face-api/dist/face-api.js

/*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
*/

var faceapi=(()=>{var Qy=Object.defineProperty;var T$=Object.getOwnPropertyDescriptor;var C$=Object.getOwnPropertyNames;var E$=Object.prototype.hasOwnProperty;var _$=e=>Qy(e,"__esModule",{value:!0});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 Zd=(e,t)=>{for(var n in t)Qy(e,n,{get:t[n],enumerable:!0})},$$=(e,t,n,a)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of C$(t))!E$.call(e,r)&&(n||r!=="default")&&Qy(e,r,{get:()=>t[r],enumerable:!(a=T$(t,r))||a.enumerable});return e};var F$=(e=>(t,n)=>e&&e.get(t)||(n=$$(_$({}),t,1),e&&e.set(t,n),n))(typeof WeakMap!="undefined"?new WeakMap:0);var hpe={};Zd(hpe,{AgeGenderNet:()=>og,BoundingBox:()=>Gu,Box:()=>ut,ComposableTask:()=>$a,ComputeAllFaceDescriptorsTask:()=>Ts,ComputeFaceDescriptorsTaskBase:()=>fg,ComputeSingleFaceDescriptorTask:()=>Cs,DetectAllFaceLandmarksTask:()=>yg,DetectAllFacesTask:()=>gd,DetectFaceLandmarksTaskBase:()=>gg,DetectFacesTaskBase:()=>xg,DetectSingleFaceLandmarksTask:()=>bg,DetectSingleFaceTask:()=>vg,Dimensions:()=>An,FACE_EXPRESSION_LABELS:()=>q0,FaceDetection:()=>wt,FaceDetectionNet:()=>gA,FaceExpressionNet:()=>rg,FaceExpressions:()=>Ns,FaceLandmark68Net:()=>Qu,FaceLandmark68TinyNet:()=>lg,FaceLandmarkNet:()=>rA,FaceLandmarks:()=>ba,FaceLandmarks5:()=>V_,FaceLandmarks68:()=>ju,FaceMatch:()=>rd,FaceMatcher:()=>kg,FaceRecognitionNet:()=>ep,Gender:()=>ig,LabeledBox:()=>sd,LabeledFaceDescriptors:()=>Mr,NetInput:()=>Pr,NeuralNetwork:()=>cn,ObjectDetection:()=>Is,Point:()=>Le,PredictedBox:()=>G_,Rect:()=>Hu,SsdMobilenetv1:()=>Ao,SsdMobilenetv1Options:()=>Aa,TinyFaceDetector:()=>sp,TinyFaceDetectorOptions:()=>mg,TinyYolov2:()=>ap,TinyYolov2Options:()=>xr,allFaces:()=>ppe,allFacesSsdMobilenetv1:()=>$A,allFacesTinyYolov2:()=>upe,awaitMediaLoaded:()=>W0,bufferToImage:()=>U0,computeFaceDescriptor:()=>Xue,createCanvas:()=>To,createCanvasFromMedia:()=>ud,createFaceDetectionNet:()=>Uue,createFaceRecognitionNet:()=>$ue,createSsdMobilenetv1:()=>fA,createTinyFaceDetector:()=>cpe,createTinyYolov2:()=>Hue,detectAllFaces:()=>wg,detectFaceLandmarks:()=>_A,detectFaceLandmarksTiny:()=>Kue,detectLandmarks:()=>ope,detectSingleFace:()=>lpe,draw:()=>J0,env:()=>et,euclideanDistance:()=>o1,extendWithAge:()=>cg,extendWithFaceDescriptor:()=>pg,extendWithFaceDetection:()=>Io,extendWithFaceExpressions:()=>sg,extendWithFaceLandmarks:()=>Zu,extendWithGender:()=>dg,extractFaceTensors:()=>Ku,extractFaces:()=>qu,fetchImage:()=>yue,fetchJson:()=>H0,fetchNetWeights:()=>bue,fetchOrThrow:()=>Or,fetchVideo:()=>xue,getContext2dOrThrow:()=>Gn,getMediaDimensions:()=>No,imageTensorToCanvas:()=>V0,imageToSquare:()=>G0,inverseSigmoid:()=>pue,iou:()=>A0,isMediaElement:()=>Xf,isMediaLoaded:()=>ld,isWithAge:()=>Fue,isWithFaceDetection:()=>br,isWithFaceExpressions:()=>K0,isWithFaceLandmarks:()=>Eo,isWithGender:()=>Due,loadAgeGenderModel:()=>rpe,loadFaceDetectionModel:()=>spe,loadFaceExpressionModel:()=>ape,loadFaceLandmarkModel:()=>epe,loadFaceLandmarkTinyModel:()=>tpe,loadFaceRecognitionModel:()=>npe,loadSsdMobilenetv1Model:()=>AA,loadTinyFaceDetectorModel:()=>Zue,loadTinyYolov2Model:()=>Que,loadWeightMap:()=>j0,locateFaces:()=>ipe,matchDimensions:()=>vue,minBbox:()=>$0,nets:()=>tt,nonMaxSuppression:()=>F0,normalize:()=>tr,padToSquare:()=>D0,predictAgeAndGender:()=>Jue,recognizeFaceExpressions:()=>Yue,resizeResults:()=>FA,resolveInput:()=>So,shuffleArray:()=>uue,sigmoid:()=>ad,ssdMobilenetv1:()=>EA,tf:()=>Oe,tinyFaceDetector:()=>jue,tinyYolov2:()=>que,toNetInput:()=>bt,utils:()=>_0,validateConfig:()=>a1,version:()=>dpe});var Oe={};Zd(Oe,{Abs:()=>wl,Acos:()=>kl,Acosh:()=>Il,AdadeltaOptimizer:()=>pf,AdagradOptimizer:()=>cf,AdamOptimizer:()=>df,AdamaxOptimizer:()=>hf,Add:()=>cs,AddN:()=>mi,All:()=>Sl,Any:()=>Nl,ArgMax:()=>fi,ArgMin:()=>rc,Asin:()=>Tl,Asinh:()=>Cl,Atan:()=>El,Atan2:()=>Al,Atanh:()=>_l,AvgPool:()=>gi,AvgPool3D:()=>sc,AvgPool3DGrad:()=>tm,AvgPoolGrad:()=>em,BackendWasm:()=>W_,BatchMatMul:()=>yi,BatchToSpaceND:()=>$l,Bincount:()=>nm,BroadcastArgs:()=>am,BroadcastTo:()=>yI,Callback:()=>vN,CallbackList:()=>S2,Cast:()=>bi,Ceil:()=>xi,ClipByValue:()=>ds,Complex:()=>rm,ComplexAbs:()=>ic,Concat:()=>Fl,Conv2D:()=>vi,Conv2DBackpropFilter:()=>sm,Conv2DBackpropInput:()=>wi,Conv3D:()=>oc,Conv3DBackpropFilterV2:()=>im,Conv3DBackpropInputV2:()=>om,Cos:()=>ki,Cosh:()=>Ii,CropAndResize:()=>Dl,Cumsum:()=>Si,CustomCallback:()=>T2,DataStorage:()=>Jh,DenseBincount:()=>lm,DepthToSpace:()=>Rl,DepthwiseConv2dNative:()=>Ni,DepthwiseConv2dNativeBackpropFilter:()=>um,DepthwiseConv2dNativeBackpropInput:()=>pm,Diag:()=>cm,Dilation2D:()=>lc,Dilation2DBackpropFilter:()=>Nh,Dilation2DBackpropInput:()=>Sh,ENV:()=>bx,EarlyStopping:()=>wN,Einsum:()=>dm,Elu:()=>Ci,EluGrad:()=>hm,Environment:()=>fI,Equal:()=>Pl,Erf:()=>Ml,Exp:()=>Ei,ExpandDims:()=>Ol,Expm1:()=>Ll,FFT:()=>mm,Fill:()=>uc,FlipLeftRight:()=>zl,Floor:()=>_i,FloorDiv:()=>Ai,FromPixels:()=>Th,FusedBatchNorm:()=>$i,FusedConv2D:()=>Js,FusedDepthwiseConv2D:()=>Zs,GPGPUContext:()=>xh,GatherNd:()=>Wl,GatherV2:()=>Bl,GraphModel:()=>HN,Greater:()=>Ul,GreaterEqual:()=>Fi,History:()=>N2,IFFT:()=>fm,Identity:()=>Di,Imag:()=>gm,InputSpec:()=>Bt,IsFinite:()=>Vl,IsInf:()=>Gl,IsNan:()=>Hl,KernelBackend:()=>ac,LRN:()=>dc,LRNGrad:()=>bm,LayerVariable:()=>v2,LayersModel:()=>Cr,LeakyRelu:()=>Ri,Less:()=>jl,LessEqual:()=>ql,LinSpace:()=>ym,Log:()=>Mi,Log1p:()=>Kl,LogSoftmax:()=>bI,LogicalAnd:()=>Xl,LogicalNot:()=>pc,LogicalOr:()=>cc,MathBackendWebGL:()=>Lf,Max:()=>Pi,MaxPool:()=>Li,MaxPool3D:()=>hc,MaxPool3DGrad:()=>vm,MaxPoolGrad:()=>xm,MaxPoolWithArgmax:()=>wm,Maximum:()=>Oi,Mean:()=>zi,Min:()=>Bi,Minimum:()=>Wi,MirrorPad:()=>Ui,Mod:()=>Yl,MomentumOptimizer:()=>mf,Multinomial:()=>km,Multiply:()=>Vi,Neg:()=>Jl,NonMaxSuppressionV3:()=>Ql,NonMaxSuppressionV4:()=>eu,NonMaxSuppressionV5:()=>tu,NotEqual:()=>Zl,OP_SCOPE_SUFFIX:()=>EI,OneHot:()=>Gi,OnesLike:()=>nu,Optimizer:()=>$r,OptimizerConstructors:()=>Hr,Pack:()=>au,PadV2:()=>Hi,Pool:()=>EF,Pow:()=>ji,Prelu:()=>qi,Prod:()=>ru,RMSPropOptimizer:()=>ff,RNN:()=>gr,Range:()=>mc,Rank:()=>bb,Real:()=>Im,RealDiv:()=>Ti,Reciprocal:()=>su,Reduction:()=>wn,Relu:()=>Ki,Relu6:()=>Yi,Reshape:()=>iu,ResizeBilinear:()=>Xi,ResizeBilinearGrad:()=>Nm,ResizeNearestNeighbor:()=>fc,ResizeNearestNeighborGrad:()=>Sm,Reverse:()=>Ji,RotateWithOffset:()=>ku,Round:()=>Zi,Rsqrt:()=>Qi,SGDOptimizer:()=>Pc,ScatterNd:()=>ou,Select:()=>lu,Selu:()=>uu,Sequential:()=>ml,Sigmoid:()=>to,Sign:()=>du,Sin:()=>eo,Sinh:()=>cu,Slice:()=>pu,Softmax:()=>ro,Softplus:()=>hu,SpaceToBatchND:()=>mu,SparseFillEmptyRows:()=>gc,SparseReshape:()=>gu,SparseSegmentMean:()=>yc,SparseSegmentSum:()=>bc,SparseToDense:()=>Tm,SplitV:()=>fu,Sqrt:()=>no,Square:()=>xc,SquaredDifference:()=>so,Step:()=>ms,StridedSlice:()=>yu,StringNGrams:()=>Cm,StringSplit:()=>Em,StringToHashBucketFast:()=>_m,Sub:()=>io,Sum:()=>ao,SymbolicTensor:()=>Ua,Tan:()=>oo,Tanh:()=>lo,Tensor:()=>Ae,TensorBuffer:()=>jt,Tile:()=>hs,TopK:()=>bu,Transform:()=>xu,Transpose:()=>uo,Unique:()=>Am,Unpack:()=>vu,UnsortedSegmentSum:()=>vc,Variable:()=>ts,ZerosLike:()=>wu,_FusedMatMul:()=>Ys,abs:()=>zt,acos:()=>Rx,acosh:()=>Mx,add:()=>J,addN:()=>oS,all:()=>Pm,any:()=>jp,argMax:()=>ti,argMin:()=>Px,asin:()=>Ox,asinh:()=>Lx,atan:()=>zx,atan2:()=>Bx,atanh:()=>Wx,avgPool:()=>ma,avgPool3d:()=>Vx,backend:()=>iS,backend_util:()=>E,basicLSTMCell:()=>vM,batchNorm:()=>Er,batchNorm2d:()=>cS,batchNorm3d:()=>dS,batchNorm4d:()=>hS,batchToSpaceND:()=>Cc,bincount:()=>Gx,booleanMaskAsync:()=>N3,broadcastArgs:()=>mS,broadcastTo:()=>rl,broadcast_util:()=>Iu,browser:()=>po,buffer:()=>He,callbacks:()=>JG,cast:()=>oe,ceil:()=>Hx,clipByValue:()=>nn,clone:()=>Tr,complex:()=>ns,concat:()=>Qe,concat1d:()=>fS,concat2d:()=>gS,concat3d:()=>yS,concat4d:()=>bS,constraints:()=>o2,conv1d:()=>Om,conv2d:()=>Rt,conv2dTranspose:()=>Lm,conv3d:()=>qx,conv3dTranspose:()=>vS,copyRegisteredKernels:()=>FF,cos:()=>Ec,cosh:()=>zm,cosineWindow:()=>xv,cumsum:()=>Bm,customGrad:()=>pr,data:()=>jN,denseBincount:()=>wS,deprecationWarn:()=>Dx,depthToSpace:()=>Kx,depthwiseConv2d:()=>ys,deregisterOp:()=>e6,device_util:()=>Sc,diag:()=>YM,dilation2d:()=>Xx,disableDeprecationWarnings:()=>FR,dispose:()=>De,disposeVariables:()=>DR,div:()=>fe,divNoNan:()=>Yx,dot:()=>kS,dropout:()=>VS,einsum:()=>IS,elu:()=>Su,enableDebugMode:()=>$R,enableProdMode:()=>AR,enclosingPowerOfTwo:()=>GS,engine:()=>rr,env:()=>Y,equal:()=>Zn,erf:()=>Jx,exp:()=>fn,expandDims:()=>hn,expm1:()=>Zx,eye:()=>Qx,fft:()=>Rc,fill:()=>Cn,findBackend:()=>BR,findBackendFactory:()=>WR,floor:()=>Nu,floorDiv:()=>Mm,forceHalfFloat:()=>pE,fused:()=>rs,gather:()=>ai,gatherND:()=>US,gather_util:()=>Cx,getBackend:()=>LR,getGradient:()=>gb,getKernel:()=>Ch,getKernelsForBackend:()=>Eh,getThreadsCount:()=>Yle,gpgpu_util:()=>UC,grad:()=>NP,grads:()=>TP,greater:()=>Wn,greaterEqual:()=>bs,ifft:()=>cl,imag:()=>Wm,image:()=>Yn,inTopKAsync:()=>P3,initializers:()=>c2,input:()=>B2,io:()=>Qt,irfft:()=>tf,isFinite:()=>SS,isInf:()=>NS,isNaN:()=>ev,keep:()=>en,kernel_impls:()=>fr,layers:()=>b2,leakyRelu:()=>_c,less:()=>Um,lessEqual:()=>xs,linalg:()=>e2,linspace:()=>TS,loadGraphModel:()=>aH,loadLayersModel:()=>oV,localResponseNormalization:()=>tv,log:()=>Qn,log1p:()=>Ac,logSigmoid:()=>ES,logSoftmax:()=>Gm,logSumExp:()=>rv,logicalAnd:()=>Na,logicalNot:()=>$c,logicalOr:()=>Hm,logicalXor:()=>FS,losses:()=>bz,matMul:()=>Fe,math:()=>UI,max:()=>Ia,maxPool:()=>Pt,maxPool3d:()=>sv,maxPoolWithArgmax:()=>DS,maximum:()=>mr,mean:()=>_t,memory:()=>$h,meshgrid:()=>KP,metrics:()=>yN,min:()=>qp,minimum:()=>Tu,mirrorPad:()=>iv,mod:()=>ov,model:()=>sV,models:()=>bN,moments:()=>jm,movingAverage:()=>E3,mul:()=>B,multiRNNCell:()=>nO,multinomial:()=>RS,neg:()=>St,nextFrame:()=>t2,norm:()=>rf,notEqual:()=>si,oneHot:()=>ul,ones:()=>Xn,onesLike:()=>ea,op:()=>z,outerProduct:()=>oO,pad:()=>fa,pad1d:()=>pO,pad2d:()=>dO,pad3d:()=>mO,pad4d:()=>gO,pool:()=>MS,pow:()=>_r,prelu:()=>Dc,print:()=>LI,prod:()=>qm,profile:()=>RR,rand:()=>NO,randomGamma:()=>_O,randomNormal:()=>PS,randomUniform:()=>Cu,range:()=>pl,ready:()=>OR,real:()=>Kp,reciprocal:()=>pv,registerBackend:()=>Rm,registerCallbackConstructor:()=>lV,registerGradient:()=>xI,registerKernel:()=>wc,registerOp:()=>QG,regularizers:()=>xN,relu:()=>Xe,relu6:()=>Km,removeBackend:()=>zR,reshape:()=>W,reverse:()=>ta,reverse1d:()=>LO,reverse2d:()=>BO,reverse3d:()=>UO,reverse4d:()=>GO,rfft:()=>Mc,round:()=>Xm,rsqrt:()=>Ym,scalar:()=>ke,scatterND:()=>WS,scatter_util:()=>Ex,selu:()=>Jm,separableConv2d:()=>ho,sequential:()=>iV,serialization:()=>se,setBackend:()=>PR,setPlatform:()=>UR,setThreadsCount:()=>Xle,setWasmPath:()=>qle,setWasmPaths:()=>Kle,setWebGLContext:()=>hC,setdiff1dAsync:()=>OS,sigmoid:()=>da,sign:()=>cv,signal:()=>yz,sin:()=>Zm,sinh:()=>Qm,slice:()=>Ge,slice1d:()=>ef,slice2d:()=>dv,slice3d:()=>Eu,slice4d:()=>Xp,slice_util:()=>qt,softmax:()=>Ja,softplus:()=>co,spaceToBatchND:()=>Fc,sparse:()=>_p,sparseToDense:()=>bv,spectral:()=>gz,split:()=>Ln,sqrt:()=>ln,square:()=>lt,squaredDifference:()=>nf,squeeze:()=>cr,stack:()=>Mt,step:()=>_u,stridedSlice:()=>hv,string:()=>dh,sub:()=>ce,sum:()=>be,sumOutType:()=>Fm,tan:()=>mv,tanh:()=>ni,tensor:()=>Jn,tensor1d:()=>qe,tensor2d:()=>Ga,tensor3d:()=>Dm,tensor4d:()=>Za,tensor5d:()=>g3,tensor6d:()=>y3,tensor_util:()=>Va,test_util:()=>aS,tidy:()=>O,tile:()=>On,time:()=>MR,topk:()=>fv,train:()=>Ls,transpose:()=>Me,truncatedNormal:()=>af,unique:()=>Dh,unregisterGradient:()=>$F,unregisterKernel:()=>AF,unsortedSegmentSum:()=>gv,unstack:()=>ht,upcastType:()=>Sa,util:()=>k,valueAndGrad:()=>CP,valueAndGrads:()=>EP,variable:()=>LS,variableGrads:()=>CS,version:()=>oue,version_converter:()=>rH,version_core:()=>_R,version_layers:()=>Vv,version_wasm:()=>Jle,version_webgl:()=>d9,webgl:()=>h9,webgl_util:()=>dC,where:()=>mn,whereAsync:()=>yv,zeros:()=>kt,zerosLike:()=>Ke});var D$=Object.create,Yh=Object.defineProperty,R$=Object.getOwnPropertyDescriptor,M$=Object.getOwnPropertyNames,P$=Object.getPrototypeOf,O$=Object.prototype.hasOwnProperty,L$=e=>Yh(e,"__esModule",{value:!0}),ft=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Re=(e,t)=>{for(var n in t)Yh(e,n,{get:t[n],enumerable:!0})},z$=(e,t,n,a)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of M$(t))!O$.call(e,r)&&(n||r!=="default")&&Yh(e,r,{get:()=>t[r],enumerable:!(a=R$(t,r))||a.enumerable});return e},di=(e,t)=>z$(L$(Yh(e!=null?D$(P$(e)):{},"default",!t&&e&&e.__esModule?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),B$=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 _}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 _=u(-1,2147483647,!1);a.MAX_VALUE=_;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)}}),W$=ft(()=>{}),U$=ft(()=>{}),V$=ft((e,t)=>{(function(n,a,r){function s(u){var p=this,d=l();p.next=function(){var c=2091639*p.s0+p.c*23283064365386963e-26;return p.s0=p.s1,p.s1=p.s2,p.s2=c-(p.c=c|0)},p.c=1,p.s0=d(" "),p.s1=d(" "),p.s2=d(" "),p.s0-=d(u),p.s0<0&&(p.s0+=1),p.s1-=d(u),p.s1<0&&(p.s1+=1),p.s2-=d(u),p.s2<0&&(p.s2+=1),d=null}function i(u,p){return p.c=u.c,p.s0=u.s0,p.s1=u.s1,p.s2=u.s2,p}function o(u,p){var d=new s(u),c=p&&p.state,h=d.next;return h.int32=function(){return d.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,c&&(typeof c=="object"&&i(c,d),h.state=function(){return i(d,{})}),h}function l(){var u=4022871197,p=function(d){d=d.toString();for(var c=0;c<d.length;c++){u+=d.charCodeAt(c);var h=.02519603282416938*u;u=h>>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return p}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),G$=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)}),H$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.next=function(){var c=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(c^c<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:p+=l;for(var d=0;d<p.length+64;d++)u.x^=p.charCodeAt(d)|0,d==p.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),j$=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)}),q$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var d=u.w,c=u.X,h=u.i,m,f;return u.w=d=d+1640531527|0,f=c[h+34&127],m=c[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[h]=f^m,u.i=h,f+(d^d>>>16)|0};function p(d,c){var h,m,f,g,y,b=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,g=-32;g<x;++g)c&&(m^=c.charCodeAt((g+32)%c.length)),g===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,g>=0&&(y=y+1640531527|0,h=b[g&127]^=m+y,f=h==0?f+1:0);for(f>=128&&(b[(c&&c.length||0)&127]=-1),f=127,g=4*128;g>0;--g)m=b[f+34&127],h=b[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,b[f]=m^h;d.w=y,d.X=b,d.i=f}p(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(d.X&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),K$=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)}),tI=ft(()=>{}),X$=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 _=[];T=T==!0?{entropy:!0}:T||{};var $=b(y(T.entropy?[w,v(n)]:w==null?x():w,3),_),P=new f(_),F=function(){for(var S=P.g(i),M=u,V=0;S<p;)S=(S+V)*s,M*=s,V=P.g(1);for(;S>=d;)S/=2,M/=2,V>>>=1;return(S+V)/M};return F.int32=function(){return P.g(4)|0},F.quick=function(){return P.g(4)/4294967296},F.double=F,b(v(P.S),n),(T.pass||C||function(S,M,V,j){return j&&(j.S&&g(j,P),S.state=function(){return g(P,{})}),V?(a[l]=S,M):S})(F,$,"global"in T?T.global:this==a,T.state)}a["seed"+l]=m;function f(w){var T,C=w.length,_=this,$=0,P=_.i=_.j=0,F=_.S=[];for(C||(w=[C++]);$<s;)F[$]=$++;for($=0;$<s;$++)F[$]=F[P=c&P+w[$%C]+(T=F[$])],F[P]=T;(_.g=function(S){for(var M,V=0,j=_.i,q=_.j,K=_.S;S--;)M=K[j=c&j+1],V=V*s+K[c&(K[j]=K[q=c&q+M])+(K[q]=M)];return _.i=j,_.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=[],_=typeof w,$;if(T&&_=="object")for($ in w)try{C.push(y(w[$],T-1))}catch(P){}return C.length?C:_=="string"?w:w+"\0"}function b(w,T){for(var C=w+"",_,$=0;$<C.length;)T[c&$]=c&(_^=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(_){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=tI()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return m})})([],Math)}),nI=ft((e,t)=>{var n=V$(),a=G$(),r=H$(),s=j$(),i=q$(),o=K$(),l=X$();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),Y$=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)}),J$=ft((e,t)=>{(function(n,a,r){function s(l){var u=this,p="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var c=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^c^c>>>8},l===(l|0)?u.x=l:p+=l;for(var d=0;d<p.length+64;d++)u.x^=p.charCodeAt(d)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var p=new s(l),d=u&&u.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var h=p.next()>>>11,m=(p.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=p.next,c.quick=c,d&&(typeof d=="object"&&i(d,p),c.state=function(){return i(p,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Z$=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)}),Q$=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)}),eF=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)}),tF=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)}),nF=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 _=[];T=T==!0?{entropy:!0}:T||{};var $=b(y(T.entropy?[w,v(a)]:w==null?x():w,3),_),P=new f(_),F=function(){for(var S=P.g(i),M=u,V=0;S<p;)S=(S+V)*s,M*=s,V=P.g(1);for(;S>=d;)S/=2,M/=2,V>>>=1;return(S+V)/M};return F.int32=function(){return P.g(4)|0},F.quick=function(){return P.g(4)/4294967296},F.double=F,b(v(P.S),a),(T.pass||C||function(S,M,V,j){return j&&(j.S&&g(j,P),S.state=function(){return g(P,{})}),V?(r[l]=S,M):S})(F,$,"global"in T?T.global:this==r,T.state)}function f(w){var T,C=w.length,_=this,$=0,P=_.i=_.j=0,F=_.S=[];for(C||(w=[C++]);$<s;)F[$]=$++;for($=0;$<s;$++)F[$]=F[P=c&P+w[$%C]+(T=F[$])],F[P]=T;(_.g=function(S){for(var M,V=0,j=_.i,q=_.j,K=_.S;S--;)M=K[j=c&j+1],V=V*s+K[c&(K[j]=K[q=c&q+M])+(K[q]=M)];return _.i=j,_.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=[],_=typeof w,$;if(T&&_=="object")for($ in w)try{C.push(y(w[$],T-1))}catch(P){}return C.length?C:_=="string"?w:w+"\0"}function b(w,T){for(var C=w+"",_,$=0;$<C.length;)T[c&$]=c&(_^=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(_){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=tI()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return m}):r["seed"+l]=m})(typeof self!="undefined"?self:e,[],Math)}),aI=ft((e,t)=>{var n=Y$(),a=J$(),r=Z$(),s=Q$(),i=eF(),o=tF(),l=nF();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),rI=ft(()=>{}),mx=ft(()=>{}),wh=ft(()=>{}),aF=ft(()=>{}),rF=ft(()=>{}),sF=ft(()=>{}),iF=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!=yn&&Da(Te.buffer),bd}function i(){return Te.buffer!=yn&&Da(Te.buffer),xd}function o(){return Te.buffer!=yn&&Da(Te.buffer),cp}function l(){return Te.buffer!=yn&&Da(Te.buffer),vd}function u(){return Te.buffer!=yn&&Da(Te.buffer),wd}function p(){return Te.buffer!=yn&&Da(Te.buffer),kd}function d(){return Te.buffer!=yn&&Da(Te.buffer),Id}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,_="";function $(N){return c.locateFile?c.locateFile(N,_):_+N}var P,F,S,M;function V(N){N instanceof wp||Z("exiting due to exception: "+N)}var j,q,K;if(T){w?_=wh().dirname(_)+"/":_=__dirname+"/",K=()=>{q||(j=mx(),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,X)=>{K(),D=q.normalize(D),j.readFile(D,function(pe,he){pe?X(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 wp))throw D}),process.on("unhandledRejection",function(D){throw D}),x=(D,U)=>{if(As())throw process.exitCode=D,U;V(U),process.exit(D)},c.inspect=function(){return"[Emscripten Module object]"};let N;try{N=aF()}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?_=self.location.href:typeof document!="undefined"&&document.currentScript&&(_=document.currentScript.src),typeof a!="undefined"&&a&&(_=a),_.indexOf("blob:")!==0?_=_.substr(0,_.replace(/[?#].*/,"").lastIndexOf("/")+1):_="",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 X=new XMLHttpRequest;X.open("GET",N,!0),X.responseType="arraybuffer",X.onload=()=>{if(X.status==200||X.status==0&&X.response){D(X.response);return}U()},X.onerror=U,X.send(null)}),M=N=>document.title=N);T&&typeof performance=="undefined"&&(global.performance=rF().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"},X={parameters:[],results:D[0]=="v"?[]:[U[D[0]]]},pe=1;pe<D.length;++pe)X.parameters.push(U[D[pe]]);return new WebAssembly.Function(X,N)}var he=[1,0,1,96],xe=D.slice(0,1),Ce=D.slice(1),Et={i:127,j:126,f:125,d:124};he.push(Ce.length);for(var pe=0;pe<Ce.length;++pe)he.push(Et[Ce[pe]]);xe=="v"?he.push(0):he=he.concat([1,Et[xe]]),he[1]=he.length-2;var Oa=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])),La=new WebAssembly.Module(Oa),Jd=new WebAssembly.Instance(La,{e:{f:N}}),kp=Jd.exports.f;return kp}var ue=[],we;function ye(){if(ue.length)return ue.pop();try{ia.grow(1)}catch(N){throw N instanceof RangeError?"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.":N}return ia.length-1}function Ie(N,D){for(var U=N;U<N+D;U++){var X=Wo(U);X&&we.set(X,U)}}var _e=0,$e=N=>{_e=N},Be=Atomics.load,je=Atomics.store,st=Atomics.compareExchange,nt;c.wasmBinary&&(nt=c.wasmBinary);var at=c.noExitRuntime||!0;typeof WebAssembly!="object"&&Lo("no native wasm support detected");var Te,gt,pt=!1,gn;function Yt(N,D){N||Lo(D)}function Dn(N){var D=c["_"+N];return D}function Vt(N,D,U,X,pe){var he={string:function(oa){var Ko=0;if(oa!=null&&oa!==0){var E1=(oa.length<<2)+1;Ko=qo(E1),Es(oa,Ko,E1)}return Ko},array:function(oa){var Ko=qo(oa.length);return vr(oa,Ko),Ko}};function xe(oa){return D==="string"?sa(oa):D==="boolean"?Boolean(oa):oa}var Ce=Dn(N),Et=[],Oa=0;if(X)for(var La=0;La<X.length;La++){var Jd=he[U[La]];Jd?(Oa===0&&(Oa=Jy()),Et[La]=Jd(X[La])):Et[La]=X[La]}var kp=Ce.apply(null,Et);function N$(oa){return Oa!==0&&qd(Oa),xe(oa)}return kp=N$(kp),kp}function Jt(N,D,U,X){U=U||[];var pe=U.every(function(xe){return xe==="number"}),he=D!=="string";return he&&pe&&!X?Dn(N):function(){return Vt(N,D,U,arguments,X)}}var Fa=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 ra(N,D,U){for(var X=D+U,pe=D;N[pe]&&!(pe>=X);)++pe;if(pe-D>16&&N.subarray&&Gt)return Gt.decode(N.subarray(D,pe));for(var he="";D<pe;){var xe=N[D++];if(!(xe&128)){he+=String.fromCharCode(xe);continue}var Ce=N[D++]&63;if((xe&224)==192){he+=String.fromCharCode((xe&31)<<6|Ce);continue}var Et=N[D++]&63;if((xe&240)==224?xe=(xe&15)<<12|Ce<<6|Et:xe=(xe&7)<<18|Ce<<12|Et<<6|N[D++]&63,xe<65536)he+=String.fromCharCode(xe);else{var Oa=xe-65536;he+=String.fromCharCode(55296|Oa>>10,56320|Oa&1023)}}return he}function sa(N,D){return N?ra(i(),N,D):""}function Br(N,D,U,X){if(!(X>0))return 0;for(var pe=U,he=U+X-1,xe=0;xe<N.length;++xe){var Ce=N.charCodeAt(xe);if(Ce>=55296&&Ce<=57343){var Et=N.charCodeAt(++xe);Ce=65536+((Ce&1023)<<10)|Et&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 Es(N,D,U){return Br(N,i(),D,U)}function yd(N){for(var D=0,U=0;U<N.length;++U){var X=N.charCodeAt(U);X>=55296&&X<=57343&&(X=65536+((X&1023)<<10)|N.charCodeAt(++U)&1023),X<=127?++D:X<=2047?D+=2:X<=65535?D+=3:D+=4}return D}var Wr=typeof TextDecoder!="undefined"?new Rn("utf-16le"):void 0;function vr(N,D){s().set(N,D)}function pp(N,D,U){for(var X=0;X<N.length;++X)s()[D++>>0]=N.charCodeAt(X);U||(s()[D>>0]=0)}function Po(N,D){return N%D>0&&(N+=D-N%D),N}var yn,bd,xd,cp,vd,wd,l1,kd,Id;C&&(yn=c.buffer);function Da(N){yn=N,c.HEAP8=bd=new Int8Array(N),c.HEAP16=cp=new Int16Array(N),c.HEAP32=wd=new Int32Array(N),c.HEAPU8=xd=new Uint8Array(N),c.HEAPU16=vd=new Uint16Array(N),c.HEAPU32=l1=new Uint32Array(N),c.HEAPF32=kd=new Float32Array(N),c.HEAPF64=Id=new Float64Array(N)}var Sd=c.INITIAL_MEMORY||16777216;if(C)Te=c.wasmMemory,yn=c.buffer;else if(c.wasmMemory)Te=c.wasmMemory;else if(Te=new WebAssembly.Memory({initial:Sd/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&&(yn=Te.buffer),Sd=yn.byteLength,Da(yn);var ia,Oo=[],Ur=[],Ig=[],Nd=[],_s=!1,Sg=!1,Td=0;function As(){return at||Td>0}function bn(){if(c.preRun)for(typeof c.preRun=="function"&&(c.preRun=[c.preRun]);c.preRun.length;)u1(c.preRun.shift());Ad(Oo)}function dp(){_s=!0,!C&&Ad(Ur)}function Ng(){C||(Ee.terminateAllThreads(),Sg=!0)}function Tg(){if(!C){if(c.postRun)for(typeof c.postRun=="function"&&(c.postRun=[c.postRun]);c.postRun.length;)hp(c.postRun.shift());Ad(Nd)}}function u1(N){Oo.unshift(N)}function p1(N){Ur.unshift(N)}function hp(N){Nd.unshift(N)}var Vr=0,Cd=null,Ra=null;function mp(N){Vr++,c.monitorRunDependencies&&c.monitorRunDependencies(Vr)}function c1(N){if(Vr--,c.monitorRunDependencies&&c.monitorRunDependencies(Vr),Vr==0&&(Cd!==null&&(clearInterval(Cd),Cd=null),Ra)){var D=Ra;Ra=null,D()}}c.preloadedImages={},c.preloadedAudios={};function Lo(N){C?postMessage({cmd:"onAbort",arg:N}):c.onAbort&&c.onAbort(N),N="Aborted("+N+")",Z(N),pt=!0,gn=1,N+=". Build with -s ASSERTIONS=1 for more info.";var D=new WebAssembly.RuntimeError(N);throw m(D),D}var Cg="data:application/octet-stream;base64,";function fp(N){return N.startsWith(Cg)}function Ed(N){return N.startsWith("file://")}var xn;xn="tfjs-backend-wasm-threaded-simd.wasm",fp(xn)||(xn=$(xn));function _d(N){try{if(N==xn&&nt)return new Uint8Array(nt);if(S)return S(N);throw"both async and sync fetching of the wasm failed"}catch(D){Lo(D)}}function zo(){if(!nt&&(v||w)){if(typeof fetch=="function"&&!Ed(xn))return fetch(xn,{credentials:"same-origin"}).then(function(N){if(!N.ok)throw"failed to load wasm binary file at '"+xn+"'";return N.arrayBuffer()}).catch(function(){return _d(xn)});if(F)return new Promise(function(N,D){F(xn,function(U){N(new Uint8Array(U))},D)})}return Promise.resolve().then(function(){return _d(xn)})}function Eg(){var N={env:Ud,wasi_snapshot_preview1:Ud};function D(xe,Ce){var Et=xe.exports;if(c.asm=Et,Mg(c.asm.emscripten_tls_init),ia=c.asm.__indirect_function_table,p1(c.asm.__wasm_call_ctors),gt=Ce,!C){var Oa=Ee.unusedWorkers.length;Ee.unusedWorkers.forEach(function(La){Ee.loadWasmModuleToWorker(La,function(){--Oa||c1("wasm-instantiate")})})}}C||mp("wasm-instantiate");function U(xe){D(xe.instance,xe.module)}function X(xe){return zo().then(function(Ce){return WebAssembly.instantiate(Ce,N)}).then(function(Ce){return Ce}).then(xe,function(Ce){Z("failed to asynchronously prepare wasm: "+Ce),Lo(Ce)})}function pe(){return!nt&&typeof WebAssembly.instantiateStreaming=="function"&&!fp(xn)&&!Ed(xn)&&typeof fetch=="function"?fetch(xn,{credentials:"same-origin"}).then(function(xe){var Ce=WebAssembly.instantiateStreaming(xe,N);return Ce.then(U,function(Et){return Z("wasm streaming compile failed: "+Et),Z("falling back to ArrayBuffer instantiation"),X(U)})}):X(U)}if(c.instantiateWasm)try{var he=c.instantiateWasm(N,D);return he}catch(xe){return Z("Module.instantiateWasm callback failed with error: "+xe),!1}return pe().catch(m),{}}var d1,h1,_g={};function Ad(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?Wo(U)():Wo(U)(D.arg):U(D.arg===void 0?null:D.arg)}}function Bo(N){var D=Jy(),U=N();return qd(D),U}function DA(N){return N}function m1(N){var D=/\b_Z[\w\d_]+/g;return N.replace(D,function(U){var X=U;return U===X?U:X+" ["+U+"]"})}function Ag(N){u()[N>>2]=0;var D=Ee.pthreads[N];delete Ee.pthreads[N],D.worker.terminate(),Yy(N),Ee.runningWorkers.splice(Ee.runningWorkers.indexOf(D.worker),1),D.worker.pthread=void 0}function $g(N){var D=Ee.pthreads[N];D.worker.postMessage({cmd:"cancel"})}function $d(N){var D=Ee.pthreads[N];if(D){u()[N>>2]=0;var U=D.worker;Ee.returnWorkerToPool(U)}}function Fd(N){k$(N)}function Fg(N){if(N instanceof wp||N=="unwind")return gn;x(1,N)}var Ee={unusedWorkers:[],runningWorkers:[],tlsInitFunctions:[],init:function(){C?Ee.initWorker():Ee.initMainThread()},initMainThread:function(){for(var N=8,D=0;D<N;++D)Ee.allocateUnusedWorker()},initWorker:function(){at=!1},pthreads:{},setExitStatus:function(N){gn=N},terminateAllThreads:function(){for(var N in Ee.pthreads){var D=Ee.pthreads[N];D&&D.worker&&Ee.returnWorkerToPool(D.worker)}for(var U=0;U<Ee.unusedWorkers.length;++U){var X=Ee.unusedWorkers[U];X.terminate()}Ee.unusedWorkers=[]},returnWorkerToPool:function(N){Ee.runWithoutMainThreadQueuedCalls(function(){delete Ee.pthreads[N.pthread.threadInfoStruct],Ee.unusedWorkers.push(N),Ee.runningWorkers.splice(Ee.runningWorkers.indexOf(N),1),Yy(N.pthread.threadInfoStruct),N.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(N){u()[C1>>2]=0;try{N()}finally{u()[C1>>2]=1}},receiveObjectTransfer:function(N){},threadInit:function(){for(var N in Ee.tlsInitFunctions)Ee.tlsInitFunctions[N]()},loadWasmModuleToWorker:function(N,D){N.onmessage=U=>{var X=U.data,pe=X.cmd;if(N.pthread&&(Ee.currentProxiedOperationCallerThread=N.pthread.threadInfoStruct),X.targetThread&&X.targetThread!=jd()){var he=Ee.pthreads[X.targetThread];he?he.worker.postMessage(X,X.transferList):Z('Internal error! Worker sent a message "'+pe+'" to target pthread '+X.targetThread+", but that thread no longer exists!"),Ee.currentProxiedOperationCallerThread=void 0;return}pe==="processQueuedMainThreadWork"?k1():pe==="spawnThread"?Rd(X):pe==="cleanupThread"?$d(X.thread):pe==="killThread"?Ag(X.thread):pe==="cancelThread"?$g(X.thread):pe==="loaded"?(N.loaded=!0,D&&D(N),N.runPthread&&(N.runPthread(),delete N.runPthread)):pe==="print"?re("Thread "+X.threadId+": "+X.text):pe==="printErr"?Z("Thread "+X.threadId+": "+X.text):pe==="alert"?alert("Thread "+X.threadId+": "+X.text):X.target==="setimmediate"?N.postMessage(X):pe==="onAbort"?c.onAbort&&c.onAbort(X.arg):Z("worker sent an unknown command "+pe),Ee.currentProxiedOperationCallerThread=void 0},N.onerror=U=>{var X="worker sent an error!";throw Z(X+" "+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");Ee.unusedWorkers.push(new Worker(N))},getNewWorker:function(){return Ee.unusedWorkers.length==0&&(Ee.allocateUnusedWorker(),Ee.loadWasmModuleToWorker(Ee.unusedWorkers[0])),Ee.unusedWorkers.pop()}};function Dg(){var N=jd(),D=u()[N+44>>2],U=u()[N+48>>2],X=D-U;T1(D,X),qd(D)}c.establishStackSpace=Dg;function Dd(N){if(C)return Ds(1,0,N);try{Fd(N)}catch(D){Fg(D)}}var $s=[];function Wo(N){var D=$s[N];return D||(N>=$s.length&&($s.length=N+1),$s[N]=D=ia.get(N)),D}function Rg(N,D){return Wo(N)(D)}c.invokeEntryPoint=Rg;function f1(){var N=new Error;if(!N.stack){try{throw new Error}catch(D){N=D}if(!N.stack)return"(no stack trace available)"}return N.stack.toString()}function Mg(N,D,U){Ee.tlsInitFunctions.push(N)}function g1(N,D){ia.set(N,D),$s[N]=D}var Fs;T?Fs=()=>{var N=process.hrtime();return N[0]*1e3+N[1]/1e6}:C?Fs=()=>performance.now()-c.__performance_now_clock_drift:Fs=()=>performance.now();var Pg=!0;function Og(N){return u()[w1()>>2]=N,N}function Lg(N,D){var U;if(N===0)U=Date.now();else if((N===1||N===4)&&Pg)U=Fs();else return Og(28),-1;return u()[D>>2]=U/1e3|0,u()[D+4>>2]=U%1e3*1e3*1e3|0,0}function zg(N,D){return Lg(N,D)}function Bg(N){I1(N,!w,1,!v),Ee.threadInit()}function Wg(N){C?postMessage({cmd:"cleanupThread",thread:N}):$d(N)}function Rd(N){var D=Ee.getNewWorker();if(!D)return 6;Ee.runningWorkers.push(D);var U=Ee.pthreads[N.pthread_ptr]={worker:D,threadInfoStruct:N.pthread_ptr};D.pthread=U;var X={cmd:"run",start_routine:N.startRoutine,arg:N.arg,threadInfoStruct:N.pthread_ptr};return D.runPthread=()=>{X.time=performance.now(),D.postMessage(X,N.transferList)},D.loaded&&(D.runPthread(),delete D.runPthread),0}function Ug(N,D,U,X){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 S1(687865856,N,D,U,X);if(he)return he;var xe={startRoutine:U,pthread_ptr:N,arg:X,transferList:pe};return C?(xe.cmd="spawnThread",postMessage(xe,pe),0):Rd(xe)}function Vg(){return 2097152}function Gg(N,D){if(N==D)postMessage({cmd:"processQueuedMainThreadWork"});else if(C)postMessage({targetThread:N,cmd:"processThreadQueue"});else{var U=Ee.pthreads[N],X=U&&U.worker;if(!X)return;X.postMessage({cmd:"processThreadQueue"})}return 1}function Hg(){Lo("")}function jg(){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 Md(){return 2147483648}function qg(N,D,U){i().copyWithin(N,D,D+U)}function Kg(){return T?sF().cpus().length:navigator.hardwareConcurrency}function Ds(N,D){var U=arguments.length-2,X=arguments;return Bo(function(){for(var pe=U,he=qo(pe*8),xe=he>>3,Ce=0;Ce<U;Ce++){var Et=X[2+Ce];d()[xe+Ce]=Et}return N1(N,pe,he,D)})}var gp=[];function Xg(N,D,U){gp.length=D;for(var X=U>>3,pe=0;pe<D;pe++)gp[pe]=d()[X+pe];var he=N<0,xe=he?_g[-N-1]:my[N];return xe.apply(null,gp)}function Yg(N){try{return Te.grow(N-yn.byteLength+65535>>>16),Da(Te.buffer),1}catch(D){}}function Jg(N){var D=i().length;if(N=N>>>0,N<=D)return!1;var U=Md();if(N>U)return!1;for(var X=1;X<=4;X*=2){var pe=D*(1+.2/X);pe=Math.min(pe,N+100663296);var he=Math.min(U,Po(Math.max(N,pe),65536)),xe=Yg(he);if(xe)return!0}return!1}var Ue={inEventHandler:0,removeAllEventListeners:function(){for(var N=Ue.eventHandlers.length-1;N>=0;--N)Ue._removeHandler(N);Ue.eventHandlers=[],Ue.deferredCalls=[]},registerRemoveEventListeners:function(){Ue.removeEventListenersRegistered||(Ig.push(Ue.removeAllEventListeners),Ue.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(N,D,U){function X(xe,Ce){if(xe.length!=Ce.length)return!1;for(var Et in xe)if(xe[Et]!=Ce[Et])return!1;return!0}for(var pe in Ue.deferredCalls){var he=Ue.deferredCalls[pe];if(he.targetFunction==N&&X(he.argsList,U))return}Ue.deferredCalls.push({targetFunction:N,precedence:D,argsList:U}),Ue.deferredCalls.sort(function(xe,Ce){return xe.precedence<Ce.precedence})},removeDeferredCalls:function(N){for(var D=0;D<Ue.deferredCalls.length;++D)Ue.deferredCalls[D].targetFunction==N&&(Ue.deferredCalls.splice(D,1),--D)},canPerformEventHandlerRequests:function(){return Ue.inEventHandler&&Ue.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Ue.canPerformEventHandlerRequests())for(var N=0;N<Ue.deferredCalls.length;++N){var D=Ue.deferredCalls[N];Ue.deferredCalls.splice(N,1),--N,D.targetFunction.apply(null,D.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(N,D){for(var U=0;U<Ue.eventHandlers.length;++U)Ue.eventHandlers[U].target==N&&(!D||D==Ue.eventHandlers[U].eventTypeString)&&Ue._removeHandler(U--)},_removeHandler:function(N){var D=Ue.eventHandlers[N];D.target.removeEventListener(D.eventTypeString,D.eventListenerFunc,D.useCapture),Ue.eventHandlers.splice(N,1)},registerOrRemoveHandler:function(N){var D=function(X){++Ue.inEventHandler,Ue.currentEventHandler=N,Ue.runDeferredCalls(),N.handlerFunc(X),Ue.runDeferredCalls(),--Ue.inEventHandler};if(N.callbackfunc)N.eventListenerFunc=D,N.target.addEventListener(N.eventTypeString,D,N.useCapture),Ue.eventHandlers.push(N),Ue.registerRemoveEventListeners();else for(var U=0;U<Ue.eventHandlers.length;++U)Ue.eventHandlers[U].target==N.target&&Ue.eventHandlers[U].eventTypeString==N.eventTypeString&&Ue._removeHandler(U--)},queueEventHandlerOnThread_iiii:function(N,D,U,X,pe){Bo(function(){var he=qo(12);u()[he>>2]=U,u()[he+4>>2]=X,u()[he+8>>2]=pe,Xy(N,637534208,D,X,he)})},getTargetThreadForEventCallback:function(N){switch(N){case 1:return 0;case 2:return Ee.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 Zg(N){var D=yd(N)+1,U=Ky(D);return Es(N,U,D),U}function Qg(N,D,U,X){Bo(function(){var pe=qo(12),he=0;D&&(he=Zg(D)),u()[pe>>2]=he,u()[pe+4>>2]=U,u()[pe+8>>2]=X,Xy(N,657457152,0,he,pe)})}function ey(N,D,U,X){D=D?sa(D):"",Qg(N,D,U,X)}function ty(N){return N>2?sa(N):N}var ny=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function ay(N){N=ty(N);var D=ny[N]||(typeof document!="undefined"?document.querySelector(N):void 0);return D}function yp(N){return ay(N)}function Pd(N,D,U){var X=yp(N);if(!X)return-4;if(X.canvasSharedPtr&&(u()[X.canvasSharedPtr>>2]=D,u()[X.canvasSharedPtr+4>>2]=U),X.offscreenCanvas||!X.controlTransferredOffscreen){X.offscreenCanvas&&(X=X.offscreenCanvas);var pe=!1;if(X.GLctxObject&&X.GLctxObject.GLctx){var he=X.GLctxObject.GLctx.getParameter(2978);pe=he[0]===0&&he[1]===0&&he[2]===X.width&&he[3]===X.height}X.width=D,X.height=U,pe&&X.GLctxObject.GLctx.viewport(0,0,D,U)}else if(X.canvasSharedPtr){var xe=u()[X.canvasSharedPtr+8>>2];return ey(xe,N,D,U),1}else return-4;return 0}function Od(N,D,U){return C?Ds(2,1,N,D,U):Pd(N,D,U)}function ry(N,D,U){var X=yp(N);return X?Pd(N,D,U):Od(N,D,U)}function sy(){throw"unwind"}function iy(N){var D=N.getExtension("ANGLE_instanced_arrays");if(D)return N.vertexAttribDivisor=function(U,X){D.vertexAttribDivisorANGLE(U,X)},N.drawArraysInstanced=function(U,X,pe,he){D.drawArraysInstancedANGLE(U,X,pe,he)},N.drawElementsInstanced=function(U,X,pe,he,xe){D.drawElementsInstancedANGLE(U,X,pe,he,xe)},1}function oy(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 ly(N){var D=N.getExtension("WEBGL_draw_buffers");if(D)return N.drawBuffers=function(U,X){D.drawBuffersWEBGL(U,X)},1}function uy(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,X){for(var pe="",he=0;he<D;++he){var xe=X?u()[X+he*4>>2]:-1;pe+=sa(u()[U+he*4>>2],xe<0?void 0:xe)}return pe},createContext:function(N,D){N.getContextSafariWebGL2Fixed||(N.getContextSafariWebGL2Fixed=N.getContext,N.getContext=function(pe,he){var xe=N.getContextSafariWebGL2Fixed(pe,he);return pe=="webgl"==xe instanceof WebGLRenderingContext?xe:null});var U=N.getContext("webgl",D);if(!U)return 0;var X=Ct.registerContext(U,D);return X},registerContext:function(N,D){var U=Ky(8);u()[U+4>>2]=jd();var X={handle:U,attributes:D,version:D.majorVersion,GLctx:N};return N.canvas&&(N.canvas.GLctxObject=X),Ct.contexts[U]=X,(typeof D.enableExtensionsByDefault=="undefined"||D.enableExtensionsByDefault)&&Ct.initExtensions(X),U},makeContextCurrent:function(N){return Ct.currentContext=Ct.contexts[N],c.ctx=Wd=Ct.currentContext&&Ct.currentContext.GLctx,!(N&&!Wd)},getContext:function(N){return Ct.contexts[N]},deleteContext:function(N){Ct.currentContext===Ct.contexts[N]&&(Ct.currentContext=null),typeof Ue=="object"&&Ue.removeAllHandlersOnTarget(Ct.contexts[N].GLctx.canvas),Ct.contexts[N]&&Ct.contexts[N].GLctx.canvas&&(Ct.contexts[N].GLctx.canvas.GLctxObject=void 0),v1(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;iy(D),oy(D),ly(D),D.disjointTimerQueryExt=D.getExtension("EXT_disjoint_timer_query"),uy(D);var U=D.getSupportedExtensions()||[];U.forEach(function(X){!X.includes("lose_context")&&!X.includes("debug")&&D.getExtension(X)})}}},py=["default","low-power","high-performance"];function cy(N,D){var U=D>>2,X=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:py[X],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=yp(N);if(!he||pe.explicitSwapControl)return 0;var xe=Ct.createContext(he,pe);return xe}function dy(N,D){return cy(N,D)}var Uo={mappings:{},buffers:[null,[],[]],printChar:function(N,D){var U=Uo.buffers[N];D===0||D===10?((N===1?re:Z)(ra(U,0)),U.length=0):U.push(D)},varargs:void 0,get:function(){Uo.varargs+=4;var N=u()[Uo.varargs-4>>2];return N},getStr:function(N){var D=sa(N);return D},get64:function(N,D){return N}};function Ld(N){return C?Ds(3,1,N):0}function zd(N,D,U,X,pe){if(C)return Ds(4,1,N,D,U,X,pe)}function Bd(N,D,U,X){if(C)return Ds(5,1,N,D,U,X);for(var pe=0,he=0;he<U;he++){var xe=u()[D>>2],Ce=u()[D+4>>2];D+=8;for(var Et=0;Et<Ce;Et++)Uo.printChar(N,i()[xe+Et]);pe+=Ce}return u()[X>>2]=pe,0}function hy(N){$e(N)}Ee.init();var Wd,my=[null,Dd,Od,Ld,zd,Bd],y1=!1,Ud={__clock_gettime:zg,__emscripten_init_main_thread_js:Bg,__emscripten_thread_cleanup:Wg,__pthread_create_js:Ug,_emscripten_default_pthread_stack_size:Vg,_emscripten_notify_thread_queue:Gg,abort:Hg,emscripten_check_blocking_allowed:jg,emscripten_get_heap_max:Md,emscripten_get_now:Fs,emscripten_memcpy_big:qg,emscripten_num_logical_cores:Kg,emscripten_receive_on_main_thread_js:Xg,emscripten_resize_heap:Jg,emscripten_set_canvas_element_size:ry,emscripten_unwind_to_js_event_loop:sy,emscripten_webgl_create_context:dy,exit:Fd,fd_close:Ld,fd_seek:zd,fd_write:Bd,memory:Te||c.wasmMemory,setTempRet0:hy},b1=Eg(),fy=c.___wasm_call_ctors=function(){return(fy=c.___wasm_call_ctors=c.asm.__wasm_call_ctors).apply(null,arguments)},gy=c._init=function(){return(gy=c._init=c.asm.init).apply(null,arguments)},yy=c._init_with_threads_count=function(){return(yy=c._init_with_threads_count=c.asm.init_with_threads_count).apply(null,arguments)},by=c._get_threads_count=function(){return(by=c._get_threads_count=c.asm.get_threads_count).apply(null,arguments)},xy=c._register_tensor=function(){return(xy=c._register_tensor=c.asm.register_tensor).apply(null,arguments)},vy=c._dispose_data=function(){return(vy=c._dispose_data=c.asm.dispose_data).apply(null,arguments)},wy=c._dispose=function(){return(wy=c._dispose=c.asm.dispose).apply(null,arguments)},ky=c._Abs=function(){return(ky=c._Abs=c.asm.Abs).apply(null,arguments)},Iy=c._Add=function(){return(Iy=c._Add=c.asm.Add).apply(null,arguments)},Sy=c._AddN=function(){return(Sy=c._AddN=c.asm.AddN).apply(null,arguments)},Ny=c._All=function(){return(Ny=c._All=c.asm.All).apply(null,arguments)},Ty=c._Any=function(){return(Ty=c._Any=c.asm.Any).apply(null,arguments)},Cy=c._ArgMax=function(){return(Cy=c._ArgMax=c.asm.ArgMax).apply(null,arguments)},Ey=c._AvgPool=function(){return(Ey=c._AvgPool=c.asm.AvgPool).apply(null,arguments)},_y=c._BatchMatMul=function(){return(_y=c._BatchMatMul=c.asm.BatchMatMul).apply(null,arguments)},Ay=c._Ceil=function(){return(Ay=c._Ceil=c.asm.Ceil).apply(null,arguments)},$y=c._ClipByValue=function(){return($y=c._ClipByValue=c.asm.ClipByValue).apply(null,arguments)},Fy=c._Conv2D=function(){return(Fy=c._Conv2D=c.asm.Conv2D).apply(null,arguments)},Dy=c._Conv2DBackpropInput=function(){return(Dy=c._Conv2DBackpropInput=c.asm.Conv2DBackpropInput).apply(null,arguments)},Ry=c._Cos=function(){return(Ry=c._Cos=c.asm.Cos).apply(null,arguments)},My=c._Cosh=function(){return(My=c._Cosh=c.asm.Cosh).apply(null,arguments)},Py=c._CropAndResize=function(){return(Py=c._CropAndResize=c.asm.CropAndResize).apply(null,arguments)},Oy=c._Cumsum=function(){return(Oy=c._Cumsum=c.asm.Cumsum).apply(null,arguments)},Ly=c._DepthToSpace=function(){return(Ly=c._DepthToSpace=c.asm.DepthToSpace).apply(null,arguments)},zy=c._DepthwiseConv2dNative=function(){return(zy=c._DepthwiseConv2dNative=c.asm.DepthwiseConv2dNative).apply(null,arguments)},By=c._Elu=function(){return(By=c._Elu=c.asm.Elu).apply(null,arguments)},Wy=c._Equal=function(){return(Wy=c._Equal=c.asm.Equal).apply(null,arguments)},Uy=c._Exp=function(){return(Uy=c._Exp=c.asm.Exp).apply(null,arguments)},Vy=c._FlipLeftRight=function(){return(Vy=c._FlipLeftRight=c.asm.FlipLeftRight).apply(null,arguments)},Vd=c._Floor=function(){return(Vd=c._Floor=c.asm.Floor).apply(null,arguments)},Gd=c._FloorDiv=function(){return(Gd=c._FloorDiv=c.asm.FloorDiv).apply(null,arguments)},bp=c._FusedBatchNorm=function(){return(bp=c._FusedBatchNorm=c.asm.FusedBatchNorm).apply(null,arguments)},Gy=c._FusedConv2D=function(){return(Gy=c._FusedConv2D=c.asm.FusedConv2D).apply(null,arguments)},Hy=c._FusedDepthwiseConv2D=function(){return(Hy=c._FusedDepthwiseConv2D=c.asm.FusedDepthwiseConv2D).apply(null,arguments)},Vo=c._Gather=function(){return(Vo=c._Gather=c.asm.Gather).apply(null,arguments)},xp=c._GatherNd=function(){return(xp=c._GatherNd=c.asm.GatherNd).apply(null,arguments)},vp=c._Greater=function(){return(vp=c._Greater=c.asm.Greater).apply(null,arguments)},x1=c._GreaterEqual=function(){return(x1=c._GreaterEqual=c.asm.GreaterEqual).apply(null,arguments)},Go=c._LeakyRelu=function(){return(Go=c._LeakyRelu=c.asm.LeakyRelu).apply(null,arguments)},Ho=c._Less=function(){return(Ho=c._Less=c.asm.Less).apply(null,arguments)},jy=c._LessEqual=function(){return(jy=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)},rt=c._Mean=function(){return(rt=c._Mean=c.asm.Mean).apply(null,arguments)},Ve=c._Min=function(){return(Ve=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)},Ma=c._Multiply=function(){return(Ma=c._Multiply=c.asm.Multiply).apply(null,arguments)},Pa=c._Neg=function(){return(Pa=c._Neg=c.asm.Neg).apply(null,arguments)},jo=c._NonMaxSuppressionV3=function(){return(jo=c._NonMaxSuppressionV3=c.asm.NonMaxSuppressionV3).apply(null,arguments)},Rs=c._NonMaxSuppressionV4=function(){return(Rs=c._NonMaxSuppressionV4=c.asm.NonMaxSuppressionV4).apply(null,arguments)},qy=c._NonMaxSuppressionV5=function(){return(qy=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)},Hd=c._PadV2=function(){return(Hd=c._PadV2=c.asm.PadV2).apply(null,arguments)},RA=c._Pow=function(){return(RA=c._Pow=c.asm.Pow).apply(null,arguments)},MA=c._Prelu=function(){return(MA=c._Prelu=c.asm.Prelu).apply(null,arguments)},PA=c._Prod=function(){return(PA=c._Prod=c.asm.Prod).apply(null,arguments)},OA=c._RealDiv=function(){return(OA=c._RealDiv=c.asm.RealDiv).apply(null,arguments)},LA=c._Relu=function(){return(LA=c._Relu=c.asm.Relu).apply(null,arguments)},zA=c._Relu6=function(){return(zA=c._Relu6=c.asm.Relu6).apply(null,arguments)},BA=c._ResizeBilinear=function(){return(BA=c._ResizeBilinear=c.asm.ResizeBilinear).apply(null,arguments)},WA=c._Reverse=function(){return(WA=c._Reverse=c.asm.Reverse).apply(null,arguments)},UA=c._RotateWithOffset=function(){return(UA=c._RotateWithOffset=c.asm.RotateWithOffset).apply(null,arguments)},VA=c._Round=function(){return(VA=c._Round=c.asm.Round).apply(null,arguments)},GA=c._Rsqrt=function(){return(GA=c._Rsqrt=c.asm.Rsqrt).apply(null,arguments)},HA=c._ScatterNd=function(){return(HA=c._ScatterNd=c.asm.ScatterNd).apply(null,arguments)},jA=c._SelectV2=function(){return(jA=c._SelectV2=c.asm.SelectV2).apply(null,arguments)},qA=c._Sigmoid=function(){return(qA=c._Sigmoid=c.asm.Sigmoid).apply(null,arguments)},KA=c._Sin=function(){return(KA=c._Sin=c.asm.Sin).apply(null,arguments)},XA=c._Softmax=function(){return(XA=c._Softmax=c.asm.Softmax).apply(null,arguments)},YA=c._SparseFillEmptyRows=function(){return(YA=c._SparseFillEmptyRows=c.asm.SparseFillEmptyRows).apply(null,arguments)},JA=c._SparseReshape=function(){return(JA=c._SparseReshape=c.asm.SparseReshape).apply(null,arguments)},ZA=c._SparseSegmentReduction=function(){return(ZA=c._SparseSegmentReduction=c.asm.SparseSegmentReduction).apply(null,arguments)},QA=c._Sqrt=function(){return(QA=c._Sqrt=c.asm.Sqrt).apply(null,arguments)},e$=c._Square=function(){return(e$=c._Square=c.asm.Square).apply(null,arguments)},t$=c._SquaredDifference=function(){return(t$=c._SquaredDifference=c.asm.SquaredDifference).apply(null,arguments)},n$=c._Step=function(){return(n$=c._Step=c.asm.Step).apply(null,arguments)},a$=c._StridedSlice=function(){return(a$=c._StridedSlice=c.asm.StridedSlice).apply(null,arguments)},r$=c._Sub=function(){return(r$=c._Sub=c.asm.Sub).apply(null,arguments)},s$=c._Sum=function(){return(s$=c._Sum=c.asm.Sum).apply(null,arguments)},i$=c._Tan=function(){return(i$=c._Tan=c.asm.Tan).apply(null,arguments)},o$=c._Tanh=function(){return(o$=c._Tanh=c.asm.Tanh).apply(null,arguments)},l$=c._Tile=function(){return(l$=c._Tile=c.asm.Tile).apply(null,arguments)},u$=c._TopK=function(){return(u$=c._TopK=c.asm.TopK).apply(null,arguments)},p$=c._Transform=function(){return(p$=c._Transform=c.asm.Transform).apply(null,arguments)},c$=c._Transpose=function(){return(c$=c._Transpose=c.asm.Transpose).apply(null,arguments)},d$=c.__FusedMatMul=function(){return(d$=c.__FusedMatMul=c.asm._FusedMatMul).apply(null,arguments)},Ky=c._malloc=function(){return(Ky=c._malloc=c.asm.malloc).apply(null,arguments)},v1=c._free=function(){return(v1=c._free=c.asm.free).apply(null,arguments)},h$=c._emscripten_tls_init=function(){return(h$=c._emscripten_tls_init=c.asm.emscripten_tls_init).apply(null,arguments)},w1=c.___errno_location=function(){return(w1=c.___errno_location=c.asm.__errno_location).apply(null,arguments)},jd=c._pthread_self=function(){return(jd=c._pthread_self=c.asm.pthread_self).apply(null,arguments)},k1=c._emscripten_main_thread_process_queued_calls=function(){return(k1=c._emscripten_main_thread_process_queued_calls=c.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},m$=c.__emscripten_thread_crashed=function(){return(m$=c.__emscripten_thread_crashed=c.asm._emscripten_thread_crashed).apply(null,arguments)},I1=c.__emscripten_thread_init=function(){return(I1=c.__emscripten_thread_init=c.asm._emscripten_thread_init).apply(null,arguments)},f$=c._emscripten_current_thread_process_queued_calls=function(){return(f$=c._emscripten_current_thread_process_queued_calls=c.asm.emscripten_current_thread_process_queued_calls).apply(null,arguments)},g$=c._emscripten_main_browser_thread_id=function(){return(g$=c._emscripten_main_browser_thread_id=c.asm.emscripten_main_browser_thread_id).apply(null,arguments)},y$=c._emscripten_sync_run_in_main_thread_2=function(){return(y$=c._emscripten_sync_run_in_main_thread_2=c.asm.emscripten_sync_run_in_main_thread_2).apply(null,arguments)},S1=c._emscripten_sync_run_in_main_thread_4=function(){return(S1=c._emscripten_sync_run_in_main_thread_4=c.asm.emscripten_sync_run_in_main_thread_4).apply(null,arguments)},N1=c._emscripten_run_in_main_runtime_thread_js=function(){return(N1=c._emscripten_run_in_main_runtime_thread_js=c.asm.emscripten_run_in_main_runtime_thread_js).apply(null,arguments)},Xy=c._emscripten_dispatch_to_thread_=function(){return(Xy=c._emscripten_dispatch_to_thread_=c.asm.emscripten_dispatch_to_thread_).apply(null,arguments)},Yy=c.__emscripten_thread_free_data=function(){return(Yy=c.__emscripten_thread_free_data=c.asm._emscripten_thread_free_data).apply(null,arguments)},b$=c.__emscripten_thread_exit=function(){return(b$=c.__emscripten_thread_exit=c.asm._emscripten_thread_exit).apply(null,arguments)},x$=c._memalign=function(){return(x$=c._memalign=c.asm.memalign).apply(null,arguments)},T1=c._emscripten_stack_set_limits=function(){return(T1=c._emscripten_stack_set_limits=c.asm.emscripten_stack_set_limits).apply(null,arguments)},Jy=c.stackSave=function(){return(Jy=c.stackSave=c.asm.stackSave).apply(null,arguments)},qd=c.stackRestore=function(){return(qd=c.stackRestore=c.asm.stackRestore).apply(null,arguments)},qo=c.stackAlloc=function(){return(qo=c.stackAlloc=c.asm.stackAlloc).apply(null,arguments)},v$=c.dynCall_iijjiiii=function(){return(v$=c.dynCall_iijjiiii=c.asm.dynCall_iijjiiii).apply(null,arguments)},w$=c.dynCall_jiji=function(){return(w$=c.dynCall_jiji=c.asm.dynCall_jiji).apply(null,arguments)},C1=c.__emscripten_allow_main_runtime_queued_calls=21416;c.cwrap=Jt,c.keepRuntimeAlive=As,c.PThread=Ee,c.PThread=Ee,c.wasmMemory=Te,c.ExitStatus=wp;var Kd;function wp(N){this.name="ExitStatus",this.message="Program terminated with exit("+N+")",this.status=N}Ra=function N(){Kd||Zy(),Kd||(Ra=N)};function Zy(N){if(N=N||y,Vr>0)return;if(C){h(c),dp(),postMessage({cmd:"loaded"});return}if(bn(),Vr>0)return;function D(){Kd||(Kd=!0,c.calledRun=!0,!pt&&(dp(),h(c),c.onRuntimeInitialized&&c.onRuntimeInitialized(),Tg()))}c.setStatus?(c.setStatus("Running..."),setTimeout(function(){setTimeout(function(){c.setStatus("")},1),D()},1)):D()}c.run=Zy;function k$(N,D){if(gn=N,!D&&C)throw Dd(N),"unwind";As()||Ng(),I$(N)}function I$(N){gn=N,As()||(Ee.terminateAllThreads(),c.onExit&&c.onExit(N),pt=!0),x(N,new wp(N))}if(c.preInit)for(typeof c.preInit=="function"&&(c.preInit=[c.preInit]);c.preInit.length>0;)c.preInit.pop()();Zy();var Xd;f&&(Xd={uncaughtException:process.listeners("uncaughtException").filter(function(N){return!f.uncaughtException.indexOf(N)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(N){return!f.unhandledRejection.indexOf(N)>-1})});var Yd;if(typeof WasmBackendModule!="undefined")Yd=WasmBackendModule;else if(typeof r!="undefined")Yd=r;else throw new Error("Could not find wasm module in post.js");if(Xd){var S$=Yd._dispose;Yd._dispose=function(){S$(),Xd.uncaughtException.forEach(function(N){process.removeListener("uncaughtException",N)}),Xd.unhandledRejection.forEach(function(N){process.removeListener("unhandledRejection",N)})}}return r.ready}})();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}),oF=ft((e,t)=>{var n=(()=>{var a=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(a=a||__filename),function(r){r=r||{};var s=typeof r!="undefined"?r:{},i,o;s.ready=new Promise(function(G,te){i=G,o=te});var l;typeof process!="undefined"&&process.listeners&&(l={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var u=Object.assign({},s),p=[],d="./this.program",c=(G,te)=>{throw te},h=typeof window=="object",m=typeof importScripts=="function",f=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g="";function y(G){return s.locateFile?s.locateFile(G,g):g+G}var b,x,v,w;function T(G){G instanceof xp||F("exiting due to exception: "+G)}var C,_,$;f?(m?g=wh().dirname(g)+"/":g=__dirname+"/",$=()=>{_||(C=mx(),_=wh())},b=function(G,te){return $(),G=_.normalize(G),C.readFileSync(G,te?void 0:"utf8")},v=G=>{var te=b(G,!0);return te.buffer||(te=new Uint8Array(te)),te},x=(G,te,de)=>{$(),G=_.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 xp))throw G}),process.on("unhandledRejection",function(G){throw G}),c=(G,te)=>{if(cp())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 rt=[1,0,1,96],Ve=te.slice(0,1),We=te.slice(1),Lt={i:127,j:126,f:125,d:124};rt.push(We.length);for(var Ze=0;Ze<We.length;++Ze)rt.push(Lt[We[Ze]]);Ve=="v"?rt.push(0):rt=rt.concat([1,Lt[Ve]]),rt[1]=rt.length-2;var Ma=new Uint8Array([0,97,115,109,1,0,0,0].concat(rt,[2,7,1,1,101,1,102,0,0,7,5,1,1,102,0,0])),Pa=new WebAssembly.Module(Ma),jo=new WebAssembly.Instance(Pa,{e:{f:G}}),Rs=jo.exports.f;return Rs}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=mp(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"&&_s("no native wasm support detected");var ae,le=!1,ue;function we(G,te){G||_s(te)}function ye(G){var te=s["_"+G];return te}function Ie(G,te,de,Se,Ze){var rt={string:function(Mn){var Gr=0;if(Mn!=null&&Mn!==0){var Hd=(Mn.length<<2)+1;Gr=bp(Hd),at(Mn,Gr,Hd)}return Gr},array:function(Mn){var Gr=bp(Mn.length);return pt(Mn,Gr),Gr}};function Ve(Mn){return te==="string"?st(Mn):te==="boolean"?Boolean(Mn):Mn}var We=ye(G),Lt=[],Ma=0;if(Se)for(var Pa=0;Pa<Se.length;Pa++){var jo=rt[de[Pa]];jo?(Ma===0&&(Ma=Vd()),Lt[Pa]=jo(Se[Pa])):Lt[Pa]=Se[Pa]}var Rs=We.apply(null,Lt);function qy(Mn){return Ma!==0&&Gd(Ma),Ve(Mn)}return Rs=qy(Rs),Rs}function _e(G,te,de,Se){de=de||[];var Ze=de.every(function(Ve){return Ve==="number"}),rt=te!=="string";return rt&&Ze&&!Se?ye(G):function(){return Ie(G,te,de,arguments,Se)}}var $e=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 rt="";te<Ze;){var Ve=G[te++];if(!(Ve&128)){rt+=String.fromCharCode(Ve);continue}var We=G[te++]&63;if((Ve&224)==192){rt+=String.fromCharCode((Ve&31)<<6|We);continue}var Lt=G[te++]&63;if((Ve&240)==224?Ve=(Ve&15)<<12|We<<6|Lt:Ve=(Ve&7)<<18|We<<12|Lt<<6|G[te++]&63,Ve<65536)rt+=String.fromCharCode(Ve);else{var Ma=Ve-65536;rt+=String.fromCharCode(55296|Ma>>10,56320|Ma&1023)}}return rt}function st(G,te){return G?je(Jt,G,te):""}function nt(G,te,de,Se){if(!(Se>0))return 0;for(var Ze=de,rt=de+Se-1,Ve=0;Ve<G.length;++Ve){var We=G.charCodeAt(Ve);if(We>=55296&&We<=57343){var Lt=G.charCodeAt(++Ve);We=65536+((We&1023)<<10)|Lt&1023}if(We<=127){if(de>=rt)break;te[de++]=We}else if(We<=2047){if(de+1>=rt)break;te[de++]=192|We>>6,te[de++]=128|We&63}else if(We<=65535){if(de+2>=rt)break;te[de++]=224|We>>12,te[de++]=128|We>>6&63,te[de++]=128|We&63}else{if(de+3>=rt)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 at(G,te,de){return nt(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 pt(G,te){Vt.set(G,te)}function gn(G,te,de){for(var Se=0;Se<G.length;++Se)Vt[te++>>0]=G.charCodeAt(Se);de||(Vt[te>>0]=0)}function Yt(G,te){return G%te>0&&(G+=te-G%te),G}var Dn,Vt,Jt,Fa,Rn,Gt,ra,sa,Br;function Es(G){Dn=G,s.HEAP8=Vt=new Int8Array(G),s.HEAP16=Fa=new Int16Array(G),s.HEAP32=Gt=new Int32Array(G),s.HEAPU8=Jt=new Uint8Array(G),s.HEAPU16=Rn=new Uint16Array(G),s.HEAPU32=ra=new Uint32Array(G),s.HEAPF32=sa=new Float32Array(G),s.HEAPF64=Br=new Float64Array(G)}var yd=s.INITIAL_MEMORY||16777216,Wr,vr=[],pp=[],Po=[],yn=!1,bd=!1,xd=0;function cp(){return ie||xd>0}function vd(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Id(s.preRun.shift());hp(vr)}function wd(){yn=!0,hp(pp)}function l1(){bd=!0}function kd(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)Sd(s.postRun.shift());hp(Po)}function Id(G){vr.unshift(G)}function Da(G){pp.unshift(G)}function Sd(G){Po.unshift(G)}var ia=0,Oo=null,Ur=null;function Ig(G){ia++,s.monitorRunDependencies&&s.monitorRunDependencies(ia)}function Nd(G){if(ia--,s.monitorRunDependencies&&s.monitorRunDependencies(ia),ia==0&&(Oo!==null&&(clearInterval(Oo),Oo=null),Ur)){var te=Ur;Ur=null,te()}}s.preloadedImages={},s.preloadedAudios={};function _s(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 Sg="data:application/octet-stream;base64,";function Td(G){return G.startsWith(Sg)}function As(G){return G.startsWith("file://")}var bn;bn="tfjs-backend-wasm.wasm",Td(bn)||(bn=y(bn));function dp(G){try{if(G==bn&&Z)return new Uint8Array(Z);if(v)return v(G);throw"both async and sync fetching of the wasm failed"}catch(te){_s(te)}}function Ng(){if(!Z&&(h||m)){if(typeof fetch=="function"&&!As(bn))return fetch(bn,{credentials:"same-origin"}).then(function(G){if(!G.ok)throw"failed to load wasm binary file at '"+bn+"'";return G.arrayBuffer()}).catch(function(){return dp(bn)});if(x)return new Promise(function(G,te){x(bn,function(de){G(new Uint8Array(de))},te)})}return Promise.resolve().then(function(){return dp(bn)})}function Tg(){var G={env:Bo,wasi_snapshot_preview1:Bo};function te(Ve,We){var Lt=Ve.exports;s.asm=Lt,ae=s.asm.memory,Es(ae.buffer),Wr=s.asm.__indirect_function_table,Da(s.asm.__wasm_call_ctors),Nd("wasm-instantiate")}Ig("wasm-instantiate");function de(Ve){te(Ve.instance)}function Se(Ve){return Ng().then(function(We){return WebAssembly.instantiate(We,G)}).then(function(We){return We}).then(Ve,function(We){F("failed to asynchronously prepare wasm: "+We),_s(We)})}function Ze(){return!Z&&typeof WebAssembly.instantiateStreaming=="function"&&!Td(bn)&&!As(bn)&&typeof fetch=="function"?fetch(bn,{credentials:"same-origin"}).then(function(Ve){var We=WebAssembly.instantiateStreaming(Ve,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 rt=s.instantiateWasm(G,te);return rt}catch(Ve){return F("Module.instantiateWasm callback failed with error: "+Ve),!1}return Ze().catch(o),{}}var u1,p1;function hp(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?mp(de)():mp(de)(te.arg):de(te.arg===void 0?null:te.arg)}}function Vr(G){return G}function Cd(G){var te=/\b_Z[\w\d_]+/g;return G.replace(te,function(de){var Se=de;return de===Se?de:Se+" ["+de+"]"})}var Ra=[];function mp(G){var te=Ra[G];return te||(G>=Ra.length&&(Ra.length=G+1),Ra[G]=te=Wr.get(G)),te}function c1(){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 Lo(G,te){Wr.set(G,te),Ra[G]=te}function Cg(){_s("")}function fp(){return 2147483648}function Ed(G,te,de){Jt.copyWithin(G,te,te+de)}function xn(G){try{return ae.grow(G-Dn.byteLength+65535>>>16),Es(ae.buffer),1}catch(te){}}function _d(G){var te=Jt.length;G=G>>>0;var de=fp();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 rt=Math.min(de,Yt(Math.max(G,Ze),65536)),Ve=xn(rt);if(Ve)return!0}return!1}var zo={mappings:{},buffers:[null,[],[]],printChar:function(G,te){var de=zo.buffers[G];te===0||te===10?((G===1?P:F)(je(de,0)),de.length=0):de.push(te)},varargs:void 0,get:function(){zo.varargs+=4;var G=Gt[zo.varargs-4>>2];return G},getStr:function(G){var te=st(G);return te},get64:function(G,te){return G}};function Eg(G){return 0}function d1(G,te,de,Se,Ze){}function h1(G,te,de,Se){for(var Ze=0,rt=0;rt<de;rt++){var Ve=Gt[te>>2],We=Gt[te+4>>2];te+=8;for(var Lt=0;Lt<We;Lt++)zo.printChar(G,Jt[Ve+Lt]);Ze+=We}return Gt[Se>>2]=Ze,0}function _g(G){re(G)}var Ad=!1,Bo={abort:Cg,emscripten_get_heap_max:fp,emscripten_memcpy_big:Ed,emscripten_resize_heap:_d,fd_close:Eg,fd_seek:d1,fd_write:h1,setTempRet0:_g},DA=Tg(),m1=s.___wasm_call_ctors=function(){return(m1=s.___wasm_call_ctors=s.asm.__wasm_call_ctors).apply(null,arguments)},Ag=s._init=function(){return(Ag=s._init=s.asm.init).apply(null,arguments)},$g=s._init_with_threads_count=function(){return($g=s._init_with_threads_count=s.asm.init_with_threads_count).apply(null,arguments)},$d=s._get_threads_count=function(){return($d=s._get_threads_count=s.asm.get_threads_count).apply(null,arguments)},Fd=s._register_tensor=function(){return(Fd=s._register_tensor=s.asm.register_tensor).apply(null,arguments)},Fg=s._dispose_data=function(){return(Fg=s._dispose_data=s.asm.dispose_data).apply(null,arguments)},Ee=s._dispose=function(){return(Ee=s._dispose=s.asm.dispose).apply(null,arguments)},Dg=s._Abs=function(){return(Dg=s._Abs=s.asm.Abs).apply(null,arguments)},Dd=s._Add=function(){return(Dd=s._Add=s.asm.Add).apply(null,arguments)},$s=s._AddN=function(){return($s=s._AddN=s.asm.AddN).apply(null,arguments)},Wo=s._All=function(){return(Wo=s._All=s.asm.All).apply(null,arguments)},Rg=s._Any=function(){return(Rg=s._Any=s.asm.Any).apply(null,arguments)},f1=s._ArgMax=function(){return(f1=s._ArgMax=s.asm.ArgMax).apply(null,arguments)},Mg=s._AvgPool=function(){return(Mg=s._AvgPool=s.asm.AvgPool).apply(null,arguments)},g1=s._BatchMatMul=function(){return(g1=s._BatchMatMul=s.asm.BatchMatMul).apply(null,arguments)},Fs=s._Ceil=function(){return(Fs=s._Ceil=s.asm.Ceil).apply(null,arguments)},Pg=s._ClipByValue=function(){return(Pg=s._ClipByValue=s.asm.ClipByValue).apply(null,arguments)},Og=s._Conv2D=function(){return(Og=s._Conv2D=s.asm.Conv2D).apply(null,arguments)},Lg=s._Conv2DBackpropInput=function(){return(Lg=s._Conv2DBackpropInput=s.asm.Conv2DBackpropInput).apply(null,arguments)},zg=s._Cos=function(){return(zg=s._Cos=s.asm.Cos).apply(null,arguments)},Bg=s._Cosh=function(){return(Bg=s._Cosh=s.asm.Cosh).apply(null,arguments)},Wg=s._CropAndResize=function(){return(Wg=s._CropAndResize=s.asm.CropAndResize).apply(null,arguments)},Rd=s._Cumsum=function(){return(Rd=s._Cumsum=s.asm.Cumsum).apply(null,arguments)},Ug=s._DepthToSpace=function(){return(Ug=s._DepthToSpace=s.asm.DepthToSpace).apply(null,arguments)},Vg=s._DepthwiseConv2dNative=function(){return(Vg=s._DepthwiseConv2dNative=s.asm.DepthwiseConv2dNative).apply(null,arguments)},Gg=s._Elu=function(){return(Gg=s._Elu=s.asm.Elu).apply(null,arguments)},Hg=s._Equal=function(){return(Hg=s._Equal=s.asm.Equal).apply(null,arguments)},jg=s._Exp=function(){return(jg=s._Exp=s.asm.Exp).apply(null,arguments)},Md=s._FlipLeftRight=function(){return(Md=s._FlipLeftRight=s.asm.FlipLeftRight).apply(null,arguments)},qg=s._Floor=function(){return(qg=s._Floor=s.asm.Floor).apply(null,arguments)},Kg=s._FloorDiv=function(){return(Kg=s._FloorDiv=s.asm.FloorDiv).apply(null,arguments)},Ds=s._FusedBatchNorm=function(){return(Ds=s._FusedBatchNorm=s.asm.FusedBatchNorm).apply(null,arguments)},gp=s._FusedConv2D=function(){return(gp=s._FusedConv2D=s.asm.FusedConv2D).apply(null,arguments)},Xg=s._FusedDepthwiseConv2D=function(){return(Xg=s._FusedDepthwiseConv2D=s.asm.FusedDepthwiseConv2D).apply(null,arguments)},Yg=s._Gather=function(){return(Yg=s._Gather=s.asm.Gather).apply(null,arguments)},Jg=s._GatherNd=function(){return(Jg=s._GatherNd=s.asm.GatherNd).apply(null,arguments)},Ue=s._Greater=function(){return(Ue=s._Greater=s.asm.Greater).apply(null,arguments)},Zg=s._GreaterEqual=function(){return(Zg=s._GreaterEqual=s.asm.GreaterEqual).apply(null,arguments)},Qg=s._LeakyRelu=function(){return(Qg=s._LeakyRelu=s.asm.LeakyRelu).apply(null,arguments)},ey=s._Less=function(){return(ey=s._Less=s.asm.Less).apply(null,arguments)},ty=s._LessEqual=function(){return(ty=s._LessEqual=s.asm.LessEqual).apply(null,arguments)},ny=s._Log=function(){return(ny=s._Log=s.asm.Log).apply(null,arguments)},ay=s._LogicalAnd=function(){return(ay=s._LogicalAnd=s.asm.LogicalAnd).apply(null,arguments)},yp=s._Max=function(){return(yp=s._Max=s.asm.Max).apply(null,arguments)},Pd=s._MaxPool=function(){return(Pd=s._MaxPool=s.asm.MaxPool).apply(null,arguments)},Od=s._Maximum=function(){return(Od=s._Maximum=s.asm.Maximum).apply(null,arguments)},ry=s._Mean=function(){return(ry=s._Mean=s.asm.Mean).apply(null,arguments)},sy=s._Min=function(){return(sy=s._Min=s.asm.Min).apply(null,arguments)},iy=s._Minimum=function(){return(iy=s._Minimum=s.asm.Minimum).apply(null,arguments)},oy=s._MirrorPad=function(){return(oy=s._MirrorPad=s.asm.MirrorPad).apply(null,arguments)},ly=s._Multiply=function(){return(ly=s._Multiply=s.asm.Multiply).apply(null,arguments)},uy=s._Neg=function(){return(uy=s._Neg=s.asm.Neg).apply(null,arguments)},Ct=s._NonMaxSuppressionV3=function(){return(Ct=s._NonMaxSuppressionV3=s.asm.NonMaxSuppressionV3).apply(null,arguments)},py=s._NonMaxSuppressionV4=function(){return(py=s._NonMaxSuppressionV4=s.asm.NonMaxSuppressionV4).apply(null,arguments)},cy=s._NonMaxSuppressionV5=function(){return(cy=s._NonMaxSuppressionV5=s.asm.NonMaxSuppressionV5).apply(null,arguments)},dy=s._NotEqual=function(){return(dy=s._NotEqual=s.asm.NotEqual).apply(null,arguments)},Uo=s._OneHot=function(){return(Uo=s._OneHot=s.asm.OneHot).apply(null,arguments)},Ld=s._PadV2=function(){return(Ld=s._PadV2=s.asm.PadV2).apply(null,arguments)},zd=s._Pow=function(){return(zd=s._Pow=s.asm.Pow).apply(null,arguments)},Bd=s._Prelu=function(){return(Bd=s._Prelu=s.asm.Prelu).apply(null,arguments)},hy=s._Prod=function(){return(hy=s._Prod=s.asm.Prod).apply(null,arguments)},Wd=s._RealDiv=function(){return(Wd=s._RealDiv=s.asm.RealDiv).apply(null,arguments)},my=s._Relu=function(){return(my=s._Relu=s.asm.Relu).apply(null,arguments)},y1=s._Relu6=function(){return(y1=s._Relu6=s.asm.Relu6).apply(null,arguments)},Ud=s._ResizeBilinear=function(){return(Ud=s._ResizeBilinear=s.asm.ResizeBilinear).apply(null,arguments)},b1=s._Reverse=function(){return(b1=s._Reverse=s.asm.Reverse).apply(null,arguments)},fy=s._RotateWithOffset=function(){return(fy=s._RotateWithOffset=s.asm.RotateWithOffset).apply(null,arguments)},gy=s._Round=function(){return(gy=s._Round=s.asm.Round).apply(null,arguments)},yy=s._Rsqrt=function(){return(yy=s._Rsqrt=s.asm.Rsqrt).apply(null,arguments)},by=s._ScatterNd=function(){return(by=s._ScatterNd=s.asm.ScatterNd).apply(null,arguments)},xy=s._SelectV2=function(){return(xy=s._SelectV2=s.asm.SelectV2).apply(null,arguments)},vy=s._Sigmoid=function(){return(vy=s._Sigmoid=s.asm.Sigmoid).apply(null,arguments)},wy=s._Sin=function(){return(wy=s._Sin=s.asm.Sin).apply(null,arguments)},ky=s._Softmax=function(){return(ky=s._Softmax=s.asm.Softmax).apply(null,arguments)},Iy=s._SparseFillEmptyRows=function(){return(Iy=s._SparseFillEmptyRows=s.asm.SparseFillEmptyRows).apply(null,arguments)},Sy=s._SparseReshape=function(){return(Sy=s._SparseReshape=s.asm.SparseReshape).apply(null,arguments)},Ny=s._SparseSegmentReduction=function(){return(Ny=s._SparseSegmentReduction=s.asm.SparseSegmentReduction).apply(null,arguments)},Ty=s._Sqrt=function(){return(Ty=s._Sqrt=s.asm.Sqrt).apply(null,arguments)},Cy=s._Square=function(){return(Cy=s._Square=s.asm.Square).apply(null,arguments)},Ey=s._SquaredDifference=function(){return(Ey=s._SquaredDifference=s.asm.SquaredDifference).apply(null,arguments)},_y=s._Step=function(){return(_y=s._Step=s.asm.Step).apply(null,arguments)},Ay=s._StridedSlice=function(){return(Ay=s._StridedSlice=s.asm.StridedSlice).apply(null,arguments)},$y=s._Sub=function(){return($y=s._Sub=s.asm.Sub).apply(null,arguments)},Fy=s._Sum=function(){return(Fy=s._Sum=s.asm.Sum).apply(null,arguments)},Dy=s._Tan=function(){return(Dy=s._Tan=s.asm.Tan).apply(null,arguments)},Ry=s._Tanh=function(){return(Ry=s._Tanh=s.asm.Tanh).apply(null,arguments)},My=s._Tile=function(){return(My=s._Tile=s.asm.Tile).apply(null,arguments)},Py=s._TopK=function(){return(Py=s._TopK=s.asm.TopK).apply(null,arguments)},Oy=s._Transform=function(){return(Oy=s._Transform=s.asm.Transform).apply(null,arguments)},Ly=s._Transpose=function(){return(Ly=s._Transpose=s.asm.Transpose).apply(null,arguments)},zy=s.__FusedMatMul=function(){return(zy=s.__FusedMatMul=s.asm._FusedMatMul).apply(null,arguments)},By=s._malloc=function(){return(By=s._malloc=s.asm.malloc).apply(null,arguments)},Wy=s._free=function(){return(Wy=s._free=s.asm.free).apply(null,arguments)},Uy=s.___errno_location=function(){return(Uy=s.___errno_location=s.asm.__errno_location).apply(null,arguments)},Vy=s._emscripten_main_thread_process_queued_calls=function(){return(Vy=s._emscripten_main_thread_process_queued_calls=s.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},Vd=s.stackSave=function(){return(Vd=s.stackSave=s.asm.stackSave).apply(null,arguments)},Gd=s.stackRestore=function(){return(Gd=s.stackRestore=s.asm.stackRestore).apply(null,arguments)},bp=s.stackAlloc=function(){return(bp=s.stackAlloc=s.asm.stackAlloc).apply(null,arguments)},Gy=s.dynCall_iijjiiii=function(){return(Gy=s.dynCall_iijjiiii=s.asm.dynCall_iijjiiii).apply(null,arguments)},Hy=s.dynCall_jiji=function(){return(Hy=s.dynCall_jiji=s.asm.dynCall_jiji).apply(null,arguments)};s.cwrap=_e;var Vo;function xp(G){this.name="ExitStatus",this.message="Program terminated with exit("+G+")",this.status=G}Ur=function G(){Vo||vp(),Vo||(Ur=G)};function vp(G){if(G=G||p,ia>0||(vd(),ia>0))return;function te(){Vo||(Vo=!0,s.calledRun=!0,!le&&(wd(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),kd()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),te()},1)):te()}s.run=vp;function x1(G){ue=G,cp()||(s.onExit&&s.onExit(G),le=!0),c(G,new xp(G))}if(s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();vp();var Go;l&&(Go={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 Ho;if(typeof r!="undefined")Ho=r;else if(typeof WasmBackendModuleThreadedSimd!="undefined")Ho=WasmBackendModuleThreadedSimd;else throw new Error("Could not find wasm module in post.js");if(Go){var jy=Ho._dispose;Ho._dispose=function(){jy(),Go.uncaughtException.forEach(function(G){process.removeListener("uncaughtException",G)}),Go.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)}),Jh=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}},ac=class{refCount(e){return la("refCount")}incRef(e){return la("incRef")}timerAvailable(){return!0}time(e){return la("time")}read(e){return la("read")}readSync(e){return la("readSync")}readToGPU(e,t){return la("readToGPU")}numDataIds(){return la("numDataIds")}disposeData(e,t){return la("disposeData")}write(e,t,n){return la("write")}move(e,t,n,a,r){return la("move")}memory(){return la("memory")}floatPrecision(){return la("floatPrecision")}epsilon(){return this.floatPrecision()===32?1e-7:1e-4}dispose(){return la("dispose")}};function la(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 sI(e){let t=e.length,n=0;for(;t>0;)n=Math.random()*t|0,t--,kh(e,t,n)}function lF(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 Wp(e,t,n){return Math.max(e,Math.min(t,n))}function uF(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 pF(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function cF(e,t){let n=Math.random();return t*n+(1-n)*e}function dF(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(ps(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function hi(e){R(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function Xs(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||dn(e)&&!n)for(let a=0;a<e.length;++a)Xs(e[a],t,n);else t.push(e);return t}function vt(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 hF(e){return e.length===0}function ps(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 il(e){return e%1===0}function mF(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 fF(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function gF(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return sI(t),t}function Op(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function yF(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 bF(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} / ${n}`);let r=e.slice();return r[a]=t/n,r}function Ta(e,t){let n=t.length;return e=e==null?t.map((a,r)=>r):[].concat(e),R(e.every(a=>a>=-n&&a<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),R(e.every(a=>il(a)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(a=>a<0?n+a:a)}function iI(e,t){let n=[],a=[],r=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||r?null:Ta(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 oI(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 lI(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 uI(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 pI(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function xF(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function dn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray}function fb(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function cI(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Kr(e){return typeof e=="string"||e instanceof String}function dI(e){return typeof e=="boolean"}function hI(e){return typeof e=="number"}function Zh(e){return Array.isArray(e)?Zh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray?"int32":hI(e)?"float32":Kr(e)?"string":dI(e)?"bool":"float32"}function es(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Ih(e,t){for(let n=t;n<e;++n)if(e%n===0)return n;return e}function vl(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let a=t-3;a>=0;--a)n[a]=n[a+1]*e[a+1];return n}function mI(e,t,n,a=!1){let r=new Array;if(t.length===1){let s=t[0]*(a?2:1);for(let i=0;i<s;i++)r[i]=n[e+i]}else{let s=t[0],i=t.slice(1),o=i.reduce((l,u)=>l*u)*(a?2:1);for(let l=0;l<s;l++)r[l]=mI(e+l*o,i,n,a)}return r}function tl(e,t,n=!1){if(e.length===0)return t[0];let a=e.reduce((r,s)=>r*s)*(n?2:1);if(a===0)return[];if(a!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return mI(0,e,t,n)}function fx(e,t){let n=Qh(e,t);for(let a=0;a<n.length;a++)n[a]=1;return n}function Qh(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 vF(e,t){let n=e.reduce((a,r)=>a*r,1);if(t==null||t==="float32")return tl(e,new Float32Array(n));if(t==="int32")return tl(e,new Int32Array(n));if(t==="bool")return tl(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function gx(e){e.forEach(t=>{R(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function wF(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 kF(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 yx(e){return e&&e.then&&typeof e.then=="function"}var _1="tfjsflags",fI=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=IF,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&(Y().getBool("IS_TEST")||Y().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];Y().getBool("IS_TEST")||Y().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(yx(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);_1 in e&&e[_1].split(",").forEach(t=>{let[n,a]=t.split(":");this.urlFlags[n]=NF(n,a)})}};function IF(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...a)=>(SF(t,a[0],a[1]),a.join("="))),t}function SF(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function NF(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 ${e}.