e.next=="function"}async function YV(e,t,n){n=n||{};let a=n.batches!=null,r=e.testFunction,s=[];if(n.verbose>0)throw new Pe("Verbose mode is not implemented yet.");k.assert(!a||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=XV(t)?t:await t.iterator(),o=0,l=0;for(;!a||l<n.batches;){let u=await i.next();if(s=O(()=>{if(u.value){let{xs:p,ys:d}=_2(e,u.value),c=p.concat(d),h=O(()=>r(c));if(Re(c),l===0)for(let f=0;f<h.length;++f)s.push(we(0));let m=c[0].shape[0];for(let f=0;f<h.length;++f){let g=h[f],y=s[f];s[f]=O(()=>J(s[f],z(m,g))),l>0&&Re(y)}Re(h),o+=m,++l}return s}),u.done){a&&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<s.length;++u){let p=s[u];s[u]=fe(s[u],o),Re(p)}return Pn(s)}function Wb(e){k.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Rp(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(a=>Qs(a,t,n-t)):Qs(e,t,n-t)}function Kv(e,t){return O(()=>e==null?null:Array.isArray(e)?e.map(n=>Kv(n,t)):i2(e,t.dtype==="int32"?t:oe(t,"int32")))}function Vb(e,t){let n=[],a=0,r=null;for(;a<e;)r=a+t,r>=e&&(r=e),n.push([a,r]),a=r;return n}async function JV(e,t,n,a,r,s,i,o,l,u,p,d,c,h,m){r==null&&(r=32),s==null&&(s=1),p==null&&(p=!0),c==null&&(c=0);let f=!1;if(l!=null&&u!=null&&(f=!0),m!=null&&(f=!0,h==null))throw new H("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,r,h,"steps_per_epoch"),y;g!=null&&(y=Ka(0,g)),i==null&&(i=1);let{callbackList:b,history:x}=x2(o,i,s,c,g,h,r,f,d);b.setModel(e),e.history=x,await b.onTrainBegin(),e.stopTraining_=!1;for(let v=c;v<s;++v){await b.onEpochBegin(v);let w={};if(h!=null)throw new Pe("stepsPerEpoch mode is not implemented yet.");{if(p==="batch")throw new Pe("batch shuffling is not implemneted yet");p&&k.shuffle(y);let T=qe(y),C=Vb(g,r);for(let E=0;E<C.length;++E){let $={};if(await b.onBatchBegin(E,$),O(()=>{let P=C[E][0],F=C[E][1],S=Qs(T,P,F-P);$.batch=E,$.size=F-P;let M=Kv(n,S),V=t(M);for(let j=0;j<a.length;++j){let q=a[j],K=V[j];$[q]=K,en(K)}if(E===C.length-1&&f){let j=e.testLoop(l,u,r);for(let q=0;q<a.length;++q){let K=a[q],Q=j[q];en(Q),w["val_"+K]=Q}}}),await b.onBatchEnd(E,$),m2($),e.stopTraining_)break}T.dispose()}if(await b.onEpochEnd(v,w),e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}async function ZV(e,t,n,a={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let r,s,i,o,l,u,p,d,c;try{let h=a.batchSize==null?32:a.batchSize;Wb(h);let m=!1,f=await e.standardizeUserData(t,n,a.sampleWeight,a.classWeight,m,h);r=f[0],s=f[1],c=f[2];let g=!1,y;if(a.validationData!=null&&a.validationData.length>0){if(g=!0,a.validationData.length===2)l=a.validationData[0],u=a.validationData[1];else throw a.validationData.length===3?new Pe("validationData including sample weights is not supported yet."):new H(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${a.validationData} is invalid.`);let E=!0,$=await e.standardizeUserData(l,u,null,null,E,h);p=$[0],d=$[1],y=p.concat(d)}else if(a.validationSplit!=null&&a.validationSplit>0&&a.validationSplit<1){g=!0;let E=Math.floor(r[0].shape[0]*(1-a.validationSplit)),$=r[0].shape[0];p=Rp(r,E,$),i=r,r=Rp(r,0,E),d=Rp(s,E,$),o=s,s=Rp(s,0,E),y=p.concat(d)}else a.validationSteps!=null&&(g=!0);let b=r.concat(s).concat(c);e.checkTrainableWeightsConsistency();let x=e.makeTrainFunction(),v=e.getDedupedMetricsNames(),w,T;g?(e.makeTestFunction(),w=e.testFunction,T=v.slice().concat(v.map(E=>"val_"+E))):(w=null,y=[],T=v.slice());let C=b2(a.callbacks,a.yieldEvery);return await JV(e,x,b,v,h,a.epochs,a.verbose,C,w,y,a.shuffle,T,a.initialEpoch,null,null)}finally{e.isTraining=!1,Wa(r,t),Wa(s,n),Wa(i,t),Wa(o,n),Wa(p,l),Wa(d,u),c!=null&&Re(c)}}function E2(e){let t=[];e instanceof Ae&&(e=[e]);for(let n=0;n<e.length;++n){let a=e[n];if(a.rank===1)t.push(Vc(a,1));else{if(a.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(a)}}return t}function Wa(e,t){if(e==null)return;let n=[];if(t instanceof Ae)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 s=t[r];n.push(s.id)}let a=[];if(e instanceof Ae)n.indexOf(e.id)===-1&&a.push(e);else if(Array.isArray(e))e.forEach(r=>{n.indexOf(r.id)===-1&&a.push(r)});else if(e!=null)for(let r in e){let s=e[r];n.indexOf(s.id)===-1&&a.push(s)}a.forEach(r=>{r.isDisposed||r.dispose()})}function QV(e){return e instanceof Ae}function Ub(e){return Array.isArray(e)}function Z1(e){return!QV(e)&&!Ub(e)}function Q1(e,t,n,a=!0,r=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(Ub(e)&&e.length>0)i=!0;else if(Z1(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new H(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(Z1(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new H(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(Ub(e)){if(e=e,e.length!==t.length)throw new H(`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}`);s=e}else{if(e=e,t.length>1)throw new H(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=E2(s),n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new H(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);for(let l=0;l<n[i].length;++l){if(l===0&&!a)continue;let u=o.shape[l],p=n[i][l];if(p!=null&&p>=0&&u!==p)throw new H(`${r} expected a batch of elements where each example has shape [${n[i].slice(1,n[i].length)}] (i.e.,tensor shape [*,${n[i].slice(1,n[i].length)}]) but the ${r} received an input with ${o.shape[0]} examples, each with shape [${o.shape.slice(1,o.shape.length)}] (tensor shape [${o.shape}])`)}}return s}function eU(e,t,n){let a=Zr(e.map(s=>s.shape[0]));a.sort();let r=Zr(t.map(s=>s.shape[0]));if(r.sort(),a.length>1)throw new H(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(r.length>1)throw new H(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(a.length>0&&r.length>0&&!k.arraysEqual(a,r))throw new H(`Input Tensors should have the same number of samples as target Tensors. Found ${a[0]} input sample(s) and ${r[0]} target sample(s).`)}function tU(e,t,n){let a=[wo,Ef,ec];for(let r=0;r<e.length;++r){let s=e[r],i=t[r],o=n[r];if(i!=null){if(i===ec&&s.shape[s.shape.length-1]===1)throw new H(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(a.indexOf(i)!==-1){let l=s.shape.slice(1),u=o.slice(1);for(let p=0;p<l.length;++p){let d=l[p],c=u[p];if(c!=null&&d!==c)throw new H(`A target Tensor with shape ${s.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function ek(e,t,n,a=!0,r=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new H(`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).`);s=e}else{if(t.length>1)throw new H(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new H(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);for(let l=0;l<n[i].length;++l){if(l===0&&!a)continue;let u=o.shape[l],p=n[i][l];if(p!=null&&p!==u)throw new H(`Error when checking ${r}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function nU(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>[]);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: ${e}`);if(Array.isArray(n))return t.map(a=>n);{let a=[];for(let r of t){let s=n.hasOwnProperty(r)?n[r]:[];Array.isArray(s)||(s=[s]),a.push(s)}return a}}var aU="layers-model",Tr=class extends nr{constructor(e){super(e),this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new H("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");LV(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=OV(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof Ar))throw new H("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new H(`Unknown entry in loss dictionary: "${s}". Only expected the following keys: ${this.outputNames}`);for(let s of this.outputNames)e.loss[s]==null&&console.warn(`Output "${s}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${s} during training`),t.push(cb(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new H(`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(s=>cb(s))}else{let s=cb(e.loss);this.outputs.forEach(i=>{t.push(s)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let s=0;s<this.outputs.length;++s){let i=this.internalOutputShapes[s],o=this.outputNames[s];this.feedOutputNames.push(o),this.feedOutputShapes.push(i),this.feedLossFns.push(this.lossFunctions[s])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],Zs("loss",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=this.lossFunctions[s];this.outputs.length>1&&(this.metricsTensors.push([i,s]),this.metricsNames.push(this.outputNames[s]+"_loss"))}});let a=nU(e.metrics,this.outputNames),r=(s,i,o)=>{this.outputNames.length>1&&(i=this.outputNames[s]+"_"+i),this.metricsNames.push(i),this.metricsTensors.push([o,s])};Zs("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=a[s];(o=>{let l="",u,p,d;for(let c of o){if(typeof c=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(c)!==-1){let m=this.internalOutputShapes[s];m[m.length-1]===1||this.lossFunctions[s]===Ef?["accuracy","acc"].indexOf(c)!==-1?p=Gv:["crossentropy","ce"].indexOf(c)!==-1&&(p=k2):this.lossFunctions[s]===zh?["accuracy","acc"].indexOf(c)!==-1?p=I2:["crossentropy","ce"].indexOf(c)!==-1&&(p=S2):["accuracy","acc"].indexOf(c)!==-1?p=Hv:["crossentropy","ce"].indexOf(c)!==-1&&(p=jv);let f;["accuracy","acc"].indexOf(c)!==-1?f="acc":["crossentropy","ce"].indexOf(c)!==-1&&(f="ce"),d=p,u=l+f}else d=PV(c),u=l+ah(c);let h;Zs(u,()=>{h=d}),r(s,u,h)}})(i)}}),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 a=n.batchSize==null?32:n.batchSize;Wb(a);let r=!0,s=this.standardizeUserDataXY(e,t,r,a);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,l=this.testLoop(o,i,a,n.verbose,n.steps);return Pn(l)}finally{Wa(s[0],e),Wa(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),YV(this,e,t)}checkNumSamples(e,t,n,a="steps"){let r;if(n!=null){if(r=null,t!=null)throw new H(`If ${a} 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 H(`Either the input data should have a defined shape, or ${a} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new H("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),a=n?t:[t],r=this.retrieveSymbolicTensors(a),s=new Xs;if(e instanceof Ae&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new H(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;o<this.inputs.length;++o)s.add(this.inputs[o],e[o])}else for(let o of this.inputs){let l=e[o.name];if(l==null)throw new H(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=Dp(r,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=ci(null,e.length),n=e.length;for(let a of this.layers){let r=Array.isArray(a.output)?a.output:[a.output],s=r.map(i=>i.name);for(let i=0;i<e.length;++i){let o=s.indexOf(e[i]);if(o!==-1&&(t[i]=r[o],n--),n===0)break}if(n===0)break}if(n>0){let a=[];throw t.forEach((r,s)=>{r==null&&a.push(e[s])}),new H(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(a)}`)}return t}predictLoop(e,t=32,n=!1){return O(()=>{let a=this.checkNumSamples(e);if(n)throw new Pe("Verbose predictLoop() is not implemented yet.");let r=Vb(a,t),s=this.outputs.map(i=>[]);for(let i=0;i<r.length;++i)O(()=>{let o=r[i][0],l=r[i][1],u=Rp(e,o,l),p=[];if(Array.isArray(u))for(let c=0;c<u.length;++c)p.push({key:this.inputs[c],value:u[c]});else p.push({key:this.inputs[0],value:u});let d=new Xs(p);return Dp(this.outputs,d)}).forEach((o,l)=>s[l].push(o));return Pn(s.map(i=>Qe(i,0)))})}predict(e,t={}){let n=E2(e);ek(n,this.inputNames,this.feedInputShapes,!1);try{let a=t.batchSize==null?32:t.batchSize;return Wb(a),this.predictLoop(n,a)}finally{Wa(n,e)}}predictOnBatch(e){ek(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,a){if(this.optimizer_==null)throw new Va("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===zh?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Q1(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Q1(t,this.feedOutputNames,r,!1,"target"),eU(e,t,null),tU(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&a!=null&&a>0&&e[0].shape[0]%a!==0)throw new H(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${a}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,a,r=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,r,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(a!=null){let u=T2(a,this.outputNames);l=[];for(let p=0;p<u.length;++p)l.push(await C2(o[p],null,u[p]))}return[i,o,l]}testLoop(e,t,n,a=0,r){return O(()=>{let s=this.checkNumSamples(t,n,r,"steps"),i=[];if(a>0)throw new Pe("Verbose mode is not implemented yet.");if(r!=null)throw new Pe("steps mode in testLoop() is not implemented yet");{let o=Vb(s,n),l=qe(Ka(0,s));for(let u=0;u<o.length;++u){let p=o[u][0],d=o[u][1],c=Qs(l,p,d-p),h=Kv(t,c),m=e(h);if(u===0)for(let f=0;f<m.length;++f)i.push(we(0));for(let f=0;f<m.length;++f){let g=m[f];i[f]=J(i[f],z(d-p,g))}}for(let u=0;u<i.length;++u)i[u]=fe(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let a=e[n],r=a;z1(e,a)>1&&(r+=`_${z1(e.slice(0,n),a)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=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),s=[],i=()=>{let u=[];for(let h=0;h<this.inputs.length;++h)u.push({key:this.inputs[h],value:n[h]});let p=new Xs(u),d=Dp(this.outputs,p,{training:!0}),c;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],d[h]);r[h]!=null&&(m=GV(m,r[h]));let f=Et(m);t.push(f),h===0?c=m:c=J(c,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Et(f(a[g],d[g]))}en(m),s.push(m)}return c=Et(c),this.calculateLosses().forEach(h=>{c=J(c,h)}),c},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>O(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new Xs(s),o=Dp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],p=Et(u(r[l],o[l]));l===0?n=p:n=J(n,p),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],p=this.metricsTensors[l][1],d=Et(u(r[p],o[p]));t.push(d)}return t})}async fit(e,t,n={}){return ZV(this,e,t,n)}async fitDataset(e,t){return qV(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Re(s),Wa(n[0],e),Wa(n[1],t),Pn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});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=wr(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=>wr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=wr(n[a]);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[wr(ah(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>wr(ah(e)));{let e={};for(let t in this.metrics)e[t]=wr(ah(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=tc(e.optimizer_config),n=ja(t),a;if(typeof e.loss=="string")a=js(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>js(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=js(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>js(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=js(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Qt.getSaveHandlers(e);if(i.length===0)throw new H(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new H(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new H("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Qt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:aU,generatedBy:`TensorFlow.js tfjs-layers v${qv}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Qt.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Qt.concatenateArrayBuffers([n.data,o])}return this.userDefinedMetadata!=null&&(X1(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){X1(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Tr.className="Model";se.registerClass(Tr);var A2=class extends Tr{};A2.className="Functional";se.registerClass(A2);async function rU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=tc(n),r=ja(a,t);if(e.weightsManifest!=null){let s=await Qt.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Re(s)}return r}async function sU(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Qt.getLoadHandlers(e,t);if(n.length===0)n.push(Qt.browserHTTPRequest(e,t));else if(n.length>1)throw new H(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return iU(e,void 0,t)}async function iU(e,t,n){if(n==null&&(n={}),e.load==null)throw new H("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=ja(tc(r),t,i),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new H("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:p}=oU(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&p.length>0&&await o.optimizer.setWeights(p),Re(u),Re(p.map(d=>d.tensor))}return o}function oU(e,t){let n=Qt.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var gl=class extends Tr{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:yf("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new H(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof gl||e instanceof Tr,n;if(t){if(n=e,n.outputs.length!==1)throw new H("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 H("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 H("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=p2({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new H(`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 H("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=u2(this.outputs[0])}this.inboundNodes=[],new Cf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ci(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))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=[a],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(it(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 Tr({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 Va("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 Va("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 Va("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 Va("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={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new H("Legacy serialization format not supported yet.");r=t}else k.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,s=t;let i=new e(s);if(!(i instanceof gl))throw new Pe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ja(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new H("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 H("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}}};gl.className="Sequential";se.registerClass(gl);function lU(e){return new Tr(e)}function uU(e){return new gl(e)}function pU(e,t){return t==null&&(t={}),sU(e,t)}function $2(e){return p2(e)}function cU(e,t){ka.registerCallbackConstructor(e,t)}var Hn=class extends se.Serializable{getConfig(){return{}}},F2=class extends Hn{apply(e,t=1){return F4(e,t)}};F2.className="elu";se.registerClass(F2);var D2=class extends Hn{apply(e){return Zm(e)}};D2.className="selu";se.registerClass(D2);var R2=class extends Hn{apply(e){return Xe(e)}};R2.className="relu";se.registerClass(R2);var M2=class extends Hn{apply(e){return O(()=>_u(6,Xe(e)))}};M2.className="relu6";se.registerClass(M2);var P2=class extends Hn{apply(e){return e}};P2.className="linear";se.registerClass(P2);var O2=class extends Hn{apply(e){return ha(e)}};O2.className="sigmoid";se.registerClass(O2);var L2=class extends Hn{apply(e){return R4(e)}};L2.className="hardSigmoid";se.registerClass(L2);var z2=class extends Hn{apply(e){return bo(e)}};z2.className="softplus";se.registerClass(z2);var B2=class extends Hn{apply(e){return D4(e)}};B2.className="softsign";se.registerClass(B2);var W2=class extends Hn{apply(e){return oi(e)}};W2.className="tanh";se.registerClass(W2);var Xv=class extends Hn{apply(e,t=-1){return Ja(e,t)}};Xv.className="softmax";se.registerClass(Xv);var V2=class extends Hn{apply(e,t=-1){return Hm(e,t)}};V2.className="logSoftmax";se.registerClass(V2);var U2=class extends Hn{apply(e,t=1){return O(()=>z(ha(z(e,t)),e))}};U2.className="swish";se.registerClass(U2);var G2=class extends Hn{apply(e){return O(()=>z(e,oi(bo(e))))}};G2.className="mish";se.registerClass(G2);function is(e){return e.getClassName()}function db(e,t={}){return Wc(e,se.SerializationMap.getMap().classNameMap,t,"activation")}function os(e){if(e==null){let t={};return t.className="linear",t.config={},db(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},db(t)}else return e instanceof Hn?e:db(e)}function Yv(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 H2=class extends se.Serializable{},jc=class extends H2{constructor(e){super(),Yv(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 O(()=>{let t=kt([1]);return this.hasL1&&(t=J(t,be(z(this.l1,zt(e))))),this.hasL2&&(t=J(t,be(z(this.l2,Uc(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};jc.className="L1L2";se.registerClass(jc);function dU(e){return Yv(e),new jc({l1:e!=null?e.l1:null,l2:0})}function hU(e){return Yv(e),new jc({l2:e!=null?e.l2:null,l1:0})}var tk={l1l2:"L1L2"};function dt(e){return Cv(e)}function nk(e,t={}){return Wc(e,se.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in tk?tk[e]:e,config:{}};return nk(t)}else return e instanceof H2?e:nk(e)}var Jv=class extends Ye{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Le(e);let n=Xe(e);return this.maxValue!=null&&(n=nn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Jv.className="ReLU";se.registerClass(Jv);var Zv=class extends Ye{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=Le(e);return Dc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Zv.className="LeakyReLU";se.registerClass(Zv);var Qv=class extends Ye{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=It(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Xt(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 H(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=it(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new Bt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Le(e),Oc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:At(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:Kt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Qv.className="PReLU";se.registerClass(Qv);var ew=class extends Ye{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Pe(`Non-default alpha value (${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=Le(e);return Tu(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};ew.className="ELU";se.registerClass(ew);var tw=class extends Ye{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=Le(e);return z(n,oe(Gn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};tw.className="ThresholdedReLU";se.registerClass(tw);var nw=class extends Ye{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Xv().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Le(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}};nw.className="Softmax";se.registerClass(nw);function ol(e,t,n){if(typeof e=="number")return ci(e,t);if(e.length!==t)throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!_4(r))throw new H(`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 qa(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function sr(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+ss([n-t,0]);else if(a==="same")e=e*t;else throw new H(`Unsupport padding mode: ${a}.`);return e}function aw(e,t){return O(()=>(Ot(t),t==="channelsFirst"?$e(e,[0,2,3,1]):e))}function j2(e,t){return O(()=>(Ot(t),t==="channelsFirst"?$e(e,[0,2,3,4,1]):e))}function mU(e,t,n,a=1,r="valid",s,i=1){return O(()=>{if(s==null&&(s=Xa()),Ot(s),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=$e(e,[0,2,1])),r==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Lm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Qa(o,n)),o})}function ak(e,t,n,a=[1,1],r="valid",s,i,o=null){return O(()=>{if(s==null&&(s=Xa()),Ot(s),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=aw(e,s);if(r==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=rs.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=$e(l,[0,3,1,2])),l})}function fU(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Xa()),Ot(s),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=j2(e,s);if(r==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Yx(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Qa(o,n)),s==="channelsFirst"&&(o=$e(o,[0,4,1,2,3])),o})}var rw=class extends Ye{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",rw.verifyArgs(t),this.rank=e,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ol(t.kernelSize,e,"kernelSize"),this.strides=ol(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ya(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ot(this.dataFormat),this.activation=os(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=It(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Xt(t.biasConstraint),this.biasRegularizer=Nt(t.biasRegularizer),this.activityRegularizer=Nt(t.activityRegularizer),this.dilationRate=ol(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`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 H(`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 H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!_v(e.kernelSize,"number",1,3))throw new H(`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:is(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Kt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},qc=class extends rw{constructor(e,t){super(e,t),this.kernel=null,qc.verifyArgs(t),this.filters=t.filters,tn(this.filters,"filters"),this.kernelInitializer=It(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Xt(t.kernelConstraint),this.kernelRegularizer=Nt(t.kernelRegularizer)}build(e){e=it(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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 O(()=>{e=Le(e);let n,a=this.bias==null?null:this.bias.read(),r=e2(this.activation.getClassName());if(r!=null&&this.rank===2)n=ak(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=mU(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=ak(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=fU(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=it(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 s=qa(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:At(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Kt(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 H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Kc=class extends qc{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"&&!_v(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Kc.className="Conv2D";se.registerClass(Kc);var Xc=class extends qc{constructor(e){super(3,e),Xc.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 H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Xc.className="Conv3D";se.registerClass(Xc);var sw=class extends Kc{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=it(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Le(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=sr(o,d,u,this.padding),m=sr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=$e(n,[0,2,3,1]));let g=zm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=$e(g,[0,3,1,2])),this.bias!=null&&(g=Qa(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=it(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=sr(t[a],o,s,this.padding),t[r]=sr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};sw.className="Conv2DTranspose";se.registerClass(sw);var iw=class extends Xc{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=it(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Le(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=sr(l,m,d,this.padding),b=sr(u,f,c,this.padding),x=sr(p,g,h,this.padding),v=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=$e(n,[0,2,3,4,1]));let w=cS(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=$e(w,[0,4,1,2,3])),this.bias!==null&&(w=Qa(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=it(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=sr(t[a],u,i,this.padding),t[r]=sr(t[r],p,o,this.padding),t[s]=sr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};iw.className="Conv3DTranspose";se.registerClass(iw);var q2=class extends qc{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("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 H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=It(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Xt(t.depthwiseConstraint),this.pointwiseInitializer=It(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Xt(t.pointwiseConstraint)}build(e){if(e=it(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${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 H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Bt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{e=Le(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=$e(e,[0,2,3,1])),n=xo(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Qa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=$e(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=At(this.depthwiseInitializer),e.pointwiseInitializer=At(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Kt(this.depthwiseConstraint),e.pointwiseConstraint=Kt(this.pointwiseConstraint),e}};q2.className="SeparableConv";var ow=class extends q2{constructor(e){super(2,e)}};ow.className="SeparableConv2D";se.registerClass(ow);var Af=class extends qc{constructor(e){super(1,e),Af.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"&&!_v(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Af.className="Conv1D";se.registerClass(Af);var lw=class extends Ye{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Le(e),this.dataFormat==="channelsLast"){let n=nh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return nh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=nh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return nh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};lw.className="Cropping2D";se.registerClass(lw);var uw=class extends Ye{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,N4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Le(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=$e(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Ln.resizeNearestNeighbor(n,[r,s]):Ln.resizeBilinear(n,[r,s]);return $e(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Ln.resizeNearestNeighbor(n,[r,s]):Ln.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};uw.className="UpSampling2D";se.registerClass(uw);function gU(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Xa()),Ot(r);let i=aw(e,r);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=bs(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=$e(i,[0,3,1,2])),i})}var pw=class extends rw{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=It(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Xt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=it(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 O(()=>{e=Le(e);let n=gU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Qa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=it(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=qa(t,this.kernelSize[0],this.padding,this.strides[0]),s=qa(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=At(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Kt(this.depthwiseRegularizer),e}};pw.className="DepthwiseConv2D";se.registerClass(pw);function K2(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function X2(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new H(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ka(2,l));if(t=$e(t,u),s!=null)throw new Pe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=oe(oe(r,"bool"),"float32"),r.rank===l-1&&(r=mn(r,-1)),r=$e(r,u)),a&&(t=na(t,0),r!=null&&(r=na(r,0)));let p=[],d,c=n,h=t.shape[0],m=mt(t),f;r!=null&&(f=mt(r));for(let y=0;y<h;++y){let b=m[y],x=O(()=>e(b,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let w=f[y],T=ce(ta(w),w),C=J(z(x[0],w),z(c[0],T)),E=c.map(($,P)=>J(z(x[1][P],w),z($,T)));return{output:C,newStates:E}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Mt(p,1)),[d,g,c]})}var fr=class extends Ye{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Df({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("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 Bt({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 Ka(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ob(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return O(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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){if(this.numConstants!=null)throw new Pe("Constants support is not implemented in RNN yet.");Ob(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new Bt({shape:[t,null,...n]});let a=[e[0]].concat(e.slice(2));this.cell.build(a);let r;if(Array.isArray(this.cell.stateSize)?r=this.cell.stateSize:r=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),r))throw new H(`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=r.map(s=>new Bt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("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(a=>kt([n,a])):this.states_=[kt([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>kt([n,a])):this.states_[0]=kt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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()):Re(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new H(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>en(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=K2(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Bt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ua){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return O(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Le(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new H(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=X2((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return O(()=>{let t=kt(e.shape);return t=be(t,[1,2]),t=Vc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Mb(t,[1,n]):t):this.cell.stateSize>1?[Mb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===fr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=ja(a,n);return new e(Object.assign(t,{cell:r}))}};fr.className="RNN";se.registerClass(fr);var Yc=class extends Ye{},$f=class extends Yc{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,tn(this.units,"units"),this.activation=os(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=It(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=ml([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ml([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=it(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 O(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>ta(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ta(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Qa(r,this.bias.read())),i!=null&&(n=z(n,i));let o=J(r,or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:is(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),recurrentConstraint:Kt(this.recurrentConstraint),biasConstraint:Kt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};$f.className="SimpleRNNCell";se.registerClass($f);var cw=class extends fr{constructor(e){e.cell=new $f(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};cw.className="SimpleRNN";se.registerClass(cw);var Ff=class extends Yc{constructor(e){if(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",e.resetAfter)throw new H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=os(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=os(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=It(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=ml([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ml([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=it(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 O(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>ta(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ta(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=z(e,r[0]));let u=or(e,this.kernel.read());this.useBias&&(u=Qa(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=z(a,s[0]));let p=this.recurrentKernel.read(),[d,c]=zn(p,[2*this.units,this.units],p.rank-1),h=or(a,d),[m,f,g]=zn(u,3,u.rank-1),[y,b]=zn(h,2,h.rank-1);i=this.recurrentActivation.apply(J(m,y)),o=this.recurrentActivation.apply(J(f,b));let x=or(z(o,a),c);l=this.activation.apply(J(g,x));let v=J(z(i,a),z(J(1,St(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:is(this.activation),recurrentActivation:is(this.recurrentActivation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),recurrentConstraint:Kt(this.recurrentConstraint),biasConstraint:Kt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Ff.className="GRUCell";se.registerClass(Ff);var dw=class extends fr{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 Ff(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};dw.className="GRU";se.registerClass(dw);var Jc=class extends Yc{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,tn(this.units,"units"),this.activation=os(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=os(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=It(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=ml([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ml([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=it(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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends _a{apply(i,o){let l=r.apply([s]),u=new vf().apply([s]),p=r.apply([s*2]);return W1(W1(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>ta(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ta(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0<this.dropout&&this.dropout<1&&(e=z(e,s[0]));let d=or(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=z(a,i[0])),d=J(d,or(a,this.recurrentKernel.read())),this.useBias&&(d=Qa(d,this.bias.read()));let[c,h,m,f]=zn(d,4,d.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),u=J(z(l,r),z(o,this.activation.apply(m))),p=this.recurrentActivation.apply(f);let g=z(p,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:is(this.activation),recurrentActivation:is(this.recurrentActivation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),recurrentConstraint:Kt(this.recurrentConstraint),biasConstraint:Kt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Jc.className="LSTMCell";se.registerClass(Jc);var hw=class extends fr{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 Jc(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};hw.className="LSTM";se.registerClass(hw);var Df=class extends Yc{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 O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Ob(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Zs(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(ja(r,n));return new e({cells:a})}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 Lb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Ov(t)}};Df.className="StackedRNNCells";se.registerClass(Df);function ls(e){let{ones:t,rate:n,training:a=!1,count:r=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),n):o2(t(),n),o=()=>Gc(i,t,a);return!r||r<=1?en(o().clone()):Array(r).fill(void 0).map(o).map(l=>en(l.clone()))}var yU=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},Y2=class extends fr{constructor(e){if(e.unroll)throw new Pe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Pe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Bt({ndim:5})]}call(e,t){return O(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,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 O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=kt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new H("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(()=>kt(r)):this.states_=[kt(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>kt(r)):this.states_[0]=kt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new H(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>en(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],p=qa(l,a[0],r,s[0],i[0]),d=qa(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};Y2.className="ConvRNN2D";var Rf=class extends Jc{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t})),this.filters=t,tn(this.filters,"filters"),this.kernelSize=ol(n,2,"kernelSize"),this.kernelSize.forEach(o=>tn(o,"kernelSize")),this.strides=ol(a||1,2,"strides"),this.strides.forEach(o=>tn(o,"strides")),this.padding=r||"valid",ya(this.padding),this.dataFormat=s||"channelsLast",Ot(this.dataFormat),this.dilationRate=ol(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>tn(o,"dilationRate"))}build(e){var t;e=it(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends _a{apply(p,d){let c=l.apply([u]),h=Jn([u]),m=l.apply([u*2]);return Ev([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>ta(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(ee,re,Z)=>!re||!re[Z]?ee:z(re[Z],ee),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ta(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,w,T]=zn(this.kernel.read(),i,b),[C,E,$,P]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,E,this.padding),d=this.inputConv(d,w,$,this.padding),c=this.inputConv(c,T,P,this.padding);let[F,S,M,V]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,F),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),y=this.recurrentConv(y,V);let j=this.recurrentActivation.apply(J(u,m)),q=this.recurrentActivation.apply(J(p,f)),K=J(z(q,s),z(j,this.activation.apply(J(d,g)))),Q=z(this.recurrentActivation.apply(J(c,y)),this.activation.apply(K));return[Q,Q,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=yU(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Qa(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Rf.className="ConvLSTM2DCell";se.registerClass(Rf);var mw=class extends Y2{constructor(e){let t=new Rf(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};mw.className="ConvLSTM2D";se.registerClass(mw);var Mf=class extends Ye{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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Le(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Gc(()=>o2(n,this.rate,r,this.seed),()=>n,a)}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()}};Mf.className="Dropout";se.registerClass(Mf);var fw=class extends Mf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};fw.className="SpatialDropout1D";se.registerClass(fw);var gw=class extends Ye{constructor(e){if(super(e),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,tn(this.units,"units"),this.activation=os(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Xt(e.kernelConstraint),this.biasConstraint=Xt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=it(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=it(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Le(e),a=e2(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Qa(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:is(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),biasConstraint:Kt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};gw.className="Dense";se.registerClass(gw);var yw=class extends Ye{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=it(e);for(let t of e.slice(1))if(t==null)throw new H(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Qr(e,1)]}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Le(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=$e(n,a)}return $4(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};yw.className="Flatten";se.registerClass(yw);var bw=class extends Ye{constructor(e){super(e),this.supportsMasking=!0,this.activation=os(e.activation)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.activation.apply(n)})}getConfig(){let e={activation:is(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};bw.className="Activation";se.registerClass(bw);var xw=class extends Ye{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 O(()=>(e=Le(e),E4(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};xw.className="RepeatVector";se.registerClass(xw);var vw=class extends Ye{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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new H("Can only specifiy one unknown dimension.");else r*=l}let i=Qr(e);if(s!==null){if(r===0||i%r!==0)throw new H(n);a[s]=i/r}else if(i!==r)throw new H(n);return a}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 O(()=>{this.invokeCallHook(e,t);let n=Le(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return W(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};vw.className="Reshape";se.registerClass(vw);var ww=class extends Ye{constructor(e){if(super(e),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=Ka(1,e.dims.length+1);if(!k.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 Bt({ndim:this.dims.length+1})]}computeOutputShape(e){e=it(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return $e(Le(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};ww.className="Permute";se.registerClass(ww);var kw=class extends Ye{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=Le(e),a=-1;return Xp(pi(n,this.maskValue),a)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Le(e),a=-1,r=!0,s=Xp(pi(n,this.maskValue),a,r);return z(n,oe(s,n.dtype))})}};kw.className="Masking";se.registerClass(kw);var Iw=class extends Ye{constructor(e){if(super(e),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(bt(e.inputLength))}this.inputDim=e.inputDim,tn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,tn(this.outputDim,"outputDim"),this.embeddingsInitializer=It(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Nt(e.embeddingsRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.embeddingsConstraint=Xt(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 O(()=>this.maskZero?(e=Le(e),pi(e,Ke(e))):null)}computeOutputShape(e){if(e=it(e),this.inputLength==null)return[...e,this.outputDim];let t=bt(this.inputLength);if(t.length!==e.length-1)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Le(e);n.dtype!=="int32"&&(n=bf(n,"int32"));let a=i2(this.embeddings.read(),W(n,[n.size]));return W(a,it(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:At(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:Kt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Iw.className="Embedding";se.registerClass(Iw);var ko=class extends Ye{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Pe}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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new H("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=[it(e)]),e=e,e.length<2)throw new H(`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=Zr(t),t.length>1)throw new H(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&Zr(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return O(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ss(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Vc(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,p=u[0],d=u.slice(1).concat([p]),c=W(o,[p].concat(Qr(u.slice(1))));c=$e(c,[1,0]),c=W(c,d),n.push(c),r=!0}else if(l>1){let u=Ka(1,l).concat([0]);n.push($e(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],p=[u].concat(o.slice(0,o.length-1));s=W($e(W(s,[-1,u]),[1,0]),p)}else if(i>1){let o=[i-1].concat(Ka(0,i-1));s=$e(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=Zr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return O(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:mn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=Ta(n,t[a]);return n})}},Sw=class extends ko{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=J(t,e[n]);return t})}};Sw.className="Add";se.registerClass(Sw);var Nw=class extends ko{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=z(t,e[n]);return t})}};Nw.className="Multiply";se.registerClass(Nw);var Tw=class extends ko{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=J(t,e[n]);return z(1/e.length,t)})}};Tw.className="Average";se.registerClass(Tw);var Cw=class extends ko{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=hr(t,e[n]);return t})}};Cw.className="Maximum";se.registerClass(Cw);var _w=class extends ko{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_u(t,e[n]);return t})}};_w.className="Minimum";se.registerClass(_w);var Ew=class extends ko{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 H("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return O(()=>Ev(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new H("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new H("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new H(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return O(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(oe(ta(e[s]),"bool")):t[s].rank<e[s].rank?a.push(mn(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return Om(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ew.className="Concatenate";se.registerClass(Ew);function Cp(e,t){for(;e<0;)e+=t;return e}function bU(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Pe("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Pe("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return O(()=>{let i;if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);t=W(t,t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);e=W(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=be(z(e,t),s[0]):o=be(z($e(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=De(e,t,l,u)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let p=l;p<l+i;++p)u.push(p);o=pr(o,u)}return o.shape.length===1&&(o=mn(o,1)),o})}var Aw=class extends ko{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 Pe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new H(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>Cp(r,e[s].shape.length)):a=[Cp(this.axes,t.shape.length),Cp(this.axes,n.shape.length)],this.normalize&&(t=Lh(t,a[0]),n=Lh(n,a[1])),bU(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cp(this.axes,e.length),Cp(this.axes,t.length)],n}computeOutputShape(e){k.