human/dist/human.js

    /*
      Human library
      homepage: <https://github.com/vladmandic/human>
      author: <https://github.com/vladmandic>'
    */
var Human=(()=>{var R3=Object.defineProperty;var rR=e=>R3(e,"__esModule",{value:!0});var Qg=e=>{if(typeof require!="undefined")return require(e);throw new Error('Dynamic require of "'+e+'" is not supported')};var e2=(e,t)=>{rR(e);for(var n in t)R3(e,n,{get:t[n],enumerable:!0})};var D3=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var Fn=(e,t,n)=>(D3(e,t,"read from private field"),n?n.call(e):t.get(e)),Ir=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},Qr=(e,t,n,r)=>(D3(e,t,"write to private field"),r?r.call(e,n):t.set(e,n),n);var fwe={};e2(fwe,{Human:()=>f_,default:()=>f_});function $t(e,t){let n=e.endsWith("/")?"":"/",s=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!s.toLocaleLowerCase().includes(".json"))throw new Error(`Human: ModelPath Error: ${s} Expecting JSON file`);return s}function me(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}var at=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function lr(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(s=>{let a=n[s],o=r[s];Array.isArray(a)&&Array.isArray(o)?n[s]=a.concat(...o):t(a)&&t(o)?n[s]=lr(a,o):n[s]=o}),n),{})}var F3={backend:"webgl",modelBasePath:"../models/",wasmPath:"../node_modules/@tensorflow/tfjs-backend-wasm/dist/",debug:!0,async:!0,warmup:"full",cacheSensitivity:.75,skipFrame:!1,filter:{enabled:!0,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:15,skipFrames:15,minConfidence:.2,iouThreshold:.1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:11,minConfidence:.1},emotion:{enabled:!0,minConfidence:.1,skipFrames:17,modelPath:"emotion.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:1,minConfidence:.2,skipFrames:1},hand:{enabled:!0,rotation:!0,skipFrames:18,minConfidence:.1,iouThreshold:.1,maxDetected:2,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:19},segmentation:{enabled:!1,modelPath:"selfie.json"}};function M3(){let e,t;if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);if(n&&n[0]){let r=n[0].match(/\(([^()]+)\)/g);e=r?r[0].replace(/\(|\)/g,""):"",t=navigator.userAgent.replace(n[0],""),e[1]&&(t=t.replace(n[1],"")),t=t.replace(/  /g," ")}}else typeof process!="undefined"&&(e=`${process.platform} ${process.arch}`,t=`NodeJS ${process.version}`);return{platform:e,agent:t}}var bh={};e2(bh,{Abs:()=>tv,Acos:()=>nv,Acosh:()=>rv,AdadeltaOptimizer:()=>Fp,AdagradOptimizer:()=>Mp,AdamOptimizer:()=>Op,AdamaxOptimizer:()=>Pp,Add:()=>l2,AddN:()=>sv,All:()=>av,Any:()=>ov,ArgMax:()=>iv,ArgMin:()=>lv,Asin:()=>uv,Asinh:()=>cv,Atan:()=>dv,Atan2:()=>pv,Atanh:()=>hv,AvgPool:()=>fv,AvgPool3D:()=>mv,AvgPool3DGrad:()=>iD,AvgPoolGrad:()=>oD,BackendWasm:()=>d$,BatchMatMul:()=>gv,BatchToSpaceND:()=>yv,Bincount:()=>Av,BroadcastTo:()=>lD,Callback:()=>B8,CallbackList:()=>CS,Cast:()=>u2,Ceil:()=>xv,ClipByValue:()=>bv,Complex:()=>vv,ComplexAbs:()=>wv,Concat:()=>kv,Conv2D:()=>Iv,Conv2DBackpropFilter:()=>Sv,Conv2DBackpropInput:()=>Tv,Conv3D:()=>Nv,Conv3DBackpropFilterV2:()=>uD,Conv3DBackpropInputV2:()=>Cv,Cos:()=>Ev,Cosh:()=>$v,CropAndResize:()=>Rv,Cumsum:()=>_v,CustomCallback:()=>$S,DataStorage:()=>LR,DenseBincount:()=>Dv,DepthToSpace:()=>Fv,DepthwiseConv2dNative:()=>Mv,DepthwiseConv2dNativeBackpropFilter:()=>Ov,DepthwiseConv2dNativeBackpropInput:()=>Pv,Diag:()=>zv,Dilation2D:()=>Lv,Dilation2DBackpropFilter:()=>dD,Dilation2DBackpropInput:()=>cD,ENV:()=>Sr,EarlyStopping:()=>V8,Einsum:()=>Wv,Elu:()=>Vv,EluGrad:()=>hD,Environment:()=>Q3,Equal:()=>Hv,Erf:()=>Uv,Exp:()=>Gv,ExpandDims:()=>jv,Expm1:()=>qv,FFT:()=>Kv,Fill:()=>Xv,FlipLeftRight:()=>Zv,Floor:()=>Yv,FloorDiv:()=>Jv,FromPixels:()=>h2,FusedBatchNorm:()=>Qv,FusedConv2D:()=>f2,FusedDepthwiseConv2D:()=>m2,GPGPUContext:()=>Bm,GatherNd:()=>tw,GatherV2:()=>ew,GraphModel:()=>bT,Greater:()=>nw,GreaterEqual:()=>rw,History:()=>ES,IFFT:()=>sw,Identity:()=>c2,Imag:()=>aw,InputSpec:()=>tn,IsFinite:()=>ow,IsInf:()=>iw,IsNan:()=>lw,KernelBackend:()=>W3,LRN:()=>Aw,LRNGrad:()=>fD,LayerVariable:()=>kS,LayersModel:()=>pa,LeakyRelu:()=>uw,Less:()=>cw,LessEqual:()=>dw,LinSpace:()=>hw,Log:()=>pw,Log1p:()=>fw,LogSoftmax:()=>pD,LogicalAnd:()=>mw,LogicalNot:()=>gw,LogicalOr:()=>yw,MathBackendCPU:()=>_5,MathBackendWebGL:()=>dh,Max:()=>xw,MaxPool:()=>vw,MaxPool3D:()=>ww,MaxPool3DGrad:()=>gD,MaxPoolGrad:()=>mD,MaxPoolWithArgmax:()=>kw,Maximum:()=>bw,Mean:()=>Iw,Min:()=>Sw,Minimum:()=>Tw,MirrorPad:()=>Nw,Mod:()=>Cw,MomentumOptimizer:()=>zp,Multinomial:()=>Ew,Multiply:()=>$w,Neg:()=>_w,NonMaxSuppressionV3:()=>Dw,NonMaxSuppressionV4:()=>Fw,NonMaxSuppressionV5:()=>Mw,NotEqual:()=>Rw,OP_SCOPE_SUFFIX:()=>J7,OneHot:()=>Pw,OnesLike:()=>Ow,Optimizer:()=>Ra,Pack:()=>zw,PadV2:()=>Lw,Pool:()=>yD,Pow:()=>Bw,Prelu:()=>Ww,Prod:()=>Vw,RMSPropOptimizer:()=>Lp,RNN:()=>fa,Range:()=>Uw,Rank:()=>b2,Real:()=>Hw,RealDiv:()=>Bv,Reciprocal:()=>Gw,Reduction:()=>Pn,Relu:()=>jw,Relu6:()=>Zw,Reshape:()=>qw,ResizeBilinear:()=>Xw,ResizeBilinearGrad:()=>xD,ResizeNearestNeighbor:()=>Kw,ResizeNearestNeighborGrad:()=>AD,Reverse:()=>Yw,RotateWithOffset:()=>M7,Round:()=>Jw,Rsqrt:()=>Qw,SGDOptimizer:()=>mc,ScatterNd:()=>e7,Select:()=>t7,Selu:()=>n7,Sequential:()=>pm,Sigmoid:()=>i7,Sign:()=>o7,Sin:()=>s7,Sinh:()=>a7,Slice:()=>r7,Softmax:()=>p7,Softplus:()=>l7,SpaceToBatchND:()=>d7,SparseFillEmptyRows:()=>f7,SparseReshape:()=>m7,SparseSegmentMean:()=>g7,SparseSegmentSum:()=>y7,SparseToDense:()=>A7,SplitV:()=>h7,Sqrt:()=>u7,Square:()=>bD,SquaredDifference:()=>x7,Step:()=>F7,StridedSlice:()=>b7,StringNGrams:()=>v7,StringSplit:()=>w7,StringToHashBucketFast:()=>k7,Sub:()=>I7,Sum:()=>c7,SymbolicTensor:()=>ps,Tan:()=>S7,Tanh:()=>T7,Tensor:()=>Tt,TensorBuffer:()=>up,Tile:()=>d2,TopK:()=>N7,Transform:()=>C7,Transpose:()=>E7,Unique:()=>$7,Unpack:()=>_7,UnsortedSegmentSum:()=>R7,Variable:()=>rc,ZerosLike:()=>D7,_FusedMatMul:()=>p2,abs:()=>Nr,acos:()=>BM,acosh:()=>VM,add:()=>Me,addN:()=>Z2,all:()=>GM,any:()=>qM,argMax:()=>Y2,argMin:()=>ZM,asin:()=>JM,asinh:()=>eO,atan:()=>nO,atan2:()=>sO,atanh:()=>oO,avgPool:()=>Vk,avgPool3d:()=>gO,backend:()=>RM,backend_util:()=>_4,basicLSTMCell:()=>wO,batchNorm:()=>Ap,batchNorm2d:()=>NO,batchNorm3d:()=>EO,batchNorm4d:()=>_O,batchToSpaceND:()=>Uk,bincount:()=>Hk,booleanMaskAsync:()=>BB,broadcastTo:()=>xp,browser:()=>Hr,buffer:()=>Ys,callbacks:()=>Fre,cast:()=>Pt,ceil:()=>MO,clipByValue:()=>PO,clone:()=>Js,complex:()=>go,concat:()=>an,concat1d:()=>LO,concat2d:()=>lc,concat3d:()=>VO,concat4d:()=>HO,constraints:()=>nS,conv1d:()=>qO,conv2d:()=>bp,conv2dTranspose:()=>ZO,conv3d:()=>JO,conv3dTranspose:()=>nP,copyRegisteredKernels:()=>ID,cos:()=>sP,cosh:()=>oP,cosineWindow:()=>dy,cumsum:()=>lP,customGrad:()=>Ns,data:()=>vT,denseBincount:()=>cP,deprecationWarn:()=>Ok,depthToSpace:()=>hP,depthwiseConv2d:()=>ty,deregisterOp:()=>Ore,device_util:()=>K7,diag:()=>mP,dilation2d:()=>yP,disableDeprecationWarnings:()=>vM,dispose:()=>Ve,disposeVariables:()=>wM,div:()=>Qe,divNoNan:()=>kP,dot:()=>SP,dropout:()=>QB,einsum:()=>NP,elu:()=>Kk,enableDebugMode:()=>bM,enableProdMode:()=>xM,enclosingPowerOfTwo:()=>w4,engine:()=>kM,env:()=>ct,equal:()=>qk,erf:()=>$P,exp:()=>wo,expandDims:()=>ea,expm1:()=>FP,eye:()=>Xk,fft:()=>ly,fill:()=>wp,findBackend:()=>K2,findBackendFactory:()=>_M,floor:()=>Zk,floorDiv:()=>zk,forceHalfFloat:()=>wE,fused:()=>k4,gather:()=>Yk,gatherND:()=>ZB,gather_util:()=>yk,getBackend:()=>EM,getGradient:()=>g2,getKernel:()=>rp,getKernelsForBackend:()=>Li,gpgpu_util:()=>kC,grad:()=>rz,grads:()=>sz,greater:()=>kp,greaterEqual:()=>Jk,ifft:()=>Ep,imag:()=>ny,image:()=>Ye,inTopKAsync:()=>tW,initializers:()=>uS,input:()=>QS,io:()=>uk,irfft:()=>g4,isFinite:()=>UP,isInf:()=>GP,isNaN:()=>qP,keep:()=>Pk,kernel_impls:()=>M4,layers:()=>bS,leakyRelu:()=>Qk,less:()=>ZP,lessEqual:()=>ry,linalg:()=>BV,linspace:()=>JP,loadGraphModel:()=>Et,loadLayersModel:()=>Gte,localResponseNormalization:()=>ez,log:()=>uc,log1p:()=>e4,logSigmoid:()=>cz,logSoftmax:()=>mz,logSumExp:()=>s4,logicalAnd:()=>Sp,logicalNot:()=>a4,logicalOr:()=>o4,logicalXor:()=>Tz,losses:()=>WV,matMul:()=>yt,math:()=>mk,max:()=>_a,maxPool:()=>i4,maxPool3d:()=>Ez,maxPoolWithArgmax:()=>_z,maximum:()=>l4,mean:()=>Tp,memory:()=>IM,meshgrid:()=>Fz,metrics:()=>P8,min:()=>ay,minimum:()=>u4,mirrorPad:()=>zz,mod:()=>Bz,model:()=>Ute,models:()=>z8,moments:()=>Uz,movingAverage:()=>UB,mul:()=>fe,multiRNNCell:()=>Gz,multinomial:()=>qz,neg:()=>$a,nextFrame:()=>jV,norm:()=>cy,notEqual:()=>c4,oneHot:()=>W2,ones:()=>ko,onesLike:()=>Zz,op:()=>H,outerProduct:()=>Jz,pad:()=>dc,pad1d:()=>tL,pad2d:()=>rL,pad3d:()=>aL,pad4d:()=>iL,pool:()=>hL,pow:()=>hc,prelu:()=>h4,print:()=>lk,prod:()=>gL,profile:()=>SM,rand:()=>AL,randomGamma:()=>wL,randomNormal:()=>IL,randomUniform:()=>p4,range:()=>pc,ready:()=>CM,real:()=>Np,reciprocal:()=>CL,registerBackend:()=>X2,registerCallbackConstructor:()=>jte,registerGradient:()=>vD,registerKernel:()=>sp,registerOp:()=>Mre,regularizers:()=>L8,relu:()=>Cp,relu6:()=>f4,removeBackend:()=>$M,reshape:()=>ue,reverse:()=>Io,reverse1d:()=>DL,reverse2d:()=>ML,reverse3d:()=>PL,reverse4d:()=>LL,rfft:()=>uy,round:()=>m4,rsqrt:()=>VL,scalar:()=>ut,scatterND:()=>GB,scatter_util:()=>xk,selu:()=>HL,separableConv2d:()=>jL,sequential:()=>Hte,serialization:()=>_k,setBackend:()=>NM,setPlatform:()=>DM,setWasmPath:()=>$ve,setWasmPaths:()=>_ve,setWebGLContext:()=>Dm,setdiff1dAsync:()=>KL,shared:()=>GT,sigmoid:()=>Ts,sign:()=>ZL,signal:()=>LV,sin:()=>JL,sinh:()=>eB,slice:()=>Ze,slice1d:()=>nB,slice2d:()=>sB,slice3d:()=>oB,slice4d:()=>lB,slice_util:()=>H2,softmax:()=>cB,softplus:()=>n4,spaceToBatchND:()=>d4,sparse:()=>VV,sparseToDense:()=>KB,spectral:()=>zV,split:()=>ta,sqrt:()=>na,square:()=>ns,squaredDifference:()=>y4,squeeze:()=>Zn,stack:()=>So,step:()=>A4,stridedSlice:()=>wB,string:()=>UV,sub:()=>He,sum:()=>_t,sumOutType:()=>jD,tan:()=>IB,tanh:()=>ey,tensor:()=>ts,tensor1d:()=>ur,tensor2d:()=>ra,tensor3d:()=>mp,tensor4d:()=>SB,tensor5d:()=>TB,tensor6d:()=>NB,tensor_util:()=>U7,test_util:()=>Dk,tidy:()=>Ue,tile:()=>vp,time:()=>TM,topk:()=>EB,train:()=>HV,transpose:()=>fp,truncatedNormal:()=>_B,unique:()=>DB,unregisterGradient:()=>kD,unregisterKernel:()=>wD,unsortedSegmentSum:()=>MB,unstack:()=>fc,upcastType:()=>cp,util:()=>O7,valueAndGrad:()=>az,valueAndGrads:()=>oz,variable:()=>PB,variableGrads:()=>t4,version:()=>Fve,version_converter:()=>Lse,version_core:()=>AM,version_cpu:()=>boe,version_layers:()=>tx,version_wasm:()=>Rve,version_webgl:()=>gfe,webgl:()=>yfe,webgl_util:()=>YN,where:()=>Gi,whereAsync:()=>b4,zeros:()=>ji,zerosLike:()=>Cr});var sR=Object.create,Qh=Object.defineProperty,aR=Object.getOwnPropertyDescriptor,oR=Object.getOwnPropertyNames,iR=Object.getPrototypeOf,lR=Object.prototype.hasOwnProperty,O3=e=>Qh(e,"__esModule",{value:!0}),co=e=>{if(typeof Qg!="undefined")return Qg(e);throw new Error('Dynamic require of "'+e+'" is not supported')},Ot=(e,t)=>function(){return t||(0,e[Object.keys(e)[0]])((t={exports:{}}).exports,t),t.exports},De=(e,t)=>{O3(e);for(var n in t)Qh(e,n,{get:t[n],enumerable:!0})},uR=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of oR(t))!lR.call(e,r)&&r!=="default"&&Qh(e,r,{get:()=>t[r],enumerable:!(n=aR(t,r))||n.enumerable});return e},Ks=e=>uR(O3(Qh(e!=null?sR(iR(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),P3=Ot({"node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(e,t){t.exports=r;var n=null;try{n=new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0,97,115,109,1,0,0,0,1,13,2,96,0,1,127,96,4,127,127,127,127,1,127,3,7,6,0,1,1,1,1,1,6,6,1,127,1,65,0,11,7,50,6,3,109,117,108,0,1,5,100,105,118,95,115,0,2,5,100,105,118,95,117,0,3,5,114,101,109,95,115,0,4,5,114,101,109,95,117,0,5,8,103,101,116,95,104,105,103,104,0,0,10,191,1,6,4,0,35,0,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,126,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,127,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,128,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,129,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,130,34,4,66,32,135,167,36,0,32,4,167,11])),{}).exports}catch(_){}function r(_,N,P){this.low=_|0,this.high=N|0,this.unsigned=!!P}r.prototype.__isLong__,Object.defineProperty(r.prototype,"__isLong__",{value:!0});function s(_){return(_&&_.__isLong__)===!0}r.isLong=s;var a={},o={};function i(_,N){var P,B,j;return N?(_>>>=0,(j=0<=_&&_<256)&&(B=o[_],B)?B:(P=u(_,(_|0)<0?-1:0,!0),j&&(o[_]=P),P)):(_|=0,(j=-128<=_&&_<128)&&(B=a[_],B)?B:(P=u(_,_<0?-1:0,!1),j&&(a[_]=P),P))}r.fromInt=i;function l(_,N){if(isNaN(_))return N?b:x;if(N){if(_<0)return b;if(_>=g)return E}else{if(_<=-y)return F;if(_+1>=y)return I}return _<0?l(-_,N).neg():u(_%m|0,_/m|0,N)}r.fromNumber=l;function u(_,N,P){return new r(_,N,P)}r.fromBits=u;var c=Math.pow;function d(_,N,P){if(_.length===0)throw Error("empty string");if(_==="NaN"||_==="Infinity"||_==="+Infinity"||_==="-Infinity")return x;if(typeof N=="number"?(P=N,N=!1):N=!!N,P=P||10,P<2||36<P)throw RangeError("radix");var B;if((B=_.indexOf("-"))>0)throw Error("interior hyphen");if(B===0)return d(_.substring(1),N,P).neg();for(var j=l(c(P,8)),X=x,Y=0;Y<_.length;Y+=8){var ee=Math.min(8,_.length-Y),oe=parseInt(_.substring(Y,Y+ee),P);if(ee<8){var se=l(c(P,ee));X=X.mul(se).add(l(oe))}else X=X.mul(j),X=X.add(l(oe))}return X.unsigned=N,X}r.fromString=d;function h(_,N){return typeof _=="number"?l(_,N):typeof _=="string"?d(_,N):u(_.low,_.high,typeof N=="boolean"?N:_.unsigned)}r.fromValue=h;var p=1<<16,f=1<<24,m=p*p,g=m*m,y=g/2,A=i(f),x=i(0);r.ZERO=x;var b=i(0,!0);r.UZERO=b;var v=i(1);r.ONE=v;var w=i(1,!0);r.UONE=w;var S=i(-1);r.NEG_ONE=S;var I=u(4294967295|0,2147483647|0,!1);r.MAX_VALUE=I;var E=u(4294967295|0,4294967295|0,!0);r.MAX_UNSIGNED_VALUE=E;var F=u(0,2147483648|0,!1);r.MIN_VALUE=F;var $=r.prototype;$.toInt=function(){return this.unsigned?this.low>>>0:this.low},$.toNumber=function(){return this.unsigned?(this.high>>>0)*m+(this.low>>>0):this.high*m+(this.low>>>0)},$.toString=function(N){if(N=N||10,N<2||36<N)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(F)){var P=l(N),B=this.div(P),j=B.mul(P).sub(this);return B.toString(N)+j.toInt().toString(N)}else return"-"+this.neg().toString(N);for(var X=l(c(N,6),this.unsigned),Y=this,ee="";;){var oe=Y.div(X),se=Y.sub(oe.mul(X)).toInt()>>>0,ie=se.toString(N);if(Y=oe,Y.isZero())return ie+ee;for(;ie.length<6;)ie="0"+ie;ee=""+ie+ee}},$.getHighBits=function(){return this.high},$.getHighBitsUnsigned=function(){return this.high>>>0},$.getLowBits=function(){return this.low},$.getLowBitsUnsigned=function(){return this.low>>>0},$.getNumBitsAbs=function(){if(this.isNegative())return this.eq(F)?64:this.neg().getNumBitsAbs();for(var N=this.high!=0?this.high:this.low,P=31;P>0&&(N&1<<P)==0;P--);return this.high!=0?P+33:P+1},$.isZero=function(){return this.high===0&&this.low===0},$.eqz=$.isZero,$.isNegative=function(){return!this.unsigned&&this.high<0},$.isPositive=function(){return this.unsigned||this.high>=0},$.isOdd=function(){return(this.low&1)==1},$.isEven=function(){return(this.low&1)==0},$.equals=function(N){return s(N)||(N=h(N)),this.unsigned!==N.unsigned&&this.high>>>31==1&&N.high>>>31==1?!1:this.high===N.high&&this.low===N.low},$.eq=$.equals,$.notEquals=function(N){return!this.eq(N)},$.neq=$.notEquals,$.ne=$.notEquals,$.lessThan=function(N){return this.comp(N)<0},$.lt=$.lessThan,$.lessThanOrEqual=function(N){return this.comp(N)<=0},$.lte=$.lessThanOrEqual,$.le=$.lessThanOrEqual,$.greaterThan=function(N){return this.comp(N)>0},$.gt=$.greaterThan,$.greaterThanOrEqual=function(N){return this.comp(N)>=0},$.gte=$.greaterThanOrEqual,$.ge=$.greaterThanOrEqual,$.compare=function(N){if(s(N)||(N=h(N)),this.eq(N))return 0;var P=this.isNegative(),B=N.isNegative();return P&&!B?-1:!P&&B?1:this.unsigned?N.high>>>0>this.high>>>0||N.high===this.high&&N.low>>>0>this.low>>>0?-1:1:this.sub(N).isNegative()?-1:1},$.comp=$.compare,$.negate=function(){return!this.unsigned&&this.eq(F)?F:this.not().add(v)},$.neg=$.negate,$.add=function(N){s(N)||(N=h(N));var P=this.high>>>16,B=this.high&65535,j=this.low>>>16,X=this.low&65535,Y=N.high>>>16,ee=N.high&65535,oe=N.low>>>16,se=N.low&65535,ie=0,ne=0,de=0,he=0;return he+=X+se,de+=he>>>16,he&=65535,de+=j+oe,ne+=de>>>16,de&=65535,ne+=B+ee,ie+=ne>>>16,ne&=65535,ie+=P+Y,ie&=65535,u(de<<16|he,ie<<16|ne,this.unsigned)},$.subtract=function(N){return s(N)||(N=h(N)),this.add(N.neg())},$.sub=$.subtract,$.multiply=function(N){if(this.isZero())return x;if(s(N)||(N=h(N)),n){var P=n.mul(this.low,this.high,N.low,N.high);return u(P,n.get_high(),this.unsigned)}if(N.isZero())return x;if(this.eq(F))return N.isOdd()?F:x;if(N.eq(F))return this.isOdd()?F:x;if(this.isNegative())return N.isNegative()?this.neg().mul(N.neg()):this.neg().mul(N).neg();if(N.isNegative())return this.mul(N.neg()).neg();if(this.lt(A)&&N.lt(A))return l(this.toNumber()*N.toNumber(),this.unsigned);var B=this.high>>>16,j=this.high&65535,X=this.low>>>16,Y=this.low&65535,ee=N.high>>>16,oe=N.high&65535,se=N.low>>>16,ie=N.low&65535,ne=0,de=0,he=0,ge=0;return ge+=Y*ie,he+=ge>>>16,ge&=65535,he+=X*ie,de+=he>>>16,he&=65535,he+=Y*se,de+=he>>>16,he&=65535,de+=j*ie,ne+=de>>>16,de&=65535,de+=X*se,ne+=de>>>16,de&=65535,de+=Y*oe,ne+=de>>>16,de&=65535,ne+=B*ie+j*se+X*oe+Y*ee,ne&=65535,u(he<<16|ge,ne<<16|de,this.unsigned)},$.mul=$.multiply,$.divide=function(N){if(s(N)||(N=h(N)),N.isZero())throw Error("division by zero");if(n){if(!this.unsigned&&this.high===-2147483648&&N.low===-1&&N.high===-1)return this;var P=(this.unsigned?n.div_u:n.div_s)(this.low,this.high,N.low,N.high);return u(P,n.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?b:x;var B,j,X;if(this.unsigned){if(N.unsigned||(N=N.toUnsigned()),N.gt(this))return b;if(N.gt(this.shru(1)))return w;X=b}else{if(this.eq(F)){if(N.eq(v)||N.eq(S))return F;if(N.eq(F))return v;var Y=this.shr(1);return B=Y.div(N).shl(1),B.eq(x)?N.isNegative()?v:S:(j=this.sub(N.mul(B)),X=B.add(j.div(N)),X)}else if(N.eq(F))return this.unsigned?b:x;if(this.isNegative())return N.isNegative()?this.neg().div(N.neg()):this.neg().div(N).neg();if(N.isNegative())return this.div(N.neg()).neg();X=x}for(j=this;j.gte(N);){B=Math.max(1,Math.floor(j.toNumber()/N.toNumber()));for(var ee=Math.ceil(Math.log(B)/Math.LN2),oe=ee<=48?1:c(2,ee-48),se=l(B),ie=se.mul(N);ie.isNegative()||ie.gt(j);)B-=oe,se=l(B,this.unsigned),ie=se.mul(N);se.isZero()&&(se=v),X=X.add(se),j=j.sub(ie)}return X},$.div=$.divide,$.modulo=function(N){if(s(N)||(N=h(N)),n){var P=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,N.low,N.high);return u(P,n.get_high(),this.unsigned)}return this.sub(this.div(N).mul(N))},$.mod=$.modulo,$.rem=$.modulo,$.not=function(){return u(~this.low,~this.high,this.unsigned)},$.and=function(N){return s(N)||(N=h(N)),u(this.low&N.low,this.high&N.high,this.unsigned)},$.or=function(N){return s(N)||(N=h(N)),u(this.low|N.low,this.high|N.high,this.unsigned)},$.xor=function(N){return s(N)||(N=h(N)),u(this.low^N.low,this.high^N.high,this.unsigned)},$.shiftLeft=function(N){return s(N)&&(N=N.toInt()),(N&=63)===0?this:N<32?u(this.low<<N,this.high<<N|this.low>>>32-N,this.unsigned):u(0,this.low<<N-32,this.unsigned)},$.shl=$.shiftLeft,$.shiftRight=function(N){return s(N)&&(N=N.toInt()),(N&=63)===0?this:N<32?u(this.low>>>N|this.high<<32-N,this.high>>N,this.unsigned):u(this.high>>N-32,this.high>=0?0:-1,this.unsigned)},$.shr=$.shiftRight,$.shiftRightUnsigned=function(N){if(s(N)&&(N=N.toInt()),N&=63,N===0)return this;var P=this.high;if(N<32){var B=this.low;return u(B>>>N|P<<32-N,P>>>N,this.unsigned)}else return N===32?u(P,0,this.unsigned):u(P>>>N-32,0,this.unsigned)},$.shru=$.shiftRightUnsigned,$.shr_u=$.shiftRightUnsigned,$.toSigned=function(){return this.unsigned?u(this.low,this.high,!1):this},$.toUnsigned=function(){return this.unsigned?this:u(this.low,this.high,!0)},$.toBytes=function(N){return N?this.toBytesLE():this.toBytesBE()},$.toBytesLE=function(){var N=this.high,P=this.low;return[P&255,P>>>8&255,P>>>16&255,P>>>24,N&255,N>>>8&255,N>>>16&255,N>>>24]},$.toBytesBE=function(){var N=this.high,P=this.low;return[N>>>24,N>>>16&255,N>>>8&255,N&255,P>>>24,P>>>16&255,P>>>8&255,P&255]},r.fromBytes=function(N,P,B){return B?r.fromBytesLE(N,P):r.fromBytesBE(N,P)},r.fromBytesLE=function(N,P){return new r(N[0]|N[1]<<8|N[2]<<16|N[3]<<24,N[4]|N[5]<<8|N[6]<<16|N[7]<<24,P)},r.fromBytesBE=function(N,P){return new r(N[4]<<24|N[5]<<16|N[6]<<8|N[7],N[0]<<24|N[1]<<16|N[2]<<8|N[3],P)}}}),z3=Ot({"(disabled):node_modules/.pnpm/node-fetch@2.6.1/node_modules/node-fetch/browser.js"(){}}),cR=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,r,s){function a(u){var c=this,d=l();c.next=function(){var h=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=h-(c.c=h|0)},c.c=1,c.s0=d(" "),c.s1=d(" "),c.s2=d(" "),c.s0-=d(u),c.s0<0&&(c.s0+=1),c.s1-=d(u),c.s1<0&&(c.s1+=1),c.s2-=d(u),c.s2<0&&(c.s2+=1),d=null}function o(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function i(u,c){var d=new a(u),h=c&&c.state,p=d.next;return p.int32=function(){return d.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,h&&(typeof h=="object"&&o(h,d),p.state=function(){return o(d,{})}),p}function l(){var u=4022871197,c=function(d){d=d.toString();for(var h=0;h<d.length;h++){u+=d.charCodeAt(h);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.alea=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),dR=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,r,s){function a(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var h=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^h^h>>>8},l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xor128=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),hR=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,r,s){function a(l){var u=this,c="";u.next=function(){var h=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(h^h<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,d==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xorwow=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),pR=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,r,s){function a(l){var u=this;u.next=function(){var d=u.x,h=u.i,p,f,m;return p=d[h],p^=p>>>7,f=p^p<<24,p=d[h+1&7],f^=p^p>>>10,p=d[h+3&7],f^=p^p>>>3,p=d[h+4&7],f^=p^p<<7,p=d[h+7&7],p=p^p<<13,f^=p^p<<9,d[h]=f,u.i=h+1&7,f};function c(d,h){var p,f,m=[];if(h===(h|0))f=m[0]=h;else for(h=""+h,p=0;p<h.length;++p)m[p&7]=m[p&7]<<15^h.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],d.x=m,d.i=0,p=256;p>0;--p)d.next()}c(u,l)}function o(l,u){return u.x=l.x.slice(),u.i=l.i,u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.x&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xorshift7=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),fR=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,r,s){function a(l){var u=this;u.next=function(){var d=u.w,h=u.X,p=u.i,f,m;return u.w=d=d+1640531527|0,m=h[p+34&127],f=h[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=h[p]=m^f,u.i=p,m+(d^d>>>16)|0};function c(d,h){var p,f,m,g,y,A=[],x=128;for(h===(h|0)?(f=h,h=null):(h=h+"\0",f=0,x=Math.max(x,h.length)),m=0,g=-32;g<x;++g)h&&(f^=h.charCodeAt((g+32)%h.length)),g===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,g>=0&&(y=y+1640531527|0,p=A[g&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(A[(h&&h.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=A[m+34&127],p=A[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,A[m]=f^p;d.w=y,d.X=A,d.i=m}c(u,l)}function o(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.X&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xor4096=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),mR=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,r,s){function a(l){var u=this,c="";u.next=function(){var h=u.b,p=u.c,f=u.d,m=u.a;return h=h<<25^h>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-h|0,u.b=h=h<<20^h>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-h|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var d=0;d<c.length+20;d++)u.b^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.tychei=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),L3=Ot({"(disabled):crypto"(){}}),gR=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,r){var s=this,a=256,o=6,i=52,l="random",u=r.pow(a,o),c=r.pow(2,i),d=c*2,h=a-1,p;function f(v,w,S){var I=[];w=w==!0?{entropy:!0}:w||{};var E=A(y(w.entropy?[v,b(n)]:v==null?x():v,3),I),F=new m(I),$=function(){for(var _=F.g(o),N=u,P=0;_<c;)_=(_+P)*a,N*=a,P=F.g(1);for(;_>=d;)_/=2,N/=2,P>>>=1;return(_+P)/N};return $.int32=function(){return F.g(4)|0},$.quick=function(){return F.g(4)/4294967296},$.double=$,A(b(F.S),n),(w.pass||S||function(_,N,P,B){return B&&(B.S&&g(B,F),_.state=function(){return g(F,{})}),P?(r[l]=_,N):_})($,E,"global"in w?w.global:this==r,w.state)}r["seed"+l]=f;function m(v){var w,S=v.length,I=this,E=0,F=I.i=I.j=0,$=I.S=[];for(S||(v=[S++]);E<a;)$[E]=E++;for(E=0;E<a;E++)$[E]=$[F=h&F+v[E%S]+(w=$[E])],$[F]=w;(I.g=function(_){for(var N,P=0,B=I.i,j=I.j,X=I.S;_--;)N=X[B=h&B+1],P=P*a+X[h&(X[B]=X[j=h&j+N])+(X[j]=N)];return I.i=B,I.j=j,P})(a)}function g(v,w){return w.i=v.i,w.j=v.j,w.S=v.S.slice(),w}function y(v,w){var S=[],I=typeof v,E;if(w&&I=="object")for(E in v)try{S.push(y(v[E],w-1))}catch(F){}return S.length?S:I=="string"?v:v+"\0"}function A(v,w){for(var S=v+"",I,E=0;E<S.length;)w[h&E]=h&(I^=w[h&E]*19)+S.charCodeAt(E++);return b(w)}function x(){try{var v;return p&&(v=p.randomBytes)?v=v(a):(v=new Uint8Array(a),(s.crypto||s.msCrypto).getRandomValues(v)),b(v)}catch(I){var w=s.navigator,S=w&&w.plugins;return[+new Date,s,S,s.screen,b(n)]}}function b(v){return String.fromCharCode.apply(0,v)}if(A(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=L3()}catch(v){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}}),t2=Ot({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(e,t){var n=cR(),r=dR(),s=hR(),a=pR(),o=fR(),i=mR(),l=gR();l.alea=n,l.xor128=r,l.xorwow=s,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),yR=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,r,s){function a(u){var c=this,d=l();c.next=function(){var h=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=h-(c.c=h|0)},c.c=1,c.s0=d(" "),c.s1=d(" "),c.s2=d(" "),c.s0-=d(u),c.s0<0&&(c.s0+=1),c.s1-=d(u),c.s1<0&&(c.s1+=1),c.s2-=d(u),c.s2<0&&(c.s2+=1),d=null}function o(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function i(u,c){var d=new a(u),h=c&&c.state,p=d.next;return p.int32=function(){return d.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,h&&(typeof h=="object"&&o(h,d),p.state=function(){return o(d,{})}),p}function l(){var u=4022871197,c=function(d){d=String(d);for(var h=0;h<d.length;h++){u+=d.charCodeAt(h);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.alea=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),AR=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,r,s){function a(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var h=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^h^h>>>8},l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xor128=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),xR=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,r,s){function a(l){var u=this,c="";u.next=function(){var h=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(h^h<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,d==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xorwow=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),bR=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,r,s){function a(l){var u=this;u.next=function(){var d=u.x,h=u.i,p,f,m;return p=d[h],p^=p>>>7,f=p^p<<24,p=d[h+1&7],f^=p^p>>>10,p=d[h+3&7],f^=p^p>>>3,p=d[h+4&7],f^=p^p<<7,p=d[h+7&7],p=p^p<<13,f^=p^p<<9,d[h]=f,u.i=h+1&7,f};function c(d,h){var p,f,m=[];if(h===(h|0))f=m[0]=h;else for(h=""+h,p=0;p<h.length;++p)m[p&7]=m[p&7]<<15^h.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],d.x=m,d.i=0,p=256;p>0;--p)d.next()}c(u,l)}function o(l,u){return u.x=l.x.slice(),u.i=l.i,u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.x&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xorshift7=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),vR=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,r,s){function a(l){var u=this;u.next=function(){var d=u.w,h=u.X,p=u.i,f,m;return u.w=d=d+1640531527|0,m=h[p+34&127],f=h[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=h[p]=m^f,u.i=p,m+(d^d>>>16)|0};function c(d,h){var p,f,m,g,y,A=[],x=128;for(h===(h|0)?(f=h,h=null):(h=h+"\0",f=0,x=Math.max(x,h.length)),m=0,g=-32;g<x;++g)h&&(f^=h.charCodeAt((g+32)%h.length)),g===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,g>=0&&(y=y+1640531527|0,p=A[g&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(A[(h&&h.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=A[m+34&127],p=A[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,A[m]=f^p;d.w=y,d.X=A,d.i=m}c(u,l)}function o(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.X&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.xor4096=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),wR=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,r,s){function a(l){var u=this,c="";u.next=function(){var h=u.b,p=u.c,f=u.d,m=u.a;return h=h<<25^h>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-h|0,u.b=h=h<<20^h>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-h|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var d=0;d<c.length+20;d++)u.b^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}r&&r.exports?r.exports=i:s&&s.amd?s(function(){return i}):this.tychei=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),kR=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,r,s){var a=256,o=6,i=52,l="random",u=s.pow(a,o),c=s.pow(2,i),d=c*2,h=a-1,p;function f(v,w,S){var I=[];w=w==!0?{entropy:!0}:w||{};var E=A(y(w.entropy?[v,b(r)]:v==null?x():v,3),I),F=new m(I),$=function(){for(var _=F.g(o),N=u,P=0;_<c;)_=(_+P)*a,N*=a,P=F.g(1);for(;_>=d;)_/=2,N/=2,P>>>=1;return(_+P)/N};return $.int32=function(){return F.g(4)|0},$.quick=function(){return F.g(4)/4294967296},$.double=$,A(b(F.S),r),(w.pass||S||function(_,N,P,B){return B&&(B.S&&g(B,F),_.state=function(){return g(F,{})}),P?(s[l]=_,N):_})($,E,"global"in w?w.global:this==s,w.state)}function m(v){var w,S=v.length,I=this,E=0,F=I.i=I.j=0,$=I.S=[];for(S||(v=[S++]);E<a;)$[E]=E++;for(E=0;E<a;E++)$[E]=$[F=h&F+v[E%S]+(w=$[E])],$[F]=w;(I.g=function(_){for(var N,P=0,B=I.i,j=I.j,X=I.S;_--;)N=X[B=h&B+1],P=P*a+X[h&(X[B]=X[j=h&j+N])+(X[j]=N)];return I.i=B,I.j=j,P})(a)}function g(v,w){return w.i=v.i,w.j=v.j,w.S=v.S.slice(),w}function y(v,w){var S=[],I=typeof v,E;if(w&&I=="object")for(E in v)try{S.push(y(v[E],w-1))}catch(F){}return S.length?S:I=="string"?v:v+"\0"}function A(v,w){for(var S=v+"",I,E=0;E<S.length;)w[h&E]=h&(I^=w[h&E]*19)+S.charCodeAt(E++);return b(w)}function x(){try{var v;return p&&(v=p.randomBytes)?v=v(a):(v=new Uint8Array(a),(n.crypto||n.msCrypto).getRandomValues(v)),b(v)}catch(I){var w=n.navigator,S=w&&w.plugins;return[+new Date,n,S,n.screen,b(r)]}}function b(v){return String.fromCharCode.apply(0,v)}if(A(s.random(),r),typeof t=="object"&&t.exports){t.exports=f;try{p=L3()}catch(v){}}else typeof define=="function"&&define.amd?define(function(){return f}):s["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}}),B3=Ot({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(e,t){var n=yR(),r=AR(),s=xR(),a=bR(),o=vR(),i=wR(),l=kR();l.alea=n,l.xor128=r,l.xorwow=s,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),IR=Ot({"(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"(){}}),ju=Ot({"(disabled):path"(){}}),SR=Ot({"(disabled):worker_threads"(){}}),TR=Ot({"(disabled):perf_hooks"(){}}),NR=Ot({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.7.0_@tensorflow+tfjs-core@3.7.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e,t){var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(s){s=s||{};function a(){return ne.buffer!=Je&&wn(ne.buffer),jn}function o(){return ne.buffer!=Je&&wn(ne.buffer),Wt}function i(){return ne.buffer!=Je&&wn(ne.buffer),Vr}function l(){return ne.buffer!=Je&&wn(ne.buffer),Rn}function u(){return ne.buffer!=Je&&wn(ne.buffer),vr}var c=typeof s!="undefined"?s:{},d,h;c.ready=new Promise(function(C,D){d=C,h=D});var p={},f;for(f in c)c.hasOwnProperty(f)&&(p[f]=c[f]);var m=[],g="./this.program",y=function(C,D){throw D},A=!1,x=!1,b=!1,v=!1;A=typeof window=="object",x=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",v=!A&&!b&&!x;var w=c.ENVIRONMENT_IS_PTHREAD||!1;w&&(Je=c.buffer);var S="";function I(C){return c.locateFile?c.locateFile(C,S):S+C}var E,F,$,_,N,P;if(b){x?S=ju().dirname(S)+"/":S=__dirname+"/",E=function(D,W){return N||(N=co("fs")),P||(P=ju()),D=P.normalize(D),N.readFileSync(D,W?null:"utf8")},$=function(D){var W=E(D,!0);return W.buffer||(W=new Uint8Array(W)),be(W.buffer),W},process.argv.length>1&&(g=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(C){if(!(C instanceof Gu))throw C}),process.on("unhandledRejection",Gs),y=function(C){process.exit(C)},c.inspect=function(){return"[Emscripten Module object]"};var B;try{B=SR()}catch(C){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),C}global.Worker=B.Worker}else v?(typeof read!="undefined"&&(E=function(D){return read(D)}),$=function(D){var W;return typeof readbuffer=="function"?new Uint8Array(readbuffer(D)):(W=read(D,"binary"),be(typeof W=="object"),W)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(y=function(C){quit(C)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(A||x)&&(x?S=self.location.href:typeof document!="undefined"&&document.currentScript&&(S=document.currentScript.src),typeof r!="undefined"&&r&&(S=r),S.indexOf("blob:")!==0?S=S.substr(0,S.lastIndexOf("/")+1):S="",b?(E=function(D,W){return N||(N=co("fs")),P||(P=ju()),D=P.normalize(D),N.readFileSync(D,W?null:"utf8")},$=function(D){var W=E(D,!0);return W.buffer||(W=new Uint8Array(W)),be(W.buffer),W}):(E=function(C){var D=new XMLHttpRequest;return D.open("GET",C,!1),D.send(null),D.responseText},x&&($=function(C){var D=new XMLHttpRequest;return D.open("GET",C,!1),D.responseType="arraybuffer",D.send(null),new Uint8Array(D.response)}),F=function(C,D,W){var Q=new XMLHttpRequest;Q.open("GET",C,!0),Q.responseType="arraybuffer",Q.onload=function(){if(Q.status==200||Q.status==0&&Q.response){D(Q.response);return}W()},Q.onerror=W,Q.send(null)}),_=function(C){document.title=C});b&&typeof performance=="undefined"&&(global.performance=TR().performance);var j=c.print||console.log.bind(console),X=c.printErr||console.warn.bind(console);for(f in p)p.hasOwnProperty(f)&&(c[f]=p[f]);p=null,c.arguments&&(m=c.arguments),c.thisProgram&&(g=c.thisProgram),c.quit&&(y=c.quit);var Y=Atomics.load,ee=Atomics.store,oe=Atomics.compareExchange,se;c.wasmBinary&&(se=c.wasmBinary);var ie=c.noExitRuntime||!0;typeof WebAssembly!="object"&&Gs("no native wasm support detected");var ne,de,he=!1,ge;function be(C,D){C||Gs("Assertion failed: "+D)}function Ee(C){var D=c["_"+C];return be(D,"Cannot call unknown function "+C+", make sure it is exported"),D}function $e(C,D,W,Q,xe){var ye={string:function(Dn){var Mi=0;if(Dn!=null&&Dn!==0){var _3=(Dn.length<<2)+1;Mi=Ri(_3),ft(Dn,Mi,_3)}return Mi},array:function(Dn){var Mi=Ri(Dn.length);return dt(Dn,Mi),Mi}};function Ae(Dn){return D==="string"?We(Dn):D==="boolean"?Boolean(Dn):Dn}var Se=Ee(C),gt=[],pn=0;if(Q)for(var sn=0;sn<Q.length;sn++){var ka=ye[W[sn]];ka?(pn===0&&(pn=Hu()),gt[sn]=ka(Q[sn])):gt[sn]=Q[sn]}var Fi=Se.apply(null,gt);return Fi=Ae(Fi),pn!==0&&_i(pn),Fi}function ze(C,D,W,Q){W=W||[];var xe=W.every(function(Ae){return Ae==="number"}),ye=D!=="string";return ye&&xe&&!Q?Ee(C):function(){return $e(C,D,W,arguments,Q)}}function qe(C,D,W){for(var Q=D+W,xe="";!(D>=Q);){var ye=C[D++];if(!ye)return xe;if(!(ye&128)){xe+=String.fromCharCode(ye);continue}var Ae=C[D++]&63;if((ye&224)==192){xe+=String.fromCharCode((ye&31)<<6|Ae);continue}var Se=C[D++]&63;if((ye&240)==224?ye=(ye&15)<<12|Ae<<6|Se:ye=(ye&7)<<18|Ae<<12|Se<<6|C[D++]&63,ye<65536)xe+=String.fromCharCode(ye);else{var gt=ye-65536;xe+=String.fromCharCode(55296|gt>>10,56320|gt&1023)}}return xe}function We(C,D){return C?qe(o(),C,D):""}function vt(C,D,W,Q){if(!(Q>0))return 0;for(var xe=W,ye=W+Q-1,Ae=0;Ae<C.length;++Ae){var Se=C.charCodeAt(Ae);if(Se>=55296&&Se<=57343){var gt=C.charCodeAt(++Ae);Se=65536+((Se&1023)<<10)|gt&1023}if(Se<=127){if(W>=ye)break;D[W++]=Se}else if(Se<=2047){if(W+1>=ye)break;D[W++]=192|Se>>6,D[W++]=128|Se&63}else if(Se<=65535){if(W+2>=ye)break;D[W++]=224|Se>>12,D[W++]=128|Se>>6&63,D[W++]=128|Se&63}else{if(W+3>=ye)break;D[W++]=240|Se>>18,D[W++]=128|Se>>12&63,D[W++]=128|Se>>6&63,D[W++]=128|Se&63}}return D[W]=0,W-xe}function ft(C,D,W){return vt(C,o(),D,W)}function mt(C){for(var D=0,W=0;W<C.length;++W){var Q=C.charCodeAt(W);Q>=55296&&Q<=57343&&(Q=65536+((Q&1023)<<10)|C.charCodeAt(++W)&1023),Q<=127?++D:Q<=2047?D+=2:Q<=65535?D+=3:D+=4}return D}function dt(C,D){a().set(C,D)}function bt(C,D){return C%D>0&&(C+=D-C%D),C}var Je,jn,Wt,ar,vn,Vr,Rn,br,vr;function wn(C){Je=C,c.HEAP8=jn=new Int8Array(C),c.HEAP16=ar=new Int16Array(C),c.HEAP32=Vr=new Int32Array(C),c.HEAPU8=Wt=new Uint8Array(C),c.HEAPU16=vn=new Uint16Array(C),c.HEAPU32=Rn=new Uint32Array(C),c.HEAPF32=br=new Float32Array(C),c.HEAPF64=vr=new Float64Array(C)}var wr=c.INITIAL_MEMORY||16777216;if(w)ne=c.wasmMemory,Je=c.buffer;else if(c.wasmMemory)ne=c.wasmMemory;else if(ne=new WebAssembly.Memory({initial:wr/65536,maximum:2147483648/65536,shared:!0}),!(ne.buffer instanceof SharedArrayBuffer))throw X("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");ne&&(Je=ne.buffer),wr=Je.byteLength,wn(Je);var kr,or=[],ws=[],Us=[],Aa=[],Si=[],ks=!1,_h=!1;w||ws.push({func:function(){jh()}});function E0(){if(!w){if(c.preRun)for(typeof c.preRun=="function"&&(c.preRun=[c.preRun]);c.preRun.length;)Dh(c.preRun.shift());Ni(or)}}function Mu(){ks=!0,!w&&Ni(ws)}function $0(){w||Ni(Us)}function Rh(){w||(_h=!0)}function qn(){if(!w){if(c.postRun)for(typeof c.postRun=="function"&&(c.postRun=[c.postRun]);c.postRun.length;)_0(c.postRun.shift());Ni(Si)}}function Dh(C){or.unshift(C)}function _0(C){Si.unshift(C)}var Hs=0,xa=null,io=null;function R0(C){be(!w,"addRunDependency cannot be used in a pthread worker"),Hs++,c.monitorRunDependencies&&c.monitorRunDependencies(Hs)}function D0(C){if(Hs--,c.monitorRunDependencies&&c.monitorRunDependencies(Hs),Hs==0&&(xa!==null&&(clearInterval(xa),xa=null),io)){var D=io;io=null,D()}}c.preloadedImages={},c.preloadedAudios={};function Gs(C){c.onAbort&&c.onAbort(C),w&&console.error("Pthread aborting at "+new Error().stack),C+="",X(C),he=!0,ge=1,C="abort("+C+"). Build with -s ASSERTIONS=1 for more info.";var D=new WebAssembly.RuntimeError(C);throw h(D),D}function Fh(C,D){return String.prototype.startsWith?C.startsWith(D):C.indexOf(D)===0}var Ti="data:application/octet-stream;base64,";function Mh(C){return Fh(C,Ti)}var F0="file://";function Oh(C){return Fh(C,F0)}var Kn="tfjs-backend-wasm-threaded-simd.wasm";Mh(Kn)||(Kn=I(Kn));function Ph(C){try{if(C==Kn&&se)return new Uint8Array(se);if($)return $(C);throw"both async and sync fetching of the wasm failed"}catch(D){Gs(D)}}function M0(){if(!se&&(A||x)){if(typeof fetch=="function"&&!Oh(Kn))return fetch(Kn,{credentials:"same-origin"}).then(function(C){if(!C.ok)throw"failed to load wasm binary file at '"+Kn+"'";return C.arrayBuffer()}).catch(function(){return Ph(Kn)});if(F)return new Promise(function(C,D){F(Kn,function(W){C(new Uint8Array(W))},D)})}return Promise.resolve().then(function(){return Ph(Kn)})}function O0(){var C={a:Tg};function D(Ae,Se){var gt=Ae.exports;if(c.asm=gt,kr=c.asm.F,de=Se,!w){var pn=Ce.unusedWorkers.length;Ce.unusedWorkers.forEach(function(sn){Ce.loadWasmModuleToWorker(sn,function(){--pn||D0("wasm-instantiate")})})}}w||R0("wasm-instantiate");function W(Ae){D(Ae.instance,Ae.module)}function Q(Ae){return M0().then(function(Se){return WebAssembly.instantiate(Se,C)}).then(Ae,function(Se){X("failed to asynchronously prepare wasm: "+Se),Gs(Se)})}function xe(){return!se&&typeof WebAssembly.instantiateStreaming=="function"&&!Mh(Kn)&&!Oh(Kn)&&typeof fetch=="function"?fetch(Kn,{credentials:"same-origin"}).then(function(Ae){var Se=WebAssembly.instantiateStreaming(Ae,C);return Se.then(W,function(gt){return X("wasm streaming compile failed: "+gt),X("falling back to ArrayBuffer instantiation"),Q(W)})}):Q(W)}if(c.instantiateWasm)try{var ye=c.instantiateWasm(C,D);return ye}catch(Ae){return X("Module.instantiateWasm callback failed with error: "+Ae),!1}return xe().catch(h),{}}var P0={9816:function(){throw"Canceled!"},9834:function(C,D){setTimeout(function(){S3(C,D)},0)}};function zh(){Ce.initRuntime()}function Ni(C){for(;C.length>0;){var D=C.shift();if(typeof D=="function"){D(c);continue}var W=D.func;typeof W=="number"?D.arg===void 0?kr.get(W)():kr.get(W)(D.arg):W(D.arg===void 0?null:D.arg)}}function Ou(C,D){if(C<=0||C>a().length||C&!0||D<0)return-28;if(D==0)return 0;D>=2147483647&&(D=Infinity);var W=Atomics.load(i(),Di>>2),Q=0;if(W==C){var xe=Atomics.compareExchange(i(),Di>>2,W,0);if(xe==W&&(--D,Q=1,D<=0))return 1}var ye=Atomics.notify(i(),C>>2,D);if(ye>=0)return ye+Q;throw"Atomics.notify returned an unexpected value "+ye}c._emscripten_futex_wake=Ou;function z0(C){if(w)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in killThread!";i()[C+12>>2]=0;var D=Ce.pthreads[C];D.worker.terminate(),Ce.freeThreadData(D),Ce.runningWorkers.splice(Ce.runningWorkers.indexOf(D.worker),1),D.worker.pthread=void 0}function L0(C){if(w)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in cancelThread!";var D=Ce.pthreads[C];D.worker.postMessage({cmd:"cancel"})}function B0(C){if(w)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in cleanupThread!";var D=Ce.pthreads[C];if(D){i()[C+12>>2]=0;var W=D.worker;Ce.returnWorkerToPool(W)}}var Ce={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var C=Math.min(4,Math.max(1,(navigator.hardwareConcurrency||1)/2)),D=0;D<C;++D)Ce.allocateUnusedWorker()},initRuntime:function(){for(var C=uo(228),D=0;D<228/4;++D)l()[C/4+D]=0;i()[C+12>>2]=C;var W=C+152;i()[W>>2]=W;for(var Q=uo(512),D=0;D<128;++D)l()[Q/4+D]=0;Atomics.store(l(),C+100>>2,Q),Atomics.store(l(),C+40>>2,C),Yg(C,!x,1),I3(C)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ce.threadExitHandlers.length>0;)Ce.threadExitHandlers.pop()();w&&$i()&&k3()},runExitHandlersAndDeinitThread:function(C,D){Atomics.store(l(),C+56>>2,1),Atomics.store(l(),C+60>>2,0),Ce.runExitHandlers(),Atomics.store(l(),C+4>>2,D),Atomics.store(l(),C+0>>2,1),Ou(C+0,2147483647),Yg(0,0,0)},threadExit:function(C){var D=$i();D&&(Ce.runExitHandlersAndDeinitThread(D,C),w&&postMessage({cmd:"exit"}))},threadCancel:function(){Ce.runExitHandlersAndDeinitThread($i(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var C in Ce.pthreads){var D=Ce.pthreads[C];D&&D.worker&&Ce.returnWorkerToPool(D.worker)}Ce.pthreads={};for(var W=0;W<Ce.unusedWorkers.length;++W){var Q=Ce.unusedWorkers[W];Q.terminate()}Ce.unusedWorkers=[];for(var W=0;W<Ce.runningWorkers.length;++W){var Q=Ce.runningWorkers[W],D=Q.pthread;Ce.freeThreadData(D),Q.terminate()}Ce.runningWorkers=[]},freeThreadData:function(C){if(!!C){if(C.threadInfoStruct){var D=i()[C.threadInfoStruct+100>>2];i()[C.threadInfoStruct+100>>2]=0,Uu(D),Uu(C.threadInfoStruct)}C.threadInfoStruct=0,C.allocatedOwnStack&&C.stackBase&&Uu(C.stackBase),C.stackBase=0,C.worker&&(C.worker.pthread=null)}},returnWorkerToPool:function(C){Ce.runWithoutMainThreadQueuedCalls(function(){delete Ce.pthreads[C.pthread.threadInfoStruct],Ce.unusedWorkers.push(C),Ce.runningWorkers.splice(Ce.runningWorkers.indexOf(C),1),Ce.freeThreadData(C.pthread),C.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(C){i()[$3>>2]=0;try{C()}finally{i()[$3>>2]=1}},receiveObjectTransfer:function(C){},loadWasmModuleToWorker:function(C,D){C.onmessage=function(W){var Q=W.data,xe=Q.cmd;if(C.pthread&&(Ce.currentProxiedOperationCallerThread=C.pthread.threadInfoStruct),Q.targetThread&&Q.targetThread!=$i()){var ye=Ce.pthreads[Q.targetThread];ye?ye.worker.postMessage(W.data,Q.transferList):console.error('Internal error! Worker sent a message "'+xe+'" to target pthread '+Q.targetThread+", but that thread no longer exists!"),Ce.currentProxiedOperationCallerThread=void 0;return}if(xe==="processQueuedMainThreadWork")Xg();else if(xe==="spawnThread")Hh(W.data);else if(xe==="cleanupThread")B0(Q.thread);else if(xe==="killThread")z0(Q.thread);else if(xe==="cancelThread")L0(Q.thread);else if(xe==="loaded")C.loaded=!0,D&&D(C),C.runPthread&&(C.runPthread(),delete C.runPthread);else if(xe==="print")j("Thread "+Q.threadId+": "+Q.text);else if(xe==="printErr")X("Thread "+Q.threadId+": "+Q.text);else if(xe==="alert")alert("Thread "+Q.threadId+": "+Q.text);else if(xe==="exit"){var Ae=C.pthread&&Atomics.load(l(),C.pthread.threadInfoStruct+64>>2);Ae&&Ce.returnWorkerToPool(C)}else if(xe==="exitProcess")try{nR(Q.returnCode)}catch(Se){if(Se instanceof Gu)return;throw Se}else xe==="cancelDone"?Ce.returnWorkerToPool(C):xe==="objectTransfer"?Ce.receiveObjectTransfer(W.data):W.data.target==="setimmediate"?C.postMessage(W.data):X("worker sent an unknown command "+xe);Ce.currentProxiedOperationCallerThread=void 0},C.onerror=function(W){X("pthread sent an error! "+W.filename+":"+W.lineno+": "+W.message)},b&&(C.on("message",function(W){C.onmessage({data:W})}),C.on("error",function(W){C.onerror(W)}),C.on("exit",function(W){})),C.postMessage({cmd:"load",urlOrBlob:c.mainScriptUrlOrBlob||r,wasmMemory:ne,wasmModule:de})},allocateUnusedWorker:function(){var C=I("tfjs-backend-wasm-threaded-simd.worker.js");Ce.unusedWorkers.push(new Worker(C))},getNewWorker:function(){return Ce.unusedWorkers.length==0&&(Ce.allocateUnusedWorker(),Ce.loadWasmModuleToWorker(Ce.unusedWorkers[0])),Ce.unusedWorkers.length>0?Ce.unusedWorkers.pop():null},busySpinWait:function(C){for(var D=performance.now()+C;performance.now()<D;);}};function W0(C,D){C3(C,D),_i(C)}c.establishStackSpace=W0;function V0(){return ie}c.getNoExitRuntime=V0;function U0(C,D){return kr.get(C)(D)}c.invokeEntryPoint=U0;function H0(C,D,W,Q){Gs("Assertion failed: "+We(C)+", at: "+[D?We(D):"unknown filename",W,Q?We(Q):"unknown function"])}function G0(C,D){var W=_main(C,D)}var lo;b?lo=function(){var C=process.hrtime();return C[0]*1e3+C[1]/1e6}:w?lo=function(){return performance.now()-c.__performance_now_clock_drift}:typeof dateNow!="undefined"?lo=dateNow:lo=function(){return performance.now()};function j0(C){return i()[v3()>>2]=C,C}function q0(C,D){if(w)return ba(1,1,C,D)}function K0(C,D){if(C==D)postMessage({cmd:"processQueuedMainThreadWork"});else if(w)postMessage({targetThread:C,cmd:"processThreadQueue"});else{var W=Ce.pthreads[C],Q=W&&W.worker;if(!Q)return;Q.postMessage({cmd:"processThreadQueue"})}return 1}function X0(){Gs()}function Z0(C,D,W){var Q=tg(D,W);return P0[C].apply(null,Q)}function Y0(C,D){}function J0(C,D,W){if(C<=0||C>a().length||C&!0)return-28;if(A){if(Atomics.load(i(),C>>2)!=D)return-6;for(var xe=performance.now(),ye=xe+W,Ae=Atomics.exchange(i(),Di>>2,C);;){if(xe=performance.now(),xe>ye)return Ae=Atomics.exchange(i(),Di>>2,0),-73;if(Ae=Atomics.exchange(i(),Di>>2,0),Ae==0)break;if(Xg(),Atomics.load(i(),C>>2)!=D)return-6;Ae=Atomics.exchange(i(),Di>>2,C)}return 0}else{var Q=Atomics.wait(i(),C>>2,D,W);if(Q==="timed-out")return-73;if(Q==="not-equal")return-6;if(Q==="ok")return 0;throw"Atomics.wait returned an unexpected value "+Q}}function Q0(C,D,W){o().copyWithin(C,D,D+W)}function eg(){return b?co("os").cpus().length:navigator.hardwareConcurrency}function ba(C,D){for(var W=arguments.length-2,Q=Hu(),xe=W,ye=Ri(xe*8),Ae=ye>>3,Se=0;Se<W;Se++){var gt=arguments[2+Se];u()[Ae+Se]=gt}var pn=N3(C,xe,ye,D);return _i(Q),pn}var Pu=[],zu=[];function tg(C,D){zu.length=0;var W;for(D>>=2;W=o()[C++];){var Q=W<105;Q&&D&1&&D++,zu.push(Q?u()[D++>>1]:i()[D]),++D}return zu}function ng(C,D,W){Pu.length=D;for(var Q=W>>3,xe=0;xe<D;xe++)Pu[xe]=u()[Q+xe];var ye=C<0,Ae=ye?P0[-C-1]:Sg[C];return Ae.apply(null,Pu)}function rg(){return o().length}function sg(C){try{return ne.grow(C-Je.byteLength+65535>>>16),wn(ne.buffer),1}catch(D){}}function ag(C){var D=rg();if(C<=D)return!1;var W=2147483648;if(C>W)return!1;for(var Q=1;Q<=4;Q*=2){var xe=D*(1+.2/Q);xe=Math.min(xe,C+100663296);var ye=Math.min(W,bt(Math.max(C,xe),65536)),Ae=sg(ye);if(Ae)return!0}return!1}var Xe={inEventHandler:0,removeAllEventListeners:function(){for(var C=Xe.eventHandlers.length-1;C>=0;--C)Xe._removeHandler(C);Xe.eventHandlers=[],Xe.deferredCalls=[]},registerRemoveEventListeners:function(){Xe.removeEventListenersRegistered||(Aa.push(Xe.removeAllEventListeners),Xe.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(C,D,W){function Q(Ae,Se){if(Ae.length!=Se.length)return!1;for(var gt in Ae)if(Ae[gt]!=Se[gt])return!1;return!0}for(var xe in Xe.deferredCalls){var ye=Xe.deferredCalls[xe];if(ye.targetFunction==C&&Q(ye.argsList,W))return}Xe.deferredCalls.push({targetFunction:C,precedence:D,argsList:W}),Xe.deferredCalls.sort(function(Ae,Se){return Ae.precedence<Se.precedence})},removeDeferredCalls:function(C){for(var D=0;D<Xe.deferredCalls.length;++D)Xe.deferredCalls[D].targetFunction==C&&(Xe.deferredCalls.splice(D,1),--D)},canPerformEventHandlerRequests:function(){return Xe.inEventHandler&&Xe.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!!Xe.canPerformEventHandlerRequests())for(var C=0;C<Xe.deferredCalls.length;++C){var D=Xe.deferredCalls[C];Xe.deferredCalls.splice(C,1),--C,D.targetFunction.apply(null,D.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(C,D){for(var W=0;W<Xe.eventHandlers.length;++W)Xe.eventHandlers[W].target==C&&(!D||D==Xe.eventHandlers[W].eventTypeString)&&Xe._removeHandler(W--)},_removeHandler:function(C){var D=Xe.eventHandlers[C];D.target.removeEventListener(D.eventTypeString,D.eventListenerFunc,D.useCapture),Xe.eventHandlers.splice(C,1)},registerOrRemoveHandler:function(C){var D=function(xe){++Xe.inEventHandler,Xe.currentEventHandler=C,Xe.runDeferredCalls(),C.handlerFunc(xe),Xe.runDeferredCalls(),--Xe.inEventHandler};if(C.callbackfunc)C.eventListenerFunc=D,C.target.addEventListener(C.eventTypeString,D,C.useCapture),Xe.eventHandlers.push(C),Xe.registerRemoveEventListeners();else for(var W=0;W<Xe.eventHandlers.length;++W)Xe.eventHandlers[W].target==C.target&&Xe.eventHandlers[W].eventTypeString==C.eventTypeString&&Xe._removeHandler(W--)},queueEventHandlerOnThread_iiii:function(C,D,W,Q,xe){var ye=Hu(),Ae=Ri(12);i()[Ae>>2]=W,i()[Ae+4>>2]=Q,i()[Ae+8>>2]=xe,Zg(0,C,637534208,D,Q,Ae),_i(ye)},getTargetThreadForEventCallback:function(C){switch(C){case 1:return 0;case 2:return Ce.currentProxiedOperationCallerThread;default:return C}},getNodeNameForTarget:function(C){return C?C==window?"#window":C==screen?"#screen":C&&C.nodeName?C.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function og(C){var D=mt(C)+1,W=uo(D);return ft(C,W,D),W}function ig(C,D,W,Q){var xe=Hu(),ye=Ri(12),Ae=0;D&&(Ae=og(D)),i()[ye>>2]=Ae,i()[ye+4>>2]=W,i()[ye+8>>2]=Q,Zg(0,C,657457152,0,Ae,ye),_i(xe)}function lg(C,D,W,Q){D=D?We(D):"",ig(C,D,W,Q)}function ug(C){return C>2?We(C):C}var cg=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function dg(C){C=ug(C);var D=cg[C]||(typeof document!="undefined"?document.querySelector(C):void 0);return D}function Lu(C){return dg(C)}function Lh(C,D,W){var Q=Lu(C);if(!Q)return-4;if(Q.canvasSharedPtr&&(i()[Q.canvasSharedPtr>>2]=D,i()[Q.canvasSharedPtr+4>>2]=W),Q.offscreenCanvas||!Q.controlTransferredOffscreen){Q.offscreenCanvas&&(Q=Q.offscreenCanvas);var xe=!1;if(Q.GLctxObject&&Q.GLctxObject.GLctx){var ye=Q.GLctxObject.GLctx.getParameter(2978);xe=ye[0]===0&&ye[1]===0&&ye[2]===Q.width&&ye[3]===Q.height}Q.width=D,Q.height=W,xe&&Q.GLctxObject.GLctx.viewport(0,0,D,W)}else if(Q.canvasSharedPtr){var Ae=i()[Q.canvasSharedPtr+8>>2];return lg(Ae,C,D,W),1}else return-4;return 0}function Bh(C,D,W){return w?ba(2,1,C,D,W):Lh(C,D,W)}function hg(C,D,W){var Q=Lu(C);return Q?Lh(C,D,W):Bh(C,D,W)}function pg(C){}function fg(C,D){}function mg(C){var D=C.getExtension("ANGLE_instanced_arrays");if(D)return C.vertexAttribDivisor=function(W,Q){D.vertexAttribDivisorANGLE(W,Q)},C.drawArraysInstanced=function(W,Q,xe,ye){D.drawArraysInstancedANGLE(W,Q,xe,ye)},C.drawElementsInstanced=function(W,Q,xe,ye,Ae){D.drawElementsInstancedANGLE(W,Q,xe,ye,Ae)},1}function gg(C){var D=C.getExtension("OES_vertex_array_object");if(D)return C.createVertexArray=function(){return D.createVertexArrayOES()},C.deleteVertexArray=function(W){D.deleteVertexArrayOES(W)},C.bindVertexArray=function(W){D.bindVertexArrayOES(W)},C.isVertexArray=function(W){return D.isVertexArrayOES(W)},1}function yg(C){var D=C.getExtension("WEBGL_draw_buffers");if(D)return C.drawBuffers=function(W,Q){D.drawBuffersWEBGL(W,Q)},1}function Ag(C){return!!(C.multiDrawWebgl=C.getExtension("WEBGL_multi_draw"))}var ht={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(D){ht.lastError||(ht.lastError=D)},getNewId:function(C){for(var D=ht.counter++,W=C.length;W<D;W++)C[W]=null;return D},getSource:function(C,D,W,Q){for(var xe="",ye=0;ye<D;++ye){var Ae=Q?i()[Q+ye*4>>2]:-1;xe+=We(i()[W+ye*4>>2],Ae<0?void 0:Ae)}return xe},createContext:function(C,D){var W=C.getContext("webgl",D);if(!W)return 0;var Q=ht.registerContext(W,D);return Q},registerContext:function(C,D){var W=uo(8);i()[W+4>>2]=$i();var Q={handle:W,attributes:D,version:D.majorVersion,GLctx:C};return C.canvas&&(C.canvas.GLctxObject=Q),ht.contexts[W]=Q,(typeof D.enableExtensionsByDefault=="undefined"||D.enableExtensionsByDefault)&&ht.initExtensions(Q),W},makeContextCurrent:function(C){return ht.currentContext=ht.contexts[C],c.ctx=va=ht.currentContext&&ht.currentContext.GLctx,!(C&&!va)},getContext:function(C){return ht.contexts[C]},deleteContext:function(C){ht.currentContext===ht.contexts[C]&&(ht.currentContext=null),typeof Xe=="object"&&Xe.removeAllHandlersOnTarget(ht.contexts[C].GLctx.canvas),ht.contexts[C]&&ht.contexts[C].GLctx.canvas&&(ht.contexts[C].GLctx.canvas.GLctxObject=void 0),Uu(ht.contexts[C].handle),ht.contexts[C]=null},initExtensions:function(C){if(C||(C=ht.currentContext),!C.initExtensionsDone){C.initExtensionsDone=!0;var D=C.GLctx;mg(D),gg(D),yg(D),D.disjointTimerQueryExt=D.getExtension("EXT_disjoint_timer_query"),Ag(D);var W=D.getSupportedExtensions()||[];W.forEach(function(Q){Q.indexOf("lose_context")<0&&Q.indexOf("debug")<0&&D.getExtension(Q)})}},populateUniformTable:function(C){for(var D=ht.programs[C],W=ht.programInfos[C]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},Q=W.uniforms,xe=va.getProgramParameter(D,35718),ye=0;ye<xe;++ye){var Ae=va.getActiveUniform(D,ye),Se=Ae.name;W.maxUniformLength=Math.max(W.maxUniformLength,Se.length+1),Se.slice(-1)=="]"&&(Se=Se.slice(0,Se.lastIndexOf("[")));var gt=va.getUniformLocation(D,Se);if(gt){var pn=ht.getNewId(ht.uniforms);Q[Se]=[Ae.size,pn],ht.uniforms[pn]=gt;for(var sn=1;sn<Ae.size;++sn){var ka=Se+"["+sn+"]";gt=va.getUniformLocation(D,ka),pn=ht.getNewId(ht.uniforms),ht.uniforms[pn]=gt}}}}},xg=["default","low-power","high-performance"];function bg(C,D){var W=D>>2,Q=i()[W+(24>>2)],xe={alpha:!!i()[W+(0>>2)],depth:!!i()[W+(4>>2)],stencil:!!i()[W+(8>>2)],antialias:!!i()[W+(12>>2)],premultipliedAlpha:!!i()[W+(16>>2)],preserveDrawingBuffer:!!i()[W+(20>>2)],powerPreference:xg[Q],failIfMajorPerformanceCaveat:!!i()[W+(28>>2)],majorVersion:i()[W+(32>>2)],minorVersion:i()[W+(36>>2)],enableExtensionsByDefault:i()[W+(40>>2)],explicitSwapControl:i()[W+(44>>2)],proxyContextToMainThread:i()[W+(48>>2)],renderViaOffscreenBackBuffer:i()[W+(52>>2)]},ye=Lu(C);if(!ye||xe.explicitSwapControl)return 0;var Ae=ht.createContext(ye,xe);return Ae}function vg(C,D){return bg(C,D)}var Ci={mappings:{},buffers:[null,[],[]],printChar:function(C,D){var W=Ci.buffers[C];D===0||D===10?((C===1?j:X)(qe(W,0)),W.length=0):W.push(D)},varargs:void 0,get:function(){Ci.varargs+=4;var C=i()[Ci.varargs-4>>2];return C},getStr:function(C){var D=We(C);return D},get64:function(C,D){return C}};function Wh(C){return w?ba(3,1,C):0}function Vh(C,D,W,Q,xe){if(w)return ba(4,1,C,D,W,Q,xe)}function Uh(C,D,W,Q){if(w)return ba(5,1,C,D,W,Q);for(var xe=0,ye=0;ye<W;ye++){for(var Ae=i()[D+ye*8>>2],Se=i()[D+(ye*8+4)>>2],gt=0;gt<Se;gt++)Ci.printChar(C,o()[Ae+gt]);xe+=Se}return i()[Q>>2]=xe,0}function wg(C){var D=Ce.threadExitHandlers.pop();C&&D()}function kg(C,D){Ce.threadExitHandlers.push(function(){kr.get(C)(D)})}function Hh(C){if(w)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var D=Ce.getNewWorker();if(D.pthread!==void 0)throw"Internal error!";if(!C.pthread_ptr)throw"Internal error, no pthread ptr!";Ce.runningWorkers.push(D);for(var W=uo(128*4),Q=0;Q<128;++Q)i()[W+Q*4>>2]=0;var xe=C.stackBase+C.stackSize,ye=Ce.pthreads[C.pthread_ptr]={worker:D,stackBase:C.stackBase,stackSize:C.stackSize,allocatedOwnStack:C.allocatedOwnStack,threadInfoStruct:C.pthread_ptr},Ae=ye.threadInfoStruct>>2;Atomics.store(l(),Ae+(64>>2),C.detached),Atomics.store(l(),Ae+(100>>2),W),Atomics.store(l(),Ae+(40>>2),ye.threadInfoStruct),Atomics.store(l(),Ae+(80>>2),C.stackSize),Atomics.store(l(),Ae+(76>>2),xe),Atomics.store(l(),Ae+(104>>2),C.stackSize),Atomics.store(l(),Ae+(104+8>>2),xe),Atomics.store(l(),Ae+(104+12>>2),C.detached);var Se=w3(),gt=Se+40;Atomics.store(l(),Ae+(172>>2),gt),D.pthread=ye;var pn={cmd:"run",start_routine:C.startRoutine,arg:C.arg,threadInfoStruct:C.pthread_ptr,stackBase:C.stackBase,stackSize:C.stackSize};D.runPthread=function(){pn.time=performance.now(),D.postMessage(pn,C.transferList)},D.loaded&&(D.runPthread(),delete D.runPthread)}function Ig(C,D,W,Q){if(typeof SharedArrayBuffer=="undefined")return X("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!C)return X("pthread_create called with a null thread pointer!"),28;var xe=[],ye=0;if(w&&(xe.length===0||ye))return T3(687865856,C,D,W,Q);if(ye)return ye;var Ae=0,Se=0,gt=0;D&&D!=-1?(Ae=i()[D>>2],Ae+=81920,Se=i()[D+8>>2],gt=i()[D+12>>2]!==0):Ae=2097152;var pn=Se==0;pn?Se=E3(16,Ae):(Se-=Ae,be(Se>0));for(var sn=uo(228),ka=0;ka<228>>2;++ka)l()[(sn>>2)+ka]=0;i()[C>>2]=sn,i()[sn+12>>2]=sn;var Fi=sn+152;i()[Fi>>2]=Fi;var Dn={stackBase:Se,stackSize:Ae,allocatedOwnStack:pn,detached:gt,startRoutine:W,pthread_ptr:sn,arg:Q,transferList:xe};return w?(Dn.cmd="spawnThread",postMessage(Dn,xe)):Hh(Dn),0}function Gh(C){if(w)return ba(6,1,C);switch(C){case 30:return 16384;case 85:var D=2147483648;return D/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return j0(28),-1}w||Ce.initMainThreadBlock();var va,Sg=[null,q0,Bh,Wh,Vh,Uh,Gh],Tg={e:H0,r:G0,x:K0,b:X0,y:Z0,j:Y0,c:J0,d:Ou,f:lo,p:Q0,z:eg,u:ng,q:ag,v:hg,i:pg,t:fg,w:vg,m:Wh,n:Vh,g:Uh,o:zh,a:ne||c.wasmMemory,k:wg,l:kg,h:Ig,s:Gh},b3=O0(),jh=c.___wasm_call_ctors=function(){return(jh=c.___wasm_call_ctors=c.asm.A).apply(null,arguments)},Ng=c._init=function(){return(Ng=c._init=c.asm.B).apply(null,arguments)},Cg=c._register_tensor=function(){return(Cg=c._register_tensor=c.asm.C).apply(null,arguments)},Eg=c._dispose_data=function(){return(Eg=c._dispose_data=c.asm.D).apply(null,arguments)},$g=c._dispose=function(){return($g=c._dispose=c.asm.E).apply(null,arguments)},_g=c._Abs=function(){return(_g=c._Abs=c.asm.G).apply(null,arguments)},Rg=c._Add=function(){return(Rg=c._Add=c.asm.H).apply(null,arguments)},Dg=c._AddN=function(){return(Dg=c._AddN=c.asm.I).apply(null,arguments)},Fg=c._All=function(){return(Fg=c._All=c.asm.J).apply(null,arguments)},Mg=c._Any=function(){return(Mg=c._Any=c.asm.K).apply(null,arguments)},Og=c._ArgMax=function(){return(Og=c._ArgMax=c.asm.L).apply(null,arguments)},Pg=c._AvgPool=function(){return(Pg=c._AvgPool=c.asm.M).apply(null,arguments)},zg=c._BatchMatMul=function(){return(zg=c._BatchMatMul=c.asm.N).apply(null,arguments)},Lg=c._Ceil=function(){return(Lg=c._Ceil=c.asm.O).apply(null,arguments)},Bg=c._ClipByValue=function(){return(Bg=c._ClipByValue=c.asm.P).apply(null,arguments)},Wg=c._Conv2D=function(){return(Wg=c._Conv2D=c.asm.Q).apply(null,arguments)},Vg=c._Conv2DBackpropInput=function(){return(Vg=c._Conv2DBackpropInput=c.asm.R).apply(null,arguments)},Ug=c._Cos=function(){return(Ug=c._Cos=c.asm.S).apply(null,arguments)},Hg=c._CropAndResize=function(){return(Hg=c._CropAndResize=c.asm.T).apply(null,arguments)},Gg=c._Cumsum=function(){return(Gg=c._Cumsum=c.asm.U).apply(null,arguments)},jg=c._DepthToSpace=function(){return(jg=c._DepthToSpace=c.asm.V).apply(null,arguments)},qg=c._DepthwiseConv2dNative=function(){return(qg=c._DepthwiseConv2dNative=c.asm.W).apply(null,arguments)},qh=c._Equal=function(){return(qh=c._Equal=c.asm.X).apply(null,arguments)},Kh=c._Exp=function(){return(Kh=c._Exp=c.asm.Y).apply(null,arguments)},Xh=c._FlipLeftRight=function(){return(Xh=c._FlipLeftRight=c.asm.Z).apply(null,arguments)},Bu=c._Floor=function(){return(Bu=c._Floor=c.asm._).apply(null,arguments)},Ei=c._FloorDiv=function(){return(Ei=c._FloorDiv=c.asm.$).apply(null,arguments)},Kg=c._FusedBatchNorm=function(){return(Kg=c._FusedBatchNorm=c.asm.aa).apply(null,arguments)},Wu=c._FusedConv2D=function(){return(Wu=c._FusedConv2D=c.asm.ba).apply(null,arguments)},te=c._FusedDepthwiseConv2D=function(){return(te=c._FusedDepthwiseConv2D=c.asm.ca).apply(null,arguments)},le=c._Gather=function(){return(le=c._Gather=c.asm.da).apply(null,arguments)},we=c._GatherNd=function(){return(we=c._GatherNd=c.asm.ea).apply(null,arguments)},lt=c._Greater=function(){return(lt=c._Greater=c.asm.fa).apply(null,arguments)},Gt=c._GreaterEqual=function(){return(Gt=c._GreaterEqual=c.asm.ga).apply(null,arguments)},Mt=c._LeakyRelu=function(){return(Mt=c._LeakyRelu=c.asm.ha).apply(null,arguments)},et=c._Less=function(){return(et=c._Less=c.asm.ia).apply(null,arguments)},tt=c._LessEqual=function(){return(tt=c._LessEqual=c.asm.ja).apply(null,arguments)},kn=c._Log=function(){return(kn=c._Log=c.asm.ka).apply(null,arguments)},js=c._LogicalAnd=function(){return(js=c._LogicalAnd=c.asm.la).apply(null,arguments)},qs=c._Max=function(){return(qs=c._Max=c.asm.ma).apply(null,arguments)},Zh=c._MaxPool=function(){return(Zh=c._MaxPool=c.asm.na).apply(null,arguments)},Vu=c._Maximum=function(){return(Vu=c._Maximum=c.asm.oa).apply(null,arguments)},ir=c._Mean=function(){return(ir=c._Mean=c.asm.pa).apply(null,arguments)},wa=c._Min=function(){return(wa=c._Min=c.asm.qa).apply(null,arguments)},Yh=c._Minimum=function(){return(Yh=c._Minimum=c.asm.ra).apply(null,arguments)},m_=c._MirrorPad=function(){return(m_=c._MirrorPad=c.asm.sa).apply(null,arguments)},g_=c._Multiply=function(){return(g_=c._Multiply=c.asm.ta).apply(null,arguments)},y_=c._Neg=function(){return(y_=c._Neg=c.asm.ua).apply(null,arguments)},A_=c._NonMaxSuppressionV3=function(){return(A_=c._NonMaxSuppressionV3=c.asm.va).apply(null,arguments)},x_=c._NonMaxSuppressionV4=function(){return(x_=c._NonMaxSuppressionV4=c.asm.wa).apply(null,arguments)},b_=c._NonMaxSuppressionV5=function(){return(b_=c._NonMaxSuppressionV5=c.asm.xa).apply(null,arguments)},v_=c._NotEqual=function(){return(v_=c._NotEqual=c.asm.ya).apply(null,arguments)},w_=c._OneHot=function(){return(w_=c._OneHot=c.asm.za).apply(null,arguments)},k_=c._PadV2=function(){return(k_=c._PadV2=c.asm.Aa).apply(null,arguments)},I_=c._Pow=function(){return(I_=c._Pow=c.asm.Ba).apply(null,arguments)},S_=c._Prelu=function(){return(S_=c._Prelu=c.asm.Ca).apply(null,arguments)},T_=c._Prod=function(){return(T_=c._Prod=c.asm.Da).apply(null,arguments)},N_=c._RealDiv=function(){return(N_=c._RealDiv=c.asm.Ea).apply(null,arguments)},C_=c._Relu=function(){return(C_=c._Relu=c.asm.Fa).apply(null,arguments)},E_=c._Relu6=function(){return(E_=c._Relu6=c.asm.Ga).apply(null,arguments)},$_=c._ResizeBilinear=function(){return($_=c._ResizeBilinear=c.asm.Ha).apply(null,arguments)},__=c._Reverse=function(){return(__=c._Reverse=c.asm.Ia).apply(null,arguments)},R_=c._RotateWithOffset=function(){return(R_=c._RotateWithOffset=c.asm.Ja).apply(null,arguments)},D_=c._Round=function(){return(D_=c._Round=c.asm.Ka).apply(null,arguments)},F_=c._Rsqrt=function(){return(F_=c._Rsqrt=c.asm.La).apply(null,arguments)},M_=c._ScatterNd=function(){return(M_=c._ScatterNd=c.asm.Ma).apply(null,arguments)},O_=c._SelectV2=function(){return(O_=c._SelectV2=c.asm.Na).apply(null,arguments)},P_=c._Sigmoid=function(){return(P_=c._Sigmoid=c.asm.Oa).apply(null,arguments)},z_=c._Sin=function(){return(z_=c._Sin=c.asm.Pa).apply(null,arguments)},L_=c._Softmax=function(){return(L_=c._Softmax=c.asm.Qa).apply(null,arguments)},B_=c._Sqrt=function(){return(B_=c._Sqrt=c.asm.Ra).apply(null,arguments)},W_=c._Square=function(){return(W_=c._Square=c.asm.Sa).apply(null,arguments)},V_=c._SquaredDifference=function(){return(V_=c._SquaredDifference=c.asm.Ta).apply(null,arguments)},U_=c._Step=function(){return(U_=c._Step=c.asm.Ua).apply(null,arguments)},H_=c._StridedSlice=function(){return(H_=c._StridedSlice=c.asm.Va).apply(null,arguments)},G_=c._Sub=function(){return(G_=c._Sub=c.asm.Wa).apply(null,arguments)},j_=c._Sum=function(){return(j_=c._Sum=c.asm.Xa).apply(null,arguments)},q_=c._Tan=function(){return(q_=c._Tan=c.asm.Ya).apply(null,arguments)},K_=c._Tanh=function(){return(K_=c._Tanh=c.asm.Za).apply(null,arguments)},X_=c._Tile=function(){return(X_=c._Tile=c.asm._a).apply(null,arguments)},Z_=c._TopK=function(){return(Z_=c._TopK=c.asm.$a).apply(null,arguments)},Y_=c._Transform=function(){return(Y_=c._Transform=c.asm.ab).apply(null,arguments)},J_=c._Transpose=function(){return(J_=c._Transpose=c.asm.bb).apply(null,arguments)},Q_=c.__FusedMatMul=function(){return(Q_=c.__FusedMatMul=c.asm.cb).apply(null,arguments)},uo=c._malloc=function(){return(uo=c._malloc=c.asm.db).apply(null,arguments)},Uu=c._free=function(){return(Uu=c._free=c.asm.eb).apply(null,arguments)},v3=c.___errno_location=function(){return(v3=c.___errno_location=c.asm.fb).apply(null,arguments)},w3=c._emscripten_get_global_libc=function(){return(w3=c._emscripten_get_global_libc=c.asm.gb).apply(null,arguments)},$i=c._pthread_self=function(){return($i=c._pthread_self=c.asm.hb).apply(null,arguments)},k3=c.___pthread_tsd_run_dtors=function(){return(k3=c.___pthread_tsd_run_dtors=c.asm.ib).apply(null,arguments)},Xg=c._emscripten_main_thread_process_queued_calls=function(){return(Xg=c._emscripten_main_thread_process_queued_calls=c.asm.jb).apply(null,arguments)},eR=c._emscripten_current_thread_process_queued_calls=function(){return(eR=c._emscripten_current_thread_process_queued_calls=c.asm.kb).apply(null,arguments)},I3=c._emscripten_register_main_browser_thread_id=function(){return(I3=c._emscripten_register_main_browser_thread_id=c.asm.lb).apply(null,arguments)},S3=c.__emscripten_do_dispatch_to_thread=function(){return(S3=c.__emscripten_do_dispatch_to_thread=c.asm.mb).apply(null,arguments)},T3=c._emscripten_sync_run_in_main_thread_4=function(){return(T3=c._emscripten_sync_run_in_main_thread_4=c.asm.nb).apply(null,arguments)},N3=c._emscripten_run_in_main_runtime_thread_js=function(){return(N3=c._emscripten_run_in_main_runtime_thread_js=c.asm.ob).apply(null,arguments)},Zg=c.__emscripten_call_on_thread=function(){return(Zg=c.__emscripten_call_on_thread=c.asm.pb).apply(null,arguments)},tR=c._emscripten_tls_init=function(){return(tR=c._emscripten_tls_init=c.asm.qb).apply(null,arguments)},Yg=c.__emscripten_thread_init=function(){return(Yg=c.__emscripten_thread_init=c.asm.rb).apply(null,arguments)},Hu=c.stackSave=function(){return(Hu=c.stackSave=c.asm.sb).apply(null,arguments)},_i=c.stackRestore=function(){return(_i=c.stackRestore=c.asm.tb).apply(null,arguments)},Ri=c.stackAlloc=function(){return(Ri=c.stackAlloc=c.asm.ub).apply(null,arguments)},C3=c._emscripten_stack_set_limits=function(){return(C3=c._emscripten_stack_set_limits=c.asm.vb).apply(null,arguments)},E3=c._memalign=function(){return(E3=c._memalign=c.asm.wb).apply(null,arguments)},$3=c.__emscripten_allow_main_runtime_queued_calls=9808,Di=c.__emscripten_main_thread_futex=11432;c.cwrap=ze,c.PThread=Ce,c.PThread=Ce,c.wasmMemory=ne,c.ExitStatus=Gu;var Jh;function Gu(C){this.name="ExitStatus",this.message="Program terminated with exit("+C+")",this.status=C}io=function C(){Jh||Jg(),Jh||(io=C)};function Jg(C){if(C=C||m,Hs>0)return;if(w){d(c),Mu(),postMessage({cmd:"loaded"});return}if(E0(),Hs>0)return;function D(){Jh||(Jh=!0,c.calledRun=!0,!he&&(Mu(),$0(),d(c),c.onRuntimeInitialized&&c.onRuntimeInitialized(),qn()))}c.setStatus?(c.setStatus("Running..."),setTimeout(function(){setTimeout(function(){c.setStatus("")},1),D()},1)):D()}c.run=Jg;function nR(C,D){if(!(D&&ie&&C===0)){if(!D&&w)throw postMessage({cmd:"exitProcess",returnCode:C}),new Gu(C);ie||(Ce.terminateAllThreads(),ge=C,Rh(),c.onExit&&c.onExit(C),he=!0),y(C,new Gu(C))}}if(c.preInit)for(typeof c.preInit=="function"&&(c.preInit=[c.preInit]);c.preInit.length>0;)c.preInit.pop()();return w&&(ie=!1,Ce.initWorker()),Jg(),s.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)}}),CR=Ot({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.7.0_@tensorflow+tfjs-core@3.7.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e,t){var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(s){s=s||{};var a=typeof s!="undefined"?s:{},o,i;a.ready=new Promise(function(te,le){o=te,i=le});var l={},u;for(u in a)a.hasOwnProperty(u)&&(l[u]=a[u]);var c=[],d="./this.program",h=function(te,le){throw le},p=!1,f=!1,m=!1,g=!1;p=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g=!p&&!m&&!f;var y="";function A(te){return a.locateFile?a.locateFile(te,y):y+te}var x,b,v,w,S,I;m?(f?y=ju().dirname(y)+"/":y=__dirname+"/",x=function(le,we){return S||(S=co("fs")),I||(I=ju()),le=I.normalize(le),S.readFileSync(le,we?null:"utf8")},v=function(le){var we=x(le,!0);return we.buffer||(we=new Uint8Array(we)),j(we.buffer),we},process.argv.length>1&&(d=process.argv[1].replace(/\\/g,"/")),c=process.argv.slice(2),process.on("uncaughtException",function(te){if(!(te instanceof Kg))throw te}),process.on("unhandledRejection",ks),h=function(te){process.exit(te)},a.inspect=function(){return"[Emscripten Module object]"}):g?(typeof read!="undefined"&&(x=function(le){return read(le)}),v=function(le){var we;return typeof readbuffer=="function"?new Uint8Array(readbuffer(le)):(we=read(le,"binary"),j(typeof we=="object"),we)},typeof scriptArgs!="undefined"?c=scriptArgs:typeof arguments!="undefined"&&(c=arguments),typeof quit=="function"&&(h=function(te){quit(te)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(p||f)&&(f?y=self.location.href:typeof document!="undefined"&&document.currentScript&&(y=document.currentScript.src),r&&(y=r),y.indexOf("blob:")!==0?y=y.substr(0,y.lastIndexOf("/")+1):y="",x=function(te){var le=new XMLHttpRequest;return le.open("GET",te,!1),le.send(null),le.responseText},f&&(v=function(te){var le=new XMLHttpRequest;return le.open("GET",te,!1),le.responseType="arraybuffer",le.send(null),new Uint8Array(le.response)}),b=function(te,le,we){var lt=new XMLHttpRequest;lt.open("GET",te,!0),lt.responseType="arraybuffer",lt.onload=function(){if(lt.status==200||lt.status==0&&lt.response){le(lt.response);return}we()},lt.onerror=we,lt.send(null)},w=function(te){document.title=te});var E=a.print||console.log.bind(console),F=a.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(a[u]=l[u]);l=null,a.arguments&&(c=a.arguments),a.thisProgram&&(d=a.thisProgram),a.quit&&(h=a.quit);var $;a.wasmBinary&&($=a.wasmBinary);var _=a.noExitRuntime||!0;typeof WebAssembly!="object"&&ks("no native wasm support detected");var N,P=!1,B;function j(te,le){te||ks("Assertion failed: "+le)}function X(te){var le=a["_"+te];return j(le,"Cannot call unknown function "+te+", make sure it is exported"),le}function Y(te,le,we,lt,Gt){var Mt={string:function(ir){var wa=0;if(ir!=null&&ir!==0){var Yh=(ir.length<<2)+1;wa=Bu(Yh),de(ir,wa,Yh)}return wa},array:function(ir){var wa=Bu(ir.length);return he(ir,wa),wa}};function et(ir){return le==="string"?ie(ir):le==="boolean"?Boolean(ir):ir}var tt=X(te),kn=[],js=0;if(lt)for(var qs=0;qs<lt.length;qs++){var Zh=Mt[we[qs]];Zh?(js===0&&(js=Kh()),kn[qs]=Zh(lt[qs])):kn[qs]=lt[qs]}var Vu=tt.apply(null,kn);return Vu=et(Vu),js!==0&&Xh(js),Vu}function ee(te,le,we,lt){we=we||[];var Gt=we.every(function(et){return et==="number"}),Mt=le!=="string";return Mt&&Gt&&!lt?X(te):function(){return Y(te,le,we,arguments,lt)}}var oe=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function se(te,le,we){for(var lt=le+we,Gt=le;te[Gt]&&!(Gt>=lt);)++Gt;if(Gt-le>16&&te.subarray&&oe)return oe.decode(te.subarray(le,Gt));for(var Mt="";le<Gt;){var et=te[le++];if(!(et&128)){Mt+=String.fromCharCode(et);continue}var tt=te[le++]&63;if((et&224)==192){Mt+=String.fromCharCode((et&31)<<6|tt);continue}var kn=te[le++]&63;if((et&240)==224?et=(et&15)<<12|tt<<6|kn:et=(et&7)<<18|tt<<12|kn<<6|te[le++]&63,et<65536)Mt+=String.fromCharCode(et);else{var js=et-65536;Mt+=String.fromCharCode(55296|js>>10,56320|js&1023)}}return Mt}function ie(te,le){return te?se($e,te,le):""}function ne(te,le,we,lt){if(!(lt>0))return 0;for(var Gt=we,Mt=we+lt-1,et=0;et<te.length;++et){var tt=te.charCodeAt(et);if(tt>=55296&&tt<=57343){var kn=te.charCodeAt(++et);tt=65536+((tt&1023)<<10)|kn&1023}if(tt<=127){if(we>=Mt)break;le[we++]=tt}else if(tt<=2047){if(we+1>=Mt)break;le[we++]=192|tt>>6,le[we++]=128|tt&63}else if(tt<=65535){if(we+2>=Mt)break;le[we++]=224|tt>>12,le[we++]=128|tt>>6&63,le[we++]=128|tt&63}else{if(we+3>=Mt)break;le[we++]=240|tt>>18,le[we++]=128|tt>>12&63,le[we++]=128|tt>>6&63,le[we++]=128|tt&63}}return le[we]=0,we-Gt}function de(te,le,we){return ne(te,$e,le,we)}function he(te,le){Ee.set(te,le)}function ge(te,le){return te%le>0&&(te+=le-te%le),te}var be,Ee,$e,ze,qe,We,vt,ft,mt;function dt(te){be=te,a.HEAP8=Ee=new Int8Array(te),a.HEAP16=ze=new Int16Array(te),a.HEAP32=We=new Int32Array(te),a.HEAPU8=$e=new Uint8Array(te),a.HEAPU16=qe=new Uint16Array(te),a.HEAPU32=vt=new Uint32Array(te),a.HEAPF32=ft=new Float32Array(te),a.HEAPF64=mt=new Float64Array(te)}var bt=a.INITIAL_MEMORY||16777216,Je,jn=[],Wt=[],ar=[],vn=[],Vr=!1;Wt.push({func:function(){zh()}});function Rn(){if(a.preRun)for(typeof a.preRun=="function"&&(a.preRun=[a.preRun]);a.preRun.length;)wr(a.preRun.shift());xa(jn)}function br(){Vr=!0,xa(Wt)}function vr(){xa(ar)}function wn(){if(a.postRun)for(typeof a.postRun=="function"&&(a.postRun=[a.postRun]);a.postRun.length;)kr(a.postRun.shift());xa(vn)}function wr(te){jn.unshift(te)}function kr(te){vn.unshift(te)}var or=0,ws=null,Us=null;function Aa(te){or++,a.monitorRunDependencies&&a.monitorRunDependencies(or)}function Si(te){if(or--,a.monitorRunDependencies&&a.monitorRunDependencies(or),or==0&&(ws!==null&&(clearInterval(ws),ws=null),Us)){var le=Us;Us=null,le()}}a.preloadedImages={},a.preloadedAudios={};function ks(te){a.onAbort&&a.onAbort(te),te+="",F(te),P=!0,B=1,te="abort("+te+"). Build with -s ASSERTIONS=1 for more info.";var le=new WebAssembly.RuntimeError(te);throw i(le),le}function _h(te,le){return String.prototype.startsWith?te.startsWith(le):te.indexOf(le)===0}var E0="data:application/octet-stream;base64,";function Mu(te){return _h(te,E0)}var $0="file://";function Rh(te){return _h(te,$0)}var qn="tfjs-backend-wasm.wasm";Mu(qn)||(qn=A(qn));function Dh(te){try{if(te==qn&&$)return new Uint8Array($);if(v)return v(te);throw"both async and sync fetching of the wasm failed"}catch(le){ks(le)}}function _0(){if(!$&&(p||f)){if(typeof fetch=="function"&&!Rh(qn))return fetch(qn,{credentials:"same-origin"}).then(function(te){if(!te.ok)throw"failed to load wasm binary file at '"+qn+"'";return te.arrayBuffer()}).catch(function(){return Dh(qn)});if(b)return new Promise(function(te,le){b(qn,function(we){te(new Uint8Array(we))},le)})}return Promise.resolve().then(function(){return Dh(qn)})}function Hs(){var te={a:O0};function le(et,tt){var kn=et.exports;a.asm=kn,N=a.asm.i,dt(N.buffer),Je=a.asm.o,Si("wasm-instantiate")}Aa("wasm-instantiate");function we(et){le(et.instance)}function lt(et){return _0().then(function(tt){return WebAssembly.instantiate(tt,te)}).then(et,function(tt){F("failed to asynchronously prepare wasm: "+tt),ks(tt)})}function Gt(){return!$&&typeof WebAssembly.instantiateStreaming=="function"&&!Mu(qn)&&!Rh(qn)&&typeof fetch=="function"?fetch(qn,{credentials:"same-origin"}).then(function(et){var tt=WebAssembly.instantiateStreaming(et,te);return tt.then(we,function(kn){return F("wasm streaming compile failed: "+kn),F("falling back to ArrayBuffer instantiation"),lt(we)})}):lt(we)}if(a.instantiateWasm)try{var Mt=a.instantiateWasm(te,le);return Mt}catch(et){return F("Module.instantiateWasm callback failed with error: "+et),!1}return Gt().catch(i),{}}function xa(te){for(;te.length>0;){var le=te.shift();if(typeof le=="function"){le(a);continue}var we=le.func;typeof we=="number"?le.arg===void 0?Je.get(we)():Je.get(we)(le.arg):we(le.arg===void 0?null:le.arg)}}function io(){ks()}function R0(te,le,we){$e.copyWithin(te,le,le+we)}function D0(){return $e.length}function Gs(te){try{return N.grow(te-be.byteLength+65535>>>16),dt(N.buffer),1}catch(le){}}function Fh(te){var le=D0(),we=2147483648;if(te>we)return!1;for(var lt=1;lt<=4;lt*=2){var Gt=le*(1+.2/lt);Gt=Math.min(Gt,te+100663296);var Mt=Math.min(we,ge(Math.max(te,Gt),65536)),et=Gs(Mt);if(et)return!0}return!1}var Ti={mappings:{},buffers:[null,[],[]],printChar:function(te,le){var we=Ti.buffers[te];le===0||le===10?((te===1?E:F)(se(we,0)),we.length=0):we.push(le)},varargs:void 0,get:function(){Ti.varargs+=4;var te=We[Ti.varargs-4>>2];return te},getStr:function(te){var le=ie(te);return le},get64:function(te,le){return te}};function Mh(te){return 0}function F0(te,le,we,lt,Gt){}function Oh(te,le,we,lt){for(var Gt=0,Mt=0;Mt<we;Mt++){for(var et=We[le+Mt*8>>2],tt=We[le+(Mt*8+4)>>2],kn=0;kn<tt;kn++)Ti.printChar(te,$e[et+kn]);Gt+=tt}return We[lt>>2]=Gt,0}function Kn(){return 6}function Ph(te){return We[qh()>>2]=te,te}function M0(te){switch(te){case 30:return 16384;case 85:var le=2147483648;return le/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return Ph(28),-1}var O0={a:io,d:R0,e:Fh,f:Mh,c:F0,b:Oh,g:Kn,h:M0},P0=Hs(),zh=a.___wasm_call_ctors=function(){return(zh=a.___wasm_call_ctors=a.asm.j).apply(null,arguments)},Ni=a._init=function(){return(Ni=a._init=a.asm.k).apply(null,arguments)},Ou=a._register_tensor=function(){return(Ou=a._register_tensor=a.asm.l).apply(null,arguments)},z0=a._dispose_data=function(){return(z0=a._dispose_data=a.asm.m).apply(null,arguments)},L0=a._dispose=function(){return(L0=a._dispose=a.asm.n).apply(null,arguments)},B0=a._Abs=function(){return(B0=a._Abs=a.asm.p).apply(null,arguments)},Ce=a._Add=function(){return(Ce=a._Add=a.asm.q).apply(null,arguments)},W0=a._AddN=function(){return(W0=a._AddN=a.asm.r).apply(null,arguments)},V0=a._All=function(){return(V0=a._All=a.asm.s).apply(null,arguments)},U0=a._Any=function(){return(U0=a._Any=a.asm.t).apply(null,arguments)},H0=a._ArgMax=function(){return(H0=a._ArgMax=a.asm.u).apply(null,arguments)},G0=a._AvgPool=function(){return(G0=a._AvgPool=a.asm.v).apply(null,arguments)},lo=a._BatchMatMul=function(){return(lo=a._BatchMatMul=a.asm.w).apply(null,arguments)},j0=a._Ceil=function(){return(j0=a._Ceil=a.asm.x).apply(null,arguments)},q0=a._ClipByValue=function(){return(q0=a._ClipByValue=a.asm.y).apply(null,arguments)},K0=a._Conv2D=function(){return(K0=a._Conv2D=a.asm.z).apply(null,arguments)},X0=a._Conv2DBackpropInput=function(){return(X0=a._Conv2DBackpropInput=a.asm.A).apply(null,arguments)},Z0=a._Cos=function(){return(Z0=a._Cos=a.asm.B).apply(null,arguments)},Y0=a._CropAndResize=function(){return(Y0=a._CropAndResize=a.asm.C).apply(null,arguments)},J0=a._Cumsum=function(){return(J0=a._Cumsum=a.asm.D).apply(null,arguments)},Q0=a._DepthToSpace=function(){return(Q0=a._DepthToSpace=a.asm.E).apply(null,arguments)},eg=a._DepthwiseConv2dNative=function(){return(eg=a._DepthwiseConv2dNative=a.asm.F).apply(null,arguments)},ba=a._Equal=function(){return(ba=a._Equal=a.asm.G).apply(null,arguments)},Pu=a._Exp=function(){return(Pu=a._Exp=a.asm.H).apply(null,arguments)},zu=a._FlipLeftRight=function(){return(zu=a._FlipLeftRight=a.asm.I).apply(null,arguments)},tg=a._Floor=function(){return(tg=a._Floor=a.asm.J).apply(null,arguments)},ng=a._FloorDiv=function(){return(ng=a._FloorDiv=a.asm.K).apply(null,arguments)},rg=a._FusedBatchNorm=function(){return(rg=a._FusedBatchNorm=a.asm.L).apply(null,arguments)},sg=a._FusedConv2D=function(){return(sg=a._FusedConv2D=a.asm.M).apply(null,arguments)},ag=a._FusedDepthwiseConv2D=function(){return(ag=a._FusedDepthwiseConv2D=a.asm.N).apply(null,arguments)},Xe=a._Gather=function(){return(Xe=a._Gather=a.asm.O).apply(null,arguments)},og=a._GatherNd=function(){return(og=a._GatherNd=a.asm.P).apply(null,arguments)},ig=a._Greater=function(){return(ig=a._Greater=a.asm.Q).apply(null,arguments)},lg=a._GreaterEqual=function(){return(lg=a._GreaterEqual=a.asm.R).apply(null,arguments)},ug=a._LeakyRelu=function(){return(ug=a._LeakyRelu=a.asm.S).apply(null,arguments)},cg=a._Less=function(){return(cg=a._Less=a.asm.T).apply(null,arguments)},dg=a._LessEqual=function(){return(dg=a._LessEqual=a.asm.U).apply(null,arguments)},Lu=a._Log=function(){return(Lu=a._Log=a.asm.V).apply(null,arguments)},Lh=a._LogicalAnd=function(){return(Lh=a._LogicalAnd=a.asm.W).apply(null,arguments)},Bh=a._Max=function(){return(Bh=a._Max=a.asm.X).apply(null,arguments)},hg=a._MaxPool=function(){return(hg=a._MaxPool=a.asm.Y).apply(null,arguments)},pg=a._Maximum=function(){return(pg=a._Maximum=a.asm.Z).apply(null,arguments)},fg=a._Mean=function(){return(fg=a._Mean=a.asm._).apply(null,arguments)},mg=a._Min=function(){return(mg=a._Min=a.asm.$).apply(null,arguments)},gg=a._Minimum=function(){return(gg=a._Minimum=a.asm.aa).apply(null,arguments)},yg=a._MirrorPad=function(){return(yg=a._MirrorPad=a.asm.ba).apply(null,arguments)},Ag=a._Multiply=function(){return(Ag=a._Multiply=a.asm.ca).apply(null,arguments)},ht=a._Neg=function(){return(ht=a._Neg=a.asm.da).apply(null,arguments)},xg=a._NonMaxSuppressionV3=function(){return(xg=a._NonMaxSuppressionV3=a.asm.ea).apply(null,arguments)},bg=a._NonMaxSuppressionV4=function(){return(bg=a._NonMaxSuppressionV4=a.asm.fa).apply(null,arguments)},vg=a._NonMaxSuppressionV5=function(){return(vg=a._NonMaxSuppressionV5=a.asm.ga).apply(null,arguments)},Ci=a._NotEqual=function(){return(Ci=a._NotEqual=a.asm.ha).apply(null,arguments)},Wh=a._OneHot=function(){return(Wh=a._OneHot=a.asm.ia).apply(null,arguments)},Vh=a._PadV2=function(){return(Vh=a._PadV2=a.asm.ja).apply(null,arguments)},Uh=a._Pow=function(){return(Uh=a._Pow=a.asm.ka).apply(null,arguments)},wg=a._Prelu=function(){return(wg=a._Prelu=a.asm.la).apply(null,arguments)},kg=a._Prod=function(){return(kg=a._Prod=a.asm.ma).apply(null,arguments)},Hh=a._RealDiv=function(){return(Hh=a._RealDiv=a.asm.na).apply(null,arguments)},Ig=a._Relu=function(){return(Ig=a._Relu=a.asm.oa).apply(null,arguments)},Gh=a._Relu6=function(){return(Gh=a._Relu6=a.asm.pa).apply(null,arguments)},va=a._ResizeBilinear=function(){return(va=a._ResizeBilinear=a.asm.qa).apply(null,arguments)},Sg=a._Reverse=function(){return(Sg=a._Reverse=a.asm.ra).apply(null,arguments)},Tg=a._RotateWithOffset=function(){return(Tg=a._RotateWithOffset=a.asm.sa).apply(null,arguments)},b3=a._Round=function(){return(b3=a._Round=a.asm.ta).apply(null,arguments)},jh=a._Rsqrt=function(){return(jh=a._Rsqrt=a.asm.ua).apply(null,arguments)},Ng=a._ScatterNd=function(){return(Ng=a._ScatterNd=a.asm.va).apply(null,arguments)},Cg=a._SelectV2=function(){return(Cg=a._SelectV2=a.asm.wa).apply(null,arguments)},Eg=a._Sigmoid=function(){return(Eg=a._Sigmoid=a.asm.xa).apply(null,arguments)},$g=a._Sin=function(){return($g=a._Sin=a.asm.ya).apply(null,arguments)},_g=a._Softmax=function(){return(_g=a._Softmax=a.asm.za).apply(null,arguments)},Rg=a._Sqrt=function(){return(Rg=a._Sqrt=a.asm.Aa).apply(null,arguments)},Dg=a._Square=function(){return(Dg=a._Square=a.asm.Ba).apply(null,arguments)},Fg=a._SquaredDifference=function(){return(Fg=a._SquaredDifference=a.asm.Ca).apply(null,arguments)},Mg=a._Step=function(){return(Mg=a._Step=a.asm.Da).apply(null,arguments)},Og=a._StridedSlice=function(){return(Og=a._StridedSlice=a.asm.Ea).apply(null,arguments)},Pg=a._Sub=function(){return(Pg=a._Sub=a.asm.Fa).apply(null,arguments)},zg=a._Sum=function(){return(zg=a._Sum=a.asm.Ga).apply(null,arguments)},Lg=a._Tan=function(){return(Lg=a._Tan=a.asm.Ha).apply(null,arguments)},Bg=a._Tanh=function(){return(Bg=a._Tanh=a.asm.Ia).apply(null,arguments)},Wg=a._Tile=function(){return(Wg=a._Tile=a.asm.Ja).apply(null,arguments)},Vg=a._TopK=function(){return(Vg=a._TopK=a.asm.Ka).apply(null,arguments)},Ug=a._Transform=function(){return(Ug=a._Transform=a.asm.La).apply(null,arguments)},Hg=a._Transpose=function(){return(Hg=a._Transpose=a.asm.Ma).apply(null,arguments)},Gg=a.__FusedMatMul=function(){return(Gg=a.__FusedMatMul=a.asm.Na).apply(null,arguments)},jg=a._malloc=function(){return(jg=a._malloc=a.asm.Oa).apply(null,arguments)},qg=a._free=function(){return(qg=a._free=a.asm.Pa).apply(null,arguments)},qh=a.___errno_location=function(){return(qh=a.___errno_location=a.asm.Qa).apply(null,arguments)},Kh=a.stackSave=function(){return(Kh=a.stackSave=a.asm.Ra).apply(null,arguments)},Xh=a.stackRestore=function(){return(Xh=a.stackRestore=a.asm.Sa).apply(null,arguments)},Bu=a.stackAlloc=function(){return(Bu=a.stackAlloc=a.asm.Ta).apply(null,arguments)};a.cwrap=ee;var Ei;function Kg(te){this.name="ExitStatus",this.message="Program terminated with exit("+te+")",this.status=te}Us=function te(){Ei||Wu(),Ei||(Us=te)};function Wu(te){if(te=te||c,or>0||(Rn(),or>0))return;function le(){Ei||(Ei=!0,a.calledRun=!0,!P&&(br(),vr(),o(a),a.onRuntimeInitialized&&a.onRuntimeInitialized(),wn()))}a.setStatus?(a.setStatus("Running..."),setTimeout(function(){setTimeout(function(){a.setStatus("")},1),le()},1)):le()}if(a.run=Wu,a.preInit)for(typeof a.preInit=="function"&&(a.preInit=[a.preInit]);a.preInit.length>0;)a.preInit.pop()();return Wu(),s.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)}}),ER="3.7.0",$R="3.7.0",_R="3.7.0",RR="3.7.0",DR="3.7.0",FR="3.7.0",MR="3.7.0",OR="3.7.0",PR=1e-7,zR=1e-4,LR=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}},W3=class{refCount(e){return Ur("refCount")}incRef(e){return Ur("incRef")}timerAvailable(){return!0}time(e){return Ur("time")}read(e){return Ur("read")}readSync(e){return Ur("readSync")}numDataIds(){return Ur("numDataIds")}disposeData(e,t){return Ur("disposeData")}write(e,t,n){return Ur("write")}move(e,t,n,r,s){return Ur("move")}memory(){return Ur("memory")}floatPrecision(){return Ur("floatPrecision")}epsilon(){return this.floatPrecision()===32?PR:zR}dispose(){return Ur("dispose")}};function Ur(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 V3(e){let t=e.length,n=0,r=0;for(;t>0;)r=Math.random()*t|0,t--,n=e[t],e[t]=e[r],e[r]=n}function BR(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,r,s,a=0;for(;n>0;)a=Math.random()*n|0,n--,r=e[n],s=t[n],e[n]=e[a],t[n]=t[a],e[a]=r,t[a]=s}function qu(e,t,n){return Math.max(e,Math.min(t,n))}function WR(e){return e%2==0?e:e+1}function VR(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function UR(e,t){let n=Math.random();return t*n+(1-n)*e}function HR(e,t){let n=0;for(let r=0;r<e.length;r++){let s=Number(e[r])-Number(t[r]);n+=s*s}return n}function L(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function Mn(e,t,n=""){L(Xs(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ho(e){L(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function po(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||Cn(e)&&!n)for(let r=0;r<e.length;++r)po(e[r],t,n);else t.push(e);return t}function Jt(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 GR(e){return e.length===0}function Xs(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 Xn(e){return e%1==0}function jR(e){if(Math.tanh!=null)return Math.tanh(e);if(e===Infinity)return 1;if(e===-Infinity)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function qR(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function KR(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return V3(t),t}function Ku(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function XR(e,t=r=>0,n){return new Promise((r,s)=>{let a=0,o=()=>{if(e()){r();return}a++;let i=t(a);if(n!=null&&a>=n){s();return}setTimeout(o,i)};o()})}function ZR(e,t){let n=1,r=-1;for(let a=0;a<e.length;++a)if(e[a]>=0)n*=e[a];else if(e[a]===-1){if(r!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${r} and dim ${a}`);r=a}else if(e[a]<0)throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`);if(r===-1){if(t>0&&t!==n)throw Error(`Size(${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} / ${n}`);let s=e.slice();return s[r]=t/n,s}function Xu(e,t){let n=t.length;return e=e==null?t.map((r,s)=>s):[].concat(e),L(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),L(e.every(r=>Xn(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function U3(e,t){let n=[],r=[],s=t!=null&&Array.isArray(t)&&t.length===0,a=t==null||s?null:Xu(t,e).sort(),o=0;for(let i=0;i<e.length;++i){if(a!=null){if(a[o]===i&&e[i]!==1)throw new Error(`Can't squeeze axis ${i} since its dim '${e[i]}' is not 1`);(a[o]==null||a[o]>i)&&e[i]===1&&(n.push(e[i]),r.push(i)),a[o]<=i&&o++}e[i]!==1&&(n.push(e[i]),r.push(i))}return{newShape:n,keptDims:r}}function H3(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 G3(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 j3(e,t){for(let n=0;n<e.length;n++){let r=e[n];if(isNaN(r)||!isFinite(r))throw Error(`A tensor of type ${t} being uploaded contains ${r}.`)}}function q3(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function YR(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function Cn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function n2(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function K3(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Ia(e){return typeof e=="string"||e instanceof String}function X3(e){return typeof e=="boolean"}function Z3(e){return typeof e=="number"}function ep(e){return Array.isArray(e)?ep(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Z3(e)?"float32":Ia(e)?"string":X3(e)?"bool":"float32"}function Sa(e){return!!(e&&e.constructor&&e.call&&e.apply)}function tp(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function Oi(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function Y3(e,t,n,r=!1){let s=new Array;if(t.length===1){let a=t[0]*(r?2:1);for(let o=0;o<a;o++)s[o]=n[e+o]}else{let a=t[0],o=t.slice(1),i=o.reduce((l,u)=>l*u)*(r?2:1);for(let l=0;l<a;l++)s[l]=Y3(e+l*i,o,n,r)}return s}function Pi(e,t,n=!1){if(e.length===0)return t[0];let r=e.reduce((s,a)=>s*a)*(n?2:1);if(r===0)return[];if(r!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return Y3(0,e,t,n)}function r2(e,t){let n=np(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function np(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 JR(e,t){let n=e.reduce((r,s)=>r*s,1);if(t==null||t==="float32")return Pi(e,new Float32Array(n));if(t==="int32")return Pi(e,new Int32Array(n));if(t==="bool")return Pi(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function s2(e){e.forEach(t=>{L(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function QR(e,t,n){if(t===0)return 0;if(t===1)return e[0];let r=e[e.length-1];for(let s=0;s<e.length-1;++s)r+=n[s]*e[s];return r}function eD(e,t,n){if(t===0)return[];if(t===1)return[e];let r=new Array(t);for(let s=0;s<r.length-1;++s)r[s]=Math.floor(e/n[s]),e-=r[s]*n[s];return r[r.length-1]=e,r}function a2(e){return e&&e.then&&typeof e.then=="function"}var J3="tfjsflags",Q3=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=tD,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let r=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${r}.`),this.set(e,r)}}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(a2(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);J3 in e&&e[J3].split(",").forEach(n=>{let[r,s]=n.split(":");this.urlFlags[r]=rD(r,s)})}};function tD(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(nD(t,r[0],r[1]),r.join("="))),t}function nD(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function rD(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 ${e}.`)}function ct(){return Sr}var Sr=null;function sD(e){Sr=e}var o2;function ev(){if(o2==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");o2=e}return o2}function aD(){let e=ev();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function i2(e,t){let n=aD();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var tv="Abs",nv="Acos",rv="Acosh",l2="Add",sv="AddN",av="All",ov="Any",iv="ArgMax",lv="ArgMin",uv="Asin",cv="Asinh",dv="Atan",hv="Atanh",pv="Atan2",fv="AvgPool",oD="AvgPoolGrad",mv="AvgPool3D",iD="AvgPool3DGrad",gv="BatchMatMul",yv="BatchToSpaceND",Av="Bincount",lD="BroadcastTo",u2="Cast",xv="Ceil",bv="ClipByValue",vv="Complex",wv="ComplexAbs",kv="Concat",Iv="Conv2D",Sv="Conv2DBackpropFilter",Tv="Conv2DBackpropInput",Nv="Conv3D",uD="Conv3DBackpropFilterV2",Cv="Conv3DBackpropInputV2",Ev="Cos",$v="Cosh",_v="Cumsum",Rv="CropAndResize",Dv="DenseBincount",Fv="DepthToSpace",Mv="DepthwiseConv2dNative",Ov="DepthwiseConv2dNativeBackpropFilter",Pv="DepthwiseConv2dNativeBackpropInput",zv="Diag",Lv="Dilation2D",cD="Dilation2DBackpropInput",dD="Dilation2DBackpropFilter",Bv="RealDiv",Wv="Einsum",Vv="Elu",hD="EluGrad",Uv="Erf",Hv="Equal",Gv="Exp",jv="ExpandDims",qv="Expm1",Kv="FFT",Xv="Fill",Zv="FlipLeftRight",Yv="Floor",Jv="FloorDiv",Qv="FusedBatchNorm",ew="GatherV2",tw="GatherNd",nw="Greater",rw="GreaterEqual",c2="Identity",sw="IFFT",aw="Imag",ow="IsFinite",iw="IsInf",lw="IsNan",uw="LeakyRelu",cw="Less",dw="LessEqual",hw="LinSpace",pw="Log",fw="Log1p",mw="LogicalAnd",gw="LogicalNot",yw="LogicalOr",pD="LogSoftmax",Aw="LRN",fD="LRNGrad",xw="Max",bw="Maximum",vw="MaxPool",mD="MaxPoolGrad",ww="MaxPool3D",gD="MaxPool3DGrad",kw="MaxPoolWithArgmax",Iw="Mean",Sw="Min",Tw="Minimum",Nw="MirrorPad",Cw="Mod",Ew="Multinomial",$w="Multiply",_w="Neg",Rw="NotEqual",Dw="NonMaxSuppressionV3",Fw="NonMaxSuppressionV4",Mw="NonMaxSuppressionV5",Ow="OnesLike",Pw="OneHot",zw="Pack",Lw="PadV2",yD="Pool",Bw="Pow",Ww="Prelu",Vw="Prod",Uw="Range",Hw="Real",Gw="Reciprocal",jw="Relu",qw="Reshape",Kw="ResizeNearestNeighbor",AD="ResizeNearestNeighborGrad",Xw="ResizeBilinear",xD="ResizeBilinearGrad",Zw="Relu6",Yw="Reverse",Jw="Round",Qw="Rsqrt",e7="ScatterNd",t7="Select",n7="Selu",r7="Slice",s7="Sin",a7="Sinh",o7="Sign",i7="Sigmoid",l7="Softplus",u7="Sqrt",c7="Sum",d7="SpaceToBatchND",h7="SplitV",p7="Softmax",f7="SparseFillEmptyRows",m7="SparseReshape",g7="SparseSegmentMean",y7="SparseSegmentSum",A7="SparseToDense",x7="SquaredDifference",bD="Square",b7="StridedSlice",v7="StringNGrams",w7="StringSplit",k7="StringToHashBucketFast",I7="Sub",S7="Tan",T7="Tanh",d2="Tile",N7="TopK",C7="Transform",E7="Transpose",$7="Unique",_7="Unpack",R7="UnsortedSegmentSum",D7="ZerosLike",F7="Step",h2="FromPixels",M7="RotateWithOffset",p2="_FusedMatMul",f2="FusedConv2D",m2="FusedDepthwiseConv2D",zi=i2("kernelRegistry",()=>new Map),Zu=i2("gradRegistry",()=>new Map);function rp(e,t){let n=y2(e,t);return zi.get(n)}function g2(e){return Zu.get(e)}function Li(e){let t=zi.entries(),n=[];for(;;){let{done:r,value:s}=t.next();if(r)break;let[a,o]=s,[i]=a.split("_");i===e&&n.push(o)}return n}function sp(e){let{kernelName:t,backendName:n}=e,r=y2(t,n);zi.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),zi.set(r,e)}function vD(e){let{kernelName:t}=e;Zu.has(t)&&ct().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Zu.set(t,e)}function wD(e,t){let n=y2(e,t);if(!zi.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);zi.delete(n)}function kD(e){if(!Zu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Zu.delete(e)}function ID(e,t){Li(e).forEach(r=>{let s=Object.assign({},r,{backendName:t});sp(s)})}function y2(e,t){return`${t}_${e}`}var O7={};De(O7,{arraysEqual:()=>Xs,assert:()=>L,assertNonNegativeIntegerDimensions:()=>s2,assertNonNull:()=>ho,assertShapesMatch:()=>Mn,bytesFromStringArray:()=>K3,bytesPerElement:()=>n2,checkConversionForErrors:()=>j3,clamp:()=>qu,computeStrides:()=>Oi,createScalarValue:()=>$D,createShuffledIndices:()=>KR,decodeString:()=>ip,distSquared:()=>HR,encodeString:()=>Qu,fetch:()=>RD,fingerPrint64:()=>ED,flatten:()=>po,getArrayFromDType:()=>G3,getTypedArrayFromDType:()=>H3,hasEncodingLoss:()=>YR,hexToLong:()=>Yu,indexToLoc:()=>eD,inferDtype:()=>ep,inferFromImplicitShape:()=>ZR,isBoolean:()=>X3,isFunction:()=>Sa,isInt:()=>Xn,isNumber:()=>Z3,isPromise:()=>a2,isScalarShape:()=>GR,isString:()=>Ia,isTypedArray:()=>Cn,isValidDtype:()=>q3,locToIndex:()=>QR,makeOnesTypedArray:()=>r2,makeZerosNestedTypedArray:()=>JR,makeZerosTypedArray:()=>np,nearestDivisor:()=>tp,nearestLargerEven:()=>WR,now:()=>Ju,parseAxisParam:()=>Xu,randUniform:()=>UR,repeatedTry:()=>XR,rightPad:()=>Ku,shuffle:()=>V3,shuffleCombo:()=>BR,sizeFromShape:()=>Jt,sizeToSquarishShape:()=>qR,squeezeShape:()=>U3,sum:()=>VR,tanh:()=>jR,toNestedArray:()=>Pi,toTypedArray:()=>op});var P7=Ks(P3()),fo=P7.default||P7;function Yu(e){return fo.fromString(e,!0,16)}var z7=Yu("c3a5c85c97cb3127"),mo=Yu("b492b66fbe98f273"),On=Yu("9ae16a3b2f90404f");function A2(e){return e.xor(e.shru(47))}function L7(e,t,n){let r=e.slice(t,t+n);return fo.fromBytes(Array.from(r),!0,!0)}function St(e,t){return L7(e,t,8)}function B7(e,t){return L7(e,t,4)}function fn(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function Ta(e,t,n=Yu("9ddfea08eb382d69")){let r=e.xor(t).mul(n);r=r.xor(r.shru(47));let s=t.xor(r).mul(n);return s=s.xor(s.shru(47)),s=s.mul(n),s}function SD(e,t,n,r,s,a){s=s.add(e),a=fn(a.add(s).add(r),21);let o=s;return s=s.add(t),s=s.add(n),a=a.add(fn(s,44)),[s.add(r),a.add(o)]}function ap(e,t,n,r){return SD(St(e,t),St(e,t+8),St(e,t+16),St(e,t+24),n,r)}function TD(e,t=e.length){if(t>=8){let n=On.add(t*2),r=St(e,0).add(On),s=St(e,t-8),a=fn(s,37).mul(n).add(r),o=fn(r,25).add(s).mul(n);return Ta(a,o,n)}if(t>=4){let n=On.add(t*2),r=B7(e,0);return Ta(r.shl(3).add(t),B7(e,t-4),n)}if(t>0){let n=e[0],r=e[t>>1],s=e[t-1],a=n+(r<<8),o=t+(s<<2);return A2(On.mul(a).xor(z7.mul(o))).mul(On)}return On}function ND(e,t=e.length){let n=On.add(t*2),r=St(e,0).mul(mo),s=St(e,8),a=St(e,t-8).mul(n),o=St(e,t-16).mul(On);return Ta(fn(r.add(s),43).add(fn(a,30)).add(o),r.add(fn(s.add(On),18)).add(a),n)}function CD(e,t=e.length){let n=On.add(t*2),r=St(e,0).mul(On),s=St(e,8),a=St(e,t-8).mul(n),o=St(e,t-16).mul(On),i=fn(r.add(s),43).add(fn(a,30)).add(o),l=Ta(i,r.add(fn(s.add(On),18)).add(a),n),u=St(e,16).mul(n),c=St(e,24),d=i.add(St(e,t-32)).mul(n),h=l.add(St(e,t-24)).mul(n);return Ta(fn(u.add(c),43).add(fn(d,30)).add(h),u.add(fn(c.add(r),18)).add(d),n)}function ED(e,t=e.length){let n=fo.fromNumber(81,!0);if(t<=32)return t<=16?TD(e,t):ND(e,t);if(t<=64)return CD(e,t);let r=n,s=n.mul(mo).add(113),a=A2(s.mul(On).add(113)).mul(On),o=[fo.UZERO,fo.UZERO],i=[fo.UZERO,fo.UZERO];r=r.mul(On).add(St(e,0));let l=0,u=(t-1>>6)*64,c=u+(t-1&63)-63;do r=fn(r.add(s).add(o[0]).add(St(e,l+8)),37).mul(mo),s=fn(s.add(o[1]).add(St(e,l+48)),42).mul(mo),r=r.xor(i[1]),s=s.add(o[0]).add(St(e,l+40)),a=fn(a.add(i[0]),33).mul(mo),o=ap(e,l,o[1].mul(mo),r.add(i[0])),i=ap(e,l+32,a.add(i[1]),s.add(St(e,l+16))),[a,r]=[r,a],l+=64;while(l!==u);let d=mo.add(a.and(255).shl(1));return l=c,i[0]=i[0].add(t-1&63),o[0]=o[0].add(i[0]),i[0]=i[0].add(o[0]),r=fn(r.add(s).add(o[0]).add(St(e,l+8)),37).mul(d),s=fn(s.add(o[1]).add(St(e,l+48)),42).mul(d),r=r.xor(i[1].mul(9)),s=s.add(o[0].mul(9).add(St(e,l+40))),a=fn(a.add(i[0]),33).mul(d),o=ap(e,l,o[1].mul(d),r.add(i[0])),i=ap(e,l+32,a.add(i[1]),s.add(St(e,l+16))),[a,r]=[r,a],Ta(Ta(o[0],i[0],d).add(A2(s).mul(z7)).add(a),Ta(o[1],i[1],d).add(r),d)}function $D(e,t){return t==="string"?Qu(e):op([e],t)}function _D(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function op(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=po(e)),ct().getBool("DEBUG")&&j3(e,t),_D(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 r=0;r<n.length;++r)Math.round(e[r])!==0&&(n[r]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Ju(){return ct().platform.now()}function RD(e,t){return ct().platform.fetch(e,t)}function Qu(e,t="utf-8"){return t=t||"utf-8",ct().platform.encode(e,t)}function ip(e,t="utf-8"){return t=t||"utf-8",ct().platform.decode(e,t)}var DD=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new MD)}profileKernel(e,t,n){let r,s=()=>{r=n()},a,o=Ju();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(s);else{s();for(let l of r)l.dataSync();a=Promise.resolve({kernelMs:Ju()-o})}if(ct().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<r.length;l++){let u=r[l];u.data().then(c=>{FD(c,u.dtype,e)})}return{kernelName:e,outputs:r,inputs:t,timeMs:a.then(l=>l.kernelMs),extraInfo:a.then(l=>l.getExtraProfileInfo!=null?l.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:r,inputs:s,extraInfo:a}=e;n.forEach(o=>{Promise.all([o.data(),r,a]).then(i=>{this.logger.logKernelProfile(t,o,i[0],i[1],s,i[2])})})}};function FD(e,t,n){if(t!=="float32")return!1;for(let r=0;r<e.length;r++){let s=e[r];if(isNaN(s)||!isFinite(s))return console.warn(`Found ${s} in the result of '${n}'`),!0}return!1}var MD=class{logKernelProfile(e,t,n,r,s,a){let o=typeof r=="number"?Ku(`${r}ms`,9):r.error,i=Ku(e,25),l=t.rank,u=t.size,c=Ku(t.shape.toString(),14),d="";for(let h in s){let p=s[h];if(p!=null){let f=p.shape||t.shape,m=f.length;d+=`${h}: ${m}D ${m>0?f:""} `}}console.log(`%c${i}	%c${o}	%c${l}D ${c}	%c${u}	%c${d}	%c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function OD(e,t,n){let r={},s={};for(let l=0;l<t.length;l++)r[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],c=u.inputs;for(let d in c){let h=c[d],p=!1;for(let f=0;f<t.length;f++)if(r[h.id]){u.outputs.forEach(m=>r[m.id]=!0),p=!0,s[u.id]=!0;break}if(p)break}}let a={};a[n.id]=!0;let o={};for(let l=e.length-1;l>=0;l--){let u=e[l],c=u.inputs;for(let d=0;d<u.outputs.length;d++)if(a[u.outputs[d].id]){for(let h in c)a[c[h].id]=!0,o[u.id]=!0;break}}let i=[];for(let l=0;l<e.length;l++){let u=e[l];if(s[u.id]&&o[u.id]){let c={};for(let h in u.inputs){let p=u.inputs[h];r[p.id]&&(c[h]=p)}let d=Object.assign({},u);d.inputs=c,d.outputs=u.outputs,i.push(d)}}return i}function PD(e,t,n,r){for(let s=t.length-1;s>=0;s--){let a=t[s],o=[];if(a.outputs.forEach(l=>{let u=e[l.id];u!=null?o.push(u):o.push(null)}),a.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);let i=a.gradient(o);for(let l in a.inputs){if(!(l in i))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(i)}.`);let u=n(()=>i[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=a.inputs[l];if(!Xs(u.shape,c.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=u;else{let d=e[c.id];e[c.id]=r(d,u),d.dispose()}}}}var W7=20,ec=3,x2=7;function zD(e,t,n,r){let s=Oi(t),a=LD(e,t,n,s),o=t.length,i=lp(e,t,n,s,a),l=["Tensor"];return r&&(l.push(`  dtype: ${n}`),l.push(`  rank: ${o}`),l.push(`  shape: [${t}]`),l.push("  values:")),l.push(i.map(u=>"    "+u).join(`
`)),l.join(`