`)}function Y(){return bx}var bx=null;function TF(e){bx=e}var eb;function gI(){if(eb==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");eb=e}return eb}function CF(){let e=gI();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function xx(e,t){let n=CF();if(n.has(e))return n.get(e);{let a=t();return n.set(e,a),n.get(e)}}var wl="Abs",kl="Acos",Il="Acosh",cs="Add",mi="AddN",Sl="All",Nl="Any",fi="ArgMax",rc="ArgMin",Tl="Asin",Cl="Asinh",El="Atan",_l="Atanh",Al="Atan2",gi="AvgPool",em="AvgPoolGrad",sc="AvgPool3D",tm="AvgPool3DGrad",yi="BatchMatMul",$l="BatchToSpaceND",nm="Bincount",yI="BroadcastTo",am="BroadcastArgs",bi="Cast",xi="Ceil",ds="ClipByValue",rm="Complex",ic="ComplexAbs",Fl="Concat",vi="Conv2D",sm="Conv2DBackpropFilter",wi="Conv2DBackpropInput",oc="Conv3D",im="Conv3DBackpropFilterV2",om="Conv3DBackpropInputV2",ki="Cos",Ii="Cosh",Si="Cumsum",Dl="CropAndResize",lm="DenseBincount",Rl="DepthToSpace",Ni="DepthwiseConv2dNative",um="DepthwiseConv2dNativeBackpropFilter",pm="DepthwiseConv2dNativeBackpropInput",cm="Diag",lc="Dilation2D",Sh="Dilation2DBackpropInput",Nh="Dilation2DBackpropFilter",Ti="RealDiv",dm="Einsum",Ci="Elu",hm="EluGrad",Ml="Erf",Pl="Equal",Ei="Exp",Ol="ExpandDims",Ll="Expm1",mm="FFT",uc="Fill",zl="FlipLeftRight",_i="Floor",Ai="FloorDiv",$i="FusedBatchNorm",Bl="GatherV2",Wl="GatherNd",Ul="Greater",Fi="GreaterEqual",Di="Identity",fm="IFFT",gm="Imag",Vl="IsFinite",Gl="IsInf",Hl="IsNan",Ri="LeakyRelu",jl="Less",ql="LessEqual",ym="LinSpace",Mi="Log",Kl="Log1p",Xl="LogicalAnd",pc="LogicalNot",cc="LogicalOr",bI="LogSoftmax",dc="LRN",bm="LRNGrad",Pi="Max",Oi="Maximum",Li="MaxPool",xm="MaxPoolGrad",hc="MaxPool3D",vm="MaxPool3DGrad",wm="MaxPoolWithArgmax",zi="Mean",Bi="Min",Wi="Minimum",Ui="MirrorPad",Yl="Mod",km="Multinomial",Vi="Multiply",Jl="Neg",Zl="NotEqual",Ql="NonMaxSuppressionV3",eu="NonMaxSuppressionV4",tu="NonMaxSuppressionV5",nu="OnesLike",Gi="OneHot",au="Pack",Hi="PadV2",EF="Pool",ji="Pow",qi="Prelu",ru="Prod",mc="Range",Im="Real",su="Reciprocal",Ki="Relu",iu="Reshape",fc="ResizeNearestNeighbor",Sm="ResizeNearestNeighborGrad",Xi="ResizeBilinear",Nm="ResizeBilinearGrad",Yi="Relu6",Ji="Reverse",Zi="Round",Qi="Rsqrt",ou="ScatterNd",lu="Select",uu="Selu",pu="Slice",eo="Sin",cu="Sinh",du="Sign",to="Sigmoid",hu="Softplus",no="Sqrt",ao="Sum",mu="SpaceToBatchND",fu="SplitV",ro="Softmax",gc="SparseFillEmptyRows",gu="SparseReshape",yc="SparseSegmentMean",bc="SparseSegmentSum",Tm="SparseToDense",so="SquaredDifference",xc="Square",yu="StridedSlice",Cm="StringNGrams",Em="StringSplit",_m="StringToHashBucketFast",io="Sub",oo="Tan",lo="Tanh",hs="Tile",bu="TopK",xu="Transform",uo="Transpose",Am="Unique",vu="Unpack",vc="UnsortedSegmentSum",wu="ZerosLike",ms="Step",Th="FromPixels",ku="RotateWithOffset",Ys="_FusedMatMul",Js="FusedConv2D",Zs="FusedDepthwiseConv2D";function qr(...e){Y().getBool("IS_TEST")||Y().getBool("PROD")||console.warn(...e)}function _F(...e){Y().getBool("IS_TEST")||Y().getBool("PROD")||console.log(...e)}var ol=xx("kernelRegistry",()=>new Map),Up=xx("gradRegistry",()=>new Map);function Ch(e,t){let n=vx(e,t);return ol.get(n)}function gb(e){return Up.get(e)}function Eh(e){let t=ol.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 wc(e){let{kernelName:t,backendName:n}=e,a=vx(t,n);ol.has(a)&&qr(`The kernel '${t}' for backend '${n}' is already registered`),ol.set(a,e)}function xI(e){let{kernelName:t}=e;Up.has(t)&&Y().getBool("DEBUG")&&qr(`Overriding the gradient for '${t}'`),Up.set(t,e)}function AF(e,t){let n=vx(e,t);if(!ol.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);ol.delete(n)}function $F(e){if(!Up.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Up.delete(e)}function FF(e,t){Eh(e).forEach(n=>{let a=Object.assign({},n,{backendName:t});wc(a)})}function vx(e,t){return`${t}_${e}`}var k={};Re(k,{arraysEqual:()=>ps,assert:()=>R,assertNonNegativeIntegerDimensions:()=>gx,assertNonNull:()=>hi,assertShapesMatch:()=>Nn,bytesFromStringArray:()=>cI,bytesPerElement:()=>fb,checkConversionForErrors:()=>uI,clamp:()=>Wp,computeStrides:()=>vl,createScalarValue:()=>LF,createShuffledIndices:()=>gF,decodeString:()=>_h,distSquared:()=>dF,encodeString:()=>Ic,fetch:()=>BF,fingerPrint64:()=>OF,flatten:()=>Xs,getArrayFromDType:()=>lI,getTypedArrayFromDType:()=>oI,hasEncodingLoss:()=>xF,hexToLong:()=>kc,indexToLoc:()=>kF,inferDtype:()=>Zh,inferFromImplicitShape:()=>bF,isBoolean:()=>dI,isFunction:()=>es,isInt:()=>il,isNumber:()=>hI,isPromise:()=>yx,isScalarShape:()=>hF,isString:()=>Kr,isTypedArray:()=>dn,isValidDtype:()=>pI,locToIndex:()=>wF,makeOnesTypedArray:()=>fx,makeZerosNestedTypedArray:()=>vF,makeZerosTypedArray:()=>Qh,nearestDivisor:()=>Ih,nearestLargerEven:()=>uF,now:()=>Vp,parseAxisParam:()=>Ta,randUniform:()=>cF,repeatedTry:()=>yF,rightPad:()=>Op,shuffle:()=>sI,shuffleCombo:()=>lF,sizeFromShape:()=>vt,sizeToSquarishShape:()=>fF,squeezeShape:()=>iI,sum:()=>pF,swap:()=>kh,tanh:()=>mF,toNestedArray:()=>tl,toTypedArray:()=>$m});var A1=di(B$()),zs=A1.default||A1;function kc(e){return zs.fromString(e,!0,16)}var vI=kc("c3a5c85c97cb3127"),Os=kc("b492b66fbe98f273"),vn=kc("9ae16a3b2f90404f");function yb(e){return e.xor(e.shru(47))}function wI(e,t,n){let a=e.slice(t,t+n);return zs.fromBytes(Array.from(a),!0,!0)}function yt(e,t){return wI(e,t,8)}function $1(e,t){return wI(e,t,4)}function Zt(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function Jr(e,t,n=kc("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 DF(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 DF(yt(e,t),yt(e,t+8),yt(e,t+16),yt(e,t+24),n,a)}function RF(e,t=e.length){if(t>=8){let n=vn.add(t*2),a=yt(e,0).add(vn),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=vn.add(t*2),a=$1(e,0);return Jr(a.shl(3).add(t),$1(e,t-4),n)}if(t>0){let n=e[0],a=e[t>>1],r=e[t-1],s=n+(a<<8),i=t+(r<<2);return yb(vn.mul(s).xor(vI.mul(i))).mul(vn)}return vn}function MF(e,t=e.length){let n=vn.add(t*2),a=yt(e,0).mul(Os),r=yt(e,8),s=yt(e,t-8).mul(n),i=yt(e,t-16).mul(vn);return Jr(Zt(a.add(r),43).add(Zt(s,30)).add(i),a.add(Zt(r.add(vn),18)).add(s),n)}function PF(e,t=e.length){let n=vn.add(t*2),a=yt(e,0).mul(vn),r=yt(e,8),s=yt(e,t-8).mul(n),i=yt(e,t-16).mul(vn),o=Zt(a.add(r),43).add(Zt(s,30)).add(i),l=Jr(o,a.add(Zt(r.add(vn),18)).add(s),n),u=yt(e,16).mul(n),p=yt(e,24),d=o.add(yt(e,t-32)).mul(n),c=l.add(yt(e,t-24)).mul(n);return Jr(Zt(u.add(p),43).add(Zt(d,30)).add(c),u.add(Zt(p.add(a),18)).add(d),n)}function OF(e,t=e.length){let n=zs.fromNumber(81,!0);if(t<=32)return t<=16?RF(e,t):MF(e,t);if(t<=64)return PF(e,t);let a=n,r=n.mul(Os).add(113),s=yb(r.mul(vn).add(113)).mul(vn),i=[zs.UZERO,zs.UZERO],o=[zs.UZERO,zs.UZERO];a=a.mul(vn).add(yt(e,0));let l=0,u=(t-1>>6)*64,p=u+(t-1&63)-63;do a=Zt(a.add(r).add(i[0]).add(yt(e,l+8)),37).mul(Os),r=Zt(r.add(i[1]).add(yt(e,l+48)),42).mul(Os),a=a.xor(o[1]),r=r.add(i[0]).add(yt(e,l+40)),s=Zt(s.add(o[0]),33).mul(Os),i=Qd(e,l,i[1].mul(Os),a.add(o[0])),o=Qd(e,l+32,s.add(o[1]),r.add(yt(e,l+16))),[s,a]=[a,s],l+=64;while(l!==u);let d=Os.add(s.and(255).shl(1));return l=p,o[0]=o[0].add(t-1&63),i[0]=i[0].add(o[0]),o[0]=o[0].add(i[0]),a=Zt(a.add(r).add(i[0]).add(yt(e,l+8)),37).mul(d),r=Zt(r.add(i[1]).add(yt(e,l+48)),42).mul(d),a=a.xor(o[1].mul(9)),r=r.add(i[0].mul(9).add(yt(e,l+40))),s=Zt(s.add(o[0]),33).mul(d),i=Qd(e,l,i[1].mul(d),a.add(o[0])),o=Qd(e,l+32,s.add(o[1]),r.add(yt(e,l+16))),[s,a]=[a,s],Jr(Jr(i[0],o[0],d).add(yb(r).mul(vI)).add(s),Jr(i[1],o[1],d).add(a),d)}function LF(e,t){return t==="string"?Ic(e):$m([e],t)}function zF(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function $m(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=Xs(e)),Y().getBool("DEBUG")&&uI(e,t),zF(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 Vp(){return Y().platform.now()}function BF(e,t){return Y().platform.fetch(e,t)}function Ic(e,t="utf-8"){return t=t||"utf-8",Y().platform.encode(e,t)}function _h(e,t="utf-8"){return t=t||"utf-8",Y().platform.decode(e,t)}var WF=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new VF)}profileKernel(e,t,n){let a,r=()=>{a=n()},s,i=Vp();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(r);else{r();for(let o of a)o.dataSync();s=Promise.resolve({kernelMs:Vp()-i})}if(Y().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<a.length;o++){let l=a[o];l.data().then(u=>{UF(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 UF(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 VF=class{logKernelProfile(e,t,n,a,r,s){let i=typeof a=="number"?Op(`${a}ms`,9):a.error,o=Op(e,25),l=t.rank,u=t.size,p=Op(t.shape.toString(),14),d="";for(let c in r){let h=r[c];if(h!=null){let m=h.shape||t.shape,f=m.length;d+=`${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 GF(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 HF(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(!ps(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 F1=20,Ip=3,tb=7;function jF(e,t,n,a){let r=vl(t),s=qF(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 qF(e,t,n,a){let r=vt(t),s=a[a.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Ep(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],Cp(l[p+d],0,n).length)}return i}function Cp(e,t,n){let a;return Array.isArray(e)?a=`${parseFloat(e[0].toFixed(tb))} + ${parseFloat(e[1].toFixed(tb))}j`:Kr(e)?a=`'${e}'`:n==="bool"?a=kI(e):a=parseFloat(e.toFixed(tb)).toString(),Op(a,t)}function kI(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=Ep(e);return[Cp(f[0],0,n)]}return n==="bool"?[kI(e[0])]:[e[0].toString()]}if(l===1){if(o>F1){let g=Ip*i,y=Array.from(e.slice(0,g)),b=Array.from(e.slice((o-Ip)*i,o*i));return n==="complex64"&&(y=Ep(y),b=Ep(b)),["["+y.map((x,v)=>Cp(x,r[v],n)).join(", ")+", ..., "+b.map((x,v)=>Cp(x,r[o-Ip+v],n)).join(", ")+"]"]}let f=n==="complex64"?Ep(e):Array.from(e);return["["+f.map((g,y)=>Cp(g,r[y],n)).join(", ")+"]"]}let u=t.slice(1),p=a.slice(1),d=a[0]*i,c=[];if(o>F1){for(let f=0;f<Ip;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-Ip;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 Ep(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=vt(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||lI(t,this.size),this.strides=vl(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 za().makeTensor(this.values,this.shape,this.dtype)}},za=null,Zo=null,KF=null;function XF(e){za=e}function YF(e){Zo=e}function JF(e){KF=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=vt(e),this.strides=vl(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 Zo.buffer(this.shape,this.dtype,e)}bufferSync(){return Zo.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return tl(this.shape,e,this.dtype==="complex64")}arraySync(){return tl(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=za().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>_h(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(),za().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=za().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>_h(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 za().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(za().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Zo.print(this,e)}clone(){return this.throwIfDisposed(),Zo.clone(this)}toString(e=!1){let t=this.dataSync();return jF(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Zo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),za().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 xx("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(!ps(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);za().disposeTensor(this),this.dataId=e.dataId,za().incRef(this,null)}dispose(){za().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(ts,Symbol.hasInstance,{value:e=>e instanceof Ae&&e.assign!=null&&e.assign instanceof Function});var Va={};Re(Va,{assertTypesMatch:()=>II,getTensorsInContainer:()=>wx,isTensorInList:()=>QF,makeTypesMatch:()=>$t});var bb;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(bb||(bb={}));var xb;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(xb||(xb={}));var vb;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(vb||(vb={}));var wb;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(wb||(wb={}));var kb;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(kb||(kb={}));var ZF={float32:wb,int32:xb,bool:vb,complex64:kb};function Sa(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return ZF[e][t]}function Fm(e){return Sa(e,"int32")}function $t(e,t){if(e.dtype===t.dtype)return[e,t];let n=Sa(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function II(e,t){R(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function QF(e,t){return t.some(n=>n.id===e.id)}function wx(e){let t=[];return SI(e,t,new Set),t}function SI(e,t,n){if(e==null)return;if(e instanceof Ae){t.push(e);return}if(!eD(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),SI(s,t,n))}}function eD(e){return Array.isArray(e)||typeof e=="object"}function nb(e){return e.kernelName!=null}var D1=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Gp=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new D1}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 WF(this.backendInstance),!0}setupRegisteredKernels(){Eh(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Eh(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 ac)&&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 Gp.nextTensorId++}nextVariableId(){return Gp.nextVariableId++}clone(e){let t=L.runKernel(Di,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return L.runKernel(bi,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 '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=nb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(nb(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Ch(h,this.backendName);R(g!=null,()=>`Cannot find registered kernel '${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=nb(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=gb(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=>Ic(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=cI(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*fb(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*fb(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=gb(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,p)=>{if(u==null){let d=n[p],c=Qh(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=wx(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=GF(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?tD(r.shape):n,HF(i,s,l=>this.tidy(l),nD);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=Vp(),n=await this.backend.time(e);return n.wallMs=Vp()-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 D1;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}};Gp.nextTensorId=0;Gp.nextVariableId=0;function tD(e){let t=fx(vt(e),"float32");return L.makeTensor(t,e,"float32")}function NI(){let e=gI();if(e._tfengine==null){let t=new fI(e);e._tfengine=new Gp(t)}return TF(e._tfengine.ENV),XF(()=>e._tfengine),e._tfengine}var L=NI();function nD(e,t){let n={a:e,b:t};return L.runKernel(cs,n)}var Sc={};Re(Sc,{isBrowser:()=>TI,isMobile:()=>sD,mockIsMobile:()=>rD});function aD(){return typeof navigator!="undefined"&&navigator!=null}var Ib;function rD(e){Ib=e}function sD(e){if(Ib!==void 0)return Ib;if(e||aD()){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 TI(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var qa=Y();qa.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.")});qa.registerFlag("IS_BROWSER",()=>TI());qa.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");qa.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));qa.registerFlag("PROD",()=>!1);qa.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>qa.getBool("DEBUG"));qa.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);qa.registerFlag("IS_TEST",()=>!1);qa.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);qa.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function ur(e,t){let n=e;if(dn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let a=[];for(;Array.isArray(n)||dn(n)&&t!=="string";)a.push(n.length),n=n[0];return Array.isArray(e)&&Y().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&CI(e,a,[]),a}function CI(e,t,n){if(n=n||[],!Array.isArray(e)&&!dn(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 ${e.length} elements`);let a=t.slice(1);for(let r=0;r<e.length;++r)CI(e[r],a,n.concat(r))}function R1(e,t,n,a){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${a}' must be ${e} tensor, but got ${t} tensor`)}}function A(e,t,n,a="numeric"){if(e instanceof Ae)return R1(a,e.dtype,t,n),e;let r=Zh(e);if(r!=="string"&&["bool","int32","float32"].indexOf(a)>=0&&(r=a),R1(a,r,t,n),e==null||!dn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=ur(e,r);!dn(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?$m(e,r):Xs(e,[],!0);return L.makeTensor(i,s,r)}function Hp(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 EI="__op";function z(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+EI;let r=(...s)=>{L.startScope(n);try{let i=a(...s);return yx(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 iD(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 ${a.shape}, must match in call to tf.complex().`);let r={real:n,imag:a};return L.runKernel(rm,r)}var ns=z({complex_:iD});function fs(e,t,n,a){if(a==null&&(a=Zh(e)),a==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!dn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){gx(t);let r=vt(t),s=vt(n);R(r===s,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==vt(t.slice(i)):!0;R(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!dn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=a!=="string"?$m(e,a):Xs(e,[],!0),L.makeTensor(e,t,a)}function Jn(e,t,n){let a=ur(e,n);return fs(e,t,a,n)}var Sb={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},Ah=4;async function oD(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:lD(s),specs:n}}function _I(e,t){let n={},a,r=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,u=vt(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=Sb[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=mD()),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}': ${o}`);r+=u*c}else if(o==="string"){let d=vt(s.shape);p=[];for(let c=0;c<d;c++){let h=new Uint32Array(e.slice(r,r+Ah))[0];r+=Ah;let m=new Uint8Array(e.slice(r,r+h));p.push(m),r+=h}}else{let d=Sb[o],c=e.slice(r,r+u*d);if(o==="float32")p=new Float32Array(c);else if(o==="int32")p=new Int32Array(c);else if(o==="bool")p=new Uint8Array(c);else if(o==="complex64"){p=new Float32Array(c);let h=new Float32Array(p.length/2),m=new Float32Array(p.length/2);for(let y=0;y<h.length;y++)h[y]=p[y*2],m[y]=p[y*2+1];let f=Jn(h,l,"float32"),g=Jn(m,l,"float32");n[i]=ns(f,g),f.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=u*d}o!=="complex64"&&(n[i]=Jn(p,l,o))}return n}function lD(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 kx=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function M1(e){return kx?Buffer.byteLength(e):new Blob([e]).size}function uD(e){if(kx)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 pD(e){if(kx){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 Ix(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 P1(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 AI(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 Sx(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 Nc(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:M1(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:M1(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function cD(){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 dD(){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 hD(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function mD(){let e=cD(),t=dD(),n=hD();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 Ft=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Ft.instance==null&&(Ft.instance=new Ft),Ft.instance}static registerSaveRouter(e){Ft.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Ft.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Ft.getHandlers(e,"save")}static getLoadHandlers(e,t){return Ft.getHandlers(e,"load",t)}static getHandlers(e,t,n){let a=[];return(t==="load"?Ft.getInstance().loadRouters:Ft.getInstance().saveRouters).forEach(r=>{let s=r(e,n);s!==null&&a.push(s)}),a}},fD=e=>Ft.registerSaveRouter(e),gD=e=>Ft.registerLoadRouter(e),yD=e=>Ft.getSaveHandlers(e),bD=(e,t)=>Ft.getLoadHandlers(e,t),Nb="tensorflowjs",Tb=1,Vs="models_store",Xr="model_info_store";function $I(){if(!Y().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Cb(e){let t=e.result;t.createObjectStore(Vs,{keyPath:"modelPath"}),t.createObjectStore(Xr,{keyPath:"modelPath"})}var Qs=class{constructor(e){if(this.indexedDB=$I(),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(Nb,Tb);r.onupgradeneeded=()=>Cb(r),r.onsuccess=()=>{let s=r.result;if(t==null){let i=s.transaction(Vs,"readonly"),o=i.objectStore(Vs).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),a(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),a(o.error)),i.oncomplete=()=>s.close()}else{let i=Nc(t),o=s.transaction(Xr,"readwrite"),l=o.objectStore(Xr),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),p;u.onsuccess=()=>{p=s.transaction(Vs,"readwrite");let d=p.objectStore(Vs).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});d.onsuccess=()=>n({modelArtifactsInfo:i}),d.onerror=c=>{l=o.objectStore(Xr);let h=l.delete(this.modelPath);h.onsuccess=()=>(s.close(),a(d.error)),h.onerror=m=>(s.close(),a(d.error))}},u.onerror=d=>(s.close(),a(u.error)),o.oncomplete=()=>{p==null?s.close():p.oncomplete=()=>s.close()}}},r.onerror=s=>a(r.error)})}};Qs.URL_SCHEME="indexeddb://";var FI=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Qs.URL_SCHEME)?xD(e.slice(Qs.URL_SCHEME.length)):null;Ft.registerSaveRouter(FI);Ft.registerLoadRouter(FI);function xD(e){return new Qs(e)}function vD(e){return e.startsWith(Qs.URL_SCHEME)?e.slice(Qs.URL_SCHEME.length):e}var wD=class{constructor(){this.indexedDB=$I()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(Nb,Tb);n.onupgradeneeded=()=>Cb(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=vD(e),new Promise((t,n)=>{let a=this.indexedDB.open(Nb,Tb);a.onupgradeneeded=()=>Cb(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(Vs,"readwrite");let d=l.objectStore(Vs).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)})}},Sr="/",Qo="tensorflowjs_models",DI="info",kD="model_topology",ID="weight_specs",SD="weight_data",ND="model_metadata";function RI(e){return{info:[Qo,e,DI].join(Sr),topology:[Qo,e,kD].join(Sr),weightSpecs:[Qo,e,ID].join(Sr),weightData:[Qo,e,SD].join(Sr),modelMetadata:[Qo,e,ND].join(Sr)}}function MI(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function TD(e){let t=e.split(Sr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Sr)}function CD(e){return e.startsWith(ei.URL_SCHEME)?e.slice(ei.URL_SCHEME.length):e}var ei=class{constructor(e){if(!Y().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=RI(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),a=Nc(e);try{this.LS.setItem(this.keys.info,JSON.stringify(a)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,uD(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:a}}catch(r){throw MI(this.keys),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=pD(s),t}};ei.URL_SCHEME="localstorage://";var PI=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ei.URL_SCHEME)?ED(e.slice(ei.URL_SCHEME.length)):null;Ft.registerSaveRouter(PI);Ft.registerLoadRouter(PI);function ED(e){return new ei(e)}var _D=class{constructor(){R(Y().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),R(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Qo+Sr,n=Sr+DI;for(let a=0;a<this.LS.length;++a){let r=this.LS.key(a);if(r.startsWith(t)&&r.endsWith(n)){let s=TD(r);e[s]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=CD(e);let t=RI(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return MI(t),n}},nl="://",ua=class{constructor(){this.managers={}}static getInstance(){return ua.instance==null&&(ua.instance=new ua),ua.instance}static registerManager(e,t){R(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(nl)&&(e=e.slice(0,e.indexOf(nl))),R(e.length>0,()=>"scheme must not be an empty string.");let n=ua.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(nl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ua.getSchemes().join(",")}`);return{scheme:e.split(nl)[0],path:e.split(nl)[1]}}async function OI(e,t,n=!1){R(e!==t,()=>`Old path and new path are the same: '${e}'`);let a=Ft.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=Ft.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 ua.getManager(o).removeModel(l);let d=await i.save(p);return n&&!u&&await ua.getManager(o).removeModel(l),d.modelArtifactsInfo}async function AD(){let e=ua.getSchemes(),t={};for(let n of e){let a=await ua.getManager(n).listModels();for(let r in a){let s=n+nl+r;t[s]=a[r]}}return t}async function $D(e){let t=ch(e);return ua.getManager(t.scheme).removeModel(t.path)}async function FD(e,t){return OI(e,t,!1)}async function DD(e,t){return OI(e,t,!0)}var RD=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 ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}};if(Y().get("IS_BROWSER")){Y().setPlatform("browser",new RD);try{ua.registerManager(ei.URL_SCHEME,new _D)}catch(e){}try{ua.registerManager(Qs.URL_SCHEME,new wD)}catch(e){}}var MD={importFetch:()=>W$()},ab,PD=class{constructor(){this.util=U$(),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return Y().global.fetch!=null?Y().global.fetch(e,t):(ab==null&&(ab=MD.importFetch()),ab(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)}};Y().get("IS_NODE")&&!Y().get("IS_BROWSER")&&Y().setPlatform("node",new PD);function He(e,t="float32",n){return t=t||"float32",gx(e),new jt(e,t,n)}function OD(e,t){let n=A(e,"x","cast");if(!pI(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let a={x:n},r={dtype:t};return L.runKernel(bi,a,r)}var oe=z({cast_:OD});function LD(e){let t={x:A(e,"x","clone","string_or_numeric")};return L.runKernel(Di,t)}var Tr=z({clone_:LD});function LI(e,t=!1){console.log(e.toString(t))}NI();var zD={buffer:He,cast:oe,clone:Tr,print:LI};YF(zD);var Qt={};Re(Qt,{browserFiles:()=>jD,browserHTTPRequest:()=>JD,concatenateArrayBuffers:()=>Ix,copyModel:()=>FD,decodeWeights:()=>_I,encodeWeights:()=>oD,fromMemory:()=>QD,getLoadHandlers:()=>bD,getModelArtifactsForJSON:()=>Sx,getModelArtifactsInfoForJSON:()=>Nc,getSaveHandlers:()=>yD,http:()=>Tx,isHTTPScheme:()=>Eb,listModels:()=>AD,loadWeights:()=>qD,moveModel:()=>DD,registerLoadRouter:()=>gD,registerSaveRouter:()=>fD,removeModel:()=>$D,weightsLoaderFactory:()=>BI,withSaveHandler:()=>eR});var BD="model",WD=".json",UD=".weights.bin";function O1(e){return new Promise(t=>setTimeout(t)).then(e)}var ll=class{constructor(e){if(!Y().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(ll.URL_SCHEME)&&(e=e.slice(ll.URL_SCHEME.length)),(e==null||e.length===0)&&(e=BD),this.modelJsonFileName=e+WD,this.weightDataFileName=e+UD}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=AI(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 O1(()=>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 O1(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Nc(e)}}}};ll.URL_SCHEME="downloads://";var VD=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 ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:s});return}let i=Sx(r,o=>this.loadWeights(o));e(i)},n.onerror=a=>t(`Failed to read model topology and weights manifest JSON from file '${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,Ix(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 '${e}'.`),r.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],n=this.weightsFiles.map(r=>P1(r.name)),a={};for(let r of e)r.paths.forEach(s=>{let i=P1(s);if(t.indexOf(i)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${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}},GD=e=>Y().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ll.URL_SCHEME)?HD(e.slice(ll.URL_SCHEME.length)):null;Ft.registerSaveRouter(GD);function HD(e="model"){return new ll(e)}function jD(e){return new VD(e)}function L1(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 zI(e,t){t==null&&(t={});let n=t.fetchFunc==null?Y().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 L1(a,t.onProgress,r,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await L1(i,t.onProgress,o,l)}async function qD(e,t="",n,a){return BI(r=>zI(r,{requestInit:a}))(e,t,n)}function BI(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=Sb[y]*vt(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=_I(v,[x.manifestEntry]);for(let T in w)d[T]=w[T]}),c+=m}),d}}var KD="application/octet-stream",XD="application/json",Nx=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=Y().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=AI(e,n);t.body.append("model.json",new Blob([JSON.stringify(a)],{type:XD}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:KD}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Nc(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 Sx(t,r=>this.loadWeights(r))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,a]=YD(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 zI(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,Ix(l)]}};Nx.URL_SCHEME_REGEX=/^https?:\/\//;function YD(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),a=e.substring(0,t),r=n>t?e.substring(n):"";return[a+"/",r]}function Eb(e){return e.match(Nx.URL_SCHEME_REGEX)!=null}var WI=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(a=>Eb(a)):n=Eb(e),n)return Tx(e,t)}return null};Ft.registerSaveRouter(WI);Ft.registerLoadRouter(WI);function Tx(e,t){return new Nx(e,t)}function JD(e,t){return Tx(e,t)}var rb=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},ZD=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function QD(e,t,n,a){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new rb(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 rb({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 rb({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:a}))}function eR(e){return new ZD(e)}var UI={};Re(UI,{confusionMatrix:()=>sR});function tR(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(yi,i,o)}var Fe=z({matMul_:tR});function nR(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(Gi,r,s)}var ul=z({oneHot_:nR});function aR(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(uo,a,r)}var Me=z({transpose_:aR});function rR(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=ul(oe(a,"int32"),n),i=ul(oe(r,"int32"),n),o=Me(s),l=Fe(o,i);return oe(l,"int32")}var sR=z({confusionMatrix_:rR}),Iu={};Re(Iu,{assertAndGetBroadcastShape:()=>dt,getBroadcastDims:()=>VI,getReductionAxes:()=>Wt});function VI(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 dt(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 po={};Re(po,{fromPixels:()=>dR,fromPixelsAsync:()=>pR,toPixels:()=>cR});function Dm(e,t,n){if(hi(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let a=ur(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 fs(e,t,a,n)}var Ms;function GI(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(Ms==null)if(typeof document=="undefined")if(typeof OffscreenCanvas!="undefined"&&typeof OffscreenCanvasRenderingContext2D!="undefined")Ms=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else Ms=document.createElement("canvas").getContext("2d");Ms.canvas.width=l,Ms.canvas.height=u,Ms.drawImage(e,0,0,l,u),p=Ms.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 Dm(d,[u,l,t],"int32")}function iR(e){return e!=null&&e.data instanceof Uint8Array}function oR(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function lR(e){return e!=null&&e.width!==0&&e.height!==0}function uR(e){return oR()&&!(e instanceof ImageBitmap)&&lR(e)&&!iR(e)}async function pR(e,t=3){let n=null;if(Y().getBool("WRAP_TO_IMAGEBITMAP")&&uR(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 GI(n,t)}async function cR(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 dR=z({fromPixels_:GI}),Cx={};Re(Cx,{prepareAndValidate:()=>HI});function HI(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(vt(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=[...vl(e.shape).map(d=>d/u),1].slice(0,s);return[l,i,u,p]}var Ex={};Re(Ex,{calculateShapes:()=>jI,validateInput:()=>Ax,validateUpdateShape:()=>_x});function _x(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 Ax(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: ${e.shape}`)}_x(n,t,e)}function jI(e,t,n){let a=t.shape.length,r=a>1?t.shape[a-1]:1,s=n.length,i=1;for(let d=r;d<s;++d)i*=n[d];let o=r<1?1:r,l=vt(t.shape)/o,u=[...vl(n.slice(0,r)),1],p=vt(n);return{sliceRank:r,numUpdates:l,sliceSize:i,strides:u,outputSize:p}}var qt={};Re(qt,{assertParamsValid:()=>mR,computeFlatOffset:()=>xR,computeOutShape:()=>gR,getNormalizedAxes:()=>yR,isSliceContinous:()=>bR,maskToAxes:()=>fR,parseSliceParams:()=>tS,sliceInfo:()=>vR,startForAxis:()=>QI,startIndicesWithElidedDims:()=>YI,stopForAxis:()=>eS,stopIndicesWithElidedDims:()=>JI,stridesForAxis:()=>ZI,stridesWithElidedDims:()=>qI});var _b=-2,hR=-1;function mR(e,t,n){let a=e.shape.length;R(a===t.length,()=>`Error in slice${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 fR(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function gR(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 qI(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 KI(e,t,n){return n<=e?n:n-(t-1)}function XI(e,t){let n=[];for(let a=0;a<e;a++)n.push(t+a);return n}function yR(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=YI(i,h,m,a,e),d=JI(o,h,m,r,e),c=qI(s,h,m,e)}else for(let h=0;h<u;h++)p[h]=QI(i,a,s,e,h,l),d[h]=eS(o,r,s,e,h,l),c[h]=ZI(s,h,l);return{begin:p,end:d,strides:c}}function YI(e,t,n,a,r){let s=[...r],i=XI(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=KI(t,n,o),u=a[l];e&1<<l&&(u=0),s[o]=u}return s}function JI(e,t,n,a,r){let s=[...r],i=XI(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=KI(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]=Wp(0,s[o],r[o])}return s}function ZI(e,t,n){let a=e[t];return(n&1<<t||a==null)&&(a=1),a}function QI(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=Wp(0,i,l-1),i}function eS(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=Wp(0,i,l):i=Wp(-1,i,l-1),i}function bR(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 xR(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 tS(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 ${o}.`),e.shape[o]-a[o])),[a,s]}function vR(e,t,n,a,r,s,i,o,l){let u;if(a==null?(u=new Array(t.length),u.fill(1)):u=a,i!=null&&(i&i-1)!==0)throw new Error("Multiple ellipses in slice is not allowed.");let p=!1,d={dims:u.length,numAddAxisAfterEllipsis:0,begin:t.slice(),end:n.slice(),strides:u.slice(),beginMask:r,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};for(let b=0;b<d.dims;b++)p&&(1<<b&o)!==0&&d.numAddAxisAfterEllipsis++,1<<b&i&&(p=!0);p||(d.ellipsisMask|=1<<d.dims,d.dims++);let c={dims:e.length,beginMask:0,endMask:0,beginValid:!1,endValid:!1};wR(d,c);let h=!0,m=!0,f=!0,g=[],y=[];for(let b=0;b<e.length;++b){if(c.strides[b]===0)throw Error(`strides[${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 ${b} out of bounds.`)}else c.begin[b]=z1(c.begin[b],0,c.strides[b],v,w,T),c.end[b]=z1(c.end[b],1,c.strides[b],v,w,T);let P=c.strides[b]===1&&c.begin[b]===0&&c.end[b]===v;h=h&&P,m=m&&(b===0&&c.strides[b]===1||P)}else h=h&&c.strides[b]===1&&C,m=m&&(b===0&&c.strides[b]===1||C);let _,$=!1;if(c.beginValid&&c.endValid?(_=c.end[b]-c.begin[b],$=!0):x?(_=1,$=!0):C&&v>=0&&(c.strides[b]<0?_=-v:_=v,$=!0),$){let P;_===0||_<0!=c.strides[b]<0?P=0:P=Math.trunc(_/c.strides[b])+(_%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 wR(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, ${t.begin.length}.`);e.begin!=null&&(t.begin[n]=e.begin[a]),e.end!=null&&(t.end[n]=e.end[a]),t.strides[n]=e.strides[a],e.beginMask&1<<a&&(t.beginMask|=1<<n),e.endMask&1<<a&&(t.endMask|=1<<n),e.shrinkAxisMask&1<<a?(t.finalShapeGatherIndices.push(hR),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<n):(t.finalShapeGatherIndices.push(n),t.finalShapeGatherIndicesSparse.push(a)),t.inputShapeGatherIndicesSparse[n]=a,n++}}function z1(e,t,n,a,r,s){if(r[t])return n>0?s[t]:s[t+1&1];{let i=e<0?a+e:e;return i<s[0]?s[0]:i>s[1]?s[1]:i}}var se={};Re(se,{Serializable:()=>nS,SerializationMap:()=>Bs,registerClass:()=>gs});var nS=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Bs=class{constructor(){this.classNameMap={}}static getMap(){return Bs.instance==null&&(Bs.instance=new Bs),Bs.instance}static register(e){Bs.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function gs(e){R(e.className!=null,()=>"Class being registered does not have the static className property defined."),R(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),R(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Bs.register(e)}var aS={};Re(aS,{TEST_EPSILON_FLOAT16:()=>rS,encodeStrings:()=>sS,expectArrayBuffersEqual:()=>ER,expectArraysClose:()=>IR,expectArraysEqual:()=>NR,expectNumbersClose:()=>TR,expectPromiseToFail:()=>SR,expectValuesInRange:()=>CR,testEpsilon:()=>$x});var kR=.001,rS=.1;function IR(e,t,n){return n==null&&(n=$x()),Ab(e,t,(a,r)=>Fx(a,r,n))}function $x(){return L.backend.floatPrecision()===32?kR:rS}function Ab(e,t,n){let a=!0;if((dn(e)||dn(t))&&(a=!1),dn(e)&&dn(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: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=ur(e),o=ur(t);if(!ps(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let r=dn(e)?e:Xs(e),s=dn(t)?t:Xs(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: ${s}.`)}}function SR(e,t){e().then(()=>t.fail(),()=>t())}function NR(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Kr(e)||Kr(e[0])||Kr(t)||Kr(t[0])?Ab(e,n,(a,r)=>a==r):Ab(e,t,(a,r)=>Fx(a,r,0))}function TR(e,t,n){if(n==null&&(n=$x()),!Fx(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Fx(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function CR(e,t,n){for(let a=0;a<e.length;a++)if(e[a]<t||e[a]>n)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function ER(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 sS(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?sS(n):e[t]=Ic(n)}return e}var _R="3.14.0";function AR(){Y().set("PROD",!0)}function $R(){Y().set("DEBUG",!0)}function FR(){Y().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Dx(e){Y().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}JF(Dx);function DR(){L.disposeVariables()}function rr(){return L}function $h(){return L.memory()}function RR(e){return L.profile(e)}function O(e,t){return L.tidy(e,t)}function De(e){wx(e).forEach(t=>t.dispose())}function en(e){return L.keep(e)}function MR(e){return L.time(e)}function PR(e){return L.setBackend(e)}function OR(){return L.ready()}function LR(){return L.backendName}function zR(e){L.removeBackend(e)}function BR(e){return L.findBackend(e)}function WR(e){return L.findBackendFactory(e)}function Rm(e,t,n=1){return L.registerBackend(e,t,n)}function iS(){return L.backend}function UR(e,t){Y().setPlatform(e,t)}function VR(e,t){let n=A(e,"a","add"),a=A(t,"b","add");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(cs,r)}var J=z({add_:VR});function GR(e,t){let n=A(e,"a","floorDiv"),a=A(t,"b","floorDiv");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(Ai,r)}var Mm=z({floorDiv_:GR});function HR(e,t){let n=A(e,"a","div"),a=A(t,"b","div");if([n,a]=$t(n,a),n.dtype==="int32"&&a.dtype==="int32")return Mm(n,a);let r={a:n,b:a},s={};return L.runKernel(Ti,r,s)}var fe=z({div_:HR});function jR(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(Vi,r)}var B=z({mul_:jR});function qR(e){let t=A(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return L.runKernel(ic,n)}else{let n={x:t};return L.runKernel(wl,n)}}var zt=z({abs_:qR});function KR(e){let t={x:A(e,"x","acos")};return L.runKernel(kl,t)}var Rx=z({acos_:KR});function XR(e){let t={x:A(e,"x","acosh")};return L.runKernel(Il,t)}var Mx=z({acosh_:XR});function YR(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(!ps(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let a=t;return L.runKernel(mi,a)}var oS=z({addN_:YR});function JR(e,t=null,n=!1){let a={x:A(e,"x","all","bool")},r={axis:t,keepDims:n};return L.runKernel(Sl,a,r)}var Pm=z({all_:JR});function ZR(e,t=null,n=!1){let a={x:A(e,"x","any","bool")},r={axis:t,keepDims:n};return L.runKernel(Nl,a,r)}var jp=z({any_:ZR});function QR(e,t=0){let n={x:A(e,"x","argMax")},a={axis:t};return L.runKernel(fi,n,a)}var ti=z({argMax_:QR});function eM(e,t=0){let n={x:A(e,"x","argMin")},a={axis:t};return L.runKernel(rc,n,a)}var Px=z({argMin_:eM});function tM(e){let t={x:A(e,"x","asin")};return L.runKernel(Tl,t)}var Ox=z({asin_:tM});function nM(e){let t={x:A(e,"x","asinh")};return L.runKernel(Cl,t)}var Lx=z({asinh_:nM});function aM(e){let t={x:A(e,"x","atan")};return L.runKernel(El,t)}var zx=z({atan_:aM});function rM(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(Al,r)}var Bx=z({atan2_:rM});function sM(e){let t={x:A(e,"x","atanh")};return L.runKernel(_l,t)}var Wx=z({atanh_:sM});function iM(e,t,n,a,r="NHWC",s){let i=e[3],o=[...t,i],l=pS(r);return Tc(e,o,n,s,a,null,null,l)}function lS(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 ${i}`);return Tc(e,u,n,a,r,s,!1,i)}function oM(e,t,n,a,r,s,i="NDHWC"){let[o,l,u]=$b(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 uS(e,p,n,a,r,!1,d,s)}function Tc(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 ${o}`);let[c,h,,m]=t,[f,g]=Fh(n),[y,b]=Fh(a),x=al(c,y),v=al(h,b),{padInfo:w,outHeight:T,outWidth:C}=pM(r,u,p,f,g,x,v,s,o),_=i?m*d:m,$;return o==="channelsFirst"?$=[l,_,T,C]:o==="channelsLast"&&($=[l,T,C,_]),{batchSize:l,dataFormat:o,inHeight:u,inWidth:p,inChannels:d,outHeight:T,outWidth:C,outChannels:_,padInfo:w,strideHeight:f,strideWidth:g,filterHeight:c,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:y,dilationWidth:b,inShape:e,outShape:$,filterShape:t}}function uS(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 ${i}`);let[h,m,f,,g]=t,[y,b,x]=$b(n),[v,w,T]=$b(a),C=al(h,v),_=al(m,w),$=al(f,T),{padInfo:P,outDepth:F,outHeight:S,outWidth:M}=cM(r,u,p,d,y,b,x,C,_,$,o),V=s?g*c:g,j;return i==="channelsFirst"?j=[l,V,F,S,M]:i==="channelsLast"&&(j=[l,F,S,M,V]),{batchSize:l,dataFormat:i,inDepth:u,inHeight:p,inWidth:d,inChannels:c,outDepth:F,outHeight:S,outWidth:M,outChannels:V,padInfo:P,strideDepth:y,strideHeight:b,strideWidth:x,filterDepth:h,filterHeight:m,filterWidth:f,effectiveFilterDepth:C,effectiveFilterHeight:_,effectiveFilterWidth:$,dilationDepth:v,dilationHeight:w,dilationWidth:T,inShape:e,outShape:j,filterShape:t}}function lM(e,t,n,a,r){a==null&&(a=Ux(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 uM(e,t,n,a,r,s){r==null&&(r=Ux(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 Ux(e,t,n,a=1){let r=al(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 $b(e){return typeof e=="number"?[e,e,e]:e}function al(e,t){return t<=1?e:e+(e-1)*(t-1)}function pM(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=lM([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 cM(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=uM([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 hr(e,t){return as(e)||as(t)}function pS(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(il(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(il(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 dM(e,t){let n={x:A(e,"x","reshape","string_or_numeric")},a={shape:t};return L.runKernel(iu,n,a)}var W=z({reshape_:dM});function hM(e,t,n,a,r){let s=A(e,"x","avgPool","float32"),i=1;R(hr(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(gi,u,p);return d=oe(d,s.dtype),l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var ma=z({avgPool_:hM});function mM(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(sc,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 Vx=z({avgPool3d_:mM});function fM(e,t=0){R(e.length>=1,()=>"Pass at least one tensor to concat");let n=Hp(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 Tr(n[0]);let a=n,r={axis:t};return L.runKernel(Fl,a,r)}var Qe=z({concat_:fM});function gM(e){let t={x:A(e,"x","sigmoid","float32")};return L.runKernel(to,t)}var da=z({sigmoid_:gM});function yM(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(pu,r,s)}var Ge=z({slice_:yM});function bM(e){let t={x:A(e,"x","tanh","float32")};return L.runKernel(lo,t)}var ni=z({tanh_:bM});function xM(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=Fe(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(B(da(b),ni(x)),B(p,da(J(i,v)))),C=B(ni(T),da(w));return[T,C]}var vM=z({basicLSTMCell_:xM});function wM(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(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return L.runKernel($l,s,i)}var Cc=z({batchToSpaceND_:wM});function kM(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 IM(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:kM(i),scale:u,offset:p,mean:o,variance:l},c={varianceEpsilon:s},h=L.runKernel($i,d,c);return W(h,i.shape)}var Er=z({batchNorm_:IM});function SM(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}.`),Er(i,o,l,p,u,s)}var cS=z({batchNorm2d_:SM});function NM(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}.`),Er(i,o,l,p,u,s)}var dS=z({batchNorm3d_:NM});function TM(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}.`),Er(i,o,l,p,u,s)}var hS=z({batchNorm4d_:TM});function CM(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(nm,s,i)}var Gx=z({bincount_:CM});function EM(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 ${a.rank}`);let r={s0:n,s1:a};return L.runKernel(am,r)}var mS=z({broadcastArgs_:EM});function _M(e,t){let n=A(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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 Tr(n);let i={x:n},o={reps:s};return L.runKernel(hs,i,o)}var rl=z({broadcastTo_:_M});function AM(e){let t={x:A(e,"x","ceil","float32")};return L.runKernel(xi,t)}var Hx=z({ceil_:AM});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 (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return L.runKernel(ds,r,s)}var nn=z({clipByValue_:$M});function FM(e){return Qe(e,0)}var fS=z({concat1d_:FM});function DM(e,t){return Qe(e,t)}var gS=z({concat2d_:DM});function RM(e,t){return Qe(e,t)}var yS=z({concat3d_:RM});function MM(e,t){return Qe(e,t)}var bS=z({concat4d_:MM});function PM(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(hr(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(vi,c,h);return p?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Rt=z({conv2d_:PM});function OM(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(hr(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 ${r} but only NWC is currently supported.`);let d=W(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=Rt(c,d,[1,n],a,"NHWC",[1,s],i);return p?W(h,[h.shape[2],h.shape[3]]):W(h,[h.shape[0],h.shape[2],h.shape[3]])}var Om=z({conv1d_:OM});function LM(e,t,n,a,r,s="NHWC",i){R(e.length===t.rank,()=>`Length of inShape (${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(wi,c,h);return u?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var jx=z({conv2DBackpropInput_:LM});function zM(e,t,n,a,r,s){let i=A(e,"x","conv2dTranspose"),o=A(t,"filter","conv2dTranspose");return jx(n,i,o,a,r,"NHWC",s)}var Lm=z({conv2dTranspose_:zM});function BM(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(hr(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 ${r} but only NDHWC is currently supported.`);let p={x:l,filter:o},d={strides:n,pad:a,dataFormat:r,dilations:s},c=L.runKernel(oc,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var qx=z({conv3d_:BM});function WM(e,t,n,a,r){R(e.length===t.rank,()=>`Length of inShape (${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(om,p,d);return o?W(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var xS=z({conv3DBackpropInput_:WM});function UM(e,t,n,a,r){let s=A(e,"x","conv3dTranspose"),i=A(t,"filter","conv3dTranspose");return xS(n,s,i,a,r)}var vS=z({conv3dTranspose_:UM});function VM(e){let t={x:A(e,"x","cos","float32")};return L.runKernel(ki,t)}var Ec=z({cos_:VM});function GM(e){let t={x:A(e,"x","cosh","float32")};return L.runKernel(Ii,t)}var zm=z({cosh_:GM});function HM(e,t=0,n=!1,a=!1){let r={x:A(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return L.runKernel(Si,r,s)}var Bm=z({cumsum_:HM});function jM(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(lm,i,o)}var wS=z({denseBincount_:jM});function qM(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(Rl,o,l)}var Kx=z({depthToSpace_:qM});function KM(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(Ni,d,c);return p?W(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var ys=z({depthwiseConv2d_:KM});function XM(e){let t={x:A(e,"x","diag")};return L.runKernel(cm,t)}var YM=z({diag_:XM});function JM(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 ${s}`);let l=i,u=!1;i.rank===3&&(l=W(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let p={x:l,filter:o},d={strides:n,pad:a,dilations:r},c=L.runKernel(lc,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Xx=z({dilation2d_:JM});function ZM(e,t){let n=A(e,"a","equal","string_or_numeric"),a=A(t,"b","equal","string_or_numeric");[n,a]=$t(n,a),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Pl,r)}var Zn=z({equal_:ZM});function QM(e,t,n){let a=A(t,"a","where"),r=A(n,"b","where"),s=A(e,"condition","where","bool"),i=dt(dt(s.shape,a.shape),r.shape),o=rl(s,i),l=rl(a,i),u=rl(r,i),p={condition:o,t:l,e:u};return L.runKernel(lu,p)}var mn=z({where_:QM});function eP(e){let t={x:A(e,"x","zerosLike")};return L.runKernel(wu,t)}var Ke=z({zerosLike_:eP});function tP(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=Zn(a,s);return mn(i,s,r)}var Yx=z({divNoNan_:tP});function nP(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 ${s}.`),n.rank===1&&a.rank===1){let i=W(n,[1,-1]),o=W(a,[-1,1]),l=Fe(i,o);return W(l,[])}else if(n.rank===1&&a.rank===2){let i=W(n,[1,-1]),o=W(a,[a.shape[0],a.shape[1]]),l=Fe(i,o);return W(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=W(a,[-1,1]),o=Fe(n,i);return W(o,[o.size])}else{let i=W(a,[a.shape[0],a.shape[1]]);return Fe(n,i)}}var kS=z({dot_:nP});function aP(e,...t){let n=t.map((r,s)=>A(r,`tensors${s}`,"einsum")),a={equation:e};return L.runKernel(dm,n,a)}var IS=z({einsum_:aP});function rP(e){let t={x:A(e,"x","elu","float32")};return L.runKernel(Ci,t)}var Su=z({elu_:rP});function sP(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(Ml,n)}var Jx=z({erf_:sP});function iP(e){let t={x:A(e,"x","exp")};return L.runKernel(Ei,t)}var fn=z({exp_:iP});function oP(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(Ol,a,r)}var hn=z({expandDims_:oP});function lP(e){let t={x:A(e,"x","expm1")};return L.runKernel(Ll,t)}var Zx=z({expm1_:lP});function uP(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(hs,a,r)}var On=z({tile_:uP});function pP(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(hn(i,0),[n[0],1,1]);if(n.length===2)return On(hn(hn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return On(hn(hn(hn(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 ${n.length}D.`)}var Qx=z({eye_:pP});function Cn(e,t,n){let a={shape:e,value:t,dtype:n};return L.runKernel(uc,{},a)}function cP(e){let t={x:A(e,"x","floor","float32")};return L.runKernel(_i,t)}var Nu=z({floor_:cP});function dP(e,t,n=0,a=0){let r=A(e,"x","gather"),s=A(t,"indices","gather","int32"),i={x:r,indices:s},o={axis:n,batchDims:a};return L.runKernel(Bl,i,o)}var ai=z({gather_:dP});function hP(e,t){let n=A(e,"a","greater","string_or_numeric"),a=A(t,"b","greater","string_or_numeric");[n,a]=$t(n,a),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Ul,r)}var Wn=z({greater_:hP});function mP(e,t){let n=A(e,"a","greaterEqual","string_or_numeric"),a=A(t,"b","greaterEqual","string_or_numeric");[n,a]=$t(n,a),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Fi,r)}var bs=z({greaterEqual_:mP});function fP(e){let t={input:A(e,"input","imag")};return L.runKernel(gm,t)}var Wm=z({imag_:fP});function gP(e){let t={x:A(e,"x","isFinite")};return L.runKernel(Vl,t)}var SS=z({isFinite_:gP});function yP(e){let t={x:A(e,"x","isInf")};return L.runKernel(Gl,t)}var NS=z({isInf_:yP});function bP(e){let t={x:A(e,"x","isNaN")};return L.runKernel(Hl,t)}var ev=z({isNaN_:bP});function xP(e,t=.2){let n={x:A(e,"x","leakyRelu")},a={alpha:t};return L.runKernel(Ri,n,a)}var _c=z({leakyRelu_:xP});function vP(e,t){let n=A(e,"a","less","string_or_numeric"),a=A(t,"b","less","string_or_numeric");[n,a]=$t(n,a),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(jl,r)}var Um=z({less_:vP});function wP(e,t){let n=A(e,"a","lessEqual","string_or_numeric"),a=A(t,"b","lessEqual","string_or_numeric");[n,a]=$t(n,a),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(ql,r)}var xs=z({lessEqual_:wP});function TS(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(ym,{},a)}function kP(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(il(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(dc,l,u);return o?W(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var tv=z({localResponseNormalization_:kP});function IP(e){let t={x:A(e,"x","log","float32")};return L.runKernel(Mi,t)}var Qn=z({log_:IP});function SP(e){let t={x:A(e,"x","log1p")};return L.runKernel(Kl,t)}var Ac=z({log1p_:SP});function NP(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)"),Vm(i),i[0]})}}function TP(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=Hp(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,...])"),Vm(i),i})}}function CP(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 Vm(a),{grad:a[0],value:r}}}function EP(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,...])"),Vm(a.grads),a}}function CS(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 pr(e){return L.customGrad(e)}function Vm(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 _P(e){let t={x:A(e,"x","neg")};return L.runKernel(Jl,t)}var St=z({neg_:_P});function AP(e){let t={x:A(e,"x","softplus")};return L.runKernel(hu,t)}var co=z({softplus_:AP});function $P(e){let t=A(e,"x","logSigmoid");return pr(n=>({value:St(co(St(n))),gradFunc:a=>B(a,da(St(n)))}))(t)}var ES=z({logSigmoid_:$P});function FP(e,t=null,n=!1){let a={x:A(e,"x","max")},r={reductionIndices:t,keepDims:n};return L.runKernel(Pi,a,r)}var Ia=z({max_:FP});function DP(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(io,r)}var ce=z({sub_:DP});function RP(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(ao,r,s)}var be=z({sum_:RP});function MP(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 ${t}`);return pr((a,r)=>{let s=Ia(a,t,!0),i=ce(a,s),o=ce(oe(i,"float32"),Qn(be(fn(i),t,!0)));return r([o]),{value:o,gradFunc:(l,u)=>{let[p]=u,d=!0,c=fn(p);return ce(l,B(be(l,t,d),c))}}})(n)}var Gm=z({logSoftmax_:MP});function nv(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function _S(e,t,n){let a=e.length+t.length,r=[],s=0,i=0;for(let o=0;o<a;o++)n.indexOf(o)===-1?r.push(e[s++]):r.push(t[i++]);return r}function AS(e,t){let n=[],a=e.length;for(let s=0;s<a;s++)t.indexOf(s)===-1&&n.push(e[s]);let r=t.map(s=>e[s]);return[n,r]}function ri(e,t){let n=t.map(a=>1);return _S(e,n,t)}function PP(e,t,n){R(nv(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function $S(e,t){if(nv(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 av(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function OP(e,t){let n=[];for(let a=t-e;a<t;++a)n.push(a);return n}function LP(e,t=null,n=!1){let a=A(e,"x","logSumExp"),r=Ta(t,a.shape),s=Ia(a,r,!0),i=ce(a,s),o=fn(i),l=be(o,r),u=Qn(l),p=J(W(s,u.shape),u);if(n){let d=ri(p.shape,r);return W(p,d)}return p}var rv=z({logSumExp_:LP});function zP(e,t){let n=A(e,"a","logicalAnd","bool"),a=A(t,"b","logicalAnd","bool");dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Xl,r)}var Na=z({logicalAnd_:zP});function BP(e){let t={x:A(e,"x","logicalNot","bool")};return L.runKernel(pc,t)}var $c=z({logicalNot_:BP});function WP(e,t){let n=A(e,"a","logicalOr","bool"),a=A(t,"b","logicalOr","bool");dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(cc,r)}var Hm=z({logicalOr_:WP});function UP(e,t){let n=A(e,"a","logicalXor","bool"),a=A(t,"b","logicalXor","bool");return dt(n.shape,a.shape),Na(Hm(e,t),$c(Na(e,t)))}var FS=z({logicalXor_:UP});function VP(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(hr(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(Li,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Pt=z({maxPool_:VP});function GP(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 ${s}`),Tn("maxPool3d",a,r);let u={x:o},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},d=L.runKernel(hc,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var sv=z({maxPool3d_:GP});function HP(e,t,n,a,r=!1){let s={x:A(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:a,includeBatchInIndex:r},o=L.runKernel(wm,s,i);return{result:o[0],indexes:o[1]}}var DS=z({maxPoolWithArgmax_:HP});function jP(e,t){let n=A(e,"a","maximum"),a=A(t,"b","maximum");[n,a]=$t(n,a),n.dtype==="bool"&&(n=oe(n,"int32"),a=oe(a,"int32")),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Oi,r)}var mr=z({maximum_:jP});function qP(e,t=null,n=!1){let a={x:A(e,"x","mean")},r={axis:t,keepDims:n};return L.runKernel(zi,a,r)}var _t=z({mean_:qP});function kt(e,t="float32"){if(t==="complex64"){let a=kt(e,"float32"),r=kt(e,"float32");return ns(a,r)}let n=Qh(vt(e),t);return L.makeTensor(n,e,t)}function Xn(e,t="float32"){if(t==="complex64"){let a=Xn(e,"float32"),r=kt(e,"float32");return ns(a,r)}let n=fx(vt(e),t);return L.makeTensor(n,e,t)}function KP(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let a=A(e,"x","meshgrid",e instanceof Ae?e.dtype:"float32");if(t===void 0)return[a];let r=A(t,"y","meshgrid",t instanceof Ae?t.dtype:"float32"),s=vt(a.shape),i=vt(r.shape);return n==="xy"?(a=W(a,[1,-1]),r=W(r,[-1,1]),[Fe(Xn([i,1],a.dtype),a),Fe(r,Xn([1,s],r.dtype))]):(a=W(a,[-1,1]),r=W(r,[1,-1]),[Fe(a,Xn([1,i],a.dtype)),Fe(Xn([s,1],r.dtype),r)])}function XP(e,t=null,n=!1){let a={x:A(e,"x","min")},r={axis:t,keepDims:n};return L.runKernel(Bi,a,r)}var qp=z({min_:XP});function YP(e,t){let n=A(e,"a","minimum"),a=A(t,"b","minimum");[n,a]=$t(n,a),n.dtype==="bool"&&(n=oe(n,"int32"),a=oe(a,"int32")),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Wi,r)}var Tu=z({minimum_:YP});function JP(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 ${a.shape}`);let s={paddings:t,mode:n},i={x:a};return L.runKernel(Ui,i,s)}var iv=z({mirrorPad_:JP});function ZP(e,t){let n=A(e,"a","mod"),a=A(t,"b","mod");[n,a]=$t(n,a);let r={a:n,b:a};return L.runKernel(Yl,r)}var ov=z({mod_:ZP});function QP(e){let t=A(e,"x","square"),n={};return L.runKernel("Square",{x:t},n)}var lt=z({square_:QP});function eO(e,t=null,n=!1){e=A(e,"x","moments");let a=Ta(t,e.shape),r=_t(e,a,n),s=r.shape;n||(s=ri(r.shape,a));let i=lt(ce(oe(e,"float32"),W(r,s))),o=_t(i,a,n);return{mean:r,variance:o}}var jm=z({moments_:eO});function tO(e,t,n,a){let r=A(t,"data","multiRNNCell"),s=Hp(n,"c","multiRNNCell"),i=Hp(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 nO=z({multiRNNCell_:tO});function aO(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 ${i}`);n=n||Math.random();let o={logits:i===1?W(r,[1,-1]):r},l={numSamples:t,seed:n,normalized:a},u=L.runKernel(km,o,l);return i===1?W(u,[u.size]):u}var RS=z({multinomial_:aO});function rO(e,t){let n=A(e,"a","notEqual","string_or_numeric"),a=A(t,"b","notEqual","string_or_numeric");[n,a]=$t(n,a),dt(n.shape,a.shape);let r={a:n,b:a};return L.runKernel(Zl,r)}var si=z({notEqual_:rO});function sO(e){let t={x:A(e,"x","onesLike")};return L.runKernel(nu,t)}var ea=z({onesLike_:sO});function iO(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 Fe(r,s)}var oO=z({outerProduct_:iO});function lO(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(Hi,s,r)}var fa=z({pad_:lO});function uO(e,t,n=0){return R(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),fa(e,[t],n)}var pO=z({pad1d_:uO});function cO(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."),fa(e,t,n)}var dO=z({pad2d_:cO});function hO(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."),fa(e,t,n)}var mO=z({pad3d_:hO});function fO(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."),fa(e,t,n)}var gO=z({pad4d_:fO});function yO(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(mu,r,s)}var Fc=z({spaceToBatchND_:yO});function bO(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(hr(s,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${r}'`);let p=lS(l.shape,t,s,r,a),d=[p.dilationHeight,p.dilationWidth],c;a==="same"?c=vO([p.filterHeight,p.filterWidth],d):c=[[0,0],[0,0]];let h=d[0]===1&&d[1]===1,[m,f]=xO([p.inHeight,p.inWidth],d,c),g=h?a:"valid",y=h?l:Fc(l,d,m),b=(n==="avg"?()=>ma(y,t,s,g,i):()=>Pt(y,t,s,g,i))(),x=h?b:Cc(b,d,f);return u?W(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function xO(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 vO(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 MS=z({pool_:bO});function wO(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=z({pow_:wO});function kO(e,t){let n=A(e,"x","prelu"),a=A(t,"alpha","prelu"),r={x:n,alpha:a};return L.runKernel(qi,r)}var Dc=z({prelu_:kO});function IO(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(ru,r,s)}var qm=z({prod_:IO});function SO(e,t,n){let a=vt(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 NO=z({rand_:SO}),lv=di(nI()),uv=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=lv.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}},TO=class{constructor(e,t,n,a){this.alpha=e,this.beta=1/t,this.dtype=n;let r=a||Math.random();this.randu=lv.alea(r.toString()),this.randn=new uv(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)}},CO=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=lv.alea(a)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function EO(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 TO(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 _O=z({randomGamma_:EO});function AO(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error(`Unsupported data type ${a}`);let s=new uv(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 PS=z({randomNormal_:AO});function $O(e,t=0,n=1,a="float32",r){let s=He(e,a),i=new CO(t,n,null,r);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var Cu=z({randomUniform_:$O});function pl(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(mc,{},r)}function FO(e){let t={input:A(e,"input","real")};return L.runKernel(Im,t)}var Kp=z({real_:FO});function DO(e){let t={x:A(e,"x","reciprocal")};return L.runKernel(su,t)}var pv=z({reciprocal_:DO});function RO(e){let t={x:A(e,"x","relu")};return L.runKernel(Ki,t)}var Xe=z({relu_:RO});function MO(e){let t={x:A(e,"x","relu6")};return L.runKernel(Yi,t)}var Km=z({relu6_:MO});function PO(e,t){let n={x:A(e,"x","reverse")},a={dims:t};return L.runKernel(Ji,n,a)}var ta=z({reverse_:PO});function OO(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}.`),ta(t,0)}var LO=z({reverse1d_:OO});function zO(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}.`),ta(n,t)}var BO=z({reverse2d_:zO});function WO(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}.`),ta(n,t)}var UO=z({reverse3d_:WO});function VO(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}.`),ta(n,t)}var GO=z({reverse4d_:VO});function HO(e){let t={x:A(e,"x","round")};return L.runKernel(Zi,t)}var Xm=z({round_:HO});function jO(e){let t={x:A(e,"x","rsqrt","float32")};return L.runKernel(Qi,t)}var Ym=z({rsqrt_:jO});function ke(e,t){if((dn(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"&&dn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return fs(e,[],[],t)}function qO(e){let t={x:A(e,"x","selu")};return L.runKernel(uu,t)}var Jm=z({selu_:qO});function KO(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 ${u.shape[2]}.`);let m=ys(p,l,a,r,i,s),f=Rt(m,u,1,"valid",i);return d?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ho=z({separableConv2d_:KO});async function XO(e,t){let n=A(e,"x","setdiff1d"),a=A(t,"y","setdiff1d");R(n.dtype===a.dtype,()=>`x and y should have the same dtype, but got x (${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 (${a.shape}).`);let r=await n.data(),s=await a.data(),i=new Set(s),o=0;for(let p=0;p<r.length;p++)i.has(r[p])||o++;let l=new jt([o],n.dtype),u=new jt([o],"int32");for(let p=0,d=0;p<r.length;p++)i.has(r[p])||(l.values[d]=r[p],u.values[d]=p,d++);return[l.toTensor(),u.toTensor()]}var OS=XO;function YO(e){let t={x:A(e,"x","sign")};return L.runKernel(du,t)}var cv=z({sign_:YO});function JO(e){let t={x:A(e,"x","sin","float32")};return L.runKernel(eo,t)}var Zm=z({sin_:JO});function ZO(e){let t={x:A(e,"x","sinh")};return L.runKernel(cu,t)}var Qm=z({sinh_:ZO});function QO(e,t,n){let a=A(e,"x","slice1d");return R(a.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${a.rank} tensor`),Ge(a,[t],[n])}var ef=z({slice1d_:QO});function e3(e,t,n){let a=A(e,"x","slice2d");return R(a.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${a.rank} tensor`),Ge(a,t,n)}var dv=z({slice2d_:e3});function t3(e,t,n){let a=A(e,"x","slice3d");return R(a.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${a.rank} tensor`),Ge(a,t,n)}var Eu=z({slice3d_:t3});function n3(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 Xp=z({slice4d_:n3});function a3(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(ro,a,r)}var Ja=z({softmax_:a3});function r3(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(mm,t)}var Rc=z({fft_:r3});function s3(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(fm,t)}var cl=z({ifft_:s3});function i3(e){let t=e.shape[e.shape.length-1],n=e.size/t,a;if(t<=2){let r=W(e,[n,t]);a=cl(r)}else{let r=[n,2*(t-1)],s=W(Kp(e),[n,t]),i=W(Wm(e),[n,t]),o=ta(Ge(s,[0,1],[n,t-2]),1),l=B(ta(Ge(i,[0,1],[n,t-2]),1),ke(-1)),u=Qe([s,o],1),p=Qe([i,l],1),d=W(ns(u,p),[r[0],r[1]]);a=cl(d)}if(a=Kp(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 tf=z({irfft_:i3});function o3(e,t,n=0){let a={x:A(e,"x","split")},r={numOrSizeSplits:t,axis:n};return L.runKernel(fu,a,r)}var Ln=z({split_:o3});function l3(e,t){R(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],a=e.size/n,r;if(t!=null&&t<n){let m=e.shape.map(g=>0),f=e.shape.map(g=>g);f[e.shape.length-1]=t,r=Ge(e,m,f),n=t}else if(t!=null&&t>n){let m=e.shape.map(f=>f);m[e.shape.length-1]=t-n,r=Qe([e,kt(m)],e.shape.length-1),n=t}else r=e;let s=Ke(r),i=W(ns(r,s),[a,n]),o=Rc(i),l=Math.floor(n/2)+1,u=Kp(o),p=Wm(o),d=Ln(u,[l,n-l],u.shape.length-1),c=Ln(p,[l,n-l],p.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,W(ns(d[0],c[0]),h)}var Mc=z({rfft_:l3});function u3(e){let t={x:A(e,"x","sqrt","float32")};return L.runKernel(no,t)}var ln=z({sqrt_:u3});function p3(e,t){let n=A(e,"a","squaredDifference"),a=A(t,"b","squaredDifference");[n,a]=$t(n,a),dt(n.shape,a.shape);let r={a:n,b:a},s={};return L.runKernel(so,r,s)}var nf=z({squaredDifference_:p3});function c3(e,t){let n=A(e,"x","squeeze");return W(n,iI(n.shape,t).newShape)}var cr=z({squeeze_:c3});function d3(e,t=0){let n=Hp(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(au,a,r)}var Mt=z({stack_:d3});function h3(e,t=0){let n={x:A(e,"x","step")},a={alpha:t};return L.runKernel(ms,n,a)}var _u=z({step_:h3});function m3(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(yu,u,p)}var hv=z({stridedSlice_:m3});function f3(e){let t={x:A(e,"x","tan","float32")};return L.runKernel(oo,t)}var mv=z({tan_:f3});function qe(e,t){hi(e);let n=ur(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return fs(e,null,n,t)}function Ga(e,t,n){if(hi(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let a=ur(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 fs(e,t,a,n)}function Za(e,t,n){if(hi(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let a=ur(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 fs(e,t,a,n)}function g3(e,t,n){if(hi(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let a=ur(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 fs(e,t,a,n)}function y3(e,t,n){if(hi(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let a=ur(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,fs(e,t,a,n)}function b3(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(bu,s,i);return{values:o,indices:l}}var fv=z({topk_:b3});function x3(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new uv(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 af=z({truncatedNormal_:x3});function v3(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(Am,a,r);return{values:s,indices:i}}var Dh=z({unique_:v3});function w3(e,t,n){let a=A(e,"x","unsortedSegmentSum"),r=A(t,"segmentIds","unsortedSegmentSum","int32");R(il(n),()=>"numSegments must be of dtype int");let s={x:a,segmentIds:r},i={numSegments:n};return L.runKernel(vc,s,i)}var gv=z({unsortedSegmentSum_:w3});function k3(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(vu,a,r)}var ht=z({unstack_:k3});function LS(e,t=!0,n,a){return L.makeVariable(e,t,n,a)}function zS(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 I3(e){let t=A(e,"condition","whereAsync","bool"),n=await t.data(),a=zS(t.shape,n);return e!==t&&t.dispose(),a}var yv=I3;async function S3(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 yv(d),h=cr(c,[1]),m=ai(p,h,s);return e!==a&&a.dispose(),t!==r&&r.dispose(),h.dispose(),p.dispose(),d.dispose(),c.dispose(),m}var N3=S3;function T3(e,t="euclidean",n=null,a=!1){e=A(e,"x","norm");let r=BS(e,t,n),s=r.shape;if(a){let i=Ta(n,e.shape);s=ri(r.shape,i)}return W(r,s)}function BS(e,t,n=null){if(e.rank===0)return zt(e);if(e.rank!==1&&n===null)return BS(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 Ia(zt(e),n);if(t===-1/0)return qp(zt(e),n);if(t==="euclidean"||t===2)return ln(be(_r(zt(e),ke(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Ia(be(zt(e),n[0]),n[1]-1);if(t===1/0)return Ia(be(zt(e),n[1]),n[0]);if(t===-1/0)return qp(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 rf=z({norm_:T3});function C3(e,t,n,a,r=!0){let s=A(e,"v","movingAverage"),i=A(t,"x","movingAverage"),o=A(n,"decay","movingAverage");II(s,i),R(ps(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=ke(1),u=ce(l,o),p=B(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 E3=z({movingAverage_:C3});function _3(e,t,n){let a=A(e,"indices","scatterND","int32"),r=A(t,"updates","scatterND");Ax(r,a,n);let s={indices:a,updates:r},i={shape:n};return L.runKernel(ou,s,i)}var WS=z({scatterND_:_3});function A3(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 $3(e,t,n,a=0){let r=A(e,"sparseIndices","sparseToDense","int32"),s=A(t,"sparseValues","sparseToDense"),i=A(a,"defaultValue","sparseToDense",s.dtype);A3(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:n};return L.runKernel(Tm,o,l)}var bv=z({sparseToDense_:$3});function F3(e,t){let n=A(t,"indices","gatherND","int32"),a={params:A(e,"x","gatherND","string_or_numeric"),indices:n};return L.runKernel(Wl,a)}var US=z({gatherND_:F3});function D3(e,t){if(t==null)return e.shape.slice();if(ps(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 R3(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=D3(r,n),i=1-t,o=fe(Nu(J(Cu(s,0,1,"float32",a),i)),i);return B(r,o)}var VS=z({dropout_:R3});function GS(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function xv(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 M3(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=oI("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(),Jn(p,r.shape,"bool")}var P3=M3,rs={};Re(rs,{conv2d:()=>z3,depthwiseConv2d:()=>V3,matMul:()=>H3});function O3(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(sm,d,c)}var vv=z({conv2DBackpropFilter_:O3});function sf(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return B(e,_u(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function of(e,t){let n=t,a=Wt(e.shape,t.shape);return a.length>0&&(n=be(n,a)),W(n,e.shape)}function lf(e,t,n,a){if(t==="linear")return e;if(t==="relu")return Xe(e);if(t==="elu")return Su(e);if(t==="relu6")return Km(e);if(t==="prelu")return Dc(e,n);if(t==="leakyrelu")return _c(e,a);if(t==="sigmoid")return da(e);throw new Error(`Unknown fused activation ${t}.`)}var uf=(e,t)=>!(e>0)||t==="linear";function L3({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",uf(L.state.gradientDepth,l)===!1){let w=Rt(e,t,n,a,r,s,i);return o!=null&&(w=J(w,o)),lf(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(hr(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 ${r} but only NHWC is currently supported.`);let f=Tc(h.shape,c.shape,n,s,a,i),g;o!=null&&(g=A(o,"bias","fused conv2d"),[g]=$t(g,d),dt(f.outShape,g.shape));let y;u!=null&&(y=A(u,"prelu weights","fused conv2d"));let b=(w,T)=>{let[C,_,$,P]=T,F=sf(w,$,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=jx(_.shape,F,C,n,a),M=vv(_,F,C.shape,n,a),V=[S,M];if(P!=null){let j=of(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?pr((w,T,C)=>{let _=L.runKernel(Js,x,v);return C([T,w,_]),m&&(_=W(_,[_.shape[1],_.shape[2],_.shape[3]])),{value:_,gradFunc:b}})(h,c):pr((w,T,C,_)=>{let $=L.runKernel(Js,x,v);return _([T,w,$,C]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,c,g)}var z3=z({fusedConv2d_:L3});function B3(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(um,u,p)}var HS=z({depthwiseConv2dNativeBackpropFilter_:B3});function W3(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(pm,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var jS=z({depthwiseConv2dNativeBackpropInput_:W3});function U3({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(uf(L.state.gradientDepth,l)===!1){let w=ys(e,t,n,a,r,s,i);return o!=null&&(w=J(w,o)),lf(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(hr(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=Tc(h.shape,c.shape,n,s,a,i,!0),g;o!=null&&(g=A(o,"bias","fused conv2d"),[g]=$t(g,d),dt(f.outShape,g.shape));let y;u!=null&&(y=A(u,"prelu weights","fused depthwiseConv2d"));let b=(w,T)=>{R(as(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[C,_,$,P]=T,F=sf(w,$,l),S=jS(_.shape,F,C,n,a,s,i),M=HS(_,F,C.shape,n,a,s,i);if(P!=null){let V=of(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?pr((w,T,C)=>{let _=L.runKernel(Zs,x,v);return C([T,w,_]),m&&(_=W(_,[_.shape[1],_.shape[2],_.shape[3]])),{value:_,gradFunc:b}})(h,c):pr((w,T,C,_)=>{let $=L.runKernel(Zs,x,v);return _([T,w,$,C]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,c,g)}var V3=z({fusedDepthwiseConv2d_:U3});function G3({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(uf(L.state.gradientDepth,s)===!1){let P=Fe(e,t,n,a);return r!=null&&(P=J(P,r)),lf(P,s,i,o)}let l=A(e,"a","fused matMul"),u=A(t,"b","fused matMul");[l,u]=$t(l,u);let p=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=vt(m),y=vt(f);R(p===d,()=>`Error in fused matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=dt(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([c,h]),x=n?W(l,[g,p,c]):W(l,[g,c,p]),v=a?W(u,[y,h,d]):W(u,[y,d,h]),w;r!