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 Pe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[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}};Aw.className="Dot";se.registerClass(Aw);var $w=class extends Ye{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Le(e);return Gc(()=>J(xf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};$w.className="GaussianNoise";se.registerClass($w);var Fw=class extends Ye{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 O(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.rate>0&&this.rate<1?Gc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,xf(n.shape,1,a))},()=>n,t.training||!1):n})}};Fw.className="GaussianDropout";se.registerClass(Fw);var Dw=class extends Ye{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Le(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 O(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Gc(()=>{let a=Le(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=xs(Eu(n),this.rate);o=bf(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,p=J(z(a,o),z(J(o,-1),i));return J(z(p,l),u)},()=>Le(e),t.training||!1)}return e})}};Dw.className="AlphaDropout";se.registerClass(Dw);function nc(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=nS(e,t,n,a,r,s);else if(e.rank===3)i=aS(e,t,n,a,r,s);else if(e.rank===4)i=rS(e,t,n,a,r,s);else throw new Pe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function xU(e,t,n,a,r=.001){return O(()=>{let s=qm(e,a),i=s.mean,o=s.variance;return[nc(e,i,o,n,t,r),i,o]})}function vU(e,t,n,a,r=.001){return O(()=>{let s=qm(e,a),i=s.mean,o=s.variance,l=[];for(let h of Ka(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=W(i,l),p=W(o,l),d=t==null?null:W(t,l),c=n==null?null:W(n,l);return[nc(e,u,p,c,d,r),i,o]})}function wU(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Ka(0,e.rank-1))?xU(e,t,n,a,r):vU(e,t,n,a,r)}var Rw=class extends Ye{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=It(e.betaInitializer||"zeros"),this.gammaInitializer=It(e.gammaInitializer||"ones"),this.movingMeanInitializer=It(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=It(e.movingVarianceInitializer||"ones"),this.betaConstraint=Xt(e.betaConstraint),this.gammaConstraint=Xt(e.gammaConstraint),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer)}build(e){e=it(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Bt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training,a=Le(e),r=a.shape,s=r.length,i=Ka(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ci(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!k.arraysEqual(u,Ka(0,s).slice(0,s-1)),d=()=>{if(p){let g=W(this.movingMean.read(),l),y=W(this.movingVariance.read(),l),b=this.center?W(this.beta.read(),l):null,x=this.scale?W(this.gamma.read(),l):null;return nc(a,g,y,b,x,this.epsilon)}else return nc(a,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[c,h,m]=wU(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{O(()=>{let x=1-b,v=g.read(),w=z(ce(v,y),x);g.write(ce(v,w))})};return f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),movingMeanInitializer:At(this.movingMeanInitializer),movingVarianceInitializer:At(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:Kt(this.betaConstraint),gammaConstraint:Kt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Rw.className="BatchNormalization";se.registerClass(Rw);var Mw=class extends Ye{constructor(e){if(e==null&&(e={}),super(e),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=It(e.betaInitializer||"zeros"),this.gammaInitializer=It(e.gammaInitializer||"ones"),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=it(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!==Zr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Le(e),a=n.shape,r=a.length;return O(()=>{let{mean:s,variance:i}=qm(n,this.axis,!0),o=ci(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=l(this.gamma.read()),p=l(this.beta.read()),d=[],c=[];for(let h=0;h<r;++h)this.axis.indexOf(h)!==-1?(d.push(a[h]),c.push(1)):(d.push(1),c.push(a[h]));return s=On(s,d),i=On(i,d),u=On(u,c),p=On(p,c),nc(n,s,i,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Mw.className="LayerNormalization";se.registerClass(Mw);function kU(e,t,n){return O(()=>{if(e.rank!==4)throw new H(`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 H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Xa()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ga(e,a)})}var Pw=class extends Ye{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Xa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 H(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new H(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=it(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 O(()=>kU(Le(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Pw.className="ZeroPadding2D";se.registerClass(Pw);function Pf(e,t,n,a,r,s){return O(()=>{Ot(r),n2(s),ya(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Xa()),s==null&&(s="max"),e=aw(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Pt(e,t,n,o):i=fa(e,t,n,o),r==="channelsFirst"&&(i=$e(i,[0,3,1,2])),i})}function J2(e,t,n,a,r,s){return O(()=>{Ot(r),n2(s),ya(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Xa()),s==null&&(s="max"),e=j2(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=lv(e,t,n,o):i=jx(e,t,n,o),r==="channelsFirst"&&(i=$e(i,[0,4,1,2,3])),i})}var Z2=class extends Ye{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),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 H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(tn(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 H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ya(this.padding),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){e=it(e);let t=qa(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return O(()=>{this.invokeCallHook(e,t),e=Vc(Le(e),2);let n=this.poolingFunction(Le(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return pr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Ow=class extends Z2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ya(a),Pf(e,t,n,a,r,"max")}};Ow.className="MaxPooling1D";se.registerClass(Ow);var Lw=class extends Z2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ya(a),Pf(e,t,n,a,r,"avg")}};Lw.className="AveragePooling1D";se.registerClass(Lw);var Q2=class extends Ye{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),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 H(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),ya(this.padding),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=it(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=qa(t,this.poolSize[0],this.padding,this.strides[0]),n=qa(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(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}},zw=class extends Q2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ya(a),Pf(e,t,n,a,r,"max")}};zw.className="MaxPooling2D";se.registerClass(zw);var Bw=class extends Q2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ya(a),Pf(e,t,n,a,r,"avg")}};Bw.className="AveragePooling2D";se.registerClass(Bw);var eN=class extends Ye{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),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 H(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),ya(this.padding),this.inputSpec=[new Bt({ndim:5})]}computeOutputShape(e){e=it(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=qa(t,this.poolSize[0],this.padding,this.strides[0]),n=qa(n,this.poolSize[1],this.padding,this.strides[1]),a=qa(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(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}},Ww=class extends eN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ya(a),J2(e,t,n,a,r,"max")}};Ww.className="MaxPooling3D";se.registerClass(Ww);var Vw=class extends eN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ya(a),J2(e,t,n,a,r,"avg")}};Vw.className="AveragePooling3D";se.registerClass(Vw);var tN=class extends Ye{constructor(e){super(e),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Pe}},Uw=class extends tN{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=Le(e);return Et(n,1)})}};Uw.className="GlobalAveragePooling1D";se.registerClass(Uw);var Gw=class extends tN{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=Le(e);return Sa(n,1)})}};Gw.className="GlobalMaxPooling1D";se.registerClass(Gw);var nN=class extends Ye{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Pe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Hw=class extends nN{call(e,t){return O(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Et(n,[1,2]):Et(n,[2,3])})}};Hw.className="GlobalAveragePooling2D";se.registerClass(Hw);var jw=class extends nN{call(e,t){return O(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Sa(n,[1,2]):Sa(n,[2,3])})}};jw.className="GlobalMaxPooling2D";se.registerClass(jw);var aN=class extends Ye{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 a=t.layer,r=ja(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},qw=class extends aN{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=it(e),e.length<3)throw new H(`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=it(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return O(()=>(e=Le(e),X2((n,a)=>[Le(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};qw.className="TimeDistributed";se.registerClass(qw);function IU(e){vo(S4,"BidirectionalMergeMode",e)}var SU="concat",Kw=class extends aN{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ja(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=ja(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?SU:e.mergeMode,IU(this.mergeMode),e.weights)throw new Pe("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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Pn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=K2(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new H("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(p=>new Bt({shape:p.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new Pe("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ua;for(let l of s)if(l instanceof Ua!==o)throw new H("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=p,d}else return super.apply(e,t)}call(e,t){return O(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=na(r,1));let i;return this.mergeMode==="concat"?i=Ev([a,r]):this.mergeMode==="sum"?i=J(a,r):this.mergeMode==="ave"?i=z(.5,J(a,r)):this.mergeMode==="mul"?i=z(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Zs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Zs(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 a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}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=ja(t.layer);if(delete t.layer,t.numConstants!=null)throw new Pe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};Kw.className="Bidirectional";se.registerClass(Kw);function NU(e){return new Du(e)}function TU(e){return new ew(e)}function CU(e){return new Jv(e)}function _U(e){return new Zv(e)}function EU(e){return new Qv(e)}function AU(e){return new nw(e)}function $U(e){return new tw(e)}function FU(e){return new Af(e)}function DU(e){return new Kc(e)}function RU(e){return new sw(e)}function MU(e){return new Xc(e)}function PU(e){return new iw(e)}function OU(e){return new ow(e)}function LU(e){return new lw(e)}function zU(e){return new uw(e)}function BU(e){return new pw(e)}function WU(e){return new bw(e)}function VU(e){return new gw(e)}function UU(e){return new Mf(e)}function GU(e){return new fw(e)}function HU(e){return new yw(e)}function jU(e){return new xw(e)}function qU(e){return new vw(e)}function KU(e){return new ww(e)}function XU(e){return new Iw(e)}function YU(e){return new Sw(e)}function JU(e){return new Tw(e)}function ZU(e){return new Ew(e)}function QU(e){return new Cw(e)}function eG(e){return new _w(e)}function tG(e){return new Nw(e)}function nG(e){return new Aw(e)}function aG(e){return new Rw(e)}function rG(e){return new Mw(e)}function sG(e){return new Pw(e)}function Xw(e){return new Lw(e)}function iG(e){return Xw(e)}function oG(e){return Xw(e)}function Yw(e){return new Bw(e)}function lG(e){return Yw(e)}function uG(e){return Yw(e)}function Jw(e){return new Vw(e)}function pG(e){return Jw(e)}function cG(e){return Jw(e)}function dG(e){return new Uw(e)}function hG(e){return new Hw(e)}function rN(e){return new Gw(e)}function sN(e){return new jw(e)}function iN(e){return new Ow(e)}function oN(e){return new zw(e)}function mG(e){return new Ww(e)}function fG(e){return new dw(e)}function gG(e){return new Ff(e)}function yG(e){return new hw(e)}function bG(e){return new Jc(e)}function xG(e){return new cw(e)}function vG(e){return new $f(e)}function wG(e){return new mw(e)}function kG(e){return new Rf(e)}function IG(e){return new fr(e)}function SG(e){return new Df(e)}function NG(e){return new Kw(e)}function TG(e){return new qw(e)}var CG=rN,_G=sN,EG=iN,AG=oN;function $G(e){return new $w(e)}function FG(e){return new Fw(e)}function DG(e){return new Dw(e)}function RG(e){return new kw(e)}var lN={};Me(lN,{MAPE:()=>HG,MSE:()=>KG,binaryAccuracy:()=>MG,binaryCrossentropy:()=>PG,categoricalAccuracy:()=>LG,categoricalCrossentropy:()=>zG,cosineProximity:()=>VG,mape:()=>jG,meanAbsoluteError:()=>UG,meanAbsolutePercentageError:()=>GG,meanSquaredError:()=>qG,mse:()=>XG,precision:()=>BG,recall:()=>WG,sparseCategoricalAccuracy:()=>OG});function MG(e,t){return Gv(e,t)}function PG(e,t){return k2(e,t)}function OG(e,t){return I2(e,t)}function LG(e,t){return Hv(e,t)}function zG(e,t){return jv(e,t)}function BG(e,t){return w2(e,t)}function WG(e,t){return _V(e,t)}function VG(e,t){return Uv(e,t)}function UG(e,t){return _f(e,t)}function GG(e,t){return Ru(e,t)}function HG(e,t){return Ru(e,t)}function jG(e,t){return Ru(e,t)}function qG(e,t){return wo(e,t)}function KG(e,t){return wo(e,t)}function XG(e,t){return wo(e,t)}var uN={};Me(uN,{modelFromJSON:()=>rU});var pN={};Me(pN,{l1:()=>JG,l1l2:()=>YG,l2:()=>ZG});function YG(e){return new jc(e)}function JG(e){return dU(e)}function ZG(e){return hU(e)}var cN=class extends fl{constructor(){super(...arguments),this.model=null}setModel(e){if(!(e instanceof Tr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function rh(e,t){return e<t}function rk(e,t){return e>t}var dN=class extends cN{constructor(e){if(super(),e==null&&(e={}),e.restoreBestWeights)throw new Pe("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=rh:this.mode==="max"?this.monitorFunc=rk:this.monitor.indexOf("acc")!==-1?this.monitorFunc=rk:this.monitorFunc=rh,this.monitorFunc===rh&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===rh?1/0:-1/0}async onEpochEnd(e,t){await jr(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 QG(e){return new dN(e)}var e6={earlyStopping:QG},t6=X();t6.registerFlag("KEEP_INTERMEDIATE_TENSORS",()=>!1,e=>{e&&console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.")});var wa;(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_QINT16=15]="DT_QINT16",e[e.DT_QUINT16=16]="DT_QUINT16",e[e.DT_UINT16=17]="DT_UINT16",e[e.DT_COMPLEX128=18]="DT_COMPLEX128",e[e.DT_HALF=19]="DT_HALF",e[e.DT_RESOURCE=20]="DT_RESOURCE",e[e.DT_VARIANT=21]="DT_VARIANT",e[e.DT_UINT32=22]="DT_UINT32",e[e.DT_UINT64=23]="DT_UINT64",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",e[e.DT_QINT16_REF=115]="DT_QINT16_REF",e[e.DT_QUINT16_REF=116]="DT_QUINT16_REF",e[e.DT_UINT16_REF=117]="DT_UINT16_REF",e[e.DT_COMPLEX128_REF=118]="DT_COMPLEX128_REF",e[e.DT_HALF_REF=119]="DT_HALF_REF",e[e.DT_RESOURCE_REF=120]="DT_RESOURCE_REF",e[e.DT_VARIANT_REF=121]="DT_VARIANT_REF",e[e.DT_UINT32_REF=122]="DT_UINT32_REF",e[e.DT_UINT64_REF=123]="DT_UINT64_REF"})(wa||(wa={}));var sk;(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={}))})(sk||(sk={}));var Zw={};function n6(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Zw[e]=n}function hN(e){return Zw[e]}function a6(e){delete Zw[e]}function I(e,t,n,a,r){let s=t.inputParams[e];if(s&&s.inputIndexStart!==void 0){let o=s.inputIndexStart,l=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?o+1:s.inputIndexEnd;if(s.type==="tensor")return In(t.inputNames[s.inputIndexStart],n,a,r);if(s.type==="tensors")return t.inputNames.slice(o,l).map(d=>In(d,n,a,r));let u=In(t.inputNames.slice(o)[0],n,a,r),p=u.dataSync();return s.type==="number"?p[0]:k.toNestedArray(u.shape,p)}let i=t.attrParams[e];return i&&i.value}function In(e,t,n,a){let[r,s]=Xn(e);if(a!=null){let o=a.getHashTableHandleByName(r);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[Uh(r,o)]);return i!==void 0?t[Uh(r,i)][s]:void 0}function r6(e,t,n){return t[Uh(e,n.currentContextId)]}function ir(e,t){let[n,a,r]=Xn(e);return[Uh(n,t&&t.currentContextId),a,r]}function Uh(e,t){return t?`${e}-${t}`:e}function Xn(e){let t=e.split(":");if(t.length===1)return[e,0,void 0];let n=t[0],a=t.length===3?t[1]:void 0,r=Number(t[t.length-1]);return[n,r,a]}function hh(e,t,n){let a=I("pad",e,t,n);if(a==="explicit"){a=I("explicitPaddings",e,t,n);let r=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)r[s][0]=a[s*2],r[s][1]=a[s*2+1];return r}return a}function kr(e){return e.kept?e:Nr(e)}var mN={};Me(mN,{json:()=>s6});var s6=[{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}]}],fN={};Me(fN,{json:()=>i6});var i6=[{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}]}],gN={};Me(gN,{json:()=>o6});var o6=[{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"}]},{tfOpName:"TensorListLength",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"}]},{tfOpName:"TensorListResize",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"size",type:"number"}]}],yN={};Me(yN,{json:()=>l6});var l6=[{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"}]}],bN={};Me(bN,{json:()=>u6});var u6=[{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"}]}],xN={};Me(xN,{json:()=>p6});var p6=[{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}]}],vN={};Me(vN,{json:()=>c6});var c6=[{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"}]}],wN={};Me(wN,{json:()=>d6});var d6=[{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"}]}],kN={};Me(kN,{json:()=>h6});var h6=[{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"}]}],IN={};Me(IN,{json:()=>m6});var m6=[{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"}]},{tfOpName:"ImageProjectiveTransformV3",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"transforms",type:"tensor"},{start:2,name:"outputShape",type:"number[]"},{start:3,name:"fillValue",type:"number"}],attrs:[{tfName:"interpolation",name:"interpolation",type:"string"},{tfName:"fill_mode",name:"fillMode",type:"string"}]}],SN={};Me(SN,{json:()=>f6});var f6=[{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}]}],NN={};Me(NN,{json:()=>g6});var g6=[{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"}]}],TN={};Me(TN,{json:()=>y6});var y6=[{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}]}],CN={};Me(CN,{json:()=>b6});var b6=[{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:"Cumprod",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"}]},{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"}]}],_N={};Me(_N,{json:()=>x6});var x6=[{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}]}],EN={};Me(EN,{json:()=>v6});var v6=[{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"}]}],AN={};Me(AN,{json:()=>w6});var w6=[{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}]}],$N={};Me($N,{json:()=>k6});var k6=[{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"}]}],FN={};Me(FN,{json:()=>I6});var I6=[{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:[]},{tfOpName:"BroadcastArgs",category:"transformation",inputs:[{start:0,name:"s0",type:"tensor"},{start:1,name:"s1",type:"tensor"}],attrs:[]}],ik=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[mN,fN,gN,yN,bN,xN,vN,wN,kN,IN,SN,NN,TN,CN,_N,EN,AN,$N,FN],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,a)=>(n[a.tfOpName]=a,n),{})}transformGraph(e,t={}){let n=e.node,a=[],r=[],s=[],i=n.reduce((m,f)=>(m[f.name]=this.mapNode(f),f.op.startsWith("Placeholder")?a.push(m[f.name]):f.op==="Const"?r.push(m[f.name]):(f.input==null||f.input.length===0)&&s.push(m[f.name]),m),{}),o=[],l=[],u={},p={};t!=null&&(u=this.mapSignatureEntries(t.inputs),p=this.mapSignatureEntries(t.outputs));let d=Object.keys(i);d.forEach(m=>{let f=i[m];f.inputNames.forEach((g,y)=>{let[b,,x]=ir(g),v=i[b];if(v.outputs!=null){let w=v.outputs.indexOf(x);if(w!==-1){let T=`${b}:${w}`;f.inputNames[y]=T}}f.inputs.push(v),v.children.push(f)})}),Object.keys(p).length===0?d.forEach(m=>{let f=i[m];f.children.length===0&&l.push(f)}):Object.keys(p).forEach(m=>{let[f]=ir(m),g=i[f];g!=null&&(g.signatureKey=p[m],l.push(g))}),Object.keys(u).length>0?Object.keys(u).forEach(m=>{let[f]=ir(m),g=i[f];g&&(g.signatureKey=u[m],o.push(g))}):o=a;let c={};e.library!=null&&e.library.function!=null&&(c=e.library.function.reduce((m,f)=>(m[f.signature.name]=this.mapFunction(f),m),{}));let h={nodes:i,inputs:o,outputs:l,weights:r,placeholders:a,signature:t,functions:c};return s.length>0&&(h.initNodes=s),h}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=hN(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(a=>a.startsWith("^")?a.slice(1):a),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((a,r)=>(a[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},a),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((a,r)=>{let s=r.type,i;switch(r.type){case"string":i=Gb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Gb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":i=Jb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Jb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":i=jb(e.attr,r.tfName,r.defaultValue||0),i===void 0&&!!r.tfDeprecatedName&&(i=jb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":i=Yb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Yb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":i=Hb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Hb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":i=Qb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Qb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":i=Xb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Xb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":i=Zb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Zb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":i=qb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=qb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":i=Kb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Kb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":i=ok(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=ok(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return a[r.name]={value:i,type:s},a},{})),n}mapFunction(e){let t=e.nodeDef,n=[],a=[],r={};t!=null&&(r=t.reduce((u,p)=>(u[p.name]=this.mapNode(p),p.op==="Const"&&a.push(u[p.name]),u),{}));let s=[],i=[];e.signature.inputArg.forEach(u=>{let[p]=ir(u.name),d={name:p,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Qw(u.type),type:"dtype"}},children:[]};d.signatureKey=u.name,s.push(d),r[p]=d}),Object.keys(r).forEach(u=>{let p=r[u];p.inputNames.forEach((d,c)=>{let[h,,m]=ir(d),f=r[h];if(f.outputs!=null){let g=f.outputs.indexOf(m);if(g!==-1){let y=`${h}:${g}`;p.inputNames[c]=y}}p.inputs.push(f),f.children.push(p)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[p,d]=ir(o[u.name]),c=r[p];c!=null&&(c.defaultOutput=d,i.push(c))});let l=this.mapArgsToSignature(e);return{nodes:r,inputs:s,outputs:i,weights:a,placeholders:n,signature:l}}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 S6(e){let t=X().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 DN(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):S6(e);return t?n:n.toLowerCase()}function Gb(e,t,n,a=!1){let r=e[t];return r!=null?DN(r.s,a):n}function Hb(e,t,n){let a=e[t];return a?a.b:n}function jb(e,t,n){let a=e[t]||{},r=a.i!=null?a.i:a.f!=null?a.f:n;return typeof r=="number"?r:parseInt(r,10)}function Qw(e){switch(typeof e=="string"&&(e=wa[e]),e){case wa.DT_FLOAT:case wa.DT_HALF:return"float32";case wa.DT_INT32:case wa.DT_INT64:case wa.DT_INT8:case wa.DT_UINT8:return"int32";case wa.DT_BOOL:return"bool";case wa.DT_DOUBLE:return"float32";case wa.DT_STRING:return"string";default:return null}}function ok(e,t,n){let a=e[t];return a&&a.func?a.func.name:n}function qb(e,t,n){let a=e[t];return a&&a.type?Qw(a.type):n}function Kb(e,t,n){let a=e[t];return a&&a.list&&a.list.type?a.list.type.map(r=>Qw(r)):n}function RN(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function Xb(e,t,n){let a=e[t];return a&&a.shape?RN(a.shape):n}function Yb(e,t,n){let a=e[t];return a?((a.list.f&&a.list.f.length?a.list.f:a.list.i)||[]).map(r=>typeof r=="number"?r:parseInt(r,10)):n}function Jb(e,t,n,a=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(s=>DN(s,a)):n}function Zb(e,t,n){let a=e[t];return a&&a.list&&a.list.shape?a.list.shape.map(r=>RN(r)):n}function Qb(e,t,n){let a=e[t];return a&&a.list&&a.list.b?a.list.b:n}var N6=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(a=>this.getInput(a)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((a,r)=>(a[r]=this.getAttr(r),a),{}))}getInput(e){return In(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return In(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return jb(this.node.rawAttrs,e,t);if(n.s!=null)return Gb(this.node.rawAttrs,e,t);if(n.b!=null)return Hb(this.node.rawAttrs,e,t);if(n.shape!=null)return Xb(this.node.rawAttrs,e,t);if(n.type!=null)return qb(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return Yb(this.node.rawAttrs,e,t);if(n.list.s!=null)return Jb(this.node.rawAttrs,e,t);if(n.list.shape!=null)return Zb(this.node.rawAttrs,e,t);if(n.list.b!=null)return Qb(this.node.rawAttrs,e,t);if(n.list.type!=null)return Kb(this.node.rawAttrs,e,t)}return t}},T6=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[J(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[ZI(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[pv(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[z(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[fe(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[Qx(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[Pm(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[ce(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[_u(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[hr(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[_r(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[af(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},C6=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[zt(I("x",e,t,n))];case"Acos":return[Ox(I("x",e,t,n))];case"Acosh":return[Lx(I("x",e,t,n))];case"Asin":return[Bx(I("x",e,t,n))];case"Asinh":return[Wx(I("x",e,t,n))];case"Atan":return[Vx(I("x",e,t,n))];case"Atan2":return[Ux(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[Gx(I("x",e,t,n))];case"Ceil":return[Kx(I("x",e,t,n))];case"Complex":return[ns(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[Fc(I("x",e,t,n))];case"Cosh":return[Bm(I("x",e,t,n))];case"Elu":return[Tu(I("x",e,t,n))];case"Erf":return[ev(I("x",e,t,n))];case"Exp":return[gn(I("x",e,t,n))];case"Expm1":return[tv(I("x",e,t,n))];case"Floor":return[Cu(I("x",e,t,n))];case"Log":return[ea(I("x",e,t,n))];case"Log1p":return[Rc(I("x",e,t,n))];case"Imag":return[Vm(I("x",e,t,n))];case"Neg":return[St(I("x",e,t,n))];case"Reciprocal":return[hv(I("x",e,t,n))];case"Real":return[Zp(I("x",e,t,n))];case"Relu":return[Xe(I("x",e,t,n))];case"Round":return[Ym(I("x",e,t,n))];case"Selu":return[Zm(I("x",e,t,n))];case"Sigmoid":return[ha(I("x",e,t,n))];case"Sin":return[Qm(I("x",e,t,n))];case"Sign":return[mv(I("x",e,t,n))];case"Sinh":return[ef(I("x",e,t,n))];case"Softplus":return[bo(I("x",e,t,n))];case"Sqrt":return[ln(I("x",e,t,n))];case"Square":return[lt(I("x",e,t,n))];case"Tanh":return[oi(I("x",e,t,n))];case"Tan":return[yv(I("x",e,t,n))];case"ClipByValue":return[nn(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Xm(I("x",e,t,n))];case"Rsqrt":return[Jm(In(e.inputNames[0],t,n))];case"Prod":return[Km(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[Dc(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[Oc(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[av(In(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ia(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){k.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let a=0;a<e.length;a++){let r=e[a],s=t[a];k.assert(r<0||s<0||r===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function lk(e){return!(typeof e=="number"||e.some(t=>t<0))}function _p(e,t,n){let a=ex(e,n),r=!lk(a);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${a}`);if(r&&t.forEach(s=>{a=ex(s.shape,a)}),!lk(a))throw new Error(`Non-fully-defined elementShape: ${a}`);return a}function ex(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 a=0;a<e.length;++a){let r=e[a],s=t[a];if(r>=0&&s>=0&&r!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[a]=r>=0?r:s}return n}var _6=class{constructor(e,t,n,a,r,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=a,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=we(0),en(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),Ia(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,en(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,a)=>this.write(n,t[a]))}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 a=0;a<this.size();a++)e.push(a)}if(e.length===0)return Zn([],[0].concat(this.elementShape));let n=this.readMany(e);return Ia(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Mt(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Zn([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return Ia(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Qe(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,mt(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,a=e.map(o=>(n+=o,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,s=[];O(()=>{t=W(t,[1,n,r]);for(let o=0;o<e.length;++o){let l=o===0?0:a[o-1],u=[0,l,0],p=[1,e[o],r];s[o]=W(Ge(t,u,p),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},yl=class{constructor(e,t,n,a=-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}`);Ia(t,r.shape,"TensorList shape mismatch: "),en(r)}),this.idTensor=we(0),this.maxNumElements=a,en(this.idTensor)}get id(){return this.idTensor.id}copy(){return new yl([...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.`);Ia(e,this.elementShape,"TensorList shape mismatch: ");let a=_p(this.elementShape,this.tensors,e);return O(()=>{let r=this.tensors.map(s=>W(s,a));return Mt(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=_p(this.elementShape,this.tensors,e),a=this.tensors.pop();return Ia(a.shape,e,"TensorList shape mismatch: "),W(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Ia(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");en(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}.`);let t=new yl([],this.elementShape,this.elementDtype,this.maxNumElements);t.tensors.length=e;for(let n=0;n<Math.min(this.tensors.length,e);++n)t.tensors[n]=this.tensors[n];return t}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.`);Ia(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=_p(this.elementShape,this.tensors,t);return W(this.tensors[e],a)}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.`);Ia(this.elementShape,t.shape,"TensorList shape mismatch: "),en(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}`);Ia(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=_p(this.elementShape,this.tensors,n);return e.length===0?Zn([],[0].concat(a)):O(()=>{let r=e.map(s=>W(this.tensors[s],a));return Mt(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Ia(this.elementShape,t,"TensorList shape mismatch: ");let n=_p(this.elementShape,this.tensors,t);return this.size()===0?Zn([],[0].concat(n)):O(()=>{let a=this.tensors.map(r=>W(r,n));return Qe(a,0)})}};function E6(e,t,n){let a=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);Ia(r,t,"TensorList shape mismatch: ");let s=mt(e);return new yl(s,t,a)}function A6(e,t,n){return new yl([],e,t,n)}function $6(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new yl([],n,e.dtype,a),i=mt(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function F6(e,t,n){let a=0,r=t.map(p=>(a+=p,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=ex(s,n),o=a===0?0:e.size/a,l=O(()=>{let p=[];e=W(e,[1,a,o]);for(let d=0;d<t.length;++d){let c=d===0?0:r[d-1],h=[0,c,0],m=[1,t[d],o];p[d]=W(Ge(e,h,m),i)}return e.dispose(),p}),u=new yl([],n,e.dtype,t.length);for(let p=0;p<l.length;p++)u.setItem(p,l[p]);return u}var D6=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let a=I("pred",e,t,n);return[kr(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=kr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>In(r,t,n)!==void 0);if(a){let r=In(a,t,n);return[kr(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[kr(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[kr(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[kr(a)]}case"TensorArrayV3":{let a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),u=I("name",e,t,n),p=new _6(u,r,a,s,l,i,o);return n.addTensorArray(p),[p.idTensor,we(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[we(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=I("indices",e,t,n),r=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=$6(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=A6(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=E6(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(a.id).popBack(r,s)]}case"TensorListSplit":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=F6(a,s,r);return n.addTensorList(i),[i.idTensor]}case"TensorListLength":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id);return[we(r.size(),"int32")]}case"TensorListResize":{let a=I("tensorListId",e,t,n),r=I("size",e,t,n),s=n.getTensorList(a.id).resize(r);return n.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function uk(e,t,n){let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=!s,o=r==="prelu",l=a==="fusedbatchnorm",u=I("numArgs",e,t,n);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&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 p=I("strides",e,t,n),d=hh(e,t,n),c=I("dataFormat",e,t,n).toUpperCase(),h=I("dilations",e,t,n),[m,f]=I("args",e,t,n);i&&(f=m,m=void 0);let g=I("leakyreluAlpha",e,t,n);return{stride:p,pad:d,dataFormat:c,dilations:h,biasArg:m,preluArg:f,activationFunc:r,leakyreluAlpha:g}}var R6=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Lm(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=hh(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Rt(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:p}=uk(e,t,n);return[rs.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:p}=uk(e,t,n);return[rs.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let a=I("outputShape",e,t,n),r=I("strides",e,t,n),s=hh(e,t,n);return[zm(I("x",e,t,n),I("filter",e,t,n),a,[r[1],r[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let a=I("strides",e,t,n),r=hh(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[bs(I("input",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,i,[s[1],s[2]])]}case"Conv3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Yx(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2],a[3]],r,s,[i[1],i[2],i[3]])]}case"AvgPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[fa(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Pt(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPoolWithArgmax":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=SS(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r,i);return[o,l]}case"AvgPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[jx(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"MaxPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[lv(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"Dilation2D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dilations",e,t,n),i=a[1],o=a[2],l=s[1],u=s[2];return[Zx(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},M6=(e,t,n)=>{switch(e.op){case"Fill":{let a=I("shape",e,t,n),r=I("dtype",e,t,n),s=I("value",e,t,n);return[Cn(a,s,r)]}case"LinSpace":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("num",e,t,n);return[yS(a,r,s)]}case"Multinomial":{let a=I("logits",e,t,n),r=I("numSamples",e,t,n),s=I("seed",e,t,n);return[NS(a,r,s)]}case"OneHot":{let a=I("indices",e,t,n),r=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[cl(a,r,s,i)]}case"Ones":return[Jn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[ta(I("x",e,t,n))];case"RandomUniform":return[Eu(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("step",e,t,n);return[dl(a,r,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let a=I("shape",e,t,n),r=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[rf(a,r,s,I("dtype",e,t,n),i)]}case"Zeros":return[kt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[Ke(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function hb(e,t,n){let a=I("boxes",e,t,n),r=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var P6=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=hb(e,t,n),u=await Ln.nonMaxSuppressionWithScoreAsync(a,r,s,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=hb(e,t,n),l=I("padToMaxOutputSize",e,t,n),u=await Ln.nonMaxSuppressionPaddedAsync(a,r,s,i,o,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=hb(e,t,n);return[await Ln.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=oe(I("condition",e,t,n),"bool"),r=[await vv(a)];return a.dispose(),r}case"ListDiff":return _S(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},O6=(e,t,n)=>{switch(e.op){case"TopKV2":{let a=I("x",e,t,n),r=I("k",e,t,n),s=I("sorted",e,t,n),i=bv(a,r,s);return[i.values,i.indices]}case"Unique":{let a=I("x",e,t,n),r=Dh(a);return[r.values,r.indices]}case"UniqueV2":{let a=I("x",e,t,n),r=I("axis",e,t,n),s=Dh(a,r);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},L6=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let a=I("default",e,t,n);return[In(e.name,t,n)||a];case"Placeholder":return[In(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=I("x",e,t,n);return[kr(u)]}case"IdentityN":return I("x",e,t,n).map(u=>kr(u));case"Snapshot":let r=I("x",e,t,n);return[kr(r)];case"Shape":return[qe(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(u=>qe(u.shape));case"Size":return[we(I("x",e,t,n).size,"int32")];case"Rank":return[we(I("x",e,t,n).rank,"int32")];case"NoOp":return[we(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;u<i.length;u++)console.log(Array.prototype.slice.call(i[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},z6=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=we(0),this.tensorMap=new Map,en(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 we(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),O(()=>{let a=mt(t),r=n.length,s=a.length;k.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i<r;i++){let o=n[i],l=a[i];en(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return O(()=>{let a=[];for(let r=0;r<n.length;r++){let s=n[r],i=this.findWithDefault(s,t);a.push(i)}return Mt(a)})}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}`)}},B6=async(e,t,n,a)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new z6(r,s);return a.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=I("tableHandle",e,t,n,a),s=I("keys",e,t,n),i=I("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=I("tableHandle",e,t,n,a),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await a.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=I("tableHandle",e,t,n,a);return[a.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},W6=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let a=I("images",e,t,n),r=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Ln.resizeBilinear(a,[r[0],r[1]],s,i)]}case"ResizeNearestNeighbor":{let a=I("images",e,t,n),r=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Ln.resizeNearestNeighbor(a,[r[0],r[1]],s,i)]}case"CropAndResize":{let a=I("image",e,t,n),r=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[Ln.cropAndResize(a,r,s,i,o,l)]}case"ImageProjectiveTransformV3":{let a=I("images",e,t,n),r=I("transforms",e,t,n),s=I("outputShape",e,t,n),i=I("fillValue",e,t,n),o=I("interpolation",e,t,n),l=I("fillMode",e,t,n);return[Ln.transform(a,r,o.toLowerCase(),l.toLowerCase(),i,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},V6=(e,t,n)=>{switch(e.op){case"Equal":return[Qn(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[pi(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Gn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[xs(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Um(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[vs(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[Ta(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Mc(I("a",e,t,n))];case"LogicalOr":return[jm(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[fn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},U6=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[De(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[mS(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[$e(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,p]=I("args",e,t,n);return[rs.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:u,activation:r,preluActivationWeights:p,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},G6=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Cr(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[Cr(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[rv(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[Ja(I("x",e,t,n))];case"LogSoftmax":return[Hm(I("x",e,t,n))];case"SparseToDense":return[wv(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},H6=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Sa(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Et(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Jp(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[be(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Om(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Xp(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[ii(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[zx(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Km(I("x",e,t,n),i,o)]}case"Cumprod":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[Yp(I("x",e,t,n),i,o,l)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[Wm(I("x",e,t,n),i,o,l)]}case"Bincount":let a=I("x",e,t,n),r=I("weights",e,t,n),s=I("size",e,t,n);return[qx(a,r,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),l=I("size",e,t,n),u=I("binaryOutput",e,t,n);return[dS(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},j6=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let a=I("n",e,t,n),r=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,a),[Qe(s,r)]}case"Gather":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[li(a,oe(r,"int32"),0)]}case"GatherV2":{let a=I("axis",e,t,n),r=I("batchDims",e,t,n),s=I("x",e,t,n),i=I("indices",e,t,n);return[li(s,oe(i,"int32"),a,r)]}case"Reverse":{let a=I("dims",e,t,n),r=[];for(let i=0;i<a.length;i++)a[i]&&r.push(i);let s=I("x",e,t,n);return[na(s,r)]}case"ReverseV2":{let a=I("axis",e,t,n),r=I("x",e,t,n);return[na(r,a)]}case"Slice":{let a=I("begin",e,t,n),r=I("size",e,t,n);return[Ge(I("x",e,t,n),a,r)]}case"StridedSlice":{let a=I("begin",e,t,n),r=I("end",e,t,n),s=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),u=I("newAxisMask",e,t,n),p=I("shrinkAxisMask",e,t,n),d=I("x",e,t,n);return[gv(d,a,r,s,i,o,l,u,p)]}case"Pack":return O(()=>{let a=I("axis",e,t,n),r=I("tensors",e,t,n),s=r[0].shape,i=pr(r[0]).shape,o=r.map(l=>{let u=k.arraysEqual(l.shape,s);if(!u&&!k.arraysEqual(pr(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:W(l,s)});return[Mt(o,a)]});case"Unpack":{let a=I("axis",e,t,n),r=I("tensor",e,t,n);return mt(r,a)}case"Tile":{let a=I("reps",e,t,n);return[On(I("x",e,t,n),a)]}case"Split":case"SplitV":{let a=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return zn(s,r,a)}case"ScatterNd":{let a=I("indices",e,t,n),r=I("values",e,t,n),s=I("shape",e,t,n);return[FS(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[DS(a,r)]}case"SparseToDense":{let a=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[wv(a,s,r,s.dtype===i.dtype?i:oe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},q6=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:a,outputValues:r,emptyRowIndicator:s,reverseIndexMap:i}=Fp.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[a,r,s,i]}case"SparseReshape":{let{outputIndices:a,outputShape:r}=Fp.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[a,r]}case"SparseSegmentMean":return[Fp.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Fp.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},K6=(e,t,n)=>{switch(e.op){case"FFT":return[Lc(I("x",e,t,n))];case"IFFT":return[hl(I("x",e,t,n))];case"RFFT":return[zc(I("x",e,t,n))];case"IRFFT":return[nf(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},X6=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:a,nGramsSplits:r}=dh.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[a,r]}case"StringSplit":{let{indices:a,values:r,shape:s}=dh.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[a,r,s]}case"StringToHashBucketFast":return[dh.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Y6=(e,t,n)=>{switch(e.op){case"Cast":return[oe(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let a=I("axis",e,t,n);return[mn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[pr(I("x",e,t,n),a)]}case"Reshape":return[W(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[uv(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ga(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let a=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[Pc(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[$c(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[Jx(I("x",e,t,n),a,r)]}case"BroadcastTo":return[il(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[sS(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function pk(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return O(()=>T6(s,i,o));case"basic_math":return O(()=>C6(s,i,o));case"control":return D6(s,i,o);case"convolution":return O(()=>R6(s,i,o));case"creation":return O(()=>M6(s,i,o));case"dynamic":return P6(s,i,o);case"evaluation":return O(()=>O6(s,i,o));case"image":return O(()=>W6(s,i,o));case"graph":return O(()=>L6(s,i,o));case"logical":return O(()=>V6(s,i,o));case"matrices":return O(()=>U6(s,i,o));case"normalization":return O(()=>G6(s,i,o));case"reduction":return O(()=>H6(s,i,o));case"slice_join":return O(()=>j6(s,i,o));case"sparse":return O(()=>q6(s,i,o));case"spectral":return O(()=>K6(s,i,o));case"string":return O(()=>X6(s,i,o));case"transformation":return O(()=>Y6(s,i,o));case"hash_table":return B6(s,i,o,a);case"custom":let l=hN(s.op);if(l&&l.customExecutor)return l.customExecutor(new N6(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.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 k.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var ck=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,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 dk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Xn(c)[0]),p=[];a!=null&&(p=a.map(c=>Xn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((MN(c)||tH(c)||nH(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&p.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function J6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>Xn(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{a.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{a.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{a.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(d=>{!l.has(d.name)&&a.has(d.name)&&d.inputs.every(c=>l.has(c.name))&&s.push(d)})}return u}var Z6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Q6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],eH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function MN(e){return Z6.indexOf(e.op)>=0}function tH(e){return Q6.indexOf(e.op)>=0}function nH(e){return eH.indexOf(e.op)>=0}var tx=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,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 tx(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(a=>a.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(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=dk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=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 [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return J6(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 a=n.map(p=>this.graph.nodes[Xn(p)[0]]),r=t.map(p=>Xn(p)[0]),s=r.map(p=>this.graph.nodes[p]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return O(()=>{let p=new ck(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Xn(m),y=[];y[g]=e[m],d[f]=y});let c=this.getFrozenTensorIds(d),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!d[f.name]){let g=pk(f,d,p,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);d[f.name]=g,this.checkTensorForDisposal(f.name,f,d,p,c,r,h)}}return this.parent==null&&p.dispose(c),t.map(m=>In(m,d,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=r6(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];if(p===1){if(!this.keepTensorForDebug)u.dispose();else{let[d,c]=ir(t.name,a);this.intermediateTensors[d]?this.intermediateTensors[d][c]=u:(this.intermediateTensors[d]=[],this.intermediateTensors[d][c]=u)}delete i[u.id]}else p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new ck(this.weightMap,a,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,n);let i=t.map(u=>In(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(b=>this.graph.nodes[Xn(b)[0]]),i=n.map(b=>Xn(b)[0]),o=i.map(b=>this.graph.nodes[b]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:d}=dk(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{let[x,v]=Xn(b),w=[];w[v]=e[b],h[x]=w});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let b=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(b)}p==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(b=>!MN(b)&&!In(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&I("isConstant",p.node,a,n)&&([d]=ir(p.node.name,n)),a[p.node.name]==null){let c=pk(p.node,a,n,this._resourceManager);d||([d]=ir(p.node.name,n));let h=n.currentContext;k.isPromise(c)?u.push(c.then(m=>(a[d]=m,n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=ir(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!In(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!In(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Xn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Xn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Xn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},aH=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},rH="?tfjs-format=file",sH="model.json",PN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new aH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Qt.browserHTTPRequest(e,this.loadOptions);else{let t=Qt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Qt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Qt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new tx(ik.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=ik.Instance.transformGraph(e.modelInitializer);this.initializer=new tx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Qt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ae)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function iH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${sH}${rH}`);let n=new PN(e,t);return await n.load(),n}var oH="3.16.0",ON={};Me(ON,{CSVDataset:()=>jN,Dataset:()=>Mu,FileDataSource:()=>QN,TextLineDataset:()=>HN,URLDataSource:()=>eT,array:()=>AH,csv:()=>WH,func:()=>VH,generator:()=>UH,microphone:()=>HH,version_data:()=>jH,webcam:()=>GH,zip:()=>$H});var lH=yi(Kk()),uH=yi(Kk());function pH(e,t){return Gh(e,t)}function Gh(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(a.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(bl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=Gh(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function cH(e,t=zN){return LN(e,t)}function LN(e,t,n=new Set){let a=e[0];if(n.has(a))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(bl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=LN(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function zN(e){return e===null?null:bl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function BN(e,t){let n=new Map;Gh(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return Gh(e,t,n)}function bl(e){let t=!1;if(X().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=Xk();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ae)&&!(e instanceof Promise)&&!t)}function dH(e){return e==null||hH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ae||k.isTypedArray(e)}function hH(e){return e===null||typeof e!