`)}function LD(e,t,n,r){let s=Jt(t),a=r[r.length-1],o=new Array(a).fill(0),i=t.length,l=n==="complex64"?nc(e):e;if(i>1)for(let u=0;u<s/a;u++){let c=u*a;for(let d=0;d<a;d++)o[d]=Math.max(o[d],tc(l[c+d],0,n).length)}return o}function tc(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(x2))} + ${parseFloat(e[1].toFixed(x2))}j`:Ia(e)?r=`'${e}'`:n==="bool"?r=V7(e):r=parseFloat(e.toFixed(x2)).toString(),Ku(r,t)}function V7(e){return e===0?"false":"true"}function lp(e,t,n,r,s,a=!0){let o=n==="complex64"?2:1,i=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=nc(e);return[tc(m[0],0,n)]}return n==="bool"?[V7(e[0])]:[e[0].toString()]}if(l===1){if(i>W7){let g=ec*o,y=Array.from(e.slice(0,g)),A=Array.from(e.slice((i-ec)*o,i*o));return n==="complex64"&&(y=nc(y),A=nc(A)),["["+y.map((x,b)=>tc(x,s[b],n)).join(", ")+", ..., "+A.map((x,b)=>tc(x,s[i-ec+b],n)).join(", ")+"]"]}let m=n==="complex64"?nc(e):Array.from(e);return["["+m.map((g,y)=>tc(g,s[y],n)).join(", ")+"]"]}let u=t.slice(1),c=r.slice(1),d=r[0]*o,h=[];if(i>W7){for(let m=0;m<ec;m++){let g=m*d,y=g+d;h.push(...lp(e.slice(g,y),u,n,c,s,!1))}h.push("...");for(let m=i-ec;m<i;m++){let g=m*d,y=g+d;h.push(...lp(e.slice(g,y),u,n,c,s,m===i-1))}}else for(let m=0;m<i;m++){let g=m*d,y=g+d;h.push(...lp(e.slice(g,y),u,n,c,s,m===i-1))}let p=l===2?",":"";h[0]="["+h[0]+p;for(let m=1;m<h.length-1;m++)h[m]=" "+h[m]+p;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return h[h.length-1]=" "+h[h.length-1]+"]"+(a?"":f),h}function nc(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var up=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Jt(e),n!=null){let r=n.length;L(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${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||G3(t,this.size),this.strides=Oi(e)}set(e,...t){t.length===0&&(t=[0]),L(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 r of e){if(r<0||r>=this.shape[t]){let s=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(s)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];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 Is().makeTensor(this.values,this.shape,this.dtype)}},Is=null,Bi=null,BD=null;function WD(e){Is=e}function VD(e){Bi=e}function UD(e){BD=e}var Tt=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Jt(e),this.strides=Oi(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Bi.buffer(this.shape,this.dtype,e)}bufferSync(){return Bi.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Pi(this.shape,e,this.dtype==="complex64")}arraySync(){return Pi(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Is().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>ip(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Is().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>ip(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 Is().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Is().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Bi.print(this,e)}clone(){return this.throwIfDisposed(),Bi.clone(this)}toString(e=!1){let t=this.dataSync();return zD(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Bi.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Is().makeVariable(this,e,t,n)}};Object.defineProperty(Tt,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function HD(){return i2("Tensor",()=>Tt)}HD();var rc=class extends Tt{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);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(!Xs(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Is().disposeTensor(this),this.dataId=e.dataId,Is().incRef(this,null)}dispose(){Is().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(rc,Symbol.hasInstance,{value:e=>e instanceof Tt&&e.assign!=null&&e.assign instanceof Function});var U7={};De(U7,{assertTypesMatch:()=>H7,getTensorsInContainer:()=>S2,isTensorInList:()=>qD,makeTypesMatch:()=>Vt});var b2;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(b2||(b2={}));var v2;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(v2||(v2={}));var w2;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(w2||(w2={}));var k2;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(k2||(k2={}));var I2;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(I2||(I2={}));var GD={float32:k2,int32:v2,bool:w2,complex64:I2};function cp(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return GD[e][t]}function jD(e){return cp(e,"int32")}function Vt(e,t){if(e.dtype===t.dtype)return[e,t];let n=cp(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function H7(e,t){L(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function qD(e,t){return t.some(n=>n.id===e.id)}function S2(e){let t=[],n=new Set;return G7(e,t,n),t}function G7(e,t,n){if(e==null)return;if(e instanceof Tt){t.push(e);return}if(!KD(e))return;let r=e;for(let s in r){let a=r[s];n.has(a)||(n.add(a),G7(a,t,n))}}function KD(e){return Array.isArray(e)||typeof e=="object"}function T2(e){return e.kernelName!=null}var j7=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},N2=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new j7}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new DD(this.backendInstance),!0}setupRegisteredKernels(){Li(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Li(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof W3)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,s=n.then(a=>r<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,s=this.readSync(t),a=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return N2.nextTensorId++}nextVariableId(){return N2.nextVariableId++}clone(e){let t=U.runKernel(c2,{x:e}),n={x:e},r=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return U.runKernel(u2,i,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,s,{}),t}runKernel(e,t,n){if(!(rp(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 r=this.backend.numDataIds(),s=0;n.forEach(i=>{s+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-s-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=T2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(T2(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=rp(p,this.backendName);L(g!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),o=()=>{let y=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:w,dtype:S}=b;return this.makeTensorFromDataId(v,w,S)});if(r){let b=this.getTensorsForGradient(p,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>p(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,d=T2(e)?null:e.backwardsFunc,h;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(h=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),t=h.outputs)}),r&&this.addTapeNode(l,u,t,d,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let r=g2(e);if(r!=null){let s=r.inputsToSave||[],a=r.outputsToSave||[],o;r.saveAllInputs?(L(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=s.map(l=>t[l]);let i=n.filter((l,u)=>a[u]);return o.concat(i)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let s=e;n==="string"&&Ia(e[0])&&(s=e.map(i=>Qu(i)));let a=r.write(s,t,n),o=new Tt(t,n,a,this.nextTensorId());if(this.trackTensor(o,r),n==="string"){let i=this.state.tensorInfo.get(a),l=K3(s);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,r){n=n||"float32";let s=new Tt(t,n,e,this.nextTensorId());return this.trackTensor(s,r),s}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let s=new rc(e,t,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*n2(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 rc||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*n2(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(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,s,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},i=g2(e);i!=null&&(r=i.gradFunc),r!=null&&(o.gradient=l=>(l=l.map((u,c)=>{if(u==null){let d=n[c],h=np(d.size,d.dtype);return this.makeTensor(h,d.shape,d.dtype)}return u}),r(l.length>1?l:l[0],s,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=S2(e),n=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!n.has(a.id)&&a.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(s=>{!s.kept&&s.scopeId===r.id&&this.track(s)})}gradients(e,t,n,r=!1){if(L(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 s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));L(s instanceof Tt,()=>"The result y returned by f() must be a tensor.");let a=OD(this.state.activeTape,t,s);if(!r&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let o={};o[s.id]=n==null?XD(s.shape):n,PD(o,a,l=>this.tidy(l),ZD);let i=t.map(l=>o[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:s,grads:i}})}customGrad(e){return L(Sa(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{L(t.every(o=>o instanceof Tt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((o,i)=>{r[i]=o});let s=(o,i)=>(n=e(...t,i),L(n.value instanceof Tt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),L(Sa(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(o,i)=>{let l=n.gradFunc(o,i),u=Array.isArray(l)?l:[l];L(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(...)."),L(u.every(d=>d instanceof Tt),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let c={};return u.forEach((d,h)=>{c[h]=()=>d}),c};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=Ju(),n=await this.backend.time(e);return n.wallMs=Ju()-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 j7;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}},C2=N2;C2.nextTensorId=0;C2.nextVariableId=0;function XD(e){let t=r2(Jt(e),"float32");return U.makeTensor(t,e,"float32")}function q7(){let e=ev();if(e._tfengine==null){let t=new Q3(e);e._tfengine=new C2(t)}return sD(e._tfengine.ENV),WD(()=>e._tfengine),e._tfengine}var U=q7();function ZD(e,t){let n={a:e,b:t};return U.runKernel(l2,n)}var K7={};De(K7,{isBrowser:()=>X7,isMobile:()=>JD});function YD(){return typeof navigator!="undefined"&&navigator!=null}function JD(e){if(e||YD()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function X7(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var es=ct();es.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.")});es.registerFlag("IS_BROWSER",()=>X7());es.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");es.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));es.registerFlag("PROD",()=>!1);es.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>es.getBool("DEBUG"));es.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);es.registerFlag("IS_TEST",()=>!1);es.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);es.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Ss(e,t){let n=e;if(Cn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||Cn(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&ct().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Z7(e,r,[]),r}function Z7(e,t,n){if(n=n||[],!Array.isArray(e)&&!Cn(e)){L(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}L(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),L(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let s=0;s<e.length;++s)Z7(e[s],r,n.concat(s))}function Y7(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function O(e,t,n,r="numeric"){if(e instanceof Tt)return Y7(r,e.dtype,t,n),e;let s=ep(e);if(s!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(s=r),Y7(r,s,t,n),e==null||!Cn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let l=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${l}'`)}let a=Ss(e,s);!Cn(e)&&!Array.isArray(e)&&(e=[e]);let i=s!=="string"?op(e,s):po(e,[],!0);return U.makeTensor(i,a,s)}function sc(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,o)=>O(a,`${t}[${o}]`,n,r))}var J7="__op";function H(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],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+J7;let s=(...a)=>{U.startScope(n);try{let o=r(...a);return a2(o)&&console.error("Cannot return a Promise inside of tidy."),U.endScope(o),o}catch(o){throw U.endScope(null),o}};return Object.defineProperty(s,"name",{value:n,configurable:!0}),s}function QD(e,t){let n=O(e,"real","complex"),r=O(t,"imag","complex");Mn(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);let s={real:n,imag:r};return U.runKernel(vv,s)}var go=H({complex_:QD});function Na(e,t,n,r){if(r==null&&(r=ep(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!Cn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){s2(t);let s=Jt(t),a=Jt(n);L(s===a,()=>`Based on the provided shape, [${t}], the tensor should have ${s} values but has ${a}`);for(let o=0;o<n.length;++o){let i=n[o],l=o===n.length-1?i!==Jt(t.slice(o)):!0;L(n[o]===t[o]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!Cn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?op(e,r):po(e,[],!0),U.makeTensor(e,t,r)}function ts(e,t,n){let r=Ss(e,n);return Na(e,t,r,n)}var E2={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},dp=4;async function eF(e,t){let n=[],r=[],s=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);for(let o=0;o<s.length;++o){let i=s[o],l=Array.isArray(e)?e[o].tensor:e[i];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${i}': ${l.dtype}`);let u={name:i,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let c=new Promise(async d=>{let h=await l.bytes(),p=h.reduce((g,y)=>g+y.length,0)+dp*h.length,f=new Uint8Array(p),m=0;for(let g=0;g<h.length;g++){let y=h[g],A=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(A,m),m+=dp,f.set(y,m),m+=y.length}d(f)});r.push(c)}else r.push(l.data());t!=null&&(u.group=t),n.push(u)}let a=await Promise.all(r);return{data:tF(a),specs:n}}function Q7(e,t){let n={},r,s=0;for(let a of t){let o=a.name,i=a.dtype,l=a.shape,u=Jt(l),c;if("quantization"in a){let d=a.quantization;if(d.dtype==="uint8"||d.dtype==="uint16"){if(!("min"in d&&"scale"in d))throw new Error(`Weight ${a.name} with quantization ${d.dtype} doesn't have corresponding metadata min and scale.`)}else if(d.dtype==="float16"){if(i!=="float32")throw new Error(`Weight ${a.name} is quantized with ${d.dtype} which only supports weights of type float32 not ${i}.`)}else throw new Error(`Weight ${a.name} has unknown quantization dtype ${d.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let h=E2[d.dtype],p=e.slice(s,s+u*h),f=d.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(i==="float32")if(d.dtype==="uint8"||d.dtype==="uint16"){c=new Float32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];c[m]=g*d.scale+d.min}}else if(d.dtype==="float16")r===void 0&&(r=iF()),c=r(f);else throw new Error(`Unsupported quantization type ${d.dtype} for weight type float32.`);else if(i==="int32"){if(d.dtype!=="uint8"&&d.dtype!=="uint16")throw new Error(`Unsupported quantization type ${d.dtype} for weight type int32.`);c=new Int32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];c[m]=Math.round(g*d.scale+d.min)}}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);s+=u*h}else if(i==="string"){let d=Jt(a.shape);c=[];for(let h=0;h<d;h++){let p=new Uint32Array(e.slice(s,s+dp))[0];s+=dp;let f=new Uint8Array(e.slice(s,s+p));c.push(f),s+=p}}else{let d=E2[i],h=e.slice(s,s+u*d);if(i==="float32")c=new Float32Array(h);else if(i==="int32")c=new Int32Array(h);else if(i==="bool")c=new Uint8Array(h);else if(i==="complex64"){c=new Float32Array(h);let p=new Float32Array(c.length/2),f=new Float32Array(c.length/2);for(let y=0;y<p.length;y++)p[y]=c[y*2],f[y]=c[y*2+1];let m=ts(p,l,"float32"),g=ts(f,l,"float32");n[o]=go(m,g),m.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);s+=u*d}i!=="complex64"&&(n[o]=ts(c,l,i))}return n}function tF(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(a=>{if(t+=a.byteLength,n.push(a.byteLength===a.buffer.byteLength?a:new a.constructor(a)),!(a instanceof Float32Array||a instanceof Int32Array||a instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${a.constructor.name}`)});let r=new Uint8Array(t),s=0;return n.forEach(a=>{r.set(new Uint8Array(a.buffer),s),s+=a.byteLength}),r.buffer}var $2=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function ek(e){return $2?Buffer.byteLength(e):new Blob([e]).size}function nF(e){if($2)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let r=0,s=t.length;r<s;r++)n+=String.fromCharCode(t[r]);return btoa(n)}function rF(e){if($2){let r=Buffer.from(e,"base64");return r.buffer.slice(r.byteOffset,r.byteOffset+r.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let r=0;r<t.length;++r)n.set([t.charCodeAt(r)],r);return n.buffer}function _2(e){if(e.length===1)return e[0];let t=0;e.forEach(s=>{t+=s.byteLength});let n=new Uint8Array(t),r=0;return e.forEach(s=>{n.set(new Uint8Array(s),r),r+=s.byteLength}),n.buffer}function tk(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 ac(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:ek(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:ek(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function sF(){let e=n=>{let r=n<<13,s=0;for(;(r&8388608)==0;)s-=8388608,r<<=1;return r&=~8388608,s+=947912704,r|s},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 aF(){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 oF(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function iF(){let e=sF(),t=aF(),n=oF();return r=>{let s=new ArrayBuffer(4*r.length),a=new Uint32Array(s);for(let o=0;o<r.length;o++){let i=r[o],l=e[n[i>>10]+(i&1023)]+t[i>>10];a[o]=l}return new Float32Array(s)}}var jt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return jt.instance==null&&(jt.instance=new jt),jt.instance}static registerSaveRouter(e){jt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){jt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return jt.getHandlers(e,"save")}static getLoadHandlers(e,t){return jt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?jt.getInstance().loadRouters:jt.getInstance().saveRouters).forEach(a=>{let o=a(e,n);o!==null&&r.push(o)}),r}},lF=e=>jt.registerSaveRouter(e),uF=e=>jt.registerLoadRouter(e),cF=e=>jt.getSaveHandlers(e),dF=(e,t)=>jt.getLoadHandlers(e,t),R2="tensorflowjs",D2=1,yo="models_store",Ca="model_info_store";function nk(){if(!ct().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 F2(e){let t=e.result;t.createObjectStore(yo,{keyPath:"modelPath"}),t.createObjectStore(Ca,{keyPath:"modelPath"})}var Ao=class{constructor(e){if(this.indexedDB=nk(),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,r)=>{let s=this.indexedDB.open(R2,D2);s.onupgradeneeded=()=>F2(s),s.onsuccess=()=>{let a=s.result;if(t==null){let o=a.transaction(yo,"readonly"),l=o.objectStore(yo).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return a.close(),r(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(l.result.modelArtifacts)},l.onerror=u=>(a.close(),r(l.error)),o.oncomplete=()=>a.close()}else{let o=ac(t),i=a.transaction(Ca,"readwrite"),l=i.objectStore(Ca),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:o}),c;u.onsuccess=()=>{c=a.transaction(yo,"readwrite");let h=c.objectStore(yo).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:o});h.onsuccess=()=>n({modelArtifactsInfo:o}),h.onerror=p=>{l=i.objectStore(Ca);let f=l.delete(this.modelPath);f.onsuccess=()=>(a.close(),r(h.error)),f.onerror=m=>(a.close(),r(h.error))}},u.onerror=d=>(a.close(),r(u.error)),i.oncomplete=()=>{c==null?a.close():c.oncomplete=()=>a.close()}}},s.onerror=a=>r(s.error)})}};Ao.URL_SCHEME="indexeddb://";var rk=e=>ct().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Ao.URL_SCHEME)?hF(e.slice(Ao.URL_SCHEME.length)):null;jt.registerSaveRouter(rk);jt.registerLoadRouter(rk);function hF(e){return new Ao(e)}function pF(e){return e.startsWith(Ao.URL_SCHEME)?e.slice(Ao.URL_SCHEME.length):e}var fF=class{constructor(){this.indexedDB=nk()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(R2,D2);n.onupgradeneeded=()=>F2(n),n.onsuccess=()=>{let r=n.result,s=r.transaction(Ca,"readonly"),o=s.objectStore(Ca).getAll();o.onsuccess=()=>{let i={};for(let l of o.result)i[l.modelPath]=l.modelArtifactsInfo;e(i)},o.onerror=i=>(r.close(),t(o.error)),s.oncomplete=()=>r.close()},n.onerror=r=>t(n.error)})}async removeModel(e){return e=pF(e),new Promise((t,n)=>{let r=this.indexedDB.open(R2,D2);r.onupgradeneeded=()=>F2(r),r.onsuccess=()=>{let s=r.result,a=s.transaction(Ca,"readwrite"),o=a.objectStore(Ca),i=o.get(e),l;i.onsuccess=()=>{if(i.result==null)return s.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=o.delete(e),c=()=>{l=s.transaction(yo,"readwrite");let h=l.objectStore(yo).delete(e);h.onsuccess=()=>t(i.result.modelArtifactsInfo),h.onerror=p=>n(i.error)};u.onsuccess=c,u.onerror=d=>(c(),s.close(),n(i.error))}},i.onerror=u=>(s.close(),n(i.error)),a.oncomplete=()=>{l==null?s.close():l.oncomplete=()=>s.close()}},r.onerror=s=>n(r.error)})}},Zs="/",Wi="tensorflowjs_models",sk="info",mF="model_topology",gF="weight_specs",yF="weight_data",AF="model_metadata";function ak(e){return{info:[Wi,e,sk].join(Zs),topology:[Wi,e,mF].join(Zs),weightSpecs:[Wi,e,gF].join(Zs),weightData:[Wi,e,yF].join(Zs),modelMetadata:[Wi,e,AF].join(Zs)}}function xF(e){let t=e.split(Zs);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Zs)}function bF(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}var xo=class{constructor(e){if(!ct().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=ak(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=ac(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,nF(e.weightData));let s={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(s.signature=e.signature),e.userDefinedMetadata!=null&&(s.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(s.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(s)),{modelArtifactsInfo:r}}catch(s){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${r.modelTopologyBytes}, weightSpecsBytes=${r.weightSpecsBytes}, weightDataBytes=${r.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${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 '${this.modelPath}' is missing.`);t.modelTopology=n;let r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(r==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;let s=this.LS.getItem(this.keys.modelMetadata);if(s!=null){let o=JSON.parse(s);t.format=o.format,t.generatedBy=o.generatedBy,t.convertedBy=o.convertedBy,o.signature!=null&&(t.signature=o.signature),o.userDefinedMetadata!=null&&(t.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(t.modelInitializer=o.modelInitializer)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=rF(a),t}};xo.URL_SCHEME="localstorage://";var ok=e=>ct().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME)?vF(e.slice(xo.URL_SCHEME.length)):null;jt.registerSaveRouter(ok);jt.registerLoadRouter(ok);function vF(e){return new xo(e)}var wF=class{constructor(){L(ct().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),L(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Wi+Zs,n=Zs+sk;for(let r=0;r<this.LS.length;++r){let s=this.LS.key(r);if(s.startsWith(t)&&s.endsWith(n)){let a=xF(s);e[a]=JSON.parse(this.LS.getItem(s))}}return e}async removeModel(e){e=bF(e);let t=ak(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}},Vi="://",Tr=class{constructor(){this.managers={}}static getInstance(){return Tr.instance==null&&(Tr.instance=new Tr),Tr.instance}static registerManager(e,t){L(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Vi)&&(e=e.slice(0,e.indexOf(Vi))),L(e.length>0,()=>"scheme must not be an empty string.");let n=Tr.getInstance();L(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 hp(e){if(e.indexOf(Vi)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Tr.getSchemes().join(",")}`);return{scheme:e.split(Vi)[0],path:e.split(Vi)[1]}}async function ik(e,t,n=!1){L(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=jt.getLoadHandlers(e);L(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),L(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let s=r[0],a=jt.getSaveHandlers(t);L(a.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),L(a.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let o=a[0],i=hp(e).scheme,l=hp(e).path,u=i===hp(e).scheme,c=await s.load();n&&u&&await Tr.getManager(i).removeModel(l);let d=await o.save(c);return n&&!u&&await Tr.getManager(i).removeModel(l),d.modelArtifactsInfo}async function kF(){let e=Tr.getSchemes(),t={};for(let n of e){let r=await Tr.getManager(n).listModels();for(let s in r){let a=n+Vi+s;t[a]=r[s]}}return t}async function IF(e){let t=hp(e);return Tr.getManager(t.scheme).removeModel(t.path)}async function SF(e,t){return ik(e,t,!1)}async function TF(e,t){return ik(e,t,!0)}var NF=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(ct().get("IS_BROWSER")){ct().setPlatform("browser",new NF);try{Tr.registerManager(xo.URL_SCHEME,new wF)}catch(e){}try{Tr.registerManager(Ao.URL_SCHEME,new fF)}catch(e){}}var CF={importFetch:()=>z3()},M2,EF=class{constructor(){this.util=co("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return ct().global.fetch!=null?ct().global.fetch(e,t):(M2==null&&(M2=CF.importFetch()),M2(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)}};ct().get("IS_NODE")&&ct().setPlatform("node",new EF);function Ys(e,t="float32",n){return t=t||"float32",s2(e),new up(e,t,n)}function $F(e,t){let n=O(e,"x","cast");if(!q3(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let r={x:n},s={dtype:t};return U.runKernel(u2,r,s)}var Pt=H({cast_:$F});function _F(e){let n={x:O(e,"x","clone","string_or_numeric")};return U.runKernel(c2,n)}var Js=H({clone_:_F});function lk(e,t=!1){console.log(e.toString(t))}q7();var RF={buffer:Ys,cast:Pt,clone:Js,print:lk};VD(RF);var uk={};De(uk,{browserFiles:()=>LF,browserHTTPRequest:()=>HF,concatenateArrayBuffers:()=>_2,copyModel:()=>SF,decodeWeights:()=>Q7,encodeWeights:()=>eF,fromMemory:()=>jF,getLoadHandlers:()=>dF,getModelArtifactsInfoForJSON:()=>ac,getSaveHandlers:()=>cF,http:()=>L2,isHTTPScheme:()=>z2,listModels:()=>kF,loadWeights:()=>BF,moveModel:()=>TF,registerLoadRouter:()=>uF,registerSaveRouter:()=>lF,removeModel:()=>IF,weightsLoaderFactory:()=>pk,withSaveHandler:()=>qF});var DF="model",FF=".json",MF=".weights.bin";function ck(e){return new Promise(t=>setTimeout(t)).then(e)}var O2=class{constructor(e){if(!ct().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(O2.URL_SCHEME)&&(e=e.slice(O2.URL_SCHEME.length)),(e==null||e.length===0)&&(e=DF),this.modelTopologyFileName=e+FF,this.weightDataFileName=e+MF}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}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer);let s=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),a=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(a.download=this.modelTopologyFileName,a.href=s,await ck(()=>a.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let o=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;o.download=this.weightDataFileName,o.href=t,await ck(()=>o.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:ac(e)}}}},pp=O2;pp.URL_SCHEME="downloads://";var OF=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.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,r)=>{let s=new FileReader;s.onload=a=>{let o=JSON.parse(a.target.result),i=o.modelTopology;if(i==null){r(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:i});let l=o.weightsManifest;if(l==null){r(new Error(`weightManifest field is missing from file ${e.name}`));return}let u;try{u=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let c=[],d=[],h=[];l.forEach(p=>{p.paths.forEach(f=>{d.push(f),h.push(null)}),c.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(f=>{let m=new FileReader;m.onload=g=>{let y=g.target.result,A=d.indexOf(f);if(h[A]=y,h.indexOf(null)===-1){let x={modelTopology:i,weightSpecs:c,weightData:_2(h),format:o.format,generatedBy:o.generatedBy,convertedBy:o.convertedBy};o.signature!=null&&(x.signature=o.signature),o.userDefinedMetadata!=null&&(x.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(x.modelInitializer=o.modelInitializer),n(x)}},m.onerror=g=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(u[f])})})},s.onerror=a=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),s.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(a=>tk(a.name)),s={};for(let a of e)a.paths.forEach(o=>{let i=tk(o);if(n.indexOf(i)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);if(n.push(i),r.indexOf(i)===-1)throw new Error(`Weight file with basename '${i}' is not provided.`);s[o]=t[r.indexOf(i)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return s}},PF=e=>ct().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(pp.URL_SCHEME)?zF(e.slice(pp.URL_SCHEME.length)):null;jt.registerSaveRouter(PF);function zF(e="model"){return new pp(e)}function LF(e){return new OF(e)}function dk(e,t,n,r){o(e),n=n==null?0:n,r=r==null?1:r,i(n,r);let s=0,a=l=>(l.then(u=>{let c=n+ ++s/e.length*(r-n);return t(c),u}),l);function o(l){L(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function i(l,u){L(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),L(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),L(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(a))}async function hk(e,t){t==null&&(t={});let n=t.fetchFunc==null?ct().platform.fetch:t.fetchFunc,r=e.map(d=>n(d,t.requestInit,{isBinary:!0})),s=0,a=.5,i=(t.onProgress==null?await Promise.all(r):await dk(r,t.onProgress,s,a)).map(d=>d.arrayBuffer()),l=.5,u=1;return t.onProgress==null?await Promise.all(i):await dk(i,t.onProgress,l,u)}async function BF(e,t="",n,r){return pk(o=>hk(o,{requestInit:r}))(e,t,n)}function pk(e){return async(t,n="",r)=>{let s=t.map(()=>!1),a={},o=r!=null?r.map(()=>!1):[],i=[];if(t.forEach((p,f)=>{let m=0;p.weights.forEach(g=>{let y="quantization"in g?g.quantization.dtype:g.dtype,A=E2[y]*Jt(g.shape),x=()=>{s[f]=!0,a[f]==null&&(a[f]=[]),a[f].push({manifestEntry:g,groupOffset:m,sizeBytes:A})};r!=null?r.forEach((b,v)=>{b===g.name&&(x(),o[v]=!0)}):x(),i.push(g.name),m+=A})}),!o.every(p=>p)){let p=r.filter((f,m)=>!o[m]);throw new Error(`Could not find weights in manifest with names: ${p.join(", ")}. 
Manifest JSON has weights with names: ${i.join(", ")}.`)}let l=s.reduce((p,f,m)=>(f&&p.push(m),p),[]),u=[];l.forEach(p=>{t[p].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;u.push(m)})});let c=await e(u),d={},h=0;return l.forEach(p=>{let f=t[p].paths.length,m=0;for(let b=0;b<f;b++)m+=c[h+b].byteLength;let g=new ArrayBuffer(m),y=new Uint8Array(g),A=0;for(let b=0;b<f;b++){let v=new Uint8Array(c[h+b]);y.set(v,A),A+=v.byteLength}a[p].forEach(b=>{let v=g.slice(b.groupOffset,b.groupOffset+b.sizeBytes),w=Q7(v,[b.manifestEntry]);for(let S in w)d[S]=w[S]}),h+=f}),d}}var WF="application/octet-stream",VF="application/json",P2=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?(L(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=ct().platform.fetch,L(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&L(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}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:VF}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:WF}),"model.weights.bin");let s=await this.fetch(this.path,t);if(s.ok)return{modelArtifactsInfo:ac(e),responses:[s]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.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(p){let f=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?f+=" 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.":f+=" Please make sure the server is serving valid JSON for this request.",new Error(f)}let n=t.modelTopology,r=t.weightsManifest,s=t.generatedBy,a=t.convertedBy,o=t.format,i=t.signature,l=t.userDefinedMetadata;if(n==null&&r==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let u,c;r!=null&&([u,c]=await this.loadWeights(r));let d={modelTopology:n,weightSpecs:u,weightData:c,generatedBy:s,convertedBy:a,format:o};i!=null&&(d.signature=i),l!=null&&(d.userDefinedMetadata=l);let h=t.modelInitializer;return h&&(d.modelInitializer=h),d}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=UF(t),s=this.weightPathPrefix||n,a=[];for(let u of e)a.push(...u.weights);let o=[],i=[];for(let u of e)for(let c of u.paths)this.weightUrlConverter!=null?i.push(this.weightUrlConverter(c)):o.push(s+c+r);this.weightUrlConverter&&o.push(...await Promise.all(i));let l=await hk(o,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,_2(l)]}};P2.URL_SCHEME_REGEX=/^https?:\/\//;function UF(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),s=n>t?e.substring(n):"";return[r+"/",s]}function z2(e){return e.match(P2.URL_SCHEME_REGEX)!=null}var fk=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>z2(r)):n=z2(e),n)return L2(e,t)}return null};jt.registerSaveRouter(fk);jt.registerLoadRouter(fk);function L2(e,t){return new P2(e,t)}function HF(e,t){return L2(e,t)}var B2=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},GF=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function jF(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new B2(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 B2({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 B2({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function qF(e){return new GF(e)}var mk={};De(mk,{confusionMatrix:()=>JF});function KF(e,t,n=!1,r=!1){let s=O(e,"a","matMul"),a=O(t,"b","matMul");[s,a]=Vt(s,a);let o={a:s,b:a},i={transposeA:n,transposeB:r};return U.runKernel(gv,o,i)}var yt=H({matMul_:KF});function XF(e,t,n=1,r=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:O(e,"indices","oneHot","int32")},o={depth:t,onValue:n,offValue:r};return U.runKernel(Pw,a,o)}var W2=H({oneHot_:XF});function ZF(e,t){let n=O(e,"x","transpose");if(t==null&&(t=n.shape.map((a,o)=>o).reverse()),L(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(a=>{L(a>=0&&a<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let r={x:n},s={perm:t};return U.runKernel(E7,r,s)}var fp=H({transpose_:ZF});function YF(e,t,n){let r=O(e,"labels","confusionMatrix"),s=O(t,"predictions","confusionMatrix");L(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),L(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),L(s.rank===1,()=>`Expected the rank of predictions to be 1, but got ${s.rank}`),L(r.shape[0]===s.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${s.shape[0]}. Labels and predictions should have the same number of elements.`),L(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let a=W2(Pt(r,"int32"),n),o=W2(Pt(s,"int32"),n),i=fp(a),l=yt(i,o);return Pt(l,"int32")}var JF=H({confusionMatrix_:YF}),Hr={};De(Hr,{fromPixels:()=>aM,fromPixelsAsync:()=>rM,toPixels:()=>sM});function mp(e,t,n){if(ho(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=Ss(e,n);if(r.length!==3&&r.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Na(e,t,r,n)}var Ui;function gk(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,r=!1,s=!1,a=!1,o=!1,i=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)s=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)a=!0;else if(e.getContext!=null)o=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)i=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(s){let f=2;if(s&&e.readyState<f)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(rp(h2,U.backendName)!=null){let f={pixels:e},m={numChannels:t};return U.runKernel(h2,f,m)}let[u,c]=s?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;o?d=e.getContext("2d").getImageData(0,0,u,c).data:r||n?d=e.data:(a||s||i)&&(Ui==null&&(Ui=document.createElement("canvas").getContext("2d")),Ui.canvas.width=u,Ui.canvas.height=c,Ui.drawImage(e,0,0,u,c),d=Ui.getImageData(0,0,u,c).data);let h;if(t===4)h=new Int32Array(d);else{let f=u*c;h=new Int32Array(f*t);for(let m=0;m<f;m++)for(let g=0;g<t;++g)h[m*t+g]=d[m*4+g]}return mp(h,[c,u,t],"int32")}function QF(e){return e!=null&&e.data instanceof Uint8Array}function eM(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function tM(e){return e!=null&&e.width!==0&&e.height!==0}function nM(e){return eM()&&!(e instanceof ImageBitmap)&&tM(e)&&!QF(e)}async function rM(e,t=3){let n=null;if(ct().getBool("WRAP_TO_IMAGEBITMAP")&&nM(e)){let r;try{r=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(s){r=null}r!=null&&r.width===e.width&&r.height===e.height?n=r:n=e}else n=e;return gk(n,t)}async function sM(e,t){let n=O(e,"img","toPixels");if(!(e instanceof Tt)){let u=n;n=Pt(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[r,s]=n.shape.slice(0,2),a=n.rank===2?1:n.shape[2];if(a>4||a===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let o=await n.data(),i=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(s*r*4);for(let u=0;u<r*s;++u){let c=[0,0,0,255];for(let h=0;h<a;h++){let p=o[u*a+h];if(n.dtype==="float32"){if(p<0||p>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${p}.`)}else if(n.dtype==="int32"&&(p<0||p>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${p}.`);a===1?(c[0]=p*i,c[1]=p*i,c[2]=p*i):c[h]=p*i}let d=u*4;l[d+0]=Math.round(c[0]),l[d+1]=Math.round(c[1]),l[d+2]=Math.round(c[2]),l[d+3]=Math.round(c[3])}if(t!=null){t.width=s,t.height=r;let u=t.getContext("2d"),c=new ImageData(l,s,r);u.putImageData(c,0,0)}return n!==e&&n.dispose(),l}var aM=H({fromPixels_:gk}),yk={};De(yk,{prepareAndValidate:()=>Ak});function Ak(e,t){let n=e.shape.length,r=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(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);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[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(Jt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let s=t.shape,a=s[s.length-1],o=1;for(let d=0;d<s.length-1;++d)o*=s[d];let i=e.shape,l=s.slice();l.pop();let u=1;for(let d=a;d<n;++d)u*=i[d],l.push(i[d]);let c=[...Oi(e.shape).map(d=>d/u),1].slice(0,a);return[l,o,u,c]}var xk={};De(xk,{calculateShapes:()=>bk,validateInput:()=>U2,validateUpdateShape:()=>V2});function V2(e,t,n){let r=t.rank>1?t.shape[t.rank-1]:1,s=t.rank>1?t.rank-1:1,a=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${r}, and batchDim: ${s}.`;if(n.rank<s)throw new Error(a+` update.rank < ${s}. `);if(e.length<r+(n.rank-s))throw new Error(a+` Output shape length < ${r+(n.rank-s)}`);if(n.rank!==s+e.length-r)throw new Error(a+` update.rank != ${s+e.length-r}`);for(let o=0;o<s;++o)if(n.shape[o]!==t.shape[o])throw new Error(a+` updates.shape[${o}] (${n.shape[o]}) != indices.shape[${o}] (${t.shape[o]}).`);for(let o=0;o<n.rank-s;++o)if(n.shape[o+s]!==e[o+r])throw new Error(a+` updates.shape[${o+s}] (${n.shape[o+s]}) != shape[${o+s}] (${e[o+s]})`)}function U2(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: ${e.shape}`)}V2(n,t,e)}function bk(e,t,n){let r=t.shape.length,s=r>1?t.shape[r-1]:1,a=n.length,o=1;for(let d=s;d<a;++d)o*=n[d];let i=s<1?1:s,l=Jt(t.shape)/i,u=[...Oi(n.slice(0,s)),1],c=Jt(n);return{sliceRank:s,numUpdates:l,sliceSize:o,strides:u,outputSize:c}}var H2={};De(H2,{assertParamsValid:()=>oM,computeFlatOffset:()=>lM,computeOutShape:()=>vk,getNormalizedAxes:()=>Sk,isSliceContinous:()=>iM,maskToAxes:()=>gp,parseSliceParams:()=>uM,sliceInfo:()=>cM,startForAxis:()=>Ek,startIndicesWithElidedDims:()=>Tk,stopForAxis:()=>$k,stopIndicesWithElidedDims:()=>Nk,stridesForAxis:()=>Ck,stridesWithElidedDims:()=>wk});function oM(e,t,n){let r=e.shape.length;L(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),L(r===n.length,()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`);for(let s=0;s<r;++s)L(t[s]+n[s]<=e.shape[s],()=>`Error in slice${r}D: begin[${s}] + size[${s}] (${t[s]+n[s]}) would overflow input.shape[${s}] (${e.shape[s]})`)}function gp(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function vk(e,t,n){let r=[];for(let s=0;s<e.length;s++)r[s]=Math.ceil((t[s]-e[s])/n[s]);return r}function wk(e,t,n,r){let s=[...e];for(let a=s.length;a<r.length;a++)s.push(1);for(let a=0;a<n;a++)a===0?s[t]=1:(s.splice(t,0,1),s.pop());return s}function kk(e,t,n){return n<=e?n:n-(t-1)}function Ik(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function Sk(e,t,n,r,s,a,o,i,l){let u=e.length,c=new Array(u),d=new Array(u),h=new Array(u);if(t.length&&n>0){let p=t[0],f=n+1;c=Tk(o,p,f,r,e),d=Nk(i,p,f,s,e),h=wk(a,p,f,e)}else for(let p=0;p<u;p++)c[p]=Ek(o,r,a,e,p,l),d[p]=$k(i,s,a,e,p,l),h[p]=Ck(a,p,l);return{begin:c,end:d,strides:h}}function Tk(e,t,n,r,s){let a=[...s],o=Ik(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=0;else{let l=kk(t,n,i),u=r[l];e&1<<l&&(u=0),a[i]=u}return a}function Nk(e,t,n,r,s){let a=[...s],o=Ik(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=Number.MAX_SAFE_INTEGER;else{let l=kk(t,n,i),u=r[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),a[i]=u}for(let i=0;i<a.length;i++){let l=s[i];a[i]<0&&(a[i]+=l),a[i]=qu(0,a[i],s[i])}return a}function Ck(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function Ek(e,t,n,r,s,a){let o=t[s],i=n[s]||1;(e&1<<s||a&1<<s||o==null)&&(i>0?o=Number.MIN_SAFE_INTEGER:o=Number.MAX_SAFE_INTEGER);let l=r[s];return o<0&&(o+=l),o=qu(0,o,l-1),o}function $k(e,t,n,r,s,a){let o=t[s],i=n[s]||1;(e&1<<s||a&1<<s||o==null)&&(i>0?o=Number.MAX_SAFE_INTEGER:o=Number.MIN_SAFE_INTEGER);let l=r[s];return o<0&&(o+=l),i>0?o=qu(0,o,l):o=qu(-1,o,l-1),o}function iM(e,t,n){let r=n.length;for(let s=0;s<n.length;s++)if(n[s]>1){r=s;break}for(let s=r+1;s<n.length;s++)if(t[s]>0||n[s]!==e[s])return!1;return!0}function lM(e,t){let n=e.length>0?e[e.length-1]:1;for(let r=0;r<e.length-1;r++)n+=e[r]*t[r];return n}function uM(e,t,n){let r,s=e.shape.length;typeof t=="number"?r=[t,...new Array(s-1).fill(0)]:t.length<s?r=t.concat(new Array(s-t.length).fill(0)):r=t.slice(),r.forEach(o=>{L(o!==-1,()=>"slice() does not support negative begin indexing.")});let a;return n==null?a=new Array(s).fill(-1):typeof n=="number"?a=[n,...new Array(s-1).fill(-1)]:n.length<s?a=n.concat(new Array(s-n.length).fill(-1)):a=n,a=a.map((o,i)=>o>=0?o:(L(o===-1,()=>`Negative size values should be exactly -1 but got ${o} for the slice() size at index ${i}.`),e.shape[i]-r[i])),[r,a]}function cM(e,t,n,r,s,a,o,i,l){let u=t.slice(),c=n.slice(),d=r;r==null&&(d=new Array(u.length));let h=gp(o);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(o!==0&&i!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(o!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-u.length,f=gp(i),m=e.slice();f.forEach(S=>{u[S]=0,c[S]=1,m.splice(S,0,1)});let{begin:g,end:y,strides:A}=Sk(m,h,p,u,c,d,s,a,o);u=g,c=y,d=A;let x=gp(l);x.forEach(S=>{c[S]=u[S]+1,d[S]=1});let b=vk(u,c,d),v=b.filter((S,I)=>x.indexOf(I)===-1);return{nonStrided:d.every(S=>S===1),$begin:u,$end:c,$strides:d,size:b,newShape:m,outShape:v}}var _k={};De(_k,{Serializable:()=>Rk,SerializationMap:()=>bo,registerClass:()=>Ea});var Rk=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},bo=class{constructor(){this.classNameMap={}}static getMap(){return bo.instance==null&&(bo.instance=new bo),bo.instance}static register(e){bo.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Ea(e){L(e.className!=null,()=>"Class being registered does not have the static className property defined."),L(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),L(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),bo.register(e)}var Dk={};De(Dk,{TEST_EPSILON_FLOAT16:()=>Fk,encodeStrings:()=>Mk,expectArrayBuffersEqual:()=>yM,expectArraysClose:()=>hM,expectArraysEqual:()=>fM,expectNumbersClose:()=>mM,expectPromiseToFail:()=>pM,expectValuesInRange:()=>gM,testEpsilon:()=>G2});var dM=.001,Fk=.1;function hM(e,t,n){return n==null&&(n=G2()),j2(e,t,(r,s)=>q2(r,s,n))}function G2(){return U.backend.floatPrecision()===32?dM:Fk}function j2(e,t,n){let r=!0;if((Cn(e)||Cn(t))&&(r=!1),Cn(e)&&Cn(t)&&(r=!0),r){let o=e.constructor.name,i=t.constructor.name;if(o!==i)throw new Error(`Arrays are of different type. Actual: ${o}. Expected: ${i}`)}if(Array.isArray(e)&&Array.isArray(t)){let o=Ss(e),i=Ss(t);if(!Xs(o,i))throw new Error(`Arrays have different shapes. Actual: [${o}]. Expected: [${i}]`)}let s=Cn(e)?e:po(e),a=Cn(t)?t:po(t);if(s.length!==a.length)throw new Error(`Arrays have different lengths actual: ${s.length} vs expected: ${a.length}.
Actual:   ${s}.
Expected: ${a}.`);for(let o=0;o<a.length;++o){let i=s[o],l=a[o];if(!n(i,l))throw new Error(`Arrays differ: actual[${o}] = ${i}, expected[${o}] = ${l}.
Actual:   ${s}.
Expected: ${a}.`)}}function pM(e,t){e().then(()=>t.fail(),()=>t())}function fM(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Ia(e)||Ia(e[0])||Ia(t)||Ia(t[0])?j2(e,n,(r,s)=>r==s):j2(e,t,(r,s)=>q2(r,s,0))}function mM(e,t,n){if(n==null&&(n=G2()),!q2(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function q2(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function gM(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function yM(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function Mk(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?Mk(n):e[t]=Qu(n)}return e}var AM="3.7.0";function xM(){ct().set("PROD",!0)}function bM(){ct().set("DEBUG",!0)}function vM(){ct().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Ok(e){ct().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}UD(Ok);function wM(){U.disposeVariables()}function kM(){return U}function IM(){return U.memory()}function SM(e){return U.profile(e)}function Ue(e,t){return U.tidy(e,t)}function Ve(e){S2(e).forEach(n=>n.dispose())}function Pk(e){return U.keep(e)}function TM(e){return U.time(e)}function NM(e){return U.setBackend(e)}function CM(){return U.ready()}function EM(){return U.backendName}function $M(e){U.removeBackend(e)}function K2(e){return U.findBackend(e)}function _M(e){return U.findBackendFactory(e)}function X2(e,t,n=1){return U.registerBackend(e,t,n)}function RM(){return U.backend}function DM(e,t){ct().setPlatform(e,t)}function FM(e,t){let n=O(e,"a","add"),r=O(t,"b","add");[n,r]=Vt(n,r);let s={a:n,b:r};return U.runKernel(l2,s)}var Me=H({add_:FM});function MM(e,t){let n=O(e,"a","floorDiv"),r=O(t,"b","floorDiv");[n,r]=Vt(n,r);let s={a:n,b:r};return U.runKernel(Jv,s)}var zk=H({floorDiv_:MM});function OM(e,t){let n=O(e,"a","div"),r=O(t,"b","div");if([n,r]=Vt(n,r),n.dtype==="int32"&&r.dtype==="int32")return zk(n,r);let s={a:n,b:r},a={};return U.runKernel(Bv,s,a)}var Qe=H({div_:OM});function PM(e,t){let n=O(e,"a","mul"),r=O(t,"b","mul");[n,r]=Vt(n,r);let s={a:n,b:r};return U.runKernel($w,s)}var fe=H({mul_:PM});function zM(e){let t=O(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return U.runKernel(wv,n)}else{let n={x:t};return U.runKernel(tv,n)}}var Nr=H({abs_:zM});function LM(e){let n={x:O(e,"x","acos")};return U.runKernel(nv,n)}var BM=H({acos_:LM});function WM(e){let n={x:O(e,"x","acosh")};return U.runKernel(rv,n)}var VM=H({acosh_:WM});function UM(e){L(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),L(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((s,a)=>O(s,`tensors${a}`,"addN")),n=t[0];t.forEach(s=>{if(s.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(s=>{if(!Xs(s.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return U.runKernel(sv,r)}var Z2=H({addN_:UM});function HM(e,t=null,n=!1){let s={x:O(e,"x","all","bool")},a={axis:t,keepDims:n};return U.runKernel(av,s,a)}var GM=H({all_:HM});function jM(e,t=null,n=!1){let s={x:O(e,"x","any","bool")},a={axis:t,keepDims:n};return U.runKernel(ov,s,a)}var qM=H({any_:jM});function KM(e,t=0){let r={x:O(e,"x","argMax")},s={axis:t};return U.runKernel(iv,r,s)}var Y2=H({argMax_:KM});function XM(e,t=0){let r={x:O(e,"x","argMin")},s={axis:t};return U.runKernel(lv,r,s)}var ZM=H({argMin_:XM});function YM(e){let n={x:O(e,"x","asin")};return U.runKernel(uv,n)}var JM=H({asin_:YM});function QM(e){let n={x:O(e,"x","asinh")};return U.runKernel(cv,n)}var eO=H({asinh_:QM});function tO(e){let n={x:O(e,"x","atan")};return U.runKernel(dv,n)}var nO=H({atan_:tO});function rO(e,t){let n=O(e,"a","atan2"),r=O(t,"b","atan2");[n,r]=Vt(n,r);let s={a:n,b:r};return U.runKernel(pv,s)}var sO=H({atan2_:rO});function aO(e){let n={x:O(e,"x","atanh")};return U.runKernel(hv,n)}var oO=H({atanh_:aO});function iO(e,t,n,r,s="NHWC",a){let o=e[3],i=[...t,o],l=Wk(s);return oc(e,i,n,a,r,null,null,l)}function Lk(e,t,n,r,s,a,o="channelsLast"){let[i,l]=yp(t),u;if(o==="channelsLast")u=[i,l,e[3],e[3]];else if(o==="channelsFirst")u=[i,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return oc(e,u,n,r,s,a,!1,o)}function lO(e,t,n,r,s,a,o="NDHWC"){let[i,l,u]=Q2(t),c,d;if(o==="NDHWC")d="channelsLast",c=[i,l,u,e[4],e[4]];else if(o==="NCDHW")d="channelsFirst",c=[i,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return Bk(e,c,n,r,s,!1,d,a)}function oc(e,t,n,r,s,a,o=!1,i="channelsLast"){let[l,u,c,d]=[-1,-1,-1,-1];if(i==="channelsLast")[l,u,c,d]=e;else if(i==="channelsFirst")[l,d,u,c]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,p,,f]=t,[m,g]=yp(n),[y,A]=yp(r),x=Hi(h,y),b=Hi(p,A),{padInfo:v,outHeight:w,outWidth:S}=dO(s,u,c,m,g,x,b,a,i),I=o?f*d:f,E;return i==="channelsFirst"?E=[l,I,w,S]:i==="channelsLast"&&(E=[l,w,S,I]),{batchSize:l,dataFormat:i,inHeight:u,inWidth:c,inChannels:d,outHeight:w,outWidth:S,outChannels:I,padInfo:v,strideHeight:m,strideWidth:g,filterHeight:h,filterWidth:p,effectiveFilterHeight:x,effectiveFilterWidth:b,dilationHeight:y,dilationWidth:A,inShape:e,outShape:E,filterShape:t}}function Bk(e,t,n,r,s,a=!1,o="channelsLast",i){let[l,u,c,d,h]=[-1,-1,-1,-1,-1];if(o==="channelsLast")[l,u,c,d,h]=e;else if(o==="channelsFirst")[l,h,u,c,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[p,f,m,,g]=t,[y,A,x]=Q2(n),[b,v,w]=Q2(r),S=Hi(p,b),I=Hi(f,v),E=Hi(m,w),{padInfo:F,outDepth:$,outHeight:_,outWidth:N}=hO(s,u,c,d,y,A,x,S,I,E,i),P=a?g*h:g,B;return o==="channelsFirst"?B=[l,P,$,_,N]:o==="channelsLast"&&(B=[l,$,_,N,P]),{batchSize:l,dataFormat:o,inDepth:u,inHeight:c,inWidth:d,inChannels:h,outDepth:$,outHeight:_,outWidth:N,outChannels:P,padInfo:F,strideDepth:y,strideHeight:A,strideWidth:x,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:S,effectiveFilterHeight:I,effectiveFilterWidth:E,dilationDepth:b,dilationHeight:v,dilationWidth:w,inShape:e,outShape:B,filterShape:t}}function uO(e,t,n,r,s){r==null&&(r=J2(e,t,n));let a=e[0],o=e[1],i=vo((a-t+2*r)/n+1,s),l=vo((o-t+2*r)/n+1,s);return[i,l]}function cO(e,t,n,r,s,a){s==null&&(s=J2(e,t,r));let o=e[0],i=e[1],l=e[2],u=vo((o-t+2*s)/r+1,a),c=vo((i-t+2*s)/r+1,a),d=vo((l-t+2*s)/r+1,a);return[u,c,d,n]}function J2(e,t,n,r=1){let s=Hi(t,r);return Math.floor((e[0]*(n-1)-n+s)/2)}function yp(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Q2(e){return typeof e=="number"?[e,e,e]:e}function Hi(e,t){return t<=1?e:e+(e-1)*(t-1)}function dO(e,t,n,r,s,a,o,i,l){let u,c,d;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let p=uO([t,n],a,r,e,i);c=p[0],d=p[1]}else if(e==="same"){c=Math.ceil(t/r),d=Math.ceil(n/s);let h=Math.max(0,(c-1)*r+a-t),p=Math.max(0,(d-1)*s+o-n),f=Math.floor(h/2),m=h-f,g=Math.floor(p/2),y=p-g;u={top:f,bottom:m,left:g,right:y,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-a+1)/r),d=Math.ceil((n-o+1)/s);else if(typeof e=="object"){let h=l==="channelsLast"?e[1][0]:e[2][0],p=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];u={top:h,bottom:p,left:f,right:m,type:h===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},c=vo((t-a+h+p)/r+1,i),d=vo((n-o+f+m)/s+1,i)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:c,outWidth:d}}function hO(e,t,n,r,s,a,o,i,l,u,c){let d,h,p,f;if(typeof e=="number"){d={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let g=cO([t,n,r,1],i,1,s,e,c);h=g[0],p=g[1],f=g[2]}else if(e==="same"){h=Math.ceil(t/s),p=Math.ceil(n/a),f=Math.ceil(r/o);let m=(h-1)*s+i-t,g=(p-1)*a+l-n,y=(f-1)*o+u-r,A=Math.floor(m/2),x=m-A,b=Math.floor(g/2),v=g-b,w=Math.floor(y/2),S=y-w;d={top:b,bottom:v,left:w,right:S,front:A,back:x,type:"SAME"}}else if(e==="valid")d={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},h=Math.ceil((t-i+1)/s),p=Math.ceil((n-l+1)/a),f=Math.ceil((r-u+1)/o);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:d,outDepth:h,outHeight:p,outWidth:f}}function vo(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 ic(e){let[t,n,r]=yp(e);return t===1&&n===1&&r===1}function Qs(e,t){return ic(e)||ic(t)}function Wk(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function pO(e,t){let r={x:O(e,"x","reshape","string_or_numeric")},s={shape:t};return U.runKernel(qw,r,s)}var ue=H({reshape_:pO});function fO(e,t,n,r,s){let a=O(e,"x","avgPool","float32"),o=1;L(Qs(n,o),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`);let i=a,l=!1;a.rank===3&&(l=!0,i=ue(a,[1,a.shape[0],a.shape[1],a.shape[2]])),L(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),s!=null&&L(Xn(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s},d=U.runKernel(fv,u,c);return d=Pt(d,a.dtype),l?ue(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Vk=H({avgPool_:fO});function mO(e,t,n,r,s,a="NDHWC"){let o=O(e,"x","avgPool3d","float32"),i=o,l=!1;o.rank===4&&(l=!0,i=ue(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),L(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),L(a==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),s!=null&&L(Xn(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:a},d=U.runKernel(mv,u,c);return d=Pt(d,i.dtype),l?ue(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var gO=H({avgPool3d_:mO});function yO(e,t=0){L(e.length>=1,()=>"Pass at least one tensor to concat");let n=sc(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(a=>{if(a.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${a.dtype}. `)}),n.length===1)return Js(n[0]);let r=n,s={axis:t};return U.runKernel(kv,r,s)}var an=H({concat_:yO});function AO(e){let n={x:O(e,"x","sigmoid")};return U.runKernel(i7,n)}var Ts=H({sigmoid_:AO});function xO(e,t,n){let r=O(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let s={x:r},a={begin:t,size:n};return U.runKernel(r7,s,a)}var Ze=H({slice_:xO});function bO(e){let n={x:O(e,"x","tanh")};return U.runKernel(T7,n)}var ey=H({tanh_:bO});function vO(e,t,n,r,s,a){let o=O(e,"forgetBias","basicLSTMCell"),i=O(t,"lstmKernel","basicLSTMCell"),l=O(n,"lstmBias","basicLSTMCell"),u=O(r,"data","basicLSTMCell"),c=O(s,"c","basicLSTMCell"),d=O(a,"h","basicLSTMCell"),h=an([u,d],1),p=yt(h,i),f=Me(p,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],A=Ze(f,[0,0],y),x=Ze(f,[0,g],y),b=Ze(f,[0,g*2],y),v=Ze(f,[0,g*3],y),w=Me(fe(Ts(A),ey(x)),fe(c,Ts(Me(o,b)))),S=fe(ey(w),Ts(v));return[w,S]}var wO=H({basicLSTMCell_:vO});function kO(e,t,n){let r=O(e,"x","batchToSpaceND"),s=t.reduce((i,l)=>i*l);L(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),L(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),L(r.shape[0]%s==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${s}`);let a={x:r},o={blockShape:t,crops:n};return U.runKernel(yv,a,o)}var Uk=H({batchToSpaceND_:kO});function IO(e){let t;return e.rank===0||e.rank===1?t=ue(e,[1,1,1,e.size]):e.rank===2?t=ue(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=ue(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function SO(e,t,n,r,s,a){a==null&&(a=.001);let o=O(e,"x","batchNorm"),i=O(t,"mean","batchNorm"),l=O(n,"variance","batchNorm"),u;s!=null&&(u=O(s,"scale","batchNorm"));let c;r!=null&&(c=O(r,"offset","batchNorm")),L(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),L(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),L(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:IO(o),scale:u,offset:c,mean:i,variance:l},p={varianceEpsilon:a},f=U.runKernel(Qv,h,p);return ue(f,o.shape)}var Ap=H({batchNorm_:SO});function TO(e,t,n,r,s,a){let o=O(e,"x","batchNorm"),i=O(t,"mean","batchNorm"),l=O(n,"variance","batchNorm"),u;s!=null&&(u=O(s,"scale","batchNorm"));let c;return r!=null&&(c=O(r,"offset","batchNorm")),L(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),L(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),L(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&L(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&L(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Ap(o,i,l,c,u,a)}var NO=H({batchNorm2d_:TO});function CO(e,t,n,r,s,a){let o=O(e,"x","batchNorm"),i=O(t,"mean","batchNorm"),l=O(n,"variance","batchNorm"),u;s!=null&&(u=O(s,"scale","batchNorm"));let c;return r!=null&&(c=O(r,"offset","batchNorm")),L(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),L(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),L(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&L(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&L(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Ap(o,i,l,c,u,a)}var EO=H({batchNorm3d_:CO});function $O(e,t,n,r,s,a){let o=O(e,"x","batchNorm"),i=O(t,"mean","batchNorm"),l=O(n,"variance","batchNorm"),u;s!=null&&(u=O(s,"scale","batchNorm"));let c;return r!=null&&(c=O(r,"offset","batchNorm")),L(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),L(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),L(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&L(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&L(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Ap(o,i,l,c,u,a)}var _O=H({batchNorm4d_:$O});function RO(e,t,n){let r=O(e,"x","bincount"),s=O(t,"weights","bincount");L(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),L(n>=0,()=>`size must be non-negative, but got ${n}.`),L(s.size===r.size||s.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${s.shape}.`);let a={x:r,weights:s},o={size:n};return U.runKernel(Av,a,o)}var Hk=H({bincount_:RO});function DO(e,t){let n=O(e,"broadcastTo","x"),r=n.shape;if(t.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let u=n.shape.slice();for(;u.length<t.length;)u.unshift(1);n=ue(n,u)}let s=n.shape,a=Array.from(t);for(let u=t.length-1;u>=0;u--)if(s[u]===t[u])a[u]=1;else if(n.shape[u]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(a.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Js(n);let i={x:n},l={reps:a};return U.runKernel(d2,i,l)}var xp=H({broadcastTo_:DO});function FO(e){let n={x:O(e,"x","ceil")};return U.runKernel(xv,n)}var MO=H({ceil_:FO});function OO(e,t,n){let r=O(e,"x","clipByValue");L(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let s={x:r},a={clipValueMin:t,clipValueMax:n};return U.runKernel(bv,s,a)}var PO=H({clipByValue_:OO});function zO(e){return an(e,0)}var LO=H({concat1d_:zO});function BO(e,t){return an(e,t)}var lc=H({concat2d_:BO});function WO(e,t){return an(e,t)}var VO=H({concat3d_:WO});function UO(e,t){return an(e,t)}var HO=H({concat4d_:UO});function GO(e,t,n,r,s="NHWC",a=[1,1],o){let i=O(e,"x","conv2d"),l=O(t,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=ue(i,[1,i.shape[0],i.shape[1],i.shape[2]])),L(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),L(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),o!=null&&L(Xn(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let d=s==="NHWC"?u.shape[3]:u.shape[1];L(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),L(Qs(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let h={x:u,filter:l},p={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o},f=U.runKernel(Iv,h,p);return c?ue(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var bp=H({conv2d_:GO});function jO(e,t,n,r,s="NWC",a=1,o){let i=O(e,"x","conv1d"),l=O(t,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=ue(i,[1,i.shape[0],i.shape[1]])),L(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),L(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),o!=null&&L(Xn(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`),L(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),L(Qs(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),L(s==="NWC",()=>`Error in conv1d: got dataFormat of ${s} but only NWC is currently supported.`);let d=ue(l,[1,l.shape[0],l.shape[1],l.shape[2]]),h=ue(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=bp(h,d,[1,n],r,"NHWC",[1,a],o);return c?ue(g,[g.shape[2],g.shape[3]]):ue(g,[g.shape[0],g.shape[2],g.shape[3]])}var qO=H({conv1d_:jO});function KO(e,t,n,r,s,a="NHWC",o){L(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,u=!1;t.rank===3&&(u=!0,l=ue(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),L(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),L(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),L(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];L(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),L(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),o!=null&&L(Xn(s),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let h={dy:l,filter:n},p={strides:r,pad:s,dataFormat:a,dimRoundingMode:o,inputShape:i},f=U.runKernel(Tv,h,p);return u?ue(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Gk=H({conv2DBackpropInput_:KO});function XO(e,t,n,r,s,a){let o=O(e,"x","conv2dTranspose"),i=O(t,"filter","conv2dTranspose");return Gk(n,o,i,r,s,"NHWC",a)}var ZO=H({conv2dTranspose_:XO});function YO(e,t,n,r,s="NDHWC",a=[1,1,1]){let o=O(e,"x","conv3d"),i=O(t,"filter","conv3d"),l=o,u=!1;o.rank===4&&(u=!0,l=ue(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),L(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),L(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),L(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),L(Qs(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),L(s==="NDHWC",()=>`Error in conv3d: got dataFormat of ${s} but only NDHWC is currently supported.`);let c={x:l,filter:i},d={strides:n,pad:r,dataFormat:s,dilations:a},h=U.runKernel(Nv,c,d);return u?ue(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var JO=H({conv3d_:YO});function QO(e,t,n,r,s){L(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=ue(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],u=o.shape[4];L(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),L(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),L(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),L(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),L(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},d={pad:s,strides:r,inputShape:a},h=U.runKernel(Cv,c,d);return i?ue(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var eP=H({conv3DBackpropInput_:QO});function tP(e,t,n,r,s){let a=O(e,"x","conv3dTranspose"),o=O(t,"filter","conv3dTranspose");return eP(n,a,o,r,s)}var nP=H({conv3dTranspose_:tP});function rP(e){let n={x:O(e,"x","cos")};return U.runKernel(Ev,n)}var sP=H({cos_:rP});function aP(e){let n={x:O(e,"x","cosh")};return U.runKernel($v,n)}var oP=H({cosh_:aP});function iP(e,t=0,n=!1,r=!1){let a={x:O(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:r};return U.runKernel(_v,a,o)}var lP=H({cumsum_:iP});function uP(e,t,n,r=!1){let s=O(e,"x","denseBincount"),a=O(t,"weights","denseBincount");L(s.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${s.dtype}`),L(s.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${s.rank}.`),L(n>=0,()=>`size must be non-negative, but got ${n}.`),L(a.size===s.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${s.shape}, weights shape: ${a.shape}.`);let o={x:s,weights:a},i={size:n,binaryOutput:r};return U.runKernel(Dv,o,i)}var cP=H({denseBincount_:uP});function dP(e,t,n="NHWC"){let r=O(e,"x","depthToSpace"),s=n==="NHWC"?r.shape[1]:r.shape[2],a=n==="NHWC"?r.shape[2]:r.shape[3],o=n==="NHWC"?r.shape[3]:r.shape[1];L(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${s} and ${t}  for depthToSpace with input shape
    ${r.shape}`),L(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${a} and ${t} for depthToSpace with input shape
        ${r.shape}`),L(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${r.shape}`);let i={x:r},l={blockSize:t,dataFormat:n};return U.runKernel(Fv,i,l)}var hP=H({depthToSpace_:dP});function pP(e,t,n,r,s="NHWC",a=[1,1],o){let i=O(e,"x","depthwiseConv2d"),l=O(t,"filter","depthwiseConv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=ue(i,[1,i.shape[0],i.shape[1],i.shape[2]])),L(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),L(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),L(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),o!=null&&L(Xn(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let d={x:u,filter:l},h={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o},p=U.runKernel(Mv,d,h);return c?ue(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ty=H({depthwiseConv2d_:pP});function fP(e){let n={x:O(e,"x","diag")};return U.runKernel(zv,n)}var mP=H({diag_:fP});function gP(e,t,n,r,s=[1,1],a="NHWC"){let o=O(e,"x","dilation2d"),i=O(t,"filter","dilation2d");L(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),L(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),L(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,u=!1;o.rank===3&&(l=ue(o,[1,o.shape[0],o.shape[1],o.shape[2]]),u=!0);let c={x:l,filter:i},d={strides:n,pad:r,dilations:s},h=U.runKernel(Lv,c,d);return u?ue(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var yP=H({dilation2d_:gP});function AP(e,t){let n=e.length,r=[];for(let s=0;s<n;s++){let a=n-1-s,o=e[a]||1;(t[t.length-1-s]||1)>1&&o===1&&r.unshift(a)}return r}function jk(e,t){let n=[];for(let r=0;r<t.length;r++){let s=e[e.length-r-1],a=t.length-r-1,o=t[a];(s==null||s===1&&o>1)&&n.unshift(a)}return n}function In(e,t){let n=[],r=Math.max(e.length,t.length);for(let s=0;s<r;s++){let a=e[e.length-s-1];a==null&&(a=1);let o=t[t.length-s-1];if(o==null&&(o=1),a===1)n.unshift(o);else if(o===1)n.unshift(a);else if(a!==o){let i=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(i)}else n.unshift(a)}return n}function xP(e,t){let n=O(e,"a","equal","string_or_numeric"),r=O(t,"b","equal","string_or_numeric");[n,r]=Vt(n,r),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(Hv,s)}var qk=H({equal_:xP});function bP(e,t,n){let r=O(t,"a","where"),s=O(n,"b","where"),a=O(e,"condition","where","bool"),o=In(In(a.shape,r.shape),s.shape),i=xp(a,o),l=xp(r,o),u=xp(s,o),c={condition:i,t:l,e:u};return U.runKernel(t7,c)}var Gi=H({where_:bP});function vP(e){let n={x:O(e,"x","zerosLike")};return U.runKernel(D7,n)}var Cr=H({zerosLike_:vP});function wP(e,t){let n=O(e,"a","div"),r=O(t,"b","div");[n,r]=Vt(n,r);let s=Qe(n,r),a=Cr(s),o=qk(r,a);return Gi(o,a,s)}var kP=H({divNoNan_:wP});function IP(e,t){let n=O(e,"t1","dot"),r=O(t,"t2","dot");L((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let s=n.rank===1?n.size:n.shape[1],a=r.rank===1?r.size:r.shape[0];if(L(s===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${s} and ${a}.`),n.rank===1&&r.rank===1){let o=ue(n,[1,-1]),i=ue(r,[-1,1]),l=yt(o,i);return ue(l,[])}else if(n.rank===1&&r.rank===2){let o=ue(n,[1,-1]),i=ue(r,[r.shape[0],r.shape[1]]),l=yt(o,i);return ue(l,[l.size])}else if(n.rank===2&&r.rank===1){let o=ue(r,[-1,1]),i=yt(n,o);return ue(i,[i.size])}else{let o=ue(r,[r.shape[0],r.shape[1]]);return yt(n,o)}}var SP=H({dot_:IP});function TP(e,...t){let n=t.map((s,a)=>O(s,`tensors${a}`,"einsum")),r={equation:e};return U.runKernel(Wv,n,r)}var NP=H({einsum_:TP});function CP(e){let n={x:O(e,"x","elu")};return U.runKernel(Vv,n)}var Kk=H({elu_:CP});function EP(e){let t=O(e,"x","erf");L(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=Pt(t,"float32"));let n={x:t};return U.runKernel(Uv,n)}var $P=H({erf_:EP});function _P(e){let n={x:O(e,"x","exp")};return U.runKernel(Gv,n)}var wo=H({exp_:_P});function RP(e,t=0){let n=O(e,"x","expandDims","string_or_numeric");L(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},s={dim:t};return U.runKernel(jv,r,s)}var ea=H({expandDims_:RP});function DP(e){let n={x:O(e,"x","expm1")};return U.runKernel(qv,n)}var FP=H({expm1_:DP});function MP(e,t){let n=O(e,"x","tile","string_or_numeric");L(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},s={reps:t};return U.runKernel(d2,r,s)}var vp=H({tile_:MP});function OP(e,t,n,r="float32"){t==null&&(t=e);let s=Ys([e,t],r),a=e<=t?e:t;for(let i=0;i<a;++i)s.set(1,i,i);let o=ue(s.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return vp(ea(o,0),[n[0],1,1]);if(n.length===2)return vp(ea(ea(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return vp(ea(ea(ea(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var Xk=H({eye_:OP});function wp(e,t,n){let r={shape:e,value:t,dtype:n};return U.runKernel(Xv,{},r)}function PP(e){let n={x:O(e,"x","floor")};return U.runKernel(Yv,n)}var Zk=H({floor_:PP});function zP(e,t,n=0,r=0){let s=O(e,"x","gather"),a=O(t,"indices","gather","int32"),o={x:s,indices:a},i={axis:n,batchDims:r};return U.runKernel(ew,o,i)}var Yk=H({gather_:zP});function LP(e,t){let n=O(e,"a","greater","string_or_numeric"),r=O(t,"b","greater","string_or_numeric");[n,r]=Vt(n,r),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(nw,s)}var kp=H({greater_:LP});function BP(e,t){let n=O(e,"a","greaterEqual","string_or_numeric"),r=O(t,"b","greaterEqual","string_or_numeric");[n,r]=Vt(n,r),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(rw,s)}var Jk=H({greaterEqual_:BP});function WP(e){let n={input:O(e,"input","imag")};return U.runKernel(aw,n)}var ny=H({imag_:WP});function VP(e){let n={x:O(e,"x","isFinite")};return U.runKernel(ow,n)}var UP=H({isFinite_:VP});function HP(e){let n={x:O(e,"x","isInf")};return U.runKernel(iw,n)}var GP=H({isInf_:HP});function jP(e){let n={x:O(e,"x","isNaN")};return U.runKernel(lw,n)}var qP=H({isNaN_:jP});function KP(e,t=.2){let r={x:O(e,"x","leakyRelu")},s={alpha:t};return U.runKernel(uw,r,s)}var Qk=H({leakyRelu_:KP});function XP(e,t){let n=O(e,"a","less","string_or_numeric"),r=O(t,"b","less","string_or_numeric");[n,r]=Vt(n,r),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(cw,s)}var ZP=H({less_:XP});function YP(e,t){let n=O(e,"a","lessEqual","string_or_numeric"),r=O(t,"b","lessEqual","string_or_numeric");[n,r]=Vt(n,r),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(dw,s)}var ry=H({lessEqual_:YP});function JP(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let r={start:e,stop:t,num:n};return U.runKernel(hw,{},r)}function QP(e,t=5,n=1,r=1,s=.5){let a=O(e,"x","localResponseNormalization");L(a.rank===4||a.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${a.rank}.`),L(Xn(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=ue(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},u={depthRadius:t,bias:n,alpha:r,beta:s},c=U.runKernel(Aw,l,u);return i?ue(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var ez=H({localResponseNormalization_:QP});function tz(e){let n={x:O(e,"x","log")};return U.runKernel(pw,n)}var uc=H({log_:tz});function nz(e){let n={x:O(e,"x","log1p")};return U.runKernel(fw,n)}var e4=H({log1p_:nz});function rz(e){return L(Sa(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=O(t,"x","tf.grad","string_or_numeric"),s=n!=null?O(n,"dy","tf.grad"):null;return U.tidy(()=>{let{value:a,grads:o}=U.gradients(()=>e(r),[r],s);return s!=null&&Mn(a.shape,s.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Ip(o),o[0]})}}function sz(e){return L(Sa(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{L(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let r=sc(t,"args","tf.grads","string_or_numeric"),s=n!=null?O(n,"dy","tf.grads"):null;return U.tidy(()=>{let{value:a,grads:o}=U.gradients(()=>e(...r),r,s);return s!=null&&Mn(a.shape,s.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Ip(o),o})}}function az(e){return L(Sa(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{L(t instanceof Tt,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),L(n==null||n instanceof Tt,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:s}=U.gradients(()=>e(t),[t],n);return Ip(r),{grad:r[0],value:s}}}function oz(e){return L(Sa(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{L(Array.isArray(t)&&t.every(s=>s instanceof Tt),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),L(n==null||n instanceof Tt,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=U.gradients(()=>e(...t),t,n);return n!=null&&Mn(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Ip(r.grads),r}}function t4(e,t){L(Sa(e),()=>"The f passed in variableGrads(f) must be a function"),L(t==null||Array.isArray(t)&&t.every(u=>u instanceof rc),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in U.registeredVariables)t.push(U.registeredVariables[u])}let r=n?t.filter(u=>!u.trainable):null,s=t.length;t=t.filter(u=>u.trainable),L(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${s} variables is trainable.`);let a=!0,{value:o,grads:i}=U.gradients(e,t,null,a);L(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),L(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),r!=null&&r.forEach(u=>l[u.name]=null),{value:o,grads:l}}function Ns(e){return U.customGrad(e)}function Ip(e){if(e.filter(n=>n==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
    the f you passed encloses all operations that lead from x to y.`)}function iz(e){let n={x:O(e,"x","neg")};return U.runKernel(_w,n)}var $a=H({neg_:iz});function lz(e){let n={x:O(e,"x","softplus")};return U.runKernel(l7,n)}var n4=H({softplus_:lz});function uz(e){let t=O(e,"x","logSigmoid");return Ns(r=>({value:$a(n4($a(r))),gradFunc:o=>fe(o,Ts($a(r)))}))(t)}var cz=H({logSigmoid_:uz});function dz(e,t=null,n=!1){let s={x:O(e,"x","max")},a={reductionIndices:t,keepDims:n};return U.runKernel(xw,s,a)}var _a=H({max_:dz});function hz(e,t){let n=O(e,"a","sub"),r=O(t,"b","sub");[n,r]=Vt(n,r);let s={a:n,b:r};return U.runKernel(I7,s)}var He=H({sub_:hz});function pz(e,t=null,n=!1){let r=O(e,"x","sum");r.dtype==="bool"&&(r=Pt(r,"int32"));let s={x:r},a={axis:t,keepDims:n};return U.runKernel(c7,s,a)}var _t=H({sum_:pz});function fz(e,t=-1){let n=O(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 ${t}`);return Ns((s,a)=>{let o=!0,i=_a(s,t,!0),l=He(s,i),u=He(Pt(l,"float32"),uc(_t(wo(l),t,o)));return a([u]),{value:u,gradFunc:(d,h)=>{let[p]=h,f=!0,m=wo(p);return He(d,fe(_t(d,t,f),m))}}})(n)}var mz=H({logSoftmax_:fz});function sy(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function r4(e,t,n){let r=e.length+t.length,s=[],a=0,o=0;for(let i=0;i<r;i++)n.indexOf(i)===-1?s.push(e[a++]):s.push(t[o++]);return s}function gz(e,t){let n=[],r=e.length;for(let a=0;a<r;a++)t.indexOf(a)===-1&&n.push(e[a]);let s=t.map(a=>e[a]);return[n,s]}function cc(e,t){let n=t.map(r=>1);return r4(e,n,t)}function yz(e,t,n){L(sy(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Az(e,t){if(sy(e,t))return null;let n=[];for(let r=0;r<t;++r)e.indexOf(r)===-1&&n.push(r);return e.forEach(r=>n.push(r)),n}function xz(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function bz(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function vz(e,t=null,n=!1){let r=O(e,"x","logSumExp"),s=Xu(t,r.shape),a=_a(r,s,!0),o=He(r,a),i=wo(o),l=_t(i,s),u=uc(l),c=Me(ue(a,u.shape),u);if(n){let d=cc(c.shape,s);return ue(c,d)}return c}var s4=H({logSumExp_:vz});function wz(e,t){let n=O(e,"a","logicalAnd","bool"),r=O(t,"b","logicalAnd","bool");In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(mw,s)}var Sp=H({logicalAnd_:wz});function kz(e){let n={x:O(e,"x","logicalNot","bool")};return U.runKernel(gw,n)}var a4=H({logicalNot_:kz});function Iz(e,t){let n=O(e,"a","logicalOr","bool"),r=O(t,"b","logicalOr","bool");In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(yw,s)}var o4=H({logicalOr_:Iz});function Sz(e,t){let n=O(e,"a","logicalXor","bool"),r=O(t,"b","logicalXor","bool");return In(n.shape,r.shape),Sp(o4(e,t),a4(Sp(e,t)))}var Tz=H({logicalXor_:Sz});function Nz(e,t,n,r,s){let a=O(e,"x","maxPool"),o=1,i=a,l=!1;a.rank===3&&(l=!0,i=ue(a,[1,a.shape[0],a.shape[1],a.shape[2]])),L(i.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`),L(Qs(n,o),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`),s!=null&&L(Xn(r),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s},d=U.runKernel(vw,u,c);return l?ue(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var i4=H({maxPool_:Nz});function Cz(e,t=[1,1,1],n,r,s,a="NDHWC"){let o=O(e,"x","maxPool3d"),i=o,l=!1;o.rank===4&&(l=!0,i=ue(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),L(i.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`),L(a==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),s!=null&&L(Xn(r),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:a},d=U.runKernel(ww,u,c);return l?ue(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Ez=H({maxPool3d_:Cz});function $z(e,t,n,r,s=!1){let o={x:O(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:s},l=U.runKernel(kw,o,i);return{result:l[0],indexes:l[1]}}var _z=H({maxPoolWithArgmax_:$z});function Rz(e,t){let n=O(e,"a","maximum"),r=O(t,"b","maximum");[n,r]=Vt(n,r),n.dtype==="bool"&&(n=Pt(n,"int32"),r=Pt(r,"int32")),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(bw,s)}var l4=H({maximum_:Rz});function Dz(e,t=null,n=!1){let s={x:O(e,"x","mean")},a={axis:t,keepDims:n};return U.runKernel(Iw,s,a)}var Tp=H({mean_:Dz});function ji(e,t="float32"){if(t==="complex64"){let r=ji(e,"float32"),s=ji(e,"float32");return go(r,s)}let n=np(Jt(e),t);return U.makeTensor(n,e,t)}function ko(e,t="float32"){if(t==="complex64"){let r=ko(e,"float32"),s=ji(e,"float32");return go(r,s)}let n=r2(Jt(e),t);return U.makeTensor(n,e,t)}function Fz(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 r=O(e,"x","meshgrid",e instanceof Tt?e.dtype:"float32");if(t===void 0)return[r];let s=O(t,"y","meshgrid",t instanceof Tt?t.dtype:"float32"),a=Jt(r.shape),o=Jt(s.shape);return n==="xy"?(r=ue(r,[1,-1]),s=ue(s,[-1,1]),[yt(ko([o,1],r.dtype),r),yt(s,ko([1,a],s.dtype))]):(r=ue(r,[-1,1]),s=ue(s,[1,-1]),[yt(r,ko([1,o],r.dtype)),yt(ko([a,1],s.dtype),s)])}function Mz(e,t=null,n=!1){let s={x:O(e,"x","min")},a={axis:t,keepDims:n};return U.runKernel(Sw,s,a)}var ay=H({min_:Mz});function Oz(e,t){let n=O(e,"a","minimum"),r=O(t,"b","minimum");[n,r]=Vt(n,r),n.dtype==="bool"&&(n=Pt(n,"int32"),r=Pt(r,"int32")),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(Tw,s)}var u4=H({minimum_:Oz});function Pz(e,t,n){L(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=O(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");L(t.length===r.rank,()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`);let s=n==="reflect"?1:0;for(let i=0;i<r.rank;i++)L(t[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),L(t[i][0]>=0&&t[i][0]<=r.shape[i]-s&&t[i][1]>=0&&t[i][1]<=r.shape[i]-s,()=>`Padding in dimension ${i} cannot be greater than or equal to ${r.shape[i]-s} or less than 0 for input of shape ${r.shape}`);let a={paddings:t,mode:n},o={x:r};return U.runKernel(Nw,o,a)}var zz=H({mirrorPad_:Pz});function Lz(e,t){let n=O(e,"a","mod"),r=O(t,"b","mod");[n,r]=Vt(n,r);let s={a:n,b:r};return U.runKernel(Cw,s)}var Bz=H({mod_:Lz});function Wz(e){let t=O(e,"x","square"),n={};return U.runKernel("Square",{x:t},n)}var ns=H({square_:Wz});function Vz(e,t=null,n=!1){e=O(e,"x","moments");let r=Xu(t,e.shape),s=Tp(e,r,n),a=s.shape;n||(a=cc(s.shape,r));let o=ns(He(Pt(e,"float32"),ue(s,a))),i=Tp(o,r,n);return{mean:s,variance:i}}var Uz=H({moments_:Vz});function Hz(e,t,n,r){let s=O(t,"data","multiRNNCell"),a=sc(n,"c","multiRNNCell"),o=sc(r,"h","multiRNNCell"),i=s,l=[];for(let d=0;d<e.length;d++){let h=e[d](i,a[d],o[d]);l.push(h[0]),l.push(h[1]),i=h[1]}let u=[],c=[];for(let d=0;d<l.length;d+=2)u.push(l[d]),c.push(l[d+1]);return[u,c]}var Gz=H({multiRNNCell_:Hz});function jz(e,t,n,r=!1){let s=O(e,"logits","multinomial"),a=s.size,o=s.rank;if(a<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);if(o>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${o}`);n=n||Math.random();let l={logits:o===1?ue(s,[1,-1]):s},u={numSamples:t,seed:n,normalized:r},c=U.runKernel(Ew,l,u);return o===1?ue(c,[c.size]):c}var qz=H({multinomial_:jz});function Kz(e,t){let n=O(e,"a","notEqual","string_or_numeric"),r=O(t,"b","notEqual","string_or_numeric");[n,r]=Vt(n,r),In(n.shape,r.shape);let s={a:n,b:r};return U.runKernel(Rw,s)}var c4=H({notEqual_:Kz});function Xz(e){let n={x:O(e,"x","onesLike")};return U.runKernel(Ow,n)}var Zz=H({onesLike_:Xz});function Yz(e,t){let n=O(e,"v1","outerProduct"),r=O(t,"v2","outerProduct");L(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let s=ue(n,[-1,1]),a=ue(r,[1,-1]);return yt(s,a)}var Jz=H({outerProduct_:Yz});function Qz(e,t,n=0){let r=O(e,"x","pad");if(r.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let s={paddings:t,constantValue:n},a={x:r};return U.runKernel(Lw,a,s)}var dc=H({pad_:Qz});function eL(e,t,n=0){return L(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),dc(e,[t],n)}var tL=H({pad1d_:eL});function nL(e,t,n=0){return L(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),dc(e,t,n)}var rL=H({pad2d_:nL});function sL(e,t,n=0){return L(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."),dc(e,t,n)}var aL=H({pad3d_:sL});function oL(e,t,n=0){return L(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."),dc(e,t,n)}var iL=H({pad4d_:oL});function lL(e,t,n){let r=O(e,"x","spaceToBatchND");L(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),L(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),L(r.shape.reduce((o,i,l)=>l>0&&l<=t.length?o&&(i+n[l-1][0]+n[l-1][1])%t[l-1]==0:o,!0),()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let s={x:r},a={blockShape:t,paddings:n};return U.runKernel(d7,s,a)}var d4=H({spaceToBatchND_:lL});function uL(e,t,n,r,s,a){s==null&&(s=[1,1]),a==null&&(a=1),r===0&&(r="valid");let o=O(e,"x","maxPool"),i=o,l=!1;o.rank===3&&(l=!0,i=ue(o,[1,o.shape[0],o.shape[1],o.shape[2]])),L(Qs(a,s),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${s}'`);let u=Lk(i.shape,t,a,s,r),c=[u.dilationHeight,u.dilationWidth],d;r==="same"?d=dL([u.filterHeight,u.filterWidth],c):d=[[0,0],[0,0]];let h=c[0]===1&&c[1]===1,[p,f]=cL([u.inHeight,u.inWidth],c,d),m=h?r:"valid",g=h?i:d4(i,c,p),A=(n==="avg"?()=>Vk(g,t,a,m):()=>i4(g,t,a,m))(),x=h?A:Uk(A,c,f);return l?ue(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function cL(e,t,n){let r=n.map(c=>c[0]),s=n.map(c=>c[1]),a=e.concat(r,s),o=t.map((c,d)=>(c-a[d]%c)%c),i=s.map((c,d)=>c+o[d]),l=t.map((c,d)=>[r[d],i[d]]),u=t.map((c,d)=>[0,o[d]]);return[l,u]}function dL(e,t){let r=e.map((o,i)=>o+(o-1)*(t[i]-1)).map(o=>o-1),s=r.map(o=>Math.floor(o/2)),a=r.map((o,i)=>o-s[i]);return r.map((o,i)=>[s[i],a[i]])}var hL=H({pool_:uL});function pL(e,t){let n=O(e,"base","pow"),r=O(t,"exp","pow");[n,r]=Vt(n,r);let s={a:n,b:r};return U.runKernel(Bw,s)}var hc=H({pow_:pL});function fL(e,t){let n=O(e,"x","prelu"),r=O(t,"alpha","prelu"),s={x:n,alpha:r};return U.runKernel(Ww,s)}var h4=H({prelu_:fL});function mL(e,t=null,n=!1){let r=O(e,"x","prod");r.dtype==="bool"&&(r=Pt(r,"int32"));let s={x:r},a={axis:t,keepDims:n};return U.runKernel(Vw,s,a)}var gL=H({prod_:mL});function yL(e,t,n){let r=Jt(e),s=null;if(n==null||n==="float32")s=new Float32Array(r);else if(n==="int32")s=new Int32Array(r);else if(n==="bool")s=new Uint8Array(r);else throw new Error(`Unknown data type ${n}`);for(let a=0;a<r;a++)s[a]=t();return U.makeTensor(s,e,n)}var AL=H({rand_:yL}),oy=Ks(t2()),iy=class{constructor(e,t,n,r,s){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=r,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let a=s||Math.random();this.random=oy.alea(a.toString())}nextValue(){if(!isNaN(this.nextVal)){let r=this.nextVal;return this.nextVal=NaN,r}let e,t,n=!1;for(;!n;){let r,s,a;do r=2*this.random()-1,s=2*this.random()-1,a=r*r+s*s;while(a>=1||a===0);let o=Math.sqrt(-2*Math.log(a)/a);e=this.mean+this.stdDev*r*o,t=this.mean+this.stdDev*s*o,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},xL=class{constructor(e,t,n,r){this.alpha=e,this.beta=1/t,this.dtype=n;let s=r||Math.random();this.randu=oy.alea(s.toString()),this.randn=new iy(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,r,s,a;for(;;){do r=this.randn.nextValue(),a=1+this.c*r;while(a<=0);if(a*=a*a,e=r*r,t=1-.331*e*e,n=.5*e+this.d*(1-a+Math.log(a)),s=this.randu(),s<t||Math.log(s)<n)break}return a=1/this.beta*this.d*a,this.alpha<1&&(a*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(a)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},bL=class{constructor(e=0,t=1,n,r){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,r==null&&(r=Math.random()),typeof r=="number"&&(r=r.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=oy.alea(r)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function vL(e,t,n=1,r="float32",s){if(n==null&&(n=1),r==null&&(r="float32"),r!=="float32"&&r!=="int32")throw new Error(`Unsupported data type ${r}`);let a=new xL(t,n,r,s),o=Ys(e,r);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var wL=H({randomGamma_:vL});function kL(e,t=0,n=1,r,s){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let a=new iy(t,n,r,!1,s),o=Ys(e,r);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var IL=H({randomNormal_:kL});function SL(e,t=0,n=1,r="float32",s){let a=Ys(e,r),o=new bL(t,n,null,s);for(let i=0;i<a.values.length;i++)a.values[i]=o.nextValue();return a.toTensor()}var p4=H({randomUniform_:SL});function pc(e,t,n=1,r="float32"){if(n===0)throw new Error("Cannot have a step of zero");let s={start:e,stop:t,step:n,dtype:r};return U.runKernel(Uw,{},s)}function TL(e){let n={input:O(e,"input","real")};return U.runKernel(Hw,n)}var Np=H({real_:TL});function NL(e){let n={x:O(e,"x","reciprocal")};return U.runKernel(Gw,n)}var CL=H({reciprocal_:NL});function EL(e){let n={x:O(e,"x","relu")};return U.runKernel(jw,n)}var Cp=H({relu_:EL});function $L(e){let n={x:O(e,"x","relu6")};return U.runKernel(Zw,n)}var f4=H({relu6_:$L});function _L(e,t){let r={x:O(e,"x","reverse")},s={dims:t};return U.runKernel(Yw,r,s)}var Io=H({reverse_:_L});function RL(e){let t=O(e,"x","reverse");return L(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Io(t,0)}var DL=H({reverse1d_:RL});function FL(e,t){let n=O(e,"x","reverse");return L(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Io(n,t)}var ML=H({reverse2d_:FL});function OL(e,t){let n=O(e,"x","reverse");return L(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Io(n,t)}var PL=H({reverse3d_:OL});function zL(e,t){let n=O(e,"x","reverse");return L(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Io(n,t)}var LL=H({reverse4d_:zL});function BL(e){let n={x:O(e,"x","round")};return U.runKernel(Jw,n)}var m4=H({round_:BL});function WL(e){let n={x:O(e,"x","rsqrt")};return U.runKernel(Qw,n)}var VL=H({rsqrt_:WL});function ut(e,t){if((Cn(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"&&Cn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Na(e,[],[],t)}function UL(e){let n={x:O(e,"x","selu")};return U.runKernel(n7,n)}var HL=H({selu_:UL});function GL(e,t,n,r,s,a=[1,1],o="NHWC"){let i=O(e,"x","separableConv2d"),l=O(t,"depthwiseFilter","separableConv2d"),u=O(n,"pointwiseFilter","separableConv2d"),c=i,d=!1;if(i.rank===3&&(d=!0,c=ue(i,[1,i.shape[0],i.shape[1],i.shape[2]])),o==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");L(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),L(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),L(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),L(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${u.shape[0]}.`),L(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let h=l.shape[2],p=l.shape[3];L(u.shape[2]===h*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${h*p}, but got ${u.shape[2]}.`);let f=ty(c,l,r,s,o,a),g=bp(f,u,1,"valid",o);return d?ue(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var jL=H({separableConv2d_:GL});async function qL(e,t){let n=O(e,"x","setdiff1d"),r=O(t,"y","setdiff1d");L(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),L(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),L(r.rank===1,()=>`y should be 1D tensor, but got y (${r.shape}).`);let s=await n.data(),a=await r.data(),o=new Set(a),i=0;for(let c=0;c<s.length;c++)o.has(s[c])||i++;let l=new up([i],n.dtype),u=new up([i],"int32");for(let c=0,d=0;c<s.length;c++)o.has(s[c])||(l.values[d]=s[c],u.values[d]=c,d++);return[l.toTensor(),u.toTensor()]}var KL=qL;function XL(e){let n={x:O(e,"x","sign")};return U.runKernel(o7,n)}var ZL=H({sign_:XL});function YL(e){let n={x:O(e,"x","sin")};return U.runKernel(s7,n)}var JL=H({sin_:YL});function QL(e){let n={x:O(e,"x","sinh")};return U.runKernel(a7,n)}var eB=H({sinh_:QL});function tB(e,t,n){let r=O(e,"x","slice1d");return L(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Ze(r,[t],[n])}var nB=H({slice1d_:tB});function rB(e,t,n){let r=O(e,"x","slice2d");return L(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Ze(r,t,n)}var sB=H({slice2d_:rB});function aB(e,t,n){let r=O(e,"x","slice3d");return L(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Ze(r,t,n)}var oB=H({slice3d_:aB});function iB(e,t,n){let r=O(e,"x","slice4d");return L(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Ze(r,t,n)}var lB=H({slice4d_:iB});function uB(e,t=-1){let n=O(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 r={logits:n},s={dim:t};return U.runKernel(p7,r,s)}var cB=H({softmax_:uB});function dB(e){L(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return U.runKernel(Kv,t)}var ly=H({fft_:dB});function hB(e){L(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return U.runKernel(sw,t)}var Ep=H({ifft_:hB});function pB(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let s=ue(e,[n,t]);r=Ep(s)}else{let s=[n,2*(t-1)],a=ue(Np(e),[n,t]),o=ue(ny(e),[n,t]),i=Io(Ze(a,[0,1],[n,t-2]),1),l=fe(Io(Ze(o,[0,1],[n,t-2]),1),ut(-1)),u=an([a,i],1),c=an([o,l],1),d=ue(go(u,c),[s[0],s[1]]);r=Ep(d)}if(r=Np(r),e.rank===3&&e.shape[0]!==0){let s=r,a=e.shape[0];r=ue(r,[a,r.shape[0]/a,r.shape[1]]),s.dispose()}return r}var g4=H({irfft_:pB});function fB(e,t,n=0){let s={x:O(e,"x","split")},a={numOrSizeSplits:t,axis:n};return U.runKernel(h7,s,a)}var ta=H({split_:fB});function mB(e,t){L(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,s;if(t!=null&&t<n){let f=e.shape.map(g=>0),m=e.shape.map(g=>g);m[e.shape.length-1]=t,s=Ze(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,s=an([e,ji(f)],e.shape.length-1),n=t}else s=e;let a=Cr(s),o=ue(go(s,a),[r,n]),i=ly(o),l=Math.floor(n/2)+1,u=Np(i),c=ny(i),d=ta(u,[l,n-l],u.shape.length-1),h=ta(c,[l,n-l],c.shape.length-1),p=s.shape.slice();return p[s.shape.length-1]=l,ue(go(d[0],h[0]),p)}var uy=H({rfft_:mB});function gB(e){let n={x:O(e,"x","sqrt")};return U.runKernel(u7,n)}var na=H({sqrt_:gB});function yB(e,t){let n=O(e,"a","squaredDifference"),r=O(t,"b","squaredDifference");[n,r]=Vt(n,r),In(n.shape,r.shape);let s={a:n,b:r},a={};return U.runKernel(x7,s,a)}var y4=H({squaredDifference_:yB});function AB(e,t){let n=O(e,"x","squeeze");return ue(n,U3(n.shape,t).newShape)}var Zn=H({squeeze_:AB});function xB(e,t=0){let n=sc(e,"tensors","stack","string_or_numeric");L(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&L(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,s={axis:t};return U.runKernel(zw,r,s)}var So=H({stack_:xB});function bB(e,t=0){let r={x:O(e,"x","step")},s={alpha:t};return U.runKernel(F7,r,s)}var A4=H({step_:bB});function vB(e,t,n,r,s=0,a=0,o=0,i=0,l=0){let c={x:O(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:r,beginMask:s,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:l};return U.runKernel(b7,c,d)}var wB=H({stridedSlice_:vB});function kB(e){let n={x:O(e,"x","tan")};return U.runKernel(S7,n)}var IB=H({tan_:kB});function ur(e,t){ho(e);let n=Ss(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Na(e,null,n,t)}function ra(e,t,n){if(ho(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=Ss(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Na(e,t,r,n)}function SB(e,t,n){if(ho(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=Ss(e,n);if(r.length!==4&&r.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Na(e,t,r,n)}function TB(e,t,n){if(ho(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=Ss(e,n);if(r.length!==5&&r.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Na(e,t,r,n)}function NB(e,t,n){if(ho(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=Ss(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,Na(e,t,r,n)}function CB(e,t=1,n=!0){let r=O(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let s=r.shape[r.shape.length-1];if(t>s)throw new Error(`'k' passed to topk() must be <= the last dimension (${s}) but got ${t}`);let a={x:r},o={k:t,sorted:n},[i,l]=U.runKernel(N7,a,o);return{values:i,indices:l}}var EB=H({topk_:CB});function $B(e,t=0,n=1,r,s){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let a=new iy(t,n,r,!0,s),o=Ys(e,r);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var _B=H({truncatedNormal_:$B});function RB(e,t=0){let n=O(e,"x","unique","string_or_numeric");L(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},s={axis:t},[a,o]=U.runKernel($7,r,s);return{values:a,indices:o}}var DB=H({unique_:RB});function FB(e,t,n){let r=O(e,"x","unsortedSegmentSum"),s=O(t,"segmentIds","unsortedSegmentSum","int32");L(Xn(n),()=>"numSegments must be of dtype int");let a={x:r,segmentIds:s},o={numSegments:n};return U.runKernel(R7,a,o)}var MB=H({unsortedSegmentSum_:FB});function OB(e,t=0){let n=O(e,"x","unstack","string_or_numeric");L(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},s={axis:t};return U.runKernel(_7,r,s)}var fc=H({unstack_:OB});function PB(e,t=!0,n,r){return U.makeVariable(e,t,n,r)}function x4(e,t){let n=[];for(let a=0;a<t.length;a++)t[a]&&n.push(a);let r=Ys(e,"int32"),s=Ys([n.length,e.length],"int32");for(let a=0;a<n.length;a++){let o=r.indexToLoc(n[a]),i=a*e.length;s.values.set(o,i)}return s.toTensor()}async function zB(e){let t=O(e,"condition","whereAsync","bool"),n=await t.data(),r=x4(t.shape,n);return e!==t&&t.dispose(),r}var b4=zB;async function LB(e,t,n){let r=O(e,"tensor","boolMask"),s=O(t,"mask","boolMask","bool"),a=n==null?0:n,o=s.rank,i=r.shape;L(o>0,()=>"mask cannot be scalar"),Mn(i.slice(a,a+o),s.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=a;m<a+o;m++)l*=i[m];let u=i.slice(0,a).concat([l],i.slice(a+o)),c=ue(r,u),d=ue(s,[-1]),h=await b4(d),p=Zn(h,[1]),f=Yk(c,p,a);return e!==r&&r.dispose(),t!==s&&s.dispose(),p.dispose(),c.dispose(),d.dispose(),h.dispose(),f}var BB=LB;function WB(e,t="euclidean",n=null,r=!1){e=O(e,"x","norm");let s=v4(e,t,n),a=s.shape;if(r){let o=Xu(n,e.shape);a=cc(s.shape,o)}return ue(s,a)}function v4(e,t,n=null){if(e.rank===0)return Nr(e);if(e.rank!==1&&n===null)return v4(ue(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return _t(Nr(e),n);if(t===Infinity)return _a(Nr(e),n);if(t===-Infinity)return ay(Nr(e),n);if(t==="euclidean"||t===2)return na(_t(hc(Nr(e),ut(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return _a(_t(Nr(e),n[0]),n[1]-1);if(t===Infinity)return _a(_t(Nr(e),n[1]),n[0]);if(t===-Infinity)return ay(_t(Nr(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return na(_t(ns(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var cy=H({norm_:WB});function VB(e,t,n,r,s=!0){let a=O(e,"v","movingAverage"),o=O(t,"x","movingAverage"),i=O(n,"decay","movingAverage");H7(a,o),L(Xs(a.shape,o.shape),()=>"Shape mismatch in v and x");let l=ut(1),u=He(l,i),c=fe(He(o,a),u);if(s){L(r!=null,()=>"When using zeroDebias: true, step is required.");let d=O(r,"step","movingAverage");c=Qe(c,He(l,hc(i,d)))}return Me(a,c)}var UB=H({movingAverage_:VB});function HB(e,t,n){let r=O(e,"indices","scatterND","int32"),s=O(t,"updates","scatterND");U2(s,r,n);let a={indices:r,updates:s},o={shape:n};return U.runKernel(e7,a,o)}var GB=H({scatterND_:HB});function jB(e,t,n,r){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 s=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${a}.`);let o=t.size;if(!(t.rank===0||t.rank===1&&o===s))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${s}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function qB(e,t,n,r=0){let s=O(e,"sparseIndices","sparseToDense","int32"),a=O(t,"sparseValues","sparseToDense"),o=O(r,"defaultValue","sparseToDense",a.dtype);jB(s,a,n,o);let i={sparseIndices:s,sparseValues:a,defaultValue:o},l={outputShape:n};return U.runKernel(A7,i,l)}var KB=H({sparseToDense_:qB});function XB(e,t){let n=O(t,"indices","gatherND","int32"),s={params:O(e,"x","gatherND","string_or_numeric"),indices:n};return U.runKernel(tw,s)}var ZB=H({gatherND_:XB});function YB(e,t){if(t==null)return e.shape.slice();if(Xs(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let r=0;r<e.shape.length;r++)t[r]==null&&e.shape[r]!=null?n.push(e.shape[r]):n.push(t[r]);return n}return t}function JB(e,t,n,r){let s=O(e,"x","dropout");if(L(s.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${s.dtype} tensor instead.`),L(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Tt?s.clone():s;let a=YB(s,n),o=1-t,i=Qe(Zk(Me(p4(a,0,1,"float32",r),o)),o);return fe(s,i)}var QB=H({dropout_:JB});function w4(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function dy(e,t,n){let r=1-e%2,s=new Float32Array(e);for(let a=0;a<e;++a){let o=2*Math.PI*a/(e+r-1);s[a]=t-n*Math.cos(o)}return ur(s,"float32")}async function eW(e,t,n=1){let r=O(e,"predictions","inTopK"),s=O(t,"targets","inTopK");L(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),L(r.rank-1===s.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${s.rank}`),Mn(r.shape.slice(0,r.shape.length-1),s.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let a=r.shape[r.shape.length-1];L(n>0&&n<=a,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);let o=await r.data(),i=await s.data(),[l,u]=[o.length/a,a],c=H3("bool",l);for(let d=0;d<l;d++){let h=d*u,p=o.subarray(h,h+u),f=[];for(let m=0;m<p.length;m++)f.push({value:p[m],index:m});f.sort((m,g)=>g.value-m.value),c[d]=0;for(let m=0;m<n;m++)if(f[m].index===i[d]){c[d]=1;break}}return e!==r&&r.dispose(),t!==s&&s.dispose(),ts(c,s.shape,"bool")}var tW=eW,k4={};De(k4,{conv2d:()=>aW,depthwiseConv2d:()=>dW,matMul:()=>pW});function nW(e,t,n,r,s,a="NHWC",o){let i=e;e.rank===3&&(i=ue(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=ue(t,[1,t.shape[0],t.shape[1],t.shape[2]])),L(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),L(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),L(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=a==="NHWC"?i.shape[3]:i.shape[1],c=a==="NHWC"?l.shape[3]:l.shape[1];L(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),L(c===n[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`),o!=null&&L(Xn(s),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:i,dy:l},h={strides:r,pad:s,dataFormat:a,dimRoundingMode:o,filterShape:n};return U.runKernel(Sv,d,h)}var rW=H({conv2DBackpropFilter_:nW});function $p(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return fe(e,A4(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function _p(e,t){let n=t,r=jk(e.shape,t.shape);return r.length>0&&(n=_t(n,r)),ue(n,e.shape)}function Rp(e,t,n,r){if(t==="linear")return e;if(t==="relu")return Cp(e);if(t==="elu")return Kk(e);if(t==="relu6")return f4(e);if(t==="prelu")return h4(e,n);if(t==="leakyrelu")return Qk(e,r);if(t==="sigmoid")return Ts(e);throw new Error(`Unknown fused activation ${t}.`)}var Dp=(e,t)=>!(e>0)||t==="linear";function sW({x:e,filter:t,strides:n,pad:r,dataFormat:s="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",Dp(U.state.gradientDepth,l)===!1){let v=bp(e,t,n,r,s,a,o);return i!=null&&(v=Me(v,i)),Rp(v,l,u,c)}let d=O(e,"x","conv2d"),h=O(t,"filter","conv2d"),p=d,f=!1;d.rank===3&&(f=!0,p=ue(d,[1,d.shape[0],d.shape[1],d.shape[2]])),L(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),L(h.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${h.rank}.`),o!=null&&L(Xn(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`),L(p.shape[3]===h.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${h.shape[2]}.`),L(Qs(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),L(s==="NHWC",()=>`Error in conv2d: got dataFormat of ${s} but only NHWC is currently supported.`);let m=oc(p.shape,h.shape,n,a,r,o),g;i!=null&&(g=O(i,"bias","fused conv2d"),[g]=Vt(g,d),In(m.outShape,g.shape));let y;u!=null&&(y=O(u,"prelu weights","fused conv2d"));let A=(v,w)=>{let[S,I,E,F]=w,$=$p(v,E,l);L(ic(a),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let _=Gk(I.shape,$,S,n,r),N=rW(I,$,S.shape,n,r),P=[_,N];if(F!=null){let B=_p(F,$);P.push(B)}return P},x={x:p,filter:h,bias:g,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?Ns((w,S,I)=>{let E=U.runKernel(f2,x,b);return I([S,w,E]),f&&(E=ue(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(p,h):Ns((w,S,I,E)=>{let F=U.runKernel(f2,x,b);return E([S,w,F,I]),f&&(F=ue(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:A}})(p,h,g)}var aW=H({fusedConv2d_:sW});function oW(e,t,n,r,s,a=[1,1],o){let i=e;e.rank===3&&(i=ue(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=ue(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:i,dy:l},c={strides:r,pad:s,dimRoundingMode:o,dilations:a,filterShape:n};return U.runKernel(Ov,u,c)}var iW=H({depthwiseConv2dNativeBackpropFilter_:oW});function lW(e,t,n,r,s,a=[1,1],o){let i=t,l=!1;t.rank===3&&(l=!0,i=ue(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:i,filter:n},c={strides:r,pad:s,dimRoundingMode:o,dilations:a,inputShape:e},d=U.runKernel(Pv,u,c);return l?ue(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var uW=H({depthwiseConv2dNativeBackpropInput_:lW});function cW({x:e,filter:t,strides:n,pad:r,dataFormat:s="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(Dp(U.state.gradientDepth,l)===!1){let v=ty(e,t,n,r,s,a,o);return i!=null&&(v=Me(v,i)),Rp(v,l,u,c)}let d=O(e,"x","depthwiseConv2d"),h=O(t,"filter","depthwiseConv2d"),p=d,f=!1;d.rank===3&&(f=!0,p=ue(d,[1,d.shape[0],d.shape[1],d.shape[2]])),L(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),L(h.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${h.rank}.`),L(p.shape[3]===h.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${h.shape[2]}.`),a==null&&(a=[1,1]),L(Qs(n,a),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),o!=null&&L(Xn(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${o} but got pad ${r}.`);let m=oc(p.shape,h.shape,n,a,r,o,!0),g;i!=null&&(g=O(i,"bias","fused conv2d"),[g]=Vt(g,d),In(m.outShape,g.shape));let y;u!=null&&(y=O(u,"prelu weights","fused depthwiseConv2d"));let A=(v,w)=>{L(ic(a),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[S,I,E,F]=w,$=$p(v,E,l),_=uW(I.shape,$,S,n,r,a,o),N=iW(I,$,S.shape,n,r,a,o);if(F!=null){let P=_p(g,$);return[_,N,P]}return[_,N]},x={x:p,filter:h,bias:g,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?Ns((w,S,I)=>{let E=U.runKernel(m2,x,b);return I([S,w,E]),f&&(E=ue(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(p,h):Ns((w,S,I,E)=>{let F=U.runKernel(m2,x,b);return E([S,w,F,I]),f&&(F=ue(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:A}})(p,h,g)}var dW=H({fusedDepthwiseConv2d_:cW});function hW({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:s,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i}){if(Dp(U.state.gradientDepth,a)===!1){let F=yt(e,t,n,r);return s!=null&&(F=Me(F,s)),Rp(F,a,o,i)}let l=O(e,"a","fused matMul"),u=O(t,"b","fused matMul");[l,u]=Vt(l,u);let c=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=r?u.shape[u.rank-1]:u.shape[u.rank-2],h=n?l.shape[l.rank-1]:l.shape[l.rank-2],p=r?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),g=Jt(f),y=Jt(m);L(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),L(Xs(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),L(c===d,()=>`Error in fused matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${r} must match.`);let A=l.shape.slice(0,-2).concat([h,p]),x=n?ue(l,[g,c,h]):ue(l,[g,h,c]),b=r?ue(u,[y,p,d]):ue(u,[y,d,p]),v;s!=null&&(v=O(s,"bias","fused matMul"),[v]=Vt(v,l),In(A,v.shape));let w;o!=null&&(w=O(o,"prelu weights","fused matMul"));let S=(F,$)=>{let[_,N,P,B]=$,j=$p(ue(F,P.shape),P,a),X,Y;if(!n&&!r?(X=yt(j,N,!1,!0),Y=yt(_,j,!0,!1)):!n&&r?(X=yt(j,N,!1,!1),Y=yt(j,_,!0,!1)):n&&!r?(X=yt(N,j,!1,!0),Y=yt(_,j,!1,!1)):(X=yt(N,j,!0,!0),Y=yt(j,_,!0,!0)),s!=null){let ee=_p(B,j);return[X,Y,ee]}else return[X,Y]},I={a:x,b,bias:v,preluActivationWeights:w},E={transposeA:n,transposeB:r,activation:a,leakyreluAlpha:i};return s==null?Ns(($,_,N)=>{let P=U.runKernel(p2,I,E);return N([$,_,P]),{value:ue(P,A),gradFunc:S}})(x,b):Ns(($,_,N,P)=>{let B=U.runKernel(p2,I,E);return P([$,_,B,N]),{value:ue(B,A),gradFunc:S}})(x,b,v)}var pW=H({fusedMatMul_:hW});function fW(e){return dy(e,.54,.46)}var mW=H({hammingWindow_:fW});function gW(e){return dy(e,.5,.5)}var I4=H({hannWindow_:gW});function yW(e,t,n,r=!1,s=0){let a=0,o=[];for(;a+t<=e.size;)o.push(Ze(e,a,t)),a+=n;if(r)for(;a<e.size;){let i=a+t-e.size,l=an([Ze(e,a,t-i),wp([i],s)]);o.push(l),a+=n}return o.length===0?ra([],[0,t]):ue(an(o),[o.length,t])}var S4=H({frame_:yW});function AW(e,t,n,r,s=I4){r==null&&(r=w4(t));let a=S4(e,t,n),o=fe(a,s(t));return uy(o,r)}var xW=H({stft_:AW});function bW(e,t,n,r,s="bilinear",a=0){let o=O(e,"image","cropAndResize"),i=O(t,"boxes","cropAndResize","float32"),l=O(n,"boxInd","cropAndResize","int32"),u=i.shape[0];L(o.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),L(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`),L(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`),L(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),L(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),L(s==="bilinear"||s==="nearest",()=>`method must be bilinear or nearest, but was ${s}`);let c={image:o,boxes:i,boxInd:l},d={method:s,extrapolationValue:a,cropSize:r};return U.runKernel(Rv,c,d)}var vW=H({cropAndResize_:bW});function wW(e){let t=O(e,"image","flipLeftRight","float32");L(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return U.runKernel(Zv,n,{})}var kW=H({flipLeftRight_:wW});function IW(e,t,n=0,r=.5){let s=O(e,"image","rotateWithOffset","float32");L(s.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${s.rank}.`);let a={image:s},o={radians:t,fillValue:n,center:r};return U.runKernel(M7,a,o)}var SW=H({rotateWithOffset_:IW});function qi(e,t,n,r,s,a){r==null&&(r=.5),s==null&&(s=Number.NEGATIVE_INFINITY),a==null&&(a=0);let o=e.shape[0];return n=Math.min(n,o),L(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),L(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),L(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),L(t.rank===1,()=>"scores must be a 1D tensor"),L(t.shape[0]===o,()=>`scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`),L(0<=a&&a<=1,()=>`softNmsSigma must be in [0, 1], but was '${a}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:s,softNmsSigma:a}}function TW(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){let a=O(e,"boxes","nonMaxSuppression"),o=O(t,"scores","nonMaxSuppression"),i=qi(a,o,n,r,s);n=i.maxOutputSize,r=i.iouThreshold,s=i.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:s};return U.runKernel(Dw,{boxes:a,scores:o},l)}var NW=H({nonMaxSuppression_:TW});function CW(e,t,n){let r=EW(e,t,n),s=r<0?-(r+1):r;e.splice(s,0,t)}function EW(e,t,n){return _W(e,t,n||$W)}function $W(e,t){return e>t?1:e<t?-1:0}function _W(e,t,n){let r=0,s=e.length,a=0,o=!1;for(;r<s;){a=r+(s-r>>>1);let i=n(t,e[a]);i>0?r=a+1:(s=a,o=!i)}return o?r:-r-1}function T4(e,t,n,r,s){return hy(e,t,n,r,s,0)}function N4(e,t,n,r,s,a){return hy(e,t,n,r,s,0,!1,a,!0)}function C4(e,t,n,r,s,a){return hy(e,t,n,r,s,a,!0)}function hy(e,t,n,r,s,a,o=!1,i=!1,l=!1){let u=[];for(let g=0;g<t.length;g++)t[g]>s&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(E4);let c=a>0?-.5/a:0,d=[],h=[];for(;d.length<n&&u.length>0;){let g=u.pop(),{score:y,boxIndex:A,suppressBeginIndex:x}=g;if(y<s)break;let b=!1;for(let v=d.length-1;v>=x;--v){let w=RW(e,A,d[v]);if(w>=r){b=!0;break}if(g.score=g.score*DW(r,c,w),g.score<=s)break}g.suppressBeginIndex=d.length,b||(g.score===y?(d.push(A),h.push(g.score)):g.score>s&&CW(u,g,E4))}let p=d.length,f=n-p;i&&f>0&&(d.push(...new Array(f).fill(0)),h.push(...new Array(f).fill(0)));let m={selectedIndices:d};return o&&(m.selectedScores=h),l&&(m.validOutputs=p),m}function RW(e,t,n){let r=e.subarray(t*4,t*4+4),s=e.subarray(n*4,n*4+4),a=Math.min(r[0],r[2]),o=Math.min(r[1],r[3]),i=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),u=Math.min(s[0],s[2]),c=Math.min(s[1],s[3]),d=Math.max(s[0],s[2]),h=Math.max(s[1],s[3]),p=(i-a)*(l-o),f=(d-u)*(h-c);if(p<=0||f<=0)return 0;let m=Math.max(a,u),g=Math.max(o,c),y=Math.min(i,d),A=Math.min(l,h),x=Math.max(y-m,0)*Math.max(A-g,0);return x/(p+f-x)}function DW(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function E4(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function FW(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){let a=O(e,"boxes","nonMaxSuppressionAsync"),o=O(t,"scores","nonMaxSuppressionAsync"),i=qi(a,o,n,r,s);n=i.maxOutputSize,r=i.iouThreshold,s=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),u=l[0],c=l[1],{selectedIndices:d}=T4(u,c,n,r,s);return a!==e&&a.dispose(),o!==t&&o.dispose(),ur(d,"int32")}var MW=FW;function OW(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=0){let o=O(e,"boxes","nonMaxSuppression"),i=O(t,"scores","nonMaxSuppression"),l=qi(o,i,n,r,s,a);n=l.maxOutputSize,r=l.iouThreshold,s=l.scoreThreshold,a=l.softNmsSigma;let u={boxes:o,scores:i},c={maxOutputSize:n,iouThreshold:r,scoreThreshold:s,softNmsSigma:a},d=U.runKernel(Mw,u,c);return{selectedIndices:d[0],selectedScores:d[1]}}var PW=H({nonMaxSuppressionWithScore_:OW});async function zW(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=0){let o=O(e,"boxes","nonMaxSuppressionAsync"),i=O(t,"scores","nonMaxSuppressionAsync"),l=qi(o,i,n,r,s,a);n=l.maxOutputSize,r=l.iouThreshold,s=l.scoreThreshold,a=l.softNmsSigma;let u=await Promise.all([o.data(),i.data()]),c=u[0],d=u[1],{selectedIndices:h,selectedScores:p}=C4(c,d,n,r,s,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:ur(h,"int32"),selectedScores:ur(p)}}var LW=zW;function BW(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=!1){let o=O(e,"boxes","nonMaxSuppression"),i=O(t,"scores","nonMaxSuppression"),l=qi(o,i,n,r,s,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,h={boxes:o,scores:i},p={maxOutputSize:u,iouThreshold:c,scoreThreshold:d,padToMaxOutputSize:a},f=U.runKernel(Fw,h,p);return{selectedIndices:f[0],validOutputs:f[1]}}var WW=H({nonMaxSuppressionPadded_:BW});async function VW(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=!1){let o=O(e,"boxes","nonMaxSuppressionAsync"),i=O(t,"scores","nonMaxSuppressionAsync"),l=qi(o,i,n,r,s,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,[h,p]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=N4(h,p,u,c,d,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:ur(f,"int32"),validOutputs:ut(m,"int32")}}var UW=VW;function HW(e,t,n=!1,r=!1){let s=O(e,"images","resizeBilinear");L(s.rank===3||s.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`),L(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),L(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let a=s,o=!1;s.rank===3&&(o=!0,a=ue(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:r,size:t},u=U.runKernel(Xw,i,l);return o?ue(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var GW=H({resizeBilinear_:HW});function jW(e,t,n=!1,r=!1){let s=O(e,"images","resizeNearestNeighbor");L(s.rank===3||s.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`),L(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),L(s.dtype==="float32"||s.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),L(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let a=s,o=!1;s.rank===3&&(o=!0,a=ue(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:r,size:t},u=U.runKernel(Kw,i,l);return o?ue(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var qW=H({resizeNearestNeighbor_:jW});function KW(e,t="binary",n=!1,r=.5){let s=O(e,"image","threshold"),a=.2989,o=.587,i=.114,l=s.shape[0]*s.shape[1],u=fe(ur([r]),255),c,d,h,p;if(L(s.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${s.rank}.`),L(s.shape[2]===3||s.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${s.shape[2]}.`),L(s.dtype==="int32"||s.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${s.dtype}.`),L(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),s.shape[2]===3){[c,d,h]=ta(s,[1,1,1],-1);let g=fe(c,a),y=fe(d,o),A=fe(h,i);p=Me(Me(g,y),A)}else p=e;if(t==="otsu"){let g=Hk(Pt(m4(p),"int32"),ts([]),256);u=XW(g,l)}let f=n?ry(p,u):kp(p,u);return Pt(fe(f,255),"int32")}function XW(e,t){let n=ur([-1]),r=ur([0]),s=ur([0]),a,o,i,l,u,c;for(let d=0;d<e.size-1;d++){a=Ze(e,0,d+1),o=Ze(e,d+1),u=Qe(_t(a),t),c=Qe(_t(o),t);let h=_t(fe(a,pc(0,a.size)));i=Qe(h,_t(a));let p=wp(o.shape,a.size),f=Me(pc(0,o.size),p),m=fe(o,f);l=Qe(_t(m),_t(o));let g=He(i,l),y=He(i,l),A=fe(u,c);s=fe(fe(A,g),y);let x=kp(s,r);r=Gi(x,s,r),n=Gi(x,ur([d]),n)}return n}var ZW=H({threshold_:KW});function YW(e,t,n="nearest",r="constant",s=0,a){let o=O(e,"image","transform","float32"),i=O(t,"transforms","transform","float32");L(o.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${o.rank}.`),L(i.rank===2&&(i.shape[0]===o.shape[0]||i.shape[0]===1)&&i.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),L(a==null||a.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${a}.`);let l={image:o,transforms:i},u={interpolation:n,fillMode:r,fillValue:s,outputShape:a};return U.runKernel(C7,l,u)}var JW=H({transform_:YW});function QW(e,t,n){L(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),L(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=O(e,"a","bandPart");L(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let s=r.shape,[a,o]=r.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=ue(pc(0,a,1,"int32"),[-1,1]),l=pc(0,o,1,"int32"),u=He(i,l),c=Sp(ry(u,ut(+t,"int32")),Jk(u,ut(-n,"int32"))),d=ji([a,o],r.dtype);return ue(So(fc(ue(r,[-1,a,o])).map(h=>Gi(c,h,d))),s)}var eV=H({bandPart_:QW});function tV(e){let t;if(Array.isArray(e)){t=!1,L(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let s=e[0].shape[0];for(let a=1;a<e.length;++a)L(e[a].shape[0]===s,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${s})`)}else t=!0,e=ta(e,e.shape[0],0).map(s=>Zn(s,[0]));L(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let s=0;s<e.length;++s)n.push(U.tidy(()=>{let a=r[s];if(s>0)for(let o=0;o<s;++o){let i=fe(_t(fe(n[o],a)),n[o]);a=He(a,i)}return Qe(a,cy(a,"euclidean"))}));return t?So(n,0):n}var nV=H({gramSchmidt_:tV});function rV(e,t=!1){if(L(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return $4(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),r=fc(ue(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),s=[],a=[];r.forEach(l=>{let[u,c]=$4(l,t);s.push(u),a.push(c)});let o=ue(So(s,0),e.shape),i=ue(So(a,0),e.shape);return[o,i]}}function $4(e,t=!1){return U.tidy(()=>{L(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],s=Xk(n),a=Js(e),o=ra([[1]],[1,1]),i=Js(o),l=n>=r?r:n;for(let u=0;u<l;++u){let c=a,d=i,h=s;[i,a,s]=U.tidy(()=>{let p=Ze(a,[u,u],[n-u,1]),f=cy(p),m=Ze(a,[u,u],[1,1]),g=Gi(kp(m,0),ra([[-1]]),ra([[1]])),y=He(m,fe(g,f)),A=Qe(p,y);A.shape[0]===1?i=Js(o):i=an([o,Ze(A,[1,0],[A.shape[0]-1,A.shape[1]])],0);let x=$a(Qe(yt(g,y),f)),b=Ze(a,[u,0],[n-u,r]),v=fe(x,i),w=fp(i);if(u===0)a=He(b,yt(v,yt(w,b)));else{let E=He(b,yt(v,yt(w,b)));a=an([Ze(a,[0,0],[u,r]),E],0)}let S=fp(v),I=Ze(s,[0,u],[n,s.shape[1]-u]);if(u===0)s=He(I,yt(yt(I,i),S));else{let E=He(I,yt(yt(I,i),S));s=an([Ze(s,[0,0],[n,u]),E],1)}return[i,a,s]}),Ve([c,d,h])}return!t&&n>r&&(s=Ze(s,[0,0],[n,r]),a=Ze(a,[0,0],[r,r])),[s,a]})}var sV=H({qr_:rV}),Pn;(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"})(Pn||(Pn={}));function aV(e,t,n=Pn.SUM_BY_NONZERO_WEIGHTS){let r=O(e,"losses","computeWeightedLoss"),s=null;t!=null&&(s=O(t,"weights","computeWeightedLoss"));let a=s==null?r:fe(r,s);if(n===Pn.NONE)return a;if(n===Pn.SUM)return _t(a);if(n===Pn.MEAN){if(s==null)return Tp(a);{let o=r.size/s.size,i=Qe(_t(a),_t(s));return o>1?Qe(i,ut(o)):i}}if(n===Pn.SUM_BY_NONZERO_WEIGHTS){if(s==null)return Qe(_t(a),ut(r.size));{let o=fe(s,ko(r.shape)),i=Pt(_t(c4(o,ut(0))),"float32");return Qe(_t(a),i)}}throw Error(`Unknown reduction: ${n}`)}var sa=H({computeWeightedLoss_:aV});function oV(e,t,n,r=Pn.SUM_BY_NONZERO_WEIGHTS){let s=O(e,"labels","absoluteDifference"),a=O(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=O(n,"weights","absoluteDifference")),Mn(s.shape,a.shape,"Error in absoluteDifference: ");let i=Nr(He(s,a));return sa(i,o,r)}var iV=H({absoluteDifference_:oV});function lV(e,t,n,r,s=Pn.SUM_BY_NONZERO_WEIGHTS){let a=O(e,"labels","cosineDistance"),o=O(t,"predictions","cosineDistance"),i=null;r!=null&&(i=O(r,"weights","cosineDistance")),Mn(a.shape,o.shape,"Error in cosineDistance: ");let l=ut(1),u=He(l,_t(fe(a,o),n,!0));return sa(u,i,s)}var uV=H({cosineDistance_:lV});function cV(e,t,n,r=Pn.SUM_BY_NONZERO_WEIGHTS){let s=O(e,"labels","hingeLoss"),a=O(t,"predictions","hingeLoss"),o=null;n!=null&&(o=O(n,"weights","hingeLoss")),Mn(s.shape,a.shape,"Error in hingeLoss: ");let i=ut(1);s=He(fe(ut(2),s),i);let l=Cp(He(i,fe(s,a)));return sa(l,o,r)}var dV=H({hingeLoss_:cV});function hV(e,t,n,r=1,s=Pn.SUM_BY_NONZERO_WEIGHTS){let a=O(e,"labels","huberLoss"),o=O(t,"predictions","huberLoss"),i=null;n!=null&&(i=O(n,"weights","huberLoss")),Mn(a.shape,o.shape,"Error in huberLoss: ");let l=ut(r),u=Nr(He(o,a)),c=u4(u,l),d=He(u,c),h=Me(fe(ut(.5),ns(c)),fe(l,d));return sa(h,i,s)}var pV=H({huberLoss_:hV});function fV(e,t,n,r=1e-7,s=Pn.SUM_BY_NONZERO_WEIGHTS){let a=O(e,"labels","logLoss"),o=O(t,"predictions","logLoss"),i=null;n!=null&&(i=O(n,"weights","logLoss")),Mn(a.shape,o.shape,"Error in logLoss: ");let l=ut(1),u=ut(r),c=$a(fe(a,uc(Me(o,u)))),d=fe(He(l,a),uc(Me(He(l,o),u))),h=He(c,d);return sa(h,i,s)}var mV=H({logLoss_:fV});function gV(e,t,n,r=Pn.SUM_BY_NONZERO_WEIGHTS){let s=O(e,"labels","meanSquaredError"),a=O(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=O(n,"weights","meanSquaredError")),Mn(s.shape,a.shape,"Error in meanSquaredError: ");let i=y4(s,a);return sa(i,o,r)}var yV=H({meanSquaredError_:gV});function AV(e,t){let n=O(e,"labels","sigmoidCrossEntropyWithLogits"),r=O(t,"logits","sigmoidCrossEntropyWithLogits");Mn(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let s=Cp(r),a=fe(r,n),o=e4(wo($a(Nr(r))));return Me(He(s,a),o)}function xV(e,t,n,r=0,s=Pn.SUM_BY_NONZERO_WEIGHTS){let a=O(e,"multiClassLabels","sigmoidCrossEntropy"),o=O(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=O(n,"weights","sigmoidCrossEntropy")),Mn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),r>0){let u=ut(r),c=ut(1),d=ut(.5);a=Me(fe(a,He(c,u)),fe(d,u))}let l=AV(a,o);return sa(l,i,s)}var bV=H({sigmoidCrossEntropy_:xV});function vV(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 ${n}`);return Ns((s,a,o)=>{let l=s4(a,[n],!0),u=He(Pt(a,"float32"),l);o([s,u]);let c=$a(fe(u,s));return{value:_t(c,[n]),gradFunc:(p,f)=>{let[m,g]=f,y=cc(p.shape,[n]);return[fe(ue(p,y),He(Pt(m,"float32"),wo(g))),fe(ue(p,y),He(wo(g),Pt(m,"float32")))]}}})(e,t)}function wV(e,t,n,r=0,s=Pn.SUM_BY_NONZERO_WEIGHTS){let a=O(e,"onehotLabels","softmaxCrossEntropy"),o=O(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=O(n,"weights","softmaxCrossEntropy")),Mn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),r>0){let u=ut(r),c=ut(1),d=ut(a.shape[1]);a=Me(fe(a,He(c,u)),Qe(u,d))}let l=vV(a,o);return sa(l,i,s)}var kV=H({softmaxCrossEntropy_:wV});function IV(e,t,n,r){let s=O(e,"indices","sparseFillEmptyRows"),a=O(t,"values","sparseFillEmptyRows"),o=O(n,"denseShape","sparseFillEmptyRows"),i=O(r,"defaultValue","sparseFillEmptyRows",a.dtype);if(s.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
        ${s.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:s,values:a,denseShape:o,defaultValue:i},u=U.runKernel(f7,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var SV=H({sparseFillEmptyRows_:IV});function TV(e,t,n){let r=O(e,"inputIndices","sparseReshape"),s=O(t,"inputShape","sparseReshape"),a=O(n,"newShape","sparseReshape");if(r.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
        ${r.shape}`);if(s.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:r,inputShape:s,newShape:a},i=U.runKernel(m7,o);return{outputIndices:i[0],outputShape:i[1]}}var NV=H({sparseReshape_:TV});function CV(e,t,n){let r=O(e,"data","sparseSegmentMean"),s=O(t,"indices","sparseSegmentMean"),a=O(n,"segmentIds","sparseSegmentMean");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${s.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);let o={data:r,indices:s,segmentIds:a};return U.runKernel(g7,o)}var EV=H({sparseSegmentMean_:CV});function $V(e,t,n){let r=O(e,"data","sparseSegmentSum"),s=O(t,"indices","sparseSegmentSum"),a=O(n,"segmentIds","sparseSegmentSum");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
         ${s.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
         ${a.shape}`);let o={data:r,indices:s,segmentIds:a};return U.runKernel(y7,o)}var _V=H({sparseSegmentSum_:$V});function RV(e,t,n,r,s,a,o,i){let l=O(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=O(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:r,leftPad:s,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:u},h=U.runKernel(v7,d,c);return{nGrams:h[0],nGramsSplits:h[1]}}var DV=H({stringNGrams_:RV});function FV(e,t,n=!0){let r=O(e,"input","stringSplit","string"),s=O(t,"delimiter","stringSplit","string");if(r.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${r.shape}`);if(s.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${s.shape}`);let a={skipEmpty:n},o={input:r,delimiter:s},i=U.runKernel(w7,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var MV=H({stringSplit_:FV});function OV(e,t){let n=O(e,"input","stringToHashBucketFast","string"),r={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let s={input:n};return U.runKernel(k7,s,r)}var PV=H({stringToHashBucketFast_:OV}),zV={fft:ly,ifft:Ep,rfft:uy,irfft:g4},LV={hammingWindow:mW,hannWindow:I4,frame:S4,stft:xW},Ye={flipLeftRight:kW,resizeNearestNeighbor:qW,resizeBilinear:GW,rotateWithOffset:SW,cropAndResize:vW,nonMaxSuppression:NW,nonMaxSuppressionAsync:MW,nonMaxSuppressionWithScore:PW,nonMaxSuppressionWithScoreAsync:LW,nonMaxSuppressionPadded:WW,nonMaxSuppressionPaddedAsync:UW,threshold:ZW,transform:JW},BV={bandPart:eV,gramSchmidt:nV,qr:sV},WV={absoluteDifference:iV,computeWeightedLoss:sa,cosineDistance:uV,hingeLoss:dV,huberLoss:pV,logLoss:mV,meanSquaredError:yV,sigmoidCrossEntropy:bV,softmaxCrossEntropy:kV},VV={sparseFillEmptyRows:SV,sparseReshape:NV,sparseSegmentMean:EV,sparseSegmentSum:_V},UV={stringNGrams:DV,stringSplit:MV,stringToHashBucketFast:PV},Ra=class extends Rk{minimize(e,t=!1,n){let{value:r,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:s[o.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ve(s),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return t4(e,t)}dispose(){this.iterations_!=null&&Ve(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ut(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ra,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Fp=class extends Ra{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=U.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=U.registeredVariables[n],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${n}/accum_grad`,variable:Ue(()=>Cr(s).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${n}/accum_var`,variable:Ue(()=>Cr(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[r].variable,l=this.accumulatedUpdates[r].variable;Ue(()=>{let u=Me(fe(i,this.rho),fe(ns(o),1-this.rho)),c=fe(Qe(na(Me(l,this.epsilon)),na(Me(i,this.epsilon))),o),d=Me(fe(l,this.rho),fe(ns(c),1-this.rho));i.assign(u),l.assign(d);let h=Me(fe(c,-this.learningRate),s);s.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ve(this.accumulatedGrads.map(e=>e.variable)),Ve(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Fp.className="Adadelta";Ea(Fp);var Mp=class extends Ra{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=U.registeredVariables[n];if(this.accumulatedGrads[r]==null){let i=!1;this.accumulatedGrads[r]={originalName:`${n}/accumulator`,variable:Ue(()=>wp(s.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[r].variable;Ue(()=>{let i=Me(o,ns(a));o.assign(i);let l=Me(fe(Qe(a,na(Me(i,U.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ve(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)}};Mp.className="Adagrad";Ea(Mp);var Op=class extends Ra{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ue(()=>{this.accBeta1=ut(t).variable(),this.accBeta2=ut(n).variable()}),r==null&&(this.epsilon=U.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Ue(()=>{let n=He(1,this.accBeta1),r=He(1,this.accBeta2);t.forEach((s,a)=>{let o=U.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ue(()=>Cr(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ue(()=>Cr(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[s];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,d=Me(fe(u,this.beta1),fe(l,1-this.beta1)),h=Me(fe(c,this.beta2),fe(ns(l),1-this.beta2)),p=Qe(d,n),f=Qe(h,r);u.assign(d),c.assign(h);let m=Me(fe(Qe(p,Me(na(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(fe(this.accBeta1,this.beta1)),this.accBeta2.assign(fe(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ve(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ve(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),Ue(()=>{this.accBeta1.assign(hc(this.beta1,this.iterations_+1)),this.accBeta2.assign(hc(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.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)}};Op.className="Adam";Ea(Op);var Pp=class extends Ra{constructor(e,t,n,r=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ue(()=>{this.iteration=ut(0).variable(),this.accBeta1=ut(t).variable()}),r==null&&(this.epsilon=U.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Ue(()=>{let n=He(1,this.accBeta1),r=Qe(-this.learningRate,Me(fe(this.iteration,this.decay),1));t.forEach((s,a)=>{let o=U.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Cr(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Cr(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[s];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,d=Me(fe(u,this.beta1),fe(l,1-this.beta1)),h=fe(c,this.beta2),p=Nr(l),f=l4(h,p);u.assign(d),c.assign(f);let m=Me(fe(Qe(r,n),Qe(d,Me(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(Me(this.iteration,1)),this.accBeta1.assign(fe(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ve(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ve(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)}};Pp.className="Adamax";Ea(Pp);var mc=class extends Ra{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=Array.isArray(e)?e[r].tensor:e[n];if(s==null)return;let a=U.registeredVariables[n];Ue(()=>{let o=Me(fe(this.c,s),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Pk(ut(-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)}};mc.className="SGD";Ea(mc);var zp=class extends mc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ut(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=U.registeredVariables[n];if(this.accumulations[r]==null){let i=!1;this.accumulations[r]={originalName:`${n}/momentum`,variable:Ue(()=>Cr(s).variable(i))}}let a=this.accumulations[r].variable,o=Array.isArray(e)?e[r].tensor:e[n];o!=null&&Ue(()=>{let i,l=Me(fe(this.m,a),o);this.useNesterov?i=Me(fe(this.c,Me(o,fe(l,this.m))),s):i=Me(fe(this.c,l),s),a.assign(l),s.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ve(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};zp.className="Momentum";Ea(zp);var Lp=class extends Ra{constructor(e,t=.9,n=0,r=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,r==null&&(this.epsilon=U.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=U.registeredVariables[n],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:Ue(()=>Cr(s).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:Ue(()=>Cr(s).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:Ue(()=>Cr(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[r].variable,l=this.accumulatedMoments[r].variable;Ue(()=>{let u=Me(fe(i,this.decay),fe(ns(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[r].variable,d=Me(fe(c,this.decay),fe(o,1-this.decay)),h=Qe(fe(o,this.learningRate),na(He(u,Me(ns(d),this.epsilon)))),p=Me(fe(l,this.momentum),h);i.assign(u),c.assign(d),l.assign(p);let f=He(s,p);s.assign(f)}else{let c=Me(fe(i,this.decay),fe(ns(o),1-this.decay)),d=Me(fe(l,this.momentum),Qe(fe(o,this.learningRate),na(Me(c,this.epsilon))));i.assign(c),l.assign(d);let h=He(s,d);s.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ve(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ve(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ve(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(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.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)}};Lp.className="RMSProp";Ea(Lp);var To=class{static sgd(e){return new mc(e)}static momentum(e,t,n=!1){return new zp(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,s=!1){return new Lp(e,t,n,r,s)}static adam(e=.001,t=.9,n=.999,r=null){return new Op(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Fp(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,s=0){return new Pp(e,t,n,r,s)}static adagrad(e,t=.1){return new Mp(e,t)}},HV={sgd:To.sgd,momentum:To.momentum,adadelta:To.adadelta,adagrad:To.adagrad,rmsprop:To.rmsprop,adamax:To.adamax,adam:To.adam},GV=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function jV(){return new Promise(e=>GV(()=>e()))}var _4={};De(_4,{ERF_A1:()=>aU,ERF_A2:()=>oU,ERF_A3:()=>iU,ERF_A4:()=>lU,ERF_A5:()=>uU,ERF_P:()=>sU,PARALLELIZE_THRESHOLD:()=>py,SELU_SCALE:()=>rU,SELU_SCALEALPHA:()=>nU,applyActivation:()=>Rp,assertAndGetBroadcastShape:()=>In,assertAxesAreInnerMostDims:()=>yz,assertParamsConsistent:()=>qV,assignToTypedArray:()=>yU,axesAreInnerMostDims:()=>sy,calculateShapes:()=>bk,checkEinsumDimSizes:()=>kU,combineLocations:()=>r4,complexWithEvenIndex:()=>fU,complexWithOddIndex:()=>mU,computeConv2DInfo:()=>oc,computeConv3DInfo:()=>Bk,computeDefaultPad:()=>J2,computeDilation2DInfo:()=>iO,computeOptimalWindowSize:()=>XV,computeOutAndReduceShapes:()=>gz,computeOutShape:()=>KV,computePool2DInfo:()=>Lk,computePool3DInfo:()=>lO,convertConv2DDataFormat:()=>Wk,decodeEinsumEquation:()=>vU,eitherStridesOrDilationsAreOne:()=>Qs,expandShapeToKeepDim:()=>cc,exponent:()=>xU,exponents:()=>AU,fromStringArrayToUint8:()=>RU,fromUint8ToStringArray:()=>_U,getAxesPermutation:()=>Az,getBroadcastDims:()=>AP,getComplexWithIndex:()=>gU,getEinsumComputePath:()=>IU,getEinsumPermutation:()=>wU,getFusedBiasGradient:()=>_p,getFusedDyActivation:()=>$p,getImageCenter:()=>ZV,getInnerMostAxes:()=>bz,getPermuted:()=>JV,getReductionAxes:()=>jk,getReshaped:()=>YV,getReshapedPermuted:()=>QV,getSliceBeginCoords:()=>eU,getSliceSize:()=>tU,getUndoAxesPermutation:()=>xz,isIdentityPermutation:()=>SU,log:()=>dU,mergeRealAndImagArrays:()=>hU,prepareAndValidate:()=>Ak,prepareSplitSize:()=>NU,segment_util:()=>F4,shouldFuse:()=>Dp,slice_util:()=>H2,splitRealAndImagArrays:()=>pU,tupleValuesAreOne:()=>ic,upcastType:()=>cp,validateInput:()=>U2,validateUpdateShape:()=>V2,warn:()=>cU});function qV(e,t){let n=e[0].length;e.forEach((s,a)=>{L(s.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),L(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((s,a)=>{for(let o=0;o<n;o++)L(o===t||s[o]===r[o],()=>`Error in concat${n}D: Shape of tensors[${a}] (${s}) does not match the shape of the rest (${r}) along the non-concatenated axis ${a}.`)})}function KV(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var py=30;function XV(e){return e<=py?e:tp(e,Math.floor(Math.sqrt(e)))}function ZV(e,t,n){let r=n*(typeof e=="number"?e:e[0]),s=t*(typeof e=="number"?e:e[1]);return[r,s]}function YV(e,t,n,r=!0){let s=[];if(r)s=s.concat(t.slice(0)),s.push(e[0]/n),s=s.concat(e.slice(1));else{s=s.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)s=s.concat([e[o+1]/t[o],t[o]]);s=s.concat(e.slice(a+1))}return s}function JV(e,t,n=!0){let r=[];if(n){r.push(t);for(let s=t+1;s<e;++s)s<=2*t?(r.push(s),r.push(s-(t+1))):r.push(s)}else{let s=[],a=[];for(let o=1;o<e;++o)o>=t*2+1||o%2==1?a.push(o):s.push(o);r.push(...s),r.push(0),r.push(...a)}return r}function QV(e,t,n,r=!0){let s=[];r?s.push(e[0]/n):s.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?r?s.push(t[a-1]*e[a]):s.push(e[a]/t[a-1]):s.push(e[a]);return s}function eU(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function tU(e,t,n){let r=e.slice(0,1);for(let s=0;s<n;++s)r.push(e[s+1]-t[s][0]-t[s][1]);return r}var nU=1.7580993408473768,rU=1.0507009873554805,sU=.3275911,aU=.254829592,oU=-.284496736,iU=1.421413741,lU=-1.453152027,uU=1.061405429;function cU(...e){ct().getBool("IS_TEST")||console.warn(...e)}function dU(...e){ct().getBool("IS_TEST")||console.log(...e)}function hU(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function pU(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function fU(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let s=0;s<e.length;s+=4)n[Math.floor(s/4)]=e[s],r[Math.floor(s/4)]=e[s+1];return{real:n,imag:r}}function mU(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let s=2;s<e.length;s+=4)n[Math.floor(s/4)]=e[s],r[Math.floor(s/4)]=e[s+1];return{real:n,imag:r}}function gU(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function yU(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function AU(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let s=0;s<Math.ceil(e/2);s++){let a=(t?2:-2)*Math.PI*(s/e);n[s]=Math.cos(a),r[s]=Math.sin(a)}return{real:n,imag:r}}function xU(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),s=Math.cos(r),a=Math.sin(r);return{real:s,imag:a}}var fy="->",bU=/->/g,R4=",",D4="...";function vU(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(bU,"").length)/fy.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${fy}").`);let[r,s]=e.split(fy);L(r.indexOf(D4)===-1,()=>`The ellipsis notation ("${D4}") is not supported yet.`);let a=r.split(R4),o=a.length;if(t!==o)throw new Error(`Expected ${o} input tensors, received ${t}`);if(o>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let h=0;h<s.length;++h){let p=s[h];if(!a.some(f=>f.indexOf(p)!==-1))throw new Error(`Output subscripts contain the label ${p} not present in the input subscripts.`);i.indexOf(p)===-1&&i.push(p)}for(let h=0;h<r.length;++h){let p=r[h];i.indexOf(p)===-1&&p!==R4&&i.push(p)}let l=new Array(a.length);for(let h=0;h<o;++h){if(new Set(a[h].split("")).size!==a[h].length)throw new Error(`Found duplicate axes in input component ${a[h]}. Support for duplicate axes in input is not implemented yet.`);l[h]=[];for(let p=0;p<a[h].length;++p)l[h].push(i.indexOf(a[h][p]))}let u=i.length,c=s.length,d=[];for(let h=c;h<u;++h)d.push(h);return{allDims:i,summedDims:d,idDims:l}}function wU(e,t){let n=new Array(e);n.fill(-1);for(let s=0;s<t.length;++s)n[t[s]]=s;let r=[];for(let s=0;s<e;++s)n[s]===-1&&r.push(s);return n=n.filter(s=>s!==-1),{permutationIndices:n,expandDims:r}}function kU(e,t,n){let r=new Array(e);for(let s=0;s<n.length;++s){let a=n[s].shape;for(let o=0;o<t[s].length;++o)r[t[s][o]]===void 0?r[t[s][o]]=a[o]:L(r[t[s][o]]===a[o],()=>`Expected dimension ${r[t[s][o]]} at axis ${o} of input shaped ${JSON.stringify(a)}, but got dimension ${a[o]}`)}}function IU(e,t){let n=e,r=[],s=0;e.length===0&&n.push(-1),s=e.length+1;for(let o=0;o<s;++o)r.push([]);let a=[];for(let o=0;o<n.length;++o){let i=n[o],l=TU(t,i);for(let u of l)a.indexOf(u)===-1&&(r[o].push(u),a.push(u))}return{path:n,steps:r}}function SU(e){return e.every((t,n)=>t===n)}function TU(e,t){let n=[];for(let r=0;r<e.length;++r)(e[r].length===0||e[r].indexOf(t)!==-1||t===-1)&&n.push(r);return n}function NU(e,t,n=0){let r=[];if(typeof t=="number")L(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let s=t.reduce((o,i)=>(i===-1&&(o+=1),o),0);L(s<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let o=t.reduce((i,l)=>l>0?i+l:i);t[a]=e.shape[n]-o}L(e.shape[n]===t.reduce((o,i)=>o+i),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var F4={};De(F4,{collectGatherOpShapeInfo:()=>$U,computeOutShape:()=>EU,segOpComputeOptimalWindowSize:()=>CU});function CU(e,t){let n=!1,r;for(e<=py?(r=e,n=!0):r=tp(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=tp(e,r+1);return r}function EU(e,t,n){let r=[],s=e.length;for(let a=0;a<s;a++)a!==t?r.push(e[a]):r.push(n);return r}function $U(e,t,n,r){let s=t.shape.length,a=e.shape.length;if(r!==0&&(r<-s||r>s))throw new Error(`Expect batchDims in the range of [-${s}, ${s}], but got ${r}`);if(r<0&&(r+=s),r>a)throw new Error(`batchDims (${r}) must be less than rank(x) (