=null&&(w=A(r,"bias","fused matMul"),[w]=$t(w,l),dt(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=sf(W(P,V.shape),V,s),K,Q;if(!n&&!a?(K=Fe(q,M,!1,!0),Q=Fe(S,q,!0,!1)):!n&&a?(K=Fe(q,M,!1,!1),Q=Fe(q,S,!0,!1)):n&&!a?(K=Fe(M,q,!1,!0),Q=Fe(S,q,!1,!1)):(K=Fe(M,q,!0,!0),Q=Fe(q,S,!0,!0)),r!=null){let ee=of(j,q);return[K,Q,ee]}else return[K,Q]},_={a:x,b:v,bias:w,preluActivationWeights:T},$={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?pr((P,F,S)=>{let M=L.runKernel(Ys,_,$);return S([P,F,M]),{value:W(M,b),gradFunc:C}})(x,v):pr((P,F,S,M)=>{let V=L.runKernel(Ys,_,$);return M([P,F,V,S]),{value:W(V,b),gradFunc:C}})(x,v,w)}var H3=z({fusedMatMul_:G3});function j3(e){return xv(e,.54,.46)}var q3=z({hammingWindow_:j3});function K3(e){return xv(e,.5,.5)}var qS=z({hannWindow_:K3});function X3(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?Ga([],[0,t]):W(Qe(i),[i.length,t])}var KS=z({frame_:X3});function Y3(e,t,n,a,r=qS){a==null&&(a=GS(t));let s=KS(e,t,n),i=B(s,r(t));return Mc(i,a)}var J3=z({stft_:Y3});function Z3(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(Dl,p,d)}var Q3=z({cropAndResize_:Z3});function eL(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(zl,n,{})}var tL=z({flipLeftRight_:eL});function nL(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 aL=z({grayscaleToRGB_:nL});function rL(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 ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return L.runKernel(ku,s,i)}var sL=z({rotateWithOffset_:rL});function Au(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),R(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${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 iL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=A(e,"boxes","nonMaxSuppression","float32"),i=A(t,"scores","nonMaxSuppression","float32"),o=Au(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(Ql,{boxes:s,scores:i},l)}var oL=z({nonMaxSuppression_:iL});function lL(e,t,n){let a=uL(e,t,n),r=a<0?-(a+1):a;e.splice(r,0,t)}function uL(e,t,n){return cL(e,t,n||pL)}function pL(e,t){return e>t?1:e<t?-1:0}function cL(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 XS(e,t,n,a,r){return wv(e,t,n,a,r,0)}function YS(e,t,n,a,r,s){return wv(e,t,n,a,r,0,!1,s,!0)}function JS(e,t,n,a,r,s){return wv(e,t,n,a,r,s,!0)}function wv(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(B1);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=dL(e,b,d[w]);if(T>=a){v=!0;break}if(g.score=g.score*hL(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&&lL(u,g,B1))}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 dL(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 hL(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function B1(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function mL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=A(e,"boxes","nonMaxSuppressionAsync"),i=A(t,"scores","nonMaxSuppressionAsync"),o=Au(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}=XS(u,p,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),qe(d,"int32")}var fL=mL;function gL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=A(e,"boxes","nonMaxSuppression"),o=A(t,"scores","nonMaxSuppression"),l=Au(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(tu,u,p);return{selectedIndices:d[0],selectedScores:d[1]}}var yL=z({nonMaxSuppressionWithScore_:gL});async function bL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=A(e,"boxes","nonMaxSuppressionAsync"),o=A(t,"scores","nonMaxSuppressionAsync"),l=Au(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}=JS(p,d,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:qe(c,"int32"),selectedScores:qe(h)}}var xL=bL;function vL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=A(e,"boxes","nonMaxSuppression"),o=A(t,"scores","nonMaxSuppression"),l=Au(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(eu,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var wL=z({nonMaxSuppressionPadded_:vL});async function kL(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=A(e,"boxes","nonMaxSuppressionAsync"),o=A(t,"scores","nonMaxSuppressionAsync"),l=Au(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}=YS(c,h,u,p,d,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:qe(m,"int32"),validOutputs:ke(f,"int32")}}var IL=kL;function SL(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(Xi,o,l);return i?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var ZS=z({resizeBilinear_:SL});function NL(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(fc,o,l);return i?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var QS=z({resizeNearestNeighbor_:NL});function TL(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=B(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]=Ln(r,[1,1,1],-1);let f=B(p,s),g=B(d,i),y=B(c,o);h=J(J(f,g),y)}else h=e;if(t==="otsu"){let f=Gx(oe(Xm(h),"int32"),Jn([]),256);u=CL(f,l)}let m=n?xs(h,u):Wn(h,u);return oe(B(m,255),"int32")}function CL(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(B(s,pl(0,s.size)));o=fe(c,be(s));let h=Cn(i.shape,s.size),m=J(pl(0,i.size),h),f=B(i,m);l=fe(be(f),be(i));let g=ce(o,l),y=ce(o,l),b=B(u,p);r=B(B(b,g),y);let x=Wn(r,a);a=mn(x,r,a),n=mn(x,qe([d]),n)}return n}var EL=z({threshold_:TL});function _L(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(xu,l,u)}var AL=z({transform_:_L});function $L(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 (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=W(pl(0,s,1,"int32"),[-1,1]),l=pl(0,i,1,"int32"),u=ce(o,l),p=Na(xs(u,ke(+t,"int32")),bs(u,ke(-n,"int32"))),d=kt([s,i],a.dtype);return W(Mt(ht(W(a,[-1,s,i])).map(c=>mn(p,c,d))),r)}var FL=z({bandPart_:$L});function DL(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. ${r})`)}else t=!0,e=Ln(e,e.shape[0],0).map(r=>cr(r,[0]));R(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${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=B(be(B(n[i],s)),n[i]);s=ce(s,o)}return fe(s,rf(s,"euclidean"))}));return t?Mt(n,0):n}var RL=z({gramSchmidt_:DL});function ML(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 W1(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),a=ht(W(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(l=>{let[u,p]=W1(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 W1(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=Qx(n),s=Tr(e),i=Ga([[1]],[1,1]),o=Tr(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=rf(h),f=Ge(s,[u,u],[1,1]),g=mn(Wn(f,0),Ga([[-1]]),Ga([[1]])),y=ce(f,B(g,m)),b=fe(h,y);b.shape[0]===1?o=Tr(i):o=Qe([i,Ge(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let x=St(fe(Fe(g,y),m)),v=Ge(s,[u,0],[n-u,a]),w=B(x,o),T=Me(o);if(u===0)s=ce(v,Fe(w,Fe(T,v)));else{let $=ce(v,Fe(w,Fe(T,v)));s=Qe([Ge(s,[0,0],[u,a]),$],0)}let C=Me(w),_=Ge(r,[0,u],[n,r.shape[1]-u]);if(u===0)r=ce(_,Fe(Fe(_,o),C));else{let $=ce(_,Fe(Fe(_,o),C));r=Qe([Ge(r,[0,0],[n,u]),$],1)}return[o,s,r]}),De([p,d,c])}return!t&&n>a&&(r=Ge(r,[0,0],[n,a]),s=Ge(s,[0,0],[a,a])),[r,s]})}var PL=z({qr_:ML}),wn;(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"})(wn||(wn={}));function OL(e,t,n=wn.SUM_BY_NONZERO_WEIGHTS){let a=A(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=A(t,"weights","computeWeightedLoss"));let s=r==null?a:B(a,r);if(n===wn.NONE)return s;if(n===wn.SUM)return be(s);if(n===wn.MEAN){if(r==null)return _t(s);{let i=a.size/r.size,o=fe(be(s),be(r));return i>1?fe(o,ke(i)):o}}if(n===wn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return fe(be(s),ke(a.size));{let i=B(r,Xn(a.shape)),o=oe(be(si(i,ke(0))),"float32");return fe(be(s),o)}}throw Error(`Unknown reduction: ${n}`)}var Ar=z({computeWeightedLoss_:OL});function LL(e,t,n,a=wn.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 Ar(o,i,a)}var zL=z({absoluteDifference_:LL});function BL(e,t,n,a,r=wn.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=ke(1),u=ce(l,be(B(s,i),n,!0));return Ar(u,o,r)}var WL=z({cosineDistance_:BL});function UL(e,t,n,a=wn.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=ke(1);r=ce(B(ke(2),r),o);let l=Xe(ce(o,B(r,s)));return Ar(l,i,a)}var VL=z({hingeLoss_:UL});function GL(e,t,n,a=1,r=wn.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=ke(a),u=zt(ce(i,s)),p=Tu(u,l),d=ce(u,p),c=J(B(ke(.5),lt(p)),B(l,d));return Ar(c,o,r)}var HL=z({huberLoss_:GL});function jL(e,t,n,a=1e-7,r=wn.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=ke(1),u=ke(a),p=St(B(s,Qn(J(i,u)))),d=B(ce(l,s),Qn(J(ce(l,i),u))),c=ce(p,d);return Ar(c,o,r)}var qL=z({logLoss_:jL});function KL(e,t,n,a=wn.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=nf(r,s);return Ar(o,i,a)}var XL=z({meanSquaredError_:KL});function YL(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=B(a,n),i=Ac(fn(St(zt(a))));return J(ce(r,s),i)}function JL(e,t,n,a=0,r=wn.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=ke(a),p=ke(1),d=ke(.5);s=J(B(s,ce(p,u)),B(d,u))}let l=YL(s,i);return Ar(l,o,r)}var ZL=z({sigmoidCrossEntropy_:JL});function QL(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 pr((a,r,s)=>{let i=rv(r,[n],!0),o=ce(oe(r,"float32"),i);s([a,o]);let l=St(B(o,a));return{value:be(l,[n]),gradFunc:(u,p)=>{let[d,c]=p,h=ri(u.shape,[n]);return[B(W(u,h),ce(oe(d,"float32"),fn(c))),B(W(u,h),ce(fn(c),oe(d,"float32")))]}}})(e,t)}function ez(e,t,n,a=0,r=wn.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=ke(a),p=ke(1),d=ke(s.shape[1]);s=J(B(s,ce(p,u)),fe(u,d))}let l=QL(s,i);return Ar(l,o,r)}var tz=z({softmaxCrossEntropy_:ez});function nz(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(gc,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var az=z({sparseFillEmptyRows_:nz});function rz(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(gu,i);return{outputIndices:o[0],outputShape:o[1]}}var sz=z({sparseReshape_:rz});function iz(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(yc,i)}var oz=z({sparseSegmentMean_:iz});function lz(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(bc,i)}var uz=z({sparseSegmentSum_:lz});function pz(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(Cm,d,p);return{nGrams:c[0],nGramsSplits:c[1]}}var cz=z({stringNGrams_:pz});function dz(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 ${r.shape}`);let s={skipEmpty:n},i={input:a,delimiter:r},o=L.runKernel(Em,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var hz=z({stringSplit_:dz});function mz(e,t){let n=A(e,"input","stringToHashBucketFast","string"),a={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return L.runKernel(_m,r,a)}var fz=z({stringToHashBucketFast_:mz}),gz={fft:Rc,ifft:cl,rfft:Mc,irfft:tf},yz={hammingWindow:q3,hannWindow:qS,frame:KS,stft:J3},Yn={flipLeftRight:tL,grayscaleToRGB:aL,resizeNearestNeighbor:QS,resizeBilinear:ZS,rotateWithOffset:sL,cropAndResize:Q3,nonMaxSuppression:oL,nonMaxSuppressionAsync:fL,nonMaxSuppressionWithScore:yL,nonMaxSuppressionWithScoreAsync:xL,nonMaxSuppressionPadded:wL,nonMaxSuppressionPaddedAsync:IL,threshold:EL,transform:AL},e2={bandPart:FL,gramSchmidt:RL,qr:PL},bz={absoluteDifference:zL,computeWeightedLoss:Ar,cosineDistance:WL,hingeLoss:VL,huberLoss:HL,logLoss:qL,meanSquaredError:XL,sigmoidCrossEntropy:ZL,softmaxCrossEntropy:tz},_p={sparseFillEmptyRows:az,sparseReshape:sz,sparseSegmentMean:oz,sparseSegmentSum:uz},dh={stringNGrams:cz,stringSplit:hz,stringToHashBucketFast:fz},$r=class extends nS{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 De(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 CS(e,t)}dispose(){this.iterations_!=null&&De(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty($r,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var pf=class extends $r{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${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(B(i,this.rho),B(lt(s),1-this.rho)),u=B(fe(ln(J(o,this.epsilon)),ln(J(i,this.epsilon))),s),p=J(B(o,this.rho),B(lt(u),1-this.rho));i.assign(l),o.assign(p);let d=J(B(u,-this.learningRate),a);a.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(De(this.accumulatedGrads.map(e=>e.variable)),De(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)}};pf.className="Adadelta";gs(pf);var cf=class extends $r{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${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(B(fe(r,ln(J(i,L.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&De(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)}};cf.className="Adagrad";gs(cf);var df=class extends $r{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=ce(1,this.accBeta1),a=ce(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${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(B(u,this.beta1),B(l,1-this.beta1)),c=J(B(p,this.beta2),B(lt(l),1-this.beta2)),h=fe(d,n),m=fe(c,a);u.assign(d),p.assign(c);let f=J(B(fe(h,J(ln(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(B(this.accBeta1,this.beta1)),this.accBeta2.assign(B(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&De(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)}};df.className="Adam";gs(df);var hf=class extends $r{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=ce(1,this.accBeta1),a=fe(-this.learningRate,J(B(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${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(B(u,this.beta1),B(l,1-this.beta1)),c=B(p,this.beta2),h=zt(l),m=mr(c,h);u.assign(d),p.assign(m);let f=J(B(fe(a,n),fe(d,J(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(J(this.iteration,1)),this.accBeta1.assign(B(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&De(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)}};hf.className="Adamax";gs(hf);var Pc=class extends $r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=L.registeredVariables[t];O(()=>{let s=J(B(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=en(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Pc.className="SGD";gs(Pc);var mf=class extends Pc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${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(B(this.m,r),s);this.useNesterov?i=J(B(this.c,J(s,B(o,this.m))),a):i=J(B(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&De(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)}};mf.className="Momentum";gs(mf);var ff=class extends $r{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=L.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=L.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${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(B(i,this.decay),B(lt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,p=J(B(u,this.decay),B(s,1-this.decay)),d=fe(B(s,this.learningRate),ln(ce(l,J(lt(p),this.epsilon)))),c=J(B(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(B(i,this.decay),B(lt(s),1-this.decay)),p=J(B(o,this.momentum),fe(B(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&&De(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&De(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&De(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)}};ff.className="RMSProp";gs(ff);var Hr=class{static sgd(e){return new Pc(e)}static momentum(e,t,n=!1){return new mf(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new ff(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new df(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new pf(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new hf(e,t,n,a,r)}static adagrad(e,t=.1){return new cf(e,t)}},Ls={sgd:Hr.sgd,momentum:Hr.momentum,adadelta:Hr.adadelta,adagrad:Hr.adagrad,rmsprop:Hr.rmsprop,adamax:Hr.adamax,adam:Hr.adam},xz=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function t2(){return new Promise(e=>xz(()=>e()))}var E={};Re(E,{ERF_A1:()=>Az,ERF_A2:()=>$z,ERF_A3:()=>Fz,ERF_A4:()=>Dz,ERF_A5:()=>Rz,ERF_P:()=>_z,PARALLELIZE_THRESHOLD:()=>kv,SELU_SCALE:()=>a2,SELU_SCALEALPHA:()=>n2,applyActivation:()=>lf,assertAndGetBroadcastShape:()=>dt,assertAxesAreInnerMostDims:()=>PP,assertParamsConsistent:()=>vz,assignToTypedArray:()=>Bz,axesAreInnerMostDims:()=>nv,calculateShapes:()=>jI,checkEinsumDimSizes:()=>jz,checkPadOnDimRoundingMode:()=>Tn,combineLocations:()=>_S,complexWithEvenIndex:()=>Oz,complexWithOddIndex:()=>Lz,computeConv2DInfo:()=>Tc,computeConv3DInfo:()=>uS,computeDefaultPad:()=>Ux,computeDilation2DInfo:()=>iM,computeOptimalWindowSize:()=>kz,computeOutAndReduceShapes:()=>AS,computeOutShape:()=>wz,computePool2DInfo:()=>lS,computePool3DInfo:()=>oM,convertConv2DDataFormat:()=>pS,decodeEinsumEquation:()=>Gz,eitherStridesOrDilationsAreOne:()=>hr,expandShapeToKeepDim:()=>ri,exponent:()=>Uz,exponents:()=>Wz,fromStringArrayToUint8:()=>hB,fromUint8ToStringArray:()=>dB,getAxesPermutation:()=>$S,getBroadcastDims:()=>VI,getComplexWithIndex:()=>zz,getEinsumComputePath:()=>qz,getEinsumPermutation:()=>Hz,getFusedBiasGradient:()=>of,getFusedDyActivation:()=>sf,getImageCenter:()=>Iz,getInnerMostAxes:()=>OP,getPermuted:()=>Nz,getReductionAxes:()=>Wt,getReshaped:()=>Sz,getReshapedPermuted:()=>Tz,getSliceBeginCoords:()=>Cz,getSliceSize:()=>Ez,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>Jz,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>Zz,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>Qz,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>nB,getSparseReshapeInputOutputMismatchErrorMessage:()=>rB,getSparseReshapeInputOutputMultipleErrorMessage:()=>aB,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>eB,getSparseReshapeNegativeOutputDimErrorMessage:()=>tB,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>lB,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>sB,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>iB,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>oB,getUndoAxesPermutation:()=>av,isIdentityPermutation:()=>Kz,log:()=>_F,mergeRealAndImagArrays:()=>Mz,prepareAndValidate:()=>HI,prepareSplitSize:()=>Yz,segment_util:()=>r2,shouldFuse:()=>uf,slice_util:()=>qt,splitRealAndImagArrays:()=>Pz,tupleValuesAreOne:()=>as,upcastType:()=>Sa,validateInput:()=>Ax,validateUpdateShape:()=>_x,warn:()=>qr});function vz(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 ${s}.`)})}function wz(e,t){let n=e[0].slice();for(let a=1;a<e.length;a++)n[t]+=e[a][t];return n}var kv=30;function kz(e){return e<=kv?e:Ih(e,Math.floor(Math.sqrt(e)))}function Iz(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function Sz(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(s+1))}return r}function Nz(e,t,n=!0){let a=[];if(n){a.push(t);for(let r=t+1;r<e;++r)r<=2*t?(a.push(r),a.push(r-(t+1))):a.push(r)}else{let r=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2===1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function Tz(e,t,n,a=!0){let r=[];a?r.push(e[0]/n):r.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?a?r.push(t[s-1]*e[s]):r.push(e[s]/t[s-1]):r.push(e[s]);return r}function Cz(e,t){let n=[0];for(let a=0;a<t;++a)n.push(e[a][0]);return n}function Ez(e,t,n){let a=e.slice(0,1);for(let r=0;r<n;++r)a.push(e[r+1]-t[r][0]-t[r][1]);return a}var n2=1.7580993408473768,a2=1.0507009873554805,_z=.3275911,Az=.254829592,$z=-.284496736,Fz=1.421413741,Dz=-1.453152027,Rz=1.061405429;function Mz(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let a=0;a<n.length;a+=2)n[a]=e[a/2],n[a+1]=t[a/2];return n}function Pz(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let a=0;a<e.length;a+=2)t[a/2]=e[a],n[a/2]=e[a+1];return{real:t,imag:n}}function Oz(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function Lz(e){let t=Math.floor(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function zz(e,t){let n=e[t*2],a=e[t*2+1];return{real:n,imag:a}}function Bz(e,t,n,a){e[a*2]=t,e[a*2+1]=n}function Wz(e,t){let n=new Float32Array(e/2),a=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let s=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(s),a[r]=Math.sin(s)}return{real:n,imag:a}}function Uz(e,t,n){let a=(n?2:-2)*Math.PI*(e/t),r=Math.cos(a),s=Math.sin(a);return{real:r,imag:s}}var sb="->",Vz=/->/g,U1=",",V1="...";function Gz(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(Vz,"").length)/sb.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${sb}").`);let[a,r]=e.split(sb);R(a.indexOf(V1)===-1,()=>`The ellipsis notation ("${V1}") is not supported yet.`);let s=a.split(U1),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!==U1&&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 Hz(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 jz(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 qz(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=Xz(t,o);for(let u of l)s.indexOf(u)===-1&&(a[i].push(u),s.push(u))}return{path:n,steps:a}}function Kz(e){return e.every((t,n)=>t===n)}function Xz(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 Yz(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 Jz(e){return`Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${e}`}function Zz(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function Qz(e,t,n){return`indices(${e}, 0) is invalid: ${t} >= ${n}`}function eB(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function tB(e,t){return`size ${e} must be non-negative, not ${t}`}function nB(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function aB(e,t){let n=vt(e),a=vt(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 rB(e,t){let n=vt(e),a=vt(t);return`Input to reshape is a tensor with ${n} dense values, but the requested shape has ${a}. inputShape=${e} outputShape=${t}`}function sB(){return"segment ids must be >= 0"}function iB(){return"segment ids are not increasing"}function oB(e,t){return`Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function lB(e,t,n){return`Bad: indices[${e}] == ${t} out of range [0, ${n})`}var r2={};Re(r2,{collectGatherOpShapeInfo:()=>cB,computeOutShape:()=>pB,segOpComputeOptimalWindowSize:()=>uB});function uB(e,t){let n=!1,a;for(e<=kv?(a=e,n=!0):a=Ih(e,Math.floor(Math.sqrt(e)));!n;)a>t||a===e?n=!0:a=Ih(e,a+1);return a}function pB(e,t,n){let a=[],r=e.length;for(let s=0;s<r;s++)s!==t?a.push(e[s]):a.push(n);return a}function cB(e,t,n,a){let r=t.shape.length,s=e.shape.length;if(a!==0&&(a<-r||a>r))throw new Error(`Expect batchDims in the range of [-${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 dB(e){try{return e.map(t=>_h(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function hB(e){return e.map(t=>Ic(t))}var fr={};Re(fr,{nonMaxSuppressionV3Impl:()=>XS,nonMaxSuppressionV4Impl:()=>YS,nonMaxSuppressionV5Impl:()=>JS,whereImpl:()=>zS});var s2={kernelName:wl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,_u(oe(n,"float32"),-1))}}},mB={kernelName:kl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=lt(oe(n,"float32")),r=ln(ce(ke(1),a));return St(fe(e,r))}}}},fB={kernelName:Il,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ln(ce(lt(oe(n,"float32")),1));return fe(e,a)}}}},gB={kernelName:cs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=dt(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)}}}},yB={kernelName:mi,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((a,r)=>{n[r]=()=>e.clone()}),n}},bB={kernelName:fi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ke(n)}}},xB={kernelName:rc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ke(n)}}},vB={kernelName:Tl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ln(ce(ke(1),lt(oe(n,"float32")))))}}},wB={kernelName:Cl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ln(J(ke(1),lt(oe(n,"float32"))));return fe(e,a)}}}},kB={kernelName:Al,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=dt(n.shape,a.shape);return{a:()=>{let s=J(lt(n),lt(a)),i=B(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(B(e,fe(n,s))),o=Wt(a.shape,r);return o.length>0&&(i=be(i,o)),W(i,a.shape)}}}},IB={kernelName:El,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,J(lt(oe(n,"float32")),1))}}},SB={kernelName:_l,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ce(ke(1),lt(oe(n,"float32"))))}}};function NB(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 ${u.rank}.`),Tn("avgPool3dGrad",r,s);let d={dy:l,input:u},c={filterSize:n,strides:a,pad:r,dimRoundingMode:s},h=L.runKernel(tm,d,c);return p?W(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var TB=z({avgPool3dGrad_:NB}),CB={kernelName:sc,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>TB(e,a,r,s,i,o)}}};function EB(e,t,n,a,r){let s=A(e,"dy","avgPoolGrad"),i=A(t,"input","avgPoolGrad");R(i.rank===s.rank,()=>`Rank of input (${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 ${o.rank}.`);let p={dy:l,input:o},d={filterSize:n,strides:a,pad:r},c=L.runKernel(em,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var _B=z({avgPoolGrad_:EB}),AB={kernelName:gi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i}=n;return{x:()=>_B(e,a,r,s,i)}}},$B={kernelName:yi,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[a,r]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>Fe(e,r,!1,!0),b:()=>Fe(a,e,!0,!1)}:!s&&i?{a:()=>Fe(e,r,!1,!1),b:()=>Fe(e,a,!0,!1)}:s&&!i?{a:()=>Fe(r,e,!1,!0),b:()=>Fe(a,e,!1,!1)}:{a:()=>Fe(r,e,!0,!0),b:()=>Fe(e,a,!0,!0)}}},FB={kernelName:$l,gradFunc:(e,t,n)=>{let{blockShape:a,crops:r}=n;return{x:()=>Fc(e,a,r)}}},DB={kernelName:yI,gradFunc:(e,t,n)=>{let a=n,r=a.inputShape,s=a.shape,i=Array.from(s);for(let l=r.length-1;l>=0;l--)if(r[l]===s[l])i[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l<i.length;l++)i[l]>1&&o.push(l);return{x:()=>be(e,o,!0)}}},RB={kernelName:bi,gradFunc:e=>({x:()=>e.clone()})},MB={kernelName:xi,gradFunc:e=>({x:()=>Ke(e)})},PB={kernelName:ds,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{clipValueMin:r,clipValueMax:s}=n;return{x:()=>mn(Na(bs(a,r),xs(a,s)),e,Ke(e))}}},OB={kernelName:ic,inputsToSave:["x"],gradFunc:s2.gradFunc},LB={kernelName:Fl,saveAllInputs:!0,gradFunc:(e,t,n)=>{let a=t.map(o=>o.shape),{axis:r}=n,s=Ta(r,t[0].shape)[0],i=a.map(o=>o[s]);return Ln(e,i,s).map(o=>()=>o)}},zB={kernelName:vi,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return R(as(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>jx(a.shape,e,r,i,o,l),filter:()=>vv(a,e,r.shape,i,o,l)}}},BB={kernelName:wi,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:()=>vv(e,a,r.shape,s,i,o,l)}}};function WB(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(im,o,l)}var UB=z({conv3DBackpropFilter_:WB}),VB={kernelName:oc,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 '${a}'`);let[i,o]=t;return{x:()=>xS(i.shape,e,o,r,s),filter:()=>UB(i,e,o.shape,r,s)}}},GB={kernelName:ki,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(St(Zm(oe(n,"float32"))),e)}}},HB={kernelName:Ii,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(Qm(oe(n,"float32")),e)}}},jB={kernelName:Si,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r,exclusive:s,reverse:i}=n;return{x:()=>{let o=$S([r],a.rank),l=Bm(e,r,s,!i);return o!=null&&(l=Me(l,o)),l}}}},qB={kernelName:Ni,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(hr(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:()=>jS(l.shape,e,u,r,s,o,i),filter:()=>HS(l,e,u.shape,r,s,o,i)}}},KB={kernelName:lc,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)}}},XB={kernelName:Ci,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,a={dy:e,y:n};return{x:()=>L.runKernel(hm,a)}}},YB={kernelName:Ml,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=B(fn(St(lt(n))),2/Math.sqrt(Math.PI));return{x:()=>B(e,a)}}},JB={kernelName:Ei,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,n)}}},ZB={kernelName:Ol,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>W(e,n.shape)}}},QB={kernelName:Ll,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,fn(n))}}},eW={kernelName:_i,gradFunc:e=>({x:()=>Ke(e)})},tW={kernelName:Ai,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=dt(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=B(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")))}}}},nW={kernelName:$i,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:a}=n,[r,s,i,o]=t,l=o==null?ke(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=B(e,l),h=Ym(J(i,ke(a))),m=B(B(B(h,h),h),ke(-.5));return{x:()=>s.rank===1?W(B(B(e,On(W(h,[1,1,1,s.shape[0]]),p)),l),r.shape):W(B(B(e,h),l),r.shape),mean:()=>{let f=B(B(h,ke(-1)),c);return s.rank===1&&(f=be(f,u)),W(f,s.shape)},variance:()=>{let f=B(B(m,d),c);return s.rank===1&&(f=be(f,u)),W(f,s.shape)},scale:()=>{let f=B(d,h),g=B(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)}}}},aW={kernelName:Bl,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[a,r]=t,{axis:s}=n,i=Ta(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=G1(0,p),m=G1(p+1,p+1+c),f=H1([u,[l],d]),g=W(e,f),y=W(r,[l]),b=H1([[p],h,m]),x=Me(g,b),v=gv(x,y,a.shape[i]),w=av(b);return v=Me(v,w),v},indices:()=>r}}};function G1(e,t){let n=[];for(let a=e;a<t;++a)n.push(a);return n}function H1(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 rW={kernelName:Fi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>Ke(n),b:()=>Ke(a)}}},sW={kernelName:Di,gradFunc:e=>({x:()=>oe(e,"float32")})},iW={kernelName:Vl,gradFunc:e=>({x:()=>Ke(e)})},oW={kernelName:Gl,gradFunc:e=>({x:()=>Ke(e)})},lW={kernelName:Hl,gradFunc:e=>({x:()=>Ke(e)})},uW={kernelName:Ri,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{alpha:r}=n,s=Wn(a,0);return{x:()=>mn(s,e,B(e,r))}}},pW={kernelName:Kl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,J(n,1))}}},cW={kernelName:Mi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,oe(n,"float32"))}}},dW={kernelName:bI,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n;return{logits:()=>{let s=fn(a);return ce(e,B(be(e,r,!0),s))}}}};function hW(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(bm,o,l)}var mW=z({localResponseNormalizationBackprop_:hW}),fW={kernelName:dc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>mW(a,r,e,s,i,o,l)}}};function i2(e,t,n,a){return t.rank<n.rank&&(t=W(t,ri(t.shape,a))),e.rank<n.rank&&(e=W(e,ri(e.shape,a))),{x:()=>B(e,oe(Zn(n,t),e.dtype))}}var j1={kernelName:Pi,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{reductionIndices:r}=a,s=t[0],i=t[1],o=Ta(r,s.shape),l=i2(e,i,s,o);return{x:()=>l.x()}}},gW={kernelName:Oi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>B(e,oe(bs(n,a),"float32")),b:()=>B(e,oe(Um(n,a),"float32"))}}};function yW(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 ${c.rank}.`),Tn("maxPool3dGrad",s,i);let m={dy:p,input:d,output:c},f={filterSize:a,strides:r,pad:s,dimRoundingMode:i},g=L.runKernel(vm,m,f);return h?W(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var bW=z({maxPool3dGrad_:yW}),xW={kernelName:hc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>bW(e,a,r,s,i,o,l)}}};function vW(e,t,n,a,r,s,i){let o=A(e,"dy","maxPoolGrad"),l=A(t,"input","maxPoolGrad"),u=A(n,"output","maxPoolGrad");R(l.rank===o.rank,()=>`Rank of input (${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 ${l.rank}.`),Tn("maxPoolGrad",s,i);let p={dy:o,input:l,output:u},d={filterSize:a,strides:r,pad:s,dimRoundingMode:i};return L.runKernel(xm,p,d)}var wW=z({maxPoolGrad_:vW}),kW={kernelName:Li,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>wW(e,a,r,s,i,o)}}},IW={kernelName:zi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=Ta(r,a.shape),i=AS(a.shape,s)[1],o=vt(i);return{x:()=>{let l=a.shape.slice();s.forEach(p=>{l[p]=1});let u=W(e,l);return fe(B(u,Xn(a.shape,"float32")),o)}}}},SW={kernelName:Bi,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{axis:r}=a,[s,i]=t,o=Ta(r,s.shape),l=i2(e,i,s,o);return{x:()=>l.x()}}},NW={kernelName:Wi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>B(e,oe(xs(n,a),"float32")),b:()=>B(e,oe(Wn(n,a),"float32"))}}},TW={kernelName:Ui,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Ge(e,s,a.shape)}}},CW={kernelName:Yl,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=dt(n.shape,a.shape);return{a:()=>{let s=Wt(n.shape,r);return s.length>0?W(be(e,s),n.shape):e},b:()=>{let s=B(e,St(Nu(fe(n,a)))),i=Wt(a.shape,r);return i.length>0?W(be(s,i),a.shape):s}}}},EW={kernelName:Vi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=dt(n.shape,a.shape);return{a:()=>{let s=B(e,oe(a,"float32")),i=Wt(n.shape,r);return i.length>0?W(be(s,i),n.shape):s},b:()=>{let s=B(e,oe(n,"float32")),i=Wt(a.shape,r);return i.length>0?W(be(s,i),a.shape):s}}}},_W={kernelName:Jl,gradFunc:e=>({x:()=>St(e)})},AW={kernelName:Gi,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>kt(n.shape,"float32")}}},$W={kernelName:nu,gradFunc:e=>({x:()=>Ke(e)})},FW={kernelName:au,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:a}=n;return ht(e,a).map(r=>()=>r)}},q1={kernelName:Hi,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)}}},DW={kernelName:ji,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,a,r]=t,s=n,i=a,o=dt(s.shape,i.shape);return{a:()=>{let l=oe(i,"float32"),u=B(e,B(l,_r(s,ce(l,ke(1))))),p=Wt(s.shape,o);return p.length>0&&(u=be(u,p)),W(u,s.shape)},b:()=>{let l=Wn(s,0),u=mn(l,Qn(s),Ke(s)),p=B(e,B(r,u)),d=Wt(i.shape,o);return d.length>0&&(p=be(p,d)),W(p,i.shape)}}}},RW={kernelName:qi,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,a]=t,r=Wn(n,0);return{x:()=>mn(r,e,B(e,a)),alpha:()=>{let s=mn(r,Ke(e),B(e,n)),i=Wt(a.shape,e.shape);return i.length>0&&(s=be(s,i)),W(s,a.shape)}}}},MW={kernelName:Ti,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=dt(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=B(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")))}}}},PW={kernelName:su,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,St(lt(n)))}}},OW={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=B(xs(n,6),_u(n));return{x:()=>B(e,oe(a,"float32"))}}},LW={kernelName:Ki,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,oe(_u(n),"float32"))}}},zW={kernelName:iu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>W(e,n.shape)}}},BW={kernelName:Xi,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>L.runKernel(Nm,r,n)}}},WW={kernelName:fc,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>L.runKernel(Sm,r,n)}}},UW={kernelName:Ji,gradFunc:(e,t,n)=>{let{dims:a}=n,r=Ta(a,e.shape);return{x:()=>ta(e,r)}}},VW={kernelName:Zi,gradFunc:e=>({x:()=>Ke(e)})},GW={kernelName:Qi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>St(fe(e,B(_r(n,1.5),2)))}}},HW={kernelName:lu,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>oe(Ke(n),"float32"),t:()=>B(e,oe(n,e.dtype)),e:()=>B(e,oe($c(n),e.dtype))}}},jW={kernelName:uu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=Wn(n,ke(0)),r=ke(n2),s=ke(a2),i=B(e,s),o=B(B(e,r),fn(oe(n,"float32")));return mn(a,i,o)}}}},qW={kernelName:to,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(n,ce(ke(1),n)))}}},KW={kernelName:du,gradFunc:e=>({x:()=>Ke(e)})},XW={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(Ec(oe(n,"float32")),e)}}},YW={kernelName:cu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(zm(oe(n,"float32")),e)}}},JW={kernelName:pu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{begin:r,size:s}=n,i=a.shape,[o,l]=tS(a,r,s),u=[];for(let p=0;p<e.rank;p++)u.push([o[p],i[p]-o[p]-l[p]]);return{x:()=>fa(e,u)}}},ZW={kernelName:ro,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{dim:r}=n,s=!0,i=B(e,a);return{logits:()=>ce(i,B(be(i,[r],s),a))}}},QW={kernelName:hu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,da(n))}}},K1={kernelName:mu,gradFunc:(e,t,n)=>{let{blockShape:a,paddings:r}=n;return{x:()=>Cc(e,a,r)}}},X1={kernelName:fu,gradFunc:(e,t,n)=>{let{axis:a}=n;return{x:()=>Qe(e,a)}}},e4={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,B(ln(oe(n,"float32")),2))}}},t4={kernelName:xc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(oe(n,"float32"),2))}}},n4={kernelName:so,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ke(2);return{a:()=>B(e,B(r,ce(n,a))),b:()=>B(e,B(r,ce(a,n)))}}},a4={kernelName:ms,gradFunc:e=>({x:()=>Ke(e)})},r4={kernelName:io,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=dt(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)}}}},s4={kernelName:ao,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,r=a.shape.slice(),{axis:s}=n;Ta(s,a.shape).forEach(l=>{r[l]=1});let i=W(e,r),o=B(i,Xn(a.shape,"float32"));return{x:()=>o}}},i4={kernelName:oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,lt(Ec(n)))}}},o4={kernelName:lo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(ce(ke(1),lt(n)),e)}}},l4={kernelName:hs,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}}}},u4={kernelName:uo,gradFunc:(e,t,n)=>{let a=n,{perm:r}=a,s=av(r);return{x:()=>Me(e,s)}}},p4={kernelName:vu,gradFunc:(e,t,n)=>{let a=n,{axis:r}=a;return{value:()=>Mt(e,r)}}},c4={kernelName:vc,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>d4(e,n)}}};function d4(e,t){let n=mr(t,Ke(t)),a=ai(e,n),r=bs(t,ke(0,"int32")),s=a.rank-r.rank;for(let o=0;o<s;++o)r=hn(r,o+1);r=Na(r,Xn(a.shape,"bool"));let i=Ke(a);return mn(r,a,i)}var h4={kernelName:wu,gradFunc:e=>({x:()=>Ke(e)})},m4=[s2,mB,fB,gB,yB,bB,xB,vB,wB,kB,IB,SB,CB,AB,$B,FB,DB,RB,MB,PB,OB,LB,BB,zB,VB,GB,HB,jB,qB,KB,MW,XB,YB,JB,ZB,QB,tW,eW,nW,aW,rW,sW,iW,oW,lW,uW,pW,cW,dW,fW,j1,j1,gW,xW,kW,IW,SW,NW,TW,CW,EW,_W,AW,$W,FW,q1,q1,DW,RW,PW,OW,LW,zW,BW,WW,UW,VW,GW,HW,jW,qW,KW,XW,YW,JW,ZW,QW,K1,K1,X1,X1,e4,n4,t4,a4,r4,s4,i4,o4,l4,u4,p4,c4,h4];for(let e of m4)xI(e);ne().prototype.abs=function(){return this.throwIfDisposed(),zt(this)};ne().prototype.acos=function(){return this.throwIfDisposed(),Rx(this)};ne().prototype.acosh=function(){return this.throwIfDisposed(),Mx(this)};ne().prototype.add=function(e){return this.throwIfDisposed(),J(this,e)};ne().prototype.all=function(e,t){return this.throwIfDisposed(),Pm(this,e,t)};ne().prototype.any=function(e,t){return this.throwIfDisposed(),jp(this,e,t)};ne().prototype.argMax=function(e){return this.throwIfDisposed(),ti(this,e)};ne().prototype.argMin=function(e){return this.throwIfDisposed(),Px(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(),Ox(this)};ne().prototype.asinh=function(){return this.throwIfDisposed(),Lx(this)};ne().prototype.atan=function(){return this.throwIfDisposed(),zx(this)};ne().prototype.atan2=function(e){return this.throwIfDisposed(),Bx(this,e)};ne().prototype.atanh=function(){return this.throwIfDisposed(),Wx(this)};ne().prototype.avgPool=function(e,t,n,a){return this.throwIfDisposed(),ma(this,e,t,n,a)};ne().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Cc(this,e,t)};ne().prototype.batchNorm=function(e,t,n,a,r){return this.throwIfDisposed(),Er(this,e,t,n,a,r)};ne().prototype.broadcastTo=function(e){return this.throwIfDisposed(),rl(this,e)};ne().prototype.cast=function(e){return this.throwIfDisposed(),oe(this,e)};ne().prototype.ceil=function(){return this.throwIfDisposed(),Hx(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(),Om(this,e,t,n,a,r,s)};ne().prototype.conv2dTranspose=function(e,t,n,a,r){return this.throwIfDisposed(),Lm(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(),Ec(this)};ne().prototype.cosh=function(){return this.throwIfDisposed(),zm(this)};ne().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),Bm(this,e,t,n)};ne().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),Kx(this,e,t)};ne().prototype.depthwiseConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),ys(this,e,t,n,a,r,s)};ne().prototype.dilation2d=function(e,t,n,a,r){return this.throwIfDisposed(),Xx(this,e,t,n,a,r)};ne().prototype.divNoNan=function(e){return this.throwIfDisposed(),Yx(this,e)};ne().prototype.div=function(e){return this.throwIfDisposed(),fe(this,e)};ne().prototype.dot=function(e){return this.throwIfDisposed(),kS(this,e)};ne().prototype.elu=function(){return this.throwIfDisposed(),Su(this)};ne().prototype.equal=function(e){return this.throwIfDisposed(),Zn(this,e)};ne().prototype.erf=function(){return this.throwIfDisposed(),Jx(this)};ne().prototype.exp=function(){return this.throwIfDisposed(),fn(this)};ne().prototype.expandDims=function(e){return this.throwIfDisposed(),hn(this,e)};ne().prototype.expm1=function(){return this.throwIfDisposed(),Zx(this)};ne().prototype.fft=function(){return this.throwIfDisposed(),Rc(this)};ne().prototype.flatten=function(){return this.throwIfDisposed(),W(this,[this.size])};ne().prototype.floor=function(){return this.throwIfDisposed(),Nu(this)};ne().prototype.floorDiv=function(e){return this.throwIfDisposed(),Mm(this,e)};ne().prototype.gather=function(e,t){return this.throwIfDisposed(),ai(this,e,t)};ne().prototype.greaterEqual=function(e){return this.throwIfDisposed(),bs(this,e)};ne().prototype.greater=function(e){return this.throwIfDisposed(),Wn(this,e)};ne().prototype.ifft=function(){return this.throwIfDisposed(),cl(this)};ne().prototype.irfft=function(){return this.throwIfDisposed(),tf(this)};ne().prototype.isFinite=function(){return this.throwIfDisposed(),SS(this)};ne().prototype.isInf=function(){return this.throwIfDisposed(),NS(this)};ne().prototype.isNaN=function(){return this.throwIfDisposed(),ev(this)};ne().prototype.leakyRelu=function(e){return this.throwIfDisposed(),_c(this,e)};ne().prototype.lessEqual=function(e){return this.throwIfDisposed(),xs(this,e)};ne().prototype.less=function(e){return this.throwIfDisposed(),Um(this,e)};ne().prototype.localResponseNormalization=function(e,t,n,a){return this.throwIfDisposed(),tv(this,e,t,n,a)};ne().prototype.logSigmoid=function(){return this.throwIfDisposed(),ES(this)};ne().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Gm(this,e)};ne().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),rv(this,e,t)};ne().prototype.log=function(){return this.throwIfDisposed(),Qn(this)};ne().prototype.log1p=function(){return this.throwIfDisposed(),Ac(this)};ne().prototype.logicalAnd=function(e){return this.throwIfDisposed(),Na(this,e)};ne().prototype.logicalNot=function(){return this.throwIfDisposed(),$c(this)};ne().prototype.logicalOr=function(e){return this.throwIfDisposed(),Hm(this,e)};ne().prototype.logicalXor=function(e){return this.throwIfDisposed(),FS(this,e)};ne().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Fe(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(),Ia(this,e,t)};ne().prototype.maximum=function(e){return this.throwIfDisposed(),mr(this,e)};ne().prototype.mean=function(e,t){return this.throwIfDisposed(),_t(this,e,t)};ne().prototype.min=function(e,t){return this.throwIfDisposed(),qp(this,e,t)};ne().prototype.minimum=function(e){return this.throwIfDisposed(),Tu(this,e)};ne().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),iv(this,e,t)};ne().prototype.mod=function(e){return this.throwIfDisposed(),ov(this,e)};ne().prototype.mul=function(e){return this.throwIfDisposed(),B(this,e)};ne().prototype.neg=function(){return this.throwIfDisposed(),St(this)};ne().prototype.norm=function(e,t,n){return this.throwIfDisposed(),rf(this,e,t,n)};ne().prototype.notEqual=function(e){return this.throwIfDisposed(),si(this,e)};ne().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),ul(this,e,t,n)};ne().prototype.onesLike=function(){return this.throwIfDisposed(),ea(this)};ne().prototype.pad=function(e,t){return this.throwIfDisposed(),fa(this,e,t)};ne().prototype.pool=function(e,t,n,a,r,s){return this.throwIfDisposed(),MS(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(),Dc(this,e)};ne().prototype.prod=function(e,t){return this.throwIfDisposed(),qm(this,e,t)};ne().prototype.reciprocal=function(){return this.throwIfDisposed(),pv(this)};ne().prototype.relu=function(){return this.throwIfDisposed(),Xe(this)};ne().prototype.relu6=function(){return this.throwIfDisposed(),Km(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(),ZS(this,e,t,n)};ne().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),QS(this,e,t,n)};ne().prototype.reverse=function(e){return this.throwIfDisposed(),ta(this,e)};ne().prototype.rfft=function(){return this.throwIfDisposed(),Mc(this)};ne().prototype.round=function(){return this.throwIfDisposed(),Xm(this)};ne().prototype.rsqrt=function(){return this.throwIfDisposed(),Ym(this)};ne().prototype.selu=function(){return this.throwIfDisposed(),Jm(this)};ne().prototype.separableConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),ho(this,e,t,n,a,r,s)};ne().prototype.sigmoid=function(){return this.throwIfDisposed(),da(this)};ne().prototype.sign=function(){return this.throwIfDisposed(),cv(this)};ne().prototype.sin=function(){return this.throwIfDisposed(),Zm(this)};ne().prototype.sinh=function(){return this.throwIfDisposed(),Qm(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(),co(this)};ne().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Fc(this,e,t)};ne().prototype.split=function(e,t){return this.throwIfDisposed(),Ln(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(),nf(this,e)};ne().prototype.squeeze=function(e){return this.throwIfDisposed(),cr(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(),hv(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(),mv(this)};ne().prototype.tanh=function(){return this.throwIfDisposed(),ni(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(),fv(this,e,t)};ne().prototype.transpose=function(e){return this.throwIfDisposed(),Me(this,e)};ne().prototype.unique=function(e){return this.throwIfDisposed(),Dh(this,e)};ne().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),gv(this,e,t)};ne().prototype.unstack=function(e){return this.throwIfDisposed(),ht(this,e)};ne().prototype.where=function(e,t){return this.throwIfDisposed(),mn(e,this,t)};ne().prototype.zerosLike=function(){return this.throwIfDisposed(),Ke(this)};var o2={};Re(o2,{maxNorm:()=>b4,minMaxNorm:()=>w4,nonNeg:()=>v4,unitNorm:()=>x4});var ib;function Ht(){return ib==null&&(ib=iS().epsilon()),ib}function Ka(){return"channelsLast"}var wr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,wr.prototype)}},Wa=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Wa.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)}},l2=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,l2.prototype)}};function ii(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 sr(e,t){if(!e)throw new l2(t)}function Y1(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 xt(e){return Array.isArray(e)?e:[e]}function kr(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function Ws(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var xa={};function Iv(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function Fb(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>Fb(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:Fb(a))}}}function Oc(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 xa)i=xa[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 xa?[o,l]=xa.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(xa))u[h]=xa[h];for(let h of Object.keys(n))u[h]=n[h];let p=s.config;p.customObjects=u;let d=Object.assign({},xa);for(let h of Object.keys(n))xa[h]=n[h];Fb(s.config);let c=l(o,s.config,n,r);return xa=Object.assign({},d),c}else{let u=Object.assign({},xa);for(let d of Object.keys(n))xa[d]=n[d];let p=new o(s.config);return xa=Object.assign({},u),p}}}function f4(e,t){return e<t?-1:e>t?1:0}function eh(e,t){return-1*f4(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 g4(e){if(e==null)throw new H(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function mo(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new H(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`)}function Sv(e,t,n=0,a=1/0){return sr(n>=0),sr(a>=n),Array.isArray(e)&&e.length>=n&&e.length<=a&&e.every(r=>typeof r===t)}function tn(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${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 ${u2(e)}.`)}function u2(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>u2(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function y4(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 p2(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function Nv(e,t){return O(()=>ln(be(B(e,e),t,!0)))}var Lc=class extends se.Serializable{getConfig(){return{}}},Tv=class extends Lc{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=Nv(e,this.axis),n=nn(t,0,this.maxValue);return B(e,fe(n,J(Ht(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};Tv.className="MaxNorm";se.registerClass(Tv);var Cv=class extends Lc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return O(()=>fe(e,J(Ht(),Nv(e,this.axis))))}getConfig(){return{axis:this.axis}}};Cv.className="UnitNorm";se.registerClass(Cv);var Ev=class extends Lc{apply(e){return Xe(e)}};Ev.className="NonNeg";se.registerClass(Ev);var _v=class extends Lc{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=Nv(e,this.axis),n=J(B(this.rate,nn(t,this.minValue,this.maxValue)),B(1-this.rate,t));return B(e,fe(n,J(Ht(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};_v.className="MinMaxNorm";se.registerClass(_v);var J1={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Kt(e){return Iv(e)}function Z1(e,t={}){return Oc(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 Z1(t)}else return e instanceof Lc?e:Z1(e)}function b4(e){return new Tv(e)}function x4(e){return new Cv(e)}function v4(){return new Ev}function w4(e){return new _v(e)}var c2={};Re(c2,{constant:()=>G4,glorotNormal:()=>J4,glorotUniform:()=>Y4,heNormal:()=>Z4,heUniform:()=>Q4,identity:()=>K4,leCunNormal:()=>eU,leCunUniform:()=>tU,ones:()=>V4,orthogonal:()=>nU,randomNormal:()=>j4,randomUniform:()=>H4,truncatedNormal:()=>q4,varianceScaling:()=>X4,zeros:()=>U4});var k4=["channelsFirst","channelsLast"],I4=["nearest","bilinear"],S4=["valid","same","causal"],N4=["max","avg"],T4=["sum","mul","concat","ave"],Xo=new Map;function Ot(e){mo(k4,"DataFormat",e)}function C4(e){mo(I4,"InterpolationFormat",e)}function ga(e){mo(S4,"PaddingMode",e)}function d2(e){mo(N4,"PoolMode",e)}var Lp=[],Q1="/";function qs(e,t){Lp.push(e);try{let n=t();return Lp.pop(),n}catch(n){throw Lp.pop(),n}}function E4(){return Lp.length===0?"":Lp.join(Q1)+Q1}function h2(e){if(!f2(e))throw new Error("Not a valid tensor name: '"+e+"'");return E4()+e}function m2(e){if(!f2(e))throw new Error("Not a valid tensor name: '"+e+"'");Xo.has(e)||Xo.set(e,0);let t=Xo.get(e);if(Xo.set(e,Xo.get(e)+1),t>0){let n=`${e}_${t}`;return Xo.set(n,1),n}else return e}var _4=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function f2(e){return!!e.match(_4)}function A4(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 dl(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 Xa(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}function gf(e,t){return oe(e,t)}function zc(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 $4(e,t){return O(()=>{if(e.shape.length!==2)throw new H(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=zc(e,1);return Db(n,[1,t,1])})}function F4(e){let t=[Qr(e.shape)];return W(e,t)}function D4(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 Ks(e,t,n){return O(()=>{switch(e.rank){case 1:return ef(e,t,n);case 2:return dv(e,[t,0],[n,e.shape[1]]);case 3:return Eu(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Xp(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 ob(e,t,n){return O(()=>{switch(e.rank){case 1:return ef(e,t,n);case 2:return dv(e,[0,t],[e.shape[0],n]);case 3:return Eu(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Xp(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 th(e,t,n,a){return O(()=>{switch(e.rank){case 1:return ef(e,t,n);case 2:switch(a){case 1:return Ks(e,t,n);case 2:return ob(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 Ks(e,t,n);case 2:return Eu(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return ob(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 Ks(e,t,n);case 2:return Xp(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Xp(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return ob(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 Av(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 ek(e,t){switch(e.rank){case 1:return fS([e,t]);case 2:return gS([e,t],0);case 3:return yS([e,t],0);case 4:return bS([e,t],0);default:throw new H(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function Db(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new H(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return On(e,t)}function yf(e,t=0,n=1,a,r){return PS(e,t,n,a,r)}function lr(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?Rb(e.rank,a,Ka()):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(Me(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?Rb(e.rank,a,Ka()):null,activation:n}),d)}}function g2(e,t,n){return O(()=>(Array.isArray(t)?t=qe(t,"int32"):t=oe(t,"int32"),ai(e,t,n)))}function Bc(e){return B(e,e)}function Rb(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: ${t.rank}`)}function Qa(e,t,n){return O(()=>(n==null&&(n=Ka()),Ot(n),J(e,Rb(e.rank,t,n))))}function R4(e,t=1){if(t!==1)throw new Pe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Su(e)}function M4(e){return O(()=>fe(e,J(zt(e),1)))}function y2(e,t,n,a){return O(()=>VS(e,t,n,a))}function P4(e){return O(()=>{let t=J(.5,B(.2,e));return nn(t,0,1)})}function Wc(e,t,n=!1){return n?e():t()}var O4=["fanIn","fanOut","fanAvg"],L4=["normal","uniform","truncatedNormal"];function z4(e){mo(O4,"FanMode",e)}function B4(e){mo(L4,"Distribution",e)}var Ca=class extends se.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},$v=class extends Ca{apply(e,t){return kt(e,t)}};$v.className="Zeros";se.registerClass($v);var bf=class extends Ca{apply(e,t){return Xn(e,t)}};bf.className="Ones";se.registerClass(bf);var Fv=class extends Ca{constructor(e){super();if(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(()=>B(ke(this.value),Xn(e,t)))}getConfig(){return{value:this.value}}};Fv.className="Constant";se.registerClass(Fv);var Dv=class extends Ca{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 Cu(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};Dv.className="RandomUniform";se.registerClass(Dv);var Rv=class extends Ca{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 yf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Rv.className="RandomNormal";se.registerClass(Rv);var Mv=class extends Ca{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 af(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Mv.className="TruncatedNormal";se.registerClass(Mv);var Pv=class extends Ca{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 B(this.gain,Qx(e[0]))})}getConfig(){return{gain:this.gain}}};Pv.className="Identity";se.registerClass(Pv);function W4(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 Bn=class extends Ca{constructor(e){super();if(e.scale<0)throw new H(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,z4(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,B4(this.distribution),this.seed=e.seed}apply(e,t){let n=W4(e),a=n[0],r=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,a):this.mode==="fanOut"?s/=Math.max(1,r):s/=Math.max(1,(a+r)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Pe(`${this.getClassName()} does not support dType ${t}.`);return af(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return Cu(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Bn.className="VarianceScaling";se.registerClass(Bn);var xf=class extends Bn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Bn.className}};xf.className="GlorotUniform";se.registerClass(xf);var vf=class extends Bn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Bn.className}};vf.className="GlorotNormal";se.registerClass(vf);var wf=class extends Bn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Bn.className}};wf.className="HeNormal";se.registerClass(wf);var kf=class extends Bn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Bn.className}};kf.className="HeUniform";se.registerClass(kf);var If=class extends Bn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Bn.className}};If.className="LeCunNormal";se.registerClass(If);var Sf=class extends Bn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Bn.className}};Sf.className="LeCunNormal";se.registerClass(Sf);var Ov=class extends Ca{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Pe("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return O(()=>{if(e.length<2)throw new Pe("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,a=yf(n,0,1,"float32"),r=e2.gramSchmidt(a);return e[0]>e[1]&&(r=Me(r)),B(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Ov.className="Orthogonal";se.registerClass(Ov);var tk={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 nk(e,t={}){return Oc(e,se.SerializationMap.getMap().classNameMap,t,"initializer")}function At(e){return Iv(e)}function It(e){if(typeof e=="string"){let t=e in tk?tk[e]:e;if(t==="GlorotNormal")return new vf;if(t==="GlorotUniform")return new xf;if(t==="HeNormal")return new wf;if(t==="HeUniform")return new kf;if(t==="LeCunNormal")return new If;if(t==="LeCunUniform")return new Sf;{let n={};return n.className=t,n.config={},nk(n)}}else return e instanceof Ca?e:nk(e)}function U4(){return new $v}function V4(){return new bf}function G4(e){return new Fv(e)}function H4(e){return new Dv(e)}function j4(e){return new Rv(e)}function q4(e){return new Mv(e)}function K4(e){return new Pv(e)}function X4(e){return new Bn(e)}function Y4(e){return new xf(e)}function J4(e){return new vf(e)}function Z4(e){return new wf(e)}function Q4(e){return new kf(e)}function eU(e){return new If(e)}function tU(e){return new Sf(e)}function nU(e){return new Ov(e)}var b2={};Re(b2,{Layer:()=>Ye,RNN:()=>gr,RNNCell:()=>jc,activation:()=>LV,add:()=>qV,alphaDropout:()=>AG,average:()=>KV,averagePooling1d:()=>Hw,averagePooling2d:()=>jw,averagePooling3d:()=>qw,avgPool1d:()=>aG,avgPool2d:()=>sG,avgPool3d:()=>oG,avgPooling1d:()=>rG,avgPooling2d:()=>iG,avgPooling3d:()=>lG,batchNormalization:()=>eG,bidirectional:()=>kG,concatenate:()=>XV,conv1d:()=>_V,conv2d:()=>AV,conv2dTranspose:()=>$V,conv3d:()=>FV,conv3dTranspose:()=>DV,convLstm2d:()=>bG,convLstm2dCell:()=>xG,cropping2D:()=>MV,dense:()=>zV,depthwiseConv2d:()=>OV,dot:()=>QV,dropout:()=>BV,elu:()=>IV,embedding:()=>jV,flatten:()=>UV,gaussianDropout:()=>_G,gaussianNoise:()=>EG,globalAveragePooling1d:()=>uG,globalAveragePooling2d:()=>pG,globalMaxPool1d:()=>SG,globalMaxPool2d:()=>NG,globalMaxPooling1d:()=>hN,globalMaxPooling2d:()=>mN,gru:()=>dG,gruCell:()=>hG,input:()=>B2,inputLayer:()=>kV,layerNormalization:()=>tG,leakyReLU:()=>NV,lstm:()=>mG,lstmCell:()=>fG,masking:()=>$G,maxPool1d:()=>TG,maxPool2d:()=>CG,maxPooling1d:()=>fN,maxPooling2d:()=>gN,maxPooling3d:()=>cG,maximum:()=>YV,minimum:()=>JV,multiply:()=>ZV,permute:()=>HV,prelu:()=>TV,reLU:()=>SV,repeatVector:()=>VV,reshape:()=>GV,rnn:()=>vG,separableConv2d:()=>RV,simpleRNN:()=>gG,simpleRNNCell:()=>yG,softmax:()=>CV,spatialDropout1d:()=>WV,stackedRNNCells:()=>wG,thresholdedReLU:()=>EV,timeDistributed:()=>IG,upSampling2d:()=>PV,zeroPadding2d:()=>nG});var aU=0;function x2(){return aU++}var nh={};function Nf(e=""){return e in nh||(nh[e]=0),nh[e]+=1,e+nh[e].toString()}function Mb(e){return Array.isArray(e)&&Array.isArray(e[0])}function Rh(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function ze(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new 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 ak="Variable",v2=class{constructor(e,t="float32",n=ak,a=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=x2(),n=n==null?ak:n,this.originalName=h2(n),this.name=m2(this.originalName),this.trainable_=a,this.constraint=r,this.val=LS(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),rU(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 rU(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function Pb(e){return e.map(t=>t.read())}function Lv(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=x2(),s!=null&&(this.originalName=h2(s),this.name=m2(this.originalName)),this.rank=t.length}},sU=0,Tf=class{constructor(e,t){this.callArgs=t,this.id=sU++,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}}},iU=0,Ye=class extends se.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=iU++,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=kr(n)+"_"+Nf(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 Wa(`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 wr(`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 wr(`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 wr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new wr(`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=xt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=xt(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=xt(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 qs(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of xt(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=xt(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=oU(e),i=this.computeOutputShape(s),o,l=lU(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,xt(e),t,this.name,p)):o=new Ua(l,i,this,xt(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 wr(`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 wr(`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 Wa(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Mh(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Pb(e?this.trainableWeights:this.weights)}setWeights(e){O(()=>{let t=this.weights;if(t.length!==e.length)throw new H(`You called setWeights(weights) on layer "${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=Pb(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])}Lv(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 v2(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=xt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=xt(e);t=xt(t),n=xt(n),a=xt(a),r=Rh(r),s=Rh(s);let l=[],u=[],p=[];for(let d of o)l.push(d.sourceLayer),u.push(d.nodeIndex),p.push(d.tensorIndex);new Tf({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:p,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 oU(e){e=xt(e);let t=[];for(let n of e)t.push(n.shape);return Pn(t)}function lU(e){return"float32"}function w2(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=w2(i,o,l);for(let p of u)r.indexOf(p)===-1&&r.push(p)}return r}}}var $u=class extends Ye{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Nf("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new 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 Tf({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: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};$u.className="InputLayer";se.registerClass($u);function k2(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 $u({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}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];De(a)}}function I2(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var rk;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(rk||(rk={}));var uU=125,hl=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){}},S2=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)}},pU=class extends hl{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],B(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=B(fe(1,this.seen),this.totals[n]);t[n]=a,this.totals[n].dispose(),en(t[n])}))}},N2=class extends hl{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]}},T2=class extends hl{constructor(e,t){super();if(this.currentEpoch=0,this.nowFunc=e.nowFunc,this.nextFrameFunc=e.nextFrameFunc||t2,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=uU),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=y4(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 C2(e,t){return e==null&&(e={}),e instanceof hl?[e]:Array.isArray(e)&&e[0]instanceof hl?e:xt(e).map(n=>new T2(n,t))}var wa=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}`),wa.checkForDuplicate(t),wa.constructors[e]==null&&(wa.constructors[e]=[]),wa.constructors[e].push(t)}static checkForDuplicate(e){for(let t in wa.constructors)wa.constructors[+t].forEach(n=>{if(n===e)throw new H("Duplicate callback constructor.")})}static clear(){wa.constructors={}}static createCallbacks(e){let t=[];for(let n in wa.constructors){let a=+n;e>=a&&t.push(...wa.constructors[a])}return t.map(n=>new n)}};wa.constructors={};function E2(e,t,n,a,r,s,i,o,l){let u=new N2,p=[new pU,...wa.createCallbacks(t)];e!=null&&p.push(...e),p.push(u);let d=new S2(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 Ha(e,t={},n=!1){return Oc(e,se.SerializationMap.getMap().classNameMap,t,"layer",n)}function Ph(e,t){return O(()=>{e.dtype!=="float32"&&(e=oe(e,"float32"));let n=be(Bc(e),t,!0),a=Cn(n.shape,Ht()),r=ln(mr(n,a));return fe(e,r)})}function fo(e,t){return O(()=>_t(Bc(ce(t,e)),-1))}function Cf(e,t){return O(()=>_t(zt(ce(t,e)),-1))}function Fu(e,t){return O(()=>{let n=ce(e,t),a=nn(zt(e),Ht(),Number.MAX_VALUE),r=zt(fe(n,a));return B(100,_t(r,-1))})}function cU(e,t){return O(()=>{let n=nn(t,Ht(),Number.MAX_VALUE),a=Qn(J(1,n)),r=nn(e,Ht(),Number.MAX_VALUE),s=Qn(J(1,r));return _t(Bc(ce(a,s)),-1)})}function dU(e,t){return O(()=>{let n=mr(0,ce(1,B(e,t)));return _t(Bc(n),-1)})}function hU(e,t){return O(()=>{let n=mr(0,ce(1,B(e,t)));return _t(n,-1)})}function mU(e,t){return O(()=>{let n=be(B(e,t),-1),a=Ia(B(ce(1,e),t),-1);return mr(0,J(1,ce(a,n)))})}function fU(e,t){return O(()=>{let n=Math.log(2),a=ce(t,e),r=ce(J(a,co(B(-2,a))),n);return _t(r,-1)})}function Yp(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(B(oe(e,"float32"),Qn(t)),t.shape.length-1))})}function Oh(e,t,n=!1){return O(()=>{let a=oe(Nu(F4(e)),"int32");t=nn(t,Ht(),1-Ht());let r=t.shape,s=W(ul(a,r[r.length-1]),r);return Yp(s,t,n)})}function gU(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,B(t,e)),Ac(fn(a)))})}function Ef(e,t){return O(()=>{let n;return n=nn(t,Ht(),1-Ht()),n=Qn(fe(n,ce(1,n))),_t(gU(e,n),-1)})}function yU(e,t){return O(()=>{let n=nn(e,Ht(),1),a=nn(t,Ht(),1);return be(B(e,Qn(fe(n,a))),-1)})}function bU(e,t){return O(()=>{let n=Qn(J(Ht(),t));return _t(ce(t,B(e,n)),-1)})}function zv(e,t){return O(()=>{let n=Ph(e,-1),a=Ph(t,-1),r=B(n,a);return St(be(r,-1))})}var Lh={meanSquaredError:fo,meanAbsoluteError:Cf,meanAbsolutePercentageError:Fu,meanSquaredLogarithmicError:cU,squaredHinge:dU,hinge:hU,categoricalHinge:mU,logcosh:fU,categoricalCrossentropy:Yp,sparseCategoricalCrossentropy:Oh,binaryCrossentropy:Ef,kullbackLeiblerDivergence:yU,poisson:bU,cosineProximity:zv};function lb(e){if(typeof e=="string"){if(e in Lh)return Lh[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 Bv(e,t){return O(()=>{let n=B(.5,ea(t)),a=gf(Wn(t,n),e.dtype);return _t(Zn(e,a),-1)})}function Wv(e,t){return O(()=>gf(Zn(ti(e,-1),ti(t,-1)),"float32"))}function _2(e,t){return O(()=>oe(be(Na(Zn(e,1),Zn(t,1))),"float32"))}function xU(e,t){return O(()=>oe(be(Na(Zn(e,1),Zn(t,0))),"float32"))}function vU(e,t){return O(()=>oe(be(Na(Zn(e,0),Zn(t,1))),"float32"))}function A2(e,t){return O(()=>{let n=_2(e,t),a=vU(e,t),r=J(n,a);return oe(mn(Wn(r,0),fe(n,r),0),"float32")})}function wU(e,t){return O(()=>{let n=_2(e,t),a=xU(e,t),r=J(n,a);return oe(mn(Wn(r,0),fe(n,r),0),"float32")})}function $2(e,t){return Ef(e,t)}function F2(e,t){return e.rank===t.rank&&(e=cr(e,[e.rank-1])),t=ti(t,-1),t.dtype!==e.dtype&&(t=oe(t,e.dtype)),oe(Zn(e,t),"float32")}var kU=fo,IU=fo,SU=Cf,NU=Cf,TU=Fu,CU=Fu,Uv=Yp,EU=zv,D2=Oh,zh={binaryAccuracy:Bv,categoricalAccuracy:Wv,precision:A2,categoricalCrossentropy:Uv,sparseCategoricalCrossentropy:D2,mse:kU,MSE:IU,mae:SU,MAE:NU,mape:TU,MAPE:CU,cosine:EU};function _U(e){if(typeof e=="string"&&e in zh)return zh[e];if(typeof e!="string"&&e!=null)return e;throw new H(`Unknown metric ${e}`)}function ah(e){if(sr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Lh))if(Lh[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(zh))if(zh[n]===e){t=n;break}return t!==void 0?t:e.name}}function AU(e){let t={Adagrad:()=>Ls.adagrad(.01),Adadelta:()=>Ls.adadelta(1,.95,Ht()),Adam:()=>Ls.adam(.001,.9,.999,Ht()),Adamax:()=>Ls.adamax(.002,.9,.999,Ht(),0),RMSProp:()=>Ls.rmsprop(.001,.9,0,Ht()),SGD:()=>Ls.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 ${e}`)}function sk(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!Ob(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let a=JSON.stringify(e);a.length>1048576&&console.warn(`User-defined metadata of model "${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 Ob(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"||!Ob(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!Ob(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function $U(e,t,n,a=console.log){let r=DU(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)),Bh(s,n,a),a("=".repeat(t));let o=e.layers;for(let p=0;p<o.length;++p)r?RU(o[p],n,a):MU(o[p],n,i,a),a((p===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=FU(e),u=Mh(e.nonTrainableWeights);a(`Total params: ${l+u}`),a(`Trainable params: ${l}`),a(`Non-trainable params: ${u}`),a("_".repeat(t))}function FU(e){let t;return e.collectedTrainableWeights!=null?t=Mh(e.collectedTrainableWeights):t=Mh(e.trainableWeights),t}function DU(e){let t=!0,n=[],a=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}a.push(...r)}if(t)for(let r of e.layers){let s=!1;for(let i of r.inboundNodes)if(a.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function Bh(e,t,n=console.log){let a="";for(let r=0;r<e.length;++r)r>0&&(a=a.slice(0,a.length-1)+" "),a+=e[r],a=a.slice(0,t[r]),a+=" ".repeat(t[r]-a.length);n(a)}function RU(e,t,n){let a,r;try{r=e.inboundNodes.map(l=>JSON.stringify(l.inputShapes)).join(",")}catch(l){r="multiple"}try{a=JSON.stringify(e.outputShape)}catch(l){a="multiple"}let s=e.name,i=e.getClassName(),o=[`${s} (${i})`,r,a,e.countParams().toString()];Bh(o,t,n)}function MU(e,t,n,a){let r,s;try{s=e.inboundNodes.map(d=>JSON.stringify(d.inputShapes)).join(",")}catch(d){s="multiple"}try{r=JSON.stringify(e.outputShape)}catch(d){r="multiple"}let i=[];for(let d of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(d)===-1))for(let c=0;c<d.inboundLayers.length;++c){let h=d.inboundLayers[c].name,m=d.nodeIndices[c],f=d.tensorIndices[c];i.push(`${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];Bh(p,t,a);for(let d=1;d<i.length;++d)Bh(["","","","",i[d]],t,a)}function R2(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Jp(e,t){if(e===null)return null;if(typeof e=="string")return Ws(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];R2(t,r,s)?n.push(s):n.push(Jp(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=Ws(a);n[s]=Jp(r,s)}}return n}}function Lb(e,t){if(e==null)return null;if(typeof e=="string")return kr(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];R2(t,r,s)?n.push(s):n.push(Lb(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a],s=kr(a);(a==="name"||a==="className")&&typeof r=="string"?n[s]=r:n[s]=Lb(r,a)}return n}}var Vv="3.14.0";function PU(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}' (${e.dtype}).`)}}var Gs=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Gs)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=PU(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new H(`Duplicate key: name=${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&&De(this.id2Mask)}},ub={},ik={};function Ap(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,c;if(ub[p]==null){let m=OU(i,t);d=m.sorted,c=m.recipientCounts,ub[p]=d,ik[p]=c}d=ub[p],c={},r||Object.assign(c,ik[p]);let h=new Gs(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 $u)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=xt(g.apply(y,n)),T=null;g.supportsMasking&&(T=g.computeMask(y,b));let C=zU(f),_=Array.isArray(C)?C:[C];for(let $=0;$<_.length;++$){h.hasKey(_[$])||h.add(_[$],w[$],Array.isArray(T)?T[0]:T);let P=o.indexOf(_[$].name);P!==-1&&(l[P]=w[$])}r||De(x)}return h.disposeMasks(),s?l:l[0]}function OU(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=ok(e[0],t);n=r.sorted,a=r.recipientMap}else{let r=new Set;for(let s of e){let{sorted:i,recipientMap:o}=ok(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:LU(a)}}function LU(e){let t={};for(let n in e)t[n]=e[n].size;return t}function ok(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 zU(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 ar=class extends Ye{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Nf(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;sr(x===0,"input layer has >1 nodes"),sr(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 $u))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 Wa(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(ar.nodeKey(v,w)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(C)===-1&&x.push(C);let _=C.inboundLayers.length;for(let $=0;$<_;$++){let P=C.inputTensors[$],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],_=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,_),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 ar&&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 Wa(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of b.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(x=>x===y).length;if(b!==1)throw new Wa(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Tf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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}`)}Lv(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Vv}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Lb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return O(()=>{e=xt(e);let n=new Gs;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Ap(this.outputs,n,t)})}computeMask(e,t){return O(()=>{e=xt(e);let n;return t==null?n=ii(null,e.length):n=xt(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];sr(o in n),r.push(n[o])}return Pn(r)}runInternalGraph(e,t){t==null&&(t=ii(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=xt(p.call(x,m)),b=xt(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=xt(p.call(f,m)),b=xt(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){sr(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 ar?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=ar.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=ar.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=ar.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=ar.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=ar.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=ar.