="object"&&typeof e!="function"}function mH(e){return pH(e,fH)}function fH(e){return e instanceof Ae?{value:e.clone(),recurse:!1}:bl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var WN=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}},e0=class extends WN{constructor(){super(e0.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 a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};e0.INITIAL_CAPACITY=32;function VN(e){return new bH(e)}function t0(e){return new xH(e)}function gH(e,t){return new UN(e,t)}function yH(e,t=Yr.FAIL){return new _H(e,t)}var an=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 TH(this,e)}filter(e){return new SH(this,e)}map(e){return new NH(this,e)}mapAsync(e){return new hk(this,e)}serialMapAsync(e){return new hk(this,e).serial()}flatmap(e){return new CH(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 IH(this,e,t)}columnMajorBatch(e,t=!0,n=zN){return this.rowMajorBatch(e,t).map(a=>cH(a,n))}concatenate(e,t){return new UN(VN([this,e]),t)}take(e){return e<0||e==null?this:new kH(this,e)}skip(e){return e<0||e==null?this:new wH(this,e)}prefetch(e){return new GN(this,e)}shuffle(e,t){return new EH(this,e,t)}serial(){return new vH(this)}},bH=class extends an{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:mH(e),done:!1}}},xH=class extends an{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: ${e.message}`,e}}},vH=class extends an{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},wH=class extends an{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Re(e.value)}return this.upstream.next()}},kH=class extends an{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},IH=class extends an{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},SH=class extends an{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Re(e.value)}}},NH=class extends an{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ga.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ga.getTensorsInContainer(n);for(let r of t)Ga.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},TH=class extends an{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},hk=class extends an{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ga.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ga.getTensorsInContainer(n);for(let r of t)Ga.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},n0=class extends an{constructor(){super(),this.outputQueue=new e0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},CH=class extends n0{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ga.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ga.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ga.isTensorInList(r,a)||r.dispose();return!0}},UN=class extends an{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}},Yr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Yr||(Yr={}));var _H=class extends an{constructor(e,t=Yr.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 a(s){return s instanceof an?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await BN(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Yr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Yr.SHORTEST:return{value:null,done:!0};case Yr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},GN=class extends an{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new WN(t)}summary(){return`${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()}},EH=class extends GN{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=uH.alea(n||k.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}}},Mu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Kn(async()=>(await n.iterator()).columnMajorBatch(e,t,FH),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Kn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Kn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Kn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Kn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Kn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Kn(async()=>{let a=t0(async()=>({value:await t.iterator(),done:!1}));return gH(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Kn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=lH.alea(t||k.now().toString());return Kn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.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,Kn(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()}};Mu.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends Mu{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function AH(e){return Kn(async()=>VN(e),e.length)}function $H(e){if(!bl(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 Kn(async()=>{let n=await BN(e,a=>{if(a instanceof Mu)return{value:a.iterator(),recurse:!1};if(bl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return yH(n,Yr.SHORTEST)},t)}function FH(e){if(e===null)return null;let t=e[0];return dH(t)?{value:DH(e),recurse:!1}:{value:null,recurse:!0}}function DH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ae?Mt(e):Zn(e)}var HN=class extends Mu{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},sh='"',Ep=Symbol("out"),mk=Symbol("field"),ih=Symbol("quote"),mb=Symbol("quoteafterquote"),fk=Symbol("quoteinquote"),jN=class extends Mu{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 HN(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.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&&k.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((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!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={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=Ep;for(let i=0;i<r;i++)switch(s){case Ep:switch(e.charAt(i)){case sh:a=i+1,s=ih;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Ep;break;default:s=mk,a=i;break}break;case mk:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=Ep,a=i+1;break;default:}break;case ih:switch(e.charAt(i)){case sh:s=mb;break;default:}break;case mb:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=Ep,a=i+1;break;case sh:s=ih;break;default:s=fk;break}break;case fk:switch(e.charAt(i)){case sh:s=ih;break;default:}break;default:}if(s===mb?n.push(e.substring(a,r-1)):n.push(e.substring(a)),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}},qN=class extends an{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(!X().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new qN(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({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),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),Zn(n,t)}},KN=class extends an{constructor(e,t){if(super(),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=qe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Ha([s,r,o,i],[1,4])}else this.cropBox=Ha([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!X().get("IS_BROWSER"))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 KN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=yo.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return O(()=>{let t=mn(oe(e,"float32"),0),n;n=Ln.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},XN=class{},YN=class extends an{split(e){return new RH(this,e)}},RH=class extends YN{constructor(e,t){super(),this.upstream=e,this.impl=new MH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},MH=class extends n0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},PH=class extends an{decodeUTF8(){return new OH(this)}},OH=class extends YN{constructor(e){super(),this.upstream=e,this.impl=new LH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},LH=class extends n0{constructor(e){if(super(),this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Xk();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},JN=class extends PH{constructor(e,t={}){super(),this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(X().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function zH(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=BH(e));let s=await(n||k.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new JN(i,t)}else throw new Error(s.statusText)}var BH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function ZN(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var QN=class extends XN{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(ZN(this.input)&&X().get("IS_NODE")){let e=yx();this.input=e.readFileSync(this.input.slice(7))}return new JN(this.input,this.options)}},eT=class extends XN{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return ZN(this.url)?new QN(this.url,this.fileOptions).iterator():zH(this.url,this.fileOptions)}};function WH(e,t={}){return new jN(new eT(e),t)}function VH(e){let t=t0(e);return Kn(async()=>t)}function UH(e){return Kn(async()=>{let t=await e();return t0(()=>t.next())})}async function GH(e,t){return KN.create(e,t)}async function HH(e){return qN.create(e)}var jH="3.16.0";function xe(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var qH=mr.whereImpl,a0=class extends oc{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Zh(this,ar())}nextDataId(){return a0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().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 a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,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,a,r){this.data.set(e,{values:t,dtype:a,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 a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return _.mergeRealAndImagArrays(a,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(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return He(e.shape,e.dtype,n)}makeOutput(e,t,n){let a=this.write(e,t,n);return ar().makeTensorFromDataId(a,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=k.now();return e(),{kernelMs:k.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){xe([e],"where");let t=this.readSync(e.dataId);return qH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};a0.nextDataId=0;var tT={};Me(tT,{addImpl:()=>aT,bincountImpl:()=>s0,bincountReduceImpl:()=>rT,ceilImpl:()=>sT,concatImpl:()=>i0,equalImpl:()=>iT,expImpl:()=>lT,expm1Impl:()=>pT,floorImpl:()=>cT,gatherNdImpl:()=>dT,gatherV2Impl:()=>hT,greaterEqualImpl:()=>fT,greaterImpl:()=>mT,lessEqualImpl:()=>yT,lessImpl:()=>gT,linSpaceImpl:()=>bT,logImpl:()=>xT,maxImpl:()=>vT,maximumImpl:()=>wT,minimumImpl:()=>kT,multiplyImpl:()=>o0,negImpl:()=>IT,notEqualImpl:()=>ST,prodImpl:()=>NT,rangeImpl:()=>u0,rsqrtImpl:()=>TT,sigmoidImpl:()=>Mj,simpleAbsImpl:()=>nT,sliceImpl:()=>jh,sparseFillEmptyRowsImpl:()=>_T,sparseReshapeImpl:()=>ET,sparseSegmentReductionImpl:()=>p0,sqrtImpl:()=>Lj,squaredDifferenceImpl:()=>AT,stridedSliceImpl:()=>$T,stringNGramsImpl:()=>FT,stringSplitImpl:()=>DT,stringToHashBucketFastImpl:()=>RT,subImpl:()=>MT,tileImpl:()=>PT,topKImpl:()=>LT,transposeImpl:()=>l0,uniqueImpl:()=>zT});function nT(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var KH=e=>{let{x:t}=e.inputs,n=e.backend;xe(t,"abs");let a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=nT(r),n.makeOutput(a,t.shape,t.dtype)},XH={kernelName:Il,backendName:"cpu",kernelFunc:KH};function Vt(e){return(t,n,a,r,s)=>{let i=_.assertAndGetBroadcastShape(t,n),o=i.length,l=k.computeStrides(i),u=k.sizeFromShape(i),p=k.getTypedArrayFromDType(s,u),d=t.length,c=n.length,h=k.computeStrides(t),m=k.computeStrides(n),f=_.getBroadcastDims(t,i),g=_.getBroadcastDims(n,i);if(f.length+g.length===0)for(let y=0;y<p.length;++y)p[y]=e(a[y%a.length],r[y%r.length]);else for(let y=0;y<p.length;++y){let b=k.indexToLoc(y,o,l),x=b.slice(-d);f.forEach(C=>x[C]=0);let v=k.locToIndex(x,d,h),w=b.slice(-c);g.forEach(C=>w[C]=0);let T=k.locToIndex(w,c,m);p[y]=e(a[v],r[T])}return[p,i]}}function Yn(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=n.makeTensorInfo(a.shape,"complex64"),l=n.data.get(o.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(a.shape,"float32",s),imag:n.makeTensorInfo(r.shape,"float32",i)},o}var YH={kernelName:sm,backendName:"cpu",kernelFunc:Yn};function Hh(e,t,n="float32"){if(n==="complex64"){let r=Hh(e,t,"float32"),s=Hh(e,t,"float32");return Yn({inputs:{real:r,imag:s},backend:e})}let a=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,a)}function cr(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var JH={kernelName:Li,backendName:"cpu",kernelFunc:cr};function di(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.real,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var ZH={kernelName:Sm,backendName:"cpu",kernelFunc:di};function us(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return cr({inputs:{x:r},backend:n});let i=Hh(n,r.shape,r.dtype),o=us({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Yn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=di({inputs:{input:r},backend:n}),o=us({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=cr({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(r.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(r.shape,"int32",o)}if(s==="bool"){let i=n.data.get(r.dataId).values,o=k.toTypedArray([0],r.dtype),[l,u]=Vt((p,d)=>p!==d?1:0)(r.shape,[],i,o,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var QH={kernelName:Ii,backendName:"cpu",kernelFunc:us};function rn(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;xe([i,o],e);let u=l.data.get(i.dataId).values,p=l.data.get(o.dataId).values,d=i.dtype==="string"?_.fromUint8ToStringArray(u):u,c=i.dtype==="string"?_.fromUint8ToStringArray(p):p,h=a||i.dtype,[m,f]=t(i.shape,o.shape,d,c,h);return l.makeTensorInfo(f,h,m)}:({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let u=us({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),p=l.data.get(u.dataId),d=p.complexTensorInfos.real,c=p.complexTensorInfos.imag,h=l.data.get(d.dataId).values,m=l.data.get(c.dataId).values,f=us({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(f.dataId),y=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,v=l.data.get(b.dataId).values,[w,T,C]=n(i.shape,o.shape,h,m,x,v),E=l.makeTensorInfo(C,"float32",w),$=l.makeTensorInfo(C,"float32",T),P=Yn({inputs:{real:E,imag:$},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(f),l.disposeIntermediateTensorInfo(E),l.disposeIntermediateTensorInfo($),P}else{let u=l.data.get(i.dataId).values,p=l.data.get(o.dataId).values,d=a||i.dtype,[c,h]=t(i.shape,o.shape,u,p,d);return l.makeTensorInfo(h,d,c)}}}function r0(e){return(t,n,a,r,s,i)=>{let o=_.assertAndGetBroadcastShape(t,n),l=k.sizeFromShape(o),u=o.length,p=k.computeStrides(o),d=k.getTypedArrayFromDType("float32",l),c=k.getTypedArrayFromDType("float32",l),h=_.getBroadcastDims(t,o),m=_.getBroadcastDims(n,o),f=_.mergeRealAndImagArrays(a,r),g=_.mergeRealAndImagArrays(s,i),y=t.length,b=k.computeStrides(t),x=n.length,v=k.computeStrides(n);if(h.length+m.length===0)for(let w=0;w<d.length;w++){let T=w%f.length,C=w%g.length,E=e(f[T*2],f[T*2+1],g[C*2],g[C*2+1]);d[w]=E.real,c[w]=E.imag}else for(let w=0;w<d.length;w++){let T=k.indexToLoc(w,u,p),C=T.slice(-y);h.forEach(S=>C[S]=0);let E=k.locToIndex(C,y,b),$=T.slice(-x);m.forEach(S=>$[S]=0);let P=k.locToIndex($,x,v),F=e(f[E*2],f[E*2+1],g[P*2],g[P*2+1]);d[w]=F.real,c[w]=F.imag}return[d,c,o]}}var aT=Vt((e,t)=>e+t),ej=r0((e,t,n,a)=>({real:e+n,imag:t+a})),Zc=rn(ds,aT,ej),tj={kernelName:ds,backendName:"cpu",kernelFunc:Zc};function s0(e,t,n,a,r){let s=k.sizeFromShape(a),i=k.makeZerosTypedArray(r,n);for(let o=0;o<e.length;o++){let l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=r||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function rT(e,t,n,a=!1){let r=e.shape[0],s=e.shape[1],i=He([r,n],t.dtype);for(let o=0;o<r;o++)for(let l=0;l<s;l++){let u=e.get(o,l);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(a?i.set(1,o,u):t.size>0?i.set(i.get(o,u)+t.get(o,l),o,u):i.set(i.get(o,u)+1,o,u))}return i}function ws(e){return(t,n,a)=>{let r=k.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)r[s]=e(t[s],a);return r}}function ot(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(xe(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=k.sizeFromShape(i.shape),p=n||i.dtype,d=k.getArrayFromDType(p,u);for(let c=0;c<u;++c)d[c]=t(l[c],r);return o.makeTensorInfo(i.shape,p,d)}}function Pu(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(xe(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=n||i.dtype,p=t(l,u,r);return o.makeTensorInfo(i.shape,u,p)}}var sT=ws(e=>Math.ceil(e)),nj=Pu(Si,sT),aj={kernelName:Si,backendName:"cpu",kernelFunc:nj};function i0(e,t,n,a){let r=k.getArrayFromDType(n,k.sizeFromShape(t));if(a&&n!=="string"){let s=0;e.forEach(i=>{let o=k.sizeFromShape(i.shape);r.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?_.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let u=0;u<i.shape[0];++u){let p=u*t[1]+s;for(let d=0;d<i.shape[1];++d)r[p+d]=o[l++]}s+=i.shape[1]})}return r}var iT=Vt((e,t)=>e===t?1:0),oT=rn(zl,iT,null,"bool"),rj={kernelName:zl,backendName:"cpu",kernelFunc:oT},lT=ws(e=>Math.exp(e)),uT=Pu(Di,lT,"float32"),sj={kernelName:Di,backendName:"cpu",kernelFunc:uT},pT=ws(e=>Math.expm1(e)),ij=Pu(Wl,pT),oj={kernelName:Wl,backendName:"cpu",kernelFunc:ij},cT=ws(e=>Math.floor(e)),lj=Pu(Ri,cT),uj={kernelName:Ri,backendName:"cpu",kernelFunc:lj};function dT(e,t,n,a,r,s,i,o,l){let u=He([a,s],n);for(let p=0;p<a;p++){let d=[],c=0;for(let h=0;h<r;h++){let m=e[p*r+h];c+=m*i[h],d.push(m)}if(c<0||c>=l/s)throw new Error(`Invalid indices: ${d} does not index into ${o}`);for(let h=0;h<s;h++)u.values[p*s+h]=t.get(...t.indexToLoc(c*s+h))}return u}function hT(e,t,n){let a=He(n,e.dtype);for(let r=0;r<a.size;++r){let s=a.indexToLoc(r).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let u=e.locToIndex(s);0<=u&&u<e.values.length&&(a.values[r]=e.values[u])}return a}var mT=Vt((e,t)=>e>t?1:0),pj=rn(Hl,mT,null,"bool"),cj={kernelName:Hl,backendName:"cpu",kernelFunc:pj},fT=Vt((e,t)=>e>=t?1:0),dj=rn(Oi,fT,null,"bool"),hj={kernelName:Oi,backendName:"cpu",kernelFunc:dj},gT=Vt((e,t)=>e<t?1:0),mj=rn(Xl,gT,null,"bool"),fj={kernelName:Xl,backendName:"cpu",kernelFunc:mj},yT=Vt((e,t)=>e<=t?1:0),gj=rn(Yl,yT,null,"bool"),yj={kernelName:Yl,backendName:"cpu",kernelFunc:gj};function bT(e,t,n){let a=(t-e)/(n-1),r=k.makeZerosTypedArray(n,"float32");r[0]=e;for(let s=1;s<r.length;s++)r[s]=r[s-1]+a;return r}var xT=ws(e=>Math.log(e)),bj=Pu(Bi,xT),xj={kernelName:Bi,backendName:"cpu",kernelFunc:bj};function vT(e,t,n,a){let r=k.getTypedArrayFromDType(a,k.sizeFromShape(n));for(let s=0;s<r.length;++s){let i=s*t,o=e[i];for(let l=0;l<t;++l){let u=e[i+l];(Number.isNaN(u)||u>o)&&(o=u)}r[s]=o}return r}var wT=Vt((e,t)=>Math.max(e,t)),vj=rn(Vi,wT),wj={kernelName:Vi,backendName:"cpu",kernelFunc:vj},kT=Vt((e,t)=>Math.min(e,t)),kj=rn(ji,kT),Ij={kernelName:ji,backendName:"cpu",kernelFunc:kj},o0=Vt((e,t)=>e*t),Sj=r0((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),Of=rn(Ki,o0,Sj),Nj={kernelName:Ki,backendName:"cpu",kernelFunc:Of};function IT(e,t,n){let a=k.createScalarValue(-1,n);return o0([],t,a,e,n)}function Tj(e){let{inputs:t,backend:n}=e,{x:a}=t;xe(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=IT(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var Cj={kernelName:eu,backendName:"cpu",kernelFunc:Tj},ST=Vt((e,t)=>e!==t?1:0),_j=rn(tu,ST,null,"bool"),Ej={kernelName:tu,backendName:"cpu",kernelFunc:_j};function l0(e,t,n,a,r){let s=t.length,i=k.sizeFromShape(t),o=k.computeStrides(t),l=k.computeStrides(r),u=k.getTypedArrayFromDType(n,k.sizeFromShape(r));for(let p=0;p<i;++p){let d=k.indexToLoc(p,s,o),c=new Array(d.length);for(let m=0;m<c.length;m++)c[m]=d[a[m]];let h=k.locToIndex(c,s,l);u[h]=e[p]}return u}function Vn(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{perm:s}=n;xe(r,"transpose");let i=r.shape.length,o=new Array(i);for(let p=0;p<o.length;p++)o[p]=r.shape[s[p]];let l=a.data.get(r.dataId).values,u=l0(l,r.shape,r.dtype,s,o);return{dataId:a.write(u,o,r.dtype),shape:o,dtype:r.dtype}}var Aj={kernelName:go,backendName:"cpu",kernelFunc:Vn};function NT(e,t,n,a){let[r,s]=_.computeOutAndReduceShapes(e,a),i=ma(t,"int32"),o=k.makeZerosTypedArray(k.sizeFromShape(r),i),l=k.sizeFromShape(s);for(let u=0;u<o.length;++u){let p=u*l,d=1;for(let c=0;c<l;++c)d*=n[p+c];o[u]=d}return{outVals:o,outShape:r,outDtype:i}}function $j(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"prod");let o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=_.getAxesPermutation(l,o),p=l,d=r,c=[];u!=null&&(d=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}),c.push(d),p=_.getInnerMostAxes(p.length,o));let h=n.data.get(d.dataId).values,{outVals:m,outShape:f,outDtype:g}=NT(d.shape,d.dtype,h,p),y=f;return i&&(y=_.expandShapeToKeepDim(f,l)),c.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(y,g,m)}var Fj={kernelName:Qi,backendName:"cpu",kernelFunc:$j};function u0(e,t,n,a){let r=e===t,s=e<t&&n<0,i=t<e&&n>1;if(r||s||i)return k.makeZerosTypedArray(0,a);let o=Math.abs(Math.ceil((t-e)/n)),l=k.makeZerosTypedArray(o,a);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 TT=ws(e=>1/Math.sqrt(e)),Dj=Pu(so,TT),Rj={kernelName:so,backendName:"cpu",kernelFunc:Dj},Mj=ws(e=>1/(1+Math.exp(-e))),CT=ot(oo,e=>1/(1+Math.exp(-e))),Pj={kernelName:oo,backendName:"cpu",kernelFunc:CT};function jh(e,t,n,a,r){let s=qt.isSliceContinous(a,t,n),i=k.sizeFromShape(n),o=k.computeStrides(a);if(s){let d=qt.computeFlatOffset(t,o);return r==="string"?e.slice(d,d+i):e.subarray(d,d+i)}let l=r==="string"?_.fromUint8ToStringArray(e):e,u=He(a,r,l),p=He(n,r);for(let d=0;d<p.size;++d){let c=p.indexToLoc(d),h=c.map((m,f)=>m+t[f]);p.set(u.get(...h),...c)}return r==="string"?_.fromStringArrayToUint8(p.values):p.values}function hi(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;xe(r,"slice");let[o,l]=qt.parseSliceParams(r,s,i);qt.assertParamsValid(r,o,l);let u=n.data.get(r.dataId).values,p=jh(u,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}var Oj={kernelName:du,backendName:"cpu",kernelFunc:hi};function _T(e,t,n,a,r,s,i){let o=t[0],l=s[0],u=new Array(l),p=new Array(o),d=t[1];if(l===0){if(o!==0)throw new Error(_.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));let g=k.getArrayFromDType(n,0),y=k.getArrayFromDType(r,0);return[g,[0,d],y,u,p]}let c=!0,h=0,m=new Array(l).fill(0);for(let g=0;g<o;++g){let y=e[g*d];if(y<0)throw new Error(_.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,y));if(y>=l)throw new Error(_.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,y,l));++m[y],c=c&&y>=h,h=y}let f=!0;for(let g=0;g<l;++g){let y=m[g]===0;u[g]=y,f=f&&!y,m[g]=Math.max(m[g],1),g>0&&(m[g]+=m[g-1])}if(f&&c){let g=e,y=a;for(let b=0;b<o;++b)p[b]=b;return[g,[o,d],y,u,p]}else{let g=m[l-1],y=k.getArrayFromDType(n,g*d),b=k.getArrayFromDType(r,g),x=new Array(l).fill(0);for(let v=0;v<o;++v){let w=e[v*d],T=x[w],C=(w===0?0:m[w-1])+T;x[w]++;for(let E=0;E<d;++E)y[C*d+E]=e[v*d+E];b[C]=a[v],p[v]=C}for(let v=0;v<l;++v)if(x[v]===0){let w=v===0?0:m[v-1];y[w*d+0]=v;for(let T=1;T<d;++T)y[w*d+T]=0;b[w]=i}return[y,[g,d],b,u,p]}}function ET(e,t,n,a,r){let s=k.sizeFromShape(a),i=t[0],o=r.length,l=[],u=1,p=-1;for(let f=0;f<o;++f){let g=r[f];if(g===-1){if(p!==-1)throw new Error(_.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(p,f));p=f,l.push(1)}else{if(g<0)throw new Error(_.getSparseReshapeNegativeOutputDimErrorMessage(f,g));u*=g,l.push(g)}}if(p!==-1){if(u<=0)throw new Error(_.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let f=Math.trunc(s/u);if(u*f!==s)throw new Error(_.getSparseReshapeInputOutputMultipleErrorMessage(a,l));l[p]=f}if(k.sizeFromShape(l)!==s)throw new Error(_.getSparseReshapeInputOutputMismatchErrorMessage(a,l));let d=a.length,c=[];if(d>0){c[d-1]=1;for(let f=d-2;f>=0;--f)c[f]=c[f+1]*a[f+1]}let h=[];if(o>0){h[o-1]=1;for(let f=o-2;f>=0;--f)h[f]=h[f+1]*l[f+1]}let m=k.getArrayFromDType(n,i*o);for(let f=0;f<i;++f){let g=0;for(let y=0;y<d;++y)g+=e[f*d+y]*c[y];for(let y=0;y<o;++y)m[f*o+y]=Math.trunc(g/h[y]),g%=h[y]}return[m,[i,o],l]}function p0(e,t,n,a,r,s=!1,i=0){let o=a.length,l=[t[0],e.length/t[0]],u=l[1],p=o>0?r[o-1]+1:0;if(p<0)throw new Error(_.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let d=t.slice();d[0]=p;let c=d.reduce((b,x)=>b*x,1),h=k.getArrayFromDType(n,c);if(o===0)return p>0&&h.fill(i),[h,d];if(p<=0)throw new Error(_.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,f=1,g=0,y=r[m];for(;;){let b=0;if(f<o){if(b=r[f],y===b){++f;continue}if(y>=b)throw new Error(_.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(y<0||y>=p)throw new Error(_.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y,p));y>g&&h.fill(i,g*u,y*u);for(let x=m;x<f;++x){let v=a[x];if(v<0||v>=l[0])throw new Error(_.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x,a[x],l[0]));for(let w=0;w<u;w++)h[y*u+w]+=e[v*u+w]}if(s)for(let x=0;x<u;x++)h[y*u+x]/=f-m;if(m=f,++f,g=y+1,y=b,f>o)break}return g<p&&h.fill(i,g*u,p*u),[h,d]}var Lj=ws(e=>Math.sqrt(e)),zj=ot(lo,e=>Math.sqrt(e)),Bj={kernelName:lo,backendName:"cpu",kernelFunc:zj},AT=Vt((e,t)=>{let n=e-t;return n*n}),Wj=rn(co,AT),Vj={kernelName:co,backendName:"cpu",kernelFunc:Wj};function $T(e,t,n,a){let r=He(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*n[l]+a[l];r.set(t.get(...o),...i)}return r}var Uj=class{constructor(e,t,n,a,r,s){this.separator=k.encodeString(e),this.nGramWidths=t,this.leftPad=k.encodeString(n),this.rightPad=k.encodeString(a),this.padWidth=r,this.preserveShort=s}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,a,r,s){for(let i=0;i<r;++i){let o=this.getPadWidth(s),l=Math.max(0,o-i),u=Math.max(0,o-(r-(i+1))),p=s-(l+u),d=t+(l>0?0:i-o),c=0;c+=l*this.leftPad.length;for(let g=0;g<p;++g)c+=e[d+g].length;c+=u*this.rightPad.length,c+=(l+u+p-1)*this.separator.length,n[a+i]=new Uint8Array(c);let h=n[a+i],m=0,f=g=>g.forEach(y=>h[m++]=y);for(let g=0;g<l;++g)f(this.leftPad),f(this.separator);for(let g=0;g<p-1;++g)f(e[d+g]),f(this.separator);if(p>0){f(e[d+p-1]);for(let g=0;g<u;++g)f(this.separator),f(this.rightPad)}else{for(let g=0;g<u-1;++g)f(this.rightPad),f(this.separator);f(this.rightPad)}}}compute(e,t){let n=e.length,a=t.length;if(a>0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let l=1;l<a;++l){let u=t[l]>=o;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${o}, ${n}]`);o=t[l]}if(o!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${o}`)}let r=a-1,s=k.getArrayFromDType("int32",a);if(n===0||a===0){let o=new Array(n);for(let l=0;l<=r;++l)s[l]=0;return[o,s]}s[0]=0;for(let o=1;o<=r;++o){let l=t[o]-t[o-1],u=0;this.nGramWidths.forEach(p=>{u+=this.getNumNGrams(l,p)}),this.preserveShort&&l>0&&u===0&&(u=1),s[o]=s[o-1]+u}let i=new Array(s[r]);for(let o=0;o<r;++o){let l=t[o],u=s[o];if(this.nGramWidths.forEach(p=>{let d=t[o+1]-t[o],c=this.getNumNGrams(d,p);this.createNGrams(e,l,i,u,c,p),u+=c}),this.preserveShort&&u===s[o]){let p=t[o+1]-t[o];if(p===0)continue;let d=p+2*this.padWidth,c=1;this.createNGrams(e,l,i,u,c,d)}}return[i,s]}};function FT(e,t,n,a,r,s,i,o){return new Uj(n,a,r,s,i,o).compute(e,t)}function Gj(e,t,n,a){if(!e.length)return;if(t.length===0){for(let s=0;s<e.length;++s)a.push(e.subarray(s,s+1));return}if(t.length===1){let s=t[0],i=e.indexOf(s);for(;i!==-1;){let o=e.subarray(0,i);(!n||o.length!==0)&&a.push(o),e=e.subarray(i+1),i=e.indexOf(s)}(!n||e.length!==0)&&a.push(e);return}let r=0;for(let s=0;s<e.length+1;s++)if(s===e.length||t.indexOf(e[s])!==-1){let i=e.subarray(r,s);(!n||i.length!==0)&&a.push(i),r=s+1}}function DT(e,t,n){let a=e.length,r=[],s=0,i=0,o=new Array(a);for(let c=0;c<a;++c){let h=r.length;Gj(e[c],t,n,r);let m=r.length-h;o[c]=m,s+=m,i=Math.max(i,m)}let l=k.getArrayFromDType("int32",s*2),u=new Array(s),p=[a,i],d=0;for(let c=0;c<a;++c)for(let h=0;h<o[c];++h)l[d*2]=c,l[d*2+1]=h,u[d]=r[d],++d;return[l,u,p]}function RT(e,t){let n=k.getArrayFromDType("int32",e.length);for(let a=0;a<e.length;++a)n[a]=k.fingerPrint64(e[a]).modulo(t).getLowBitsUnsigned();return n}var MT=Vt((e,t)=>e-t),Hj=r0((e,t,n,a)=>({real:e-n,imag:t-a})),c0=rn(ho,MT,Hj),jj={kernelName:ho,backendName:"cpu",kernelFunc:c0};function PT(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let a=He(n,e.dtype);for(let r=0;r<a.values.length;++r){let s=a.indexToLoc(r),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}var Mp=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function OT(e,t,n=0,a=e.length-1){for(;a>n;){if(a-n>600){let o=a-n+1,l=t-n+1,u=Math.log(o),p=.5*Math.exp(2*u/3),d=.5*Math.sqrt(u*p*(o-p)/o)*Math.sign(l-o/2),c=Math.max(n,Math.floor(t-l*p/o+d)),h=Math.min(a,Math.floor(t+(o-l)*p/o+d));OT(e,t,c,h)}let r=e[t],s=n,i=a;for(k.swap(e,n,t),Mp(e[a],r)>0&&k.swap(e,n,a);s<i;){for(k.swap(e,s,i),s++,i--;Mp(e[s],r)<0;)s=s+1;for(;Mp(e[i],r)>0;)i=i-1}Mp(e[n],r)===0?k.swap(e,n,i):(i=i+1,k.swap(e,i,a)),i<=t&&(n=i+1),t<=i&&(a=i-1)}}function LT(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],l=k.getTypedArrayFromDType(n,i*a),u=k.getTypedArrayFromDType("int32",i*a);for(let d=0;d<i;d++){let c=d*o,h=e.subarray(c,c+o),m=new Array(h.length);h.forEach((b,x)=>m[x]={value:b,index:x}),a<m.length&&(OT(m,a),m=m.slice(0,a)),r&&m.sort(Mp);let f=d*a,g=l.subarray(f,f+a),y=u.subarray(f,f+a);for(let b=0;b<a;b++)g[b]=m[b].value,y[b]=m[b].index}let p=t.slice();return p[p.length-1]=a,[He(p,n,l),He(p,"int32",u)]}function zT(e,t,n,a){let r=k.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let m=0;m<r;m++)s[0]*=n[m];s[1]=n[r];for(let m=r+1;m<n.length;m++)s[2]*=n[m];let i={},o=new Int32Array(n[r]),l=new jt(s,a,e),u=[],p=s[0]===1&&s[2]===1;for(let m=0;m<n[r];m++){let f;if(p)f=e[m].toString();else{let g=[];for(let y=0;y<s[0];y++)for(let b=0;b<s[2];b++)g.push(l.get(y,m,b));f=g.join(",")}if(i[f]!==void 0)o[m]=i[f];else{let g=Object.keys(i).length;i[f]=g,o[m]=g,u.push(m)}}let d=s.slice();d[1]=Object.keys(i).length;let c=new jt(d,a);u.forEach((m,f)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)c.set(l.get(g,m,y),g,f,y)});let h=n.slice();return h[r]=d[1],{outputValues:c.values,outputShape:h,indices:o}}Mm("cpu",()=>new a0,1);var BT=ot(Fi,e=>e>=0?e:Math.exp(e)-1),qj={kernelName:Fi,backendName:"cpu",kernelFunc:BT};function WT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;xe([r],"leakyRelu");let i=k.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,l=k.getTypedArrayFromDType("float32",i);for(let u=0;u<o.length;u++)l[u]=o[u]<0?s*o[u]:o[u];return n.makeTensorInfo(r.shape,"float32",l)}var Kj={kernelName:zi,backendName:"cpu",kernelFunc:WT},Xj=Vt((e,t)=>e<0?t*e:e);function VT(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;xe([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=Xj(a.shape,r.shape,s,i,"float32");return n.makeTensorInfo(l,"float32",o)}var Yj={kernelName:Zi,backendName:"cpu",kernelFunc:VT},UT=ot(eo,e=>Math.max(0,e)),Jj={kernelName:eo,backendName:"cpu",kernelFunc:UT},GT=ot(no,e=>Math.min(Math.max(0,e),6)),Zj={kernelName:no,backendName:"cpu",kernelFunc:GT};function d0(e,t,n,a,r){if(n==="linear")return cr({inputs:{x:t},backend:e});if(n==="relu")return UT({inputs:{x:t},backend:e});if(n==="elu")return BT({inputs:{x:t},backend:e});if(n==="relu6")return GT({inputs:{x:t},backend:e});if(n==="prelu")return VT({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return WT({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return CT({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Tt(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let u=n.data.get(r.dataId);if(u.complexTensorInfos!=null){let p=u.complexTensorInfos.real,d=u.complexTensorInfos.imag;p.shape=o,d.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var Qj={kernelName:lu,backendName:"cpu",kernelFunc:Tt};function HT(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;xe([r,s],"matMul");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=Nu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,p,c]:[g,c,p],v=o?[y,h,d]:[y,d,h],w=Tt({inputs:{x:r},backend:n,attrs:{shape:x}}),T=Tt({inputs:{x:s},backend:n,attrs:{shape:v}}),C=i?w.shape[1]:w.shape[2],E=i?w.shape[2]:w.shape[1],$=o?T.shape[1]:T.shape[2],P=Math.max(g,y),F=n.data.get(w.dataId).values,S=n.data.get(T.dataId).values,M=k.computeStrides(w.shape),V=k.computeStrides(T.shape),[j,q,K]=i?[M[0],1,M[1]]:[M[0],M[1],1],[Q,ee,re]=o?[1,V[1],V[0]]:[V[1],1,V[0]],Z=E*$,ie=He([P,E,$],w.dtype),ae=ie.values,le=n.blockSize;for(let ue=0;ue<P;ue++)for(let ke=0;ke<E;ke+=le)for(let ye=0;ye<$;ye+=le)for(let Ie=0;Ie<C;Ie+=le){let Ee=Math.min(ke+le,E),Fe=Math.min(ye+le,$),Be=Math.min(Ie+le,C);for(let je=ke;je<Ee;je++)for(let st=ye;st<Fe;st++){let et=0;for(let tt=Ie;tt<Be;tt++){let Te=Math.min(ue,g-1)*j,gt=Math.min(ue,y-1)*re,ct=F[Te+je*q+tt*K],yn=S[tt*Q+st*ee+gt];et+=ct*yn}ae[ue*Z+(je*$+st)]+=et}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(T),n.makeTensorInfo(b,ie.dtype,ie.values)}var e5={kernelName:ki,backendName:"cpu",kernelFunc:HT};function t5(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c,h,m,f=[];c=HT({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(h=Zc({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),p&&(m=d0(n,c,p,o,d),f.push(c),c=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return c}var n5={kernelName:ti,backendName:"cpu",kernelFunc:t5},a5=ot(Sl,e=>Math.acos(e)),r5={kernelName:Sl,backendName:"cpu",kernelFunc:a5},s5=ot(Nl,e=>Math.acosh(e)),i5={kernelName:Nl,backendName:"cpu",kernelFunc:s5};function o5(e){let{inputs:t,backend:n}=e,a=t;xe(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=He(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var l5={kernelName:xi,backendName:"cpu",kernelFunc:o5};function u5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,u=_.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("all",l,p.shape.length);let[d,c]=_.computeOutAndReduceShapes(p.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,x=f[b];for(let v=0;v<h;++v){let w=f[b+v];x=x&&w}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(p);let g=n.makeTensorInfo(d,p.dtype,m);if(i){let y=_.expandShapeToKeepDim(d,o),b=Tt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var p5={kernelName:Tl,backendName:"cpu",kernelFunc:u5};function c5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"any");let o=k.parseAxisParam(s,r.shape),l=o,u=_.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("any",l,p.shape.length);let[d,c]=_.computeOutAndReduceShapes(p.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,x=f[b];for(let v=0;v<h;++v){let w=f[b+v];x=x||w}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(p);let g=n.makeTensorInfo(d,p.dtype,m);if(i){let y=_.expandShapeToKeepDim(d,o),b=Tt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var d5={kernelName:Cl,backendName:"cpu",kernelFunc:c5};function h5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;xe(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],_.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[p,d]=_.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(p),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let v=0;v<m;++v){let w=f[y+v];w>b&&(b=w,x=v)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var m5={kernelName:vi,backendName:"cpu",kernelFunc:h5};function f5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;xe(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],_.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,d]=_.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(p),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let v=0;v<m;++v){let w=f[y+v];w<b&&(b=w,x=v)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var g5={kernelName:lc,backendName:"cpu",kernelFunc:f5},y5=ot(_l,e=>Math.asin(e)),b5={kernelName:_l,backendName:"cpu",kernelFunc:y5},x5=ot(El,e=>Math.asinh(e)),v5={kernelName:El,backendName:"cpu",kernelFunc:x5},w5=ot(Al,e=>Math.atan(e)),k5={kernelName:Al,backendName:"cpu",kernelFunc:w5},I5=Vt((e,t)=>Math.atan2(e,t)),S5=rn(Fl,I5),N5={kernelName:Fl,backendName:"cpu",kernelFunc:S5},T5=ot($l,e=>Math.atanh(e)),C5={kernelName:$l,backendName:"cpu",kernelFunc:T5};function h0(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=He(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let v=0;v<r.batchSize;++v){let w=v*y,T=v*a[0];for(let C=0;C<r.inChannels;++C)for(let E=0;E<r.outHeight;++E){let $=E*i-c,P=Math.max(0,$),F=Math.min(r.inHeight,p+$),S=w+E*b;for(let M=0;M<r.outWidth;++M){let V=M*o-h,j=Math.max(0,V),q=Math.min(r.inWidth,d+V),K=m,Q=0,ee=0;for(let Z=P;Z<F;Z+=l){let ie=T+Z*a[1];for(let ae=j;ae<q;ae+=u){let le=ie+ae*a[2],ue=e[le+C];s==="max"&&ue>K?K=ue:s==="avg"&&(Q+=ue,ee++)}if(isNaN(K))break}let re=S+M*x+C;g[re]=s==="avg"?Q/ee:K}}}return f}function jT(e,t,n,a,r=!1,s=!1){let i=He(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=He(t,n,e);for(let g=0;g<a.batchSize;++g)for(let y=0;y<a.inChannels;++y)for(let b=0;b<a.outHeight;++b){let x=b*o-h,v=x;for(;v<0;)v+=u;let w=Math.min(a.inHeight,d+x);for(let T=0;T<a.outWidth;++T){let C=T*l-m,E=C;for(;E<0;)E+=p;let $=Math.min(a.inWidth,c+C),P=Number.NEGATIVE_INFINITY,F=-1;for(let S=v;S<w;S+=u){let M=S-x;for(let V=E;V<$;V+=p){let j=V-C,q=f.get(g,S,V,y);q>P&&(P=q,r?F=s?((g*a.inHeight+S)*a.inWidth+V)*a.inChannels+y:(S*a.inWidth+V)*a.inChannels+y:F=M*c+j)}}i.set(F,g,b,T,y)}}return i}function qT(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=He(r.outShape,n),v=x.values,w=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],E=r.outShape[4];for(let $=0;$<r.batchSize;++$){let P=$*w,F=$*a[0];for(let S=0;S<r.inChannels;++S)for(let M=0;M<r.outDepth;++M){let V=M*i-f,j=V;for(;j<0;)j+=u;let q=Math.min(r.inDepth,c+V),K=P+M*T;for(let Q=0;Q<r.outHeight;++Q){let ee=Q*o-g,re=ee;for(;re<0;)re+=p;let Z=Math.min(r.inHeight,h+ee),ie=K+Q*C;for(let ae=0;ae<r.outWidth;++ae){let le=ae*l-y,ue=le;for(;ue<0;)ue+=d;let ke=Math.min(r.inWidth,m+le),ye=ie+ae*E,Ie=b,Ee=0,Fe=0;for(let je=j;je<q;je+=u){let st=F+je*a[1];for(let et=re;et<Z;et+=p){let tt=st+et*a[2];for(let Te=ue;Te<ke;Te+=d){let gt=tt+Te*a[3],ct=e[gt+S];if(s==="max"&&ct>Ie?Ie=ct:s==="avg"&&(Ee+=ct,Fe++),isNaN(Ie))break}if(isNaN(Ie))break}if(isNaN(Ie))break}let Be=ye+S;v[Be]=s==="avg"?Ee/Fe:Ie}}}}return x}function _5(e,t){let n=He(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-c,x=b;for(;x<0;)x+=i;let v=Math.min(t.inDepth,u+b);for(let w=0;w<t.outHeight;++w){let T=w*r-h,C=T;for(;C<0;)C+=o;let E=Math.min(t.inHeight,p+T);for(let $=0;$<t.outWidth;++$){let P=$*s-m,F=P;for(;F<0;)F+=l;let S=Math.min(t.inWidth,d+P),M=Number.NEGATIVE_INFINITY,V=-1;for(let j=x;j<v;j+=i){let q=j-b;for(let K=C;K<E;K+=o){let Q=K-T;for(let ee=F;ee<S;ee+=l){let re=ee-P,Z=e.get(f,j,K,ee,g);Z>=M&&(M=Z,V=q*p*d+Q*p+re)}}}n.set(V,f,y,w,$,g)}}}return n}function E5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;xe(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))d=cr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=h0(c,r.shape,r.dtype,h,p,"avg");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var A5={kernelName:wi,backendName:"cpu",kernelFunc:E5};function $5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;xe(r,"avgPool3d");let p=_.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=qT(d,r.shape,r.dtype,k.computeStrides(r.shape),p,"avg");return n.makeTensorInfo(c.shape,"float32",c.values)}var F5={kernelName:uc,backendName:"cpu",kernelFunc:$5};function D5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;xe([r,s],"avgPool3DGrad");let p=_.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,b=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,w=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=v-1-p.padInfo.front,E=T-1-p.padInfo.left,$=w-1-p.padInfo.top,P=He(s.shape,"float32"),F=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M<p.batchSize;++M)for(let V=0;V<p.inChannels;++V)for(let j=0;j<p.inDepth;++j)for(let q=0;q<p.inHeight;++q)for(let K=0;K<p.inWidth;++K){let Q=j-C,ee=q-$,re=K-E,Z=0;for(let ie=0;ie<v;ie+=y){let ae=(Q+ie)/d;if(!(ae<0||ae>=p.outDepth||Math.floor(ae)!==ae))for(let le=0;le<w;le+=b){let ue=(ee+le)/c;if(!(ue<0||ue>=p.outHeight||Math.floor(ue)!==ue))for(let ke=0;ke<T;ke+=x){let ye=(re+ke)/h;ye<0||ye>=p.outWidth||Math.floor(ye)!==ye||(Z+=S.get(M,ae,ue,ye,V))}}}P.set(Z*F,M,j,q,K,V)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var R5={kernelName:nm,backendName:"cpu",kernelFunc:D5};function M5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;xe([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=_.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,x=b-1-p.padInfo.left,v=y-1-p.padInfo.top,w=He(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=He(r.shape,"float32",C);for(let $=0;$<p.batchSize;++$)for(let P=0;P<p.inChannels;++P)for(let F=0;F<p.inHeight;++F)for(let S=0;S<p.inWidth;++S){let M=F-v,V=S-x,j=0;for(let q=0;q<y;q+=f){let K=(M+q)/d;if(!(K<0||K>=p.outHeight||Math.floor(K)!==K))for(let Q=0;Q<b;Q+=g){let ee=(V+Q)/c;ee<0||ee>=p.outWidth||Math.floor(ee)!==ee||(j+=E.get($,K,ee,P))}}w.set(j*T,$,F,S,P)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var P5={kernelName:tm,backendName:"cpu",kernelFunc:M5};function O5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),xe([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=a;u==null&&(u=.001);let p=n.data.get(r.dataId).values,d=n.data.get(o.dataId).values,c=n.data.get(l.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(p.length),g=m.length,y=h.length,b=c.length,x=d.length,v=0,w=0,T=0,C=0;for(let E=0;E<p.length;++E)f[E]=m[v++]+(p[E]-d[w++])*h[T++]/Math.sqrt(c[C++]+u),v>=g&&(v=0),w>=x&&(w=0),T>=y&&(T=0),C>=b&&(C=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var L5={kernelName:Pi,backendName:"cpu",kernelFunc:O5};function z5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;xe([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),u=_.getPermuted(l.length,s.length),p=_.getReshapedPermuted(r.shape,s,o),d=_.getSliceBeginCoords(i,s.length),c=_.getSliceSize(p,i,s.length),h=Tt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Tt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=hi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var B5={kernelName:Dl,backendName:"cpu",kernelFunc:z5};function W5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=s0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var V5={kernelName:am,backendName:"cpu",kernelFunc:W5};function U5(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=_.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var G5={kernelName:rm,backendName:"cpu",kernelFunc:U5},H5=ot(hs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),j5={kernelName:hs,backendName:"cpu",kernelFunc:H5},q5=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let p=o[u],d=l[u];a[u]=Math.hypot(p,d)}return n.makeOutput(a,t.shape,"float32")},K5={kernelName:pc,backendName:"cpu",kernelFunc:q5};function xl(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var X5={kernelName:ym,backendName:"cpu",kernelFunc:xl};function vl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return cr({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(_.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>di({inputs:{input:v},backend:n})),g=o.map(v=>xl({inputs:{input:v},backend:n})),y=vl({inputs:f,backend:n,attrs:{axis:s}}),b=vl({inputs:g,backend:n,attrs:{axis:s}}),x=Yn({inputs:{real:y,imag:b},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),x}let u=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return Tt({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=_.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=i0(p,i,t[0].dtype,d),h=_.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var Y5={kernelName:Rl,backendName:"cpu",kernelFunc:vl};function KT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;xe([r,s],"conv2d");let d=_.convertConv2DDataFormat(l),c=_.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,b=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new jt(c.outShape,r.dtype),w=k.computeStrides(r.shape),T=k.computeStrides(s.shape),C=w[0],E=x?w[1]:w[2],$=x?w[2]:1,P=x?1:w[1],F=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,V=x?1:v.strides[1],j=n.data.get(r.dataId).values,q=n.data.get(s.dataId).values,K=v.values;for(let Q=0;Q<c.batchSize;++Q){let ee=Q*C,re=Q*F;for(let Z=0;Z<c.outHeight;++Z){let ie=re+Z*S,ae=Z*c.strideHeight-b;for(let le=0;le<h;++le){let ue=ae+le*f;if(ue<0||ue>=c.inHeight)continue;let ke=le*T[0],ye=ee+ue*E;for(let Ie=0;Ie<c.outWidth;++Ie){let Ee=ie+Ie*M,Fe=Ie*c.strideWidth-y;for(let Be=0;Be<m;++Be){let je=Fe+Be*g;if(je<0||je>=c.inWidth)continue;let st=ke+Be*T[1],et=ye+je*$,tt=st;for(let Te=0;Te<c.inChannels;++Te){let gt=j[et+Te*P];for(let ct=0;ct<c.outChannels;++ct)K[Ee+ct*V]+=gt*q[tt+ct];tt+=c.outChannels}}}}}}return n.makeTensorInfo(v.shape,v.dtype,K)}var J5={kernelName:Ni,backendName:"cpu",kernelFunc:KT};function Z5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a;xe([r,s],"conv2dBackpropFilter");let d=_.convertConv2DDataFormat(l),c=_.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),{strideHeight:h,strideWidth:m,filterHeight:f,filterWidth:g}=c,y=c.dataFormat==="channelsLast",b=new jt(c.filterShape,"float32"),x=c.padInfo.left,v=c.padInfo.top,w=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=new jt(r.shape,r.dtype,w),E=new jt(s.shape,s.dtype,T);for(let $=0;$<f;++$){let P=Math.max(0,Math.ceil((v-$)/h)),F=Math.min(c.outHeight,(c.inHeight+v-$)/h);for(let S=0;S<g;++S){let M=Math.max(0,Math.ceil((x-S)/m)),V=Math.min(c.outWidth,(c.inWidth+x-S)/m);for(let j=0;j<c.inChannels;++j)for(let q=0;q<c.outChannels;++q){let K=0;for(let Q=0;Q<c.batchSize;++Q)for(let ee=P;ee<F;++ee){let re=$+ee*h-v;for(let Z=M;Z<V;++Z){let ie=S+Z*m-x;y?K+=C.get(Q,re,ie,j)*E.get(Q,ee,Z,q):K+=C.get(Q,j,re,ie)*E.get(Q,q,ee,Z)}}b.set(K,$,S,j,q)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var Q5={kernelName:im,backendName:"cpu",kernelFunc:Z5};function eq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a;xe([r,s],"conv2dBackpropInput");let d=k.computeStrides(s.shape),c=k.computeStrides(r.shape),h=_.convertConv2DDataFormat(u),m=_.computeConv2DInfo(i,s.shape,o,1,l,p,!1,h),f=new jt(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,b=n.data.get(s.dataId).values,[x,v,w]=d,{batchSize:T,filterHeight:C,filterWidth:E,inChannels:$,inHeight:P,inWidth:F,outChannels:S,outHeight:M,outWidth:V,strideHeight:j,strideWidth:q}=m;h=m.dataFormat;let K=C-1-m.padInfo.top,Q=E-1-m.padInfo.left,ee=h==="channelsLast",re=f.strides[0],Z=ee?f.strides[1]:f.strides[2],ie=ee?f.strides[2]:1,ae=ee?1:f.strides[1],le=c[0],ue=ee?c[1]:c[2],ke=ee?c[2]:1,ye=ee?1:c[1];for(let Ie=0;Ie<T;++Ie)for(let Ee=0;Ee<$;++Ee)for(let Fe=0;Fe<P;++Fe){let Be=Fe-K,je=Math.max(0,Math.ceil(Be/j)),st=Math.min(M,(C+Be)/j);for(let et=0;et<F;++et){let tt=et-Q,Te=Math.max(0,Math.ceil(tt/q)),gt=Math.min(V,(E+tt)/q),ct=0;for(let Yt=je;Yt<st;++Yt){let Dn=Yt*j-Be;for(let Ut=Te;Ut<gt;++Ut){let Jt=Ut*q-tt,Da=le*Ie+ue*Yt+ke*Ut,Rn=x*(C-1-Dn)+v*(E-1-Jt)+w*Ee;for(let Gt=0;Gt<S;++Gt){let sa=y[Da+ye*Gt],ia=b[Rn+Gt];ct+=sa*ia}}}let yn=re*Ie+Z*Fe+ie*et+ae*Ee;g[yn]=ct}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var tq={kernelName:Ti,backendName:"cpu",kernelFunc:eq};function nq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;xe([r,s],"conv3d");let u=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:p,filterHeight:d,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,b=g.left,x=g.top,v=new jt(u.outShape,r.dtype),w=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=v.values,E=k.computeStrides(r.shape),$=k.computeStrides(s.shape);for(let P=0;P<u.batchSize;++P){let F=P*E[0],S=P*v.strides[0];for(let M=0;M<u.outDepth;++M){let V=S+M*v.strides[1],j=M*u.strideDepth-y;for(let q=0;q<p;++q){let K=j+q*h;if(K<0||K>=u.inDepth)continue;let Q=q*$[0],ee=F+K*E[1];for(let re=0;re<u.outHeight;++re){let Z=V+re*v.strides[2],ie=re*u.strideHeight-x;for(let ae=0;ae<d;++ae){let le=ie+ae*m;if(le<0||le>=u.inHeight)continue;let ue=Q+ae*$[1],ke=ee+le*E[2];for(let ye=0;ye<u.outWidth;++ye){let Ie=Z+ye*u.outChannels,Ee=ye*u.strideWidth-b;for(let Fe=0;Fe<c;++Fe){let Be=Ee+Fe*f;if(Be<0||Be>=u.inWidth)continue;let je=ue+Fe*$[2],st=ke+Be*u.inChannels,et=je;for(let tt=0;tt<u.inChannels;++tt){let Te=w[st+tt];for(let gt=0;gt<u.outChannels;++gt)C[Ie+gt]+=Te*T[et+gt];et+=u.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var aq={kernelName:cc,backendName:"cpu",kernelFunc:nq};function rq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;xe([r,s],"conv3dBackpropFilterV2");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=_.computeConv3DInfo(r.shape,l,i,1,o),c=d.strideDepth,h=d.strideHeight,m=d.strideWidth,f=d.filterDepth,g=d.filterHeight,y=d.filterWidth,b=new jt(d.filterShape,"float32"),x=b.values,[v,w,T,C]=b.strides,E=n.data.get(s.dataId).values,[$,P,F,S]=p,M=n.data.get(r.dataId).values,[V,j,q,K]=u,Q=d.padInfo.front,ee=d.padInfo.left,re=d.padInfo.top;for(let Z=0;Z<f;++Z){let ie=Math.max(0,Math.ceil((Q-Z)/c)),ae=Math.min(d.outDepth,(d.inDepth+Q-Z)/c),le=Z*v;for(let ue=0;ue<g;++ue){let ke=Math.max(0,Math.ceil((re-ue)/h)),ye=Math.min(d.outHeight,(d.inHeight+re-ue)/h),Ie=ue*w+le;for(let Ee=0;Ee<y;++Ee){let Fe=Math.max(0,Math.ceil((ee-Ee)/m)),Be=Math.min(d.outWidth,(d.inWidth+ee-Ee)/m),je=Ee*T+Ie;for(let st=0;st<d.inChannels;++st){let et=st*C+je;for(let tt=0;tt<d.outChannels;++tt){let Te=0;for(let gt=0;gt<d.batchSize;++gt){let ct=gt*V,yn=gt*$;for(let Yt=ie;Yt<ae;++Yt){let Dn=(Z+Yt*c-Q)*j+ct,Ut=Yt*P+yn;for(let Jt=ke;Jt<ye;++Jt){let Da=(ue+Jt*h-re)*q+Dn,Rn=Jt*F+Ut;for(let Gt=Fe;Gt<Be;++Gt){let sa=(Ee+Gt*m-ee)*K+Da,ia=Gt*S+Rn;Te+=M[sa+st]*E[ia+tt]}}}}x[et+tt]=Te}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var sq={kernelName:om,backendName:"cpu",kernelFunc:rq};function iq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;xe([r],"conv3dBackpropInputV2");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=_.computeConv3DInfo(l,s.shape,o,1,i),c=new jt(d.inShape,"float32"),h=c.values,[m,f,g,y]=c.strides,b=n.data.get(r.dataId).values,[x,v,w,T]=u,C=n.data.get(s.dataId).values,[E,$,P,F]=p,{batchSize:S,filterDepth:M,filterHeight:V,filterWidth:j,inChannels:q,inDepth:K,inHeight:Q,inWidth:ee,outChannels:re,outDepth:Z,outHeight:ie,outWidth:ae,strideDepth:le,strideHeight:ue,strideWidth:ke}=d,ye=M-1-d.padInfo.front,Ie=V-1-d.padInfo.top,Ee=j-1-d.padInfo.left;for(let Fe=0;Fe<S;++Fe)for(let Be=0;Be<q;++Be)for(let je=0;je<K;++je){let st=je-ye,et=Math.max(0,Math.ceil(st/le)),tt=Math.min(Z,(M+st)/le);for(let Te=0;Te<Q;++Te){let gt=Te-Ie,ct=Math.max(0,Math.ceil(gt/ue)),yn=Math.min(ie,(V+gt)/ue);for(let Yt=0;Yt<ee;++Yt){let Dn=Yt-Ee,Ut=Math.max(0,Math.ceil(Dn/ke)),Jt=Math.min(ae,(j+Dn)/ke),Da=0;for(let Rn=et;Rn<tt;++Rn){let Gt=Rn*le-st;for(let sa=ct;sa<yn;++sa){let ia=sa*ue-gt;for(let Br=Ut;Br<Jt;++Br){let Ds=Br*ke-Dn,bd=x*Fe+v*Rn+w*sa+T*Br,Wr=E*(M-1-Gt)+$*(V-1-ia)+P*(j-1-Ds)+F*Be;for(let xr=0;xr<re;++xr){let hp=b[bd+xr],Lo=C[Wr+xr];Da+=hp*Lo}}}}h[m*Fe+f*je+g*Te+y*Yt+Be]=Da}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var oq={kernelName:lm,backendName:"cpu",kernelFunc:iq},lq=ot(Ci,e=>Math.cos(e)),uq={kernelName:Ci,backendName:"cpu",kernelFunc:lq},pq=ot(_i,e=>Math.cosh(e)),cq={kernelName:_i,backendName:"cpu",kernelFunc:pq};function dq(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=He([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,w=k.computeStrides(r.shape),T=k.computeStrides(y.shape);for(let C=0;C<m;C++){let E=C*4,$=b[E],P=b[E+1],F=b[E+2],S=b[E+3],M=x[C];if(M>=p)continue;let V=f>1?(F-$)*(d-1)/(f-1):0,j=g>1?(S-P)*(c-1)/(g-1):0;for(let q=0;q<f;q++){let K=f>1?