    ${a}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let d=0;d<r;++d)if(e.shape[d]!==t.shape[d])throw new Error(`x.shape[${d}]: ${e.shape[d]} should be equal to indices.shape[${d}]: ${t.shape[d]}.`);let o=e.shape[n],i=[],l=1,u=1,c=1;for(let d=0;d<r;++d)i.push(e.shape[d]),l*=e.shape[d];for(let d=r;d<n;d++)i.push(e.shape[d]),u*=e.shape[d];for(let d=r;d<s;d++)i.push(t.shape[d]);for(let d=n+1;d<a;d++)i.push(e.shape[d]),c*=e.shape[d];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:o,outputShape:i}}function _U(e){try{return e.map(t=>ip(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function RU(e){return e.map(t=>Qu(t))}var M4={};De(M4,{nonMaxSuppressionV3Impl:()=>T4,nonMaxSuppressionV4Impl:()=>N4,nonMaxSuppressionV5Impl:()=>C4,whereImpl:()=>x4});var DU=1e-7,FU=1e-4,my=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},Bp=class{refCount(e){return Gr("refCount")}incRef(e){return Gr("incRef")}timerAvailable(){return!0}time(e){return Gr("time")}read(e){return Gr("read")}readSync(e){return Gr("readSync")}numDataIds(){return Gr("numDataIds")}disposeData(e,t){return Gr("disposeData")}write(e,t,n){return Gr("write")}move(e,t,n,r,s){return Gr("move")}memory(){return Gr("memory")}floatPrecision(){return Gr("floatPrecision")}epsilon(){return this.floatPrecision()===32?DU:FU}dispose(){return Gr("dispose")}};function Gr(e){throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}function O4(e){let t=e.length,n=0,r=0;for(;t>0;)r=Math.random()*t|0,t--,n=e[t],e[t]=e[r],e[r]=n}function MU(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,r,s,a=0;for(;n>0;)a=Math.random()*n|0,n--,r=e[n],s=t[n],e[n]=e[a],t[n]=t[a],e[a]=r,t[a]=s}function gc(e,t,n){return Math.max(e,Math.min(t,n))}function OU(e){return e%2==0?e:e+1}function PU(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function zU(e,t){let n=Math.random();return t*n+(1-n)*e}function LU(e,t){let n=0;for(let r=0;r<e.length;r++){let s=Number(e[r])-Number(t[r]);n+=s*s}return n}function z(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function rs(e,t,n=""){z(Da(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function Wp(e){z(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function yc(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||ss(e)&&!n)for(let r=0;r<e.length;++r)yc(e[r],t,n);else t.push(e);return t}function on(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 BU(e){return e.length===0}function Da(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 mn(e){return e%1==0}function WU(e){if(Math.tanh!=null)return Math.tanh(e);if(e===Infinity)return 1;if(e===-Infinity)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function VU(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function UU(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return O4(t),t}function Ac(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function HU(e,t=r=>0,n){return new Promise((r,s)=>{let a=0,o=()=>{if(e()){r();return}a++;let i=t(a);if(n!=null&&a>=n){s();return}setTimeout(o,i)};o()})}function GU(e,t){let n=1,r=-1;for(let a=0;a<e.length;++a)if(e[a]>=0)n*=e[a];else if(e[a]===-1){if(r!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${r} and dim ${a}`);r=a}else if(e[a]<0)throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`);if(r===-1){if(t>0&&t!==n)throw Error(`Size(${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} / ${n}`);let s=e.slice();return s[r]=t/n,s}function jr(e,t){let n=t.length;return e=e==null?t.map((r,s)=>s):[].concat(e),z(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),z(e.every(r=>mn(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function P4(e,t){let n=[],r=[],s=t!=null&&Array.isArray(t)&&t.length===0,a=t==null||s?null:jr(t,e).sort(),o=0;for(let i=0;i<e.length;++i){if(a!=null){if(a[o]===i&&e[i]!==1)throw new Error(`Can't squeeze axis ${i} since its dim '${e[i]}' is not 1`);(a[o]==null||a[o]>i)&&e[i]===1&&(n.push(e[i]),r.push(i)),a[o]<=i&&o++}e[i]!==1&&(n.push(e[i]),r.push(i))}return{newShape:n,keptDims:r}}function jU(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 z4(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 L4(e,t){for(let n=0;n<e.length;n++){let r=e[n];if(isNaN(r)||!isFinite(r))throw Error(`A tensor of type ${t} being uploaded contains ${r}.`)}}function B4(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function qU(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function ss(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function gy(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function W4(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Vp(e){return typeof e=="string"||e instanceof String}function V4(e){return typeof e=="boolean"}function U4(e){return typeof e=="number"}function Up(e){return Array.isArray(e)?Up(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":U4(e)?"float32":Vp(e)?"string":V4(e)?"bool":"float32"}function Hp(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Gp(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function Ki(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function H4(e,t,n,r=!1){let s=new Array;if(t.length===1){let a=t[0]*(r?2:1);for(let o=0;o<a;o++)s[o]=n[e+o]}else{let a=t[0],o=t.slice(1),i=o.reduce((l,u)=>l*u)*(r?2:1);for(let l=0;l<a;l++)s[l]=H4(e+l*i,o,n,r)}return s}function Xi(e,t,n=!1){if(e.length===0)return t[0];let r=e.reduce((s,a)=>s*a)*(n?2:1);if(r===0)return[];if(r!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return H4(0,e,t,n)}function yy(e,t){let n=jp(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function jp(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 KU(e,t){let n=e.reduce((r,s)=>r*s,1);if(t==null||t==="float32")return Xi(e,new Float32Array(n));if(t==="int32")return Xi(e,new Int32Array(n));if(t==="bool")return Xi(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function Ay(e){e.forEach(t=>{z(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function XU(e,t,n){if(t===0)return 0;if(t===1)return e[0];let r=e[e.length-1];for(let s=0;s<e.length-1;++s)r+=n[s]*e[s];return r}function ZU(e,t,n){if(t===0)return[];if(t===1)return[e];let r=new Array(t);for(let s=0;s<r.length-1;++s)r[s]=Math.floor(e/n[s]),e-=r[s]*n[s];return r[r.length-1]=e,r}function xy(e){return e&&e.then&&typeof e.then=="function"}var G4="tfjsflags",YU=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=JU,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let r=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${r}.`),this.set(e,r)}}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(xy(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);G4 in e&&e[G4].split(",").forEach(n=>{let[r,s]=n.split(":");this.urlFlags[r]=eH(r,s)})}};function JU(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(QU(t,r[0],r[1]),r.join("="))),t}function QU(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function eH(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 ${e}.`)}function ae(){return j4}var j4=null;function tH(e){j4=e}var by;function q4(){if(by==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");by=e}return by}function nH(){let e=q4();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function vy(e,t){let n=nH();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var xc="Abs",bc="Acos",vc="Acosh",Fa="Add",Zi="AddN",wc="All",kc="Any",Yi="ArgMax",qp="ArgMin",Ic="Asin",Sc="Asinh",Tc="Atan",Nc="Atanh",Cc="Atan2",Ji="AvgPool",wy="AvgPoolGrad",Kp="AvgPool3D",ky="AvgPool3DGrad",Qi="BatchMatMul",Xp="BatchToSpaceND",Iy="Bincount",rH="BroadcastTo",el="Cast",No="Ceil",Co="ClipByValue",Sy="Complex",Zp="ComplexAbs",Ec="Concat",tl="Conv2D",Ty="Conv2DBackpropFilter",nl="Conv2DBackpropInput",Yp="Conv3D",Ny="Conv3DBackpropFilterV2",Cy="Conv3DBackpropInputV2",rl="Cos",$c="Cosh",sl="Cumsum",_c="CropAndResize",Ey="DenseBincount",Rc="DepthToSpace",al="DepthwiseConv2dNative",$y="DepthwiseConv2dNativeBackpropFilter",_y="DepthwiseConv2dNativeBackpropInput",Ry="Diag",Jp="Dilation2D",Dy="Dilation2DBackpropInput",Fy="Dilation2DBackpropFilter",ol="RealDiv",My="Einsum",Dc="Elu",Oy="EluGrad",Fc="Erf",il="Equal",Eo="Exp",Mc="ExpandDims",ll="Expm1",Py="FFT",Qp="Fill",Oc="FlipLeftRight",$o="Floor",ul="FloorDiv",cl="FusedBatchNorm",Pc="GatherV2",zc="GatherNd",dl="Greater",_o="GreaterEqual",hl="Identity",zy="IFFT",Ly="Imag",Lc="IsFinite",Bc="IsInf",Wc="IsNan",pl="LeakyRelu",fl="Less",ml="LessEqual",By="LinSpace",Ro="Log",Vc="Log1p",Uc="LogicalAnd",ef="LogicalNot",tf="LogicalOr",sH="LogSoftmax",nf="LRN",Wy="LRNGrad",gl="Max",Do="Maximum",yl="MaxPool",Vy="MaxPoolGrad",rf="MaxPool3D",Uy="MaxPool3DGrad",Hy="MaxPoolWithArgmax",Al="Mean",xl="Min",Fo="Minimum",bl="MirrorPad",Hc="Mod",Gy="Multinomial",Mo="Multiply",Gc="Neg",vl="NotEqual",jc="NonMaxSuppressionV3",qc="NonMaxSuppressionV4",Kc="NonMaxSuppressionV5",Xc="OnesLike",wl="OneHot",Zc="Pack",kl="PadV2",Il="Pow",Sl="Prelu",Yc="Prod",sf="Range",jy="Real",Jc="Reciprocal",Tl="Relu",Qc="Reshape",af="ResizeNearestNeighbor",qy="ResizeNearestNeighborGrad",Nl="ResizeBilinear",Ky="ResizeBilinearGrad",Cl="Relu6",El="Reverse",$l="Round",Oo="Rsqrt",ed="ScatterNd",td="Select",nd="Selu",rd="Slice",_l="Sin",sd="Sinh",ad="Sign",Rl="Sigmoid",od="Softplus",Dl="Sqrt",Fl="Sum",of="SpaceToBatchND",id="SplitV",Ml="Softmax",Xy="SparseFillEmptyRows",Zy="SparseReshape",Yy="SparseSegmentMean",Jy="SparseSegmentSum",Qy="SparseToDense",Po="SquaredDifference",lf="Square",ld="StridedSlice",eA="StringNGrams",tA="StringSplit",nA="StringToHashBucketFast",zo="Sub",Ol="Tan",Pl="Tanh",Lo="Tile",ud="TopK",cd="Transform",zl="Transpose",rA="Unique",dd="Unpack",uf="UnsortedSegmentSum",hd="ZerosLike",Bo="Step",sA="FromPixels",pd="RotateWithOffset",Ll="_FusedMatMul",Bl="FusedConv2D",Wl="FusedDepthwiseConv2D",cf=vy("kernelRegistry",()=>new Map),aA=vy("gradRegistry",()=>new Map);function oA(e,t){let n=Z4(e,t);return cf.get(n)}function K4(e){return aA.get(e)}function X4(e){let t=cf.entries(),n=[];for(;;){let{done:r,value:s}=t.next();if(r)break;let[a,o]=s,[i]=a.split("_");i===e&&n.push(o)}return n}function iA(e){let{kernelName:t,backendName:n}=e,r=Z4(t,n);cf.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),cf.set(r,e)}function aH(e){let{kernelName:t}=e;aA.has(t)&&ae().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),aA.set(t,e)}function Z4(e,t){return`${t}_${e}`}var k={};De(k,{arraysEqual:()=>Da,assert:()=>z,assertNonNegativeIntegerDimensions:()=>Ay,assertNonNull:()=>Wp,assertShapesMatch:()=>rs,bytesFromStringArray:()=>W4,bytesPerElement:()=>gy,checkConversionForErrors:()=>L4,clamp:()=>gc,computeStrides:()=>Ki,createScalarValue:()=>dH,createShuffledIndices:()=>UU,decodeString:()=>ff,distSquared:()=>LU,encodeString:()=>pf,fetch:()=>pH,fingerPrint64:()=>cH,flatten:()=>yc,getArrayFromDType:()=>z4,getTypedArrayFromDType:()=>jU,hasEncodingLoss:()=>qU,hexToLong:()=>fd,indexToLoc:()=>ZU,inferDtype:()=>Up,inferFromImplicitShape:()=>GU,isBoolean:()=>V4,isFunction:()=>Hp,isInt:()=>mn,isNumber:()=>U4,isPromise:()=>xy,isScalarShape:()=>BU,isString:()=>Vp,isTypedArray:()=>ss,isValidDtype:()=>B4,locToIndex:()=>XU,makeOnesTypedArray:()=>yy,makeZerosNestedTypedArray:()=>KU,makeZerosTypedArray:()=>jp,nearestDivisor:()=>Gp,nearestLargerEven:()=>OU,now:()=>md,parseAxisParam:()=>jr,randUniform:()=>zU,repeatedTry:()=>HU,rightPad:()=>Ac,shuffle:()=>O4,shuffleCombo:()=>MU,sizeFromShape:()=>on,sizeToSquarishShape:()=>VU,squeezeShape:()=>P4,sum:()=>PU,tanh:()=>WU,toNestedArray:()=>Xi,toTypedArray:()=>hf});var Y4=Ks(P3()),Wo=Y4.default||Y4;function fd(e){return Wo.fromString(e,!0,16)}var J4=fd("c3a5c85c97cb3127"),Vo=fd("b492b66fbe98f273"),zn=fd("9ae16a3b2f90404f");function lA(e){return e.xor(e.shru(47))}function Q4(e,t,n){let r=e.slice(t,t+n);return Wo.fromBytes(Array.from(r),!0,!0)}function Nt(e,t){return Q4(e,t,8)}function e6(e,t){return Q4(e,t,4)}function gn(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function Ma(e,t,n=fd("9ddfea08eb382d69")){let r=e.xor(t).mul(n);r=r.xor(r.shru(47));let s=t.xor(r).mul(n);return s=s.xor(s.shru(47)),s=s.mul(n),s}function oH(e,t,n,r,s,a){s=s.add(e),a=gn(a.add(s).add(r),21);let o=s;return s=s.add(t),s=s.add(n),a=a.add(gn(s,44)),[s.add(r),a.add(o)]}function df(e,t,n,r){return oH(Nt(e,t),Nt(e,t+8),Nt(e,t+16),Nt(e,t+24),n,r)}function iH(e,t=e.length){if(t>=8){let n=zn.add(t*2),r=Nt(e,0).add(zn),s=Nt(e,t-8),a=gn(s,37).mul(n).add(r),o=gn(r,25).add(s).mul(n);return Ma(a,o,n)}if(t>=4){let n=zn.add(t*2),r=e6(e,0);return Ma(r.shl(3).add(t),e6(e,t-4),n)}if(t>0){let n=e[0],r=e[t>>1],s=e[t-1],a=n+(r<<8),o=t+(s<<2);return lA(zn.mul(a).xor(J4.mul(o))).mul(zn)}return zn}function lH(e,t=e.length){let n=zn.add(t*2),r=Nt(e,0).mul(Vo),s=Nt(e,8),a=Nt(e,t-8).mul(n),o=Nt(e,t-16).mul(zn);return Ma(gn(r.add(s),43).add(gn(a,30)).add(o),r.add(gn(s.add(zn),18)).add(a),n)}function uH(e,t=e.length){let n=zn.add(t*2),r=Nt(e,0).mul(zn),s=Nt(e,8),a=Nt(e,t-8).mul(n),o=Nt(e,t-16).mul(zn),i=gn(r.add(s),43).add(gn(a,30)).add(o),l=Ma(i,r.add(gn(s.add(zn),18)).add(a),n),u=Nt(e,16).mul(n),c=Nt(e,24),d=i.add(Nt(e,t-32)).mul(n),h=l.add(Nt(e,t-24)).mul(n);return Ma(gn(u.add(c),43).add(gn(d,30)).add(h),u.add(gn(c.add(r),18)).add(d),n)}function cH(e,t=e.length){let n=Wo.fromNumber(81,!0);if(t<=32)return t<=16?iH(e,t):lH(e,t);if(t<=64)return uH(e,t);let r=n,s=n.mul(Vo).add(113),a=lA(s.mul(zn).add(113)).mul(zn),o=[Wo.UZERO,Wo.UZERO],i=[Wo.UZERO,Wo.UZERO];r=r.mul(zn).add(Nt(e,0));let l=0,u=(t-1>>6)*64,c=u+(t-1&63)-63;do r=gn(r.add(s).add(o[0]).add(Nt(e,l+8)),37).mul(Vo),s=gn(s.add(o[1]).add(Nt(e,l+48)),42).mul(Vo),r=r.xor(i[1]),s=s.add(o[0]).add(Nt(e,l+40)),a=gn(a.add(i[0]),33).mul(Vo),o=df(e,l,o[1].mul(Vo),r.add(i[0])),i=df(e,l+32,a.add(i[1]),s.add(Nt(e,l+16))),[a,r]=[r,a],l+=64;while(l!==u);let d=Vo.add(a.and(255).shl(1));return l=c,i[0]=i[0].add(t-1&63),o[0]=o[0].add(i[0]),i[0]=i[0].add(o[0]),r=gn(r.add(s).add(o[0]).add(Nt(e,l+8)),37).mul(d),s=gn(s.add(o[1]).add(Nt(e,l+48)),42).mul(d),r=r.xor(i[1].mul(9)),s=s.add(o[0].mul(9).add(Nt(e,l+40))),a=gn(a.add(i[0]),33).mul(d),o=df(e,l,o[1].mul(d),r.add(i[0])),i=df(e,l+32,a.add(i[1]),s.add(Nt(e,l+16))),[a,r]=[r,a],Ma(Ma(o[0],i[0],d).add(lA(s).mul(J4)).add(a),Ma(o[1],i[1],d).add(r),d)}function dH(e,t){return t==="string"?pf(e):hf([e],t)}function hH(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function hf(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=yc(e)),ae().getBool("DEBUG")&&L4(e,t),hH(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 r=0;r<n.length;++r)Math.round(e[r])!==0&&(n[r]=1);return n}else throw new Error(`Unknown data type ${t}`)}function md(){return ae().platform.now()}function pH(e,t){return ae().platform.fetch(e,t)}function pf(e,t="utf-8"){return t=t||"utf-8",ae().platform.encode(e,t)}function ff(e,t="utf-8"){return t=t||"utf-8",ae().platform.decode(e,t)}var fH=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new gH)}profileKernel(e,t,n){let r,s=()=>{r=n()},a,o=md();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(s);else{s();for(let l of r)l.dataSync();a=Promise.resolve({kernelMs:md()-o})}if(ae().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<r.length;l++){let u=r[l];u.data().then(c=>{mH(c,u.dtype,e)})}return{kernelName:e,outputs:r,inputs:t,timeMs:a.then(l=>l.kernelMs),extraInfo:a.then(l=>l.getExtraProfileInfo!=null?l.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:r,inputs:s,extraInfo:a}=e;n.forEach(o=>{Promise.all([o.data(),r,a]).then(i=>{this.logger.logKernelProfile(t,o,i[0],i[1],s,i[2])})})}};function mH(e,t,n){if(t!=="float32")return!1;for(let r=0;r<e.length;r++){let s=e[r];if(isNaN(s)||!isFinite(s))return console.warn(`Found ${s} in the result of '${n}'`),!0}return!1}var gH=class{logKernelProfile(e,t,n,r,s,a){let o=typeof r=="number"?Ac(`${r}ms`,9):r.error,i=Ac(e,25),l=t.rank,u=t.size,c=Ac(t.shape.toString(),14),d="";for(let h in s){let p=s[h];if(p!=null){let f=p.shape||t.shape,m=f.length;d+=`${h}: ${m}D ${m>0?f:""} `}}console.log(`%c${i}	%c${o}	%c${l}D ${c}	%c${u}	%c${d}	%c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function yH(e,t,n){let r={},s={};for(let l=0;l<t.length;l++)r[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],c=u.inputs;for(let d in c){let h=c[d],p=!1;for(let f=0;f<t.length;f++)if(r[h.id]){u.outputs.forEach(m=>r[m.id]=!0),p=!0,s[u.id]=!0;break}if(p)break}}let a={};a[n.id]=!0;let o={};for(let l=e.length-1;l>=0;l--){let u=e[l],c=u.inputs;for(let d=0;d<u.outputs.length;d++)if(a[u.outputs[d].id]){for(let h in c)a[c[h].id]=!0,o[u.id]=!0;break}}let i=[];for(let l=0;l<e.length;l++){let u=e[l];if(s[u.id]&&o[u.id]){let c={};for(let h in u.inputs){let p=u.inputs[h];r[p.id]&&(c[h]=p)}let d=Object.assign({},u);d.inputs=c,d.outputs=u.outputs,i.push(d)}}return i}function AH(e,t,n,r){for(let s=t.length-1;s>=0;s--){let a=t[s],o=[];if(a.outputs.forEach(l=>{let u=e[l.id];u!=null?o.push(u):o.push(null)}),a.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);let i=a.gradient(o);for(let l in a.inputs){if(!(l in i))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(i)}.`);let u=n(()=>i[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=a.inputs[l];if(!Da(u.shape,c.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=u;else{let d=e[c.id];e[c.id]=r(d,u),d.dispose()}}}}var t6=20,gd=3,uA=7;function xH(e,t,n,r){let s=Ki(t),a=bH(e,t,n,s),o=t.length,i=mf(e,t,n,s,a),l=["Tensor"];return r&&(l.push(`  dtype: ${n}`),l.push(`  rank: ${o}`),l.push(`  shape: [${t}]`),l.push("  values:")),l.push(i.map(u=>"    "+u).join(`
`)),l.join(`
`)}function bH(e,t,n,r){let s=on(t),a=r[r.length-1],o=new Array(a).fill(0),i=t.length,l=n==="complex64"?Ad(e):e;if(i>1)for(let u=0;u<s/a;u++){let c=u*a;for(let d=0;d<a;d++)o[d]=Math.max(o[d],yd(l[c+d],0,n).length)}return o}function yd(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(uA))} + ${parseFloat(e[1].toFixed(uA))}j`:Vp(e)?r=`'${e}'`:n==="bool"?r=n6(e):r=parseFloat(e.toFixed(uA)).toString(),Ac(r,t)}function n6(e){return e===0?"false":"true"}function mf(e,t,n,r,s,a=!0){let o=n==="complex64"?2:1,i=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=Ad(e);return[yd(m[0],0,n)]}return n==="bool"?[n6(e[0])]:[e[0].toString()]}if(l===1){if(i>t6){let g=gd*o,y=Array.from(e.slice(0,g)),A=Array.from(e.slice((i-gd)*o,i*o));return n==="complex64"&&(y=Ad(y),A=Ad(A)),["["+y.map((x,b)=>yd(x,s[b],n)).join(", ")+", ..., "+A.map((x,b)=>yd(x,s[i-gd+b],n)).join(", ")+"]"]}let m=n==="complex64"?Ad(e):Array.from(e);return["["+m.map((g,y)=>yd(g,s[y],n)).join(", ")+"]"]}let u=t.slice(1),c=r.slice(1),d=r[0]*o,h=[];if(i>t6){for(let m=0;m<gd;m++){let g=m*d,y=g+d;h.push(...mf(e.slice(g,y),u,n,c,s,!1))}h.push("...");for(let m=i-gd;m<i;m++){let g=m*d,y=g+d;h.push(...mf(e.slice(g,y),u,n,c,s,m===i-1))}}else for(let m=0;m<i;m++){let g=m*d,y=g+d;h.push(...mf(e.slice(g,y),u,n,c,s,m===i-1))}let p=l===2?",":"";h[0]="["+h[0]+p;for(let m=1;m<h.length-1;m++)h[m]=" "+h[m]+p;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return h[h.length-1]=" "+h[h.length-1]+"]"+(a?"":f),h}function Ad(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Qt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=on(e),n!=null){let r=n.length;z(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${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||z4(t,this.size),this.strides=Ki(e)}set(e,...t){t.length===0&&(t=[0]),z(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 r of e){if(r<0||r>=this.shape[t]){let s=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(s)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];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 Cs().makeTensor(this.values,this.shape,this.dtype)}},Cs=null,Vl=null,vH=null;function wH(e){Cs=e}function kH(e){Vl=e}function IH(e){vH=e}var Ct=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=on(e),this.strides=Ki(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Vl.buffer(this.shape,this.dtype,e)}bufferSync(){return Vl.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Xi(this.shape,e,this.dtype==="complex64")}arraySync(){return Xi(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Cs().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>ff(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Cs().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>ff(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 Cs().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Cs().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Vl.print(this,e)}clone(){return this.throwIfDisposed(),Vl.clone(this)}toString(e=!1){let t=this.dataSync();return xH(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Vl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Cs().makeVariable(this,e,t,n)}};Object.defineProperty(Ct,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function re(){return vy("Tensor",()=>Ct)}re();var gf=class extends Ct{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);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(!Da(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Cs().disposeTensor(this),this.dataId=e.dataId,Cs().incRef(this,null)}dispose(){Cs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(gf,Symbol.hasInstance,{value:e=>e instanceof Ct&&e.assign!=null&&e.assign instanceof Function});var Es={};De(Es,{assertTypesMatch:()=>s6,getTensorsInContainer:()=>mA,isTensorInList:()=>TH,makeTypesMatch:()=>Ut});var r6;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r6||(r6={}));var cA;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(cA||(cA={}));var dA;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(dA||(dA={}));var hA;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(hA||(hA={}));var pA;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(pA||(pA={}));var SH={float32:hA,int32:cA,bool:dA,complex64:pA};function qr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return SH[e][t]}function fA(e){return qr(e,"int32")}function Ut(e,t){if(e.dtype===t.dtype)return[e,t];let n=qr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function s6(e,t){z(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function TH(e,t){return t.some(n=>n.id===e.id)}function mA(e){let t=[],n=new Set;return a6(e,t,n),t}function a6(e,t,n){if(e==null)return;if(e instanceof Ct){t.push(e);return}if(!NH(e))return;let r=e;for(let s in r){let a=r[s];n.has(a)||(n.add(a),a6(a,t,n))}}function NH(e){return Array.isArray(e)||typeof e=="object"}function gA(e){return e.kernelName!=null}var o6=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},xd=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new o6}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new fH(this.backendInstance),!0}setupRegisteredKernels(){X4(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){X4(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof Bp)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,s=n.then(a=>r<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,s=this.readSync(t),a=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return xd.nextTensorId++}nextVariableId(){return xd.nextVariableId++}clone(e){let t=G.runKernel(hl,{x:e}),n={x:e},r=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return G.runKernel(el,i,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,s,{}),t}runKernel(e,t,n){if(!(oA(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 r=this.backend.numDataIds(),s=0;n.forEach(i=>{s+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-s-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=gA(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(gA(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=oA(p,this.backendName);z(g!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),o=()=>{let y=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:w,dtype:S}=b;return this.makeTensorFromDataId(v,w,S)});if(r){let b=this.getTensorsForGradient(p,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>p(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,d=gA(e)?null:e.backwardsFunc,h;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(h=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),t=h.outputs)}),r&&this.addTapeNode(l,u,t,d,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let r=K4(e);if(r!=null){let s=r.inputsToSave||[],a=r.outputsToSave||[],o;r.saveAllInputs?(z(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=s.map(l=>t[l]);let i=n.filter((l,u)=>a[u]);return o.concat(i)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let s=e;n==="string"&&Vp(e[0])&&(s=e.map(i=>pf(i)));let a=r.write(s,t,n),o=new Ct(t,n,a,this.nextTensorId());if(this.trackTensor(o,r),n==="string"){let i=this.state.tensorInfo.get(a),l=W4(s);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,r){n=n||"float32";let s=new Ct(t,n,e,this.nextTensorId());return this.trackTensor(s,r),s}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let s=new gf(e,t,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*gy(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 gf||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*gy(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(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,s,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},i=K4(e);i!=null&&(r=i.gradFunc),r!=null&&(o.gradient=l=>(l=l.map((u,c)=>{if(u==null){let d=n[c],h=jp(d.size,d.dtype);return this.makeTensor(h,d.shape,d.dtype)}return u}),r(l.length>1?l:l[0],s,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=mA(e),n=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!n.has(a.id)&&a.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(s=>{!s.kept&&s.scopeId===r.id&&this.track(s)})}gradients(e,t,n,r=!1){if(z(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 s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));z(s instanceof Ct,()=>"The result y returned by f() must be a tensor.");let a=yH(this.state.activeTape,t,s);if(!r&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let o={};o[s.id]=n==null?CH(s.shape):n,AH(o,a,l=>this.tidy(l),EH);let i=t.map(l=>o[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:s,grads:i}})}customGrad(e){return z(Hp(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{z(t.every(o=>o instanceof Ct),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((o,i)=>{r[i]=o});let s=(o,i)=>(n=e(...t,i),z(n.value instanceof Ct,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),z(Hp(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(o,i)=>{let l=n.gradFunc(o,i),u=Array.isArray(l)?l:[l];z(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(...)."),z(u.every(d=>d instanceof Ct),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let c={};return u.forEach((d,h)=>{c[h]=()=>d}),c};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=md(),n=await this.backend.time(e);return n.wallMs=md()-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 o6;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}};xd.nextTensorId=0;xd.nextVariableId=0;function CH(e){let t=yy(on(e),"float32");return G.makeTensor(t,e,"float32")}function i6(){let e=q4();if(e._tfengine==null){let t=new YU(e);e._tfengine=new xd(t)}return tH(e._tfengine.ENV),wH(()=>e._tfengine),e._tfengine}var G=i6();function EH(e,t){let n={a:e,b:t};return G.runKernel(Fa,n)}var yf={};De(yf,{isBrowser:()=>l6,isMobile:()=>_H});function $H(){return typeof navigator!="undefined"&&navigator!=null}function _H(e){if(e||$H()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function l6(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var as=ae();as.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.")});as.registerFlag("IS_BROWSER",()=>l6());as.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");as.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));as.registerFlag("PROD",()=>!1);as.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>as.getBool("DEBUG"));as.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);as.registerFlag("IS_TEST",()=>!1);as.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);as.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function bd(e,t){let n=e;if(ss(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||ss(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&ae().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&u6(e,r,[]),r}function u6(e,t,n){if(n=n||[],!Array.isArray(e)&&!ss(e)){z(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}z(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),z(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let s=0;s<e.length;++s)u6(e[s],r,n.concat(s))}function c6(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function M(e,t,n,r="numeric"){if(e instanceof Ct)return c6(r,e.dtype,t,n),e;let s=Up(e);if(s!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(s=r),c6(r,s,t,n),e==null||!ss(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let l=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${l}'`)}let a=bd(e,s);!ss(e)&&!Array.isArray(e)&&(e=[e]);let i=s!=="string"?hf(e,s):yc(e,[],!0);return G.makeTensor(i,a,s)}function Af(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,o)=>M(a,`${t}[${o}]`,n,r))}var RH="__op";function V(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+RH;let s=(...a)=>{G.startScope(n);try{let o=r(...a);return xy(o)&&console.error("Cannot return a Promise inside of tidy."),G.endScope(o),o}catch(o){throw G.endScope(null),o}};return Object.defineProperty(s,"name",{value:n,configurable:!0}),s}function DH(e,t){let n=M(e,"real","complex"),r=M(t,"imag","complex");rs(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);let s={real:n,imag:r};return G.runKernel(Sy,s)}var Uo=V({complex_:DH});function vd(e,t,n,r){if(r==null&&(r=Up(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!ss(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Ay(t);let s=on(t),a=on(n);z(s===a,()=>`Based on the provided shape, [${t}], the tensor should have ${s} values but has ${a}`);for(let o=0;o<n.length;++o){let i=n[o],l=o===n.length-1?i!==on(t.slice(o)):!0;z(n[o]===t[o]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!ss(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?hf(e,r):yc(e,[],!0),G.makeTensor(e,t,r)}function $s(e,t,n){let r=bd(e,n);return vd(e,t,r,n)}var yA={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},xf=4;async function FH(e,t){let n=[],r=[],s=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);for(let o=0;o<s.length;++o){let i=s[o],l=Array.isArray(e)?e[o].tensor:e[i];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${i}': ${l.dtype}`);let u={name:i,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let c=new Promise(async d=>{let h=await l.bytes(),p=h.reduce((g,y)=>g+y.length,0)+xf*h.length,f=new Uint8Array(p),m=0;for(let g=0;g<h.length;g++){let y=h[g],A=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(A,m),m+=xf,f.set(y,m),m+=y.length}d(f)});r.push(c)}else r.push(l.data());t!=null&&(u.group=t),n.push(u)}let a=await Promise.all(r);return{data:MH(a),specs:n}}function d6(e,t){let n={},r,s=0;for(let a of t){let o=a.name,i=a.dtype,l=a.shape,u=on(l),c;if("quantization"in a){let d=a.quantization;if(d.dtype==="uint8"||d.dtype==="uint16"){if(!("min"in d&&"scale"in d))throw new Error(`Weight ${a.name} with quantization ${d.dtype} doesn't have corresponding metadata min and scale.`)}else if(d.dtype==="float16"){if(i!=="float32")throw new Error(`Weight ${a.name} is quantized with ${d.dtype} which only supports weights of type float32 not ${i}.`)}else throw new Error(`Weight ${a.name} has unknown quantization dtype ${d.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let h=yA[d.dtype],p=e.slice(s,s+u*h),f=d.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(i==="float32")if(d.dtype==="uint8"||d.dtype==="uint16"){c=new Float32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];c[m]=g*d.scale+d.min}}else if(d.dtype==="float16")r===void 0&&(r=WH()),c=r(f);else throw new Error(`Unsupported quantization type ${d.dtype} for weight type float32.`);else if(i==="int32"){if(d.dtype!=="uint8"&&d.dtype!=="uint16")throw new Error(`Unsupported quantization type ${d.dtype} for weight type int32.`);c=new Int32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];c[m]=Math.round(g*d.scale+d.min)}}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);s+=u*h}else if(i==="string"){let d=on(a.shape);c=[];for(let h=0;h<d;h++){let p=new Uint32Array(e.slice(s,s+xf))[0];s+=xf;let f=new Uint8Array(e.slice(s,s+p));c.push(f),s+=p}}else{let d=yA[i],h=e.slice(s,s+u*d);if(i==="float32")c=new Float32Array(h);else if(i==="int32")c=new Int32Array(h);else if(i==="bool")c=new Uint8Array(h);else if(i==="complex64"){c=new Float32Array(h);let p=new Float32Array(c.length/2),f=new Float32Array(c.length/2);for(let y=0;y<p.length;y++)p[y]=c[y*2],f[y]=c[y*2+1];let m=$s(p,l,"float32"),g=$s(f,l,"float32");n[o]=Uo(m,g),m.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);s+=u*d}i!=="complex64"&&(n[o]=$s(c,l,i))}return n}function MH(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(a=>{if(t+=a.byteLength,n.push(a.byteLength===a.buffer.byteLength?a:new a.constructor(a)),!(a instanceof Float32Array||a instanceof Int32Array||a instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${a.constructor.name}`)});let r=new Uint8Array(t),s=0;return n.forEach(a=>{r.set(new Uint8Array(a.buffer),s),s+=a.byteLength}),r.buffer}var AA=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function h6(e){return AA?Buffer.byteLength(e):new Blob([e]).size}function OH(e){if(AA)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let r=0,s=t.length;r<s;r++)n+=String.fromCharCode(t[r]);return btoa(n)}function PH(e){if(AA){let r=Buffer.from(e,"base64");return r.buffer.slice(r.byteOffset,r.byteOffset+r.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let r=0;r<t.length;++r)n.set([t.charCodeAt(r)],r);return n.buffer}function xA(e){if(e.length===1)return e[0];let t=0;e.forEach(s=>{t+=s.byteLength});let n=new Uint8Array(t),r=0;return e.forEach(s=>{n.set(new Uint8Array(s),r),r+=s.byteLength}),n.buffer}function p6(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 wd(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:h6(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:h6(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function zH(){let e=n=>{let r=n<<13,s=0;for(;(r&8388608)==0;)s-=8388608,r<<=1;return r&=~8388608,s+=947912704,r|s},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 LH(){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 BH(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function WH(){let e=zH(),t=LH(),n=BH();return r=>{let s=new ArrayBuffer(4*r.length),a=new Uint32Array(s);for(let o=0;o<r.length;o++){let i=r[o],l=e[n[i>>10]+(i&1023)]+t[i>>10];a[o]=l}return new Float32Array(s)}}var qt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return qt.instance==null&&(qt.instance=new qt),qt.instance}static registerSaveRouter(e){qt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){qt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return qt.getHandlers(e,"save")}static getLoadHandlers(e,t){return qt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?qt.getInstance().loadRouters:qt.getInstance().saveRouters).forEach(a=>{let o=a(e,n);o!==null&&r.push(o)}),r}},VH=e=>qt.registerSaveRouter(e),UH=e=>qt.registerLoadRouter(e),HH=e=>qt.getSaveHandlers(e),GH=(e,t)=>qt.getLoadHandlers(e,t),bA="tensorflowjs",vA=1,Ho="models_store",Oa="model_info_store";function f6(){if(!ae().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 wA(e){let t=e.result;t.createObjectStore(Ho,{keyPath:"modelPath"}),t.createObjectStore(Oa,{keyPath:"modelPath"})}var Go=class{constructor(e){if(this.indexedDB=f6(),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,r)=>{let s=this.indexedDB.open(bA,vA);s.onupgradeneeded=()=>wA(s),s.onsuccess=()=>{let a=s.result;if(t==null){let o=a.transaction(Ho,"readonly"),l=o.objectStore(Ho).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return a.close(),r(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(l.result.modelArtifacts)},l.onerror=u=>(a.close(),r(l.error)),o.oncomplete=()=>a.close()}else{let o=wd(t),i=a.transaction(Oa,"readwrite"),l=i.objectStore(Oa),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:o}),c;u.onsuccess=()=>{c=a.transaction(Ho,"readwrite");let h=c.objectStore(Ho).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:o});h.onsuccess=()=>n({modelArtifactsInfo:o}),h.onerror=p=>{l=i.objectStore(Oa);let f=l.delete(this.modelPath);f.onsuccess=()=>(a.close(),r(h.error)),f.onerror=m=>(a.close(),r(h.error))}},u.onerror=d=>(a.close(),r(u.error)),i.oncomplete=()=>{c==null?a.close():c.oncomplete=()=>a.close()}}},s.onerror=a=>r(s.error)})}};Go.URL_SCHEME="indexeddb://";var m6=e=>ae().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Go.URL_SCHEME)?jH(e.slice(Go.URL_SCHEME.length)):null;qt.registerSaveRouter(m6);qt.registerLoadRouter(m6);function jH(e){return new Go(e)}function qH(e){return e.startsWith(Go.URL_SCHEME)?e.slice(Go.URL_SCHEME.length):e}var KH=class{constructor(){this.indexedDB=f6()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(bA,vA);n.onupgradeneeded=()=>wA(n),n.onsuccess=()=>{let r=n.result,s=r.transaction(Oa,"readonly"),o=s.objectStore(Oa).getAll();o.onsuccess=()=>{let i={};for(let l of o.result)i[l.modelPath]=l.modelArtifactsInfo;e(i)},o.onerror=i=>(r.close(),t(o.error)),s.oncomplete=()=>r.close()},n.onerror=r=>t(n.error)})}async removeModel(e){return e=qH(e),new Promise((t,n)=>{let r=this.indexedDB.open(bA,vA);r.onupgradeneeded=()=>wA(r),r.onsuccess=()=>{let s=r.result,a=s.transaction(Oa,"readwrite"),o=a.objectStore(Oa),i=o.get(e),l;i.onsuccess=()=>{if(i.result==null)return s.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=o.delete(e),c=()=>{l=s.transaction(Ho,"readwrite");let h=l.objectStore(Ho).delete(e);h.onsuccess=()=>t(i.result.modelArtifactsInfo),h.onerror=p=>n(i.error)};u.onsuccess=c,u.onerror=d=>(c(),s.close(),n(i.error))}},i.onerror=u=>(s.close(),n(i.error)),a.oncomplete=()=>{l==null?s.close():l.oncomplete=()=>s.close()}},r.onerror=s=>n(r.error)})}},aa="/",Ul="tensorflowjs_models",g6="info",XH="model_topology",ZH="weight_specs",YH="weight_data",JH="model_metadata";function y6(e){return{info:[Ul,e,g6].join(aa),topology:[Ul,e,XH].join(aa),weightSpecs:[Ul,e,ZH].join(aa),weightData:[Ul,e,YH].join(aa),modelMetadata:[Ul,e,JH].join(aa)}}function QH(e){let t=e.split(aa);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(aa)}function eG(e){return e.startsWith(jo.URL_SCHEME)?e.slice(jo.URL_SCHEME.length):e}var jo=class{constructor(e){if(!ae().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=y6(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=wd(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,OH(e.weightData));let s={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(s.signature=e.signature),e.userDefinedMetadata!=null&&(s.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(s.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(s)),{modelArtifactsInfo:r}}catch(s){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${r.modelTopologyBytes}, weightSpecsBytes=${r.weightSpecsBytes}, weightDataBytes=${r.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${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 '${this.modelPath}' is missing.`);t.modelTopology=n;let r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(r==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;let s=this.LS.getItem(this.keys.modelMetadata);if(s!=null){let o=JSON.parse(s);t.format=o.format,t.generatedBy=o.generatedBy,t.convertedBy=o.convertedBy,o.signature!=null&&(t.signature=o.signature),o.userDefinedMetadata!=null&&(t.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(t.modelInitializer=o.modelInitializer)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=PH(a),t}};jo.URL_SCHEME="localstorage://";var A6=e=>ae().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(jo.URL_SCHEME)?tG(e.slice(jo.URL_SCHEME.length)):null;qt.registerSaveRouter(A6);qt.registerLoadRouter(A6);function tG(e){return new jo(e)}var nG=class{constructor(){z(ae().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),z(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Ul+aa,n=aa+g6;for(let r=0;r<this.LS.length;++r){let s=this.LS.key(r);if(s.startsWith(t)&&s.endsWith(n)){let a=QH(s);e[a]=JSON.parse(this.LS.getItem(s))}}return e}async removeModel(e){e=eG(e);let t=y6(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}},Hl="://",Er=class{constructor(){this.managers={}}static getInstance(){return Er.instance==null&&(Er.instance=new Er),Er.instance}static registerManager(e,t){z(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Hl)&&(e=e.slice(0,e.indexOf(Hl))),z(e.length>0,()=>"scheme must not be an empty string.");let n=Er.getInstance();z(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 bf(e){if(e.indexOf(Hl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Er.getSchemes().join(",")}`);return{scheme:e.split(Hl)[0],path:e.split(Hl)[1]}}async function x6(e,t,n=!1){z(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=qt.getLoadHandlers(e);z(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),z(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let s=r[0],a=qt.getSaveHandlers(t);z(a.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),z(a.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let o=a[0],i=bf(e).scheme,l=bf(e).path,u=i===bf(e).scheme,c=await s.load();n&&u&&await Er.getManager(i).removeModel(l);let d=await o.save(c);return n&&!u&&await Er.getManager(i).removeModel(l),d.modelArtifactsInfo}async function rG(){let e=Er.getSchemes(),t={};for(let n of e){let r=await Er.getManager(n).listModels();for(let s in r){let a=n+Hl+s;t[a]=r[s]}}return t}async function sG(e){let t=bf(e);return Er.getManager(t.scheme).removeModel(t.path)}async function aG(e,t){return x6(e,t,!1)}async function oG(e,t){return x6(e,t,!0)}var iG=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(ae().get("IS_BROWSER")){ae().setPlatform("browser",new iG);try{Er.registerManager(jo.URL_SCHEME,new nG)}catch(e){}try{Er.registerManager(Go.URL_SCHEME,new KH)}catch(e){}}var lG={importFetch:()=>z3()},kA,uG=class{constructor(){this.util=co("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return ae().global.fetch!=null?ae().global.fetch(e,t):(kA==null&&(kA=lG.importFetch()),kA(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)}};ae().get("IS_NODE")&&ae().setPlatform("node",new uG);function Le(e,t="float32",n){return t=t||"float32",Ay(e),new Qt(e,t,n)}function cG(e,t){let n=M(e,"x","cast");if(!B4(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 r={x:n},s={dtype:t};return G.runKernel(el,r,s)}var ke=V({cast_:cG});function dG(e){let n={x:M(e,"x","clone","string_or_numeric")};return G.runKernel(hl,n)}var qo=V({clone_:dG});function hG(e,t=!1){console.log(e.toString(t))}i6();var pG={buffer:Le,cast:ke,clone:qo,print:hG};kH(pG);var cr={};De(cr,{browserFiles:()=>bG,browserHTTPRequest:()=>SG,concatenateArrayBuffers:()=>xA,copyModel:()=>aG,decodeWeights:()=>d6,encodeWeights:()=>FH,fromMemory:()=>NG,getLoadHandlers:()=>GH,getModelArtifactsInfoForJSON:()=>wd,getSaveHandlers:()=>HH,http:()=>TA,isHTTPScheme:()=>SA,listModels:()=>rG,loadWeights:()=>vG,moveModel:()=>oG,registerLoadRouter:()=>UH,registerSaveRouter:()=>VH,removeModel:()=>sG,weightsLoaderFactory:()=>k6,withSaveHandler:()=>CG});var fG="model",mG=".json",gG=".weights.bin";function b6(e){return new Promise(t=>setTimeout(t)).then(e)}var Gl=class{constructor(e){if(!ae().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Gl.URL_SCHEME)&&(e=e.slice(Gl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=fG),this.modelTopologyFileName=e+mG,this.weightDataFileName=e+gG}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}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer);let s=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),a=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(a.download=this.modelTopologyFileName,a.href=s,await b6(()=>a.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let o=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;o.download=this.weightDataFileName,o.href=t,await b6(()=>o.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:wd(e)}}}};Gl.URL_SCHEME="downloads://";var yG=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.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,r)=>{let s=new FileReader;s.onload=a=>{let o=JSON.parse(a.target.result),i=o.modelTopology;if(i==null){r(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:i});let l=o.weightsManifest;if(l==null){r(new Error(`weightManifest field is missing from file ${e.name}`));return}let u;try{u=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let c=[],d=[],h=[];l.forEach(p=>{p.paths.forEach(f=>{d.push(f),h.push(null)}),c.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(f=>{let m=new FileReader;m.onload=g=>{let y=g.target.result,A=d.indexOf(f);if(h[A]=y,h.indexOf(null)===-1){let x={modelTopology:i,weightSpecs:c,weightData:xA(h),format:o.format,generatedBy:o.generatedBy,convertedBy:o.convertedBy};o.signature!=null&&(x.signature=o.signature),o.userDefinedMetadata!=null&&(x.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(x.modelInitializer=o.modelInitializer),n(x)}},m.onerror=g=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(u[f])})})},s.onerror=a=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),s.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(a=>p6(a.name)),s={};for(let a of e)a.paths.forEach(o=>{let i=p6(o);if(n.indexOf(i)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);if(n.push(i),r.indexOf(i)===-1)throw new Error(`Weight file with basename '${i}' is not provided.`);s[o]=t[r.indexOf(i)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return s}},AG=e=>ae().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Gl.URL_SCHEME)?xG(e.slice(Gl.URL_SCHEME.length)):null;qt.registerSaveRouter(AG);function xG(e="model"){return new Gl(e)}function bG(e){return new yG(e)}function v6(e,t,n,r){o(e),n=n==null?0:n,r=r==null?1:r,i(n,r);let s=0,a=l=>(l.then(u=>{let c=n+ ++s/e.length*(r-n);return t(c),u}),l);function o(l){z(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function i(l,u){z(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),z(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),z(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(a))}async function w6(e,t){t==null&&(t={});let n=t.fetchFunc==null?ae().platform.fetch:t.fetchFunc,r=e.map(d=>n(d,t.requestInit,{isBinary:!0})),s=0,a=.5,i=(t.onProgress==null?await Promise.all(r):await v6(r,t.onProgress,s,a)).map(d=>d.arrayBuffer()),l=.5,u=1;return t.onProgress==null?await Promise.all(i):await v6(i,t.onProgress,l,u)}async function vG(e,t="",n,r){return k6(o=>w6(o,{requestInit:r}))(e,t,n)}function k6(e){return async(t,n="",r)=>{let s=t.map(()=>!1),a={},o=r!=null?r.map(()=>!1):[],i=[];if(t.forEach((p,f)=>{let m=0;p.weights.forEach(g=>{let y="quantization"in g?g.quantization.dtype:g.dtype,A=yA[y]*on(g.shape),x=()=>{s[f]=!0,a[f]==null&&(a[f]=[]),a[f].push({manifestEntry:g,groupOffset:m,sizeBytes:A})};r!=null?r.forEach((b,v)=>{b===g.name&&(x(),o[v]=!0)}):x(),i.push(g.name),m+=A})}),!o.every(p=>p)){let p=r.filter((f,m)=>!o[m]);throw new Error(`Could not find weights in manifest with names: ${p.join(", ")}. 
Manifest JSON has weights with names: ${i.join(", ")}.`)}let l=s.reduce((p,f,m)=>(f&&p.push(m),p),[]),u=[];l.forEach(p=>{t[p].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;u.push(m)})});let c=await e(u),d={},h=0;return l.forEach(p=>{let f=t[p].paths.length,m=0;for(let b=0;b<f;b++)m+=c[h+b].byteLength;let g=new ArrayBuffer(m),y=new Uint8Array(g),A=0;for(let b=0;b<f;b++){let v=new Uint8Array(c[h+b]);y.set(v,A),A+=v.byteLength}a[p].forEach(b=>{let v=g.slice(b.groupOffset,b.groupOffset+b.sizeBytes),w=d6(v,[b.manifestEntry]);for(let S in w)d[S]=w[S]}),h+=f}),d}}var wG="application/octet-stream",kG="application/json",IA=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?(z(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=ae().platform.fetch,z(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&z(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}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:kG}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:wG}),"model.weights.bin");let s=await this.fetch(this.path,t);if(s.ok)return{modelArtifactsInfo:wd(e),responses:[s]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.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(p){let f=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?f+=" 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.":f+=" Please make sure the server is serving valid JSON for this request.",new Error(f)}let n=t.modelTopology,r=t.weightsManifest,s=t.generatedBy,a=t.convertedBy,o=t.format,i=t.signature,l=t.userDefinedMetadata;if(n==null&&r==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let u,c;r!=null&&([u,c]=await this.loadWeights(r));let d={modelTopology:n,weightSpecs:u,weightData:c,generatedBy:s,convertedBy:a,format:o};i!=null&&(d.signature=i),l!=null&&(d.userDefinedMetadata=l);let h=t.modelInitializer;return h&&(d.modelInitializer=h),d}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=IG(t),s=this.weightPathPrefix||n,a=[];for(let u of e)a.push(...u.weights);let o=[],i=[];for(let u of e)for(let c of u.paths)this.weightUrlConverter!=null?i.push(this.weightUrlConverter(c)):o.push(s+c+r);this.weightUrlConverter&&o.push(...await Promise.all(i));let l=await w6(o,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,xA(l)]}};IA.URL_SCHEME_REGEX=/^https?:\/\//;function IG(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),s=n>t?e.substring(n):"";return[r+"/",s]}function SA(e){return e.match(IA.URL_SCHEME_REGEX)!=null}var I6=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>SA(r)):n=SA(e),n)return TA(e,t)}return null};qt.registerSaveRouter(I6);qt.registerLoadRouter(I6);function TA(e,t){return new IA(e,t)}function SG(e,t){return TA(e,t)}var NA=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},TG=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function NG(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new NA(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 NA({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 NA({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function CG(e){return new TG(e)}function EG(e,t,n=!1,r=!1){let s=M(e,"a","matMul"),a=M(t,"b","matMul");[s,a]=Ut(s,a);let o={a:s,b:a},i={transposeA:n,transposeB:r};return G.runKernel(Qi,o,i)}var ot=V({matMul_:EG});function $G(e,t,n=1,r=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:M(e,"indices","oneHot","int32")},o={depth:t,onValue:n,offValue:r};return G.runKernel(wl,a,o)}var kd=V({oneHot_:$G});function _G(e,t){let n=M(e,"x","transpose");if(t==null&&(t=n.shape.map((a,o)=>o).reverse()),z(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(a=>{z(a>=0&&a<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let r={x:n},s={perm:t};return G.runKernel(zl,r,s)}var pt=V({transpose_:_G});function RG(e,t,n){let r=M(e,"labels","confusionMatrix"),s=M(t,"predictions","confusionMatrix");z(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),z(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),z(s.rank===1,()=>`Expected the rank of predictions to be 1, but got ${s.rank}`),z(r.shape[0]===s.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${s.shape[0]}. Labels and predictions should have the same number of elements.`),z(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let a=kd(ke(r,"int32"),n),o=kd(ke(s,"int32"),n),i=pt(a),l=ot(i,o);return ke(l,"int32")}var xwe=V({confusionMatrix_:RG}),S6={};De(S6,{fromPixels:()=>BG,fromPixelsAsync:()=>zG,toPixels:()=>LG});function DG(e,t,n){if(Wp(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=bd(e,n);if(r.length!==3&&r.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return vd(e,t,r,n)}var jl;function T6(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,r=!1,s=!1,a=!1,o=!1,i=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)s=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)a=!0;else if(e.getContext!=null)o=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)i=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(s){let f=2;if(s&&e.readyState<f)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(oA(sA,G.backendName)!=null){let f={pixels:e},m={numChannels:t};return G.runKernel(sA,f,m)}let[u,c]=s?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;o?d=e.getContext("2d").getImageData(0,0,u,c).data:r||n?d=e.data:(a||s||i)&&(jl==null&&(jl=document.createElement("canvas").getContext("2d")),jl.canvas.width=u,jl.canvas.height=c,jl.drawImage(e,0,0,u,c),d=jl.getImageData(0,0,u,c).data);let h;if(t===4)h=new Int32Array(d);else{let f=u*c;h=new Int32Array(f*t);for(let m=0;m<f;m++)for(let g=0;g<t;++g)h[m*t+g]=d[m*4+g]}return DG(h,[c,u,t],"int32")}function FG(e){return e!=null&&e.data instanceof Uint8Array}function MG(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function OG(e){return e!=null&&e.width!==0&&e.height!==0}function PG(e){return MG()&&!(e instanceof ImageBitmap)&&OG(e)&&!FG(e)}async function zG(e,t=3){let n=null;if(ae().getBool("WRAP_TO_IMAGEBITMAP")&&PG(e)){let r;try{r=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(s){r=null}r!=null&&r.width===e.width&&r.height===e.height?n=r:n=e}else n=e;return T6(n,t)}async function LG(e,t){let n=M(e,"img","toPixels");if(!(e instanceof Ct)){let u=n;n=ke(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[r,s]=n.shape.slice(0,2),a=n.rank===2?1:n.shape[2];if(a>4||a===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let o=await n.data(),i=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(s*r*4);for(let u=0;u<r*s;++u){let c=[0,0,0,255];for(let h=0;h<a;h++){let p=o[u*a+h];if(n.dtype==="float32"){if(p<0||p>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${p}.`)}else if(n.dtype==="int32"&&(p<0||p>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${p}.`);a===1?(c[0]=p*i,c[1]=p*i,c[2]=p*i):c[h]=p*i}let d=u*4;l[d+0]=Math.round(c[0]),l[d+1]=Math.round(c[1]),l[d+2]=Math.round(c[2]),l[d+3]=Math.round(c[3])}if(t!=null){t.width=s,t.height=r;let u=t.getContext("2d"),c=new ImageData(l,s,r);u.putImageData(c,0,0)}return n!==e&&n.dispose(),l}var BG=V({fromPixels_:T6}),N6={};De(N6,{prepareAndValidate:()=>C6});function C6(e,t){let n=e.shape.length,r=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(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);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[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(on(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let s=t.shape,a=s[s.length-1],o=1;for(let d=0;d<s.length-1;++d)o*=s[d];let i=e.shape,l=s.slice();l.pop();let u=1;for(let d=a;d<n;++d)u*=i[d],l.push(i[d]);let c=[...Ki(e.shape).map(d=>d/u),1].slice(0,a);return[l,o,u,c]}var E6={};De(E6,{calculateShapes:()=>$6,validateInput:()=>EA,validateUpdateShape:()=>CA});function CA(e,t,n){let r=t.rank>1?t.shape[t.rank-1]:1,s=t.rank>1?t.rank-1:1,a=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${r}, and batchDim: ${s}.`;if(n.rank<s)throw new Error(a+` update.rank < ${s}. `);if(e.length<r+(n.rank-s))throw new Error(a+` Output shape length < ${r+(n.rank-s)}`);if(n.rank!==s+e.length-r)throw new Error(a+` update.rank != ${s+e.length-r}`);for(let o=0;o<s;++o)if(n.shape[o]!==t.shape[o])throw new Error(a+` updates.shape[${o}] (${n.shape[o]}) != indices.shape[${o}] (${t.shape[o]}).`);for(let o=0;o<n.rank-s;++o)if(n.shape[o+s]!==e[o+r])throw new Error(a+` updates.shape[${o+s}] (${n.shape[o+s]}) != shape[${o+s}] (${e[o+s]})`)}function EA(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}`)}CA(n,t,e)}function $6(e,t,n){let r=t.shape.length,s=r>1?t.shape[r-1]:1,a=n.length,o=1;for(let d=s;d<a;++d)o*=n[d];let i=s<1?1:s,l=on(t.shape)/i,u=[...Ki(n.slice(0,s)),1],c=on(n);return{sliceRank:s,numUpdates:l,sliceSize:o,strides:u,outputSize:c}}var En={};De(En,{assertParamsValid:()=>WG,computeFlatOffset:()=>UG,computeOutShape:()=>_6,getNormalizedAxes:()=>M6,isSliceContinous:()=>VG,maskToAxes:()=>vf,parseSliceParams:()=>W6,sliceInfo:()=>HG,startForAxis:()=>L6,startIndicesWithElidedDims:()=>O6,stopForAxis:()=>B6,stopIndicesWithElidedDims:()=>P6,stridesForAxis:()=>z6,stridesWithElidedDims:()=>R6});function WG(e,t,n){let r=e.shape.length;z(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),z(r===n.length,()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`);for(let s=0;s<r;++s)z(t[s]+n[s]<=e.shape[s],()=>`Error in slice${r}D: begin[${s}] + size[${s}] (${t[s]+n[s]}) would overflow input.shape[${s}] (${e.shape[s]})`)}function vf(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function _6(e,t,n){let r=[];for(let s=0;s<e.length;s++)r[s]=Math.ceil((t[s]-e[s])/n[s]);return r}function R6(e,t,n,r){let s=[...e];for(let a=s.length;a<r.length;a++)s.push(1);for(let a=0;a<n;a++)a===0?s[t]=1:(s.splice(t,0,1),s.pop());return s}function D6(e,t,n){return n<=e?n:n-(t-1)}function F6(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function M6(e,t,n,r,s,a,o,i,l){let u=e.length,c=new Array(u),d=new Array(u),h=new Array(u);if(t.length&&n>0){let p=t[0],f=n+1;c=O6(o,p,f,r,e),d=P6(i,p,f,s,e),h=R6(a,p,f,e)}else for(let p=0;p<u;p++)c[p]=L6(o,r,a,e,p,l),d[p]=B6(i,s,a,e,p,l),h[p]=z6(a,p,l);return{begin:c,end:d,strides:h}}function O6(e,t,n,r,s){let a=[...s],o=F6(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=0;else{let l=D6(t,n,i),u=r[l];e&1<<l&&(u=0),a[i]=u}return a}function P6(e,t,n,r,s){let a=[...s],o=F6(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=Number.MAX_SAFE_INTEGER;else{let l=D6(t,n,i),u=r[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),a[i]=u}for(let i=0;i<a.length;i++){let l=s[i];a[i]<0&&(a[i]+=l),a[i]=gc(0,a[i],s[i])}return a}function z6(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function L6(e,t,n,r,s,a){let o=t[s],i=n[s]||1;(e&1<<s||a&1<<s||o==null)&&(i>0?o=Number.MIN_SAFE_INTEGER:o=Number.MAX_SAFE_INTEGER);let l=r[s];return o<0&&(o+=l),o=gc(0,o,l-1),o}function B6(e,t,n,r,s,a){let o=t[s],i=n[s]||1;(e&1<<s||a&1<<s||o==null)&&(i>0?o=Number.MAX_SAFE_INTEGER:o=Number.MIN_SAFE_INTEGER);let l=r[s];return o<0&&(o+=l),i>0?o=gc(0,o,l):o=gc(-1,o,l-1),o}function VG(e,t,n){let r=n.length;for(let s=0;s<n.length;s++)if(n[s]>1){r=s;break}for(let s=r+1;s<n.length;s++)if(t[s]>0||n[s]!==e[s])return!1;return!0}function UG(e,t){let n=e.length>0?e[e.length-1]:1;for(let r=0;r<e.length-1;r++)n+=e[r]*t[r];return n}function W6(e,t,n){let r,s=e.shape.length;typeof t=="number"?r=[t,...new Array(s-1).fill(0)]:t.length<s?r=t.concat(new Array(s-t.length).fill(0)):r=t.slice(),r.forEach(o=>{z(o!==-1,()=>"slice() does not support negative begin indexing.")});let a;return n==null?a=new Array(s).fill(-1):typeof n=="number"?a=[n,...new Array(s-1).fill(-1)]:n.length<s?a=n.concat(new Array(s-n.length).fill(-1)):a=n,a=a.map((o,i)=>o>=0?o:(z(o===-1,()=>`Negative size values should be exactly -1 but got ${o} for the slice() size at index ${i}.`),e.shape[i]-r[i])),[r,a]}function HG(e,t,n,r,s,a,o,i,l){let u=t.slice(),c=n.slice(),d=r;r==null&&(d=new Array(u.length));let h=vf(o);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(o!==0&&i!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(o!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-u.length,f=vf(i),m=e.slice();f.forEach(S=>{u[S]=0,c[S]=1,m.splice(S,0,1)});let{begin:g,end:y,strides:A}=M6(m,h,p,u,c,d,s,a,o);u=g,c=y,d=A;let x=vf(l);x.forEach(S=>{c[S]=u[S]+1,d[S]=1});let b=_6(u,c,d),v=b.filter((S,I)=>x.indexOf(I)===-1);return{nonStrided:d.every(S=>S===1),$begin:u,$end:c,$strides:d,size:b,newShape:m,outShape:v}}var ce={};De(ce,{Serializable:()=>V6,SerializationMap:()=>Ko,registerClass:()=>Pa});var V6=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Ko=class{constructor(){this.classNameMap={}}static getMap(){return Ko.instance==null&&(Ko.instance=new Ko),Ko.instance}static register(e){Ko.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Pa(e){z(e.className!=null,()=>"Class being registered does not have the static className property defined."),z(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),z(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Ko.register(e)}function U6(e){ae().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}IH(U6);function za(){return G}function $A(){return G.memory()}function Z(e,t){return G.tidy(e,t)}function je(e){mA(e).forEach(n=>n.dispose())}function Sn(e){return G.keep(e)}function _A(e,t,n=1){return G.registerBackend(e,t,n)}function GG(){return G.backend}function jG(e,t){let n=M(e,"a","add"),r=M(t,"b","add");[n,r]=Ut(n,r);let s={a:n,b:r};return G.runKernel(Fa,s)}var pe=V({add_:jG});function qG(e,t){let n=M(e,"a","floorDiv"),r=M(t,"b","floorDiv");[n,r]=Ut(n,r);let s={a:n,b:r};return G.runKernel(ul,s)}var RA=V({floorDiv_:qG});function KG(e,t){let n=M(e,"a","div"),r=M(t,"b","div");if([n,r]=Ut(n,r),n.dtype==="int32"&&r.dtype==="int32")return RA(n,r);let s={a:n,b:r},a={};return G.runKernel(ol,s,a)}var Re=V({div_:KG});function XG(e,t){let n=M(e,"a","mul"),r=M(t,"b","mul");[n,r]=Ut(n,r);let s={a:n,b:r};return G.runKernel(Mo,s)}var K=V({mul_:XG});function ZG(e){let t=M(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return G.runKernel(Zp,n)}else{let n={x:t};return G.runKernel(xc,n)}}var yn=V({abs_:ZG});function YG(e){let n={x:M(e,"x","acos")};return G.runKernel(bc,n)}var H6=V({acos_:YG});function JG(e){let n={x:M(e,"x","acosh")};return G.runKernel(vc,n)}var G6=V({acosh_:JG});function QG(e){z(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),z(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((s,a)=>M(s,`tensors${a}`,"addN")),n=t[0];t.forEach(s=>{if(s.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(s=>{if(!Da(s.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return G.runKernel(Zi,r)}var ej=V({addN_:QG});function tj(e,t=null,n=!1){let s={x:M(e,"x","all","bool")},a={axis:t,keepDims:n};return G.runKernel(wc,s,a)}var DA=V({all_:tj});function nj(e,t=null,n=!1){let s={x:M(e,"x","any","bool")},a={axis:t,keepDims:n};return G.runKernel(kc,s,a)}var wf=V({any_:nj});function rj(e,t=0){let r={x:M(e,"x","argMax")},s={axis:t};return G.runKernel(Yi,r,s)}var kf=V({argMax_:rj});function sj(e,t=0){let r={x:M(e,"x","argMin")},s={axis:t};return G.runKernel(qp,r,s)}var j6=V({argMin_:sj});function aj(e){let n={x:M(e,"x","asin")};return G.runKernel(Ic,n)}var q6=V({asin_:aj});function oj(e){let n={x:M(e,"x","asinh")};return G.runKernel(Sc,n)}var K6=V({asinh_:oj});function ij(e){let n={x:M(e,"x","atan")};return G.runKernel(Tc,n)}var X6=V({atan_:ij});function lj(e,t){let n=M(e,"a","atan2"),r=M(t,"b","atan2");[n,r]=Ut(n,r);let s={a:n,b:r};return G.runKernel(Cc,s)}var Z6=V({atan2_:lj});function uj(e){let n={x:M(e,"x","atanh")};return G.runKernel(Nc,n)}var Y6=V({atanh_:uj});function cj(e,t,n,r,s="NHWC",a){let o=e[3],i=[...t,o],l=eI(s);return Id(e,i,n,a,r,null,null,l)}function J6(e,t,n,r,s,a,o="channelsLast"){let[i,l]=If(t),u;if(o==="channelsLast")u=[i,l,e[3],e[3]];else if(o==="channelsFirst")u=[i,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return Id(e,u,n,r,s,a,!1,o)}function dj(e,t,n,r,s,a,o="NDHWC"){let[i,l,u]=MA(t),c,d;if(o==="NDHWC")d="channelsLast",c=[i,l,u,e[4],e[4]];else if(o==="NCDHW")d="channelsFirst",c=[i,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return Q6(e,c,n,r,s,!1,d,a)}function Id(e,t,n,r,s,a,o=!1,i="channelsLast"){let[l,u,c,d]=[-1,-1,-1,-1];if(i==="channelsLast")[l,u,c,d]=e;else if(i==="channelsFirst")[l,d,u,c]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,p,,f]=t,[m,g]=If(n),[y,A]=If(r),x=ql(h,y),b=ql(p,A),{padInfo:v,outHeight:w,outWidth:S}=fj(s,u,c,m,g,x,b,a,i),I=o?f*d:f,E;return i==="channelsFirst"?E=[l,I,w,S]:i==="channelsLast"&&(E=[l,w,S,I]),{batchSize:l,dataFormat:i,inHeight:u,inWidth:c,inChannels:d,outHeight:w,outWidth:S,outChannels:I,padInfo:v,strideHeight:m,strideWidth:g,filterHeight:h,filterWidth:p,effectiveFilterHeight:x,effectiveFilterWidth:b,dilationHeight:y,dilationWidth:A,inShape:e,outShape:E,filterShape:t}}function Q6(e,t,n,r,s,a=!1,o="channelsLast",i){let[l,u,c,d,h]=[-1,-1,-1,-1,-1];if(o==="channelsLast")[l,u,c,d,h]=e;else if(o==="channelsFirst")[l,h,u,c,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[p,f,m,,g]=t,[y,A,x]=MA(n),[b,v,w]=MA(r),S=ql(p,b),I=ql(f,v),E=ql(m,w),{padInfo:F,outDepth:$,outHeight:_,outWidth:N}=mj(s,u,c,d,y,A,x,S,I,E,i),P=a?g*h:g,B;return o==="channelsFirst"?B=[l,P,$,_,N]:o==="channelsLast"&&(B=[l,$,_,N,P]),{batchSize:l,dataFormat:o,inDepth:u,inHeight:c,inWidth:d,inChannels:h,outDepth:$,outHeight:_,outWidth:N,outChannels:P,padInfo:F,strideDepth:y,strideHeight:A,strideWidth:x,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:S,effectiveFilterHeight:I,effectiveFilterWidth:E,dilationDepth:b,dilationHeight:v,dilationWidth:w,inShape:e,outShape:B,filterShape:t}}function hj(e,t,n,r,s){r==null&&(r=FA(e,t,n));let a=e[0],o=e[1],i=Xo((a-t+2*r)/n+1,s),l=Xo((o-t+2*r)/n+1,s);return[i,l]}function pj(e,t,n,r,s,a){s==null&&(s=FA(e,t,r));let o=e[0],i=e[1],l=e[2],u=Xo((o-t+2*s)/r+1,a),c=Xo((i-t+2*s)/r+1,a),d=Xo((l-t+2*s)/r+1,a);return[u,c,d,n]}function FA(e,t,n,r=1){let s=ql(t,r);return Math.floor((e[0]*(n-1)-n+s)/2)}function If(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function MA(e){return typeof e=="number"?[e,e,e]:e}function ql(e,t){return t<=1?e:e+(e-1)*(t-1)}function fj(e,t,n,r,s,a,o,i,l){let u,c,d;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let p=hj([t,n],a,r,e,i);c=p[0],d=p[1]}else if(e==="same"){c=Math.ceil(t/r),d=Math.ceil(n/s);let h=Math.max(0,(c-1)*r+a-t),p=Math.max(0,(d-1)*s+o-n),f=Math.floor(h/2),m=h-f,g=Math.floor(p/2),y=p-g;u={top:f,bottom:m,left:g,right:y,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-a+1)/r),d=Math.ceil((n-o+1)/s);else if(typeof e=="object"){let h=l==="channelsLast"?e[1][0]:e[2][0],p=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];u={top:h,bottom:p,left:f,right:m,type:h===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},c=Xo((t-a+h+p)/r+1,i),d=Xo((n-o+f+m)/s+1,i)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:c,outWidth:d}}function mj(e,t,n,r,s,a,o,i,l,u,c){let d,h,p,f;if(typeof e=="number"){d={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let g=pj([t,n,r,1],i,1,s,e,c);h=g[0],p=g[1],f=g[2]}else if(e==="same"){h=Math.ceil(t/s),p=Math.ceil(n/a),f=Math.ceil(r/o);let m=(h-1)*s+i-t,g=(p-1)*a+l-n,y=(f-1)*o+u-r,A=Math.floor(m/2),x=m-A,b=Math.floor(g/2),v=g-b,w=Math.floor(y/2),S=y-w;d={top:b,bottom:v,left:w,right:S,front:A,back:x,type:"SAME"}}else if(e==="valid")d={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},h=Math.ceil((t-i+1)/s),p=Math.ceil((n-l+1)/a),f=Math.ceil((r-u+1)/o);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:d,outDepth:h,outHeight:p,outWidth:f}}function Xo(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 La(e){let[t,n,r]=If(e);return t===1&&n===1&&r===1}function _s(e,t){return La(e)||La(t)}function eI(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function gj(e,t){let r={x:M(e,"x","reshape","string_or_numeric")},s={shape:t};return G.runKernel(Qc,r,s)}var J=V({reshape_:gj});function yj(e,t,n,r,s){let a=M(e,"x","avgPool","float32"),o=1;z(_s(n,o),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`);let i=a,l=!1;a.rank===3&&(l=!0,i=J(a,[1,a.shape[0],a.shape[1],a.shape[2]])),z(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),s!=null&&z(mn(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s},d=G.runKernel(Ji,u,c);return d=ke(d,a.dtype),l?J(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Sf=V({avgPool_:yj});function Aj(e,t,n,r,s,a="NDHWC"){let o=M(e,"x","avgPool3d","float32"),i=o,l=!1;o.rank===4&&(l=!0,i=J(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),z(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),z(a==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),s!=null&&z(mn(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:a},d=G.runKernel(Kp,u,c);return d=ke(d,i.dtype),l?J(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var tI=V({avgPool3d_:Aj});function xj(e,t=0){z(e.length>=1,()=>"Pass at least one tensor to concat");let n=Af(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(a=>{if(a.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${a.dtype}. `)}),n.length===1)return qo(n[0]);let r=n,s={axis:t};return G.runKernel(Ec,r,s)}var en=V({concat_:xj});function bj(e){let n={x:M(e,"x","sigmoid")};return G.runKernel(Rl,n)}var Rs=V({sigmoid_:bj});function vj(e,t,n){let r=M(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let s={x:r},a={begin:t,size:n};return G.runKernel(rd,s,a)}var nt=V({slice_:vj});function wj(e){let n={x:M(e,"x","tanh")};return G.runKernel(Pl,n)}var Kl=V({tanh_:wj});function kj(e,t,n,r,s,a){let o=M(e,"forgetBias","basicLSTMCell"),i=M(t,"lstmKernel","basicLSTMCell"),l=M(n,"lstmBias","basicLSTMCell"),u=M(r,"data","basicLSTMCell"),c=M(s,"c","basicLSTMCell"),d=M(a,"h","basicLSTMCell"),h=en([u,d],1),p=ot(h,i),f=pe(p,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],A=nt(f,[0,0],y),x=nt(f,[0,g],y),b=nt(f,[0,g*2],y),v=nt(f,[0,g*3],y),w=pe(K(Rs(A),Kl(x)),K(c,Rs(pe(o,b)))),S=K(Kl(w),Rs(v));return[w,S]}var bwe=V({basicLSTMCell_:kj});function Ij(e,t,n){let r=M(e,"x","batchToSpaceND"),s=t.reduce((i,l)=>i*l);z(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),z(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),z(r.shape[0]%s==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${s}`);let a={x:r},o={blockShape:t,crops:n};return G.runKernel(Xp,a,o)}var Tf=V({batchToSpaceND_:Ij});function Sj(e){let t;return e.rank===0||e.rank===1?t=J(e,[1,1,1,e.size]):e.rank===2?t=J(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=J(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function Tj(e,t,n,r,s,a){a==null&&(a=.001);let o=M(e,"x","batchNorm"),i=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;s!=null&&(u=M(s,"scale","batchNorm"));let c;r!=null&&(c=M(r,"offset","batchNorm")),z(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),z(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),z(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:Sj(o),scale:u,offset:c,mean:i,variance:l},p={varianceEpsilon:a},f=G.runKernel(cl,h,p);return J(f,o.shape)}var Xl=V({batchNorm_:Tj});function Nj(e,t,n,r,s,a){let o=M(e,"x","batchNorm"),i=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;s!=null&&(u=M(s,"scale","batchNorm"));let c;return r!=null&&(c=M(r,"offset","batchNorm")),z(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),z(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),z(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&z(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&z(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Xl(o,i,l,c,u,a)}var Cj=V({batchNorm2d_:Nj});function Ej(e,t,n,r,s,a){let o=M(e,"x","batchNorm"),i=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;s!=null&&(u=M(s,"scale","batchNorm"));let c;return r!=null&&(c=M(r,"offset","batchNorm")),z(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),z(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),z(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&z(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&z(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Xl(o,i,l,c,u,a)}var $j=V({batchNorm3d_:Ej});function _j(e,t,n,r,s,a){let o=M(e,"x","batchNorm"),i=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;s!=null&&(u=M(s,"scale","batchNorm"));let c;return r!=null&&(c=M(r,"offset","batchNorm")),z(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),z(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),z(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&z(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&z(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Xl(o,i,l,c,u,a)}var Rj=V({batchNorm4d_:_j});function Dj(e,t,n){let r=M(e,"x","bincount"),s=M(t,"weights","bincount");z(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),z(n>=0,()=>`size must be non-negative, but got ${n}.`),z(s.size===r.size||s.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${s.shape}.`);let a={x:r,weights:s},o={size:n};return G.runKernel(Iy,a,o)}var nI=V({bincount_:Dj});function Fj(e,t){let n=M(e,"broadcastTo","x"),r=n.shape;if(t.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let u=n.shape.slice();for(;u.length<t.length;)u.unshift(1);n=J(n,u)}let s=n.shape,a=Array.from(t);for(let u=t.length-1;u>=0;u--)if(s[u]===t[u])a[u]=1;else if(n.shape[u]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(a.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return qo(n);let i={x:n},l={reps:a};return G.runKernel(Lo,i,l)}var Sd=V({broadcastTo_:Fj});function Mj(e){let n={x:M(e,"x","ceil")};return G.runKernel(No,n)}var rI=V({ceil_:Mj});function Oj(e,t,n){let r=M(e,"x","clipByValue");z(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let s={x:r},a={clipValueMin:t,clipValueMax:n};return G.runKernel(Co,s,a)}var dr=V({clipByValue_:Oj});function Pj(e){return en(e,0)}var zj=V({concat1d_:Pj});function Lj(e,t){return en(e,t)}var Bj=V({concat2d_:Lj});function Wj(e,t){return en(e,t)}var Vj=V({concat3d_:Wj});function Uj(e,t){return en(e,t)}var Hj=V({concat4d_:Uj});function Gj(e,t,n,r,s="NHWC",a=[1,1],o){let i=M(e,"x","conv2d"),l=M(t,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=J(i,[1,i.shape[0],i.shape[1],i.shape[2]])),z(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),z(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),o!=null&&z(mn(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let d=s==="NHWC"?u.shape[3]:u.shape[1];z(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),z(_s(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let h={x:u,filter:l},p={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o},f=G.runKernel(tl,h,p);return c?J(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Ba=V({conv2d_:Gj});function jj(e,t,n,r,s="NWC",a=1,o){let i=M(e,"x","conv1d"),l=M(t,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=J(i,[1,i.shape[0],i.shape[1]])),z(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),z(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),o!=null&&z(mn(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`),z(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),z(_s(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),z(s==="NWC",()=>`Error in conv1d: got dataFormat of ${s} but only NWC is currently supported.`);let d=J(l,[1,l.shape[0],l.shape[1],l.shape[2]]),h=J(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Ba(h,d,[1,n],r,"NHWC",[1,a],o);return c?J(g,[g.shape[2],g.shape[3]]):J(g,[g.shape[0],g.shape[2],g.shape[3]])}var OA=V({conv1d_:jj});function qj(e,t,n,r,s,a="NHWC",o){z(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,u=!1;t.rank===3&&(u=!0,l=J(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),z(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),z(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),z(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];z(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),z(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),o!=null&&z(mn(s),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let h={dy:l,filter:n},p={strides:r,pad:s,dataFormat:a,dimRoundingMode:o,inputShape:i},f=G.runKernel(nl,h,p);return u?J(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var PA=V({conv2DBackpropInput_:qj});function Kj(e,t,n,r,s,a){let o=M(e,"x","conv2dTranspose"),i=M(t,"filter","conv2dTranspose");return PA(n,o,i,r,s,"NHWC",a)}var zA=V({conv2dTranspose_:Kj});function Xj(e,t,n,r,s="NDHWC",a=[1,1,1]){let o=M(e,"x","conv3d"),i=M(t,"filter","conv3d"),l=o,u=!1;o.rank===4&&(u=!0,l=J(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),z(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),z(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),z(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),z(_s(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),z(s==="NDHWC",()=>`Error in conv3d: got dataFormat of ${s} but only NDHWC is currently supported.`);let c={x:l,filter:i},d={strides:n,pad:r,dataFormat:s,dilations:a},h=G.runKernel(Yp,c,d);return u?J(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var sI=V({conv3d_:Xj});function Zj(e,t,n,r,s){z(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=J(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],u=o.shape[4];z(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),z(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),z(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),z(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),z(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},d={pad:s,strides:r,inputShape:a},h=G.runKernel(Cy,c,d);return i?J(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var aI=V({conv3DBackpropInput_:Zj});function Yj(e,t,n,r,s){let a=M(e,"x","conv3dTranspose"),o=M(t,"filter","conv3dTranspose");return aI(n,a,o,r,s)}var Jj=V({conv3dTranspose_:Yj});function Qj(e){let n={x:M(e,"x","cos")};return G.runKernel(rl,n)}var Nf=V({cos_:Qj});function eq(e){let n={x:M(e,"x","cosh")};return G.runKernel($c,n)}var LA=V({cosh_:eq});function tq(e,t=0,n=!1,r=!1){let a={x:M(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:r};return G.runKernel(sl,a,o)}var BA=V({cumsum_:tq});function nq(e,t,n,r=!1){let s=M(e,"x","denseBincount"),a=M(t,"weights","denseBincount");z(s.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${s.dtype}`),z(s.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${s.rank}.`),z(n>=0,()=>`size must be non-negative, but got ${n}.`),z(a.size===s.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${s.shape}, weights shape: ${a.shape}.`);let o={x:s,weights:a},i={size:n,binaryOutput:r};return G.runKernel(Ey,o,i)}var rq=V({denseBincount_:nq});function sq(e,t,n="NHWC"){let r=M(e,"x","depthToSpace"),s=n==="NHWC"?r.shape[1]:r.shape[2],a=n==="NHWC"?r.shape[2]:r.shape[3],o=n==="NHWC"?r.shape[3]:r.shape[1];z(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${s} and ${t}  for depthToSpace with input shape
    ${r.shape}`),z(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${a} and ${t} for depthToSpace with input shape
        ${r.shape}`),z(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${r.shape}`);let i={x:r},l={blockSize:t,dataFormat:n};return G.runKernel(Rc,i,l)}var oI=V({depthToSpace_:sq});function aq(e,t,n,r,s="NHWC",a=[1,1],o){let i=M(e,"x","depthwiseConv2d"),l=M(t,"filter","depthwiseConv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=J(i,[1,i.shape[0],i.shape[1],i.shape[2]])),z(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),z(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),z(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),o!=null&&z(mn(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let d={x:u,filter:l},h={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o},p=G.runKernel(al,d,h);return c?J(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Td=V({depthwiseConv2d_:aq});function oq(e){let n={x:M(e,"x","diag")};return G.runKernel(Ry,n)}var vwe=V({diag_:oq});function iq(e,t,n,r,s=[1,1],a="NHWC"){let o=M(e,"x","dilation2d"),i=M(t,"filter","dilation2d");z(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),z(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),z(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,u=!1;o.rank===3&&(l=J(o,[1,o.shape[0],o.shape[1],o.shape[2]]),u=!0);let c={x:l,filter:i},d={strides:n,pad:r,dilations:s},h=G.runKernel(Jp,c,d);return u?J(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var iI=V({dilation2d_:iq});function lq(e,t){let n=e.length,r=[];for(let s=0;s<n;s++){let a=n-1-s,o=e[a]||1;(t[t.length-1-s]||1)>1&&o===1&&r.unshift(a)}return r}function ln(e,t){let n=[];for(let r=0;r<t.length;r++){let s=e[e.length-r-1],a=t.length-r-1,o=t[a];(s==null||s===1&&o>1)&&n.unshift(a)}return n}function Rt(e,t){let n=[],r=Math.max(e.length,t.length);for(let s=0;s<r;s++){let a=e[e.length-s-1];a==null&&(a=1);let o=t[t.length-s-1];if(o==null&&(o=1),a===1)n.unshift(o);else if(o===1)n.unshift(a);else if(a!==o){let i=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(i)}else n.unshift(a)}return n}function uq(e,t){let n=M(e,"a","equal","string_or_numeric"),r=M(t,"b","equal","string_or_numeric");[n,r]=Ut(n,r),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(il,s)}var Zo=V({equal_:uq});function cq(e,t,n){let r=M(t,"a","where"),s=M(n,"b","where"),a=M(e,"condition","where","bool"),o=Rt(Rt(a.shape,r.shape),s.shape),i=Sd(a,o),l=Sd(r,o),u=Sd(s,o),c={condition:i,t:l,e:u};return G.runKernel(td,c)}var Ln=V({where_:cq});function dq(e){let n={x:M(e,"x","zerosLike")};return G.runKernel(hd,n)}var rt=V({zerosLike_:dq});function hq(e,t){let n=M(e,"a","div"),r=M(t,"b","div");[n,r]=Ut(n,r);let s=Re(n,r),a=rt(s),o=Zo(r,a);return Ln(o,a,s)}var lI=V({divNoNan_:hq});function pq(e,t){let n=M(e,"t1","dot"),r=M(t,"t2","dot");z((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let s=n.rank===1?n.size:n.shape[1],a=r.rank===1?r.size:r.shape[0];if(z(s===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${s} and ${a}.`),n.rank===1&&r.rank===1){let o=J(n,[1,-1]),i=J(r,[-1,1]),l=ot(o,i);return J(l,[])}else if(n.rank===1&&r.rank===2){let o=J(n,[1,-1]),i=J(r,[r.shape[0],r.shape[1]]),l=ot(o,i);return J(l,[l.size])}else if(n.rank===2&&r.rank===1){let o=J(r,[-1,1]),i=ot(n,o);return J(i,[i.size])}else{let o=J(r,[r.shape[0],r.shape[1]]);return ot(n,o)}}var fq=V({dot_:pq});function mq(e,...t){let n=t.map((s,a)=>M(s,`tensors${a}`,"einsum")),r={equation:e};return G.runKernel(My,n,r)}var gq=V({einsum_:mq});function yq(e){let n={x:M(e,"x","elu")};return G.runKernel(Dc,n)}var Nd=V({elu_:yq});function Aq(e){let t=M(e,"x","erf");z(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=ke(t,"float32"));let n={x:t};return G.runKernel(Fc,n)}var uI=V({erf_:Aq});function xq(e){let n={x:M(e,"x","exp")};return G.runKernel(Eo,n)}var Kr=V({exp_:xq});function bq(e,t=0){let n=M(e,"x","expandDims","string_or_numeric");z(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},s={dim:t};return G.runKernel(Mc,r,s)}var $r=V({expandDims_:bq});function vq(e){let n={x:M(e,"x","expm1")};return G.runKernel(ll,n)}var cI=V({expm1_:vq});function wq(e,t){let n=M(e,"x","tile","string_or_numeric");z(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},s={reps:t};return G.runKernel(Lo,r,s)}var Yo=V({tile_:wq});function kq(e,t,n,r="float32"){t==null&&(t=e);let s=Le([e,t],r),a=e<=t?e:t;for(let i=0;i<a;++i)s.set(1,i,i);let o=J(s.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return Yo($r(o,0),[n[0],1,1]);if(n.length===2)return Yo($r($r(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Yo($r($r($r(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var dI=V({eye_:kq});function Cd(e,t,n){let r={shape:e,value:t,dtype:n};return G.runKernel(Qp,{},r)}function Iq(e){let n={x:M(e,"x","floor")};return G.runKernel($o,n)}var Ed=V({floor_:Iq});function Sq(e,t,n=0,r=0){let s=M(e,"x","gather"),a=M(t,"indices","gather","int32"),o={x:s,indices:a},i={axis:n,batchDims:r};return G.runKernel(Pc,o,i)}var $d=V({gather_:Sq});function Tq(e,t){let n=M(e,"a","greater","string_or_numeric"),r=M(t,"b","greater","string_or_numeric");[n,r]=Ut(n,r),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(dl,s)}var _r=V({greater_:Tq});function Nq(e,t){let n=M(e,"a","greaterEqual","string_or_numeric"),r=M(t,"b","greaterEqual","string_or_numeric");[n,r]=Ut(n,r),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(_o,s)}var Jo=V({greaterEqual_:Nq});function Cq(e){let n={input:M(e,"input","imag")};return G.runKernel(Ly,n)}var WA=V({imag_:Cq});function Eq(e){let n={x:M(e,"x","isFinite")};return G.runKernel(Lc,n)}var $q=V({isFinite_:Eq});function _q(e){let n={x:M(e,"x","isInf")};return G.runKernel(Bc,n)}var Rq=V({isInf_:_q});function Dq(e){let n={x:M(e,"x","isNaN")};return G.runKernel(Wc,n)}var hI=V({isNaN_:Dq});function Fq(e,t=.2){let r={x:M(e,"x","leakyRelu")},s={alpha:t};return G.runKernel(pl,r,s)}var Cf=V({leakyRelu_:Fq});function Mq(e,t){let n=M(e,"a","less","string_or_numeric"),r=M(t,"b","less","string_or_numeric");[n,r]=Ut(n,r),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(fl,s)}var VA=V({less_:Mq});function Oq(e,t){let n=M(e,"a","lessEqual","string_or_numeric"),r=M(t,"b","lessEqual","string_or_numeric");[n,r]=Ut(n,r),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(ml,s)}var Qo=V({lessEqual_:Oq});function Pq(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let r={start:e,stop:t,num:n};return G.runKernel(By,{},r)}function zq(e,t=5,n=1,r=1,s=.5){let a=M(e,"x","localResponseNormalization");z(a.rank===4||a.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${a.rank}.`),z(mn(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=J(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},u={depthRadius:t,bias:n,alpha:r,beta:s},c=G.runKernel(nf,l,u);return i?J(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var pI=V({localResponseNormalization_:zq});function Lq(e){let n={x:M(e,"x","log")};return G.runKernel(Ro,n)}var Rr=V({log_:Lq});function Bq(e){let n={x:M(e,"x","log1p")};return G.runKernel(Vc,n)}var UA=V({log1p_:Bq});function Wq(e,t){z(Hp(e),()=>"The f passed in variableGrads(f) must be a function"),z(t==null||Array.isArray(t)&&t.every(u=>u instanceof gf),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in G.registeredVariables)t.push(G.registeredVariables[u])}let r=n?t.filter(u=>!u.trainable):null,s=t.length;t=t.filter(u=>u.trainable),z(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${s} variables is trainable.`);let a=!0,{value:o,grads:i}=G.gradients(e,t,null,a);z(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),z(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),r!=null&&r.forEach(u=>l[u.name]=null),{value:o,grads:l}}function oa(e){return G.customGrad(e)}function Vq(e){let n={x:M(e,"x","neg")};return G.runKernel(Gc,n)}var Kt=V({neg_:Vq});function Uq(e){let n={x:M(e,"x","softplus")};return G.runKernel(od,n)}var Zl=V({softplus_:Uq});function Hq(e){let t=M(e,"x","logSigmoid");return oa(r=>({value:Kt(Zl(Kt(r))),gradFunc:o=>K(o,Rs(Kt(r)))}))(t)}var Gq=V({logSigmoid_:Hq});function jq(e,t=null,n=!1){let s={x:M(e,"x","max")},a={reductionIndices:t,keepDims:n};return G.runKernel(gl,s,a)}var os=V({max_:jq});function qq(e,t){let n=M(e,"a","sub"),r=M(t,"b","sub");[n,r]=Ut(n,r);let s={a:n,b:r};return G.runKernel(zo,s)}var Ne=V({sub_:qq});function Kq(e,t=null,n=!1){let r=M(e,"x","sum");r.dtype==="bool"&&(r=ke(r,"int32"));let s={x:r},a={axis:t,keepDims:n};return G.runKernel(Fl,s,a)}var _e=V({sum_:Kq});function Xq(e,t=-1){let n=M(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 ${t}`);return oa((s,a)=>{let o=!0,i=os(s,t,!0),l=Ne(s,i),u=Ne(ke(l,"float32"),Rr(_e(Kr(l),t,o)));return a([u]),{value:u,gradFunc:(d,h)=>{let[p]=h,f=!0,m=Kr(p);return Ne(d,K(_e(d,t,f),m))}}})(n)}var HA=V({logSoftmax_:Xq});function GA(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function fI(e,t,n){let r=e.length+t.length,s=[],a=0,o=0;for(let i=0;i<r;i++)n.indexOf(i)===-1?s.push(e[a++]):s.push(t[o++]);return s}function mI(e,t){let n=[],r=e.length;for(let a=0;a<r;a++)t.indexOf(a)===-1&&n.push(e[a]);let s=t.map(a=>e[a]);return[n,s]}function ei(e,t){let n=t.map(r=>1);return fI(e,n,t)}function Zq(e,t,n){z(GA(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function gI(e,t){if(GA(e,t))return null;let n=[];for(let r=0;r<t;++r)e.indexOf(r)===-1&&n.push(r);return e.forEach(r=>n.push(r)),n}function jA(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function Yq(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function Jq(e,t=null,n=!1){let r=M(e,"x","logSumExp"),s=jr(t,r.shape),a=os(r,s,!0),o=Ne(r,a),i=Kr(o),l=_e(i,s),u=Rr(l),c=pe(J(a,u.shape),u);if(n){let d=ei(c.shape,s);return J(c,d)}return c}var yI=V({logSumExp_:Jq});function Qq(e,t){let n=M(e,"a","logicalAnd","bool"),r=M(t,"b","logicalAnd","bool");Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(Uc,s)}var is=V({logicalAnd_:Qq});function eK(e){let n={x:M(e,"x","logicalNot","bool")};return G.runKernel(ef,n)}var Ef=V({logicalNot_:eK});function tK(e,t){let n=M(e,"a","logicalOr","bool"),r=M(t,"b","logicalOr","bool");Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(tf,s)}var qA=V({logicalOr_:tK});function nK(e,t){let n=M(e,"a","logicalXor","bool"),r=M(t,"b","logicalXor","bool");return Rt(n.shape,r.shape),is(qA(e,t),Ef(is(e,t)))}var rK=V({logicalXor_:nK});function sK(e,t,n,r,s){let a=M(e,"x","maxPool"),o=1,i=a,l=!1;a.rank===3&&(l=!0,i=J(a,[1,a.shape[0],a.shape[1],a.shape[2]])),z(i.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`),z(_s(n,o),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`),s!=null&&z(mn(r),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s},d=G.runKernel(yl,u,c);return l?J(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var $f=V({maxPool_:sK});function aK(e,t=[1,1,1],n,r,s,a="NDHWC"){let o=M(e,"x","maxPool3d"),i=o,l=!1;o.rank===4&&(l=!0,i=J(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),z(i.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`),z(a==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),s!=null&&z(mn(r),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:a},d=G.runKernel(rf,u,c);return l?J(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var AI=V({maxPool3d_:aK});function oK(e,t,n,r,s=!1){let o={x:M(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:s},l=G.runKernel(Hy,o,i);return{result:l[0],indexes:l[1]}}var iK=V({maxPoolWithArgmax_:oK});function lK(e,t){let n=M(e,"a","maximum"),r=M(t,"b","maximum");[n,r]=Ut(n,r),n.dtype==="bool"&&(n=ke(n,"int32"),r=ke(r,"int32")),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(Do,s)}var ia=V({maximum_:lK});function uK(e,t=null,n=!1){let s={x:M(e,"x","mean")},a={axis:t,keepDims:n};return G.runKernel(Al,s,a)}var Xt=V({mean_:uK});function un(e,t="float32"){if(t==="complex64"){let r=un(e,"float32"),s=un(e,"float32");return Uo(r,s)}let n=jp(on(e),t);return G.makeTensor(n,e,t)}function la(e,t="float32"){if(t==="complex64"){let r=la(e,"float32"),s=un(e,"float32");return Uo(r,s)}let n=yy(on(e),t);return G.makeTensor(n,e,t)}function cK(e,t=null,n=!1){let s={x:M(e,"x","min")},a={axis:t,keepDims:n};return G.runKernel(xl,s,a)}var _f=V({min_:cK});function dK(e,t){let n=M(e,"a","minimum"),r=M(t,"b","minimum");[n,r]=Ut(n,r),n.dtype==="bool"&&(n=ke(n,"int32"),r=ke(r,"int32")),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(Fo,s)}var _d=V({minimum_:dK});function hK(e,t,n){z(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=M(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");z(t.length===r.rank,()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`);let s=n==="reflect"?1:0;for(let i=0;i<r.rank;i++)z(t[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),z(t[i][0]>=0&&t[i][0]<=r.shape[i]-s&&t[i][1]>=0&&t[i][1]<=r.shape[i]-s,()=>`Padding in dimension ${i} cannot be greater than or equal to ${r.shape[i]-s} or less than 0 for input of shape ${r.shape}`);let a={paddings:t,mode:n},o={x:r};return G.runKernel(bl,o,a)}var xI=V({mirrorPad_:hK});function pK(e,t){let n=M(e,"a","mod"),r=M(t,"b","mod");[n,r]=Ut(n,r);let s={a:n,b:r};return G.runKernel(Hc,s)}var bI=V({mod_:pK});function fK(e){let t=M(e,"x","square"),n={};return G.runKernel("Square",{x:t},n)}var wt=V({square_:fK});function mK(e,t=null,n=!1){e=M(e,"x","moments");let r=jr(t,e.shape),s=Xt(e,r,n),a=s.shape;n||(a=ei(s.shape,r));let o=wt(Ne(ke(e,"float32"),J(s,a))),i=Xt(o,r,n);return{mean:s,variance:i}}var KA=V({moments_:mK});function gK(e,t,n,r){let s=M(t,"data","multiRNNCell"),a=Af(n,"c","multiRNNCell"),o=Af(r,"h","multiRNNCell"),i=s,l=[];for(let d=0;d<e.length;d++){let h=e[d](i,a[d],o[d]);l.push(h[0]),l.push(h[1]),i=h[1]}let u=[],c=[];for(let d=0;d<l.length;d+=2)u.push(l[d]),c.push(l[d+1]);return[u,c]}var wwe=V({multiRNNCell_:gK});function yK(e,t,n,r=!1){let s=M(e,"logits","multinomial"),a=s.size,o=s.rank;if(a<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);if(o>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${o}`);n=n||Math.random();let l={logits:o===1?J(s,[1,-1]):s},u={numSamples:t,seed:n,normalized:r},c=G.runKernel(Gy,l,u);return o===1?J(c,[c.size]):c}var AK=V({multinomial_:yK});function xK(e,t){let n=M(e,"a","notEqual","string_or_numeric"),r=M(t,"b","notEqual","string_or_numeric");[n,r]=Ut(n,r),Rt(n.shape,r.shape);let s={a:n,b:r};return G.runKernel(vl,s)}var Yl=V({notEqual_:xK});function bK(e){let n={x:M(e,"x","onesLike")};return G.runKernel(Xc,n)}var Dr=V({onesLike_:bK});function vK(e,t){let n=M(e,"v1","outerProduct"),r=M(t,"v2","outerProduct");z(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let s=J(n,[-1,1]),a=J(r,[1,-1]);return ot(s,a)}var kwe=V({outerProduct_:vK});function wK(e,t,n=0){let r=M(e,"x","pad");if(r.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let s={paddings:t,constantValue:n},a={x:r};return G.runKernel(kl,a,s)}var Wa=V({pad_:wK});function kK(e,t,n=0){return z(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),Wa(e,[t],n)}var Iwe=V({pad1d_:kK});function IK(e,t,n=0){return z(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),Wa(e,t,n)}var Swe=V({pad2d_:IK});function SK(e,t,n=0){return z(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."),Wa(e,t,n)}var Twe=V({pad3d_:SK});function TK(e,t,n=0){return z(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."),Wa(e,t,n)}var Nwe=V({pad4d_:TK});function NK(e,t,n){let r=M(e,"x","spaceToBatchND");z(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),z(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),z(r.shape.reduce((o,i,l)=>l>0&&l<=t.length?o&&(i+n[l-1][0]+n[l-1][1])%t[l-1]==0:o,!0),()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let s={x:r},a={blockShape:t,paddings:n};return G.runKernel(of,s,a)}var Rf=V({spaceToBatchND_:NK});function CK(e,t,n,r,s,a){s==null&&(s=[1,1]),a==null&&(a=1),r===0&&(r="valid");let o=M(e,"x","maxPool"),i=o,l=!1;o.rank===3&&(l=!0,i=J(o,[1,o.shape[0],o.shape[1],o.shape[2]])),z(_s(a,s),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${s}'`);let u=J6(i.shape,t,a,s,r),c=[u.dilationHeight,u.dilationWidth],d;r==="same"?d=$K([u.filterHeight,u.filterWidth],c):d=[[0,0],[0,0]];let h=c[0]===1&&c[1]===1,[p,f]=EK([u.inHeight,u.inWidth],c,d),m=h?r:"valid",g=h?i:Rf(i,c,p),A=(n==="avg"?()=>Sf(g,t,a,m):()=>$f(g,t,a,m))(),x=h?A:Tf(A,c,f);return l?J(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function EK(e,t,n){let r=n.map(c=>c[0]),s=n.map(c=>c[1]),a=e.concat(r,s),o=t.map((c,d)=>(c-a[d]%c)%c),i=s.map((c,d)=>c+o[d]),l=t.map((c,d)=>[r[d],i[d]]),u=t.map((c,d)=>[0,o[d]]);return[l,u]}function $K(e,t){let r=e.map((o,i)=>o+(o-1)*(t[i]-1)).map(o=>o-1),s=r.map(o=>Math.floor(o/2)),a=r.map((o,i)=>o-s[i]);return r.map((o,i)=>[s[i],a[i]])}var _K=V({pool_:CK});function RK(e,t){let n=M(e,"base","pow"),r=M(t,"exp","pow");[n,r]=Ut(n,r);let s={a:n,b:r};return G.runKernel(Il,s)}var Va=V({pow_:RK});function DK(e,t){let n=M(e,"x","prelu"),r=M(t,"alpha","prelu"),s={x:n,alpha:r};return G.runKernel(Sl,s)}var Df=V({prelu_:DK});function FK(e,t=null,n=!1){let r=M(e,"x","prod");r.dtype==="bool"&&(r=ke(r,"int32"));let s={x:r},a={axis:t,keepDims:n};return G.runKernel(Yc,s,a)}var XA=V({prod_:FK});function MK(e,t,n){let r=on(e),s=null;if(n==null||n==="float32")s=new Float32Array(r);else if(n==="int32")s=new Int32Array(r);else if(n==="bool")s=new Uint8Array(r);else throw new Error(`Unknown data type ${n}`);for(let a=0;a<r;a++)s[a]=t();return G.makeTensor(s,e,n)}var Cwe=V({rand_:MK}),ZA=Ks(t2()),YA=class{constructor(e,t,n,r,s){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=r,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let a=s||Math.random();this.random=ZA.alea(a.toString())}nextValue(){if(!isNaN(this.nextVal)){let r=this.nextVal;return this.nextVal=NaN,r}let e,t,n=!1;for(;!n;){let r,s,a;do r=2*this.random()-1,s=2*this.random()-1,a=r*r+s*s;while(a>=1||a===0);let o=Math.sqrt(-2*Math.log(a)/a);e=this.mean+this.stdDev*r*o,t=this.mean+this.stdDev*s*o,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},OK=class{constructor(e,t,n,r){this.alpha=e,this.beta=1/t,this.dtype=n;let s=r||Math.random();this.randu=ZA.alea(s.toString()),this.randn=new YA(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,r,s,a;for(;;){do r=this.randn.nextValue(),a=1+this.c*r;while(a<=0);if(a*=a*a,e=r*r,t=1-.331*e*e,n=.5*e+this.d*(1-a+Math.log(a)),s=this.randu(),s<t||Math.log(s)<n)break}return a=1/this.beta*this.d*a,this.alpha<1&&(a*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(a)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},PK=class{constructor(e=0,t=1,n,r){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,r==null&&(r=Math.random()),typeof r=="number"&&(r=r.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=ZA.alea(r)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function zK(e,t,n=1,r="float32",s){if(n==null&&(n=1),r==null&&(r="float32"),r!=="float32"&&r!=="int32")throw new Error(`Unsupported data type ${r}`);let a=new OK(t,n,r,s),o=Le(e,r);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var Ewe=V({randomGamma_:zK});function LK(e,t=0,n=1,r,s){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let a=new YA(t,n,r,!1,s),o=Le(e,r);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var BK=V({randomNormal_:LK});function WK(e,t=0,n=1,r="float32",s){let a=Le(e,r),o=new PK(t,n,null,s);for(let i=0;i<a.values.length;i++)a.values[i]=o.nextValue();return a.toTensor()}var Rd=V({randomUniform_:WK});function Dd(e,t,n=1,r="float32"){if(n===0)throw new Error("Cannot have a step of zero");let s={start:e,stop:t,step:n,dtype:r};return G.runKernel(sf,{},s)}function VK(e){let n={input:M(e,"input","real")};return G.runKernel(jy,n)}var Ff=V({real_:VK});function UK(e){let n={x:M(e,"x","reciprocal")};return G.runKernel(Jc,n)}var vI=V({reciprocal_:UK});function HK(e){let n={x:M(e,"x","relu")};return G.runKernel(Tl,n)}var ua=V({relu_:HK});function GK(e){let n={x:M(e,"x","relu6")};return G.runKernel(Cl,n)}var JA=V({relu6_:GK});function jK(e,t){let r={x:M(e,"x","reverse")},s={dims:t};return G.runKernel(El,r,s)}var Fr=V({reverse_:jK});function qK(e){let t=M(e,"x","reverse");return z(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Fr(t,0)}var $we=V({reverse1d_:qK});function KK(e,t){let n=M(e,"x","reverse");return z(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Fr(n,t)}var _we=V({reverse2d_:KK});function XK(e,t){let n=M(e,"x","reverse");return z(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Fr(n,t)}var Rwe=V({reverse3d_:XK});function ZK(e,t){let n=M(e,"x","reverse");return z(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Fr(n,t)}var Dwe=V({reverse4d_:ZK});function YK(e){let n={x:M(e,"x","round")};return G.runKernel($l,n)}var QA=V({round_:YK});function JK(e){let n={x:M(e,"x","rsqrt")};return G.runKernel(Oo,n)}var e1=V({rsqrt_:JK});function Fe(e,t){if((ss(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"&&ss(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return vd(e,[],[],t)}function QK(e){let n={x:M(e,"x","selu")};return G.runKernel(nd,n)}var t1=V({selu_:QK});function eX(e,t,n,r,s,a=[1,1],o="NHWC"){let i=M(e,"x","separableConv2d"),l=M(t,"depthwiseFilter","separableConv2d"),u=M(n,"pointwiseFilter","separableConv2d"),c=i,d=!1;if(i.rank===3&&(d=!0,c=J(i,[1,i.shape[0],i.shape[1],i.shape[2]])),o==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");z(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),z(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),z(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),z(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${u.shape[0]}.`),z(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let h=l.shape[2],p=l.shape[3];z(u.shape[2]===h*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${h*p}, but got ${u.shape[2]}.`);let f=Td(c,l,r,s,o,a),g=Ba(f,u,1,"valid",o);return d?J(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var wI=V({separableConv2d_:eX});async function tX(e,t){let n=M(e,"x","setdiff1d"),r=M(t,"y","setdiff1d");z(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),z(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),z(r.rank===1,()=>`y should be 1D tensor, but got y (${r.shape}).`);let s=await n.data(),a=await r.data(),o=new Set(a),i=0;for(let c=0;c<s.length;c++)o.has(s[c])||i++;let l=new Qt([i],n.dtype),u=new Qt([i],"int32");for(let c=0,d=0;c<s.length;c++)o.has(s[c])||(l.values[d]=s[c],u.values[d]=c,d++);return[l.toTensor(),u.toTensor()]}var nX=tX;function rX(e){let n={x:M(e,"x","sign")};return G.runKernel(ad,n)}var kI=V({sign_:rX});function sX(e){let n={x:M(e,"x","sin")};return G.runKernel(_l,n)}var n1=V({sin_:sX});function aX(e){let n={x:M(e,"x","sinh")};return G.runKernel(sd,n)}var r1=V({sinh_:aX});function oX(e,t,n){let r=M(e,"x","slice1d");return z(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),nt(r,[t],[n])}var s1=V({slice1d_:oX});function iX(e,t,n){let r=M(e,"x","slice2d");return z(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),nt(r,t,n)}var II=V({slice2d_:iX});function lX(e,t,n){let r=M(e,"x","slice3d");return z(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),nt(r,t,n)}var a1=V({slice3d_:lX});function uX(e,t,n){let r=M(e,"x","slice4d");return z(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),nt(r,t,n)}var Mf=V({slice4d_:uX});function cX(e,t=-1){let n=M(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 r={logits:n},s={dim:t};return G.runKernel(Ml,r,s)}var Of=V({softmax_:cX});function dX(e){z(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return G.runKernel(Py,t)}var o1=V({fft_:dX});function hX(e){z(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return G.runKernel(zy,t)}var Pf=V({ifft_:hX});function pX(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let s=J(e,[n,t]);r=Pf(s)}else{let s=[n,2*(t-1)],a=J(Ff(e),[n,t]),o=J(WA(e),[n,t]),i=Fr(nt(a,[0,1],[n,t-2]),1),l=K(Fr(nt(o,[0,1],[n,t-2]),1),Fe(-1)),u=en([a,i],1),c=en([o,l],1),d=J(Uo(u,c),[s[0],s[1]]);r=Pf(d)}if(r=Ff(r),e.rank===3&&e.shape[0]!==0){let s=r,a=e.shape[0];r=J(r,[a,r.shape[0]/a,r.shape[1]]),s.dispose()}return r}var SI=V({irfft_:pX});function fX(e,t,n=0){let s={x:M(e,"x","split")},a={numOrSizeSplits:t,axis:n};return G.runKernel(id,s,a)}var hr=V({split_:fX});function mX(e,t){z(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,s;if(t!=null&&t<n){let f=e.shape.map(g=>0),m=e.shape.map(g=>g);m[e.shape.length-1]=t,s=nt(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,s=en([e,un(f)],e.shape.length-1),n=t}else s=e;let a=rt(s),o=J(Uo(s,a),[r,n]),i=o1(o),l=Math.floor(n/2)+1,u=Ff(i),c=WA(i),d=hr(u,[l,n-l],u.shape.length-1),h=hr(c,[l,n-l],c.shape.length-1),p=s.shape.slice();return p[s.shape.length-1]=l,J(Uo(d[0],h[0]),p)}var i1=V({rfft_:mX});function gX(e){let n={x:M(e,"x","sqrt")};return G.runKernel(Dl,n)}var $n=V({sqrt_:gX});function yX(e,t){let n=M(e,"a","squaredDifference"),r=M(t,"b","squaredDifference");[n,r]=Ut(n,r),Rt(n.shape,r.shape);let s={a:n,b:r},a={};return G.runKernel(Po,s,a)}var l1=V({squaredDifference_:yX});function AX(e,t){let n=M(e,"x","squeeze");return J(n,P4(n.shape,t).newShape)}var Jl=V({squeeze_:AX});function xX(e,t=0){let n=Af(e,"tensors","stack","string_or_numeric");z(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&z(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,s={axis:t};return G.runKernel(Zc,r,s)}var Mr=V({stack_:xX});function bX(e,t=0){let r={x:M(e,"x","step")},s={alpha:t};return G.runKernel(Bo,r,s)}var Fd=V({step_:bX});function vX(e,t,n,r,s=0,a=0,o=0,i=0,l=0){let c={x:M(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:r,beginMask:s,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:l};return G.runKernel(ld,c,d)}var TI=V({stridedSlice_:vX});function wX(e){let n={x:M(e,"x","tan")};return G.runKernel(Ol,n)}var NI=V({tan_:wX});function _n(e,t){Wp(e);let n=bd(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return vd(e,null,n,t)}function Ql(e,t,n){if(Wp(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=bd(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return vd(e,t,r,n)}function kX(e,t=1,n=!0){let r=M(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let s=r.shape[r.shape.length-1];if(t>s)throw new Error(`'k' passed to topk() must be <= the last dimension (${s}) but got ${t}`);let a={x:r},o={k:t,sorted:n},[i,l]=G.runKernel(ud,a,o);return{values:i,indices:l}}var CI=V({topk_:kX});function IX(e,t=0,n=1,r,s){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let a=new YA(t,n,r,!0,s),o=Le(e,r);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var u1=V({truncatedNormal_:IX});function SX(e,t=0){let n=M(e,"x","unique","string_or_numeric");z(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},s={axis:t},[a,o]=G.runKernel(rA,r,s);return{values:a,indices:o}}var c1=V({unique_:SX});function TX(e,t,n){let r=M(e,"x","unsortedSegmentSum"),s=M(t,"segmentIds","unsortedSegmentSum","int32");z(mn(n),()=>"numSegments must be of dtype int");let a={x:r,segmentIds:s},o={numSegments:n};return G.runKernel(uf,a,o)}var EI=V({unsortedSegmentSum_:TX});function NX(e,t=0){let n=M(e,"x","unstack","string_or_numeric");z(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},s={axis:t};return G.runKernel(dd,r,s)}var ls=V({unstack_:NX});function CX(e,t=!0,n,r){return G.makeVariable(e,t,n,r)}function $I(e,t){let n=[];for(let a=0;a<t.length;a++)t[a]&&n.push(a);let r=Le(e,"int32"),s=Le([n.length,e.length],"int32");for(let a=0;a<n.length;a++){let o=r.indexToLoc(n[a]),i=a*e.length;s.values.set(o,i)}return s.toTensor()}async function EX(e){let t=M(e,"condition","whereAsync","bool"),n=await t.data(),r=$I(t.shape,n);return e!==t&&t.dispose(),r}var $X=EX;function _X(e,t="euclidean",n=null,r=!1){e=M(e,"x","norm");let s=_I(e,t,n),a=s.shape;if(r){let o=jr(n,e.shape);a=ei(s.shape,o)}return J(s,a)}function _I(e,t,n=null){if(e.rank===0)return yn(e);if(e.rank!==1&&n===null)return _I(J(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return _e(yn(e),n);if(t===Infinity)return os(yn(e),n);if(t===-Infinity)return _f(yn(e),n);if(t==="euclidean"||t===2)return $n(_e(Va(yn(e),Fe(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return os(_e(yn(e),n[0]),n[1]-1);if(t===Infinity)return os(_e(yn(e),n[1]),n[0]);if(t===-Infinity)return _f(_e(yn(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return $n(_e(wt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var d1=V({norm_:_X});function RX(e,t,n,r,s=!0){let a=M(e,"v","movingAverage"),o=M(t,"x","movingAverage"),i=M(n,"decay","movingAverage");s6(a,o),z(Da(a.shape,o.shape),()=>"Shape mismatch in v and x");let l=Fe(1),u=Ne(l,i),c=K(Ne(o,a),u);if(s){z(r!=null,()=>"When using zeroDebias: true, step is required.");let d=M(r,"step","movingAverage");c=Re(c,Ne(l,Va(i,d)))}return pe(a,c)}var Fwe=V({movingAverage_:RX});function DX(e,t,n){let r=M(e,"indices","scatterND","int32"),s=M(t,"updates","scatterND");EA(s,r,n);let a={indices:r,updates:s},o={shape:n};return G.runKernel(ed,a,o)}var FX=V({scatterND_:DX});function MX(e,t,n,r){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 s=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${a}.`);let o=t.size;if(!(t.rank===0||t.rank===1&&o===s))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${s}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function OX(e,t,n,r=0){let s=M(e,"sparseIndices","sparseToDense","int32"),a=M(t,"sparseValues","sparseToDense"),o=M(r,"defaultValue","sparseToDense",a.dtype);MX(s,a,n,o);let i={sparseIndices:s,sparseValues:a,defaultValue:o},l={outputShape:n};return G.runKernel(Qy,i,l)}var RI=V({sparseToDense_:OX});function PX(e,t){let n=M(t,"indices","gatherND","int32"),s={params:M(e,"x","gatherND","string_or_numeric"),indices:n};return G.runKernel(zc,s)}var zX=V({gatherND_:PX});function LX(e,t){if(t==null)return e.shape.slice();if(Da(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let r=0;r<e.shape.length;r++)t[r]==null&&e.shape[r]!