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 _=C.inboundNodes[w];y.push(_.outputTensors[T])}y.length>0&&f.apply(Pn(y),b)}function l(f){let g=f.name,y=Ha(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(;!g4(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];sr(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];sr(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 BU(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: ${JSON.stringify(e)}`)}function M2(e,t){return BU(e,t,"classWeight")}async function P2(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=O(()=>{if(e.shape.length===1)return Tr(e);if(e.shape.length===2){if(e.shape[1]>1)return ti(e,1);if(e.shape[1]===1)return W(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());De(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 WU(e,t){return B(e,t)}var UU=32;function O2(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=lk("input",e.inputNames,n),i=lk("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 lk(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 VU(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 GU(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(uk(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=VU(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=C2(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=E2(p,d,n.epochs,null,null,HU(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, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:v,ys:w}=O2(e,x.value),T={};T.batch=b,T.size=v[0].shape[0],await c.onBatchBegin(b,T);let C=[];if(n.classWeight!=null){let P=M2(n.classWeight,e.outputNames);for(let F=0;F<P.length;++F)C.push(await P2(w[F],null,P[F]))}let _=v.concat(w).concat(C),$=o(_);De(_);for(let P=0;P<l.length;++P){let F=l[P],S=$[P];T[F]=S,en(S)}await c.onBatchEnd(b,T),I2(T),b++,y++}if(a?y>=n.batchesPerEpoch:x.done){if(r){let v;uk(n.validationData)?v=xt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):v=xt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?UU:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${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 HU(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function uk(e){return typeof e.iterator=="function"}function jU(e){return typeof e.next=="function"}async function qU(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=jU(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}=O2(e,u.value),c=p.concat(d),h=O(()=>r(c));if(De(c),l===0)for(let f=0;f<h.length;++f)s.push(ke(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],B(m,g))),l>0&&De(y)}De(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),De(p)}return Pn(s)}function zb(e){k.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function $p(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(a=>Ks(a,t,n-t)):Ks(e,t,n-t)}function Gv(e,t){return O(()=>e==null?null:Array.isArray(e)?e.map(n=>Gv(n,t)):g2(e,t.dtype==="int32"?t:oe(t,"int32")))}function Bb(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 KU(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=Xa(0,g)),i==null&&(i=1);let{callbackList:b,history:x}=E2(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=Bb(g,r);for(let _=0;_<C.length;++_){let $={};if(await b.onBatchBegin(_,$),O(()=>{let P=C[_][0],F=C[_][1],S=Ks(T,P,F-P);$.batch=_,$.size=F-P;let M=Gv(n,S),V=t(M);for(let j=0;j<a.length;++j){let q=a[j],K=V[j];$[q]=K,en(K)}if(_===C.length-1&&f){let j=e.testLoop(l,u,r);for(let q=0;q<a.length;++q){let K=a[q],Q=j[q];en(Q),w["val_"+K]=Q}}}),await b.onBatchEnd(_,$),I2($),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 XU(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;zb(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; ${a.validationData} is invalid.`);let _=!0,$=await e.standardizeUserData(l,u,null,null,_,h);p=$[0],d=$[1],y=p.concat(d)}else if(a.validationSplit!=null&&a.validationSplit>0&&a.validationSplit<1){g=!0;let _=Math.floor(r[0].shape[0]*(1-a.validationSplit)),$=r[0].shape[0];p=$p(r,_,$),i=r,r=$p(r,0,_),d=$p(s,_,$),o=s,s=$p(s,0,_),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(_=>"val_"+_))):(w=null,y=[],T=v.slice());let C=C2(a.callbacks,a.yieldEvery);return await KU(e,x,b,v,h,a.epochs,a.verbose,C,w,y,a.shuffle,T,a.initialEpoch,null,null)}finally{e.isTraining=!1,Ba(r,t),Ba(s,n),Ba(i,t),Ba(o,n),Ba(p,l),Ba(d,u),c!=null&&De(c)}}function L2(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(zc(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 Ba(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 YU(e){return e instanceof Ae}function Wb(e){return Array.isArray(e)}function pk(e){return!YU(e)&&!Wb(e)}function ck(e,t,n,a=!0,r=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(Wb(e)&&e.length>0)i=!0;else if(pk(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 ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(pk(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new H(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(Wb(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=L2(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 JU(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 ZU(e,t,n){let a=[fo,Ef,Yp];for(let r=0;r<e.length;++r){let s=e[r],i=t[r],o=n[r];if(i!=null){if(i===Yp&&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 dk(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 QU(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: ${e}`);if(Array.isArray(n))return t.map(a=>n);{let a=[];for(let r of t){let s=n.hasOwnProperty(r)?n[r]:[];Array.isArray(s)||(s=[s]),a.push(s)}return a}}var eV="layers-model",Cr=class extends ar{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new H("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");$U(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=AU(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof $r))throw new H("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new H(`Unknown entry in loss dictionary: "${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(lb(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=>lb(s))}else{let s=lb(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=[],qs("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=QU(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])};qs("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=Bv:["crossentropy","ce"].indexOf(c)!==-1&&(p=$2):this.lossFunctions[s]===Oh?["accuracy","acc"].indexOf(c)!==-1?p=F2:["crossentropy","ce"].indexOf(c)!==-1&&(p=D2):["accuracy","acc"].indexOf(c)!==-1?p=Wv:["crossentropy","ce"].indexOf(c)!==-1&&(p=Uv);let f;["accuracy","acc"].indexOf(c)!==-1?f="acc":["crossentropy","ce"].indexOf(c)!==-1&&(f="ce"),d=p,u=l+f}else d=_U(c),u=l+ah(c);let h;qs(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;zb(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{Ba(s[0],e),Ba(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),qU(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 ${a} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new H("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),a=n?t:[t],r=this.retrieveSymbolicTensors(a),s=new Gs;if(e instanceof Ae&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new H(`The number of inputs provided (${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=Ap(r,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=ii(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): ${JSON.stringify(a)}`)}return t}predictLoop(e,t=32,n=!1){return O(()=>{let a=this.checkNumSamples(e);if(n)throw new Pe("Verbose predictLoop() is not implemented yet.");let r=Bb(a,t),s=this.outputs.map(i=>[]);for(let i=0;i<r.length;++i)O(()=>{let o=r[i][0],l=r[i][1],u=$p(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 Gs(p);return Ap(this.outputs,d)}).forEach((o,l)=>s[l].push(o));return Pn(s.map(i=>Qe(i,0)))})}predict(e,t={}){let n=L2(e);dk(n,this.inputNames,this.feedInputShapes,!1);try{let a=t.batchSize==null?32:t.batchSize;return zb(a),this.predictLoop(n,a)}finally{Ba(n,e)}}predictOnBatch(e){dk(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 Wa("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]===Oh?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=ck(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=ck(t,this.feedOutputNames,r,!1,"target"),JU(e,t,null),ZU(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: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,a,r=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,r,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(a!=null){let u=M2(a,this.outputNames);l=[];for(let p=0;p<u.length;++p)l.push(await P2(o[p],null,u[p]))}return[i,o,l]}testLoop(e,t,n,a=0,r){return O(()=>{let s=this.checkNumSamples(t,n,r,"steps"),i=[];if(a>0)throw new Pe("Verbose mode is not implemented yet.");if(r!=null)throw new Pe("steps mode in testLoop() is not implemented yet");{let o=Bb(s,n),l=qe(Xa(0,s));for(let u=0;u<o.length;++u){let p=o[u][0],d=o[u][1],c=Ks(l,p,d-p),h=Gv(t,c),m=e(h);if(u===0)for(let f=0;f<m.length;++f)i.push(ke(0));for(let f=0;f<m.length;++f){let g=m[f];i[f]=J(i[f],B(d-p,g))}}for(let u=0;u<i.length;++u)i[u]=fe(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let a=e[n],r=a;Y1(e,a)>1&&(r+=`_${Y1(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 Gs(u),d=Ap(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=WU(m,r[h]));let f=_t(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=_t(f(a[g],d[g]))}en(m),s.push(m)}return c=_t(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 Gs(s),o=Ap(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],p=_t(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=_t(u(r[p],o[p]));t.push(d)}return t})}async fit(e,t,n={}){return XU(this,e,t,n)}async fitDataset(e,t){return GU(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 De(s),Ba(n[0],e),Ba(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=kr(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=>kr(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]=kr(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[kr(ah(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>kr(ah(e)));{let e={};for(let t in this.metrics)e[t]=kr(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=Jp(e.optimizer_config),n=Ha(t),a;if(typeof e.loss=="string")a=Ws(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Ws(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Ws(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Ws(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Ws(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 '${e}'`);e=i[0]}if(e.save==null)throw new H("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Qt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:eV,generatedBy:`TensorFlow.js tfjs-layers v${Vv}`,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&&(sk(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){sk(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Cr.className="Model";se.registerClass(Cr);var z2=class extends Cr{};z2.className="Functional";se.registerClass(z2);async function tV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=Jp(n),r=Ha(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),De(s)}return r}async function nV(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 aV(e,void 0,t)}async function aV(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=Ha(Jp(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}=rV(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&p.length>0&&await o.optimizer.setWeights(p),De(u),De(p.map(d=>d.tensor))}return o}function rV(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 ml=class extends Cr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Nf("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 ml||e instanceof Cr,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=k2({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=w2(this.outputs[0])}this.inboundNodes=[],new Tf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ii(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 Cr({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 Wa("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 Wa("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 Wa("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 Wa("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 ml))throw new Pe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ha(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}}};ml.className="Sequential";se.registerClass(ml);function sV(e){return new Cr(e)}function iV(e){return new ml(e)}function oV(e,t){return t==null&&(t={}),nV(e,t)}function B2(e){return k2(e)}function lV(e,t){wa.registerCallbackConstructor(e,t)}var Un=class extends se.Serializable{getConfig(){return{}}},W2=class extends Un{apply(e,t=1){return R4(e,t)}};W2.className="elu";se.registerClass(W2);var U2=class extends Un{apply(e){return Jm(e)}};U2.className="selu";se.registerClass(U2);var V2=class extends Un{apply(e){return Xe(e)}};V2.className="relu";se.registerClass(V2);var G2=class extends Un{apply(e){return O(()=>Tu(6,Xe(e)))}};G2.className="relu6";se.registerClass(G2);var H2=class extends Un{apply(e){return e}};H2.className="linear";se.registerClass(H2);var j2=class extends Un{apply(e){return da(e)}};j2.className="sigmoid";se.registerClass(j2);var q2=class extends Un{apply(e){return P4(e)}};q2.className="hardSigmoid";se.registerClass(q2);var K2=class extends Un{apply(e){return co(e)}};K2.className="softplus";se.registerClass(K2);var X2=class extends Un{apply(e){return M4(e)}};X2.className="softsign";se.registerClass(X2);var Y2=class extends Un{apply(e){return ni(e)}};Y2.className="tanh";se.registerClass(Y2);var Hv=class extends Un{apply(e,t=-1){return Ja(e,t)}};Hv.className="softmax";se.registerClass(Hv);var J2=class extends Un{apply(e,t=-1){return Gm(e,t)}};J2.className="logSoftmax";se.registerClass(J2);var Z2=class extends Un{apply(e,t=1){return O(()=>B(da(B(e,t)),e))}};Z2.className="swish";se.registerClass(Z2);var Q2=class extends Un{apply(e){return O(()=>B(e,ni(co(e))))}};Q2.className="mish";se.registerClass(Q2);function is(e){return e.getClassName()}function pb(e,t={}){return Oc(e,se.SerializationMap.getMap().classNameMap,t,"activation")}function os(e){if(e==null){let t={};return t.className="linear",t.config={},pb(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},pb(t)}else return e instanceof Un?e:pb(e)}function jv(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 eN=class extends se.Serializable{},Uc=class extends eN{constructor(e){super();jv(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(B(this.l1,zt(e))))),this.hasL2&&(t=J(t,be(B(this.l2,Bc(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Uc.className="L1L2";se.registerClass(Uc);function uV(e){return jv(e),new Uc({l1:e!=null?e.l1:null,l2:0})}function pV(e){return jv(e),new Uc({l2:e!=null?e.l2:null,l1:0})}var hk={l1l2:"L1L2"};function ct(e){return Iv(e)}function mk(e,t={}){return Oc(e,se.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in hk?hk[e]:e,config:{}};return mk(t)}else return e instanceof eN?e:mk(e)}var qv=class extends Ye{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(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}};qv.className="ReLU";se.registerClass(qv);var Kv=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=ze(e);return _c(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Kv.className="LeakyReLU";se.registerClass(Kv);var Xv=class extends Ye{constructor(e){super(e==null?{}:e);if(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 ${e.sharedAxes}`)}build(e){e=it(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new Bt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=ze(e),Dc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:At(this.alphaInitializer),alphaRegularizer:ct(this.alphaRegularizer),alphaConstraint:Kt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Xv.className="PReLU";se.registerClass(Xv);var Yv=class extends Ye{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Pe(`Non-default alpha value (${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=ze(e);return Su(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Yv.className="ELU";se.registerClass(Yv);var Jv=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=ze(e);return B(n,oe(Wn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Jv.className="ThresholdedReLU";se.registerClass(Jv);var Zv=class extends Ye{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Hv().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=ze(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Zv.className="Softmax";se.registerClass(Zv);function sl(e,t,n){if(typeof e=="number")return ii(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(!A4(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 ja(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 ir(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 Qv(e,t){return O(()=>(Ot(t),t==="channelsFirst"?Me(e,[0,2,3,1]):e))}function tN(e,t){return O(()=>(Ot(t),t==="channelsFirst"?Me(e,[0,2,3,4,1]):e))}function cV(e,t,n,a=1,r="valid",s,i=1){return O(()=>{if(s==null&&(s=Ka()),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=Me(e,[0,2,1])),r==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Om(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Qa(o,n)),o})}function fk(e,t,n,a=[1,1],r="valid",s,i,o=null){return O(()=>{if(s==null&&(s=Ka()),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=Qv(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=Me(l,[0,3,1,2])),l})}function dV(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ka()),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 ${e.rank}.`);let o=tN(e,s);if(r==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=qx(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Qa(o,n)),s==="channelsFirst"&&(o=Me(o,[0,4,1,2,3])),o})}var ew=class extends Ye{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ew.verifyArgs(t),this.rank=e,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=sl(t.kernelSize,e,"kernelSize"),this.strides=sl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ga(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=sl(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(sr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Sv(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:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Kt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Vc=class extends ew{constructor(e,t){super(e,t);this.kernel=null,Vc.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=ze(e);let n,a=this.bias==null?null:this.bias.read(),r=p2(this.activation.getClassName());if(r!=null&&this.rank===2)n=fk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=cV(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=fk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=dV(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=ja(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:ct(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)}`)}},Gc=class extends Vc{constructor(e){super(2,e);Gc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Sv(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)}.`)}};Gc.className="Conv2D";se.registerClass(Gc);var Hc=class extends Vc{constructor(e){super(3,e);Hc.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)}.`)}};Hc.className="Conv3D";se.registerClass(Hc);var tw=class extends Gc{constructor(e){super(e);if(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 ${this.padding}`)}build(e){if(e=it(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=ze(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=ir(o,d,u,this.padding),m=ir(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,1]));let g=Lm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Me(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]=ir(t[a],o,s,this.padding),t[r]=ir(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};tw.className="Conv2DTranspose";se.registerClass(tw);var nw=class extends Hc{constructor(e){super(e);if(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 ${this.padding}`)}build(e){if(e=it(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=ze(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=ir(l,m,d,this.padding),b=ir(u,f,c,this.padding),x=ir(p,g,h,this.padding),v=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,4,1]));let w=vS(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Me(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]=ir(t[a],u,i,this.padding),t[r]=ir(t[r],p,o,this.padding),t[s]=ir(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};nw.className="Conv3DTranspose";se.registerClass(nw);var nN=class extends Vc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new 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=ze(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=Me(e,[0,2,3,1])),n=ho(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=Me(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=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Kt(this.depthwiseConstraint),e.pointwiseConstraint=Kt(this.pointwiseConstraint),e}};nN.className="SeparableConv";var aw=class extends nN{constructor(e){super(2,e)}};aw.className="SeparableConv2D";se.registerClass(aw);var _f=class extends Vc{constructor(e){super(1,e);_f.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"&&!Sv(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)}.`)}};_f.className="Conv1D";se.registerClass(_f);var rw=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=ze(e),this.dataFormat==="channelsLast"){let n=th(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return th(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=th(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return th(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}};rw.className="Cropping2D";se.registerClass(rw);var sw=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,C4(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=ze(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Me(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Yn.resizeNearestNeighbor(n,[r,s]):Yn.resizeBilinear(n,[r,s]);return Me(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Yn.resizeNearestNeighbor(n,[r,s]):Yn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};sw.className="UpSampling2D";se.registerClass(sw);function hV(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ka()),Ot(r);let i=Qv(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=ys(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Me(i,[0,3,1,2])),i})}var iw=class extends ew{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=ze(e);let n=hV(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=ja(t,this.kernelSize[0],this.padding,this.strides[0]),s=ja(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=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Kt(this.depthwiseRegularizer),e}};iw.className="DepthwiseConv2D";se.registerClass(iw);function aN(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 rN(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 ${l}D.`);let u=[1,0].concat(Xa(2,l));if(t=Me(t,u),s!=null)throw new Pe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=oe(oe(r,"bool"),"float32"),r.rank===l-1&&(r=hn(r,-1)),r=Me(r,u)),a&&(t=ta(t,0),r!=null&&(r=ta(r,0)));let p=[],d,c=n,h=t.shape[0],m=ht(t),f;r!=null&&(f=ht(r));for(let y=0;y<h;++y){let b=m[y],x=O(()=>e(b,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let w=f[y],T=ce(ea(w),w),C=J(B(x[0],w),B(c[0],T)),_=c.map(($,P)=>J(B(x[1][P],w),B($,T)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Mt(p,1)),[d,g,c]})}var gr=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 Ff({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 Xa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Mb(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.");Mb(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 wr("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)De(this.states_),this.keptStates!=null&&(De(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()):De(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=aN(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=ze(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=rN((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=zc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Db(t,[1,n]):t):this.cell.stateSize>1?[Db(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()===gr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ha(a,n);return new e(Object.assign(t,{cell:r}))}};gr.className="RNN";se.registerClass(gr);var jc=class extends Ye{},Af=class extends jc{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=dl([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=dl([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:()=>ea(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ea(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=lr(B(e,s),this.kernel.read()):r=lr(e,this.kernel.read()),this.bias!=null&&(r=Qa(r,this.bias.read())),i!=null&&(n=B(n,i));let o=J(r,lr(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:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(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)}};Af.className="SimpleRNNCell";se.registerClass(Af);var ow=class extends gr{constructor(e){e.cell=new Af(e);super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(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)}};ow.className="SimpleRNN";se.registerClass(ow);var $f=class extends jc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new 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=dl([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=dl([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 ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>ea(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ea(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=B(e,r[0]));let u=lr(e,this.kernel.read());this.useBias&&(u=Qa(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,s[0]));let p=this.recurrentKernel.read(),[d,c]=Ln(p,[2*this.units,this.units],p.rank-1),h=lr(a,d),[m,f,g]=Ln(u,3,u.rank-1),[y,b]=Ln(h,2,h.rank-1);i=this.recurrentActivation.apply(J(m,y)),o=this.recurrentActivation.apply(J(f,b));let x=lr(B(o,a),c);l=this.activation.apply(J(g,x));let v=J(B(i,a),B(J(1,St(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:is(this.activation),recurrentActivation:is(this.recurrentActivation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),recurrentConstraint:Kt(this.recurrentConstraint),biasConstraint:Kt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};$f.className="GRUCell";se.registerClass($f);var lw=class extends gr{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 $f(e);super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(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)}};lw.className="GRU";se.registerClass(lw);var qc=class extends jc{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=dl([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=dl([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 Ca{apply(i,o){let l=r.apply([s]),u=new bf().apply([s]),p=r.apply([s*2]);return ek(ek(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:()=>ea(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ea(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=B(e,s[0]));let d=lr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,i[0])),d=J(d,lr(a,this.recurrentKernel.read())),this.useBias&&(d=Qa(d,this.bias.read()));let[c,h,m,f]=Ln(d,4,d.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),u=J(B(l,r),B(o,this.activation.apply(m))),p=this.recurrentActivation.apply(f);let g=B(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:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(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)}};qc.className="LSTMCell";se.registerClass(qc);var uw=class extends gr{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 qc(e);super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(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)}};uw.className="LSTM";se.registerClass(uw);var Ff=class extends jc{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){Mb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{qs(`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(Ha(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 Pb(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]])}Lv(t)}};Ff.className="StackedRNNCells";se.registerClass(Ff);function ls(e){let{ones:t,rate:n,training:a=!1,count:r=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),n):y2(t(),n),o=()=>Wc(i,t,a);return!r||r<=1?en(o().clone()):Array(r).fill(void 0).map(o).map(l=>en(l.clone()))}var mV=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},sN=class extends gr{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&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(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 wr("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)De(this.states_),this.keptStates!=null&&(De(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()):De(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=ja(l,a[0],r,s[0],i[0]),d=ja(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};sN.className="ConvRNN2D";var Df=class extends qc{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=sl(n,2,"kernelSize"),this.kernelSize.forEach(o=>tn(o,"kernelSize")),this.strides=sl(a||1,2,"strides"),this.strides.forEach(o=>tn(o,"strides")),this.padding=r||"valid",ga(this.padding),this.dataFormat=s||"channelsLast",Ot(this.dataFormat),this.dilationRate=sl(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 Ca{apply(p,d){let c=l.apply([u]),h=Xn([u]),m=l.apply([u*2]);return Av([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 ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>ea(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(ee,re,Z)=>!re||!re[Z]?ee:B(re[Z],ee),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>ea(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,w,T]=Ln(this.kernel.read(),i,b),[C,_,$,P]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,w,$,this.padding),c=this.inputConv(c,T,P,this.padding);let[F,S,M,V]=Ln(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(B(q,s),B(j,this.activation.apply(J(d,g)))),Q=B(this.recurrentActivation.apply(J(c,y)),this.activation.apply(K));return[Q,Q,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=mV(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")}};Df.className="ConvLSTM2DCell";se.registerClass(Df);var pw=class extends sN{constructor(e){let t=new Df(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};pw.className="ConvLSTM2D";se.registerClass(pw);var Rf=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=ze(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wc(()=>y2(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()}};Rf.className="Dropout";se.registerClass(Rf);var cw=class extends Rf{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};cw.className="SpatialDropout1D";se.registerClass(cw);var dw=class extends Ye{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,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=ze(e),a=p2(this.activation.getClassName()),r;return a!=null?r=lr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=lr(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:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),biasConstraint:Kt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};dw.className="Dense";se.registerClass(dw);var hw=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=ze(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=Me(n,a)}return D4(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};hw.className="Flatten";se.registerClass(hw);var mw=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=ze(e);return this.activation.apply(n)})}getConfig(){let e={activation:is(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};mw.className="Activation";se.registerClass(mw);var fw=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=ze(e),$4(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};fw.className="RepeatVector";se.registerClass(fw);var gw=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=ze(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}};gw.className="Reshape";se.registerClass(gw);var yw=class extends Ye{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Xa(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 Me(ze(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};yw.className="Permute";se.registerClass(yw);var bw=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=ze(e),a=-1;return jp(si(n,this.maskValue),a)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=ze(e),a=-1,r=!0,s=jp(si(n,this.maskValue),a,r);return B(n,oe(s,n.dtype))})}};bw.className="Masking";se.registerClass(bw);var xw=class extends Ye{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(xt(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=ze(e),si(e,Ke(e))):null)}computeOutputShape(e){if(e=it(e),this.inputLength==null)return[...e,this.outputDim];let t=xt(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=ze(e);n.dtype!=="int32"&&(n=gf(n,"int32"));let a=g2(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:ct(this.embeddingsRegularizer),activityRegularizer:ct(this.activityRegularizer),embeddingsConstraint:Kt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};xw.className="Embedding";se.registerClass(xw);var go=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: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&Zr(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return O(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ss(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=zc(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,p=u[0],d=u.slice(1).concat([p]),c=W(o,[p].concat(Qr(u.slice(1))));c=Me(c,[1,0]),c=W(c,d),n.push(c),r=!0}else if(l>1){let u=Xa(1,l).concat([0]);n.push(Me(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],p=[u].concat(o.slice(0,o.length-1));s=W(Me(W(s,[-1,u]),[1,0]),p)}else if(i>1){let o=[i-1].concat(Xa(0,i-1));s=Me(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=Zr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return O(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:hn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=Na(n,t[a]);return n})}},vw=class extends go{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})}};vw.className="Add";se.registerClass(vw);var ww=class extends go{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=B(t,e[n]);return t})}};ww.className="Multiply";se.registerClass(ww);var kw=class extends go{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 B(1/e.length,t)})}};kw.className="Average";se.registerClass(kw);var Iw=class extends go{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=mr(t,e[n]);return t})}};Iw.className="Maximum";se.registerClass(Iw);var Sw=class extends go{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Tu(t,e[n]);return t})}};Sw.className="Minimum";se.registerClass(Sw);var Nw=class extends go{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(()=>Av(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(ea(e[s]),"bool")):t[s].rank<e[s].rank?a.push(hn(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return Pm(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Nw.className="Concatenate";se.registerClass(Nw);function Sp(e,t){for(;e<0;)e+=t;return e}function fV(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(B(e,t),s[0]):o=be(B(Me(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Fe(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=cr(o,u)}return o.shape.length===1&&(o=hn(o,1)),o})}var Tw=class extends go{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)=>Sp(r,e[s].shape.length)):a=[Sp(this.axes,t.shape.length),Sp(this.axes,n.shape.length)],this.normalize&&(t=Ph(t,a[0]),n=Ph(n,a[1])),fV(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Sp(this.axes,e.length),Sp(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}};Tw.className="Dot";se.registerClass(Tw);var Cw=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=ze(e);return Wc(()=>J(yf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Cw.className="GaussianNoise";se.registerClass(Cw);var Ew=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=ze(e);return this.rate>0&&this.rate<1?Wc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return B(n,yf(n.shape,1,a))},()=>n,t.training||!1):n})}};Ew.className="GaussianDropout";se.registerClass(Ew);var _w=class extends Ye{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return O(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wc(()=>{let a=ze(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=bs(Cu(n),this.rate);o=gf(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,p=J(B(a,o),B(J(o,-1),i));return J(B(p,l),u)},()=>ze(e),t.training||!1)}return e})}};_w.className="AlphaDropout";se.registerClass(_w);function Zp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=cS(e,t,n,a,r,s);else if(e.rank===3)i=dS(e,t,n,a,r,s);else if(e.rank===4)i=hS(e,t,n,a,r,s);else throw new Pe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function gV(e,t,n,a,r=.001){return O(()=>{let s=jm(e,a),i=s.mean,o=s.variance;return[Zp(e,i,o,n,t,r),i,o]})}function yV(e,t,n,a,r=.001){return O(()=>{let s=jm(e,a),i=s.mean,o=s.variance,l=[];for(let h of Xa(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[Zp(e,u,p,c,d,r),i,o]})}function bV(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Xa(0,e.rank-1))?gV(e,t,n,a,r):yV(e,t,n,a,r)}var Aw=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=ze(e),r=a.shape,s=r.length,i=Xa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ii(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!k.arraysEqual(u,Xa(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 Zp(a,g,y,b,x,this.epsilon)}else return Zp(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]=bV(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{O(()=>{let x=1-b,v=g.read(),w=B(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:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Kt(this.betaConstraint),gammaConstraint:Kt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Aw.className="BatchNormalization";se.registerClass(Aw);var $w=class extends Ye{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=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: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=ze(e),a=n.shape,r=a.length;return O(()=>{let{mean:s,variance:i}=jm(n,this.axis,!0),o=ii(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=l(this.gamma.read()),p=l(this.beta.read()),d=[],c=[];for(let h=0;h<r;++h)this.axis.indexOf(h)!==-1?(d.push(a[h]),c.push(1)):(d.push(1),c.push(a[h]));return s=On(s,d),i=On(i,d),u=On(u,c),p=On(p,c),Zp(n,s,i,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};$w.className="LayerNormalization";se.registerClass($w);function xV(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=Ka()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],fa(e,a)})}var Fw=class extends Ye{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?Ka():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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(()=>xV(ze(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Fw.className="ZeroPadding2D";se.registerClass(Fw);function Mf(e,t,n,a,r,s){return O(()=>{Ot(r),d2(s),ga(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ka()),s==null&&(s="max"),e=Qv(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Pt(e,t,n,o):i=ma(e,t,n,o),r==="channelsFirst"&&(i=Me(i,[0,3,1,2])),i})}function iN(e,t,n,a,r,s){return O(()=>{Ot(r),d2(s),ga(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ka()),s==null&&(s="max"),e=tN(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=sv(e,t,n,o):i=Vx(e,t,n,o),r==="channelsFirst"&&(i=Me(i,[0,4,1,2,3])),i})}var oN=class extends Ye{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new 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,ga(this.padding),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){e=it(e);let t=ja(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=zc(ze(e),2);let n=this.poolingFunction(ze(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return cr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Dw=class extends oN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ga(a),Mf(e,t,n,a,r,"max")}};Dw.className="MaxPooling1D";se.registerClass(Dw);var Rw=class extends oN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ga(a),Mf(e,t,n,a,r,"avg")}};Rw.className="AveragePooling1D";se.registerClass(Rw);var lN=class extends Ye{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new 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),ga(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=ja(t,this.poolSize[0],this.padding,this.strides[0]),n=ja(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(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Mw=class extends lN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ga(a),Mf(e,t,n,a,r,"max")}};Mw.className="MaxPooling2D";se.registerClass(Mw);var Pw=class extends lN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ga(a),Mf(e,t,n,a,r,"avg")}};Pw.className="AveragePooling2D";se.registerClass(Pw);var uN=class extends Ye{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new 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),ga(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=ja(t,this.poolSize[0],this.padding,this.strides[0]),n=ja(n,this.poolSize[1],this.padding,this.strides[1]),a=ja(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(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ow=class extends uN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ga(a),iN(e,t,n,a,r,"max")}};Ow.className="MaxPooling3D";se.registerClass(Ow);var Lw=class extends uN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),ga(a),iN(e,t,n,a,r,"avg")}};Lw.className="AveragePooling3D";se.registerClass(Lw);var pN=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}},zw=class extends pN{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=ze(e);return _t(n,1)})}};zw.className="GlobalAveragePooling1D";se.registerClass(zw);var Bw=class extends pN{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=ze(e);return Ia(n,1)})}};Bw.className="GlobalMaxPooling1D";se.registerClass(Bw);var cN=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}},Ww=class extends cN{call(e,t){return O(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?_t(n,[1,2]):_t(n,[2,3])})}};Ww.className="GlobalAveragePooling2D";se.registerClass(Ww);var Uw=class extends cN{call(e,t){return O(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?Ia(n,[1,2]):Ia(n,[2,3])})}};Uw.className="GlobalMaxPooling2D";se.registerClass(Uw);var dN=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=Ha(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},Vw=class extends dN{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=ze(e),rN((n,a)=>[ze(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Vw.className="TimeDistributed";se.registerClass(Vw);function vV(e){mo(T4,"BidirectionalMergeMode",e)}var wV="concat",Gw=class extends dN{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ha(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ha(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wV:e.mergeMode,vV(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=aN(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=ta(r,1));let i;return this.mergeMode==="concat"?i=Av([a,r]):this.mergeMode==="sum"?i=J(a,r):this.mergeMode==="ave"?i=B(.5,J(a,r)):this.mergeMode==="mul"?i=B(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){qs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),qs(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=Ha(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)}};Gw.className="Bidirectional";se.registerClass(Gw);function kV(e){return new $u(e)}function IV(e){return new Yv(e)}function SV(e){return new qv(e)}function NV(e){return new Kv(e)}function TV(e){return new Xv(e)}function CV(e){return new Zv(e)}function EV(e){return new Jv(e)}function _V(e){return new _f(e)}function AV(e){return new Gc(e)}function $V(e){return new tw(e)}function FV(e){return new Hc(e)}function DV(e){return new nw(e)}function RV(e){return new aw(e)}function MV(e){return new rw(e)}function PV(e){return new sw(e)}function OV(e){return new iw(e)}function LV(e){return new mw(e)}function zV(e){return new dw(e)}function BV(e){return new Rf(e)}function WV(e){return new cw(e)}function UV(e){return new hw(e)}function VV(e){return new fw(e)}function GV(e){return new gw(e)}function HV(e){return new yw(e)}function jV(e){return new xw(e)}function qV(e){return new vw(e)}function KV(e){return new kw(e)}function XV(e){return new Nw(e)}function YV(e){return new Iw(e)}function JV(e){return new Sw(e)}function ZV(e){return new ww(e)}function QV(e){return new Tw(e)}function eG(e){return new Aw(e)}function tG(e){return new $w(e)}function nG(e){return new Fw(e)}function Hw(e){return new Rw(e)}function aG(e){return Hw(e)}function rG(e){return Hw(e)}function jw(e){return new Pw(e)}function sG(e){return jw(e)}function iG(e){return jw(e)}function qw(e){return new Lw(e)}function oG(e){return qw(e)}function lG(e){return qw(e)}function uG(e){return new zw(e)}function pG(e){return new Ww(e)}function hN(e){return new Bw(e)}function mN(e){return new Uw(e)}function fN(e){return new Dw(e)}function gN(e){return new Mw(e)}function cG(e){return new Ow(e)}function dG(e){return new lw(e)}function hG(e){return new $f(e)}function mG(e){return new uw(e)}function fG(e){return new qc(e)}function gG(e){return new ow(e)}function yG(e){return new Af(e)}function bG(e){return new pw(e)}function xG(e){return new Df(e)}function vG(e){return new gr(e)}function wG(e){return new Ff(e)}function kG(e){return new Gw(e)}function IG(e){return new Vw(e)}var SG=hN,NG=mN,TG=fN,CG=gN;function EG(e){return new Cw(e)}function _G(e){return new Ew(e)}function AG(e){return new _w(e)}function $G(e){return new bw(e)}var yN={};Re(yN,{MAPE:()=>UG,MSE:()=>HG,binaryAccuracy:()=>FG,binaryCrossentropy:()=>DG,categoricalAccuracy:()=>MG,categoricalCrossentropy:()=>PG,cosineProximity:()=>zG,mape:()=>VG,meanAbsoluteError:()=>BG,meanAbsolutePercentageError:()=>WG,meanSquaredError:()=>GG,mse:()=>jG,precision:()=>OG,recall:()=>LG,sparseCategoricalAccuracy:()=>RG});function FG(e,t){return Bv(e,t)}function DG(e,t){return $2(e,t)}function RG(e,t){return F2(e,t)}function MG(e,t){return Wv(e,t)}function PG(e,t){return Uv(e,t)}function OG(e,t){return A2(e,t)}function LG(e,t){return wU(e,t)}function zG(e,t){return zv(e,t)}function BG(e,t){return Cf(e,t)}function WG(e,t){return Fu(e,t)}function UG(e,t){return Fu(e,t)}function VG(e,t){return Fu(e,t)}function GG(e,t){return fo(e,t)}function HG(e,t){return fo(e,t)}function jG(e,t){return fo(e,t)}var bN={};Re(bN,{modelFromJSON:()=>tV});var xN={};Re(xN,{l1:()=>KG,l1l2:()=>qG,l2:()=>XG});function qG(e){return new Uc(e)}function KG(e){return uV(e)}function XG(e){return pV(e)}var vN=class extends hl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof Cr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function rh(e,t){return e<t}function gk(e,t){return e>t}var wN=class extends vN{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Pe("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=rh:this.mode==="max"?this.monitorFunc=gk:this.monitor.indexOf("acc")!==-1?this.monitorFunc=gk: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 YG(e){return new wN(e)}var JG={earlyStopping:YG},ZG=Y();ZG.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 va;(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"})(va||(va={}));var yk;(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={}))})(yk||(yk={}));var Kw={};function QG(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Kw[e]=n}function kN(e){return Kw[e]}function e6(e){delete Kw[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 kn(t.inputNames[s.inputIndexStart],n,a,r);if(s.type==="tensors")return t.inputNames.slice(o,l).map(d=>kn(d,n,a,r));let u=kn(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 kn(e,t,n,a){let[r,s]=qn(e);if(a!=null){let o=a.getHashTableHandleByName(r);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[Wh(r,o)]);return i!==void 0?t[Wh(r,i)][s]:void 0}function t6(e,t,n){return t[Wh(e,n.currentContextId)]}function or(e,t){let[n,a,r]=qn(e);return[Wh(n,t&&t.currentContextId),a,r]}function Wh(e,t){return t?`${e}-${t}`:e}function qn(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 Ir(e){return e.kept?e:Tr(e)}var IN={};Re(IN,{json:()=>n6});var n6=[{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}]}],SN={};Re(SN,{json:()=>a6});var a6=[{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}]}],NN={};Re(NN,{json:()=>r6});var r6=[{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"}]}],TN={};Re(TN,{json:()=>s6});var s6=[{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"}]}],CN={};Re(CN,{json:()=>i6});var i6=[{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"}]}],EN={};Re(EN,{json:()=>o6});var o6=[{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}]}],_N={};Re(_N,{json:()=>l6});var l6=[{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"}]}],AN={};Re(AN,{json:()=>u6});var u6=[{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"}]}],$N={};Re($N,{json:()=>p6});var p6=[{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"}]}],FN={};Re(FN,{json:()=>c6});var c6=[{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"}]}],DN={};Re(DN,{json:()=>d6});var d6=[{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}]}],RN={};Re(RN,{json:()=>h6});var h6=[{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"}]}],MN={};Re(MN,{json:()=>m6});var m6=[{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}]}],PN={};Re(PN,{json:()=>f6});var f6=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],ON={};Re(ON,{json:()=>g6});var g6=[{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}]}],LN={};Re(LN,{json:()=>y6});var y6=[{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"}]}],zN={};Re(zN,{json:()=>b6});var b6=[{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}]}],BN={};Re(BN,{json:()=>x6});var x6=[{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"}]}],WN={};Re(WN,{json:()=>v6});var v6=[{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:[]}],bk=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[IN,SN,NN,TN,CN,EN,_N,AN,$N,FN,DN,RN,MN,PN,ON,LN,zN,BN,WN],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]=or(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]=or(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]=or(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=kN(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.substr(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=Ub(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Ub(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":i=Xb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Xb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":i=Gb(e.attr,r.tfName,r.defaultValue||0),i===void 0&&!!r.tfDeprecatedName&&(i=Gb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":i=Kb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Kb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":i=Vb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Vb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":i=Jb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Jb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":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=Yb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Yb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":i=Hb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Hb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":i=jb(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=jb(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":i=xk(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=xk(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return a[r.name]={value:i,type:s},a},{})),n}mapFunction(e){let t=e.nodeDef,n=[],a=[],r={};t!=null&&(r=t.reduce((u,p)=>(u[p.name]=this.mapNode(p),p.op==="Const"&&a.push(u[p.name]),u),{}));let s=[],i=[];e.signature.inputArg.forEach(u=>{let[p]=or(u.name),d={name:p,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Xw(u.type),type:"dtype"}},children:[]};d.signatureKey=u.name,s.push(d),r[p]=d}),Object.keys(r).forEach(u=>{let p=r[u];p.inputNames.forEach((d,c)=>{let[h,,m]=or(d),f=r[h];if(f.outputs!=null){let g=f.outputs.indexOf(m);if(g!==-1){let y=`${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]=or(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 w6(e){let t=Y().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 UN(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):w6(e);return t?n:n.toLowerCase()}function Ub(e,t,n,a=!1){let r=e[t];return r!=null?UN(r.s,a):n}function Vb(e,t,n){let a=e[t];return a?a.b:n}function Gb(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 Xw(e){switch(typeof e=="string"&&(e=va[e]),e){case va.DT_FLOAT:case va.DT_HALF:return"float32";case va.DT_INT32:case va.DT_INT64:case va.DT_INT8:case va.DT_UINT8:return"int32";case va.DT_BOOL:return"bool";case va.DT_DOUBLE:return"float32";case va.DT_STRING:return"string";default:return null}}function xk(e,t,n){let a=e[t];return a&&a.func?a.func.name:n}function Hb(e,t,n){let a=e[t];return a&&a.type?Xw(a.type):n}function jb(e,t,n){let a=e[t];return a&&a.list&&a.list.type?a.list.type.map(r=>Xw(r)):n}function VN(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function qb(e,t,n){let a=e[t];return a&&a.shape?VN(a.shape):n}function Kb(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 Xb(e,t,n,a=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(s=>UN(s,a)):n}function Yb(e,t,n){let a=e[t];return a&&a.list&&a.list.shape?a.list.shape.map(r=>VN(r)):n}function Jb(e,t,n){let a=e[t];return a&&a.list&&a.list.b?a.list.b:n}var k6=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 kn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return kn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Gb(this.node.rawAttrs,e,t);if(n.s!=null)return Ub(this.node.rawAttrs,e,t);if(n.b!=null)return Vb(this.node.rawAttrs,e,t);if(n.shape!=null)return qb(this.node.rawAttrs,e,t);if(n.type!=null)return Hb(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return Kb(this.node.rawAttrs,e,t);if(n.list.s!=null)return Xb(this.node.rawAttrs,e,t);if(n.list.shape!=null)return Yb(this.node.rawAttrs,e,t);if(n.list.b!=null)return Jb(this.node.rawAttrs,e,t);if(n.list.type!=null)return jb(this.node.rawAttrs,e,t)}return t}},I6=(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[oS(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[ov(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[B(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[Yx(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[Mm(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[Tu(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[mr(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[nf(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},S6=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[zt(I("x",e,t,n))];case"Acos":return[Rx(I("x",e,t,n))];case"Acosh":return[Mx(I("x",e,t,n))];case"Asin":return[Ox(I("x",e,t,n))];case"Asinh":return[Lx(I("x",e,t,n))];case"Atan":return[zx(I("x",e,t,n))];case"Atan2":return[Bx(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[Wx(I("x",e,t,n))];case"Ceil":return[Hx(I("x",e,t,n))];case"Complex":return[ns(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[Ec(I("x",e,t,n))];case"Cosh":return[zm(I("x",e,t,n))];case"Elu":return[Su(I("x",e,t,n))];case"Erf":return[Jx(I("x",e,t,n))];case"Exp":return[fn(I("x",e,t,n))];case"Expm1":return[Zx(I("x",e,t,n))];case"Floor":return[Nu(I("x",e,t,n))];case"Log":return[Qn(I("x",e,t,n))];case"Log1p":return[Ac(I("x",e,t,n))];case"Imag":return[Wm(I("x",e,t,n))];case"Neg":return[St(I("x",e,t,n))];case"Reciprocal":return[pv(I("x",e,t,n))];case"Real":return[Kp(I("x",e,t,n))];case"Relu":return[Xe(I("x",e,t,n))];case"Round":return[Xm(I("x",e,t,n))];case"Selu":return[Jm(I("x",e,t,n))];case"Sigmoid":return[da(I("x",e,t,n))];case"Sin":return[Zm(I("x",e,t,n))];case"Sign":return[cv(I("x",e,t,n))];case"Sinh":return[Qm(I("x",e,t,n))];case"Softplus":return[co(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[ni(I("x",e,t,n))];case"Tan":return[mv(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[Km(I("x",e,t,n))];case"Rsqrt":return[Ym(kn(e.inputNames[0],t,n))];case"Prod":return[qm(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[_c(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[Dc(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[ev(kn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ka(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 vk(e){return!(typeof e=="number"||e.some(t=>t<0))}function Np(e,t,n){let a=Zb(e,n),r=!vk(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=Zb(s.shape,a)}),!vk(a))throw new Error(`Non-fully-defined elementShape: ${a}`);return a}function Zb(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 N6=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=ke(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),ka(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 Jn([],[0].concat(this.elementShape));let n=this.readMany(e);return ka(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 ${e}`);if(this.size()===0)return Jn([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return ka(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${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,ht(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)}},Kc=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}`);ka(t,r.shape,"TensorList shape mismatch: "),en(r)}),this.idTensor=ke(0),this.maxNumElements=a,en(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Kc([...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.`);ka(e,this.elementShape,"TensorList shape mismatch: ");let a=Np(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=Np(this.elementShape,this.tensors,e),a=this.tensors.pop();return ka(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(ka(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}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);ka(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=Np(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.`);ka(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}`);ka(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=Np(this.elementShape,this.tensors,n);return e.length===0?Jn([],[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}`);ka(this.elementShape,t,"TensorList shape mismatch: ");let n=Np(this.elementShape,this.tensors,t);return this.size()===0?Jn([],[0].concat(n)):O(()=>{let a=this.tensors.map(r=>W(r,n));return Qe(a,0)})}};function T6(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);ka(r,t,"TensorList shape mismatch: ");let s=ht(e);return new Kc(s,t,a)}function C6(e,t,n){return new Kc([],e,t,n)}function E6(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 Kc([],n,e.dtype,a),i=ht(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function _6(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=Zb(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 Kc([],n,e.dtype,t.length);for(let p=0;p<l.length;p++)u.setItem(p,l[p]);return u}var A6=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[Ir(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Ir(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>kn(r,t,n)!==void 0);if(a){let r=kn(a,t,n);return[Ir(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Ir(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Ir(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Ir(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 N6(u,r,a,s,l,i,o);return n.addTensorArray(p),[p.idTensor,ke(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[ke(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=E6(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=C6(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=T6(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=_6(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function wk(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 $6=(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[Om(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}=wk(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}=wk(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[Lm(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[ys(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[qx(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[ma(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}=DS(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[Vx(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[sv(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[Xx(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`)}},F6=(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[TS(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[RS(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[ul(a,r,s,i)]}case"Ones":return[Xn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[ea(I("x",e,t,n))];case"RandomUniform":return[Cu(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[pl(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[af(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 cb(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 D6=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=cb(e,t,n),u=await Yn.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}=cb(e,t,n),l=I("padToMaxOutputSize",e,t,n),u=await Yn.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}=cb(e,t,n);return[await Yn.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=oe(I("condition",e,t,n),"bool"),r=[await yv(a)];return a.dispose(),r}case"ListDiff":return OS(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},R6=(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=fv(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`)}},M6=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let a=I("default",e,t,n);return[kn(e.name,t,n)||a];case"Placeholder":return[kn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=I("x",e,t,n);return[Ir(u)]}case"IdentityN":return I("x",e,t,n).map(u=>Ir(u));case"Snapshot":let r=I("x",e,t,n);return[Ir(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[ke(I("x",e,t,n).size,"int32")];case"Rank":return[ke(I("x",e,t,n).rank,"int32")];case"NoOp":return[ke(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`)}},P6=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=ke(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 ke(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=ht(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}`)}},O6=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 P6(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`)}},L6=(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[Yn.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[Yn.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[Yn.cropAndResize(a,r,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},z6=(e,t,n)=>{switch(e.op){case"Equal":return[Zn(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[si(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Wn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[bs(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Um(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[xs(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[Na(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[$c(I("a",e,t,n))];case"LogicalOr":return[Hm(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[mn(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`)}},B6=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Fe(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[IS(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Me(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`)}},W6=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Er(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[Er(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[tv(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[Gm(I("x",e,t,n))];case"SparseToDense":return[bv(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`)}},U6=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ia(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[_t(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[qp(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[Pm(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[jp(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[ti(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[Px(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[qm(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[Bm(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[Gx(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[wS(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},V6=(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[ai(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[ai(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[ta(s,r)]}case"ReverseV2":{let a=I("axis",e,t,n),r=I("x",e,t,n);return[ta(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[hv(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=cr(r[0]).shape,o=r.map(l=>{let u=k.arraysEqual(l.shape,s);if(!u&&!k.arraysEqual(cr(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 ht(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 Ln(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[WS(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[US(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[bv(a,s,r,s.dtype===i.dtype?i:oe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},G6=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:a,outputValues:r,emptyRowIndicator:s,reverseIndexMap:i}=_p.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}=_p.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[a,r]}case"SparseSegmentMean":return[_p.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[_p.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`)}},H6=(e,t,n)=>{switch(e.op){case"FFT":return[Rc(I("x",e,t,n))];case"IFFT":return[cl(I("x",e,t,n))];case"RFFT":return[Mc(I("x",e,t,n))];case"IRFFT":return[tf(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},j6=(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`)}},q6=(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[hn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[cr(I("x",e,t,n),a)]}case"Reshape":return[W(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[iv(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[fa(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[Fc(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Cc(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[Kx(I("x",e,t,n),a,r)]}case"BroadcastTo":return[rl(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[mS(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function kk(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return O(()=>I6(s,i,o));case"basic_math":return O(()=>S6(s,i,o));case"control":return A6(s,i,o);case"convolution":return O(()=>$6(s,i,o));case"creation":return O(()=>F6(s,i,o));case"dynamic":return D6(s,i,o);case"evaluation":return O(()=>R6(s,i,o));case"image":return O(()=>L6(s,i,o));case"graph":return O(()=>M6(s,i,o));case"logical":return O(()=>z6(s,i,o));case"matrices":return O(()=>B6(s,i,o));case"normalization":return O(()=>W6(s,i,o));case"reduction":return O(()=>U6(s,i,o));case"slice_join":return O(()=>V6(s,i,o));case"sparse":return O(()=>G6(s,i,o));case"spectral":return O(()=>H6(s,i,o));case"string":return O(()=>j6(s,i,o));case"transformation":return O(()=>q6(s,i,o));case"hash_table":return O6(s,i,o,a);case"custom":let l=kN(s.op);if(l&&l.customExecutor)return l.customExecutor(new k6(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 Ik=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 Sk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>qn(c)[0]),p=[];a!=null&&(p=a.map(c=>qn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((GN(c)||Z6(c)||Q6(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 K6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>qn(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 X6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Y6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],J6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function GN(e){return X6.indexOf(e.op)>=0}function Z6(e){return Y6.indexOf(e.op)>=0}function Q6(e){return J6.indexOf(e.op)>=0}var Qb=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 Qb(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=Sk(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 K6(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[qn(p)[0]]),r=t.map(p=>qn(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 Ik(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=qn(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=kk(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=>kn(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=t6(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]=or(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=Y().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new Ik(this.weightMap,a,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,n);let i=t.map(u=>kn(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[qn(b)[0]]),i=n.map(b=>qn(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}=Sk(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]=qn(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=>!GN(b)&&!kn(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}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&I("isConstant",p.node,a,n)&&([d]=or(p.node.name,n)),a[p.node.name]==null){let c=kk(p.node,a,n,this._resourceManager);d||([d]=or(p.node.name,n));let h=n.currentContext;k.isPromise(c)?u.push(c.then(m=>(a[d]=m,n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=or(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=qn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=qn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=qn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},eH=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},tH="?tfjs-format=file",nH="model.json",HN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new eH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Qt.browserHTTPRequest(e,this.loadOptions);else{let t=Qt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Qt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Qt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Qb(bk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=bk.Instance.transformGraph(e.modelInitializer);this.initializer=new Qb(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Qt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function aH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${nH}${tH}`);let n=new HN(e,t);return await n.load(),n}var rH="3.14.0",jN={};Re(jN,{CSVDataset:()=>tT,Dataset:()=>Du,FileDataSource:()=>lT,TextLineDataset:()=>eT,URLDataSource:()=>uT,array:()=>CH,csv:()=>LH,func:()=>zH,generator:()=>BH,microphone:()=>UH,version_data:()=>VH,webcam:()=>WH,zip:()=>EH});var sH=di(aI()),iH=di(aI());function oH(e,t){return Uh(e,t)}function Uh(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(fl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=Uh(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 lH(e,t=KN){return qN(e,t)}function qN(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(fl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=qN(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 KN(e){return e===null?null:fl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function XN(e,t){let n=new Map;Uh(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 Uh(e,t,n)}function fl(e){let t=!1;if(Y().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=rI();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ae)&&!(e instanceof Promise)&&!t)}function uH(e){return e==null||pH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ae||k.isTypedArray(e)}function pH(e){return e===null||typeof e!="object"&&typeof e!="function"}function cH(e){return oH(e,dH)}function dH(e){return e instanceof Ae?{value:e.clone(),recurse:!1}:fl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var YN=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}},Yw=class extends YN{constructor(){super(Yw.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}};Yw.INITIAL_CAPACITY=32;function JN(e){return new fH(e)}function Jw(e){return new gH(e)}function hH(e,t){return new ZN(e,t)}function mH(e,t=Yr.FAIL){return new NH(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 IH(this,e)}filter(e){return new wH(this,e)}map(e){return new kH(this,e)}mapAsync(e){return new Nk(this,e)}serialMapAsync(e){return new Nk(this,e).serial()}flatmap(e){return new SH(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 vH(this,e,t)}columnMajorBatch(e,t=!0,n=KN){return this.rowMajorBatch(e,t).map(a=>lH(a,n))}concatenate(e,t){return new ZN(JN([this,e]),t)}take(e){return e<0||e==null?this:new xH(this,e)}skip(e){return e<0||e==null?this:new bH(this,e)}prefetch(e){return new QN(this,e)}shuffle(e,t){return new TH(this,e,t)}serial(){return new yH(this)}},fH=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:cH(e),done:!1}}},gH=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: ${e.message}`,e}}},yH=class extends an{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},bH=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`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;De(e.value)}return this.upstream.next()}},xH=class extends an{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},vH=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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},wH=class extends an{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;De(e.value)}}},kH=class extends an{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Va.getTensorsInContainer(e.value),n=this.transform(e.value),a=Va.getTensorsInContainer(n);for(let r of t)Va.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},IH=class extends an{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Nk=class extends an{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Va.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Va.getTensorsInContainer(n);for(let r of t)Va.