$*(d-1)+q*V:.5*($+F)*(d-1);if(K<0||K>d-1){for(let Q=0;Q<g;Q++)for(let ee=0;ee<h;ee++){let re=ee+Q*T[2]+q*T[1]+C*T[0];y.values[re]=u}continue}if(l==="bilinear"){let Q=Math.floor(K),ee=Math.ceil(K),re=K-Q;for(let Z=0;Z<g;Z++){let ie=g>1?P*(c-1)+Z*j:.5*(P+S)*(c-1);if(ie<0||ie>c-1){for(let ke=0;ke<h;ke++){let ye=ke+Z*T[2]+q*T[1]+C*T[0];y.values[ye]=u}continue}let ae=Math.floor(ie),le=Math.ceil(ie),ue=ie-ae;for(let ke=0;ke<h;ke++){let ye=ke+ae*w[2]+Q*w[1]+M*w[0],Ie=v[ye];ye=ke+le*w[2]+Q*w[1]+M*w[0];let Ee=v[ye];ye=ke+ae*w[2]+ee*w[1]+M*w[0];let Fe=v[ye];ye=ke+le*w[2]+ee*w[1]+M*w[0];let Be=v[ye],je=Ie+(Ee-Ie)*ue,st=Fe+(Be-Fe)*ue;ye=ke+Z*T[2]+q*T[1]+C*T[0],y.values[ye]=je+(st-je)*re}}}else for(let Q=0;Q<g;++Q){let ee=g>1?P*(c-1)+Q*j:.5*(P+S)*(c-1);if(ee<0||ee>c-1){for(let ie=0;ie<h;ie++){let ae=ie+Q*T[2]+q*T[1]+C*T[0];y.values[ae]=u}continue}let re=Math.round(ee),Z=Math.round(K);for(let ie=0;ie<h;ie++){let ae=ie+re*w[2]+Z*w[1]+M*w[0],le=ie+Q*T[2]+q*T[1]+C*T[0];y.values[le]=v[ae]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var hq={kernelName:Pl,backendName:"cpu",kernelFunc:dq};function mq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;xe(r,"cumprod");let l=_.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=Vn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=_.getInnerMostAxes(1,r.shape.length)[0];if(p!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${p}`);let d=ma(u.dtype,"int32"),c=k.makeOnesTypedArray(k.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)c[x]=i?1:h[x];else{let v=f(y,b-1);c[x]=i?h[v]*c[v]:h[x]*c[v]}}let g=n.makeTensorInfo(u.shape,d,c);if(l!=null){let y=_.getUndoAxesPermutation(l),b=Vn({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var fq={kernelName:Ml,backendName:"cpu",kernelFunc:mq};function gq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;xe(r,"cumsum");let l=_.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=Vn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=_.getInnerMostAxes(1,r.shape.length)[0];if(p!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${p}`);let d=ma(u.dtype,"int32"),c=k.makeZerosTypedArray(k.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)c[x]=i?0:h[x];else{let v=f(y,b-1);c[x]=i?h[v]+c[v]:h[x]+c[v]}}let g=n.makeTensorInfo(u.shape,d,c);if(l!=null){let y=_.getUndoAxesPermutation(l),b=Vn({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var yq={kernelName:Ei,backendName:"cpu",kernelFunc:gq};function bq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,p=s0(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=rT(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var xq={kernelName:um,backendName:"cpu",kernelFunc:bq};function vq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let y=0;y<o;++y)for(let b=0;b<d;++b){let x=Math.floor(b/s),v=b%s;for(let w=0;w<c;++w){let T=Math.floor(w/s),C=w%s,E=(v*s+C)*h;for(let $=0;$<h;++$){let P=$+E+p*(T+u*(x+l*y));f[g++]=m[P]}}}return n.makeTensorInfo([o,d,c,h],r.dtype,f)}var wq={kernelName:Ol,backendName:"cpu",kernelFunc:vq};function XT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a;xe([r,s],"depthwiseConv2DNative");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),c=l;c==null&&(c=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,v=b.top,w=h.outChannels/h.inChannels,T=new jt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,E=n.data.get(s.dataId).values,$=T.values;for(let P=0;P<h.batchSize;++P){let F=P*p[0],S=P*T.strides[0];for(let M=0;M<h.outHeight;++M){let V=S+M*T.strides[1],j=M*h.strideHeight-v;for(let q=0;q<m;++q){let K=j+q*g;if(K<0||K>=h.inHeight)continue;let Q=q*d[0],ee=F+K*p[1];for(let re=0;re<h.outWidth;++re){let Z=V+re*T.strides[2],ie=re*h.strideWidth-x;for(let ae=0;ae<f;++ae){let le=ie+ae*y;if(le<0||le>=h.inWidth)continue;let ue=Q+ae*d[1],ke=ee+le*h.inChannels,ye=Z,Ie=ue;for(let Ee=0;Ee<h.inChannels;++Ee){let Fe=C[ke+Ee];for(let Be=0;Be<w;++Be)$[ye+Be]+=Fe*E[Ie+Be];ye+=w,Ie+=w}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var kq={kernelName:Ai,backendName:"cpu",kernelFunc:XT};function Iq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a;xe([r,s],"depthwiseConv2dNativeBackpropFilter");let d=_.computeConv2DInfo(r.shape,p,i,o,l,u,!0),{strideHeight:c,strideWidth:h,filterHeight:m,filterWidth:f}=d,g=new jt(d.filterShape,"float32"),y=d.padInfo.left,b=d.padInfo.top,x=d.outChannels/d.inChannels,v=n.data.get(r.dataId).values,w=new jt(r.shape,r.dtype,v),T=n.data.get(s.dataId).values,C=new jt(s.shape,s.dtype,T);for(let E=0;E<m;++E){let $=Math.max(0,Math.ceil((b-E)/c)),P=Math.min(d.outHeight,(d.inHeight+b-E)/c);for(let F=0;F<f;++F){let S=Math.max(0,Math.ceil((y-F)/h)),M=Math.min(d.outWidth,(d.inWidth+y-F)/h);for(let V=0;V<d.outChannels;++V){let j=Math.trunc(V/x),q=V%x,K=0;for(let Q=0;Q<d.batchSize;++Q)for(let ee=$;ee<P;++ee){let re=E+ee*c-b;for(let Z=S;Z<M;++Z){let ie=F+Z*h-y;K+=w.get(Q,re,ie,j)*C.get(Q,ee,Z,V)}}g.set(K,E,F,j,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Sq={kernelName:pm,backendName:"cpu",kernelFunc:Iq};function Nq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a;xe([r,s],"depthwiseConv2DNativeBackpropInput");let d=k.computeStrides(r.shape),c=k.computeStrides(s.shape),h=_.computeConv2DInfo(p,s.shape,i,o,l,u,!0),m=new jt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,x=n.data.get(r.dataId).values,[v,w,T]=d,C=n.data.get(s.dataId).values,[E,$,P]=c,{batchSize:F,filterHeight:S,filterWidth:M,inChannels:V,inHeight:j,inWidth:q,outChannels:K,outHeight:Q,outWidth:ee,strideHeight:re,strideWidth:Z}=h,ie=S-1-h.padInfo.top,ae=M-1-h.padInfo.left,le=K/V;for(let ue=0;ue<F;++ue)for(let ke=0;ke<V;++ke)for(let ye=0;ye<j;++ye){let Ie=ye-ie,Ee=Math.max(0,Math.ceil(Ie/re)),Fe=Math.min(Q,(S+Ie)/re);for(let Be=0;Be<q;++Be){let je=Be-ae,st=Math.max(0,Math.ceil(je/Z)),et=Math.min(ee,(M+je)/Z),tt=0;for(let Te=Ee;Te<Fe;++Te){let gt=Te*re-Ie;for(let ct=st;ct<et;++ct){let yn=ct*Z-je,Yt=v*ue+w*Te+T*ct,Dn=E*(S-1-gt)+$*(M-1-yn)+P*ke;for(let Ut=0;Ut<le;++Ut){let Jt=ke*le+Ut,Da=x[Yt+Jt],Rn=C[Dn+Ut];tt+=Da*Rn}}}f[g*ue+y*ye+b*Be+ke]=tt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var Tq={kernelName:cm,backendName:"cpu",kernelFunc:Nq};function Cq(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=He([r,r],a.dtype),o=i.values;for(let u=0;u<s.length;u++)o[u*r+u]=s[u];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var _q={kernelName:dm,backendName:"cpu",kernelFunc:Cq},Eq={kernelName:dc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:v,strideWidth:w,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:$,outShape:P}=_.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),F=k.sizeFromShape(P),S=P.length,M=k.getArrayFromDType(a.dtype,F);for(let V=0;V<h;++V)for(let j=0;j<y;++j){let q=j*v-x.top;for(let K=0;K<b;++K){let Q=K*w-x.left;for(let ee=0;ee<g;++ee){let re=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<T;++ie){let ae=q+ie*E;if(ae>=0&&ae<m)for(let le=0;le<C;++le){let ue=Q+le*$;if(ue>=0&&ue<f){let ke=k.locToIndex([V,ae,ue,ee],p,k.computeStrides(a.shape)),ye=k.locToIndex([ie,le,ee],c,k.computeStrides(r.shape)),Ie=u[ke]+d[ye];Ie>re&&(re=Ie)}}}let Z=k.locToIndex([V,j,K,ee],S,k.computeStrides(P));M[Z]=re}}}return{dataId:l.write(k.toTypedArray(M,a.dtype),P,a.dtype),shape:P,dtype:a.dtype}}},Aq={kernelName:Nh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=k.toNestedArray(a.shape,u.data.get(a.dataId).values),d=k.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Nh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let P=k.toNestedArray($,u.data.get(s.dataId).values),F=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S<c;++S)for(let M=0;M<g;++M){let V=M*x-b.top;for(let j=0;j<y;++j){let q=j*v-b.left;for(let K=0;K<f;++K){let Q=Number.MIN_SAFE_INTEGER,ee=0,re=0;for(let Z=0;Z<w;++Z){let ie=V+Z*C;if(ie>=0&&ie<h)for(let ae=0;ae<T;++ae){let le=q+ae*E;if(le>=0&&le<m){let ue=p[S][ie][le][K]+d[Z][ae][K];ue>Q&&(Q=ue,ee=Z,re=ae)}}}F[ee][re][K]+=P[S][M][j][K]}}}return{dataId:u.write(k.toTypedArray(F,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},$q={kernelName:Sh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=k.toNestedArray(a.shape,u.data.get(a.dataId).values),d=k.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Sh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let P=k.toNestedArray($,u.data.get(s.dataId).values),F=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S<c;++S)for(let M=0;M<g;++M){let V=M*x-b.top;for(let j=0;j<y;++j){let q=j*v-b.left;for(let K=0;K<f;++K){let Q=Number.MIN_SAFE_INTEGER,ee=V<0?0:V,re=q<0?0:q;for(let Z=0;Z<w;++Z){let ie=V+Z*C;if(ie>=0&&ie<h)for(let ae=0;ae<T;++ae){let le=q+ae*E;if(le>=0&&le<m){let ue=p[S][ie][le][K]+d[Z][ae][K];ue>Q&&(Q=ue,ee=ie,re=le)}}}F[S][ee][re][K]+=P[S][M][j][K]}}}return{dataId:u.write(k.toTypedArray(F,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function Qc(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"sum");let o;r.dtype==="bool"?o=us({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=cr({inputs:{x:r},backend:n});let l=o.shape.length,u=k.parseAxisParam(s,o.shape),p=_.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Vn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=_.getInnerMostAxes(d.length,l)),_.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=_.computeOutAndReduceShapes(c.shape,d),f=_.upcastType(c.dtype,"int32"),g=Hh(n,h,f),y=k.sizeFromShape(m),b=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let v=0;v<b.length;++v){let w=v*y,T=0;for(let C=0;C<y;++C)T+=x[w+C];b[v]=T}if(i){let v=_.expandShapeToKeepDim(g.shape,u),w=g;g=Tt({inputs:{x:g},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),p!=null&&n.disposeIntermediateTensorInfo(c),g}var Fq={kernelName:uo,backendName:"cpu",kernelFunc:Qc};function Dq(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=_.decodeEinsumEquation(r,s.length);_.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=_.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f<d;++f){for(let g of p[f]){let{permutationIndices:y,expandDims:b}=_.getEinsumPermutation(h,l[g]),x;_.isIdentityPermutation(y)?x=s[g]:(x=Vn({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let w=0;w<b.length;++w)v.splice(b[w],0,1);k.arraysEqual(x.shape,v)||(x=Tt({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=Of({inputs:{a:x,b:c},backend:n}),m.push(c))}f<d-1&&(u[f]>=0&&(c=Qc({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Rq={kernelName:hm,backendName:"cpu",kernelFunc:Dq};function Mq(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;xe([a,r],"eluGrad");let s=new Float32Array(k.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l<i.length;++l){let u=i[l];u>=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var Pq={kernelName:mm,backendName:"cpu",kernelFunc:Mq},Oq=_.ERF_P,Lq=_.ERF_A1,zq=_.ERF_A2,Bq=_.ERF_A3,Wq=_.ERF_A4,Vq=_.ERF_A5,Uq=ot(Ll,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+Oq*n);return t*(1-((((Vq*a+Wq)*a+Bq)*a+zq)*a+Lq)*a*Math.exp(-n*n))}),Gq={kernelName:Ll,backendName:"cpu",kernelFunc:Uq};function qh(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Tt({inputs:{x:r},backend:n,attrs:{shape:o}})}var Hq={kernelName:Bl,backendName:"cpu",kernelFunc:qh},jq=Vt((e,t)=>e/t),m0=rn($i,jq),nx={kernelName:$i,backendName:"cpu",kernelFunc:m0};function YT(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=k.sizeFromShape(u),d=k.getTypedArrayFromDType("float32",p),c=k.getTypedArrayFromDType("float32",p);for(let g=0;g<r;g++){let y=hi({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,s]}}),b=hi({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,s]}}),x=Yn({inputs:{real:y,imag:b},backend:n}),{real:v,imag:w}=qq(x,t,n),T=_.mergeRealAndImagArrays(v,w);for(let C=0;C<s;C++){let E=_.getComplexWithIndex(T,C);d[g*s+C]=E.real,c[g*s+C]=E.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(x)}let h=n.makeTensorInfo(u,"float32",d),m=n.makeTensorInfo(u,"float32",c),f=Yn({inputs:{real:h,imag:m},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}function qq(e,t,n){let a=k.sizeFromShape(e.shape),r=n.data.get(e.dataId),s=n.data.get(r.complexTensorInfos.real.dataId).values,i=n.data.get(r.complexTensorInfos.imag.dataId).values;if(Kq(a)){let o=ax(s,i,a,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",o.real),p=n.makeTensorInfo(l,"float32",o.imag),d=n.makeTensorInfo([],"float32",k.createScalarValue(a,"float32")),c=cr({inputs:{x:d},backend:n}),h=nx.kernelFunc({inputs:{a:u,b:d},backend:n}),m=nx.kernelFunc({inputs:{a:p,b:c},backend:n}),f=n.data.get(h.dataId).values,g=n.data.get(m.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),{real:f,imag:g}}return o}else{let o=_.mergeRealAndImagArrays(s,i),l=Xq(o,a,t);return _.splitRealAndImagArrays(l)}}function Kq(e){return(e&e-1)===0}function ax(e,t,n,a,r){if(n===1)return{real:e,imag:t};let s=_.mergeRealAndImagArrays(e,t),i=n/2,o=_.complexWithEvenIndex(s),l=o.real,u=o.imag,p=[l.length],d=r.makeTensorInfo(p,"float32",l),c=r.makeTensorInfo(p,"float32",u),h=Yn({inputs:{real:d,imag:c},backend:r}),m=_.complexWithOddIndex(s),f=m.real,g=m.imag,y=[f.length],b=r.makeTensorInfo(y,"float32",f),x=r.makeTensorInfo(y,"float32",g),v=Yn({inputs:{real:b,imag:x},backend:r}),w=ax(l,u,i,a,r),T=w.real,C=w.imag,E=[T.length],$=r.makeTensorInfo(E,"float32",T),P=r.makeTensorInfo(E,"float32",C),F=Yn({inputs:{real:$,imag:P},backend:r}),S=ax(f,g,i,a,r),M=S.real,V=S.imag,j=[M.length],q=r.makeTensorInfo(j,"float32",M),K=r.makeTensorInfo(j,"float32",V),Q=Yn({inputs:{real:q,imag:K},backend:r}),ee=_.exponents(n,a),re=[ee.real.length],Z=r.makeTensorInfo(re,"float32",ee.real),ie=r.makeTensorInfo(re,"float32",ee.imag),ae=Yn({inputs:{real:Z,imag:ie},backend:r}),le=Of({inputs:{a:ae,b:Q},backend:r}),ue=Zc({inputs:{a:F,b:le},backend:r}),ke=c0({inputs:{a:F,b:le},backend:r}),ye=di({inputs:{input:ue},backend:r}),Ie=di({inputs:{input:ke},backend:r}),Ee=xl({inputs:{input:ue},backend:r}),Fe=xl({inputs:{input:ke},backend:r}),Be=vl({inputs:[ye,Ie],backend:r,attrs:{axis:0}}),je=vl({inputs:[Ee,Fe],backend:r,attrs:{axis:0}}),st=r.data.get(Be.dataId).values,et=r.data.get(je.dataId).values;return r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo($),r.disposeIntermediateTensorInfo(P),r.disposeIntermediateTensorInfo(F),r.disposeIntermediateTensorInfo(q),r.disposeIntermediateTensorInfo(K),r.disposeIntermediateTensorInfo(Q),r.disposeIntermediateTensorInfo(Z),r.disposeIntermediateTensorInfo(ie),r.disposeIntermediateTensorInfo(ae),r.disposeIntermediateTensorInfo(le),r.disposeIntermediateTensorInfo(ue),r.disposeIntermediateTensorInfo(ke),r.disposeIntermediateTensorInfo(ye),r.disposeIntermediateTensorInfo(Ee),r.disposeIntermediateTensorInfo(Ie),r.disposeIntermediateTensorInfo(Fe),r.disposeIntermediateTensorInfo(Be),r.disposeIntermediateTensorInfo(je),{real:st,imag:et}}function Xq(e,t,n){let a=new Float32Array(t*2);for(let r=0;r<t;r++){let s=0,i=0;for(let o=0;o<t;o++){let l=_.exponent(r*o,t,n),u=_.getComplexWithIndex(e,o);s+=u.real*l.real-u.imag*l.imag,i+=u.real*l.imag+u.imag*l.real}n&&(s/=t,i/=t),_.assignToTypedArray(a,s,i,r)}return a}function Yq(e){let{inputs:t,backend:n}=e,{input:a}=t,r=k.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=Tt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=YT(o,!1,n),u=Tt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var Jq={kernelName:fm,backendName:"cpu",kernelFunc:Yq};function f0(e){let{backend:t,attrs:n}=e,{shape:a,value:r,dtype:s}=n,i=s||k.inferDtype(r),o=k.getArrayFromDType(i,k.sizeFromShape(a));return Qq(o,r,i),t.makeTensorInfo(a,i,o)}var Zq={kernelName:hc,backendName:"cpu",kernelFunc:f0};function Qq(e,t,n){e.fill(t)}var e8={kernelName:Vl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,r=n,s=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d<i;d++){let c=d*l*o*u;for(let h=0;h<o;h++){let m=h*(l*u);for(let f=0;f<l;f++){let g=f*u;for(let y=0;y<u;y++){let b=Math.round(l-f-1),x=c+m+g+y,v=p[x];if(b>=0&&b<l){let w=b*u,T=c+m+w+y;v=p[T]}s[x]=v}}}}return{dataId:r.write(s,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},t8=Vt((e,t)=>Math.floor(e/t)),n8=rn(Mi,t8,null,"int32"),a8={kernelName:Mi,backendName:"cpu",kernelFunc:n8};function r8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=KT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=Zc({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=d0(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var s8={kernelName:ni,backendName:"cpu",kernelFunc:r8};function i8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=XT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=Zc({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=d0(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var o8={kernelName:ai,backendName:"cpu",kernelFunc:i8};function l8(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=k.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,u,p,d]=_.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let c=n.data.get(r.dataId).values,h=n.bufferSync(a),m=dT(c,h,a.dtype,u,o,p,d,a.shape,s);return n.makeTensorInfo(l,a.dtype,m.values)}var u8={kernelName:Gl,backendName:"cpu",kernelFunc:l8};function p8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;xe([r,s],"gatherV2");let l=k.parseAxisParam(i,r.shape)[0],u=n.data.get(s.dataId).values,p=r.shape[l];for(let v=0;v<u.length;++v){let w=u[v];k.assert(w<=p-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=k.sizeFromShape(s.shape),h=_.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=Tt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=Tt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],y=n.bufferSync(f),b=n.bufferSync(m),x=hT(b,y,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var c8={kernelName:Ul,backendName:"cpu",kernelFunc:p8};function d8(e){let{inputs:t,backend:n}=e,{input:a}=t,r=k.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=Tt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=YT(o,!0,n),u=Tt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var h8={kernelName:gm,backendName:"cpu",kernelFunc:d8},m8=ot(jl,e=>Number.isFinite(e)?1:0,"bool"),f8={kernelName:jl,backendName:"cpu",kernelFunc:m8},g8=ot(ql,e=>Math.abs(e)===1/0?1:0,"bool"),y8={kernelName:ql,backendName:"cpu",kernelFunc:g8},b8=ot(Kl,e=>Number.isNaN(e)?1:0,"bool"),x8={kernelName:Kl,backendName:"cpu",kernelFunc:b8};function v8(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=bT(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var w8={kernelName:bm,backendName:"cpu",kernelFunc:v8},k8=ot(Jl,e=>Math.log1p(e)),I8={kernelName:Jl,backendName:"cpu",kernelFunc:k8},S8=Vt((e,t)=>e&&t),N8=rn(Zl,S8,null,"bool"),T8={kernelName:Zl,backendName:"cpu",kernelFunc:N8},C8=ot(mc,e=>e?0:1,"bool"),_8={kernelName:mc,backendName:"cpu",kernelFunc:C8},E8=Vt((e,t)=>e||t),A8=rn(fc,E8,null,"bool"),$8={kernelName:fc,backendName:"cpu",kernelFunc:A8};function F8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;xe(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=k.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),b=f-g+Math.min(g+s,p),x=0;for(;y<=b;y++){let v=d[y];x+=v*v}return x}for(let f=0;f<c;f++){let g=m(f),y=d[f]*Math.pow(i+o*g,-l);h[f]=y}return n.makeTensorInfo(r.shape,r.dtype,h)}var D8={kernelName:gc,backendName:"cpu",kernelFunc:F8};function R8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;xe(i,"LRNGrad");let d=k.sizeFromShape(i.shape),c=i.shape[3],h=n.data.get(i.dataId).values,m=n.data.get(r.dataId).values,f=n.data.get(s.dataId).values,g=new Float32Array(d),y=d;for(let b=0;b<y;b++){let x=b%c,v=b-x+Math.max(0,x-o),w=b-x+Math.min(c,x+o+1),T=0;for(let C=v;C<w;C++)T+=Math.pow(m[C],2);T=u*T+l;for(let C=v;C<w;C++){let E=-2*u*p*m[C]*f[b]/T;b===C&&(E+=Math.pow(T,-p)),E*=h[b],g[C]+=E}}return n.makeTensorInfo(i.shape,r.dtype,g)}var M8={kernelName:xm,backendName:"cpu",kernelFunc:R8};function JT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=n,l=r.shape,u=l.length,p=k.parseAxisParam(s,l),d=p,c=_.getAxesPermutation(d,u),h=o.data.get(r.dataId).values;if(c!=null){let v=new Array(u);for(let w=0;w<v.length;w++)v[w]=l[c[w]];h=l0(h,l,r.dtype,c,v),d=_.getInnerMostAxes(d.length,u),l=v}xe(r,"max"),_.assertAxesAreInnerMostDims("max",d,u);let[m,f]=_.computeOutAndReduceShapes(l,d),g=k.sizeFromShape(f),y=vT(h,g,m,r.dtype),b=o.write(y,m,r.dtype),x=m;return i&&(x=_.expandShapeToKeepDim(m,p)),{dataId:b,shape:x,dtype:r.dtype}}var P8={kernelName:Wi,backendName:"cpu",kernelFunc:JT};function O8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;xe(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))d=cr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=h0(c,r.shape,r.dtype,h,p,"max");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var L8={kernelName:Ui,backendName:"cpu",kernelFunc:O8};function z8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;xe(r,"maxPool3d");let p=_.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=qT(d,r.shape,r.dtype,k.computeStrides(r.shape),p,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var B8={kernelName:yc,backendName:"cpu",kernelFunc:z8};function W8(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;xe([r,s],"maxPool3DGrad");let p=_.computePool3DInfo(s.shape,i,o,1,l,u),d=n.bufferSync(s),c=_5(d,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,y=p.dilationHeight,b=p.dilationWidth,x=p.effectiveFilterDepth,v=p.effectiveFilterHeight,w=p.effectiveFilterWidth,T=x-1-p.padInfo.front,C=w-1-p.padInfo.left,E=v-1-p.padInfo.top,$=He(s.shape,"float32"),P=n.bufferSync(r);for(let F=0;F<p.batchSize;++F)for(let S=0;S<p.inChannels;++S)for(let M=0;M<p.inDepth;++M)for(let V=0;V<p.inHeight;++V)for(let j=0;j<p.inWidth;++j){let q=M-T,K=V-E,Q=j-C,ee=0;for(let re=0;re<x;re+=g){let Z=(q+re)/h;if(!(Z<0||Z>=p.outDepth||Math.floor(Z)!==Z))for(let ie=0;ie<v;ie+=y){let ae=(K+ie)/m;if(!(ae<0||ae>=p.outHeight||Math.floor(ae)!==ae))for(let le=0;le<w;le+=b){let ue=(Q+le)/f;if(ue<0||ue>=p.outWidth||Math.floor(ue)!==ue)continue;let ke=x*v*w-1-c.get(F,Z,ae,ue,S),ye=re*v*w+ie*w+le,Ie=ke===ye?1:0;Ie!==0&&(ee+=P.get(F,Z,ae,ue,S)*Ie)}}}$.set(ee,F,M,V,j,S)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var V8={kernelName:wm,backendName:"cpu",kernelFunc:W8};function U8(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;xe([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=_.computePool2DInfo(o.shape,l,u,1,p,d),h=n.data.get(o.dataId).values,m=He(c.outShape,o.dtype,jT(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,y=c.dilationHeight,b=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,w=v-1-c.padInfo.left,T=x-1-c.padInfo.top,C=He(o.shape,"float32"),E=n.data.get(r.dataId).values,$=He(r.shape,"float32",E);for(let P=0;P<c.batchSize;++P)for(let F=0;F<c.inChannels;++F)for(let S=0;S<c.inHeight;++S)for(let M=0;M<c.inWidth;++M){let V=S-T,j=M-w,q=0;for(let K=0;K<x;K+=y){let Q=(V+K)/f;if(!(Q<0||Q>=c.outHeight||Math.floor(Q)!==Q))for(let ee=0;ee<v;ee+=b){let re=(j+ee)/g;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;let Z=x*v-1-m.get(P,Q,re,F),ie=K*v+ee,ae=Z===ie?1:0;ae!==0&&(q+=$.get(P,Q,re,F)*ae)}}C.set(q,P,S,M,F)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var G8={kernelName:vm,backendName:"cpu",kernelFunc:U8};function H8(e,t,n,a,r){let s=k.computeStrides(t),i=h0(e,t,n,s,r,"max"),o=jT(e,t,n,r,!0,a);return[i.values,o.values]}var j8={kernelName:km,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;xe(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,p=_.computePool2DInfo(a.shape,r,s,[1,1],i),[d,c]=H8(u,a.shape,a.dtype,o,p),h=l.write(d,p.outShape,a.dtype),m=l.write(c,p.outShape,a.dtype);return[{dataId:h,shape:p.outShape,dtype:a.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function q8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),l=_.computeOutAndReduceShapes(r.shape,o)[1],u=k.sizeFromShape(l),p=[],d=n.makeTensorInfo([],"float32",new Float32Array([u]));p.push(d);let c=us({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});p.push(c);let h=m0({inputs:{a:c,b:d},backend:n});p.push(h);let m=Qc({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var K8={kernelName:Gi,backendName:"cpu",kernelFunc:q8};function X8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"min");let o=k.parseAxisParam(s,r.shape),l=o,u=_.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",l,p.shape.length);let[d,c]=_.computeOutAndReduceShapes(p.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,x=f[b];for(let v=0;v<h;++v){let w=f[b+v];(Number.isNaN(w)||w<x)&&(x=w)}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(p);let g=n.makeTensorInfo(d,p.dtype,m);if(i){let y=_.expandShapeToKeepDim(d,o),b=Tt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var Y8={kernelName:Hi,backendName:"cpu",kernelFunc:X8};function J8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,mode:i}=a;xe(r,"mirrorPad");let o=s.map((b,x)=>b[0]+r.shape[x]+b[1]),l=s.map(b=>b[0]),u=s.map((b,x)=>b[0]+r.shape[x]),p=i==="reflect"?0:1,d=n.data.get(r.dataId).values,c=r.shape.length,h=k.computeStrides(r.shape),m=k.sizeFromShape(o),f=o.length,g=k.computeStrides(o),y=k.getTypedArrayFromDType(r.dtype,m);for(let b=0;b<m;b++){let x=k.indexToLoc(b,f,g);for(let w=0;w<f;w++)x[w]<l[w]?x[w]=l[w]*2-x[w]-p:x[w]>=u[w]&&(x[w]=(u[w]-1)*2-x[w]+p);x=x.map((w,T)=>w-l[T]);let v=k.locToIndex(x,c,h);y[b]=d[v]}return{dataId:n.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var Z8={kernelName:qi,backendName:"cpu",kernelFunc:J8},Q8=Vt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),eK=rn(Ql,Q8),tK={kernelName:Ql,backendName:"cpu",kernelFunc:eK},nK=yi(qk());function ZT(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=k.parseAxisParam([o],r.shape),u=JT({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),p=_.expandShapeToKeepDim(u.shape,l),d=Tt({inputs:{x:u},backend:n,attrs:{shape:p}}),c=c0({inputs:{a:r,b:d},backend:n}),h=uT({inputs:{x:c},backend:n}),m=Qc({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=Tt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=m0({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var aK={kernelName:po,backendName:"cpu",kernelFunc:ZT};function rK(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;xe(r,"multinomial");let l=o?r:ZT({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],d=n.data.get(l.dataId).values,c=[u,s],h=k.makeZerosTypedArray(k.sizeFromShape(c),"int32");for(let m=0;m<u;++m){let f=m*p,g=new Float32Array(p-1);g[0]=d[f];for(let x=1;x<g.length;++x)g[x]=g[x-1]+d[f+x];let y=nK.alea(i.toString()),b=m*s;for(let x=0;x<s;++x){let v=y();h[b+x]=g.length;for(let w=0;w<g.length;w++)if(v<g[w]){h[b+x]=w;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(c,"int32",h)}var sK={kernelName:Im,backendName:"cpu",kernelFunc:rK},iK=mr.nonMaxSuppressionV3Impl;function oK(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a;xe(r,"NonMaxSuppression");let u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:d}=iK(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var lK={kernelName:nu,backendName:"cpu",kernelFunc:oK},uK=mr.nonMaxSuppressionV4Impl;function pK(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a;xe(r,"NonMaxSuppressionPadded");let p=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,{selectedIndices:c,validOutputs:h}=uK(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var cK={kernelName:au,backendName:"cpu",kernelFunc:pK},dK=mr.nonMaxSuppressionV5Impl;function hK(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a;xe(r,"NonMaxSuppressionWithScore");let p=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=dK(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var mK={kernelName:ru,backendName:"cpu",kernelFunc:hK};function fK(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;xe(r,"oneHot");let l=k.sizeFromShape(r.shape),u=new Float32Array(l*s);u.fill(o);let p=n.data.get(r.dataId).values;for(let d=0;d<l;++d)p[d]>=0&&p[d]<s&&(u[d*s+p[d]]=i);return n.makeTensorInfo([...r.shape,s],"int32",u)}var gK={kernelName:Xi,backendName:"cpu",kernelFunc:fK};function Kh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(a.dtype==="complex64"){let r=di({inputs:{input:a},backend:n}),s=Kh({inputs:{x:r},backend:n}),i=xl({inputs:{input:a},backend:n}),o=Kh({inputs:{x:i},backend:n}),l=Yn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return f0({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var yK={kernelName:Iu,backendName:"cpu",kernelFunc:Kh};function QT(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(a.dtype==="complex64"){let r=di({inputs:{input:a},backend:n}),s=QT({inputs:{x:r},backend:n}),i=xl({inputs:{input:a},backend:n}),o=Kh({inputs:{x:i},backend:n}),l=Yn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return f0({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var bK={kernelName:su,backendName:"cpu",kernelFunc:QT};function eC(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return qh({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{k.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=qh({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=vl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var xK={kernelName:iu,backendName:"cpu",kernelFunc:eC};function vK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;xe(r,"pad");let o=s.map((y,b)=>y[0]+r.shape[b]+y[1]),l=s.map(y=>y[0]),u=n.data.get(r.dataId).values,p=k.sizeFromShape(r.shape),d=r.shape.length,c=k.computeStrides(r.shape),h=k.sizeFromShape(o),m=o.length,f=k.computeStrides(o),g=k.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let y=0;y<p;y++){let b=k.indexToLoc(y,d,c).map((v,w)=>v+l[w]),x=k.locToIndex(b,m,f);g[x]=u[y]}return{dataId:n.write(g,o,r.dtype),shape:o,dtype:r.dtype}}var tC={kernelName:Yi,backendName:"cpu",kernelFunc:vK},wK=Vt((e,t)=>Math.pow(e,t)),kK=rn(Ji,wK),IK={kernelName:Ji,backendName:"cpu",kernelFunc:kK};function SK(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=u0(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var NK={kernelName:bc,backendName:"cpu",kernelFunc:SK},TK=ot(ou,e=>1/e),CK={kernelName:ou,backendName:"cpu",kernelFunc:TK};function _K(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;xe(r,"resizeBilinear");let l=k.computeStrides(r.shape),[u,p]=o,[d,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(k.sizeFromShape([d,u,p,m])),y=[s&&u>1?c-1:c,s&&p>1?h-1:h],b=[s&&u>1?u-1:u,s&&p>1?p-1:p],x=0,v=y[0]/b[0],w=y[1]/b[1];for(let T=0;T<d;T++)for(let C=0;C<u;C++){let E;i?E=v*(C+.5)-.5:E=v*C;let $=Math.max(0,Math.floor(E)),P=E-$,F=Math.min(c-1,Math.ceil(E)),S=T*l[0]+$*l[1],M=T*l[0]+F*l[1];for(let V=0;V<p;V++){let j;i?j=w*(V+.5)-.5:j=w*V;let q=Math.max(0,Math.floor(j)),K=j-q,Q=Math.min(h-1,Math.ceil(j)),ee=S+q*l[2],re=M+q*l[2],Z=S+Q*l[2],ie=M+Q*l[2];for(let ae=0;ae<m;ae++){let le=f[ee+ae],ue=f[re+ae],ke=f[Z+ae],ye=f[ie+ae],Ie=le+(ke-le)*K,Ee=ue+(ye-ue)*K,Fe=Ie+(Ee-Ie)*P;g[x++]=Fe}}}return n.makeTensorInfo([d,u,p,m],"float32",g)}var EK={kernelName:to,backendName:"cpu",kernelFunc:_K};function AK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;xe([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,u,p,d]=r.shape,[,c,h]=s.shape,m=new Float32Array(l*u*p*d),f=[i&&c>1?u-1:u,i&&h>1?p-1:p],g=[i&&c>1?c-1:c,i&&h>1?h-1:h],y=f[0]/g[0],b=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let w=0;w<l;w++){let T=w*o[0];for(let C=0;C<c;C++){let E=C*y,$=Math.floor(E),P=Math.min(Math.ceil(E),u-1),F=T+$*o[1],S=T+P*o[1],M=E-$,V=1-M;for(let j=0;j<h;j++){let q=j*b,K=Math.floor(q),Q=Math.min(Math.ceil(q),p-1),ee=q-K,re=1-ee,Z=F+K*o[2],ie=F+Q*o[2],ae=S+K*o[2],le=S+Q*o[2],ue=V*re,ke=V*ee,ye=M*re,Ie=M*ee;for(let Ee=0;Ee<d;Ee++){let Fe=x[v++];m[Z+Ee]+=Fe*ue,m[ie+Ee]+=Fe*ke,m[ae+Ee]+=Fe*ye,m[le+Ee]+=Fe*Ie}}}}return n.makeTensorInfo([l,p,u,d],"float32",m)}var $K={kernelName:Tm,backendName:"cpu",kernelFunc:AK};function FK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;xe(r,"resizeNearestNeighbor");let l=k.computeStrides(r.shape),[u,p]=o,[d,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(d*u*p*m),y=[s&&u>1?c-1:c,s&&p>1?h-1:h],b=[s&&u>1?u-1:u,s&&p>1?p-1:p],x=y[0]/b[0],v=y[1]/b[1],w=0;for(let T=0;T<d;T++){let C=T*l[0];for(let E=0;E<u;E++){let $=i?x*(E+.5):x*E,P=Math.min(c-1,s?Math.round($):Math.floor($));i&&(P=Math.max(0,P));let F=C+P*l[1];for(let S=0;S<p;S++){let M=i?v*(S+.5):v*S,V=Math.min(h-1,s?Math.round(M):Math.floor(M));i&&(V=Math.max(0,V));let j=F+V*l[2];for(let q=0;q<m;q++){let K=f[j+q];g[w++]=K}}}}return n.makeTensorInfo([d,u,p,m],r.dtype,g)}var DK={kernelName:xc,backendName:"cpu",kernelFunc:FK};function RK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;xe([s,r],"resizeNearestNeighborGrad");let o=k.computeStrides(r.shape),l=k.computeStrides(s.shape),[u,p,d,c]=r.shape,[,h,m]=s.shape,f=new Float32Array(u*p*d*c),g=n.data.get(s.dataId).values,y=[i&&h>1?p-1:p,i&&m>1?d-1:d],b=[i&&h>1?h-1:h,i&&m>1?m-1:m],x=y[0]/b[0],v=y[1]/b[1],w=1/x,T=1/v,C=Math.ceil(w)*2+2,E=Math.ceil(T)*2+2;for(let $=0;$<u;$++){let P=$*o[0];for(let F=0;F<p;F++){let S=P+F*o[1],M=Math.floor(F*w),V=Math.floor(M-C/2);for(let j=0;j<d;j++){let q=S+j*o[2],K=Math.floor(j*T),Q=Math.floor(K-E/2);for(let ee=0;ee<c;ee++){let re=0;for(let Z=0;Z<C;Z++){let ie=Z+V;if(ie<0||ie>=h)continue;let ae=P+ie*l[1],le=ie*x,ue=Math.min(p-1,i?Math.round(le):Math.floor(le));if(F===ue)for(let ke=0;ke<E;ke++){let ye=ke+Q;if(ye<0||ye>=m)continue;let Ie=ae+ye*l[2],Ee=ye*v,Fe=Math.min(d-1,i?Math.round(Ee):Math.floor(Ee));j===Fe&&(re+=g[Ie+ee])}}f[q+ee]=re}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var MK={kernelName:Nm,backendName:"cpu",kernelFunc:RK};function PK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;xe(r,"reverse");let i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return cr({inputs:{x:r},backend:n});let l=new jt(r.shape,r.dtype),u=n.bufferSync(r);for(let p=0;p<l.size;p++){let d=l.indexToLoc(p),c=d.slice();o.forEach(h=>c[h]=r.shape[h]-1-c[h]),l.set(u.get(...c),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var OK={kernelName:ao,backendName:"cpu",kernelFunc:PK},LK={kernelName:Su,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[u,p,d,c]=a.shape,[h,m]=_.getImageCenter(i,p,d),f=255,g=Math.sin(r),y=Math.cos(r),b=o.data.get(a.dataId).values;for(let x=0;x<u;x++){let v=x*d*p*c;for(let w=0;w<p;w++){let T=w*(d*c);for(let C=0;C<d;C++){let E=C*c;for(let $=0;$<c;$++){let P=[u,w,C,$],F=P[2],S=P[1],M=(F-h)*y-(S-m)*g,V=(F-h)*g+(S-m)*y;M=Math.round(M+h),V=Math.round(V+m);let j=s;if(typeof s!="number"&&($===3?j=f:j=s[$]),M>=0&&M<d&&V>=0&&V<p){let K=V*(d*c),Q=M*c,ee=v+K+Q+$;j=b[ee]}let q=v+T+E+$;l[q]=j}}}}return{dataId:o.write(l,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},zK=ot(ro,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}),BK={kernelName:ro,backendName:"cpu",kernelFunc:zK};function nC(e,t,n,a,r,s,i,o,l,u){let p=[a/r,r],d=e.values,c=t.values;if(a===0)return He(n,t.dtype);let h=He(p,t.dtype);h.values.fill(l);for(let m=0;m<s;m++){let f=[],g=0;for(let y=0;y<i;y++){let b=d[m*i+y];f.push(b),g+=b*o[y]}if(g<0||g>=a/r)throw new Error(`Invalid indices: ${f} does not index into ${n}`);for(let y=0;y<r;y++)u?h.values[g*r+y]+=c[m*r+y]:h.values[g*r+y]=t.rank===0?c[0]:c[m*r+y]}return h}function WK(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=_.calculateShapes(s,r,i),c=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=nC(h,m,i,d,u,l,o,p,0,c);return n.makeTensorInfo(i,f.dtype,f.values)}var VK={kernelName:uu,backendName:"cpu",kernelFunc:WK};function UK(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t;xe([a,r,s],"select");let i=a.shape.length,o=n.data.get(a.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,p=ma(r.dtype,s.dtype),d=k.makeZerosTypedArray(k.sizeFromShape(r.shape),p),c=0,h=i===0||i>1||r.shape.length===1?1:k.sizeFromShape(r.shape.slice(1));for(let m=0;m<o.length;m++)for(let f=0;f<h;f++)o[m]===1?d[c++]=l[m]:d[c++]=u[m];return n.makeTensorInfo(r.shape,p,d)}var GK={kernelName:pu,backendName:"cpu",kernelFunc:UK},HK=_.SELU_SCALEALPHA,jK=_.SELU_SCALE,qK=ot(cu,e=>e>=0?jK*e:HK*(Math.exp(e)-1)),KK={kernelName:cu,backendName:"cpu",kernelFunc:qK},XK=ot(mu,e=>e<0?-1:e>0?1:0),YK={kernelName:mu,backendName:"cpu",kernelFunc:XK},JK=ot(io,e=>Math.sin(e)),ZK={kernelName:io,backendName:"cpu",kernelFunc:JK},QK=ot(hu,e=>Math.sinh(e)),eX={kernelName:hu,backendName:"cpu",kernelFunc:QK},tX=11920928955078125e-23,gk=Math.log(tX)+2,nX=ot(fu,e=>{let t=e>-gk,n=e<gk,a=Math.exp(e),r;return n?r=a:t?r=e:r=Math.log(1+a),r}),aX={kernelName:fu,backendName:"cpu",kernelFunc:nX};function rX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;xe([r],"spaceToBatchND");let o=k.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<r.shape.length;++g)l.push([0,0]);let u=tC.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=_.getReshaped(u.shape,s,o,!1),d=_.getPermuted(p.length,s.length,!1),c=_.getReshapedPermuted(u.shape,s,o,!1),h=Tt({inputs:{x:u},backend:n,attrs:{shape:p}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:d}}),f=Tt({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}var sX={kernelName:gu,backendName:"cpu",kernelFunc:rX};function iX(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let o=n.data.get(a.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,p=n.data.get(i.dataId).values[0],[d,c,h,m,f]=_T(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var oX={kernelName:vc,backendName:"cpu",kernelFunc:iX};function lX(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.data.get(r.dataId).values),o=n.data.get(a.dataId).values,l=Array.from(n.data.get(s.dataId).values),[u,p,d]=ET(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var uX={kernelName:bu,backendName:"cpu",kernelFunc:lX};function pX(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);if(r.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,[u,p]=p0(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var cX={kernelName:wc,backendName:"cpu",kernelFunc:pX};function dX(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);if(r.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,[u,p]=p0(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var hX={kernelName:kc,backendName:"cpu",kernelFunc:dX};function mX(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=_.calculateShapes(s,r,o),h=!1,m=n.bufferSync(r),f=n.bufferSync(s),g=n.data.get(i.dataId).values[0],y=nC(m,f,o,c,p,u,l,d,g,h);return n.makeTensorInfo(o,y.dtype,y.values)}var fX={kernelName:Cm,backendName:"cpu",kernelFunc:mX};function gX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),p=r.shape.slice();return l.map(d=>{let c=[...p];c[o]=d;let h=hi({inputs:{x:r},backend:n,attrs:{begin:u,size:c}});return u[o]+=d,h})}var yX={kernelName:yu,backendName:"cpu",kernelFunc:gX},bX={kernelName:Ic,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;xe(n,"square");let r=a.data.get(n.dataId).values,s=new Float32Array(r.length);for(let i=0;i<r.length;++i){let o=r[i];s[i]=o*o}return{dataId:a.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},xX=ot(fs,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),vX={kernelName:fs,backendName:"cpu",kernelFunc:xX};function wX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a;xe(r,"stridedSlice");let{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:v}=qt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),w;if(f)w=Tt({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){k.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=qt.computeOutShape(b,x,v),C=hi({inputs:{x:r},backend:n,attrs:{begin:b,size:T}});w=Tt({inputs:{x:C},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(C)}else{let T=n.bufferSync(r),C=$T(h,T,v,b);w=n.makeTensorInfo(m,C.dtype,C.values)}return w}var kX={kernelName:xu,backendName:"cpu",kernelFunc:wX};function IX(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.data.get(p.dataId).values,h=n.data.get(d.dataId).values,[m,f]=FT(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var SX={kernelName:_m,backendName:"cpu",kernelFunc:IX};function NX(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values[0],[u,p,d]=DT(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var TX={kernelName:Em,backendName:"cpu",kernelFunc:NX};function CX(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.data.get(s.dataId).values,o=RT(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var _X={kernelName:Am,backendName:"cpu",kernelFunc:CX},EX=ot(mo,e=>Math.tan(e)),AX={kernelName:mo,backendName:"cpu",kernelFunc:EX},$X=ot(fo,e=>Math.tanh(e)),FX={kernelName:fo,backendName:"cpu",kernelFunc:$X};function DX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;xe(r,"tile");let i=PT(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var RX={kernelName:ms,backendName:"cpu",kernelFunc:DX};function MX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;xe(r,"topk");let o=n.data.get(r.dataId).values,[l,u]=LT(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var PX={kernelName:vu,backendName:"cpu",kernelFunc:MX};function OX(e){let{inputs:t,attrs:n,backend:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=k.computeStrides(r.shape),b=y[0],x=y[1],v=y[2],w=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(g));w.fill(l);let T=a.data.get(r.dataId).values,C=a.data.get(s.dataId).values;for(let E=0;E<p;++E){let $=s.shape[0]===1?C:C.subarray(E*8,E*8+8);for(let P=0;P<m;++P)for(let F=0;F<f;++F)for(let S=0;S<h;++S){let M,V=$[6]*F+$[7]*P+1;if(V===0)continue;let j=($[0]*F+$[1]*P+$[2])/V,q=($[3]*F+$[4]*P+$[5])/V,K=yk(j,c,o),Q=yk(q,d,o);switch(i){case"nearest":M=UX(T,d,c,b,x,v,E,Q,K,S,l);break;case"bilinear":M=GX(T,d,c,b,x,v,E,Q,K,S,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let ee=E*b+P*x+F*v+S;w[ee]=M}return a.makeTensorInfo(g,r.dtype,w)}return{dataId:a.write(w,g,r.dtype),shape:r.shape,dtype:r.dtype}}var LX={kernelName:wu,backendName:"cpu",kernelFunc:OX};function yk(e,t,n){switch(n){case"reflect":return zX(e,t);case"wrap":return BX(e,t);case"nearest":return VX(e,t);case"constant":default:return WX(e,t)}}function zX(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let a=2*t;n<a&&(n=a*Math.trunc(-n/a)+n),n=n<-t?n+a:-n-1}else if(n>t-1)if(t<=1)n=0;else{let a=2*t;n-=a*Math.trunc(n/a),n>=t&&(n=a-n-1)}return k.clamp(0,n,t-1)}function BX(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let a=t-1;n+=t*(Math.trunc(-n/a)+1)}else if(n>t-1)if(t<=1)n=0;else{let a=t-1;n-=t*Math.trunc(n/a)}return k.clamp(0,n,t-1)}function WX(e,t){return e}function VX(e,t){return k.clamp(0,e,t-1)}function Pp(e,t,n,a,r,s,i,o,l,u,p){let d=i*a+o*r+l*s+u;return 0<=o&&o<t&&0<=l&&l<n?e[d]:p}function UX(e,t,n,a,r,s,i,o,l,u,p){let d=Math.round(o),c=Math.round(l);return Pp(e,t,n,a,r,s,i,d,c,u,p)}function GX(e,t,n,a,r,s,i,o,l,u,p){let d=Math.floor(o),c=Math.floor(l),h=d+1,m=c+1,f=(m-l)*Pp(e,t,n,a,r,s,i,d,c,u,p)+(l-c)*Pp(e,t,n,a,r,s,i,d,m,u,p),g=(m-l)*Pp(e,t,n,a,r,s,i,h,c,u,p)+(l-c)*Pp(e,t,n,a,r,s,i,h,m,u,p);return(h-o)*f+(o-d)*g}function HX(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;xe(s,"unique");let i=a.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:u}=zT(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var jX={kernelName:$m,backendName:"cpu",kernelFunc:HX};function qX(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape.length,o=r.shape[s],l=new Array(i-1),u=0;for(let h=0;h<i;h++)h!==s&&(l[u++]=r.shape[h]);let p=new Array(i).fill(0),d=r.shape.slice();d[s]=1;let c=new Array(o);for(let h=0;h<c.length;h++){p[s]=h;let m=hi({inputs:{x:r},backend:n,attrs:{begin:p,size:d}});c[h]=Tt({inputs:{x:m},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(m)}return c}var KX={kernelName:ku,backendName:"cpu",kernelFunc:qX};function XX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a;xe(r,"unsortedSegmentSum");let o=r.shape.length,l=s.shape.length,u=[],p=[],d=o-l,c=s;for(let m=0;m<d;++m){let f=qh({inputs:{input:c},backend:n,attrs:{dim:m+1}});c=f,p.push(f)}for(let m=0;m<i;++m){let f=k.createScalarValue(m,"int32"),g=n.makeTensorInfo([],"int32",f),y=oT({inputs:{a:g,b:c},backend:n}),b=us({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=Of({inputs:{a:b,b:r},backend:n}),v=Qc({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});u.push(v),p.push(g),p.push(y),p.push(b),p.push(x),p.push(v)}let h=eC({inputs:u,backend:n,attrs:{axis:0}});return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var YX={kernelName:Sc,backendName:"cpu",kernelFunc:XX},JX=[n5,XH,r5,i5,tj,l5,p5,d5,m5,g5,b5,v5,k5,N5,C5,A5,F5,R5,P5,e5,L5,B5,V5,G5,QH,aj,j5,YH,K5,Y5,J5,Q5,tq,aq,sq,oq,uq,cq,hq,fq,yq,xq,wq,kq,Sq,Tq,_q,Eq,Aq,$q,Rq,qj,Pq,rj,Gq,sj,Hq,oj,Jq,Zq,e8,uj,a8,s8,o8,u8,c8,cj,hj,JH,h8,X5,f8,y8,x8,Kj,fj,yj,w8,xj,I8,T8,_8,$8,D8,M8,P8,wj,L8,B8,V8,G8,j8,K8,Y8,Ij,Z8,tK,sK,Nj,Cj,lK,cK,mK,Ej,gK,bK,xK,tC,IK,Yj,Fj,NK,ZH,nx,CK,Jj,Zj,Qj,EK,$K,DK,MK,OK,LK,BK,Rj,VK,GK,KK,Pj,YK,ZK,eX,Oj,aK,aX,sX,oX,uX,cX,hX,fX,yX,Bj,bX,Vj,vX,kX,SX,TX,_X,jj,Fq,AX,FX,RX,PX,LX,Aj,jX,KX,YX,yK];for(let e of JX)Nc(e);var aC={};Me(aC,{assertNotComplex:()=>Lu,bindCanvasToFramebuffer:()=>l7,bindColorTextureToFramebuffer:()=>fh,bindTextureToProgramUniformSampler:()=>xC,bindTextureUnit:()=>gC,bindVertexBufferToProgramAttribute:()=>rx,callAndCheck:()=>ge,canBeRepresented:()=>sC,createFragmentShader:()=>lC,createFramebuffer:()=>fC,createProgram:()=>uC,createStaticIndexBuffer:()=>dC,createStaticVertexBuffer:()=>cC,createTexture:()=>hC,createVertexShader:()=>oC,getBatchDim:()=>mi,getExtensionOrThrow:()=>Op,getFramebufferErrorMessage:()=>vC,getMaxTexturesInShader:()=>SC,getNumChannels:()=>i7,getProgramUniformLocation:()=>bC,getProgramUniformLocationOrThrow:()=>yC,getRowsCols:()=>fi,getShapeAs3D:()=>gh,getTextureShapeFromLogicalShape:()=>kC,getWebGLDisjointQueryTimerVersion:()=>NC,getWebGLErrorMessage:()=>iC,getWebGLMaxTextureSize:()=>IC,hasExtension:()=>da,isCapableOfRenderingToFloatTexture:()=>TC,isDownloadFloatTextureEnabled:()=>CC,isReshapeFree:()=>rc,isWebGLFenceEnabled:()=>_C,isWebGLVersionEnabled:()=>ix,linkProgram:()=>pC,logShaderSourceAndInfoLog:()=>y0,resetMaxTextureSize:()=>u7,resetMaxTexturesInShader:()=>p7,unbindColorTextureFromFramebuffer:()=>sx,unbindTextureUnit:()=>o7,validateFramebuffer:()=>Lp,validateProgram:()=>mh,validateTextureSize:()=>mC});var qs={},fb={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function rC(e,t){qs[e]=t}function Ya(e,t){if(!(e in qs)||t!=null){let a=QX(e,t);if(a!==null)qs[e]=a;else return console.log("Could not get context for WebGL version",e),null}let n=qs[e];return n==null||n.isContextLost()?(delete qs[e],Ya(e)):(n.disable(n.DEPTH_TEST),n.disable(n.STENCIL_TEST),n.disable(n.BLEND),n.disable(n.DITHER),n.disable(n.POLYGON_OFFSET_FILL),n.disable(n.SAMPLE_COVERAGE),n.enable(n.SCISSOR_TEST),n.enable(n.CULL_FACE),n.cullFace(n.BACK),qs[e])}function ZX(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 QX(e,t){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let n=t==null?ZX(e):t;return n.addEventListener("webglcontextlost",a=>{a.preventDefault(),delete qs[e]},!1),e===1?n.getContext("webgl",fb)||n.getContext("experimental-webgl",fb):n.getContext("webgl2",fb)}var ac;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(ac||(ac={}));var ca;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(ca||(ca={}));var on;(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"})(on||(on={}));function ed(e,t){return[t,e]}function e7(e,t){return e*t}function oh(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Ou(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function t7(e,t){let[n,a]=Ou(e,t);return n*a*4}function g0(e,t){let n=e,a,r,s,i,o,l,u,p,d,c;return X().getNumber("WEBGL_VERSION")===2?(a=n.R32F,r=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,u=4,p=1,d=n.HALF_FLOAT,c=n.FLOAT,l=n.RGBA8):(a=e.RGBA,r=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,u=4,p=4,d=t!=null?t.HALF_FLOAT_OES:null,c=e.FLOAT,l=e.RGBA),{internalFormatFloat:a,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:p,textureTypeHalfFloat:d,textureTypeFloat:c}}function ge(e,t){let n=t();return X().getBool("DEBUG")&&n7(e),n}function n7(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+iC(e,t))}var a7=596e-10,r7=65504;function sC(e){return!!(X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||a7<Math.abs(e)&&Math.abs(e)<r7)}function iC(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 Op(e,t){return $r(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function oC(e,t){let n=$r(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(ge(e,()=>e.shaderSource(n,t)),ge(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 lC(e,t){let n=$r(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(ge(e,()=>e.shaderSource(n,t)),ge(e,()=>e.compileShader(n)),X().get("ENGINE_COMPILE_ONLY"))return n;if(e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw y0(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var s7=/ERROR: [0-9]+:([0-9]+):/g;function y0(e,t){let n=s7.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let a=+n[1],r=e.split(`
`),s=r.length.toString().length+2,i=r.map((d,c)=>k.rightPad((c+1).toString(),s)+d),o=0;for(let d=0;d<i.length;d++)o=Math.max(i[d].length,o);let l=i.slice(0,a-1),u=i.slice(a-1,a),p=i.slice(a);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${k.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(p.join(`
`))}function uC(e){return $r(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function pC(e,t){if(ge(e,()=>e.linkProgram(t)),!X().get("ENGINE_COMPILE_ONLY")&&e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function mh(e,t){if(ge(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function cC(e,t){let n=$r(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ge(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function dC(e,t){let n=$r(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ge(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),ge(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function i7(){return X().getNumber("WEBGL_VERSION")===2?1:4}function hC(e){return $r(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function mC(e,t){let n=X().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${e}x${t}]`;throw new Error("Requested texture size "+a+" is invalid.")}if(e>n||t>n){let a=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+a+" greater than WebGL maximum on this browser / GPU "+r+".")}}function fC(e){return $r(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function rx(e,t,n,a,r,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),ge(e,()=>e.vertexAttribPointer(o,r,e.FLOAT,!1,s,i)),ge(e,()=>e.enableVertexAttribArray(o)),!0)}function gC(e,t,n){wC(e,n),ge(e,()=>e.activeTexture(e.TEXTURE0+n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function o7(e,t){wC(e,t),ge(e,()=>e.activeTexture(e.TEXTURE0+t)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function yC(e,t,n){return $r(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function bC(e,t,n){return e.getUniformLocation(t,n)}function xC(e,t,n,a){ge(e,()=>gC(e,t,a)),ge(e,()=>e.uniform1i(n,a))}function l7(e){ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ge(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),ge(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function fh(e,t,n){ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),ge(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function sx(e,t){ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),ge(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Lp(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+vC(e,t))}function vC(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 $r(e,t,n){let a=ge(e,()=>t());if(a==null)throw new Error(n);return a}function wC(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function mi(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function fi(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 gh(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[mi(e),...fi(e)]),t}function kC(e,t=!1){let n=X().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,s)=>s>=e.length-2?k.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=k.squeezeShape(e).newShape);let a=k.sizeFromShape(e);if(e.length<=1&&a<=n)return[1,a];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=mi(e),s=2,i=2;return e.length&&([s,i]=fi(e)),a=r*(s/2)*(i/2),k.sizeToSquarishShape(a).map(o=>o*2)}return k.sizeToSquarishShape(a)}function lh(e){return e%2===0}function rc(e,t){if(e=e.slice(-2),t=t.slice(-2),k.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],a=t.slice(-1)[0];if(n===a||lh(n)&&lh(a)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&lh(e[0])&&lh(t[0])}var yh,bh;function IC(e){if(yh==null){let t=Ya(e);yh=t.getParameter(t.MAX_TEXTURE_SIZE)}return yh}function u7(){yh=null}function p7(){bh=null}function SC(e){if(bh==null){let t=Ya(e);bh=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,bh)}function NC(e){if(e===0)return 0;let t,n=Ya(e);return da(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:da(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function da(e,t){return e.getExtension(t)!=null}function ix(e){try{if(Ya(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function TC(e){if(e===0)return!1;let t=Ya(e);if(e===1){if(!da(t,"OES_texture_float"))return!1}else if(!da(t,"EXT_color_buffer_float"))return!1;return ox(t)}function CC(e){if(e===0)return!1;let t=Ya(e);if(e===1){if(!da(t,"OES_texture_float")||!da(t,"WEBGL_color_buffer_float"))return!1}else{if(da(t,"EXT_color_buffer_float"))return ox(t);let n="EXT_color_buffer_half_float";if(da(t,n)){let a=t.getExtension(n);return c7(t,a)}return!1}return ox(t)}function ox(e){let t=g0(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function c7(e,t){let n=g0(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function _C(e){return e!==2?!1:Ya(e).fenceSync!=null}function Lu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=X();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>ix(2)?2:ix(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",()=>IC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>SC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:NC(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!_c.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>TC(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",()=>CC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>_C(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",()=>_c.isMobile()?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 _n(){let e,t,n,a,r,s,i,o,l,u;return X().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