=null?n.push(e.shape[r]):n.push(t[r]);return n}return t}function BX(e,t,n,r){let s=M(e,"x","dropout");if(z(s.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${s.dtype} tensor instead.`),z(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Ct?s.clone():s;let a=LX(s,n),o=1-t,i=Re(Ed(pe(Rd(a,0,1,"float32",r),o)),o);return K(s,i)}var WX=V({dropout_:BX});function VX(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function DI(e,t,n){let r=1-e%2,s=new Float32Array(e);for(let a=0;a<e;++a){let o=2*Math.PI*a/(e+r-1);s[a]=t-n*Math.cos(o)}return _n(s,"float32")}var ti={};De(ti,{conv2d:()=>GX,depthwiseConv2d:()=>XX,matMul:()=>YX});function UX(e,t,n,r,s,a="NHWC",o){let i=e;e.rank===3&&(i=J(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=J(t,[1,t.shape[0],t.shape[1],t.shape[2]])),z(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),z(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),z(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=a==="NHWC"?i.shape[3]:i.shape[1],c=a==="NHWC"?l.shape[3]:l.shape[1];z(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),z(c===n[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`),o!=null&&z(mn(s),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:i,dy:l},h={strides:r,pad:s,dataFormat:a,dimRoundingMode:o,filterShape:n};return G.runKernel(Ty,d,h)}var h1=V({conv2DBackpropFilter_:UX});function zf(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return K(e,Fd(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Lf(e,t){let n=t,r=ln(e.shape,t.shape);return r.length>0&&(n=_e(n,r)),J(n,e.shape)}function Bf(e,t,n,r){if(t==="linear")return e;if(t==="relu")return ua(e);if(t==="elu")return Nd(e);if(t==="relu6")return JA(e);if(t==="prelu")return Df(e,n);if(t==="leakyrelu")return Cf(e,r);if(t==="sigmoid")return Rs(e);throw new Error(`Unknown fused activation ${t}.`)}var Wf=(e,t)=>!(e>0)||t==="linear";function HX({x:e,filter:t,strides:n,pad:r,dataFormat:s="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",Wf(G.state.gradientDepth,l)===!1){let v=Ba(e,t,n,r,s,a,o);return i!=null&&(v=pe(v,i)),Bf(v,l,u,c)}let d=M(e,"x","conv2d"),h=M(t,"filter","conv2d"),p=d,f=!1;d.rank===3&&(f=!0,p=J(d,[1,d.shape[0],d.shape[1],d.shape[2]])),z(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),z(h.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${h.rank}.`),o!=null&&z(mn(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`),z(p.shape[3]===h.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${h.shape[2]}.`),z(_s(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),z(s==="NHWC",()=>`Error in conv2d: got dataFormat of ${s} but only NHWC is currently supported.`);let m=Id(p.shape,h.shape,n,a,r,o),g;i!=null&&(g=M(i,"bias","fused conv2d"),[g]=Ut(g,d),Rt(m.outShape,g.shape));let y;u!=null&&(y=M(u,"prelu weights","fused conv2d"));let A=(v,w)=>{let[S,I,E,F]=w,$=zf(v,E,l);z(La(a),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let _=PA(I.shape,$,S,n,r),N=h1(I,$,S.shape,n,r),P=[_,N];if(F!=null){let B=Lf(F,$);P.push(B)}return P},x={x:p,filter:h,bias:g,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?oa((w,S,I)=>{let E=G.runKernel(Bl,x,b);return I([S,w,E]),f&&(E=J(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(p,h):oa((w,S,I,E)=>{let F=G.runKernel(Bl,x,b);return E([S,w,F,I]),f&&(F=J(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:A}})(p,h,g)}var GX=V({fusedConv2d_:HX});function jX(e,t,n,r,s,a=[1,1],o){let i=e;e.rank===3&&(i=J(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=J(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:i,dy:l},c={strides:r,pad:s,dimRoundingMode:o,dilations:a,filterShape:n};return G.runKernel($y,u,c)}var FI=V({depthwiseConv2dNativeBackpropFilter_:jX});function qX(e,t,n,r,s,a=[1,1],o){let i=t,l=!1;t.rank===3&&(l=!0,i=J(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:i,filter:n},c={strides:r,pad:s,dimRoundingMode:o,dilations:a,inputShape:e},d=G.runKernel(_y,u,c);return l?J(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var MI=V({depthwiseConv2dNativeBackpropInput_:qX});function KX({x:e,filter:t,strides:n,pad:r,dataFormat:s="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(Wf(G.state.gradientDepth,l)===!1){let v=Td(e,t,n,r,s,a,o);return i!=null&&(v=pe(v,i)),Bf(v,l,u,c)}let d=M(e,"x","depthwiseConv2d"),h=M(t,"filter","depthwiseConv2d"),p=d,f=!1;d.rank===3&&(f=!0,p=J(d,[1,d.shape[0],d.shape[1],d.shape[2]])),z(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),z(h.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${h.rank}.`),z(p.shape[3]===h.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${h.shape[2]}.`),a==null&&(a=[1,1]),z(_s(n,a),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),o!=null&&z(mn(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${o} but got pad ${r}.`);let m=Id(p.shape,h.shape,n,a,r,o,!0),g;i!=null&&(g=M(i,"bias","fused conv2d"),[g]=Ut(g,d),Rt(m.outShape,g.shape));let y;u!=null&&(y=M(u,"prelu weights","fused depthwiseConv2d"));let A=(v,w)=>{z(La(a),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[S,I,E,F]=w,$=zf(v,E,l),_=MI(I.shape,$,S,n,r,a,o),N=FI(I,$,S.shape,n,r,a,o);if(F!=null){let P=Lf(g,$);return[_,N,P]}return[_,N]},x={x:p,filter:h,bias:g,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?oa((w,S,I)=>{let E=G.runKernel(Wl,x,b);return I([S,w,E]),f&&(E=J(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:A}})(p,h):oa((w,S,I,E)=>{let F=G.runKernel(Wl,x,b);return E([S,w,F,I]),f&&(F=J(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:A}})(p,h,g)}var XX=V({fusedDepthwiseConv2d_:KX});function ZX({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:s,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i}){if(Wf(G.state.gradientDepth,a)===!1){let F=ot(e,t,n,r);return s!=null&&(F=pe(F,s)),Bf(F,a,o,i)}let l=M(e,"a","fused matMul"),u=M(t,"b","fused matMul");[l,u]=Ut(l,u);let c=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=r?u.shape[u.rank-1]:u.shape[u.rank-2],h=n?l.shape[l.rank-1]:l.shape[l.rank-2],p=r?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),g=on(f),y=on(m);z(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),z(Da(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),z(c===d,()=>`Error in fused matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${r} must match.`);let A=l.shape.slice(0,-2).concat([h,p]),x=n?J(l,[g,c,h]):J(l,[g,h,c]),b=r?J(u,[y,p,d]):J(u,[y,d,p]),v;s!=null&&(v=M(s,"bias","fused matMul"),[v]=Ut(v,l),Rt(A,v.shape));let w;o!=null&&(w=M(o,"prelu weights","fused matMul"));let S=(F,$)=>{let[_,N,P,B]=$,j=zf(J(F,P.shape),P,a),X,Y;if(!n&&!r?(X=ot(j,N,!1,!0),Y=ot(_,j,!0,!1)):!n&&r?(X=ot(j,N,!1,!1),Y=ot(j,_,!0,!1)):n&&!r?(X=ot(N,j,!1,!0),Y=ot(_,j,!1,!1)):(X=ot(N,j,!0,!0),Y=ot(j,_,!0,!0)),s!=null){let ee=Lf(B,j);return[X,Y,ee]}else return[X,Y]},I={a:x,b,bias:v,preluActivationWeights:w},E={transposeA:n,transposeB:r,activation:a,leakyreluAlpha:i};return s==null?oa(($,_,N)=>{let P=G.runKernel(Ll,I,E);return N([$,_,P]),{value:J(P,A),gradFunc:S}})(x,b):oa(($,_,N,P)=>{let B=G.runKernel(Ll,I,E);return P([$,_,B,N]),{value:J(B,A),gradFunc:S}})(x,b,v)}var YX=V({fusedMatMul_:ZX});function JX(e){return DI(e,.54,.46)}var Mwe=V({hammingWindow_:JX});function QX(e){return DI(e,.5,.5)}var eZ=V({hannWindow_:QX});function tZ(e,t,n,r=!1,s=0){let a=0,o=[];for(;a+t<=e.size;)o.push(nt(e,a,t)),a+=n;if(r)for(;a<e.size;){let i=a+t-e.size,l=en([nt(e,a,t-i),Cd([i],s)]);o.push(l),a+=n}return o.length===0?Ql([],[0,t]):J(en(o),[o.length,t])}var nZ=V({frame_:tZ});function rZ(e,t,n,r,s=eZ){r==null&&(r=VX(t));let a=nZ(e,t,n),o=K(a,s(t));return i1(o,r)}var Owe=V({stft_:rZ});function sZ(e,t,n,r,s="bilinear",a=0){let o=M(e,"image","cropAndResize"),i=M(t,"boxes","cropAndResize","float32"),l=M(n,"boxInd","cropAndResize","int32"),u=i.shape[0];z(o.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),z(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`),z(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`),z(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),z(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),z(s==="bilinear"||s==="nearest",()=>`method must be bilinear or nearest, but was ${s}`);let c={image:o,boxes:i,boxInd:l},d={method:s,extrapolationValue:a,cropSize:r};return G.runKernel(_c,c,d)}var aZ=V({cropAndResize_:sZ});function oZ(e){let t=M(e,"image","flipLeftRight","float32");z(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return G.runKernel(Oc,n,{})}var iZ=V({flipLeftRight_:oZ});function lZ(e,t,n=0,r=.5){let s=M(e,"image","rotateWithOffset","float32");z(s.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${s.rank}.`);let a={image:s},o={radians:t,fillValue:n,center:r};return G.runKernel(pd,a,o)}var uZ=V({rotateWithOffset_:lZ});function eu(e,t,n,r,s,a){r==null&&(r=.5),s==null&&(s=Number.NEGATIVE_INFINITY),a==null&&(a=0);let o=e.shape[0];return n=Math.min(n,o),z(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),z(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),z(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),z(t.rank===1,()=>"scores must be a 1D tensor"),z(t.shape[0]===o,()=>`scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`),z(0<=a&&a<=1,()=>`softNmsSigma must be in [0, 1], but was '${a}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:s,softNmsSigma:a}}function cZ(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){let a=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),i=eu(a,o,n,r,s);n=i.maxOutputSize,r=i.iouThreshold,s=i.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:s};return G.runKernel(jc,{boxes:a,scores:o},l)}var dZ=V({nonMaxSuppression_:cZ});function hZ(e,t,n){let r=pZ(e,t,n),s=r<0?-(r+1):r;e.splice(s,0,t)}function pZ(e,t,n){return mZ(e,t,n||fZ)}function fZ(e,t){return e>t?1:e<t?-1:0}function mZ(e,t,n){let r=0,s=e.length,a=0,o=!1;for(;r<s;){a=r+(s-r>>>1);let i=n(t,e[a]);i>0?r=a+1:(s=a,o=!i)}return o?r:-r-1}function OI(e,t,n,r,s){return p1(e,t,n,r,s,0)}function PI(e,t,n,r,s,a){return p1(e,t,n,r,s,0,!1,a,!0)}function zI(e,t,n,r,s,a){return p1(e,t,n,r,s,a,!0)}function p1(e,t,n,r,s,a,o=!1,i=!1,l=!1){let u=[];for(let g=0;g<t.length;g++)t[g]>s&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(LI);let c=a>0?-.5/a:0,d=[],h=[];for(;d.length<n&&u.length>0;){let g=u.pop(),{score:y,boxIndex:A,suppressBeginIndex:x}=g;if(y<s)break;let b=!1;for(let v=d.length-1;v>=x;--v){let w=gZ(e,A,d[v]);if(w>=r){b=!0;break}if(g.score=g.score*yZ(r,c,w),g.score<=s)break}g.suppressBeginIndex=d.length,b||(g.score===y?(d.push(A),h.push(g.score)):g.score>s&&hZ(u,g,LI))}let p=d.length,f=n-p;i&&f>0&&(d.push(...new Array(f).fill(0)),h.push(...new Array(f).fill(0)));let m={selectedIndices:d};return o&&(m.selectedScores=h),l&&(m.validOutputs=p),m}function gZ(e,t,n){let r=e.subarray(t*4,t*4+4),s=e.subarray(n*4,n*4+4),a=Math.min(r[0],r[2]),o=Math.min(r[1],r[3]),i=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),u=Math.min(s[0],s[2]),c=Math.min(s[1],s[3]),d=Math.max(s[0],s[2]),h=Math.max(s[1],s[3]),p=(i-a)*(l-o),f=(d-u)*(h-c);if(p<=0||f<=0)return 0;let m=Math.max(a,u),g=Math.max(o,c),y=Math.min(i,d),A=Math.min(l,h),x=Math.max(y-m,0)*Math.max(A-g,0);return x/(p+f-x)}function yZ(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function LI(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function AZ(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){let a=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),i=eu(a,o,n,r,s);n=i.maxOutputSize,r=i.iouThreshold,s=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),u=l[0],c=l[1],{selectedIndices:d}=OI(u,c,n,r,s);return a!==e&&a.dispose(),o!==t&&o.dispose(),_n(d,"int32")}var xZ=AZ;function bZ(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=0){let o=M(e,"boxes","nonMaxSuppression"),i=M(t,"scores","nonMaxSuppression"),l=eu(o,i,n,r,s,a);n=l.maxOutputSize,r=l.iouThreshold,s=l.scoreThreshold,a=l.softNmsSigma;let u={boxes:o,scores:i},c={maxOutputSize:n,iouThreshold:r,scoreThreshold:s,softNmsSigma:a},d=G.runKernel(Kc,u,c);return{selectedIndices:d[0],selectedScores:d[1]}}var vZ=V({nonMaxSuppressionWithScore_:bZ});async function wZ(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=0){let o=M(e,"boxes","nonMaxSuppressionAsync"),i=M(t,"scores","nonMaxSuppressionAsync"),l=eu(o,i,n,r,s,a);n=l.maxOutputSize,r=l.iouThreshold,s=l.scoreThreshold,a=l.softNmsSigma;let u=await Promise.all([o.data(),i.data()]),c=u[0],d=u[1],{selectedIndices:h,selectedScores:p}=zI(c,d,n,r,s,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:_n(h,"int32"),selectedScores:_n(p)}}var kZ=wZ;function IZ(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=!1){let o=M(e,"boxes","nonMaxSuppression"),i=M(t,"scores","nonMaxSuppression"),l=eu(o,i,n,r,s,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,h={boxes:o,scores:i},p={maxOutputSize:u,iouThreshold:c,scoreThreshold:d,padToMaxOutputSize:a},f=G.runKernel(qc,h,p);return{selectedIndices:f[0],validOutputs:f[1]}}var SZ=V({nonMaxSuppressionPadded_:IZ});async function TZ(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=!1){let o=M(e,"boxes","nonMaxSuppressionAsync"),i=M(t,"scores","nonMaxSuppressionAsync"),l=eu(o,i,n,r,s,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,[h,p]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=PI(h,p,u,c,d,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:_n(f,"int32"),validOutputs:Fe(m,"int32")}}var NZ=TZ;function CZ(e,t,n=!1,r=!1){let s=M(e,"images","resizeBilinear");z(s.rank===3||s.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`),z(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),z(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let a=s,o=!1;s.rank===3&&(o=!0,a=J(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:r,size:t},u=G.runKernel(Nl,i,l);return o?J(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var BI=V({resizeBilinear_:CZ});function EZ(e,t,n=!1,r=!1){let s=M(e,"images","resizeNearestNeighbor");z(s.rank===3||s.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`),z(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),z(s.dtype==="float32"||s.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),z(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let a=s,o=!1;s.rank===3&&(o=!0,a=J(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:r,size:t},u=G.runKernel(af,i,l);return o?J(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var WI=V({resizeNearestNeighbor_:EZ});function $Z(e,t="binary",n=!1,r=.5){let s=M(e,"image","threshold"),a=.2989,o=.587,i=.114,l=s.shape[0]*s.shape[1],u=K(_n([r]),255),c,d,h,p;if(z(s.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${s.rank}.`),z(s.shape[2]===3||s.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${s.shape[2]}.`),z(s.dtype==="int32"||s.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${s.dtype}.`),z(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),s.shape[2]===3){[c,d,h]=hr(s,[1,1,1],-1);let g=K(c,a),y=K(d,o),A=K(h,i);p=pe(pe(g,y),A)}else p=e;if(t==="otsu"){let g=nI(ke(QA(p),"int32"),$s([]),256);u=_Z(g,l)}let f=n?Qo(p,u):_r(p,u);return ke(K(f,255),"int32")}function _Z(e,t){let n=_n([-1]),r=_n([0]),s=_n([0]),a,o,i,l,u,c;for(let d=0;d<e.size-1;d++){a=nt(e,0,d+1),o=nt(e,d+1),u=Re(_e(a),t),c=Re(_e(o),t);let h=_e(K(a,Dd(0,a.size)));i=Re(h,_e(a));let p=Cd(o.shape,a.size),f=pe(Dd(0,o.size),p),m=K(o,f);l=Re(_e(m),_e(o));let g=Ne(i,l),y=Ne(i,l),A=K(u,c);s=K(K(A,g),y);let x=_r(s,r);r=Ln(x,s,r),n=Ln(x,_n([d]),n)}return n}var RZ=V({threshold_:$Z});function DZ(e,t,n="nearest",r="constant",s=0,a){let o=M(e,"image","transform","float32"),i=M(t,"transforms","transform","float32");z(o.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${o.rank}.`),z(i.rank===2&&(i.shape[0]===o.shape[0]||i.shape[0]===1)&&i.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),z(a==null||a.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${a}.`);let l={image:o,transforms:i},u={interpolation:n,fillMode:r,fillValue:s,outputShape:a};return G.runKernel(cd,l,u)}var FZ=V({transform_:DZ});function MZ(e,t,n){z(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),z(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=M(e,"a","bandPart");z(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let s=r.shape,[a,o]=r.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=J(Dd(0,a,1,"int32"),[-1,1]),l=Dd(0,o,1,"int32"),u=Ne(i,l),c=is(Qo(u,Fe(+t,"int32")),Jo(u,Fe(-n,"int32"))),d=un([a,o],r.dtype);return J(Mr(ls(J(r,[-1,a,o])).map(h=>Ln(c,h,d))),s)}var OZ=V({bandPart_:MZ});function PZ(e){let t;if(Array.isArray(e)){t=!1,z(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let s=e[0].shape[0];for(let a=1;a<e.length;++a)z(e[a].shape[0]===s,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${s})`)}else t=!0,e=hr(e,e.shape[0],0).map(s=>Jl(s,[0]));z(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let s=0;s<e.length;++s)n.push(G.tidy(()=>{let a=r[s];if(s>0)for(let o=0;o<s;++o){let i=K(_e(K(n[o],a)),n[o]);a=Ne(a,i)}return Re(a,d1(a,"euclidean"))}));return t?Mr(n,0):n}var zZ=V({gramSchmidt_:PZ});function LZ(e,t=!1){if(z(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return VI(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),r=ls(J(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),s=[],a=[];r.forEach(l=>{let[u,c]=VI(l,t);s.push(u),a.push(c)});let o=J(Mr(s,0),e.shape),i=J(Mr(a,0),e.shape);return[o,i]}}function VI(e,t=!1){return G.tidy(()=>{z(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],s=dI(n),a=qo(e),o=Ql([[1]],[1,1]),i=qo(o),l=n>=r?r:n;for(let u=0;u<l;++u){let c=a,d=i,h=s;[i,a,s]=G.tidy(()=>{let p=nt(a,[u,u],[n-u,1]),f=d1(p),m=nt(a,[u,u],[1,1]),g=Ln(_r(m,0),Ql([[-1]]),Ql([[1]])),y=Ne(m,K(g,f)),A=Re(p,y);A.shape[0]===1?i=qo(o):i=en([o,nt(A,[1,0],[A.shape[0]-1,A.shape[1]])],0);let x=Kt(Re(ot(g,y),f)),b=nt(a,[u,0],[n-u,r]),v=K(x,i),w=pt(i);if(u===0)a=Ne(b,ot(v,ot(w,b)));else{let E=Ne(b,ot(v,ot(w,b)));a=en([nt(a,[0,0],[u,r]),E],0)}let S=pt(v),I=nt(s,[0,u],[n,s.shape[1]-u]);if(u===0)s=Ne(I,ot(ot(I,i),S));else{let E=Ne(I,ot(ot(I,i),S));s=en([nt(s,[0,0],[n,u]),E],1)}return[i,a,s]}),je([c,d,h])}return!t&&n>r&&(s=nt(s,[0,0],[n,r]),a=nt(a,[0,0],[r,r])),[s,a]})}var BZ=V({qr_:LZ}),Yn;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Yn||(Yn={}));function WZ(e,t,n=Yn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"losses","computeWeightedLoss"),s=null;t!=null&&(s=M(t,"weights","computeWeightedLoss"));let a=s==null?r:K(r,s);if(n===Yn.NONE)return a;if(n===Yn.SUM)return _e(a);if(n===Yn.MEAN){if(s==null)return Xt(a);{let o=r.size/s.size,i=Re(_e(a),_e(s));return o>1?Re(i,Fe(o)):i}}if(n===Yn.SUM_BY_NONZERO_WEIGHTS){if(s==null)return Re(_e(a),Fe(r.size));{let o=K(s,la(r.shape)),i=ke(_e(Yl(o,Fe(0))),"float32");return Re(_e(a),i)}}throw Error(`Unknown reduction: ${n}`)}var Ua=V({computeWeightedLoss_:WZ});function VZ(e,t,n,r=Yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","absoluteDifference"),a=M(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=M(n,"weights","absoluteDifference")),rs(s.shape,a.shape,"Error in absoluteDifference: ");let i=yn(Ne(s,a));return Ua(i,o,r)}var Pwe=V({absoluteDifference_:VZ});function UZ(e,t,n,r,s=Yn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"labels","cosineDistance"),o=M(t,"predictions","cosineDistance"),i=null;r!=null&&(i=M(r,"weights","cosineDistance")),rs(a.shape,o.shape,"Error in cosineDistance: ");let l=Fe(1),u=Ne(l,_e(K(a,o),n,!0));return Ua(u,i,s)}var zwe=V({cosineDistance_:UZ});function HZ(e,t,n,r=Yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","hingeLoss"),a=M(t,"predictions","hingeLoss"),o=null;n!=null&&(o=M(n,"weights","hingeLoss")),rs(s.shape,a.shape,"Error in hingeLoss: ");let i=Fe(1);s=Ne(K(Fe(2),s),i);let l=ua(Ne(i,K(s,a)));return Ua(l,o,r)}var Lwe=V({hingeLoss_:HZ});function GZ(e,t,n,r=1,s=Yn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"labels","huberLoss"),o=M(t,"predictions","huberLoss"),i=null;n!=null&&(i=M(n,"weights","huberLoss")),rs(a.shape,o.shape,"Error in huberLoss: ");let l=Fe(r),u=yn(Ne(o,a)),c=_d(u,l),d=Ne(u,c),h=pe(K(Fe(.5),wt(c)),K(l,d));return Ua(h,i,s)}var Bwe=V({huberLoss_:GZ});function jZ(e,t,n,r=1e-7,s=Yn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"labels","logLoss"),o=M(t,"predictions","logLoss"),i=null;n!=null&&(i=M(n,"weights","logLoss")),rs(a.shape,o.shape,"Error in logLoss: ");let l=Fe(1),u=Fe(r),c=Kt(K(a,Rr(pe(o,u)))),d=K(Ne(l,a),Rr(pe(Ne(l,o),u))),h=Ne(c,d);return Ua(h,i,s)}var Wwe=V({logLoss_:jZ});function qZ(e,t,n,r=Yn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","meanSquaredError"),a=M(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=M(n,"weights","meanSquaredError")),rs(s.shape,a.shape,"Error in meanSquaredError: ");let i=l1(s,a);return Ua(i,o,r)}var Vwe=V({meanSquaredError_:qZ});function KZ(e,t){let n=M(e,"labels","sigmoidCrossEntropyWithLogits"),r=M(t,"logits","sigmoidCrossEntropyWithLogits");rs(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let s=ua(r),a=K(r,n),o=UA(Kr(Kt(yn(r))));return pe(Ne(s,a),o)}function XZ(e,t,n,r=0,s=Yn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"multiClassLabels","sigmoidCrossEntropy"),o=M(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=M(n,"weights","sigmoidCrossEntropy")),rs(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),r>0){let u=Fe(r),c=Fe(1),d=Fe(.5);a=pe(K(a,Ne(c,u)),K(d,u))}let l=KZ(a,o);return Ua(l,i,s)}var Uwe=V({sigmoidCrossEntropy_:XZ});function ZZ(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 oa((s,a,o)=>{let l=yI(a,[n],!0),u=Ne(ke(a,"float32"),l);o([s,u]);let c=Kt(K(u,s));return{value:_e(c,[n]),gradFunc:(p,f)=>{let[m,g]=f,y=ei(p.shape,[n]);return[K(J(p,y),Ne(ke(m,"float32"),Kr(g))),K(J(p,y),Ne(Kr(g),ke(m,"float32")))]}}})(e,t)}function YZ(e,t,n,r=0,s=Yn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"onehotLabels","softmaxCrossEntropy"),o=M(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=M(n,"weights","softmaxCrossEntropy")),rs(a.shape,o.shape,"Error in softmaxCrossEntropy: "),r>0){let u=Fe(r),c=Fe(1),d=Fe(a.shape[1]);a=pe(K(a,Ne(c,u)),Re(u,d))}let l=ZZ(a,o);return Ua(l,i,s)}var Hwe=V({softmaxCrossEntropy_:YZ});function JZ(e,t,n,r){let s=M(e,"indices","sparseFillEmptyRows"),a=M(t,"values","sparseFillEmptyRows"),o=M(n,"denseShape","sparseFillEmptyRows"),i=M(r,"defaultValue","sparseFillEmptyRows",a.dtype);if(s.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
        ${s.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:s,values:a,denseShape:o,defaultValue:i},u=G.runKernel(Xy,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var QZ=V({sparseFillEmptyRows_:JZ});function eY(e,t,n){let r=M(e,"inputIndices","sparseReshape"),s=M(t,"inputShape","sparseReshape"),a=M(n,"newShape","sparseReshape");if(r.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
        ${r.shape}`);if(s.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:r,inputShape:s,newShape:a},i=G.runKernel(Zy,o);return{outputIndices:i[0],outputShape:i[1]}}var tY=V({sparseReshape_:eY});function nY(e,t,n){let r=M(e,"data","sparseSegmentMean"),s=M(t,"indices","sparseSegmentMean"),a=M(n,"segmentIds","sparseSegmentMean");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${s.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);let o={data:r,indices:s,segmentIds:a};return G.runKernel(Yy,o)}var rY=V({sparseSegmentMean_:nY});function sY(e,t,n){let r=M(e,"data","sparseSegmentSum"),s=M(t,"indices","sparseSegmentSum"),a=M(n,"segmentIds","sparseSegmentSum");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
         ${s.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
         ${a.shape}`);let o={data:r,indices:s,segmentIds:a};return G.runKernel(Jy,o)}var aY=V({sparseSegmentSum_:sY});function oY(e,t,n,r,s,a,o,i){let l=M(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=M(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:r,leftPad:s,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:u},h=G.runKernel(eA,d,c);return{nGrams:h[0],nGramsSplits:h[1]}}var iY=V({stringNGrams_:oY});function lY(e,t,n=!0){let r=M(e,"input","stringSplit","string"),s=M(t,"delimiter","stringSplit","string");if(r.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${r.shape}`);if(s.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${s.shape}`);let a={skipEmpty:n},o={input:r,delimiter:s},i=G.runKernel(tA,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var uY=V({stringSplit_:lY});function cY(e,t){let n=M(e,"input","stringToHashBucketFast","string"),r={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let s={input:n};return G.runKernel(nA,s,r)}var dY=V({stringToHashBucketFast_:cY}),ni={flipLeftRight:iZ,resizeNearestNeighbor:WI,resizeBilinear:BI,rotateWithOffset:uZ,cropAndResize:aZ,nonMaxSuppression:dZ,nonMaxSuppressionAsync:xZ,nonMaxSuppressionWithScore:vZ,nonMaxSuppressionWithScoreAsync:kZ,nonMaxSuppressionPadded:SZ,nonMaxSuppressionPaddedAsync:NZ,threshold:RZ,transform:FZ},hY={bandPart:OZ,gramSchmidt:zZ,qr:BZ},Vf={sparseFillEmptyRows:QZ,sparseReshape:tY,sparseSegmentMean:rY,sparseSegmentSum:aY},f1={stringNGrams:iY,stringSplit:uY,stringToHashBucketFast:dY},Ha=class extends V6{minimize(e,t=!1,n){let{value:r,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:s[o.name]}));this.applyGradients(a)}else this.applyGradients(s);return je(s),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Wq(e,t)}dispose(){this.iterations_!=null&&je(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Fe(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ha,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var m1=class extends Ha{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=G.registeredVariables[n],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${n}/accum_grad`,variable:Z(()=>rt(s).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${n}/accum_var`,variable:Z(()=>rt(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[r].variable,l=this.accumulatedUpdates[r].variable;Z(()=>{let u=pe(K(i,this.rho),K(wt(o),1-this.rho)),c=K(Re($n(pe(l,this.epsilon)),$n(pe(i,this.epsilon))),o),d=pe(K(l,this.rho),K(wt(c),1-this.rho));i.assign(u),l.assign(d);let h=pe(K(c,-this.learningRate),s);s.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(je(this.accumulatedGrads.map(e=>e.variable)),je(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};m1.className="Adadelta";Pa(m1);var g1=class extends Ha{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=G.registeredVariables[n];if(this.accumulatedGrads[r]==null){let i=!1;this.accumulatedGrads[r]={originalName:`${n}/accumulator`,variable:Z(()=>Cd(s.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[r].variable;Z(()=>{let i=pe(o,wt(a));o.assign(i);let l=pe(K(Re(a,$n(pe(i,G.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&je(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};g1.className="Adagrad";Pa(g1);var y1=class extends Ha{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Z(()=>{this.accBeta1=Fe(t).variable(),this.accBeta2=Fe(n).variable()}),r==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Z(()=>{let n=Ne(1,this.accBeta1),r=Ne(1,this.accBeta2);t.forEach((s,a)=>{let o=G.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Z(()=>rt(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Z(()=>rt(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[s];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,d=pe(K(u,this.beta1),K(l,1-this.beta1)),h=pe(K(c,this.beta2),K(wt(l),1-this.beta2)),p=Re(d,n),f=Re(h,r);u.assign(d),c.assign(h);let m=pe(K(Re(p,pe($n(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(K(this.accBeta1,this.beta1)),this.accBeta2.assign(K(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&je(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&je(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),Z(()=>{this.accBeta1.assign(Va(this.beta1,this.iterations_+1)),this.accBeta2.assign(Va(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.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)}};y1.className="Adam";Pa(y1);var A1=class extends Ha{constructor(e,t,n,r=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Z(()=>{this.iteration=Fe(0).variable(),this.accBeta1=Fe(t).variable()}),r==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Z(()=>{let n=Ne(1,this.accBeta1),r=Re(-this.learningRate,pe(K(this.iteration,this.decay),1));t.forEach((s,a)=>{let o=G.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:rt(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:rt(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[s];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,d=pe(K(u,this.beta1),K(l,1-this.beta1)),h=K(c,this.beta2),p=yn(l),f=ia(h,p);u.assign(d),c.assign(f);let m=pe(K(Re(r,n),Re(d,pe(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(pe(this.iteration,1)),this.accBeta1.assign(K(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&je(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&je(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)}};A1.className="Adamax";Pa(A1);var Uf=class extends Ha{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=Array.isArray(e)?e[r].tensor:e[n];if(s==null)return;let a=G.registeredVariables[n];Z(()=>{let o=pe(K(this.c,s),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Sn(Fe(-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)}};Uf.className="SGD";Pa(Uf);var x1=class extends Uf{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Fe(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=G.registeredVariables[n];if(this.accumulations[r]==null){let i=!1;this.accumulations[r]={originalName:`${n}/momentum`,variable:Z(()=>rt(s).variable(i))}}let a=this.accumulations[r].variable,o=Array.isArray(e)?e[r].tensor:e[n];o!=null&&Z(()=>{let i,l=pe(K(this.m,a),o);this.useNesterov?i=pe(K(this.c,pe(o,K(l,this.m))),s):i=pe(K(this.c,l),s),a.assign(l),s.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&je(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};x1.className="Momentum";Pa(x1);var b1=class extends Ha{constructor(e,t=.9,n=0,r=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,r==null&&(this.epsilon=G.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=G.registeredVariables[n],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:Z(()=>rt(s).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:Z(()=>rt(s).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:Z(()=>rt(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[r].variable,l=this.accumulatedMoments[r].variable;Z(()=>{let u=pe(K(i,this.decay),K(wt(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[r].variable,d=pe(K(c,this.decay),K(o,1-this.decay)),h=Re(K(o,this.learningRate),$n(Ne(u,pe(wt(d),this.epsilon)))),p=pe(K(l,this.momentum),h);i.assign(u),c.assign(d),l.assign(p);let f=Ne(s,p);s.assign(f)}else{let c=pe(K(i,this.decay),K(wt(o),1-this.decay)),d=pe(K(l,this.momentum),Re(K(o,this.learningRate),$n(pe(c,this.epsilon))));i.assign(c),l.assign(d);let h=Ne(s,d);s.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&je(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&je(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&je(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(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.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)}};b1.className="RMSProp";Pa(b1);var ri=class{static sgd(e){return new Uf(e)}static momentum(e,t,n=!1){return new x1(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,s=!1){return new b1(e,t,n,r,s)}static adam(e=.001,t=.9,n=.999,r=null){return new y1(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new m1(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,s=0){return new A1(e,t,n,r,s)}static adagrad(e,t=.1){return new g1(e,t)}},tu={sgd:ri.sgd,momentum:ri.momentum,adadelta:ri.adadelta,adagrad:ri.adagrad,rmsprop:ri.rmsprop,adamax:ri.adamax,adam:ri.adam},pY=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function v1(){return new Promise(e=>pY(()=>e()))}var R={};De(R,{ERF_A1:()=>IY,ERF_A2:()=>SY,ERF_A3:()=>TY,ERF_A4:()=>NY,ERF_A5:()=>CY,ERF_P:()=>kY,PARALLELIZE_THRESHOLD:()=>w1,SELU_SCALE:()=>HI,SELU_SCALEALPHA:()=>UI,applyActivation:()=>Bf,assertAndGetBroadcastShape:()=>Rt,assertAxesAreInnerMostDims:()=>Zq,assertParamsConsistent:()=>fY,assignToTypedArray:()=>OY,axesAreInnerMostDims:()=>GA,calculateShapes:()=>$6,checkEinsumDimSizes:()=>VY,combineLocations:()=>fI,complexWithEvenIndex:()=>DY,complexWithOddIndex:()=>FY,computeConv2DInfo:()=>Id,computeConv3DInfo:()=>Q6,computeDefaultPad:()=>FA,computeDilation2DInfo:()=>cj,computeOptimalWindowSize:()=>gY,computeOutAndReduceShapes:()=>mI,computeOutShape:()=>mY,computePool2DInfo:()=>J6,computePool3DInfo:()=>dj,convertConv2DDataFormat:()=>eI,decodeEinsumEquation:()=>BY,eitherStridesOrDilationsAreOne:()=>_s,expandShapeToKeepDim:()=>ei,exponent:()=>zY,exponents:()=>PY,fromStringArrayToUint8:()=>YY,fromUint8ToStringArray:()=>ZY,getAxesPermutation:()=>gI,getBroadcastDims:()=>lq,getComplexWithIndex:()=>MY,getEinsumComputePath:()=>UY,getEinsumPermutation:()=>WY,getFusedBiasGradient:()=>Lf,getFusedDyActivation:()=>zf,getImageCenter:()=>yY,getInnerMostAxes:()=>Yq,getPermuted:()=>xY,getReductionAxes:()=>ln,getReshaped:()=>AY,getReshapedPermuted:()=>bY,getSliceBeginCoords:()=>vY,getSliceSize:()=>wY,getUndoAxesPermutation:()=>jA,isIdentityPermutation:()=>HY,log:()=>$Y,mergeRealAndImagArrays:()=>_Y,prepareAndValidate:()=>C6,prepareSplitSize:()=>jY,segment_util:()=>qI,shouldFuse:()=>Wf,slice_util:()=>En,splitRealAndImagArrays:()=>RY,tupleValuesAreOne:()=>La,upcastType:()=>qr,validateInput:()=>EA,validateUpdateShape:()=>CA,warn:()=>EY});function fY(e,t){let n=e[0].length;e.forEach((s,a)=>{z(s.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),z(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((s,a)=>{for(let o=0;o<n;o++)z(o===t||s[o]===r[o],()=>`Error in concat${n}D: Shape of tensors[${a}] (${s}) does not match the shape of the rest (${r}) along the non-concatenated axis ${a}.`)})}function mY(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var w1=30;function gY(e){return e<=w1?e:Gp(e,Math.floor(Math.sqrt(e)))}function yY(e,t,n){let r=n*(typeof e=="number"?e:e[0]),s=t*(typeof e=="number"?e:e[1]);return[r,s]}function AY(e,t,n,r=!0){let s=[];if(r)s=s.concat(t.slice(0)),s.push(e[0]/n),s=s.concat(e.slice(1));else{s=s.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)s=s.concat([e[o+1]/t[o],t[o]]);s=s.concat(e.slice(a+1))}return s}function xY(e,t,n=!0){let r=[];if(n){r.push(t);for(let s=t+1;s<e;++s)s<=2*t?(r.push(s),r.push(s-(t+1))):r.push(s)}else{let s=[],a=[];for(let o=1;o<e;++o)o>=t*2+1||o%2==1?a.push(o):s.push(o);r.push(...s),r.push(0),r.push(...a)}return r}function bY(e,t,n,r=!0){let s=[];r?s.push(e[0]/n):s.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?r?s.push(t[a-1]*e[a]):s.push(e[a]/t[a-1]):s.push(e[a]);return s}function vY(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function wY(e,t,n){let r=e.slice(0,1);for(let s=0;s<n;++s)r.push(e[s+1]-t[s][0]-t[s][1]);return r}var UI=1.7580993408473768,HI=1.0507009873554805,kY=.3275911,IY=.254829592,SY=-.284496736,TY=1.421413741,NY=-1.453152027,CY=1.061405429;function EY(...e){ae().getBool("IS_TEST")||console.warn(...e)}function $Y(...e){ae().getBool("IS_TEST")||console.log(...e)}function _Y(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function RY(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function DY(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let s=0;s<e.length;s+=4)n[Math.floor(s/4)]=e[s],r[Math.floor(s/4)]=e[s+1];return{real:n,imag:r}}function FY(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let s=2;s<e.length;s+=4)n[Math.floor(s/4)]=e[s],r[Math.floor(s/4)]=e[s+1];return{real:n,imag:r}}function MY(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function OY(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function PY(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let s=0;s<Math.ceil(e/2);s++){let a=(t?2:-2)*Math.PI*(s/e);n[s]=Math.cos(a),r[s]=Math.sin(a)}return{real:n,imag:r}}function zY(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),s=Math.cos(r),a=Math.sin(r);return{real:s,imag:a}}var k1="->",LY=/->/g,GI=",",jI="...";function BY(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(LY,"").length)/k1.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${k1}").`);let[r,s]=e.split(k1);z(r.indexOf(jI)===-1,()=>`The ellipsis notation ("${jI}") is not supported yet.`);let a=r.split(GI),o=a.length;if(t!==o)throw new Error(`Expected ${o} input tensors, received ${t}`);if(o>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let h=0;h<s.length;++h){let p=s[h];if(!a.some(f=>f.indexOf(p)!==-1))throw new Error(`Output subscripts contain the label ${p} not present in the input subscripts.`);i.indexOf(p)===-1&&i.push(p)}for(let h=0;h<r.length;++h){let p=r[h];i.indexOf(p)===-1&&p!==GI&&i.push(p)}let l=new Array(a.length);for(let h=0;h<o;++h){if(new Set(a[h].split("")).size!==a[h].length)throw new Error(`Found duplicate axes in input component ${a[h]}. Support for duplicate axes in input is not implemented yet.`);l[h]=[];for(let p=0;p<a[h].length;++p)l[h].push(i.indexOf(a[h][p]))}let u=i.length,c=s.length,d=[];for(let h=c;h<u;++h)d.push(h);return{allDims:i,summedDims:d,idDims:l}}function WY(e,t){let n=new Array(e);n.fill(-1);for(let s=0;s<t.length;++s)n[t[s]]=s;let r=[];for(let s=0;s<e;++s)n[s]===-1&&r.push(s);return n=n.filter(s=>s!==-1),{permutationIndices:n,expandDims:r}}function VY(e,t,n){let r=new Array(e);for(let s=0;s<n.length;++s){let a=n[s].shape;for(let o=0;o<t[s].length;++o)r[t[s][o]]===void 0?r[t[s][o]]=a[o]:z(r[t[s][o]]===a[o],()=>`Expected dimension ${r[t[s][o]]} at axis ${o} of input shaped ${JSON.stringify(a)}, but got dimension ${a[o]}`)}}function UY(e,t){let n=e,r=[],s=0;e.length===0&&n.push(-1),s=e.length+1;for(let o=0;o<s;++o)r.push([]);let a=[];for(let o=0;o<n.length;++o){let i=n[o],l=GY(t,i);for(let u of l)a.indexOf(u)===-1&&(r[o].push(u),a.push(u))}return{path:n,steps:r}}function HY(e){return e.every((t,n)=>t===n)}function GY(e,t){let n=[];for(let r=0;r<e.length;++r)(e[r].length===0||e[r].indexOf(t)!==-1||t===-1)&&n.push(r);return n}function jY(e,t,n=0){let r=[];if(typeof t=="number")z(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let s=t.reduce((o,i)=>(i===-1&&(o+=1),o),0);z(s<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let o=t.reduce((i,l)=>l>0?i+l:i);t[a]=e.shape[n]-o}z(e.shape[n]===t.reduce((o,i)=>o+i),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var qI={};De(qI,{collectGatherOpShapeInfo:()=>XY,computeOutShape:()=>KY,segOpComputeOptimalWindowSize:()=>qY});function qY(e,t){let n=!1,r;for(e<=w1?(r=e,n=!0):r=Gp(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=Gp(e,r+1);return r}function KY(e,t,n){let r=[],s=e.length;for(let a=0;a<s;a++)a!==t?r.push(e[a]):r.push(n);return r}function XY(e,t,n,r){let s=t.shape.length,a=e.shape.length;if(r!==0&&(r<-s||r>s))throw new Error(`Expect batchDims in the range of [-${s}, ${s}], but got ${r}`);if(r<0&&(r+=s),r>a)throw new Error(`batchDims (${r}) must be less than rank(x) (
    ${a}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let d=0;d<r;++d)if(e.shape[d]!==t.shape[d])throw new Error(`x.shape[${d}]: ${e.shape[d]} should be equal to indices.shape[${d}]: ${t.shape[d]}.`);let o=e.shape[n],i=[],l=1,u=1,c=1;for(let d=0;d<r;++d)i.push(e.shape[d]),l*=e.shape[d];for(let d=r;d<n;d++)i.push(e.shape[d]),u*=e.shape[d];for(let d=r;d<s;d++)i.push(t.shape[d]);for(let d=n+1;d<a;d++)i.push(e.shape[d]),c*=e.shape[d];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:o,outputShape:i}}function ZY(e){try{return e.map(t=>ff(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function YY(e){return e.map(t=>pf(t))}var ca={};De(ca,{nonMaxSuppressionV3Impl:()=>OI,nonMaxSuppressionV4Impl:()=>PI,nonMaxSuppressionV5Impl:()=>zI,whereImpl:()=>$I});re().prototype.abs=function(){return this.throwIfDisposed(),yn(this)};re().prototype.acos=function(){return this.throwIfDisposed(),H6(this)};re().prototype.acosh=function(){return this.throwIfDisposed(),G6(this)};re().prototype.add=function(e){return this.throwIfDisposed(),pe(this,e)};re().prototype.all=function(e,t){return this.throwIfDisposed(),DA(this,e,t)};re().prototype.any=function(e,t){return this.throwIfDisposed(),wf(this,e,t)};re().prototype.argMax=function(e){return this.throwIfDisposed(),kf(this,e)};re().prototype.argMin=function(e){return this.throwIfDisposed(),j6(this,e)};re().prototype.asScalar=function(){return this.throwIfDisposed(),z(this.size===1,()=>"The array must have only 1 element."),J(this,[])};re().prototype.asType=function(e){return this.throwIfDisposed(),ke(this,e)};re().prototype.as1D=function(){return this.throwIfDisposed(),J(this,[this.size])};re().prototype.as2D=function(e,t){return this.throwIfDisposed(),J(this,[e,t])};re().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),J(this,[e,t,n])};re().prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),J(this,[e,t,n,r])};re().prototype.as5D=function(e,t,n,r,s){return this.throwIfDisposed(),J(this,[e,t,n,r,s])};re().prototype.asin=function(){return this.throwIfDisposed(),q6(this)};re().prototype.asinh=function(){return this.throwIfDisposed(),K6(this)};re().prototype.atan=function(){return this.throwIfDisposed(),X6(this)};re().prototype.atan2=function(e){return this.throwIfDisposed(),Z6(this,e)};re().prototype.atanh=function(){return this.throwIfDisposed(),Y6(this)};re().prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),Sf(this,e,t,n,r)};re().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Tf(this,e,t)};re().prototype.batchNorm=function(e,t,n,r,s){return this.throwIfDisposed(),Xl(this,e,t,n,r,s)};re().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Sd(this,e)};re().prototype.cast=function(e){return this.throwIfDisposed(),ke(this,e)};re().prototype.ceil=function(){return this.throwIfDisposed(),rI(this)};re().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),dr(this,e,t)};re().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Ct&&(e=[e]),en([this,...e],t)};re().prototype.conv1d=function(e,t,n,r,s,a){return this.throwIfDisposed(),OA(this,e,t,n,r,s,a)};re().prototype.conv2dTranspose=function(e,t,n,r,s){return this.throwIfDisposed(),zA(this,e,t,n,r,s)};re().prototype.conv2d=function(e,t,n,r,s,a){return this.throwIfDisposed(),Ba(this,e,t,n,r,s,a)};re().prototype.cos=function(){return this.throwIfDisposed(),Nf(this)};re().prototype.cosh=function(){return this.throwIfDisposed(),LA(this)};re().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),BA(this,e,t,n)};re().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),oI(this,e,t)};re().prototype.depthwiseConv2d=function(e,t,n,r,s,a){return this.throwIfDisposed(),Td(this,e,t,n,r,s,a)};re().prototype.dilation2d=function(e,t,n,r,s){return this.throwIfDisposed(),iI(this,e,t,n,r,s)};re().prototype.divNoNan=function(e){return this.throwIfDisposed(),lI(this,e)};re().prototype.div=function(e){return this.throwIfDisposed(),Re(this,e)};re().prototype.dot=function(e){return this.throwIfDisposed(),fq(this,e)};re().prototype.elu=function(){return this.throwIfDisposed(),Nd(this)};re().prototype.equal=function(e){return this.throwIfDisposed(),Zo(this,e)};re().prototype.erf=function(){return this.throwIfDisposed(),uI(this)};re().prototype.exp=function(){return this.throwIfDisposed(),Kr(this)};re().prototype.expandDims=function(e){return this.throwIfDisposed(),$r(this,e)};re().prototype.expm1=function(){return this.throwIfDisposed(),cI(this)};re().prototype.fft=function(){return this.throwIfDisposed(),o1(this)};re().prototype.flatten=function(){return this.throwIfDisposed(),J(this,[this.size])};re().prototype.floor=function(){return this.throwIfDisposed(),Ed(this)};re().prototype.floorDiv=function(e){return this.throwIfDisposed(),RA(this,e)};re().prototype.gather=function(e,t){return this.throwIfDisposed(),$d(this,e,t)};re().prototype.greaterEqual=function(e){return this.throwIfDisposed(),Jo(this,e)};re().prototype.greater=function(e){return this.throwIfDisposed(),_r(this,e)};re().prototype.ifft=function(){return this.throwIfDisposed(),Pf(this)};re().prototype.irfft=function(){return this.throwIfDisposed(),SI(this)};re().prototype.isFinite=function(){return this.throwIfDisposed(),$q(this)};re().prototype.isInf=function(){return this.throwIfDisposed(),Rq(this)};re().prototype.isNaN=function(){return this.throwIfDisposed(),hI(this)};re().prototype.leakyRelu=function(e){return this.throwIfDisposed(),Cf(this,e)};re().prototype.lessEqual=function(e){return this.throwIfDisposed(),Qo(this,e)};re().prototype.less=function(e){return this.throwIfDisposed(),VA(this,e)};re().prototype.localResponseNormalization=function(e,t,n,r){return this.throwIfDisposed(),pI(this,e,t,n,r)};re().prototype.logSigmoid=function(){return this.throwIfDisposed(),Gq(this)};re().prototype.logSoftmax=function(e){return this.throwIfDisposed(),HA(this,e)};re().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),yI(this,e,t)};re().prototype.log=function(){return this.throwIfDisposed(),Rr(this)};re().prototype.log1p=function(){return this.throwIfDisposed(),UA(this)};re().prototype.logicalAnd=function(e){return this.throwIfDisposed(),is(this,e)};re().prototype.logicalNot=function(){return this.throwIfDisposed(),Ef(this)};re().prototype.logicalOr=function(e){return this.throwIfDisposed(),qA(this,e)};re().prototype.logicalXor=function(e){return this.throwIfDisposed(),rK(this,e)};re().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),ot(this,e,t,n)};re().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),$f(this,e,t,n,r)};re().prototype.max=function(e,t){return this.throwIfDisposed(),os(this,e,t)};re().prototype.maximum=function(e){return this.throwIfDisposed(),ia(this,e)};re().prototype.mean=function(e,t){return this.throwIfDisposed(),Xt(this,e,t)};re().prototype.min=function(e,t){return this.throwIfDisposed(),_f(this,e,t)};re().prototype.minimum=function(e){return this.throwIfDisposed(),_d(this,e)};re().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),xI(this,e,t)};re().prototype.mod=function(e){return this.throwIfDisposed(),bI(this,e)};re().prototype.mul=function(e){return this.throwIfDisposed(),K(this,e)};re().prototype.neg=function(){return this.throwIfDisposed(),Kt(this)};re().prototype.norm=function(e,t,n){return this.throwIfDisposed(),d1(this,e,t,n)};re().prototype.notEqual=function(e){return this.throwIfDisposed(),Yl(this,e)};re().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),kd(this,e,t,n)};re().prototype.onesLike=function(){return this.throwIfDisposed(),Dr(this)};re().prototype.pad=function(e,t){return this.throwIfDisposed(),Wa(this,e,t)};re().prototype.pool=function(e,t,n,r,s){return this.throwIfDisposed(),_K(this,e,t,n,r,s)};re().prototype.pow=function(e){return this.throwIfDisposed(),Va(this,e)};re().prototype.prelu=function(e){return this.throwIfDisposed(),Df(this,e)};re().prototype.prod=function(e,t){return this.throwIfDisposed(),XA(this,e,t)};re().prototype.reciprocal=function(){return this.throwIfDisposed(),vI(this)};re().prototype.relu=function(){return this.throwIfDisposed(),ua(this)};re().prototype.relu6=function(){return this.throwIfDisposed(),JA(this)};re().prototype.reshapeAs=function(e){return this.throwIfDisposed(),J(this,e.shape)};re().prototype.reshape=function(e){return this.throwIfDisposed(),J(this,e)};re().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),BI(this,e,t,n)};re().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),WI(this,e,t,n)};re().prototype.reverse=function(e){return this.throwIfDisposed(),Fr(this,e)};re().prototype.rfft=function(){return this.throwIfDisposed(),i1(this)};re().prototype.round=function(){return this.throwIfDisposed(),QA(this)};re().prototype.rsqrt=function(){return this.throwIfDisposed(),e1(this)};re().prototype.selu=function(){return this.throwIfDisposed(),t1(this)};re().prototype.separableConv2d=function(e,t,n,r,s,a){return this.throwIfDisposed(),wI(this,e,t,n,r,s,a)};re().prototype.sigmoid=function(){return this.throwIfDisposed(),Rs(this)};re().prototype.sign=function(){return this.throwIfDisposed(),kI(this)};re().prototype.sin=function(){return this.throwIfDisposed(),n1(this)};re().prototype.sinh=function(){return this.throwIfDisposed(),r1(this)};re().prototype.slice=function(e,t){return this.throwIfDisposed(),nt(this,e,t)};re().prototype.softmax=function(e){return this.throwIfDisposed(),Of(this,e)};re().prototype.softplus=function(){return this.throwIfDisposed(),Zl(this)};re().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Rf(this,e,t)};re().prototype.split=function(e,t){return this.throwIfDisposed(),hr(this,e,t)};re().prototype.sqrt=function(){return this.throwIfDisposed(),$n(this)};re().prototype.square=function(){return this.throwIfDisposed(),wt(this)};re().prototype.squaredDifference=function(e){return this.throwIfDisposed(),l1(this,e)};re().prototype.squeeze=function(e){return this.throwIfDisposed(),Jl(this,e)};re().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Ct?[this,e]:[this,...e];return Mr(n,t)};re().prototype.step=function(e){return this.throwIfDisposed(),Fd(this,e)};re().prototype.stridedSlice=function(e,t,n,r,s,a,o,i){return this.throwIfDisposed(),TI(this,e,t,n,r,s,a,o,i)};re().prototype.sub=function(e){return this.throwIfDisposed(),Ne(this,e)};re().prototype.sum=function(e,t){return this.throwIfDisposed(),_e(this,e,t)};re().prototype.tan=function(){return this.throwIfDisposed(),NI(this)};re().prototype.tanh=function(){return this.throwIfDisposed(),Kl(this)};re().prototype.tile=function(e){return this.throwIfDisposed(),Yo(this,e)};re().prototype.toBool=function(){return this.throwIfDisposed(),ke(this,"bool")};re().prototype.toFloat=function(){return this.throwIfDisposed(),ke(this,"float32")};re().prototype.toInt=function(){return this.throwIfDisposed(),ke(this,"int32")};re().prototype.topk=function(e,t){return this.throwIfDisposed(),CI(this,e,t)};re().prototype.transpose=function(e){return this.throwIfDisposed(),pt(this,e)};re().prototype.unique=function(e){return this.throwIfDisposed(),c1(this,e)};re().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),EI(this,e,t)};re().prototype.unstack=function(e){return this.throwIfDisposed(),ls(this,e)};re().prototype.where=function(e,t){return this.throwIfDisposed(),Ln(e,this,t)};re().prototype.zerosLike=function(){return this.throwIfDisposed(),rt(this)};var KI={kernelName:xc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(e,Fd(ke(n,"float32"),-1))}}},JY={kernelName:bc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=wt(ke(n,"float32")),s=$n(Ne(Fe(1),r));return Kt(Re(e,s))}}}},QY={kernelName:vc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=$n(Ne(wt(ke(n,"float32")),1));return Re(e,r)}}}},eJ={kernelName:Fa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Rt(n.shape,r.shape);return{a:()=>{let i=e,l=ln(n.shape,s);return l.length>0&&(i=_e(i,l)),J(i,n.shape)},b:()=>{let i=e,l=ln(r.shape,s);return l.length>0&&(i=_e(i,l)),J(i,r.shape)}}}},tJ={kernelName:Zi,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,s)=>{n[s]=()=>e.clone()}),n}},nJ={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>rt(n)}}},rJ={kernelName:qp,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>rt(n)}}},sJ={kernelName:Ic,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,$n(Ne(Fe(1),wt(ke(n,"float32")))))}}},aJ={kernelName:Sc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=$n(pe(Fe(1),wt(ke(n,"float32"))));return Re(e,r)}}}},oJ={kernelName:Cc,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Rt(n.shape,r.shape);return{a:()=>{let i=pe(wt(n),wt(r)),l=K(e,Re(r,i)),u=ln(n.shape,s);return u.length>0&&(l=_e(l,u)),J(l,n.shape)},b:()=>{let i=pe(wt(n),wt(r)),l=Kt(K(e,Re(n,i))),u=ln(r.shape,s);return u.length>0&&(l=_e(l,u)),J(l,r.shape)}}}},iJ={kernelName:Tc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,pe(wt(ke(n,"float32")),1))}}},lJ={kernelName:Nc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,Ne(Fe(1),wt(ke(n,"float32"))))}}};function uJ(e,t,n,r,s,a){let o=M(e,"dy","avgPool3dGrad"),i=M(t,"input","avgPool3dGrad"),l=o,u=i,c=!1;i.rank===4&&(c=!0,l=J(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),u=J(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),z(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),z(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),a!=null&&z(mn(s),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${s}.`);let d={dy:l,input:u},h={filterSize:n,strides:r,pad:s,dimRoundingMode:a},p=G.runKernel(ky,d,h);return c?J(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var cJ=V({avgPool3dGrad_:uJ}),dJ={kernelName:Kp,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:s,strides:a,pad:o,dimRoundingMode:i}=n;return{x:()=>cJ(e,r,s,a,o,i)}}};function hJ(e,t,n,r,s){let a=M(e,"dy","avgPoolGrad"),o=M(t,"input","avgPoolGrad");z(o.rank===a.rank,()=>`Rank of input (${o.rank}) does not match rank of dy (${a.rank})`);let i=o,l=a,u=!1;o.rank===3&&(u=!0,i=J(o,[1,o.shape[0],o.shape[1],o.shape[2]]),l=J(a,[1,a.shape[0],a.shape[1],a.shape[2]])),z(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),z(i.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${i.rank}.`);let c={dy:l,input:i},d={filterSize:n,strides:r,pad:s},h=G.runKernel(wy,c,d);return u?J(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var pJ=V({avgPoolGrad_:hJ}),fJ={kernelName:Ji,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:s,strides:a,pad:o}=n;return{x:()=>pJ(e,r,s,a,o)}}},mJ={kernelName:Qi,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[r,s]=t,{transposeA:a,transposeB:o}=n;return!a&&!o?{a:()=>ot(e,s,!1,!0),b:()=>ot(r,e,!0,!1)}:!a&&o?{a:()=>ot(e,s,!1,!1),b:()=>ot(e,r,!0,!1)}:a&&!o?{a:()=>ot(s,e,!1,!0),b:()=>ot(r,e,!1,!1)}:{a:()=>ot(s,e,!0,!0),b:()=>ot(e,r,!0,!0)}}},gJ={kernelName:Xp,gradFunc:(e,t,n)=>{let{blockShape:r,crops:s}=n;return{x:()=>Rf(e,r,s)}}},yJ={kernelName:rH,gradFunc:(e,t,n)=>{let r=n,s=r.inputShape,a=r.shape,o=Array.from(a);for(let l=s.length-1;l>=0;l--)if(s[l]===a[l])o[l]=1;else if(s[l]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${a}].`);let i=[];for(let l=0;l<o.length;l++)o[l]>1&&i.push(l);return{x:()=>_e(e,i,!0)}}},AJ={kernelName:el,gradFunc:e=>({x:()=>e.clone()})},xJ={kernelName:No,gradFunc:e=>({x:()=>rt(e)})},bJ={kernelName:Co,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:s,clipValueMax:a}=n;return{x:()=>Ln(is(Jo(r,s),Qo(r,a)),e,rt(e))}}},vJ={kernelName:Zp,inputsToSave:["x"],gradFunc:KI.gradFunc},wJ={kernelName:Ec,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(l=>l.shape),{axis:s}=n,a=jr(s,t[0].shape)[0],o=r.map(l=>l[a]);return hr(e,o,a).map(l=>()=>l)}},kJ={kernelName:tl,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,s]=t,{dilations:a,strides:o,pad:i,dataFormat:l}=n;return z(La(a),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`),{x:()=>PA(r.shape,e,s,o,i,l),filter:()=>h1(r,e,s.shape,o,i,l)}}},IJ={kernelName:nl,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[r,s]=t,{strides:a,pad:o,dataFormat:i,dimRoundingMode:l}=n;return{dy:()=>Ba(e,s,a,o,i,1,l),filter:()=>h1(e,r,s.shape,a,o,i,l)}}};function SJ(e,t,n,r,s){let a=e;e.rank===4&&(a=J(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let o=t;o.rank===4&&(o=J(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),z(a.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`),z(o.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${o.shape}.`),z(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),z(a.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`),z(o.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${o.shape[4]}) must match output depth for filter (${n[4]}).`);let i={x:a,dy:o},l={strides:r,pad:s,filterShape:n};return G.runKernel(Ny,i,l)}var TJ=V({conv3DBackpropFilter_:SJ}),NJ={kernelName:Yp,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:s,pad:a}=n;z(La(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[o,i]=t;return{x:()=>aI(o.shape,e,i,s,a),filter:()=>TJ(o,e,i.shape,s,a)}}},CJ={kernelName:rl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(Kt(n1(ke(n,"float32"))),e)}}},EJ={kernelName:$c,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(r1(ke(n,"float32")),e)}}},$J={kernelName:sl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:s,exclusive:a,reverse:o}=n;return{x:()=>{let i=gI([s],r.rank),l=BA(e,s,a,!o);return i!=null&&(l=pt(l,i)),l}}}},_J={kernelName:al,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:s,pad:a,dimRoundingMode:o}=n,i=r==null?[1,1]:r;z(La(i),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${i}'`);let[l,u]=t;return z(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),z(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),z(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),z(_s(s,i),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${s} and dilations '${i}'.`),o!=null&&z(mn(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`),{x:()=>MI(l.shape,e,u,s,a,r,o),filter:()=>FI(l,e,u.shape,s,a,r,o)}}},RJ={kernelName:Jp,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,s]=t,a={x:r,filter:s,dy:e},o={x:r,filter:s,dy:e};return{x:()=>G.runKernel(Dy,a,n),filter:()=>G.runKernel(Fy,o,n)}}},DJ={kernelName:Dc,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>G.runKernel(Oy,r)}}},FJ={kernelName:Fc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=K(Kr(Kt(wt(n))),2/Math.sqrt(Math.PI));return{x:()=>K(e,r)}}},MJ={kernelName:Eo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(e,n)}}},OJ={kernelName:Mc,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>J(e,n.shape)}}},PJ={kernelName:ll,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(e,Kr(n))}}},zJ={kernelName:$o,gradFunc:e=>({x:()=>rt(e)})},LJ={kernelName:ul,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Rt(n.shape,r.shape);return{a:()=>{let i=Re(e,ke(r,"float32")),l=ln(n.shape,s);return l.length>0?J(_e(i,l),n.shape):i},b:()=>{let i=K(e,ke(n,"float32")),l=ln(r.shape,s);l.length>0&&(i=J(_e(i,l),r.shape));let u=wt(r);return Kt(Re(i,ke(u,"float32")))}}}},BJ={kernelName:cl,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:r}=n,[s,a,o,i]=t,l=i==null?Fe(1):i,u=ln(a.shape,s.shape),c=[];if(a.rank===1){for(let b=0;b<s.shape.length-1;++b)c.push(s.shape[b]);c.push(1)}let d=Ne(s,a),h=K(e,l),p=e1(pe(o,Fe(r))),f=K(K(K(p,p),p),Fe(-.5));return{x:()=>a.rank===1?J(K(K(e,Yo(J(p,[1,1,1,a.shape[0]]),c)),l),s.shape):J(K(K(e,p),l),s.shape),mean:()=>{let b=K(K(p,Fe(-1)),h);return a.rank===1&&(b=_e(b,u)),J(b,a.shape)},variance:()=>{let b=K(K(f,d),h);return a.rank===1&&(b=_e(b,u)),J(b,a.shape)},scale:()=>{let b=K(d,p),v=K(e,b);return a.rank===1&&(v=_e(v,u)),J(v,a.shape)},offset:()=>{let b=e;return a.rank===1&&(b=_e(b,u)),J(b,a.shape)}}}},WJ={kernelName:Pc,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,s]=t,{axis:a}=n,o=jr(a,r.shape)[0];return{x:()=>{let l=r.shape,u=s.size,c=l.slice(0,o),d=c.length,h=l.slice(a,l.length).slice(1),p=h.length,f=XI(0,d),m=XI(d+1,d+1+p),g=ZI([c,[u],h]),y=J(e,g),A=J(s,[u]),x=ZI([[d],f,m]),b=pt(y,x),v=EI(b,A,r.shape[o]),w=jA(x);return v=pt(v,w),v},indices:()=>s}}};function XI(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function ZI(e){let t=[];for(let n=0;n<e.length;++n)for(let r=0;r<e[n].length;++r)t.push(e[n][r]);return t}var VJ={kernelName:_o,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>rt(n),b:()=>rt(r)}}},UJ={kernelName:hl,gradFunc:e=>({x:()=>ke(e,"float32")})},HJ={kernelName:Lc,gradFunc:e=>({x:()=>rt(e)})},GJ={kernelName:Bc,gradFunc:e=>({x:()=>rt(e)})},jJ={kernelName:Wc,gradFunc:e=>({x:()=>rt(e)})},qJ={kernelName:pl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:s}=n,a=_r(r,0);return{x:()=>Ln(a,e,K(e,s))}}},KJ={kernelName:Vc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,pe(n,1))}}},XJ={kernelName:Ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,ke(n,"float32"))}}},ZJ={kernelName:sH,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:s}=n;return{logits:()=>{let a=!0,o=Kr(r);return Ne(e,K(_e(e,s,a),o))}}}};function YJ(e,t,n,r=5,s=1,a=1,o=.5){let i={x:e,y:t,dy:n},l={depthRadius:r,bias:s,alpha:a,beta:o};return G.runKernel(Wy,i,l)}var JJ=V({localResponseNormalizationBackprop_:YJ}),QJ={kernelName:nf,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,s]=t,{depthRadius:a,bias:o,alpha:i,beta:l}=n;return{x:()=>JJ(r,s,e,a,o,i,l)}}};function YI(e,t,n,r){return t.rank<n.rank&&(t=J(t,ei(t.shape,r))),e.rank<n.rank&&(e=J(e,ei(e.shape,r))),{x:()=>K(e,ke(Zo(n,t),e.dtype))}}var JI={kernelName:gl,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{reductionIndices:s}=r,a=t[0],o=t[1],i=jr(s,a.shape),l=YI(e,o,a,i);return{x:()=>l.x()}}},eQ={kernelName:Do,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>K(e,ke(Jo(n,r),"float32")),b:()=>K(e,ke(VA(n,r),"float32"))}}};function tQ(e,t,n,r,s,a,o){let i=M(e,"dy","maxPool3dGrad"),l=M(t,"input","maxPool3dGrad"),u=M(n,"output","maxPool3dGrad"),c=i,d=l,h=u,p=!1;l.rank===4&&(p=!0,c=J(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),d=J(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),h=J(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),z(c.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),z(d.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${d.rank}.`),z(h.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${h.rank}.`),o!=null&&z(mn(a),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let f={dy:c,input:d,output:h},m={filterSize:r,strides:s,pad:a,dimRoundingMode:o},g=G.runKernel(Uy,f,m);return p?J(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var nQ=V({maxPool3dGrad_:tQ}),rQ={kernelName:rf,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,s]=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=n;return{x:()=>nQ(e,r,s,a,o,i,l)}}};function sQ(e,t,n,r,s,a,o){let i=M(e,"dy","maxPoolGrad"),l=M(t,"input","maxPoolGrad"),u=M(n,"output","maxPoolGrad");z(l.rank===i.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${i.rank})`),z(i.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${i.rank}.`),z(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),o!=null&&z(mn(a),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let c={dy:i,input:l,output:u},d={filterSize:r,strides:s,pad:a,dimRoundingMode:o};return G.runKernel(Vy,c,d)}var aQ=V({maxPoolGrad_:sQ}),oQ={kernelName:yl,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,s]=t,{filterSize:a,strides:o,pad:i}=n;return{x:()=>aQ(e,r,s,a,o,i)}}},iQ={kernelName:Al,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:s}=n,a=jr(s,r.shape),i=mI(r.shape,a)[1],l=on(i);return{x:()=>{let c=r.shape.slice();a.forEach(p=>{c[p]=1});let d=J(e,c);return Re(K(d,la(r.shape,"float32")),l)}}}},lQ={kernelName:xl,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:s}=r,[a,o]=t,i=jr(s,a.shape),l=YI(e,o,a,i);return{x:()=>l.x()}}},uQ={kernelName:Fo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>K(e,ke(Qo(n,r),"float32")),b:()=>K(e,ke(_r(n,r),"float32"))}}},cQ={kernelName:bl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:s}=n,a=s.map(o=>o[0]);return{x:()=>nt(e,a,r.shape)}}},dQ={kernelName:Hc,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Rt(n.shape,r.shape);return{a:()=>{let i=ln(n.shape,s);return i.length>0?J(_e(e,i),n.shape):e},b:()=>{let i=K(e,Kt(Ed(Re(n,r)))),l=ln(r.shape,s);return l.length>0?J(_e(i,l),r.shape):i}}}},hQ={kernelName:Mo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Rt(n.shape,r.shape);return{a:()=>{let i=K(e,ke(r,"float32")),l=ln(n.shape,s);return l.length>0?J(_e(i,l),n.shape):i},b:()=>{let i=K(e,ke(n,"float32")),l=ln(r.shape,s);return l.length>0?J(_e(i,l),r.shape):i}}}},pQ={kernelName:Gc,gradFunc:e=>({x:()=>Kt(e)})},fQ={kernelName:wl,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>un(n.shape,"float32")}}},mQ={kernelName:Xc,gradFunc:e=>({x:()=>rt(e)})},gQ={kernelName:Zc,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return ls(e,r).map(a=>()=>a)}},QI={kernelName:kl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:s}=n,a=s.map(o=>o[0]);return{x:()=>nt(e,a,r.shape)}}},yQ={kernelName:Il,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,s]=t,a=n,o=r,i=Rt(a.shape,o.shape);return{a:()=>{let c=ke(o,"float32"),d=K(e,K(c,Va(a,Ne(c,Fe(1))))),h=ln(a.shape,i);return h.length>0&&(d=_e(d,h)),J(d,a.shape)},b:()=>{let c=_r(a,0),d=Ln(c,Rr(a),rt(a)),h=K(e,K(s,d)),p=ln(o.shape,i);return p.length>0&&(h=_e(h,p)),J(h,o.shape)}}}},AQ={kernelName:Sl,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,s=_r(n,0);return{x:()=>Ln(s,e,K(e,r)),alpha:()=>{let a=Ln(s,rt(e),K(e,n)),o=ln(r.shape,e.shape);return o.length>0&&(a=_e(a,o)),J(a,r.shape)}}}},xQ={kernelName:ol,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Rt(n.shape,r.shape);return{a:()=>{let i=Re(e,ke(r,"float32")),l=ln(n.shape,s);return l.length>0?J(_e(i,l),n.shape):i},b:()=>{let i=K(e,ke(n,"float32")),l=ln(r.shape,s);l.length>0&&(i=J(_e(i,l),r.shape));let u=wt(r);return Kt(Re(i,ke(u,"float32")))}}}},bQ={kernelName:Jc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,Kt(wt(n)))}}},vQ={kernelName:Cl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=K(Qo(n,6),Fd(n));return{x:()=>K(e,ke(r,"float32"))}}},wQ={kernelName:Tl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(e,ke(Fd(n),"float32"))}}},kQ={kernelName:Qc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>J(e,n.shape)}}},IQ={kernelName:Nl,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,s={dy:e,images:r};return{images:()=>G.runKernel(Ky,s,n)}}},SQ={kernelName:af,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,s={dy:e,images:r};return{images:()=>G.runKernel(qy,s,n)}}},TQ={kernelName:El,gradFunc:(e,t,n)=>{let{dims:r}=n,s=jr(r,e.shape);return{x:()=>Fr(e,s)}}},NQ={kernelName:$l,gradFunc:e=>({x:()=>rt(e)})},CQ={kernelName:Oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Kt(Re(e,K(Va(n,1.5),2)))}}},EQ={kernelName:td,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>ke(rt(n),"float32"),t:()=>K(e,ke(n,e.dtype)),e:()=>K(e,ke(Ef(n),e.dtype))}}},$Q={kernelName:nd,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=_r(n,Fe(0)),s=Fe(UI),a=Fe(HI),o=K(e,a),i=K(K(e,s),Kr(ke(n,"float32")));return Ln(r,o,i)}}}},_Q={kernelName:Rl,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(e,K(n,Ne(Fe(1),n)))}}},RQ={kernelName:ad,gradFunc:e=>({x:()=>rt(e)})},DQ={kernelName:_l,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(Nf(ke(n,"float32")),e)}}},FQ={kernelName:sd,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(LA(ke(n,"float32")),e)}}},MQ={kernelName:rd,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:s,size:a}=n,o=r.shape,[i,l]=W6(r,s,a),u=[];for(let c=0;c<e.rank;c++)u.push([i[c],o[c]-i[c]-l[c]]);return{x:()=>Wa(e,u)}}},OQ={kernelName:Ml,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:s}=n,a=!0,o=K(e,r);return{logits:()=>Ne(o,K(_e(o,[s],a),r))}}},PQ={kernelName:od,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(e,Rs(n))}}},eS={kernelName:of,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:s}=n;return{x:()=>Tf(e,r,s)}}},tS={kernelName:id,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>en(e,r)}}},zQ={kernelName:Dl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,K($n(ke(n,"float32")),2))}}},LQ={kernelName:lf,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(e,K(ke(n,"float32"),2))}}},BQ={kernelName:Po,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Fe(2);return{a:()=>K(e,K(s,Ne(n,r))),b:()=>K(e,K(s,Ne(r,n)))}}},WQ={kernelName:Bo,gradFunc:e=>({x:()=>rt(e)})},VQ={kernelName:zo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,s=Rt(n.shape,r.shape);return{a:()=>{let i=e,l=ln(n.shape,s);return l.length>0&&(i=_e(i,l)),J(i,n.shape)},b:()=>{let i=e,l=ln(r.shape,s);return l.length>0&&(i=_e(i,l)),J(Kt(i),r.shape)}}}},UQ={kernelName:Fl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,s=r.shape.slice(),{axis:a}=n;jr(a,r.shape).forEach(u=>{s[u]=1});let i=J(e,s),l=K(i,la(r.shape,"float32"));return{x:()=>l}}},HQ={kernelName:Ol,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Re(e,wt(Nf(n)))}}},GQ={kernelName:Pl,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>K(Ne(Fe(1),wt(n)),e)}}},jQ={kernelName:Lo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{reps:s}=n;return{x:()=>{let o=rt(r);if(r.rank===1)for(let i=0;i<s[0];++i)o=pe(o,nt(e,[i*r.shape[0]],[r.shape[0]]));else if(r.rank===2)for(let i=0;i<s[0];++i)for(let l=0;l<s[1];++l)o=pe(o,nt(e,[i*r.shape[0],l*r.shape[1]],[r.shape[0],r.shape[1]]));else if(r.rank===3)for(let i=0;i<s[0];++i)for(let l=0;l<s[1];++l)for(let u=0;u<s[2];++u)o=pe(o,nt(e,[i*r.shape[0],l*r.shape[1],u*r.shape[2]],[r.shape[0],r.shape[1],r.shape[2]]));else if(r.rank===4)for(let i=0;i<s[0];++i)for(let l=0;l<s[1];++l)for(let u=0;u<s[2];++u)for(let c=0;c<s[3];++c)o=pe(o,nt(e,[i*r.shape[0],l*r.shape[1],u*r.shape[2],c*r.shape[3]],[r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${r.rank} tensors yet.`);return o}}}},qQ={kernelName:zl,gradFunc:(e,t,n)=>{let r=n,{perm:s}=r,a=jA(s);return{x:()=>pt(e,a)}}},KQ={kernelName:dd,gradFunc:(e,t,n)=>{let r=n,{axis:s}=r;return{value:()=>Mr(e,s)}}},XQ={kernelName:uf,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ZQ(e,n)}}};function ZQ(e,t){let n=ia(t,rt(t)),r=$d(e,n),s=Jo(t,Fe(0,"int32")),a=r.rank-s.rank;for(let i=0;i<a;++i)s=$r(s,i+1);s=is(s,la(r.shape,"bool"));let o=rt(r);return Ln(s,r,o)}var YQ={kernelName:hd,gradFunc:e=>({x:()=>rt(e)})},JQ=[KI,JY,QY,eJ,tJ,nJ,rJ,sJ,aJ,oJ,iJ,lJ,dJ,fJ,mJ,gJ,yJ,AJ,xJ,bJ,vJ,wJ,IJ,kJ,NJ,CJ,EJ,$J,_J,RJ,xQ,DJ,FJ,MJ,OJ,PJ,LJ,zJ,BJ,WJ,VJ,UJ,HJ,GJ,jJ,qJ,KJ,XJ,ZJ,QJ,JI,JI,eQ,rQ,oQ,iQ,lQ,uQ,cQ,dQ,hQ,pQ,fQ,mQ,gQ,QI,QI,yQ,AQ,bQ,vQ,wQ,kQ,IQ,SQ,TQ,NQ,CQ,EQ,$Q,_Q,RQ,DQ,FQ,MQ,OQ,PQ,eS,eS,tS,tS,zQ,BQ,LQ,WQ,VQ,UQ,HQ,GQ,jQ,qQ,KQ,XQ,YQ];for(let e of JQ)aH(e);var nS={};De(nS,{maxNorm:()=>nee,minMaxNorm:()=>aee,nonNeg:()=>see,unitNorm:()=>ree});var I1;function cn(){return I1==null&&(I1=GG().epsilon()),I1}function us(){return"channelsLast"}var da=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,da.prototype)}},cs=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,cs.prototype)}},q=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,q.prototype)}},Ge=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Ge.prototype)}},rS=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,rS.prototype)}};function si(e,t){if(Array.isArray(e)){let n=[];for(let r=0;r<t;r++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function Ds(e,t){if(!e)throw new rS(t)}function sS(e,t){let n=0;for(let r of e)r===t&&n++;return n}function Jn(e){return e.length===1?e[0]:e}function Dt(e){return Array.isArray(e)?e:[e]}function ha(e){let n=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return n[0]!=="_"?n:"private"+n}function ai(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var Xr={};function S1(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function T1(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>T1(t));else{let t=Object.keys(e);for(let n of t){let r=e[n];r!=null&&typeof r=="object"&&(!Array.isArray(r)&&r.type==="ndarray"&&typeof r.value=="number"?e[n]=r.value:T1(r))}}}function Md(e,t={},n={},r="object",s=!1){if(typeof e=="string"){let a=e,o;if(a in n)o=n[a];else if(a in Xr)o=Xr[a];else if(o=t[a],o==null)throw new q(`Unknown ${r}: ${e}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return o}else{let a=e;if(a.className==null||a.config==null)throw new q(`${r}: Improper config format: ${JSON.stringify(a)}.
'className' and 'config' must set.`);let o=a.className,i,l;if(o in n?[i,l]=n[o]:o in Xr?[i,l]=Xr.className:o in t&&([i,l]=t[o]),i==null)throw new q(`Unknown ${r}: ${o}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let p of Object.keys(Xr))u[p]=Xr[p];for(let p of Object.keys(n))u[p]=n[p];let c=a.config;c.customObjects=u;let d={...Xr};for(let p of Object.keys(n))Xr[p]=n[p];T1(a.config);let h=l(i,a.config,n,s);return Xr={...d},h}else{let u={...Xr};for(let d of Object.keys(n))Xr[d]=n[d];let c=new i(a.config);return Xr={...u},c}}}function QQ(e,t){return e<t?-1:e>t?1:0}function Hf(e,t){return-1*QQ(e,t)}function Ga(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function eee(e){if(e==null)throw new q(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function oi(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new q(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`)}function N1(e,t,n=0,r=Infinity){return Ds(n>=0),Ds(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(s=>typeof s===t)}function An(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>An(n,`element ${r+1} of ${t}`))):k.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${aS(e)}.`)}function aS(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>aS(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function tee(e,t){let n=k.now(),r;return(...a)=>{let o=k.now();return o-n<t||(n=o,r=e(...a)),r}}function oS(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function C1(e,t){return Z(()=>$n(_e(K(e,e),t,!0)))}var Od=class extends ce.Serializable{getConfig(){return{}}},E1=class extends Od{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 Z(()=>{let t=C1(e,this.axis),n=dr(t,0,this.maxValue);return K(e,Re(n,pe(cn(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};E1.className="MaxNorm";ce.registerClass(E1);var $1=class extends Od{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return Z(()=>Re(e,pe(cn(),C1(e,this.axis))))}getConfig(){return{axis:this.axis}}};$1.className="UnitNorm";ce.registerClass($1);var _1=class extends Od{apply(e){return ua(e)}};_1.className="NonNeg";ce.registerClass(_1);var R1=class extends Od{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 Z(()=>{let t=C1(e,this.axis),n=pe(K(this.rate,dr(t,this.minValue,this.maxValue)),K(1-this.rate,t));return K(e,Re(n,pe(cn(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};R1.className="MinMaxNorm";ce.registerClass(R1);var iS={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function dn(e){return S1(e)}function lS(e,t={}){return Md(e,ce.SerializationMap.getMap().classNameMap,t,"constraint")}function hn(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in iS?iS[e]:e,config:{}};return lS(n)}else return e instanceof Od?e:lS(e)}function nee(e){return new E1(e)}function ree(e){return new $1(e)}function see(){return new _1}function aee(e){return new R1(e)}var uS={};De(uS,{constant:()=>Cee,glorotNormal:()=>Mee,glorotUniform:()=>Fee,heNormal:()=>Oee,heUniform:()=>Pee,identity:()=>Ree,leCunNormal:()=>zee,leCunUniform:()=>Lee,ones:()=>Nee,orthogonal:()=>Bee,randomNormal:()=>$ee,randomUniform:()=>Eee,truncatedNormal:()=>_ee,varianceScaling:()=>Dee,zeros:()=>Tee});var oee=["channelsFirst","channelsLast"],iee=["nearest","bilinear"],lee=["valid","same","causal"],uee=["max","avg"],cee=["sum","mul","concat","ave"],nu=new Map;function Yt(e){oi(oee,"DataFormat",e)}function dee(e){oi(iee,"InterpolationFormat",e)}function Or(e){oi(lee,"PaddingMode",e)}function cS(e){oi(uee,"PoolMode",e)}var Pd=[],dS="/";function ii(e,t){Pd.push(e);try{let n=t();return Pd.pop(),n}catch(n){throw Pd.pop(),n}}function hee(){return Pd.length===0?"":Pd.join(dS)+dS}function hS(e){if(!fS(e))throw new Error("Not a valid tensor name: '"+e+"'");return hee()+e}function pS(e){if(!fS(e))throw new Error("Not a valid tensor name: '"+e+"'");nu.has(e)||nu.set(e,0);let t=nu.get(e);if(nu.set(e,nu.get(e)+1),t>0){let n=`${e}_${t}`;return nu.set(n,1),n}else return e}var pee=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function fS(e){return!!e.match(pee)}function fee(e){return e===parseInt(e.toString(),10)}function ja(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let r=1;for(let s=t;s<n;++s)r*=e[s];return r}function ru(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){let r=e[n];r<t&&(t=r)}return t}function qa(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){let r=e[n];r>t&&(t=r)}return t}function ds(e,t){if(t<e)throw new q(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let r=e;r<t;++r)n.push(r);return n}function zd(e,t){return e.asType(t)}function Ld(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function mee(e,t){return Z(()=>{if(e.shape.length!==2)throw new q(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Ld(e,1);return M1(n,[1,t,1])})}function gee(e){let t=[ja(e.shape)];return e.reshape(t)}function yee(e){if(e.rank<=1)throw new q(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],ja(e.shape,1)];return e.reshape(t)}function li(e,t,n){return Z(()=>{switch(e.rank){case 1:return s1(e,t,n);case 2:return II(e,[t,0],[n,e.shape[1]]);case 3:return a1(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Mf(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return nt(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return nt(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 q(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function D1(e,t,n){return Z(()=>{switch(e.rank){case 1:return s1(e,t,n);case 2:return II(e,[0,t],[e.shape[0],n]);case 3:return a1(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Mf(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new q(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Gf(e,t,n,r){return Z(()=>{switch(e.rank){case 1:return s1(e,t,n);case 2:switch(r){case 1:return li(e,t,n);case 2:return D1(e,t,n);default:throw new q(`The axis is not within the rank of the tensor ${r}`)}case 3:switch(r){case 1:return li(e,t,n);case 2:return a1(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return D1(e,t,n);default:throw new q(`The axis is not within the rank of the tensor ${r}`)}case 4:switch(r){case 1:return li(e,t,n);case 2:return Mf(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Mf(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return D1(e,t,n);default:throw new q(`The axis is not within the rank of the tensor ${r}`)}default:throw new q(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function F1(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),en(e,t)}function mS(e,t){switch(e.rank){case 1:return zj([e,t]);case 2:return Bj([e,t],0);case 3:return Vj([e,t],0);case 4:return Hj([e,t],0);default:throw new q(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function M1(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new q(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Yo(e,t)}function jf(e,t=0,n=1,r,s){return BK(e,t,n,r,s)}function Fs(e,t,n,r){if(e.rank<2||t.rank<2)throw new Ge(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let s=e.shape.slice(-1)[0],a=t.shape.slice(-2)[0];if(s!==a)throw new Ge(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and  y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let s=!1,a=!1;return ti.matMul({a:e,b:t,transposeA:s,transposeB:a,bias:r?O1(e.rank,r,us()):null,activation:n})}else{let s=e.shape.slice(),a=s.pop();e=e.reshape([-1,a]);let o=t.shape.slice(),i=o.pop(),l=o.pop(),u=[...o,i],c=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=t.transpose(c).reshape([l,-1]);let d=[...s,...u],h=!1,p=!1;return ti.matMul({a:e,b:t,transposeA:h,transposeB:p,bias:r?O1(e.rank,r,us()):null,activation:n}).reshape(d)}}function gS(e,t,n){return Z(()=>(Array.isArray(t)?t=_n(t,"int32"):t=t.toInt(),$d(e,t,n)))}function Bd(e){return K(e,e)}function O1(e,t,n){let r=t.shape;if(t.rank!==1&&t.rank!==e)throw new q(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1,1]):t.reshape([1,r[3],r[0],r[1],r[2]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===4){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1]):t.reshape([1,r[2],r[0],r[1]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===3){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1]):t.reshape([1,r[1],r[0]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,r[0]]):t.reshape([1].concat(r))}else if(e<3)return t;throw new q(`Unsupported input rank by biasAdd: ${t.rank}`)}function hs(e,t,n){return Z(()=>(n==null&&(n=us()),Yt(n),e.add(O1(e.rank,t,n))))}function Aee(e,t=1){if(t!==1)throw new Ge(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Nd(e)}function xee(e){return Z(()=>Re(e,yn(e).add(1)))}function yS(e,t,n,r){return Z(()=>WX(e,t,n,r))}function bee(e){return Z(()=>{let t=pe(.5,K(.2,e));return dr(t,0,1)})}function Wd(e,t,n=!1){return n?e():t()}var vee=["fanIn","fanOut","fanAvg"],wee=["normal","uniform","truncatedNormal"];function kee(e){oi(vee,"FanMode",e)}function Iee(e){oi(wee,"Distribution",e)}var Zr=class extends ce.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},P1=class extends Zr{apply(e,t){return un(e,t)}};P1.className="Zeros";ce.registerClass(P1);var qf=class extends Zr{apply(e,t){return la(e,t)}};qf.className="Ones";ce.registerClass(qf);var z1=class extends Zr{constructor(e){super();if(typeof e!="object")throw new q(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new q(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return Z(()=>K(Fe(this.value),la(e,t)))}getConfig(){return{value:this.value}}};z1.className="Constant";ce.registerClass(z1);var L1=class extends Zr{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 Rd(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};L1.className="RandomUniform";ce.registerClass(L1);var B1=class extends Zr{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 Ge(`randomNormal does not support dType ${t}.`);return jf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};B1.className="RandomNormal";ce.registerClass(B1);var W1=class extends Zr{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 Ge(`truncatedNormal does not support dType ${t}.`);return u1(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};W1.className="TruncatedNormal";ce.registerClass(W1);var V1=class extends Zr{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return Z(()=>{if(e.length!==2||e[0]!==e[1])throw new q("Identity matrix initializer can only be used for 2D square matrices.");return K(this.gain,dI(e[0]))})}getConfig(){return{gain:this.gain}}};V1.className="Identity";ce.registerClass(V1);function See(e,t="channelsLast"){let n,r;if(Yt(t),e.length===2)n=e[0],r=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let s=ja(e,2);n=e[1]*s,r=e[0]*s}else if(t==="channelsLast"){let s=ja(e,0,e.length-2);n=e[e.length-2]*s,r=e[e.length-1]*s}}else{let s=ja(e);n=Math.sqrt(s),r=Math.sqrt(s)}return[n,r]}var Qn=class extends Zr{constructor(e){super();if(e.scale<0)throw new q(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,kee(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,Iee(this.distribution),this.seed=e.seed}apply(e,t){let n=See(e),r=n[0],s=n[1],a=this.scale;if(this.mode==="fanIn"?a/=Math.max(1,r):this.mode==="fanOut"?a/=Math.max(1,s):a/=Math.max(1,(r+s)/2),this.distribution==="normal"){let o=Math.sqrt(a);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Ge(`${this.getClassName()} does not support dType ${t}.`);return u1(e,0,o,t,this.seed)}else{let o=Math.sqrt(3*a);return Rd(e,-o,o,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Qn.className="VarianceScaling";ce.registerClass(Qn);var Kf=class extends Qn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Qn.className}};Kf.className="GlorotUniform";ce.registerClass(Kf);var Xf=class extends Qn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Qn.className}};Xf.className="GlorotNormal";ce.registerClass(Xf);var Zf=class extends Qn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Qn.className}};Zf.className="HeNormal";ce.registerClass(Zf);var Yf=class extends Qn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Qn.className}};Yf.className="HeUniform";ce.registerClass(Yf);var Jf=class extends Qn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Qn.className}};Jf.className="LeCunNormal";ce.registerClass(Jf);var Qf=class extends Qn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Qn.className}};Qf.className="LeCunNormal";ce.registerClass(Qf);var U1=class extends Zr{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Ge("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return Z(()=>{if(e.length<2)throw new Ge("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=jf(n,0,1,"float32"),s=hY.gramSchmidt(r);return e[0]>e[1]&&(s=s.transpose()),K(this.gain,s)})}getConfig(){return{gain:this.gain,seed:this.seed}}};U1.className="Orthogonal";ce.registerClass(U1);var AS={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 xS(e,t={}){return Md(e,ce.SerializationMap.getMap().classNameMap,t,"initializer")}function Ht(e){return S1(e)}function zt(e){if(typeof e=="string"){let t=e in AS?AS[e]:e;if(t==="GlorotNormal")return new Xf;if(t==="GlorotUniform")return new Kf;if(t==="HeNormal")return new Zf;if(t==="HeUniform")return new Yf;if(t==="LeCunNormal")return new Jf;if(t==="LeCunUniform")return new Qf;{let n={};return n.className=t,n.config={},xS(n)}}else return e instanceof Zr?e:xS(e)}function Tee(){return new P1}function Nee(){return new qf}function Cee(e){return new z1(e)}function Eee(e){return new L1(e)}function $ee(e){return new B1(e)}function _ee(e){return new W1(e)}function Ree(e){return new V1(e)}function Dee(e){return new Qn(e)}function Fee(e){return new Kf(e)}function Mee(e){return new Xf(e)}function Oee(e){return new Zf(e)}function Pee(e){return new Yf(e)}function zee(e){return new Jf(e)}function Lee(e){return new Qf(e)}function Bee(e){return new U1(e)}var bS={};De(bS,{Layer:()=>st,RNN:()=>fa,RNNCell:()=>Kd,activation:()=>vne,add:()=>$ne,alphaDropout:()=>pre,average:()=>_ne,averagePooling1d:()=>c5,averagePooling2d:()=>d5,averagePooling3d:()=>h5,avgPool1d:()=>Bne,avgPool2d:()=>Vne,avgPool3d:()=>Hne,avgPooling1d:()=>Wne,avgPooling2d:()=>Une,avgPooling3d:()=>Gne,batchNormalization:()=>Pne,bidirectional:()=>are,concatenate:()=>Rne,conv1d:()=>hne,conv2d:()=>pne,conv2dTranspose:()=>fne,conv3d:()=>mne,conv3dTranspose:()=>gne,convLstm2d:()=>tre,convLstm2dCell:()=>nre,cropping2D:()=>Ane,dense:()=>wne,depthwiseConv2d:()=>bne,dot:()=>One,dropout:()=>kne,elu:()=>one,embedding:()=>Ene,flatten:()=>Sne,gaussianDropout:()=>hre,gaussianNoise:()=>dre,globalAveragePooling1d:()=>jne,globalAveragePooling2d:()=>qne,globalMaxPool1d:()=>ire,globalMaxPool2d:()=>lre,globalMaxPooling1d:()=>D8,globalMaxPooling2d:()=>F8,gru:()=>Xne,gruCell:()=>Zne,input:()=>QS,inputLayer:()=>ane,layerNormalization:()=>zne,leakyReLU:()=>lne,lstm:()=>Yne,lstmCell:()=>Jne,masking:()=>fre,maxPool1d:()=>ure,maxPool2d:()=>cre,maxPooling1d:()=>M8,maxPooling2d:()=>O8,maxPooling3d:()=>Kne,maximum:()=>Dne,minimum:()=>Fne,multiply:()=>Mne,permute:()=>Cne,prelu:()=>une,reLU:()=>ine,repeatVector:()=>Tne,reshape:()=>Nne,rnn:()=>rre,separableConv2d:()=>yne,simpleRNN:()=>Qne,simpleRNNCell:()=>ere,softmax:()=>cne,spatialDropout1d:()=>Ine,stackedRNNCells:()=>sre,thresholdedReLU:()=>dne,timeDistributed:()=>ore,upSampling2d:()=>xne,zeroPadding2d:()=>Lne});var Wee=0;function vS(){return Wee++}var em={};function tm(e=""){return e in em||(em[e]=0),em[e]+=1,e+em[e].toString()}function H1(e){return Array.isArray(e)&&Array.isArray(e[0])}function nm(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Ke(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new q(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function At(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new q(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function rm(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((r,s)=>r*s);return t}var wS="Variable",kS=class{constructor(e,t="float32",n=wS,r=!0,s=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=vS(),n=n==null?wS:n,this.originalName=hS(n),this.name=pS(this.originalName),this.trainable_=r,this.constraint=s,this.val=CX(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Vee(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 Vee(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function G1(e){return e.map(t=>t.read())}function j1(e){e.forEach(t=>{t[0].write(t[1])})}var tn=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},ps=class{constructor(e,t,n,r,s,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=s,this.outputTensorIndex=o,this.id=vS(),a!=null&&(this.originalName=hS(a),this.name=pS(this.originalName)),this.rank=t.length}},Uee=0,sm=class{constructor(e,t){this.callArgs=t,this.id=Uee++,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}}},Hee=0,st=class extends ce.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Hee++,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=ha(n)+"_"+tm(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 s=null;e.batchSize!=null&&(s=e.batchSize),n=[s].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}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 cs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Jn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Jn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new da(`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 da(`Layer ${this.name} is not connected, no input to return.`);return Jn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new da(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new da(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Jn(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=Dt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=Dt(this.inputSpec);if(e.length!==t.length)throw new q(`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 r=e[n],s=t[n];if(s==null)continue;let a=r.rank;if(s.ndim!=null&&a!==s.ndim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${a}`);if(s.maxNDim!=null&&a>s.maxNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${a}`);if(s.minNDim!=null&&a<s.minNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${a}.`);if(s.dtype!=null&&r.dtype!==s.dtype)throw new q(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${r.dtype}.`);if(s.axes){let o=r.shape;for(let i in s.axes){let l=Number(i),u=s.axes[i],c=l>=0?o[l]:o[o.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${o}.`)}}if(s.shape!=null)for(let o=0;o<s.shape.length;++o){let i=s.shape[o],l=r.shape[o];if(i!=null&&l!=null&&i!==l)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${r.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=Dt(e),r=!0;for(let a of n)if(!(a instanceof ps)){r=!1;break}let s=!0;for(let a of n)if(a instanceof ps){s=!1;break}if(r===s)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return ii(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of Dt(e))a.push(o.shape);this.build(Jn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(e),s){let a=this.call(e,t),o=Dt(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=Jn(i),this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=Gee(e),o=this.computeOutputShape(a),i,l=jee(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((u,c)=>new ps(l,u,this,Dt(e),t,this.name,c)):i=new ps(l,o,this,Dt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 da(`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 da(`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 cs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return rm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return G1(e?this.trainableWeights:this.weights)}setWeights(e){Z(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${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=[],r=G1(t);for(let s=0;s<r.length;++s){let a=r[s],o=t[s],i=e[s];if(!k.arraysEqual(a.shape,i.shape))throw new q(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}j1(n)})}addWeight(e,t,n,r,s,a,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(r=zt("zeros"));let i=r.apply(t,n),l=new kS(i,n,e,a,o);return i.dispose(),s!=null&&this.addLoss(()=>s.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=Dt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,s,a,o=null){let i=Dt(e);t=Dt(t),n=Dt(n),r=Dt(r),s=nm(s),a=nm(a);let l=[],u=[],c=[];for(let d of i)l.push(d.sourceLayer),u.push(d.nodeIndex),c.push(d.tensorIndex);new sm({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:s,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function Gee(e){e=Dt(e);let t=[];for(let n of e)t.push(n.shape);return Jn(t)}function jee(e){return"float32"}function IS(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let r=t.inboundNodes[n];if(r.inboundLayers.length===0)return r.inputTensors;{let s=[];for(let a=0;a<r.inboundLayers.length;a++){let o=r.inputTensors[a],i=r.inboundLayers[a],l=r.nodeIndices[a],u=IS(o,i,l);for(let c of u)s.indexOf(c)===-1&&s.push(c)}return s}}}var su=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:tm("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 q("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 q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("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 r=new ps(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`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}}};su.className="InputLayer";ce.registerClass(su);function SS(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 q("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 su({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Ka(e){if(e==null)return;let t=[],n=[],r=[];for(let s in e){let a=e[s];if(typeof a!="number"){let o=a;t.push(o.data()),n.push(s),r.push(o)}}if(t.length>0){let s=await Promise.all(t);for(let a=0;a<s.length;++a)e[n[a]]=s[a][0];je(r)}}function TS(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var NS;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(NS||(NS={}));var qee=125,au=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){}},CS=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)}},Kee=class extends au{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 r in t){let s=t[r];if(typeof s=="number")this.totals.hasOwnProperty(r)||(this.totals[r]=0),this.totals[r]=this.totals[r]+s*n;else{let a;r in this.totals?a=this.totals[r]:this.totals[r]=0;let o=Z(()=>pe(this.totals[r],K(s,n)));this.totals[r]=o,a!=null&&a.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:Z(()=>{let r=K(Re(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),Sn(t[n])}))}},ES=class extends au{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 s in this.history){let a=this.history[s];for(let o=0;o<a.length;++o)if(typeof a[o]!="number"){let i=a[o];e.push(i.data()),t.push(s),n.push(o)}}let r=await Promise.all(e);for(let s=0;s<r.length;++s)this.history[t[s]][n[s]].dispose(),this.history[t[s]][n[s]]=r[s][0]}},$S=class extends au{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=qee),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=tee(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let r=[];this.yield!=null&&(await Ka(n),r.push(this.yield(e,t,n))),r.push(v1()),await Promise.all(r)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Ka(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Ka(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(v1()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Ka(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Ka(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(v1()):k.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Ka(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Ka(e),await this.trainEnd(e))}};function _S(e,t){return e==null&&(e={}),e instanceof au?[e]:Array.isArray(e)&&e[0]instanceof au?e:Dt(e).map(r=>new $S(r,t))}var Ms=class{constructor(){}static registerCallbackConstructor(e,t){k.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),Ms.checkForDuplicate(t),Ms.constructors[e]==null&&(Ms.constructors[e]=[]),Ms.constructors[e].push(t)}static checkForDuplicate(e){for(let t in Ms.constructors)Ms.constructors[+t].forEach(r=>{if(r===e)throw new q("Duplicate callback constructor.")})}static clear(){Ms.constructors={}}static createCallbacks(e){let t=[];for(let n in Ms.constructors){let r=+n;e>=r&&t.push(...Ms.constructors[r])}return t.map(n=>new n)}},q1=Ms;q1.constructors={};function RS(e,t,n,r,s,a,o,i,l){let u=new ES,c=[new Kee,...q1.createCallbacks(t)];e!=null&&c.push(...e),c.push(u);let d=new CS(c);return d.setParams({epochs:n,initialEpoch:r,samples:s,steps:a,batchSize:o,verbose:t,doValidation:i,metrics:l}),{callbackList:d,history:u}}function fs(e,t={},n=!1){return Md(e,ce.SerializationMap.getMap().classNameMap,t,"layer",n)}function am(e,t){return Z(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=_e(Bd(e),t,!0),r=Cd(n.shape,cn()),s=$n(ia(n,r));return Re(e,s)})}function ui(e,t){return Z(()=>Xt(Bd(Ne(t,e)),-1))}function om(e,t){return Z(()=>Xt(yn(Ne(t,e)),-1))}function ou(e,t){return Z(()=>{let n=Ne(e,t),r=dr(yn(e),cn(),Number.MAX_VALUE),s=yn(Re(n,r));return K(100,Xt(s,-1))})}function Xee(e,t){return Z(()=>{let n=dr(t,cn(),Number.MAX_VALUE),r=Rr(pe(1,n)),s=dr(e,cn(),Number.MAX_VALUE),a=Rr(pe(1,s));return Xt(Bd(Ne(r,a)),-1)})}function Zee(e,t){return Z(()=>{let n=ia(0,Ne(1,K(e,t)));return Xt(Bd(n),-1)})}function Yee(e,t){return Z(()=>{let n=ia(0,Ne(1,K(e,t)));return Xt(n,-1)})}function Jee(e,t){return Z(()=>{let n=_e(K(e,t),-1),r=os(K(Ne(1,e),t),-1);return ia(0,pe(1,Ne(r,n)))})}function Qee(e,t){return Z(()=>{let n=Math.log(2),r=Ne(t,e),s=Ne(pe(r,Zl(K(-2,r))),n);return Xt(s,-1)})}function Vd(e,t,n=!1){return Z(()=>{if(n)t=Of(t);else{let r=_e(t,t.shape.length-1,!0);t=Re(t,r)}return t=dr(t,cn(),1-cn()),Kt(_e(K(e.toFloat(),Rr(t)),t.shape.length-1))})}function im(e,t,n=!1){return Z(()=>{let r=Ed(gee(e)).toInt();t=dr(t,cn(),1-cn());let s=t.shape,a=kd(r,s[s.length-1]).reshape(s);return Vd(a,t,n)})}function ete(e,t){if(!k.arraysEqual(e.shape,t.shape))throw new q(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return Z(()=>{let n=t.relu(),r=t.abs().neg();return n.sub(t.mul(e)).add(r.exp().log1p())})}function lm(e,t){return Z(()=>{let n;return n=dr(t,cn(),1-cn()),n=Rr(Re(n,Ne(1,n))),Xt(ete(e,n),-1)})}function tte(e,t){return Z(()=>{let n=dr(e,cn(),1),r=dr(t,cn(),1);return _e(K(e,Rr(Re(n,r))),-1)})}function nte(e,t){return Z(()=>{let n=Rr(pe(cn(),t));return Xt(Ne(t,K(e,n)),-1)})}function K1(e,t){return Z(()=>{let n=am(e,-1),r=am(t,-1),s=K(n,r);return Kt(_e(s,-1))})}var um={meanSquaredError:ui,meanAbsoluteError:om,meanAbsolutePercentageError:ou,meanSquaredLogarithmicError:Xee,squaredHinge:Zee,hinge:Yee,categoricalHinge:Jee,logcosh:Qee,categoricalCrossentropy:Vd,sparseCategoricalCrossentropy:im,binaryCrossentropy:lm,kullbackLeiblerDivergence:tte,poisson:nte,cosineProximity:K1};function X1(e){if(typeof e=="string"){if(e in um)return um[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 q(t)}else return e}function Z1(e,t){return Z(()=>{let n=K(.5,Dr(t)),r=zd(_r(t,n),e.dtype);return Xt(Zo(e,r),-1)})}function Y1(e,t){return Z(()=>zd(Zo(kf(e,-1),kf(t,-1)),"float32"))}function DS(e,t){return Z(()=>is(e.equal(1),t.equal(1)).sum().cast("float32"))}function rte(e,t){return Z(()=>is(e.equal(1),t.equal(0)).sum().cast("float32"))}function ste(e,t){return Z(()=>is(e.equal(0),t.equal(1)).sum().cast("float32"))}function FS(e,t){return Z(()=>{let n=DS(e,t),r=ste(e,t),s=n.add(r);return Ln(_r(s,0),n.div(s),0).cast("float32")})}function ate(e,t){return Z(()=>{let n=DS(e,t),r=rte(e,t),s=n.add(r);return Ln(_r(s,0),n.div(s),0).cast("float32")})}function MS(e,t){return lm(e,t)}function OS(e,t){return e.rank===t.rank&&(e=e.squeeze([e.rank-1])),t=t.argMax(-1),t.dtype!==e.dtype&&(t=t.asType(e.dtype)),Zo(e,t).asType("float32")}var ote=ui,ite=ui,lte=om,ute=om,cte=ou,dte=ou,J1=Vd,hte=K1,PS=im,cm={binaryAccuracy:Z1,categoricalAccuracy:Y1,precision:FS,categoricalCrossentropy:J1,sparseCategoricalCrossentropy:PS,mse:ote,MSE:ite,mae:lte,MAE:ute,mape:cte,MAPE:dte,cosine:hte};function pte(e){if(typeof e=="string"&&e in cm)return cm[e];if(typeof e!="string"&&e!=null)return e;throw new q(`Unknown metric ${e}`)}function dm(e){if(Ds(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(um))if(um[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(cm))if(cm[n]===e){t=n;break}return t!==void 0?t:e.name}}function fte(e){let t={Adagrad:()=>tu.adagrad(.01),Adadelta:()=>tu.adadelta(1,.95,cn()),Adam:()=>tu.adam(.001,.9,.999,cn()),Adamax:()=>tu.adamax(.002,.9,.999,cn(),0),RMSProp:()=>tu.rmsprop(.001,.9,0,cn()),SGD:()=>tu.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 q(`Unknown Optimizer ${e}`)}var zS=1*1024*1024;function LS(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!Q1(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>zS&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${r.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${zS}.`)}}function Q1(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"||!Q1(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!Q1(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function mte(e,t,n,r=console.log){let s=yte(e),a=["Layer (type)","Output shape","Param #"];s?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(c=>Math.floor(t*c)));let o;if(!s){a.push("Receives inputs"),o=[];for(let c in e.nodesByDepth)o.push(...e.nodesByDepth[c])}r("_".repeat(t)),hm(a,n,r),r("=".repeat(t));let i=e.layers;for(let c=0;c<i.length;++c)s?Ate(i[c],n,r):xte(i[c],n,o,r),r((c===i.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=gte(e),u=rm(e.nonTrainableWeights);r(`Total params: ${l+u}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${u}`),r("_".repeat(t))}function gte(e){let t;return e.collectedTrainableWeights!=null?t=rm(e.collectedTrainableWeights):t=rm(e.trainableWeights),t}function yte(e){let t=!0,n=[],r=[];for(let s in e.nodesByDepth)n.push(e.nodesByDepth[s]);for(let s of n){if(s.length>1||s.length===1&&s[0].inboundLayers.length>1){t=!1;break}r.push(...s)}if(t)for(let s of e.layers){let a=!1;for(let o of s.inboundNodes)if(r.indexOf(o)!==-1)if(a){t=!1;break}else a=!0;if(!t)break}return t}function hm(e,t,n=console.log){let r="";for(let s=0;s<e.length;++s)s>0&&(r=r.slice(0,r.length-1)+" "),r+=e[s],r=r.slice(0,t[s]),r+=" ".repeat(t[s]-r.length);n(r)}function Ate(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(i){r="multiple"}let s=e.name,a=e.getClassName(),o=[`${s} (${a})`,r,e.countParams().toString()];hm(o,t,n)}function xte(e,t,n,r){let s;try{s=JSON.stringify(e.outputShape)}catch(c){s="multiple"}let a=[];for(let c of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(c)===-1))for(let d=0;d<c.inboundLayers.length;++d){let h=c.inboundLayers[d].name,p=c.nodeIndices[d],f=c.tensorIndices[d];a.push(`${h}[${p}][${f}]`)}let o=e.name,i=e.getClassName(),l=a.length===0?"":a[0],u=[`${o} (${i})`,s,e.countParams().toString(),l];hm(u,t,r);for(let c=1;c<a.length;++c)hm(["","","",a[c]],t,r)}function BS(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Ud(e,t){if(e===null)return null;if(typeof e=="string")return ai(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let s=0;s<r;++s){let a=e[s];BS(t,s,a)?n.push(a):n.push(Ud(a,t))}return n}else{let n={};for(let r of Object.keys(e)){let s=e[r];if(r==="name"&&typeof s=="string")n[r]=s;else{let a=ai(r);n[a]=Ud(s,a)}}return n}}function ex(e,t){if(e==null)return null;if(typeof e=="string")return ha(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let s=0;s<r;++s){let a=e[s];BS(t,s,a)?n.push(a):n.push(ex(a,t))}return n}else{let n={};for(let r of Object.keys(e)){let s=e[r],a=ha(r);(r==="name"||r==="className")&&typeof s=="string"?n[a]=s:n[a]=ex(s,r)}return n}}var tx="3.7.0";function bte(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return ke(t,e.dtype)}catch(n){throw new q(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var ci=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof ci)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=bte(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new q(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof ps){if(this.id2Value[e.id]==null)throw new q(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new q(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof ps){if(this.id2Value[e.id]==null)throw new q(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new q(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&je(this.id2Mask)}},nx={},WS={};function Hd(e,t,n,r){let s=n==null?!1:n.training,a=Array.isArray(e),o=a?e:[e],i=o.map(f=>f.name),l=[],u=t.names();for(let f of i)u.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let c=i.join(",")+"|"+t.names().join(","),d,h;if(nx[c]==null){let f=vte(o,t);d=f.sorted,h=f.recipientCounts,nx[c]=d,WS[c]=h}d=nx[c],h={},s||Object.assign(h,WS[c]);let p=new ci(t);for(let f=0;f<d.length;++f){if(r!=null){let E=$A().numTensors;E>r.maxNumTensors&&(r.maxNumTensors=E),E<r.minNumTensors&&(r.minNumTensors=E)}let m=d[f],g=m.sourceLayer;if(g instanceof su)continue;let y=[],A=[],x=[],b=!1;for(let E of m.inputs){let F=p.getValue(E),$=p.getMask(E);y.push(F),A.push($),$!=null&&(b=!0),s||(h[E.name]--,h[E.name]===0&&!t.hasKey(E)&&i.indexOf(E.name)===-1&&!F.isDisposed&&E.sourceLayer.stateful!==!0&&x.push(F))}b&&(n=n||{},n.mask=A[0]);let v=Dt(g.apply(y,n)),w=null;g.supportsMasking&&(w=g.computeMask(y,A));let S=kte(m),I=Array.isArray(S)?S:[S];for(let E=0;E<I.length;++E){p.hasKey(I[E])||p.add(I[E],v[E],Array.isArray(w)?w[0]:w);let F=i.indexOf(I[E].name);F!==-1&&(l[F]=v[E])}s||je(x)}return p.disposeMasks(),a?l:l[0]}function vte(e,t){k.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let s=VS(e[0],t);n=s.sorted,r=s.recipientMap}else{let s=new Set;for(let a of e){let{sorted:o,recipientMap:i}=VS(a,t);for(let l of o)s.has(l.name)||(n.push(l),s.add(l.name));for(let l in i)r[l]==null&&(r[l]=new Set),i[l].forEach(u=>r[l].add(u))}}return{sorted:n,recipientCounts:wte(r)}}function wte(e){let t={};for(let n in e)t[n]=e[n].size;return t}function VS(e,t){let n=new Set,r=[],s={};for(let i of t.names())n.add(i);let a=[],o=[];for(a.push(e);a.length>0;){let i=a[a.length-1];if(n.has(i.name)){a.pop();continue}let l=o[o.length-1]===a.length-1;if(i.inputs.length===0||l)a.pop(),r.push(i),n.add(i.name),l&&o.pop();else{o.push(a.length-1);for(let u of i.inputs)s[u.name]==null&&(s[u.name]=new Set),s[u.name].add(i.name),!n.has(u.name)&&a.push(u)}}return{sorted:r,recipientMap:s}}function kte(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let r=0;r<e.sourceLayer.inboundNodes.length;++r)for(let s of e.sourceLayer.inboundNodes[r].outputTensors)if(s.id===e.id){n=r;break}t=e.sourceLayer.getOutputAt(n)}return t}var Os=class extends st{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=tm(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],Ga(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Ga(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 A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;Ds(x===0,"input layer has >1 nodes"),Ds(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof su))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.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={},r={},s={},a={},o=[],i=(y,A,x,b,v,w)=>{(b==null||v==null||w==null)&&(b=y.sourceLayer,v=y.nodeIndex,w=y.tensorIndex);let S=b.inboundNodes[v];if(x.indexOf(S)!==-1)throw new cs(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(Os.nodeKey(b,v)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(S)===-1&&x.push(S);let I=S.inboundLayers.length;for(let E=0;E<I;E++){let F=S.inputTensors[E],$=S.inboundLayers[E],_=S.nodeIndices[E],N=S.tensorIndices[E];i(F,A,x,$,_,N)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);o.push(S)},l=[],u=[];for(let y of this.outputs)i(y,l,u);let c=o.slice().reverse();for(let y of c){n[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];A=Math.max(A,x),r[y.outboundLayer.id]=A,s[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let v=y.inboundLayers[b],w=y.nodeIndices[b],S=v.inboundNodes[w],I=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,I),n[S.id]=S}}let d={};for(let y in t){let A=t[y];A in d||(d[A]=[]),d[A].push(n[y])}let h={};for(let y in r){let A=r[y];A in h||(h[A]=[]),h[A].push(s[y])}let p=Object.keys(h).map(y=>parseInt(y,10)).sort(Hf);this.layers=[];for(let y of p){let A=h[y];A.sort((x,b)=>{let v=a[x.id],w=a[b.id];return v<w?-1:v>w?1:0});for(let x of A)x instanceof Os&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=h,p=Object.keys(d).map(y=>parseInt(y,10)).sort(Hf);let f=this.inputs.slice(),m=[];for(let y of p)for(let A of d[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new cs(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new cs(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 q("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={},r=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new q(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,r++}let s=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)s.push([n[o],e[a]]);else if(t)throw new q(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new q(`${a.length} of ${r} weights are not set: ${a}`)}j1(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${tx}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=ex(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return Z(()=>{e=Dt(e);let n=new ci;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Hd(this.outputs,n,t)})}computeMask(e,t){return Z(()=>{e=Dt(e);let n;return t==null?n=si(null,e.length):n=Dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=nm(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],u=i.name+"_0_0";n[u]=l}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Hf);if(r.length>1)for(let o of r){let i=this.nodesByDepth[o];for(let l of i){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=n[A];c.push(x)}let d=u.computeOutputShape(Jn(c)),h=nm(d),p=u.inboundNodes.indexOf(l);for(let f=0;f<h.length;f++){let m=`${u.name}_${p}_${f}`;n[m]=h[f]}}}let s=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],c=`${i.name}_${l}_${u}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];Ds(i in n),s.push(n[i])}return Jn(s)}runInternalGraph(e,t){t==null&&(t=si(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],u=e[i],c=t[i];n[l.id]=[u,c]}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Hf);for(let i of r){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer,d=u.inputTensors,h=u.outputTensors,p=new Array;for(let f of d)f.id in n&&p.push(n[f.id]);if(p.length===d.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),p.length===1){let[x,b]=p[0];f.mask==null&&(f.mask=b),y=Dt(c.call(x,f)),A=Dt(c.computeMask(x,b)),m=[x],g=[b]}else m=p.map(x=>x[0]),g=p.map(x=>x[1]),f.mask==null&&(f.mask=g),y=Dt(c.call(m,f)),A=Dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Ge("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<h.length;++x){let b=h[x],v=y[x],w=A[x];n[b.id]=[v,w]}}}}let s=[],a=[],o=[];for(let i of this.outputs){Ds(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,u]=n[i.id];o.push(l.shape),s.push(l),a.push(u)}return[s,a,o]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof Os?1:0;for(let s=0;s<r.inboundNodes.length;s++){let a=Os.nodeKey(r,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`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 q("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return Z(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=Os.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let c=0;c<a.inboundNodes.length;c++){let d=a.inboundNodes[c],h=Os.nodeKey(a,c),p={};if(this.containerNodes.has(h)){if(d.callArgs)try{JSON.stringify(d.callArgs),p=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),p={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],y=d.nodeIndices[m],A=d.tensorIndices[m],x=Os.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,p])}l.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=l,n.push(u)}e.layers=n;let r=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=Os.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[a];r.push([o.name,u,c])}e.inputLayers=r;let s=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=Os.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[a];s.push([o.name,u,c])}return e.outputLayers=s,e}static fromConfig(e,t,n={},r=!1){let s={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],w=x[2];if(A=x[3]==null?{}:x[3],!(b in s)){o(m,g);return}let S=s[b];if(S.inboundNodes.length<=v){o(m,g);return}let I=S.inboundNodes[v];y.push(I.outputTensors[w])}y.length>0&&m.apply(Jn(y),A)}function l(m){let g=m.name,y=fs(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),s[g]=y,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${x}`);o(y,x)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!eee(a);)for(let m of c){let g=s[m.name];if(g.name in a){let y=a[g.name];delete a[g.name];for(let A of y)i(g,A)}}let d=[],h=[],p=t.inputLayers;for(let m of p){let g=m[0],y=m[1],A=m[2];Ds(g in s);let b=s[g].inboundNodes[y].outputTensors;d.push(b[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ds(g in s);let b=s[g].inboundNodes[y].outputTensors;h.push(b[A])}return new e({inputs:d,outputs:h,name:u})}get stateful(){if(this._stateful)throw new q("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(){Z(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function Ite(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>null);if(r===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!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let s=[];return t.forEach(a=>{a in e?s.push(e[a]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function US(e,t){return Ite(e,t,"classWeight")}async function HS(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=Z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let i=1;return e.argMax(i)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await s.data());je(s);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),_n(o,"float32")}else return null}function Ste(e,t){return K(e,t)}var Tte=32;function GS(e,t){let n,r,s=t;n=s.xs,r=s.ys,k.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=jS("input",e.inputNames,n),o=jS("output",e.outputNames,r),i=a[0].shape[0];k.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)k.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)k.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function jS(e,t,n){if(n instanceof Ct)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 r=[];for(let s of t){if(n[s]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}function Nte(e){if(e.length===3)throw new Ge("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Cte(e,t,n){let r=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(!r||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 s=n.validationData!=null,a,o;if(s)if(qS(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=Nte(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;s?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=_S(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:h,history:p}=RS(c,d,n.epochs,null,null,Ete(t,n),null,s,u);h.setModel(e),e.history=p,await h.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await h.onEpochBegin(f);let y=0,A=0;for(r||(m=await t.iterator());r?y<n.batchesPerEpoch:!0;){let x=await m.next();if(r&&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:b,ys:v}=GS(e,x.value),w={};w.batch=A,w.size=b[0].shape[0],await h.onBatchBegin(A,w);let S=[];if(n.classWeight!=null){let F=US(n.classWeight,e.outputNames);for(let $=0;$<F.length;++$)S.push(await HS(v[$],null,F[$]))}let I=b.concat(v).concat(S),E=i(I);je(I);for(let F=0;F<l.length;++F){let $=l[F],_=E[F];w[$]=_,Sn(_)}await h.onBatchEnd(A,w),TS(w),A++,y++}if(r?y>=n.batchesPerEpoch:x.done){if(s){let b;qS(n.validationData)?b=Dt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=Dt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?Tte:n.validationBatchSize,verbose:0}));for(let v=0;v<e.metricsNames.length;++v)g[`val_${e.metricsNames[v]}`]=b[v]}break}if(e.stopTraining_)break}if(await h.onEpochEnd(f,g),f++,e.stopTraining_)break}return await h.