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Zw=class extends an{constructor(){super();this.outputQueue=new Yw,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},SH=class extends Zw{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Va.getTensorsInContainer(e.value),n=this.transform(e.value),a=Va.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Va.isTensorInList(r,a)||r.dispose();return!0}},ZN=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 NH=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 XN(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 ${this.count}.`);case Yr.SHORTEST:return{value:null,done:!0};case Yr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},QN=class extends an{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new YN(t)}summary(){return`${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()}},TH=class extends QN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=iH.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}}},Du=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),jn(async()=>(await n.iterator()).columnMajorBatch(e,t,_H),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,jn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,jn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return jn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return jn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return jn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,jn(async()=>{let a=Jw(async()=>({value:await t.iterator(),done:!1}));return hH(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,jn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=sH.alea(t||k.now().toString());return jn(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,jn(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()}};Du.MAX_BUFFER_SIZE=1e4;function jn(e,t=null){return new class extends Du{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function CH(e){return jn(async()=>JN(e),e.length)}function EH(e){if(!fl(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 jn(async()=>{let n=await XN(e,a=>{if(a instanceof Du)return{value:a.iterator(),recurse:!1};if(fl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return mH(n,Yr.SHORTEST)},t)}function _H(e){if(e===null)return null;let t=e[0];return uH(t)?{value:AH(e),recurse:!1}:{value:null,recurse:!0}}function AH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ae?Mt(e):Jn(e)}var eT=class extends Du{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='"',Tp=Symbol("out"),Tk=Symbol("field"),ih=Symbol("quote"),db=Symbol("quoteafterquote"),Ck=Symbol("quoteinquote"),tT=class extends Du{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 eT(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=Tp;for(let i=0;i<r;i++)switch(s){case Tp: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=Tp;break;default:s=Tk,a=i;break}break;case Tk:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=Tp,a=i+1;break;default:}break;case ih:switch(e.charAt(i)){case sh:s=db;break;default:}break;case db:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=Tp,a=i+1;break;case sh:s=ih;break;default:s=Ck;break}break;case Ck:switch(e.charAt(i)){case sh:s=ih;break;default:}break;default:}if(s===db?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}},nT=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(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new nT(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),Jn(n,t)}},aT=class extends an{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=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=Ga([s,r,o,i],[1,4])}else this.cropBox=Ga([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new aT(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=po.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=hn(oe(e,"float32"),0),n;n=Yn.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.")}},rT=class{},sT=class extends an{split(e){return new $H(this,e)}},$H=class extends sT{constructor(e,t){super();this.upstream=e,this.impl=new FH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},FH=class extends Zw{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}},DH=class extends an{decodeUTF8(){return new RH(this)}},RH=class extends sT{constructor(e){super();this.upstream=e,this.impl=new MH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},MH=class extends Zw{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=rI();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 Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},iT=class extends DH{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(Y().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function PH(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=OH(e));let s=await(n||k.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new iT(i,t)}else throw new Error(s.statusText)}var OH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function oT(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var lT=class extends rT{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(oT(this.input)&&Y().get("IS_NODE")){let e=mx();this.input=e.readFileSync(this.input.substr(7))}return new iT(this.input,this.options)}},uT=class extends rT{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return oT(this.url)?new lT(this.url,this.fileOptions).iterator():PH(this.url,this.fileOptions)}};function LH(e,t={}){return new tT(new uT(e),t)}function zH(e){let t=Jw(e);return jn(async()=>t)}function BH(e){return jn(async()=>{let t=await e();return Jw(()=>t.next())})}async function WH(e,t){return aT.create(e,t)}async function UH(e){return nT.create(e)}var VH="3.14.0";function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var GH=fr.whereImpl,Qw=class extends ac{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Jh(this,rr())}nextDataId(){return Qw.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&E.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 E.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 rr().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){ve([e],"where");let t=this.readSync(e.dataId);return GH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Qw.nextDataId=0;var pT={};Re(pT,{addImpl:()=>dT,bincountImpl:()=>t0,bincountReduceImpl:()=>hT,ceilImpl:()=>mT,concatImpl:()=>n0,equalImpl:()=>fT,expImpl:()=>yT,expm1Impl:()=>xT,floorImpl:()=>vT,gatherNdImpl:()=>wT,gatherV2Impl:()=>kT,greaterEqualImpl:()=>ST,greaterImpl:()=>IT,lessEqualImpl:()=>TT,lessImpl:()=>NT,linSpaceImpl:()=>CT,logImpl:()=>ET,maxImpl:()=>_T,maximumImpl:()=>AT,minimumImpl:()=>$T,multiplyImpl:()=>a0,negImpl:()=>FT,notEqualImpl:()=>DT,prodImpl:()=>RT,rangeImpl:()=>s0,rsqrtImpl:()=>MT,sigmoidImpl:()=>Fj,simpleAbsImpl:()=>cT,sliceImpl:()=>Gh,sparseFillEmptyRowsImpl:()=>OT,sparseReshapeImpl:()=>LT,sparseSegmentReductionImpl:()=>i0,sqrtImpl:()=>Mj,squaredDifferenceImpl:()=>zT,stridedSliceImpl:()=>BT,stringNGramsImpl:()=>WT,stringSplitImpl:()=>UT,stringToHashBucketFastImpl:()=>VT,subImpl:()=>GT,tileImpl:()=>HT,topKImpl:()=>qT,transposeImpl:()=>r0,uniqueImpl:()=>KT});function cT(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var HH=e=>{let{x:t}=e.inputs,n=e.backend;ve(t,"abs");let a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=cT(r),n.makeOutput(a,t.shape,t.dtype)},jH={kernelName:wl,backendName:"cpu",kernelFunc:HH};function Ut(e){return(t,n,a,r,s)=>{let i=E.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=E.getBroadcastDims(t,i),g=E.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 Kn(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 qH={kernelName:rm,backendName:"cpu",kernelFunc:Kn};function Vh(e,t,n="float32"){if(n==="complex64"){let r=Vh(e,t,"float32"),s=Vh(e,t,"float32");return Kn({inputs:{real:r,imag:s},backend:e})}let a=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,a)}function dr(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 KH={kernelName:Di,backendName:"cpu",kernelFunc:dr};function oi(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 XH={kernelName:Im,backendName:"cpu",kernelFunc:oi};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 dr({inputs:{x:r},backend:n});let i=Vh(n,r.shape,r.dtype),o=us({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Kn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=oi({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=dr({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]=Ut((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 ${s}`)}var YH={kernelName:bi,backendName:"cpu",kernelFunc:us};function rn(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;ve([i,o],e);let u=l.data.get(i.dataId).values,p=l.data.get(o.dataId).values,d=i.dtype==="string"?E.fromUint8ToStringArray(u):u,c=i.dtype==="string"?E.fromUint8ToStringArray(p):p,h=a||i.dtype,[m,f]=t(i.shape,o.shape,d,c,h);return l.makeTensorInfo(f,h,m)}:({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let u=us({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),p=l.data.get(u.dataId),d=p.complexTensorInfos.real,c=p.complexTensorInfos.imag,h=l.data.get(d.dataId).values,m=l.data.get(c.dataId).values,f=us({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(f.dataId),y=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,v=l.data.get(b.dataId).values,[w,T,C]=n(i.shape,o.shape,h,m,x,v),_=l.makeTensorInfo(C,"float32",w),$=l.makeTensorInfo(C,"float32",T),P=Kn({inputs:{real:_,imag:$},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(f),l.disposeIntermediateTensorInfo(_),l.disposeIntermediateTensorInfo($),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 e0(e){return(t,n,a,r,s,i)=>{let o=E.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=E.getBroadcastDims(t,o),m=E.getBroadcastDims(n,o),f=E.mergeRealAndImagArrays(a,r),g=E.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(f[T*2],f[T*2+1],g[C*2],g[C*2+1]);d[w]=_.real,c[w]=_.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 _=k.locToIndex(C,y,b),$=T.slice(-x);m.forEach(S=>$[S]=0);let P=k.locToIndex($,x,v),F=e(f[_*2],f[_*2+1],g[P*2],g[P*2+1]);d[w]=F.real,c[w]=F.imag}return[d,c,o]}}var dT=Ut((e,t)=>e+t),JH=e0((e,t,n,a)=>({real:e+n,imag:t+a})),Xc=rn(cs,dT,JH),ZH={kernelName:cs,backendName:"cpu",kernelFunc:Xc};function t0(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 hT(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 vs(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(ve(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 Ru(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(ve(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 mT=vs(e=>Math.ceil(e)),QH=Ru(xi,mT),ej={kernelName:xi,backendName:"cpu",kernelFunc:QH};function n0(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"?E.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 fT=Ut((e,t)=>e===t?1:0),gT=rn(Pl,fT,null,"bool"),tj={kernelName:Pl,backendName:"cpu",kernelFunc:gT},yT=vs(e=>Math.exp(e)),bT=Ru(Ei,yT,"float32"),nj={kernelName:Ei,backendName:"cpu",kernelFunc:bT},xT=vs(e=>Math.expm1(e)),aj=Ru(Ll,xT),rj={kernelName:Ll,backendName:"cpu",kernelFunc:aj},vT=vs(e=>Math.floor(e)),sj=Ru(_i,vT),ij={kernelName:_i,backendName:"cpu",kernelFunc:sj};function wT(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 ${o}`);for(let h=0;h<s;h++)u.values[p*s+h]=t.get(...t.indexToLoc(c*s+h))}return u}function kT(e,t,n){let a=He(n,e.dtype);for(let r=0;r<a.size;++r){let s=a.indexToLoc(r).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let u=e.locToIndex(s);0<=u&&u<e.values.length&&(a.values[r]=e.values[u])}return a}var IT=Ut((e,t)=>e>t?1:0),oj=rn(Ul,IT,null,"bool"),lj={kernelName:Ul,backendName:"cpu",kernelFunc:oj},ST=Ut((e,t)=>e>=t?1:0),uj=rn(Fi,ST,null,"bool"),pj={kernelName:Fi,backendName:"cpu",kernelFunc:uj},NT=Ut((e,t)=>e<t?1:0),cj=rn(jl,NT,null,"bool"),dj={kernelName:jl,backendName:"cpu",kernelFunc:cj},TT=Ut((e,t)=>e<=t?1:0),hj=rn(ql,TT,null,"bool"),mj={kernelName:ql,backendName:"cpu",kernelFunc:hj};function CT(e,t,n){let a=(t-e)/(n-1),r=k.makeZerosTypedArray(n,"float32");r[0]=e;for(let s=1;s<r.length;s++)r[s]=r[s-1]+a;return r}var ET=vs(e=>Math.log(e)),fj=Ru(Mi,ET),gj={kernelName:Mi,backendName:"cpu",kernelFunc:fj};function _T(e,t,n,a){let r=k.getTypedArrayFromDType(a,k.sizeFromShape(n));for(let s=0;s<r.length;++s){let i=s*t,o=e[i];for(let l=0;l<t;++l){let u=e[i+l];(Number.isNaN(u)||u>o)&&(o=u)}r[s]=o}return r}var AT=Ut((e,t)=>Math.max(e,t)),yj=rn(Oi,AT),bj={kernelName:Oi,backendName:"cpu",kernelFunc:yj},$T=Ut((e,t)=>Math.min(e,t)),xj=rn(Wi,$T),vj={kernelName:Wi,backendName:"cpu",kernelFunc:xj},a0=Ut((e,t)=>e*t),wj=e0((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),Pf=rn(Vi,a0,wj),kj={kernelName:Vi,backendName:"cpu",kernelFunc:Pf};function FT(e,t,n){let a=k.createScalarValue(-1,n);return a0([],t,a,e,n)}function Ij(e){let{inputs:t,backend:n}=e,{x:a}=t;ve(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=FT(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var Sj={kernelName:Jl,backendName:"cpu",kernelFunc:Ij},DT=Ut((e,t)=>e!==t?1:0),Nj=rn(Zl,DT,null,"bool"),Tj={kernelName:Zl,backendName:"cpu",kernelFunc:Nj};function r0(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 ha(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{perm:s}=n;ve(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=r0(l,r.shape,r.dtype,s,o);return{dataId:a.write(u,o,r.dtype),shape:o,dtype:r.dtype}}var Cj={kernelName:uo,backendName:"cpu",kernelFunc:ha};function RT(e,t,n,a){let[r,s]=E.computeOutAndReduceShapes(e,a),i=Sa(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 Ej(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"prod");let o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=E.getAxesPermutation(l,o),p=l,d=r,c=[];u!=null&&(d=ha({inputs:{x:r},backend:n,attrs:{perm:u}}),c.push(d),p=E.getInnerMostAxes(p.length,o));let h=n.data.get(d.dataId).values,{outVals:m,outShape:f,outDtype:g}=RT(d.shape,d.dtype,h,p),y=f;return i&&(y=E.expandShapeToKeepDim(f,l)),c.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(y,g,m)}var _j={kernelName:ru,backendName:"cpu",kernelFunc:Ej};function s0(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 MT=vs(e=>1/Math.sqrt(e)),Aj=Ru(Qi,MT),$j={kernelName:Qi,backendName:"cpu",kernelFunc:Aj},Fj=vs(e=>1/(1+Math.exp(-e))),PT=ot(to,e=>1/(1+Math.exp(-e))),Dj={kernelName:to,backendName:"cpu",kernelFunc:PT};function Gh(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"?E.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"?E.fromStringArrayToUint8(p.values):p.values}function li(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;ve(r,"slice");let[o,l]=qt.parseSliceParams(r,s,i);qt.assertParamsValid(r,o,l);let u=n.data.get(r.dataId).values,p=Gh(u,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}var Rj={kernelName:pu,backendName:"cpu",kernelFunc:li};function OT(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(E.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(E.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,y));if(y>=l)throw new Error(E.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 _=0;_<d;++_)y[C*d+_]=e[v*d+_];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 LT(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(E.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(p,f));p=f,l.push(1)}else{if(g<0)throw new Error(E.getSparseReshapeNegativeOutputDimErrorMessage(f,g));u*=g,l.push(g)}}if(p!==-1){if(u<=0)throw new Error(E.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let f=Math.trunc(s/u);if(u*f!==s)throw new Error(E.getSparseReshapeInputOutputMultipleErrorMessage(a,l));l[p]=f}if(k.sizeFromShape(l)!==s)throw new Error(E.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 i0(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(E.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(E.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(E.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(y<0||y>=p)throw new Error(E.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(E.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 Mj=vs(e=>Math.sqrt(e)),Pj=ot(no,e=>Math.sqrt(e)),Oj={kernelName:no,backendName:"cpu",kernelFunc:Pj},zT=Ut((e,t)=>{let n=e-t;return n*n}),Lj=rn(so,zT),zj={kernelName:so,backendName:"cpu",kernelFunc:Lj};function BT(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 Bj=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 WT(e,t,n,a,r,s,i,o){return new Bj(n,a,r,s,i,o).compute(e,t)}function Wj(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 UT(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;Wj(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 VT(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 GT=Ut((e,t)=>e-t),Uj=e0((e,t,n,a)=>({real:e-n,imag:t-a})),o0=rn(io,GT,Uj),Vj={kernelName:io,backendName:"cpu",kernelFunc:o0};function HT(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 Fp=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function jT(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));jT(e,t,c,h)}let r=e[t],s=n,i=a;for(k.swap(e,n,t),Fp(e[a],r)>0&&k.swap(e,n,a);s<i;){for(k.swap(e,s,i),s++,i--;Fp(e[s],r)<0;)s=s+1;for(;Fp(e[i],r)>0;)i=i-1}Fp(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 qT(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&&(jT(m,a),m=m.slice(0,a)),r&&m.sort(Fp);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 KT(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}}Rm("cpu",()=>new Qw,1);var XT=ot(Ci,e=>e>=0?e:Math.exp(e)-1),Gj={kernelName:Ci,backendName:"cpu",kernelFunc:XT};function YT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;ve([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 Hj={kernelName:Ri,backendName:"cpu",kernelFunc:YT},jj=Ut((e,t)=>e<0?t*e:e);function JT(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;ve([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=jj(a.shape,r.shape,s,i,"float32");return n.makeTensorInfo(l,"float32",o)}var qj={kernelName:qi,backendName:"cpu",kernelFunc:JT},ZT=ot(Ki,e=>Math.max(0,e)),Kj={kernelName:Ki,backendName:"cpu",kernelFunc:ZT},QT=ot(Yi,e=>Math.min(Math.max(0,e),6)),Xj={kernelName:Yi,backendName:"cpu",kernelFunc:QT};function l0(e,t,n,a,r){if(n==="linear")return dr({inputs:{x:t},backend:e});if(n==="relu")return ZT({inputs:{x:t},backend:e});if(n==="elu")return XT({inputs:{x:t},backend:e});if(n==="relu6")return QT({inputs:{x:t},backend:e});if(n==="prelu")return JT({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return YT({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return PT({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 ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let u=n.data.get(r.dataId);if(u.complexTensorInfos!=null){let p=u.complexTensorInfos.real,d=u.complexTensorInfos.imag;p.shape=o,d.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var Yj={kernelName:iu,backendName:"cpu",kernelFunc:Tt};function eC(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;ve([r,s],"matMul");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=Iu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,p,c]:[g,c,p],v=o?[y,h,d]:[y,d,h],w=Tt({inputs:{x:r},backend:n,attrs:{shape:x}}),T=Tt({inputs:{x:s},backend:n,attrs:{shape:v}}),C=i?w.shape[1]:w.shape[2],_=i?w.shape[2]:w.shape[1],$=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=_*$,ie=He([P,_,$],w.dtype),ae=ie.values,le=n.blockSize;for(let ue=0;ue<P;ue++)for(let we=0;we<_;we+=le)for(let ye=0;ye<$;ye+=le)for(let Ie=0;Ie<C;Ie+=le){let _e=Math.min(we+le,_),$e=Math.min(ye+le,$),Be=Math.min(Ie+le,C);for(let je=we;je<_e;je++)for(let st=ye;st<$e;st++){let nt=0;for(let at=Ie;at<Be;at++){let Te=Math.min(ue,g-1)*j,gt=Math.min(ue,y-1)*re,pt=F[Te+je*q+at*K],gn=S[at*Q+st*ee+gt];nt+=pt*gn}ae[ue*Z+(je*$+st)]+=nt}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(T),n.makeTensorInfo(b,ie.dtype,ie.values)}var Jj={kernelName:yi,backendName:"cpu",kernelFunc:eC};function Zj(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=eC({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(h=Xc({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),p&&(m=l0(n,c,p,o,d),f.push(c),c=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return c}var Qj={kernelName:Ys,backendName:"cpu",kernelFunc:Zj},e5=ot(kl,e=>Math.acos(e)),t5={kernelName:kl,backendName:"cpu",kernelFunc:e5},n5=ot(Il,e=>Math.acosh(e)),a5={kernelName:Il,backendName:"cpu",kernelFunc:n5};function r5(e){let{inputs:t,backend:n}=e,a=t;ve(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 s5={kernelName:mi,backendName:"cpu",kernelFunc:r5};function i5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,u=E.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=ha({inputs:{x:r},backend:n,attrs:{perm:u}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("all",l,p.shape.length);let[d,c]=E.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=E.expandShapeToKeepDim(d,o),b=Tt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var o5={kernelName:Sl,backendName:"cpu",kernelFunc:i5};function l5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"any");let o=k.parseAxisParam(s,r.shape),l=o,u=E.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=ha({inputs:{x:r},backend:n,attrs:{perm:u}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("any",l,p.shape.length);let[d,c]=E.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=E.expandShapeToKeepDim(d,o),b=Tt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var u5={kernelName:Nl,backendName:"cpu",kernelFunc:l5};function p5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ve(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ha({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],E.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[p,d]=E.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 c5={kernelName:fi,backendName:"cpu",kernelFunc:p5};function d5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ve(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ha({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],E.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,d]=E.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 h5={kernelName:rc,backendName:"cpu",kernelFunc:d5},m5=ot(Tl,e=>Math.asin(e)),f5={kernelName:Tl,backendName:"cpu",kernelFunc:m5},g5=ot(Cl,e=>Math.asinh(e)),y5={kernelName:Cl,backendName:"cpu",kernelFunc:g5},b5=ot(El,e=>Math.atan(e)),x5={kernelName:El,backendName:"cpu",kernelFunc:b5},v5=Ut((e,t)=>Math.atan2(e,t)),w5=rn(Al,v5),k5={kernelName:Al,backendName:"cpu",kernelFunc:w5},I5=ot(_l,e=>Math.atanh(e)),S5={kernelName:_l,backendName:"cpu",kernelFunc:I5};function u0(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 _=0;_<r.outHeight;++_){let $=_*i-c,P=Math.max(0,$),F=Math.min(r.inHeight,p+$),S=w+_*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 tC(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,_=C;for(;_<0;)_+=p;let $=Math.min(a.inWidth,c+C),P=Number.NEGATIVE_INFINITY,F=-1;for(let S=v;S<w;S+=u){let M=S-x;for(let V=_;V<$;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 nC(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],_=r.outShape[4];for(let $=0;$<r.batchSize;++$){let P=$*w,F=$*a[0];for(let S=0;S<r.inChannels;++S)for(let M=0;M<r.outDepth;++M){let V=M*i-f,j=V;for(;j<0;)j+=u;let q=Math.min(r.inDepth,c+V),K=P+M*T;for(let Q=0;Q<r.outHeight;++Q){let ee=Q*o-g,re=ee;for(;re<0;)re+=p;let Z=Math.min(r.inHeight,h+ee),ie=K+Q*C;for(let ae=0;ae<r.outWidth;++ae){let le=ae*l-y,ue=le;for(;ue<0;)ue+=d;let we=Math.min(r.inWidth,m+le),ye=ie+ae*_,Ie=b,_e=0,$e=0;for(let je=j;je<q;je+=u){let st=F+je*a[1];for(let nt=re;nt<Z;nt+=p){let at=st+nt*a[2];for(let Te=ue;Te<we;Te+=d){let gt=at+Te*a[3],pt=e[gt+S];if(s==="max"&&pt>Ie?Ie=pt:s==="avg"&&(_e+=pt,$e++),isNaN(Ie))break}if(isNaN(Ie))break}if(isNaN(Ie))break}let Be=ye+S;v[Be]=s==="avg"?_e/$e:Ie}}}}return x}function N5(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 _=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<_;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 T5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ve(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=E.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))d=dr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=u0(c,r.shape,r.dtype,h,p,"avg");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var C5={kernelName:gi,backendName:"cpu",kernelFunc:T5};function E5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;ve(r,"avgPool3d");let p=E.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=nC(d,r.shape,r.dtype,k.computeStrides(r.shape),p,"avg");return n.makeTensorInfo(c.shape,"float32",c.values)}var _5={kernelName:sc,backendName:"cpu",kernelFunc:E5};function A5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;ve([r,s],"avgPool3DGrad");let p=E.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,_=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-_,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 we=0;we<T;we+=x){let ye=(re+we)/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 $5={kernelName:tm,backendName:"cpu",kernelFunc:A5};function F5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ve([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=E.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,_=He(r.shape,"float32",C);for(let $=0;$<p.batchSize;++$)for(let P=0;P<p.inChannels;++P)for(let F=0;F<p.inHeight;++F)for(let S=0;S<p.inWidth;++S){let M=F-v,V=S-x,j=0;for(let q=0;q<y;q+=f){let K=(M+q)/d;if(!(K<0||K>=p.outHeight||Math.floor(K)!==K))for(let Q=0;Q<b;Q+=g){let ee=(V+Q)/c;ee<0||ee>=p.outWidth||Math.floor(ee)!==ee||(j+=_.get($,K,ee,P))}}w.set(j*T,$,F,S,P)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var D5={kernelName:em,backendName:"cpu",kernelFunc:F5};function R5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ve([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=a;u==null&&(u=.001);let p=n.data.get(r.dataId).values,d=n.data.get(o.dataId).values,c=n.data.get(l.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(p.length),g=m.length,y=h.length,b=c.length,x=d.length,v=0,w=0,T=0,C=0;for(let _=0;_<p.length;++_)f[_]=m[v++]+(p[_]-d[w++])*h[T++]/Math.sqrt(c[C++]+u),v>=g&&(v=0),w>=x&&(w=0),T>=y&&(T=0),C>=b&&(C=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var M5={kernelName:$i,backendName:"cpu",kernelFunc:R5};function P5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ve([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=E.getReshaped(r.shape,s,o),u=E.getPermuted(l.length,s.length),p=E.getReshapedPermuted(r.shape,s,o),d=E.getSliceBeginCoords(i,s.length),c=E.getSliceSize(p,i,s.length),h=Tt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ha({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Tt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=li({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var O5={kernelName:$l,backendName:"cpu",kernelFunc:P5};function L5(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=t0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var z5={kernelName:nm,backendName:"cpu",kernelFunc:L5};function B5(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=E.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var W5={kernelName:am,backendName:"cpu",kernelFunc:B5},U5=ot(ds,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),V5={kernelName:ds,backendName:"cpu",kernelFunc:U5},G5=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")},H5={kernelName:ic,backendName:"cpu",kernelFunc:G5};function gl(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 j5={kernelName:gm,backendName:"cpu",kernelFunc:gl};function yl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=E.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 dr({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(E.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>oi({inputs:{input:v},backend:n})),g=o.map(v=>gl({inputs:{input:v},backend:n})),y=yl({inputs:f,backend:n,attrs:{axis:s}}),b=yl({inputs:g,backend:n,attrs:{axis:s}}),x=Kn({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=E.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=n0(p,i,t[0].dtype,d),h=E.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var q5={kernelName:Fl,backendName:"cpu",kernelFunc:yl};function aC(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;ve([r,s],"conv2d");let d=E.convertConv2DDataFormat(l),c=E.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],_=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 we=le*T[0],ye=ee+ue*_;for(let Ie=0;Ie<c.outWidth;++Ie){let _e=ie+Ie*M,$e=Ie*c.strideWidth-y;for(let Be=0;Be<m;++Be){let je=$e+Be*g;if(je<0||je>=c.inWidth)continue;let st=we+Be*T[1],nt=ye+je*$,at=st;for(let Te=0;Te<c.inChannels;++Te){let gt=j[nt+Te*P];for(let pt=0;pt<c.outChannels;++pt)K[_e+pt*V]+=gt*q[at+pt];at+=c.outChannels}}}}}}return n.makeTensorInfo(v.shape,v.dtype,K)}var K5={kernelName:vi,backendName:"cpu",kernelFunc:aC};function X5(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;ve([r,s],"conv2dBackpropFilter");let d=E.convertConv2DDataFormat(l),c=E.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),_=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)*_.get(Q,ee,Z,q):K+=C.get(Q,j,re,ie)*_.get(Q,q,ee,Z)}}b.set(K,$,S,j,q)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var Y5={kernelName:sm,backendName:"cpu",kernelFunc:X5};function J5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a;ve([r,s],"conv2dBackpropInput");let d=k.computeStrides(s.shape),c=k.computeStrides(r.shape),h=E.convertConv2DDataFormat(u),m=E.computeConv2DInfo(i,s.shape,o,1,l,p,!1,h),f=new jt(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,b=n.data.get(s.dataId).values,[x,v,w]=d,{batchSize:T,filterHeight:C,filterWidth:_,inChannels:$,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=_-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],we=ee?c[2]:1,ye=ee?1:c[1];for(let Ie=0;Ie<T;++Ie)for(let _e=0;_e<$;++_e)for(let $e=0;$e<P;++$e){let Be=$e-K,je=Math.max(0,Math.ceil(Be/j)),st=Math.min(M,(C+Be)/j);for(let nt=0;nt<F;++nt){let at=nt-Q,Te=Math.max(0,Math.ceil(at/q)),gt=Math.min(V,(_+at)/q),pt=0;for(let Yt=je;Yt<st;++Yt){let Dn=Yt*j-Be;for(let Vt=Te;Vt<gt;++Vt){let Jt=Vt*q-at,Fa=le*Ie+ue*Yt+we*Vt,Rn=x*(C-1-Dn)+v*(_-1-Jt)+w*_e;for(let Gt=0;Gt<S;++Gt){let ra=y[Fa+ye*Gt],sa=b[Rn+Gt];pt+=ra*sa}}}let gn=re*Ie+Z*$e+ie*nt+ae*_e;g[gn]=pt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var Z5={kernelName:wi,backendName:"cpu",kernelFunc:J5};function Q5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;ve([r,s],"conv3d");let u=E.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:p,filterHeight:d,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,b=g.left,x=g.top,v=new jt(u.outShape,r.dtype),w=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=v.values,_=k.computeStrides(r.shape),$=k.computeStrides(s.shape);for(let P=0;P<u.batchSize;++P){let F=P*_[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*$[0],ee=F+K*_[1];for(let re=0;re<u.outHeight;++re){let Z=V+re*v.strides[2],ie=re*u.strideHeight-x;for(let ae=0;ae<d;++ae){let le=ie+ae*m;if(le<0||le>=u.inHeight)continue;let ue=Q+ae*$[1],we=ee+le*_[2];for(let ye=0;ye<u.outWidth;++ye){let Ie=Z+ye*u.outChannels,_e=ye*u.strideWidth-b;for(let $e=0;$e<c;++$e){let Be=_e+$e*f;if(Be<0||Be>=u.inWidth)continue;let je=ue+$e*$[2],st=we+Be*u.inChannels,nt=je;for(let at=0;at<u.inChannels;++at){let Te=w[st+at];for(let gt=0;gt<u.outChannels;++gt)C[Ie+gt]+=Te*T[nt+gt];nt+=u.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var eq={kernelName:oc,backendName:"cpu",kernelFunc:Q5};function tq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;ve([r,s],"conv3dBackpropFilterV2");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=E.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,_=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 we=Math.max(0,Math.ceil((re-ue)/h)),ye=Math.min(d.outHeight,(d.inHeight+re-ue)/h),Ie=ue*w+le;for(let _e=0;_e<y;++_e){let $e=Math.max(0,Math.ceil((ee-_e)/m)),Be=Math.min(d.outWidth,(d.inWidth+ee-_e)/m),je=_e*T+Ie;for(let st=0;st<d.inChannels;++st){let nt=st*C+je;for(let at=0;at<d.outChannels;++at){let Te=0;for(let gt=0;gt<d.batchSize;++gt){let pt=gt*V,gn=gt*$;for(let Yt=ie;Yt<ae;++Yt){let Dn=(Z+Yt*c-Q)*j+pt,Vt=Yt*P+gn;for(let Jt=we;Jt<ye;++Jt){let Fa=(ue+Jt*h-re)*q+Dn,Rn=Jt*F+Vt;for(let Gt=$e;Gt<Be;++Gt){let ra=(_e+Gt*m-ee)*K+Fa,sa=Gt*S+Rn;Te+=M[ra+st]*_[sa+at]}}}}x[nt+at]=Te}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var nq={kernelName:im,backendName:"cpu",kernelFunc:tq};function aq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;ve([r],"conv3dBackpropInputV2");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=E.computeConv3DInfo(l,s.shape,o,1,i),c=new jt(d.inShape,"float32"),h=c.values,[m,f,g,y]=c.strides,b=n.data.get(r.dataId).values,[x,v,w,T]=u,C=n.data.get(s.dataId).values,[_,$,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:we}=d,ye=M-1-d.padInfo.front,Ie=V-1-d.padInfo.top,_e=j-1-d.padInfo.left;for(let $e=0;$e<S;++$e)for(let Be=0;Be<q;++Be)for(let je=0;je<K;++je){let st=je-ye,nt=Math.max(0,Math.ceil(st/le)),at=Math.min(Z,(M+st)/le);for(let Te=0;Te<Q;++Te){let gt=Te-Ie,pt=Math.max(0,Math.ceil(gt/ue)),gn=Math.min(ie,(V+gt)/ue);for(let Yt=0;Yt<ee;++Yt){let Dn=Yt-_e,Vt=Math.max(0,Math.ceil(Dn/we)),Jt=Math.min(ae,(j+Dn)/we),Fa=0;for(let Rn=nt;Rn<at;++Rn){let Gt=Rn*le-st;for(let ra=pt;ra<gn;++ra){let sa=ra*ue-gt;for(let Br=Vt;Br<Jt;++Br){let Es=Br*we-Dn,yd=x*$e+v*Rn+w*ra+T*Br,Wr=_*(M-1-Gt)+$*(V-1-sa)+P*(j-1-Es)+F*Be;for(let vr=0;vr<re;++vr){let pp=b[yd+vr],Po=C[Wr+vr];Fa+=pp*Po}}}}h[m*$e+f*je+g*Te+y*Yt+Be]=Fa}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var rq={kernelName:om,backendName:"cpu",kernelFunc:aq},sq=ot(ki,e=>Math.cos(e)),iq={kernelName:ki,backendName:"cpu",kernelFunc:sq},oq=ot(Ii,e=>Math.cosh(e)),lq={kernelName:Ii,backendName:"cpu",kernelFunc:oq};function uq(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 _=C*4,$=b[_],P=b[_+1],F=b[_+2],S=b[_+3],M=x[C];if(M>=p)continue;let V=f>1?(F-$)*(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 we=0;we<h;we++){let ye=we+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 we=0;we<h;we++){let ye=we+ae*w[2]+Q*w[1]+M*w[0],Ie=v[ye];ye=we+le*w[2]+Q*w[1]+M*w[0];let _e=v[ye];ye=we+ae*w[2]+ee*w[1]+M*w[0];let $e=v[ye];ye=we+le*w[2]+ee*w[1]+M*w[0];let Be=v[ye],je=Ie+(_e-Ie)*ue,st=$e+(Be-$e)*ue;ye=we+Z*T[2]+q*T[1]+C*T[0],y.values[ye]=je+(st-je)*re}}}else for(let Q=0;Q<g;++Q){let ee=g>1?P*(c-1)+Q*j:.5*(P+S)*(c-1);if(ee<0||ee>c-1){for(let ie=0;ie<h;ie++){let ae=ie+Q*T[2]+q*T[1]+C*T[0];y.values[ae]=u}continue}let re=Math.round(ee),Z=Math.round(K);for(let ie=0;ie<h;ie++){let ae=ie+re*w[2]+Z*w[1]+M*w[0],le=ie+Q*T[2]+q*T[1]+C*T[0];y.values[le]=v[ae]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var pq={kernelName:Dl,backendName:"cpu",kernelFunc:uq};function cq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;ve(r,"cumsum");let l=E.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=ha({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=E.getInnerMostAxes(1,r.shape.length)[0];if(p!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${p}`);let d=Sa(u.dtype,"int32"),c=k.makeZerosTypedArray(k.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)c[x]=i?0:h[x];else{let v=f(y,b-1);c[x]=i?h[v]+c[v]:h[x]+c[v]}}let g=n.makeTensorInfo(u.shape,d,c);if(l!=null){let y=E.getUndoAxesPermutation(l),b=ha({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var dq={kernelName:Si,backendName:"cpu",kernelFunc:cq};function hq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,p=t0(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=hT(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var mq={kernelName:lm,backendName:"cpu",kernelFunc:hq};function fq(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,_=(v*s+C)*h;for(let $=0;$<h;++$){let P=$+_+p*(T+u*(x+l*y));f[g++]=m[P]}}}return n.makeTensorInfo([o,d,c,h],r.dtype,f)}var gq={kernelName:Rl,backendName:"cpu",kernelFunc:fq};function rC(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a;ve([r,s],"depthwiseConv2DNative");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),c=l;c==null&&(c=[1,1]),k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=E.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,_=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],we=ee+le*h.inChannels,ye=Z,Ie=ue;for(let _e=0;_e<h.inChannels;++_e){let $e=C[we+_e];for(let Be=0;Be<w;++Be)$[ye+Be]+=$e*_[Ie+Be];ye+=w,Ie+=w}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var yq={kernelName:Ni,backendName:"cpu",kernelFunc:rC};function bq(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;ve([r,s],"depthwiseConv2dNativeBackpropFilter");let d=E.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 _=0;_<m;++_){let $=Math.max(0,Math.ceil((b-_)/c)),P=Math.min(d.outHeight,(d.inHeight+b-_)/c);for(let F=0;F<f;++F){let S=Math.max(0,Math.ceil((y-F)/h)),M=Math.min(d.outWidth,(d.inWidth+y-F)/h);for(let V=0;V<d.outChannels;++V){let j=Math.trunc(V/x),q=V%x,K=0;for(let Q=0;Q<d.batchSize;++Q)for(let ee=$;ee<P;++ee){let re=_+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,_,F,j,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var xq={kernelName:um,backendName:"cpu",kernelFunc:bq};function vq(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;ve([r,s],"depthwiseConv2DNativeBackpropInput");let d=k.computeStrides(r.shape),c=k.computeStrides(s.shape),h=E.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,[_,$,P]=c,{batchSize:F,filterHeight:S,filterWidth:M,inChannels:V,inHeight:j,inWidth:q,outChannels:K,outHeight:Q,outWidth:ee,strideHeight:re,strideWidth:Z}=h,ie=S-1-h.padInfo.top,ae=M-1-h.padInfo.left,le=K/V;for(let ue=0;ue<F;++ue)for(let we=0;we<V;++we)for(let ye=0;ye<j;++ye){let Ie=ye-ie,_e=Math.max(0,Math.ceil(Ie/re)),$e=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)),nt=Math.min(ee,(M+je)/Z),at=0;for(let Te=_e;Te<$e;++Te){let gt=Te*re-Ie;for(let pt=st;pt<nt;++pt){let gn=pt*Z-je,Yt=v*ue+w*Te+T*pt,Dn=_*(S-1-gt)+$*(M-1-gn)+P*we;for(let Vt=0;Vt<le;++Vt){let Jt=we*le+Vt,Fa=x[Yt+Jt],Rn=C[Dn+Vt];at+=Fa*Rn}}}f[g*ue+y*ye+b*Be+we]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var wq={kernelName:pm,backendName:"cpu",kernelFunc:vq};function kq(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 Iq={kernelName:cm,backendName:"cpu",kernelFunc:kq},Sq={kernelName:lc,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:_,dilationWidth:$,outShape:P}=E.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),F=k.sizeFromShape(P),S=P.length,M=k.getArrayFromDType(a.dtype,F);for(let V=0;V<h;++V)for(let j=0;j<y;++j){let q=j*v-x.top;for(let K=0;K<b;++K){let Q=K*w-x.left;for(let ee=0;ee<g;++ee){let re=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<T;++ie){let ae=q+ie*_;if(ae>=0&&ae<m)for(let le=0;le<C;++le){let ue=Q+le*$;if(ue>=0&&ue<f){let we=k.locToIndex([V,ae,ue,ee],p,k.computeStrides(a.shape)),ye=k.locToIndex([ie,le,ee],c,k.computeStrides(r.shape)),Ie=u[we]+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}}},Nq={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:_,outShape:$}=E.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.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*_;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}}},Tq={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:_,outShape:$}=E.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.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*_;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 Yc(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"sum");let o;r.dtype==="bool"?o=us({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=dr({inputs:{x:r},backend:n});let l=o.shape.length,u=k.parseAxisParam(s,o.shape),p=E.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=ha({inputs:{x:o},backend:n,attrs:{perm:p}}),d=E.getInnerMostAxes(d.length,l)),E.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=E.computeOutAndReduceShapes(c.shape,d),f=E.upcastType(c.dtype,"int32"),g=Vh(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=E.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 Cq={kernelName:ao,backendName:"cpu",kernelFunc:Yc};function Eq(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=E.decodeEinsumEquation(r,s.length);E.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=E.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}=E.getEinsumPermutation(h,l[g]),x;E.isIdentityPermutation(y)?x=s[g]:(x=ha({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=Pf({inputs:{a:x,b:c},backend:n}),m.push(c))}f<d-1&&(u[f]>=0&&(c=Yc({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 _q={kernelName:dm,backendName:"cpu",kernelFunc:Eq};function Aq(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ve([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 $q={kernelName:hm,backendName:"cpu",kernelFunc:Aq},Fq=E.ERF_P,Dq=E.ERF_A1,Rq=E.ERF_A2,Mq=E.ERF_A3,Pq=E.ERF_A4,Oq=E.ERF_A5,Lq=ot(Ml,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+Fq*n);return t*(1-((((Oq*a+Pq)*a+Mq)*a+Rq)*a+Dq)*a*Math.exp(-n*n))}),zq={kernelName:Ml,backendName:"cpu",kernelFunc:Lq};function Hh(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Tt({inputs:{x:r},backend:n,attrs:{shape:o}})}var Bq={kernelName:Ol,backendName:"cpu",kernelFunc:Hh},Wq=Ut((e,t)=>e/t),p0=rn(Ti,Wq),ex={kernelName:Ti,backendName:"cpu",kernelFunc:p0};function sC(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=li({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,s]}}),b=li({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,s]}}),x=Kn({inputs:{real:y,imag:b},backend:n}),{real:v,imag:w}=Uq(x,t,n),T=E.mergeRealAndImagArrays(v,w);for(let C=0;C<s;C++){let _=E.getComplexWithIndex(T,C);d[g*s+C]=_.real,c[g*s+C]=_.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(x)}let h=n.makeTensorInfo(u,"float32",d),m=n.makeTensorInfo(u,"float32",c),f=Kn({inputs:{real:h,imag:m},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}function Uq(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(Vq(a)){let o=tx(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=dr({inputs:{x:d},backend:n}),h=ex.kernelFunc({inputs:{a:u,b:d},backend:n}),m=ex.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=E.mergeRealAndImagArrays(s,i),l=Gq(o,a,t);return E.splitRealAndImagArrays(l)}}function Vq(e){return(e&e-1)===0}function tx(e,t,n,a,r){if(n===1)return{real:e,imag:t};let s=E.mergeRealAndImagArrays(e,t),i=n/2,o=E.complexWithEvenIndex(s),l=o.real,u=o.imag,p=[l.length],d=r.makeTensorInfo(p,"float32",l),c=r.makeTensorInfo(p,"float32",u),h=Kn({inputs:{real:d,imag:c},backend:r}),m=E.complexWithOddIndex(s),f=m.real,g=m.imag,y=[f.length],b=r.makeTensorInfo(y,"float32",f),x=r.makeTensorInfo(y,"float32",g),v=Kn({inputs:{real:b,imag:x},backend:r}),w=tx(l,u,i,a,r),T=w.real,C=w.imag,_=[T.length],$=r.makeTensorInfo(_,"float32",T),P=r.makeTensorInfo(_,"float32",C),F=Kn({inputs:{real:$,imag:P},backend:r}),S=tx(f,g,i,a,r),M=S.real,V=S.imag,j=[M.length],q=r.makeTensorInfo(j,"float32",M),K=r.makeTensorInfo(j,"float32",V),Q=Kn({inputs:{real:q,imag:K},backend:r}),ee=E.exponents(n,a),re=[ee.real.length],Z=r.makeTensorInfo(re,"float32",ee.real),ie=r.makeTensorInfo(re,"float32",ee.imag),ae=Kn({inputs:{real:Z,imag:ie},backend:r}),le=Pf({inputs:{a:ae,b:Q},backend:r}),ue=Xc({inputs:{a:F,b:le},backend:r}),we=o0({inputs:{a:F,b:le},backend:r}),ye=oi({inputs:{input:ue},backend:r}),Ie=oi({inputs:{input:we},backend:r}),_e=gl({inputs:{input:ue},backend:r}),$e=gl({inputs:{input:we},backend:r}),Be=yl({inputs:[ye,Ie],backend:r,attrs:{axis:0}}),je=yl({inputs:[_e,$e],backend:r,attrs:{axis:0}}),st=r.data.get(Be.dataId).values,nt=r.data.get(je.dataId).values;return r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo($),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(we),r.disposeIntermediateTensorInfo(ye),r.disposeIntermediateTensorInfo(_e),r.disposeIntermediateTensorInfo(Ie),r.disposeIntermediateTensorInfo($e),r.disposeIntermediateTensorInfo(Be),r.disposeIntermediateTensorInfo(je),{real:st,imag:nt}}function Gq(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=E.exponent(r*o,t,n),u=E.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),E.assignToTypedArray(a,s,i,r)}return a}function Hq(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=sC(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:mm,backendName:"cpu",kernelFunc:Hq};function c0(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 Kq(o,r,i),t.makeTensorInfo(a,i,o)}var qq={kernelName:uc,backendName:"cpu",kernelFunc:c0};function Kq(e,t,n){e.fill(t)}var Xq={kernelName:zl,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}}},Yq=Ut((e,t)=>Math.floor(e/t)),Jq=rn(Ai,Yq,null,"int32"),Zq={kernelName:Ai,backendName:"cpu",kernelFunc:Jq};function Qq(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=aC({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=Xc({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=l0(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var e8={kernelName:Js,backendName:"cpu",kernelFunc:Qq};function t8(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=rC({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=Xc({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=l0(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var n8={kernelName:Zs,backendName:"cpu",kernelFunc:t8};function a8(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]=E.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=wT(c,h,a.dtype,u,o,p,d,a.shape,s);return n.makeTensorInfo(l,a.dtype,m.values)}var r8={kernelName:Wl,backendName:"cpu",kernelFunc:a8};function s8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;ve([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=E.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=kT(b,y,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var i8={kernelName:Bl,backendName:"cpu",kernelFunc:s8};function o8(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=sC(o,!0,n),u=Tt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var l8={kernelName:fm,backendName:"cpu",kernelFunc:o8},u8=ot(Vl,e=>Number.isFinite(e)?1:0,"bool"),p8={kernelName:Vl,backendName:"cpu",kernelFunc:u8},c8=ot(Gl,e=>Math.abs(e)===1/0?1:0,"bool"),d8={kernelName:Gl,backendName:"cpu",kernelFunc:c8},h8=ot(Hl,e=>Number.isNaN(e)?1:0,"bool"),m8={kernelName:Hl,backendName:"cpu",kernelFunc:h8};function f8(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=CT(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var g8={kernelName:ym,backendName:"cpu",kernelFunc:f8},y8=ot(Kl,e=>Math.log1p(e)),b8={kernelName:Kl,backendName:"cpu",kernelFunc:y8},x8=Ut((e,t)=>e&&t),v8=rn(Xl,x8,null,"bool"),w8={kernelName:Xl,backendName:"cpu",kernelFunc:v8},k8=ot(pc,e=>e?0:1,"bool"),I8={kernelName:pc,backendName:"cpu",kernelFunc:k8},S8=Ut((e,t)=>e||t),N8=rn(cc,S8,null,"bool"),T8={kernelName:cc,backendName:"cpu",kernelFunc:N8};function C8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ve(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 E8={kernelName:dc,backendName:"cpu",kernelFunc:C8};function _8(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;ve(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 _=-2*u*p*m[C]*f[b]/T;b===C&&(_+=Math.pow(T,-p)),_*=h[b],g[C]+=_}}return n.makeTensorInfo(i.shape,r.dtype,g)}var A8={kernelName:bm,backendName:"cpu",kernelFunc:_8};function iC(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=E.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=r0(h,l,r.dtype,c,v),d=E.getInnerMostAxes(d.length,u),l=v}ve(r,"max"),E.assertAxesAreInnerMostDims("max",d,u);let[m,f]=E.computeOutAndReduceShapes(l,d),g=k.sizeFromShape(f),y=_T(h,g,m,r.dtype),b=o.write(y,m,r.dtype),x=m;return i&&(x=E.expandShapeToKeepDim(m,p)),{dataId:b,shape:x,dtype:r.dtype}}var $8={kernelName:Pi,backendName:"cpu",kernelFunc:iC};function F8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ve(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=E.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))d=dr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=u0(c,r.shape,r.dtype,h,p,"max");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var D8={kernelName:Li,backendName:"cpu",kernelFunc:F8};function R8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;ve(r,"maxPool3d");let p=E.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=nC(d,r.shape,r.dtype,k.computeStrides(r.shape),p,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var M8={kernelName:hc,backendName:"cpu",kernelFunc:R8};function P8(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;ve([r,s],"maxPool3DGrad");let p=E.computePool3DInfo(s.shape,i,o,1,l,u),d=n.bufferSync(s),c=N5(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,_=v-1-p.padInfo.top,$=He(s.shape,"float32"),P=n.bufferSync(r);for(let F=0;F<p.batchSize;++F)for(let S=0;S<p.inChannels;++S)for(let M=0;M<p.inDepth;++M)for(let V=0;V<p.inHeight;++V)for(let j=0;j<p.inWidth;++j){let q=M-T,K=V-_,Q=j-C,ee=0;for(let re=0;re<x;re+=g){let Z=(q+re)/h;if(!(Z<0||Z>=p.outDepth||Math.floor(Z)!==Z))for(let ie=0;ie<v;ie+=y){let ae=(K+ie)/m;if(!(ae<0||ae>=p.outHeight||Math.floor(ae)!==ae))for(let le=0;le<w;le+=b){let ue=(Q+le)/f;if(ue<0||ue>=p.outWidth||Math.floor(ue)!==ue)continue;let we=x*v*w-1-c.get(F,Z,ae,ue,S),ye=re*v*w+ie*w+le,Ie=we===ye?1:0;Ie!==0&&(ee+=P.get(F,Z,ae,ue,S)*Ie)}}}$.set(ee,F,M,V,j,S)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var O8={kernelName:vm,backendName:"cpu",kernelFunc:P8};function L8(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ve([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=E.computePool2DInfo(o.shape,l,u,1,p,d),h=n.data.get(o.dataId).values,m=He(c.outShape,o.dtype,tC(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,y=c.dilationHeight,b=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,w=v-1-c.padInfo.left,T=x-1-c.padInfo.top,C=He(o.shape,"float32"),_=n.data.get(r.dataId).values,$=He(r.shape,"float32",_);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 z8={kernelName:xm,backendName:"cpu",kernelFunc:L8};function B8(e,t,n,a,r){let s=k.computeStrides(t),i=u0(e,t,n,s,r,"max"),o=tC(e,t,n,r,!0,a);return[i.values,o.values]}var W8={kernelName:wm,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ve(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,p=E.computePool2DInfo(a.shape,r,s,[1,1],i),[d,c]=B8(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 U8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),l=E.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=p0({inputs:{a:c,b:d},backend:n});p.push(h);let m=Yc({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var V8={kernelName:zi,backendName:"cpu",kernelFunc:U8};function G8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"min");let o=k.parseAxisParam(s,r.shape),l=o,u=E.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=ha({inputs:{x:r},backend:n,attrs:{perm:u}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",l,p.shape.length);let[d,c]=E.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=E.expandShapeToKeepDim(d,o),b=Tt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var H8={kernelName:Bi,backendName:"cpu",kernelFunc:G8};function j8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,mode:i}=a;ve(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 q8={kernelName:Ui,backendName:"cpu",kernelFunc:j8},K8=Ut((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),X8=rn(Yl,K8),Y8={kernelName:Yl,backendName:"cpu",kernelFunc:X8},J8=di(nI());function oC(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=iC({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),p=E.expandShapeToKeepDim(u.shape,l),d=Tt({inputs:{x:u},backend:n,attrs:{shape:p}}),c=o0({inputs:{a:r,b:d},backend:n}),h=bT({inputs:{x:c},backend:n}),m=Yc({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=Tt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=p0({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 Z8={kernelName:ro,backendName:"cpu",kernelFunc:oC};function Q8(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;ve(r,"multinomial");let l=o?r:oC({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=J8.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 eK={kernelName:km,backendName:"cpu",kernelFunc:Q8},tK=fr.nonMaxSuppressionV3Impl;function nK(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a;ve(r,"NonMaxSuppression");let u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:d}=tK(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var aK={kernelName:Ql,backendName:"cpu",kernelFunc:nK},rK=fr.nonMaxSuppressionV4Impl;function sK(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a;ve(r,"NonMaxSuppressionPadded");let p=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,{selectedIndices:c,validOutputs:h}=rK(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var iK={kernelName:eu,backendName:"cpu",kernelFunc:sK},oK=fr.nonMaxSuppressionV5Impl;function lK(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a;ve(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}=oK(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var uK={kernelName:tu,backendName:"cpu",kernelFunc:lK};function pK(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;ve(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 cK={kernelName:Gi,backendName:"cpu",kernelFunc:pK};function jh(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=oi({inputs:{input:a},backend:n}),s=jh({inputs:{x:r},backend:n}),i=gl({inputs:{input:a},backend:n}),o=jh({inputs:{x:i},backend:n}),l=Kn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return c0({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var dK={kernelName:wu,backendName:"cpu",kernelFunc:jh};function lC(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=oi({inputs:{input:a},backend:n}),s=lC({inputs:{x:r},backend:n}),i=gl({inputs:{input:a},backend:n}),o=jh({inputs:{x:i},backend:n}),l=Kn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return c0({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var hK={kernelName:nu,backendName:"cpu",kernelFunc:lC};function uC(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Hh({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=Hh({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=yl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var mK={kernelName:au,backendName:"cpu",kernelFunc:uC};function fK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ve(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 pC={kernelName:Hi,backendName:"cpu",kernelFunc:fK},gK=Ut((e,t)=>Math.pow(e,t)),yK=rn(ji,gK),bK={kernelName:ji,backendName:"cpu",kernelFunc:yK};function xK(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=s0(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var vK={kernelName:mc,backendName:"cpu",kernelFunc:xK},wK=ot(su,e=>1/e),kK={kernelName:su,backendName:"cpu",kernelFunc:wK};function IK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ve(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 _;i?_=v*(C+.5)-.5:_=v*C;let $=Math.max(0,Math.floor(_)),P=_-$,F=Math.min(c-1,Math.ceil(_)),S=T*l[0]+$*l[1],M=T*l[0]+F*l[1];for(let V=0;V<p;V++){let j;i?j=w*(V+.5)-.5:j=w*V;let q=Math.max(0,Math.floor(j)),K=j-q,Q=Math.min(h-1,Math.ceil(j)),ee=S+q*l[2],re=M+q*l[2],Z=S+Q*l[2],ie=M+Q*l[2];for(let ae=0;ae<m;ae++){let le=f[ee+ae],ue=f[re+ae],we=f[Z+ae],ye=f[ie+ae],Ie=le+(we-le)*K,_e=ue+(ye-ue)*K,$e=Ie+(_e-Ie)*P;g[x++]=$e}}}return n.makeTensorInfo([d,u,p,m],"float32",g)}var SK={kernelName:Xi,backendName:"cpu",kernelFunc:IK};function NK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ve([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,u,p,d]=r.shape,[,c,h]=s.shape,m=new Float32Array(l*u*p*d),f=[i&&c>1?u-1:u,i&&h>1?p-1:p],g=[i&&c>1?c-1:c,i&&h>1?h-1:h],y=f[0]/g[0],b=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let w=0;w<l;w++){let T=w*o[0];for(let C=0;C<c;C++){let _=C*y,$=Math.floor(_),P=Math.min(Math.ceil(_),u-1),F=T+$*o[1],S=T+P*o[1],M=_-$,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,we=V*ee,ye=M*re,Ie=M*ee;for(let _e=0;_e<d;_e++){let $e=x[v++];m[Z+_e]+=$e*ue,m[ie+_e]+=$e*we,m[ae+_e]+=$e*ye,m[le+_e]+=$e*Ie}}}}return n.makeTensorInfo([l,p,u,d],"float32",m)}var TK={kernelName:Nm,backendName:"cpu",kernelFunc:NK};function CK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ve(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 _=0;_<u;_++){let $=i?x*(_+.5):x*_,P=Math.min(c-1,s?Math.round($):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 EK={kernelName:fc,backendName:"cpu",kernelFunc:CK};function _K(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ve([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,_=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-_/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 we=0;we<_;we++){let ye=we+Q;if(ye<0||ye>=m)continue;let Ie=ae+ye*l[2],_e=ye*v,$e=Math.min(d-1,i?Math.round(_e):Math.floor(_e));j===$e&&(re+=g[Ie+ee])}}f[q+ee]=re}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var AK={kernelName:Sm,backendName:"cpu",kernelFunc:_K};function $K(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;ve(r,"reverse");let i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return dr({inputs:{x:r},backend:n});let l=new jt(r.shape,r.dtype),u=n.bufferSync(r);for(let p=0;p<l.size;p++){let d=l.indexToLoc(p),c=d.slice();o.forEach(h=>c[h]=r.shape[h]-1-c[h]),l.set(u.get(...c),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var FK={kernelName:Ji,backendName:"cpu",kernelFunc:$K},DK={kernelName:ku,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]=E.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 _=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+$;j=b[ee]}let q=v+T+_+$;l[q]=j}}}}return{dataId:o.write(l,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},RK=ot(Zi,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}),MK={kernelName:Zi,backendName:"cpu",kernelFunc:RK};function cC(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 PK(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}=E.calculateShapes(s,r,i),c=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=cC(h,m,i,d,u,l,o,p,0,c);return n.makeTensorInfo(i,f.dtype,f.values)}var OK={kernelName:ou,backendName:"cpu",kernelFunc:PK};function LK(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t;ve([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=Sa(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 zK={kernelName:lu,backendName:"cpu",kernelFunc:LK},BK=E.SELU_SCALEALPHA,WK=E.SELU_SCALE,UK=ot(uu,e=>e>=0?WK*e:BK*(Math.exp(e)-1)),VK={kernelName:uu,backendName:"cpu",kernelFunc:UK},GK=ot(du,e=>e<0?-1:e>0?1:0),HK={kernelName:du,backendName:"cpu",kernelFunc:GK},jK=ot(eo,e=>Math.sin(e)),qK={kernelName:eo,backendName:"cpu",kernelFunc:jK},KK=ot(cu,e=>Math.sinh(e)),XK={kernelName:cu,backendName:"cpu",kernelFunc:KK},YK=11920928955078125e-23,Ek=Math.log(YK)+2,JK=ot(hu,e=>{let t=e>-Ek,n=e<Ek,a=Math.exp(e),r;return n?r=a:t?r=e:r=Math.log(1+a),r}),ZK={kernelName:hu,backendName:"cpu",kernelFunc:JK};function QK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;ve([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=pC.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=E.getReshaped(u.shape,s,o,!1),d=E.getPermuted(p.length,s.length,!1),c=E.getReshapedPermuted(u.shape,s,o,!1),h=Tt({inputs:{x:u},backend:n,attrs:{shape:p}}),m=ha({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 eX={kernelName:mu,backendName:"cpu",kernelFunc:QK};function tX(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]=OT(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 nX={kernelName:gc,backendName:"cpu",kernelFunc:tX};function aX(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]=LT(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var rX={kernelName:gu,backendName:"cpu",kernelFunc:aX};function sX(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]=i0(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var iX={kernelName:yc,backendName:"cpu",kernelFunc:sX};function oX(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]=i0(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var lX={kernelName:bc,backendName:"cpu",kernelFunc:oX};function uX(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}=E.calculateShapes(s,r,o),h=!1,m=n.bufferSync(r),f=n.bufferSync(s),g=n.data.get(i.dataId).values[0],y=cC(m,f,o,c,p,u,l,d,g,h);return n.makeTensorInfo(o,y.dtype,y.values)}var pX={kernelName:Tm,backendName:"cpu",kernelFunc:uX};function cX(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=E.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=li({inputs:{x:r},backend:n,attrs:{begin:u,size:c}});return u[o]+=d,h})}var dX={kernelName:fu,backendName:"cpu",kernelFunc:cX},hX={kernelName:xc,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;ve(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}}},mX=ot(ms,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),fX={kernelName:ms,backendName:"cpu",kernelFunc:mX};function gX(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;ve(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=li({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=BT(h,T,v,b);w=n.makeTensorInfo(m,C.dtype,C.values)}return w}var yX={kernelName:yu,backendName:"cpu",kernelFunc:gX};function bX(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]=WT(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var xX={kernelName:Cm,backendName:"cpu",kernelFunc:bX};function vX(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]=UT(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 wX={kernelName:Em,backendName:"cpu",kernelFunc:vX};function kX(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=VT(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var IX={kernelName:_m,backendName:"cpu",kernelFunc:kX},SX=ot(oo,e=>Math.tan(e)),NX={kernelName:oo,backendName:"cpu",kernelFunc:SX},TX=ot(lo,e=>Math.tanh(e)),CX={kernelName:lo,backendName:"cpu",kernelFunc:TX};function EX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;ve(r,"tile");let i=HT(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var _X={kernelName:hs,backendName:"cpu",kernelFunc:EX};function AX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;ve(r,"topk");let o=n.data.get(r.dataId).values,[l,u]=qT(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 $X={kernelName:bu,backendName:"cpu",kernelFunc:AX};function FX(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 _=0;_<p;++_){let $=s.shape[0]===1?C:C.subarray(_*8,_*8+8);for(let P=0;P<m;++P)for(let F=0;F<f;++F)for(let S=0;S<h;++S){let M,V=$[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=_k(j,c,o),Q=_k(q,d,o);switch(i){case"nearest":M=LX(T,d,c,b,x,v,_,Q,K,S,l);break;case"bilinear":M=zX(T,d,c,b,x,v,_,Q,K,S,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let ee=_*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 DX={kernelName:xu,backendName:"cpu",kernelFunc:FX};function _k(e,t,n){switch(n){case"reflect":return RX(e,t);case"wrap":return MX(e,t);case"nearest":return OX(e,t);case"constant":default:return PX(e,t)}}function RX(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 MX(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 PX(e,t){return e}function OX(e,t){return k.clamp(0,e,t-1)}function Dp(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 LX(e,t,n,a,r,s,i,o,l,u,p){let d=Math.round(o),c=Math.round(l);return Dp(e,t,n,a,r,s,i,d,c,u,p)}function zX(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)*Dp(e,t,n,a,r,s,i,d,c,u,p)+(l-c)*Dp(e,t,n,a,r,s,i,d,m,u,p),g=(m-l)*Dp(e,t,n,a,r,s,i,h,c,u,p)+(l-c)*Dp(e,t,n,a,r,s,i,h,m,u,p);return(h-o)*f+(o-d)*g}function BX(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;ve(s,"unique");let i=a.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:u}=KT(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var WX={kernelName:Am,backendName:"cpu",kernelFunc:BX};function UX(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=li({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 VX={kernelName:vu,backendName:"cpu",kernelFunc:UX};function GX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a;ve(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=Hh({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=gT({inputs:{a:g,b:c},backend:n}),b=us({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=Pf({inputs:{a:b,b:r},backend:n}),v=Yc({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=uC({inputs:u,backend:n,attrs:{axis:0}});return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var HX={kernelName:vc,backendName:"cpu",kernelFunc:GX},jX=[Qj,jH,t5,a5,ZH,s5,o5,u5,c5,h5,f5,y5,x5,k5,S5,C5,_5,$5,D5,Jj,M5,O5,z5,W5,YH,ej,V5,qH,H5,q5,K5,Y5,Z5,eq,nq,rq,iq,lq,pq,dq,mq,gq,yq,xq,wq,Iq,Sq,Nq,Tq,_q,Gj,$q,tj,zq,nj,Bq,rj,jq,qq,Xq,ij,Zq,e8,n8,r8,i8,lj,pj,KH,l8,j5,p8,d8,m8,Hj,dj,mj,g8,gj,b8,w8,I8,T8,E8,A8,$8,bj,D8,M8,O8,z8,W8,V8,H8,vj,q8,Y8,eK,kj,Sj,aK,iK,uK,Tj,cK,hK,mK,pC,bK,qj,_j,vK,XH,ex,kK,Kj,Xj,Yj,SK,TK,EK,AK,FK,DK,MK,$j,OK,zK,VK,Dj,HK,qK,XK,Rj,Z8,ZK,eX,nX,rX,iX,lX,pX,dX,Oj,hX,zj,fX,yX,xX,wX,IX,Vj,Cq,NX,CX,_X,$X,DX,Cj,WX,VX,HX,dK];for(let e of jX)wc(e);var dC={};Re(dC,{assertNotComplex:()=>Pu,bindCanvasToFramebuffer:()=>r7,bindColorTextureToFramebuffer:()=>fh,bindTextureToProgramUniformSampler:()=>EC,bindTextureUnit:()=>NC,bindVertexBufferToProgramAttribute:()=>nx,callAndCheck:()=>ge,canBeRepresented:()=>mC,createFragmentShader:()=>yC,createFramebuffer:()=>SC,createProgram:()=>bC,createStaticIndexBuffer:()=>wC,createStaticVertexBuffer:()=>vC,createTexture:()=>kC,createVertexShader:()=>gC,getBatchDim:()=>ui,getExtensionOrThrow:()=>Rp,getFramebufferErrorMessage:()=>_C,getMaxTexturesInShader:()=>DC,getNumChannels:()=>n7,getProgramUniformLocation:()=>CC,getProgramUniformLocationOrThrow:()=>TC,getRowsCols:()=>pi,getShapeAs3D:()=>gh,getTextureShapeFromLogicalShape:()=>$C,getWebGLDisjointQueryTimerVersion:()=>RC,getWebGLErrorMessage:()=>fC,getWebGLMaxTextureSize:()=>FC,hasExtension:()=>ca,isCapableOfRenderingToFloatTexture:()=>MC,isDownloadFloatTextureEnabled:()=>PC,isReshapeFree:()=>ec,isWebGLFenceEnabled:()=>OC,isWebGLVersionEnabled:()=>rx,linkProgram:()=>xC,resetMaxTextureSize:()=>s7,resetMaxTexturesInShader:()=>i7,unbindColorTextureFromFramebuffer:()=>ax,unbindTextureUnit:()=>a7,validateFramebuffer:()=>Mp,validateProgram:()=>mh,validateTextureSize:()=>IC});var Us={},hb={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function hC(e,t){Us[e]=t}function Ya(e,t){if(!(e in Us)||t!=null){let a=KX(e,t);if(a!==null)Us[e]=a;else return console.log("Could not get context for WebGL version",e),null}let n=Us[e];return n==null||n.isContextLost()?(delete Us[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),Us[e])}function qX(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 KX(e,t){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let n=t==null?qX(e):t;return n.addEventListener("webglcontextlost",a=>{a.preventDefault(),delete Us[e]},!1),e===1?n.getContext("webgl",hb)||n.getContext("experimental-webgl",hb):n.getContext("webgl2",hb)}var Qp;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(Qp||(Qp={}));var pa;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(pa||(pa={}));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 Jc(e,t){return[t,e]}function XX(e,t){return e*t}function oh(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Mu(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function YX(e,t){let[n,a]=Mu(e,t);return n*a*4}function d0(e,t){let n=e,a,r,s,i,o,l,u,p,d,c;return Y().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 Y().getBool("DEBUG")&&JX(e),n}function JX(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+fC(e,t))}var ZX=596e-10,QX=65504;function mC(e){return!!(Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||ZX<Math.abs(e)&&Math.abs(e)<QX)}function fC(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 Rp(e,t){return Fr(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function gC(e,t){let n=Fr(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 yC(e,t){let n=Fr(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(ge(e,()=>e.shaderSource(n,t)),ge(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw t7(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var e7=/ERROR: [0-9]+:([0-9]+):/g;function t7(e,t){let n=e7.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 ${k.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(p.join(`
`))}function bC(e){return Fr(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function xC(e,t){if(ge(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function mh(e,t){if(ge(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function vC(e,t){let n=Fr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ge(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function wC(e,t){let n=Fr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ge(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),ge(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function n7(){return Y().getNumber("WEBGL_VERSION")===2?1:4}function kC(e){return Fr(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function IC(e,t){let n=Y().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${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 SC(e){return Fr(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function nx(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 NC(e,t,n){AC(e,n),ge(e,()=>e.activeTexture(e.TEXTURE0+n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function a7(e,t){AC(e,t),ge(e,()=>e.activeTexture(e.TEXTURE0+t)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function TC(e,t,n){return Fr(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function CC(e,t,n){return e.getUniformLocation(t,n)}function EC(e,t,n,a){ge(e,()=>NC(e,t,a)),ge(e,()=>e.uniform1i(n,a))}function r7(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 ax(e,t){ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),ge(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Mp(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+_C(e,t))}function _C(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 ${t}`}}function Fr(e,t,n){let a=ge(e,()=>t());if(a==null)throw new Error(n);return a}function AC(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function ui(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function pi(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=[ui(e),...pi(e)]),t}function $C(e,t=!1){let n=Y().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=ui(e),s=2,i=2;return e.length&&([s,i]=pi(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 ec(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 FC(e){if(yh==null){let t=Ya(e);yh=t.getParameter(t.MAX_TEXTURE_SIZE)}return yh}function s7(){yh=null}function i7(){bh=null}function DC(e){if(bh==null){let t=Ya(e);bh=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,bh)}function RC(e){if(e===0)return 0;let t,n=Ya(e);return ca(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:ca(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function ca(e,t){return e.getExtension(t)!=null}function rx(e){try{if(Ya(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function MC(e){if(e===0)return!1;let t=Ya(e);if(e===1){if(!ca(t,"OES_texture_float"))return!1}else if(!ca(t,"EXT_color_buffer_float"))return!1;return sx(t)}function PC(e){if(e===0)return!1;let t=Ya(e);if(e===1){if(!ca(t,"OES_texture_float")||!ca(t,"WEBGL_color_buffer_float"))return!1}else{if(ca(t,"EXT_color_buffer_float"))return sx(t);let n="EXT_color_buffer_half_float";if(ca(t,n)){let a=t.getExtension(n);return o7(t,a)}return!1}return sx(t)}function sx(e){let t=d0(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 o7(e,t){let n=d0(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 OC(e){return e!==2?!1:Ya(e).fenceSync!=null}function Pu(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=Y();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>rx(2)?2:rx(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",()=>FC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>DC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:RC(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Sc.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>MC(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",()=>PC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>OC(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",()=>Sc.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 En(){let e,t,n,a,r,s,i,o,l,u;return Y().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 yo(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 Of(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}; ${o};`}).join("")}function l7(e,t){let n=e.length,a=e.map(s=>`${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 u7(e,t,n="index"){let a=e.map((s,i)=>i),r=l7(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 h0(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 m0(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var LC=`
  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:zC}=E;function p7(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}=f0(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?`[${c.arrayIndex}]`:""};`)});let r=a.join(`
`),s=e.map(c=>c7(c,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),i=t.texShape,o=En(),l=m7(o),u,p,d=y7(o);return t.isPacked?(u=d7(t.logicalShape,i,n.enableShapeUniforms),p=g7(o)):(u=h7(t.logicalShape,i,n.enableShapeUniforms),p=f7(o)),n.packedInputs&&(d+=w7),[d,l,p,r,u,s,n.userCode].join(`
`)}function Ou(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return D7(e,t);case 1:return M7(e,t);case 2:return O7(e,t);case 3:return z7(e,t);case 4:return W7(e,t);case 5:return U7(e);case 6:return V7(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function BC(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return F7(e);case 1:return R7(e,t);case 2:return P7(e,t);case 3:return L7(e,t);default:return B7(e,t)}}function c7(e,t,n=!1,a){let r="";n?r+=BC(e,a):r+=Ou(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=G7(e,t):r+=H7(e,t)),r}function d7(e,t,n){switch(e.length){case 0:return WC();case 1:return k7(e,t,n);case 2:return A7(e,t,n);case 3:return S7(e,t,n);default:return T7(e,t,n)}}function h7(e,t,n){switch(e.length){case 0:return WC();case 1:return I7(e,t,n);case 2:return $7(e,t,n);case 3:return N7(e,t,n);case 4:return C7(e,t,n);case 5:return E7(e,t);case 6:return _7(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function m7(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function f7(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function g7(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function y7(e){return`${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);
    }