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",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
#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:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Io(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Lf(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function d7(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function h7(e,t,n="index"){let a=e.map((s,i)=>i),r=d7(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function b0(e){let t=k.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function x0(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var EC=`
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;
}
`,{getBroadcastDims:AC}=_;function m7(e,t,n){let a=[];if(e.forEach(c=>{let h=k.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=v0(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(`
`),s=e.map(c=>f7(c,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),i=t.texShape,o=_n(),l=b7(o),u,p,d=w7(o);return t.isPacked?(u=g7(t.logicalShape,i,n.enableShapeUniforms),p=v7(o)):(u=y7(t.logicalShape,i,n.enableShapeUniforms),p=x7(o)),n.packedInputs&&(d+=N7),[d,l,p,r,u,s,n.userCode].join(`
`)}function zu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return O7(e,t);case 1:return z7(e,t);case 2:return W7(e,t);case 3:return U7(e,t);case 4:return H7(e,t);case 5:return j7(e);case 6:return q7(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function $C(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return P7(e);case 1:return L7(e,t);case 2:return B7(e,t);case 3:return V7(e,t);default:return G7(e,t)}}function f7(e,t,n=!1,a){let r="";n?r+=$C(e,a):r+=zu(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=K7(e,t):r+=X7(e,t)),r}function g7(e,t,n){switch(e.length){case 0:return FC();case 1:return T7(e,t,n);case 2:return R7(e,t,n);case 3:return _7(e,t,n);default:return A7(e,t,n)}}function y7(e,t,n){switch(e.length){case 0:return FC();case 1:return C7(e,t,n);case 2:return M7(e,t,n);case 3:return E7(e,t,n);case 4:return $7(e,t,n);case 5:return F7(e,t);case 6:return D7(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function b7(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function x7(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function v7(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function w7(e){return`${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);
}
${k7}
${I7}
${S7}
`}var k7=`
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);
}
`,I7=`
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);
}
`,S7=`
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);
}
`,N7=`
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 FC(){return`
int getOutputCoords() {
return 0;
}
`}function T7(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${a[1]}.0);
}
`:a[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${a[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
return 2 * (resTexRC.x * ${a[1]} + resTexRC.y);
}
`}function C7(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 * ${t[1]} + resTexRC.y;
}
`}function _7(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
int index = resTexRC.x * ${a[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function E7(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;
${Lf(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let a=Io(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${a}
return ivec3(r, c, d);
}
`}function A7(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
int b${u} = index / ${i};
index -= b${u} * ${i};
`+o,l=`b${u}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
int index = resTexRC.x * ${a[1]} + resTexRC.y;
${o}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${l});
}
`}function $7(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;
${Lf(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let a=Io(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${a}
return ivec4(r, c, d, d2);
}
`}function F7(e,t){let n=Io(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function D7(e,t){let n=Io(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function R7(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${a[0]}, ${a[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
int index = resTexRC.x * ${a[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function M7(e,t,n){return k.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
return ivec2(r, c);
}
`}function So(e){return`offset${e}`}function P7(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=_n();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function O7(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return`
float ${a}() {
return sampleTexture(${n}, halfCR);
}
`;let i=So(n);if(t)return`
float ${a}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
return sampleTexture(${n}, uv);
}
`;let[o,l]=e.shapeInfo.texShape;return`
float ${a}() {
vec2 uv = uvFromFlat(${o}, ${l}, ${i});
return sampleTexture(${n}, uv);
}
`}function L7(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=_n();if(t)return`
vec4 ${a}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${n}, uv);
}
`;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${a}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${s.texture2D}(${n}, uv);
}
`}function z7(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${a}(int index) {
${Bu(e)}
}
`;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return`
float ${a}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let o=So(n);return i===1?t?`
float ${a}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
return sampleTexture(${n}, uv);
}
`:s===1?t?`
float ${a}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${a}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int index) {
vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
return sampleTexture(${n}, uv);
}
`}function B7(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=_n();if(s!=null&&k.arraysEqual(n,s))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
return ${l.texture2D}(${a}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
return ${l.texture2D}(${a}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${a}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${a}, uv);
}
`;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],p=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col);
return ${l.texture2D}(${a}, uv);
}
`}function W7(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&k.arraysEqual(n,s)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`;let c=s[0],h=s[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${c}.0);
return sampleTexture(${a}, uv);
}
`}let{newShape:i,keptDims:o}=k.squeezeShape(n),l=i;if(l.length<n.length){let c=Wu(e,l),h=["row","col"];return`
${zu(c,t)}
float ${r}(int row, int col) {
return ${r}(${Vu(h,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${Bu(e)}
}
`;let u=s[0],p=s[1],d=So(a);return p===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0]));
return sampleTexture(${a}, 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) / ${u}.0);
return sampleTexture(${a}, uv);
}
`:u===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${a}TexShape[1]), 0.5);
return sampleTexture(${a}, 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) / ${p}.0, 0.5);
return sampleTexture(${a}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
return sampleTexture(${a}, 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}, ${p}, index);
return sampleTexture(${a}, uv);
}
`}function V7(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=Wu(e,c),f=["b","row","col"];return`
${$C(m,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${Vu(f,h)});
}
`}let o=_n();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${a}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${o.texture2D}(${a}, uv);
}
`;let l=i[0],u=i[1],p=Math.ceil(n[2]/2),d=p*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${u}, ${d}, ${p}, b, row, col);
return ${o.texture2D}(${a}, uv);
}
`}function U7(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=k.squeezeShape(n),u=o;if(u.length<n.length){let f=Wu(e,u),g=["row","col","depth"];return`
${zu(f,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${Vu(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${i}, 1)));
${Bu(e)}
}
`;let p=e.shapeInfo.texShape,d=p[0],c=p[1],h=e.shapeInfo.flatOffset;if(c===s&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${a}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${d}.0);
return sampleTexture(${a}, uv);
}
`;if(c===i&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${a}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, 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(${c}.0, ${d}.0);
return sampleTexture(${a}, uv);
}
`;let m=So(a);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${a}Shape[1] * ${a}Shape[2];
int stride1 = ${a}Shape[2];
int index = row * ${s} + col * ${i} + depth + ${m};
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${i} + depth + ${m};
vec2 uv = uvFromFlat(${d}, ${c}, index);
return sampleTexture(${a}, uv);
}
`}function G7(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=_n();if(t)return`
vec4 ${a}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${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 s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],p=l[1],d=Math.ceil(s[i-1]/2),c=d*Math.ceil(s[i-2]/2),h="int b, int row, int col",m=`b * ${c} + (row / 2) * ${d} + (col / 2)`;for(let f=2;f<i-1;f++)h=`int b${f}, `+h,c*=s[i-f-1],m=`b${f} * ${c} + `+m;return`
vec4 ${a}(${h}) {
int index = ${m};
int texR = index / ${p};
int texC = index - texR * ${p};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${u});
return ${r.texture2D}(${n}, uv);
}
`}function H7(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[3],i=n[2]*s,o=n[1]*i,{newShape:l,keptDims:u}=k.squeezeShape(n);if(l.length<n.length){let b=Wu(e,l),x=["row","col","depth","depth2"];return`
${zu(b,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${Vu(x,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, 1)));
${Bu(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,c=d[0],h=d[1],m=`int stride2 = ${a}Shape[3];`,f=`int stride1 = ${a}Shape[2] * stride2;`,g=`int stride0 = ${a}Shape[1] * stride1;`;if(h===o&&p==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${m}
${f}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${a}, uv);
}
`;if(h===s&&p==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${a}Shape[1] * ${a}Shape[2], ${a}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${a}, uv);
}
`;let y=So(a);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${m}
${f}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index + ${y});
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} +
depth * ${s} + depth2;
vec2 uv = uvFromFlat(${c}, ${h}, index + ${y});
return sampleTexture(${a}, uv);
}
`}function j7(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=k.squeezeShape(t);if(l.length<t.length){let f=Wu(e,l),g=["row","col","depth","depth2","depth3"];return`
${zu(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${Vu(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${r})) +
depth3;
${Bu(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,c=d[0],h=d[1];if(h===o&&p==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&p==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;let m=So(n);return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${r} + depth3 + ${m};
vec2 uv = uvFromFlat(${c}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function q7(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=Wu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${zu(g)}
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${a}(${Vu(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,p=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${p}, ${u}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${Bu(e)}
}
`;let d=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===p&&d==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&d==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let f=So(n);return`
float ${a}(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 * ${p} + col * ${u} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${h}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function Bu(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function K7(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=AC(e.shapeInfo.logicalShape,t.logicalShape),l=ut(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(`
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,y)=>`coords.${d[y+u]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${p}
vec4 outputValue = get${a}(${c});
${h}
}
`}function X7(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let u=ut(l),p=AC(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),`
float ${r}() {
${u} coords = getOutputCoords();
${c}
return get${a}(${m});
}
`}function ut(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 ${e} is not yet supported`)}function v0(e,t,n){let{newShape:a,keptDims:r}=k.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!k.arraysEqual(t,n)&&a.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function Wu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Vu(e,t){return t.map(n=>e[n]).join(", ")}function Y7(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=m7(r,i,t),l=lC(e.gl,o),u=e.createProgram(l);return X().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},DC(e,t,u))}function DC(e,t,n){let a={},r={},s={},i=[],o,l,u,p=null,d=null;d=e.getUniformLocation(n,"NAN",!1),X().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(n,"INFINITY",!1));let c=!1;for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h];a[m]=e.getUniformLocation(n,m,c),a[`offset${m}`]=e.getUniformLocation(n,`offset${m}`,c),t.enableShapeUniforms&&(r[`${m}Shape`]=e.getUniformLocation(n,`${m}Shape`,c),s[`${m}TexShape`]=e.getUniformLocation(n,`${m}TexShape`,c))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",c),u=e.getUniformLocation(n,"outShapeStrides",c),l=e.getUniformLocation(n,"outTexShape",c)),t.customUniforms&&t.customUniforms.forEach((h,m)=>{i[m]=e.getUniformLocation(n,h.name,c)}),{uniformLocations:a,customUniformLocations:i,infLoc:p,nanLoc:d,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function bk(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,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function J7(e,t,n,a,r){t.program.enableShapeUniforms||(bk(t.inShapeInfos,n),bk([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let p=t.program.variableNames[u],d=t.uniformLocations[p],c=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],m=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:f}=v0(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(h,new Int32Array(f));break;case 2:e.gl.uniform2iv(h,new Int32Array(f));break;case 3:e.gl.uniform3iv(h,new Int32Array(f));break;case 4:e.gl.uniform4iv(h,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(k.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(d,f)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,d,u)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=k.computeStrides(a.shape);switch(a.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,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(p,d);else if(l.type==="vec2")e.gl.uniform2fv(p,d);else if(l.type==="vec3")e.gl.uniform3fv(p,d);else if(l.type==="vec4")e.gl.uniform4fv(p,d);else if(l.type==="int")e.gl.uniform1iv(p,d);else if(l.type==="ivec2")e.gl.uniform2iv(p,d);else if(l.type==="ivec3")e.gl.uniform3iv(p,d);else if(l.type==="ivec4")e.gl.uniform4iv(p,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Z7(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=v0(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${w[0]>1}_${w[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let w=k.computeStrides(p);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&k.arraysEqual(i.shape,l),y=k.sizeFromShape(i.shape)===1,b=_.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&k.arraysEqual(l,n.texData.texShape),v=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${y}_${b}_${g}_${c}_${h}_${m}_${v}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,s}function jn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var Q7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ac.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Lf(["r","c","d"],e):Io(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[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);
}
${t.output} = result;
}
`}},eY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ac.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Lf(["r","c","d"],e):Io(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[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));
}
${t.output} = result;
}
`}},tY=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ca.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=`
${EC}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},nY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ca.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=`
${EC}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},aY=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=_n();this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?x0():b0(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.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];
}
${n.output} = vec4(${a}, 0., 0., 0.);
}
`}},rY=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=_n();this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length);let a="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;a+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${s};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${o}] = values[0];
} else if (offset == 1) {
result[${o}] = values[1];
} else if (offset == 2) {
result[${o}] = values[2];
} else {
result[${o}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?x0():b0(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${a}
${n.output} = ${r};
}
`}},RC={};Me(RC,{bindVertexProgramAttributeStreams:()=>UC,createBufferFromOutputTexture:()=>jC,createFloat16MatrixTexture:()=>zC,createFloat16PackedMatrixTexture:()=>VC,createFloat32MatrixTexture:()=>LC,createIndexBuffer:()=>OC,createPackedMatrixTexture:()=>WC,createUnsignedBytesMatrixTexture:()=>BC,createVertexBuffer:()=>PC,createVertexShader:()=>MC,downloadByteEncodedFloatMatrixFromOutputTexture:()=>KC,downloadFloat32MatrixFromBuffer:()=>qC,downloadMatrixFromPackedOutputTexture:()=>YC,downloadPackedMatrixFromBuffer:()=>XC,getInternalFormatForFloat16MatrixTexture:()=>k0,getInternalFormatForFloat16PackedMatrixTexture:()=>N0,getInternalFormatForFloat32MatrixTexture:()=>w0,getInternalFormatForPackedMatrixTexture:()=>S0,getInternalFormatForUnsignedBytesMatrixTexture:()=>I0,uploadDenseMatrixToTexture:()=>GC,uploadPixelDataToTexture:()=>HC});function MC(e){let t=_n(),n=`${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 oC(e,n)}function PC(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 cC(e,t)}function OC(e){let t=new Uint16Array([0,1,2,2,1,3]);return dC(e,t)}function td(e,t,n,a,r,s){mC(t,n);let i=hC(e),o=e.TEXTURE_2D;return ge(e,()=>e.bindTexture(o,i)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber("WEBGL_VERSION")===1?ge(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):ge(e,()=>e.texStorage2D(o,1,a,t,n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function w0(e){return e.internalFormatFloat}function LC(e,t,n,a){let[r,s]=ed(t,n);return td(e,r,s,w0(a),a.textureFormatFloat,e.FLOAT)}function k0(e){return e.internalFormatHalfFloat}function zC(e,t,n,a){let[r,s]=ed(t,n);return td(e,r,s,k0(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function I0(e){return e.downloadTextureFormat}function BC(e,t,n,a){let[r,s]=ed(t,n);return td(e,r,s,I0(a),e.RGBA,e.UNSIGNED_BYTE)}function S0(e){return e.internalFormatPackedFloat}function WC(e,t,n,a){let[r,s]=Ou(t,n);return td(e,r,s,S0(a),e.RGBA,e.FLOAT)}function N0(e){return e.internalFormatPackedHalfFloat}function VC(e,t,n,a){let[r,s]=Ou(t,n);return td(e,r,s,N0(a),e.RGBA,a.textureTypeHalfFloat)}function UC(e,t,n){return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),rx(e,t,"clipSpacePos",n,3,20,0)&&rx(e,t,"uv",n,2,20,12)}function GC(e,t,n,a,r,s){ge(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),X().getNumber("WEBGL_VERSION")===2?ge(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,a,e.RGBA,o,i)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function HC(e,t,n){ge(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?X().getNumber("WEBGL_VERSION")===2?ge(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):X().getNumber("WEBGL_VERSION")===2?ge(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function jC(e,t,n,a){let r=e.createBuffer();ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return ge(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function qC(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function KC(e,t,n,a){let[r,s]=ed(t,n),i=4,o=new Uint8Array(e7(t*n,i));return ge(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function XC(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(t7(s,i));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 YC(e,t,n){let a=new Float32Array(t*n*4);return ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var xh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,rC(t,e)):this.gl=Ya(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),X().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Op(this.gl,r),da(this.gl,s))this.textureHalfFloatExtension=Op(this.gl,s);else if(X().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),da(this.gl,a))this.colorBufferHalfFloatExtension=Op(this.gl,a);else if(X().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",da(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(da(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=PC(this.gl),this.indexBuffer=OC(this.gl),this.framebuffer=fC(this.gl),this.textureConfig=g0(this.gl,this.textureHalfFloatExtension)}get debug(){return X().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;ge(e,()=>e.finish()),ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ge(e,()=>e.deleteFramebuffer(this.framebuffer)),ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ge(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ge(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),LC(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),zC(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),BC(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),HC(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),GC(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),VC(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),WC(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(sx(this.gl,this.framebuffer),this.outputTexture=null),ge(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>KC(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return XC(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return qC(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=jC(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(X().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>YC(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=MC(t));let n=uC(t);return ge(t,()=>t.attachShader(n,this.vertexShader)),ge(t,()=>t.attachShader(n,e)),pC(t,n),this.debug&&mh(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=UC(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ge(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&mh(this.gl,this.program),ge(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?yC(this.gl,e,t):bC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ge(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(),xC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Ou(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&mh(this.gl,this.program),Lp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ge(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ge(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Op(this.gl,X().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(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,X().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,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=sY(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)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),fh(this.gl,e,this.framebuffer),this.debug&&Lp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(fh(this.gl,this.outputTexture,this.framebuffer),this.debug&&Lp(this.gl)):sx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;fh(a,e,this.framebuffer),this.debug&&Lp(a),this.outputTexture=e,ge(a,()=>a.viewport(0,0,t,n)),ge(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),ge(this.gl,()=>this.gl.scissor(e,t,n,a))}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 sY(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:iY,bincountImpl:JC,bincountReduceImpl:oY,ceilImpl:lY,concatImpl:uY,equalImpl:pY,expImpl:cY,expm1Impl:dY,floorImpl:hY,gatherNdImpl:mY,gatherV2Impl:fY,greaterImpl:gY,greaterEqualImpl:yY,lessImpl:bY,lessEqualImpl:xY,linSpaceImpl:vY,logImpl:wY,maxImpl:kY,maximumImpl:IY,minimumImpl:SY,multiplyImpl:NY,negImpl:TY,notEqualImpl:CY,prodImpl:_Y,rangeImpl:EY,rsqrtImpl:AY,sigmoidImpl:$Y,simpleAbsImpl:ZC,sliceImpl:FY,sparseFillEmptyRowsImpl:DY,sparseReshapeImpl:RY,sparseSegmentReductionImpl:QC,sqrtImpl:MY,stridedSliceImpl:PY,stringNGramsImpl:OY,stringSplitImpl:LY,stringToHashBucketFastImpl:zY,subImpl:BY,tileImpl:WY,topKImpl:VY,transposeImpl:T0,uniqueImpl:UY}=tT;function e_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Sn(e,t){return t===1?[e]:e_(e,t)}function GY(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var HY=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=jn(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=Sn("rc",this.rank),n=ut(this.rank),a=this.getOutOfBoundsCondition(t),r=this.getSetup(t),s=this.getOutput(t);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${a}) {
setOutput(vec4(0));
} else {
${r}
setOutput(vec4(${s}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let a=0;a<=1;a++){let r=`${n===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)r=`${e[e.length-1-s]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],a=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${t[0]};
int c = ${t[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${n};
bool rEdge = rp1 >= ${a};
`}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
cEdge ? 0. : getA(${t[1]}),
rEdge ? 0. : getA(${t[2]}),
rEdge || cEdge ? 0. : getA(${t[3]})`}},t_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${a}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${a>0?"}":""}
`}this.userCode=`
${jY(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?x0():b0(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function jY(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?h7(["r","c","d"],"inputShape"):Io(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var qY=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=vk(t,n),r=wk(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=xk(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===on.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===on.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===on.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=vk(n,a),s=wk(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=xk(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function KY(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function xk(e,t,n,a,r){let s=XY(t,a),i;if(r){let[l,u]=Ou(e[0],e[1]);i=l*u}else{let[l,u]=ed(e[0],e[1]);i=l*u}let o=KY(n,s);return i*o}function XY(e,t){switch(e){case on.PACKED_2X2_FLOAT32:return S0(t);case on.PACKED_2X2_FLOAT16:return N0(t);case on.UNPACKED_FLOAT32:return w0(t);case on.UNPACKED_FLOAT16:return k0(t);case on.PACKED_4X1_UNSIGNED_BYTE:return I0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function YY(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?on.PACKED_2X2_FLOAT32:on.UNPACKED_FLOAT32:e?on.PACKED_2X2_FLOAT16:on.UNPACKED_FLOAT16}function vk(e,t){if(e===ca.UPLOAD)return on.PACKED_2X2_FLOAT32;if(e===ca.RENDER||e==null)return YY(t);if(e===ca.DOWNLOAD||e===ca.PIXELS)return on.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function wk(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Sr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Ea="if (isnan(x)) return x;",JY="return x;",kk="return abs(x);",ZY="return (x >= 0.0) ? x : (exp(x) - 1.0);",QY=Ea+`
return (x < 0.0) ? 0.0 : x;
`,e9=Ea+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Zo="return x;",t9="return 1.0 / (1.0 + exp(-1.0 * x));",n9="return x;",a9=`
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;
`,r9=`
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;
`,s9=`
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;
`,i9="return 1.0 / (1.0 + exp(-1.0 * x));",Ys=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},o9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length);let t=e.length,n=Sn("rc",t),a=ut(t),r=GY(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${i}));
}
`}},l9=mr.whereImpl,u9=1e-7,p9=1e-4,gb={};function c9(e){return e in gb||(gb[e]={}),gb[e]}var d9=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),h9=600;function m9(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*h9/1024/1024}var zf=class extends oc{constructor(e){if(super(),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,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof xh)t=e;else{let n=Ya(X().getNumber("WEBGL_VERSION"),e);t=new xh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ya(X().getNumber("WEBGL_VERSION"));t=new xh(n),this.binaryCache=c9(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new qY(this.gpgpu),this.numMBBeforeWarning=m9(),this.texData=new Zh(this,ar())}nextDataId(){return zf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().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 a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ca.UPLOAD,refCount:1}),a}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,a,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:ca.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let d;o?d=new Ys(i,Zo):d=new Sr(i,Zo);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=k.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=_.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Ys(a,Zo):h=new Sr(a,Zo);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().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(s!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...oh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=_.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ge(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ar().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new Ys(r,Zo):c=new Sr(r,Zo);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=ar().makeTensorFromDataId(u.dataId,u.shape,u.dtype),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return He(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!sC(n))throw X().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),c=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture.texture,...oh(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let s=X().getBool("WEBGL_PACK")&&a===!0,i=s?gh(t):t,o=s?new nY(i):new tY(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(X().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:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));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.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=d9){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.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 l9(e.shape,t)}packedUnaryOp(e,t,n){let a=new Ys(e.shape,t),r=this.compileAndRun(a,[e],n);return ar().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=ZC(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,kk,e.dtype);let t=new Sr(e.shape,kk),n=this.compileAndRun(t,[e]);return ar().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return ar().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new o9(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new HY(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[mi(e.shape),...fi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[mi(t),...fi(t)],s=new t_(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=k.sizeFromShape(r),c=t[0]*t[1]*4;k.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=gh(r),o;a?o=new eY(i):o=new Q7(i);let l=!0,u=[t!=null?t:oh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===ac.DENSE){let g=s!=null?s:oh(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),k.sizeFromShape(i.shape)===0)return o.values=k.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&k.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!rc(y.shape,g.shape)){let b=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),b.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=Z7(e,u,p),c=this.getAndSaveBinary(d,()=>Y7(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),X().get("ENGINE_COMPILE_ONLY")||J7(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=X().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=k.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),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=O(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?u9:p9}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=k.now());let p=t.texShape;if(p==null&&(p=kC(n,o),t.texShape=p),r!=null){let d=gh(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=Ou(p[0],p[1])),o?c=new rY(d,f):c=new aY(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=ca.PIXELS:b.usage=ca.UPLOAD,b.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let x=[[m,h]],v=!0,w=this.runWebGLProgram(c,[y],a,x,v),T=this.texData.get(w.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=k.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=f9(t,a)),n.values}acquireTexture(e,t,n,a){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,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Nv(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(y0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=DC(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}};zf.nextDataId=0;function f9(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var g9="3.16.0";function n_(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}_c.isBrowser()&&Mm("webgl",()=>new zf,2);var y9={forceHalfFloat:n_},a_=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,wl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=jn(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Bf=`
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;
`,nd=class{constructor(e,t,n,a=!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=jn(r);let s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${ut(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?s+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Sn("coords",r);this.enableShapeUniforms?s+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:s+=`
bool nextRowOutOfBounds =
(${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);
${s}
setOutput(result);
}
`}};function Un(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var b9={kernelName:Li,backendName:"webgl",kernelFunc:Un};function ks(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Un({inputs:{x:a},backend:n}),l=Un({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var x9={kernelName:sm,backendName:"webgl",kernelFunc:ks},r_="return (a < 0.) ? b * a : a;",s_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function v9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new nd(s_,r.shape,i.shape):new wl(r_,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var w9={kernelName:zi,backendName:"webgl",kernelFunc:v9},i_="return (a < 0.) ? b * a : a;",o_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function k9(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new nd(o_,a.shape,r.shape):new wl(i_,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var I9={kernelName:Zi,backendName:"webgl",kernelFunc:k9},Uu="if (isnan(x)) return x;",S9=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,N9=`
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 Je({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Ys(i.shape,t):p=new Sr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function pn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,w]=x,T={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new wl(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ma(v.dtype,w.dtype))}),b=ks({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),b}let d=s||ma(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?_.fromUint8ToStringArray(m):m,y=l.dtype==="string"?_.fromUint8ToStringArray(f):f,[b,x]=r(l.shape,u.shape,g,y,d),v=p.makeTensorInfo(x,d),w=p.texData.get(v.dataId);return w.values=b,v}let c=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new nd(t,l.shape,u.shape,n):h=new wl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Wf(e,t=!1){if(e==="linear")return t?n9:JY;if(e==="relu")return t?r9:QY;if(e==="elu")return t?a9:ZY;if(e==="relu6")return t?s9:e9;if(e==="prelu")return t?o_:i_;if(e==="leakyrelu")return t?s_:r_;if(e==="sigmoid")return t?i9:t9;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var l_=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=jn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?f=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:f=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${p}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${p}; i++) {
int batchA = ${b};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${d});
vec4 b = getMatrixB(batchB, ${c});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${m[0]});
result += (${h[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},Ik={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Sk=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) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},Nk="return a * b;";function C0(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new Sk(Ik.REAL,a.shape,r.shape),p=new Sk(Ik.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=ks({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=NY(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new nd(Nk,a.shape,r.shape):i=new wl(Nk,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var T9={kernelName:Ki,backendName:"webgl",kernelFunc:C0};function C9(e,t,n){let a=[mi(e.shape),...fi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[mi(t),...fi(t)],i=new t_(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function me(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),u=k.sizeFromShape(l);k.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!rc(r.shape,l)&&!(p.texture!==null&&rc(p.shape,l))?C9(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var _9={kernelName:lu,backendName:"webgl",kernelFunc:me},Tk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${k.isInt(p)?p.toPrecision(2):p}, 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 < ${i}; 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 + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},E9=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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,p=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 = ${o}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${o}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,c="vec4";t==="all"?(i="1.0",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,c="bvec4"):t==="any"&&(i="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,c="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
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;
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${d}
}
int inIdx = inOffset + ${u};
if (${p===1}) {
${c} values = ${c}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${p===2}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${p===3}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${l});
}
`}};function A9(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function No(e,t,n,a){let r=A9(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],p,d;n==="mean"?p=i===0?new Tk({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Tk({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):p=new E9({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),d=s,s=a.runWebGLProgram(p,[s],t),d.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(d)}return s}var $9=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=F9(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function F9(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var D9=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 ${this.rank} is not yet supported.`);let a=ut(this.rank),r=e_("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Vf(e,t,n){let a=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new D9(e.shape,t):new $9(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function R9(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Vf(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=_.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=_.expandShapeToKeepDim(d,i));let m=k.sizeFromShape(c),f=k.sizeFromShape(e.shape)/m,g=me({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),y=Dm(e.dtype),b=No(g,y,"sum",a),x=me({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),u&&a.disposeIntermediateTensorInfo(p),x}function Uf(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return R9(r,s,i,n)}var M9={kernelName:uo,backendName:"webgl",kernelFunc:Uf};function un(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];let u;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=T0(p,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(u.dataId);c.values=d}else u=Vf(r,s,i);return u}var P9={kernelName:go,backendName:"webgl",kernelFunc:un},u_=1e3;function Xh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],c=a?t.shape[p-1]:t.shape[p-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),x=Nu.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);k.assert(d===c,()=>`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[y,d,h]:[y,h,d],w=a?[b,m,c]:[b,c,m],T=me({inputs:{x:e},backend:r,attrs:{shape:v}}),C=me({inputs:{x:t},backend:r,attrs:{shape:w}}),E=[T,C],$=Math.max(y,b),P=n?T.shape[1]:T.shape[2],F=s!=null,S=i!=null,M=l==="leakyrelu",V=l!=null?Wf(l,!0):null,j=F||S||M||V!=null,q;if((h===1||m===1)&&P>u_&&j===!1){let Q=T,ee=C;n&&(Q=un({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(Q)),a&&(ee=un({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(ee));let re=m!==1,Z=m===1,ie=Q;re&&(ie=me({inputs:{x:Q},backend:r,attrs:{shape:[$,P,1]}}),E.push(ie));let ae=m===1?2:1,le=ee;Z&&(le=me({inputs:{x:ee},backend:r,attrs:{shape:[$,1,P]}}),E.push(le));let ue=C0({inputs:{a:ie,b:le},backend:r});q=Uf({inputs:{x:ue},backend:r,attrs:{axis:ae,keepDims:!0}}),E.push(ue)}else{let Q=ma(e.dtype,t.dtype),ee=new l_(v,w,[$,h,m],n,a,F,V,S,M),re=[T,C];if(s!=null&&re.push(s),S&&re.push(i),M){let Z=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));re.push(Z),E.push(Z)}q=r.runWebGLProgram(ee,re,Q)}let K=me({inputs:{x:q},backend:r,attrs:{shape:x}});E.push(q);for(let Q of E)r.disposeIntermediateTensorInfo(Q);return K}function O9(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Xh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var L9={kernelName:ti,backendName:"webgl",kernelFunc:O9},Ck="return abs(x);";function z9(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=ZC(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Ys(a.shape,Ck):r=new Sr(a.shape,Ck),n.runWebGLProgram(r,[a],a.dtype)}var B9={kernelName:Il,backendName:"webgl",kernelFunc:z9},W9=Ea+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,V9=Je({opSnippet:W9}),U9={kernelName:Sl,backendName:"webgl",kernelFunc:V9},G9=Ea+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,H9=Je({opSnippet:G9}),j9={kernelName:Nl,backendName:"webgl",kernelFunc:H9},_k="return a + b;",q9=pn({opSnippet:_k,packedOpSnippet:_k,supportsComplex:!0,cpuKernelImpl:iY}),K9={kernelName:ds,backendName:"webgl",kernelFunc:q9},X9=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${a};
setOutput(result);
}
`}},Y9=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${a};
setOutput(result);
}
`}};function vh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Un({inputs:{x:a[0]},backend:n});if(a.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=vh({inputs:a.slice(0,o),backend:n}),u=vh({inputs:a.slice(o),backend:n});return vh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ma(o,l)),s=a.map(o=>o.shape),i=X().getBool("WEBGL_PACK")?new Y9(a[0].shape,s):new X9(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var J9={kernelName:xi,backendName:"webgl",kernelFunc:vh};function Z9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=un({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o)),_.assertAxesAreInnerMostDims("all",u,o);let[c,h]=_.computeOutAndReduceShapes(d.shape,u),m=k.sizeFromShape(h),f=me({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=No(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(c,l);y=me({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=me({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var Q9={kernelName:Tl,backendName:"webgl",kernelFunc:Z9};function eJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=un({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o)),_.assertAxesAreInnerMostDims("any",u,o);let[c,h]=_.computeOutAndReduceShapes(d.shape,u),m=k.sizeFromShape(h),f=me({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=No(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(c,l);y=me({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=me({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var tJ={kernelName:Cl,backendName:"webgl",kernelFunc:eJ},nJ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=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 * ${a};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${a}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},aJ=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ut(o),u=Sn("coords",o),p,d;if(s===1){d=o+1;let C=ut(d);p=`
${C} sourceLocR = ${C}(${u.join()}, 0);
++${u[o-1]};
${C} sourceLocG = ${C}(${u.join()}, 0);
++${u[o-2]};
${C} sourceLocA = ${C}(${u.join()}, 0);
--${u[o-1]};
${C} sourceLocB = ${C}(${u.join()}, 0);
--${u[o-2]};`}else d=o,p=`
${l} sourceLocR = coords;
++${u[o-1]};
${l} sourceLocG = coords;
++${u[o-2]};
${l} sourceLocA = coords;
--${u[o-1]};
${l} sourceLocB = coords;
--${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(C=>"int "+C),f=Sn("sourceLocR",d-1).concat("inIdx.r"),g=Sn("sourceLocG",d-1).concat("inIdx.g"),y=Sn("sourceLocB",d-1).concat("inIdx.b"),b=Sn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${b.join()})));`,w=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,T=a?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${c.join()}),
vec2(${c.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${c.join()}),
vec2(${c.slice(-2).join()}));
}
${T}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${v}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${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 p_(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new nJ(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=p_(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function c_(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new aJ(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=c_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function d_(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=_.computeOutAndReduceShapes(l.shape,r),d=k.sizeFromShape(p),c=me({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=p_(e,c,a);s.push(h);let m=me({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return c_(e,t,a)}function rJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=un({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=d_(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var sJ={kernelName:vi,backendName:"webgl",kernelFunc:rJ};function iJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=un({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=d_(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var oJ={kernelName:lc,backendName:"webgl",kernelFunc:iJ},lJ=Ea+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,uJ=Je({opSnippet:lJ}),pJ={kernelName:_l,backendName:"webgl",kernelFunc:uJ},cJ=Ea+"return log(x + sqrt(x * x + 1.0));",dJ=Je({opSnippet:cJ}),hJ={kernelName:El,backendName:"webgl",kernelFunc:dJ},mJ=Ea+`