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function Ete(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function qS(e){return typeof e.iterator=="function"}function $te(e){return typeof e.next=="function"}async function _te(e,t,n){n=n||{};let r=n.batches!=null,s=e.testFunction,a=[];if(n.verbose>0)throw new Ge("Verbose mode is not implemented yet.");k.assert(!r||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=$te(t)?t:await t.iterator(),i=0,l=0;for(;r?l<n.batches:!0;){let u=await o.next();if(a=Z(()=>{if(u.value){let{xs:c,ys:d}=GS(e,u.value),h=c.concat(d),p=Z(()=>s(h));if(je(h),l===0)for(let m=0;m<p.length;++m)a.push(Fe(0));let f=h[0].shape[0];for(let m=0;m<p.length;++m){let g=p[m],y=a[m];a[m]=Z(()=>pe(a[m],K(f,g))),l>0&&je(y)}je(p),i+=f,++l}return a}),u.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let u=0;u<a.length;++u){let c=a[u];a[u]=Re(a[u],i),je(c)}return Jn(a)}function rx(e){k.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Gd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(r=>li(r,t,n-t)):li(e,t,n-t)}function sx(e,t){return Z(()=>e==null?null:Array.isArray(e)?e.map(n=>sx(n,t)):gS(e,t.dtype==="int32"?t:t.toInt()))}function ax(e,t){let n=[],r=0,s=null;for(;r<e;)s=r+t,s>=e&&(s=e),n.push([r,s]),r=s;return n}async function Rte(e,t,n,r,s,a,o,i,l,u,c,d,h,p,f){s==null&&(s=32),a==null&&(a=1),c==null&&(c=!0),h==null&&(h=0);let m=!1;if(l!=null&&u!=null&&(m=!0),f!=null&&(m=!0,p==null))throw new q("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,s,p,"steps_per_epoch"),y;g!=null&&(y=ds(0,g)),o==null&&(o=1);let{callbackList:A,history:x}=RS(i,o,a,h,g,p,s,m,d);A.setModel(e),e.history=x,await A.onTrainBegin(),e.stopTraining_=!1;for(let b=h;b<a;++b){await A.onEpochBegin(b);let v={};if(p!=null)throw new Ge("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new Ge("batch shuffling is not implemneted yet");c&&k.shuffle(y);let w=_n(y),S=ax(g,s);for(let I=0;I<S.length;++I){let E={};if(await A.onBatchBegin(I,E),Z(()=>{let F=S[I][0],$=S[I][1],_=li(w,F,$-F);E.batch=I,E.size=$-F;let N=sx(n,_),P=t(N);for(let B=0;B<r.length;++B){let j=r[B],X=P[B];E[j]=X,Sn(X)}if(I===S.length-1&&m){let B=e.testLoop(l,u,s);for(let j=0;j<r.length;++j){let X=r[j],Y=B[j];Sn(Y),v["val_"+X]=Y}}}),await A.onBatchEnd(I,E),TS(E),e.stopTraining_)break}w.dispose()}if(await A.onEpochEnd(b,v),e.stopTraining_)break}return await A.onTrainEnd(),await e.history.syncData(),e.history}async function Dte(e,t,n,r={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let s,a,o,i,l,u,c;try{let d=r.batchSize==null?32:r.batchSize;rx(d);let h=!1,p=await e.standardizeUserData(t,n,r.sampleWeight,r.classWeight,h,d);s=p[0],a=p[1],c=p[2];let f=!1,m;if(r.validationData!=null&&r.validationData.length>0){if(f=!0,r.validationData.length===2)o=r.validationData[0],i=r.validationData[1];else throw r.validationData.length===3?new Ge("validationData including sample weights is not supported yet."):new q(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);let S=!0,I=await e.standardizeUserData(o,i,null,null,S,d);l=I[0],u=I[1],m=l.concat(u)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){f=!0;let S=Math.floor(s[0].shape[0]*(1-r.validationSplit)),I=s[0].shape[0];l=Gd(s,S,I),s=Gd(s,0,S),u=Gd(a,S,I),a=Gd(a,0,S),m=l.concat(u)}else r.validationSteps!=null&&(f=!0);let g=s.concat(a).concat(c);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),A=e.getDedupedMetricsNames(),x,b;f?(e.makeTestFunction(),x=e.testFunction,b=A.slice().concat(A.map(S=>"val_"+S))):(x=null,m=[],b=A.slice());let v=_S(r.callbacks,r.yieldEvery);return await Rte(e,y,g,A,d,r.epochs,r.verbose,v,x,m,r.shuffle,b,r.initialEpoch,null,null)}finally{e.isTraining=!1,di(s,t),di(a,n),di(l,o),di(u,i),c!=null&&je(c)}}function KS(e){let t=[];e instanceof Ct&&(e=[e]);for(let n=0;n<e.length;++n){let r=e[n];if(r.rank===1)t.push(Ld(r,1));else{if(r.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(r)}}return t}function di(e,t){if(e==null)return;let n=[];if(t instanceof Ct)n.push(t.id);else if(Array.isArray(t))t.forEach(s=>n.push(s.id));else if(t!=null)for(let s in t){let a=t[s];n.push(a.id)}let r=[];if(e instanceof Ct)n.indexOf(e.id)===-1&&r.push(e);else if(Array.isArray(e))e.forEach(s=>{n.indexOf(s.id)===-1&&r.push(s)});else if(e!=null)for(let s in e){let a=e[s];n.indexOf(a.id)===-1&&r.push(a)}r.forEach(s=>{s.isDisposed||s.dispose()})}function Fte(e){return e instanceof Ct}function ox(e){return Array.isArray(e)}function XS(e){return!Fte(e)&&!ox(e)}function ZS(e,t,n,r=!0,s=""){if(t==null||t.length===0){if(e!=null){let o=!1;if(ox(e)&&e.length>0)o=!0;else if(XS(e)){for(let i in e)if(e.hasOwnProperty(i)){o=!0;break}}else o=!0;if(o)throw new q(`Error when checking model ${s} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(o=>null);let a;if(XS(e)){e=e,a=[];for(let o of t){if(e[o]==null)throw new q(`No data provided for "${o}". Need data for each key in: ${t}`);a.push(e[o])}}else if(ox(e)){if(e=e,e.length!==t.length)throw new q(`Error when checking model ${s}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);a=e}else{if(e=e,t.length>1)throw new q(`The model ${s} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);a=[e]}if(a=KS(a),n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new q(`Error when checking ${s}: expected ${t[o]} to have ${n[o].length} dimension(s). but got array with shape ${i.shape}`);for(let l=0;l<n[o].length;++l){if(l===0&&!r)continue;let u=i.shape[l],c=n[o][l];if(c!=null&&c>=0&&u!==c)throw new q(`Error when checking ${s}: expected ${t[o]} to have shape [${n[o]}], but got array with shape [${i.shape}].`)}}return a}function Mte(e,t,n){let r=Ga(e.map(a=>a.shape[0]));r.sort();let s=Ga(t.map(a=>a.shape[0]));if(s.sort(),r.length>1)throw new q(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(a=>a.shape))}`);if(s.length>1)throw new q(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(a=>a.shape))}`);if(r.length>0&&s.length>0&&!k.arraysEqual(r,s))throw new q(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${s[0]} target sample(s).`)}function Ote(e,t,n){let r=[ui,lm,Vd];for(let s=0;s<e.length;++s){let a=e[s],o=t[s],i=n[s];if(o!=null){if(o===Vd&&a.shape[a.shape.length-1]===1)throw new q(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(r.indexOf(o)!==-1){let l=a.shape.slice(1),u=i.slice(1);for(let c=0;c<l.length;++c){let d=l[c],h=u[c];if(h!=null&&d!==h)throw new q(`A target Tensor with shape ${a.shape} was passed for an output of shape ${i}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function YS(e,t,n,r=!0,s=""){let a;if(Array.isArray(e)){if(e.length!==t.length)throw new q(`Error when checking model ${s}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);a=e}else{if(t.length>1)throw new q(`The model expects ${t.length} ${s} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);a=[e]}if(n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new q(`Error when checking ${s}: expected ${t[o]} to have ${n[o].length} dimension(s), but got array with shape ${JSON.stringify(i.shape)}`);for(let l=0;l<n[o].length;++l){if(l===0&&!r)continue;let u=i.shape[l],c=n[o][l];if(c!=null&&c!==u)throw new q(`Error when checking ${s}: expected ${t[o]} to have shape ${JSON.stringify(n[o])} but got array with shape ${JSON.stringify(i.shape)}.`)}}}function Pte(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>[]);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: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let s of t){let a=n.hasOwnProperty(s)?n[s]:[];Array.isArray(a)||(a=[a]),r.push(a)}return r}}var zte="layers-model",pa=class extends Os{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new q("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");mte(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=fte(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof Ha))throw new q("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new q(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);for(let a of this.outputNames)e.loss[a]==null&&console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`),t.push(X1(e.loss[a]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new q(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(o=>X1(o))}else{let a=X1(e.loss);this.outputs.forEach(o=>{t.push(a)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let a=0;a<this.outputs.length;++a){let o=this.internalOutputShapes[a],i=this.outputNames[a];this.feedOutputNames.push(i),this.feedOutputShapes.push(o),this.feedLossFns.push(this.lossFunctions[a])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],ii("loss",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=this.lossFunctions[a];this.outputs.length>1&&(this.metricsTensors.push([o,a]),this.metricsNames.push(this.outputNames[a]+"_loss"))}});let r=Pte(e.metrics,this.outputNames),s=(a,o,i)=>{this.outputNames.length>1&&(o=this.outputNames[a]+"_"+o),this.metricsNames.push(o),this.metricsTensors.push([i,a])};ii("metric",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=r[a];(l=>{let u="",c,d,h;for(let p of l){if(typeof p=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(p)!==-1){let m=this.internalOutputShapes[a];m[m.length-1]===1||this.lossFunctions[a]===lm?["accuracy","acc"].indexOf(p)!==-1?d=Z1:["crossentropy","ce"].indexOf(p)!==-1&&(d=MS):this.lossFunctions[a]===im?["accuracy","acc"].indexOf(p)!==-1?d=OS:["crossentropy","ce"].indexOf(p)!==-1&&(d=PS):["accuracy","acc"].indexOf(p)!==-1?d=Y1:["crossentropy","ce"].indexOf(p)!==-1&&(d=J1);let g;["accuracy","acc"].indexOf(p)!==-1?g="acc":["crossentropy","ce"].indexOf(p)!==-1&&(g="ce"),h=d,c=u+g}else h=pte(p),c=u+dm(p);let f;ii(c,()=>{f=h}),s(a,c,f)}})(o)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let r=n.batchSize==null?32:n.batchSize;rx(r);let s=!0,a=this.standardizeUserDataXY(e,t,s,r);try{let o=a[0].concat(a[1]);this.makeTestFunction();let i=this.testFunction,l=this.testLoop(i,o,r,n.verbose,n.steps);return Jn(l)}finally{di(a[0],e),di(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),_te(this,e,t)}checkNumSamples(e,t,n,r="steps"){let s;if(n!=null){if(s=null,t!=null)throw new q(`If ${r} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?s=e[0].shape[0]:s=e.shape[0];else throw new q(`Either the input data should have a defined shape, or ${r} shoud be specified.`);return s}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new q("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],s=this.retrieveSymbolicTensors(r),a=new ci;if(e instanceof Ct&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new q(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)a.add(this.inputs[i],e[i])}else for(let i of this.inputs){let l=e[i.name];if(l==null)throw new q(`No value is provided for the model's input ${i.name}`);a.add(i,l)}let o=Hd(s,a);return n?o:o[0]}retrieveSymbolicTensors(e){let t=si(null,e.length),n=e.length;for(let r of this.layers){let s=Array.isArray(r.output)?r.output:[r.output],a=s.map(o=>o.name);for(let o=0;o<e.length;++o){let i=a.indexOf(e[o]);if(i!==-1&&(t[o]=s[i],n--),n===0)break}if(n===0)break}if(n>0){let r=[];throw t.forEach((s,a)=>{s==null&&r.push(e[a])}),new q(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(r)}`)}return t}predictLoop(e,t=32,n=!1){return Z(()=>{let r=this.checkNumSamples(e);if(n)throw new Ge("Verbose predictLoop() is not implemented yet.");let s=ax(r,t),a=this.outputs.map(o=>[]);for(let o=0;o<s.length;++o)Z(()=>{let l=s[o][0],u=s[o][1],c=Gd(e,l,u),d=[];if(Array.isArray(c))for(let p=0;p<c.length;++p)d.push({key:this.inputs[p],value:c[p]});else d.push({key:this.inputs[0],value:c});let h=new ci(d);return Hd(this.outputs,h)}).forEach((l,u)=>a[u].push(l));return Jn(a.map(o=>en(o,0)))})}predict(e,t={}){let n=KS(e);YS(n,this.inputNames,this.feedInputShapes,!1);try{let r=t.batchSize==null?32:t.batchSize;return rx(r),this.predictLoop(n,r)}finally{di(n,e)}}predictOnBatch(e){YS(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,r){if(this.optimizer_==null)throw new cs("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let s=[];for(let a=0;a<this.feedOutputShapes.length;++a){let o=this.feedOutputShapes[a];this.feedLossFns[a]===im?s.push(o.slice(0,o.length-1).concat([1])):s.push(o)}if(e=ZS(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=ZS(t,this.feedOutputNames,s,!1,"target"),Mte(e,t,null),Ote(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new q(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,s=!0,a){let[o,i]=this.standardizeUserDataXY(e,t,s,a);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let u=US(r,this.outputNames);l=[];for(let c=0;c<u.length;++c)l.push(await HS(i[c],null,u[c]))}return[o,i,l]}testLoop(e,t,n,r=0,s){return Z(()=>{let a=this.checkNumSamples(t,n,s,"steps"),o=[];if(r>0)throw new Ge("Verbose mode is not implemented yet.");if(s!=null)throw new Ge("steps mode in testLoop() is not implemented yet");{let i=ax(a,n),l=_n(ds(0,a));for(let u=0;u<i.length;++u){let c=i[u][0],d=i[u][1],h=li(l,c,d-c),p=sx(t,h),f=e(p);if(u===0)for(let m=0;m<f.length;++m)o.push(Fe(0));for(let m=0;m<f.length;++m){let g=f[m];o[m]=pe(o[m],K(d-c,g))}}for(let u=0;u<o.length;++u)o[u]=Re(o[u],a)}return o})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],s=r;sS(e,r)>1&&(s+=`_${sS(e.slice(0,n),r)}`),t.push(s)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let d=new ci(c),h=Hd(this.outputs,d,{training:!0}),p;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](r[f],h[f]);s[f]!=null&&(g=Ste(g,s[f]));let y=Xt(g);t.push(y),f===0?p=g:p=pe(p,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],y=this.metricsTensors[f][1];m=Xt(g(r[y],h[y]))}Sn(m),a.push(m)}return p=Xt(p),this.calculateLosses().forEach(f=>{p=pe(p,f)}),p},i=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>Z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:r[l]});let o=new ci(a),i=Hd(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=Xt(u(s[l],i[l]));l===0?n=c:n=pe(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],d=Xt(u(s[c],i[c]));t.push(d)}return t})}async fit(e,t,n={}){return Dte(this,e,t,n)}async fitDataset(e,t){return Cte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],s=n[1],o=this.makeTrainFunction()(r.concat(s)),i=[];for(let l of o){let u=await l.data();i.push(u[0])}return je(o),Jn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,s=this.getWeights(n);for(let a=0;a<r.length;++a)n&&!r[a].trainable||t.push({name:r[a].originalName,tensor:s[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=$A().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-$A().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ha(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=>ha(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ha(n[r]);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[ha(dm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ha(dm(e)));{let e={};for(let t in this.metrics)e[t]=ha(dm(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=Ud(e.optimizer_config),n=fs(t),r;if(typeof e.loss=="string")r=ai(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(a=>ai(a));else if(e.loss!=null){r={};for(let a in e.loss)r[a]=ai(e.loss[a])}let s;if(Array.isArray(e.metrics))s=e.metrics.map(a=>ai(a));else if(e.metrics!=null){s={};for(let a in e.metrics)s[a]=ai(e.metrics[a])}this.compile({loss:r,metrics:s,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=cr.getSaveHandlers(e);if(l.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new q(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await cr.encodeWeights(this.getNamedWeights(t)),r=!1,s=null,o={modelTopology:this.toJSON(s,r),format:zte,generatedBy:`TensorFlow.js tfjs-layers v${tx}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:u,specs:c}=await cr.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...c),n.data=cr.concatenateArrayBuffers([n.data,u])}if(this.userDefinedMetadata!=null){let l=!0;LS(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){LS(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pa.className="Model";ce.registerClass(pa);var JS=class extends pa{};JS.className="Functional";ce.registerClass(JS);async function Lte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Ud(n),s=fs(r,t);if(e.weightsManifest!=null){let a=await cr.loadWeights(e.weightsManifest,e.pathPrefix,s.weights.map(i=>i.originalName)),o={};for(let i of s.weights)o[i.originalName]=a[i.originalName];s.loadWeights(o),je(a)}return s}async function Bte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=cr.getLoadHandlers(e,t);if(n.length===0)n.push(cr.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Wte(e,void 0,t)}async function Wte(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),s=r.modelTopology;s.model_config!=null&&(s=s.model_config);let a=n.strict==null?!0:n.strict,o=r.weightData!=null&&r.weightSpecs!=null&&a,i=fs(Ud(s),t,o),l=r.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),r.userDefinedMetadata!=null&&i.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=Vte(r.weightData,r.weightSpecs);i.loadWeights(u,a),i.optimizer!=null&&c.length>0&&await i.optimizer.setWeights(c),je(u),je(c.map(d=>d.tensor))}return i}function Vte(e,t){let n=cr.decodeWeights(e,t),r={},s=[];return t.forEach(a=>{a.group==="optimizer"?s.push({name:a.name,tensor:n[a.name]}):r[a.name]=n[a.name]}),{modelWeights:r,optimizerWeights:s}}var ix=class extends pa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:tm("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof ix||e instanceof pa,n;if(t){if(n=e,n.outputs.length!==1)throw new q("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 q("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 q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=SS({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new q(`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 q("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=IS(this.outputs[0])}this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:si(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(r=>r.shape),outputShapes:this.outputs[0].shape})}else{let r=e.apply(this.outputs[0]);if(Array.isArray(r))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=[r],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(At(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 pa({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 cs("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 cs("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 cs("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 cs("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={},r=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");s=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."),s=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof ix))throw new Ge(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of s){let u=fs(i,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new q("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 q("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}}},pm=ix;pm.className="Sequential";ce.registerClass(pm);function Ute(e){return new pa(e)}function Hte(e){return new pm(e)}function Gte(e,t){return t==null&&(t={}),Bte(e,t)}function QS(e){return SS(e)}function jte(e,t){q1.registerCallbackConstructor(e,t)}var er=class extends ce.Serializable{getConfig(){return{}}},e8=class extends er{apply(e,t=1){return Aee(e,t)}};e8.className="elu";ce.registerClass(e8);var t8=class extends er{apply(e){return t1(e)}};t8.className="selu";ce.registerClass(t8);var n8=class extends er{apply(e){return ua(e)}};n8.className="relu";ce.registerClass(n8);var r8=class extends er{apply(e){return Z(()=>_d(6,ua(e)))}};r8.className="relu6";ce.registerClass(r8);var s8=class extends er{apply(e){return e}};s8.className="linear";ce.registerClass(s8);var a8=class extends er{apply(e){return Rs(e)}};a8.className="sigmoid";ce.registerClass(a8);var o8=class extends er{apply(e){return bee(e)}};o8.className="hardSigmoid";ce.registerClass(o8);var i8=class extends er{apply(e){return Zl(e)}};i8.className="softplus";ce.registerClass(i8);var l8=class extends er{apply(e){return xee(e)}};l8.className="softsign";ce.registerClass(l8);var u8=class extends er{apply(e){return Kl(e)}};u8.className="tanh";ce.registerClass(u8);var lx=class extends er{apply(e,t=-1){return Of(e,t)}};lx.className="softmax";ce.registerClass(lx);var c8=class extends er{apply(e,t=-1){return HA(e,t)}};c8.className="logSoftmax";ce.registerClass(c8);var d8=class extends er{apply(e,t=1){return Z(()=>Rs(e.mul(t)).mul(e))}};d8.className="swish";ce.registerClass(d8);var h8=class extends er{apply(e){return Z(()=>K(e,Kl(Zl(e))))}};h8.className="mish";ce.registerClass(h8);function Xa(e){return e.getClassName()}function ux(e,t={}){return Md(e,ce.SerializationMap.getMap().classNameMap,t,"activation")}function Za(e){if(e==null){let t={};return t.className="linear",t.config={},ux(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},ux(t)}else return e instanceof er?e:ux(e)}function cx(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 p8=class extends ce.Serializable{},jd=class extends p8{constructor(e){super();cx(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 Z(()=>{let t=un([1]);return this.hasL1&&(t=pe(t,_e(K(this.l1,yn(e))))),this.hasL2&&(t=pe(t,_e(K(this.l2,Bd(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};jd.className="L1L2";ce.registerClass(jd);function qte(e){return cx(e),new jd({l1:e!=null?e.l1:null,l2:0})}function Kte(e){return cx(e),new jd({l2:e!=null?e.l2:null,l1:0})}var f8={l1l2:"L1L2"};function kt(e){return S1(e)}function m8(e,t={}){return Md(e,ce.SerializationMap.getMap().classNameMap,t,"regularizer")}function Lt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in f8?f8[e]:e,config:{}};return m8(n)}else return e instanceof p8?e:m8(e)}var dx=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ke(e);let n=ua(e);return this.maxValue!=null&&(n=dr(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};dx.className="ReLU";ce.registerClass(dx);var hx=class extends st{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=Ke(e);return Cf(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};hx.className="LeakyReLU";ce.registerClass(hx);var px=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=zt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Lt(e.alphaRegularizer),this.alphaConstraint=hn(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 q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=At(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new tn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ke(e),Df(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Ht(this.alphaInitializer),alphaRegularizer:kt(this.alphaRegularizer),alphaConstraint:dn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};px.className="PReLU";ce.registerClass(px);var fx=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ge(`Non-default alpha value (${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=Ke(e);return Nd(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};fx.className="ELU";ce.registerClass(fx);var mx=class extends st{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=Ke(e);return n.mul(zd(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};mx.className="ThresholdedReLU";ce.registerClass(mx);var gx=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new lx().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ke(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}};gx.className="Softmax";ce.registerClass(gx);function iu(e,t,n){if(typeof e=="number")return si(e,t);if(e.length!==t)throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let s=e[r];if(!fee(s))throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${s}`)}return e}function ms(e,t,n,r,s=1){if(e==null)return e;let a=t+(t-1)*(s-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+r-1)/r)}function Ps(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+qa([n-t,0]);else if(r==="same")e=e*t;else throw new q(`Unsupport padding mode: ${r}.`);return e}function yx(e,t){return Z(()=>(Yt(t),t==="channelsFirst"?pt(e,[0,2,3,1]):e))}function g8(e,t){return Z(()=>(Yt(t),t==="channelsFirst"?pt(e,[0,2,3,4,1]):e))}function Xte(e,t,n,r=1,s="valid",a,o=1){return Z(()=>{if(a==null&&(a=us()),Yt(a),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=pt(e,[0,2,1])),s==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=OA(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=hs(i,n)),i})}function y8(e,t,n,r=[1,1],s="valid",a,o,i=null){return Z(()=>{if(a==null&&(a=us()),Yt(a),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=yx(e,a);if(s==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ti.conv2d({x:l,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=pt(l,[0,3,1,2])),l})}function Zte(e,t,n,r=[1,1,1],s="valid",a,o){return Z(()=>{if(a==null&&(a=us()),Yt(a),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=g8(e,a);if(s==="causal")throw new Ge("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=sI(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=hs(i,n)),a==="channelsFirst"&&(i=pt(i,[0,4,1,2,3])),i})}var Ax=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Ax.verifyArgs(t),this.rank=e,An(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ge(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=iu(t.kernelSize,e,"kernelSize"),this.strides=iu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Or(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Yt(this.dataFormat),this.activation=Za(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=zt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=hn(t.biasConstraint),this.biasRegularizer=Lt(t.biasRegularizer),this.activityRegularizer=Lt(t.activityRegularizer),this.dilationRate=iu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ds("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!N1(e.kernelSize,"number",1,3))throw new q(`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:Xa(this.activation),useBias:this.useBias,biasInitializer:Ht(this.biasInitializer),biasRegularizer:kt(this.biasRegularizer),activityRegularizer:kt(this.activityRegularizer),biasConstraint:dn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},qd=class extends Ax{constructor(e,t){super(e,t);this.kernel=null,qd.verifyArgs(t),this.filters=t.filters,An(this.filters,"filters"),this.kernelInitializer=zt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=hn(t.kernelConstraint),this.kernelRegularizer=Lt(t.kernelRegularizer)}build(e){e=At(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,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 Z(()=>{e=Ke(e);let n,r=this.bias==null?null:this.bias.read(),s=oS(this.activation.getClassName());if(s!=null&&this.rank===2)n=y8(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=Xte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=y8(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Zte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ge("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=At(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=ms(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Ht(this.kernelInitializer),kernelRegularizer:kt(this.kernelRegularizer),kernelConstraint:dn(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 q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},A8=class extends qd{constructor(e){super(2,e);A8.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!N1(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},fm=A8;fm.className="Conv2D";ce.registerClass(fm);var x8=class extends qd{constructor(e){super(3,e);x8.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 q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},mm=x8;mm.className="Conv3D";ce.registerClass(mm);var xx=class extends fm{constructor(e){super(e);if(this.inputSpec=[new tn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new tn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Z(()=>{let n=Ke(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],l=r[o],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],h=this.strides[1],p=Ps(i,d,u,this.padding),f=Ps(l,h,c,this.padding),m=[s,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=pt(n,[0,2,3,1]));let g=zA(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=pt(g,[0,3,1,2])),this.bias!=null&&(g=hs(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=At(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Ps(t[r],i,a,this.padding),t[s]=Ps(t[s],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};xx.className="Conv2DTranspose";ce.registerClass(xx);var bx=class extends mm{constructor(e){super(e);if(this.inputSpec=[new tn({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new tn({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return Z(()=>{let n=Ke(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=r[i],u=r[a],c=r[o],d=this.kernelSize[0],h=this.kernelSize[1],p=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Ps(l,f,d,this.padding),A=Ps(u,m,h,this.padding),x=Ps(c,g,p,this.padding),b=[s,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=pt(n,[0,2,3,4,1]));let v=Jj(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=pt(v,[0,4,1,2,3])),this.bias!==null&&(v=hs(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=At(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[r]=Ps(t[r],u,o,this.padding),t[s]=Ps(t[s],c,i,this.padding),t[a]=Ps(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};bx.className="Conv3DTranspose";ce.registerClass(bx);var b8=class extends qd{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 q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("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 q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=zt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Lt(t.depthwiseRegularizer),this.depthwiseConstraint=hn(t.depthwiseConstraint),this.pointwiseInitializer=zt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Lt(t.pointwiseRegularizer),this.pointwiseConstraint=hn(t.pointwiseConstraint)}build(e){if(e=At(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let o=0;o<this.rank;++o)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new tn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Z(()=>{e=Ke(e);let n;if(this.rank===1)throw new Ge("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=pt(e,[0,2,3,1])),n=wI(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=hs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=pt(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=Ht(this.depthwiseInitializer),e.pointwiseInitializer=Ht(this.pointwiseInitializer),e.depthwiseRegularizer=kt(this.depthwiseRegularizer),e.pointwiseRegularizer=kt(this.pointwiseRegularizer),e.depthwiseConstraint=dn(this.depthwiseConstraint),e.pointwiseConstraint=dn(this.pointwiseConstraint),e}};b8.className="SeparableConv";var vx=class extends b8{constructor(e){super(2,e)}};vx.className="SeparableConv2D";ce.registerClass(vx);var v8=class extends qd{constructor(e){super(1,e);v8.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"&&!N1(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},wx=v8;wx.className="Conv1D";ce.registerClass(wx);var kx=class extends st{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 Z(()=>{if(e=Ke(e),this.dataFormat==="channelsLast"){let n=Gf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gf(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}};kx.className="Cropping2D";ce.registerClass(kx);var Ix=class extends st{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,Yt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,dee(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 Z(()=>{let n=Ke(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=pt(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a]);return pt(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ix.className="UpSampling2D";ce.registerClass(Ix);function Yte(e,t,n=[1,1],r="valid",s,a){return Z(()=>{s==null&&(s=us()),Yt(s);let o=yx(e,s);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Td(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=pt(o,[0,3,1,2])),o})}var Sx=class extends Ax{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=zt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=hn(e.depthwiseConstraint),this.depthwiseRegularizer=Lt(e.depthwiseRegularizer)}build(e){if(e=At(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,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 Z(()=>{e=Ke(e);let n=Yte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=ms(t,this.kernelSize[0],this.padding,this.strides[0]),a=ms(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ht(this.depthwiseInitializer),e.depthwiseRegularizer=kt(this.depthwiseRegularizer),e.depthwiseConstraint=dn(this.depthwiseRegularizer),e}};Sx.className="DepthwiseConv2D";ce.registerClass(Sx);function w8(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function k8(e,t,n,r=!1,s,a,o=!1,i=!1){return Z(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ds(2,l));if(t=pt(t,u),a!=null)throw new Ge("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=s.asType("bool").asType("float32"),s.rank===l-1&&(s=$r(s,-1)),s=pt(s,u)),r&&(t=Fr(t,0),s!=null&&(s=Fr(s,0)));let c=[],d,h=n,p=t.shape[0],f=ls(t),m;s!=null&&(m=ls(s));for(let y=0;y<p;++y){let A=f[y],x=Z(()=>e(A,h));if(s==null)d=x[0],h=x[1];else{let b=Z(()=>{let v=m[y],w=Dr(v).sub(v),S=x[0].mul(v).add(h[0].mul(w)),I=h.map((E,F)=>x[1][F].mul(v).add(E.mul(w)));return{output:S,newStates:I}});d=b.output,h=b.newStates}i&&c.push(d)}let g;return i&&(g=Mr(c,1)),[d,g,h]})}var I8=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Am({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("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 tn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ds(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){H1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[r].concat(s)}else return r}computeMask(e,t){return Z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(s=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Ge("Constants support is not implemented in RNN yet.");H1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new tn({shape:[n,null,...r]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ge("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new tn({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){Z(()=>{if(!this.stateful)throw new da("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new q("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(r=>un([n,r])):this.states_=[un([n,this.cell.stateSize])];else if(e==null)je(this.states_),this.keptStates!=null&&(je(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>un([n,r])):this.states_[0]=un([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):je(this.states_);for(let r=0;r<this.states_.length;++r){let s=e[r],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,o=[n,a];if(!k.arraysEqual(s.shape,o))throw new q(`State ${r} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${s.shape}`);this.states_[r]=s}}this.states_=this.states_.map(r=>Sn(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=w8(e,n,r,this.numConstants);e=s.inputs,n=s.initialState,r=s.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new tn({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(t.constants=r,a=a.concat(r),this.numConstants=r.length),a[0]instanceof ps){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return Z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;e=Ke(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new q(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=k8((p,f)=>{let m=this.cell.call([p].concat(f),o);return[m[0],m.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let h=this.returnSequences?c:u;return this.returnState?[h].concat(d):h})}getInitialState(e){return Z(()=>{let t=un(e.shape);return t=_e(t,[1,2]),t=Ld(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?M1(t,[1,n]):t):this.cell.stateSize>1?[M1(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()===I8.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let r=t.cell,s=fs(r,n);return new e(Object.assign(t,{cell:s}))}},fa=I8;fa.className="RNN";ce.registerClass(fa);var Kd=class extends st{},gm=class extends Kd{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,An(this.units,"units"),this.activation=Za(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=zt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=zt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=zt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Lt(e.kernelRegularizer),this.recurrentRegularizer=Lt(e.recurrentRegularizer),this.biasRegularizer=Lt(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=ru([1,qa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,qa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 Z(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ya({ones:()=>Dr(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ya({ones:()=>Dr(n),rate:this.recurrentDropout,training:r}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=Fs(K(e,a),this.kernel.read()):s=Fs(e,this.kernel.read()),this.bias!=null&&(s=hs(s,this.bias.read())),o!=null&&(n=K(n,o));let i=pe(s,Fs(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Xa(this.activation),useBias:this.useBias,kernelInitializer:Ht(this.kernelInitializer),recurrentInitializer:Ht(this.recurrentInitializer),biasInitializer:Ht(this.biasInitializer),kernelRegularizer:kt(this.kernelRegularizer),recurrentRegularizer:kt(this.recurrentRegularizer),biasRegularizer:kt(this.biasRegularizer),activityRegularizer:kt(this.activityRegularizer),kernelConstraint:dn(this.kernelConstraint),recurrentConstraint:dn(this.recurrentConstraint),biasConstraint:dn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};gm.className="SimpleRNNCell";ce.registerClass(gm);var Tx=class extends fa{constructor(e){e.cell=new gm(e),super(e)}call(e,t){return Z(()=>{this.cell.dropoutMask!=null&&(je(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(je(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};Tx.className="SimpleRNN";ce.registerClass(Tx);var ym=class extends Kd{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 q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,An(this.units,"units"),this.activation=Za(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Za(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=zt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=zt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=zt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Lt(e.kernelRegularizer),this.recurrentRegularizer=Lt(e.recurrentRegularizer),this.biasRegularizer=Lt(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=ru([1,qa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,qa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 Z(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ya({ones:()=>Dr(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ya({ones:()=>Dr(r),rate:this.recurrentDropout,training:n,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=K(e,s[0]));let u=Fs(e,this.kernel.read());this.useBias&&(u=hs(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=K(r,a[0]));let c=this.recurrentKernel.read(),[d,h]=hr(c,[2*this.units,this.units],c.rank-1),p=Fs(r,d),[f,m,g]=hr(u,3,u.rank-1),[y,A]=hr(p,2,p.rank-1);o=this.recurrentActivation.apply(pe(f,y)),i=this.recurrentActivation.apply(pe(m,A));let x=Fs(K(i,r),h);l=this.activation.apply(pe(g,x));let b=pe(K(o,r),K(pe(1,Kt(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Xa(this.activation),recurrentActivation:Xa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ht(this.kernelInitializer),recurrentInitializer:Ht(this.recurrentInitializer),biasInitializer:Ht(this.biasInitializer),kernelRegularizer:kt(this.kernelRegularizer),recurrentRegularizer:kt(this.recurrentRegularizer),biasRegularizer:kt(this.biasRegularizer),activityRegularizer:kt(this.activityRegularizer),kernelConstraint:dn(this.kernelConstraint),recurrentConstraint:dn(this.recurrentConstraint),biasConstraint:dn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};ym.className="GRUCell";ce.registerClass(ym);var Nx=class extends fa{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 ym(e),super(e)}call(e,t){return Z(()=>{this.cell.dropoutMask!=null&&(je(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(je(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Nx.className="GRU";ce.registerClass(Nx);var Xd=class extends Kd{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,An(this.units,"units"),this.activation=Za(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Za(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=zt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=zt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=zt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Lt(e.kernelRegularizer),this.recurrentRegularizer=Lt(e.recurrentRegularizer),this.biasRegularizer=Lt(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=ru([1,qa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,qa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=At(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 r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Zr{apply(o,i){let l=s.apply([a]),u=new qf().apply([a]),c=s.apply([a*2]);return mS(mS(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return Z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ya({ones:()=>Dr(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ya({ones:()=>Dr(r),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0<this.dropout&&this.dropout<1&&(e=K(e,a[0]));let d=Fs(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=K(r,o[0])),d=pe(d,Fs(r,this.recurrentKernel.read())),this.useBias&&(d=hs(d,this.bias.read()));let[h,p,f,m]=hr(d,4,d.rank-1);i=this.recurrentActivation.apply(h),l=this.recurrentActivation.apply(p),u=pe(K(l,s),K(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=K(c,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Xa(this.activation),recurrentActivation:Xa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ht(this.kernelInitializer),recurrentInitializer:Ht(this.recurrentInitializer),biasInitializer:Ht(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:kt(this.kernelRegularizer),recurrentRegularizer:kt(this.recurrentRegularizer),biasRegularizer:kt(this.biasRegularizer),activityRegularizer:kt(this.activityRegularizer),kernelConstraint:dn(this.kernelConstraint),recurrentConstraint:dn(this.recurrentConstraint),biasConstraint:dn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Xd.className="LSTMCell";ce.registerClass(Xd);var Cx=class extends fa{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 Xd(e),super(e)}call(e,t){return Z(()=>{this.cell.dropoutMask!=null&&(je(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(je(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Cx.className="LSTM";ce.registerClass(Cx);var Am=class extends Kd{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 Z(()=>{e=e;let n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=r[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),s.push(a.slice(1))}n=[];for(let o of s.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){H1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{ii(`RNNCell_${r}`,()=>{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=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(fs(s,n));return new e({cells:r})}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 G1(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}j1(t)}};Am.className="StackedRNNCells";ce.registerClass(Am);function Ya(e){let{ones:t,rate:n,training:r=!1,count:s=1}=e,a=()=>yS(t(),n),o=()=>Wd(a,t,r);return!s||s<=1?Sn(o().clone()):Array(s).fill(void 0).map(o).map(l=>Sn(l.clone()))}var S8=class extends fa{constructor(e){if(e.unroll)throw new Ge("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ge("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new tn({ndim:5})]}call(e,t){return Z(()=>{if(this.cell.dropoutMask!=null&&(je(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(je(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}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 Z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=un(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){Z(()=>{if(!this.stateful)throw new da("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(n[0]==null)throw new q("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(()=>un(s)):this.states_=[un(s)];else if(e==null)je(this.states_),this.keptStates!=null&&(je(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>un(s)):this.states_[0]=un(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):je(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=s;if(!k.arraysEqual(i.shape,l))throw new q(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>Sn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=ms(l,r[0],s,a[0],o[0]),d=ms(u,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};S8.className="ConvRNN2D";var xm=class extends Xd{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super({...e,units:t});this.filters=t,An(this.filters,"filters"),this.kernelSize=iu(n,2,"kernelSize"),this.kernelSize.forEach(i=>An(i,"kernelSize")),this.strides=iu(r||1,2,"strides"),this.strides.forEach(i=>An(i,"strides")),this.padding=s||"valid",Or(this.padding),this.dataFormat=a||"channelsLast",Yt(this.dataFormat),this.dilationRate=iu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>An(i,"dilationRate"))}build(e){var t;e=At(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends Zr{apply(c,d){let h=l.apply([u]),p=la([u]),f=l.apply([u*2]);return F1([h,p,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return Z(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ya({ones:()=>Dr(r),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(ee,oe,se)=>!oe||!oe[se]?ee:K(oe[se],ee),u=l(r,i,0),c=l(r,i,1),d=l(r,i,2),h=l(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ya({ones:()=>Dr(s),rate:this.recurrentDropout,training:n,count:o}));let p=this.recurrentDropoutMask,f=l(s,p,0),m=l(s,p,1),g=l(s,p,2),y=l(s,p,3),A=3,[x,b,v,w]=hr(this.kernel.read(),o,A),[S,I,E,F]=this.useBias?hr(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,b,I,this.padding),d=this.inputConv(d,v,E,this.padding),h=this.inputConv(h,w,F,this.padding);let[$,_,N,P]=hr(this.recurrentKernel.read(),o,A);f=this.recurrentConv(f,$),m=this.recurrentConv(m,_),g=this.recurrentConv(g,N),y=this.recurrentConv(y,P);let B=this.recurrentActivation.apply(pe(u,f)),j=this.recurrentActivation.apply(pe(c,m)),X=pe(K(j,a),K(B,this.activation.apply(pe(d,g)))),Y=K(this.recurrentActivation.apply(pe(h,y)),this.activation.apply(X));return[Y,Y,X]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,r){let s=Ba(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?hs(s,n,this.dataFormat):s}recurrentConv(e,t){return Ba(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};xm.className="ConvLSTM2DCell";ce.registerClass(xm);var Ex=class extends S8{constructor(e){let t=new xm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};Ex.className="ConvLSTM2D";ce.registerClass(Ex);var bm=class extends st{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 r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,s=this.getNoiseShape(n);return Wd(()=>yS(n,this.rate,s,this.seed),()=>n,r)}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()}};bm.className="Dropout";ce.registerClass(bm);var $x=class extends bm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};$x.className="SpatialDropout1D";ce.registerClass($x);var _x=class extends st{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,An(this.units,"units"),this.activation=Za(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=zt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=zt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=hn(e.kernelConstraint),this.biasConstraint=hn(e.biasConstraint),this.kernelRegularizer=Lt(e.kernelRegularizer),this.biasRegularizer=Lt(e.biasRegularizer),this.activityRegularizer=Lt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=At(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=At(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e),r=oS(this.activation.getClassName()),s;return r!=null?s=Fs(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=Fs(n,this.kernel.read()),this.bias!=null&&(s=hs(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Xa(this.activation),useBias:this.useBias,kernelInitializer:Ht(this.kernelInitializer),biasInitializer:Ht(this.biasInitializer),kernelRegularizer:kt(this.kernelRegularizer),biasRegularizer:kt(this.biasRegularizer),activityRegularizer:kt(this.activityRegularizer),kernelConstraint:dn(this.kernelConstraint),biasConstraint:dn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};_x.className="Dense";ce.registerClass(_x);var Rx=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=At(e);for(let t of e.slice(1))if(t==null)throw new q(`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],ja(e,1)]}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s<n.rank;++s)r.push(s);r.push(1),n=n.transpose(r)}return yee(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Rx.className="Flatten";ce.registerClass(Rx);var Dx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=Za(e.activation)}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);return this.activation.apply(n)})}getConfig(){let e={activation:Xa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="Activation";ce.registerClass(Dx);var Fx=class extends st{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 Z(()=>(e=Ke(e),mee(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Fx.className="RepeatVector";ce.registerClass(Fx);var Mx=class extends st{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.",r=t.slice(),s=1,a=null;for(let i=0;i<r.length;++i){let l=r[i];if(this.isUnknown(l))if(a===null)a=i;else throw new q("Can only specifiy one unknown dimension.");else s*=l}let o=ja(e);if(a!==null){if(s===0||o%s!=0)throw new q(n);r[a]=o/s}else if(o!==s)throw new q(n);return r}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 Z(()=>{this.invokeCallHook(e,t);let n=Ke(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="Reshape";ce.registerClass(Mx);var Ox=class extends st{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ds(1,e.dims.length+1);if(!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 tn({ndim:this.dims.length+1})]}computeOutputShape(e){e=At(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return pt(Ke(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ox.className="Permute";ce.registerClass(Ox);var Px=class extends st{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=Ke(e),r=-1;return wf(Yl(n,this.maskValue),r)}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e),r=-1,s=!0,a=wf(Yl(n,this.maskValue),r,s);return n.mul(a.asType(n.dtype))})}};Px.className="Masking";ce.registerClass(Px);var zx=class extends st{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(Dt(e.inputLength))}this.inputDim=e.inputDim,An(this.inputDim,"inputDim"),this.outputDim=e.outputDim,An(this.outputDim,"outputDim"),this.embeddingsInitializer=zt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Lt(e.embeddingsRegularizer),this.activityRegularizer=Lt(e.activityRegularizer),this.embeddingsConstraint=hn(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 Z(()=>this.maskZero?(e=Ke(e),Yl(e,rt(e))):null)}computeOutputShape(e){if(e=At(e),this.inputLength==null)return[...e,this.outputDim];let t=Dt(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let s=t[r],a=e[r+1];if(s!=null&&a!=null&&s!==a)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);return n.dtype!=="int32"&&(n=zd(n,"int32")),gS(this.embeddings.read(),n.as1D()).reshape(At(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ht(this.embeddingsInitializer),embeddingsRegularizer:kt(this.embeddingsRegularizer),activityRegularizer:kt(this.activityRegularizer),embeddingsConstraint:dn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};zx.className="Embedding";ce.registerClass(zx);var hi=class extends st{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ge}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 r=0;r<t.length;++r){let s=e[e.length-t.length+r],a=t[r];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[At(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=Ga(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let r=e.map(s=>s.length);e.indexOf(null)===-1&&Ga(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return Z(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(s=>s.rank);if(r.indexOf(null)===-1){let s=qa(r);for(let a of e){let o=a.rank;for(let i=0;i<s-o;++i)a=Ld(a,1);n.push(a)}return this.mergeFunction(n)}else{let s=!1;for(let i of e){let l=i.rank;if(l==null){let u=i.shape,c=u[0],d=u.slice(1).concat([c]),h=i.reshape([c].concat(ja(u.slice(1))));h=pt(h,[1,0]),h=h.reshape(d),n.push(h),s=!0}else if(l>1){let u=ds(1,l).concat([0]);n.push(pt(i,u)),s=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(s){if(o==null){let i=a.shape,l=i.length,u=i[l-1],c=[u].concat(i.slice(0,i.length-1));a=pt(a.reshape([-1,u]),[1,0]).reshape(c)}else if(o>1){let i=[o-1].concat(ds(0,o-1));a=pt(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ga(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return Z(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:$r(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=is(n,t[r]);return n})}},Lx=class extends hi{constructor(e){super(e)}mergeFunction(e){return Z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=pe(t,e[n]);return t})}};Lx.className="Add";ce.registerClass(Lx);var Bx=class extends hi{constructor(e){super(e)}mergeFunction(e){return Z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=K(t,e[n]);return t})}};Bx.className="Multiply";ce.registerClass(Bx);var Wx=class extends hi{constructor(e){super(e)}mergeFunction(e){return Z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=pe(t,e[n]);return K(1/e.length,t)})}};Wx.className="Average";ce.registerClass(Wx);var Vx=class extends hi{constructor(e){super(e)}mergeFunction(e){return Z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ia(t,e[n]);return t})}};Vx.className="Maximum";ce.registerClass(Vx);var Ux=class extends hi{constructor(e){super(e)}mergeFunction(e){return Z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_d(t,e[n]);return t})}};Ux.className="Minimum";ce.registerClass(Ux);var Hx=class extends hi{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 q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let s=e[r].slice();s.splice(this.axis,1);let a=!1;for(let o of n)if(k.arraysEqual(o,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return Z(()=>F1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let s of t.slice(1)){if(n[r]==null||s[r]==null){n[r]=null;break}n[r]+=s[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return Z(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let r=[];for(let a=0;a<e.length;++a)t[a]==null?r.push(Dr(e[a]).asType("bool")):t[a].rank<e[a].rank?r.push($r(t[a],-1)):r.push(t[a]);let s=en(r,this.axis);return DA(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Hx.className="Concatenate";ce.registerClass(Hx);function Zd(e,t){for(;e<0;)e+=t;return e}function Jte(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Ge("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 Ge("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,s=t.shape.length;n==null&&(n=[r-1,s-2]);let a=n;return Z(()=>{let o;if(r>s){o=r-s;let l=[];for(let u=0;u<o;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(s>r){o=s-r;let l=[];for(let u=0;u<o;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=e.mul(t).sum(a[0]):i=e.transpose([1,0]).mul(t).sum(a[1]);else{let l=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=e.matMul(t,l,u)}if(o>0){let l;r>s?l=r+s-3:l=r-1;let u=[];for(let c=l;c<l+o;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var Gx=class extends hi{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 Ge("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new q(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((s,a)=>Zd(s,e[a].shape.length)):r=[Zd(this.axes,t.shape.length),Zd(this.axes,n.shape.length)],this.normalize&&(t=am(t,r[0]),n=am(n,r[1])),Jte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Zd(this.axes,e.length),Zd(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 Ge("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let s=t.concat(n);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Gx.className="Dot";ce.registerClass(Gx);var jx=class extends st{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 Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);return Wd(()=>jf(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};jx.className="GaussianNoise";ce.registerClass(jx);var qx=class extends st{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 Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);return this.rate>0&&this.rate<1?Wd(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return n.mul(jf(n.shape,1,s))},()=>n,t.training||!1):n})}};qx.className="GaussianDropout";ce.registerClass(qx);var Kx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ke(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 Z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wd(()=>{let s=Ke(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Jo(Rd(n),this.rate);l=zd(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate;return s.mul(l).add(l.add(-1).mul(i)).mul(u).add(c)},()=>Ke(e),t.training||!1)}return e})}};Kx.className="AlphaDropout";ce.registerClass(Kx);function Yd(e,t,n,r,s,a=.001){let o;if(e.rank===2)o=Cj(e,t,n,r,s,a);else if(e.rank===3)o=$j(e,t,n,r,s,a);else if(e.rank===4)o=Rj(e,t,n,r,s,a);else throw new Ge(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function Qte(e,t,n,r,s=.001){return Z(()=>{let a=KA(e,r),o=a.mean,i=a.variance;return[Yd(e,o,i,n,t,s),o,i]})}function ene(e,t,n,r,s=.001){return Z(()=>{let a=KA(e,r),o=a.mean,i=a.variance,l=[];for(let f of ds(0,e.rank))r.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=o.reshape(l),c=i.reshape(l),d=t==null?null:t.reshape(l),h=n==null?null:n.reshape(l);return[Yd(e,u,c,h,d,s),o,i]})}function tne(e,t,n,r,s=.001){return k.arraysEqual(r.slice().sort(),ds(0,e.rank-1))?Qte(e,t,n,r,s):ene(e,t,n,r,s)}var Xx=class extends st{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=zt(e.betaInitializer||"zeros"),this.gammaInitializer=zt(e.gammaInitializer||"ones"),this.movingMeanInitializer=zt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=zt(e.movingVarianceInitializer||"ones"),this.betaConstraint=hn(e.betaConstraint),this.gammaConstraint=hn(e.gammaConstraint),this.betaRegularizer=Lt(e.betaRegularizer),this.gammaRegularizer=Lt(e.gammaRegularizer)}build(e){e=At(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new tn({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return Z(()=>{let n=t.training==null?!1:t.training,r=Ke(e),s=r.shape,a=s.length,o=ds(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=si(1,a);l[i]=s[i];let u=o.slice();u.sort();let c=!k.arraysEqual(u,ds(0,a).slice(0,a-1)),d=()=>{if(c){let y=this.movingMean.read().reshape(l),A=this.movingVariance.read().reshape(l),x=this.center?this.beta.read().reshape(l):null,b=this.scale?this.gamma.read().reshape(l):null;return Yd(r,y,A,x,b,this.epsilon)}else return Yd(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[h,p,f]=tne(r,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(y,A,x)=>{Z(()=>{let b=1-x,v=y.read(),w=v.sub(A).mul(b);y.write(v.sub(w))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),h})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ht(this.betaInitializer),gammaInitializer:Ht(this.gammaInitializer),movingMeanInitializer:Ht(this.movingMeanInitializer),movingVarianceInitializer:Ht(this.movingVarianceInitializer),betaRegularizer:kt(this.betaRegularizer),gammaRegularizer:kt(this.gammaRegularizer),betaConstraint:dn(this.betaConstraint),gammaConstraint:dn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Xx.className="BatchNormalization";ce.registerClass(Xx);var Zx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=zt(e.betaInitializer||"zeros"),this.gammaInitializer=zt(e.gammaInitializer||"ones"),this.betaRegularizer=Lt(e.betaRegularizer),this.gammaRegularizer=Lt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=At(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Ga(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Ke(e),r=n.shape,s=r.length;return Z(()=>{let a=!0,{mean:o,variance:i}=KA(n,this.axis,a),l=si(1,s);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==s&&this.axis!==[s-1]?f.reshape(l):f,c=u(this.gamma.read()),d=u(this.beta.read()),h=[],p=[];for(let f=0;f<s;++f)this.axis.indexOf(f)!==-1?(h.push(r[f]),p.push(1)):(h.push(1),p.push(r[f]));return o=o.tile(h),i=i.tile(h),c=c.tile(p),d=d.tile(p),Yd(n,o,i,d,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ht(this.betaInitializer),gammaInitializer:Ht(this.gammaInitializer),betaRegularizer:kt(this.betaRegularizer),gammaRegularizer:kt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Zx.className="LayerNormalization";ce.registerClass(Zx);function nne(e,t,n){return Z(()=>{if(e.rank!==4)throw new q(`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 q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=us()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Wa(e,r)})}var Yx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?us():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new tn({ndim:4})]}computeOutputShape(e){e=At(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 Z(()=>nne(Ke(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Yx.className="ZeroPadding2D";ce.registerClass(Yx);function vm(e,t,n,r,s,a){return Z(()=>{Yt(s),cS(a),Or(r),n==null&&(n=[1,1]),r==null&&(r="valid"),s==null&&(s=us()),a==null&&(a="max"),e=yx(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=$f(e,t,n,i):o=Sf(e,t,n,i),s==="channelsFirst"&&(o=pt(o,[0,3,1,2])),o})}function T8(e,t,n,r,s,a){return Z(()=>{Yt(s),cS(a),Or(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=us()),a==null&&(a="max"),e=g8(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=AI(e,t,n,i):o=tI(e,t,n,i),s==="channelsFirst"&&(o=pt(o,[0,4,1,2,3])),o})}var N8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(An(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 q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);An(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Or(this.padding),this.inputSpec=[new tn({ndim:3})]}computeOutputShape(e){e=At(e);let t=ms(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return Z(()=>{this.invokeCallHook(e,t),e=Ld(Ke(e),2);let n=this.poolingFunction(Ke(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Jl(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Jx=class extends N8{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Yt(s),Or(r),vm(e,t,n,r,s,"max")}};Jx.className="MaxPooling1D";ce.registerClass(Jx);var Qx=class extends N8{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Yt(s),Or(r),vm(e,t,n,r,s,"avg")}};Qx.className="AveragePooling1D";ce.registerClass(Qx);var C8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`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];An(this.poolSize,"poolSize"),An(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Yt(this.dataFormat),Or(this.padding),this.inputSpec=[new tn({ndim:4})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(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 Z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(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}},e5=class extends C8{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Yt(s),Or(r),vm(e,t,n,r,s,"max")}};e5.className="MaxPooling2D";ce.registerClass(e5);var t5=class extends C8{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Yt(s),Or(r),vm(e,t,n,r,s,"avg")}};t5.className="AveragePooling2D";ce.registerClass(t5);var E8=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`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];An(this.poolSize,"poolSize"),An(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Yt(this.dataFormat),Or(this.padding),this.inputSpec=[new tn({ndim:5})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),r=ms(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return Z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(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}},n5=class extends E8{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Yt(s),Or(r),T8(e,t,n,r,s,"max")}};n5.className="MaxPooling3D";ce.registerClass(n5);var r5=class extends E8{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Yt(s),Or(r),T8(e,t,n,r,s,"avg")}};r5.className="AveragePooling3D";ce.registerClass(r5);var $8=class extends st{constructor(e){super(e);this.inputSpec=[new tn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ge}},s5=class extends $8{constructor(e){super(e||{})}call(e,t){return Z(()=>{let n=Ke(e);return Xt(n,1)})}};s5.className="GlobalAveragePooling1D";ce.registerClass(s5);var a5=class extends $8{constructor(e){super(e||{})}call(e,t){return Z(()=>{let n=Ke(e);return os(n,1)})}};a5.className="GlobalMaxPooling1D";ce.registerClass(a5);var _8=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Yt(this.dataFormat),this.inputSpec=[new tn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ge}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},o5=class extends _8{call(e,t){return Z(()=>{let n=Ke(e);return this.dataFormat==="channelsLast"?Xt(n,[1,2]):Xt(n,[2,3])})}};o5.className="GlobalAveragePooling2D";ce.registerClass(o5);var i5=class extends _8{call(e,t){return Z(()=>{let n=Ke(e);return this.dataFormat==="channelsLast"?os(n,[1,2]):os(n,[2,3])})}};i5.className="GlobalMaxPooling2D";ce.registerClass(i5);var R8=class extends st{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 r=t.layer,s=fs(r,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},l5=class extends R8{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=At(e),e.length<3)throw new q(`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=At(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return Z(()=>(e=Ke(e),k8((a,o)=>[Ke(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};l5.className="TimeDistributed";ce.registerClass(l5);function rne(e){oi(cee,"BidirectionalMergeMode",e)}var sne="concat",u5=class extends R8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=fs(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=fs(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?sne:e.mergeMode,rne(this.mergeMode),e.weights)throw new Ge("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,r,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Jn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=w8(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(c=>new tn({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),o.push(...u)}if(r!=null)throw new Ge("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof ps;for(let l of a)if(l instanceof ps!==i)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return Z(()=>{let n=t.initialState,r,s;if(n==null)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(r)&&(a=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=Fr(s,1));let o;return this.mergeMode==="concat"?o=F1([r,s]):this.mergeMode==="sum"?o=pe(r,s):this.mergeMode==="ave"?o=K(.5,pe(r,s)):this.mergeMode==="mul"?o=K(r,s):this.mergeMode==null&&(o=[r,s]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){ii(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ii(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 s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}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=fs(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ge("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};u5.className="Bidirectional";ce.registerClass(u5);function ane(e){return new su(e)}function one(e){return new fx(e)}function ine(e){return new dx(e)}function lne(e){return new hx(e)}function une(e){return new px(e)}function cne(e){return new gx(e)}function dne(e){return new mx(e)}function hne(e){return new wx(e)}function pne(e){return new fm(e)}function fne(e){return new xx(e)}function mne(e){return new mm(e)}function gne(e){return new bx(e)}function yne(e){return new vx(e)}function Ane(e){return new kx(e)}function xne(e){return new Ix(e)}function bne(e){return new Sx(e)}function vne(e){return new Dx(e)}function wne(e){return new _x(e)}function kne(e){return new bm(e)}function Ine(e){return new $x(e)}function Sne(e){return new Rx(e)}function Tne(e){return new Fx(e)}function Nne(e){return new Mx(e)}function Cne(e){return new Ox(e)}function Ene(e){return new zx(e)}function $ne(e){return new Lx(e)}function _ne(e){return new Wx(e)}function Rne(e){return new Hx(e)}function Dne(e){return new Vx(e)}function Fne(e){return new Ux(e)}function Mne(e){return new Bx(e)}function One(e){return new Gx(e)}function Pne(e){return new Xx(e)}function zne(e){return new Zx(e)}function Lne(e){return new Yx(e)}function c5(e){return new Qx(e)}function Bne(e){return c5(e)}function Wne(e){return c5(e)}function d5(e){return new t5(e)}function Vne(e){return d5(e)}function Une(e){return d5(e)}function h5(e){return new r5(e)}function Hne(e){return h5(e)}function Gne(e){return h5(e)}function jne(e){return new s5(e)}function qne(e){return new o5(e)}function D8(e){return new a5(e)}function F8(e){return new i5(e)}function M8(e){return new Jx(e)}function O8(e){return new e5(e)}function Kne(e){return new n5(e)}function Xne(e){return new Nx(e)}function Zne(e){return new ym(e)}function Yne(e){return new Cx(e)}function Jne(e){return new Xd(e)}function Qne(e){return new Tx(e)}function ere(e){return new gm(e)}function tre(e){return new Ex(e)}function nre(e){return new xm(e)}function rre(e){return new fa(e)}function sre(e){return new Am(e)}function are(e){return new u5(e)}function ore(e){return new l5(e)}var ire=D8,lre=F8,ure=M8,cre=O8;function dre(e){return new jx(e)}function hre(e){return new qx(e)}function pre(e){return new Kx(e)}function fre(e){return new Px(e)}var P8={};De(P8,{MAPE:()=>Sre,MSE:()=>Cre,binaryAccuracy:()=>mre,binaryCrossentropy:()=>gre,categoricalAccuracy:()=>Are,categoricalCrossentropy:()=>xre,cosineProximity:()=>wre,mape:()=>Tre,meanAbsoluteError:()=>kre,meanAbsolutePercentageError:()=>Ire,meanSquaredError:()=>Nre,mse:()=>Ere,precision:()=>bre,recall:()=>vre,sparseCategoricalAccuracy:()=>yre});function mre(e,t){return Z1(e,t)}function gre(e,t){return MS(e,t)}function yre(e,t){return OS(e,t)}function Are(e,t){return Y1(e,t)}function xre(e,t){return J1(e,t)}function bre(e,t){return FS(e,t)}function vre(e,t){return ate(e,t)}function wre(e,t){return K1(e,t)}function kre(e,t){return om(e,t)}function Ire(e,t){return ou(e,t)}function Sre(e,t){return ou(e,t)}function Tre(e,t){return ou(e,t)}function Nre(e,t){return ui(e,t)}function Cre(e,t){return ui(e,t)}function Ere(e,t){return ui(e,t)}var z8={};De(z8,{modelFromJSON:()=>Lte});var L8={};De(L8,{l1:()=>_re,l1l2:()=>$re,l2:()=>Rre});function $re(e){return new jd(e)}function _re(e){return qte(e)}function Rre(e){return Kte(e)}var B8=class extends au{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof pa))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function wm(e,t){return e<t}function W8(e,t){return e>t}var V8=class extends B8{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Ge("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=wm:this.mode==="max"?this.monitorFunc=W8:this.monitor.indexOf("acc")!==-1?this.monitorFunc=W8:this.monitorFunc=wm,this.monitorFunc===wm&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===wm?Infinity:-Infinity}async onEpochEnd(e,t){await Ka(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 Dre(e){return new V8(e)}var Fre={earlyStopping:Dre},gs;(function(e){e[e.DT_INVALID=0]="DT_INVALID",e[e.DT_FLOAT=1]="DT_FLOAT",e[e.DT_DOUBLE=2]="DT_DOUBLE",e[e.DT_INT32=3]="DT_INT32",e[e.DT_UINT8=4]="DT_UINT8",e[e.DT_INT16=5]="DT_INT16",e[e.DT_INT8=6]="DT_INT8",e[e.DT_STRING=7]="DT_STRING",e[e.DT_COMPLEX64=8]="DT_COMPLEX64",e[e.DT_INT64=9]="DT_INT64",e[e.DT_BOOL=10]="DT_BOOL",e[e.DT_QINT8=11]="DT_QINT8",e[e.DT_QUINT8=12]="DT_QUINT8",e[e.DT_QINT32=13]="DT_QINT32",e[e.DT_BFLOAT16=14]="DT_BFLOAT16",e[e.DT_FLOAT_REF=101]="DT_FLOAT_REF",e[e.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",e[e.DT_INT32_REF=103]="DT_INT32_REF",e[e.DT_UINT8_REF=104]="DT_UINT8_REF",e[e.DT_INT16_REF=105]="DT_INT16_REF",e[e.DT_INT8_REF=106]="DT_INT8_REF",e[e.DT_STRING_REF=107]="DT_STRING_REF",e[e.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",e[e.DT_INT64_REF=109]="DT_INT64_REF",e[e.DT_BOOL_REF=110]="DT_BOOL_REF",e[e.DT_QINT8_REF=111]="DT_QINT8_REF",e[e.DT_QUINT8_REF=112]="DT_QUINT8_REF",e[e.DT_QINT32_REF=113]="DT_QINT32_REF",e[e.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(gs||(gs={}));var U8;(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={}))})(U8||(U8={}));var p5={};function Mre(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};p5[e]=n}function H8(e){return p5[e]}function Ore(e){delete p5[e]}function T(e,t,n,r,s){let a=t.inputParams[e];if(a&&a.inputIndexStart!==void 0){let i=a.inputIndexStart,l=a.inputIndexEnd===0?void 0:a.inputIndexEnd===void 0?i+1:a.inputIndexEnd;if(a.type==="tensor")return Bn(t.inputNames[a.inputIndexStart],n,r,s);if(a.type==="tensors")return t.inputNames.slice(i,l).map(h=>Bn(h,n,r,s));let u=Bn(t.inputNames.slice(i)[0],n,r,s),c=u.dataSync();return a.type==="number"?c[0]:k.toNestedArray(u.shape,c)}let o=t.attrParams[e];return o&&o.value}function Bn(e,t,n,r){let[s,a]=pr(e);if(r!=null){let i=r.getHashTableHandleByName(s);if(i!=null)return i}let o=n.currentContextIds.find(i=>!!t[km(s,i)]);return o!==void 0?t[km(s,o)][a]:void 0}function Pre(e,t,n){return t[km(e,n.currentContextId)]}function ma(e,t){let[n,r,s]=pr(e);return[km(n,t&&t.currentContextId),r,s]}function km(e,t){return t?`${e}-${t}`:e}function pr(e){let t=e.split(":");if(t.length===1)return[e,0,void 0];let n=t[0],r=t.length===3?t[1]:void 0,s=Number(t[t.length-1]);return[n,s,r]}function Im(e,t,n){let r=T("pad",e,t,n);if(r==="explicit"){r=T("explicitPaddings",e,t,n);let s=[[0,0],[0,0],[0,0],[0,0]];for(let a=0;a<4;a++)s[a][0]=r[a*2],s[a][1]=r[a*2+1];return s}return r}function ga(e){return e.kept?e:qo(e)}var G8={};De(G8,{json:()=>zre});var zre=[{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}]}],j8={};De(j8,{json:()=>Lre});var Lre=[{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}]}],q8={};De(q8,{json:()=>Bre});var Bre=[{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"}]}],K8={};De(K8,{json:()=>Wre});var Wre=[{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"}]}],X8={};De(X8,{json:()=>Vre});var Vre=[{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"}]}],Z8={};De(Z8,{json:()=>Ure});var Ure=[{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}]}],Y8={};De(Y8,{json:()=>Hre});var Hre=[{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"}]}],J8={};De(J8,{json:()=>Gre});var Gre=[{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"}]}],Q8={};De(Q8,{json:()=>jre});var jre=[{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"}]}],eT={};De(eT,{json:()=>qre});var qre=[{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"}]}],tT={};De(tT,{json:()=>Kre});var Kre=[{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}]}],nT={};De(nT,{json:()=>Xre});var Xre=[{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"}]}],rT={};De(rT,{json:()=>Zre});var Zre=[{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}]}],sT={};De(sT,{json:()=>Yre});var Yre=[{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"}]}],aT={};De(aT,{json:()=>Jre});var Jre=[{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}]}],oT={};De(oT,{json:()=>Qre});var Qre=[{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"}]}],iT={};De(iT,{json:()=>ese});var ese=[{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}]}],lT={};De(lT,{json:()=>tse});var tse=[{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"}]}],uT={};De(uT,{json:()=>nse});var nse=[{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:[]}],cT=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[G8,j8,q8,K8,X8,Z8,Y8,J8,Q8,eT,tT,nT,rT,sT,aT,oT,iT,lT,uT],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,r)=>(n[r.tfOpName]=r,n),{})}transformGraph(e,t={}){let n=e.node,r=[],s=[],a=[],o=n.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?r.push(f[m.name]):m.op==="Const"?s.push(f[m.name]):(m.input==null||m.input.length===0)&&a.push(f[m.name]),f),{}),i=[],l=[],u={},c={};t!=null&&(u=this.mapSignatureEntries(t.inputs),c=this.mapSignatureEntries(t.outputs));let d=Object.keys(o);d.forEach(f=>{let m=o[f];m.inputNames.forEach((g,y)=>{let[A,,x]=ma(g),b=o[A];if(b.outputs!=null){let v=b.outputs.indexOf(x);if(v!==-1){let w=`${A}:${v}`;m.inputNames[y]=w}}m.inputs.push(b),b.children.push(m)})}),Object.keys(c).length===0?d.forEach(f=>{let m=o[f];m.children.length===0&&l.push(m)}):Object.keys(c).forEach(f=>{let[m]=ma(f),g=o[m];g!=null&&(g.signatureKey=c[f],l.push(g))}),Object.keys(u).length>0?Object.keys(u).forEach(f=>{let[m]=ma(f),g=o[m];g&&(g.signatureKey=u[f],i.push(g))}):i=r;let h={};e.library!=null&&e.library.function!=null&&(h=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let p={nodes:o,inputs:i,outputs:l,weights:s,placeholders:r,signature:t,functions:h};return a.length>0&&(p.initNodes=a),p}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=H8(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(r=>r.startsWith("^")?r.substr(1):r),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((r,s)=>(r[s.name]={type:s.type,inputIndexStart:s.start,inputIndexEnd:s.end},r),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((r,s)=>{let a=s.type,o;switch(s.type){case"string":o=f5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=f5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"string[]":o=w5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=w5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"number":o=g5(e.attr,s.tfName,s.defaultValue||0),o===void 0&&!!s.tfDeprecatedName&&(o=g5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"number[]":o=v5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=v5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool":o=m5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=m5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool[]":o=I5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=I5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":o=b5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=b5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":o=k5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=k5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":o=A5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=A5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":o=x5(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=x5(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":o=hT(e.attr,s.tfName,s.defaultValue),o===void 0&&!!s.tfDeprecatedName&&(o=hT(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${e.op}`)}return r[s.name]={value:o,type:a},r},{})),n}mapFunction(e){let t=e.nodeDef,n=[],r=[],s={};t!=null&&(s=t.reduce((c,d)=>(c[d.name]=this.mapNode(d),d.op==="Const"&&r.push(c[d.name]),c),{}));let a=[],o=[];e.signature.inputArg.forEach(c=>{let[d]=ma(c.name),h={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:y5(c.type),type:"dtype"}},children:[]};h.signatureKey=c.name,a.push(h),s[d]=h}),Object.keys(s).forEach(c=>{let d=s[c];d.inputNames.forEach((h,p)=>{let[f,,m]=ma(h),g=s[f];if(g.outputs!=null){let y=g.outputs.indexOf(m);if(y!==-1){let A=`${f}:${y}`;d.inputNames[p]=A}}d.inputs.push(g),g.children.push(d)})});let l=e.ret;e.signature.outputArg.forEach(c=>{let[d,h]=ma(l[c.name]),p=s[d];p!=null&&(p.defaultOutput=h,o.push(p))});let u=this.mapArgsToSignature(e);return{nodes:s,inputs:a,outputs:o,weights:r,placeholders:n,signature:u}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function rse(e){let t=ae().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 dT(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):rse(e);return t?n:n.toLowerCase()}function f5(e,t,n,r=!1){let s=e[t];return s!=null?dT(s.s,r):n}function m5(e,t,n){let r=e[t];return r?r.b:n}function g5(e,t,n){let r=e[t]||{},s=r.i!=null?r.i:r.f!=null?r.f:n;return typeof s=="number"?s:parseInt(s,10)}function y5(e){switch(typeof e=="string"&&(e=gs[e]),e){case gs.DT_FLOAT:return"float32";case gs.DT_INT32:case gs.DT_INT64:case gs.DT_INT8:case gs.DT_UINT8:return"int32";case gs.DT_BOOL:return"bool";case gs.DT_DOUBLE:return"float32";case gs.DT_STRING:return"string";default:return null}}function hT(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function A5(e,t,n){let r=e[t];return r&&r.type?y5(r.type):n}function x5(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(s=>y5(s)):n}function pT(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function b5(e,t,n){let r=e[t];return r&&r.shape?pT(r.shape):n}function v5(e,t,n){let r=e[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map(s=>typeof s=="number"?s:parseInt(s,10)):n}function w5(e,t,n,r=!1){let s=e[t];return s&&s.list&&s.list.s?s.list.s.map(a=>dT(a,r)):n}function k5(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(s=>pT(s)):n}function I5(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var sse=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(r=>this.getInput(r)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((r,s)=>(r[s]=this.getAttr(s),r),{}))}getInput(e){return Bn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Bn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return g5(this.node.rawAttrs,e,t);if(n.s!=null)return f5(this.node.rawAttrs,e,t);if(n.b!=null)return m5(this.node.rawAttrs,e,t);if(n.shape!=null)return b5(this.node.rawAttrs,e,t);if(n.type!=null)return A5(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return v5(this.node.rawAttrs,e,t);if(n.list.s!=null)return w5(this.node.rawAttrs,e,t);if(n.list.shape!=null)return k5(this.node.rawAttrs,e,t);if(n.list.b!=null)return I5(this.node.rawAttrs,e,t);if(n.list.type!=null)return x5(this.node.rawAttrs,e,t)}return t}},ase=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[pe(T("a",e,t,n),T("b",e,t,n))];case"AddN":return[ej(T("tensors",e,t,n))];case"FloorMod":case"Mod":return[bI(T("a",e,t,n),T("b",e,t,n))];case"Mul":return[K(T("a",e,t,n),T("b",e,t,n))];case"RealDiv":case"Div":return[Re(T("a",e,t,n),T("b",e,t,n))];case"DivNoNan":return[lI(T("a",e,t,n),T("b",e,t,n))];case"FloorDiv":return[RA(T("a",e,t,n),T("b",e,t,n))];case"Sub":return[Ne(T("a",e,t,n),T("b",e,t,n))];case"Minimum":return[_d(T("a",e,t,n),T("b",e,t,n))];case"Maximum":return[ia(T("a",e,t,n),T("b",e,t,n))];case"Pow":return[Va(T("a",e,t,n),T("b",e,t,n))];case"SquaredDifference":return[l1(T("a",e,t,n),T("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ose=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[yn(T("x",e,t,n))];case"Acos":return[H6(T("x",e,t,n))];case"Acosh":return[G6(T("x",e,t,n))];case"Asin":return[q6(T("x",e,t,n))];case"Asinh":return[K6(T("x",e,t,n))];case"Atan":return[X6(T("x",e,t,n))];case"Atan2":return[Z6(T("x",e,t,n),T("y",e,t,n))];case"Atanh":return[Y6(T("x",e,t,n))];case"Ceil":return[rI(T("x",e,t,n))];case"Complex":return[Uo(T("real",e,t,n),T("imag",e,t,n))];case"Cos":return[Nf(T("x",e,t,n))];case"Cosh":return[LA(T("x",e,t,n))];case"Elu":return[Nd(T("x",e,t,n))];case"Erf":return[uI(T("x",e,t,n))];case"Exp":return[Kr(T("x",e,t,n))];case"Expm1":return[cI(T("x",e,t,n))];case"Floor":return[Ed(T("x",e,t,n))];case"Log":return[Rr(T("x",e,t,n))];case"Log1p":return[UA(T("x",e,t,n))];case"Imag":return[WA(T("x",e,t,n))];case"Neg":return[Kt(T("x",e,t,n))];case"Reciprocal":return[vI(T("x",e,t,n))];case"Real":return[Ff(T("x",e,t,n))];case"Relu":return[ua(T("x",e,t,n))];case"Round":return[QA(T("x",e,t,n))];case"Selu":return[t1(T("x",e,t,n))];case"Sigmoid":return[Rs(T("x",e,t,n))];case"Sin":return[n1(T("x",e,t,n))];case"Sign":return[kI(T("x",e,t,n))];case"Sinh":return[r1(T("x",e,t,n))];case"Softplus":return[Zl(T("x",e,t,n))];case"Sqrt":return[$n(T("x",e,t,n))];case"Square":return[wt(T("x",e,t,n))];case"Tanh":return[Kl(T("x",e,t,n))];case"Tan":return[NI(T("x",e,t,n))];case"ClipByValue":return[dr(T("x",e,t,n),T("clipValueMin",e,t,n),T("clipValueMax",e,t,n))];case"Relu6":return[JA(T("x",e,t,n))];case"Rsqrt":return[e1(Bn(e.inputNames[0],t,n))];case"Prod":return[XA(T("x",e,t,n),T("axes",e,t,n))];case"LeakyRelu":return[Cf(T("x",e,t,n),T("alpha",e,t,n))];case"Prelu":return[Df(T("x",e,t,n),T("alpha",e,t,n))];case"IsNan":return[hI(Bn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Yr(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 r=0;r<e.length;r++){let s=e[r],a=t[r];k.assert(s<0||a<0||s===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function fT(e){return!(typeof e=="number"||e.some(t=>t<0))}function Jd(e,t,n){let r=S5(e,n),s=!fT(r);if(s&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(s&&t.forEach(a=>{r=S5(a.shape,r)}),!fT(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function S5(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 r=0;r<e.length;++r){let s=e[r],a=t[r];if(s>=0&&a>=0&&s!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[r]=s>=0?s:a}return n}var ise=class{constructor(e,t,n,r,s,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=Fe(0),Sn(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),Yr(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,Sn(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,r)=>this.write(n,t[r]))}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 r=0;r<this.size();r++)e.push(r)}if(e.length===0)return $s([],[0].concat(this.elementShape));let n=this.readMany(e);return Yr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Mr(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 $s([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return Yr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),en(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,ls(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,r=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let s=n===0?0:t.size/n,a=[];Z(()=>{t=J(t,[1,n,s]);for(let i=0;i<e.length;++i){let l=i===0?0:r[i-1],u=[0,l,0],c=[1,e[i],s];a[i]=J(nt(t,u,c),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},Qd=class{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(s=>{if(n!==s.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${s.dtype}`);Yr(t,s.shape,"TensorList shape mismatch: "),Sn(s)}),this.idTensor=Fe(0),this.maxNumElements=r,Sn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Qd([...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.`);Yr(e,this.elementShape,"TensorList shape mismatch: ");let r=Jd(this.elementShape,this.tensors,e);return Z(()=>{let s=this.tensors.map(a=>J(a,r));return Mr(s,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=Jd(this.elementShape,this.tensors,e),r=this.tensors.pop();return Yr(r.shape,e,"TensorList shape mismatch: "),J(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Yr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Sn(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.`);Yr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=Jd(this.elementShape,this.tensors,t);return J(this.tensors[e],r)}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.`);Yr(this.elementShape,t.shape,"TensorList shape mismatch: "),Sn(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 ${this.elementDtype}`);Yr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=Jd(this.elementShape,this.tensors,n);return e.length===0?$s([],[0].concat(r)):Z(()=>{let s=e.map(a=>J(this.tensors[a],r));return Mr(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Yr(this.elementShape,t,"TensorList shape mismatch: ");let n=Jd(this.elementShape,this.tensors,t);return this.size()===0?$s([],[0].concat(n)):Z(()=>{let r=this.tensors.map(s=>J(s,n));return en(r,0)})}};function lse(e,t,n){let r=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 s=e.shape.slice(1);Yr(s,t,"TensorList shape mismatch: ");let a=ls(e);return new Qd(a,t,r)}function use(e,t,n){return new Qd([],e,t,n)}function cse(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let s=Math.max(...t);if(r!=null&&r!==-1&&s>=r)throw new Error(`Max index must be < array size (${s}  vs. ${r})`);let a=new Qd([],n,e.dtype,r),o=ls(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function dse(e,t,n){let r=0,s=t.map(c=>(r+=c,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${r}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=S5(a,n),i=r===0?0:e.size/r,l=Z(()=>{let c=[];e=J(e,[1,r,i]);for(let d=0;d<t.length;++d){let h=d===0?0:s[d-1],p=[0,h,0],f=[1,t[d],i];c[d]=J(nt(e,p,f),o)}return e.dispose(),c}),u=new Qd([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var hse=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=T("thenBranch",e,t,n),s=T("elseBranch",e,t,n),a=T("cond",e,t,n),o=T("args",e,t,n);return(await a.data())[0]?n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=T("body",e,t,n),s=T("cond",e,t,n),a=T("args",e,t,n),o=await n.functionMap[s].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(c=>c.id),l=await o[0].data();o.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=a;for(;l[0];){let c=u;u=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(p=>p.id);c.forEach(p=>{!p.kept&&i.indexOf(p.id)===-1&&d.indexOf(p.id)===-1&&p.dispose()});let h=await n.functionMap[s].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await h[0].data(),h.forEach(p=>{!p.kept&&i.indexOf(p.id)===-1&&d.indexOf(p.id)===-1&&p.dispose()})}return u}case"LoopCond":{let r=T("pred",e,t,n);return[ga(r)]}case"Switch":{let r=T("pred",e,t,n),s=T("data",e,t,n);return s.kept||(s=ga(s)),(await r.data())[0]?[void 0,s]:[s,void 0]}case"Merge":{let r=e.inputNames.find(s=>Bn(s,t,n)!==void 0);if(r){let s=Bn(r,t,n);return[ga(s)]}return}case"Enter":{let r=T("frameName",e,t,n),s=T("tensor",e,t,n);return n.enterFrame(r),[ga(s)]}case"Exit":{let r=T("tensor",e,t,n);return n.exitFrame(),[ga(r)]}case"NextIteration":{let r=T("tensor",e,t,n);return n.nextIteration(),[ga(r)]}case"TensorArrayV3":{let r=T("size",e,t,n),s=T("dtype",e,t,n),a=T("elementShape",e,t,n),o=T("dynamicSize",e,t,n),i=T("clearAfterRead",e,t,n),l=T("identicalElementShapes",e,t,n),u=T("name",e,t,n),c=new ise(u,s,r,a,l,o,i);return n.addTensorArray(c),[c.idTensor,Fe(1)]}case"TensorArrayWriteV3":{let r=T("tensorArrayId",e,t,n),s=T("index",e,t,n),a=T("tensor",e,t,n),o=n.getTensorArray(r.id);return o.write(s,a),[o.idTensor]}case"TensorArrayReadV3":{let r=T("tensorArrayId",e,t,n),s=T("index",e,t,n);return[n.getTensorArray(r.id).read(s)]}case"TensorArrayGatherV3":{let r=T("tensorArrayId",e,t,n),s=T("indices",e,t,n),a=T("dtype",e,t,n);return[n.getTensorArray(r.id).gather(s,a)]}case"TensorArrayScatterV3":{let r=T("tensorArrayId",e,t,n),s=T("indices",e,t,n),a=T("tensor",e,t,n),o=n.getTensorArray(r.id);return o.scatter(s,a),[o.idTensor]}case"TensorArrayConcatV3":{let r=T("tensorArrayId",e,t,n),s=n.getTensorArray(r.id),a=T("dtype",e,t,n);return[s.concat(a)]}case"TensorArraySplitV3":{let r=T("tensorArrayId",e,t,n),s=T("tensor",e,t,n),a=T("lengths",e,t,n),o=n.getTensorArray(r.id);return o.split(a,s),[o.idTensor]}case"TensorArraySizeV3":{let r=T("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return[Fe(s.size(),"int32")]}case"TensorArrayCloseV3":{let r=T("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return s.clearAndClose(),[s.idTensor]}case"TensorListSetItem":{let r=T("tensorListId",e,t,n),s=T("index",e,t,n),a=T("tensor",e,t,n),o=n.getTensorList(r.id);return o.setItem(s,a),[o.idTensor]}case"TensorListGetItem":{let r=T("tensorListId",e,t,n),s=T("index",e,t,n),a=T("elementShape",e,t,n),o=T("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(s,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let r=T("indices",e,t,n),s=T("tensor",e,t,n),a=T("elementShape",e,t,n),o=T("numElements",e,t,n),i=cse(s,r,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=T("elementShape",e,t,n),s=T("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=T(a,e,t,n),i=use(r,s,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let r=T("tensorListId",e,t,n),s=T("indices",e,t,n),a=T("elementShape",e,t,n),o=T("elementDType",e,t,n);return[n.getTensorList(r.id).gather(s,o,a)]}case"TensorListStack":{let r=T("tensorListId",e,t,n),s=T("elementShape",e,t,n),a=T("elementDType",e,t,n),o=T("numElements",e,t,n);return[n.getTensorList(r.id).stack(s,a,o)]}case"TensorListFromTensor":{let r=T("tensor",e,t,n),s=T("elementShape",e,t,n),a=T("elementDType",e,t,n),o=lse(r,s,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let r=T("tensorListId",e,t,n),s=n.getTensorList(r.id),a=T("dtype",e,t,n),o=T("elementShape",e,t,n);return[s.concat(a,o)]}case"TensorListPushBack":{let r=T("tensorListId",e,t,n),s=T("tensor",e,t,n),a=n.getTensorList(r.id);return a.pushBack(s),[a.idTensor]}case"TensorListPopBack":{let r=T("tensorListId",e,t,n),s=T("elementShape",e,t,n),a=T("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(s,a)]}case"TensorListSplit":{let r=T("tensor",e,t,n),s=T("elementShape",e,t,n),a=T("lengths",e,t,n),o=dse(r,a,s);return n.addTensorList(o),[o.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function mT(e,t,n){let[r,s]=T("fusedOps",e,t,n),a=r==="biasadd",o=!a,i=s==="prelu",l=r==="fusedbatchnorm",u=T("numArgs",e,t,n);if(a){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&a&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=T("strides",e,t,n),d=Im(e,t,n),h=T("dataFormat",e,t,n).toUpperCase(),p=T("dilations",e,t,n),[f,m]=T("args",e,t,n);o&&(m=f,f=void 0);let g=T("leakyreluAlpha",e,t,n);return{stride:c,pad:d,dataFormat:h,dilations:p,biasArg:f,preluArg:m,activationFunc:s,leakyreluAlpha:g}}var pse=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=T("stride",e,t,n),s=T("pad",e,t,n),a=T("dataFormat",e,t,n).toUpperCase(),o=T("dilation",e,t,n);return[OA(T("x",e,t,n),T("filter",e,t,n),r,s,a,o)]}case"Conv2D":{let r=T("strides",e,t,n),s=Im(e,t,n),a=T("dataFormat",e,t,n).toUpperCase(),o=T("dilations",e,t,n);return[Ba(T("x",e,t,n),T("filter",e,t,n),[r[1],r[2]],s,a,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:r,pad:s,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=mT(e,t,n);return[ti.conv2d({x:T("x",e,t,n),filter:T("filter",e,t,n),strides:[r[1],r[2]],pad:s,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:s,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=mT(e,t,n);return[ti.depthwiseConv2d({x:T("x",e,t,n),filter:T("filter",e,t,n),strides:[r[1],r[2]],pad:s,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=T("outputShape",e,t,n),s=T("strides",e,t,n),a=Im(e,t,n);return[zA(T("x",e,t,n),T("filter",e,t,n),r,[s[1],s[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=T("strides",e,t,n),s=Im(e,t,n),a=T("dilations",e,t,n),o=T("dataFormat",e,t,n).toUpperCase();return[Td(T("input",e,t,n),T("filter",e,t,n),[r[1],r[2]],s,o,[a[1],a[2]])]}case"Conv3D":{let r=T("strides",e,t,n),s=T("pad",e,t,n),a=T("dataFormat",e,t,n).toUpperCase(),o=T("dilations",e,t,n);return[sI(T("x",e,t,n),T("filter",e,t,n),[r[1],r[2],r[3]],s,a,[o[1],o[2],o[3]])]}case"AvgPool":{let r=T("strides",e,t,n),s=T("pad",e,t,n),a=T("kernelSize",e,t,n);return[Sf(T("x",e,t,n),[a[1],a[2]],[r[1],r[2]],s)]}case"MaxPool":{let r=T("strides",e,t,n),s=T("pad",e,t,n),a=T("kernelSize",e,t,n);return[$f(T("x",e,t,n),[a[1],a[2]],[r[1],r[2]],s)]}case"MaxPoolWithArgmax":{let r=T("strides",e,t,n),s=T("pad",e,t,n),a=T("kernelSize",e,t,n),o=T("includeBatchInIndex",e,t,n),{result:i,indexes:l}=iK(T("x",e,t,n),[a[1],a[2]],[r[1],r[2]],s,o);return[i,l]}case"AvgPool3D":{let r=T("strides",e,t,n),s=T("pad",e,t,n),a=T("kernelSize",e,t,n);return[tI(T("x",e,t,n),[a[1],a[2],a[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=T("strides",e,t,n),s=T("pad",e,t,n),a=T("kernelSize",e,t,n);return[AI(T("x",e,t,n),[a[1],a[2],a[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=T("strides",e,t,n),s=T("pad",e,t,n),a=T("dilations",e,t,n),o=r[1],i=r[2],l=a[1],u=a[2];return[iI(T("x",e,t,n),T("filter",e,t,n),[o,i],s,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},fse=(e,t,n)=>{switch(e.op){case"Fill":{let r=T("shape",e,t,n),s=T("dtype",e,t,n),a=T("value",e,t,n);return[Cd(r,a,s)]}case"LinSpace":{let r=T("start",e,t,n),s=T("stop",e,t,n),a=T("num",e,t,n);return[Pq(r,s,a)]}case"Multinomial":{let r=T("logits",e,t,n),s=T("numSamples",e,t,n),a=T("seed",e,t,n);return[AK(r,s,a)]}case"OneHot":{let r=T("indices",e,t,n),s=T("depth",e,t,n),a=T("onValue",e,t,n),o=T("offValue",e,t,n);return[kd(r,s,a,o)]}case"Ones":return[la(T("shape",e,t,n),T("dtype",e,t,n))];case"OnesLike":return[Dr(T("x",e,t,n))];case"RandomUniform":return[Rd(T("shape",e,t,n),T("minval",e,t,n),T("maxval",e,t,n),T("dtype",e,t,n))];case"Range":{let r=T("start",e,t,n),s=T("stop",e,t,n),a=T("step",e,t,n);return[Dd(r,s,a,T("dtype",e,t,n))]}case"TruncatedNormal":{let r=T("shape",e,t,n),s=T("mean",e,t,n),a=T("stdDev",e,t,n),o=T("seed",e,t,n);return[u1(r,s,a,T("dtype",e,t,n),o)]}case"Zeros":return[un(T("shape",e,t,n),T("dtype",e,t,n))];case"ZerosLike":return[rt(T("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function T5(e,t,n){let r=T("boxes",e,t,n),s=T("scores",e,t,n),a=T("maxOutputSize",e,t,n),o=T("iouThreshold",e,t,n),i=T("scoreThreshold",e,t,n),l=T("softNmsSigma",e,t,n);return{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}}var mse=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}=T5(e,t,n),u=await ni.nonMaxSuppressionWithScoreAsync(r,s,a,o,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=T5(e,t,n),l=T("padToMaxOutputSize",e,t,n),u=await ni.nonMaxSuppressionPaddedAsync(r,s,a,o,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=T5(e,t,n);return[await ni.nonMaxSuppressionAsync(r,s,a,o,i)]}case"Where":{let r=ke(T("condition",e,t,n),"bool"),s=[await $X(r)];return r.dispose(),s}case"ListDiff":return nX(T("x",e,t,n),T("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},gse=(e,t,n)=>{switch(e.op){case"TopKV2":{let r=T("x",e,t,n),s=T("k",e,t,n),a=T("sorted",e,t,n),o=CI(r,s,a);return[o.values,o.indices]}case"Unique":{let r=T("x",e,t,n),s=c1(r);return[s.values,s.indices]}case"UniqueV2":{let r=T("x",e,t,n),s=T("axis",e,t,n),a=c1(r,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},yse=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=T("default",e,t,n);return[Bn(e.name,t,n)||r];case"Placeholder":return[Bn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=T("x",e,t,n);return[ga(u)]}case"IdentityN":return T("x",e,t,n).map(u=>ga(u));case"Snapshot":let s=T("x",e,t,n);return[ga(s)];case"Shape":return[_n(T("x",e,t,n).shape,"int32")];case"ShapeN":return T("x",e,t,n).map(u=>_n(u.shape));case"Size":return[Fe(T("x",e,t,n).size,"int32")];case"Rank":return[Fe(T("x",e,t,n).rank,"int32")];case"NoOp":return[Fe(1)];case"Print":let a=T("x",e,t,n),o=T("data",e,t,n),i=T("message",e,t,n),l=T("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;u<o.length;u++)console.log(Array.prototype.slice.call(o[u].dataSync()).slice(0,l));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ase=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Fe(0),this.tensorMap=new Map,Sn(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 Fe(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),Z(()=>{let r=ls(t),s=n.length,a=r.length;k.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let o=0;o<s;o++){let i=n[o],l=r[o];Sn(l),this.tensorMap.set(i,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return Z(()=>{let r=[];for(let s=0;s<n.length;s++){let a=n[s],o=this.findWithDefault(a,t);r.push(o)}return Mr(r)})}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}`)}},xse=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let s=T("keyDType",e,t,n),a=T("valueDType",e,t,n),o=new Ase(s,a);return r.addHashTable(e.name,o),[o.handle]}case"LookupTableImport":case"LookupTableImportV2":{let s=T("tableHandle",e,t,n,r),a=T("keys",e,t,n),o=T("values",e,t,n);return[await r.getHashTableById(s.id).import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{let s=T("tableHandle",e,t,n,r),a=T("keys",e,t,n),o=T("defaultValue",e,t,n);return[await r.getHashTableById(s.id).find(a,o)]}case"LookupTableSize":case"LookupTableSizeV2":{let s=T("tableHandle",e,t,n,r);return[r.getHashTableById(s.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},bse=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=T("images",e,t,n),s=T("size",e,t,n),a=T("alignCorners",e,t,n),o=T("halfPixelCenters",e,t,n);return[ni.resizeBilinear(r,[s[0],s[1]],a,o)]}case"ResizeNearestNeighbor":{let r=T("images",e,t,n),s=T("size",e,t,n),a=T("alignCorners",e,t,n),o=T("halfPixelCenters",e,t,n);return[ni.resizeNearestNeighbor(r,[s[0],s[1]],a,o)]}case"CropAndResize":{let r=T("image",e,t,n),s=T("boxes",e,t,n),a=T("boxInd",e,t,n),o=T("cropSize",e,t,n),i=T("method",e,t,n),l=T("extrapolationValue",e,t,n);return[ni.cropAndResize(r,s,a,o,i,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},vse=(e,t,n)=>{switch(e.op){case"Equal":return[Zo(T("a",e,t,n),T("b",e,t,n))];case"NotEqual":return[Yl(T("a",e,t,n),T("b",e,t,n))];case"Greater":return[_r(T("a",e,t,n),T("b",e,t,n))];case"GreaterEqual":return[Jo(T("a",e,t,n),T("b",e,t,n))];case"Less":return[VA(T("a",e,t,n),T("b",e,t,n))];case"LessEqual":return[Qo(T("a",e,t,n),T("b",e,t,n))];case"LogicalAnd":return[is(T("a",e,t,n),T("b",e,t,n))];case"LogicalNot":return[Ef(T("a",e,t,n))];case"LogicalOr":return[qA(T("a",e,t,n),T("b",e,t,n))];case"Select":case"SelectV2":return[Ln(T("condition",e,t,n),T("a",e,t,n),T("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},wse=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[ot(T("a",e,t,n),T("b",e,t,n),T("transposeA",e,t,n),T("transposeB",e,t,n))];case"Einsum":return[gq(T("equation",e,t,n),...T("tensors",e,t,n))];case"Transpose":return[pt(T("x",e,t,n),T("perm",e,t,n))];case"_FusedMatMul":let[r,s]=T("fusedOps",e,t,n),a=r==="biasadd",o=s==="prelu",i=T("numArgs",e,t,n),l=T("leakyreluAlpha",e,t,n);if(a){if(o&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=T("args",e,t,n);return[ti.matMul({a:T("a",e,t,n),b:T("b",e,t,n),transposeA:T("transposeA",e,t,n),transposeB:T("transposeB",e,t,n),bias:u,activation:s,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kse=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Xl(T("x",e,t,n),T("mean",e,t,n),T("variance",e,t,n),T("offset",e,t,n),T("scale",e,t,n),T("epsilon",e,t,n))];case"FusedBatchNormV3":return[Xl(T("x",e,t,n),T("mean",e,t,n),T("variance",e,t,n),T("offset",e,t,n),T("scale",e,t,n),T("epsilon",e,t,n))];case"LRN":return[pI(T("x",e,t,n),T("radius",e,t,n),T("bias",e,t,n),T("alpha",e,t,n),T("beta",e,t,n))];case"Softmax":return[Of(T("x",e,t,n))];case"LogSoftmax":return[HA(T("x",e,t,n))];case"SparseToDense":return[RI(T("sparseIndices",e,t,n),T("outputShape",e,t,n),T("sparseValues",e,t,n),T("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ise=(e,t,n)=>{switch(e.op){case"Max":{let o=T("axis",e,t,n),i=T("keepDims",e,t,n);return[os(T("x",e,t,n),o,i)]}case"Mean":{let o=T("axis",e,t,n),i=T("keepDims",e,t,n);return[Xt(T("x",e,t,n),o,i)]}case"Min":{let o=T("axis",e,t,n),i=T("keepDims",e,t,n);return[_f(T("x",e,t,n),o,i)]}case"Sum":{let o=T("axis",e,t,n),i=T("keepDims",e,t,n);return[_e(T("x",e,t,n),o,i)]}case"All":{let o=T("axis",e,t,n),i=T("keepDims",e,t,n);return[DA(T("x",e,t,n),o,i)]}case"Any":{let o=T("axis",e,t,n),i=T("keepDims",e,t,n);return[wf(T("x",e,t,n),o,i)]}case"ArgMax":{let o=T("axis",e,t,n);return[kf(T("x",e,t,n),o)]}case"ArgMin":{let o=T("axis",e,t,n);return[j6(T("x",e,t,n),o)]}case"Prod":{let o=T("axis",e,t,n),i=T("keepDims",e,t,n);return[XA(T("x",e,t,n),o,i)]}case"Cumsum":{let o=T("axis",e,t,n),i=T("exclusive",e,t,n),l=T("reverse",e,t,n);return[BA(T("x",e,t,n),o,i,l)]}case"Bincount":let r=T("x",e,t,n),s=T("weights",e,t,n),a=T("size",e,t,n);return[nI(r,s,a)];case"DenseBincount":{let o=T("x",e,t,n),i=T("weights",e,t,n),l=T("size",e,t,n),u=T("binaryOutput",e,t,n);return[rq(o,i,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Sse=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=T("n",e,t,n),s=T("axis",e,t,n),a=T("tensors",e,t,n);return a=a.slice(0,r),[en(a,s)]}case"Gather":{let r=T("x",e,t,n),s=T("indices",e,t,n);return[$d(r,ke(s,"int32"),0)]}case"GatherV2":{let r=T("axis",e,t,n),s=T("batchDims",e,t,n),a=T("x",e,t,n),o=T("indices",e,t,n);return[$d(a,ke(o,"int32"),r,s)]}case"Reverse":{let r=T("dims",e,t,n),s=[];for(let o=0;o<r.length;o++)r[o]&&s.push(o);let a=T("x",e,t,n);return[Fr(a,s)]}case"ReverseV2":{let r=T("axis",e,t,n),s=T("x",e,t,n);return[Fr(s,r)]}case"Slice":{let r=T("begin",e,t,n),s=T("size",e,t,n);return[nt(T("x",e,t,n),r,s)]}case"StridedSlice":{let r=T("begin",e,t,n),s=T("end",e,t,n),a=T("strides",e,t,n),o=T("beginMask",e,t,n),i=T("endMask",e,t,n),l=T("ellipsisMask",e,t,n),u=T("newAxisMask",e,t,n),c=T("shrinkAxisMask",e,t,n),d=T("x",e,t,n);return[TI(d,r,s,a,o,i,l,u,c)]}case"Pack":return Z(()=>{let r=T("axis",e,t,n),s=T("tensors",e,t,n),a=s[0].shape,o=Jl(s[0]).shape,i=s.map(l=>{let u=k.arraysEqual(l.shape,a);if(!u&&!k.arraysEqual(Jl(l).shape,o))throw new Error("the input tensors shape does not match");return u?l:J(l,a)});return[Mr(i,r)]});case"Unpack":{let r=T("axis",e,t,n),s=T("tensor",e,t,n);return ls(s,r)}case"Tile":{let r=T("reps",e,t,n);return[Yo(T("x",e,t,n),r)]}case"Split":case"SplitV":{let r=T("axis",e,t,n),s=T("numOrSizeSplits",e,t,n),a=T("x",e,t,n);return hr(a,s,r)}case"ScatterNd":{let r=T("indices",e,t,n),s=T("values",e,t,n),a=T("shape",e,t,n);return[FX(r,s,a)]}case"GatherNd":{let r=T("x",e,t,n),s=T("indices",e,t,n);return[zX(r,s)]}case"SparseToDense":{let r=T("sparseIndices",e,t,n),s=T("outputShape",e,t,n),a=T("sparseValues",e,t,n),o=T("defaultValue",e,t,n);return[RI(r,a,s,a.dtype===o.dtype?o:ke(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Tse=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:a,reverseIndexMap:o}=Vf.sparseFillEmptyRows(T("indices",e,t,n),T("values",e,t,n),T("denseShape",e,t,n),T("defaultValue",e,t,n));return[r,s,a,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=Vf.sparseReshape(T("inputIndices",e,t,n),T("inputShape",e,t,n),T("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[Vf.sparseSegmentMean(T("data",e,t,n),T("indices",e,t,n),T("segmentIds",e,t,n))];case"SparseSegmentSum":return[Vf.sparseSegmentSum(T("data",e,t,n),T("indices",e,t,n),T("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nse=(e,t,n)=>{switch(e.op){case"FFT":return[o1(T("x",e,t,n))];case"IFFT":return[Pf(T("x",e,t,n))];case"RFFT":return[i1(T("x",e,t,n))];case"IRFFT":return[SI(T("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Cse=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=f1.stringNGrams(T("data",e,t,n),T("dataSplits",e,t,n),T("separator",e,t,n),T("nGramWidths",e,t,n),T("leftPad",e,t,n),T("rightPad",e,t,n),T("padWidth",e,t,n),T("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:a}=f1.stringSplit(T("input",e,t,n),T("delimiter",e,t,n),T("skipEmpty",e,t,n));return[r,s,a]}case"StringToHashBucketFast":return[f1.stringToHashBucketFast(T("input",e,t,n),T("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ese=(e,t,n)=>{switch(e.op){case"Cast":return[ke(T("x",e,t,n),T("dtype",e,t,n))];case"ExpandDims":{let r=T("axis",e,t,n);return[$r(T("x",e,t,n),r)]}case"Squeeze":{let r=T("axis",e,t,n);return[Jl(T("x",e,t,n),r)]}case"Reshape":return[J(T("x",e,t,n),T("shape",e,t,n))];case"MirrorPad":return[xI(T("x",e,t,n),T("padding",e,t,n),T("mode",e,t,n))];case"PadV2":case"Pad":return[Wa(T("x",e,t,n),T("padding",e,t,n),T("constantValue",e,t,n))];case"SpaceToBatchND":{let r=T("blockShape",e,t,n),s=T("paddings",e,t,n);return[Rf(T("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=T("blockShape",e,t,n),s=T("crops",e,t,n);return[Tf(T("x",e,t,n),r,s)]}case"DepthToSpace":{let r=T("blockSize",e,t,n),s=T("dataFormat",e,t,n).toUpperCase();return[oI(T("x",e,t,n),r,s)]}case"BroadcastTo":return[Sd(T("x",e,t,n),T("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function gT(e,t,n,r){let s=((a,o,i)=>{switch(a.category){case"arithmetic":return Z(()=>ase(a,o,i));case"basic_math":return Z(()=>ose(a,o,i));case"control":return hse(a,o,i);case"convolution":return Z(()=>pse(a,o,i));case"creation":return Z(()=>fse(a,o,i));case"dynamic":return mse(a,o,i);case"evaluation":return Z(()=>gse(a,o,i));case"image":return Z(()=>bse(a,o,i));case"graph":return Z(()=>yse(a,o,i));case"logical":return Z(()=>vse(a,o,i));case"matrices":return Z(()=>wse(a,o,i));case"normalization":return Z(()=>kse(a,o,i));case"reduction":return Z(()=>Ise(a,o,i));case"slice_join":return Z(()=>Sse(a,o,i));case"sparse":return Z(()=>Tse(a,o,i));case"spectral":return Z(()=>Nse(a,o,i));case"string":return Z(()=>Cse(a,o,i));case"transformation":return Z(()=>Ese(a,o,i));case"hash_table":return xse(a,o,i,r);case"custom":let l=H8(a.op);if(l&&l.customExecutor)return l.customExecutor(new sse(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var yT=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,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 AT(e,t,n,r){let s=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(h=>pr(h)[0]),c=[];r!=null&&(c=r.map(h=>pr(h.name)[0]));let d=[...t];for(;d.length>0;){let h=d.pop();if((xT(h)||Fse(h)||Mse(h))&&o==null&&(o=h,i=o.children.map(p=>p.name).filter(p=>s.has(p))),s.add(h.name),n[h.name]==null&&u.indexOf(h.name)===-1&&c.indexOf(h.name)===-1){if(h.inputs.length===0){a.push(h.name);continue}h.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),d.push(p))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function $se(e,t,n){let{usedNodes:r,inputs:s}=n,a=[],o=Object.keys(s).map(c=>pr(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{r.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{r.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&r.has(d.name)&&d.inputs.every(h=>l.has(h.name))&&a.push(d)})}return u}var _se=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Rse=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Dse=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function xT(e){return _se.indexOf(e.op)>=0}function Fse(e){return Rse.indexOf(e.op)>=0}function Mse(e){return Dse.indexOf(e.op)>=0}var N5=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new N5(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(r=>r.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}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(s=>s.name).sort(),r=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=AT(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(r.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${r}]`)}return $se(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[pr(c)[0]]),s=t.map(c=>pr(c)[0]),a=s.map(c=>this.graph.nodes[c]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(r,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return Z(()=>{let c=new yT(this.weightMap,l,u,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=pr(f),y=[];y[g]=e[f],d[m]=y});let h=this.getFrozenTensorIds(d),p={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=gT(m,d,c,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,c,h,s,p)}}return this.parent==null&&c.dispose(h),t.map(f=>Bn(f,d,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=Pre(i.name,n,r);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!s.has(u.id)){let c=o[u.id];c===1?(u.dispose(),delete o[u.id]):c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},s={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new yT(this.weightMap,r,s,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>Bn(d,o,a)),l=i.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(p=>{p&&!p.kept&&!p.isDisposed&&!c.has(p.id)&&p.dispose()})}),this.parent==null&&a.dispose(c),i}async executeFunctionAsync(e,t,n){let r=e.reduce((s,a,o)=>(s[this.inputs[o].name]=a,s),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let s=Object.keys(e),a=s.map(A=>this.graph.nodes[pr(A)[0]]),o=n.map(A=>pr(A)[0]),i=o.map(A=>this.graph.nodes[A]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:d}=AT(e,i,this.weightMap,this._initNodes),h=[...a,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),p={...this.weightMap};Object.keys(e).forEach(A=>{let[x,b]=pr(A),v=[];v[b]=e[A],p[x]=v});let f={},m=this.getFrozenTensorIds(p),g={};for(;h.length>0;){let A=this.processStack(a,h,t,p,g,m,o,f,l);await Promise.all(A)}c==null&&!r&&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=i.filter(A=>!xT(A)&&!Bn(A.name,p,t)).map(A=>A.name);if(y.length>0){let A="";throw c!=null&&(A=`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 [${s}]. Consider providing the following inputs: [${u}]. ${A}`)}return p}processStack(e,t,n,r,s,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&T("isConstant",c.node,r,n)&&([d]=ma(c.node.name,n)),r[c.node.name]==null){let h=gT(c.node,r,n,this._resourceManager);d||([d]=ma(c.node.name,n));let p=n.currentContext;k.isPromise(h)?u.push(h.then(f=>(r[d]=f,n.currentContext=p,this.checkTensorForDisposal(d,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,l),f))):(r[d]=h,this.checkTensorForDisposal(d,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,l))}else this.processChildNodes(c.node,t,n,r,s,l)}return u}processChildNodes(e,t,n,r,s,a){e.children.forEach(o=>{let[i]=ma(o.name,n);s[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Bn(l,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Bn(l,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=pr(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);k.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&k.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=pr(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=pr(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ose=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Pse="?tfjs-format=file",zse="model.json",bT=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ose}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=cr.browserHTTPRequest(e,this.loadOptions);else{let t=cr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(cr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=cr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new N5(cT.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=cT.Instance.transformGraph(e.modelInitializer);this.initializer=new N5(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=cr.