    ${b7}
    ${x7}
    ${v7}
  `}var b7=`
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);
}
`,x7=`
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);
}
`,v7=`
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);
}
`,w7=`
  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 WC(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function k7(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 I7(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 S7(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 N7(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;
    ${Of(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let a=yo(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec3(r, c, d);
    }
  `}function T7(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 C7(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;
      ${Of(["r","c","d","d2"],e)}
      return ivec4(r, c, d, d2);
    }
  `;let a=yo(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec4(r, c, d, d2);
    }
  `}function E7(e,t){let n=yo(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function _7(e,t){let n=yo(["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 A7(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 $7(e,t,n){return k.arraysEqual(e,t)?n?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
      return ivec2(r, c);
    }
  `}function bo(e){return`offset${e}`}function F7(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=En();return`
    vec4 ${n}() {
      return ${a.texture2D}(${t}, halfCR);
    }
  `}function D7(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=bo(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 R7(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=En();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 M7(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
      float ${a}(int index) {
        ${Lu(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=bo(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 P7(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=En();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 O7(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=zu(e,l),h=["row","col"];return`
      ${Ou(c,t)}
      float ${r}(int row, int col) {
        return ${r}(${Bu(h,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${Lu(e)}
      }
    `;let u=s[0],p=s[1],d=bo(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 L7(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=zu(e,c),f=["b","row","col"];return`
        ${BC(m,t)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${Bu(f,h)});
        }
      `}let o=En();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 z7(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=zu(e,u),g=["row","col","depth"];return`
        ${Ou(f,t)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${Bu(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)));
        ${Lu(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=bo(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 B7(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=En();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 W7(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=zu(e,l),x=["row","col","depth","depth2"];return`
      ${Ou(b,t)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${Bu(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)));
        ${Lu(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=bo(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 U7(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=zu(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${Ou(f)}
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        return ${a}(${Bu(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;
        ${Lu(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=bo(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 V7(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=zu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${Ou(g)}
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${a}(${Bu(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)));
        ${Lu(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=bo(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 Lu(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 ${t}[i];
      }
    }
  `}function G7(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=zC(e.shapeInfo.logicalShape,t.logicalShape),l=mt(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${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 H7(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=mt(l),p=zC(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 mt(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 ${e} is not yet supported`)}function f0(e,t,n){let{newShape:a,keptDims:r}=k.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!k.arraysEqual(t,n)&&a.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function zu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Bu(e,t){return t.map(n=>e[n]).join(", ")}function j7(e,t,n,a){let r=n.map((v,w)=>{let T={logicalShape:v.shape,texShape:v.isUniform?null:v.texData.texShape,isUniform:v.isUniform,isPacked:v.isUniform?!1:v.texData.isPacked,flatOffset:null};return v.texData!=null&&v.texData.slice!=null&&v.texData.slice.flatOffset>0&&(T.flatOffset=v.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:T}}),s=r.map(v=>v.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=p7(r,i,t),l=yC(e.gl,o),u=e.createProgram(l),p=null,d=e.getUniformLocation(u,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(u,"INFINITY",!1));let c=!1,h={},m={},f={};for(let v=0;v<t.variableNames.length;v++){let w=t.variableNames[v];h[w]=e.getUniformLocation(u,w,c),h[`offset${w}`]=e.getUniformLocation(u,`offset${w}`,c),t.enableShapeUniforms&&(m[`${w}Shape`]=e.getUniformLocation(u,`${w}Shape`,c),f[`${w}TexShape`]=e.getUniformLocation(u,`${w}TexShape`,c))}let g,y,b;t.enableShapeUniforms&&(g=e.getUniformLocation(u,"outShape",c),b=e.getUniformLocation(u,"outShapeStrides",c),y=e.getUniformLocation(u,"outTexShape",c));let x=[];return t.customUniforms&&t.customUniforms.forEach((v,w)=>{x[w]=e.getUniformLocation(u,v.name,c)}),{program:t,fragmentShader:l,source:o,webGLProgram:u,uniformLocations:h,customUniformLocations:x,inShapeInfos:s,outShapeInfo:i,infLoc:p,nanLoc:d,inShapesLocations:m,inTexShapesLocations:f,outShapeLocation:g,outShapeStridesLocation:b,outTexShapeLocation:y}}function Ak(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 ${l} must match`)})}function q7(e,t,n,a,r){t.program.enableShapeUniforms||(Ak(t.inShapeInfos,n),Ak([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let p=t.program.variableNames[u],d=t.uniformLocations[p],c=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],m=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:f}=f0(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 ${l.type} is not supported yet.`)}),e.executeProgram()}function K7(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=f0(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${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]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&k.arraysEqual(i.shape,l),y=k.sizeFromShape(i.shape)===1,b=E.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&k.arraysEqual(l,n.texData.texShape),v=e.packedInputs||p.length>2?"":`${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+`${Y().getNumber("WEBGL_VERSION")}`,s}function Vn(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var X7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Qp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Of(["r","c","d"],e):yo(["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;
      }
    `}},Y7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Qp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Of(["r","c","d"],e):yo(["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;
      }
    `}},J7=class{constructor(e){this.variableNames=["A"],this.outTexUsage=pa.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=`
      ${LC}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},Z7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=pa.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=`
      ${LC}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},Q7=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=En();this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)"),this.userCode=`
      ${this.enableShapeUniforms?m0():h0(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.);
      }
    `}},eY=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=En();this.outputShape=e,this.enableShapeUniforms=Vn(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?m0():h0(e)}