return atan(x);
`,fJ=Je({opSnippet:mJ}),gJ={kernelName:Al,backendName:"webgl",kernelFunc:fJ},yJ=S9+`
return atan(a, b);
`,bJ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+N9+`
return result;
`,xJ=pn({opSnippet:yJ,packedOpSnippet:bJ}),vJ={kernelName:Fl,backendName:"webgl",kernelFunc:xJ},wJ=Ea+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,kJ=Je({opSnippet:wJ}),IJ={kernelName:$l,backendName:"webgl",kernelFunc:kJ},sc=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${c}, ${h});
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 < ${p};
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 ${C} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?f:g:`wR * ${d} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,w=s%4,T=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${c}, ${h});
const float initializationValue = ${y};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${v}; 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)
);
${T}
}
int xC = xCCorner + ${v};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
initializationValue,
initializationValue
);
${T}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
initializationValue
);
${T}
}
}
setOutput(${x});
}
`}},_0=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
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 < ${c};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
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 ${$} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?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 * ${h} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let v="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,E=`
if (${b}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${v}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
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 < ${c};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${T}; 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)
);
${E}
}
int xC = xCCorner + ${T};
if (${C===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${C===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${C===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
);
${E}
}
}
setOutput(${w});
}
}
`}};function SJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Lu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))return Un({inputs:{x:r},backend:n});let d=new sc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var NJ={kernelName:wi,backendName:"webgl",kernelFunc:SJ};function TJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=_.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new _0(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var CJ={kernelName:uc,backendName:"webgl",kernelFunc:TJ},_J=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${u}, ${p});
const float avgMultiplier = float(${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 < ${o};
wR += ${s}) {
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 < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},EJ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${h}, ${m}, ${f});
const float avgMultiplier = float(${g});
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 < ${p};
wD += ${o}) {
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) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${u}) {
float dyC = float(dyCCorner + wC) / ${i}.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 AJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=_.computePool3DInfo(i.shape,o,l,d,u,p),h=new EJ(c);return n.runWebGLProgram(h,[r],i.dtype)}var $J={kernelName:nm,backendName:"webgl",kernelFunc:AJ};function FJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Lu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=_.computePool2DInfo(i.shape,o,l,1,u),d=new _J(p);return n.runWebGLProgram(d,[r],i.dtype)}var DJ={kernelName:tm,backendName:"webgl",kernelFunc:FJ};function RJ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Xh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var MJ={kernelName:ki,backendName:"webgl",kernelFunc:RJ},PJ=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},OJ=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},LJ=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=X().getBool("WEBGL_PACK_NORMALIZATION")?new OJ(a.shape,r.shape,s.shape,p,d,l):new PJ(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},zJ={kernelName:Pi,backendName:"webgl",kernelFunc:LJ},BJ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=WJ(this.rank),a,r=e.map((s,i)=>`sourceLoc.${lx[i]} = start[${i}] + coords.${lx[i]};`);a=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${a}
setOutput(getSource(${n}));
}
`}},lx=["x","y","z","w","u","v"];function WJ(e){if(e===1)return"sourceLoc";if(e<=6)return lx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var VJ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ut(this.rank),n=Sn("coords",this.rank),a=Sn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.y = ${s};
--${a[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${a[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((u,p)=>`start[${p}]`).join()});`:e.map((u,p)=>`${a[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}};function UJ(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=qt.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Gu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=qt.parseSliceParams(r,s,i);if(qt.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=FY(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=qt.isSliceContinous(r.shape,o,l);if(u||!p){let d=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VJ(l):new BJ(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),UJ(r,o,l,n)}var GJ={kernelName:du,backendName:"webgl",kernelFunc:Gu},HJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=_.getReshaped(r.shape,s,o),u=_.getPermuted(l.length,s.length),p=_.getReshapedPermuted(r.shape,s,o),d=_.getSliceBeginCoords(i,s.length),c=_.getSliceSize(p,i,s.length),h=[],m=me({inputs:{x:r},backend:n,attrs:{shape:l}}),f=un({inputs:{x:m},backend:n,attrs:{perm:u}}),g=me({inputs:{x:f},backend:n,attrs:{shape:p}}),y=Gu({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},jJ={kernelName:Dl,backendName:"webgl",kernelFunc:HJ};function qJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=JC(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var KJ={kernelName:am,backendName:"webgl",kernelFunc:qJ};function XJ(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=_.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var YJ={kernelName:rm,backendName:"webgl",kernelFunc:XJ},JJ="return float(a != b);",h_=pn({opSnippet:JJ,cpuKernelImpl:CY,dtype:"bool"}),ZJ={kernelName:tu,backendName:"webgl",kernelFunc:h_};function ad(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Un({inputs:{x:r.complexTensorInfos.real},backend:n})}var QJ={kernelName:Sm,backendName:"webgl",kernelFunc:ad},eZ="return float(int(x));";function tZ(e,t){let n=new Sr(e.shape,eZ),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function ux(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Un({inputs:{x:r},backend:n});let i=kt(r.shape),o=ux({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ks({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=ad({inputs:{input:r},backend:n}),o=ux({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Un({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return tZ(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=h_({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var nZ={kernelName:Ii,backendName:"webgl",kernelFunc:ux},Ek="return ceil(x);",aZ=Je({opSnippet:Ek,packedOpSnippet:Ek,cpuKernelImpl:lY}),rZ={kernelName:Si,backendName:"webgl",kernelFunc:aZ},sZ=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));
}
`}},iZ=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 oZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;X().getBool("WEBGL_PACK_CLIP")?o=new iZ(r.shape):o=new sZ(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var lZ={kernelName:hs,backendName:"webgl",kernelFunc:oZ},uZ=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 Ak(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function pZ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new uZ(a.shape),i=[Ak(a,r.complexTensorInfos.real),Ak(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var cZ={kernelName:pc,backendName:"webgl",kernelFunc:pZ},dZ=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},hZ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ut(a),s=Sn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),p=i.join(),d=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${p}), vec2(${u.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];d+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${uh(i,l,f)}),
vec2(${uh(u,l,f)}));
}`}let c=o.length,h=o[o.length-1];d+=`
return getChannel(
getT${c}(${uh(i,l,h)}),
vec2(${uh(u,l,h)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${d}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[a-1]} = ${s[a-1]} + 1;
if (${s[a-1]} < ${n[a-1]}) {
result.g = getValue(${s});
}
${s[a-2]} = ${s[a-2]} + 1;
if (${s[a-2]} < ${n[a-2]}) {
result.a = getValue(${s});
}
${s[a-1]} = ${s[a-1]} - 1;
if (${s[a-2]} < ${n[a-2]} &&
${s[a-1]} < ${n[a-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function uh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Gf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Un({inputs:{x:r.complexTensorInfos.imag},backend:n})}var mZ={kernelName:ym,backendName:"webgl",kernelFunc:Gf};function nl(e,t,n){let a=e[0].dtype;if(a==="complex64"){let p=e.map(f=>ad({inputs:{input:f},backend:n})),d=e.map(f=>Gf({inputs:{input:f},backend:n})),c=nl(p,t,n),h=nl(d,t,n),m=ks({inputs:{real:c,imag:h},backend:n});return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),d.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let p=e.map(y=>{let b=k.sizeFromShape(y.shape.slice(t));return me({inputs:{x:y},backend:n,attrs:{shape:[-1,b]}})}),d=p.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),c=_.computeOutShape(p.map(y=>y.shape),1),h=p[0].shape[0]===1,m=uY(d,c,a,h),f=_.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(f,a,m);return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(e.length/2),d=nl(e.slice(0,p),t,n),c=nl(e.slice(p),t,n),h=nl([d,c],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),h}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let p=new hZ(e.map(d=>d.shape),t);return n.runWebGLProgram(p,e,a)}let{tensors2D:s,outShape:i}=fZ(e,t,n),o=new dZ(s.map(p=>p.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(p=>n.disposeIntermediateTensorInfo(p));let u=me({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),u}function fZ(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>me({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function m_(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(u=>u.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>k.sizeFromShape(u.shape)>0);if(o.length===1)return Un({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return _.assertParamsConsistent(l,s),nl(o,s,n)}var gZ={kernelName:Rl,backendName:"webgl",kernelFunc:m_},f_=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,x="",v="";n&&(a?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}
}
`,v="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${b}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * 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 < ${c}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
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 (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${f}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${f}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${w}
${v}
setOutput(result);
}
`}},yZ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${a});
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 < ${p}; wF++) {
int xF = xFCorner + wF * ${o};
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 < ${c}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${m===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},bZ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=jn(this.outputShape.length);let{dataFormat:n}=t,a=_n(),r=n==="channelsLast",s=r?0:1,i=r?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let p=0;p<=1;p++)l+=`
blockIndex = rc.y + ${p};
pos = rc.x + ${u};
${o}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${s}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${u*2+p}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${u*2+p}] = 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;
${l}
${a.output} = result;
}
`}};function g_({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(!((d===1||c===1)&&p>u_)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&k.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,k.assert(rc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=me({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(w);let T=Xh({a:x,b:w,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);k.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=Un({inputs:{x:T},backend:a}),g.shape=n.outShape,y.push(T)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=me({inputs:{x:e},backend:a,attrs:{shape:[1,b,n.inChannels]}}),v=me({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),w=Xh({a:x,b:v,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=me({inputs:{x:w},backend:a,attrs:{shape:n.outShape}}),y.push(x),y.push(v),y.push(w)}for(let b of y)a.disposeIntermediateTensorInfo(b);return g}function y_({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,y=[f,g],b=!0,x=!1,v=[],w=me({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=me({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(w),v.push(T);let C=new bZ(y,n),E=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],$=a.runWebGLProgram(C,[w],"float32",E),P=me({inputs:{x:$},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push($),v.push(P);let F=r!=null,S=s!=null,M=o==="leakyrelu",V=o?Wf(o,!0):null,j=new l_(P.shape,T.shape,[1,g,n.outChannels],b,x,F,V,S,M),q=[P,T];if(r&&q.push(r),S&&q.push(s),M){let re=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));q.push(re),v.push(re)}let K=a.runWebGLProgram(j,q,"float32"),Q=m?[1,c,d,n.outChannels]:[1,n.outChannels,c,d],ee=me({inputs:{x:K},backend:a,attrs:{shape:Q}});v.push(K);for(let re of v)a.disposeIntermediateTensorInfo(re);return ee}function xZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=_.convertConv2DDataFormat(l),c=_.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=g_({x:r,filter:s,convInfo:c,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=y_({x:r,filter:s,convInfo:c,backend:n});else{let f=new f_(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=me({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var vZ={kernelName:Ni,backendName:"webgl",kernelFunc:xZ},wZ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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} - ${a};
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 >= ${e.inWidth}) {
continue;
}
if (${s}) {
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);
}
`}},kZ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
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) / ${a}.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 < ${e.outChannels}; d2++) {
if (${s}) {
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);
}
`}},IZ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=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} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${a} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},SZ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${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) / ${s}.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 < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${a} - 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 NZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=_.convertConv2DDataFormat(l),c=_.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new wZ(c);return n.runWebGLProgram(h,[r,s],"float32")}var TZ={kernelName:im,backendName:"webgl",kernelFunc:NZ};function CZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=_.convertConv2DDataFormat(u),c=_.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d),h=new kZ(c);return n.runWebGLProgram(h,[r,s],"float32")}var _Z={kernelName:Ti,backendName:"webgl",kernelFunc:CZ};function EZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=_.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new yZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var AZ={kernelName:cc,backendName:"webgl",kernelFunc:EZ};function $Z(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=_.computeConv3DInfo(r.shape,l,i,1,o),p=new IZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var FZ={kernelName:om,backendName:"webgl",kernelFunc:$Z};function DZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=_.computeConv3DInfo(l,s.shape,o,1,i),p=new SZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var RZ={kernelName:lm,backendName:"webgl",kernelFunc:DZ},MZ=Uu+`
return cos(x);
`,PZ=Je({opSnippet:MZ}),OZ={kernelName:Ci,backendName:"webgl",kernelFunc:PZ},LZ=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,zZ=Je({opSnippet:LZ}),BZ={kernelName:_i,backendName:"webgl",kernelFunc:zZ},WZ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,v]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${b});
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 >= ${s}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${v};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${c} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},VZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new WZ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},UZ={kernelName:Pl,backendName:"webgl",kernelFunc:VZ},$k=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let a=e.length,r=t?"1.0":`getX(${Fk(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${ut(a)} coords = getOutputCoords();
int end = ${Dk(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${Dk(a,"coords")} = idx;
val *= getX(${Fk(a,"coords")});
}
setOutput(val);
}
`}};function Fk(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 product for rank ${e} is not yet supported`)}function Dk(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function GZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=_.getAxesPermutation([s],l),p=r;u!=null&&(p=un({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=_.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=p.shape[d],h=Un({inputs:{x:p},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new $k(p.shape,!1,o),g=[[m]],y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new $k(p.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=_.getUndoAxesPermutation(u),f=un({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}return h}var HZ={kernelName:Ml,backendName:"webgl",kernelFunc:GZ},Rk=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${Mk(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${ut(a)} coords = getOutputCoords();
int end = ${Pk(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${Pk(a,"coords")} = idx;
val += getX(${Mk(a,"coords")});
}
setOutput(val);
}
`}};function Mk(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 ${e} is not yet supported`)}function Pk(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function jZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=_.getAxesPermutation([s],l),p=r;u!=null&&(p=un({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=_.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=p.shape[d],h=Un({inputs:{x:p},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new Rk(p.shape,!1,o),g=[[m]],y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new Rk(p.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=_.getUndoAxesPermutation(u),f=un({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}return h}var qZ={kernelName:Ei,backendName:"webgl",kernelFunc:jZ};function KZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=JC(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=oY(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var XZ={kernelName:um,backendName:"webgl",kernelFunc:KZ},YZ=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 JZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new YZ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var ZZ={kernelName:Ol,backendName:"webgl",kernelFunc:JZ},b_=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=jn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${o};
int q = d2 - d1 * ${o};
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 < ${s}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${i}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${p}
${u}
setOutput(result);
}
`}},x_=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=jn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<p;g++)c+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;c+=`
for (int r = 0; r < ${u}; r++) {
`;for(let g=0;g<p;g++)c+=`
xTexelC${g*2} = vec4(0.0);
xTexelC${g*2}Ready = 0;
xTexelC${g*2+1} = vec4(0.0);
xTexelC${g*2+1}Ready = 0;
xC${g} = vec4(0.0);`;c+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(d+1)/2;g++){let y=g*2;if(c+=`
xC = xCCorner + ${y*l};
`,o===1){if(y<p&&(i%2===1?(c+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
`,l===1&&y>0?c+=`
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
`:c+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
} else {
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
}
`):c+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xC${y} = xTexelC${y};
`,y+1<p)){let b=i%2===0?k.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(c+=`
xCOffset = xC + imod(pads[1], 2) + ${b};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
`,l>1&&(c+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
xTexelC${y}Ready = 1;
}
`),c+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
`):b===1?c+=`
xC${y+1} = xTexelC${y};
`:c+=`
xCOffset = xC + ${b};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y+1} = xTexelC${y+1};
`}}else y<p&&(i%2===1?(c+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`,y+1<p&&(c+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
`)):(c+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(
xTexelC${y}.xy, xTexelC${y+1}.xy);
`,y+1<p&&(c+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`)));y<p&&(c+=`
wTexel = getW(r, ${y}, d1, q);
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
`,y+1<p&&(c+=`
wTexel = getW(r, ${y+1}, d1, q);
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
`))}c+=`
}
`,c+=`
}
`;let h="",m="";n&&(a?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,m="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${s};
int q = d2 - d1 * ${s};
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);
${c}
vec4 result = dotProd - vec4(0.000000000000001);
${f}
${m}
setOutput(result);
}
`}};function QZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=_.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new x_(d):c=new b_(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var eQ={kernelName:Ai,backendName:"webgl",kernelFunc:QZ},tQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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 * ${s} + 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} - ${a};
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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},nQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
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) / ${a}.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 < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function aQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=_.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new tQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var rQ={kernelName:pm,backendName:"webgl",kernelFunc:aQ};function sQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=_.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new nQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var iQ={kernelName:cm,backendName:"webgl",kernelFunc:sQ},oQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function lQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=me({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new oQ(s),l=n.runWebGLProgram(o,[i],i.dtype),u=me({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var uQ={kernelName:dm,backendName:"webgl",kernelFunc:lQ},pQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=`
const ivec2 strides = ivec2(${r}, ${s});
const ivec2 pads = ivec2(${p}, ${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 < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; 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 cQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new pQ(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=me({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var dQ={kernelName:dc,backendName:"webgl",kernelFunc:cQ};function hQ(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=_.decodeEinsumEquation(r,s.length);_.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=_.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f<d;++f){for(let g of p[f]){let{permutationIndices:y,expandDims:b}=_.getEinsumPermutation(h,l[g]),x;_.isIdentityPermutation(y)?x=s[g]:(x=un({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let w=0;w<b.length;++w)v.splice(b[w],0,1);k.arraysEqual(x.shape,v)||(x=me({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=C0({inputs:{a:x,b:c},backend:n}),m.push(c))}f<d-1&&(u[f]>=0&&(c=Uf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var mQ={kernelName:hm,backendName:"webgl",kernelFunc:hQ},fQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",gQ=`
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;
`,yQ=Je({opSnippet:fQ,packedOpSnippet:gQ}),bQ={kernelName:Fi,backendName:"webgl",kernelFunc:yQ},xQ="return (b >= 1.0) ? a : a * (b + 1.0);",vQ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,wQ=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new nd(vQ,a.shape,r.shape):new wl(xQ,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},kQ={kernelName:mm,backendName:"webgl",kernelFunc:wQ},IQ=`
return vec4(equal(a, b));
`,SQ="return float(a == b);",NQ=pn({opSnippet:SQ,packedOpSnippet:IQ,dtype:"bool",cpuKernelImpl:pY}),TQ={kernelName:zl,backendName:"webgl",kernelFunc:NQ},CQ=`
// 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 = ${_.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,_Q=Je({opSnippet:CQ}),EQ={kernelName:Ll,backendName:"webgl",kernelFunc:_Q},AQ=Uu+`
return exp(x);
`,$Q=`
vec4 result = exp(x);
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;
`,v_=Je({opSnippet:AQ,packedOpSnippet:$Q,cpuKernelImpl:cY,dtype:"float32"}),FQ={kernelName:Di,backendName:"webgl",kernelFunc:v_};function px(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),me({inputs:{x:s},backend:a,attrs:{shape:o}})}var DQ={kernelName:Bl,backendName:"webgl",kernelFunc:px},Ok="return exp(x) - 1.0;",RQ=Je({opSnippet:Ok,packedOpSnippet:Ok,cpuKernelImpl:dY}),MQ={kernelName:Wl,backendName:"webgl",kernelFunc:RQ},Lk=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="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) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${a});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${a}; 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) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function w_(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=me({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new Lk("real",l,t),p=new Lk("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=ks({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=me({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function PQ(e){let{inputs:t,backend:n}=e,{input:a}=t;return w_(a,!1,n)}var OQ={kernelName:fm,backendName:"webgl",kernelFunc:PQ},LQ=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 rd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new LQ(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var zQ={kernelName:hc,backendName:"webgl",kernelFunc:rd},BQ=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},WQ={kernelName:Vl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new BQ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},zk="return floor(x);",VQ=Je({opSnippet:zk,packedOpSnippet:zk,cpuKernelImpl:hY}),UQ={kernelName:Ri,backendName:"webgl",kernelFunc:VQ},GQ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,HQ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,jQ=pn({opSnippet:GQ,packedOpSnippet:HQ,dtype:"int32"}),qQ={kernelName:Mi,backendName:"webgl",kernelFunc:jQ},KQ=class{constructor(e){this.variableNames=["A"];let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},XQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}.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);
}
}
${t.output} = result;
}
`}},YQ={kernelName:Th,backendName:"webgl",kernelFunc:JQ},Qo;function JQ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];(o||i)&&(Qo==null&&(Qo=document.createElement("canvas").getContext("2d")),Qo.canvas.width=l,Qo.canvas.height=u,Qo.drawImage(r,0,0,l,u),r=Qo.canvas);let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=ca.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=X().getBool("WEBGL_PACK")?new XQ(d):new KQ(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function ZQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(p),g=_.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=g_({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=y_({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,w=o!=null,T=h==="leakyrelu",C=h?Wf(h,!1):null,E=new f_(g,v,C,w,T),$=[r,s];if(i&&$.push(i),o&&$.push(o),T){let P=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));$.push(P),b.push(P)}y=n.runWebGLProgram(E,$,"float32")}let x=me({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var QQ={kernelName:ni,backendName:"webgl",kernelFunc:ZQ};function eee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:d,activation:c,leakyreluAlpha:h}=a,m=[],f=p;f==null&&(f=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,u,d,!0),y=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=c?Wf(c,y):null,x=[r,s],v=i!=null,w=o!=null,T=c==="leakyrelu";if(v&&x.push(i),w&&x.push(o),T){let P=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push(P),m.push(P)}let C;y?C=new x_(g,v,b,w,T):C=new b_(g,v,b,w,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=n.runWebGLProgram(C,x,"float32",E);return m.forEach(P=>n.disposeIntermediateTensorInfo(P)),$}var tee={kernelName:ai,backendName:"webgl",kernelFunc:eee},nee=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ut(t.length),r=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${a} strides = ${a}(${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 * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function aee(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=k.sizeFromShape(a.shape),[l,u,p,d]=_.prepareAndValidate(a,r),c=me({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=me({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/p,p]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let y=n.readSync(r.dataId),b=n.bufferSync(a),x=mY(y,b,a.dtype,u,i,p,d,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new nee(i,d,[u,p]),f=n.runWebGLProgram(m,[h,c],h.dtype),g=me({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var ree={kernelName:Gl,backendName:"webgl",kernelFunc:aee},see=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=iee(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${a}));
}
`}};function iee(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("index"):a.push(`${n[r]}`);return a.join()}function k_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let b=n.readSync(s.dataId),x=r.shape[l];for(let v=0;v<b.length;++v){let w=b[v];k.assert(w<=x-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${x-1}]`)}}let u=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=k.sizeFromShape(s.shape),d=[],c=me({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=me({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,p/u.batchSize]}});d.push(c),d.push(h);let m=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),x=n.bufferSync(c),v=fY(x,b,m);return d.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new see(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);d.push(g);let y=me({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var oee={kernelName:Ul,backendName:"webgl",kernelFunc:k_},lee="return float(a > b);",uee=`
return vec4(greaterThan(a, b));
`,pee=pn({opSnippet:lee,packedOpSnippet:uee,cpuKernelImpl:gY,dtype:"bool"}),cee={kernelName:Hl,backendName:"webgl",kernelFunc:pee},dee="return float(a >= b);",hee=`
return vec4(greaterThanEqual(a, b));
`,mee=pn({opSnippet:dee,packedOpSnippet:hee,dtype:"bool",cpuKernelImpl:yY}),fee={kernelName:Oi,backendName:"webgl",kernelFunc:mee};function gee(e){let{inputs:t,backend:n}=e,{input:a}=t;return w_(a,!0,n)}var yee={kernelName:gm,backendName:"webgl",kernelFunc:gee},bee="return float(!isnan(x) && !isinf(x));",xee=Je({opSnippet:bee,dtype:"bool"}),vee={kernelName:jl,backendName:"webgl",kernelFunc:xee},wee="return float(isinf(x));",kee=Je({opSnippet:wee,dtype:"bool"}),Iee={kernelName:ql,backendName:"webgl",kernelFunc:kee},See="return float(isnan(x));",Nee=Je({opSnippet:See,dtype:"bool"}),Tee={kernelName:Kl,backendName:"webgl",kernelFunc:Nee},Cee="return float(a < b);",_ee=`
return vec4(lessThan(a, b));
`,Eee=pn({opSnippet:Cee,packedOpSnippet:_ee,cpuKernelImpl:bY,dtype:"bool"}),Aee={kernelName:Xl,backendName:"webgl",kernelFunc:Eee},$ee="return float(a <= b);",Fee=`
return vec4(lessThanEqual(a, b));
`,Dee=pn({opSnippet:$ee,packedOpSnippet:Fee,cpuKernelImpl:xY,dtype:"bool"}),Ree={kernelName:Yl,backendName:"webgl",kernelFunc:Dee};function Mee(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=vY(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Pee={kernelName:bm,backendName:"webgl",kernelFunc:Mee},Oee=Uu+`
return x < 0.0 ? 0./0. : log(x);
`,Lee=`
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`,zee=Je({opSnippet:Oee,packedOpSnippet:Lee,cpuKernelImpl:wY}),Bee={kernelName:Bi,backendName:"webgl",kernelFunc:zee},Wee=Uu+`
return log(1.0 + x);
`,Vee=Je({opSnippet:Wee}),Uee={kernelName:Jl,backendName:"webgl",kernelFunc:Vee},Gee="return float(a >= 1.0 && b >= 1.0);",Hee=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,jee=pn({opSnippet:Gee,packedOpSnippet:Hee,dtype:"bool"}),qee={kernelName:Zl,backendName:"webgl",kernelFunc:jee},Kee="return float(!(x >= 1.0));",Xee=Je({opSnippet:Kee}),Yee={kernelName:mc,backendName:"webgl",kernelFunc:Xee},Jee="return float(a >= 1.0 || b >= 1.0);",Zee=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Qee=pn({opSnippet:Jee,packedOpSnippet:Zee,dtype:"bool"}),ete={kernelName:fc,backendName:"webgl",kernelFunc:Qee},tte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},nte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${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 - ${s};
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 = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
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 * ${o};
setOutput(result);
}
`}},ate=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=X().getBool("WEBGL_PACK_NORMALIZATION")?new nte(r.shape,s,i,o,l):new tte(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},rte={kernelName:gc,backendName:"webgl",kernelFunc:ate},ste=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${a}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${a})
* 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);
}
`}},ite=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new ste(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},ote={kernelName:xm,backendName:"webgl",kernelFunc:ite};function lte(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=me({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=No(i,e.dtype,"max",a),l=me({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function I_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,p=_.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[p[T]];let v=T0(b,r.shape,r.dtype,p,x);h=n.makeTensorInfo(x,r.dtype);let w=n.texData.get(h.dataId);w.values=v}else h=Vf(r,p,n);u=_.getInnerMostAxes(u.length,o)}_.assertAxesAreInnerMostDims("max",u,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(c){let b=n.texData.get(h.dataId).values,x=kY(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=lte(h,f,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var ute={kernelName:Wi,backendName:"webgl",kernelFunc:I_},pte=a_+`
return max(a, b);
`,cte=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Bf+`
return result;
`,dte=pn({opSnippet:pte,packedOpSnippet:cte,cpuKernelImpl:IY}),hte={kernelName:Vi,backendName:"webgl",kernelFunc:dte};function mte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Lu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))return Un({inputs:{x:r},backend:n});let d=new sc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var fte={kernelName:Ui,backendName:"webgl",kernelFunc:mte};function gte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new _0(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var yte={kernelName:yc,backendName:"webgl",kernelFunc:gte},bte=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
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 += ${a}) {
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 < ${s}; 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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},xte=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${d}, ${c});
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 < ${o};
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 += ${s}) {
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 += ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.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 = ${h} -
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 vte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=_.computePool3DInfo(i.shape,o,l,d,u,p),h=new _0(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new xte(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var wte={kernelName:wm,backendName:"webgl",kernelFunc:vte};function kte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Lu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=_.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new sc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new bte(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var Ite={kernelName:vm,backendName:"webgl",kernelFunc:kte};function Ste(e,t,n,a){let r=new sc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new sc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Nte={kernelName:km,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];k.assert(_.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=_.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Ste(a,o,p,l);return[d,c]}};function Tte(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=me({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=No(i,"float32","mean",a),l=me({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Cte={kernelName:Gi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),u=l,p=_.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let C=0;C<v.length;C++)v[C]=a.shape[p[C]];let w=T0(x,a.shape,a.dtype,p,v);m=i.makeTensorInfo(v,a.dtype);let T=i.texData.get(m.dataId);T.values=w}else m=Vf(a,p,i);h.push(m),u=_.getInnerMostAxes(u.length,o)}_.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=Tte(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function _te(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=un({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",u,o);let[c,h]=_.computeOutAndReduceShapes(d.shape,u),m=k.sizeFromShape(h),f=me({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=No(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(c,l);y=me({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=me({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var Ete={kernelName:Hi,backendName:"webgl",kernelFunc:_te},Ate=a_+`
return min(a, b);
`,$te=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Bf+`
return result;
`,Fte=pn({opSnippet:Ate,packedOpSnippet:$te,cpuKernelImpl:SY}),Dte={kernelName:ji,backendName:"webgl",kernelFunc:Fte},Rte=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=ut(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
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 = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${a}; 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};
}
}
${r} coords = outC - start;
setOutput(getX(${o}));
}
`}},Mte=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ut(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${d};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${d};
}
source -= start;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${p});
${o[a-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${p});
}
`}else{let h=`
${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;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${p});
${o[a-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${p});
}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${p});
${o[a-1]} += 1;
if(${u}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${p});
}
}
`}this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${c}
setOutput(result);
}
`}},Pte=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Mte(a.shape,r,s):new Rte(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Ote={kernelName:qi,backendName:"webgl",kernelFunc:Pte},Lte=`if (b == 0.0) return NAN;
return mod(a, b);`,zte=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Bf+`
return result;
`,Bte=pn({opSnippet:Lte,packedOpSnippet:zte}),Wte={kernelName:Ql,backendName:"webgl",kernelFunc:Bte},Vte=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}));
}
`}},Ute=`
if (a == b) {
return 1.0;
};
return a / b;`,Gte=`
// 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;
`,S_=pn({opSnippet:Ute,packedOpSnippet:Gte,checkOutOfBounds:!0}),Hte={kernelName:$i,backendName:"webgl",kernelFunc:S_},Bk="return a - b;",N_=pn({opSnippet:Bk,packedOpSnippet:Bk,supportsComplex:!0,cpuKernelImpl:BY}),jte={kernelName:ho,backendName:"webgl",kernelFunc:N_};function T_(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=I_({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),u=me({inputs:{x:o},backend:n,attrs:{shape:l}}),p=N_({inputs:{a:r,b:u},backend:n}),d=v_({inputs:{x:p},backend:n}),c=Uf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=me({inputs:{x:c},backend:n,attrs:{shape:l}}),m=S_({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var qte={kernelName:po,backendName:"webgl",kernelFunc:T_};function Kte(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:T_({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Vte(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Xte={kernelName:Im,backendName:"webgl",kernelFunc:Kte},Yte=Ea+`
return -x;
`,Jte=`
vec4 result = -x;
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;
`;function Zte(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=TY(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Ys(a.shape,Jte):r=new Sr(a.shape,Yte),n.runWebGLProgram(r,[a],a.dtype)}var Qte={kernelName:eu,backendName:"webgl",kernelFunc:Zte},ene=mr.nonMaxSuppressionV3Impl;function tne(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=ene(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var nne={kernelName:nu,backendName:"webgl",kernelFunc:tne},ane=mr.nonMaxSuppressionV4Impl;function rne(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=ane(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var sne={kernelName:au,backendName:"webgl",kernelFunc:rne},ine=mr.nonMaxSuppressionV5Impl;function one(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=ine(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var lne={kernelName:ru,backendName:"webgl",kernelFunc:one},une=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${a}), float(${n}),
float(index == coords.y)));
}
`}},pne=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),u=new une(l,s,i,o),p=me({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[p],r.dtype);n.disposeIntermediateTensorInfo(p);let c=[...r.shape,s],h=me({inputs:{x:d},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(d),h},cne={kernelName:Xi,backendName:"webgl",kernelFunc:pne};function Yh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=ad({inputs:{input:a},backend:n}),s=Yh({inputs:{x:r},backend:n}),i=Gf({inputs:{input:a},backend:n}),o=Yh({inputs:{x:i},backend:n}),l=ks({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return rd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var dne={kernelName:Iu,backendName:"webgl",kernelFunc:Yh};function C_(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=ad({inputs:{input:a},backend:n}),s=C_({inputs:{x:r},backend:n}),i=Gf({inputs:{input:a},backend:n}),o=Yh({inputs:{x:i},backend:n}),l=ks({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return rd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var hne={kernelName:su,backendName:"webgl",kernelFunc:C_};function mne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return px({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{k.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=px({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=m_({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var fne={kernelName:iu,backendName:"webgl",kernelFunc:mne},gne=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 a=e.length,r=ut(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},yne=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ut(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
if(${u}) {
`,a===1?"":`}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
${d[m]}
if (${c}) {
result[${m}] = float(value);
} else {
${r} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${p});
}
`;h+=a===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},__=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(k.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return rd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yne(r.shape,s,i):new gne(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},bne={kernelName:Yi,backendName:"webgl",kernelFunc:__},xne=`
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);
`,vne=`
// 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));
`+Bf+`
return result;
`,wne=pn({opSnippet:xne,packedOpSnippet:vne}),kne={kernelName:Ji,backendName:"webgl",kernelFunc:wne};function Ine(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=k.parseAxisParam(s,r.shape),p=u,d=_.getAxesPermutation(p,o),c=r;d!=null&&(c=un({inputs:{x:r},backend:n,attrs:{perm:d}}),p=_.getInnerMostAxes(p.length,o),l.push(c)),_.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=_Y(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(c.shape,p),g=k.sizeFromShape(f),y=me({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=Dm(r.dtype),x=No(y,b,"prod",n);h=me({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,u);h=me({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Sne={kernelName:Qi,backendName:"webgl",kernelFunc:Ine},E_=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=EY(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Nne={kernelName:bc,backendName:"webgl",kernelFunc:E_},Tne="return 1.0 / x;",Cne=Je({opSnippet:Tne}),_ne={kernelName:ou,backendName:"webgl",kernelFunc:Cne},Ene=Ea+`
return (x < 0.0) ? 0.0 : x;
`,Ane=`
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;
`,$ne=Je({opSnippet:Ene,packedOpSnippet:Ane}),Fne={kernelName:eo,backendName:"webgl",kernelFunc:$ne},Dne=Ea+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Rne=`
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;
`,Mne=Je({opSnippet:Dne,packedOpSnippet:Rne}),Pne={kernelName:no,backendName:"webgl",kernelFunc:Mne},One=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&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]/p[0]},
${u[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.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);
}
`}},Lne=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&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]/p[0]},
${u[1]/p[1]},
${u[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.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 zne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Lne(r.shape,l,u,s,i):new One(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Bne={kernelName:to,backendName:"webgl",kernelFunc:zne},Wne=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*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(${p});
const float invHeightScale = float(${d});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
const int winWidth = int(${m});
// 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 >= ${s}) {
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 >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${a-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 Vne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Wne(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Une={kernelName:Tm,backendName:"webgl",kernelFunc:Vne},Gne=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/p[0]},
${u[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${c};
// 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);
}
`}},Hne=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/p[0]},
${u[1]/p[1]},
${u[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.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 = ${c};
// 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 < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function jne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Hne(r.shape,l,u,s,i):new Gne(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var qne={kernelName:xc,backendName:"webgl",kernelFunc:jne},Kne=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${p});
const float invHeightScale = float(${d});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
const int winWidth = int(${m});
// 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 >= ${s}) {
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 >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Xne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Kne(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Yne={kernelName:Nm,backendName:"webgl",kernelFunc:Xne},Jne=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ut(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},Zne=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-${n} tensor is not yet supported`);this.outputShape=e;let a=Sn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(a.slice())};