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ct)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Et(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${zse}${Pse}`);let n=new bT(e,t);return await n.load(),n}var Lse="3.7.0",vT={};De(vT,{CSVDataset:()=>MT,Dataset:()=>uu,FileDataSource:()=>VT,TextLineDataset:()=>RT,URLDataSource:()=>UT,array:()=>lae,csv:()=>xae,func:()=>bae,generator:()=>vae,microphone:()=>kae,version_data:()=>Iae,webcam:()=>wae,zip:()=>uae});var Bse=Ks(B3()),Wse=Ks(B3());function Vse(e,t){return Sm(e,t)}function Sm(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let s=t(e);if(s.recurse&&s.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(s.recurse)if(lu(e)){let a=Array.isArray(e)?[]:{};r.add(e);for(let o in e){let i=e[o],l=Sm(i,t,n,r);a[o]=l}return r.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,s.value),s.value}function Use(e,t=kT){return wT(e,t)}function wT(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let s=t(e);if(s.recurse&&s.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(s.recurse)if(lu(r)){let a=Array.isArray(r)?[]:{};n.add(r);for(let o in r){let i=e.map(u=>u[o]),l=wT(i,t,n);a[o]=l}return n.delete(r),a}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return s.value}function kT(e){return e===null?null:lu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function IT(e,t){let n=new Map;Sm(e,t,n);for(let s of Array.from(n.keys())){let a=n.get(s);if(k.isPromise(a)){let o=await a;n.set(s,o)}}return Sm(e,t,n)}function lu(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ct))}function Hse(e){return e==null||Gse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ct||k.isTypedArray(e)}function Gse(e){return e===null||typeof e!="object"&&typeof e!="function"}function jse(e){return Vse(e,qse)}function qse(e){return e instanceof Ct?{value:e.clone(),recurse:!1}:lu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var ST=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}},TT=class extends ST{constructor(){super(TT.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 r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},NT=TT;NT.INITIAL_CAPACITY=32;function CT(e){return new Zse(e)}function C5(e){return new Yse(e)}function Kse(e,t){return new $T(e,t)}function Xse(e,t=Tm.FAIL){return new oae(e,t)}var xn=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 sae(this,e)}filter(e){return new nae(this,e)}map(e){return new rae(this,e)}mapAsync(e){return new ET(this,e)}serialMapAsync(e){return new ET(this,e).serial()}flatmap(e){return new aae(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 tae(this,e,t)}columnMajorBatch(e,t=!0,n=kT){return this.rowMajorBatch(e,t).map(s=>Use(s,n))}concatenate(e,t){return new $T(CT([this,e]),t)}take(e){return e<0||e==null?this:new eae(this,e)}skip(e){return e<0||e==null?this:new Qse(this,e)}prefetch(e){return new _T(this,e)}shuffle(e,t){return new iae(this,e,t)}serial(){return new Jse(this)}},Zse=class extends xn{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:jse(e),done:!1}}},Yse=class extends xn{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: ${e.message}`,e}}},Jse=class extends xn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Qse=class extends xn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;je(e.value)}return this.upstream.next()}},eae=class extends xn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},tae=class extends xn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},nae=class extends xn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;je(e.value)}}},rae=class extends xn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Es.getTensorsInContainer(e.value),n=this.transform(e.value),r=Es.getTensorsInContainer(n);for(let s of t)Es.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},sae=class extends xn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},ET=class extends xn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Es.getTensorsInContainer(e.value),n=await this.transform(e.value),r=Es.getTensorsInContainer(n);for(let s of t)Es.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},E5=class extends xn{constructor(){super();this.outputQueue=new NT,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},aae=class extends E5{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Es.getTensorsInContainer(e.value),n=this.transform(e.value),r=Es.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)Es.isTensorInList(s,r)||s.dispose();return!0}},$T=class extends xn{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}},Tm;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Tm||(Tm={}));var oae=class extends xn{constructor(e,t=0){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 r(a){return a instanceof xn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let s=await IT(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},_T=class extends xn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new ST(t)}summary(){return`${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()}},iae=class extends _T{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Wse.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}}},uu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),fr(async()=>(await n.iterator()).columnMajorBatch(e,t,cae),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,fr(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,fr(async()=>(await t.iterator()).filter(r=>Z(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return fr(async()=>(await t.iterator()).map(n=>Z(()=>e(n))),this.size)}mapAsync(e){let t=this;return fr(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return fr(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,fr(async()=>{let r=C5(async()=>({value:await t.iterator(),done:!1}));return Kse(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,fr(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,s=Bse.alea(t||k.now().toString());return fr(async()=>{let a=s.int32();return n&&(a+=s.int32()),(await r.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,fr(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};uu.MAX_BUFFER_SIZE=1e4;function fr(e,t=null){return new class extends uu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function lae(e){return fr(async()=>CT(e),e.length)}function uae(e){if(!lu(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 fr(async()=>{let n=await IT(e,r=>{if(r instanceof uu)return{value:r.iterator(),recurse:!1};if(lu(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Xse(n,Tm.SHORTEST)},t)}function cae(e){if(e===null)return null;let t=e[0];return Hse(t)?{value:dae(e),recurse:!1}:{value:null,recurse:!0}}function dae(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ct?Mr(e):$s(e)}var RT=class extends uu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(r=>(r.endsWith("\r")&&(r=r.slice(0,-1)),r))}},Nm='"',eh=Symbol("out"),DT=Symbol("field"),Cm=Symbol("quote"),$5=Symbol("quoteafterquote"),FT=Symbol("quoteinquote"),MT=class extends uu{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 RT(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((r,s)=>(r[s]=r[s]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[s],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let u=Number(i);if(isNaN(u))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=u;else switch(o.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(i);break;default:l=u}}o&&o.isLabel?r[a]=l:n[a]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,s=e.length,a=eh;for(let o=0;o<s;o++)switch(a){case eh:switch(e.charAt(o)){case Nm:r=o+1,a=Cm;break;case this.delimiter:if(r=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=eh;break;default:a=DT,r=o;break}break;case DT:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o)),a=eh,r=o+1;break;default:}break;case Cm:switch(e.charAt(o)){case Nm:a=$5;break;default:}break;case $5:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o-1)),a=eh,r=o+1;break;case Nm:a=Cm;break;default:a=FT;break}break;case FT:switch(e.charAt(o)){case Nm:a=Cm;break;default:}break;default:}if(a===$5?n.push(e.substring(r,s-1)):n.push(e.substring(r)),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}},OT=class extends xn{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(ae().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new OT(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: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,s)=>n.set(r,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),$s(n,t)}},PT=class extends xn{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=_n([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-r)/2,o=s+n,i=r+a;this.cropBox=Ql([a,s,i,o],[1,4])}else this.cropBox=Ql([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ae().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 PT(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=S6.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return Z(()=>{let t=$r(ke(e,"float32"),0),n;n=ni.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return J(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},zT=class{},LT=class extends xn{split(e){return new hae(this,e)}},hae=class extends LT{constructor(e,t){super();this.upstream=e,this.impl=new pae(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},pae=class extends E5{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}},fae=class extends xn{decodeUTF8(){return new mae(this)}},mae=class extends LT{constructor(e){super();this.upstream=e,this.impl=new gae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},gae=class extends E5{constructor(e){super();if(this.upstream=e,ae().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=IR();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 ae().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},BT=class extends fae{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(ae().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,r)));else{let s=new FileReader;s.onload=o=>{let i=s.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},s.onabort=o=>n(new Error("Aborted")),s.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,r);s.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function yae(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=Aae(e));let s=await k.fetch(n,r);if(s.ok){let a=new Uint8Array(await s.arrayBuffer());return new BT(a,t)}else throw new Error(s.statusText)}var Aae=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function WT(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var VT=class extends zT{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(WT(this.input)&&ae().get("IS_NODE")){let e=co("fs");this.input=e.readFileSync(this.input.substr(7))}return new BT(this.input,this.options)}},UT=class extends zT{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return WT(this.url)?new VT(this.url,this.fileOptions).iterator():yae(this.url,this.fileOptions)}};function xae(e,t={}){return new MT(new UT(e),t)}function bae(e){let t=C5(e);return fr(async()=>t)}function vae(e){return fr(async()=>{let t=await e();return C5(()=>t.next())})}async function wae(e,t){return PT.create(e,t)}async function kae(e){return OT.create(e)}var Iae="3.7.0";function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Sae=ca.whereImpl,HT=class extends Bp{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new my(this,za())}nextDataId(){return HT.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,ae().get("IS_NODE")&&R.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 r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let s=n.map(a=>k.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return{dataId:r,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,r,s){this.data.set(e,{values:t,dtype:r,refCount:s})}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 r=this.readSync(n.real.dataId),s=this.readSync(n.imag.dataId);return R.mergeRealAndImagArrays(r,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return za().makeTensorFromDataId(r,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){Te([e],"where");let t=this.readSync(e.dataId);return Sae(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},_5=HT;_5.nextDataId=0;var GT={};De(GT,{addImpl:()=>qT,bincountImpl:()=>D5,bincountReduceImpl:()=>KT,ceilImpl:()=>XT,concatImpl:()=>ZT,equalImpl:()=>YT,expImpl:()=>QT,expm1Impl:()=>tN,floorImpl:()=>nN,gatherNdImpl:()=>rN,gatherV2Impl:()=>sN,greaterEqualImpl:()=>oN,greaterImpl:()=>aN,lessEqualImpl:()=>lN,lessImpl:()=>iN,linSpaceImpl:()=>uN,logImpl:()=>cN,maxImpl:()=>dN,maximumImpl:()=>hN,minimumImpl:()=>pN,multiplyImpl:()=>F5,negImpl:()=>fN,notEqualImpl:()=>mN,prodImpl:()=>gN,rangeImpl:()=>yN,rsqrtImpl:()=>AN,simpleAbsImpl:()=>jT,sliceImpl:()=>xN,sparseFillEmptyRowsImpl:()=>bN,sparseReshapeImpl:()=>vN,sparseSegmentReductionImpl:()=>O5,squaredDifferenceImpl:()=>wN,stridedSliceImpl:()=>kN,stringNGramsImpl:()=>IN,stringSplitImpl:()=>SN,stringToHashBucketFastImpl:()=>TN,subImpl:()=>NN,tileImpl:()=>CN,topKImpl:()=>EN,transposeImpl:()=>M5,uniqueImpl:()=>$N});function jT(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var Tae=e=>{let{x:t}=e.inputs,n=e.backend;Te(t,"abs");let r=new Float32Array(k.sizeFromShape(t.shape)),s=n.data.get(t.dataId).values;return r=jT(s),n.makeOutput(r,t.shape,"float32")},Nae={kernelName:xc,backendName:"cpu",kernelFunc:Tae};function nn(e){return(t,n,r,s,a)=>{let o=R.assertAndGetBroadcastShape(t,n),i=o.length,l=k.computeStrides(o),u=k.sizeFromShape(o),c=k.getTypedArrayFromDType(a,u),d=t.length,h=n.length,p=k.computeStrides(t),f=k.computeStrides(n),m=R.getBroadcastDims(t,o),g=R.getBroadcastDims(n,o);if(m.length+g.length===0)for(let y=0;y<c.length;++y)c[y]=e(r[y%r.length],s[y%s.length]);else for(let y=0;y<c.length;++y){let A=k.indexToLoc(y,i,l),x=A.slice(-d);m.forEach(S=>x[S]=0);let b=k.locToIndex(x,d,p),v=A.slice(-h);g.forEach(S=>v[S]=0);let w=k.locToIndex(v,h,f);c[y]=e(r[b],s[w])}return[c,o]}}function mr(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=n.makeTensorInfo(r.shape,"complex64"),l=n.data.get(i.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",a),imag:n.makeTensorInfo(s.shape,"float32",o)},i}var Cae={kernelName:Sy,backendName:"cpu",kernelFunc:mr};function Em(e,t,n="float32"){if(n==="complex64"){let s=Em(e,t,"float32"),a=Em(e,t,"float32");return mr({inputs:{real:s,imag:a},backend:e})}let r=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function zs(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Eae={kernelName:hl,backendName:"cpu",kernelFunc:zs};function pi(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.real,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}var $ae={kernelName:jy,backendName:"cpu",kernelFunc:pi};function Ja(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return zs({inputs:{x:s},backend:n});let o=Em(n,s.shape,s.dtype),i=Ja({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),l=mr({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(s.dtype==="complex64"){let o=pi({inputs:{input:s},backend:n}),i=Ja({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!k.hasEncodingLoss(s.dtype,a)){let o=zs({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(s.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(s.shape,"int32",i)}if(a==="bool"){let o=n.data.get(s.dataId).values,i=k.toTypedArray([0],s.dtype),[l,u]=nn((c,d)=>c!==d?1:0)(s.shape,[],o,i,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var _ae={kernelName:el,backendName:"cpu",kernelFunc:Ja};function bn(e,t,n,r){return n==null?({inputs:s,backend:a})=>{let{a:o,b:i}=s,l=a;Te([o,i],e);let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=o.dtype==="string"?R.fromUint8ToStringArray(u):u,h=o.dtype==="string"?R.fromUint8ToStringArray(c):c,p=r||o.dtype,[f,m]=t(o.shape,i.shape,d,h,p);return l.makeTensorInfo(m,p,f)}:({inputs:s,backend:a})=>{let{a:o,b:i}=s,l=a;if(o.dtype==="complex64"||i.dtype==="complex64"){let u=Ja({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),c=l.data.get(u.dataId),d=c.complexTensorInfos.real,h=c.complexTensorInfos.imag,p=l.data.get(d.dataId).values,f=l.data.get(h.dataId).values,m=Ja({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(m.dataId),y=g.complexTensorInfos.real,A=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,b=l.data.get(A.dataId).values,[v,w,S]=n(o.shape,i.shape,p,f,x,b),I=l.makeTensorInfo(S,"float32",v),E=l.makeTensorInfo(S,"float32",w),F=mr({inputs:{real:I,imag:E},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(I),l.disposeIntermediateTensorInfo(E),F}else{let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=r||o.dtype,[h,p]=t(o.shape,i.shape,u,c,d);return l.makeTensorInfo(p,d,h)}}}function R5(e){return(t,n,r,s,a,o)=>{let i=R.assertAndGetBroadcastShape(t,n),l=k.sizeFromShape(i),u=i.length,c=k.computeStrides(i),d=k.getTypedArrayFromDType("float32",l),h=k.getTypedArrayFromDType("float32",l),p=R.getBroadcastDims(t,i),f=R.getBroadcastDims(n,i),m=R.mergeRealAndImagArrays(r,s),g=R.mergeRealAndImagArrays(a,o),y=t.length,A=k.computeStrides(t),x=n.length,b=k.computeStrides(n);if(p.length+f.length===0)for(let v=0;v<d.length;v++){let w=v%m.length,S=v%g.length,I=e(m[w*2],m[w*2+1],g[S*2],g[S*2+1]);d[v]=I.real,h[v]=I.imag}else for(let v=0;v<d.length;v++){let w=k.indexToLoc(v,u,c),S=w.slice(-y);p.forEach(_=>S[_]=0);let I=k.locToIndex(S,y,A),E=w.slice(-x);f.forEach(_=>E[_]=0);let F=k.locToIndex(E,x,b),$=e(m[I*2],m[I*2+1],g[F*2],g[F*2+1]);d[v]=$.real,h[v]=$.imag}return[d,h,i]}}var qT=nn((e,t)=>e+t),Rae=R5((e,t,n,r)=>({real:e+n,imag:t+r})),th=bn(Fa,qT,Rae),Dae={kernelName:Fa,backendName:"cpu",kernelFunc:th};function D5(e,t,n,r,s){let a=k.sizeFromShape(r),o=k.makeZerosTypedArray(s,n);for(let i=0;i<e.length;i++){let l=e[i];if(l<0)throw new Error("Input x must be non-negative!");l>=s||(a>0?o[l]+=t[i]:o[l]+=1)}return o}function KT(e,t,n,r=!1){let s=e.shape[0],a=e.shape[1],o=Le([s,n],t.dtype);for(let i=0;i<s;i++)for(let l=0;l<a;l++){let u=e.get(i,l);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(r?o.set(1,i,u):t.size>0?o.set(o.get(i,u)+t.get(i,l),i,u):o.set(o.get(i,u)+1,i,u))}return o}function cu(e){return(t,n,r)=>{let s=k.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)s[a]=e(t[a],r);return s}}function xt(e,t,n){return({inputs:r,attrs:s,backend:a})=>{let{x:o}=r;if(Te(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,u=k.sizeFromShape(o.shape),c=n||o.dtype,d=k.getArrayFromDType(c,u);for(let h=0;h<u;++h)d[h]=t(l[h],s);return i.makeTensorInfo(o.shape,c,d)}}function du(e,t,n){return({inputs:r,attrs:s,backend:a})=>{let{x:o}=r;if(Te(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,u=n||o.dtype,c=t(l,u,s);return i.makeTensorInfo(o.shape,u,c)}}var XT=cu(e=>Math.ceil(e)),Fae=du(No,XT),Mae={kernelName:No,backendName:"cpu",kernelFunc:Fae};function ZT(e,t,n,r){let s=k.getArrayFromDType(n,k.sizeFromShape(t));if(r&&n!=="string"){let a=0;e.forEach(o=>{let i=k.sizeFromShape(o.shape);s.set(o.vals,a),a+=i})}else{let a=0;e.forEach(o=>{let i=n==="string"?R.fromUint8ToStringArray(o.vals):o.vals,l=0;for(let u=0;u<o.shape[0];++u){let c=u*t[1]+a;for(let d=0;d<o.shape[1];++d)s[c+d]=i[l++]}a+=o.shape[1]})}return s}var YT=nn((e,t)=>e===t?1:0),JT=bn(il,YT,null,"bool"),Oae={kernelName:il,backendName:"cpu",kernelFunc:JT},QT=cu(e=>Math.exp(e)),eN=du(Eo,QT),Pae={kernelName:Eo,backendName:"cpu",kernelFunc:eN},tN=cu(e=>Math.expm1(e)),zae=du(ll,tN),Lae={kernelName:ll,backendName:"cpu",kernelFunc:zae},nN=cu(e=>Math.floor(e)),Bae=du($o,nN),Wae={kernelName:$o,backendName:"cpu",kernelFunc:Bae};function rN(e,t,n,r,s,a,o,i,l){let u=Le([r,a],n);for(let c=0;c<r;c++){let d=[],h=0;for(let p=0;p<s;p++){let f=e[c*s+p];h+=f*o[p],d.push(f)}if(h<0||h>=l/a)throw new Error(`Invalid indices: ${d} does not index into ${i}`);for(let p=0;p<a;p++)u.values[c*a+p]=t.get(...t.indexToLoc(h*a+p))}return u}function sN(e,t,n){let r=Le(n,e.dtype);for(let s=0;s<r.size;++s){let o=r.indexToLoc(s).slice(),i=o[0],l=o[2],u=t.locToIndex([i,l]);o[2]=t.values[u];let c=e.locToIndex(o);r.values[s]=e.values[c]}return r}var aN=nn((e,t)=>e>t?1:0),Vae=bn(dl,aN,null,"bool"),Uae={kernelName:dl,backendName:"cpu",kernelFunc:Vae},oN=nn((e,t)=>e>=t?1:0),Hae=bn(_o,oN,null,"bool"),Gae={kernelName:_o,backendName:"cpu",kernelFunc:Hae},iN=nn((e,t)=>e<t?1:0),jae=bn(fl,iN,null,"bool"),qae={kernelName:fl,backendName:"cpu",kernelFunc:jae},lN=nn((e,t)=>e<=t?1:0),Kae=bn(ml,lN,null,"bool"),Xae={kernelName:ml,backendName:"cpu",kernelFunc:Kae};function uN(e,t,n){let r=(t-e)/(n-1),s=k.makeZerosTypedArray(n,"float32");s[0]=e;for(let a=1;a<s.length;a++)s[a]=s[a-1]+r;return s}var cN=cu(e=>Math.log(e)),Zae=du(Ro,cN),Yae={kernelName:Ro,backendName:"cpu",kernelFunc:Zae};function dN(e,t,n,r){let s=k.getTypedArrayFromDType(r,k.sizeFromShape(n));for(let a=0;a<s.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let u=e[o+l];(Number.isNaN(u)||u>i)&&(i=u)}s[a]=i}return s}var hN=nn((e,t)=>Math.max(e,t)),Jae=bn(Do,hN),Qae={kernelName:Do,backendName:"cpu",kernelFunc:Jae},pN=nn((e,t)=>Math.min(e,t)),eoe=bn(Fo,pN),toe={kernelName:Fo,backendName:"cpu",kernelFunc:eoe},F5=nn((e,t)=>e*t),noe=R5((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),$m=bn(Mo,F5,noe),roe={kernelName:Mo,backendName:"cpu",kernelFunc:$m};function fN(e,t,n){let r=k.createScalarValue(-1,n);return F5([],t,r,e,n)}function soe(e){let{inputs:t,backend:n}=e,{x:r}=t;Te(r,"neg");let s=n.data.get(r.dataId).values,[a,o]=fN(s,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,a)}var aoe={kernelName:Gc,backendName:"cpu",kernelFunc:soe},mN=nn((e,t)=>e!==t?1:0),ooe=bn(vl,mN,null,"bool"),ioe={kernelName:vl,backendName:"cpu",kernelFunc:ooe};function M5(e,t,n,r,s){let a=t.length,o=k.sizeFromShape(t),i=k.computeStrides(t),l=k.computeStrides(s),u=k.getTypedArrayFromDType(n,k.sizeFromShape(s));for(let c=0;c<o;++c){let d=k.indexToLoc(c,a,i),h=new Array(d.length);for(let f=0;f<h.length;f++)h[f]=d[r[f]];let p=k.locToIndex(h,a,l);u[p]=e[c]}return u}function Pr(e){let{inputs:t,attrs:n,backend:r}=e,{x:s}=t,{perm:a}=n;Te(s,"transpose");let o=s.shape.length,i=new Array(o);for(let d=0;d<i.length;d++)i[d]=s.shape[a[d]];let l=r.data.get(s.dataId).values,u=M5(l,s.shape,s.dtype,a,i);return{dataId:r.write(u,i,s.dtype),shape:i,dtype:s.dtype}}var loe={kernelName:zl,backendName:"cpu",kernelFunc:Pr};function gN(e,t,n,r){let[s,a]=R.computeOutAndReduceShapes(e,r),o=qr(t,"int32"),i=k.makeZerosTypedArray(k.sizeFromShape(s),o),l=k.sizeFromShape(a);for(let u=0;u<i.length;++u){let c=u*l,d=1;for(let h=0;h<l;++h)d*=n[c+h];i[u]=d}return{outVals:i,outShape:s,outDtype:o}}function uoe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;Te(s,"prod");let i=s.shape.length,l=k.parseAxisParam(a,s.shape),u=R.getAxesPermutation(l,i),c=l,d=s,h=[];u!=null&&(d=Pr({inputs:{x:s},backend:n,attrs:{perm:u}}),h.push(d),c=R.getInnerMostAxes(c.length,i));let p=n.data.get(d.dataId).values,{outVals:f,outShape:m,outDtype:g}=gN(d.shape,d.dtype,p,c),y=m;return o&&(y=R.expandShapeToKeepDim(m,l)),h.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(y,g,f)}var coe={kernelName:Yc,backendName:"cpu",kernelFunc:uoe};function yN(e,t,n,r){let s=e===t,a=e<t&&n<0,o=t<e&&n>1;if(s||a||o)return k.makeZerosTypedArray(0,r);let i=Math.abs(Math.ceil((t-e)/n)),l=k.makeZerosTypedArray(i,r);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 AN=cu(e=>1/Math.sqrt(e)),doe=du(Oo,AN),hoe={kernelName:Oo,backendName:"cpu",kernelFunc:doe};function xN(e,t,n,r,s){let a=En.isSliceContinous(r,t,n),o=k.sizeFromShape(n),i=k.computeStrides(r);if(a){let d=En.computeFlatOffset(t,i);return s==="string"?e.slice(d,d+o):e.subarray(d,d+o)}let l=s==="string"?R.fromUint8ToStringArray(e):e,u=Le(r,s,l),c=Le(n,s);for(let d=0;d<c.size;++d){let h=c.indexToLoc(d),p=h.map((f,m)=>f+t[m]);c.set(u.get(...p),...h)}return s==="string"?R.fromStringArrayToUint8(c.values):c.values}function fi(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r;Te(s,"slice");let[i,l]=En.parseSliceParams(s,a,o);En.assertParamsValid(s,i,l);let u=n.data.get(s.dataId).values,c=xN(u,i,l,s.shape,s.dtype);return n.makeTensorInfo(l,s.dtype,c)}var poe={kernelName:rd,backendName:"cpu",kernelFunc:fi};function bN(e,t,n,r,s,a,o){let i=t[0],l=a[0],u=new Array(l),c=new Array(i),d=t[1];if(l===0){if(i!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
         indices.shape[0] = ${i}`);let g=k.getArrayFromDType(n,0),y=k.getArrayFromDType(s,0);return[g,[0,d],y,u,c]}let h=!0,p=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let y=e[g*d];if(y<0)throw new Error(`indices(${g}, 0) is invalid: ${y} < 0`);if(y>=l)throw new Error(`indices(${g}, 0) is invalid: ${y} >= ${l}`);++f[y],h=h&&y>=p,p=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;u[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&h){let g=e,y=r;for(let A=0;A<i;++A)c[A]=A;return[g,[i,d],y,u,c]}else{let g=f[l-1],y=k.getArrayFromDType(n,g*d),A=k.getArrayFromDType(s,g),x=new Array(l).fill(0);for(let b=0;b<i;++b){let v=e[b*d],w=x[v],S=(v===0?0:f[v-1])+w;x[v]++;for(let I=0;I<d;++I)y[S*d+I]=e[b*d+I];A[S]=r[b],c[b]=S}for(let b=0;b<l;++b)if(x[b]===0){let w=b===0?0:f[b-1];y[w*d+0]=b;for(let S=1;S<d;++S)y[w*d+S]=0;A[w]=o}return[y,[g,d],A,u,c]}}function vN(e,t,n,r,s){let a=k.sizeFromShape(r),o=t[0],i=s.length,l=[],u=1,c=-1;for(let g=0;g<i;++g){let y=s[g];if(y===-1){if(c!==-1)throw new Error(`only one output dimension may be -1, not both ${c} and ${g}`);c=g,l.push(1)}else{if(y<0)throw new Error(`size ${g} must be non-negative, not ${y}`);u*=y,l.push(y)}}if(c!==-1){if(u<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(a/u);if(u*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
          dense values, but the requested shape requires a multiple of ${u}. inputShape=${r} outputShape= ${l}`);l[c]=g}let d=k.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${r} outputShape=${l}`);let h=r.length,p=[];if(h>0){p[h-1]=1;for(let g=h-2;g>=0;--g)p[g]=p[g+1]*r[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=k.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let y=0;for(let A=0;A<h;++A)y+=e[g*h+A]*p[A];for(let A=0;A<i;++A)m[g*i+A]=Math.trunc(y/f[A]),y%=f[A]}return[m,[o,i],l]}function O5(e,t,n,r,s,a=!1,o=0){let i=r.length;if(i!==s.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],u=l[1],d=i>0?s[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let h=t.slice();h[0]=d;let p=h.reduce((x,b)=>x*b,1),f=k.getArrayFromDType(n,p);if(i===0)return d>0&&f.fill(o),[f,h];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,g=1,y=0,A=s[m];for(;;){let x=0;if(g<i){if(x=s[g],A===x){++g;continue}if(A>=x)throw new Error("segment ids are not increasing")}if(A<0||A>=d)throw new Error(`Segment id ${A} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);A>y&&f.fill(o,y*u,A*u);for(let b=m;b<g;++b){let v=r[b];if(v<0||v>=l[0])throw new Error(`Bad: indices[${b}] == ${r[b]} out of range [0, ${l[0]})`);for(let w=0;w<u;w++)f[A*u+w]+=e[v*u+w]}if(a)for(let b=0;b<u;b++)f[A*u+b]/=g-m;if(m=g,++g,y=A+1,A=x,g>i)break}return y<d&&f.fill(o,y*u,d*u),[f,h]}var wN=nn((e,t)=>{let n=e-t;return n*n}),foe=bn(Po,wN),moe={kernelName:Po,backendName:"cpu",kernelFunc:foe};function kN(e,t,n,r){let s=Le(e,t.dtype);for(let a=0;a<s.size;a++){let o=s.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+r[l];s.set(t.get(...i),...o)}return s}var goe=class{constructor(e,t,n,r,s,a){this.separator=k.encodeString(e),this.nGramWidths=t,this.leftPad=k.encodeString(n),this.rightPad=k.encodeString(r),this.padWidth=s,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,r,s,a){for(let o=0;o<s;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),u=Math.max(0,i-(s-(o+1))),c=a-(l+u),d=t+(l>0?0:o-i),h=0;h+=l*this.leftPad.length;for(let y=0;y<c;++y)h+=e[d+y].length;h+=u*this.rightPad.length,h+=(l+u+c-1)*this.separator.length,n[r+o]=new Uint8Array(h);let f=n[r+o],m=0,g=y=>y.forEach(A=>f[m++]=A);for(let y=0;y<l;++y)g(this.leftPad),g(this.separator);for(let y=0;y<c-1;++y)g(e[d+y]),g(this.separator);if(c>0){g(e[d+c-1]);for(let y=0;y<u;++y)g(this.separator),g(this.rightPad)}else{for(let y=0;y<u-1;++y)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,r=t.length;if(r>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<r;++l){let u=t[l]>=i;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${i}`)}let s=r-1,a=k.getArrayFromDType("int32",r);if(n===0||r===0){let i=new Array(n);for(let l=0;l<=s;++l)a[l]=0;return[i,a]}a[0]=0;for(let i=1;i<=s;++i){let l=t[i]-t[i-1],u=0;this.nGramWidths.forEach(c=>{u+=this.getNumNGrams(l,c)}),this.preserveShort&&l>0&&u===0&&(u=1),a[i]=a[i-1]+u}let o=new Array(a[s]);for(let i=0;i<s;++i){let l=t[i],u=a[i];if(this.nGramWidths.forEach(c=>{let d=t[i+1]-t[i],h=this.getNumNGrams(d,c);this.createNGrams(e,l,o,u,h,c),u+=h}),this.preserveShort&&u===a[i]){let c=t[i+1]-t[i];if(c===0)continue;let d=c+2*this.padWidth,h=1;this.createNGrams(e,l,o,u,h,d)}}return[o,a]}};function IN(e,t,n,r,s,a,o,i){return new goe(n,r,s,a,o,i).compute(e,t)}function yoe(e,t,n){if(!e.length)return[];if(t.length===0){let a=new Array(e.length);for(let o=0;o<e.length;++o)a[o]=e.subarray(o,o+1);return a}if(t.length===1){let a=t[0],o=[],i=e.indexOf(a);for(;i!==-1;){let l=e.subarray(0,i);(!n||l.length!==0)&&o.push(l),e=e.subarray(i+1),i=e.indexOf(a)}return(!n||e.length!==0)&&o.push(e),o}let r=[],s=0;for(let a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(s,a);(!n||o.length!==0)&&r.push(o),s=a+1}return r}function SN(e,t,n){let r=e.length,s=[],a=0,o=0,i=new Array(r);for(let h=0;h<r;++h){let p=yoe(e[h],t,n),f=p.length;i[h]=f,a+=f,o=Math.max(o,f),s.push(...p)}let l=k.getArrayFromDType("int32",a*2),u=new Array(a),c=[r,o],d=0;for(let h=0;h<r;++h)for(let p=0;p<i[h];++p)l[d*2]=h,l[d*2+1]=p,u[d]=s[d],++d;return[l,u,c]}function TN(e,t){let n=k.getArrayFromDType("int32",e.length);for(let r=0;r<e.length;++r)n[r]=k.fingerPrint64(e[r]).modulo(t).getLowBitsUnsigned();return n}var NN=nn((e,t)=>e-t),Aoe=R5((e,t,n,r)=>({real:e-n,imag:t-r})),P5=bn(zo,NN,Aoe),xoe={kernelName:zo,backendName:"cpu",kernelFunc:P5};function CN(e,t){let n=new Array(e.rank);for(let s=0;s<n.length;s++)n[s]=e.shape[s]*t[s];let r=Le(n,e.dtype);for(let s=0;s<r.values.length;++s){let a=r.indexToLoc(s),o=new Array(e.rank);for(let l=0;l<o.length;l++)o[l]=a[l]%e.shape[l];let i=e.locToIndex(o);r.values[s]=e.values[i]}return r}function EN(e,t,n,r,s){let a=t[t.length-1],[o,i]=[e.length/a,a],l=k.getTypedArrayFromDType(n,o*r),u=k.getTypedArrayFromDType("int32",o*r);for(let d=0;d<o;d++){let h=d*i,p=e.subarray(h,h+i),f=[];for(let A=0;A<p.length;A++)f.push({value:p[A],index:A});f.sort((A,x)=>x.value-A.value);let m=d*r,g=l.subarray(m,m+r),y=u.subarray(m,m+r);for(let A=0;A<r;A++)g[A]=f[A].value,y[A]=f[A].index}let c=t.slice();return c[c.length-1]=r,[Le(c,n,l),Le(c,"int32",u)]}function $N(e,t,n,r){let s=k.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<s;f++)a[0]*=n[f];a[1]=n[s];for(let f=s+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[s]),l=new Qt(a,r,e),u=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[s];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let y=0;y<a[0];y++)for(let A=0;A<a[2];A++)g.push(l.get(y,f,A));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,u.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let h=new Qt(d,r);u.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let y=0;y<a[2];y++)h.set(l.get(g,f,y),g,m,y)});let p=n.slice();return p[s]=d[1],{outputValues:h.values,outputShape:p,indices:i}}var boe="3.7.0";_A("cpu",()=>new _5,1);var _N=xt(Dc,e=>e>=0?e:Math.exp(e)-1),voe={kernelName:Dc,backendName:"cpu",kernelFunc:_N};function RN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r;Te([s],"leakyRelu");let o=k.sizeFromShape(s.shape),i=n.data.get(s.dataId).values,l=k.getTypedArrayFromDType("float32",o);for(let u=0;u<i.length;u++)l[u]=i[u]<0?a*i[u]:i[u];return n.makeTensorInfo(s.shape,"float32",l)}var woe={kernelName:pl,backendName:"cpu",kernelFunc:RN},koe=nn((e,t)=>e<0?t*e:e);function DN(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t;Te([r,s],"prelu");let a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,[i,l]=koe(r.shape,s.shape,a,o,r.dtype);return n.makeTensorInfo(l,r.dtype,i)}var Ioe={kernelName:Sl,backendName:"cpu",kernelFunc:DN},FN=xt(Tl,e=>Math.max(0,e)),Soe={kernelName:Tl,backendName:"cpu",kernelFunc:FN},MN=xt(Cl,e=>Math.min(Math.max(0,e),6)),Toe={kernelName:Cl,backendName:"cpu",kernelFunc:MN},ON=xt(Rl,e=>1/(1+Math.exp(-e))),Noe={kernelName:Rl,backendName:"cpu",kernelFunc:ON};function z5(e,t,n,r,s){if(n==="linear")return zs({inputs:{x:t},backend:e});if(n==="relu")return FN({inputs:{x:t},backend:e});if(n==="elu")return _N({inputs:{x:t},backend:e});if(n==="relu6")return MN({inputs:{x:t},backend:e});if(n==="prelu")return DN({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return RN({inputs:{x:t},backend:e,attrs:{alpha:s}});if(n==="sigmoid")return ON({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Ft(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=k.sizeFromShape(s.shape),i=k.inferFromImplicitShape(a,o),l=k.sizeFromShape(i);k.assert(o===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${s.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`),n.incRef(s.dataId);let u=n.data.get(s.dataId);if(u.complexTensorInfos!=null){let c=u.complexTensorInfos.real,d=u.complexTensorInfos.imag;c.shape=i,d.shape=i}return{dataId:s.dataId,shape:i,dtype:s.dtype}}var Coe={kernelName:Qc,backendName:"cpu",kernelFunc:Ft};function PN(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;Te([s,a],"matMul");let l=s.shape.length,u=a.shape.length,c=o?s.shape[l-2]:s.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],h=o?s.shape[l-1]:s.shape[l-2],p=i?a.shape[u-2]:a.shape[u-1],f=s.shape.slice(0,-2),m=a.shape.slice(0,-2),g=k.sizeFromShape(f),y=k.sizeFromShape(m),A=g===y||g===1||y===1;k.assert(l>=2&&u>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>y?s.shape.slice(0,-2):a.shape.slice(0,-2)).concat([h,p]);k.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${s.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[g,c,h]:[g,h,c],w=i?[y,p,d]:[y,d,p],S=Ft({inputs:{x:s},backend:n,attrs:{shape:v}}),I=Ft({inputs:{x:a},backend:n,attrs:{shape:w}}),E=o?S.shape[1]:S.shape[2],F=o?S.shape[2]:S.shape[1],$=i?I.shape[1]:I.shape[2],_=Math.max(g,y),N=n.data.get(S.dataId).values,P=n.data.get(I.dataId).values,B=k.computeStrides(S.shape),j=k.computeStrides(I.shape),[X,Y,ee]=o?[B[0],1,B[1]]:[B[0],B[1],1],[oe,se,ie]=i?[1,j[1],j[0]]:[j[1],1,j[0]],ne=F*$,de=Le([_,F,$],S.dtype),he=de.values,ge=n.blockSize;for(let be=0;be<_;be++)for(let Ee=0;Ee<F;Ee+=ge)for(let $e=0;$e<$;$e+=ge)for(let ze=0;ze<E;ze+=ge){let qe=Math.min(Ee+ge,F),We=Math.min($e+ge,$),vt=Math.min(ze+ge,E);for(let ft=Ee;ft<qe;ft++)for(let mt=$e;mt<We;mt++){let dt=0;for(let bt=ze;bt<vt;bt++){let Je=Math.min(be,g-1)*X,jn=Math.min(be,y-1)*ie,Wt=N[Je+ft*Y+bt*ee],ar=P[bt*oe+mt*se+jn];dt+=Wt*ar}he[be*ne+(ft*$+mt)]+=dt}}return n.disposeIntermediateTensorInfo(S),n.disposeIntermediateTensorInfo(I),n.makeTensorInfo(b,de.dtype,de.values)}var Eoe={kernelName:Qi,backendName:"cpu",kernelFunc:PN};function $oe(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=r,h,p,f,m=[];h=PN({inputs:{a:s,b:a},attrs:{transposeA:l,transposeB:u},backend:n}),o&&(p=th({inputs:{a:h,b:o},backend:n}),m.push(h),h=p),c&&(f=z5(n,h,c,i,d),m.push(h),h=f);for(let y of m)n.disposeIntermediateTensorInfo(y);return h}var _oe={kernelName:Ll,backendName:"cpu",kernelFunc:$oe},Roe=xt(bc,e=>Math.acos(e)),Doe={kernelName:bc,backendName:"cpu",kernelFunc:Roe},Foe=xt(vc,e=>Math.acosh(e)),Moe={kernelName:vc,backendName:"cpu",kernelFunc:Foe};function Ooe(e){let{inputs:t,backend:n}=e,r=t;Te(t,"addN");let s=r.map(i=>n.data.get(i.dataId).values),a=Le(r[0].shape,r[0].dtype),o=a.values;for(let i=0;i<r.length;i++){let l=s[i];for(let u=0;u<o.length;u++)o[u]+=l[u]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var Poe={kernelName:Zi,backendName:"cpu",kernelFunc:Ooe};function zoe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;Te(s,"all");let i=k.parseAxisParam(a,s.shape),l=i,u=R.getAxesPermutation(l,s.shape.length),c=s;u!=null&&(c=Pr({inputs:{x:s},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,s.shape.length)),R.assertAxesAreInnerMostDims("all",l,c.shape.length);let[d,h]=R.computeOutAndReduceShapes(c.shape,l),p=k.sizeFromShape(h),f=k.makeZerosTypedArray(k.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let A=y*p,x=m[A];for(let b=0;b<p;++b){let v=m[A+b];x=x&&v}f[y]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let y=R.expandShapeToKeepDim(d,i),A=Ft({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var Loe={kernelName:wc,backendName:"cpu",kernelFunc:zoe};function Boe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;Te(s,"any");let i=k.parseAxisParam(a,s.shape),l=i,u=R.getAxesPermutation(l,s.shape.length),c=s;u!=null&&(c=Pr({inputs:{x:s},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,s.shape.length)),R.assertAxesAreInnerMostDims("any",l,c.shape.length);let[d,h]=R.computeOutAndReduceShapes(c.shape,l),p=k.sizeFromShape(h),f=k.makeZerosTypedArray(k.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let A=y*p,x=m[A];for(let b=0;b<p;++b){let v=m[A+b];x=x||v}f[y]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let y=R.expandShapeToKeepDim(d,i),A=Ft({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var Woe={kernelName:kc,backendName:"cpu",kernelFunc:Boe};function Voe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;Te(s,"argMax");let o=k.parseAxisParam(a,s.shape),i=R.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=Pr({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=R.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],R.assertAxesAreInnerMostDims("argMax",o,l.shape.length);let[c,d]=R.computeOutAndReduceShapes(l.shape,o),h=k.sizeFromShape(c),p=k.makeZerosTypedArray(h,"int32"),f=k.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<p.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let v=m[y+b];v>A&&(A=v,x=b)}p[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",p)}var Uoe={kernelName:Yi,backendName:"cpu",kernelFunc:Voe};function Hoe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;Te(s,"argMin");let o=k.parseAxisParam(a,s.shape),i=R.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=Pr({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=R.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],R.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[c,d]=R.computeOutAndReduceShapes(l.shape,o),h=k.sizeFromShape(c),p=k.makeZerosTypedArray(h,"int32"),f=k.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<p.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let v=m[y+b];v<A&&(A=v,x=b)}p[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",p)}var Goe={kernelName:qp,backendName:"cpu",kernelFunc:Hoe},joe=xt(Ic,e=>Math.asin(e)),qoe={kernelName:Ic,backendName:"cpu",kernelFunc:joe},Koe=xt(Sc,e=>Math.asinh(e)),Xoe={kernelName:Sc,backendName:"cpu",kernelFunc:Koe},Zoe=xt(Tc,e=>Math.atan(e)),Yoe={kernelName:Tc,backendName:"cpu",kernelFunc:Zoe},Joe=nn((e,t)=>Math.atan2(e,t)),Qoe=bn(Cc,Joe),eie={kernelName:Cc,backendName:"cpu",kernelFunc:Qoe},tie=xt(Nc,e=>Math.atanh(e)),nie={kernelName:Nc,backendName:"cpu",kernelFunc:tie};function L5(e,t,n,r,s,a){let o=s.strideHeight,i=s.strideWidth,l=s.dilationHeight,u=s.dilationWidth,c=s.effectiveFilterHeight,d=s.effectiveFilterWidth,h=s.padInfo.top,p=s.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Le(s.outShape,n),g=m.values,y=s.outShape[1]*s.outShape[2]*s.outShape[3],A=s.outShape[2]*s.outShape[3],x=s.outShape[3];for(let b=0;b<s.batchSize;++b){let v=b*y,w=b*r[0];for(let S=0;S<s.inChannels;++S)for(let I=0;I<s.outHeight;++I){let E=I*o-h,F=Math.max(0,E),$=Math.min(s.inHeight,c+E),_=v+I*A;for(let N=0;N<s.outWidth;++N){let P=N*i-p,B=Math.max(0,P),j=Math.min(s.inWidth,d+P),X=f,Y=0,ee=0;for(let se=F;se<$;se+=l){let ie=w+se*r[1];for(let ne=B;ne<j;ne+=u){let de=ie+ne*r[2],he=e[de+S];a==="max"&&he>X?X=he:a==="avg"&&(Y+=he,ee++)}if(isNaN(X))break}let oe=_+N*x+S;g[oe]=a==="avg"?Y/ee:X}}}return m}function zN(e,t,n,r,s=!1,a=!1){let o=Le(r.outShape,"int32"),i=r.strideHeight,l=r.strideWidth,u=r.dilationHeight,c=r.dilationWidth,d=r.effectiveFilterHeight,h=r.effectiveFilterWidth,p=r.padInfo.top,f=r.padInfo.left,m=Le(t,n,e);for(let g=0;g<r.batchSize;++g)for(let y=0;y<r.inChannels;++y)for(let A=0;A<r.outHeight;++A){let x=A*i-p,b=x;for(;b<0;)b+=u;let v=Math.min(r.inHeight,d+x);for(let w=0;w<r.outWidth;++w){let S=w*l-f,I=S;for(;I<0;)I+=c;let E=Math.min(r.inWidth,h+S),F=Number.NEGATIVE_INFINITY,$=-1;for(let _=b;_<v;_+=u){let N=_-x;for(let P=I;P<E;P+=c){let B=P-S,j=m.get(g,_,P,y);j>F&&(F=j,s?$=a?((g*r.inHeight+_)*r.inWidth+P)*r.inChannels+y:(_*r.inWidth+P)*r.inChannels+y:$=N*h+B)}}o.set($,g,A,w,y)}}return o}function LN(e,t,n,r,s,a){let o=s.strideDepth,i=s.strideHeight,l=s.strideWidth,u=s.dilationDepth,c=s.dilationHeight,d=s.dilationWidth,h=s.effectiveFilterDepth,p=s.effectiveFilterHeight,f=s.effectiveFilterWidth,m=s.padInfo.front,g=s.padInfo.top,y=s.padInfo.left,A=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Le(s.outShape,n),b=x.values,v=s.outShape[1]*s.outShape[2]*s.outShape[3]*s.outShape[4],w=s.outShape[2]*s.outShape[3]*s.outShape[4],S=s.outShape[3]*s.outShape[4],I=s.outShape[4];for(let E=0;E<s.batchSize;++E){let F=E*v,$=E*r[0];for(let _=0;_<s.inChannels;++_)for(let N=0;N<s.outDepth;++N){let P=N*o-m,B=P;for(;B<0;)B+=u;let j=Math.min(s.inDepth,h+P),X=F+N*w;for(let Y=0;Y<s.outHeight;++Y){let ee=Y*i-g,oe=ee;for(;oe<0;)oe+=c;let se=Math.min(s.inHeight,p+ee),ie=X+Y*S;for(let ne=0;ne<s.outWidth;++ne){let de=ne*l-y,he=de;for(;he<0;)he+=d;let ge=Math.min(s.inWidth,f+de),be=ie+ne*I,Ee=A,$e=0,ze=0;for(let We=B;We<j;We+=u){let vt=$+We*r[1];for(let ft=oe;ft<se;ft+=c){let mt=vt+ft*r[2];for(let dt=he;dt<ge;dt+=d){let bt=mt+dt*r[3],Je=e[bt+_];if(a==="max"&&Je>Ee?Ee=Je:a==="avg"&&($e+=Je,ze++),isNaN(Ee))break}if(isNaN(Ee))break}if(isNaN(Ee))break}let qe=be+_;b[qe]=a==="avg"?$e/ze:Ee}}}}return x}function rie(e,t){let n=Le(t.outShape,"int32"),r=t.strideDepth,s=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,d=t.effectiveFilterWidth,h=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let A=y*r-h,x=A;for(;x<0;)x+=o;let b=Math.min(t.inDepth,u+A);for(let v=0;v<t.outHeight;++v){let w=v*s-p,S=w;for(;S<0;)S+=i;let I=Math.min(t.inHeight,c+w);for(let E=0;E<t.outWidth;++E){let F=E*a-f,$=F;for(;$<0;)$+=l;let _=Math.min(t.inWidth,d+F),N=Number.NEGATIVE_INFINITY,P=-1;for(let B=x;B<b;B+=o){let j=B-A;for(let X=S;X<I;X+=i){let Y=X-w;for(let ee=$;ee<_;ee+=l){let oe=ee-F,se=e.get(m,B,X,ee,g);se>=N&&(N=se,P=j*c*d+Y*c+oe)}}}n.set(P,m,y,v,E,g)}}}return n}function sie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Te(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=r,u=1;k.assert(R.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=R.computePool2DInfo(s.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&k.arraysEqual(c.inShape,c.outShape))d=zs({inputs:{x:s},backend:n});else{let h=n.data.get(s.dataId).values,p=k.computeStrides(s.shape),f=L5(h,s.shape,s.dtype,p,c,"avg");d=n.makeTensorInfo(c.outShape,s.dtype,f.values)}return d}var aie={kernelName:Ji,backendName:"cpu",kernelFunc:sie};function oie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=r;Te(s,"avgPool3d");let c=R.computePool3DInfo(s.shape,a,o,1,i,l,u),d=n.data.get(s.dataId).values,h=LN(d,s.shape,s.dtype,k.computeStrides(s.shape),c,"avg");return n.makeTensorInfo(h.shape,"float32",h.values)}var iie={kernelName:Kp,backendName:"cpu",kernelFunc:oie};function lie(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=r;Te([s,a],"avgPool3DGrad");let c=R.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,h=c.strideHeight,p=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,A=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,w=c.effectiveFilterWidth,S=b-1-c.padInfo.front,I=w-1-c.padInfo.left,E=v-1-c.padInfo.top,F=Le(a.shape,"float32"),$=1/(f*m*g),_=n.bufferSync(s);for(let N=0;N<c.batchSize;++N)for(let P=0;P<c.inChannels;++P)for(let B=0;B<c.inDepth;++B)for(let j=0;j<c.inHeight;++j)for(let X=0;X<c.inWidth;++X){let Y=B-S,ee=j-E,oe=X-I,se=0;for(let ie=0;ie<b;ie+=y){let ne=(Y+ie)/d;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let de=0;de<v;de+=A){let he=(ee+de)/h;if(!(he<0||he>=c.outHeight||Math.floor(he)!==he))for(let ge=0;ge<w;ge+=x){let be=(oe+ge)/p;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;se+=_.get(N,ne,he,be,P)}}}F.set(se*$,N,B,j,X,P)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var uie={kernelName:ky,backendName:"cpu",kernelFunc:lie};function cie(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;Te([s,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=r,c=R.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,h=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,A=c.effectiveFilterWidth,x=A-1-c.padInfo.left,b=y-1-c.padInfo.top,v=Le(o.shape,"float32"),w=1/(p*f),S=n.data.get(s.dataId).values,I=Le(s.shape,"float32",S);for(let E=0;E<c.batchSize;++E)for(let F=0;F<c.inChannels;++F)for(let $=0;$<c.inHeight;++$)for(let _=0;_<c.inWidth;++_){let N=$-b,P=_-x,B=0;for(let j=0;j<y;j+=m){let X=(N+j)/d;if(!(X<0||X>=c.outHeight||Math.floor(X)!==X))for(let Y=0;Y<A;Y+=g){let ee=(P+Y)/h;if(ee<0||ee>=c.outWidth||Math.floor(ee)!==ee)continue;B+=I.get(E,X,ee,F)}}v.set(B*w,E,$,_,F)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var die={kernelName:wy,backendName:"cpu",kernelFunc:cie};function hie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,scale:a,offset:o,mean:i,variance:l}=t;k.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Te([s,i,l,a,o],"batchNorm");let{varianceEpsilon:u}=r;u==null&&(u=.001);let c=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,h=n.data.get(l.dataId).values,p=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,y=p.length,A=h.length,x=d.length,b=0,v=0,w=0,S=0;for(let I=0;I<c.length;++I)m[I]=f[b++]+(c[I]-d[v++])*p[w++]/Math.sqrt(h[S++]+u),b>=g&&(b=0),v>=x&&(v=0),w>=y&&(w=0),S>=A&&(S=0);return n.makeTensorInfo(s.shape,s.dtype,m)}var pie={kernelName:cl,backendName:"cpu",kernelFunc:hie};function fie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;Te([s],"batchToSpaceND");let i=a.reduce((y,A)=>y*A),l=R.getReshaped(s.shape,a,i),u=R.getPermuted(l.length,a.length),c=R.getReshapedPermuted(s.shape,a,i),d=R.getSliceBeginCoords(o,a.length),h=R.getSliceSize(c,o,a.length),p=Ft({inputs:{x:s},backend:n,attrs:{shape:l}}),f=Pr({inputs:{x:p},backend:n,attrs:{perm:u}}),m=Ft({inputs:{x:f},backend:n,attrs:{shape:c}}),g=fi({inputs:{x:m},backend:n,attrs:{begin:d,size:h}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var mie={kernelName:Xp,backendName:"cpu",kernelFunc:fie};function gie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.data.get(s.dataId).values,l=n.data.get(a.dataId).values,u=D5(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var yie={kernelName:Iy,backendName:"cpu",kernelFunc:gie},Aie=xt(Co,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),xie={kernelName:Co,backendName:"cpu",kernelFunc:Aie},bie=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(k.sizeFromShape(t.shape)),s=n.data.get(t.dataId),a=s.complexTensorInfos.real,o=s.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],d=l[u];r[u]=Math.hypot(c,d)}return n.makeOutput(r,t.shape,"float32")},vie={kernelName:Zp,backendName:"cpu",kernelFunc:bie};function hu(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.imag,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}var wie={kernelName:Ly,backendName:"cpu",kernelFunc:hu};function pu(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=k.parseAxisParam(s,t[0].shape)[0],o=R.computeOutShape(t.map(m=>m.shape),a);if(k.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>k.sizeFromShape(m.shape)>0);if(i.length===1)return zs({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(R.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>pi({inputs:{input:b},backend:n})),g=i.map(b=>hu({inputs:{input:b},backend:n})),y=pu({inputs:m,backend:n,attrs:{axis:a}}),A=pu({inputs:g,backend:n,attrs:{axis:a}}),x=mr({inputs:{real:y,imag:A},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(A),x}let u=i.map(m=>{let g=k.sizeFromShape(m.shape.slice(a));return Ft({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=R.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,h=ZT(c,o,t[0].dtype,d),p=R.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(p,t[0].dtype,h);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var kie={kernelName:Ec,backendName:"cpu",kernelFunc:pu};function BN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=r;Te([s,a],"conv2d");let d=R.convertConv2DDataFormat(l),h=R.computeConv2DInfo(s.shape,a.shape,o,u,i,c,!1,d),p=h.filterHeight,f=h.filterWidth,m=h.dilationHeight,g=h.dilationWidth,y=h.padInfo.left,A=h.padInfo.top,x=h.dataFormat==="channelsLast",b=new Qt(h.outShape,s.dtype),v=k.computeStrides(s.shape),w=k.computeStrides(a.shape),S=v[0],I=x?v[1]:v[2],E=x?v[2]:1,F=x?1:v[1],$=b.strides[0],_=x?b.strides[1]:b.strides[2],N=x?b.strides[2]:1,P=x?1:b.strides[1],B=n.data.get(s.dataId).values,j=n.data.get(a.dataId).values,X=b.values;for(let Y=0;Y<h.batchSize;++Y){let ee=Y*S,oe=Y*$;for(let se=0;se<h.outHeight;++se){let ie=oe+se*_,ne=se*h.strideHeight-A;for(let de=0;de<p;++de){let he=ne+de*m;if(he<0||he>=h.inHeight)continue;let ge=de*w[0],be=ee+he*I;for(let Ee=0;Ee<h.outWidth;++Ee){let $e=ie+Ee*N,ze=Ee*h.strideWidth-y;for(let qe=0;qe<f;++qe){let We=ze+qe*g;if(We<0||We>=h.inWidth)continue;let vt=ge+qe*w[1],ft=be+We*E,mt=vt;for(let dt=0;dt<h.inChannels;++dt){let bt=B[ft+dt*F];for(let Je=0;Je<h.outChannels;++Je)X[$e+Je*P]+=bt*j[mt+Je];mt+=h.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,X)}var Iie={kernelName:tl,backendName:"cpu",kernelFunc:BN};function Sie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=r;Te([s,a],"conv2dBackpropFilter");let d=R.convertConv2DDataFormat(l),h=R.computeConv2DInfo(s.shape,c,o,1,i,u,!1,d),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:g}=h,y=h.dataFormat==="channelsLast",A=new Qt(h.filterShape,"float32"),x=h.padInfo.left,b=h.padInfo.top,v=n.data.get(s.dataId).values,w=n.data.get(a.dataId).values,S=new Qt(s.shape,s.dtype,v),I=new Qt(a.shape,a.dtype,w);for(let E=0;E<m;++E){let F=Math.max(0,Math.ceil((b-E)/p)),$=Math.min(h.outHeight,(h.inHeight+b-E)/p);for(let _=0;_<g;++_){let N=Math.max(0,Math.ceil((x-_)/f)),P=Math.min(h.outWidth,(h.inWidth+x-_)/f);for(let B=0;B<h.inChannels;++B)for(let j=0;j<h.outChannels;++j){let X=0;for(let Y=0;Y<h.batchSize;++Y)for(let ee=F;ee<$;++ee){let oe=E+ee*p-b;for(let se=N;se<P;++se){let ie=_+se*f-x;y?X+=S.get(Y,oe,ie,B)*I.get(Y,ee,se,j):X+=S.get(Y,B,oe,ie)*I.get(Y,j,ee,se)}}A.set(X,E,_,B,j)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var Tie={kernelName:Ty,backendName:"cpu",kernelFunc:Sie};function Nie(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=r;Te([s,a],"conv2dBackpropInput");let d=k.computeStrides(a.shape),h=k.computeStrides(s.shape),p=R.convertConv2DDataFormat(u),f=R.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),m=new Qt(f.inShape,"float32"),g=m.values,y=n.data.get(s.dataId).values,A=n.data.get(a.dataId).values,[x,b,v]=d,{batchSize:w,filterHeight:S,filterWidth:I,inChannels:E,inHeight:F,inWidth:$,outChannels:_,outHeight:N,outWidth:P,strideHeight:B,strideWidth:j}=f;p=f.dataFormat;let X=S-1-f.padInfo.top,Y=I-1-f.padInfo.left,ee=p==="channelsLast",oe=m.strides[0],se=ee?m.strides[1]:m.strides[2],ie=ee?m.strides[2]:1,ne=ee?1:m.strides[1],de=h[0],he=ee?h[1]:h[2],ge=ee?h[2]:1,be=ee?1:h[1];for(let Ee=0;Ee<w;++Ee)for(let $e=0;$e<E;++$e)for(let ze=0;ze<F;++ze){let qe=ze-X,We=Math.max(0,Math.ceil(qe/B)),vt=Math.min(N,(S+qe)/B);for(let ft=0;ft<$;++ft){let mt=ft-Y,dt=Math.max(0,Math.ceil(mt/j)),bt=Math.min(P,(I+mt)/j),Je=0;for(let Wt=We;Wt<vt;++Wt){let ar=Wt*B-qe;for(let vn=dt;vn<bt;++vn){let Vr=vn*j-mt,Rn=de*Ee+he*Wt+ge*vn,br=x*(S-1-ar)+b*(I-1-Vr)+v*$e;for(let vr=0;vr<_;++vr){let wn=y[Rn+be*vr],wr=A[br+vr];Je+=wn*wr}}}let jn=oe*Ee+se*ze+ie*ft+ne*$e;g[jn]=Je}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var Cie={kernelName:nl,backendName:"cpu",kernelFunc:Nie};function Eie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l}=r;Te([s,a],"conv3d");let u=R.computeConv3DInfo(s.shape,a.shape,o,l,i),{filterDepth:c,filterHeight:d,filterWidth:h,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new Qt(u.outShape,s.dtype),v=n.data.get(s.dataId).values,w=n.data.get(a.dataId).values,S=b.values,I=k.computeStrides(s.shape),E=k.computeStrides(a.shape);for(let F=0;F<u.batchSize;++F){let $=F*I[0],_=F*b.strides[0];for(let N=0;N<u.outDepth;++N){let P=_+N*b.strides[1],B=N*u.strideDepth-y;for(let j=0;j<c;++j){let X=B+j*p;if(X<0||X>=u.inDepth)continue;let Y=j*E[0],ee=$+X*I[1];for(let oe=0;oe<u.outHeight;++oe){let se=P+oe*b.strides[2],ie=oe*u.strideHeight-x;for(let ne=0;ne<d;++ne){let de=ie+ne*f;if(de<0||de>=u.inHeight)continue;let he=Y+ne*E[1],ge=ee+de*I[2];for(let be=0;be<u.outWidth;++be){let Ee=se+be*u.outChannels,$e=be*u.strideWidth-A;for(let ze=0;ze<h;++ze){let qe=$e+ze*m;if(qe<0||qe>=u.inWidth)continue;let We=he+ze*E[2],vt=ge+qe*u.inChannels,ft=We;for(let mt=0;mt<u.inChannels;++mt){let dt=v[vt+mt];for(let bt=0;bt<u.outChannels;++bt)S[Ee+bt]+=dt*w[ft+bt];ft+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var $ie={kernelName:Yp,backendName:"cpu",kernelFunc:Eie};function _ie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:l}=r;Te([s,a],"conv3dBackpropFilterV2");let u=k.computeStrides(s.shape),c=k.computeStrides(a.shape),d=R.computeConv3DInfo(s.shape,l,o,1,i),h=d.strideDepth,p=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,y=d.filterWidth,A=new Qt(d.filterShape,"float32"),x=A.values,[b,v,w,S]=A.strides,I=n.data.get(a.dataId).values,[E,F,$,_]=c,N=n.data.get(s.dataId).values,[P,B,j,X]=u,Y=d.padInfo.front,ee=d.padInfo.left,oe=d.padInfo.top;for(let se=0;se<m;++se){let ie=Math.max(0,Math.ceil((Y-se)/h)),ne=Math.min(d.outDepth,(d.inDepth+Y-se)/h),de=se*b;for(let he=0;he<g;++he){let ge=Math.max(0,Math.ceil((oe-he)/p)),be=Math.min(d.outHeight,(d.inHeight+oe-he)/p),Ee=he*v+de;for(let $e=0;$e<y;++$e){let ze=Math.max(0,Math.ceil((ee-$e)/f)),qe=Math.min(d.outWidth,(d.inWidth+ee-$e)/f),We=$e*w+Ee;for(let vt=0;vt<d.inChannels;++vt){let ft=vt*S+We;for(let mt=0;mt<d.outChannels;++mt){let dt=0;for(let bt=0;bt<d.batchSize;++bt){let Je=bt*P,jn=bt*E;for(let Wt=ie;Wt<ne;++Wt){let vn=(se+Wt*h-Y)*B+Je,Vr=Wt*F+jn;for(let Rn=ge;Rn<be;++Rn){let vr=(he+Rn*p-oe)*j+vn,wn=Rn*$+Vr;for(let wr=ze;wr<qe;++wr){let or=($e+wr*f-ee)*X+vr,ws=wr*_+wn;dt+=N[or+vt]*I[ws+mt]}}}}x[ft+mt]=dt}}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var Rie={kernelName:Ny,backendName:"cpu",kernelFunc:_ie};function Die(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:l}=r;Te([s],"conv3dBackpropInputV2");let u=k.computeStrides(s.shape),c=k.computeStrides(a.shape),d=R.computeConv3DInfo(l,a.shape,i,1,o),h=new Qt(d.inShape,"float32"),p=h.values,[f,m,g,y]=h.strides,A=n.data.get(s.dataId).values,[x,b,v,w]=u,S=n.data.get(a.dataId).values,[I,E,F,$]=c,{batchSize:_,filterDepth:N,filterHeight:P,filterWidth:B,inChannels:j,inDepth:X,inHeight:Y,inWidth:ee,outChannels:oe,outDepth:se,outHeight:ie,outWidth:ne,strideDepth:de,strideHeight:he,strideWidth:ge}=d,be=N-1-d.padInfo.front,Ee=P-1-d.padInfo.top,$e=B-1-d.padInfo.left;for(let ze=0;ze<_;++ze)for(let qe=0;qe<j;++qe)for(let We=0;We<X;++We){let vt=We-be,ft=Math.max(0,Math.ceil(vt/de)),mt=Math.min(se,(N+vt)/de);for(let dt=0;dt<Y;++dt){let bt=dt-Ee,Je=Math.max(0,Math.ceil(bt/he)),jn=Math.min(ie,(P+bt)/he);for(let Wt=0;Wt<ee;++Wt){let ar=Wt-$e,vn=Math.max(0,Math.ceil(ar/ge)),Vr=Math.min(ne,(B+ar)/ge),Rn=0;for(let br=ft;br<mt;++br){let vr=br*de-vt;for(let wn=Je;wn<jn;++wn){let wr=wn*he-bt;for(let kr=vn;kr<Vr;++kr){let or=kr*ge-ar,ws=x*ze+b*br+v*wn+w*kr,Us=I*(N-1-vr)+E*(P-1-wr)+F*(B-1-or)+$*qe;for(let Aa=0;Aa<oe;++Aa){let Si=A[ws+Aa],ks=S[Us+Aa];Rn+=Si*ks}}}}p[f*ze+m*We+g*dt+y*Wt+qe]=Rn}}}return n.makeTensorInfo(h.shape,h.dtype,h.values)}var Fie={kernelName:Cy,backendName:"cpu",kernelFunc:Die},Mie=xt(rl,e=>Math.cos(e)),Oie={kernelName:rl,backendName:"cpu",kernelFunc:Mie},Pie=xt($c,e=>Math.cosh(e)),zie={kernelName:$c,backendName:"cpu",kernelFunc:Pie};function Lie(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=r,[c,d,h,p]=s.shape,f=a.shape[0],[m,g]=i,y=Le([f,m,g,p],"float32"),A=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(s.dataId).values,v=k.computeStrides(s.shape),w=k.computeStrides(y.shape);for(let S=0;S<f;S++){let I=S*4,E=A[I],F=A[I+1],$=A[I+2],_=A[I+3],N=x[S];if(N>=c)continue;let P=m>1?($-E)*(d-1)/(m-1):0,B=g>1?(_-F)*(h-1)/(g-1):0;for(let j=0;j<m;j++){let X=m>1?E*(d-1)+j*P:.5*(E+$)*(d-1);if(X<0||X>d-1){for(let Y=0;Y<g;Y++)for(let ee=0;ee<p;ee++){let oe=ee+Y*w[2]+j*w[1]+S*w[0];y.values[oe]=u}continue}if(l==="bilinear"){let Y=Math.floor(X),ee=Math.ceil(X),oe=X-Y;for(let se=0;se<g;se++){let ie=g>1?F*(h-1)+se*B:.5*(F+_)*(h-1);if(ie<0||ie>h-1){for(let ge=0;ge<p;ge++){let be=ge+se*w[2]+j*w[1]+S*w[0];y.values[be]=u}continue}let ne=Math.floor(ie),de=Math.ceil(ie),he=ie-ne;for(let ge=0;ge<p;ge++){let be=ge+ne*v[2]+Y*v[1]+N*v[0],Ee=b[be];be=ge+de*v[2]+Y*v[1]+N*v[0];let $e=b[be];be=ge+ne*v[2]+ee*v[1]+N*v[0];let ze=b[be];be=ge+de*v[2]+ee*v[1]+N*v[0];let qe=b[be],We=Ee+($e-Ee)*he,vt=ze+(qe-ze)*he;be=ge+se*w[2]+j*w[1]+S*w[0],y.values[be]=We+(vt-We)*oe}}}else for(let Y=0;Y<g;++Y){let ee=g>1?F*(h-1)+Y*B:.5*(F+_)*(h-1);if(ee<0||ee>h-1){for(let ie=0;ie<p;ie++){let ne=ie+Y*w[2]+j*w[1]+S*w[0];y.values[ne]=u}continue}let oe=Math.round(ee),se=Math.round(X);for(let ie=0;ie<p;ie++){let ne=ie+oe*v[2]+se*v[1]+N*v[0],de=ie+Y*w[2]+j*w[1]+S*w[0];y.values[de]=b[ne]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var Bie={kernelName:_c,backendName:"cpu",kernelFunc:Lie};function Wie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;Te(s,"cumsum");let l=R.getAxesPermutation([a],s.shape.length),u=s;l!=null&&(u=Pr({inputs:{x:s},backend:n,attrs:{perm:l}}));let c=R.getInnerMostAxes(1,s.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let d=qr(u.dtype,"int32"),h=k.makeZerosTypedArray(k.sizeFromShape(u.shape),d),p=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=i?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<p.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)h[x]=o?0:p[x];else{let b=m(y,A-1);h[x]=o?p[b]+h[b]:p[x]+h[b]}}let g=n.makeTensorInfo(u.shape,d,h);if(l!=null){let y=R.getUndoAxesPermutation(l),A=Pr({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),A}return g}var Vie={kernelName:sl,backendName:"cpu",kernelFunc:Wie};function Uie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let l=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,c=D5(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(s.shape.length===2){let l=n.bufferSync(s),u=n.bufferSync(a),c=KT(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var Hie={kernelName:Ey,backendName:"cpu",kernelFunc:Uie};function Gie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r;k.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),k.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=s.shape[0],l=s.shape[1],u=s.shape[2],c=s.shape[3],d=l*a,h=u*a,p=c/(a*a),f=n.data.get(s.dataId).values,m=new Float32Array(i*d*h*p),g=0;for(let y=0;y<i;++y)for(let A=0;A<d;++A){let x=Math.floor(A/a),b=A%a;for(let v=0;v<h;++v){let w=Math.floor(v/a),S=v%a,I=(b*a+S)*p;for(let E=0;E<p;++E){let $=E+I+c*(w+u*(x+l*y));m[g++]=f[$]}}}return n.makeTensorInfo([i,d,h,p],s.dtype,m)}var jie={kernelName:Rc,backendName:"cpu",kernelFunc:Gie};function WN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=r;Te([s,a],"depthwiseConv2DNative");let c=k.computeStrides(s.shape),d=k.computeStrides(a.shape),h=l;h==null&&(h=[1,1]),k.assert(R.eitherStridesOrDilationsAreOne(o,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${h}'`);let p=R.computeConv2DInfo(s.shape,a.shape,o,h,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:A}=p,x=A.left,b=A.top,v=p.outChannels/p.inChannels,w=new Qt(p.outShape,s.dtype),S=n.data.get(s.dataId).values,I=n.data.get(a.dataId).values,E=w.values;for(let F=0;F<p.batchSize;++F){let $=F*c[0],_=F*w.strides[0];for(let N=0;N<p.outHeight;++N){let P=_+N*w.strides[1],B=N*p.strideHeight-b;for(let j=0;j<f;++j){let X=B+j*g;if(X<0||X>=p.inHeight)continue;let Y=j*d[0],ee=$+X*c[1];for(let oe=0;oe<p.outWidth;++oe){let se=P+oe*w.strides[2],ie=oe*p.strideWidth-x;for(let ne=0;ne<m;++ne){let de=ie+ne*y;if(de<0||de>=p.inWidth)continue;let he=Y+ne*d[1],ge=ee+de*p.inChannels,be=se,Ee=he;for(let $e=0;$e<p.inChannels;++$e){let ze=S[ge+$e];for(let qe=0;qe<v;++qe)E[be+qe]+=ze*I[Ee+qe];be+=v,Ee+=v}}}}}}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var qie={kernelName:al,backendName:"cpu",kernelFunc:WN};function Kie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=r;Te([s,a],"depthwiseConv2dNativeBackpropFilter");let d=R.computeConv2DInfo(s.shape,c,o,i,l,u,!0),{strideHeight:h,strideWidth:p,filterHeight:f,filterWidth:m}=d,g=new Qt(d.filterShape,"float32"),y=d.padInfo.left,A=d.padInfo.top,x=d.outChannels/d.inChannels,b=n.data.get(s.dataId).values,v=new Qt(s.shape,s.dtype,b),w=n.data.get(a.dataId).values,S=new Qt(a.shape,a.dtype,w);for(let I=0;I<f;++I){let E=Math.max(0,Math.ceil((A-I)/h)),F=Math.min(d.outHeight,(d.inHeight+A-I)/h);for(let $=0;$<m;++$){let _=Math.max(0,Math.ceil((y-$)/p)),N=Math.min(d.outWidth,(d.inWidth+y-$)/p);for(let P=0;P<d.outChannels;++P){let B=Math.trunc(P/x),j=P%x,X=0;for(let Y=0;Y<d.batchSize;++Y)for(let ee=E;ee<F;++ee){let oe=I+ee*h-A;for(let se=_;se<N;++se){let ie=$+se*p-y;X+=v.get(Y,oe,ie,B)*S.get(Y,ee,se,P)}}g.set(X,I,$,B,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Xie={kernelName:$y,backendName:"cpu",kernelFunc:Kie};function Zie(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=r;Te([s,a],"depthwiseConv2DNativeBackpropInput");let d=k.computeStrides(s.shape),h=k.computeStrides(a.shape),p=R.computeConv2DInfo(c,a.shape,o,i,l,u,!0),f=new Qt(p.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=n.data.get(s.dataId).values,[b,v,w]=d,S=n.data.get(a.dataId).values,[I,E,F]=h,{batchSize:$,filterHeight:_,filterWidth:N,inChannels:P,inHeight:B,inWidth:j,outChannels:X,outHeight:Y,outWidth:ee,strideHeight:oe,strideWidth:se}=p,ie=_-1-p.padInfo.top,ne=N-1-p.padInfo.left,de=X/P;for(let he=0;he<$;++he)for(let ge=0;ge<P;++ge)for(let be=0;be<B;++be){let Ee=be-ie,$e=Math.max(0,Math.ceil(Ee/oe)),ze=Math.min(Y,(_+Ee)/oe);for(let qe=0;qe<j;++qe){let We=qe-ne,vt=Math.max(0,Math.ceil(We/se)),ft=Math.min(ee,(N+We)/se),mt=0;for(let dt=$e;dt<ze;++dt){let bt=dt*oe-Ee;for(let Je=vt;Je<ft;++Je){let jn=Je*se-We,Wt=b*he+v*dt+w*Je,ar=I*(_-1-bt)+E*(N-1-jn)+F*ge;for(let vn=0;vn<de;++vn){let Vr=ge*de+vn,Rn=x[Wt+Vr],br=S[ar+vn];mt+=Rn*br}}}m[g*he+y*be+A*qe+ge]=mt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var Yie={kernelName:_y,backendName:"cpu",kernelFunc:Zie};function Jie(e){let{inputs:t,backend:n}=e,{x:r}=t,s=k.sizeFromShape(r.shape),a=n.data.get(r.dataId).values,o=Le([s,s],r.dtype),i=o.values;for(let u=0;u<a.length;u++)i[u*s+u]=a[u];let l=[...r.shape,...r.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var Qie={kernelName:Ry,backendName:"cpu",kernelFunc:Jie},ele={kernelName:Jp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s}=e,{strides:a,pad:o,dilations:i}=n,l=t,u=l.data.get(r.dataId).values,c=r.shape.length,d=l.data.get(s.dataId).values,h=s.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:A,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:w,filterWidth:S,dilationHeight:I,dilationWidth:E,outShape:F}=R.computeDilation2DInfo(r.shape,s.shape,a,o,"NHWC",i),$=k.sizeFromShape(F),_=F.length,N=k.getArrayFromDType(r.dtype,$);for(let B=0;B<p;++B)for(let j=0;j<y;++j){let X=j*b-x.top;for(let Y=0;Y<A;++Y){let ee=Y*v-x.left;for(let oe=0;oe<g;++oe){let se=Number.MIN_SAFE_INTEGER;for(let ne=0;ne<w;++ne){let de=X+ne*I;if(de>=0&&de<f)for(let he=0;he<S;++he){let ge=ee+he*E;if(ge>=0&&ge<m){let be=k.locToIndex([B,de,ge,oe],c,k.computeStrides(r.shape)),Ee=k.locToIndex([ne,he,oe],h,k.computeStrides(s.shape)),$e=u[be]+d[Ee];$e>se&&(se=$e)}}}let ie=k.locToIndex([B,j,Y,oe],_,k.computeStrides(F));N[ie]=se}}}return{dataId:l.write(k.toTypedArray(N,r.dtype),F,r.dtype),shape:F,dtype:r.dtype}}},tle={kernelName:Fy,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=k.toNestedArray(r.shape,u.data.get(r.dataId).values),d=k.toNestedArray(s.shape,u.data.get(s.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:w,dilationHeight:S,dilationWidth:I,outShape:E}=R.computeDilation2DInfo(r.shape,s.shape,o,i,"NHWC",l);k.assert(a.rank===E.length,()=>`Error in ${Fy}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let F=k.toNestedArray(E,u.data.get(a.dataId).values),$=k.makeZerosNestedTypedArray(s.shape,s.dtype);for(let N=0;N<h;++N)for(let P=0;P<g;++P){let B=P*x-A.top;for(let j=0;j<y;++j){let X=j*b-A.left;for(let Y=0;Y<m;++Y){let ee=Number.MIN_SAFE_INTEGER,oe=0,se=0;for(let ie=0;ie<v;++ie){let ne=B+ie*S;if(ne>=0&&ne<p)for(let de=0;de<w;++de){let he=X+de*I;if(he>=0&&he<f){let ge=c[N][ne][he][Y]+d[ie][de][Y];ge>ee&&(ee=ge,oe=ie,se=de)}}}$[oe][se][Y]+=F[N][P][j][Y]}}}return{dataId:u.write(k.toTypedArray($,r.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},nle={kernelName:Dy,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=k.toNestedArray(r.shape,u.data.get(r.dataId).values),d=k.toNestedArray(s.shape,u.data.get(s.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:w,dilationHeight:S,dilationWidth:I,outShape:E}=R.computeDilation2DInfo(r.shape,s.shape,o,i,"NHWC",l);k.assert(a.rank===E.length,()=>`Error in ${Dy}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let F=k.toNestedArray(E,u.data.get(a.dataId).values),$=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let N=0;N<h;++N)for(let P=0;P<g;++P){let B=P*x-A.top;for(let j=0;j<y;++j){let X=j*b-A.left;for(let Y=0;Y<m;++Y){let ee=Number.MIN_SAFE_INTEGER,oe=B<0?0:B,se=X<0?0:X;for(let ie=0;ie<v;++ie){let ne=B+ie*S;if(ne>=0&&ne<p)for(let de=0;de<w;++de){let he=X+de*I;if(he>=0&&he<f){let ge=c[N][ne][he][Y]+d[ie][de][Y];ge>ee&&(ee=ge,oe=ne,se=he)}}}$[N][oe][se][Y]+=F[N][P][j][Y]}}}return{dataId:u.write(k.toTypedArray($,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function nh(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;Te(s,"sum");let i;s.dtype==="bool"?i=Ja({inputs:{x:s},backend:n,attrs:{dtype:"int32"}}):i=zs({inputs:{x:s},backend:n});let l=i.shape.length,u=k.parseAxisParam(a,i.shape),c=R.getAxesPermutation(u,l),d=u,h=i;c!=null&&(h=Pr({inputs:{x:i},backend:n,attrs:{perm:c}}),d=R.getInnerMostAxes(d.length,l)),R.assertAxesAreInnerMostDims("sum",d,h.shape.length);let[p,f]=R.computeOutAndReduceShapes(h.shape,d),m=R.upcastType(h.dtype,"int32"),g=Em(n,p,m),y=k.sizeFromShape(f),A=n.data.get(g.dataId).values,x=n.data.get(h.dataId).values;for(let b=0;b<A.length;++b){let v=b*y,w=0;for(let S=0;S<y;++S)w+=x[v+S];A[b]=w}if(o){let b=R.expandShapeToKeepDim(g.shape,u),v=g;g=Ft({inputs:{x:g},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(v)}return n.disposeIntermediateTensorInfo(i),c!=null&&n.disposeIntermediateTensorInfo(h),g}var rle={kernelName:Fl,backendName:"cpu",kernelFunc:nh};function sle(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:l}=R.decodeEinsumEquation(s,a.length);R.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=R.getEinsumComputePath(i,l),d=c.length,h=null,p=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:A}=R.getEinsumPermutation(p,l[g]),x;R.isIdentityPermutation(y)?x=a[g]:(x=Pr({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);k.arraysEqual(x.shape,b)||(x=Ft({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),h===null?h=x:(h=$m({inputs:{a:x,b:h},backend:n}),f.push(h))}m<d-1&&(u[m]>=0&&(h=nh({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var ale={kernelName:My,backendName:"cpu",kernelFunc:sle};function ole(e){let{inputs:t,backend:n}=e,{dy:r,y:s}=t;Te([r,s],"eluGrad");let a=new Float32Array(k.sizeFromShape(s.shape)),o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values;for(let l=0;l<o.length;++l){let u=o[l];u>=1?a[l]=i[l]:a[l]=i[l]*(u+1)}return n.makeTensorInfo(s.shape,"float32",a)}var ile={kernelName:Oy,backendName:"cpu",kernelFunc:ole},lle=R.ERF_P,ule=R.ERF_A1,cle=R.ERF_A2,dle=R.ERF_A3,hle=R.ERF_A4,ple=R.ERF_A5,fle=xt(Fc,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+lle*n);return t*(1-((((ple*r+hle)*r+dle)*r+cle)*r+ule)*r*Math.exp(-n*n))}),mle={kernelName:Fc,backendName:"cpu",kernelFunc:fle};function _m(e){let{inputs:t,backend:n,attrs:r}=e,{input:s}=t,{dim:a}=r,o=s.shape.length,i=s.shape.slice(),l=a;return a<0&&(k.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),Ft({inputs:{x:s},backend:n,attrs:{shape:i}})}var gle={kernelName:Mc,backendName:"cpu",kernelFunc:_m},yle=nn((e,t)=>e/t),B5=bn(ol,yle),W5={kernelName:ol,backendName:"cpu",kernelFunc:B5};function VN(e,t,n){let r=e.shape,s=r[0],a=r[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,u=[s,a],c=k.sizeFromShape(u),d=k.getTypedArrayFromDType("float32",c),h=k.getTypedArrayFromDType("float32",c);for(let g=0;g<s;g++){let y=fi({inputs:{x:i},backend:n,attrs:{begin:[g,0],size:[1,a]}}),A=fi({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,a]}}),x=mr({inputs:{real:y,imag:A},backend:n}),{real:b,imag:v}=Ale(x,t,n),w=R.mergeRealAndImagArrays(b,v);for(let S=0;S<a;S++){let I=R.getComplexWithIndex(w,S);d[g*a+S]=I.real,h[g*a+S]=I.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(x)}let p=n.makeTensorInfo(u,"float32",d),f=n.makeTensorInfo(u,"float32",h),m=mr({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function Ale(e,t,n){let r=k.sizeFromShape(e.shape),s=n.data.get(e.dataId),a=n.data.get(s.complexTensorInfos.real.dataId).values,o=n.data.get(s.complexTensorInfos.imag.dataId).values;if(xle(r)){let i=V5(a,o,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",i.real),c=n.makeTensorInfo(l,"float32",i.imag),d=n.makeTensorInfo([],"float32",k.createScalarValue(r,"float32")),h=zs({inputs:{x:d},backend:n}),p=W5.kernelFunc({inputs:{a:u,b:d},backend:n}),f=W5.kernelFunc({inputs:{a:c,b:h},backend:n}),m=n.data.get(p.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return i}else{let i=R.mergeRealAndImagArrays(a,o),l=ble(i,r,t);return R.splitRealAndImagArrays(l)}}function xle(e){return(e&e-1)==0}function V5(e,t,n,r,s){if(n===1)return{real:e,imag:t};let a=R.mergeRealAndImagArrays(e,t),o=n/2,i=R.complexWithEvenIndex(a),l=i.real,u=i.imag,c=[l.length],d=s.makeTensorInfo(c,"float32",l),h=s.makeTensorInfo(c,"float32",u),p=mr({inputs:{real:d,imag:h},backend:s}),f=R.complexWithOddIndex(a),m=f.real,g=f.imag,y=[m.length],A=s.makeTensorInfo(y,"float32",m),x=s.makeTensorInfo(y,"float32",g),b=mr({inputs:{real:A,imag:x},backend:s}),v=V5(l,u,o,r,s),w=v.real,S=v.imag,I=[w.length],E=s.makeTensorInfo(I,"float32",w),F=s.makeTensorInfo(I,"float32",S),$=mr({inputs:{real:E,imag:F},backend:s}),_=V5(m,g,o,r,s),N=_.real,P=_.imag,B=[N.length],j=s.makeTensorInfo(B,"float32",N),X=s.makeTensorInfo(B,"float32",P),Y=mr({inputs:{real:j,imag:X},backend:s}),ee=R.exponents(n,r),oe=[ee.real.length],se=s.makeTensorInfo(oe,"float32",ee.real),ie=s.makeTensorInfo(oe,"float32",ee.imag),ne=mr({inputs:{real:se,imag:ie},backend:s}),de=$m({inputs:{a:ne,b:Y},backend:s}),he=th({inputs:{a:$,b:de},backend:s}),ge=P5({inputs:{a:$,b:de},backend:s}),be=pi({inputs:{input:he},backend:s}),Ee=pi({inputs:{input:ge},backend:s}),$e=hu({inputs:{input:he},backend:s}),ze=hu({inputs:{input:ge},backend:s}),qe=pu({inputs:[be,Ee],backend:s,attrs:{axis:0}}),We=pu({inputs:[$e,ze],backend:s,attrs:{axis:0}}),vt=s.data.get(qe.dataId).values,ft=s.data.get(We.dataId).values;return s.disposeIntermediateTensorInfo(d),s.disposeIntermediateTensorInfo(h),s.disposeIntermediateTensorInfo(p),s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(x),s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(E),s.disposeIntermediateTensorInfo(F),s.disposeIntermediateTensorInfo($),s.disposeIntermediateTensorInfo(j),s.disposeIntermediateTensorInfo(X),s.disposeIntermediateTensorInfo(Y),s.disposeIntermediateTensorInfo(se),s.disposeIntermediateTensorInfo(ie),s.disposeIntermediateTensorInfo(ne),s.disposeIntermediateTensorInfo(de),s.disposeIntermediateTensorInfo(he),s.disposeIntermediateTensorInfo(ge),s.disposeIntermediateTensorInfo(be),s.disposeIntermediateTensorInfo($e),s.disposeIntermediateTensorInfo(Ee),s.disposeIntermediateTensorInfo(ze),s.disposeIntermediateTensorInfo(qe),s.disposeIntermediateTensorInfo(We),{real:vt,imag:ft}}function ble(e,t,n){let r=new Float32Array(t*2);for(let s=0;s<t;s++){let a=0,o=0;for(let i=0;i<t;i++){let l=R.exponent(s*i,t,n),u=R.getComplexWithIndex(e,i);a+=u.real*l.real-u.imag*l.imag,o+=u.real*l.imag+u.imag*l.real}n&&(a/=t,o/=t),R.assignToTypedArray(r,a,o,s)}return r}function vle(e){let{inputs:t,backend:n}=e,{input:r}=t,s=k.sizeFromShape(r.shape),a=r.shape[r.shape.length-1],o=s/a,i=Ft({inputs:{x:r},backend:n,attrs:{shape:[o,a]}}),l=VN(i,!1,n),u=Ft({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var wle={kernelName:Py,backendName:"cpu",kernelFunc:vle};function U5(e){let{backend:t,attrs:n}=e,{shape:r,value:s,dtype:a}=n,o=a||k.inferDtype(s),i=k.getArrayFromDType(o,k.sizeFromShape(r));return Ile(i,s,o),t.makeTensorInfo(r,o,i)}var kle={kernelName:Qp,backendName:"cpu",kernelFunc:U5};function Ile(e,t,n){e.fill(t)}var Sle={kernelName:Oc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,s=n,a=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(r.shape)),[o,i,l,u]=r.shape,c=s.data.get(r.dataId).values;for(let h=0;h<o;h++){let p=h*l*i*u;for(let f=0;f<i;f++){let m=f*(l*u);for(let g=0;g<l;g++){let y=g*u;for(let A=0;A<u;A++){let b=[o,f,g,A][2],v=Math.round(l-b),w=p+m+y+A,S=c[w];if(v>=0&&v<l){let I=v*u,E=p+m+I+A;S=c[E]}a[w]=S}}}}return{dataId:s.write(a,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Tle=nn((e,t)=>Math.floor(e/t)),Nle=bn(ul,Tle,null,"int32"),Cle={kernelName:ul,backendName:"cpu",kernelFunc:Nle};function Ele(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=r,m=BN({inputs:{x:s,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let g=m;m=th({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(p){let g=m;m=z5(n,m,p,i,f),n.disposeIntermediateTensorInfo(g)}return m}var $le={kernelName:Bl,backendName:"cpu",kernelFunc:Ele};function _le(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=r,m=WN({inputs:{x:s,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let g=m;m=th({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(p){let g=m;m=z5(n,m,p,i,f),n.disposeIntermediateTensorInfo(g)}return m}var Rle={kernelName:Wl,backendName:"cpu",kernelFunc:_le};function Dle(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=k.sizeFromShape(r.shape),o=s.shape,i=o[o.length-1],[l,u,c,d]=R.prepareAndValidate(r,s);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let h=n.data.get(s.dataId).values,p=n.bufferSync(r),f=rN(h,p,r.dtype,u,i,c,d,r.shape,a);return n.makeTensorInfo(l,r.dtype,f.values)}var Fle={kernelName:zc,backendName:"cpu",kernelFunc:Dle};function Mle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r;Te([s,a],"gatherV2");let l=i;i==null&&(l=0);let u=k.sizeFromShape(a.shape),c=k.parseAxisParam(o,s.shape)[0],d=R.segment_util.collectGatherOpShapeInfo(s,a,c,l),h=Ft({inputs:{x:s},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),p=Ft({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,u/d.batchSize]}}),f=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize],m=n.bufferSync(p),g=n.bufferSync(h),y=sN(g,m,f);return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(d.outputShape,y.dtype,y.values)}var Ole={kernelName:Pc,backendName:"cpu",kernelFunc:Mle};function Ple(e){let{inputs:t,backend:n}=e,{input:r}=t,s=k.sizeFromShape(r.shape),a=r.shape[r.shape.length-1],o=s/a,i=Ft({inputs:{x:r},backend:n,attrs:{shape:[o,a]}}),l=VN(i,!0,n),u=Ft({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var zle={kernelName:zy,backendName:"cpu",kernelFunc:Ple},Lle=xt(Lc,e=>Number.isFinite(e)?1:0,"bool"),Ble={kernelName:Lc,backendName:"cpu",kernelFunc:Lle},Wle=xt(Bc,e=>Math.abs(e)===Infinity?1:0,"bool"),Vle={kernelName:Bc,backendName:"cpu",kernelFunc:Wle},Ule=xt(Wc,e=>Number.isNaN(e)?1:0,"bool"),Hle={kernelName:Wc,backendName:"cpu",kernelFunc:Ule};function Gle(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=uN(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var jle={kernelName:By,backendName:"cpu",kernelFunc:Gle},qle=xt(Vc,e=>Math.log1p(e)),Kle={kernelName:Vc,backendName:"cpu",kernelFunc:qle},Xle=nn((e,t)=>e&&t),Zle=bn(Uc,Xle,null,"bool"),Yle={kernelName:Uc,backendName:"cpu",kernelFunc:Zle},Jle=xt(ef,e=>e?0:1,"bool"),Qle={kernelName:ef,backendName:"cpu",kernelFunc:Jle},eue=nn((e,t)=>e||t),tue=bn(tf,eue,null,"bool"),nue={kernelName:tf,backendName:"cpu",kernelFunc:tue};function rue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=r;Te(s,"LRN");let u=s.shape[3],c=u-1,d=n.data.get(s.dataId).values,h=k.sizeFromShape(s.shape),p=new Float32Array(h);function f(m){let g=m%u,y=m-g+Math.max(0,g-a),A=m-g+Math.min(g+a,c),x=0;for(;y<=A;y++){let b=d[y];x+=b*b}return x}for(let m=0;m<h;m++){let g=f(m),y=d[m]*Math.pow(o+i*g,-l);p[m]=y}return n.makeTensorInfo(s.shape,s.dtype,p)}var sue={kernelName:nf,backendName:"cpu",kernelFunc:rue};function aue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=r;Te(o,"LRNGrad");let d=k.sizeFromShape(o.shape),h=o.shape[3],p=n.data.get(o.dataId).values,f=n.data.get(s.dataId).values,m=n.data.get(a.dataId).values,g=new Float32Array(d),y=d;for(let A=0;A<y;A++){let x=A%h,b=A-x+Math.max(0,x-i),v=A-x+Math.min(h,x+i+1),w=0;for(let S=b;S<v;S++)w+=Math.pow(f[S],2);w=u*w+l;for(let S=b;S<v;S++){let I=-2*u*c*f[S]*m[A]/w;A===S&&(I+=Math.pow(w,-c)),I*=p[A],g[S]+=I}}return n.makeTensorInfo(o.shape,s.dtype,g)}var oue={kernelName:Wy,backendName:"cpu",kernelFunc:aue};function UN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=n,l=s.shape,u=l.length,c=k.parseAxisParam(a,l),d=c,h=R.getAxesPermutation(d,u),p=i.data.get(s.dataId).values;if(h!=null){let b=new Array(u);for(let v=0;v<b.length;v++)b[v]=l[h[v]];p=M5(p,l,s.dtype,h,b),d=R.getInnerMostAxes(d.length,u),l=b}Te(s,"max"),R.assertAxesAreInnerMostDims("max",d,u);let[f,m]=R.computeOutAndReduceShapes(l,d),g=k.sizeFromShape(m),y=dN(p,g,f,s.dtype),A=i.write(y,f,s.dtype),x=f;return o&&(x=R.expandShapeToKeepDim(f,c)),{dataId:A,shape:x,dtype:s.dtype}}var iue={kernelName:gl,backendName:"cpu",kernelFunc:UN};function lue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Te(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=r,u=1;k.assert(R.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=R.computePool2DInfo(s.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&k.arraysEqual(c.inShape,c.outShape))d=zs({inputs:{x:s},backend:n});else{let h=n.data.get(s.dataId).values,p=k.computeStrides(s.shape),f=L5(h,s.shape,s.dtype,p,c,"max");d=n.makeTensorInfo(c.outShape,s.dtype,f.values)}return d}var uue={kernelName:yl,backendName:"cpu",kernelFunc:lue};function cue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=r;Te(s,"maxPool3d");let c=R.computePool3DInfo(s.shape,a,o,1,i,l,u),d=n.data.get(s.dataId).values,h=LN(d,s.shape,s.dtype,k.computeStrides(s.shape),c,"max");return n.makeTensorInfo(h.shape,"float32",h.values)}var due={kernelName:rf,backendName:"cpu",kernelFunc:cue};function hue(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=r;Te([s,a],"maxPool3DGrad");let c=R.computePool3DInfo(a.shape,o,i,1,l,u),d=n.bufferSync(a),h=rie(d,c),p=c.strideDepth,f=c.strideHeight,m=c.strideWidth,g=c.dilationDepth,y=c.dilationHeight,A=c.dilationWidth,x=c.effectiveFilterDepth,b=c.effectiveFilterHeight,v=c.effectiveFilterWidth,w=x-1-c.padInfo.front,S=v-1-c.padInfo.left,I=b-1-c.padInfo.top,E=Le(a.shape,"float32"),F=n.bufferSync(s);for(let $=0;$<c.batchSize;++$)for(let _=0;_<c.inChannels;++_)for(let N=0;N<c.inDepth;++N)for(let P=0;P<c.inHeight;++P)for(let B=0;B<c.inWidth;++B){let j=N-w,X=P-I,Y=B-S,ee=0;for(let oe=0;oe<x;oe+=g){let se=(j+oe)/p;if(!(se<0||se>=c.outDepth||Math.floor(se)!==se))for(let ie=0;ie<b;ie+=y){let ne=(X+ie)/f;if(!(ne<0||ne>=c.outHeight||Math.floor(ne)!==ne))for(let de=0;de<v;de+=A){let he=(Y+de)/m;if(he<0||he>=c.outWidth||Math.floor(he)!==he)continue;let ge=x*b*v-1-h.get($,se,ne,he,_),be=oe*b*v+ie*v+de,Ee=ge===be?1:0;if(Ee===0)continue;ee+=F.get($,se,ne,he,_)*Ee}}}E.set(ee,$,N,P,B,_)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var pue={kernelName:Uy,backendName:"cpu",kernelFunc:hue};function fue(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;Te([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=r,h=R.computePool2DInfo(i.shape,l,u,1,c,d),p=n.data.get(i.dataId).values,f=Le(h.outShape,i.dtype,zN(p,i.shape,i.dtype,h).values),m=h.strideHeight,g=h.strideWidth,y=h.dilationHeight,A=h.dilationWidth,x=h.effectiveFilterHeight,b=h.effectiveFilterWidth,v=b-1-h.padInfo.left,w=x-1-h.padInfo.top,S=Le(i.shape,"float32"),I=n.data.get(s.dataId).values,E=Le(s.shape,"float32",I);for(let F=0;F<h.batchSize;++F)for(let $=0;$<h.inChannels;++$)for(let _=0;_<h.inHeight;++_)for(let N=0;N<h.inWidth;++N){let P=_-w,B=N-v,j=0;for(let X=0;X<x;X+=y){let Y=(P+X)/m;if(!(Y<0||Y>=h.outHeight||Math.floor(Y)!==Y))for(let ee=0;ee<b;ee+=A){let oe=(B+ee)/g;if(oe<0||oe>=h.outWidth||Math.floor(oe)!==oe)continue;let se=x*b-1-f.get(F,Y,oe,$),ie=X*b+ee,ne=se===ie?1:0;if(ne===0)continue;j+=E.get(F,Y,oe,$)*ne}}S.set(j,F,_,N,$)}return n.makeTensorInfo(S.shape,S.dtype,S.values)}var mue={kernelName:Vy,backendName:"cpu",kernelFunc:fue};function gue(e,t,n,r,s){let a=k.computeStrides(t),o=L5(e,t,n,a,s,"max"),i=zN(e,t,n,s,!0,r);return[o.values,i.values]}var yue={kernelName:Hy,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;Te(r,"MaxPoolWithArgmax");let u=l.data.get(r.dataId).values,c=R.computePool2DInfo(r.shape,s,a,[1,1],o),[d,h]=gue(u,r.shape,r.dtype,i,c),p=l.write(d,c.outShape,r.dtype),f=l.write(h,c.outShape,r.dtype);return[{dataId:p,shape:c.outShape,dtype:r.dtype},{dataId:f,shape:c.outShape,dtype:"int32"}]}};function Aue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=k.parseAxisParam(a,s.shape),u=R.computeOutAndReduceShapes(s.shape,i)[1],c=k.sizeFromShape(u),d=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));d.push(h);let p=Ja({inputs:{x:s},backend:n,attrs:{dtype:"float32"}});d.push(p);let f=B5({inputs:{a:p,b:h},backend:n});d.push(f);let m=nh({inputs:{x:f},backend:n,attrs:{axis:a,keepDims:o}});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),m}var xue={kernelName:Al,backendName:"cpu",kernelFunc:Aue};function bue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;Te(s,"min");let i=k.parseAxisParam(a,s.shape),l=i,u=R.getAxesPermutation(l,s.shape.length),c=s;u!=null&&(c=Pr({inputs:{x:s},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,s.shape.length)),R.assertAxesAreInnerMostDims("min",l,c.shape.length);let[d,h]=R.computeOutAndReduceShapes(c.shape,l),p=k.sizeFromShape(h),f=k.makeZerosTypedArray(k.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let A=y*p,x=m[A];for(let b=0;b<p;++b){let v=m[A+b];(Number.isNaN(v)||v<x)&&(x=v)}f[y]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let y=R.expandShapeToKeepDim(d,i),A=Ft({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var vue={kernelName:xl,backendName:"cpu",kernelFunc:bue};function wue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,mode:o}=r;Te(s,"mirrorPad");let i=a.map((x,b)=>x[0]+s.shape[b]+x[1]),l=a.map(x=>x[0]),u=a.map((x,b)=>x[0]+s.shape[b]),c=o==="reflect"?0:1,d=n.data.get(s.dataId).values,h=s.shape.length,p=k.computeStrides(s.shape),f=k.sizeFromShape(i),m=i.length,g=k.computeStrides(i),y=k.getTypedArrayFromDType(s.dtype,f);for(let x=0;x<f;x++){let b=k.indexToLoc(x,m,g);for(let w=0;w<m;w++)b[w]<l[w]?b[w]=l[w]*2-b[w]-c:b[w]>=u[w]&&(b[w]=(u[w]-1)*2-b[w]+c);b=b.map((w,S)=>w-l[S]);let v=k.locToIndex(b,h,p);y[x]=d[v]}return{dataId:n.write(y,i,s.dtype),shape:i,dtype:s.dtype}}var kue={kernelName:bl,backendName:"cpu",kernelFunc:wue},Iue=nn((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),Sue=bn(Hc,Iue),Tue={kernelName:Hc,backendName:"cpu",kernelFunc:Sue},Nue=Ks(t2());function HN(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=s.shape.length,i=a;if(i===-1&&(i=o-1),i!==o-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${o} and dim was ${i}`);let l=k.parseAxisParam([i],s.shape),u=UN({inputs:{x:s},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),c=R.expandShapeToKeepDim(u.shape,l),d=Ft({inputs:{x:u},backend:n,attrs:{shape:c}}),h=P5({inputs:{a:s,b:d},backend:n}),p=eN({inputs:{x:h},backend:n}),f=nh({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=Ft({inputs:{x:f},backend:n,attrs:{shape:c}}),g=B5({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Cue={kernelName:Ml,backendName:"cpu",kernelFunc:HN};function Eue(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r;Te(s,"multinomial");let l=i?s:HN({inputs:{logits:s},backend:n,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],d=n.data.get(l.dataId).values,h=[u,a],p=k.makeZerosTypedArray(k.sizeFromShape(h),"int32");for(let f=0;f<u;++f){let m=f*c,g=new Float32Array(c-1);g[0]=d[m];for(let x=1;x<g.length;++x)g[x]=g[x-1]+d[m+x];let y=Nue.alea(o.toString()),A=f*a;for(let x=0;x<a;++x){let b=y();p[A+x]=g.length;for(let v=0;v<g.length;v++)if(b<g[v]){p[A+x]=v;break}}}return i||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(h,"int32",p)}var $ue={kernelName:Gy,backendName:"cpu",kernelFunc:Eue},_ue=ca.nonMaxSuppressionV3Impl;function Rue(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=r;Te(s,"NonMaxSuppression");let u=n.data.get(s.dataId).values,c=n.data.get(a.dataId).values,{selectedIndices:d}=_ue(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Due={kernelName:jc,backendName:"cpu",kernelFunc:Rue},Fue=ca.nonMaxSuppressionV4Impl;function Mue(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=r;Te(s,"NonMaxSuppressionPadded");let c=n.data.get(s.dataId).values,d=n.data.get(a.dataId).values,{selectedIndices:h,validOutputs:p}=Fue(c,d,o,i,l,u);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var Oue={kernelName:qc,backendName:"cpu",kernelFunc:Mue},Pue=ca.nonMaxSuppressionV5Impl;function zue(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=r;Te(s,"NonMaxSuppressionWithScore");let c=n.data.get(s.dataId).values,d=n.data.get(a.dataId).values,h=o,p=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=Pue(c,d,h,p,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Lue={kernelName:Kc,backendName:"cpu",kernelFunc:zue};function Bue(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r;Te(s,"oneHot");let l=k.sizeFromShape(s.shape),u=new Float32Array(l*a);u.fill(i);let c=n.data.get(s.dataId).values;for(let d=0;d<l;++d)c[d]>=0&&c[d]<a&&(u[d*a+c[d]]=o);return n.makeTensorInfo([...s.shape,a],"int32",u)}var Wue={kernelName:wl,backendName:"cpu",kernelFunc:Bue};function Rm(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(r.dtype==="complex64"){let s=pi({inputs:{input:r},backend:n}),a=Rm({inputs:{x:s},backend:n}),o=hu({inputs:{input:r},backend:n}),i=Rm({inputs:{x:o},backend:n}),l=mr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return U5({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var Vue={kernelName:hd,backendName:"cpu",kernelFunc:Rm};function GN(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(r.dtype==="complex64"){let s=pi({inputs:{input:r},backend:n}),a=GN({inputs:{x:s},backend:n}),o=hu({inputs:{input:r},backend:n}),i=Rm({inputs:{x:o},backend:n}),l=mr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return U5({backend:n,attrs:{shape:r.shape,value:1,dtype:r.dtype}})}var Uue={kernelName:Xc,backendName:"cpu",kernelFunc:GN};function jN(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return _m({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{k.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),k.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=_m({inputs:{input:c},backend:n,attrs:{dim:s}});return i.push(d),d}),u=pu({inputs:l,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Hue={kernelName:Zc,backendName:"cpu",kernelFunc:jN};function Gue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;Te(s,"pad");let i=a.map((A,x)=>A[0]+s.shape[x]+A[1]),l=a.map(A=>A[0]),u=n.data.get(s.dataId).values,c=k.sizeFromShape(s.shape),d=s.shape.length,h=k.computeStrides(s.shape),p=k.sizeFromShape(i),f=i.length,m=k.computeStrides(i),g=k.getTypedArrayFromDType(s.dtype,p);o!==0&&g.fill(o);for(let A=0;A<c;A++){let b=k.indexToLoc(A,d,h).map((w,S)=>w+l[S]),v=k.locToIndex(b,f,m);g[v]=u[A]}return{dataId:n.write(g,i,s.dtype),shape:i,dtype:s.dtype}}var qN={kernelName:kl,backendName:"cpu",kernelFunc:Gue},jue=nn((e,t)=>Math.pow(e,t)),que=bn(Il,jue),Kue={kernelName:Il,backendName:"cpu",kernelFunc:que};function Xue(e){let{backend:t,attrs:n}=e,{start:r,stop:s,dtype:a,step:o}=n,i=yN(r,s,o,a);return t.makeTensorInfo([i.length],a,i)}var Zue={kernelName:sf,backendName:"cpu",kernelFunc:Xue},Yue=xt(Jc,e=>1/e),Jue={kernelName:Jc,backendName:"cpu",kernelFunc:Yue};function Que(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r;Te(s,"resizeBilinear");let l=k.computeStrides(s.shape),[u,c]=i,[d,h,p,f]=s.shape,m=n.data.get(s.dataId).values,g=new Float32Array(k.sizeFromShape([d,u,c,f])),y=[a&&u>1?h-1:h,a&&c>1?p-1:p],A=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=0,b=y[0]/A[0],v=y[1]/A[1];for(let w=0;w<d;w++)for(let S=0;S<u;S++){let I;o?I=b*(S+.5)-.5:I=b*S;let E=Math.max(0,Math.floor(I)),F=I-E,$=Math.min(h-1,Math.ceil(I)),_=w*l[0]+E*l[1],N=w*l[0]+$*l[1];for(let P=0;P<c;P++){let B;o?B=v*(P+.5)-.5:B=v*P;let j=Math.max(0,Math.floor(B)),X=B-j,Y=Math.min(p-1,Math.ceil(B)),ee=_+j*l[2],oe=N+j*l[2],se=_+Y*l[2],ie=N+Y*l[2];for(let ne=0;ne<f;ne++){let de=m[ee+ne],he=m[oe+ne],ge=m[se+ne],be=m[ie+ne],Ee=de+(ge-de)*X,$e=he+(be-he)*X,ze=Ee+($e-Ee)*F;g[x++]=ze}}}return n.makeTensorInfo([d,u,c,f],"float32",g)}var ece={kernelName:Nl,backendName:"cpu",kernelFunc:Que};function tce(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r;Te([a,s],"resizeBilinearGrad");let i=k.computeStrides(s.shape),[l,u,c,d]=s.shape,[,h,p]=a.shape,f=new Float32Array(l*u*c*d),m=[o&&h>1?u-1:u,o&&p>1?c-1:c],g=[o&&h>1?h-1:h,o&&p>1?p-1:p],y=m[0]/g[0],A=m[1]/g[1],x=n.data.get(a.dataId).values,b=0;for(let v=0;v<l;v++){let w=v*i[0];for(let S=0;S<h;S++){let I=S*y,E=Math.floor(I),F=Math.min(Math.ceil(I),u-1),$=w+E*i[1],_=w+F*i[1],N=I-E,P=1-N;for(let B=0;B<p;B++){let j=B*A,X=Math.floor(j),Y=Math.min(Math.ceil(j),c-1),ee=j-X,oe=1-ee,se=$+X*i[2],ie=$+Y*i[2],ne=_+X*i[2],de=_+Y*i[2],he=P*oe,ge=P*ee,be=N*oe,Ee=N*ee;for(let $e=0;$e<d;$e++){let ze=x[b++];f[se+$e]+=ze*he,f[ie+$e]+=ze*ge,f[ne+$e]+=ze*be,f[de+$e]+=ze*Ee}}}}return n.makeTensorInfo([l,c,u,d],"float32",f)}var nce={kernelName:Ky,backendName:"cpu",kernelFunc:tce};function rce(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r;Te(s,"resizeNearestNeighbor");let l=k.computeStrides(s.shape),[u,c]=i,[d,h,p,f]=s.shape,m=n.data.get(s.dataId).values,g=new Float32Array(d*u*c*f),y=[a&&u>1?h-1:h,a&&c>1?p-1:p],A=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=y[0]/A[0],b=y[1]/A[1],v=0;for(let w=0;w<d;w++){let S=w*l[0];for(let I=0;I<u;I++){let E=o?x*(I+.5):x*I,F=Math.min(h-1,a?Math.round(E):Math.floor(E));o&&(F=Math.max(0,F));let $=S+F*l[1];for(let _=0;_<c;_++){let N=o?b*(_+.5):b*_,P=Math.min(p-1,a?Math.round(N):Math.floor(N));o&&(P=Math.max(0,P));let B=$+P*l[2];for(let j=0;j<f;j++){let X=m[B+j];g[v++]=X}}}}return n.makeTensorInfo([d,u,c,f],s.dtype,g)}var sce={kernelName:af,backendName:"cpu",kernelFunc:rce};function ace(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r;Te([a,s],"resizeNearestNeighborGrad");let i=k.computeStrides(s.shape),l=k.computeStrides(a.shape),[u,c,d,h]=s.shape,[,p,f]=a.shape,m=new Float32Array(u*c*d*h),g=n.data.get(a.dataId).values,y=[o&&p>1?c-1:c,o&&f>1?d-1:d],A=[o&&p>1?p-1:p,o&&f>1?f-1:f],x=y[0]/A[0],b=y[1]/A[1],v=1/x,w=1/b,S=Math.ceil(v)*2+2,I=Math.ceil(w)*2+2;for(let E=0;E<u;E++){let F=E*i[0];for(let $=0;$<c;$++){let _=F+$*i[1],N=Math.floor($*v),P=Math.floor(N-S/2);for(let B=0;B<d;B++){let j=_+B*i[2],X=Math.floor(B*w),Y=Math.floor(X-I/2);for(let ee=0;ee<h;ee++){let oe=0;for(let se=0;se<S;se++){let ie=se+P;if(ie<0||ie>=p)continue;let ne=F+ie*l[1],de=ie*x,he=Math.min(c-1,o?Math.round(de):Math.floor(de));if($===he)for(let ge=0;ge<I;ge++){let be=ge+Y;if(be<0||be>=f)continue;let Ee=ne+be*l[2],$e=be*b,ze=Math.min(d-1,o?Math.round($e):Math.floor($e));B===ze&&(oe+=g[Ee+ee])}}m[j+ee]=oe}}}}return n.makeTensorInfo(s.shape,s.dtype,m)}var oce={kernelName:qy,backendName:"cpu",kernelFunc:ace};function ice(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r;Te(s,"reverse");let o=s.shape.length,i=k.parseAxisParam(a,s.shape);if(o===0)return zs({inputs:{x:s},backend:n});let l=new Qt(s.shape,s.dtype),u=n.bufferSync(s);for(let c=0;c<l.size;c++){let d=l.indexToLoc(c),h=d.slice();i.forEach(p=>h[p]=s.shape[p]-1-h[p]),l.set(u.get(...h),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var lce={kernelName:El,backendName:"cpu",kernelFunc:ice},uce={kernelName:pd,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,l=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(r.shape)),[u,c,d,h]=r.shape,[p,f]=R.getImageCenter(o,c,d),m=255,g=Math.sin(s),y=Math.cos(s),A=i.data.get(r.dataId).values;for(let b=0;b<u;b++){let v=b*d*c*h;for(let w=0;w<c;w++){let S=w*(d*h);for(let I=0;I<d;I++){let E=I*h;for(let F=0;F<h;F++){let $=[u,w,I,F],_=$[2],N=$[1],P=(_-p)*y-(N-f)*g,B=(_-p)*g+(N-f)*y;P=Math.round(P+p),B=Math.round(B+f);let j=a;if(typeof a!="number"&&(F===3?j=m:j=a[F]),P>=0&&P<d&&B>=0&&B<c){let Y=B*(d*h),ee=P*h,oe=v+Y+ee+F;j=A[oe]}let X=v+S+E+F;l[X]=j}}}}return{dataId:i.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},cce=xt($l,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}),dce={kernelName:$l,backendName:"cpu",kernelFunc:cce};function KN(e,t,n,r,s,a,o,i,l,u){let c=[r/s,s],d=e.values,h=t.values;if(r===0)return Le(n,t.dtype);let p=Le(c,t.dtype);p.values.fill(l);for(let f=0;f<a;f++){let m=[],g=0;for(let y=0;y<o;y++){let A=d[f*o+y];m.push(A),g+=A*i[y]}if(g<0||g>=r/s)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<s;y++)u?p.values[g*s+y]+=h[f*s+y]:p.values[g*s+y]=t.rank===0?h[0]:h[f*s+y]}return p}function hce(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=R.calculateShapes(a,s,o),h=!0,p=n.bufferSync(s),f=n.bufferSync(a),m=KN(p,f,o,d,u,l,i,c,0,h);return n.makeTensorInfo(o,m.dtype,m.values)}var pce={kernelName:ed,backendName:"cpu",kernelFunc:hce};function fce(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t;Te([r,s,a],"select");let o=r.shape.length,i=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,c=qr(s.dtype,a.dtype),d=k.makeZerosTypedArray(k.sizeFromShape(s.shape),c),h=0,p=o===0||o>1||s.shape.length===1?1:k.sizeFromShape(s.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<p;m++)i[f]===1?d[h++]=l[f]:d[h++]=u[f];return n.makeTensorInfo(s.shape,c,d)}var mce={kernelName:td,backendName:"cpu",kernelFunc:fce},gce=R.SELU_SCALEALPHA,yce=R.SELU_SCALE,Ace=xt(nd,e=>e>=0?yce*e:gce*(Math.exp(e)-1)),xce={kernelName:nd,backendName:"cpu",kernelFunc:Ace},bce=xt(ad,e=>e<0?-1:e>0?1:0),vce={kernelName:ad,backendName:"cpu",kernelFunc:bce},wce=xt(_l,e=>Math.sin(e)),kce={kernelName:_l,backendName:"cpu",kernelFunc:wce},Ice=xt(sd,e=>Math.sinh(e)),Sce={kernelName:sd,backendName:"cpu",kernelFunc:Ice},Tce=11920928955078125e-23,XN=Math.log(Tce)+2,Nce=xt(od,e=>{let t=e>-XN,n=e<XN,r=Math.exp(e),s;return n?s=r:t?s=e:s=Math.log(1+r),s}),Cce={kernelName:od,backendName:"cpu",kernelFunc:Nce};function Ece(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;Te([s],"spaceToBatchND");let i=k.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let w=1+a.length;w<s.shape.length;++w)l.push([0,0]);let u=qN.kernelFunc({inputs:{x:s},backend:n,attrs:{paddings:l,constantValue:0}}),c=R.getReshaped(u.shape,a,i,!1),d=R.getPermuted(c.length,a.length,!1),h=R.getReshapedPermuted(u.shape,a,i,!1),m=Ft({inputs:{x:u},backend:n,attrs:{shape:c}}),A=Pr({inputs:{x:m},backend:n,attrs:{perm:d}}),v=Ft({inputs:{x:A},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),v}var $ce={kernelName:of,backendName:"cpu",kernelFunc:Ece};function _ce(e){let{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
        ${a.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
        ${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
        ${s.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${o.shape}`);let i=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,c=n.data.get(o.dataId).values[0],[d,h,p,f,m]=bN(i,r.shape,r.dtype,l,s.dtype,u,c);return[n.makeTensorInfo(h,r.dtype,d),n.makeTensorInfo([h[0]],s.dtype,p),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}var Rce={kernelName:Xy,backendName:"cpu",kernelFunc:_ce};function Dce(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${s.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(s.dataId).values),i=n.data.get(r.dataId).values,l=Array.from(n.data.get(a.dataId).values),[u,c,d]=vN(i,r.shape,r.dtype,o,l);return[n.makeTensorInfo(c,r.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Fce={kernelName:Zy,backendName:"cpu",kernelFunc:Dce};function Mce(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
          ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
          ${a.shape}`);let o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values,l=n.data.get(a.dataId).values,[u,c]=O5(o,r.shape,r.dtype,i,l,!0);return n.makeTensorInfo(c,r.dtype,u)}var Oce={kernelName:Yy,backendName:"cpu",kernelFunc:Mce};function Pce(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
         ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
         ${a.shape}`);let o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values,l=n.data.get(a.dataId).values,[u,c]=O5(o,r.shape,r.dtype,i,l);return n.makeTensorInfo(c,r.dtype,u)}var zce={kernelName:Jy,backendName:"cpu",kernelFunc:Pce};function Lce(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:h}=R.calculateShapes(a,s,i),p=!1,f=n.bufferSync(s),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],y=KN(f,m,i,h,c,u,l,d,g,p);return n.makeTensorInfo(i,y.dtype,y.values)}var Bce={kernelName:Qy,backendName:"cpu",kernelFunc:Lce};function Wce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=k.parseAxisParam(o,s.shape)[0],l=R.prepareSplitSize(s,a,i),u=new Array(s.shape.length).fill(0),c=s.shape.slice();return l.map(d=>{let h=[...c];h[i]=d;let p=fi({inputs:{x:s},backend:n,attrs:{begin:u,size:h}});return u[i]+=d,p})}var Vce={kernelName:id,backendName:"cpu",kernelFunc:Wce},Uce=xt(Dl,e=>Math.sqrt(e)),Hce={kernelName:Dl,backendName:"cpu",kernelFunc:Uce},Gce={kernelName:lf,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;Te(n,"square");let s=r.data.get(n.dataId).values,a=new Float32Array(s.length);for(let i=0;i<s.length;++i){let l=s[i];a[i]=l*l}return{dataId:r.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},jce=xt(Bo,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),qce={kernelName:Bo,backendName:"cpu",kernelFunc:jce};function Kce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=r;Te(s,"stridedSlice");let{nonStrided:p,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=En.sliceInfo(s.shape,a,o,i,l,u,c,d,h),x=Ft({inputs:{x:s},backend:n,attrs:{shape:y}}),b;if(p){let w=fi({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=Ft({inputs:{x:w},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(w)}else if(A.some(w=>w===0))b=n.makeTensorInfo(A,s.dtype,[]);else{let w=n.bufferSync(x),S=kN(A,w,m,f);b=n.makeTensorInfo(S.shape,S.dtype,S.values)}let v=Ft({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var Xce={kernelName:ld,backendName:"cpu",kernelFunc:Kce};function Zce(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=r,{data:c,dataSplits:d}=t,h=n.data.get(c.dataId).values,p=n.data.get(d.dataId).values,[f,m]=IN(h,p,s,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Yce={kernelName:eA,backendName:"cpu",kernelFunc:Zce};function Jce(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values[0],[u,c,d]=SN(i,l,s),h=c.length;return[n.makeTensorInfo([h,2],"int32",u),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Qce={kernelName:tA,backendName:"cpu",kernelFunc:Jce};function ede(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.data.get(a.dataId).values,i=TN(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var tde={kernelName:nA,backendName:"cpu",kernelFunc:ede},nde=xt(Ol,e=>Math.tan(e)),rde={kernelName:Ol,backendName:"cpu",kernelFunc:nde},sde=xt(Pl,e=>Math.tanh(e)),ade={kernelName:Pl,backendName:"cpu",kernelFunc:sde};function ode(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;Te(s,"tile");let o=CN(n.bufferSync(s),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}var ide={kernelName:Lo,backendName:"cpu",kernelFunc:ode};function lde(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r;Te(s,"topk");let i=n.data.get(s.dataId).values,[l,u]=EN(i,s.shape,s.dtype,a,o);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var ude={kernelName:ud,backendName:"cpu",kernelFunc:lde};function cde(e){let{inputs:t,attrs:n,backend:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=n,[c,d,h,p]=s.shape,[f,m]=u!=null?u:[d,h],g=[c,f,m,p],y=k.computeStrides(s.shape),A=y[0],x=y[1],b=y[2],v=k.getTypedArrayFromDType(s.dtype,k.sizeFromShape(g));v.fill(l);let w=r.data.get(s.dataId).values,S=r.data.get(a.dataId).values;for(let E=0;E<c;++E){let F=a.shape[0]===1?S:S.subarray(E*8,E*8+8);for(let $=0;$<f;++$)for(let _=0;_<m;++_)for(let N=0;N<p;++N){let P,B=F[6]*_+F[7]*$+1;if(B===0)continue;let j=(F[0]*_+F[1]*$+F[2])/B,X=(F[3]*_+F[4]*$+F[5])/B,Y=ZN(j,h,i),ee=ZN(X,d,i);switch(o){case"nearest":P=gde(w,d,h,A,x,b,E,ee,Y,N,l);break;case"bilinear":P=yde(w,d,h,A,x,b,E,ee,Y,N,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${o}`)}let oe=E*A+$*x+_*b+N;v[oe]=P}return r.makeTensorInfo(g,s.dtype,v)}return{dataId:r.write(v,g,s.dtype),shape:s.shape,dtype:s.dtype}}var dde={kernelName:cd,backendName:"cpu",kernelFunc:cde};function ZN(e,t,n){switch(n){case"reflect":return hde(e,t);case"wrap":return pde(e,t);case"nearest":return mde(e,t);case"constant":default:return fde(e,t)}}function hde(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=2*t;n<r&&(n=r*Math.trunc(-n/r)+n),n=n<-t?n+r:-n-1}else if(n>t-1)if(t<=1)n=0;else{let r=2*t;n-=r*Math.trunc(n/r),n>=t&&(n=r-n-1)}return k.clamp(0,n,t-1)}function pde(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=t-1;n+=t*(Math.trunc(-n/r)+1)}else if(n>t-1)if(t<=1)n=0;else{let r=t-1;n-=t*Math.trunc(n/r)}return k.clamp(0,n,t-1)}function fde(e,t){return e}function mde(e,t){return k.clamp(0,e,t-1)}function rh(e,t,n,r,s,a,o,i,l,u,c){let d=o*r+i*s+l*a+u;return 0<=i&&i<t&&0<=l&&l<n?e[d]:c}function gde(e,t,n,r,s,a,o,i,l,u,c){let d=Math.round(i),h=Math.round(l);return rh(e,t,n,r,s,a,o,d,h,u,c)}function yde(e,t,n,r,s,a,o,i,l,u,c){let d=Math.floor(i),h=Math.floor(l),p=d+1,f=h+1,m=(f-l)*rh(e,t,n,r,s,a,o,d,h,u,c)+(l-h)*rh(e,t,n,r,s,a,o,d,f,u,c),g=(f-l)*rh(e,t,n,r,s,a,o,p,h,u,c)+(l-h)*rh(e,t,n,r,s,a,o,p,f,u,c);return(p-i)*m+(i-d)*g}function Ade(e){let{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;Te(a,"unique");let o=r.data.get(a.dataId).values,{outputValues:i,outputShape:l,indices:u}=$N(o,s,a.shape,a.dtype);return[r.makeTensorInfo(l,a.dtype,i),r.makeTensorInfo([u.length],"int32",u)]}var xde={kernelName:rA,backendName:"cpu",kernelFunc:Ade};function bde(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s.shape.length,i=s.shape[a],l=new Array(o-1),u=0;for(let p=0;p<o;p++)p!==a&&(l[u++]=s.shape[p]);let c=new Array(o).fill(0),d=s.shape.slice();d[a]=1;let h=new Array(i);for(let p=0;p<h.length;p++){c[a]=p;let f=fi({inputs:{x:s},backend:n,attrs:{begin:c,size:d}});h[p]=Ft({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return h}var vde={kernelName:dd,backendName:"cpu",kernelFunc:bde};function wde(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r;Te(s,"unsortedSegmentSum");let i=s.shape.length,l=a.shape.length,u=[],c=[],d=i-l,h=a;for(let f=0;f<d;++f){let m=_m({inputs:{input:h},backend:n,attrs:{dim:f+1}});h=m,c.push(m)}for(let f=0;f<o;++f){let m=k.createScalarValue(f,"int32"),g=n.makeTensorInfo([],"int32",m),y=JT({inputs:{a:g,b:h},backend:n}),A=Ja({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=$m({inputs:{a:A,b:s},backend:n}),b=nh({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});u.push(b),c.push(g),c.push(y),c.push(A),c.push(x),c.push(b)}let p=jN({inputs:u,backend:n,attrs:{axis:0}});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var kde={kernelName:uf,backendName:"cpu",kernelFunc:wde},Ide=[_oe,Nae,Doe,Moe,Dae,Poe,Loe,Woe,Uoe,Goe,qoe,Xoe,Yoe,eie,nie,aie,iie,uie,die,Eoe,pie,mie,yie,_ae,Mae,xie,Cae,vie,kie,Tie,Cie,Iie,Rie,Fie,$ie,Oie,zie,Bie,Vie,Hie,jie,qie,Xie,Yie,Qie,ele,nle,tle,W5,ale,voe,ile,Oae,mle,Pae,gle,Lae,wle,kle,Sle,Wae,Cle,$le,Rle,Fle,Ole,Uae,Gae,Eae,zle,wie,Ble,Vle,Hle,woe,qae,Xae,jle,Yae,Kle,Yle,Qle,nue,sue,oue,Qae,uue,due,pue,mue,yue,iue,xue,vue,toe,kue,Tue,$ue,roe,aoe,Due,Oue,Lue,ioe,Wue,Uue,Hue,qN,Kue,Ioe,coe,Zue,$ae,Jue,Soe,Toe,Coe,ece,nce,sce,oce,lce,uce,dce,hoe,pce,mce,xce,Noe,vce,kce,Sce,poe,Cue,Cce,$ce,Rce,Fce,Oce,zce,Bce,Vce,Hce,Gce,moe,qce,Xce,Yce,Qce,tde,xoe,rle,rde,ade,ide,ude,loe,dde,xde,vde,kde,Vue];for(let e of Ide)iA(e);var YN={};De(YN,{assertNotComplex:()=>mu,bindCanvasToFramebuffer:()=>Ode,bindColorTextureToFramebuffer:()=>Mm,bindTextureToProgramUniformSampler:()=>hC,bindTextureUnit:()=>uC,bindVertexBufferToProgramAttribute:()=>j5,callAndCheck:()=>Ie,canBeRepresented:()=>JN,createFragmentShader:()=>tC,createFramebuffer:()=>lC,createProgram:()=>nC,createStaticIndexBuffer:()=>aC,createStaticVertexBuffer:()=>sC,createTexture:()=>oC,createVertexShader:()=>eC,getBatchDim:()=>gi,getExtensionOrThrow:()=>ih,getFramebufferErrorMessage:()=>pC,getMaxTexturesInShader:()=>yC,getNumChannels:()=>Fde,getProgramUniformLocation:()=>dC,getProgramUniformLocationOrThrow:()=>cC,getRowsCols:()=>yi,getShapeAs3D:()=>Om,getTextureShapeFromLogicalShape:()=>mC,getWebGLDisjointQueryTimerVersion:()=>AC,getWebGLErrorMessage:()=>QN,getWebGLMaxTextureSize:()=>gC,hasExtension:()=>Lr,isCapableOfRenderingToFloatTexture:()=>xC,isDownloadFloatTextureEnabled:()=>bC,isReshapeFree:()=>uh,isWebGLFenceEnabled:()=>vC,isWebGLVersionEnabled:()=>K5,linkProgram:()=>rC,resetMaxTextureSize:()=>Pde,resetMaxTexturesInShader:()=>zde,unbindColorTextureFromFramebuffer:()=>q5,unbindTextureUnit:()=>Mde,validateFramebuffer:()=>lh,validateProgram:()=>Fm,validateTextureSize:()=>iC});var mi={},H5={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function Dm(e,t){mi[e]=t}function Ls(e){if(!(e in mi)){let n=Tde(e);if(n!==null)mi[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=mi[e];return t.isContextLost()?(delete mi[e],Ls(e)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),mi[e])}function Sde(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 Tde(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=Sde(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete mi[e]},!1),e===1?t.getContext("webgl",H5)||t.getContext("experimental-webgl",H5):t.getContext("webgl2",H5)}var sh;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(sh||(sh={}));var zr;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(zr||(zr={}));var Tn;(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"})(Tn||(Tn={}));function ah(e,t){return[t,e]}function Nde(e,t){return e*t}function oh(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function fu(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function Cde(e,t){let[n,r]=fu(e,t);return n*r*4}function G5(e,t){let n=e,r,s,a,o,i,l,u,c,d,h;return ae().getNumber("WEBGL_VERSION")===2?(r=n.R32F,s=n.R16F,a=n.RGBA16F,o=n.RGBA32F,i=n.RED,u=4,c=1,d=n.HALF_FLOAT,h=n.FLOAT):(r=e.RGBA,s=e.RGBA,a=e.RGBA,o=n.RGBA,i=e.RGBA,u=4,c=4,d=t!=null?t.HALF_FLOAT_OES:null,h=e.FLOAT),l=e.RGBA,{internalFormatFloat:r,internalFormatHalfFloat:s,internalFormatPackedHalfFloat:a,internalFormatPackedFloat:o,textureFormatFloat:i,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:c,textureTypeHalfFloat:d,textureTypeFloat:h}}function Ie(e,t){let n=t();return ae().getBool("DEBUG")&&Ede(e),n}function Ede(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+QN(e,t))}var $de=596e-10,_de=65504;function JN(e){return!!(ae().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||$de<Math.abs(e)&&Math.abs(e)<_de)}function QN(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 ih(e,t){return ya(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function eC(e,t){let n=ya(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(Ie(e,()=>e.shaderSource(n,t)),Ie(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 tC(e,t){let n=ya(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(Ie(e,()=>e.shaderSource(n,t)),Ie(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw Dde(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var Rde=/ERROR: [0-9]+:([0-9]+):/g;function Dde(e,t){let n=Rde.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let r=+n[1],s=e.split(`
`),a=s.length.toString().length+2,o=s.map((d,h)=>k.rightPad((h+1).toString(),a)+d),i=0;for(let d=0;d<o.length;d++)i=Math.max(o[d].length,i);let l=o.slice(0,r-1),u=o.slice(r-1,r),c=o.slice(r);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${k.rightPad(u[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
`))}function nC(e){return ya(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function rC(e,t){if(Ie(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function Fm(e,t){if(Ie(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function sC(e,t){let n=ya(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Ie(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function aC(e,t){let n=ya(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),Ie(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function Fde(){return ae().getNumber("WEBGL_VERSION")===2?1:4}function oC(e){return ya(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function iC(e,t){let n=ae().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,s=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+s+".")}}function lC(e){return ya(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function j5(e,t,n,r,s,a,o){let i=e.getAttribLocation(t,n);return i===-1?!1:(Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),Ie(e,()=>e.vertexAttribPointer(i,s,e.FLOAT,!1,a,o)),Ie(e,()=>e.enableVertexAttribArray(i)),!0)}function uC(e,t,n){fC(e,n),Ie(e,()=>e.activeTexture(e.TEXTURE0+n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function Mde(e,t){fC(e,t),Ie(e,()=>e.activeTexture(e.TEXTURE0+t)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function cC(e,t,n){return ya(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function dC(e,t,n){return e.getUniformLocation(t,n)}function hC(e,t,n,r){Ie(e,()=>uC(e,t,r)),Ie(e,()=>e.uniform1i(n,r))}function Ode(e){Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),Ie(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function Mm(e,t,n){Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),Ie(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function q5(e,t){Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),Ie(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function lh(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+pC(e,t))}function pC(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 ${t}`}}function ya(e,t,n){let r=Ie(e,()=>t());if(r==null)throw new Error(n);return r}function fC(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let s=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${s}.`)}}function gi(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function yi(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 Om(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[gi(e),...yi(e)]),t}function mC(e,t=!1){let n=ae().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((s,a)=>a>=e.length-2?k.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=k.squeezeShape(e).newShape);let r=k.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];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 s=gi(e),a=2,o=2;return e.length&&([a,o]=yi(e)),r=s*(a/2)*(o/2),k.sizeToSquarishShape(r).map(i=>i*2)}return k.sizeToSquarishShape(r)}function Pm(e){return e%2==0}function uh(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],r=t.slice(-1)[0];if(n===r||Pm(n)&&Pm(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Pm(e[0])&&Pm(t[0])}var zm,Lm;function gC(e){if(zm==null){let t=Ls(e);zm=t.getParameter(t.MAX_TEXTURE_SIZE)}return zm}function Pde(){zm=null}function zde(){Lm=null}function yC(e){if(Lm==null){let t=Ls(e);Lm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Lm)}function AC(e){if(e===0)return 0;let t,n=Ls(e);return Lr(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Lr(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Lr(e,t){return e.getExtension(t)!=null}function K5(e){try{if(Ls(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function xC(e){if(e===0)return!1;let t=Ls(e);if(e===1){if(!Lr(t,"OES_texture_float"))return!1}else if(!Lr(t,"EXT_color_buffer_float"))return!1;return X5(t)}function bC(e){if(e===0)return!1;let t=Ls(e);if(e===1){if(!Lr(t,"OES_texture_float")||!Lr(t,"WEBGL_color_buffer_float"))return!1}else{if(Lr(t,"EXT_color_buffer_float"))return X5(t);let r="EXT_color_buffer_half_float";if(Lr(t,r)){let s=t.getExtension(r);return Lde(t,s)}return!1}return X5(t)}function X5(e){let t=G5(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,s,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function Lde(e,t){let n=G5(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let s=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,s,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(o),i}function vC(e){return e!==2?!1:Ls(e).fenceSync!=null}function mu(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 Pe=ae();Pe.registerFlag("HAS_WEBGL",()=>Pe.getNumber("WEBGL_VERSION")>0);Pe.registerFlag("WEBGL_VERSION",()=>K5(2)?2:K5(1)?1:0);Pe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Pe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Pe.get("WEBGL_VERSION")===2);Pe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Pe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Pe.registerFlag("WEBGL_PACK",()=>Pe.getBool("HAS_WEBGL"));Pe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_CLIP",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_REDUCE",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_CONV_IM2COL",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>gC(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>yC(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Pe.getNumber("WEBGL_VERSION");return e===0?0:AC(e)});Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Pe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!yf.isMobile());Pe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>xC(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Pe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Pe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Pe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>bC(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>vC(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Pe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Pe.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}.`)});Pe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>yf.isMobile()&&Pe.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Pe.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);function Wn(){let e,t,n,r,s,a,o,i,l,u;return ae().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",s="texture",a="outputColor",o="out vec4 outputColor;",i=`
      bool isnan_custom(float val) {
        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
      }