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

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

          ${a}

          ${n.output} = ${r};
        }
    `}},UC={};Re(UC,{bindVertexProgramAttributeStreams:()=>JC,createBufferFromOutputTexture:()=>eE,createFloat16MatrixTexture:()=>qC,createFloat16PackedMatrixTexture:()=>YC,createFloat32MatrixTexture:()=>jC,createIndexBuffer:()=>HC,createPackedMatrixTexture:()=>XC,createUnsignedBytesMatrixTexture:()=>KC,createVertexBuffer:()=>GC,createVertexShader:()=>VC,downloadByteEncodedFloatMatrixFromOutputTexture:()=>nE,downloadFloat32MatrixFromBuffer:()=>tE,downloadMatrixFromPackedOutputTexture:()=>rE,downloadPackedMatrixFromBuffer:()=>aE,getInternalFormatForFloat16MatrixTexture:()=>y0,getInternalFormatForFloat16PackedMatrixTexture:()=>v0,getInternalFormatForFloat32MatrixTexture:()=>g0,getInternalFormatForPackedMatrixTexture:()=>x0,getInternalFormatForUnsignedBytesMatrixTexture:()=>b0,uploadDenseMatrixToTexture:()=>ZC,uploadPixelDataToTexture:()=>QC});function VC(e){let t=En(),n=`${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 gC(e,n)}function GC(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 vC(e,t)}function HC(e){let t=new Uint16Array([0,1,2,2,1,3]);return wC(e,t)}function Zc(e,t,n,a,r,s){IC(t,n);let i=kC(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)),Y().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 g0(e){return e.internalFormatFloat}function jC(e,t,n,a){let[r,s]=Jc(t,n);return Zc(e,r,s,g0(a),a.textureFormatFloat,e.FLOAT)}function y0(e){return e.internalFormatHalfFloat}function qC(e,t,n,a){let[r,s]=Jc(t,n);return Zc(e,r,s,y0(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function b0(e){return e.downloadTextureFormat}function KC(e,t,n,a){let[r,s]=Jc(t,n);return Zc(e,r,s,b0(a),e.RGBA,e.UNSIGNED_BYTE)}function x0(e){return e.internalFormatPackedFloat}function XC(e,t,n,a){let[r,s]=Mu(t,n);return Zc(e,r,s,x0(a),e.RGBA,e.FLOAT)}function v0(e){return e.internalFormatPackedHalfFloat}function YC(e,t,n,a){let[r,s]=Mu(t,n);return Zc(e,r,s,v0(a),e.RGBA,a.textureTypeHalfFloat)}function JC(e,t,n){return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),nx(e,t,"clipSpacePos",n,3,20,0)&&nx(e,t,"uv",n,2,20,12)}function ZC(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),Y().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 QC(e,t,n){ge(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Y().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)):Y().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 eE(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 tE(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 nE(e,t,n,a){let[r,s]=Jc(t,n),i=4,o=new Uint8Array(XX(t*n,i));return ge(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function aE(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(YX(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 rE(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=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,hC(t,e)):this.gl=Ya(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(Y().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Rp(this.gl,r),ca(this.gl,s))this.textureHalfFloatExtension=Rp(this.gl,s);else if(Y().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),ca(this.gl,a))this.colorBufferHalfFloatExtension=Rp(this.gl,a);else if(Y().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",ca(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ca(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=GC(this.gl),this.indexBuffer=HC(this.gl),this.framebuffer=SC(this.gl),this.textureConfig=d0(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().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(),jC(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),qC(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),KC(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),QC(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),ZC(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),YC(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),XC(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(ax(this.gl,this.framebuffer),this.outputTexture=null),ge(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>nE(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return aE(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return tE(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=eE(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(Y().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 Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>rE(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=VC(t));let n=bC(t);return ge(t,()=>t.attachShader(n,this.vertexShader)),ge(t,()=>t.attachShader(n,e)),xC(t,n),this.debug&&mh(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=JC(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?TC(this.gl,e,t):CC(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(),EC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Mu(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),Mp(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=Rp(this.gl,Y().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(Y().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(Y().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,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().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=tY(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&&Mp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(fh(this.gl,this.outputTexture,this.framebuffer),this.debug&&Mp(this.gl)):ax(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&&Mp(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 tY(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:nY,bincountImpl:sE,bincountReduceImpl:aY,ceilImpl:rY,concatImpl:sY,equalImpl:iY,expImpl:oY,expm1Impl:lY,floorImpl:uY,gatherNdImpl:pY,gatherV2Impl:cY,greaterImpl:dY,greaterEqualImpl:hY,lessImpl:mY,lessEqualImpl:fY,linSpaceImpl:gY,logImpl:yY,maxImpl:bY,maximumImpl:xY,minimumImpl:vY,multiplyImpl:wY,negImpl:kY,notEqualImpl:IY,prodImpl:SY,rangeImpl:NY,rsqrtImpl:TY,sigmoidImpl:CY,simpleAbsImpl:iE,sliceImpl:EY,sparseFillEmptyRowsImpl:_Y,sparseReshapeImpl:AY,sparseSegmentReductionImpl:oE,sqrtImpl:$Y,stridedSliceImpl:FY,stringNGramsImpl:DY,stringSplitImpl:RY,stringToHashBucketFastImpl:MY,subImpl:PY,tileImpl:OY,topKImpl:LY,transposeImpl:w0,uniqueImpl:zY}=pT;function lE(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function In(e,t){return t===1?[e]:lE(e,t)}function BY(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 WY=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Vn(this.outputShape.length),this.rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let t=In("rc",this.rank),n=mt(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]})`}},uE=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Vn(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=`
      ${UY(t,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?m0():h0(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 UY(e,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?u7(["r","c","d"],"inputShape"):yo(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var VY=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=Fk(t,n),r=Dk(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=$k(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=Fk(n,a),s=Dk(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=$k(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 GY(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 $k(e,t,n,a,r){let s=HY(t,a),i;if(r){let[l,u]=Mu(e[0],e[1]);i=l*u}else{let[l,u]=Jc(e[0],e[1]);i=l*u}let o=GY(n,s);return i*o}function HY(e,t){switch(e){case on.PACKED_2X2_FLOAT32:return x0(t);case on.PACKED_2X2_FLOAT16:return v0(t);case on.UNPACKED_FLOAT32:return g0(t);case on.UNPACKED_FLOAT16:return y0(t);case on.PACKED_4X1_UNSIGNED_BYTE:return b0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function jY(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?on.PACKED_2X2_FLOAT32:on.UNPACKED_FLOAT32:e?on.PACKED_2X2_FLOAT16:on.UNPACKED_FLOAT16}function Fk(e,t){if(e===pa.UPLOAD)return on.PACKED_2X2_FLOAT32;if(e===pa.RENDER||e==null)return jY(t);if(e===pa.DOWNLOAD||e===pa.PIXELS)return on.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Dk(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Nr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Vn(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;",qY="return x;",Rk="return abs(x);",KY="return (x >= 0.0) ? x : (exp(x) - 1.0);",XY=Ea+`
  return (x < 0.0) ? 0.0 : x;
`,YY=Ea+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Yo="return x;",JY="return 1.0 / (1.0 + exp(-1.0 * x));",ZY="return x;",QY=`
  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;
`,e9=`
  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;
`,t9=`
  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;
`,n9="return 1.0 / (1.0 + exp(-1.0 * x));",Hs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

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

        setOutput(y);
      }
    `}},a9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=mt(t),r=BY(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}));
      }
    `}},r9=fr.whereImpl,s9=1e-7,i9=1e-4,mb={};function o9(e){return e in mb||(mb[e]={}),mb[e]}var l9=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),u9=600;function p9(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*u9/1024/1024}var Lf=class extends ac{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().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(Y().getNumber("WEBGL_VERSION"),e);t=new xh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ya(Y().getNumber("WEBGL_VERSION"));t=new xh(n),this.binaryCache=o9(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new VY(this.gpgpu),this.numMBBeforeWarning=p9(),this.texData=new Jh(this,rr())}nextDataId(){return Lf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().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:pa.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(Y().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:pa.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 Hs(i,Yo):d=new Nr(i,Yo);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=E.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 Hs(a,Yo):h=new Nr(a,Yo);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(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().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"&&Y().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=E.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)&&rr().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 Hs(r,Yo):c=new Nr(r,Yo);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=rr().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(!mC(n))throw Y().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),c=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture.texture,...oh(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let s=Y().getBool("WEBGL_PACK")&&a===!0,i=s?gh(t):t,o=s?new Z7(i):new J7(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(Y().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=l9){return Y().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){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return r9(e.shape,t)}packedUnaryOp(e,t,n){let a=new Hs(e.shape,t),r=this.compileAndRun(a,[e],n);return rr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=iE(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Rk,e.dtype);let t=new Nr(e.shape,Rk),n=this.compileAndRun(t,[e]);return rr().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 rr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new a9(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new WY(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ui(e.shape),...pi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ui(t),...pi(t)],s=new uE(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 Y7(i):o=new X7(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===Qp.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)<=Y().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&&!ec(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=K7(e,u,p),c=this.getAndSaveBinary(d,()=>j7(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),q7(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=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=k.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().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||(Y().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(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?s9:i9}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=$C(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]=Mu(p[0],p[1])),o?c=new eY(d,f):c=new Q7(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=pa.PIXELS:b.usage=pa.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.texture=T.texture,t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(w.dataId),t.values=null,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=c9(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)}};Lf.nextDataId=0;function c9(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 d9="3.14.0";function pE(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Sc.isBrowser()&&Rm("webgl",()=>new Lf,2);var h9={forceHalfFloat:pE},cE=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,bl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},zf=`
  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;
`,Qc=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Vn(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=`
          ${mt(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) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=In("coords",r);this.enableShapeUniforms?s+=`
            bool nextRowOutOfBounds =
              (${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 na(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 m9={kernelName:Di,backendName:"webgl",kernelFunc:na};function ws(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=na({inputs:{x:a},backend:n}),l=na({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var f9={kernelName:rm,backendName:"webgl",kernelFunc:ws},dE="return (a < 0.) ? b * a : a;",hE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function g9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qc(hE,r.shape,i.shape):new bl(dE,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var y9={kernelName:Ri,backendName:"webgl",kernelFunc:g9},mE="return (a < 0.) ? b * a : a;",fE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function b9(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qc(fE,a.shape,r.shape):new bl(mE,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var x9={kernelName:qi,backendName:"webgl",kernelFunc:b9},Wu="if (isnan(x)) return x;",v9=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,w9=`
  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=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Hs(i.shape,t):p=new Nr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function un({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},_=new bl(e,l.shape,u.shape);return p.runWebGLProgram(_,[T,C],Sa(v.dtype,w.dtype))}),b=ws({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),b}let d=s||Sa(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"?E.fromUint8ToStringArray(m):m,y=l.dtype==="string"?E.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=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Qc(t,l.shape,u.shape,n):h=new bl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Bf(e,t=!1){if(e==="linear")return t?ZY:qY;if(e==="relu")return t?e9:XY;if(e==="elu")return t?QY:KY;if(e==="relu6")return t?t9:YY;if(e==="prelu")return t?fE:mE;if(e==="leakyrelu")return t?hE:dE;if(e==="sigmoid")return t?n9:JY;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var gE=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=Vn(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);
      }
    `}},Mk={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Pk=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.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));
      }
    `}},Ok="return a * b;";function k0(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=E.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new Pk(Mk.REAL,a.shape,r.shape),p=new Pk(Mk.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=ws({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]=wY(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 Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Qc(Ok,a.shape,r.shape):i=new bl(Ok,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var k9={kernelName:Vi,backendName:"webgl",kernelFunc:k0};function I9(e,t,n){let a=[ui(e.shape),...pi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[ui(t),...pi(t)],i=new uE(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&&!ec(r.shape,l)&&!(p.texture!==null&&ec(p.shape,l))?I9(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var S9={kernelName:iu,backendName:"webgl",kernelFunc:me},Lk=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);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},N9=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 T9(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=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function xo(e,t,n,a){let r=T9(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 Lk({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Lk({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):p=new N9({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 C9=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=mt(this.rank),r=E9(t);this.userCode=`
    void main() {
      ${a} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `}};function E9(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 _9=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 ${this.rank} is not yet supported.`);let a=mt(this.rank),r=lE("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${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 Wf(e,t,n){let a=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _9(e.shape,t):new C9(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function A9(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=E.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Wf(e,l,a),o=E.getInnerMostAxes(o.length,s)),E.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=E.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=E.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=Fm(e.dtype),b=xo(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 A9(r,s,i,n)}var $9={kernelName:ao,backendName:"webgl",kernelFunc:Uf};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];let u;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=w0(p,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(u.dataId);c.values=d}else u=Wf(r,s,i);return u}var F9={kernelName:uo,backendName:"webgl",kernelFunc:Sn},yE=1e3;function qh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],c=a?t.shape[p-1]:t.shape[p-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),x=Iu.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);k.assert(d===c,()=>`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[y,d,h]:[y,h,d],w=a?[b,m,c]:[b,c,m],T=me({inputs:{x:e},backend:r,attrs:{shape:v}}),C=me({inputs:{x:t},backend:r,attrs:{shape:w}}),_=[T,C],$=Math.max(y,b),P=n?T.shape[1]:T.shape[2],F=s!=null,S=i!=null,M=l==="leakyrelu",V=l!=null?Bf(l,!0):null,j=F||S||M||V!=null,q;if((h===1||m===1)&&P>yE&&j===!1){let Q=T,ee=C;n&&(Q=Sn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),_.push(Q)),a&&(ee=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(ee));let re=m!==1,Z=m===1,ie=Q;re&&(ie=me({inputs:{x:Q},backend:r,attrs:{shape:[$,P,1]}}),_.push(ie));let ae=m===1?2:1,le=ee;Z&&(le=me({inputs:{x:ee},backend:r,attrs:{shape:[$,1,P]}}),_.push(le));let ue=k0({inputs:{a:ie,b:le},backend:r});q=Uf({inputs:{x:ue},backend:r,attrs:{axis:ae,keepDims:!0}}),_.push(ue)}else{let Q=Sa(e.dtype,t.dtype),ee=new gE(v,w,[$,h,m],n,a,F,V,S,M),re=[T,C];if(s!=null&&re.push(s),S&&re.push(i),M){let Z=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));re.push(Z),_.push(Z)}q=r.runWebGLProgram(ee,re,Q)}let K=me({inputs:{x:q},backend:r,attrs:{shape:x}});_.push(q);for(let Q of _)r.disposeIntermediateTensorInfo(Q);return K}function D9(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return qh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var R9={kernelName:Ys,backendName:"webgl",kernelFunc:D9},zk="return abs(x);";function M9(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=iE(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Hs(a.shape,zk):r=new Nr(a.shape,zk),n.runWebGLProgram(r,[a],a.dtype)}var P9={kernelName:wl,backendName:"webgl",kernelFunc:M9},O9=Ea+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,L9=Je({opSnippet:O9}),z9={kernelName:kl,backendName:"webgl",kernelFunc:L9},B9=Ea+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,W9=Je({opSnippet:B9}),U9={kernelName:Il,backendName:"webgl",kernelFunc:W9},Bk="return a + b;",V9=un({opSnippet:Bk,packedOpSnippet:Bk,supportsComplex:!0,cpuKernelImpl:nY}),G9={kernelName:cs,backendName:"webgl",kernelFunc:V9},H9=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 = ${a};
        setOutput(result);
      }
    `}},j9=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 na({inputs:{x:a[0]},backend:n});if(a.length>Y().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)=>Sa(o,l)),s=a.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new j9(a[0].shape,s):new H9(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var q9={kernelName:mi,backendName:"webgl",kernelFunc:vh};function K9(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=E.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=E.getInnerMostAxes(u.length,o)),E.assertAxesAreInnerMostDims("all",u,o);let[c,h]=E.computeOutAndReduceShapes(d.shape,u),m=k.sizeFromShape(h),f=me({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=xo(f,f.dtype,"all",n),y;if(i){let b=E.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 X9={kernelName:Sl,backendName:"webgl",kernelFunc:K9};function Y9(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=E.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=E.getInnerMostAxes(u.length,o)),E.assertAxesAreInnerMostDims("any",u,o);let[c,h]=E.computeOutAndReduceShapes(d.shape,u),m=k.sizeFromShape(h),f=me({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=xo(f,f.dtype,"any",n),y;if(i){let b=E.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 J9={kernelName:Nl,backendName:"webgl",kernelFunc:Y9},Z9=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 ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},Q9=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${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=mt(o),u=In("coords",o),p,d;if(s===1){d=o+1;let C=mt(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=In("sourceLocR",d-1).concat("inIdx.r"),g=In("sourceLocG",d-1).concat("inIdx.g"),y=In("sourceLocB",d-1).concat("inIdx.b"),b=In("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 bE(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=E.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new Z9(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=bE(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function xE(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=E.computeOptimalWindowSize(s),o=new Q9(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=xE(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function vE(e,t,n,a){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!Y().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]=E.computeOutAndReduceShapes(l.shape,r),d=k.sizeFromShape(p),c=me({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=bE(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 xE(e,t,a)}function eJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=vE(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var tJ={kernelName:fi,backendName:"webgl",kernelFunc:eJ};function nJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=vE(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var aJ={kernelName:rc,backendName:"webgl",kernelFunc:nJ},rJ=Ea+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,sJ=Je({opSnippet:rJ}),iJ={kernelName:Tl,backendName:"webgl",kernelFunc:sJ},oJ=Ea+"return log(x + sqrt(x * x + 1.0));",lJ=Je({opSnippet:oJ}),uJ={kernelName:Cl,backendName:"webgl",kernelFunc:lJ},pJ=Ea+`
  return atan(x);
`,cJ=Je({opSnippet:pJ}),dJ={kernelName:El,backendName:"webgl",kernelFunc:cJ},hJ=v9+`
  return atan(a, b);
`,mJ=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+w9+`
  return result;
`,fJ=un({opSnippet:hJ,packedOpSnippet:mJ}),gJ={kernelName:Al,backendName:"webgl",kernelFunc:fJ},yJ=Ea+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,bJ=Je({opSnippet:yJ}),xJ={kernelName:_l,backendName:"webgl",kernelFunc:bJ},tc=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});
      }
    `}},I0=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,_=`
      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)
              );

              ${_}
            }

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

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

              ${_}
            } else if (${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
              );

              ${_}
            }
          }
          setOutput(${w});
        }
      }
    `}};function vJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Pu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=E.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new tc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var wJ={kernelName:gi,backendName:"webgl",kernelFunc:vJ};function kJ(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=E.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new I0(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var IJ={kernelName:sc,backendName:"webgl",kernelFunc:kJ},SJ=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);
      }
    `}},NJ=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 TJ(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=E.computePool3DInfo(i.shape,o,l,d,u,p),h=new NJ(c);return n.runWebGLProgram(h,[r],i.dtype)}var CJ={kernelName:tm,backendName:"webgl",kernelFunc:TJ};function EJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Pu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=E.computePool2DInfo(i.shape,o,l,1,u),d=new SJ(p);return n.runWebGLProgram(d,[r],i.dtype)}var _J={kernelName:em,backendName:"webgl",kernelFunc:EJ};function AJ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return qh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var $J={kernelName:yi,backendName:"webgl",kernelFunc:AJ},FJ=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(E.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)));
      }
    `}},DJ=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(E.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);
      }
    `}},RJ=({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=Y().getBool("WEBGL_PACK_NORMALIZATION")?new DJ(a.shape,r.shape,s.shape,p,d,l):new FJ(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},MJ={kernelName:$i,backendName:"webgl",kernelFunc:RJ},PJ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=mt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=OJ(this.rank),a,r=e.map((s,i)=>`sourceLoc.${ix[i]} = start[${i}] + coords.${ix[i]};`);a=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `,this.userCode=`
      void main() {
        ${a}
        setOutput(getSource(${n}));
      }
    `}},ix=["x","y","z","w","u","v"];function OJ(e){if(e===1)return"sourceLoc";if(e<=6)return ix.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var LJ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=mt(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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}
        ${o}
        setOutput(result);
      }
    `}};function zJ(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=qt.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Uu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=qt.parseSliceParams(r,s,i);if(qt.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=EY(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=qt.isSliceContinous(r.shape,o,l);if(u||!p){let d=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new LJ(l):new PJ(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),zJ(r,o,l,n)}var BJ={kernelName:pu,backendName:"webgl",kernelFunc:Uu},WJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=E.getReshaped(r.shape,s,o),u=E.getPermuted(l.length,s.length),p=E.getReshapedPermuted(r.shape,s,o),d=E.getSliceBeginCoords(i,s.length),c=E.getSliceSize(p,i,s.length),h=[],m=me({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=me({inputs:{x:f},backend:n,attrs:{shape:p}}),y=Uu({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},UJ={kernelName:$l,backendName:"webgl",kernelFunc:WJ};function VJ(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=sE(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var GJ={kernelName:nm,backendName:"webgl",kernelFunc:VJ};function HJ(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=E.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var jJ={kernelName:am,backendName:"webgl",kernelFunc:HJ},qJ="return float(a != b);",wE=un({opSnippet:qJ,cpuKernelImpl:IY,dtype:"bool"}),KJ={kernelName:Zl,backendName:"webgl",kernelFunc:wE};function ed(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.real},backend:n})}var XJ={kernelName:Im,backendName:"webgl",kernelFunc:ed},YJ="return float(int(x));";function JJ(e,t){let n=new Nr(e.shape,YJ),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function ox(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return na({inputs:{x:r},backend:n});let i=kt(r.shape),o=ox({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ws({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=ed({inputs:{input:r},backend:n}),o=ox({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=na({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return JJ(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=wE({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 ZJ={kernelName:bi,backendName:"webgl",kernelFunc:ox},Wk="return ceil(x);",QJ=Je({opSnippet:Wk,packedOpSnippet:Wk,cpuKernelImpl:rY}),eZ={kernelName:xi,backendName:"webgl",kernelFunc:QJ},tZ=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));
      }
    `}},nZ=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 aZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;Y().getBool("WEBGL_PACK_CLIP")?o=new nZ(r.shape):o=new tZ(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var rZ={kernelName:ds,backendName:"webgl",kernelFunc:aZ},sZ=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 Uk(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function iZ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new sZ(a.shape),i=[Uk(a,r.complexTensorInfos.real),Uk(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var oZ={kernelName:ic,backendName:"webgl",kernelFunc:iZ},lZ=class{constructor(e){this.outputShape=[],this.outputShape=E.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;

        ${n.join(`
        `)}
      }
    `}},uZ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=mt(a),s=In("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${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 Vf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.imag},backend:n})}var pZ={kernelName:gm,backendName:"webgl",kernelFunc:Vf};function el(e,t,n){let a=e[0].dtype;if(a==="complex64"){let p=e.map(f=>ed({inputs:{input:f},backend:n})),d=e.map(f=>Vf({inputs:{input:f},backend:n})),c=el(p,t,n),h=el(d,t,n),m=ws({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=E.computeOutShape(p.map(y=>y.shape),1),h=p[0].shape[0]===1,m=sY(d,c,a,h),f=E.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(f,a,m);return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(e.length/2),d=el(e.slice(0,p),t,n),c=el(e.slice(p),t,n),h=el([d,c],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let p=new uZ(e.map(d=>d.shape),t);return n.runWebGLProgram(p,e,a)}let{tensors2D:s,outShape:i}=cZ(e,t,n),o=new lZ(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 cZ(e,t,n){let a=E.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 kE(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=E.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 na({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return E.assertParamsConsistent(l,s),el(o,s,n)}var dZ={kernelName:Fl,backendName:"webgl",kernelFunc:kE},IE=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}
        ${v}
        setOutput(result);
      }
    `}},hZ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${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);
      }
    `}},mZ=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=Vn(this.outputShape.length);let{dataFormat:n}=t,a=En(),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 SE({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>yE)&&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(ec(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=me({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(w);let T=qh({a:x,b:w,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);k.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=na({inputs:{x:T},backend:a}),g.shape=n.outShape,y.push(T)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=me({inputs:{x:e},backend:a,attrs:{shape:[1,b,n.inChannels]}}),v=me({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),w=qh({a:x,b:v,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=me({inputs:{x:w},backend:a,attrs:{shape:n.outShape}}),y.push(x),y.push(v),y.push(w)}for(let b of y)a.disposeIntermediateTensorInfo(b);return g}function NE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,y=[f,g],b=!0,x=!1,v=[],w=me({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=me({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(w),v.push(T);let C=new mZ(y,n),_=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],$=a.runWebGLProgram(C,[w],"float32",_),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?Bf(o,!0):null,j=new gE(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 fZ(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=E.convertConv2DDataFormat(l),c=E.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=SE({x:r,filter:s,convInfo:c,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=NE({x:r,filter:s,convInfo:c,backend:n});else{let f=new IE(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 gZ={kernelName:vi,backendName:"webgl",kernelFunc:fZ},yZ=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);
      }
    `}},bZ=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);
      }
    `}},xZ=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);
      }
    `}},vZ=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 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function wZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=E.convertConv2DDataFormat(l),c=E.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new yZ(c);return n.runWebGLProgram(h,[r,s],"float32")}var kZ={kernelName:sm,backendName:"webgl",kernelFunc:wZ};function IZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=E.convertConv2DDataFormat(u),c=E.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d),h=new bZ(c);return n.runWebGLProgram(h,[r,s],"float32")}var SZ={kernelName:wi,backendName:"webgl",kernelFunc:IZ};function NZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=E.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new hZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var TZ={kernelName:oc,backendName:"webgl",kernelFunc:NZ};function CZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=E.computeConv3DInfo(r.shape,l,i,1,o),p=new xZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var EZ={kernelName:im,backendName:"webgl",kernelFunc:CZ};function _Z(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=E.computeConv3DInfo(l,s.shape,o,1,i),p=new vZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var AZ={kernelName:om,backendName:"webgl",kernelFunc:_Z},$Z=Wu+`
  return cos(x);
`,FZ=Je({opSnippet:$Z}),DZ={kernelName:ki,backendName:"webgl",kernelFunc:FZ},RZ=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,MZ=Je({opSnippet:RZ}),PZ={kernelName:Ii,backendName:"webgl",kernelFunc:MZ},OZ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${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);
        }
      }
    `}},LZ=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 OZ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},zZ={kernelName:Dl,backendName:"webgl",kernelFunc:LZ},Vk=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(${Gk(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() {
        ${mt(a)} coords = getOutputCoords();
        int end = ${Hk(a,"coords")};
        float val = ${r};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${Hk(a,"coords")} = idx;
          val += getX(${Gk(a,"coords")});
        }
        setOutput(val);
      }
    `}};function Gk(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 ${e} is not yet supported`)}function Hk(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function BZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=E.getAxesPermutation([s],l),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=p.shape[d],h=na({inputs:{x:p},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new Vk(p.shape,!1,o),g=[[m]],y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new Vk(p.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=E.getUndoAxesPermutation(u),f=Sn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}return h}var WZ={kernelName:Si,backendName:"webgl",kernelFunc:BZ};function UZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=sE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=aY(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var VZ={kernelName:lm,backendName:"webgl",kernelFunc:UZ},GZ=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 HZ(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 GZ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var jZ={kernelName:Rl,backendName:"webgl",kernelFunc:HZ},TE=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=Vn(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}
        ${u}
        setOutput(result);
      }
    `}},CE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<p;g++)c+=`
          vec4 xTexelC${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(E.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=E.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new CE(d):c=new TE(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 KZ={kernelName:Ni,backendName:"webgl",kernelFunc:qZ},XZ=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);
      }
    `}},YZ=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 * ${o} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function JZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=E.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new XZ(d);return n.runWebGLProgram(c,[r,s],"float32")}var ZZ={kernelName:um,backendName:"webgl",kernelFunc:JZ};function QZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=E.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new YZ(d);return n.runWebGLProgram(c,[r,s],"float32")}var eQ={kernelName:pm,backendName:"webgl",kernelFunc:QZ},tQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function nQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=me({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new tQ(s),l=n.runWebGLProgram(o,[i],i.dtype),u=me({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var aQ={kernelName:cm,backendName:"webgl",kernelFunc:nQ},rQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=`
      const ivec2 strides = ivec2(${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 sQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=E.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new rQ(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 iQ={kernelName:lc,backendName:"webgl",kernelFunc:sQ};function oQ(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=E.decodeEinsumEquation(r,s.length);E.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=E.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}=E.getEinsumPermutation(h,l[g]),x;E.isIdentityPermutation(y)?x=s[g]:(x=Sn({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=k0({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 lQ={kernelName:dm,backendName:"webgl",kernelFunc:oQ},uQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",pQ=`
  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;
`,cQ=Je({opSnippet:uQ,packedOpSnippet:pQ}),dQ={kernelName:Ci,backendName:"webgl",kernelFunc:cQ},hQ="return (b >= 1.0) ? a : a * (b + 1.0);",mQ=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,fQ=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qc(mQ,a.shape,r.shape):new bl(hQ,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},gQ={kernelName:hm,backendName:"webgl",kernelFunc:fQ},yQ=`
  return vec4(equal(a, b));
`,bQ="return float(a == b);",xQ=un({opSnippet:bQ,packedOpSnippet:yQ,dtype:"bool",cpuKernelImpl:iY}),vQ={kernelName:Pl,backendName:"webgl",kernelFunc:xQ},wQ=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${E.ERF_P};
  float a1 = ${E.ERF_A1};
  float a2 = ${E.ERF_A2};
  float a3 = ${E.ERF_A3};
  float a4 = ${E.ERF_A4};
  float a5 = ${E.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));
`,kQ=Je({opSnippet:wQ}),IQ={kernelName:Ml,backendName:"webgl",kernelFunc:kQ},SQ=Wu+`
  return exp(x);
`,NQ=`
  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;
`,EE=Je({opSnippet:SQ,packedOpSnippet:NQ,cpuKernelImpl:oY,dtype:"float32"}),TQ={kernelName:Ei,backendName:"webgl",kernelFunc:EE};function lx(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 CQ={kernelName:Ol,backendName:"webgl",kernelFunc:lx},jk="return exp(x) - 1.0;",EQ=Je({opSnippet:jk,packedOpSnippet:jk,cpuKernelImpl:lY}),_Q={kernelName:Ll,backendName:"webgl",kernelFunc:EQ},qk=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 _E(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 qk("real",l,t),p=new qk("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=ws({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 AQ(e){let{inputs:t,backend:n}=e,{input:a}=t;return _E(a,!1,n)}var $Q={kernelName:mm,backendName:"webgl",kernelFunc:AQ},FQ=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 td(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 FQ(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var DQ={kernelName:uc,backendName:"webgl",kernelFunc:td},RQ=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);
        }
    `}},MQ={kernelName:zl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new RQ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},Kk="return floor(x);",PQ=Je({opSnippet:Kk,packedOpSnippet:Kk,cpuKernelImpl:uY}),OQ={kernelName:_i,backendName:"webgl",kernelFunc:PQ},LQ=`
  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;
  }
`,zQ=`
  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);
`,BQ=un({opSnippet:LQ,packedOpSnippet:zQ,dtype:"int32"}),WQ={kernelName:Ai,backendName:"webgl",kernelFunc:BQ},UQ=class{constructor(e){this.variableNames=["A"];let t=En(),[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));
      }
    `}},VQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[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);
          }
        }