if(${r}){
result.g = ${l(a.slice())};
}
if(${s}) {
result.b = ${u(a.slice())};
if(${r}) {
result.a = ${p(a.slice())};
}
}
setOutput(result);
}
`;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((y,b)=>c(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Qne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Un({inputs:{x:r},backend:n});let l=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zne(r.shape,o):new Jne(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var eae={kernelName:ao,backendName:"webgl",kernelFunc:Qne},tae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=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 < ${a} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},nae={kernelName:Su,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new tae(a.shape,s),[u,p]=_.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},aae=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,rae=Je({opSnippet:aae}),sae={kernelName:ro,backendName:"webgl",kernelFunc:rae},iae="return inversesqrt(x);",oae=Je({opSnippet:iae,cpuKernelImpl:AY}),lae={kernelName:so,backendName:"webgl",kernelFunc:oae},A_=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let p=`getIndices(${u})`,d="";a===1?d="i":a===2&&(d="i, coords[1]");let c=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${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 < ${t}; j++) {
int index = round(${p});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${c};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function uae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=_.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=me({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=me({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new A_(l,o,h.shape.length,m.shape.length,p,c),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=me({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var pae={kernelName:uu,backendName:"webgl",kernelFunc:uae},cae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);a=o.join(),r=l.join()}let s=ut(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function dae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new cae(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ma(r.dtype,s.dtype))}var hae={kernelName:pu,backendName:"webgl",kernelFunc:dae},mae=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${_.SELU_SCALEALPHA};
float scale = ${_.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,fae=Je({opSnippet:mae}),gae={kernelName:cu,backendName:"webgl",kernelFunc:fae},yae=Uu+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,bae=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,xae=Je({opSnippet:yae,packedOpSnippet:bae,cpuKernelImpl:$Y}),vae={kernelName:oo,backendName:"webgl",kernelFunc:xae},wae=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,kae=Je({opSnippet:wae}),Iae={kernelName:mu,backendName:"webgl",kernelFunc:kae},Sae=Uu+`
return sin(x);
`,Nae=Je({opSnippet:Sae}),Tae={kernelName:io,backendName:"webgl",kernelFunc:Nae},Cae=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,_ae=Je({opSnippet:Cae}),Eae={kernelName:hu,backendName:"webgl",kernelFunc:_ae},Aae=`
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;
`,$ae=Je({opSnippet:Aae}),Fae={kernelName:fu,backendName:"webgl",kernelFunc:$ae},Dae=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],p=__({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=_.getReshaped(p.shape,s,o,!1),c=_.getPermuted(d.length,s.length,!1),h=_.getReshapedPermuted(p.shape,s,o,!1),m=me({inputs:{x:p},backend:n,attrs:{shape:d}}),f=un({inputs:{x:m},backend:n,attrs:{perm:c}}),g=me({inputs:{x:f},backend:n,attrs:{shape:h}});return u.push(p),u.push(m),u.push(f),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},Rae={kernelName:gu,backendName:"webgl",kernelFunc:Dae};function Mae(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=DY(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var Pae={kernelName:vc,backendName:"webgl",kernelFunc:Mae};function Oae(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=RY(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var Lae={kernelName:bu,backendName:"webgl",kernelFunc:Oae};function zae(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=QC(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var Bae={kernelName:wc,backendName:"webgl",kernelFunc:zae};function Wae(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=QC(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var Vae={kernelName:kc,backendName:"webgl",kernelFunc:Wae};function Uae(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:p,outputSize:d}=_.calculateShapes(s,r,o),c=!1,h=new A_(u,l,r.shape.length,s.shape.length,p,[d,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var Gae={kernelName:Cm,backendName:"webgl",kernelFunc:Uae};function Hae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Gu({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var jae={kernelName:yu,backendName:"webgl",kernelFunc:Hae},Wk="return sqrt(x);",qae=Je({opSnippet:Wk,packedOpSnippet:Wk,cpuKernelImpl:MY}),Kae={kernelName:lo,backendName:"webgl",kernelFunc:qae},Xae="return x * x;",Yae=Je({opSnippet:Xae}),Jae={kernelName:Ic,backendName:"webgl",kernelFunc:Yae},Vk="return (a - b) * (a - b);",Zae=pn({opSnippet:Vk,packedOpSnippet:Vk}),Qae={kernelName:co,backendName:"webgl",kernelFunc:Zae};function ere({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ea+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Sr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var tre={kernelName:fs,backendName:"webgl",kernelFunc:ere},nre=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ut(n.length),s=ut(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function are(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:v}=qt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),w;if(f)w=me({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){k.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=qt.computeOutShape(b,x,v),E=Gu({inputs:{x:r},backend:n,attrs:{begin:b,size:C}});w=me({inputs:{x:E},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=He(r.shape,r.dtype,C),$=PY(h,E,v,b);w=n.makeTensorInfo(m,r.dtype,$.values)}else{let C=new nre(b,v,h);w=n.runWebGLProgram(C,[r],r.dtype)}let T=me({inputs:{x:w},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(w),T}var rre={kernelName:xu,backendName:"webgl",kernelFunc:are};function sre(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=OY(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var ire={kernelName:_m,backendName:"webgl",kernelFunc:sre};function ore(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=LY(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var lre={kernelName:Em,backendName:"webgl",kernelFunc:ore};function ure(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.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 i=n.readSync(s.dataId),o=zY(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var pre={kernelName:Am,backendName:"webgl",kernelFunc:ure},cre="return tan(x);",dre=Je({opSnippet:cre}),hre={kernelName:mo,backendName:"webgl",kernelFunc:dre},mre=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,fre=Je({opSnippet:mre}),gre={kernelName:fo,backendName:"webgl",kernelFunc:fre},yre=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=bre(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function bre(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function $_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>k.decodeString(d)):o,u=He(r.shape,r.dtype,l),p=WY(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new yre(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var xre={kernelName:ms,backendName:"webgl",kernelFunc:$_},vre=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));
}
}
`}},wre=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 Ws(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Uk(e){let t=1;for(;t<e;)t*=2;return t}function kre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,p=u[u.length-1];if(n.shouldExecuteOnCPU([r])||p<o||s>l){let $=n.readSync(r.dataId),[P,F]=VY($,u,r.dtype,s,i);return[n.makeTensorInfo(P.shape,P.dtype,P.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,rd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=k.sizeFromShape(u)/p,f=me({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Ws(n,h);let g=Uk(s),y=Uk(p),b=null,x=()=>b===null?[f,f]:[f,b],v=($,P,F)=>{let S=x(),M=new vre(F),V=[[p],[b===null?1:0],[Number.NEGATIVE_INFINITY],[$],[P]],j=b;b=n.runWebGLProgram(M,S,"int32",V),Ws(n,j)};for(let $=1;$<g;$*=2){let P=$*2;for(let F=$;F>=1;F/=2)v(P,F,[m,y])}for(let $=y;$>g;$/=2){let P=x(),F=new wre([m,$/2]),S=[[p],[b===null?1:0],[g]],M=b;b=n.runWebGLProgram(F,P,"int32",S),Ws(n,M);let V=g/2,j=V*2;for(let q=V;q>=1;q/=2)v(j,q,b.shape)}let w=b;b=Gu({inputs:{x:b},backend:n,attrs:{begin:0,size:[m,s]}}),Ws(n,w);let T=k_({inputs:{x:f,indices:b},backend:n,attrs:{axis:1,batchDims:1}});Ws(n,f);let C=u.slice(0,-1);C.push(s),w=b,b=me({inputs:{x:b},attrs:{shape:C},backend:n}),Ws(n,w);let E=T;return T=me({inputs:{x:T},attrs:{shape:C},backend:n}),Ws(n,E),[T,b]}var Ire={kernelName:vu,backendName:"webgl",kernelFunc:kre},Sre=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 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 (${o} == 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(${e}));
if (${i} == 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 Nre(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new Sre(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var Tre={kernelName:wu,backendName:"webgl",kernelFunc:Nre};function Cre(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Lu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=UY(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var _re={kernelName:$m,backendName:"webgl",kernelFunc:Cre};function Ere(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;f<o;f++)f!==s&&(u[p++]=i.shape[f]);let d=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){c[s]=f;let g=Gu({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=me({inputs:{x:g},backend:n,attrs:{shape:u}});m[f]=y,d.push(g)}return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var Are={kernelName:ku,backendName:"webgl",kernelFunc:Ere},$re=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=`
sumValue += dot(values, segFilter);
`,c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
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 + ${u};
if (${p===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
);
${d}
} else if (${p===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
);
${d}
} else if (${p===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 Fre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=_.getAxesPermutation([u],o),d=r;p!=null&&(d=un({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=_.getInnerMostAxes(1,o)[0]);let c=_.segment_util.computeOutShape(d.shape,u,i),h=k.sizeFromShape([d.shape[u]]),m=me({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Dm(r.dtype),g=(v,w,T,C,E)=>{let $=v.shape[0],P=v.shape[1],F=_.segment_util.segOpComputeOptimalWindowSize(P,E),S={windowSize:F,inSize:P,batchSize:$,numSegments:E},M=new $re(S,w),V=n.compileAndRun(M,[v,T],C);if(l.push(V),V.shape[1]===E)return V;let j=E_({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=$_({inputs:{x:j},backend:n,attrs:{reps:[P/F]}});return l.push(j),l.push(q),g(V,w,q,C,E)},y=g(m,"unsortedSegmentSum",s,f,i),b=me({inputs:{x:y},backend:n,attrs:{shape:c}}),x=b;if(p!=null){l.push(b);let v=_.getUndoAxesPermutation(p);x=un({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Dre={kernelName:Sc,backendName:"webgl",kernelFunc:Fre},Rre=[L9,B9,U9,j9,K9,J9,Q9,tJ,sJ,oJ,pJ,hJ,gJ,vJ,IJ,NJ,CJ,$J,DJ,MJ,zJ,jJ,KJ,YJ,nZ,rZ,lZ,x9,cZ,gZ,vZ,TZ,_Z,AZ,FZ,RZ,OZ,BZ,UZ,HZ,qZ,XZ,ZZ,eQ,rQ,iQ,uQ,dQ,mQ,bQ,kQ,TQ,EQ,FQ,DQ,MQ,OQ,zQ,WQ,UQ,qQ,YQ,QQ,tee,ree,oee,cee,fee,b9,yee,mZ,vee,Iee,Tee,w9,Aee,Ree,Pee,Bee,Uee,qee,Yee,ete,rte,ote,ute,hte,fte,yte,wte,Ite,Nte,Cte,Ete,Dte,Ote,Wte,Xte,T9,Qte,nne,sne,lne,ZJ,cne,hne,fne,bne,kne,I9,Sne,Nne,QJ,Hte,_ne,Fne,Pne,_9,Bne,Une,qne,Yne,eae,nae,sae,lae,pae,hae,gae,vae,Iae,Tae,Eae,GJ,qte,Fae,Rae,Pae,Lae,Bae,Vae,Gae,jae,Kae,Jae,Qae,tre,rre,ire,lre,pre,jte,M9,hre,gre,xre,Ire,Tre,P9,_re,Are,Dre,dne];for(let e of Rre)Nc(e);var Ft;(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"})(Ft||(Ft={}));var ic;(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",e[e.elu=6]="elu"})(ic||(ic={}));var F_;function Mre(e){F_=e.wasm.cwrap(ti,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Pre(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);m=E.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=ic[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=Nu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)),v=n.makeOutput([...x,y,b],r.dtype),w=n.dataIdMap.get(v.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(s.shape).buffer);return F_(c,T,r.shape.length,h,C,s.shape.length,l,u,g,m,f,d||0,w),v}var Ore={kernelName:ti,backendName:"wasm",setupFunc:Mre,kernelFunc:Pre};function cn(e,t){let n;function a(s){n=s.wasm.cwrap(e,null,["number","number","number"])}function r(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),p=i.dataIdMap.get(u.dataId).id;return k.sizeFromShape(u.shape)===0||n(l,Ft[o.dtype],p),u}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:r}}var Lre=cn(Il);function En(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=_.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),b=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,y,p.shape.length,Ft[u.dtype],b),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var zre=!0,Bre=En(ds,zre),D_;function Wre(e){D_=e.wasm.cwrap(xi,null,["array","number","number","number"])}function Vre(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return D_(s,r.length,Ft[a.dtype],i),a}var Ure={kernelName:xi,backendName:"wasm",setupFunc:Wre,kernelFunc:Vre};function Hf(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Gre={kernelName:Li,backendName:"wasm",kernelFunc:Hf},R_;function Hre(e){R_=e.wasm.cwrap(go,null,["number","array","number","number","number","array","number"])}function ps(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=qre(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=jre(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Hf({inputs:t,backend:n});return m.shape=o,m}let u=n.makeOutput(o,l.dtype),p=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return R_(p,h,l.shape.length,Ft[l.dtype],d,c,s.length),u}function jre(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function qre(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Kre={kernelName:go,backendName:"wasm",kernelFunc:ps,setupFunc:Hre};function Is(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=_.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c<p.length;c++)p[c]=a[o[c]];i=_.getInnerMostAxes(i.length,r),l=ps({inputs:{x:e},attrs:{perm:o},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var M_;function Xre(e){M_=e.wasm.cwrap(Tl,null,["number, number, number"])}function Yre(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Is(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("all",p,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,p),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;M_(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=_.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Jre={kernelName:Tl,backendName:"wasm",setupFunc:Xre,kernelFunc:Yre},P_;function Zre(e){P_=e.wasm.cwrap(Cl,null,["number, number, number"])}function Qre(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Is(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("any",p,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,p),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;P_(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=_.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var ese={kernelName:Cl,backendName:"wasm",setupFunc:Zre,kernelFunc:Qre},O_;function tse(e){O_=e.wasm.cwrap(vi,null,["number","number","number","number","number"])}function nse(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:p,inputWasTransposed:d}=Is(s,r,t);if(d){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let c=l.shape.slice(0,-1),h=t.makeOutput(c,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),g=l.shape[p[0]];return O_(o,Ft[l.dtype],f,g,m),d&&t.disposeData(u.dataId),h}var ase={kernelName:vi,backendName:"wasm",kernelFunc:nse,setupFunc:tse},L_;function rse(e){L_=e.wasm.cwrap(wi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sse(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=_.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.strideHeight,b=p.strideWidth,x=p.inChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);if(p.dilationWidth!==1||p.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${p.dilationHeight}, ${p.dilationWidth}].`);let v=a.makeOutput(p.outShape,"float32"),w=a.dataIdMap.get(v.dataId).id;return L_(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,w),v}var ise={kernelName:wi,backendName:"wasm",setupFunc:rse,kernelFunc:sse};function Bn(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=k.sizeFromShape(a.shape),i=k.inferFromImplicitShape(r,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var ose={kernelName:lu,backendName:"wasm",kernelFunc:Bn},z_;function lse(e){z_=e.wasm.cwrap(ki,null,["number","array","number","number","array","number","number","number","number"])}function use(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=Nu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,p,c]:[g,c,p],v=o?[y,h,d]:[y,d,h],w=Bn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=Bn({inputs:{x:s},backend:n,attrs:{shape:v}}),C=n.dataIdMap.get(w.dataId).id,E=n.dataIdMap.get(T.dataId).id,$=i?w.shape[2]:w.shape[1],P=o?T.shape[1]:T.shape[2],F=Math.max(g,y),S=n.makeOutput([F,$,P],w.dtype),M=n.dataIdMap.get(S.dataId).id,V=new Uint8Array(new Int32Array(w.shape).buffer),j=new Uint8Array(new Int32Array(T.shape).buffer);return z_(C,V,w.shape.length,E,j,T.shape.length,i,o,M),n.disposeData(w.dataId),n.disposeData(T.dataId),S.shape=b,S}var pse={kernelName:ki,backendName:"wasm",setupFunc:lse,kernelFunc:use};function gi(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=qt.parseSliceParams(t,n,a),o=qt.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),p=k.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(o){let m=qt.computeFlatOffset(s,p);return t.dtype==="string"?d.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+k.sizeFromShape(i))),u}if(t.dtype==="string"){let m=jh(l,s,i,t.shape,t.dtype);return d.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)cse(l,p[0],c,s,i);else if(h===3)dse(l,p[0],p[1],c,s,i);else if(h===4)hse(l,p[0],p[1],p[2],c,s,i);else{let m=jh(l,s,i,t.shape,t.dtype);c.set(m)}return u}function cse(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u<l;u++){let p=u*t+o;n.set(e.subarray(p,p+r[1]),s),s+=r[1]}}function dse(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],u=r[2],p=o+s[0],d=l+s[1];for(let c=o;c<p;c++)for(let h=l;h<d;h++){let m=c*t+h*n+u;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function hse(e,t,n,a,r,s,i){let o=0,l=s[0],u=s[1],p=s[2],d=l+i[0],c=u+i[1],h=p+i[2],m=s[3];for(let f=l;f<d;f++)for(let g=u;g<c;g++)for(let y=p;y<h;y++){let b=f*t+g*n+y*a+m;r.set(e.subarray(b,b+i[3]),o),o+=i[3]}}var mse={kernelName:du,backendName:"wasm",kernelFunc:gi};function fse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a,o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),u=_.getPermuted(l.length,s.length),p=_.getReshapedPermuted(r.shape,s,o),d=_.getSliceBeginCoords(i,s.length),c=_.getSliceSize(p,i,s.length),h=Bn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ps({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Bn({inputs:{x:m},backend:n,attrs:{shape:p}}),g=gi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(h.dataId),g}var gse={kernelName:Dl,backendName:"wasm",kernelFunc:fse};function sd(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var yse={kernelName:Ii,backendName:"wasm",kernelFunc:sd},bse=cn(Si),B_;function xse(e){B_=e.wasm.cwrap(hs,null,["number","number","number","number"])}function vse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return B_(o,s,i,u),l}var wse={kernelName:hs,backendName:"wasm",setupFunc:xse,kernelFunc:vse};function W_(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=_.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return Hf({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(_.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=k.sizeFromShape(x.shape.slice(a));return Bn({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=_.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=i0(m,r,t[0].dtype,f),y=_.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=_.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=k.sizeFromShape(s[0].shape.slice(0,a)),u=0,p=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=s.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let m=h*u;for(let f=0;f<d.length;f++){let g=p[f],y=h*g,b=d[f].subarray(y,y+g);c.set(b,m),m+=g}}return i}var kse={kernelName:Rl,backendName:"wasm",kernelFunc:W_},V_;function Ise(e){V_=e.wasm.cwrap(Ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sse(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d,dataFormat:c}=n,h=_.convertConv2DDataFormat(c),m=_.computeConv2DInfo(r.shape,s.shape,l,u,p,d,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,b=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,w=m.dilationHeight,T=m.dilationWidth,C=m.strideHeight,E=m.strideWidth,$=m.inChannels,P=m.outChannels,F=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(m.outShape,"float32"),M=a.dataIdMap.get(S.dataId).id;return V_(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,v,F,w,T,C,E,$,P,M),S}var Nse={kernelName:Ni,backendName:"wasm",setupFunc:Ise,kernelFunc:Sse},U_;function Tse(e){U_=e.wasm.cwrap(Ti,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 Cse(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:p}=a,d=1,c=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(p,s.shape,i,d,o,u,!1,c),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:v,outHeight:w,outWidth:T,strideHeight:C,strideWidth:E}=h,$=f-1-h.padInfo.top,P=g-1-h.padInfo.left,F=h.dataFormat==="channelsLast",S=k.computeStrides(h.inShape),M=k.computeStrides(r.shape),[V,j,q]=k.computeStrides(s.shape),K=S[0],Q=F?S[1]:S[2],ee=F?S[2]:1,re=F?1:S[1],Z=M[0],ie=F?M[1]:M[2],ae=F?M[2]:1,le=F?1:M[1],ue=t.makeOutput(h.inShape,"float32"),ke=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,Ie=t.dataIdMap.get(s.dataId).id;return U_(ye,Ie,m,f,g,b,x,y,w,T,v,C,E,$,P,V,j,q,K,Q,ee,re,Z,ie,ae,le,ke),ue}var _se={kernelName:Ti,backendName:"wasm",setupFunc:Tse,kernelFunc:Cse},Ese=cn(Ci),Ase=cn(_i),cx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(cx||(cx={}));var G_;function $se(e){G_=e.wasm.cwrap(Pl,null,["number","number","number","number","array","number","number","number","number","number"])}function Fse(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:u}=n,p=l.shape[0],[d,c]=i,h=[p,d,c,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=sd({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(h,"float32"),v=t.dataIdMap.get(x.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return G_(g,y,b,p,w,d,c,cx[r],s,v),f!=null&&t.disposeData(f.dataId),x}var Dse={kernelName:Pl,backendName:"wasm",setupFunc:$se,kernelFunc:Fse},H_;function Rse(e){H_=e.wasm.cwrap(Ml,null,["number","number","number","number","number","number"])}function Mse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=_.getAxesPermutation([s],l),p=r;u!==null&&(p=ps({inputs:{x:r},attrs:{perm:u},backend:n}));let d=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumprod",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;H_(m,i?1:0,o?1:0,h,f,Ft[r.dtype]);let g=c;if(u!==null){let y=_.getUndoAxesPermutation(u);g=ps({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Pse={kernelName:Ml,backendName:"wasm",setupFunc:Rse,kernelFunc:Mse},j_;function Ose(e){j_=e.wasm.cwrap(Ei,null,["number","number","number","number","number","number"])}function Lse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=_.getAxesPermutation([s],l),p=r;u!==null&&(p=ps({inputs:{x:r},attrs:{perm:u},backend:n}));let d=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;j_(m,i?1:0,o?1:0,h,f,Ft[r.dtype]);let g=c;if(u!==null){let y=_.getUndoAxesPermutation(u);g=ps({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var zse={kernelName:Ei,backendName:"wasm",setupFunc:Ose,kernelFunc:Lse},q_;function Bse(e){q_=e.wasm.cwrap(Ol,null,["number","number","number","array","number","array","array","number","number"])}function Wse(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return q_(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var Vse={kernelName:Ol,backendName:"wasm",setupFunc:Bse,kernelFunc:Wse},K_;function Use(e){K_=e.wasm.cwrap(Ai,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gse(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=_.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,w=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,$=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let F=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get(F.dataId).id;return K_(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,P,v,w,T,C,E,$,S),F}var Hse={kernelName:Ai,backendName:"wasm",setupFunc:Use,kernelFunc:Gse},jse=cn(Fi),qse=!1,Kse=En(zl,qse,"bool"),Xse=cn(Di,"float32");function dx(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Bn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Yse={kernelName:Bl,backendName:"wasm",kernelFunc:dx};function X_(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Jse={kernelName:hc,backendName:"wasm",kernelFunc:X_},Y_;function Zse(e){Y_=e.wasm.cwrap(Vl,null,["number","number","number","number","number","number"])}function Qse(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return Y_(s,o,l,u,p,i),r}var eie={kernelName:Vl,backendName:"wasm",kernelFunc:Qse,setupFunc:Zse},tie=cn(Ri),nie=!1,aie=En(Mi,nie),J_;function rie(e){J_=e.wasm.cwrap(Pi,null,["number","number","number","number","number","number","number"])}function sie(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return J_(p,d,c,h,m,r,g),f}var iie={kernelName:Pi,backendName:"wasm",setupFunc:rie,kernelFunc:sie},Z_;function oie(e){Z_=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=ic[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${x})`);v=ae.id}let w=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,V=f.strideWidth,j=f.inChannels,q=f.padInfo.type==="SAME"?1:0,K=f.batchSize,Q=f.inHeight,ee=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),Z=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return Z_(y,K,Q,ee,b,w,T,v,C,E,$,P,q,F,S,M,V,j,x,g,ie,m||0,Z),re}var uie={kernelName:ni,backendName:"wasm",setupFunc:oie,kernelFunc:lie},Q_;function pie(e){Q_=e.wasm.cwrap(ai,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 cie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=ic[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${x})`);v=ae.id}let w=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,V=f.strideWidth,j=f.inChannels,q=f.padInfo.type==="SAME"?1:0,K=f.batchSize,Q=f.inHeight,ee=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),Z=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return Q_(y,K,Q,ee,b,w,T,v,C,E,$,P,q,F,S,M,V,j,x,g,ie,m||0,Z),re}var die={kernelName:ai,backendName:"wasm",setupFunc:pie,kernelFunc:cie},eE;function hie(e){eE=e.wasm.cwrap(Gl,null,["number","number","number","number","number","number","array","number"])}function mie(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ax.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return eE(c,Ft[a.dtype],h,i,d,o,m,f),u}var fie={kernelName:Gl,backendName:"wasm",setupFunc:hie,kernelFunc:mie},tE;function gie(e){tE=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function yie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C<u.length;++C){let E=u[C];k.assert(E<=p-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Bn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=k.sizeFromShape(s.shape),m=Bn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(k.sizeFromShape(r.shape)===0)return g;let y=c.shape.length-1,b=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(k.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(k.computeStrides(f)).buffer);return tE(b,Ft[r.dtype],w,y,x,d.batchSize,T,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var bie={kernelName:Ul,backendName:"wasm",setupFunc:gie,kernelFunc:yie},xie=!1,vie=En(Hl,xie,"bool"),wie=!1,kie=En(Oi,wie,"bool"),nE;function Iie(e){nE=e.wasm.cwrap(zi,null,["number","number","number","number"])}function Sie(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;nE(r,Ft[t.dtype],n,i)}return s}var Nie={kernelName:zi,backendName:"wasm",setupFunc:Iie,kernelFunc:Sie},Tie=!1,Cie=En(Xl,Tie,"bool"),_ie=!1,Eie=En(Yl,_ie,"bool"),Aie=cn(Bi),$ie=!1,Fie=En(Zl,$ie,"bool"),aE;function Die(e){aE=e.wasm.cwrap(Wi,null,["number","number","number","number"])}function Rie(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Is(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",p,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,p),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;aE(o,Ft[i.dtype],g,b)}if(c&&t.disposeData(u.dataId),s){let b=_.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Mie={kernelName:Wi,backendName:"wasm",setupFunc:Die,kernelFunc:Rie},Pie=!1,Oie=En(Vi,Pie),rE;function Lie(e){rE=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zie(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;k.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=_.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,b=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,w=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return rE(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,v,w,T,E),C}var Bie={kernelName:Ui,backendName:"wasm",setupFunc:Lie,kernelFunc:zie},sE;function Wie(e){sE=e.wasm.cwrap(Gi,null,["number, number, number"])}function Vie(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Is(i,r,t),m=d;if(h){let v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=_.getInnerMostAxes(m.length,u.shape.length))}_.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=_.computeOutAndReduceShapes(u.shape,m),y=k.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=sd({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(k.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;sE(l,y,v)}if(h&&t.disposeData(p.dataId),s){let v=_.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var Uie={kernelName:Gi,backendName:"wasm",setupFunc:Wie,kernelFunc:Vie},iE;function Gie(e){iE=e.wasm.cwrap(Hi,null,["number","number","number","number"])}function Hie(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Is(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;_.assertAxesAreInnerMostDims("min",d,m);let[f,g]=_.computeOutAndReduceShapes(u.shape,d),y=k.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;iE(l,Ft[i.dtype],y,x)}if(h&&t.disposeData(p.dataId),s){let x=_.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var jie={kernelName:Hi,backendName:"wasm",setupFunc:Gie,kernelFunc:Hie},qie=!1,Kie=En(ji,qie),hx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(hx||(hx={}));var oE;function Xie(e){oE=e.wasm.cwrap(qi,null,["number","array","number","number","array","array","number","number"])}function Yie(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return oE(i,u,t.shape.length,Ft[t.dtype],c,h,hx[r],l),o}var Jie={kernelName:qi,backendName:"wasm",kernelFunc:Yie,setupFunc:Xie},Zie=!0,Qie=En(Ki,Zie),eoe=cn(eu);function E0(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var lE;function toe(e){lE=e.wasm.cwrap(nu,"number",["number","number","number","number","number"])}function noe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=lE(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=E0(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var aoe={kernelName:nu,backendName:"wasm",setupFunc:toe,kernelFunc:noe},uE;function roe(e){uE=e.wasm.cwrap(au,"number",["number","number","number","number","number","bool"])}function soe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=uE(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=E0(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var ioe={kernelName:au,backendName:"wasm",setupFunc:roe,kernelFunc:soe},pE;function ooe(e){pE=e.wasm.cwrap(ru,"number",["number","number","number","number","number","number"])}function loe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=pE(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=E0(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var uoe={kernelName:ru,backendName:"wasm",setupFunc:ooe,kernelFunc:loe},poe=!1,coe=En(tu,poe,"bool"),cE;function doe(e){cE=e.wasm.cwrap(Xi,null,["number","number","number","number","number"])}function hoe(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(r.dataId).id;return cE(p,s,i,o,u),l}var moe={kernelName:Xi,backendName:"wasm",setupFunc:doe,kernelFunc:hoe};function foe(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var goe={kernelName:su,backendName:"wasm",kernelFunc:foe};function yoe(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return dx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{k.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=dx({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=W_({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeData(p.dataId)),u}var boe={kernelName:iu,backendName:"wasm",kernelFunc:yoe},dE;function xoe(e){dE=e.wasm.cwrap(Yi,null,["number","array","number","number","array","array","number","number"])}function voe(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]);if(k.sizeFromShape(t.shape)===0)return X_({backend:n,attrs:{shape:s,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return dE(i,u,t.shape.length,Ft[t.dtype],c,h,r,l),o}var hE={kernelName:Yi,backendName:"wasm",kernelFunc:voe,setupFunc:xoe},woe=!1,koe=En(Ji,woe),mE;function Ioe(e){mE=e.wasm.cwrap(Zi,null,["number","number","number"])}function Soe(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=s,l=a,u=l;l.dtype!=="float32"&&(u=sd({backend:n,inputs:{x:a},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(u.dataId).id);let p=n.makeOutput(a.shape,"float32"),d=n.dataIdMap.get(p.dataId).id;return mE(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var Noe={kernelName:Zi,backendName:"wasm",setupFunc:Ioe,kernelFunc:Soe},fE;function Toe(e){fE=e.wasm.cwrap(Qi,null,["number","number","number","number"])}function Coe(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Is(i,r,t),m=d;if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x,m=_.getInnerMostAxes(m.length,u.shape.length))}_.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=_.computeOutAndReduceShapes(u.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;fE(l,y,Ft[b.dtype],x)}if(h&&t.disposeData(p.dataId),s){let x=_.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var _oe={kernelName:Qi,backendName:"wasm",setupFunc:Toe,kernelFunc:Coe},Eoe=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=u0(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Aoe={kernelName:bc,backendName:"wasm",kernelFunc:Eoe},$oe=!0,Foe=En($i,$oe),Doe=cn(eo),Roe=cn(no),gE;function Moe(e){gE=e.wasm.cwrap(to,null,["number","number","number","number","number","number","number","number","number","number"])}function Poe(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[p,d,c,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=sd({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return gE(y,p,d,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var Ooe={kernelName:to,backendName:"wasm",setupFunc:Moe,kernelFunc:Poe},yE;function Loe(e){yE=e.wasm.cwrap(ao,null,["number","array","number","array","number","number"])}function zoe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return Hf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),d=new Uint8Array(new Int32Array(r.shape).buffer);yE(l,p,i.length,d,r.shape.length,u);let c=Bn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var Boe={kernelName:ao,backendName:"wasm",kernelFunc:zoe,setupFunc:Loe},bE;function Woe(e){bE=e.wasm.cwrap(Su,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Voe(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,p=n.dataIdMap.get(l.dataId).id,[d,c,h,m]=r.shape,[f,g]=_.getImageCenter(o,c,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return bE(u,d,c,h,m,s,f,g,v,x.length,p),l}var Uoe={kernelName:Su,backendName:"wasm",kernelFunc:Voe,setupFunc:Woe},Goe=cn(ro),Hoe=cn(so),xE;function joe(e){xE=e.wasm.cwrap(uu,null,["number","number","number","number","number","number","array","number","number"])}function qoe(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=$x.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(d).buffer),g=t.dataIdMap.get(o.dataId).id;return xE(h,m,Ft[s.dtype],l,u,p,f,c,g),o}var Koe={kernelName:uu,backendName:"wasm",setupFunc:joe,kernelFunc:qoe},vE;function Xoe(e){vE=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Yoe(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(r.shape,r.dtype),p=n.dataIdMap.get(u.dataId).id,d=a.shape.length,c=r.shape.length,h=d===0||d>1||c===1?1:k.sizeFromShape(r.shape.slice(1));return vE(i,o,l,h,p),u}var Joe={kernelName:pu,backendName:"wasm",kernelFunc:Yoe,setupFunc:Xoe},wE;function Zoe(e){wE=e.wasm.cwrap(oo,null,["number","number"])}function Qoe(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return k.sizeFromShape(r.shape)===0||wE(a,s),r}var ele={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Zoe,kernelFunc:Qoe},tle=cn(io),kE;function nle(e){kE=e.wasm.cwrap(po,null,["number","number","number","number"])}function ale(e){let{backend:t,inputs:{logits:n},attrs:{dim:a}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[a],l=k.sizeFromShape(n.shape)/o;return k.sizeFromShape(s.shape)===0||kE(r,i,o,l),s}var rle={kernelName:po,backendName:"wasm",setupFunc:nle,kernelFunc:ale};function sle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a,o=k.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<r.shape.length;++g)l.push([0,0]);let u=hE.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=_.getReshaped(u.shape,s,o,!1),d=_.getPermuted(p.length,s.length,!1),c=_.getReshapedPermuted(u.shape,s,o,!1),h=Bn({inputs:{x:u},backend:n,attrs:{shape:p}}),m=ps({inputs:{x:h},backend:n,attrs:{perm:d}}),f=Bn({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeData(u.dataId),n.disposeData(h.dataId),n.disposeData(m.dataId),f}var ile={kernelName:gu,backendName:"wasm",kernelFunc:sle},IE;function ole(e){IE=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function lle(e){let{backend:t,inputs:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=n,o=a.shape[0],l=a.shape[1],u=t.readSync(s.dataId)[0],p=[o+u,l],d=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,m=t.makeOutput(p,a.dtype),f=t.dataIdMap.get(m.dataId).id,g=t.makeOutput(p.slice(0,1),r.dtype),y=t.dataIdMap.get(g.dataId).id,b=t.makeOutput([u],"bool"),x=t.dataIdMap.get(b.dataId).id,v=t.makeOutput([o],a.dtype),w=t.dataIdMap.get(v.dataId).id,T=t.makeOutput([4],"int32"),C=t.dataIdMap.get(T.dataId).id,E=IE(d,c,Ft[r.dtype],o,u,l,h,f,y,x,w,C),$=t.readSync(T.dataId),P;switch($[0]){case 1:{P=_.getSparseFillEmptyRowsIndicesDenseShapeMismatch($[1]);break}case 2:{P=_.getSparseFillEmptyRowsNegativeIndexErrorMessage($[1],$[2]);break}case 3:P=_.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage($[1],$[2],$[3]);break;default:P=""}if(t.disposeData(T.dataId),P)throw t.disposeData(m.dataId),t.disposeData(g.dataId),t.disposeData(b.dataId),t.disposeData(v.dataId),new Error(P);let F=m,S=g;return E!==p[0]&&(F=gi({inputs:{x:m},attrs:{begin:0,size:[E,l]},backend:t}),S=gi({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(m.dataId),t.disposeData(g.dataId)),[F,S,b,v]}var ule={kernelName:vc,backendName:"wasm",setupFunc:ole,kernelFunc:lle},SE;function ple(e){SE=e.wasm.cwrap(bu,null,["number","number","number","number","number","number","number"])}function cle(e){let{backend:t,inputs:n}=e,{inputIndices:a,inputShape:r,newShape:s}=n;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=t.dataIdMap.get(a.dataId).id,o=t.dataIdMap.get(r.dataId).id,l=t.dataIdMap.get(s.dataId).id,u=a.shape[0],p=k.sizeFromShape(s.shape),d=t.makeOutput([u,p],a.dtype),c=t.dataIdMap.get(d.dataId).id,h=t.makeOutput([p],s.dtype),m=t.dataIdMap.get(h.dataId).id,f=t.makeOutput([3],"int32"),g=t.dataIdMap.get(f.dataId).id;SE(i,o,l,u,c,m,g);let y=t.readSync(f.dataId),b;switch(y[0]){case 0:{b=_.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1],y[2]);break}case 1:{b=_.getSparseReshapeNegativeOutputDimErrorMessage(y[1],y[2]);break}case 2:b=_.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let x=Array.from(t.readSync(r.dataId)),v=Array.from(t.readSync(h.dataId));b=_.getSparseReshapeInputOutputMultipleErrorMessage(x,v);break}case 4:{let x=Array.from(t.readSync(r.dataId)),v=Array.from(t.readSync(h.dataId));b=_.getSparseReshapeInputOutputMismatchErrorMessage(x,v);break}default:b=""}if(t.disposeData(f.dataId),b)throw t.disposeData(d.dataId),t.disposeData(h.dataId),new Error(b);return[d,h]}var dle={kernelName:bu,backendName:"wasm",setupFunc:ple,kernelFunc:cle},NE;function TE(e){NE=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function CE(e,t){let{backend:n,inputs:a}=e,{data:r,indices:s,segmentIds:i}=a,o=s.shape[0],l=n.readSync(i.dataId,o-1,o)[0],u=o>0?l+1:0;if(u<0)throw new Error(_.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=u;let d=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(i.dataId).id,m=n.makeOutput(p,r.dtype),f=n.dataIdMap.get(m.dataId).id,g=n.makeOutput([4],"int32"),y=n.dataIdMap.get(g.dataId).id;NE(d,Ft[r.dtype],r.shape[0],c,h,f,y,t,0);let b=n.readSync(g.dataId),x;switch(b[0]){case 0:{x=_.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=_.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=_.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b[1],b[2]);break;case 3:x=_.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(b[1],b[2],b[3]);break;default:x=""}if(n.disposeData(g.dataId),x)throw n.disposeData(m.dataId),new Error(x);return m}function hle(e){return CE(e,!0)}var mle={kernelName:wc,backendName:"wasm",setupFunc:TE,kernelFunc:hle};function fle(e){return CE(e,!1)}var gle={kernelName:kc,backendName:"wasm",setupFunc:TE,kernelFunc:fle};function yle(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),p=r.shape.slice();return l.map(d=>{let c=[...p];c[o]=d;let h=gi({inputs:{x:r},attrs:{begin:u,size:c},backend:a});return u[o]+=d,h})}var ble={kernelName:yu,backendName:"wasm",kernelFunc:yle},xle=cn(lo),vle=cn(Ic),wle=!0,kle=En(co,wle),_E;function Ile(e){_E=e.wasm.cwrap(fs,null,["number","number","number","number"])}function Sle(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return _E(i,r,Ft[s.dtype],l),o}var Nle={kernelName:fs,backendName:"wasm",setupFunc:Ile,kernelFunc:Sle},EE;function Tle(e){EE=e.wasm.cwrap(xu,null,["number","array","number","array","array","array","array","array","number","number"])}function Cle(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:v}=qt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),w;if(f)w=Bn({inputs:{x:r},backend:t,attrs:{shape:m}});else if(g||y){k.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=qt.computeOutShape(b,x,v),C=gi({inputs:{x:r},backend:t,attrs:{begin:b,size:T}});w=Bn({inputs:{x:C},backend:t,attrs:{shape:m}}),t.disposeData(C.dataId)}else{let T=t.makeOutput(h,"float32"),C=t.dataIdMap.get(r.dataId).id,E=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),$=new Uint8Array(new Int32Array(b).buffer),P=new Uint8Array(new Int32Array(x).buffer),F=new Uint8Array(new Int32Array(v).buffer),S=new Uint8Array(new Int32Array(h).buffer),M=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer),V=t.dataIdMap.get(T.dataId).id;EE(C,E,r.shape.length,$,P,F,S,M,h.length,V),w=Bn({inputs:{x:T},backend:t,attrs:{shape:m}}),t.disposeData(T.dataId)}return w}var _le={kernelName:xu,backendName:"wasm",setupFunc:Tle,kernelFunc:Cle},Ele=!0,Ale=En(ho,Ele),AE;function $le(e){AE=e.wasm.cwrap(uo,null,["number","number","number","number"])}function Fle(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Is(i,r,t),m=d;if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x,m=_.getInnerMostAxes(m.length,u.shape.length))}_.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=_.computeOutAndReduceShapes(u.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;AE(l,y,Ft[b.dtype],x)}if(h&&t.disposeData(p.dataId),s){let x=_.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var Dle={kernelName:uo,backendName:"wasm",setupFunc:$le,kernelFunc:Fle},Rle=cn(mo),Mle=cn(fo),$E;function Ple(e){$E=e.wasm.cwrap(ms,null,["number","array","number","array","number","number"])}function Ole(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let c=0;c<o.length;c++)o[c]=r.shape[c]*i[c];let l=new Uint8Array(new Int32Array(r.shape).buffer),u=new Uint8Array(new Int32Array(o).buffer),p=n.makeOutput(o,r.dtype),d=n.dataIdMap.get(p.dataId).id;return $E(s,l,r.shape.length,u,o.length,Ft[p.dtype],d),p}var Lle={kernelName:ms,backendName:"wasm",setupFunc:Ple,kernelFunc:Ole},FE;function zle(e){FE=e.wasm.cwrap(vu,null,["number","array","number","number","number","bool","number","number"])}var Ble=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),p=t.dataIdMap.get(u.dataId).id,d=t.makeOutput(l,"int32"),c=t.dataIdMap.get(d.dataId).id;return FE(i,o,a.shape.length,Ft[a.dtype],r,s,p,c),[u,d]},Wle={kernelName:vu,backendName:"wasm",setupFunc:zle,kernelFunc:Ble},DE;function Vle(e){DE=e.wasm.cwrap(wu,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Ule(e){let{backend:t,inputs:n,attrs:a}=e,{image:r,transforms:s}=n,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=t.makeOutput(g,r.dtype),x=t.dataIdMap.get(b.dataId).id,v=t.dataIdMap.get(r.dataId).id,w=t.dataIdMap.get(s.dataId).id,T=i==="nearest"?1:2,C;switch(o){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}return DE(v,w,s.shape[0]>1,p,m,f,h,c,d,y,r.shape.length-1,T,C,l,x),b}var Gle={kernelName:wu,backendName:"wasm",setupFunc:Vle,kernelFunc:Ule};function Hle(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h<o;h++)h!==s&&(l[u++]=r.shape[h]);let p=new Array(i),d=new Array(o).fill(0),c=r.shape.slice();c[s]=1;for(let h=0;h<p.length;h++)d[s]=h,p[h]=gi({inputs:{x:r},attrs:{begin:d,size:c},backend:n});return p.map(({dataId:h,dtype:m})=>({dataId:h,dtype:m,shape:l}))}var jle={kernelName:ku,backendName:"wasm",kernelFunc:Hle};function qle(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var Kle={kernelName:Iu,backendName:"wasm",kernelFunc:qle},Xle=[Ore,Lre,Bre,Ure,Jre,ese,ase,ise,pse,gse,yse,bse,wse,kse,Nse,_se,Ese,Ase,Dse,Pse,zse,Vse,Hse,jse,Kse,Xse,Yse,Jse,eie,tie,aie,iie,uie,die,fie,bie,vie,kie,Gre,Nie,Cie,Eie,Aie,Fie,Mie,Oie,Bie,Uie,jie,Kie,Jie,Qie,eoe,aoe,ioe,uoe,coe,moe,goe,boe,hE,koe,Noe,_oe,Aoe,Foe,Doe,Roe,ose,Ooe,Boe,Uoe,Goe,Hoe,Koe,Joe,ele,tle,mse,rle,ile,ule,dle,mle,gle,ble,xle,vle,kle,Nle,_le,Ale,Dle,Rle,Mle,Lle,Wle,Gle,Kre,jle,Kle];for(let e of Xle)Nc(e);var mx=X();mx.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])));mx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(mx.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 Gk=yi(aF()),Yle=`"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