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

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

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

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

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:s,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Ai(e,t,n="index"){let r=k.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / ${s}`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${s}`:`index -= ${e[a]} * ${s}`;return`${o}; ${i};`}).join("")}function Z5(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;
  }
`}var wC=`
  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;
  }
`,Bde=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=sh.DENSE;let t=oh(e),n=Wn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${Ai(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

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

        vec4 result = vec4(0.);

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

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

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

        vec4 result = vec4(0.);

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

        ${n.output} = result;
      }
    `}},Vde=class{constructor(e){this.variableNames=["A"],this.outTexUsage=zr.DOWNLOAD;let t=Wn();this.outputShape=e,this.userCode=`
      ${wC}

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

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},Hde=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=Wn(),[s,a]=t;this.outputShape=e;let o="result";n&&(o="floor(result * 255. + 0.5)"),this.userCode=`
      ${Z5(e)}

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

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

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

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

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

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

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

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

              if(offset == 0) {
                result[${c}] = values[0];
              } else if(offset == 1) {
                result[${c}] = values[1];
              } else if(offset == 2) {
                result[${c}] = values[2];
              } else {
                result[${c}] = values[3];
              }
            }
          }
        `}this.userCode=`
      ${Z5(e)}

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

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

        ${o}

        ${r.output} = ${i};
      }
    `}},kC={};De(kC,{bindVertexProgramAttributeStreams:()=>RC,createBufferFromOutputTexture:()=>MC,createFloat16MatrixTexture:()=>CC,createFloat16PackedMatrixTexture:()=>_C,createFloat32MatrixTexture:()=>NC,createIndexBuffer:()=>TC,createPackedMatrixTexture:()=>$C,createUnsignedBytesMatrixTexture:()=>EC,createVertexBuffer:()=>SC,createVertexShader:()=>IC,downloadByteEncodedFloatMatrixFromOutputTexture:()=>PC,downloadFloat32MatrixFromBuffer:()=>OC,downloadMatrixFromPackedOutputTexture:()=>LC,downloadPackedMatrixFromBuffer:()=>zC,getInternalFormatForFloat16MatrixTexture:()=>J5,getInternalFormatForFloat16PackedMatrixTexture:()=>tb,getInternalFormatForFloat32MatrixTexture:()=>Y5,getInternalFormatForPackedMatrixTexture:()=>eb,getInternalFormatForUnsignedBytesMatrixTexture:()=>Q5,uploadDenseMatrixToTexture:()=>DC,uploadPixelDataToTexture:()=>FC});function IC(e){let t=Wn(),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 eC(e,n)}function SC(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 sC(e,t)}function TC(e){let t=new Uint16Array([0,1,2,2,1,3]);return aC(e,t)}function ch(e,t,n,r,s,a){iC(t,n);let o=oC(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(i,0,r,t,n,0,s,a,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function Y5(e){return e.internalFormatFloat}function NC(e,t,n,r){let[s,a]=ah(t,n);return ch(e,s,a,Y5(r),r.textureFormatFloat,e.FLOAT)}function J5(e){return e.internalFormatHalfFloat}function CC(e,t,n,r){let[s,a]=ah(t,n);return ch(e,s,a,J5(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function Q5(e){return e.downloadTextureFormat}function EC(e,t,n,r){let[s,a]=ah(t,n);return ch(e,s,a,Q5(r),e.RGBA,e.UNSIGNED_BYTE)}function eb(e){return e.internalFormatPackedFloat}function $C(e,t,n,r){let[s,a]=fu(t,n);return ch(e,s,a,eb(r),e.RGBA,e.FLOAT)}function tb(e){return e.internalFormatPackedHalfFloat}function _C(e,t,n,r){let[s,a]=fu(t,n);return ch(e,s,a,tb(r),e.RGBA,r.textureTypeHalfFloat)}function RC(e,t,n){let r=0,s=3*4,a=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),j5(e,t,"clipSpacePos",n,3,a,r)&&j5(e,t,"uv",n,2,a,s)}function DC(e,t,n,r,s,a){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;s instanceof Uint8Array?(o=new Uint8Array(n*r*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*r*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(s),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function FC(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function MC(e,t,n,r){let s=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,s));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),s}function OC(e,t,n){let r=e,s=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,s),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),s}function PC(e,t,n,r){let[s,a]=ah(t,n),o=4,i=new Uint8Array(Nde(t*n,o));return Ie(e,()=>e.readPixels(0,0,s,a,r.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function zC(e,t,n,r,s,a,o,i){let l=e,u=new Float32Array(Cde(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function LC(e,t,n){let r=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var Bm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ae().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Dm(t,e)):this.gl=Ls(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(ae().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=ih(this.gl,s),Lr(this.gl,a))this.textureHalfFloatExtension=ih(this.gl,a);else if(ae().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),Lr(this.gl,r))this.colorBufferHalfFloatExtension=ih(this.gl,r);else if(ae().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",Lr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Lr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=SC(this.gl),this.indexBuffer=TC(this.gl),this.framebuffer=lC(this.gl),this.textureConfig=G5(this.gl,this.textureHalfFloatExtension)}get debug(){return ae().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;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),NC(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),CC(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),EC(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),FC(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),DC(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),_C(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),$C(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(q5(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>PC(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return zC(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return OC(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=MC(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(ae().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,s=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=r.clientWaitSync(s,0,0);return a===r.ALREADY_SIGNALED||a===r.CONDITION_SATISFIED},t=s}else ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>LC(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=tC(t,e);this.vertexShader==null&&(this.vertexShader=IC(t));let r=nC(t);return Ie(t,()=>t.attachShader(r,this.vertexShader)),Ie(t,()=>t.attachShader(r,n)),rC(t,r),this.debug&&Fm(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=RC(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Fm(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?cC(this.gl,e,t):dC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(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(),hC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=fu(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Fm(this.gl,this.program),lh(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ih(this.gl,ae().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(ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(ae().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,ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ae().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,r=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=jde(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(),Mm(this.gl,e,this.framebuffer),this.debug&&lh(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Mm(this.gl,this.outputTexture,this.framebuffer),this.debug&&lh(this.gl)):q5(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;Mm(r,e,this.framebuffer),this.debug&&lh(r),this.outputTexture=e,Ie(r,()=>r.viewport(0,0,t,n)),Ie(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,r))}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 jde(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:BC}=R;function qde(e,t,n,r){let s=[];e.forEach(f=>{let m=k.sizeFromShape(f.shapeInfo.logicalShape);f.shapeInfo.isUniform?s.push(`uniform float ${f.name}${m>1?`[${m}]`:""};`):(s.push(`uniform sampler2D ${f.name};`),s.push(`uniform int offset${f.name};`))});let a=s.join(`
`),o=e.map(f=>Kde(f,t,r)).join(`
`),i=t.texShape,l=Wn(),u=Yde(l),c,d,h=ehe(l);return t.isPacked?(c=Xde(t.logicalShape,i),d=Qde(l)):(c=Zde(t.logicalShape,i),d=Jde(l)),r&&(h+=she),[h,u,d,a,c,o,n].join(`
`)}function gu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return ghe(e);case 1:return Ahe(e);case 2:return bhe(e);case 3:return whe(e);case 4:return Ihe(e);case 5:return She(e);case 6:return The(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function WC(e){switch(e.shapeInfo.logicalShape.length){case 0:return mhe(e);case 1:return yhe(e);case 2:return xhe(e);case 3:return vhe(e);default:return khe(e)}}function Kde(e,t,n=!1){let r="";n?r+=WC(e):r+=gu(e);let s=e.shapeInfo.logicalShape,a=t.logicalShape;return s.length<=a.length&&(n?r+=Nhe(e,t):r+=Che(e,t)),r}function Xde(e,t){switch(e.length){case 0:return VC();case 1:return ahe(e,t);case 2:return phe(e,t);case 3:return ihe(e,t);default:return uhe(e,t)}}function Zde(e,t){switch(e.length){case 0:return VC();case 1:return ohe(e,t);case 2:return fhe(e,t);case 3:return lhe(e,t);case 4:return che(e,t);case 5:return dhe(e,t);case 6:return hhe(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Yde(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function Jde(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function Qde(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function ehe(e){return`${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);
    }

    ${the}
    ${nhe}
    ${rhe}
  `}var the=`
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);
}
`,nhe=`
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);
}
`,rhe=`
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);
}
`,she=`
  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 VC(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function ahe(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${n[1]}.0);
      }
    `:n[1]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${n[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
    }
  `}function ohe(e,t){return t[0]===1?`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function ihe(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[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 lhe(e,t){let n=Ai(["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;
      ${n}
      return ivec3(r, c, d);
    }
  `}function uhe(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),a=s,o="",i="b, r, c";for(let l=2;l<e.length-1;l++)a*=e[e.length-l-1],o=`
      int b${l} = index / ${a};
      index -= b${l} * ${a};
    `+o,i=`b${l}, `+i;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

      ${o}

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

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

      return ivec${e.length}(${i});
    }
  `}function che(e,t){let n=Ai(["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;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function dhe(e,t){let n=Ai(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function hhe(e,t){let n=Ai(["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 phe(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
      }
    `;let r=Math.ceil(e[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));