        ${t.output} = result;
      }
    `}},GQ={kernelName:Th,backendName:"webgl",kernelFunc:HQ},Jo;function HQ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];(o||i)&&(Jo==null&&(Jo=document.createElement("canvas").getContext("2d")),Jo.canvas.width=l,Jo.canvas.height=u,Jo.drawImage(r,0,0,l,u),r=Jo.canvas);let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=pa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=Y().getBool("WEBGL_PACK")?new VQ(d):new UQ(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function jQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=E.convertConv2DDataFormat(p),g=E.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=SE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=NE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,w=o!=null,T=h==="leakyrelu",C=h?Bf(h,!1):null,_=new IE(g,v,C,w,T),$=[r,s];if(i&&$.push(i),o&&$.push(o),T){let P=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));$.push(P),b.push(P)}y=n.runWebGLProgram(_,$,"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:Js,backendName:"webgl",kernelFunc:jQ};function KQ(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(E.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=E.computeConv2DInfo(r.shape,s.shape,l,f,u,d,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=c?Bf(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 CE(g,v,b,w,T):C=new TE(g,v,b,w,T);let _=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=n.runWebGLProgram(C,x,"float32",_);return m.forEach(P=>n.disposeIntermediateTensorInfo(P)),$}var XQ={kernelName:Zs,backendName:"webgl",kernelFunc:KQ},YQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=mt(t.length),r=mt(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 JQ(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]=E.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=pY(y,b,a.dtype,u,i,p,d,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new YQ(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 ZQ={kernelName:Wl,backendName:"webgl",kernelFunc:JQ},QQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=mt(this.rank),a=eee(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 eee(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 AE(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(Y().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=E.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=cY(x,b,m);return d.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new QQ(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 tee={kernelName:Bl,backendName:"webgl",kernelFunc:AE},nee="return float(a > b);",aee=`
  return vec4(greaterThan(a, b));
`,ree=un({opSnippet:nee,packedOpSnippet:aee,cpuKernelImpl:dY,dtype:"bool"}),see={kernelName:Ul,backendName:"webgl",kernelFunc:ree},iee="return float(a >= b);",oee=`
  return vec4(greaterThanEqual(a, b));
`,lee=un({opSnippet:iee,packedOpSnippet:oee,dtype:"bool",cpuKernelImpl:hY}),uee={kernelName:Fi,backendName:"webgl",kernelFunc:lee};function pee(e){let{inputs:t,backend:n}=e,{input:a}=t;return _E(a,!0,n)}var cee={kernelName:fm,backendName:"webgl",kernelFunc:pee},dee="return float(!isnan(x) && !isinf(x));",hee=Je({opSnippet:dee,dtype:"bool"}),mee={kernelName:Vl,backendName:"webgl",kernelFunc:hee},fee="return float(isinf(x));",gee=Je({opSnippet:fee,dtype:"bool"}),yee={kernelName:Gl,backendName:"webgl",kernelFunc:gee},bee="return float(isnan(x));",xee=Je({opSnippet:bee,dtype:"bool"}),vee={kernelName:Hl,backendName:"webgl",kernelFunc:xee},wee="return float(a < b);",kee=`
  return vec4(lessThan(a, b));
`,Iee=un({opSnippet:wee,packedOpSnippet:kee,cpuKernelImpl:mY,dtype:"bool"}),See={kernelName:jl,backendName:"webgl",kernelFunc:Iee},Nee="return float(a <= b);",Tee=`
  return vec4(lessThanEqual(a, b));
`,Cee=un({opSnippet:Nee,packedOpSnippet:Tee,cpuKernelImpl:fY,dtype:"bool"}),Eee={kernelName:ql,backendName:"webgl",kernelFunc:Cee};function _ee(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=gY(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Aee={kernelName:ym,backendName:"webgl",kernelFunc:_ee},$ee=Wu+`
  return x < 0.0 ? 0./0. : log(x);
`,Fee=`
  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;
`,Dee=Je({opSnippet:$ee,packedOpSnippet:Fee,cpuKernelImpl:yY}),Ree={kernelName:Mi,backendName:"webgl",kernelFunc:Dee},Mee=Wu+`
  return log(1.0 + x);
`,Pee=Je({opSnippet:Mee}),Oee={kernelName:Kl,backendName:"webgl",kernelFunc:Pee},Lee="return float(a >= 1.0 && b >= 1.0);",zee=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,Bee=un({opSnippet:Lee,packedOpSnippet:zee,dtype:"bool"}),Wee={kernelName:Xl,backendName:"webgl",kernelFunc:Bee},Uee="return float(!(x >= 1.0));",Vee=Je({opSnippet:Uee}),Gee={kernelName:pc,backendName:"webgl",kernelFunc:Vee},Hee="return float(a >= 1.0 || b >= 1.0);",jee=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,qee=un({opSnippet:Hee,packedOpSnippet:jee,dtype:"bool"}),Kee={kernelName:cc,backendName:"webgl",kernelFunc:qee},Xee=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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 * ${o};
        setOutput(val);
      }
    `}},Yee=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
      }
    `}},Jee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Yee(r.shape,s,i,o,l):new Xee(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Zee={kernelName:dc,backendName:"webgl",kernelFunc:Jee},Qee=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);
      }
    `}},ete=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 Qee(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},tte={kernelName:bm,backendName:"webgl",kernelFunc:ete};function nte(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=xo(i,e.dtype,"max",a),l=me({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function $E(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,p=E.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[p[T]];let v=w0(b,r.shape,r.dtype,p,x);h=n.makeTensorInfo(x,r.dtype);let w=n.texData.get(h.dataId);w.values=v}else h=Wf(r,p,n);u=E.getInnerMostAxes(u.length,o)}E.assertAxesAreInnerMostDims("max",u,o);let[m,f]=E.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=E.expandShapeToKeepDim(m,l));let y;if(c){let b=n.texData.get(h.dataId).values,x=bY(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=nte(h,f,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var ate={kernelName:Pi,backendName:"webgl",kernelFunc:$E},rte=cE+`
  return max(a, b);
`,ste=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+zf+`
  return result;
`,ite=un({opSnippet:rte,packedOpSnippet:ste,cpuKernelImpl:xY}),ote={kernelName:Oi,backendName:"webgl",kernelFunc:ite};function lte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Pu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=E.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&k.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new tc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var ute={kernelName:Li,backendName:"webgl",kernelFunc:lte};function pte(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=E.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new I0(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var cte={kernelName:hc,backendName:"webgl",kernelFunc:pte},dte=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);
      }
    `}},hte=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 mte(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=E.computePool3DInfo(i.shape,o,l,d,u,p),h=new I0(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new hte(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var fte={kernelName:vm,backendName:"webgl",kernelFunc:mte};function gte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Pu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=E.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new tc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new dte(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var yte={kernelName:xm,backendName:"webgl",kernelFunc:gte};function bte(e,t,n,a){let r=new tc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new tc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var xte={kernelName:wm,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(E.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=E.computePool2DInfo(a.shape,r,s,u,i),[d,c]=bte(a,o,p,l);return[d,c]}};function vte(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=xo(i,"float32","mean",a),l=me({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var wte={kernelName:zi,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=E.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=w0(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=Wf(a,p,i);h.push(m),u=E.getInnerMostAxes(u.length,o)}E.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=E.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=E.expandShapeToKeepDim(f,l));let b=vte(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function kte(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=E.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=E.getInnerMostAxes(u.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",u,o);let[c,h]=E.computeOutAndReduceShapes(d.shape,u),m=k.sizeFromShape(h),f=me({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=xo(f,f.dtype,"min",n),y;if(i){let b=E.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 Ite={kernelName:Bi,backendName:"webgl",kernelFunc:kte},Ste=cE+`
  return min(a, b);
`,Nte=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+zf+`
  return result;
`,Tte=un({opSnippet:Ste,packedOpSnippet:Nte,cpuKernelImpl:vY}),Cte={kernelName:Wi,backendName:"webgl",kernelFunc:Tte},Ete=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=mt(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}));
      }
    `}},_te=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=mt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=In("rc",a),l=In("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.);
        ${c}
        setOutput(result);
      }
    `}},Ate=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _te(a.shape,r,s):new Ete(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},$te={kernelName:Ui,backendName:"webgl",kernelFunc:Ate},Fte=`if (b == 0.0) return NAN;
  return mod(a, b);`,Dte=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+zf+`
  return result;
`,Rte=un({opSnippet:Fte,packedOpSnippet:Dte}),Mte={kernelName:Yl,backendName:"webgl",kernelFunc:Rte},Pte=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}));
      }
    `}},Ote=`
if (a == b) {
  return 1.0;
};
return a / b;`,Lte=`
  // 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;
`,FE=un({opSnippet:Ote,packedOpSnippet:Lte,checkOutOfBounds:!0}),zte={kernelName:Ti,backendName:"webgl",kernelFunc:FE},Xk="return a - b;",DE=un({opSnippet:Xk,packedOpSnippet:Xk,supportsComplex:!0,cpuKernelImpl:PY}),Bte={kernelName:io,backendName:"webgl",kernelFunc:DE};function RE(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=$E({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=E.expandShapeToKeepDim(o.shape,i),u=me({inputs:{x:o},backend:n,attrs:{shape:l}}),p=DE({inputs:{a:r,b:u},backend:n}),d=EE({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=FE({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 Wte={kernelName:ro,backendName:"webgl",kernelFunc:RE};function Ute(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:RE({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Pte(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Vte={kernelName:km,backendName:"webgl",kernelFunc:Ute},Gte=Ea+`
  return -x;
`,Hte=`
  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 jte(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=kY(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Hs(a.shape,Hte):r=new Nr(a.shape,Gte),n.runWebGLProgram(r,[a],a.dtype)}var qte={kernelName:Jl,backendName:"webgl",kernelFunc:jte},Kte=fr.nonMaxSuppressionV3Impl;function Xte(e){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}=Kte(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Yte={kernelName:Ql,backendName:"webgl",kernelFunc:Xte},Jte=fr.nonMaxSuppressionV4Impl;function Zte(e){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}=Jte(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Qte={kernelName:eu,backendName:"webgl",kernelFunc:Zte},ene=fr.nonMaxSuppressionV5Impl;function tne(e){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}=ene(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var nne={kernelName:tu,backendName:"webgl",kernelFunc:tne},ane=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)));
      }
    `}},rne=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 ane(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},sne={kernelName:Gi,backendName:"webgl",kernelFunc:rne};function Kh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=ed({inputs:{input:a},backend:n}),s=Kh({inputs:{x:r},backend:n}),i=Vf({inputs:{input:a},backend:n}),o=Kh({inputs:{x:i},backend:n}),l=ws({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return td({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var ine={kernelName:wu,backendName:"webgl",kernelFunc:Kh};function ME(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=ed({inputs:{input:a},backend:n}),s=ME({inputs:{x:r},backend:n}),i=Vf({inputs:{input:a},backend:n}),o=Kh({inputs:{x:i},backend:n}),l=ws({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return td({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var one={kernelName:nu,backendName:"webgl",kernelFunc:ME};function lne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return lx({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=lx({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=kE({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var une={kernelName:au,backendName:"webgl",kernelFunc:lne},pne=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=mt(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}));
        }
      }
    `}},cne=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=mt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=In("rc",a),l=In("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);
      }
    `}},PE=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 td({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cne(r.shape,s,i):new pne(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},dne={kernelName:Hi,backendName:"webgl",kernelFunc:PE},hne=`
  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);
`,mne=`
  // 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));
  `+zf+`
  return result;
`,fne=un({opSnippet:hne,packedOpSnippet:mne}),gne={kernelName:ji,backendName:"webgl",kernelFunc:fne};function yne(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=E.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=E.getInnerMostAxes(p.length,o),l.push(c)),E.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=SY(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=E.computeOutAndReduceShapes(c.shape,p),g=k.sizeFromShape(f),y=me({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=Fm(r.dtype),x=xo(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=E.expandShapeToKeepDim(h.shape,u);h=me({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var bne={kernelName:ru,backendName:"webgl",kernelFunc:yne},OE=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=NY(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},xne={kernelName:mc,backendName:"webgl",kernelFunc:OE},vne="return 1.0 / x;",wne=Je({opSnippet:vne}),kne={kernelName:su,backendName:"webgl",kernelFunc:wne},Ine=Ea+`
  return (x < 0.0) ? 0.0 : x;
`,Sne=`
  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;
`,Nne=Je({opSnippet:Ine,packedOpSnippet:Sne}),Tne={kernelName:Ki,backendName:"webgl",kernelFunc:Nne},Cne=Ea+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Ene=`
  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;
`,_ne=Je({opSnippet:Cne,packedOpSnippet:Ene}),Ane={kernelName:Yi,backendName:"webgl",kernelFunc:_ne},$ne=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);
      }
    `}},Fne=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 Dne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Fne(r.shape,l,u,s,i):new $ne(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Rne={kernelName:Xi,backendName:"webgl",kernelFunc:Dne},Mne=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 Pne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Mne(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var One={kernelName:Nm,backendName:"webgl",kernelFunc:Pne},Lne=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);
      }
    `}},zne=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 Bne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new zne(r.shape,l,u,s,i):new Lne(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var Wne={kernelName:fc,backendName:"webgl",kernelFunc:Bne},Une=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 Vne(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Une(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Gne={kernelName:Sm,backendName:"webgl",kernelFunc:Vne},Hne=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=mt(n);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `}},jne=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-${n} tensor is not yet supported`);this.outputShape=e;let a=In("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=mt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${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 na({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new jne(r.shape,o):new Hne(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Kne={kernelName:Ji,backendName:"webgl",kernelFunc:qne},Xne=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 < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},Yne={kernelName:ku,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Xne(a.shape,s),[u,p]=E.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},Jne=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,Zne=Je({opSnippet:Jne}),Qne={kernelName:Zi,backendName:"webgl",kernelFunc:Zne},eae="return inversesqrt(x);",tae=Je({opSnippet:eae,cpuKernelImpl:TY}),nae={kernelName:Qi,backendName:"webgl",kernelFunc:tae},LE=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=mt(r.length),l=mt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let p=`getIndices(${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 aae(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}=E.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 LE(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 rae={kernelName:ou,backendName:"webgl",kernelFunc:aae},sae=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=mt(n);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${a});
        if (cVal >= 1.0) {
          setOutput(getA(${r}));
        } else {
          setOutput(getB(${r}));
        }
      }
    `}};function iae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new sae(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],Sa(r.dtype,s.dtype))}var oae={kernelName:lu,backendName:"webgl",kernelFunc:iae},lae=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${E.SELU_SCALEALPHA};
  float scale = ${E.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,uae=Je({opSnippet:lae}),pae={kernelName:uu,backendName:"webgl",kernelFunc:uae},cae=Wu+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,dae=`
  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;
`,hae=Je({opSnippet:cae,packedOpSnippet:dae,cpuKernelImpl:CY}),mae={kernelName:to,backendName:"webgl",kernelFunc:hae},fae=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,gae=Je({opSnippet:fae}),yae={kernelName:du,backendName:"webgl",kernelFunc:gae},bae=Wu+`
  return sin(x);
`,xae=Je({opSnippet:bae}),vae={kernelName:eo,backendName:"webgl",kernelFunc:xae},wae=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,kae=Je({opSnippet:wae}),Iae={kernelName:cu,backendName:"webgl",kernelFunc:kae},Sae=`
  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;
`,Nae=Je({opSnippet:Sae}),Tae={kernelName:hu,backendName:"webgl",kernelFunc:Nae},Cae=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=PE({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(p.shape,s,o,!1),c=E.getPermuted(d.length,s.length,!1),h=E.getReshapedPermuted(p.shape,s,o,!1),m=me({inputs:{x:p},backend:n,attrs:{shape:d}}),f=Sn({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},Eae={kernelName:mu,backendName:"webgl",kernelFunc:Cae};function _ae(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]=_Y(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 Aae={kernelName:gc,backendName:"webgl",kernelFunc:_ae};function $ae(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 ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=AY(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var Fae={kernelName:gu,backendName:"webgl",kernelFunc:$ae};function Dae(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]=oE(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var Rae={kernelName:yc,backendName:"webgl",kernelFunc:Dae};function Mae(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
             ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=oE(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var Pae={kernelName:bc,backendName:"webgl",kernelFunc:Mae};function Oae(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:p,outputSize:d}=E.calculateShapes(s,r,o),c=!1,h=new LE(u,l,r.shape.length,s.shape.length,p,[d,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var Lae={kernelName:Tm,backendName:"webgl",kernelFunc:Oae};function zae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=E.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Uu({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var Bae={kernelName:fu,backendName:"webgl",kernelFunc:zae},Yk="return sqrt(x);",Wae=Je({opSnippet:Yk,packedOpSnippet:Yk,cpuKernelImpl:$Y}),Uae={kernelName:no,backendName:"webgl",kernelFunc:Wae},Vae="return x * x;",Gae=Je({opSnippet:Vae}),Hae={kernelName:xc,backendName:"webgl",kernelFunc:Gae},Jk="return (a - b) * (a - b);",jae=un({opSnippet:Jk,packedOpSnippet:Jk}),qae={kernelName:so,backendName:"webgl",kernelFunc:jae};function Kae({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ea+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Nr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Xae={kernelName:ms,backendName:"webgl",kernelFunc:Kae},Yae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=mt(n.length),s=mt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${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 Jae(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: ${r.shape.length}`);let C=qt.computeOutShape(b,x,v),_=Uu({inputs:{x:r},backend:n,attrs:{begin:b,size:C}});w=me({inputs:{x:_},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(_)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),_=He(r.shape,r.dtype,C),$=FY(h,_,v,b);w=n.makeTensorInfo(m,r.dtype,$.values)}else{let C=new Yae(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 Zae={kernelName:yu,backendName:"webgl",kernelFunc:Jae};function Qae(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]=DY(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var ere={kernelName:Cm,backendName:"webgl",kernelFunc:Qae};function tre(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]=RY(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 nre={kernelName:Em,backendName:"webgl",kernelFunc:tre};function are(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=MY(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var rre={kernelName:_m,backendName:"webgl",kernelFunc:are},sre="return tan(x);",ire=Je({opSnippet:sre}),ore={kernelName:oo,backendName:"webgl",kernelFunc:ire},lre=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,ure=Je({opSnippet:lre}),pre={kernelName:lo,backendName:"webgl",kernelFunc:ure},cre=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=mt(this.rank),r=dre(e);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function dre(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 zE(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=OY(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new cre(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var hre={kernelName:hs,backendName:"webgl",kernelFunc:zE},mre=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));
         }
       }
     `}},fre=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 Ps(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Zk(e){let t=1;for(;t<e;)t*=2;return t}function gre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,p=u[u.length-1];if(n.shouldExecuteOnCPU([r])||p<o||s>l){let $=n.readSync(r.dataId),[P,F]=LY($,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,td({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&&Ps(n,h);let g=Zk(s),y=Zk(p),b=null,x=()=>b===null?[f,f]:[f,b],v=($,P,F)=>{let S=x(),M=new mre(F),V=[[p],[b===null?1:0],[Number.NEGATIVE_INFINITY],[$],[P]],j=b;b=n.runWebGLProgram(M,S,"int32",V),Ps(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;$/=2){let P=x(),F=new fre([m,$/2]),S=[[p],[b===null?1:0],[g]],M=b;b=n.runWebGLProgram(F,P,"int32",S),Ps(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=Uu({inputs:{x:b},backend:n,attrs:{begin:0,size:[m,s]}}),Ps(n,w);let T=AE({inputs:{x:f,indices:b},backend:n,attrs:{axis:1,batchDims:1}});Ps(n,f);let C=u.slice(0,-1);C.push(s),w=b,b=me({inputs:{x:b},attrs:{shape:C},backend:n}),Ps(n,w);let _=T;return T=me({inputs:{x:T},attrs:{shape:C},backend:n}),Ps(n,_),[T,b]}var yre={kernelName:bu,backendName:"webgl",kernelFunc:gre},bre=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 xre(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 bre(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var vre={kernelName:xu,backendName:"webgl",kernelFunc:xre};function wre(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Pu(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}=zY(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var kre={kernelName:Am,backendName:"webgl",kernelFunc:wre};function Ire(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=Uu({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 Sre={kernelName:vu,backendName:"webgl",kernelFunc:Ire},Nre=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 Tre(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=E.getAxesPermutation([u],o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=E.getInnerMostAxes(1,o)[0]);let c=E.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=Fm(r.dtype),g=(v,w,T,C,_)=>{let $=v.shape[0],P=v.shape[1],F=E.segment_util.segOpComputeOptimalWindowSize(P,_),S={windowSize:F,inSize:P,batchSize:$,numSegments:_},M=new Nre(S,w),V=n.compileAndRun(M,[v,T],C);if(l.push(V),V.shape[1]===_)return V;let j=OE({backend:n,attrs:{start:0,stop:_,step:1,dtype:"float32"}}),q=zE({inputs:{x:j},backend:n,attrs:{reps:[P/F]}});return l.push(j),l.push(q),g(V,w,q,C,_)},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=E.getUndoAxesPermutation(p);x=Sn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Cre={kernelName:vc,backendName:"webgl",kernelFunc:Tre},Ere=[R9,P9,z9,U9,G9,q9,X9,J9,tJ,aJ,iJ,uJ,dJ,gJ,xJ,wJ,IJ,CJ,_J,$J,MJ,UJ,GJ,jJ,ZJ,eZ,rZ,f9,oZ,dZ,gZ,kZ,SZ,TZ,EZ,AZ,DZ,PZ,zZ,WZ,VZ,jZ,KZ,ZZ,eQ,aQ,iQ,lQ,dQ,gQ,vQ,IQ,TQ,CQ,_Q,$Q,DQ,MQ,OQ,WQ,GQ,qQ,XQ,ZQ,tee,see,uee,m9,cee,pZ,mee,yee,vee,y9,See,Eee,Aee,Ree,Oee,Wee,Gee,Kee,Zee,tte,ate,ote,ute,cte,fte,yte,xte,wte,Ite,Cte,$te,Mte,Vte,k9,qte,Yte,Qte,nne,KJ,sne,one,une,dne,gne,x9,bne,xne,XJ,zte,kne,Tne,Ane,S9,Rne,One,Wne,Gne,Kne,Yne,Qne,nae,rae,oae,pae,mae,yae,vae,Iae,BJ,Wte,Tae,Eae,Aae,Fae,Rae,Pae,Lae,Bae,Uae,Hae,qae,Xae,Zae,ere,nre,rre,Bte,$9,ore,pre,hre,yre,vre,F9,kre,Sre,Cre,ine];for(let e of Ere)wc(e);var Dt;(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"})(Dt||(Dt={}));var nc;(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"})(nc||(nc={}));var BE;function _re(e){BE=e.wasm.cwrap(Ys,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Are(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 _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${_.shape.length}.`);m=_.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=nc[p];if(g==null)throw new Error(`${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=Iu.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 BE(c,T,r.shape.length,h,C,s.shape.length,l,u,g,m,f,d||0,w),v}var $re={kernelName:Ys,backendName:"wasm",setupFunc:_re,kernelFunc:Are};function pn(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,Dt[o.dtype],p),u}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:r}}var Fre=pn(wl);function _n(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=E.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,Dt[u.dtype],b),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Dre=!0,Rre=_n(cs,Dre),WE;function Mre(e){WE=e.wasm.cwrap(mi,null,["array","number","number","number"])}function Pre(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 WE(s,r.length,Dt[a.dtype],i),a}var Ore={kernelName:mi,backendName:"wasm",setupFunc:Mre,kernelFunc:Pre};function Gf(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 Lre={kernelName:Di,backendName:"wasm",kernelFunc:Gf},UE;function zre(e){UE=e.wasm.cwrap(uo,null,["number","array","number","number","number","array","number"])}function xl(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Wre(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Bre(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Gf({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 UE(p,h,l.shape.length,Dt[l.dtype],d,c,s.length),u}function Bre(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Wre(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 Ure={kernelName:uo,backendName:"wasm",kernelFunc:xl,setupFunc:zre};function ks(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=E.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=E.getInnerMostAxes(i.length,r),l=xl({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 VE;function Vre(e){VE=e.wasm.cwrap(Sl,null,["number, number, number"])}function Gre(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}=ks(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("all",p,h);let[m,f]=E.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;VE(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Hre={kernelName:Sl,backendName:"wasm",setupFunc:Vre,kernelFunc:Gre},GE;function jre(e){GE=e.wasm.cwrap(Nl,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}=ks(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("any",p,h);let[m,f]=E.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;GE(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Kre={kernelName:Nl,backendName:"wasm",setupFunc:jre,kernelFunc:qre},HE;function Xre(e){HE=e.wasm.cwrap(fi,null,["number","number","number","number","number"])}function Yre(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}=ks(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 HE(o,Dt[l.dtype],f,g,m),d&&t.disposeData(u.dataId),h}var Jre={kernelName:fi,backendName:"wasm",kernelFunc:Yre,setupFunc:Xre},jE;function Zre(e){jE=e.wasm.cwrap(gi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qre(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=E.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 jE(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,w),v}var ese={kernelName:gi,backendName:"wasm",setupFunc:Zre,kernelFunc:Qre};function zn(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: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var tse={kernelName:iu,backendName:"wasm",kernelFunc:zn},qE;function nse(e){qE=e.wasm.cwrap(yi,null,["number","array","number","number","array","number","number","number","number"])}function ase(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=Iu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,p,c]:[g,c,p],v=o?[y,h,d]:[y,d,h],w=zn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=zn({inputs:{x:s},backend:n,attrs:{shape:v}}),C=n.dataIdMap.get(w.dataId).id,_=n.dataIdMap.get(T.dataId).id,$=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 qE(C,V,w.shape.length,_,j,T.shape.length,i,o,M),n.disposeData(w.dataId),n.disposeData(T.dataId),S.shape=b,S}var rse={kernelName:yi,backendName:"wasm",setupFunc:nse,kernelFunc:ase};function ci(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=Gh(l,s,i,t.shape,t.dtype);return d.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)sse(l,p[0],c,s,i);else if(h===3)ise(l,p[0],p[1],c,s,i);else if(h===4)ose(l,p[0],p[1],p[2],c,s,i);else{let m=Gh(l,s,i,t.shape,t.dtype);c.set(m)}return u}function sse(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 ise(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 ose(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 lse={kernelName:pu,backendName:"wasm",kernelFunc:ci};function use(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=E.getReshaped(r.shape,s,o),u=E.getPermuted(l.length,s.length),p=E.getReshapedPermuted(r.shape,s,o),d=E.getSliceBeginCoords(i,s.length),c=E.getSliceSize(p,i,s.length),h=zn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=xl({inputs:{x:h},backend:n,attrs:{perm:u}}),f=zn({inputs:{x:m},backend:n,attrs:{shape:p}}),g=ci({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 pse={kernelName:$l,backendName:"wasm",kernelFunc:use};function nd(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 cse={kernelName:bi,backendName:"wasm",kernelFunc:nd},dse=pn(xi),KE;function hse(e){KE=e.wasm.cwrap(ds,null,["number","number","number","number"])}function mse(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 KE(o,s,i,u),l}var fse={kernelName:ds,backendName:"wasm",setupFunc:hse,kernelFunc:mse};function XE(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return Gf({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(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=k.sizeFromShape(x.shape.slice(a));return zn({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=n0(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.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 gse={kernelName:Fl,backendName:"wasm",kernelFunc:XE},YE;function yse(e){YE=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bse(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=E.convertConv2DDataFormat(c),m=E.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,_=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 YE(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,v,F,w,T,C,_,$,P,M),S}var xse={kernelName:vi,backendName:"wasm",setupFunc:yse,kernelFunc:bse},JE;function vse(e){JE=e.wasm.cwrap(wi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wse(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:p}=a,d=1,c=E.convertConv2DDataFormat(l),h=E.computeConv2DInfo(p,s.shape,i,d,o,u,!1,c),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:v,outHeight:w,outWidth:T,strideHeight:C,strideWidth:_}=h,$=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"),we=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,Ie=t.dataIdMap.get(s.dataId).id;return JE(ye,Ie,m,f,g,b,x,y,w,T,v,C,_,$,P,V,j,q,K,Q,ee,re,Z,ie,ae,le,we),ue}var kse={kernelName:wi,backendName:"wasm",setupFunc:vse,kernelFunc:wse},Ise=pn(ki),Sse=pn(Ii),ux;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(ux||(ux={}));var ZE;function Nse(e){ZE=e.wasm.cwrap(Dl,null,["number","number","number","number","array","number","number","number","number","number"])}function Tse(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=nd({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 ZE(g,y,b,p,w,d,c,ux[r],s,v),f!=null&&t.disposeData(f.dataId),x}var Cse={kernelName:Dl,backendName:"wasm",setupFunc:Nse,kernelFunc:Tse},QE;function Ese(e){QE=e.wasm.cwrap(Si,null,["number","number","number","number","number","number"])}function _se(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=E.getAxesPermutation([s],l),p=r;u!==null&&(p=xl({inputs:{x:r},attrs:{perm:u},backend:n}));let d=E.getInnerMostAxes(1,l)[0];E.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;QE(m,i?1:0,o?1:0,h,f,Dt[r.dtype]);let g=c;if(u!==null){let y=E.getUndoAxesPermutation(u);g=xl({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Ase={kernelName:Si,backendName:"wasm",setupFunc:Ese,kernelFunc:_se},e_;function $se(e){e_=e.wasm.cwrap(Rl,null,["number","number","number","array","number","array","array","number","number"])}function Fse(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return e_(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var Dse={kernelName:Rl,backendName:"wasm",setupFunc:$se,kernelFunc:Fse},t_;function Rse(e){t_=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 Mse(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=E.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,_=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 t_(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,P,v,w,T,C,_,$,S),F}var Pse={kernelName:Ni,backendName:"wasm",setupFunc:Rse,kernelFunc:Mse},Ose=pn(Ci),Lse=!1,zse=_n(Pl,Lse,"bool"),Bse=pn(Ei,"float32");function px(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),zn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Wse={kernelName:Ol,backendName:"wasm",kernelFunc:px};function n_(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Use={kernelName:uc,backendName:"wasm",kernelFunc:n_},a_;function Vse(e){a_=e.wasm.cwrap(zl,null,["number","number","number","number","number","number"])}function Gse(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return a_(s,o,l,u,p,i),r}var Hse={kernelName:zl,backendName:"wasm",kernelFunc:Gse,setupFunc:Vse},jse=pn(_i),qse=!1,Kse=_n(Ai,qse),r_;function Xse(e){r_=e.wasm.cwrap($i,null,["number","number","number","number","number","number","number"])}function Yse(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 r_(p,d,c,h,m,r,g),f}var Jse={kernelName:$i,backendName:"wasm",setupFunc:Xse,kernelFunc:Yse},s_;function Zse(e){s_=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qse(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=nc[h];if(g==null)throw new Error(`${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 (${x})`);v=ae.id}let w=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,$=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:'${d}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),Z=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return s_(y,K,Q,ee,b,w,T,v,C,_,$,P,q,F,S,M,V,j,x,g,ie,m||0,Z),re}var eie={kernelName:Js,backendName:"wasm",setupFunc:Zse,kernelFunc:Qse},i_;function tie(e){i_=e.wasm.cwrap(Zs,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 nie(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=E.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=nc[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 (${x})`);v=ae.id}let w=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,$=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:'${d}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),Z=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return i_(y,K,Q,ee,b,w,T,v,C,_,$,P,q,F,S,M,V,j,x,g,ie,m||0,Z),re}var aie={kernelName:Zs,backendName:"wasm",setupFunc:tie,kernelFunc:nie},o_;function rie(e){o_=e.wasm.cwrap(Wl,null,["number","number","number","number","number","number","array","number"])}function sie(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Cx.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 o_(c,Dt[a.dtype],h,i,d,o,m,f),u}var iie={kernelName:Wl,backendName:"wasm",setupFunc:rie,kernelFunc:sie},l_;function oie(e){l_=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function lie(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 _=u[C];k.assert(_<=p-1&&_>=0,()=>`GatherV2: the index value ${_} is not in [0, ${p-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=zn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=k.sizeFromShape(s.shape),m=zn({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 l_(b,Dt[r.dtype],w,y,x,d.batchSize,T,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var uie={kernelName:Bl,backendName:"wasm",setupFunc:oie,kernelFunc:lie},pie=!1,cie=_n(Ul,pie,"bool"),die=!1,hie=_n(Fi,die,"bool"),u_;function mie(e){u_=e.wasm.cwrap(Ri,null,["number","number","number","number"])}function fie(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;u_(r,Dt[t.dtype],n,i)}return s}var gie={kernelName:Ri,backendName:"wasm",setupFunc:mie,kernelFunc:fie},yie=!1,bie=_n(jl,yie,"bool"),xie=!1,vie=_n(ql,xie,"bool"),wie=pn(Mi),kie=!1,Iie=_n(Xl,kie,"bool"),p_;function Sie(e){p_=e.wasm.cwrap(Pi,null,["number","number","number","number"])}function Nie(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}=ks(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",p,h);let[m,f]=E.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,Dt[i.dtype],g,b)}if(c&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Tie={kernelName:Pi,backendName:"wasm",setupFunc:Sie,kernelFunc:Nie},Cie=!1,Eie=_n(Oi,Cie),c_;function _ie(e){c_=e.wasm.cwrap(Li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Aie(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 ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=E.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,b=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,w=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),_=a.dataIdMap.get(C.dataId).id;return c_(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,v,w,T,_),C}var $ie={kernelName:Li,backendName:"wasm",setupFunc:_ie,kernelFunc:Aie},d_;function Fie(e){d_=e.wasm.cwrap(zi,null,["number, number, number"])}function Die(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}=ks(i,r,t),m=d;if(h){let v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=k.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=nd({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;d_(l,y,v)}if(h&&t.disposeData(p.dataId),s){let v=E.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var Rie={kernelName:zi,backendName:"wasm",setupFunc:Fie,kernelFunc:Die},h_;function Mie(e){h_=e.wasm.cwrap(Bi,null,["number","number","number","number"])}function Pie(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}=ks(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;E.assertAxesAreInnerMostDims("min",d,m);let[f,g]=E.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;h_(l,Dt[i.dtype],y,x)}if(h&&t.disposeData(p.dataId),s){let x=E.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var Oie={kernelName:Bi,backendName:"wasm",setupFunc:Mie,kernelFunc:Pie},Lie=!1,zie=_n(Wi,Lie),cx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(cx||(cx={}));var m_;function Bie(e){m_=e.wasm.cwrap(Ui,null,["number","array","number","number","array","array","number","number"])}function Wie(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 m_(i,u,t.shape.length,Dt[t.dtype],c,h,cx[r],l),o}var Uie={kernelName:Ui,backendName:"wasm",kernelFunc:Wie,setupFunc:Bie},Vie=!0,Gie=_n(Vi,Vie),Hie=pn(Jl);function S0(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 f_;function jie(e){f_=e.wasm.cwrap(Ql,"number",["number","number","number","number","number"])}function qie(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=f_(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=S0(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var Kie={kernelName:Ql,backendName:"wasm",setupFunc:jie,kernelFunc:qie},g_;function Xie(e){g_=e.wasm.cwrap(eu,"number",["number","number","number","number","number","bool"])}function Yie(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=g_(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=S0(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Jie={kernelName:eu,backendName:"wasm",setupFunc:Xie,kernelFunc:Yie},y_;function Zie(e){y_=e.wasm.cwrap(tu,"number",["number","number","number","number","number","number"])}function Qie(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=y_(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=S0(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var eoe={kernelName:tu,backendName:"wasm",setupFunc:Zie,kernelFunc:Qie},toe=!1,noe=_n(Zl,toe,"bool"),b_;function aoe(e){b_=e.wasm.cwrap(Gi,null,["number","number","number","number","number"])}function roe(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 b_(p,s,i,o,u),l}var soe={kernelName:Gi,backendName:"wasm",setupFunc:aoe,kernelFunc:roe};function ioe(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var ooe={kernelName:nu,backendName:"wasm",kernelFunc:ioe};function loe(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=XE({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeData(p.dataId)),u}var uoe={kernelName:au,backendName:"wasm",kernelFunc:loe},x_;function poe(e){x_=e.wasm.cwrap(Hi,null,["number","array","number","number","array","array","number","number"])}function coe(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 n_({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 x_(i,u,t.shape.length,Dt[t.dtype],c,h,r,l),o}var v_={kernelName:Hi,backendName:"wasm",kernelFunc:coe,setupFunc:poe},doe=!1,hoe=_n(ji,doe),w_;function moe(e){w_=e.wasm.cwrap(qi,null,["number","number","number"])}function foe(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=nd({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 w_(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var goe={kernelName:qi,backendName:"wasm",setupFunc:moe,kernelFunc:foe},k_;function yoe(e){k_=e.wasm.cwrap(ru,null,["number","number","number","number"])}function boe(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}=ks(i,r,t),m=d;if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=E.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;k_(l,y,Dt[b.dtype],x)}if(h&&t.disposeData(p.dataId),s){let x=E.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var xoe={kernelName:ru,backendName:"wasm",setupFunc:yoe,kernelFunc:boe},voe=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=s0(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},woe={kernelName:mc,backendName:"wasm",kernelFunc:voe},koe=!0,Ioe=_n(Ti,koe),Soe=pn(Ki),Noe=pn(Yi),I_;function Toe(e){I_=e.wasm.cwrap(Xi,null,["number","number","number","number","number","number","number","number","number","number"])}function Coe(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=nd({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 I_(y,p,d,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var Eoe={kernelName:Xi,backendName:"wasm",setupFunc:Toe,kernelFunc:Coe},S_;function _oe(e){S_=e.wasm.cwrap(Ji,null,["number","array","number","array","number","number"])}function Aoe(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 Gf({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);S_(l,p,i.length,d,r.shape.length,u);let c=zn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var $oe={kernelName:Ji,backendName:"wasm",kernelFunc:Aoe,setupFunc:_oe},N_;function Foe(e){N_=e.wasm.cwrap(ku,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Doe(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]=E.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 N_(u,d,c,h,m,s,f,g,v,x.length,p),l}var Roe={kernelName:ku,backendName:"wasm",kernelFunc:Doe,setupFunc:Foe},Moe=pn(Zi),Poe=pn(Qi),T_;function Ooe(e){T_=e.wasm.cwrap(ou,null,["number","number","number","number","number","number","array","number","number"])}function Loe(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}=Ex.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 T_(h,m,Dt[s.dtype],l,u,p,f,c,g),o}var zoe={kernelName:ou,backendName:"wasm",setupFunc:Ooe,kernelFunc:Loe},C_;function Boe(e){C_=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Woe(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 C_(i,o,l,h,p),u}var Uoe={kernelName:lu,backendName:"wasm",kernelFunc:Woe,setupFunc:Boe},E_;function Voe(e){E_=e.wasm.cwrap(to,null,["number","number"])}function Goe(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||E_(a,s),r}var Hoe={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Voe,kernelFunc:Goe},joe=pn(eo),__;function qoe(e){__=e.wasm.cwrap(ro,null,["number","number","number","number"])}function Koe(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||__(r,i,o,l),s}var Xoe={kernelName:ro,backendName:"wasm",setupFunc:qoe,kernelFunc:Koe};function Yoe(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=v_.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=E.getReshaped(u.shape,s,o,!1),d=E.getPermuted(p.length,s.length,!1),c=E.getReshapedPermuted(u.shape,s,o,!1),h=zn({inputs:{x:u},backend:n,attrs:{shape:p}}),m=xl({inputs:{x:h},backend:n,attrs:{perm:d}}),f=zn({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeData(u.dataId),n.disposeData(h.dataId),n.disposeData(m.dataId),f}var Joe={kernelName:mu,backendName:"wasm",kernelFunc:Yoe},A_;function Zoe(e){A_=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Qoe(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,_=A_(d,c,Dt[r.dtype],o,u,l,h,f,y,x,w,C),$=t.readSync(T.dataId),P;switch($[0]){case 1:{P=E.getSparseFillEmptyRowsIndicesDenseShapeMismatch($[1]);break}case 2:{P=E.getSparseFillEmptyRowsNegativeIndexErrorMessage($[1],$[2]);break}case 3:P=E.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage($[1],$[2],$[3]);break;default:P=""}if(t.disposeData(T.dataId),P)throw t.disposeData(m.dataId),t.disposeData(g.dataId),t.disposeData(b.dataId),t.disposeData(v.dataId),new Error(P);let F=m,S=g;return _!==p[0]&&(F=ci({inputs:{x:m},attrs:{begin:0,size:[_,l]},backend:t}),S=ci({inputs:{x:g},attrs:{begin:0,size:_},backend:t}),t.disposeData(m.dataId),t.disposeData(g.dataId)),[F,S,b,v]}var ele={kernelName:gc,backendName:"wasm",setupFunc:Zoe,kernelFunc:Qoe},$_;function tle(e){$_=e.wasm.cwrap(gu,null,["number","number","number","number","number","number","number"])}function nle(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 ${s.shape}`);let i=t.dataIdMap.get(a.dataId).id,o=t.dataIdMap.get(r.dataId).id,l=t.dataIdMap.get(s.dataId).id,u=a.shape[0],p=k.sizeFromShape(s.shape),d=t.makeOutput([u,p],a.dtype),c=t.dataIdMap.get(d.dataId).id,h=t.makeOutput([p],s.dtype),m=t.dataIdMap.get(h.dataId).id,f=t.makeOutput([3],"int32"),g=t.dataIdMap.get(f.dataId).id;$_(i,o,l,u,c,m,g);let y=t.readSync(f.dataId),b;switch(y[0]){case 0:{b=E.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1],y[2]);break}case 1:{b=E.getSparseReshapeNegativeOutputDimErrorMessage(y[1],y[2]);break}case 2:b=E.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let x=Array.from(t.readSync(r.dataId)),v=Array.from(t.readSync(h.dataId));b=E.getSparseReshapeInputOutputMultipleErrorMessage(x,v);break}case 4:{let x=Array.from(t.readSync(r.dataId)),v=Array.from(t.readSync(h.dataId));b=E.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 ale={kernelName:gu,backendName:"wasm",setupFunc:tle,kernelFunc:nle},F_;function D_(e){F_=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function R_(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(E.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;F_(d,Dt[r.dtype],r.shape[0],c,h,f,y,t,0);let b=n.readSync(g.dataId),x;switch(b[0]){case 0:{x=E.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=E.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=E.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b[1],b[2]);break;case 3:x=E.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 rle(e){return R_(e,!0)}var sle={kernelName:yc,backendName:"wasm",setupFunc:D_,kernelFunc:rle};function ile(e){return R_(e,!1)}var ole={kernelName:bc,backendName:"wasm",setupFunc:D_,kernelFunc:ile};function lle(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=E.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=ci({inputs:{x:r},attrs:{begin:u,size:c},backend:a});return u[o]+=d,h})}var ule={kernelName:fu,backendName:"wasm",kernelFunc:lle},ple=pn(no),cle=pn(xc),dle=!0,hle=_n(so,dle),M_;function mle(e){M_=e.wasm.cwrap(ms,null,["number","number","number","number"])}function fle(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 M_(i,r,Dt[s.dtype],l),o}var gle={kernelName:ms,backendName:"wasm",setupFunc:mle,kernelFunc:fle},P_;function yle(e){P_=e.wasm.cwrap(yu,null,["number","array","number","array","array","array","array","array","number","number"])}function ble(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=zn({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: ${r.shape.length}`);let T=qt.computeOutShape(b,x,v),C=ci({inputs:{x:r},backend:t,attrs:{begin:b,size:T}});w=zn({inputs:{x:C},backend:t,attrs:{shape:m}}),t.disposeData(C.dataId)}else{let T=t.makeOutput(h,"float32"),C=t.dataIdMap.get(r.dataId).id,_=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),$=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;P_(C,_,r.shape.length,$,P,F,S,M,h.length,V),w=zn({inputs:{x:T},backend:t,attrs:{shape:m}}),t.disposeData(T.dataId)}return w}var xle={kernelName:yu,backendName:"wasm",setupFunc:yle,kernelFunc:ble},vle=!0,wle=_n(io,vle),O_;function kle(e){O_=e.wasm.cwrap(ao,null,["number","number","number","number"])}function Ile(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}=ks(i,r,t),m=d;if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=E.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;O_(l,y,Dt[b.dtype],x)}if(h&&t.disposeData(p.dataId),s){let x=E.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var Sle={kernelName:ao,backendName:"wasm",setupFunc:kle,kernelFunc:Ile},Nle=pn(oo),Tle=pn(lo),L_;function Cle(e){L_=e.wasm.cwrap(hs,null,["number","array","number","array","number","number"])}function Ele(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 L_(s,l,r.shape.length,u,o.length,Dt[p.dtype],d),p}var _le={kernelName:hs,backendName:"wasm",setupFunc:Cle,kernelFunc:Ele},z_;function Ale(e){z_=e.wasm.cwrap(bu,null,["number","array","number","number","number","bool","number","number"])}var $le=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),p=t.dataIdMap.get(u.dataId).id,d=t.makeOutput(l,"int32"),c=t.dataIdMap.get(d.dataId).id;return z_(i,o,a.shape.length,Dt[a.dtype],r,s,p,c),[u,d]},Fle={kernelName:bu,backendName:"wasm",setupFunc:Ale,kernelFunc:$le},B_;function Dle(e){B_=e.wasm.cwrap(xu,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Rle(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 B_(v,w,s.shape[0]>1,p,m,f,h,c,d,y,r.shape.length-1,T,C,l,x),b}var Mle={kernelName:xu,backendName:"wasm",setupFunc:Dle,kernelFunc:Rle};function Ple(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]=ci({inputs:{x:r},attrs:{begin:d,size:c},backend:n});return p.map(({dataId:h,dtype:m})=>({dataId:h,dtype:m,shape:l}))}var Ole={kernelName:vu,backendName:"wasm",kernelFunc:Ple};function Lle(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var zle={kernelName:wu,backendName:"wasm",kernelFunc:Lle},Ble=[$re,Fre,Rre,Ore,Hre,Kre,Jre,ese,rse,pse,cse,dse,fse,gse,xse,kse,Ise,Sse,Cse,Ase,Dse,Pse,Ose,zse,Bse,Wse,Use,Hse,jse,Kse,Jse,eie,aie,iie,uie,cie,hie,Lre,gie,bie,vie,wie,Iie,Tie,Eie,$ie,Rie,Oie,zie,Uie,Gie,Hie,Kie,Jie,eoe,noe,soe,ooe,uoe,v_,hoe,goe,xoe,woe,Ioe,Soe,Noe,tse,Eoe,$oe,Roe,Moe,Poe,zoe,Uoe,Hoe,joe,lse,Xoe,Joe,ele,ale,sle,ole,ule,ple,cle,hle,gle,xle,wle,Sle,Nle,Tle,_le,Fle,Mle,Ure,Ole,zle];for(let e of Ble)wc(e);var dx=Y();dx.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])));dx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(dx.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 Qk=di(iF()),Wle=`"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}});`,Ule=di(oF()),W_=class extends ac{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(U_),hx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Jh(this,rr())}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 Hle(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 ${t}`)}}};function Vle(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance,s.module)})})}),{})}function eI(e,t,n){if(Xh!=null)return Xh;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),zp!=null&&zp[a]!=null?zp[a]:n+a}async function Gle(){let[e,t]=await Promise.all([Y().getAsync("WASM_HAS_SIMD_SUPPORT"),Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=Wle,p=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(p)}return o.endsWith(".wasm")?eI(e,t,Pp!=null?Pp:l):l+o},N0&&(r.instantiateWasm=Vle(eI(e,t,Pp!=null?Pp:"")));let s=!1;r.onAbort=()=>{s||Bp||(Bp=!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&&Xh==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+Qk.default.toString()],{type:"text/javascript"}),i=(0,Qk.default)(r)):i=(0,Ule.default)(r),i.then(o=>{s=!0,Bp=!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 Hle(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 jle=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Xh=null,Pp=null,zp={},Bp=!1,N0=!1;function qle(e,t=!1){if(Dx("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Bp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Xh=e,N0=t}function Kle(e,t=!1){if(Bp)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")Pp=e;else{zp=e;let n=jle.filter(a=>zp[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.`)}N0=t}var U_=-1,hx=-1;function Xle(e){U_=e}function Yle(){if(hx===-1)throw new Error("WASM backend not initialized.");return hx}var Jle="3.14.0",Zle=2;Rm("wasm",async()=>{let{wasm:e}=await Gle();return new W_(e)},Zle);var Qle="3.14.0",eue="3.14.0",tue="3.14.0",nue="3.14.0",aue="3.14.0",rue="3.14.0",sue="3.14.0",iue="3.14.0",oue={tfjs:Qle,"tfjs-core":eue,"tfjs-data":tue,"tfjs-layers":nue,"tfjs-converter":aue,"tfjs-backend-cpu":rue,"tfjs-backend-webgl":sue,"tfjs-backend-wasm":iue};var J0={};Zd(J0,{AnchorPosition:()=>z0,DrawBox:()=>Kf,DrawBoxOptions:()=>B0,DrawFaceLandmarks:()=>Y0,DrawFaceLandmarksOptions:()=>X0,DrawTextField:()=>Ss,DrawTextFieldOptions:()=>od,drawContour:()=>Dr,drawDetections:()=>gue,drawFaceExpressions:()=>wue,drawFaceLandmarks:()=>Iue});function Dr(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 _0={};Zd(_0,{computeReshapedDimensions:()=>E0,getCenterPoint:()=>ko,isDimensions:()=>jf,isEven:()=>Hf,isFloat:()=>C0,isTensor:()=>vo,isTensor1D:()=>lue,isTensor2D:()=>T0,isTensor3D:()=>Rr,isTensor4D:()=>ya,isValidNumber:()=>er,isValidProbablitiy:()=>Vu,range:()=>yr,round:()=>wo});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 ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new An(1/this.width,1/this.height)}};function vo(e,t){return e instanceof Ae&&e.shape.length===t}function lue(e){return vo(e,1)}function T0(e){return vo(e,2)}function Rr(e){return vo(e,3)}function ya(e){return vo(e,4)}function C0(e){return e%1!==0}function Hf(e){return e%2===0}function wo(e,t=2){let n=10**t;return Math.floor(e*n)/n}function jf(e){return e&&e.width&&e.height}function E0({width:e,height:t},n){let a=n/Math.max(t,e);return new An(Math.round(e*a),Math.round(t*a))}function ko(e){return e.reduce((t,n)=>t.add(n),new Le(0,0)).div(new Le(e.length,e.length))}function yr(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function er(e){return!!e&&e!==1/0&&e!==-1/0&&!Number.isNaN(e)||e===0}function Vu(e){return er(e)&&e>=0&&e<=1}var Le=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new Le(this.x+t.x,this.y+t.y)}sub(t){return new Le(this.x-t.x,this.y-t.y)}mul(t){return new Le(this.x*t.x,this.y*t.y)}div(t){return new Le(this.x/t.x,this.y/t.y)}abs(){return new Le(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new Le(Math.floor(this.x),Math.floor(this.y))}};var ut=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(er)}static assertIsValidBox(t,n,a=!1){if(!ut.isRect(t))throw new Error(`${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];ut.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 Le(this.left,this.top)}get topRight(){return new Le(this.right,this.top)}get bottomLeft(){return new Le(this.left,this.bottom)}get bottomRight(){return new Le(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 ut({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 ut({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 ut({x:t,y:n,width:a,height:r})}rescale(t){let n=jf(t)?t.width:t,a=jf(t)?t.height:t;return new ut({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 ut({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 ut({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 ut({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 ut({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 Gu=class extends ut{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var Is=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 ut(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 ut(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Is(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var wt=class extends Is{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 wt(a,r,s)}};function A0(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 $0(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 Gu(a,r,s,i)}function F0(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(A0(d,c,a))}r=r.filter((u,p)=>l[p]<=n)}return s}function tr(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 D0(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 uue(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 ad(e){return 1/(1+Math.exp(-e))}function pue(e){return Math.log(e/(1-e))}var Hu=class extends ut{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var cue=.5,due=.43,hue=.45,ba=class{constructor(t,n,a=new Le(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 Le(r,s)).add(a))}get shift(){return new Le(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 Le(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 Le(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof wt?t.box.floor():new ut(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/hue),l=ko(t),u=Math.floor(Math.max(0,l.x-cue*o)),p=Math.floor(Math.max(0,l.y-due*o));return new Hu(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=$0(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var V_=class extends ba{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],ko([t[3],t[4]])]}};var ju=class extends ba{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(ko)}};var rd=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${wo(this.distance)})`:""}`}};var sd=class extends ut{static assertIsValidLabeledBox(t,n){if(ut.assertIsValidBox(t,n),!er(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var Mr=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 Mr(t.label,n)}};var G_=class extends sd{static assertIsValidPredictedBox(t,n){if(sd.assertIsValidLabeledBox(t,n),!Vu(t.score)||!Vu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function br(e){return e.detection instanceof wt}function Io(e,t){return{...e,...{detection:t}}}function R0(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),createVideoElement:()=>document.createElement("video"),fetch:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function id(){return typeof global=="object"&&typeof process!="undefined"&&process.versions!=null&&process.versions.node!=null}function qf(e){let t="";if(!e&&id())try{e=A$("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=qf();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 mue(){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()):id()?O0(M0()):null}function fue(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 et={getEnv:mue,setEnv:O0,initialize:L0,createBrowserEnv:R0,createFileSystem:qf,createNodejsEnv:M0,monkeyPatch:fue,isBrowser:P0,isNodejs:id};L0();function So(e){return!et.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Gn(e){let{Canvas:t,CanvasRenderingContext2D:n}=et.getEnv();if(e instanceof n)return e;let a=So(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||{}),od=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}},Ss=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof Ss?t.text:t,this.anchor=n,this.options=new od(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=So(t),a=Gn(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let u=this.measureWidth(a),p=this.measureHeight();a.fillStyle=r;let d=this.getUpperLeft(a,n);a.fillRect(d.x,d.y,u,p),a.fillStyle=s,this.text.forEach((c,h)=>{let m=l+d.x,f=l+d.y+(h+1)*i;a.fillText(c,m,f)})}};var B0=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new od({...i,...s})}},Kf=class{constructor(t,n={}){this.box=new ut(t),this.options=new B0(n)}draw(t){let n=Gn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new Ss([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function gue(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof wt?a.score:br(a)?a.detection.score:void 0,s=a instanceof wt?a.box:br(a)?a.detection.box:new ut(a),i=r?`${wo(r)}`:void 0;new Kf(s,{label:i}).draw(e)})}function ld(e){let{Image:t,Video:n}=et.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function W0(e){return new Promise((t,n)=>{(e instanceof et.getEnv().Canvas||ld(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 U0(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=et.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function No(e){let{Image:t,Video:n}=et.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 To({width:e,height:t}){let{createCanvasElement:n}=et.getEnv(),a=n();return a.width=e,a.height=t,a}function ud(e,t){let{ImageData:n}=et.getEnv();if(!(e instanceof n)&&!ld(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||No(e),s=To({width:a,height:r});return e instanceof n?Gn(s).putImageData(e,0,0):Gn(s).drawImage(e,0,0,a,r),s}async function V0(e,t){let n=t||et.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ya(e)?1:0),i=O(()=>e.as3D(a,r,s).toInt());return await po.toPixels(i,n),i.dispose(),n}function Xf(e){let{Image:t,Canvas:n,Video:a}=et.getEnv();return e instanceof t||e instanceof n||e instanceof a}function G0(e,t,n=!1){let{Image:a,Canvas:r}=et.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return To({width:1,height:1});let s=No(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=To({width:t,height:t}),p=e instanceof r?e:ud(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&&Gn(u).drawImage(p,c,h,o,l),u}var Pr=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(Rr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ya(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 et.getEnv().Canvas?a:ud(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 yr(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 E0({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,O(()=>{let a=yr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Ae){let o=ya(i)?i:hn(i);return o=D0(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Yn.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof et.getEnv().Canvas)return po.fromPixels(G0(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 bt(e){if(e instanceof Pr)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 ${r}:`:"",a=t.map(So);return a.forEach((r,s)=>{if(!Xf(r)&&!Rr(r)&&!ya(r))throw typeof t[s]=="string"?new Error(`toNetInput -${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(ya(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>Xf(r)&&W0(r))),new Pr(a,Array.isArray(e))}async function qu(e,t){let{Canvas:n}=et.getEnv(),a=e;if(!(e instanceof n)){let i=await bt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await V0(o)}let r=Gn(a);return t.map(i=>i instanceof wt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let p=To({width:l,height:u});return l>0&&u>0&&Gn(p).putImageData(r.getImageData(i,o,l,u),0,0),p})}async function Ku(e,t){if(!Rr(e)&&!ya(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ya(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return O(()=>{let[n,a,r]=e.shape.slice(ya(e)?1:0);return t.map(o=>o instanceof wt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:u,height:p})=>Eu(e.as3D(n,a,r),[l,o,0],[p,u,r]))})}async function Or(e,t){let{fetch:n}=et.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function yue(e){let t=await Or(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 U0(n)}async function H0(e){return(await Or(e)).json()}async function bue(e){return new Float32Array(await(await Or(e)).arrayBuffer())}function H_(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToVideo - expected buf to be of type: Blob"));let a=et.getEnv().createVideoElement();a.oncanplay=()=>t(a),a.onerror=n,a.playsInline=!0,a.muted=!0,a.src=URL.createObjectURL(e),a.play()})}async function xue(e){let t=await Or(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: ${t.url}`);return H_(n)}function Yf(e,t){let n=`${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 j0(e,t){let{manifestUri:n,modelBaseUri:a}=Yf(e,t),r=await H0(n);return Qt.loadWeights(r,a)}function vue(e,t,n=!1){let{width:a,height:r}=n?No(t):t;return e.width=a,e.height=r,{width:a,height:r}}var cn=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=Jn(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 j0(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}=et.getEnv(),{manifestUri:a,modelBaseUri:r}=Yf(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 Hn(e,t,n){return O(()=>{let a=ho(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function Jf(e,t,n=!1){return O(()=>{let a=Xe(n?J(Rt(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Hn(e,t.conv0,[2,2])),r=Hn(a,t.conv1,[1,1]),s=Xe(J(a,r)),i=Hn(s,t.conv2,[1,1]);return Xe(J(a,J(r,i)))})}function pd(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):Hn(e,t.conv0,a?[2,2]:[1,1])),s=Hn(r,t.conv1,[1,1]),i=Xe(J(r,s)),o=Hn(i,t.conv2,[1,1]),l=Xe(J(r,J(s,o))),u=Hn(l,t.conv3,[1,1]);return Xe(J(r,J(s,J(o,u))))})}function Co(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 $n(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Xu(e,t){return(n,a,r,s)=>{let i=Za(e(n*a*r*r),[r,r,n,a]),o=qe(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Zf(e,t){return(n,a,r)=>{let s=Ga(e(n*a),[n,a]),i=qe(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Qf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function Yu(e,t){return(n,a,r)=>{let s=Za(e(9*n),[3,3,n,1]),i=Za(e(n*a),[1,1,n,a]),o=qe(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Qf(s,i,o)}}function Ju(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Qf(n,a,r)}}function aa(e,t){return(n,a,r)=>{let s=e[n];if(!vo(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function Fn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function eg(e,t){let n=Xu(e,t),a=Yu(e,t);function r(i,o,l,u=!1){let p=u?n(i,o,3,`${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 j_(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractDenseBlock4Params:r}=eg(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: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function tg(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function ng(e,t){let n=aa(e,t),a=tg(n),r=Ju(n);function s(o,l=!1){let u=l?a(`${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 q_(e){let t=[],{extractDenseBlock4Params:n}=ng(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 cd=class extends cn{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=tr(a,[122.782,117.001,104.298]).div(255),i=pd(s,n.dense0,!0);return i=pd(i,n.dense1),i=pd(i,n.dense2),i=pd(i,n.dense3),i=ma(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await bt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return q_(t)}extractParams(t){return j_(t)}};function dd(e,t){return O(()=>J(Fe(e,t.weights),t.bias))}function K_(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=Fn(e),o=Zf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function X_(e){let t=[],n=aa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return $n(e,t),{params:r,paramMappings:t}}function ag(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var hd=class extends cn{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Pr?this.faceFeatureExtractor.forwardInput(t):t;return dd(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return K_(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=ag(t);return this.faceFeatureExtractor.loadFromWeightMap(n),X_(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var q0=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ns=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: ${t.length}`);q0.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return q0.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var rg=class extends hd{constructor(t=new cd){super("FaceExpressionNet",t)}forwardInput(t){return O(()=>Ja(this.runNet(t)))}async forward(t){return this.forwardInput(await bt(t))}async predictExpressions(t){let n=await bt(t),a=await this.forwardInput(n),r=await Promise.all(ht(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Ns(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function K0(e){return e.expressions instanceof Ns}function sg(e,t){return{...e,...{expressions:t}}}function wue(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Ns?s:K0(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(d=>d.probability>n),u=br(s)?s.detection.box.bottomLeft:a||new Le(0,0);new Ss(l.map(d=>`${d.expression} (${wo(d.probability)})`),u).draw(e)})}function Eo(e){return br(e)&&e.landmarks instanceof ba&&e.unshiftedLandmarks instanceof ba&&e.alignedRect instanceof wt}function kue(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 Zu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new wt(e.detection.score,r.rescale(s.reverse()),s),o=kue(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var X0=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)"}},Y0=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new X0(n)}draw(t){let n=Gn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof ju&&(n.strokeStyle=i,n.lineWidth=s,Dr(n,this.faceLandmarks.getJawOutline()),Dr(n,this.faceLandmarks.getLeftEyeBrow()),Dr(n,this.faceLandmarks.getRightEyeBrow()),Dr(n,this.faceLandmarks.getNose()),Dr(n,this.faceLandmarks.getLeftEye(),!0),Dr(n,this.faceLandmarks.getRightEye(),!0),Dr(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 Iue(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ba?a:Eo(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new Y0(r).draw(e)})}var Y_="1.6.6";function Tue(e,t){let n=Xu(e,t),a=Yu(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,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function J_(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=Fn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Tue(a,n),u=s(3,32,3,"entry_flow/conv_in"),p=o(32,64,"entry_flow/reduction_block_0"),d=o(64,128,"entry_flow/reduction_block_1"),c={conv_in:u,reduction_block_0:p,reduction_block_1:d},h={};yr(t,0,1).forEach(y=>{h[`main_block_${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: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function Cue(e,t){let n=aa(e,t),a=tg(n),r=Ju(n);function s(o){let l=r(`${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(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function Z_(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Cue(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};yr(t,0,1).forEach(f=>{d[`main_block_${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 $n(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function Q_(e,t,n){return J(Rt(e,t.filters,n,"same"),t.bias)}function Z0(e,t,n=!0){let a=n?Xe(e):e;return a=Hn(a,t.separable_conv0,[1,1]),a=Hn(Xe(a),t.separable_conv1,[1,1]),a=Pt(a,[3,3],[2,2],"same"),a=J(a,Q_(e,t.expansion_conv,[2,2])),a}function Eue(e,t){let n=Hn(Xe(e),t.separable_conv0,[1,1]);return n=Hn(Xe(n),t.separable_conv1,[1,1]),n=Hn(Xe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var Q0=class extends cn{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return O(()=>{let a=oe(t.toBatchTensor(112,!0),"float32"),s=tr(a,[122.782,117.001,104.298]).div(255),i=Xe(Q_(s,n.entry_flow.conv_in,[2,2]));return i=Z0(i,n.entry_flow.reduction_block_0,!1),i=Z0(i,n.entry_flow.reduction_block_1),yr(this._numMainBlocks,0,1).forEach(o=>{i=Eue(i,n.middle_flow[`main_block_${o}`])}),i=Z0(i,n.exit_flow.reduction_block),i=Xe(Hn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await bt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Z_(t,this._numMainBlocks)}extractParams(t){return J_(t,this._numMainBlocks)}};function eA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),r=Zf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function tA(e){let t=[],n=aa(e,t);function a(s){let i=n(`${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 $n(e,t),{params:r,paramMappings:t}}var ig=(n=>(n.FEMALE="female",n.MALE="male",n))(ig||{});var og=class extends cn{constructor(t=new Q0(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Pr?this.faceFeatureExtractor.forwardInput(t):t,r=ma(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=dd(r,n.fc.age).as1D(),i=dd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Ja(a)}})}async forward(t){return this.forwardInput(await bt(t))}async predictAgeAndGender(t){let n=await bt(t),a=await this.forwardInput(n),r=ht(a.age),s=ht(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return eA(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=ag(t);return this.faceFeatureExtractor.loadFromWeightMap(n),tA(a)}extractParams(t){let a=t.slice(0,t.length-1539),r=t.slice(t.length-1539);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var md=class extends hd{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 bt(t))}async detectLandmarks(t){let n=await bt(t),a=O(()=>ht(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(s.dataSync()),l=o.filter((p,d)=>Hf(d)),u=o.filter((p,d)=>!Hf(d));return new ju(Array(68).fill(0).map((p,d)=>new Le(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 Qu=class extends md{constructor(t=new cd){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function nA(e){let t=[],{extractDenseBlock3Params:n}=ng(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return $n(e,t),{params:a,paramMappings:t}}function aA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractDenseBlock3Params:r}=eg(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 e1=class extends cn{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=tr(a,[122.782,117.001,104.298]).div(255),i=Jf(s,n.dense0,!0);return i=Jf(i,n.dense1),i=Jf(i,n.dense2),i=ma(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await bt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return nA(t)}extractParams(t){return aA(t)}};var lg=class extends md{constructor(t=new e1){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var rA=class extends Qu{};function sA(e,t){return J(B(e,t.weights),t.biases)}function t1(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=sA(o,t.scale),a?Xe(o):o}function iA(e,t){return t1(e,t,[1,1],!0)}function n1(e,t){return t1(e,t,[1,1],!1)}function ug(e,t){return t1(e,t,[2,2],!0,"valid")}function _ue(e,t){function n(o,l,u){let p=e(o),d=p.length/(l*u*u);if(C0(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${u}`);return O(()=>Me(Za(p,[l,d,u,u]),[2,3,1,0]))}function a(o,l,u,p){let d=n(o,l,u),c=qe(e(l));return t.push({paramPath:`${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 oA(e){let{extractWeights:t,getRemainingWeights:n}=Fn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=_ue(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(()=>Me(Ga(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:p,conv64_1:d,conv64_2:c,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:w},paramMappings:a}}function Aue(e,t){let n=aa(e,t);function a(i){let o=n(`${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 lA(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Aue(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"}),!T0(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 nr(e,t){let n=iA(e,t.conv1);return n=n1(n,t.conv2),n=J(n,e),n=Xe(n),n}function fd(e,t){let n=ug(e,t.conv1);n=n1(n,t.conv2);let a=ma(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 ep=class extends cn{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=tr(a,[122.782,117.001,104.298]).div(255),i=ug(s,n.conv32_down);i=Pt(i,3,2,"valid"),i=nr(i,n.conv32_1),i=nr(i,n.conv32_2),i=nr(i,n.conv32_3),i=fd(i,n.conv64_down),i=nr(i,n.conv64_1),i=nr(i,n.conv64_2),i=nr(i,n.conv64_3),i=fd(i,n.conv128_down),i=nr(i,n.conv128_1),i=nr(i,n.conv128_2),i=fd(i,n.conv256_down),i=nr(i,n.conv256_1),i=nr(i,n.conv256_2),i=fd(i,n.conv256_down_out);let o=i.mean([1,2]);return Fe(o,n.fc)})}async forward(t){return this.forwardInput(await bt(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 bt(t),a=O(()=>ht(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 lA(t)}extractParams(t){return oA(t)}};function $ue(e){let t=new ep;return t.extractWeights(e),t}function pg(e,t){return{...e,...{descriptor:t}}}function Fue(e){return typeof e.age=="number"}function cg(e,t){return{...e,...{age:t}}}function Due(e){return(e.gender==="male"||e.gender==="female")&&Vu(e.genderProbability)}function dg(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function Rue(e,t){function n(l,u){let p=Za(e(9*l),[3,3,l,1]),d=qe(e(l)),c=qe(e(l)),h=qe(e(l)),m=qe(e(l));return t.push({paramPath:`${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,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:c}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),p=s(64,128,"mobilenetv1/conv_2"),d=s(128,128,"mobilenetv1/conv_3"),c=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),w=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:w}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),u=r(256,512,3,"prediction_layer/conv_1"),p=r(512,128,1,"prediction_layer/conv_2"),d=r(128,256,3,"prediction_layer/conv_3"),c=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),w=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),T=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),C=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),_=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),$=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:_,class_predictor:$},box_predictor_5:{box_encoding_predictor:P,class_predictor:F}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function uA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=Rue(n,t),i=r(),o=s(),u={extra_dim:Dm(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:u},paramMappings:t}}function Mue(e,t){let n=aa(e,t);function a(u,p,d){let c=n(`${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 pA(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"}),!Rr(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 _a(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=ys(e,t.filters,n,"same");return a=Er(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 cA(e,t){return O(()=>{let n,a=_a(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=_a(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 dA(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=ht(Me(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=ht(Me(t,[1,0])),s=fe(B(fn(fe(r[2],5)),n[0]),2),i=J(B(fe(r[0],10),n[0]),a[0]),o=fe(B(fn(fe(r[3],5)),n[1]),2),l=J(B(fe(r[1],10),n[1]),a[1]);return Me(Mt([ce(i,s),ce(l,o),J(i,s),J(l,o)]),[1,0])}function hA(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=da(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=ht(r),l=ht(i);return{boxes:o,scores:l}})}function _o(e,t){return O(()=>{let n=e.shape[0],a=W(Co(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(Co(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function mA(e,t,n){return O(()=>{let a=_a(e,n.conv_0,[1,1]),r=_a(a,n.conv_1,[2,2]),s=_a(r,n.conv_2,[1,1]),i=_a(s,n.conv_3,[2,2]),o=_a(i,n.conv_4,[1,1]),l=_a(o,n.conv_5,[2,2]),u=_a(l,n.conv_6,[1,1]),p=_a(u,n.conv_7,[2,2]),d=_o(t,n.box_predictor_0),c=_o(e,n.box_predictor_1),h=_o(r,n.box_predictor_2),m=_o(i,n.box_predictor_3),f=_o(l,n.box_predictor_4),g=_o(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 Aa=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 Ao=class extends cn{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=cA(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=mA(s.out,s.conv11,n.prediction_layer);return hA(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await bt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Aa(n),s=await bt(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=dA(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(_=>_*g),[T,C]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(_=>_*f);return new wt(p[x],new Hu(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 pA(t)}extractParams(t){return uA(t)}};function fA(e){let t=new Ao;return t.extractWeights(e),t}function Uue(e){return fA(e)}var gA=class extends Ao{};var yA=.4,bA=[new Le(.738768,.874946),new Le(2.42204,2.65704),new Le(4.30971,7.04493),new Le(10.246,4.59428),new Le(12.6868,11.8741)],xA=[new Le(1.603231,2.094468),new Le(6.041143,7.080126),new Le(2.882459,3.518061),new Le(4.266906,5.178857),new Le(9.041765,10.66308)],vA=[117.001,114.697,97.404],wA="tiny_yolov2_model",kA="tiny_yolov2_separable_conv_model";var hg=e=>typeof e=="number";function a1(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(!hg(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=>hg(t.x)&&hg(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(hg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function tp(e){return O(()=>{let t=B(e,ke(.10000000149011612));return J(Xe(ce(e,t)),t)})}function Lr(e,t){return O(()=>{let n=fa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Rt(n,t.conv.filters,[1,1],"valid"),n=ce(n,t.bn.sub),n=B(n,t.bn.truediv),n=J(n,t.conv.bias),tp(n)})}function zr(e,t){return O(()=>{let n=fa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=ho(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),tp(n)})}function Vue(e,t){let n=Xu(e,t);function a(i,o){let l=qe(e(i)),u=qe(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:u,bn:p}}let s=Yu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function IA(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=Fn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Vue(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),w=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),$=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:_,conv5:$,conv6:P,conv7:F,conv8:S}}else{let[d,c,h,m,f,g,y,b,x]=a,v=l(d,c,"conv0"),w=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),$=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:_,conv5:$,conv6:P,conv7:F,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Gue(e,t){let n=aa(e,t);function a(o){let l=n(`${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=Ju(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function SA(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 $n(e,n),{params:i,paramMappings:n}}var xr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 r1=class extends cn{constructor(t){super("TinyYolov2");a1(t),this._config=t}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(t,n){let a=Lr(t,n.conv0);return a=Pt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv1),a=Pt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv2),a=Pt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv3),a=Pt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv4),a=Pt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv5),a=Pt(a,[2,2],[1,1],"same"),a=Lr(a,n.conv6),a=Lr(a,n.conv7),Co(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?tp(Co(t,n.conv0,"valid",!1)):zr(t,n.conv0);return a=Pt(a,[2,2],[2,2],"same"),a=zr(a,n.conv1),a=Pt(a,[2,2],[2,2],"same"),a=zr(a,n.conv2),a=Pt(a,[2,2],[2,2],"same"),a=zr(a,n.conv3),a=Pt(a,[2,2],[2,2],"same"),a=zr(a,n.conv4),a=Pt(a,[2,2],[2,2],"same"),a=zr(a,n.conv5),a=Pt(a,[2,2],[1,1],"same"),a=n.conv6?zr(a,n.conv6):a,a=n.conv7?zr(a,n.conv7):a,Co(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=oe(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?tr(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await bt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new xr(n),s=await bt(t),i=await this.forwardInput(s,a),o=O(()=>ht(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return F0(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new Is(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return SA(t,this.config)}extractParams(t){let n=this.config.filterSizes||r1.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return IA(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let y=t.reshape([u,u,p,this.boxEncodingSize]),b=y.slice([0,0,0,0],[u,u,p,4]),x=y.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?Ja(y.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):ke(0);return[b,x,v]}),m=[],f=await c.array(),g=await d.array();for(let y=0;y<u;y++)for(let b=0;b<u;b++)for(let x=0;x<p;x++){let v=ad(f[y][b][x][0]);if(!a||v>a){let w=(b+ad(g[y][b][x][0]))/u*o,T=(y+ad(g[y][b][x][1]))/u*l,C=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/u*l,$=w-C/2,P=T-_/2,F={row:y,col:b,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,F):{classScore:1,label:0};m.push({box:new Gu($,P,$+C,P+_),score:v,classScore:v*S,label:M,...F})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},np=r1;np.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ap=class extends np{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:yA,classes:["face"],...t?{anchors:xA,meanRgb:vA}:{anchors:bA,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 wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?kA:wA}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Hue(e,t=!0){let n=new ap(t);return n.extractWeights(e),n}var mg=class extends xr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var $a=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function $o(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Eo(l)?r(l):l.detection),i=a||(t instanceof Ae?await Ku(t,s):await qu(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function rp(e,t,n,a,r){return $o([e],t,async s=>n(s[0]),a,r)}var NA=.4,TA=[new Le(1.603231,2.094468),new Le(6.041143,7.080126),new Le(2.882459,3.518061),new Le(4.266906,5.178857),new Le(9.041765,10.66308)],CA=[117.001,114.697,97.404];var sp=class extends np{constructor(){let t={withSeparableConvs:!0,iouThreshold:NA,classes:["face"],anchors:TA,meanRgb:CA,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 wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var tt={ssdMobilenetv1:new Ao,tinyFaceDetector:new sp,tinyYolov2:new ap,faceLandmark68Net:new Qu,faceLandmark68TinyNet:new lg,faceRecognitionNet:new ep,faceExpressionNet:new rg,ageGenderNet:new og},EA=(e,t)=>tt.ssdMobilenetv1.locateFaces(e,t),jue=(e,t)=>tt.tinyFaceDetector.locateFaces(e,t),que=(e,t)=>tt.tinyYolov2.locateFaces(e,t),_A=e=>tt.faceLandmark68Net.detectLandmarks(e),Kue=e=>tt.faceLandmark68TinyNet.detectLandmarks(e),Xue=e=>tt.faceRecognitionNet.computeFaceDescriptor(e),Yue=e=>tt.faceExpressionNet.predictExpressions(e),Jue=e=>tt.ageGenderNet.predictAgeAndGender(e),AA=e=>tt.ssdMobilenetv1.load(e),Zue=e=>tt.tinyFaceDetector.load(e),Que=e=>tt.tinyYolov2.load(e),epe=e=>tt.faceLandmark68Net.load(e),tpe=e=>tt.faceLandmark68TinyNet.load(e),npe=e=>tt.faceRecognitionNet.load(e),ape=e=>tt.faceExpressionNet.load(e),rpe=e=>tt.ageGenderNet.load(e),spe=AA,ipe=EA,ope=_A;var s1=class extends $a{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},ip=class extends s1{async run(){let t=await this.parentTask,n=await $o(t,this.input,async a=>Promise.all(a.map(r=>tt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>sg(a,n[r]))}withAgeAndGender(){return new lp(this,this.input)}},op=class extends s1{async run(){let t=await this.parentTask;if(!t)return;let n=await rp(t,this.input,a=>tt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return sg(t,n)}withAgeAndGender(){return new up(this,this.input)}},Fo=class extends ip{withAgeAndGender(){return new Ro(this,this.input)}withFaceDescriptors(){return new Ts(this,this.input)}},Do=class extends op{withAgeAndGender(){return new Mo(this,this.input)}withFaceDescriptor(){return new Cs(this,this.input)}};var i1=class extends $a{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},lp=class extends i1{async run(){let t=await this.parentTask,n=await $o(t,this.input,async a=>Promise.all(a.map(r=>tt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return cg(dg(a,i,o),s)})}withFaceExpressions(){return new ip(this,this.input)}},up=class extends i1{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await rp(t,this.input,s=>tt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return cg(dg(t,a,r),n)}withFaceExpressions(){return new op(this,this.input)}},Ro=class extends lp{withFaceExpressions(){return new Fo(this,this.input)}withFaceDescriptors(){return new Ts(this,this.input)}},Mo=class extends up{withFaceExpressions(){return new Do(this,this.input)}withFaceDescriptor(){return new Cs(this,this.input)}};var fg=class extends $a{constructor(t,n){super();this.parentTask=t;this.input=n}},Ts=class extends fg{async run(){let t=await this.parentTask;return(await $o(t,this.input,a=>Promise.all(a.map(r=>tt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>pg(t[r],a))}withFaceExpressions(){return new Fo(this,this.input)}withAgeAndGender(){return new Ro(this,this.input)}},Cs=class extends fg{async run(){let t=await this.parentTask;if(!t)return;let n=await rp(t,this.input,a=>tt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return pg(t,n)}withFaceExpressions(){return new Do(this,this.input)}withAgeAndGender(){return new Mo(this,this.input)}};var gg=class extends $a{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?tt.faceLandmark68TinyNet:tt.faceLandmark68Net}},yg=class extends gg{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await Ku(this.input,n):await qu(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>Zu(s,r[i]))}withFaceExpressions(){return new Fo(this,this.input)}withAgeAndGender(){return new Ro(this,this.input)}withFaceDescriptors(){return new Ts(this,this.input)}},bg=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await Ku(this.input,[n]):await qu(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Zu(t,r)}withFaceExpressions(){return new Do(this,this.input)}withAgeAndGender(){return new Mo(this,this.input)}withFaceDescriptor(){return new Cs(this,this.input)}};var xg=class extends $a{constructor(t,n=new Aa){super();this.input=t;this.options=n}},gd=class extends xg{async run(){let{input:t,options:n}=this,a;if(n instanceof mg)a=tt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Aa)a=tt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof xr)a=tt.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=>Io({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new yg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ip(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new lp(this.runAndExtendWithFaceDetections(),this.input)}},vg=class extends xg{async run(){let t=await new gd(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?Io({},n):void 0)})}withFaceLandmarks(t=!1){return new bg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new op(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new up(this.runAndExtendWithFaceDetection(),this.input)}};function lpe(e,t=new Aa){return new vg(e,t)}function wg(e,t=new Aa){return new gd(e,t)}async function $A(e,t){return wg(e,new Aa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function upe(e,t={}){return wg(e,new xr(t)).withFaceLandmarks().withFaceDescriptors()}var ppe=$A;function o1(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 kg=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 Mr)return i;if(i instanceof Float32Array)return new Mr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Mr(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=>o1(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new rd(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 rd("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Mr.fromJSON(a));return new kg(n,t.distanceThreshold)}};function cpe(e){let t=new sp;return t.extractWeights(e),t}function FA(e,t){let{width:n,height:a}=new An(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>FA(r,{width:n,height:a}));if(Eo(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Zu(Io(e,r),s)}return br(e)?Io(e,e.detection.forSize(n,a)):e instanceof ba||e instanceof wt?e.forSize(n,a):e}var dpe=Y_;return F$(hpe);})();
/**
 * @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 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. */