");return}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;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(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})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}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);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});`,Jle=yi(rF()),RE=class extends oc{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(ME),fx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Zh(this,ar())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=k.sizeFromShape(n),o=i*k.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e,t,n){let{memoryOffset:a,dtype:r,shape:s,stringBytes:i}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(n==null||n>=i.length)?i:i.slice(t,n);t=t||0,n=n||k.sizeFromShape(s);let o=k.bytesPerElement(r),l=this.wasm.HEAPU8.slice(a+t*o,a+n*o);return eue(l.buffer,r)}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 a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=k.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=k.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function Zle(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance,s.module)})})}),{})}function Hk(e,t,n){if(Jh!=null)return Jh;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),Vp!=null&&Vp[a]!=null?Vp[a]:n+a}async function Qle(){let[e,t]=await Promise.all([X().getAsync("WASM_HAS_SIMD_SUPPORT"),X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=Yle.replace(/\n/g,"\\n"),p=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(p)}return o.endsWith(".wasm")?Hk(e,t,zp!=null?zp:l):l+o},A0&&(r.instantiateWasm=Zle(Hk(e,t,zp!=null?zp:"")));let s=!1;r.onAbort=()=>{s||Up||(Up=!0,a({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 i;t&&e&&Jh==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+Gk.default.toString()],{type:"text/javascript"}),i=(0,Gk.default)(r)):i=(0,Jle.default)(r),i.then(o=>{s=!0,Up=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),initWithThreadsCount:o.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:o.cwrap("get_threads_count","number",[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function eue(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 tue=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Jh=null,zp=null,Vp={},Up=!1,A0=!1;function nue(e,t=!1){if(Px("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Up)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Jh=e,A0=t}function aue(e,t=!1){if(Up)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")zp=e;else{Vp=e;let n=tue.filter(a=>Vp[a]==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.`)}A0=t}var ME=-1,fx=-1;function rue(e){ME=e}function sue(){if(fx===-1)throw new Error("WASM backend not initialized.");return fx}var iue="3.16.0",oue=2;Mm("wasm",async()=>{let{wasm:e}=await Qle();return new RE(e)},oue);var lue="3.16.0",uue="3.16.0",pue="3.16.0",cue="3.16.0",due="3.16.0",hue="3.16.0",mue="3.16.0",fue="3.16.0",gue={tfjs:lue,"tfjs-core":uue,"tfjs-data":pue,"tfjs-layers":cue,"tfjs-converter":due,"tfjs-backend-cpu":hue,"tfjs-backend-webgl":mue,"tfjs-backend-wasm":fue};var nA={};nb(nA,{AnchorPosition:()=>z0,DrawBox:()=>ud,DrawBoxOptions:()=>Yf,DrawFaceLandmarks:()=>lg,DrawFaceLandmarksOptions:()=>og,DrawTextField:()=>Pr,DrawTextFieldOptions:()=>Ju,drawContour:()=>Fr,drawDetections:()=>Iue,drawFaceExpressions:()=>Sue,drawFaceLandmarks:()=>Tue});function Fr(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:a,y:r},s)=>{let i=t[s];e.moveTo(i.x,i.y),e.lineTo(a,r)}),n){let a=t[t.length-1],r=t[0];if(!a||!r)return;e.moveTo(a.x,a.y),e.lineTo(r.x,r.y)}e.stroke()}var PE={};nb(PE,{computeReshapedDimensions:()=>D0,getCenterPoint:()=>_o,isDimensions:()=>qf,isEven:()=>jf,isFloat:()=>F0,isTensor:()=>To,isTensor1D:()=>yue,isTensor2D:()=>$0,isTensor3D:()=>Dr,isTensor4D:()=>ba,isValidNumber:()=>er,isValidProbablitiy:()=>Hu,range:()=>gr,round:()=>Co});var An=class{constructor(t,n){if(!er(t)||!er(n))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new An(1/this.width,1/this.height)}};function To(e,t){return e instanceof Ae&&e.shape.length===t}function yue(e){return To(e,1)}function $0(e){return To(e,2)}function Dr(e){return To(e,3)}function ba(e){return To(e,4)}function F0(e){return e%1!==0}function jf(e){return e%2===0}function Co(e,t=2){let n=10**t;return Math.floor(e*n)/n}function qf(e){return e&&e.width&&e.height}function D0({width:e,height:t},n){let a=n/Math.max(t,e);return new An(Math.round(e*a),Math.round(t*a))}function _o(e){return e.reduce((t,n)=>t.add(n),new Oe(0,0)).div(new Oe(e.length,e.length))}function gr(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function er(e){return!!e&&e!==1/0&&e!==-1/0&&!Number.isNaN(e)||e===0}function Hu(e){return er(e)&&e>=0&&e<=1}var Oe=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new Oe(this.x+t.x,this.y+t.y)}sub(t){return new Oe(this.x-t.x,this.y-t.y)}mul(t){return new Oe(this.x*t.x,this.y*t.y)}div(t){return new Oe(this.x/t.x,this.y/t.y)}abs(){return new Oe(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Oe(Math.floor(this.x),Math.floor(this.y))}};var pt=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(er)}static assertIsValidBox(t,n,a=!1){if(!pt.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!a&&(t.width<0||t.height<0))throw new Error(`${n} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,n=!0){let a=t||{},r=[a.left,a.top,a.right,a.bottom].every(er),s=[a.x,a.y,a.width,a.height].every(er);if(!s&&!r)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(a)}`);let[i,o,l,u]=s?[a.x,a.y,a.width,a.height]:[a.left,a.top,a.right-a.left,a.bottom-a.top];pt.assertIsValidBox({x:i,y:o,width:l,height:u},"Box.constructor",n),this._x=i,this._y=o,this._width=l,this._height=u}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Oe(this.left,this.top)}get topRight(){return new Oe(this.right,this.top)}get bottomLeft(){return new Oe(this.left,this.bottom)}get bottomRight(){return new Oe(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new pt({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new pt({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return a<r&&(t-=s/2,a+=s),r<a&&(n-=s/2,r+=s),new pt({x:t,y:n,width:a,height:r})}rescale(t){let n=qf(t)?t.width:t,a=qf(t)?t.height:t;return new pt({x:this.x*n,y:this.y*a,width:this.width*n,height:this.height*a})}pad(t,n){let[a,r,s,i]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new pt({x:a,y:r,width:s,height:i})}clipAtImageBorders(t,n){let{x:a,y:r,right:s,bottom:i}=this,o=Math.max(a,0),l=Math.max(r,0),u=s-o,p=i-l,d=Math.min(u,t-o),c=Math.min(p,n-l);return new pt({x:o,y:l,width:d,height:c}).floor()}shift(t,n){let{width:a,height:r}=this,s=this.x+t,i=this.y+n;return new pt({x:s,y:i,width:a,height:r})}padAtBorders(t,n){let a=this.width+1,r=this.height+1,s=1,i=1,o=a,l=r,u=this.left,p=this.top,d=this.right,c=this.bottom;return d>n&&(o=-d+n+a,d=n),c>t&&(l=-c+t+r,c=t),u<1&&(l=2-u,u=1),p<1&&(l=2-p,p=1),{dy:i,edy:l,dx:s,edx:o,y:p,ey:c,x:u,ex:d,w:a,h:r}}calibrate(t){return new pt({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var ju=class extends pt{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var Ss=class{constructor(t,n,a,r,s){this._imageDims=new An(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new pt(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new pt(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Ss(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var vt=class extends Ss{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new vt(a,r,s)}};function OE(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function LE(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>l<o?l:o,1/0),r=n.reduce((o,l)=>l<o?l:o,1/0),s=t.reduce((o,l)=>o<l?l:o,0),i=n.reduce((o,l)=>o<l?l:o,0);return new ju(a,r,s,i)}function zE(e,t,n,a=!0){let r=t.map((i,o)=>({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;u<o.length;u++){let p=o[u],d=e[i],c=e[p];l.push(OE(d,c,a))}r=r.filter((u,p)=>l[p]<=n)}return s}function yr(e,t){return O(()=>{let[n,a,r]=t,s=Cn([...e.shape.slice(0,3),1],n,"float32"),i=Cn([...e.shape.slice(0,3),1],a,"float32"),o=Cn([...e.shape.slice(0,3),1],r,"float32"),l=Qe([s,i,o],3);return ce(e,l)})}function BE(e,t=!1){return O(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=c=>{let h=e.shape.slice();return h[i]=c,Cn(h,0,"float32")},l=o(s),u=r-l.shape[i],d=[t&&u?o(u):null,e,l].filter(c=>!!c).map(c=>oe(c,"float32"));return Qe(d,i)})}function cge(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Kf(e){return 1/(1+Math.exp(-e))}function hge(e){return Math.log(e/(1-e))}var qu=class extends pt{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var bue=.5,xue=.43,vue=.45,xa=class{constructor(t,n,a=new Oe(0,0)){let{width:r,height:s}=n;this._imgDims=new An(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new Oe(r,s)).add(a))}get shift(){return new Oe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Oe(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Oe(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof vt?t.box.floor():new pt(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=d=>r.sub(d).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/vue),l=_o(t),u=Math.floor(Math.max(0,l.x-bue*o)),p=Math.floor(Math.max(0,l.y-xue*o));return new qu(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=LE(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var WE=class extends xa{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],_o([t[3],t[4]])]}};var Ku=class extends xa{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(_o)}};var id=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Co(this.distance)})`:""}`}};var od=class extends pt{constructor(n,a){super(n);this._label=a}static assertIsValidLabeledBox(n,a){if(pt.assertIsValidBox(n,a),!er(n.label))throw new Error(`${a} - expected property label (${n.label}) to be a number`)}get label(){return this._label}};var Rr=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new Rr(t.label,n)}};var VE=class extends od{constructor(n,a,r,s){super(n,a);this._score=r,this._classScore=s}static assertIsValidPredictedBox(n,a){if(od.assertIsValidLabeledBox(n,a),!Hu(n.score)||!Hu(n.classScore))throw new Error(`${a} - expected properties score (${n.score}) and (${n.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}};function Mr(e){return e.detection instanceof vt}function Xu(e,t){return{...e,...{detection:t}}}function R0(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),createVideoElement:()=>document.createElement("video"),fetch:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function ld(){return typeof global=="object"&&typeof process!="undefined"&&process.versions!=null&&process.versions.node!=null}function Xf(e){let t="";if(!e&&ld())try{e=A$("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function M0(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=global.Video||global.HTMLVideoElement,a=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},s=()=>{if(n)return new n;throw new Error("createVideoElement - missing Video implementation for nodejs environment")},i=global.fetch,o=Xf();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:a,createImageElement:r,createVideoElement:s,fetch:i,...o}}function P0(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var sn;function wue(){if(!sn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return sn}function O0(e){sn=e}function L0(){return P0()?O0(R0()):ld()?O0(M0()):null}function kue(e){if(sn||L0(),!sn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=sn.Canvas,Image:n=sn.Image}=e;sn.Canvas=t,sn.Image=n,sn.createCanvasElement=e.createCanvasElement||(()=>new t),sn.createImageElement=e.createImageElement||(()=>new n),sn.ImageData=e.ImageData||sn.ImageData,sn.Video=e.Video||sn.Video,sn.fetch=e.fetch||sn.fetch,sn.readFile=e.readFile||sn.readFile}var at={getEnv:wue,setEnv:O0,initialize:L0,createBrowserEnv:R0,createFileSystem:Xf,createNodejsEnv:M0,monkeyPatch:kue,isBrowser:P0,isNodejs:ld};L0();function Yu(e){return!at.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function aa(e){let{Canvas:t,CanvasRenderingContext2D:n}=at.getEnv();if(e instanceof n)return e;let a=Yu(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var z0=(r=>(r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT",r))(z0||{}),Ju=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||"TOP_LEFT",this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},Pr=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof Pr?t.text:t,this.anchor=n,this.options=new Ju(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a==="BOTTOM_RIGHT"||a==="TOP_RIGHT",s=a==="BOTTOM_LEFT"||a==="BOTTOM_RIGHT",i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,u=s?this.anchor.y-o:this.anchor.y;if(n){let{width:p,height:d}=n,c=Math.max(Math.min(l,p-i),0),h=Math.max(Math.min(u,d-o),0);return{x:c,y:h}}return{x:l,y:u}}draw(t){let n=Yu(t),a=aa(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let u=this.measureWidth(a),p=this.measureHeight();a.fillStyle=r;let d=this.getUpperLeft(a,n);a.fillRect(d.x,d.y,u,p),a.fillStyle=s,this.text.forEach((c,h)=>{let m=l+d.x,f=l+d.y+(h+1)*i;a.fillText(c,m,f)})}};var Yf=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new Ju({...i,...s})}},ud=class{constructor(t,n={}){this.box=new pt(t),this.options=new Yf(n)}draw(t){let n=aa(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new Pr([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Iue(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof vt?a.score:Mr(a)?a.detection.score:void 0,s=a instanceof vt?a.box:Mr(a)?a.detection.box:new pt(a),i=r?`${Co(r)}`:void 0;new ud(s,{label:i}).draw(e)})}function Jf(e){let{Image:t,Video:n}=at.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function UE(e){return new Promise((t,n)=>{(e instanceof at.getEnv().Canvas||Jf(e))&&t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function GE(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=at.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function Zu(e){let{Image:t,Video:n}=at.getEnv();return e instanceof t?new An(e.naturalWidth,e.naturalHeight):e instanceof n?new An(e.videoWidth,e.videoHeight):new An(e.width,e.height)}function Qu({width:e,height:t}){let{createCanvasElement:n}=at.getEnv(),a=n();return a.width=e,a.height=t,a}function Zf(e,t){let{ImageData:n}=at.getEnv();if(!(e instanceof n)&&!Jf(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||Zu(e),s=Qu({width:a,height:r});return e instanceof n?aa(s).putImageData(e,0,0):aa(s).drawImage(e,0,0,a,r),s}async function HE(e,t){let n=t||at.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ba(e)?1:0),i=O(()=>e.as3D(a,r,s).toInt());return await yo.toPixels(i,n),i.dispose(),n}function B0(e){let{Image:t,Canvas:n,Video:a}=at.getEnv();return e instanceof t||e instanceof n||e instanceof a}function jE(e,t,n=!1){let{Image:a,Canvas:r}=at.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Qu({width:1,height:1});let s=Zu(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=Qu({width:t,height:t}),p=e instanceof r?e:Zf(e),d=Math.abs(o-l)/2,c=n&&o<l?d:0,h=n&&l<o?d:0;return p.width>0&&p.height>0&&aa(u).drawImage(p,c,h,o,l),u}var Or=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Dr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ba(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof at.getEnv().Canvas?a:Zf(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return gr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return D0({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,O(()=>{let a=gr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Ae){let o=ba(i)?i:mn(i);return o=BE(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ln.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof at.getEnv().Canvas)return yo.fromPixels(jE(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Mt(a.map(s=>oe(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function wt(e){if(e instanceof Or)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(Yu);return a.forEach((r,s)=>{if(!B0(r)&&!Dr(r)&&!ba(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(ba(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>B0(r)&&UE(r))),new Or(a,Array.isArray(e))}async function pd(e,t){let{Canvas:n}=at.getEnv(),a=e;if(!(e instanceof n)){let i=await wt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await HE(o)}let r=aa(a);return t.map(i=>i instanceof vt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let p=Qu({width:l,height:u});return l>0&&u>0&&aa(p).putImageData(r.getImageData(i,o,l,u),0,0),p})}async function cd(e,t){if(!Dr(e)&&!ba(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ba(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return O(()=>{let[n,a,r]=e.shape.slice(ba(e)?1:0);return t.map(o=>o instanceof vt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).filter(o=>o.width>0&&o.height>0).map(({x:o,y:l,width:u,height:p})=>Au(e.as3D(n,a,r),[l,o,0],[p,u,r]))})}async function Ns(e,t){let{fetch:n}=at.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function Zbe(e){let t=await Ns(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return GE(n)}async function qE(e){return(await Ns(e)).json()}async function axe(e){return new Float32Array(await(await Ns(e)).arrayBuffer())}function KE(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToVideo - expected buf to be of type: Blob"));let a=at.getEnv().createVideoElement();a.oncanplay=()=>t(a),a.onerror=n,a.playsInline=!0,a.muted=!0,a.src=URL.createObjectURL(e),a.play()})}async function uxe(e){let t=await Ns(e),n=await t.blob();if(!n.type.startsWith("video/"))throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${n.type}, for url: ${t.url}`);return KE(n)}function Qf(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function XE(e,t){let{manifestUri:n,modelBaseUri:a}=Qf(e,t),r=await qE(n);return Qt.loadWeights(r,a)}function gxe(e,t,n=!1){let{width:a,height:r}=n?Zu(t):t;return e.width=a,e.height=r,{width:a,height:r}}var dn=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof ts)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof ts))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Zn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await XE(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=at.getEnv(),{manifestUri:a,modelBaseUri:r}=Qf(t,this.getDefaultModelName()),s=u=>Promise.all(u.map(p=>n(p).then(d=>d.buffer))),i=Qt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof Ae))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function qn(e,t,n){return O(()=>{let a=xo(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function eg(e,t,n=!1){return O(()=>{let a=Xe(n?J(Rt(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):qn(e,t.conv0,[2,2])),r=qn(a,t.conv1,[1,1]),s=Xe(J(a,r)),i=qn(s,t.conv2,[1,1]);return Xe(J(a,J(r,i)))})}function dd(e,t,n=!1,a=!0){return O(()=>{let r=Xe(n?J(Rt(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):qn(e,t.conv0,a?[2,2]:[1,1])),s=qn(r,t.conv1,[1,1]),i=Xe(J(r,s)),o=qn(i,t.conv2,[1,1]),l=Xe(J(r,J(s,o))),u=qn(l,t.conv3,[1,1]);return Xe(J(r,J(s,J(o,u))))})}function Eo(e,t,n="same",a=!1){return O(()=>{let r=J(Rt(e,t.filters,[1,1],n),t.bias);return a?Xe(r):r})}function $n(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function ep(e,t){return(n,a,r,s)=>{let i=Za(e(n*a*r*r),[r,r,n,a]),o=qe(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function tg(e,t){return(n,a,r)=>{let s=Ha(e(n*a),[n,a]),i=qe(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var hd=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function tp(e,t){return(n,a,r)=>{let s=Za(e(9*n),[3,3,n,1]),i=Za(e(n*a),[1,1,n,a]),o=qe(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new hd(s,i,o)}}function np(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new hd(n,a,r)}}function ra(e,t){return(n,a,r)=>{let s=e[n];if(!To(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function Fn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function ng(e,t){let n=ep(e,t),a=tp(e,t);function r(i,o,l,u=!1){let p=u?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),d=a(o,o,`${l}/conv1`),c=a(o,o,`${l}/conv2`);return{conv0:p,conv1:d,conv2:c}}function s(i,o,l,u=!1){let{conv0:p,conv1:d,conv2:c}=r(i,o,l,u),h=a(o,o,`${l}/conv3`);return{conv0:p,conv1:d,conv2:c,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function YE(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractDenseBlock4Params:r}=ng(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function ag(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function rg(e,t){let n=ra(e,t),a=ag(n),r=np(n);function s(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),p=r(`${o}/conv1`),d=r(`${o}/conv2`);return{conv0:u,conv1:p,conv2:d}}function i(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),p=r(`${o}/conv1`),d=r(`${o}/conv2`),c=r(`${o}/conv3`);return{conv0:u,conv1:p,conv2:d,conv3:c}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function JE(e){let t=[],{extractDenseBlock4Params:n}=rg(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return $n(e,t),{params:a,paramMappings:t}}var ap=class extends dn{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return O(()=>{let a=oe(t.toBatchTensor(112,!0),"float32"),s=yr(a,[122.782,117.001,104.298]).div(255),i=dd(s,n.dense0,!0);return i=dd(i,n.dense1),i=dd(i,n.dense2),i=dd(i,n.dense3),i=fa(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await wt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return JE(t)}extractParams(t){return YE(t)}};function md(e,t){return O(()=>J(De(e,t.weights),t.bias))}function ZE(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=Fn(e),o=tg(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function QE(e){let t=[],n=ra(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return $n(e,t),{params:r,paramMappings:t}}function sg(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var rp=class extends dn{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return O(()=>{let r=n instanceof Or?this.faceFeatureExtractor.forwardInput(n):n;return md(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return ZE(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=sg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),QE(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var eA=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ts=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);eA.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return eA.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var ig=class extends rp{constructor(t=new ap){super("FaceExpressionNet",t)}forwardInput(t){return O(()=>Ja(this.runNet(t)))}async forward(t){return this.forwardInput(await wt(t))}async predictExpressions(t){let n=await wt(t),a=await this.forwardInput(n),r=await Promise.all(mt(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Ts(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function tA(e){return e.expressions instanceof Ts}function W0(e,t){return{...e,...{expressions:t}}}function Sue(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Ts?s:tA(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(d=>d.probability>n),u=Mr(s)?s.detection.box.bottomLeft:a||new Oe(0,0);new Pr(l.map(d=>`${d.expression} (${Co(d.probability)})`),u).draw(e)})}function sp(e){return Mr(e)&&e.landmarks instanceof xa&&e.unshiftedLandmarks instanceof xa&&e.alignedRect instanceof vt}function Nue(e){let t=(o,l,u,p)=>Math.atan2(p-l,u-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>o<l._y?o:l._y,1/0),i=r.reduce((o,l)=>o>l._y?o:l._y,-1/0);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function fd(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new vt(e.detection.score,r.rescale(s.reverse()),s),o=Nue(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var og=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},lg=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new og(n)}draw(t){let n=aa(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof Ku&&(n.strokeStyle=i,n.lineWidth=s,Fr(n,this.faceLandmarks.getJawOutline()),Fr(n,this.faceLandmarks.getLeftEyeBrow()),Fr(n,this.faceLandmarks.getRightEyeBrow()),Fr(n,this.faceLandmarks.getNose()),Fr(n,this.faceLandmarks.getLeftEye(),!0),Fr(n,this.faceLandmarks.getRightEye(),!0),Fr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=p=>{n.beginPath(),n.arc(p.x,p.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function Tue(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof xa?a:sp(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new lg(r).draw(e)})}var aA="1.6.8";function Eue(e,t){let n=ep(e,t),a=tp(e,t);function r(i,o,l){let u=a(i,o,`${l}/separable_conv0`),p=a(o,o,`${l}/separable_conv1`),d=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:p,expansion_conv:d}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),p=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function rA(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=Fn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Eue(a,n),u=s(3,32,3,"entry_flow/conv_in"),p=o(32,64,"entry_flow/reduction_block_0"),d=o(64,128,"entry_flow/reduction_block_1"),c={conv_in:u,reduction_block_0:p,reduction_block_1:d},h={};gr(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function Aue(e,t){let n=ra(e,t),a=ag(n),r=np(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function sA(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Aue(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};gr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return $n(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function iA(e,t,n){return J(Rt(e,t.filters,n,"same"),t.bias)}function V0(e,t,n=!0){let a=n?Xe(e):e;return a=qn(a,t.separable_conv0,[1,1]),a=qn(Xe(a),t.separable_conv1,[1,1]),a=Pt(a,[3,3],[2,2],"same"),a=J(a,iA(e,t.expansion_conv,[2,2])),a}function $ue(e,t){let n=qn(Xe(e),t.separable_conv0,[1,1]);return n=qn(Xe(n),t.separable_conv1,[1,1]),n=qn(Xe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var ug=class extends dn{constructor(n){super("TinyXception");this._numMainBlocks=n}forwardInput(n){let{params:a}=this;if(!a)throw new Error("TinyXception - load model before inference");return O(()=>{let r=oe(n.toBatchTensor(112,!0),"float32"),i=yr(r,[122.782,117.001,104.298]).div(255),o=Xe(iA(i,a.entry_flow.conv_in,[2,2]));return o=V0(o,a.entry_flow.reduction_block_0,!1),o=V0(o,a.entry_flow.reduction_block_1),gr(this._numMainBlocks,0,1).forEach(l=>{o=$ue(o,a.middle_flow[`main_block_${l}`])}),o=V0(o,a.exit_flow.reduction_block),o=Xe(qn(o,a.exit_flow.separable_conv,[1,1])),o})}async forward(n){return this.forwardInput(await wt(n))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(n){return sA(n,this._numMainBlocks)}extractParams(n){return rA(n,this._numMainBlocks)}};function oA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),r=tg(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function lA(e){let t=[],n=ra(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return $n(e,t),{params:r,paramMappings:t}}var U0=(n=>(n.FEMALE="female",n.MALE="male",n))(U0||{});var pg=class extends dn{constructor(n=new ug(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return O(()=>{let r=n instanceof Or?this.faceFeatureExtractor.forwardInput(n):n,s=fa(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=md(s,a.fc.age).as1D(),o=md(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return O(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Ja(r)}})}async forward(n){return this.forwardInput(await wt(n))}async predictAgeAndGender(n){let a=await wt(n),r=await this.forwardInput(a),s=mt(r.age),i=mt(r.gender),o=s.map((u,p)=>({ageTensor:u,genderTensor:i[p]})),l=await Promise.all(o.map(async({ageTensor:u,genderTensor:p})=>{let d=u.dataSync()[0],c=p.dataSync()[0],h=c>.5,m=h?"male":"female",f=h?c:1-c;return u.dispose(),p.dispose(),{age:d,gender:m,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),a.isBatchInput?l:l[0]}getDefaultModelName(){return"age_gender_model"}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return oA(n)}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=sg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),lA(r)}extractParams(n){let r=n.slice(0,n.length-1539),s=n.slice(n.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var ip=class extends rp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return O(()=>{let i=(d,c)=>Mt([Cn([68],d,"float32"),Cn([68],c,"float32")],1).as2D(1,136).as1D(),o=(d,c)=>{let{width:h,height:m}=r[d];return c(h,m)?Math.abs(h-m)/2:0},l=d=>o(d,(c,h)=>c<h),u=d=>o(d,(c,h)=>h<c);return t.mul(Cn([s,136],n,"float32")).sub(Mt(Array.from(Array(s),(d,c)=>i(l(c),u(c))))).div(Mt(Array.from(Array(s),(d,c)=>i(r[c].width,r[c].height))))})}forwardInput(t){return O(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await wt(t))}async detectLandmarks(t){let n=await wt(t),a=O(()=>mt(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(s.dataSync()),l=o.filter((p,d)=>jf(d)),u=o.filter((p,d)=>!jf(d));return new Ku(Array(68).fill(0).map((p,d)=>new Oe(l[d],u[d])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var op=class extends ip{constructor(t=new ap){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function uA(e){let t=[],{extractDenseBlock3Params:n}=rg(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return $n(e,t),{params:a,paramMappings:t}}function pA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractDenseBlock3Params:r}=ng(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var cg=class extends dn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return O(()=>{let a=oe(t.toBatchTensor(112,!0),"float32"),s=yr(a,[122.782,117.001,104.298]).div(255),i=eg(s,n.dense0,!0);return i=eg(i,n.dense1),i=eg(i,n.dense2),i=fa(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await wt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return uA(t)}extractParams(t){return pA(t)}};var dg=class extends ip{constructor(t=new cg){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var cA=class extends op{};function dA(e,t){return J(z(e,t.weights),t.biases)}function G0(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Rt(e,s,n,r);return o=J(o,i),o=dA(o,t.scale),a?Xe(o):o}function hA(e,t){return G0(e,t,[1,1],!0)}function H0(e,t){return G0(e,t,[1,1],!1)}function hg(e,t){return G0(e,t,[2,2],!0,"valid")}function Fue(e,t){function n(o,l,u){let p=e(o),d=p.length/(l*u*u);if(F0(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${u}`);return O(()=>$e(Za(p,[l,d,u,u]),[2,3,1,0]))}function a(o,l,u,p){let d=n(o,l,u),c=qe(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:c}}function r(o,l){let u=qe(e(o)),p=qe(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:u,biases:p}}function s(o,l,u,p){let d=a(o,l,u,`${p}/conv`),c=r(l,`${p}/scale`);return{conv:d,scale:c}}function i(o,l,u,p,d=!1){let c=s((d?.5:1)*o,l,u,`${p}/conv1`),h=s(o,l,u,`${p}/conv2`);return{conv1:c,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function mA(e){let{extractWeights:t,getRemainingWeights:n}=Fn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=Fue(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),u=s(9216,32,3,"conv32_3"),p=s(36864,64,3,"conv64_down",!0),d=s(36864,64,3,"conv64_1"),c=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),w=O(()=>$e(Ha(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:p,conv64_1:d,conv64_2:c,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:w},paramMappings:a}}function Due(e,t){let n=ra(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),u=a(i);return{conv:{filters:o,bias:l},scale:u}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function fA(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Due(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),u=a("conv64_1"),p=a("conv64_2"),d=a("conv64_3"),c=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!$0(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:u,conv64_2:p,conv64_3:d,conv128_down:c,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return $n(e,t),{params:v,paramMappings:t}}function tr(e,t){let n=hA(e,t.conv1);return n=H0(n,t.conv2),n=J(n,e),n=Xe(n),n}function gd(e,t){let n=hg(e,t.conv1);n=H0(n,t.conv2);let a=fa(e,2,2,"valid"),r=kt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=kt(o);n=Qe([n,l],1);let u=[...n.shape];u[2]=1;let p=kt(u);n=Qe([n,p],2)}return a=s?Qe([a,r],3):a,n=J(a,n),n=Xe(n),n}var lp=class extends dn{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return O(()=>{let a=oe(t.toBatchTensor(150,!0),"float32"),s=yr(a,[122.782,117.001,104.298]).div(255),i=hg(s,n.conv32_down);i=Pt(i,3,2,"valid"),i=tr(i,n.conv32_1),i=tr(i,n.conv32_2),i=tr(i,n.conv32_3),i=gd(i,n.conv64_down),i=tr(i,n.conv64_1),i=tr(i,n.conv64_2),i=tr(i,n.conv64_3),i=gd(i,n.conv128_down),i=tr(i,n.conv128_1),i=tr(i,n.conv128_2),i=gd(i,n.conv256_down),i=tr(i,n.conv256_1),i=tr(i,n.conv256_2),i=gd(i,n.conv256_down_out);let o=i.mean([1,2]);return De(o,n.fc)})}async forward(t){return this.forwardInput(await wt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)!=null&&s.some(i=>i<=0))return new Float32Array(128);let n=await wt(t),a=O(()=>mt(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return fA(t)}extractParams(t){return mA(t)}};function V1e(e){let t=new lp;return t.extractWeights(e),t}function j0(e,t){return{...e,...{descriptor:t}}}function j1e(e){return typeof e.age=="number"}function q0(e,t){return{...e,...{age:t}}}function Y1e(e){return(e.gender==="male"||e.gender==="female")&&Hu(e.genderProbability)}function K0(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function Rue(e,t){function n(l,u){let p=Za(e(9*l),[3,3,l,1]),d=qe(e(l)),c=qe(e(l)),h=qe(e(l)),m=qe(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:c,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,p,d,c){let h=Za(e(l*u*p*p),[p,p,l,u]),m=qe(e(u));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${c?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,p,d){let{filters:c,bias:h}=a(l,u,p,d,!0);return{filters:c,batch_norm_offset:h}}function s(l,u,p){let d=n(l,`${p}/depthwise_conv`),c=r(l,u,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:c}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),p=s(64,128,"mobilenetv1/conv_2"),d=s(128,128,"mobilenetv1/conv_3"),c=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),w=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:w}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),u=r(256,512,3,"prediction_layer/conv_1"),p=r(512,128,1,"prediction_layer/conv_2"),d=r(128,256,3,"prediction_layer/conv_3"),c=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),w=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),T=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),C=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),E=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),$=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),P=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),F=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:w},box_predictor_3:{box_encoding_predictor:T,class_predictor:C},box_predictor_4:{box_encoding_predictor:E,class_predictor:$},box_predictor_5:{box_encoding_predictor:P,class_predictor:F}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function gA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=Rue(n,t),i=r(),o=s(),u={extra_dim:Rm(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:u},paramMappings:t}}function Mue(e,t){let n=ra(e,t);function a(u,p,d){let c=n(`${u}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),h=n(`${u}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:c,batch_norm_offset:h}}function r(u){let p=`mobilenetv1/conv_${u}`,d=`MobilenetV1/Conv2d_${u}_depthwise`,c=`${p}/depthwise_conv`,h=`${p}/pointwise_conv`,m=n(`${d}/depthwise_weights`,4,`${c}/filters`),f=n(`${d}/BatchNorm/gamma`,1,`${c}/batch_norm_scale`),g=n(`${d}/BatchNorm/beta`,1,`${c}/batch_norm_offset`),y=n(`${d}/BatchNorm/moving_mean`,1,`${c}/batch_norm_mean`),b=n(`${d}/BatchNorm/moving_variance`,1,`${c}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",u,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(u,p){let d=n(`${u}/weights`,4,`${p}/filters`),c=n(`${u}/biases`,1,`${p}/bias`);return{filters:d,bias:c}}function o(u){let p=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),d=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function yA(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Mue(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Dr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return $n(e,t),{params:s,paramMappings:t}}function Aa(e,t,n){return O(()=>{let a=Rt(e,t.filters,n,"same");return a=J(a,t.batch_norm_offset),nn(a,0,6)})}var Pue=.0010000000474974513;function Oue(e,t,n){return O(()=>{let a=bs(e,t.filters,n,"same");return a=Cr(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Pue),nn(a,0,6)})}function Lue(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function bA(e,t){return O(()=>{let n,a=Aa(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=Lue(o);a=Oue(a,s.depthwise_conv,l),a=Aa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function zue(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),p=Math.max(a[n][0],a[n][2]),d=Math.max(a[n][1],a[n][3]),c=(i-r)*(o-s),h=(p-l)*(d-u);if(c<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,p),y=Math.min(o,d),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(c+h-b)}function xA(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>r).sort((p,d)=>d.score-p.score),l=p=>p<=a?1:0,u=[];return o.forEach(p=>{if(u.length>=i)return;let d=p.score;for(let c=u.length-1;c>=0;--c){let h=zue(e,p.boxIndex,u[c]);if(h!==0&&(p.score*=l(h),p.score<=r))break}d===p.score&&u.push(p.boxIndex)}),u}function Bue(e){let t=mt($e(e,[1,0])),n=[ce(t[2],t[0]),ce(t[3],t[1])],a=[J(t[0],fe(n[0],2)),J(t[1],fe(n[1],2))];return{sizes:n,centers:a}}function Wue(e,t){let{sizes:n,centers:a}=Bue(e),r=mt($e(t,[1,0])),s=fe(z(gn(fe(r[2],5)),n[0]),2),i=J(z(fe(r[0],10),n[0]),a[0]),o=fe(z(gn(fe(r[3],5)),n[1]),2),l=J(z(fe(r[1],10),n[1]),a[1]);return $e(Mt([ce(i,s),ce(l,o),J(i,s),J(l,o)]),[1,0])}function vA(e,t,n){return O(()=>{let a=e.shape[0],r=Wue(W(On(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ha(Ge(t,[0,0,1],[-1,-1,-1])),i=Ge(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=mt(r),l=mt(i);return{boxes:o,scores:l}})}function Ao(e,t){return O(()=>{let n=e.shape[0],a=W(Eo(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(Eo(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function wA(e,t,n){return O(()=>{let a=Aa(e,n.conv_0,[1,1]),r=Aa(a,n.conv_1,[2,2]),s=Aa(r,n.conv_2,[1,1]),i=Aa(s,n.conv_3,[2,2]),o=Aa(i,n.conv_4,[1,1]),l=Aa(o,n.conv_5,[2,2]),u=Aa(l,n.conv_6,[1,1]),p=Aa(u,n.conv_7,[2,2]),d=Ao(t,n.box_predictor_0),c=Ao(e,n.box_predictor_1),h=Ao(r,n.box_predictor_2),m=Ao(i,n.box_predictor_3),f=Ao(l,n.box_predictor_4),g=Ao(p,n.box_predictor_5),y=Qe([d.boxPredictionEncoding,c.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Qe([d.classPrediction,c.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var $a=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var $o=class extends dn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=oe(t.toBatchTensor(512,!1),"float32"),r=ce(fe(a,127.5),1),s=bA(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=wA(s.out,s.conv11,n.prediction_layer);return vA(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await wt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new $a(n),s=await wt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x<i.length;x++)i[x].dispose(),o[x].dispose();let p=Array.from(u.dataSync()),c=xA(l,p,a,.5,r),h=s.getReshapedInputDimensions(0),m=s.inputSize,f=m/h.width,g=m/h.height,y=l.arraySync(),b=c.map(x=>{let[v,w]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(E=>E*g),[T,C]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(E=>E*f);return new vt(p[x],new qu(T,v,C-T,w-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return yA(t)}extractParams(t){return gA(t)}};function Vue(e){let t=new $o;return t.extractWeights(e),t}function Vke(e){return Vue(e)}var kA=class extends $o{};var IA=.4,SA=[new Oe(.738768,.874946),new Oe(2.42204,2.65704),new Oe(4.30971,7.04493),new Oe(10.246,4.59428),new Oe(12.6868,11.8741)],NA=[new Oe(1.603231,2.094468),new Oe(6.041143,7.080126),new Oe(2.882459,3.518061),new Oe(4.266906,5.178857),new Oe(9.041765,10.66308)],TA=[117.001,114.697,97.404],CA="tiny_yolov2_model",_A="tiny_yolov2_separable_conv_model";var mg=e=>typeof e=="number";function EA(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!mg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>mg(t.x)&&mg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(mg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function up(e){return O(()=>{let t=z(e,we(.10000000149011612));return J(Xe(ce(e,t)),t)})}function Lr(e,t){return O(()=>{let n=ga(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Rt(n,t.conv.filters,[1,1],"valid"),n=ce(n,t.bn.sub),n=z(n,t.bn.truediv),n=J(n,t.conv.bias),up(n)})}function zr(e,t){return O(()=>{let n=ga(e,[[0,0],[1,1],[1,1],[0,0]]);return n=xo(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),up(n)})}function Uue(e,t){let n=ep(e,t);function a(i,o){let l=qe(e(i)),u=qe(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=tp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function AA(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=Fn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Uue(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),w=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),E=u(f,g,"conv4"),$=u(g,y,"conv5"),P=b?u(y,b,"conv6"):void 0,F=x?u(b,x,"conv7"):void 0,S=o(x||b||y,5*n,1,"conv8");p={conv0:v,conv1:w,conv2:T,conv3:C,conv4:E,conv5:$,conv6:P,conv7:F,conv8:S}}else{let[d,c,h,m,f,g,y,b,x]=a,v=l(d,c,"conv0"),w=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),E=l(f,g,"conv4"),$=l(g,y,"conv5"),P=l(y,b,"conv6"),F=l(b,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:w,conv2:T,conv3:C,conv4:E,conv5:$,conv6:P,conv7:F,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Gue(e,t){let n=ra(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=np(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function $A(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Gue(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return $n(e,n),{params:i,paramMappings:n}}var br=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var X0=class extends dn{constructor(n){super("TinyYolov2");EA(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=Lr(n,a.conv0);return r=Pt(r,[2,2],[2,2],"same"),r=Lr(r,a.conv1),r=Pt(r,[2,2],[2,2],"same"),r=Lr(r,a.conv2),r=Pt(r,[2,2],[2,2],"same"),r=Lr(r,a.conv3),r=Pt(r,[2,2],[2,2],"same"),r=Lr(r,a.conv4),r=Pt(r,[2,2],[2,2],"same"),r=Lr(r,a.conv5),r=Pt(r,[2,2],[1,1],"same"),r=Lr(r,a.conv6),r=Lr(r,a.conv7),Eo(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?up(Eo(n,a.conv0,"valid",!1)):zr(n,a.conv0);return r=Pt(r,[2,2],[2,2],"same"),r=zr(r,a.conv1),r=Pt(r,[2,2],[2,2],"same"),r=zr(r,a.conv2),r=Pt(r,[2,2],[2,2],"same"),r=zr(r,a.conv3),r=Pt(r,[2,2],[2,2],"same"),r=zr(r,a.conv4),r=Pt(r,[2,2],[2,2],"same"),r=zr(r,a.conv5),r=Pt(r,[2,2],[1,1],"same"),r=a.conv6?zr(r,a.conv6):r,r=a.conv7?zr(r,a.conv7):r,Eo(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let s=oe(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?yr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await wt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new br(a),i=await wt(n),o=await this.forwardInput(i,r),l=O(()=>mt(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(y=>y.box),c=p.map(y=>y.score),h=p.map(y=>y.classScore),m=p.map(y=>this.config.classes[y.label]);return zE(d.map(y=>y.rescale(r)),c,this.config.iouThreshold,!0).map(y=>new Ss(c[y],h[y],m[y],d[y],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return $A(n,this.config)}extractParams(n){let a=this.config.filterSizes||X0.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return AA(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=O(()=>{let b=n.reshape([p,p,d,this.boxEncodingSize]),x=b.slice([0,0,0,0],[p,p,d,4]),v=b.slice([0,0,0,4],[p,p,d,1]),w=this.withClassScores?Ja(b.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):we(0);return[x,v,w]}),f=[],g=await h.array(),y=await c.array();for(let b=0;b<p;b++)for(let x=0;x<p;x++)for(let v=0;v<d;v++){let w=Kf(g[b][x][v][0]);if(!r||w>r){let T=(x+Kf(y[b][x][v][0]))/p*l,C=(b+Kf(y[b][x][v][1]))/p*u,E=Math.exp(y[b][x][v][2])*this.config.anchors[v].x/p*l,$=Math.exp(y[b][x][v][3])*this.config.anchors[v].y/p*u,P=T-E/2,F=C-$/2,S={row:b,col:x,anchor:v},{classScore:M,label:V}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new ju(P,F,P+E,F+$),score:w,classScore:w*M,label:V,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},Fo=X0;Fo.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var pp=class extends Fo{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:IA,classes:["face"],...t?{anchors:NA,meanRgb:TA}:{anchors:SA,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?_A:CA}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function DIe(e,t=!0){let n=new pp(t);return n.extractWeights(e),n}var fg=class extends br{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Fa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Do(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>sp(l)?r(l):l.detection),i=a||(t instanceof Ae?await cd(t,s):await pd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function cp(e,t,n,a,r){return Do([e],t,async s=>n(s[0]),a,r)}var FA=.4,DA=[new Oe(1.603231,2.094468),new Oe(6.041143,7.080126),new Oe(2.882459,3.518061),new Oe(4.266906,5.178857),new Oe(9.041765,10.66308)],RA=[117.001,114.697,97.404];var dp=class extends Fo{constructor(){let t={withSeparableConvs:!0,iouThreshold:FA,classes:["face"],anchors:DA,meanRgb:RA,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new $o,tinyFaceDetector:new dp,tinyYolov2:new pp,faceLandmark68Net:new op,faceLandmark68TinyNet:new dg,faceRecognitionNet:new lp,faceExpressionNet:new ig,ageGenderNet:new pg},Hue=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),uSe=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),pSe=(e,t)=>rt.tinyYolov2.locateFaces(e,t),jue=e=>rt.faceLandmark68Net.detectLandmarks(e),cSe=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),dSe=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),hSe=e=>rt.faceExpressionNet.predictExpressions(e),mSe=e=>rt.ageGenderNet.predictAgeAndGender(e),que=e=>rt.ssdMobilenetv1.load(e),fSe=e=>rt.tinyFaceDetector.load(e),gSe=e=>rt.tinyYolov2.load(e),ySe=e=>rt.faceLandmark68Net.load(e),bSe=e=>rt.faceLandmark68TinyNet.load(e),xSe=e=>rt.faceRecognitionNet.load(e),vSe=e=>rt.faceExpressionNet.load(e),wSe=e=>rt.ageGenderNet.load(e),kSe=que,ISe=Hue,SSe=jue;var gg=class extends Fa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Ro=class extends gg{async run(){let t=await this.parentTask,n=await Do(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>W0(a,n[r]))}withAgeAndGender(){return new Po(this,this.input)}},Mo=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let n=await cp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return W0(t,n)}withAgeAndGender(){return new Oo(this,this.input)}},Cs=class extends Ro{withAgeAndGender(){return new Es(this,this.input)}withFaceDescriptors(){return new $s(this,this.input)}},_s=class extends Mo{withAgeAndGender(){return new As(this,this.input)}withFaceDescriptor(){return new Fs(this,this.input)}};var yg=class extends Fa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Po=class extends yg{async run(){let t=await this.parentTask,n=await Do(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return q0(K0(a,i,o),s)})}withFaceExpressions(){return new Ro(this,this.input)}},Oo=class extends yg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await cp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return q0(K0(t,a,r),n)}withFaceExpressions(){return new Mo(this,this.input)}},Es=class extends Po{withFaceExpressions(){return new Cs(this,this.input)}withFaceDescriptors(){return new $s(this,this.input)}},As=class extends Oo{withFaceExpressions(){return new _s(this,this.input)}withFaceDescriptor(){return new Fs(this,this.input)}};var bg=class extends Fa{constructor(n,a){super();this.parentTask=n;this.input=a}},$s=class extends bg{async run(){let t=await this.parentTask;return(await Do(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>j0(t[r],a))}withFaceExpressions(){return new Cs(this,this.input)}withAgeAndGender(){return new Es(this,this.input)}},Fs=class extends bg{async run(){let t=await this.parentTask;if(!t)return;let n=await cp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return j0(t,n)}withFaceExpressions(){return new _s(this,this.input)}withAgeAndGender(){return new As(this,this.input)}};var xg=class extends Fa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},vg=class extends xg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ae?await cd(this.input,n):await pd(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ae&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>fd(i,r[o]))}withFaceExpressions(){return new Cs(this,this.input)}withAgeAndGender(){return new Es(this,this.input)}withFaceDescriptors(){return new $s(this,this.input)}},wg=class extends xg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await cd(this.input,[n]):await pd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),fd(t,r)}withFaceExpressions(){return new _s(this,this.input)}withAgeAndGender(){return new As(this,this.input)}withFaceDescriptor(){return new Fs(this,this.input)}};var kg=class extends Fa{constructor(n,a=new $a){super();this.input=n;this.options=a}},yd=class extends kg{async run(){let{input:t,options:n}=this,a;if(n instanceof fg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof $a)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof br)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>Xu({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new vg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ro(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Po(this.runAndExtendWithFaceDetections(),this.input)}},Ig=class extends kg{async run(){let t=await new yd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Xu({},n):void 0)})}withFaceLandmarks(t=!1){return new wg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Mo(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Oo(this.runAndExtendWithFaceDetection(),this.input)}};function w2e(e,t=new $a){return new Ig(e,t)}function Y0(e,t=new $a){return new yd(e,t)}async function Kue(e,t){return Y0(e,new $a(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function C2e(e,t={}){return Y0(e,new br(t)).withFaceLandmarks().withFaceDescriptors()}var _2e=Kue;function MA(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Sg=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Rr)return i;if(i instanceof Float32Array)return new Rr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Rr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>MA(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new id(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this._distanceThreshold?n:new id("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Rr.fromJSON(a));return new Sg(n,t.distanceThreshold)}};function j2e(e){let t=new dp;return t.extractWeights(e),t}function Xue(e,t){let{width:n,height:a}=new An(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Xue(r,{width:n,height:a}));if(sp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return fd(Xu(e,r),s)}return Mr(e)?Xu(e,e.detection.forSize(n,a)):e instanceof xa||e instanceof vt?e.forSize(n,a):e}var aNe=aA;export{pg as AgeGenderNet,ju as BoundingBox,pt as Box,Fa as ComposableTask,$s as ComputeAllFaceDescriptorsTask,bg as ComputeFaceDescriptorsTaskBase,Fs as ComputeSingleFaceDescriptorTask,vg as DetectAllFaceLandmarksTask,yd as DetectAllFacesTask,xg as DetectFaceLandmarksTaskBase,kg as DetectFacesTaskBase,wg as DetectSingleFaceLandmarksTask,Ig as DetectSingleFaceTask,An as Dimensions,eA as FACE_EXPRESSION_LABELS,vt as FaceDetection,kA as FaceDetectionNet,ig as FaceExpressionNet,Ts as FaceExpressions,op as FaceLandmark68Net,dg as FaceLandmark68TinyNet,cA as FaceLandmarkNet,xa as FaceLandmarks,WE as FaceLandmarks5,Ku as FaceLandmarks68,id as FaceMatch,Sg as FaceMatcher,lp as FaceRecognitionNet,U0 as Gender,od as LabeledBox,Rr as LabeledFaceDescriptors,Or as NetInput,dn as NeuralNetwork,Ss as ObjectDetection,Oe as Point,VE as PredictedBox,qu as Rect,$o as SsdMobilenetv1,$a as SsdMobilenetv1Options,dp as TinyFaceDetector,fg as TinyFaceDetectorOptions,pp as TinyYolov2,br as TinyYolov2Options,_2e as allFaces,Kue as allFacesSsdMobilenetv1,C2e as allFacesTinyYolov2,UE as awaitMediaLoaded,GE as bufferToImage,dSe as computeFaceDescriptor,Qu as createCanvas,Zf as createCanvasFromMedia,Vke as createFaceDetectionNet,V1e as createFaceRecognitionNet,Vue as createSsdMobilenetv1,j2e as createTinyFaceDetector,DIe as createTinyYolov2,Y0 as detectAllFaces,jue as detectFaceLandmarks,cSe as detectFaceLandmarksTiny,SSe as detectLandmarks,w2e as detectSingleFace,nA as draw,at as env,MA as euclideanDistance,q0 as extendWithAge,j0 as extendWithFaceDescriptor,Xu as extendWithFaceDetection,W0 as extendWithFaceExpressions,fd as extendWithFaceLandmarks,K0 as extendWithGender,cd as extractFaceTensors,pd as extractFaces,Zbe as fetchImage,qE as fetchJson,axe as fetchNetWeights,Ns as fetchOrThrow,uxe as fetchVideo,aa as getContext2dOrThrow,Zu as getMediaDimensions,HE as imageTensorToCanvas,jE as imageToSquare,hge as inverseSigmoid,OE as iou,B0 as isMediaElement,Jf as isMediaLoaded,j1e as isWithAge,Mr as isWithFaceDetection,tA as isWithFaceExpressions,sp as isWithFaceLandmarks,Y1e as isWithGender,wSe as loadAgeGenderModel,kSe as loadFaceDetectionModel,vSe as loadFaceExpressionModel,ySe as loadFaceLandmarkModel,bSe as loadFaceLandmarkTinyModel,xSe as loadFaceRecognitionModel,que as loadSsdMobilenetv1Model,fSe as loadTinyFaceDetectorModel,gSe as loadTinyYolov2Model,XE as loadWeightMap,ISe as locateFaces,gxe as matchDimensions,LE as minBbox,rt as nets,zE as nonMaxSuppression,yr as normalize,BE as padToSquare,mSe as predictAgeAndGender,hSe as recognizeFaceExpressions,Xue as resizeResults,Yu as resolveInput,cge as shuffleArray,Kf as sigmoid,Hue as ssdMobilenetv1,ze as tf,uSe as tinyFaceDetector,pSe as tinyYolov2,wt as toNetInput,PE as utils,EA as validateConfig,aNe as version};
/**
* @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
* Copyright 2022 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 2022 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 2022 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 2022 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 See the LICENSE file. */
//# sourceMappingURL=face-api.esm.js.map
Reference in New Issue View Git Blame Copy Permalink