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

      return ivec2(r, c);
    }
  `}function fhe(e,t){return k.arraysEqual(e,t)?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function xi(e){return`offset${e}`}function mhe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Wn();return`
    vec4 ${n}() {
      return ${r.texture2D}(${t}, halfCR);
    }
  `}function ghe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return`
      float ${n}() {
        return sampleTexture(${t}, halfCR);
      }
    `;let[a,o]=e.shapeInfo.texShape,i=xi(t);return`
    float ${n}() {
      vec2 uv = uvFromFlat(${a}, ${o}, ${i});
      return sampleTexture(${t}, uv);
    }
  `}function yhe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=Wn();return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${s[0]}, ${s[1]}, index);
      return ${a.texture2D}(${t}, uv);
    }
  `}function Ahe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${yu(e)}
      }
    `;let r=e.shapeInfo.texShape,s=r[0],a=r[1];if(a===1&&s===1)return`
      float ${n}(int index) {
        return sampleTexture(${t}, halfCR);
      }
    `;let o=xi(t);return a===1?`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
        return sampleTexture(${t}, uv);
      }
    `:s===1?`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${a}.0, 0.5);
        return sampleTexture(${t}, uv);
      }
    `:`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${s}, ${a}, index + ${o});
      return sampleTexture(${t}, uv);
    }
  `}function xhe(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,a=s[0],o=s[1],i=Wn();if(s!=null&&k.arraysEqual(t,s))return`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${a}.0);

        return ${i.texture2D}(${n}, uv);
      }
    `;let l=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],u=Math.ceil(t[1]/2);return`
    vec4 ${r}(int row, int col) {
      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
      return ${i.texture2D}(${n}, uv);
    }
  `}function bhe(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&k.arraysEqual(t,s)){let d=s[0],h=s[1];return`
    float ${r}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
      return sampleTexture(${n}, uv);
    }
  `}let{newShape:a,keptDims:o}=k.squeezeShape(t),i=a;if(i.length<t.length){let d=Au(e,i),h=["row","col"];return`
      ${gu(d)}
      float ${r}(int row, int col) {
        return ${r}(${xu(h,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${yu(e)}
      }
    `;let l=s[0],u=s[1],c=xi(n);return u===1?`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${n}, uv);
    }
  `:l===1?`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
      return sampleTexture(${n}, uv);
    }
  `:`
  float ${r}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${t[1]} + col + ${c};
    vec2 uv = uvFromFlat(${l}, ${u}, index);
    return sampleTexture(${n}, uv);
  }
`}function vhe(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(t[0]===1){let d=t.slice(1),h=[1,2],p=Au(e,d),f=["b","row","col"];return`
        ${WC(p)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${xu(f,h)});
        }
      `}let o=a[0],i=a[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),c=Wn();return`
    vec4 ${r}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${o}, ${i}, ${u}, ${l}, b, row, col);
      return ${c.texture2D}(${n}, uv);
    }
  `}function whe(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[1]*t[2],a=t[2],{newShape:o,keptDims:i}=k.squeezeShape(t),l=o;if(l.length<t.length){let f=Au(e,l),m=["row","col","depth"];return`
        ${gu(f)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${xu(m,i)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${a}, 1)));
        ${yu(e)}
      }
    `;let u=e.shapeInfo.texShape,c=u[0],d=u[1],h=e.shapeInfo.flatOffset;if(d===s&&h==null)return`
        float ${r}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${a}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${d}.0, ${c}.0);
          return sampleTexture(${n}, uv);
        }
      `;if(d===a&&h==null)return`
    float ${r}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${c}.0);
      return sampleTexture(${n}, uv);
    }
  `;let p=xi(n);return`
      float ${r}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${a} + depth + ${p};
        vec2 uv = uvFromFlat(${c}, ${d}, index);
        return sampleTexture(${n}, uv);
      }
  `}function khe(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],i=o[0],l=o[1],u=Math.ceil(t[n-1]/2),c=u*Math.ceil(t[n-2]/2),d="int b, int row, int col",h=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let f=2;f<n-1;f++)d=`int b${f}, `+d,c*=t[n-f-1],h=`b${f} * ${c} + `+h;let p=Wn();return`
    vec4 ${s}(${d}) {
      int index = ${h};
      int texR = index / ${l};
      int texC = index - texR * ${l};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
      return ${p.texture2D}(${r}, uv);
    }
  `}function Ihe(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[3],a=t[2]*s,o=t[1]*a,{newShape:i,keptDims:l}=k.squeezeShape(t);if(i.length<t.length){let f=Au(e,i),m=["row","col","depth","depth2"];return`
      ${gu(f)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${xu(m,l)});
      }
    `}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}, ${a}, ${s}, 1)));
        ${yu(e)}
      }
    `;let u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,d=c[0],h=c[1];if(h===o&&u==null)return`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${a}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(h===s&&u==null)return`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;let p=xi(n);return`
    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 * ${a} +
          depth * ${s} + depth2;
      vec2 uv = uvFromFlat(${d}, ${h}, index + ${p});
      return sampleTexture(${n}, uv);
    }
  `}function She(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[4],a=t[3]*s,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=k.squeezeShape(t);if(l.length<t.length){let m=Au(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${gu(m)}
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        return ${r}(${xu(g,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${i}, ${o}, ${a}, ${s})) +
          depth3;
        ${yu(e)}
      }
    `;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],p=d[1];if(p===i&&c==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${o}, ${a}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${p}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(p===s&&c==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${p}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;let f=xi(n);return`
    float ${r}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} + depth * ${a} +
          depth2 * ${s} + depth3 + ${f};
      vec2 uv = uvFromFlat(${h}, ${p}, index);
      return sampleTexture(${n}, uv);
    }
  `}function The(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:s,keptDims:a}=k.squeezeShape(t);if(s.length<t.length){let g=Au(e,s),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${gu(g)}
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${r}(${xu(y,a)});
      }
    `}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${u}, ${l}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${o}, 1)));
        ${yu(e)}
      }
    `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],f=h[1];if(f===c&&d==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${l}, ${i}, ${o})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(f===o&&d==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;let m=xi(n);return`
    float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${u} + depth * ${l} +
          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
      vec2 uv = uvFromFlat(${p}, ${f}, index);
      return sampleTexture(${n}, uv);
    }
  `}function yu(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 ${t}[i];
      }
    }
  `}function Nhe(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=BC(e.shapeInfo.logicalShape,t.logicalShape),l=It(o),u=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(A=>`coords.${d[A+u]} = 0;`).join(`
`);let h="";o<2&&a>0?h="coords":h=e.shapeInfo.logicalShape.map((A,x)=>`coords.${d[x+u]}`).join(", ");let p="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,y=k.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!y)p=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!y)o===1?p=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:p=`
        return vec4(outputValue.x);
      `;else if(i.length){let A=a-2,x=a-1;i.indexOf(A)>-1&&i.indexOf(x)>-1?p="return vec4(outputValue.x);":i.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${s}() {
      ${l} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${r}(${h});
      ${p}
    }
  `}function Che(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(o,a))return`
      float ${s}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let u=It(l),c=BC(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,h,p=["x","y","z","w","u","v"];i===0?h="":l<2&&c.length>=1?h="coords = 0;":h=c.map(m=>`coords.${p[m+d]} = 0;`).join(`
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${p[g+d]}`).join(", "),`
    float ${s}() {
      ${u} coords = getOutputCoords();
      ${h}
      return get${r}(${f});
    }
  `}function It(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 ${e} is not yet supported`)}function Au(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function xu(e,t){return t.map(n=>e[n]).join(", ")}function Ehe(e,t,n,r){let s=t.userCode,a=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),o=a.map(p=>p.shapeInfo),i={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=qde(a,i,s,t.packedInputs),u=e.createProgram(l),c=null,d=e.getUniformLocation(u,"NAN",!1);ae().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let h={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;h[f]=e.getUniformLocation(u,f,m),h[`offset${f}`]=e.getUniformLocation(u,`offset${f}`,m)}return{program:t,source:l,webGLProgram:u,uniformLocations:h,inShapeInfos:o,outShapeInfo:i,infLoc:c,nanLoc:d}}function UC(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,r)=>{let s=n.logicalShape,a=t[r],o=a.shape;if(!k.arraysEqual(s,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!k.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function $he(e,t,n,r,s){UC(t.inShapeInfos,n),UC([t.outShapeInfo],[r]);let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),ae().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((i,l)=>{let u=t.program.variableNames[l],c=t.uniformLocations[u],d=t.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(k.sizeFromShape(i.shape)<2)e.gl.uniform1f(c,i.uniformValues[0]);else{let h=i.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),e.gl.uniform1fv(c,h)}return}i.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,i.texData.slice.flatOffset),e.setInputMatrixTexture(i.texData.texture,c,l)}}),s!=null&&s(e,t.webGLProgram),e.executeProgram()}function _he(e,t,n){let r="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0,l=o.isUniform?"uniform":o.texData.texShape;r+=`${o.shape}_${l}_${i}`});let s=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+s,a}var HC={};De(HC,{addImpl:()=>qC,bincountImpl:()=>Mhe,bincountReduceImpl:()=>Ohe,ceilImpl:()=>KC,concatImpl:()=>XC,equalImpl:()=>ZC,expImpl:()=>YC,expm1Impl:()=>JC,floorImpl:()=>QC,gatherNdImpl:()=>Phe,gatherV2Impl:()=>zhe,greaterEqualImpl:()=>tE,greaterImpl:()=>eE,lessEqualImpl:()=>rE,lessImpl:()=>nE,linSpaceImpl:()=>Lhe,logImpl:()=>sE,maxImpl:()=>Bhe,maximumImpl:()=>aE,minimumImpl:()=>oE,multiplyImpl:()=>ab,negImpl:()=>Vhe,notEqualImpl:()=>iE,prodImpl:()=>Hhe,rangeImpl:()=>lE,rsqrtImpl:()=>uE,simpleAbsImpl:()=>Rhe,sliceImpl:()=>ob,sparseFillEmptyRowsImpl:()=>Ghe,sparseReshapeImpl:()=>jhe,sparseSegmentReductionImpl:()=>qhe,squaredDifferenceImpl:()=>cE,stridedSliceImpl:()=>Khe,stringNGramsImpl:()=>Zhe,stringSplitImpl:()=>Jhe,stringToHashBucketFastImpl:()=>Qhe,subImpl:()=>dE,tileImpl:()=>tpe,topKImpl:()=>npe,transposeImpl:()=>Uhe,uniqueImpl:()=>rpe});function GC(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}function Rhe(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}function Br(e){return(t,n,r,s,a)=>{let o=R.assertAndGetBroadcastShape(t,n),i=o.length,l=k.computeStrides(o),u=k.sizeFromShape(o),c=k.getTypedArrayFromDType(a,u),d=t.length,h=n.length,p=k.computeStrides(t),f=k.computeStrides(n),m=R.getBroadcastDims(t,o),g=R.getBroadcastDims(n,o);if(m.length+g.length===0)for(let y=0;y<c.length;++y)c[y]=e(r[y%r.length],s[y%s.length]);else for(let y=0;y<c.length;++y){let A=k.indexToLoc(y,i,l),x=A.slice(-d);m.forEach(S=>x[S]=0);let b=k.locToIndex(x,d,p),v=A.slice(-h);g.forEach(S=>v[S]=0);let w=k.locToIndex(v,h,f);c[y]=e(r[b],s[w])}return[c,o]}}function nb(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=n.makeTensorInfo(r.shape,"complex64"),l=n.data.get(i.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",a),imag:n.makeTensorInfo(s.shape,"float32",o)},i}function rb(e,t,n="float32"){if(n==="complex64"){let s=rb(e,t,"float32"),a=rb(e,t,"float32");return nb({inputs:{real:s,imag:a},backend:e})}let r=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function jC(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Dhe(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.real,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}function Wm(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return jC({inputs:{x:s},backend:n});let o=rb(n,s.shape,s.dtype),i=Wm({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),l=nb({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(s.dtype==="complex64"){let o=Dhe({inputs:{input:s},backend:n}),i=Wm({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!k.hasEncodingLoss(s.dtype,a)){let o=jC({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(s.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(s.shape,"int32",i)}if(a==="bool"){let o=n.data.get(s.dataId).values,i=k.toTypedArray([0],s.dtype),[l,u]=Br((c,d)=>c!==d?1:0)(s.shape,[],o,i,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}function Jr(e,t,n,r){return n==null?({inputs:s,backend:a})=>{let{a:o,b:i}=s,l=a;GC([o,i],e);let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=o.dtype==="string"?R.fromUint8ToStringArray(u):u,h=o.dtype==="string"?R.fromUint8ToStringArray(c):c,p=r||o.dtype,[f,m]=t(o.shape,i.shape,d,h,p);return l.makeTensorInfo(m,p,f)}:({inputs:s,backend:a})=>{let{a:o,b:i}=s,l=a;if(o.dtype==="complex64"||i.dtype==="complex64"){let u=Wm({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),c=l.data.get(u.dataId),d=c.complexTensorInfos.real,h=c.complexTensorInfos.imag,p=l.data.get(d.dataId).values,f=l.data.get(h.dataId).values,m=Wm({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(m.dataId),y=g.complexTensorInfos.real,A=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,b=l.data.get(A.dataId).values,[v,w,S]=n(o.shape,i.shape,p,f,x,b),I=l.makeTensorInfo(S,"float32",v),E=l.makeTensorInfo(S,"float32",w),F=nb({inputs:{real:I,imag:E},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(I),l.disposeIntermediateTensorInfo(E),F}else{let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=r||o.dtype,[h,p]=t(o.shape,i.shape,u,c,d);return l.makeTensorInfo(p,d,h)}}}function sb(e){return(t,n,r,s,a,o)=>{let i=R.assertAndGetBroadcastShape(t,n),l=k.sizeFromShape(i),u=i.length,c=k.computeStrides(i),d=k.getTypedArrayFromDType("float32",l),h=k.getTypedArrayFromDType("float32",l),p=R.getBroadcastDims(t,i),f=R.getBroadcastDims(n,i),m=R.mergeRealAndImagArrays(r,s),g=R.mergeRealAndImagArrays(a,o),y=t.length,A=k.computeStrides(t),x=n.length,b=k.computeStrides(n);if(p.length+f.length===0)for(let v=0;v<d.length;v++){let w=v%m.length,S=v%g.length,I=e(m[w*2],m[w*2+1],g[S*2],g[S*2+1]);d[v]=I.real,h[v]=I.imag}else for(let v=0;v<d.length;v++){let w=k.indexToLoc(v,u,c),S=w.slice(-y);p.forEach(_=>S[_]=0);let I=k.locToIndex(S,y,A),E=w.slice(-x);f.forEach(_=>E[_]=0);let F=k.locToIndex(E,x,b),$=e(m[I*2],m[I*2+1],g[F*2],g[F*2+1]);d[v]=$.real,h[v]=$.imag}return[d,h,i]}}var qC=Br((e,t)=>e+t),Fhe=sb((e,t,n,r)=>({real:e+n,imag:t+r})),Gwe=Jr(Fa,qC,Fhe);function Mhe(e,t,n,r,s){let a=k.sizeFromShape(r),o=k.makeZerosTypedArray(s,n);for(let i=0;i<e.length;i++){let l=e[i];if(l<0)throw new Error("Input x must be non-negative!");l>=s||(a>0?o[l]+=t[i]:o[l]+=1)}return o}function Ohe(e,t,n,r=!1){let s=e.shape[0],a=e.shape[1],o=Le([s,n],t.dtype);for(let i=0;i<s;i++)for(let l=0;l<a;l++){let u=e.get(i,l);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(r?o.set(1,i,u):t.size>0?o.set(o.get(i,u)+t.get(i,l),i,u):o.set(o.get(i,u)+1,i,u))}return o}function bu(e){return(t,n,r)=>{let s=k.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)s[a]=e(t[a],r);return s}}function vu(e,t,n){return({inputs:r,attrs:s,backend:a})=>{let{x:o}=r;if(GC(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,u=n||o.dtype,c=t(l,u,s);return i.makeTensorInfo(o.shape,u,c)}}var KC=bu(e=>Math.ceil(e)),jwe=vu(No,KC);function XC(e,t,n,r){let s=k.getArrayFromDType(n,k.sizeFromShape(t));if(r&&n!=="string"){let a=0;e.forEach(o=>{let i=k.sizeFromShape(o.shape);s.set(o.vals,a),a+=i})}else{let a=0;e.forEach(o=>{let i=n==="string"?R.fromUint8ToStringArray(o.vals):o.vals,l=0;for(let u=0;u<o.shape[0];++u){let c=u*t[1]+a;for(let d=0;d<o.shape[1];++d)s[c+d]=i[l++]}a+=o.shape[1]})}return s}var ZC=Br((e,t)=>e===t?1:0),qwe=Jr(il,ZC,null,"bool"),YC=bu(e=>Math.exp(e)),Kwe=vu(Eo,YC),JC=bu(e=>Math.expm1(e)),Xwe=vu(ll,JC),QC=bu(e=>Math.floor(e)),Zwe=vu($o,QC);function Phe(e,t,n,r,s,a,o,i,l){let u=Le([r,a],n);for(let c=0;c<r;c++){let d=[],h=0;for(let p=0;p<s;p++){let f=e[c*s+p];h+=f*o[p],d.push(f)}if(h<0||h>=l/a)throw new Error(`Invalid indices: ${d} does not index into ${i}`);for(let p=0;p<a;p++)u.values[c*a+p]=t.get(...t.indexToLoc(h*a+p))}return u}function zhe(e,t,n){let r=Le(n,e.dtype);for(let s=0;s<r.size;++s){let o=r.indexToLoc(s).slice(),i=o[0],l=o[2],u=t.locToIndex([i,l]);o[2]=t.values[u];let c=e.locToIndex(o);r.values[s]=e.values[c]}return r}var eE=Br((e,t)=>e>t?1:0),Ywe=Jr(dl,eE,null,"bool"),tE=Br((e,t)=>e>=t?1:0),Jwe=Jr(_o,tE,null,"bool"),nE=Br((e,t)=>e<t?1:0),Qwe=Jr(fl,nE,null,"bool"),rE=Br((e,t)=>e<=t?1:0),e7e=Jr(ml,rE,null,"bool");function Lhe(e,t,n){let r=(t-e)/(n-1),s=k.makeZerosTypedArray(n,"float32");s[0]=e;for(let a=1;a<s.length;a++)s[a]=s[a-1]+r;return s}var sE=bu(e=>Math.log(e)),t7e=vu(Ro,sE);function Bhe(e,t,n,r){let s=k.getTypedArrayFromDType(r,k.sizeFromShape(n));for(let a=0;a<s.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let u=e[o+l];(Number.isNaN(u)||u>i)&&(i=u)}s[a]=i}return s}var aE=Br((e,t)=>Math.max(e,t)),n7e=Jr(Do,aE),oE=Br((e,t)=>Math.min(e,t)),r7e=Jr(Fo,oE),ab=Br((e,t)=>e*t),Whe=sb((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),s7e=Jr(Mo,ab,Whe);function Vhe(e,t,n){let r=k.createScalarValue(-1,n);return ab([],t,r,e,n)}var iE=Br((e,t)=>e!==t?1:0),a7e=Jr(vl,iE,null,"bool");function Uhe(e,t,n,r,s){let a=t.length,o=k.sizeFromShape(t),i=k.computeStrides(t),l=k.computeStrides(s),u=k.getTypedArrayFromDType(n,k.sizeFromShape(s));for(let c=0;c<o;++c){let d=k.indexToLoc(c,a,i),h=new Array(d.length);for(let f=0;f<h.length;f++)h[f]=d[r[f]];let p=k.locToIndex(h,a,l);u[p]=e[c]}return u}function Hhe(e,t,n,r){let[s,a]=R.computeOutAndReduceShapes(e,r),o=qr(t,"int32"),i=k.makeZerosTypedArray(k.sizeFromShape(s),o),l=k.sizeFromShape(a);for(let u=0;u<i.length;++u){let c=u*l,d=1;for(let h=0;h<l;++h)d*=n[c+h];i[u]=d}return{outVals:i,outShape:s,outDtype:o}}function lE(e,t,n,r){let s=e===t,a=e<t&&n<0,o=t<e&&n>1;if(s||a||o)return k.makeZerosTypedArray(0,r);let i=Math.abs(Math.ceil((t-e)/n)),l=k.makeZerosTypedArray(i,r);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 uE=bu(e=>1/Math.sqrt(e)),o7e=vu(Oo,uE);function ob(e,t,n,r,s){let a=En.isSliceContinous(r,t,n),o=k.sizeFromShape(n),i=k.computeStrides(r);if(a){let d=En.computeFlatOffset(t,i);return s==="string"?e.slice(d,d+o):e.subarray(d,d+o)}let l=s==="string"?R.fromUint8ToStringArray(e):e,u=Le(r,s,l),c=Le(n,s);for(let d=0;d<c.size;++d){let h=c.indexToLoc(d),p=h.map((f,m)=>f+t[m]);c.set(u.get(...p),...h)}return s==="string"?R.fromStringArrayToUint8(c.values):c.values}function Ghe(e,t,n,r,s,a,o){let i=t[0],l=a[0],u=new Array(l),c=new Array(i),d=t[1];if(l===0){if(i!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
         indices.shape[0] = ${i}`);let g=k.getArrayFromDType(n,0),y=k.getArrayFromDType(s,0);return[g,[0,d],y,u,c]}let h=!0,p=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let y=e[g*d];if(y<0)throw new Error(`indices(${g}, 0) is invalid: ${y} < 0`);if(y>=l)throw new Error(`indices(${g}, 0) is invalid: ${y} >= ${l}`);++f[y],h=h&&y>=p,p=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;u[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&h){let g=e,y=r;for(let A=0;A<i;++A)c[A]=A;return[g,[i,d],y,u,c]}else{let g=f[l-1],y=k.getArrayFromDType(n,g*d),A=k.getArrayFromDType(s,g),x=new Array(l).fill(0);for(let b=0;b<i;++b){let v=e[b*d],w=x[v],S=(v===0?0:f[v-1])+w;x[v]++;for(let I=0;I<d;++I)y[S*d+I]=e[b*d+I];A[S]=r[b],c[b]=S}for(let b=0;b<l;++b)if(x[b]===0){let w=b===0?0:f[b-1];y[w*d+0]=b;for(let S=1;S<d;++S)y[w*d+S]=0;A[w]=o}return[y,[g,d],A,u,c]}}function jhe(e,t,n,r,s){let a=k.sizeFromShape(r),o=t[0],i=s.length,l=[],u=1,c=-1;for(let g=0;g<i;++g){let y=s[g];if(y===-1){if(c!==-1)throw new Error(`only one output dimension may be -1, not both ${c} and ${g}`);c=g,l.push(1)}else{if(y<0)throw new Error(`size ${g} must be non-negative, not ${y}`);u*=y,l.push(y)}}if(c!==-1){if(u<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(a/u);if(u*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
          dense values, but the requested shape requires a multiple of ${u}. inputShape=${r} outputShape= ${l}`);l[c]=g}let d=k.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${r} outputShape=${l}`);let h=r.length,p=[];if(h>0){p[h-1]=1;for(let g=h-2;g>=0;--g)p[g]=p[g+1]*r[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=k.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let y=0;for(let A=0;A<h;++A)y+=e[g*h+A]*p[A];for(let A=0;A<i;++A)m[g*i+A]=Math.trunc(y/f[A]),y%=f[A]}return[m,[o,i],l]}function qhe(e,t,n,r,s,a=!1,o=0){let i=r.length;if(i!==s.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],u=l[1],d=i>0?s[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let h=t.slice();h[0]=d;let p=h.reduce((x,b)=>x*b,1),f=k.getArrayFromDType(n,p);if(i===0)return d>0&&f.fill(o),[f,h];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,g=1,y=0,A=s[m];for(;;){let x=0;if(g<i){if(x=s[g],A===x){++g;continue}if(A>=x)throw new Error("segment ids are not increasing")}if(A<0||A>=d)throw new Error(`Segment id ${A} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);A>y&&f.fill(o,y*u,A*u);for(let b=m;b<g;++b){let v=r[b];if(v<0||v>=l[0])throw new Error(`Bad: indices[${b}] == ${r[b]} out of range [0, ${l[0]})`);for(let w=0;w<u;w++)f[A*u+w]+=e[v*u+w]}if(a)for(let b=0;b<u;b++)f[A*u+b]/=g-m;if(m=g,++g,y=A+1,A=x,g>i)break}return y<d&&f.fill(o,y*u,d*u),[f,h]}var cE=Br((e,t)=>{let n=e-t;return n*n}),i7e=Jr(Po,cE);function Khe(e,t,n,r){let s=Le(e,t.dtype);for(let a=0;a<s.size;a++){let o=s.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+r[l];s.set(t.get(...i),...o)}return s}var Xhe=class{constructor(e,t,n,r,s,a){this.separator=k.encodeString(e),this.nGramWidths=t,this.leftPad=k.encodeString(n),this.rightPad=k.encodeString(r),this.padWidth=s,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,r,s,a){for(let o=0;o<s;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),u=Math.max(0,i-(s-(o+1))),c=a-(l+u),d=t+(l>0?0:o-i),h=0;h+=l*this.leftPad.length;for(let y=0;y<c;++y)h+=e[d+y].length;h+=u*this.rightPad.length,h+=(l+u+c-1)*this.separator.length,n[r+o]=new Uint8Array(h);let f=n[r+o],m=0,g=y=>y.forEach(A=>f[m++]=A);for(let y=0;y<l;++y)g(this.leftPad),g(this.separator);for(let y=0;y<c-1;++y)g(e[d+y]),g(this.separator);if(c>0){g(e[d+c-1]);for(let y=0;y<u;++y)g(this.separator),g(this.rightPad)}else{for(let y=0;y<u-1;++y)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,r=t.length;if(r>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<r;++l){let u=t[l]>=i;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${i}`)}let s=r-1,a=k.getArrayFromDType("int32",r);if(n===0||r===0){let i=new Array(n);for(let l=0;l<=s;++l)a[l]=0;return[i,a]}a[0]=0;for(let i=1;i<=s;++i){let l=t[i]-t[i-1],u=0;this.nGramWidths.forEach(c=>{u+=this.getNumNGrams(l,c)}),this.preserveShort&&l>0&&u===0&&(u=1),a[i]=a[i-1]+u}let o=new Array(a[s]);for(let i=0;i<s;++i){let l=t[i],u=a[i];if(this.nGramWidths.forEach(c=>{let d=t[i+1]-t[i],h=this.getNumNGrams(d,c);this.createNGrams(e,l,o,u,h,c),u+=h}),this.preserveShort&&u===a[i]){let c=t[i+1]-t[i];if(c===0)continue;let d=c+2*this.padWidth,h=1;this.createNGrams(e,l,o,u,h,d)}}return[o,a]}};function Zhe(e,t,n,r,s,a,o,i){return new Xhe(n,r,s,a,o,i).compute(e,t)}function Yhe(e,t,n){if(!e.length)return[];if(t.length===0){let a=new Array(e.length);for(let o=0;o<e.length;++o)a[o]=e.subarray(o,o+1);return a}if(t.length===1){let a=t[0],o=[],i=e.indexOf(a);for(;i!==-1;){let l=e.subarray(0,i);(!n||l.length!==0)&&o.push(l),e=e.subarray(i+1),i=e.indexOf(a)}return(!n||e.length!==0)&&o.push(e),o}let r=[],s=0;for(let a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(s,a);(!n||o.length!==0)&&r.push(o),s=a+1}return r}function Jhe(e,t,n){let r=e.length,s=[],a=0,o=0,i=new Array(r);for(let h=0;h<r;++h){let p=Yhe(e[h],t,n),f=p.length;i[h]=f,a+=f,o=Math.max(o,f),s.push(...p)}let l=k.getArrayFromDType("int32",a*2),u=new Array(a),c=[r,o],d=0;for(let h=0;h<r;++h)for(let p=0;p<i[h];++p)l[d*2]=h,l[d*2+1]=p,u[d]=s[d],++d;return[l,u,c]}function Qhe(e,t){let n=k.getArrayFromDType("int32",e.length);for(let r=0;r<e.length;++r)n[r]=k.fingerPrint64(e[r]).modulo(t).getLowBitsUnsigned();return n}var dE=Br((e,t)=>e-t),epe=sb((e,t,n,r)=>({real:e-n,imag:t-r})),l7e=Jr(zo,dE,epe);function tpe(e,t){let n=new Array(e.rank);for(let s=0;s<n.length;s++)n[s]=e.shape[s]*t[s];let r=Le(n,e.dtype);for(let s=0;s<r.values.length;++s){let a=r.indexToLoc(s),o=new Array(e.rank);for(let l=0;l<o.length;l++)o[l]=a[l]%e.shape[l];let i=e.locToIndex(o);r.values[s]=e.values[i]}return r}function npe(e,t,n,r,s){let a=t[t.length-1],[o,i]=[e.length/a,a],l=k.getTypedArrayFromDType(n,o*r),u=k.getTypedArrayFromDType("int32",o*r);for(let d=0;d<o;d++){let h=d*i,p=e.subarray(h,h+i),f=[];for(let A=0;A<p.length;A++)f.push({value:p[A],index:A});f.sort((A,x)=>x.value-A.value);let m=d*r,g=l.subarray(m,m+r),y=u.subarray(m,m+r);for(let A=0;A<r;A++)g[A]=f[A].value,y[A]=f[A].index}let c=t.slice();return c[c.length-1]=r,[Le(c,n,l),Le(c,"int32",u)]}function rpe(e,t,n,r){let s=k.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<s;f++)a[0]*=n[f];a[1]=n[s];for(let f=s+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[s]),l=new Qt(a,r,e),u=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[s];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let y=0;y<a[0];y++)for(let A=0;A<a[2];A++)g.push(l.get(y,f,A));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,u.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let h=new Qt(d,r);u.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let y=0;y<a[2];y++)h.set(l.get(g,f,y),g,m,y)});let p=n.slice();return p[s]=d[1],{outputValues:h.values,outputShape:p,indices:i}}var{addImpl:spe,bincountImpl:hE,bincountReduceImpl:ape,ceilImpl:ope,concatImpl:ipe,equalImpl:lpe,expImpl:upe,expm1Impl:cpe,floorImpl:dpe,gatherNdImpl:hpe,gatherV2Impl:ppe,greaterImpl:fpe,greaterEqualImpl:mpe,lessImpl:gpe,lessEqualImpl:ype,linSpaceImpl:Ape,logImpl:xpe,maxImpl:bpe,maximumImpl:vpe,minimumImpl:wpe,multiplyImpl:kpe,negImpl:Ipe,notEqualImpl:Spe,prodImpl:Tpe,rangeImpl:Npe,rsqrtImpl:Cpe,simpleAbsImpl:pE,sliceImpl:Epe,sparseFillEmptyRowsImpl:$pe,sparseReshapeImpl:_pe,sparseSegmentReductionImpl:fE,stridedSliceImpl:Rpe,stringNGramsImpl:Dpe,stringSplitImpl:Fpe,stringToHashBucketFastImpl:Mpe,subImpl:Ope,tileImpl:Ppe,topKImpl:zpe,transposeImpl:ib,uniqueImpl:Lpe}=HC;function mE(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Vn(e,t){return t===1?[e]:mE(e,t)}function Bpe(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var Wpe=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let n=Vn("rc",t),r=It(t),s=Upe(t,e,n),a=Hpe(t,e[e.length-1],e[e.length-2],n),o=Gpe(e,n);this.userCode=`
        void main() {
          ${r} rc = getOutputCoords();

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

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

    bool cEdge = cp1 >= ${t};
    bool rEdge = rp1 >= ${n};
  `}function Gpe(e,t){let n=e.length,r=Vpe(n,t);return n===1?`getA(rc),
            rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
            0, 0`:`getA(${r[0]}),
          cEdge ? 0. : getA(${r[1]}),
          rEdge ? 0. : getA(${r[2]}),
          rEdge || cEdge ? 0. : getA(${r[3]})`}var gE=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let s="thisRC = rc;";r%2==1&&(s+="thisRC.z += 1;"),r>1&&(s+="thisRC.y += 1;"),n+=`
        ${s}
        ${r>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[${r}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${r>0?"}":""}
      `}this.userCode=`
      ${jpe(t)}
      ${Z5(e)}

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

        vec4 result = vec4(0.);

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

        ${n}

        setOutput(result);
      }
    `}};function jpe(e){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${Ai(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var qpe=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=AE(t,n),s=xE(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=yE(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].shift();return this.usedTextures[s].push(i),i}let o;return r===Tn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Tn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Tn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Tn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Tn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=AE(n,r),a=xE(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=yE(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=ae().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Kpe(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function yE(e,t,n,r,s){let a=Xpe(t,r),o;if(s){let[l,u]=fu(e[0],e[1]);o=l*u}else{let[l,u]=ah(e[0],e[1]);o=l*u}let i=Kpe(n,a);return o*i}function Xpe(e,t){switch(e){case Tn.PACKED_2X2_FLOAT32:return eb(t);case Tn.PACKED_2X2_FLOAT16:return tb(t);case Tn.UNPACKED_FLOAT32:return Y5(t);case Tn.UNPACKED_FLOAT16:return J5(t);case Tn.PACKED_4X1_UNSIGNED_BYTE:return Q5(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Zpe(e){return ae().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Tn.PACKED_2X2_FLOAT32:Tn.UNPACKED_FLOAT32:e?Tn.PACKED_2X2_FLOAT16:Tn.UNPACKED_FLOAT16}function AE(e,t){if(e===zr.UPLOAD)return Tn.PACKED_2X2_FLOAT32;if(e===zr.RENDER||e==null)return Zpe(t);if(e===zr.DOWNLOAD||e===zr.PIXELS)return Tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function xE(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Qa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

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

        setOutput(y);
      }
    `}},ys="if (isnan(x)) return x;",Ype="return x;",bE="return abs(x);",Jpe="return (x >= 0.0) ? x : (exp(x) - 1.0);",Qpe=ys+`
  return (x < 0.0) ? 0.0 : x;
`,efe=ys+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Vm="return x;",tfe="return 1.0 / (1.0 + exp(-1.0 * x));",nfe="return x;",rfe=`
  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;
`,sfe=`
  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;
`,afe=`
  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;
`,ofe="return 1.0 / (1.0 + exp(-1.0 * x));",wu=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

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

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

        setOutput(getChannel(packedInput, ${o}));
      }
    `}},lfe=ca.whereImpl,ufe=1e-7,cfe=1e-4,Um={};function dfe(e){return e in Um||(Um[e]={}),Um[e]}var hfe=ae().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),pfe=600;function ffe(){return ae().global.screen==null?1024:ae().global.screen.height*ae().global.screen.width*window.devicePixelRatio*pfe/1024/1024}var vE=class extends Bp{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,!ae().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Ls(ae().getNumber("WEBGL_VERSION"));this.binaryCache=dfe(ae().getNumber("WEBGL_VERSION")),this.gpgpu=new Bm(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new qpe(this.gpgpu),this.numMBBeforeWarning=ffe(),this.texData=new my(this,za())}nextDataId(){return vE.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((ae().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ae().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 r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:zr.UPLOAD,refCount:1}),r}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,r,s){if(ae().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:zr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:s,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new wu(o,Vm):d=new Qa(o,Vm);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:r}],r),p=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=k.now());let c;if(r==="complex64"){let d=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);c=R.mergeRealAndImagArrays(d,h)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:s,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(s!=null){let p;i?p=new wu(r,Vm):p=new Qa(r,Vm);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ae().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ae().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&ae().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...oh(r))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let p=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=p[0],m=p[1];c=R.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=k.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let d=this.convertAndCacheOnCPU(e,c),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach(p=>p(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&za().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!JN(n))throw ae().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=k.sizeFromShape(t);if(ae().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),h=this.texData.get(d.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(h.texture,...oh(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(d),p}let a=ae().getBool("WEBGL_PACK")&&r===!0,o=a?Om(t):t,i=a?new Ude(o):new Vde(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=k.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=k.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(s);o.kernelMs=k.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ae().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ae().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:r,usage:s,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=hfe){return ae().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){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return lfe(e.shape,t)}packedUnaryOp(e,t,n){let r=new wu(e.shape,t),s=this.compileAndRun(r,[e],n);return za().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=pE(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(ae().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,bE,e.dtype);let t=new Qa(e.shape,bE),n=this.compileAndRun(t,[e]);return za().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let s=n.map(a=>k.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return za().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new ife(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Wpe(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[gi(e.shape),...yi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[gi(t),...yi(t)],a=new gE(s,n),o=!0,i=this.runWebGLProgram(a,[r],e.dtype,null,o);return{dataId:i.dataId,shape:t,dtype:i.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=t,a=Om(r),o;n?o=new Wde(a):o=new Bde(a);let i=!0,l=this.runWebGLProgram(o,[{shape:a,dtype:s,dataId:e}],s,null,i);return{dtype:s,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,s=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===sh.DENSE){let m=oh(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),k.sizeFromShape(a.shape)===0)return o.values=k.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&k.sizeFromShape(m.shape)<=ae().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!uh(g.shape,m.shape)){let y=m,A=m.shape;m.shape=g.shape,m=this.packedReshape(m,A),i.push(m),g=this.texData.get(m.dataId),y.shape=A}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},c=_he(e,l,u),d=this.getAndSaveBinary(c,()=>Ehe(this.gpgpu,e,l,u)),h=this.activeTimers!=null,p;h&&(p=this.startTimer()),$he(this.gpgpu,d,l,u,r),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),h&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=ae().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=k.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!ae().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&s===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,r,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ae().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Z(()=>{if(!ae().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ae().getBool("DEBUG");ae().set("DEBUG",!1);let t=this.abs(Fe(1e-8)).dataSync()[0];if(ae().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ufe:cfe}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:s,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=k.now());let c=t.texShape;if(c==null&&(c=mC(n,i),t.texShape=c),s!=null){let d=Om(n),h,p=c[1],f=c[0],m=s instanceof Uint8Array;i?([p,f]=fu(c[0],c[1]),h=new Gde(d,[f,p],m)):h=new Hde(d,[f,p],m);let g=this.makeTensorInfo([f,p],r);m?this.texData.get(g.dataId).usage=zr.PIXELS:this.texData.get(g.dataId).usage=zr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),p,f,s);let y=!0,A=this.runWebGLProgram(h,[g],r,null,y),x=this.texData.get(A.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(A.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-u)}else{let d=this.acquireTexture(c,o,r,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=mfe(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}},dh=vE;dh.nextDataId=0;function mfe(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 r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var gfe="3.7.0";function wE(){ae().set("WEBGL_FORCE_F16_TEXTURES",!0)}yf.isBrowser()&&_A("webgl",()=>new dh,2);var yfe={forceHalfFloat:wE},kE=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,ku=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},Hm=`
  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;
`,hh=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length,a="";if(r)if(s===0||k.sizeFromShape(this.outputShape)===1)a=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(a=`
          ${It(s)} coords = getOutputCoords();
        `,s===1)a+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=Vn("coords",s);a+=`
            bool nextRowOutOfBounds =
              (${i[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
              (${i[s-1]} + 1) >= ${this.outputShape[s-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

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

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

        setOutput(result);
      }
    `}};function gr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Afe={kernelName:hl,backendName:"webgl",kernelFunc:gr};function eo(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=gr({inputs:{x:r},backend:n}),l=gr({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var xfe={kernelName:Sy,backendName:"webgl",kernelFunc:eo},IE="return (a < 0.) ? b * a : a;",SE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function bfe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",k.createScalarValue(a,"float32")),i=ae().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hh(SE,s.shape,o.shape):new ku(IE,s.shape,o.shape),l=n.runWebGLProgram(i,[s,o],s.dtype);return n.disposeIntermediateTensorInfo(o),l}var vfe={kernelName:pl,backendName:"webgl",kernelFunc:bfe},TE="return (a < 0.) ? b * a : a;",NE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function wfe(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=ae().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hh(NE,r.shape,s.shape):new ku(TE,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)}var kfe={kernelName:Sl,backendName:"webgl",kernelFunc:wfe},CE="if (isnan(x)) return x;",Ife=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,Sfe=`
  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 it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,l=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),h=n(d.values,l);return i.makeTensorInfo(o.shape,l,h)}let u=ae().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new wu(o.shape,t):c=new Qa(o.shape,e),i.runWebGLProgram(c,[o],l)}}function Nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,w={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},I=new ku(e,l.shape,u.shape);return c.runWebGLProgram(I,[w,S],qr(b.dtype,v.dtype))}),A=eo({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),A}let d=a||qr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&s!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?R.fromUint8ToStringArray(f):f,y=l.dtype==="string"?R.fromUint8ToStringArray(m):m,[A,x]=s(l.shape,u.shape,g,y,d),b=c.makeTensorInfo(x,d),v=c.texData.get(b.dataId);return v.values=A,b}let h=ae().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return h?p=new hh(t,l.shape,u.shape,n):p=new ku(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],d)}}function Gm(e,t=!1){if(e==="linear")return t?nfe:Ype;if(e==="relu")return t?sfe:Qpe;if(e==="elu")return t?rfe:Jpe;if(e==="relu6")return t?afe:efe;if(e==="prelu")return t?NE:TE;if(e==="leakyrelu")return t?SE:IE;if(e==="sigmoid")return t?ofe:tfe;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var EE=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),d=r?"i * 2, rc.y":"rc.y, i * 2",h=s?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:l?m=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:m=`vec4 activation(vec4 x) {
          ${o}
        }`,g="result = activation(result);");let y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${m}

      const float sharedDimension = ${c}.0;

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

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

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

        ${y}

        ${g}

        setOutput(result);
      }
    `}},$E={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},_E=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.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));
      }
    `}},RE="return a * b;";function lb(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=R.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),l=n.texData.get(s.dataId),u=new _E($E.REAL,r.shape,s.shape),c=new _E($E.IMAG,r.shape,s.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:s.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:s.shape}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=eo({inputs:{real:h,imag:p},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),l=n.texData.get(s.dataId),[u,c]=kpe(r.shape,s.shape,i.values,l.values,a),d=n.makeTensorInfo(c,a),h=n.texData.get(d.dataId);return h.values=u,d}let o;return ae().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new hh(RE,r.shape,s.shape):o=new ku(RE,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var Tfe={kernelName:Mo,backendName:"webgl",kernelFunc:lb};function Nfe(e,t,n){let r=[gi(e.shape),...yi(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[gi(t),...yi(t)],o=new gE(a,r),i=!0,l=n.runWebGLProgram(o,[s],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ve(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=k.sizeFromShape(s.shape),l=k.inferFromImplicitShape(a,i),u=k.sizeFromShape(l);k.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(s.dataId);return c.isPacked&&!uh(s.shape,l)&&!(c.texture!==null&&uh(c.shape,l))?Nfe(s,l,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:l,dtype:s.dtype})}var Cfe={kernelName:Qc,backendName:"webgl",kernelFunc:ve},DE=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${k.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";s%n>0&&(u=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

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

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

        float sumValue = 0.0;

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

          ${l}
        }

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

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

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

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

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

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

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

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

          ${d}
        }

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

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

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

          ${d}
        }
        setOutput(${l});
      }
    `}};function $fe(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function bi(e,t,n,r){let s=$fe(e.shape),a=e;for(let o=0;o<s.length;o++){let{inSize:i,windowSize:l,outSize:u}=s[o],c,d;n==="mean"?c=o===0?new DE({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new DE({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new Efe({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),d=a,a=r.runWebGLProgram(c,[a],t),d.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(d)}return a}var _fe=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let r=It(this.rank),s=Rfe(t);this.userCode=`
    void main() {
      ${r} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function Rfe(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"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}var Dfe=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 ${this.rank} is not yet supported.`);let r=It(this.rank),s=mE("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=s[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
    void main() {
      ${r} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${i}) {
        result[1] = ${l};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${i}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function jm(e,t,n){let r=ae().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dfe(e.shape,t):new _fe(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function Ffe(e,t,n,r){let s=t,a=e.shape.length,o=k.parseAxisParam(s,e.shape),i=o,l=R.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=jm(e,l,r),i=R.getInnerMostAxes(i.length,a)),R.assertAxesAreInnerMostDims("sum",i,a);let[d,h]=R.computeOutAndReduceShapes(c.shape,i),p=d;n&&(p=R.expandShapeToKeepDim(d,o));let f=k.sizeFromShape(h),g=k.sizeFromShape(e.shape)/f,y=ve({inputs:{x:c},attrs:{shape:[g,f]},backend:r}),A=fA(e.dtype),x=bi(y,A,"sum",r),b=ve({inputs:{x},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(x),u&&r.disposeIntermediateTensorInfo(c),b}function qm(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return Ffe(s,a,o,n)}var Mfe={kernelName:Fl,backendName:"webgl",kernelFunc:qm};function Un(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=s.shape[a[c]];let u;if(o.shouldExecuteOnCPU([s])){let d=o.texData.get(s.dataId).values,h=ib(d,s.shape,s.dtype,a,l);u=o.makeTensorInfo(l,s.dtype);let p=o.texData.get(u.dataId);p.values=h}else u=jm(s,a,o);return u}var Ofe={kernelName:zl,backendName:"webgl",kernelFunc:Un},FE=1e3;function Km({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],h=r?t.shape[c-1]:t.shape[c-2],p=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(m),A=k.sizeFromShape(g),x=y===A||y===1||A===1;k.assert(u>=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let v=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);k.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,d,p]:[y,p,d],S=r?[A,f,h]:[A,h,f],I=ve({inputs:{x:e},backend:s,attrs:{shape:w}}),E=ve({inputs:{x:t},backend:s,attrs:{shape:S}}),F=[I,E],$=Math.max(y,A),_=n?I.shape[1]:I.shape[2],N=a!=null,P=o!=null,B=l==="leakyrelu",j=l!=null?Gm(l,!0):null,X=N||P||B||j!=null,Y;if((p===1||f===1)&&_>FE&&X===!1){let oe=I,se=E;n&&(oe=Un({inputs:{x:I},backend:s,attrs:{perm:[0,2,1]}}),F.push(oe)),r&&(se=Un({inputs:{x:E},backend:s,attrs:{perm:[0,2,1]}}),F.push(se));let ie=f!==1,ne=f===1,de=oe;ie&&(de=ve({inputs:{x:oe},backend:s,attrs:{shape:[$,_,1]}}),F.push(de));let he=f===1?2:1,ge=se;ne&&(ge=ve({inputs:{x:se},backend:s,attrs:{shape:[$,1,_]}}),F.push(ge));let be=lb({inputs:{a:de,b:ge},backend:s});Y=qm({inputs:{x:be},backend:s,attrs:{axis:he,keepDims:!0}}),F.push(be)}else{let oe=qr(e.dtype,t.dtype),se=new EE(w,S,[$,p,f],n,r,N,j,P,B),ie=[I,E];if(a!=null&&ie.push(a),P&&ie.push(o),B){let ne=s.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));ie.push(ne),F.push(ne)}Y=s.runWebGLProgram(se,ie,oe)}let ee=ve({inputs:{x:Y},backend:s,attrs:{shape:v}});F.push(Y);for(let oe of F)s.disposeIntermediateTensorInfo(oe);return ee}function Pfe(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=r;return Km({a:s,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var zfe={kernelName:Ll,backendName:"webgl",kernelFunc:Pfe},ME="return abs(x);";function Lfe(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let a=n.texData.get(r.dataId),o=pE(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return ae().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new wu(r.shape,ME):s=new Qa(r.shape,ME),n.runWebGLProgram(s,[r],r.dtype)}var Bfe={kernelName:xc,backendName:"webgl",kernelFunc:Lfe},Wfe=ys+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,Vfe=it({opSnippet:Wfe}),Ufe={kernelName:bc,backendName:"webgl",kernelFunc:Vfe},Hfe=ys+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Gfe=it({opSnippet:Hfe}),jfe={kernelName:vc,backendName:"webgl",kernelFunc:Gfe},OE="return a + b;",qfe=Nn({opSnippet:OE,packedOpSnippet:OE,supportsComplex:!0,cpuKernelImpl:spe}),Kfe={kernelName:Fa,backendName:"webgl",kernelFunc:qfe},Xfe=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

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

        vec4 result = ${r};
        setOutput(result);
      }
    `}};function Xm(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return gr({inputs:{x:r[0]},backend:n});if(r.length>ae().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(r.length/2),u=Xm({inputs:r.slice(0,l),backend:n}),c=Xm({inputs:r.slice(l),backend:n});return Xm({inputs:[u,c],backend:n})}let s=r.map(l=>l.dtype).reduce((l,u)=>qr(l,u)),a=r.map(l=>l.shape),i=ae().getBool("WEBGL_PACK")?new Zfe(r[0].shape,a):new Xfe(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var Yfe={kernelName:Zi,backendName:"webgl",kernelFunc:Xm};function Jfe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=k.parseAxisParam(a,s.shape),u=l,c=R.getAxesPermutation(u,i),d=s;c!=null&&(d=Un({inputs:{x:s},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,i)),R.assertAxesAreInnerMostDims("all",u,i);let[h,p]=R.computeOutAndReduceShapes(d.shape,u),f=k.sizeFromShape(p),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=bi(m,m.dtype,"all",n),y;if(o){let A=R.expandShapeToKeepDim(h,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),y}var Qfe={kernelName:wc,backendName:"webgl",kernelFunc:Jfe};function eme(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=k.parseAxisParam(a,s.shape),u=l,c=R.getAxesPermutation(u,i),d=s;c!=null&&(d=Un({inputs:{x:s},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,i)),R.assertAxesAreInnerMostDims("any",u,i);let[h,p]=R.computeOutAndReduceShapes(d.shape,u),f=k.sizeFromShape(p),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=bi(m,m.dtype,"any",n),y;if(o){let A=R.expandShapeToKeepDim(h,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),y}var tme={kernelName:kc,backendName:"webgl",kernelFunc:eme},nme=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${r};

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

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

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

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function PE(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=R.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},l=new nme(i,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let d=PE(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function zE(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=R.computeOptimalWindowSize(a),i=new rme(s,o,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=zE(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function LE(e,t,n,r){let s=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,t.shape.length),!ae().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],[o,i]=R.computeOutAndReduceShapes(t.shape,s),l=k.sizeFromShape(i),u=ve({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});a.push(u);let c=PE(e,u,r);a.push(c);let d=ve({inputs:{x:c},backend:e,attrs:{shape:o}});return a.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}return zE(e,t,r)}function sme(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=k.parseAxisParam(a,s.shape),i=R.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=Un({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=R.getInnerMostAxes(o.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=LE(n,l,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var ame={kernelName:Yi,backendName:"webgl",kernelFunc:sme};function ome(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=k.parseAxisParam(a,s.shape),i=R.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=Un({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=R.getInnerMostAxes(o.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=LE(n,l,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var ime={kernelName:qp,backendName:"webgl",kernelFunc:ome},lme=ys+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,ume=it({opSnippet:lme}),cme={kernelName:Ic,backendName:"webgl",kernelFunc:ume},dme=ys+"return log(x + sqrt(x * x + 1.0));",hme=it({opSnippet:dme}),pme={kernelName:Sc,backendName:"webgl",kernelFunc:hme},fme=ys+`
  return atan(x);
`,mme=it({opSnippet:fme}),gme={kernelName:Tc,backendName:"webgl",kernelFunc:mme},yme=Ife+`
  return atan(a, b);
`,Ame=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Sfe+`
  return result;
`,xme=Nn({opSnippet:yme,packedOpSnippet:Ame}),bme={kernelName:Cc,backendName:"webgl",kernelFunc:xme},vme=ys+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wme=it({opSnippet:vme}),kme={kernelName:Nc,backendName:"webgl",kernelFunc:wme},ph=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
        const ivec2 strides = ivec2(${o}, ${i});
        const ivec2 pads = ivec2(${h}, ${p});

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

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

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

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

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

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

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

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

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

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

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

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

            ${w}
          }

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

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

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

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

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

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

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

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

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

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

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

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

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

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

            for (int wC = 0; wC < ${w}; 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)
              );

              ${I}
            }

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

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

              ${I}
            } else if (${S===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
              );

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

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

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

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

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

          for (int wC = 0; wC < ${l};
            wC+= ${o}) {
            float dyC = float(dyCCorner + wC) / ${s}.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);
      }
    `}},Eme=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=h-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`
      const ivec3 pads = ivec3(${p}, ${f}, ${m});
      const float avgMultiplier = float(${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 < ${c};
            wD += ${i}) {
          float dyD = float(dyDCorner + wD) / ${s}.0;

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

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

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

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

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

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

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function $me(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=r,d=[1,1,1],h=R.computePool3DInfo(o.shape,i,l,d,u,c),p=new Eme(h);return n.runWebGLProgram(p,[s],o.dtype)}var _me={kernelName:ky,backendName:"webgl",kernelFunc:$me};function Rme(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;mu([s,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=r,c=R.computePool2DInfo(o.shape,i,l,1,u),d=new Cme(c);return n.runWebGLProgram(d,[s],o.dtype)}var Dme={kernelName:wy,backendName:"webgl",kernelFunc:Rme};function Fme(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return Km({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var Mme={kernelName:Qi,backendName:"webgl",kernelFunc:Fme},Ome=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(R.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${o};
        float scale = ${i};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},Pme=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(R.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${o};
        vec4 scale = ${i};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `}},zme=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;k.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,s,a],c=null;o!=null&&(c=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let h=ae().getBool("WEBGL_PACK_NORMALIZATION")?new Pme(r.shape,s.shape,a.shape,c,d,l):new Ome(r.shape,s.shape,a.shape,c,d,l);return t.runWebGLProgram(h,u,u[0].dtype)},Lme={kernelName:cl,backendName:"webgl",kernelFunc:zme},Bme=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=It(this.rank),n=`uniform int start[${this.rank}];`,r=Wme(this.rank),s,a=e.map((o,i)=>`sourceLoc.${cb[i]} = start[${i}] + coords.${cb[i]};`);s=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${a.join(`
`)}
      `,this.userCode=`
      ${n}
      void main() {
        ${s}
        setOutput(getSource(${r}));
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},cb=["x","y","z","w","u","v"];function Wme(e){if(e===1)return"sourceLoc";if(e<=6)return cb.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Vme=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=It(this.rank),n=Vn("coords",this.rank),r=Vn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`
      result.x = ${a};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${r[this.rank-1]};
        result.y = ${a};
        --${r[this.rank-1]};
      }
    `,i=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${r[this.rank-2]};
        result.z = ${a};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${r[this.rank-1]};
          result.w = ${a};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
      uniform int start[${this.rank}];
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${o}
        ${i}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function Ume(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=En.computeFlatOffset(t,k.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let l=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,l+1),a}function fh(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,l]=En.parseSliceParams(s,a,o);if(En.assertParamsValid(s,i,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,s.dtype,[]);if(n.shouldExecuteOnCPU([s])||s.dtype==="string"){let d=n.texData.get(s.dataId),h=Epe(d.values,i,l,s.shape,s.dtype);return n.makeTensorInfo(l,s.dtype,h)}let{isPacked:u}=n.texData.get(s.dataId),c=En.isSliceContinous(s.shape,i,l);if(u||!c){let d=ae().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vme(l):new Bme(l),h=d.getCustomSetupFunc(i);return n.runWebGLProgram(d,[s],s.dtype,h)}return n.uploadToGPU(s.dataId),Ume(s,i,l,n)}var Hme={kernelName:rd,backendName:"webgl",kernelFunc:fh},Gme=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;k.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=R.getReshaped(s.shape,a,i),u=R.getPermuted(l.length,a.length),c=R.getReshapedPermuted(s.shape,a,i),d=R.getSliceBeginCoords(o,a.length),h=R.getSliceSize(c,o,a.length),p=[],f=ve({inputs:{x:s},backend:n,attrs:{shape:l}}),m=Un({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:c}}),y=fh({inputs:{x:g},backend:n,attrs:{begin:d,size:h}});return p.push(f),p.push(m),p.push(g),p.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},jme={kernelName:Xp,backendName:"webgl",kernelFunc:Gme};function qme(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),l=n.readSync(a.dataId),u=hE(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var Kme={kernelName:Iy,backendName:"webgl",kernelFunc:qme},Xme="return float(a != b);",BE=Nn({opSnippet:Xme,cpuKernelImpl:Spe,dtype:"bool"}),Zme={kernelName:vl,backendName:"webgl",kernelFunc:BE};function mh(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return gr({inputs:{x:s.complexTensorInfos.real},backend:n})}var Yme={kernelName:jy,backendName:"webgl",kernelFunc:mh},Jme="return float(int(x));";function Qme(e,t){let n=new Qa(e.shape,Jme),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function db(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return gr({inputs:{x:s},backend:n});let o=un(s.shape),i=db({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),l=eo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(s.dtype==="complex64"){let o=mh({inputs:{input:s},backend:n}),i=db({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!k.hasEncodingLoss(s.dtype,a)){let o=gr({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Qme(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),l=BE({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var e0e={kernelName:el,backendName:"webgl",kernelFunc:db},WE="return ceil(x);",t0e=it({opSnippet:WE,packedOpSnippet:WE,cpuKernelImpl:ope}),n0e={kernelName:No,backendName:"webgl",kernelFunc:t0e},r0e=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

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

        setOutput(clamp(value, minVal, maxVal));
      }
    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},s0e=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

      void main() {
        vec4 value = getAAtOutCoords();

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

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function a0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;ae().getBool("WEBGL_PACK_CLIP")?i=new s0e(s.shape):i=new r0e(s.shape);let l=i.getCustomSetupFunc(a,o);return n.runWebGLProgram(i,[s],s.dtype,l)}var o0e={kernelName:Co,backendName:"webgl",kernelFunc:a0e},i0e=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 VE(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function l0e(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new i0e(r.shape),o=[VE(r,s.complexTensorInfos.real),VE(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var u0e={kernelName:Zp,backendName:"webgl",kernelFunc:l0e},c0e=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}},d0e=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,s=It(r),a=Vn("coords",r),o=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),d=`if (${l} < ${i[0]}) {
        return getChannel(
            getT0(${c}), vec2(${u.join()}));
        }`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
        if (${l} < ${i[f]}  && ${l} >= ${i[f-1]}) {
          return getChannel(
            getT${f}(${Zm(o,l,m)}),
            vec2(${Zm(u,l,m)}));
        }`}let h=i.length,p=i[i.length-1];d+=`
        return getChannel(
          getT${h}(${Zm(o,l,p)}),
          vec2(${Zm(u,l,p)}));`,this.userCode=`
      float getValue(${o.map(f=>"int "+f)}) {
        ${d}
      }

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

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

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

        ${a[r-1]} = ${a[r-1]} - 1;
        if (${a[r-2]} < ${n[r-2]} &&
            ${a[r-1]} < ${n[r-1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `}};function Zm(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function Ym(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return gr({inputs:{x:s.complexTensorInfos.imag},backend:n})}var h0e={kernelName:Ly,backendName:"webgl",kernelFunc:Ym};function Iu(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(m=>mh({inputs:{input:m},backend:n})),d=e.map(m=>Ym({inputs:{input:m},backend:n})),h=Iu(c,t,n),p=Iu(d,t,n),f=eo({inputs:{real:h,imag:p},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let c=e.map(y=>{let A=k.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),d=c.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),h=R.computeOutShape(c.map(y=>y.shape),1),p=c[0].shape[0]===1,f=ipe(d,h,r,p),m=R.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,r,f);return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>ae().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),d=Iu(e.slice(0,c),t,n),h=Iu(e.slice(c),t,n),p=Iu([d,h],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),p}if(ae().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new d0e(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:o}=p0e(e,t,n),i=new c0e(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function p0e(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,k.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function UE(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=k.parseAxisParam(s,t[0].shape)[0],o=R.computeOutShape(t.map(u=>u.shape),a);if(k.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>k.sizeFromShape(u.shape)>0);if(i.length===1)return gr({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return R.assertParamsConsistent(l,a),Iu(i,a,n)}var f0e={kernelName:Ec,backendName:"webgl",kernelFunc:UE},HE=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";n&&(r?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:x=`
          float activation(float x) {
            ${n}
          }
        `,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

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

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

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

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

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

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

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

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

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

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

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

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

            }
          }
        }

        float result = dotProd;
        ${v}
        ${b}
        setOutput(result);
      }
    `}},m0e=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,h=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${s}, ${a}, ${o});
      const ivec3 pads = ivec3(${t}, ${n}, ${r});

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

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

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

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

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

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

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

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

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

                dotProd += dot(xValues, wValues);
              }

              if (${f===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${p}) *
                  getW(wF, wR, wC, ${p}, d2);
              } else if (${f===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${p}),
                  getX(batch, xF, xR, xC, ${p} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${p}, d2),
                  getW(wF, wR, wC, ${p} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${f===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${p}),
                  getX(batch, xF, xR, xC, ${p} + 1),
                  getX(batch, xF, xR, xC, ${p} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${p}, d2),
                  getW(wF, wR, wC, ${p} + 1, d2),
                  getW(wF, wR, wC, ${p} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},g0e=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:s,strideWidth:a,strideHeight:o,padInfo:i,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:d}=n,{left:h,top:p}=i,f=s*r,m=Wn(),g=d==="channelsLast",y=g?0:1,A=g?1:2,x="";for(let b=0;b<=1;b++)for(let v=0;v<=1;v++)x+=`
          blockIndex = rc.y + ${v};
          pos = rc.x + ${b};

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

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

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

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

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

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

        vec4 result = vec4(0);

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

        ${x}

        ${m.output} = result;
      }
    `}};function GE({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,d=l[0]*l[1]*l[2],h=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[],A=(d===1||h===1)&&c>FE,x=l[2]%2!=0&&!!u.isPacked;if(A||!ae().getBool("WEBGL_LAZILY_UNPACK")||!ae().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ve({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),w=ve({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Km({a:v,b:w,transposeA:f,transposeB:m,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:S},backend:r,attrs:{shape:n.outShape}}),y.push(v),y.push(w),y.push(S)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,k.assert(uh(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let S=ve({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let I=Km({a:v,b:S,backend:r,transposeA:f,transposeB:m,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),E=r.texData.get(I.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,E.shape=n.outShape,g=gr({inputs:{x:I},backend:r}),g.shape=n.outShape,y.push(I)}for(let b of y)r.disposeIntermediateTensorInfo(b);return g}function jE({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:d,outHeight:h,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,g=h*d,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),w=ve({inputs:{x:t},backend:r,attrs:{shape:[1,m,k.sizeFromShape(t.shape)/m]}});b.push(v),b.push(w);let S=new g0e(y,v.shape,n),I=r.runWebGLProgram(S,[v],"float32"),E=ve({inputs:{x:I},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(I),b.push(E);let F=s!=null,$=a!=null,_=i==="leakyrelu",N=i?Gm(i,!0):null,P=new EE(E.shape,w.shape,[1,g,n.outChannels],A,x,F,N,$,_),B=[E,w];if(s&&B.push(s),$&&B.push(a),_){let ee=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));B.push(ee),b.push(ee)}let j=r.runWebGLProgram(P,B,"float32"),X=f?[1,h,d,n.outChannels]:[1,n.outChannels,h,d],Y=ve({inputs:{x:j},backend:r,attrs:{shape:X}});b.push(j);for(let ee of b)r.disposeIntermediateTensorInfo(ee);return Y}function y0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=r,d=R.convertConv2DDataFormat(l),h=R.computeConv2DInfo(s.shape,a.shape,o,u,i,c,!1,d),p;if(h.filterHeight===1&&h.filterWidth===1&&h.dilationHeight===1&&h.dilationWidth===1&&h.strideHeight===1&&h.strideWidth===1&&(h.padInfo.type==="SAME"||h.padInfo.type==="VALID"))p=GE({x:s,filter:a,convInfo:h,backend:n});else if(ae().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)p=jE({x:s,filter:a,convInfo:h,backend:n});else{let m=new HE(h);p=n.runWebGLProgram(m,[s,a],"float32")}let f=ve({inputs:{x:p},backend:n,attrs:{shape:h.outShape}});return n.disposeIntermediateTensorInfo(p),f}var A0e={kernelName:tl,backendName:"webgl",kernelFunc:y0e},x0e=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

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

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

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

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

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

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

            }
          }
        }
        setOutput(dotProd);
      }
    `}},b0e=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

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

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

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

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

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

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

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

            }
          }
        }
        setOutput(dotProd);
      }
    `}},v0e=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

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

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

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

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

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

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

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},w0e=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${i}, ${l}, ${u});

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


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

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

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

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

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

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

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

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

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

              int wCPerm = ${r} - 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 k0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,d=R.convertConv2DDataFormat(l),h=R.computeConv2DInfo(s.shape,c,o,1,i,u,!1,d),p=new x0e(h);return n.runWebGLProgram(p,[s,a],"float32")}var I0e={kernelName:Ty,backendName:"webgl",kernelFunc:k0e};function S0e(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=r,d=R.convertConv2DDataFormat(u),h=R.computeConv2DInfo(o,a.shape,i,1,l,c,!1,d),p=new b0e(h);return n.runWebGLProgram(p,[s,a],"float32")}var T0e={kernelName:nl,backendName:"webgl",kernelFunc:S0e};function N0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l}=r,u=R.computeConv3DInfo(s.shape,a.shape,o,l,i),c=new m0e(u);return n.runWebGLProgram(c,[s,a],"float32")}var C0e={kernelName:Yp,backendName:"webgl",kernelFunc:N0e};function E0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:l}=r,u=R.computeConv3DInfo(s.shape,l,o,1,i),c=new v0e(u);return n.runWebGLProgram(c,[s,a],"float32")}var $0e={kernelName:Ny,backendName:"webgl",kernelFunc:E0e};function _0e(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:l}=r,u=R.computeConv3DInfo(l,a.shape,i,1,o),c=new w0e(u);return n.runWebGLProgram(c,[s,a],"float32")}var R0e={kernelName:Cy,backendName:"webgl",kernelFunc:_0e},D0e=CE+`
  return cos(x);
`,F0e=it({opSnippet:D0e}),M0e={kernelName:rl,backendName:"webgl",kernelFunc:F0e},O0e=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,P0e=it({opSnippet:O0e}),z0e={kernelName:$c,backendName:"webgl",kernelFunc:P0e},L0e=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,d]=n;this.outputShape=[u,c,d,l];let h=r==="bilinear"?1:0,[p,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[A,x,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
      const float height_ratio = float(${m});
      const float width_ratio = float(${A});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

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

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

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

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

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

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

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}},B0e=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=r,c=new L0e(s.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[s,a,o],"float32")},W0e={kernelName:_c,backendName:"webgl",kernelFunc:B0e},qE=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,s=t?"0.0":`getX(${KE(r,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
      uniform float index;
      void main() {
        ${It(r)} coords = getOutputCoords();
        int end = ${XE(r,"coords")};
        float val = ${s};
        int pow2 = int(pow(2.0, index));
        if (${o}) {
          int idx = ${i};
          ${XE(r,"coords")} = idx;
          val += getX(${KE(r,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function KE(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 ${e} is not yet supported`)}function XE(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function V0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,l=s.shape.length,u=R.getAxesPermutation([a],l),c=s;u!=null&&(c=Un({inputs:{x:s},backend:n,attrs:{perm:u}}));let d=R.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${s.shape.length-1} but got axis=${a}`);let h=c.shape[d],p=gr({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(h))-1;f++){let m=new qE(c.shape,!1,i),g=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new qE(c.shape,o,i),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=R.getUndoAxesPermutation(u),m=Un({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var U0e={kernelName:sl,backendName:"webgl",kernelFunc:V0e};function H0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let l=n.readSync(s.dataId),u=n.readSync(a.dataId),c=hE(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(s.shape.length===2){let l=n.bufferSync(s),u=n.bufferSync(a),c=ape(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var G0e={kernelName:Ey,backendName:"webgl",kernelFunc:H0e},j0e=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 q0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r;k.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=s.shape[0],l=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],d=l*a,h=u*a,p=c/(a*a),f=o==="NHWC"?[i,d,h,p]:[i,p,d,h],m=new j0e(f,a,o);return n.runWebGLProgram(m,[s],s.dtype)}var K0e={kernelName:Rc,backendName:"webgl",kernelFunc:q0e},ZE=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,o=e.inWidth,i=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,d=e.dilationHeight,h=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,g="",y="";n&&(r?g=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?g=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:g=`
          float activation(float x) {
            ${n}
          }
        `,y="result = activation(result);");let A=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${g}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

                  if (xCOffset >= 0 && xCOffset < ${i} && xTexelC${I+2}Ready == 0) {
                    xTexelC${I+2} = getX(batch, xR, xCOffset, d1);

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

                    if (xCOffset >= 0 && xCOffset < ${i} && xTexelC${I+2}Ready == 0) {
                      xTexelC${I+2} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= ${i}) {
                        xTexelC${I+2}.zw = vec2(0.0);
                      }
                      xTexelC${I+2}Ready = 1;
                    }

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

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

                xC${S} = vec4(xTexelC${I}.zw, xTexelC${I+2}.zw);
              `,I+1<m&&(y+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + ${d};
                  if(xCOffset >= 0 && xCOffset < ${i}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${S+1} = vec4(xTexelC${I+2}.xy, final.xy);
                `)):(y+=`
                if(xC >= 0 && xC < ${i} && xTexelC${I}Ready == 0) {
                  xTexelC${I} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= ${i}) {
                    xTexelC${I}.zw = vec2(0.0);
                  }
                  xTexelC${I}Ready = 1;
                }

                xCOffset = xC + ${d};
                if(xCOffset >= 0 && xCOffset < ${i} && xTexelC${I+2}Ready == 0) {
                  xTexelC${I+2} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= ${i}) {
                    xTexelC${I+2}.zw = vec2(0.);
                  }
                  xTexelC${I+2}Ready = 1;
                }

                xC${S} = vec4(
                  xTexelC${I}.xy, xTexelC${I+2}.xy);
              `,I+1<m&&(y+=`
                  xC${S+1} = vec4(xTexelC${I}.zw, xTexelC${I+2}.zw);
                `)));S<m&&(y+=`
            wTexel = getW(${v}, ${I}, d1, q);
            dotProd += xC${S} * vec4(wTexel.xz, wTexel.xz);
          `,I+1<m&&(y+=`
              wTexel = getW(${v}, ${I+1}, d1, q);
              dotProd += xC${S+1} * vec4(wTexel.xz, wTexel.xz);
            `))}y+=`
        }
      `}let A="",x="";n&&(r?A=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?A=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:A=`vec4 activation(vec4 x) {
          ${n}
        }`,x="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${A}

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

      void main() {

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

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

        ${y}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${b}
        ${x}
        setOutput(result);
      }
    `}};function X0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),k.assert(R.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=R.computeConv2DInfo(s.shape,a.shape,o,c,i,u,!0),h;return ae().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?h=new YE(d):h=new ZE(d),n.runWebGLProgram(h,[s,a],"float32")}var Z0e={kernelName:al,backendName:"webgl",kernelFunc:X0e},Y0e=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${a} + dm;

        float dotProd = 0.0;

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

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

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

              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);
      }
    `}},J0e=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${a}, ${o});

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

        float dotProd = 0.0;

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

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

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

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${i}; dm++) {
              int d2 = d1 * ${i} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Q0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=r,d=R.computeConv2DInfo(s.shape,c,o,i,l,u,!0),h=new Y0e(d);return n.runWebGLProgram(h,[s,a],"float32")}var ege={kernelName:$y,backendName:"webgl",kernelFunc:Q0e};function tge(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=r,d=R.computeConv2DInfo(c,a.shape,o,i,l,u,!0),h=new J0e(d);return n.runWebGLProgram(h,[s,a],"float32")}var nge={kernelName:_y,backendName:"webgl",kernelFunc:tge},rge=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 sge(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=k.sizeFromShape(r.shape),o=ve({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new rge(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ve({inputs:{x:l},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var age={kernelName:Ry,backendName:"webgl",kernelFunc:sge},oge=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:d}=r;this.userCode=`
      const ivec2 strides = ivec2(${s}, ${a});
      const ivec2 pads = ivec2(${c}, ${d});
      const float neg_infinity = -3.4e38;

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

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

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

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

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

        float result = curVal;
        setOutput(result);
      }
    `}};function ige(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l}=r,u=R.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",l),c,d=new oge(u);c=n.runWebGLProgram(d,[s,a],"float32");let h=ve({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),h}var lge={kernelName:Jp,backendName:"webgl",kernelFunc:ige};function uge(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:l}=R.decodeEinsumEquation(s,a.length);R.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=R.getEinsumComputePath(i,l),d=c.length,h=null,p=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:A}=R.getEinsumPermutation(p,l[g]),x;R.isIdentityPermutation(y)?x=a[g]:(x=Un({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);k.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),h===null?h=x:(h=lb({inputs:{a:x,b:h},backend:n}),f.push(h))}m<d-1&&(u[m]>=0&&(h=qm({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var cge={kernelName:My,backendName:"webgl",kernelFunc:uge},dge="return (x >= 0.0) ? x : (exp(x) - 1.0);",hge=`
  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;
`,pge=it({opSnippet:dge,packedOpSnippet:hge}),fge={kernelName:Dc,backendName:"webgl",kernelFunc:pge},mge="return (b >= 1.0) ? a : a * (b + 1.0);",gge=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,yge=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=ae().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hh(gge,r.shape,s.shape):new ku(mge,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},Age={kernelName:Oy,backendName:"webgl",kernelFunc:yge},xge=`
  return vec4(equal(a, b));
`,bge="return float(a == b);",vge=Nn({opSnippet:bge,packedOpSnippet:xge,dtype:"bool",cpuKernelImpl:lpe}),wge={kernelName:il,backendName:"webgl",kernelFunc:vge},kge=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${R.ERF_P};
  float a1 = ${R.ERF_A1};
  float a2 = ${R.ERF_A2};
  float a3 = ${R.ERF_A3};
  float a4 = ${R.ERF_A4};
  float a5 = ${R.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));
`,Ige=it({opSnippet:kge}),Sge={kernelName:Fc,backendName:"webgl",kernelFunc:Ige},JE="return exp(x);",QE=it({opSnippet:JE,packedOpSnippet:JE,cpuKernelImpl:upe}),Tge={kernelName:Eo,backendName:"webgl",kernelFunc:QE};function hb(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=s;return s<0&&(k.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+s+1),i.splice(l,0,1),ve({inputs:{x:a},backend:r,attrs:{shape:i}})}var Nge={kernelName:Mc,backendName:"webgl",kernelFunc:hb},e9="return exp(x) - 1.0;",Cge=it({opSnippet:e9,packedOpSnippet:e9,cpuKernelImpl:cpe}),Ege={kernelName:ll,backendName:"webgl",kernelFunc:Cge},t9=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${s};

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

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

        float result = 0.0;

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

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

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function n9(e,t,n){let r=n.texData.get(e.dataId),s=k.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new t9("real",l,t),c=new t9("imag",l,t),d=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=eo({inputs:{real:h,imag:p},backend:n});n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function $ge(e){let{inputs:t,backend:n}=e,{input:r}=t;return n9(r,!1,n)}var _ge={kernelName:Py,backendName:"webgl",kernelFunc:$ge},Rge=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      uniform float value;
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function pb(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||k.inferDtype(s),a==="string"){let o=k.getArrayFromDType(a,k.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new Rge(r,s),i=o.getCustomSetupFunc(s);return t.runWebGLProgram(o,[],a,i)}}var Dge={kernelName:Qp,backendName:"webgl",kernelFunc:pb},Fge=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;
          float outputValue;
          if(coordX >= 0 && coordX < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}},Mge={kernelName:Oc,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new Fge(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},r9="return floor(x);",Oge=it({opSnippet:r9,packedOpSnippet:r9,cpuKernelImpl:dpe}),Pge={kernelName:$o,backendName:"webgl",kernelFunc:Oge},zge=`
  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;
  }
`,Lge=`
  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);
`,Bge=Nn({opSnippet:zge,packedOpSnippet:Lge,dtype:"int32"}),Wge={kernelName:ul,backendName:"webgl",kernelFunc:Bge},Vge=class{constructor(e){this.variableNames=["A"];let t=Wn(),[n,r]=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(${r}.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));
      }
    `}},Uge=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Wn(),[n,r]=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(${r}.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;
      }
    `}},Hge={kernelName:sA,backendName:"webgl",kernelFunc:Gge},Su;function Gge(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[l,u]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],c=[u,l],d=[u,l,a];(i||o)&&(Su==null&&(Su=document.createElement("canvas").getContext("2d")),Su.canvas.width=l,Su.canvas.height=u,Su.drawImage(s,0,0,l,u),s=Su.canvas);let h=n.makeTensorInfo(c,"int32");n.texData.get(h.dataId).usage=zr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),s);let p=ae().getBool("WEBGL_PACK")?new Uge(d):new Vge(d),f=n.runWebGLProgram(p,[h],"int32");return n.disposeData(h.dataId),f}function jge(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(c),g=R.computeConv2DInfo(s.shape,a.shape,l,d,u,h,!1,m),y,A=[];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=GE({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else if(ae().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)y=jE({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,w=p==="leakyrelu",S=p?Gm(p,!1):null,I=new HE(g,b,S,v,w),E=[s,a];if(o&&E.push(o),i&&E.push(i),w){let F=n.makeTensorInfo([],"float32",k.createScalarValue(f,"float32"));E.push(F),A.push(F)}y=n.runWebGLProgram(I,E,"float32")}let x=ve({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var qge={kernelName:Bl,backendName:"webgl",kernelFunc:jge};function Kge(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),k.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=R.computeConv2DInfo(s.shape,a.shape,l,m,u,d,!0),y=ae().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,A=h?Gm(h,y):null,x=[s,a],b=o!=null,v=i!=null,w=h==="leakyrelu";if(b&&x.push(o),v&&x.push(i),w){let E=n.makeTensorInfo([],"float32",k.createScalarValue(p,"float32"));x.push(E),f.push(E)}let S;y?S=new YE(g,b,A,v,w):S=new ZE(g,b,A,v,w);let I=n.runWebGLProgram(S,x,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),I}var Xge={kernelName:Wl,backendName:"webgl",kernelFunc:Kge},Zge=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=It(t.length),s=It(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${r} strides = ${r}(${this.strides});
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${a};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function Yge(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=k.sizeFromShape(r.shape),[l,u,c,d]=R.prepareAndValidate(r,s),h=ve({inputs:{x:s},backend:n,attrs:{shape:[u,o]}}),p=ve({inputs:{x:r},backend:n,attrs:{shape:[k.sizeFromShape(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let y=n.readSync(s.dataId),A=n.bufferSync(r),x=hpe(y,A,r.dtype,u,o,c,d,r.shape,i);return n.makeTensorInfo(l,r.dtype,x.values)}let f=new Zge(o,d,[u,c]),m=n.runWebGLProgram(f,[p,h],p.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),g}var Jge={kernelName:zc,backendName:"webgl",kernelFunc:Yge},Qge=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=It(this.rank),r=e2e(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function e2e(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[s]}`);return r.join()}function t2e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,l=k.parseAxisParam(o,s.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(s,a,l,i),c=k.sizeFromShape(a.shape),d=[],h=ve({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),p=ve({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});d.push(h),d.push(p);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let A=n.bufferSync(p),x=n.bufferSync(h),b=ppe(x,A,f);return d.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new Qge(h.shape,f),g=n.runWebGLProgram(m,[h,p],h.dtype);d.push(g);let y=ve({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}var n2e={kernelName:Pc,backendName:"webgl",kernelFunc:t2e},r2e="return float(a > b);",s2e=`
  return vec4(greaterThan(a, b));
`,a2e=Nn({opSnippet:r2e,packedOpSnippet:s2e,cpuKernelImpl:fpe,dtype:"bool"}),o2e={kernelName:dl,backendName:"webgl",kernelFunc:a2e},i2e="return float(a >= b);",l2e=`
  return vec4(greaterThanEqual(a, b));
`,u2e=Nn({opSnippet:i2e,packedOpSnippet:l2e,dtype:"bool",cpuKernelImpl:mpe}),c2e={kernelName:_o,backendName:"webgl",kernelFunc:u2e};function d2e(e){let{inputs:t,backend:n}=e,{input:r}=t;return n9(r,!0,n)}var h2e={kernelName:zy,backendName:"webgl",kernelFunc:d2e},p2e="return float(!isnan(x) && !isinf(x));",f2e=it({opSnippet:p2e,dtype:"bool"}),m2e={kernelName:Lc,backendName:"webgl",kernelFunc:f2e},g2e="return float(isinf(x));",y2e=it({opSnippet:g2e,dtype:"bool"}),A2e={kernelName:Bc,backendName:"webgl",kernelFunc:y2e},x2e="return float(isnan(x));",b2e=it({opSnippet:x2e,dtype:"bool"}),v2e={kernelName:Wc,backendName:"webgl",kernelFunc:b2e},w2e="return float(a < b);",k2e=`
  return vec4(lessThan(a, b));
`,I2e=Nn({opSnippet:w2e,packedOpSnippet:k2e,cpuKernelImpl:gpe,dtype:"bool"}),S2e={kernelName:fl,backendName:"webgl",kernelFunc:I2e},T2e="return float(a <= b);",N2e=`
  return vec4(lessThanEqual(a, b));
`,C2e=Nn({opSnippet:T2e,packedOpSnippet:N2e,cpuKernelImpl:ype,dtype:"bool"}),E2e={kernelName:ml,backendName:"webgl",kernelFunc:C2e};function $2e(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=Ape(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var _2e={kernelName:By,backendName:"webgl",kernelFunc:$2e},R2e=`if (x < 0.0) return NAN;
  return log(x);`,D2e=`
  vec4 result = log(x);
  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
  result.r = isNaN.r == 1.0 ? NAN : result.r;
  result.g = isNaN.g == 1.0 ? NAN : result.g;
  result.b = isNaN.b == 1.0 ? NAN : result.b;
  result.a = isNaN.a == 1.0 ? NAN : result.a;

  return result;
`,F2e=it({opSnippet:R2e,packedOpSnippet:D2e,cpuKernelImpl:xpe}),M2e={kernelName:Ro,backendName:"webgl",kernelFunc:F2e},O2e="return log(1.0 + x);",P2e=it({opSnippet:O2e}),z2e={kernelName:Vc,backendName:"webgl",kernelFunc:P2e},L2e="return float(a >= 1.0 && b >= 1.0);",B2e=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,W2e=Nn({opSnippet:L2e,packedOpSnippet:B2e,dtype:"bool"}),V2e={kernelName:Uc,backendName:"webgl",kernelFunc:W2e},U2e="return float(!(x >= 1.0));",H2e=it({opSnippet:U2e}),G2e={kernelName:ef,backendName:"webgl",kernelFunc:H2e},j2e="return float(a >= 1.0 || b >= 1.0);",q2e=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,K2e=Nn({opSnippet:j2e,packedOpSnippet:q2e,dtype:"bool"}),X2e={kernelName:tf,backendName:"webgl",kernelFunc:K2e},Z2e=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${l})`:s===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${a}; j <= ${a}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${o}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${i};
        setOutput(val);
      }
    `}},Y2e=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${l})`:s===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

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

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

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

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

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

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

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${i};
        setOutput(result);
      }
    `}},J2e=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=r,u=ae().getBool("WEBGL_PACK_NORMALIZATION")?new Y2e(s.shape,a,o,i,l):new Z2e(s.shape,a,o,i,l);return n.runWebGLProgram(u,[s],s.dtype)},Q2e={kernelName:nf,backendName:"webgl",kernelFunc:J2e},eye=class{constructor(e,t,n,r,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=s,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(${r}) * norm + float(${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(${r})
                * float(${s})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},tye=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=r,d=new eye(s.shape,i,l,u,c);return n.runWebGLProgram(d,[s,a,o],s.dtype)},nye={kernelName:Wy,backendName:"webgl",kernelFunc:tye};function rye(e,t,n,r){let s=k.sizeFromShape(t),o=k.sizeFromShape(e.shape)/s,i=ve({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),l=bi(i,e.dtype,"max",r),u=ve({inputs:{x:l},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}function s9(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,l=k.parseAxisParam(a,s.shape),u=l,c=R.getAxesPermutation(u,i),d=c!=null,h=n.shouldExecuteOnCPU([s]),p=s;if(d){if(h){let x=n.texData.get(p.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=s.shape[c[S]];let v=ib(x,s.shape,s.dtype,c,b);p=n.makeTensorInfo(b,s.dtype);let w=n.texData.get(p.dataId);w.values=v}else p=jm(s,c,n);u=R.getInnerMostAxes(u.length,i)}R.assertAxesAreInnerMostDims("max",u,i);let[f,m]=R.computeOutAndReduceShapes(p.shape,u),g=f;o&&(g=R.expandShapeToKeepDim(f,l));let y;if(h){let x=n.texData.get(p.dataId).values,b=bpe(x,k.sizeFromShape(m),g,s.dtype);y=n.makeTensorInfo(g,s.dtype);let v=n.texData.get(y.dataId);v.values=b}else y=rye(p,m,g,n);return d&&n.disposeIntermediateTensorInfo(p),y}var sye={kernelName:gl,backendName:"webgl",kernelFunc:s9},aye=kE+`
  return max(a, b);
`,oye=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Hm+`
  return result;
`,iye=Nn({opSnippet:aye,packedOpSnippet:oye,cpuKernelImpl:vpe}),lye={kernelName:Do,backendName:"webgl",kernelFunc:iye};function uye(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;mu(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=r,u=1;k.assert(R.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=R.computePool2DInfo(s.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&k.arraysEqual(c.inShape,c.outShape))return gr({inputs:{x:s},backend:n});let d=new ph(c,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var cye={kernelName:yl,backendName:"webgl",kernelFunc:uye};function dye(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],d=R.computePool3DInfo(s.shape,a,o,c,i,u,l),h=new ub(d,"max",!1);return n.runWebGLProgram(h,[s],s.dtype)}var hye={kernelName:rf,backendName:"webgl",kernelFunc:dye},pye=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,l=s*a-1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function mye(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=r,d=[1,1,1],h=R.computePool3DInfo(o.shape,i,l,d,u,c),p=new ub(h,"max",!0),f=n.runWebGLProgram(p,[o],o.dtype),m=new fye(h),g=n.runWebGLProgram(m,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var gye={kernelName:Uy,backendName:"webgl",kernelFunc:mye};function yye(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;mu([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=r,h=R.computePool2DInfo(i.shape,l,u,1,c,d),p=!0,f=new ph(h,"max",p),m=n.runWebGLProgram(f,[i],i.dtype),g=new pye(h),y=n.runWebGLProgram(g,[s,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var Aye={kernelName:Vy,backendName:"webgl",kernelFunc:yye};function xye(e,t,n,r){let s=new ph(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new ph(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var bye={kernelName:Hy,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;k.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];k.assert(R.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=R.computePool2DInfo(r.shape,s,a,u,o),[d,h]=xye(r,i,c,l);return[d,h]}};function vye(e,t,n,r){let s=k.sizeFromShape(t),o=k.sizeFromShape(e.shape)/s,i=ve({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),l=bi(i,"float32","mean",r),u=ve({inputs:{x:l},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var wye={kernelName:Al,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,l=k.parseAxisParam(a,r.shape),u=l,c=R.getAxesPermutation(u,i),d=c!=null,h=o.shouldExecuteOnCPU([r]),p=[],f=r;if(d){if(h){let b=o.texData.get(f.dataId).values,v=new Array(i);for(let I=0;I<v.length;I++)v[I]=r.shape[c[I]];let w=ib(b,r.shape,r.dtype,c,v);f=o.makeTensorInfo(v,r.dtype);let S=o.texData.get(f.dataId);S.values=w}else f=jm(r,c,o);p.push(f),u=R.getInnerMostAxes(u.length,i)}R.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=R.computeOutAndReduceShapes(f.shape,u),y=m;s&&(y=R.expandShapeToKeepDim(m,l));let A=vye(f,g,y,o);for(let x of p)o.disposeIntermediateTensorInfo(x);return A}};function kye(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=k.parseAxisParam(a,s.shape),u=l,c=R.getAxesPermutation(u,i),d=s;c!=null&&(d=Un({inputs:{x:s},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,s.shape.length)),R.assertAxesAreInnerMostDims("min",u,i);let[h,p]=R.computeOutAndReduceShapes(d.shape,u),f=k.sizeFromShape(p),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=bi(m,m.dtype,"min",n),y;if(o){let A=R.expandShapeToKeepDim(h,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),y}var Iye={kernelName:xl,backendName:"webgl",kernelFunc:kye},Sye=kE+`
  return min(a, b);
`,Tye=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Hm+`
  return result;
`,Nye=Nn({opSnippet:Sye,packedOpSnippet:Tye,cpuKernelImpl:wpe}),Cye={kernelName:Fo,backendName:"webgl",kernelFunc:Nye},Eye=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,s=It(r),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};

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

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

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},_ye=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=ae().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $ye(r.shape,s,a):new Eye(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Rye={kernelName:bl,backendName:"webgl",kernelFunc:_ye},Dye=`if (b == 0.0) return NAN;
  return mod(a, b);`,Fye=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Hm+`
  return result;
`,Mye=Nn({opSnippet:Dye,packedOpSnippet:Fye}),Oye={kernelName:Hc,backendName:"webgl",kernelFunc:Mye},Pye=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
      uniform float seed;

      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}));
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},zye=`
if (a == b) {
  return 1.0;
};
return a / b;`,Lye=`
  // 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;
`,a9=Nn({opSnippet:zye,packedOpSnippet:Lye,checkOutOfBounds:!0}),Bye={kernelName:ol,backendName:"webgl",kernelFunc:a9},o9="return a - b;",i9=Nn({opSnippet:o9,packedOpSnippet:o9,supportsComplex:!0,cpuKernelImpl:Ope}),Wye={kernelName:zo,backendName:"webgl",kernelFunc:i9};function l9(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=k.parseAxisParam([a],s.shape),i=s9({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=R.expandShapeToKeepDim(i.shape,o),u=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),c=i9({inputs:{a:s,b:u},backend:n}),d=QE({inputs:{x:c},backend:n}),h=qm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),p=ve({inputs:{x:h},backend:n,attrs:{shape:l}}),f=a9({inputs:{a:d,b:p},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}var Vye={kernelName:Ml,backendName:"webgl",kernelFunc:l9};function Uye(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,l=i?s:l9({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new Pye(u,c,a),h=d.getCustomSetupFunc(o),p=n.runWebGLProgram(d,[l],"int32",h);return i||n.disposeIntermediateTensorInfo(l),p}var Hye={kernelName:Gy,backendName:"webgl",kernelFunc:Uye},u9="return -x;";function Gye(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=Ipe(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return ae().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new wu(r.shape,u9):s=new Qa(r.shape,u9),n.runWebGLProgram(s,[r],r.dtype)}var jye={kernelName:Gc,backendName:"webgl",kernelFunc:Gye},qye=ca.nonMaxSuppressionV3Impl;function Kye(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=r,u=n.readSync(s.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=qye(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Xye={kernelName:jc,backendName:"webgl",kernelFunc:Kye},Zye=ca.nonMaxSuppressionV4Impl;function Yye(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(s.dataId),d=n.readSync(a.dataId),{selectedIndices:h,validOutputs:p}=Zye(c,d,o,i,l,u);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var Jye={kernelName:qc,backendName:"webgl",kernelFunc:Yye},Qye=ca.nonMaxSuppressionV5Impl;function eAe(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(s.dataId),d=n.readSync(a.dataId),h=o,p=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=Qye(c,d,h,p,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var tAe={kernelName:Kc,backendName:"webgl",kernelFunc:eAe},nAe=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${r}), float(${n}),
                      float(index == coords.y)));
      }
    `}},rAe=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r,l=k.sizeFromShape(s.shape),u=new nAe(l,a,o,i),c=ve({inputs:{x:s},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],s.dtype);n.disposeIntermediateTensorInfo(c);let h=[...s.shape,a],p=ve({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),p},sAe={kernelName:wl,backendName:"webgl",kernelFunc:rAe};function Jm(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=mh({inputs:{input:r},backend:n}),a=Jm({inputs:{x:s},backend:n}),o=Ym({inputs:{input:r},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=eo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return pb({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var aAe={kernelName:hd,backendName:"webgl",kernelFunc:Jm};function c9(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=mh({inputs:{input:r},backend:n}),a=c9({inputs:{x:s},backend:n}),o=Ym({inputs:{input:r},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=eo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return pb({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var oAe={kernelName:Xc,backendName:"webgl",kernelFunc:c9};function iAe(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return hb({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{k.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),k.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=hb({inputs:{input:c},backend:n,attrs:{dim:s}});return i.push(d),d}),u=UE({inputs:l,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var lAe={kernelName:Zc,backendName:"webgl",kernelFunc:iAe},uAe=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,s=It(r),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};
        uniform float value;

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

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${i}));
        }
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},cAe=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,s=It(r),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Vn("rc",r),l=Vn("source",r),u=`${i[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${s} rc = outputLoc;`,`${i[r-1]} += 1;
       if(${u}) {
      `,r===1?"":`}
       rc = outputLoc;
       ${i[r-2]} += 1;
       if(${i[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":`  ${i[r-1]} += 1;
         if(${u}) {`],h=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
        ${d[f]}
        if (${h}) {
          result[${f}] = float(value);
        } else {
          ${s} source = rc - start;
          result[${f}] = getChannel(getX(${l.join()}), ${c});
        }
      `;p+=r===1?"} ":"}}",this.userCode=`
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${o});
      uniform float value;

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${p}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},d9=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r,i=ae().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cAe(s.shape,a,o):new uAe(s.shape,a,o),l=i.getCustomSetupFunc(o);return n.runWebGLProgram(i,[s],s.dtype,l)},dAe={kernelName:kl,backendName:"webgl",kernelFunc:d9},hAe=`
  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);
`,pAe=`
  // 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));
  `+Hm+`
  return result;
`,fAe=Nn({opSnippet:hAe,packedOpSnippet:pAe}),mAe={kernelName:Il,backendName:"webgl",kernelFunc:fAe};function gAe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=[],u=k.parseAxisParam(a,s.shape),c=u,d=R.getAxesPermutation(c,i),h=s;d!=null&&(h=Un({inputs:{x:s},backend:n,attrs:{perm:d}}),c=R.getInnerMostAxes(c.length,i),l.push(h)),R.assertAxesAreInnerMostDims("prod",c,i);let p;if(n.shouldExecuteOnCPU([h])){let f=n.texData.get(h.dataId).values,{outVals:m,outShape:g,outDtype:y}=Tpe(h.shape,h.dtype,f,c);p=n.makeTensorInfo(g,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(h.shape,c),g=k.sizeFromShape(m),y=ve({inputs:{x:h},backend:n,attrs:{shape:[-1,g]}}),A=fA(s.dtype),x=bi(y,A,"prod",n);p=ve({inputs:{x},backend:n,attrs:{shape:f}}),l.push(y),l.push(x)}if(o){l.push(p);let f=R.expandShapeToKeepDim(p.shape,u);p=ve({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var yAe={kernelName:Yc,backendName:"webgl",kernelFunc:gAe},h9=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=Npe(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},AAe={kernelName:sf,backendName:"webgl",kernelFunc:h9},xAe="return 1.0 / x;",bAe=it({opSnippet:xAe}),vAe={kernelName:Jc,backendName:"webgl",kernelFunc:bAe},wAe=ys+`
  return (x < 0.0) ? 0.0 : x;
`,kAe=`
  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;
`,IAe=it({opSnippet:wAe,packedOpSnippet:kAe}),SAe={kernelName:Tl,backendName:"webgl",kernelFunc:IAe},TAe=ys+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,NAe=`
  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;
`,CAe=it({opSnippet:TAe,packedOpSnippet:NAe}),EAe={kernelName:Cl,backendName:"webgl",kernelFunc:CAe},$Ae=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],d;s?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/c[0]},
          ${u[1]/c[1]});
      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);

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

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

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

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

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

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

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

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

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

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

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

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

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

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

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

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

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

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

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

        float accumulator = 0.0;

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

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

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

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

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

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

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

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

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

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${r-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), ${s-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 MAe(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new FAe(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var OAe={kernelName:Ky,backendName:"webgl",kernelFunc:MAe},PAe=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0",h;s?h="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/c[0]},
          ${u[1]/c[1]});
      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);

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

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

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

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

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

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

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

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

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

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

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

        float accumulator = 0.0;

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

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

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

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

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

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

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

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

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

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

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

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

            int sourceNearestCol = int(min(
                float(int(${s}) - 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 VAe(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new WAe(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var UAe={kernelName:qy,backendName:"webgl",kernelFunc:VAe},HAe=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 r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=It(n);this.userCode=`
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}},GAe=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-${n} tensor is not yet supported`);this.outputShape=e;let r=Vn("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=It(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${o} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${i(r.slice())};
          if(${s}){
            result.g = ${l(r.slice())};
          }
          if(${a}) {
            result.b = ${u(r.slice())};
            if(${s}) {
              result.a = ${c(r.slice())};
            }
          }
          setOutput(result);
        }
    `;function i(p){return d(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",d(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",d(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",d(p)}function d(p){let f=e.map((y,A)=>h(A,p)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function h(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function jAe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=s.shape.length,i=k.parseAxisParam(a,s.shape);if(o===0)return gr({inputs:{x:s},backend:n});let l=ae().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GAe(s.shape,i):new HAe(s.shape,i);return n.runWebGLProgram(l,[s],s.dtype)}var qAe={kernelName:El,backendName:"webgl",kernelFunc:jAe},KAe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        uniform vec4 params;
        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]));
          ${s}
          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}getCustomSetupFunc(e,t,n,r){return(s,a)=>{this.paramsLoc==null&&(this.paramsLoc=s.getUniformLocationNoThrow(a,"params")),s.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},XAe={kernelName:pd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,l=new KAe(r.shape,a),[u,c]=R.getImageCenter(o,r.shape[1],r.shape[2]),d=l.getCustomSetupFunc(u,c,Math.sin(s),Math.cos(s));return i.runWebGLProgram(l,[r],r.dtype,d)}},ZAe=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,YAe=it({opSnippet:ZAe}),JAe={kernelName:$l,backendName:"webgl",kernelFunc:YAe},QAe="return inversesqrt(x);",e1e=it({opSnippet:QAe,cpuKernelImpl:Cpe}),t1e={kernelName:Oo,backendName:"webgl",kernelFunc:e1e},p9=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=It(s.length),l=It(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,d="";r===1?d="i":r===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,p=t>1?"strides[j]":"strides";this.userCode=`
        ${i} strides = ${i}(${s});

        void main() {
          ${l} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${c});
              flattenedIndex += index * ${p};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${h};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function n1e(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=R.calculateShapes(a,s,o),h=[d/u,u];if(d===0)return n.makeTensorInfo(o,s.dtype);let p=ve({inputs:{x:s},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new p9(l,i,p.shape.length,f.shape.length,c,h),y=n.runWebGLProgram(g,[f,p,m],f.dtype),A=ve({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),A}var r1e={kernelName:ed,backendName:"webgl",kernelFunc:n1e},s1e=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);r=i.join(),s=l.join()}let a=It(n);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${r});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function a1e(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new s1e(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],qr(s.dtype,a.dtype))}var o1e={kernelName:td,backendName:"webgl",kernelFunc:a1e},i1e=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${R.SELU_SCALEALPHA};
  float scale = ${R.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,l1e=it({opSnippet:i1e}),u1e={kernelName:nd,backendName:"webgl",kernelFunc:l1e},c1e="return 1.0 / (1.0 + exp(-1.0 * x));",d1e=it({opSnippet:c1e}),h1e={kernelName:Rl,backendName:"webgl",kernelFunc:d1e},p1e=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,f1e=it({opSnippet:p1e}),m1e={kernelName:ad,backendName:"webgl",kernelFunc:f1e},g1e=CE+`
  return sin(x);
`,y1e=it({opSnippet:g1e}),A1e={kernelName:_l,backendName:"webgl",kernelFunc:y1e},x1e=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,b1e=it({opSnippet:x1e}),v1e={kernelName:sd,backendName:"webgl",kernelFunc:b1e},w1e=`
  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;
`,k1e=it({opSnippet:w1e}),I1e={kernelName:od,backendName:"webgl",kernelFunc:k1e},S1e=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;k.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,A)=>y*A),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<s.shape.length;++y)l.push([0,0]);let u=[],c=d9({inputs:{x:s},backend:n,attrs:{paddings:l,constantValue:0}}),d=R.getReshaped(c.shape,a,i,!1),h=R.getPermuted(d.length,a.length,!1),p=R.getReshapedPermuted(c.shape,a,i,!1),f=ve({inputs:{x:c},backend:n,attrs:{shape:d}}),m=Un({inputs:{x:f},backend:n,attrs:{perm:h}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:p}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},T1e={kernelName:of,backendName:"webgl",kernelFunc:S1e};function N1e(e){let{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${a.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${s.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${o.shape}`);let i=n.readSync(r.dataId),l=n.readSync(s.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,h,p,f,m]=$pe(i,r.shape,r.dtype,l,s.dtype,u,c);return[n.makeTensorInfo(h,r.dtype,d),n.makeTensorInfo([h[0]],s.dtype,p),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}var C1e={kernelName:Xy,backendName:"webgl",kernelFunc:N1e};function E1e(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(s.dataId)),i=n.readSync(r.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,d]=_pe(i,r.shape,r.dtype,o,l);return[n.makeTensorInfo(c,r.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var $1e={kernelName:Zy,backendName:"webgl",kernelFunc:E1e};function _1e(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),l=n.readSync(a.dataId),[u,c]=fE(o,r.shape,r.dtype,i,l,!0);return n.makeTensorInfo(c,r.dtype,u)}var R1e={kernelName:Yy,backendName:"webgl",kernelFunc:_1e};function D1e(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),l=n.readSync(a.dataId),[u,c]=fE(o,r.shape,r.dtype,i,l);return n.makeTensorInfo(c,r.dtype,u)}var F1e={kernelName:Jy,backendName:"webgl",kernelFunc:D1e};function M1e(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=R.calculateShapes(a,s,i),h=!1,p=new p9(u,l,s.shape.length,a.shape.length,c,[d,1],h),f=n.runWebGLProgram(p,[a,s,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var O1e={kernelName:Qy,backendName:"webgl",kernelFunc:M1e};function P1e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=k.parseAxisParam(o,s.shape)[0],l=R.prepareSplitSize(s,a,i),u=s.shape.length,c=new Array(u).fill(0),d=s.shape.slice();return l.map(h=>{let p=[...d];p[i]=h;let f=fh({inputs:{x:s},backend:n,attrs:{begin:c,size:p}});return c[i]+=h,f})}var z1e={kernelName:id,backendName:"webgl",kernelFunc:P1e},L1e="return sqrt(x);",B1e=it({opSnippet:L1e}),W1e={kernelName:Dl,backendName:"webgl",kernelFunc:B1e},V1e="return x * x;",U1e=it({opSnippet:V1e}),H1e={kernelName:lf,backendName:"webgl",kernelFunc:U1e},f9="return (a - b) * (a - b);",G1e=Nn({opSnippet:f9,packedOpSnippet:f9}),j1e={kernelName:Po,backendName:"webgl",kernelFunc:G1e};function q1e({inputs:e,attrs:t,backend:n}){let{x:r}=e,s=ys+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,a=new Qa(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}var K1e={kernelName:Bo,backendName:"webgl",kernelFunc:q1e},X1e=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,s=It(n.length),a=It(n.length),o="";if(r===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${e});
      ${s} strides = ${s}(${t});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${o}));
      }
    `}};function Z1e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=r,{nonStrided:p,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=En.sliceInfo(s.shape,a,o,i,l,u,c,d,h),x=ve({inputs:{x:s},backend:n,attrs:{shape:y}}),b;if(p){let w=fh({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=ve({inputs:{x:w},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(w)}else if(A.some(w=>w===0))b=n.makeTensorInfo(A,s.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let I=n.texData.get(x.dataId).values,E=Le(x.shape,x.dtype,I),F=Rpe(A,E,m,f);b=n.makeTensorInfo(A,x.dtype,F.values)}else{let S=new X1e(f,m,A);b=n.runWebGLProgram(S,[x],x.dtype)}let v=ve({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var Y1e={kernelName:ld,backendName:"webgl",kernelFunc:Z1e};function J1e(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=r,{data:c,dataSplits:d}=t,h=n.readSync(c.dataId),p=n.readSync(d.dataId),[f,m]=Dpe(h,p,s,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Q1e={kernelName:eA,backendName:"webgl",kernelFunc:J1e};function exe(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,d]=Fpe(i,l,s),h=c.length;return[n.makeTensorInfo([h,2],"int32",u),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var txe={kernelName:tA,backendName:"webgl",kernelFunc:exe};function nxe(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=Mpe(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var rxe={kernelName:nA,backendName:"webgl",kernelFunc:nxe},sxe="return tan(x);",axe=it({opSnippet:sxe}),oxe={kernelName:Ol,backendName:"webgl",kernelFunc:axe},ixe=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,lxe=it({opSnippet:ixe}),uxe={kernelName:Pl,backendName:"webgl",kernelFunc:lxe},cxe=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let r=It(this.rank),s=dxe(e);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function dxe(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}function m9(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let l=n.readSync(s.dataId),u=s.dtype==="string"?l.map(h=>k.decodeString(h)):l,c=Le(s.shape,s.dtype,u),d=Ppe(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new cxe(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var hxe={kernelName:Lo,backendName:"webgl",kernelFunc:m9};function pxe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=n.readSync(s.dataId),[l,u]=zpe(i,s.shape,s.dtype,a,o);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var fxe={kernelName:ud,backendName:"webgl",kernelFunc:pxe},mxe=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${i} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${s});
              }
              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(${s});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

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

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

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

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

        float sumValue = 0.0;

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

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

          ${d}
        }

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

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

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

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

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

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

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

          ${d}
        }
        setOutput(${l});
      }
    `}};function kxe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,l=[],u=0,c=R.getAxesPermutation([u],i),d=s;c!=null&&(d=Un({inputs:{x:s},backend:n,attrs:{perm:c}}),l.push(d),u=R.getInnerMostAxes(1,i)[0]);let h=R.segment_util.computeOutShape(d.shape,u,o),p=k.sizeFromShape([d.shape[u]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=fA(s.dtype),g=(b,v,w,S,I)=>{let E=b.shape[0],F=b.shape[1],$=R.segment_util.segOpComputeOptimalWindowSize(F,I),_={windowSize:$,inSize:F,batchSize:E,numSegments:I},N=new wxe(_,v),P=n.compileAndRun(N,[b,w],S);if(l.push(P),P.shape[1]===I)return P;let B=h9({backend:n,attrs:{start:0,stop:I,step:1,dtype:"float32"}}),j=m9({inputs:{x:B},backend:n,attrs:{reps:[F/$]}});return l.push(B),l.push(j),g(P,v,j,S,I)},y=g(f,"unsortedSegmentSum",a,m,o),A=ve({inputs:{x:y},backend:n,attrs:{shape:h}}),x=A;if(c!=null){l.push(A);let b=R.getUndoAxesPermutation(c);x=Un({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Ixe={kernelName:uf,backendName:"webgl",kernelFunc:kxe},Sxe=[Q2e,nye,zfe,Bfe,Ufe,jfe,Kfe,Yfe,Qfe,tme,ame,ime,cme,pme,bme,gme,kme,Nme,Sme,_me,Dme,Mme,Lme,jme,Kme,e0e,n0e,o0e,u0e,xfe,f0e,I0e,T0e,A0e,$0e,R0e,C0e,M0e,z0e,W0e,U0e,G0e,K0e,ege,nge,Z0e,age,lge,cge,fge,Age,wge,Sge,Tge,Nge,Ege,_ge,Dge,Mge,Pge,Wge,Hge,qge,Xge,Jge,n2e,o2e,c2e,Afe,h2e,h0e,m2e,A2e,v2e,vfe,S2e,E2e,_2e,z2e,M2e,V2e,G2e,X2e,sye,hye,cye,gye,Aye,bye,lye,wye,Iye,Cye,Rye,Oye,Hye,Tfe,jye,Xye,Jye,tAe,Zme,sAe,oAe,lAe,dAe,mAe,kfe,yAe,AAe,Yme,Bye,vAe,EAe,SAe,Cfe,DAe,OAe,BAe,UAe,qAe,XAe,JAe,t1e,r1e,o1e,u1e,h1e,m1e,A1e,v1e,Hme,Vye,I1e,T1e,C1e,$1e,R1e,F1e,O1e,z1e,W1e,H1e,j1e,K1e,Y1e,Q1e,txe,rxe,Wye,Mfe,oxe,uxe,hxe,fxe,yxe,Ofe,xxe,vxe,Ixe,aAe];for(let e of Sxe)iA(e);var tr;(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"})(tr||(tr={}));var gh;(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"})(gh||(gh={}));var g9;function Txe(e){g9=e.wasm.cwrap(Ll,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Nxe(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=r,h=n.dataIdMap.get(s.dataId).id,p=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let I=n.dataIdMap.get(o.dataId);if(I.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${I.shape.length}.`);f=I.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=gh[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?s.shape[2]:s.shape[1],A=u?a.shape[1]:a.shape[2],x=s.shape[0],b=n.makeOutput([x,y,A],s.dtype),v=n.dataIdMap.get(b.dataId).id,w=new Uint8Array(new Int32Array(s.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return g9(h,w,s.shape.length,p,S,a.shape.length,l,u,g,f,m,d||0,v),b}var Cxe={kernelName:Ll,backendName:"wasm",setupFunc:Txe,kernelFunc:Nxe};function Hn(e){let t;function n(s){t=s.wasm.cwrap(e,null,["number","number"])}function r(s){let{backend:a,inputs:{x:o}}=s,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),u=a.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(i,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var Exe=Hn(xc);function Gn(e,t,n){let r;function s(o){r=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:u,b:c}=l,d=i.dataIdMap.get(u.dataId).id,h=i.dataIdMap.get(c.dataId).id,p=n!=null?n:u.dtype,f=R.assertAndGetBroadcastShape(u.shape,c.shape),m=i.makeOutput(f,p);if(k.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),A=i.dataIdMap.get(m.dataId).id,x=()=>r(d,g,u.shape.length,h,y,c.shape.length,tr[u.dtype],A);if(t&&u.dtype==="float32")return x(),m;let b=R.getBroadcastDims(u.shape,f),v=R.getBroadcastDims(c.shape,f),w=b.every((I,E)=>I===E),S=v.every((I,E)=>I===E);if(w&&S)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:a}}var $xe=!0,_xe=Gn(Fa,$xe),y9;function Rxe(e){y9=e.wasm.cwrap(Zi,null,["array","number","number","number"])}function Dxe(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(r.shape)===0)return r;let s=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(s).buffer),o=n.dataIdMap.get(r.dataId).id;return y9(a,s.length,tr[r.dtype],o),r}var Fxe={kernelName:Zi,backendName:"wasm",setupFunc:Rxe,kernelFunc:Dxe};function Qm(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var Mxe={kernelName:hl,backendName:"wasm",kernelFunc:Qm},A9;function Oxe(e){A9=e.wasm.cwrap(zl,null,["number","array","number","number","number","array","number"])}function e0(e){let{inputs:t,backend:n,attrs:r}=e,[s,a]=zxe(t.x.shape,r.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Pxe(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:s,dtype:t.x.dtype};if(o){let f=Qm({inputs:t,backend:n});return f.shape=i,f}let u=n.makeOutput(i,l.dtype),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,h=new Uint8Array(new Int32Array(a).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return A9(c,p,l.shape.length,tr[l.dtype],d,h,a.length),u}function Pxe(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function zxe(e,t){let n=[],r=[];for(let s=0;s<e.length;++s)e[s]!==1&&n.push(e[s]),e[t[s]]!==1&&r.push(t[s]);for(let s=0;s<r.length;++s){let a=-1;for(let o=0;o<r.length;++o)r[o]>=s&&(a===-1||r[a]>r[o])&&(a=o);r[a]=s}return[n,r]}var Lxe={kernelName:zl,backendName:"wasm",kernelFunc:e0,setupFunc:Oxe};function to(e,t,n){let r=e.shape,s=e.shape.length,a=k.parseAxisParam(t,r),o=a,i=R.getAxesPermutation(o,s),l=null,u=!1;if(i!=null){let c=new Array(s);for(let p=0;p<c.length;p++)c[p]=r[i[p]];o=R.getInnerMostAxes(o.length,s),l=e0({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:u}}var x9;function Bxe(e){x9=e.wasm.cwrap(wc,null,["number, number, number"])}function Wxe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=to(o,s,t);if(p){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;R.assertAxesAreInnerMostDims("all",d,f);let[m,g]=R.computeOutAndReduceShapes(u.shape,d),y=k.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;x9(l,y,x)}if(p&&t.disposeData(c.dataId),a){let x=R.expandShapeToKeepDim(A.shape,h);A.shape=x}return A}var Vxe={kernelName:wc,backendName:"wasm",setupFunc:Bxe,kernelFunc:Wxe},b9;function Uxe(e){b9=e.wasm.cwrap(kc,null,["number, number, number"])}function Hxe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=to(o,s,t);if(p){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;R.assertAxesAreInnerMostDims("any",d,f);let[m,g]=R.computeOutAndReduceShapes(u.shape,d),y=k.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;b9(l,y,x)}if(p&&t.disposeData(c.dataId),a){let x=R.expandShapeToKeepDim(A.shape,h);A.shape=x}return A}var Gxe={kernelName:kc,backendName:"wasm",setupFunc:Uxe,kernelFunc:Hxe},v9;function jxe(e){v9=e.wasm.cwrap(Yi,null,["number","number","number","number","number"])}function qxe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s}=r,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:u,axes:c,inputWasTransposed:d}=to(a,s,t);if(d){let y=t.dataIdMap.get(u.dataId).id;y!==o&&(l=u,i=y)}let h=l.shape.slice(0,-1),p=t.makeOutput(h,"int32"),f=t.dataIdMap.get(p.dataId).id,m=k.sizeFromShape(p.shape),g=l.shape[c[0]];return v9(i,tr[l.dtype],m,g,f),d&&t.disposeData(u.dataId),p}var Kxe={kernelName:Yi,backendName:"wasm",kernelFunc:qxe,setupFunc:jxe},w9;function Xxe(e){w9=e.wasm.cwrap(Ji,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zxe(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=R.computePool2DInfo(s.shape,o,i,1,l,u),d=c.filterHeight,h=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,y=c.strideHeight,A=c.strideWidth,x=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=r.makeOutput(c.outShape,"float32"),v=r.dataIdMap.get(b.dataId).id;return w9(a,s.shape[0],s.shape[1],s.shape[2],d,h,p,f,m,g,y,A,x,v),b}var Yxe={kernelName:Ji,backendName:"wasm",setupFunc:Xxe,kernelFunc:Zxe};function As(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:s}=n,a=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,a);return k.assert(a===k.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:o,dtype:r.dtype}}var Jxe={kernelName:Qc,backendName:"wasm",kernelFunc:As},k9;function Qxe(e){k9=e.wasm.cwrap(Qi,null,["number","array","number","number","array","number","number","number","number"])}function e5e(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=s.shape.length,u=a.shape.length,c=o?s.shape[l-2]:s.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],h=o?s.shape[l-1]:s.shape[l-2],p=i?a.shape[u-2]:a.shape[u-1],f=s.shape.slice(0,-2),m=a.shape.slice(0,-2),g=k.sizeFromShape(f),y=k.sizeFromShape(m),A=g===y||g===1||y===1;k.assert(l>=2&&u>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>y?s.shape.slice(0,-2):a.shape.slice(0,-2)).concat([h,p]);k.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${s.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[g,c,h]:[g,h,c],w=i?[y,p,d]:[y,d,p],S=As({inputs:{x:s},backend:n,attrs:{shape:v}}),I=As({inputs:{x:a},backend:n,attrs:{shape:w}}),E=n.dataIdMap.get(S.dataId).id,F=n.dataIdMap.get(I.dataId).id,$=o?S.shape[2]:S.shape[1],_=i?I.shape[1]:I.shape[2],N=Math.max(g,y),P=n.makeOutput([N,$,_],S.dtype),B=n.dataIdMap.get(P.dataId).id,j=new Uint8Array(new Int32Array(S.shape).buffer),X=new Uint8Array(new Int32Array(I.shape).buffer);return k9(E,j,S.shape.length,F,X,I.shape.length,o,i,B),n.disposeData(S.dataId),n.disposeData(I.dataId),P.shape=b,P}var t5e={kernelName:Qi,backendName:"wasm",setupFunc:Qxe,kernelFunc:e5e};function t0(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,s=r.makeOutput(t.shape,n),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(s).set(a),s}var n5e={kernelName:el,backendName:"wasm",kernelFunc:t0},r5e=Hn(No),I9;function s5e(e){I9=e.wasm.cwrap(Co,null,["number","number","number","number"])}function a5e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i=n.dataIdMap.get(s.dataId).id,l=n.makeOutput(s.shape,s.dtype),u=n.dataIdMap.get(l.dataId).id;return I9(i,a,o,u),l}var o5e={kernelName:Co,backendName:"wasm",setupFunc:s5e,kernelFunc:a5e};function S9(e){let{inputs:t,backend:n}=e,r=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],s=R.computeOutShape(t.map(p=>p.shape),r),a=t.filter(p=>k.sizeFromShape(p.shape)>0);if(a.length===1)return Qm({inputs:{x:a[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(k.sizeFromShape(s)===0)return o;let i=a.map(p=>p.shape);if(R.assertParamsConsistent(i,r),a[0].dtype==="string"){let p=a.map(x=>{let b=k.sizeFromShape(x.shape.slice(r));return As({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));s=R.computeOutShape(p.map(x=>x.shape),1);let m=p[0].shape[0]===1,g=XC(f,s,t[0].dtype,m),y=R.computeOutShape(a.map(x=>x.shape),r);o.shape=y;let A=n.dataIdMap.get(o.dataId);return A.stringBytes=R.fromStringArrayToUint8(g),p.forEach(x=>n.disposeData(x.dataId)),o}let l=k.sizeFromShape(a[0].shape.slice(0,r)),u=0,c=a.map(p=>{let f=k.sizeFromShape(p.shape.slice(r));return u+=f,f}),d=a.map(p=>n.typedArrayFromHeap(p)),h=n.typedArrayFromHeap(o);for(let p=0;p<l;p++){let f=p*u;for(let m=0;m<d.length;m++){let g=c[m],y=p*g,A=d[m].subarray(y,y+g);h.set(A,f),f+=g}}return o}var i5e={kernelName:Ec,backendName:"wasm",kernelFunc:S9},T9;function l5e(e){T9=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function u5e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d,dataFormat:h}=n,p=R.convertConv2DDataFormat(h),f=R.computeConv2DInfo(s.shape,a.shape,l,u,c,d,!1,p),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,w=f.dilationWidth,S=f.strideHeight,I=f.strideWidth,E=f.inChannels,F=f.outChannels,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let _=r.makeOutput(f.outShape,"float32"),N=r.dataIdMap.get(_.dataId).id;return T9(o,s.shape[0],s.shape[1],s.shape[2],i,m,g,y,A,x,b,$,v,w,S,I,E,F,N),_}var c5e={kernelName:tl,backendName:"wasm",setupFunc:l5e,kernelFunc:u5e},N9;function d5e(e){N9=e.wasm.cwrap(nl,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 h5e(e){let{backend:t,inputs:n,attrs:r}=e,{dy:s,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=r,d=1,h=R.convertConv2DDataFormat(l),p=R.computeConv2DInfo(c,a.shape,o,d,i,u,!1,h),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:v,outWidth:w,strideHeight:S,strideWidth:I}=p,E=m-1-p.padInfo.top,F=g-1-p.padInfo.left,$=p.dataFormat==="channelsLast",_=k.computeStrides(p.inShape),N=k.computeStrides(s.shape),[P,B,j]=k.computeStrides(a.shape),X=_[0],Y=$?_[1]:_[2],ee=$?_[2]:1,oe=$?1:_[1],se=N[0],ie=$?N[1]:N[2],ne=$?N[2]:1,de=$?1:N[1],he=t.makeOutput(p.inShape,"float32"),ge=t.dataIdMap.get(he.dataId).id,be=t.dataIdMap.get(s.dataId).id,Ee=t.dataIdMap.get(a.dataId).id;return N9(be,Ee,f,m,g,A,x,y,v,w,b,S,I,E,F,P,B,j,X,Y,ee,oe,se,ie,ne,de,ge),he}var p5e={kernelName:nl,backendName:"wasm",setupFunc:d5e,kernelFunc:h5e},f5e=Hn(rl),fb;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(fb||(fb={}));var C9;function m5e(e){C9=e.wasm.cwrap(_c,null,["number","number","number","number","array","number","number","number","number","number"])}function g5e(e){let{backend:t,inputs:n,attrs:r}=e,{method:s,extrapolationValue:a,cropSize:o}=r,{image:i,boxes:l,boxInd:u}=n,c=l.shape[0],[d,h]=o,p=[c,d,h,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=t0({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(p,"float32"),b=t.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(i.shape).buffer);return C9(g,y,A,c,v,d,h,fb[s],a,b),m!=null&&t.disposeData(m.dataId),x}var y5e={kernelName:_c,backendName:"wasm",setupFunc:m5e,kernelFunc:g5e},E9;function A5e(e){E9=e.wasm.cwrap(sl,null,["number","number","number","number","number","number"])}function x5e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,l=s.shape.length;k.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumsum does not support ${s.dtype} tensors in the WASM backend`);let u=R.getAxesPermutation([a],l),c=s;u!==null&&(c=e0({inputs:{x:s},attrs:{perm:u},backend:n}));let d=R.getInnerMostAxes(1,l)[0];R.assertAxesAreInnerMostDims("cumsum",[d],l);let h=n.makeOutput(c.shape,c.dtype),p=c.shape[d],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(h.dataId).id;E9(f,o?1:0,i?1:0,p,m,tr[s.dtype]);let g=h;if(u!==null){let y=R.getUndoAxesPermutation(u);g=e0({inputs:{x:h},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(h.dataId)}return g}var b5e={kernelName:sl,backendName:"wasm",setupFunc:A5e,kernelFunc:x5e},$9;function v5e(e){$9=e.wasm.cwrap(Rc,null,["number","number","number","array","number","array","array","number","number"])}function w5e(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{blockSize:a,dataFormat:o}=r;k.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=s.shape[0],l=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],d=l*a,h=u*a,p=c/(a*a),f=o==="NHWC"?[i,d,h,p]:[i,p,d,h],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(s.dataId).id,A=new Uint8Array(new Int32Array(k.computeStrides(s.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(k.computeStrides(f)).buffer),v=t.dataIdMap.get(m.dataId).id;return $9(y,a,o==="NHWC"?1:0,A,s.shape.length-1,x,b,f.length,v),m}var k5e={kernelName:Rc,backendName:"wasm",setupFunc:v5e,kernelFunc:w5e},_9;function I5e(e){_9=e.wasm.cwrap(al,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function S5e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d}=n,h=u==null?[1,1]:u,p=R.computeConv2DInfo(s.shape,a.shape,l,h,c,d,!0),f=p.filterHeight,m=p.filterWidth,g=p.padInfo.top,y=p.padInfo.right,A=p.padInfo.bottom,x=p.padInfo.left,b=p.dilationHeight,v=p.dilationWidth,w=p.strideHeight,S=p.strideWidth,I=p.inChannels,E=p.outChannels,F=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let $=r.makeOutput(p.outShape,"float32"),_=r.dataIdMap.get($.dataId).id;return _9(o,s.shape[0],s.shape[1],s.shape[2],i,f,m,g,y,A,x,F,b,v,w,S,I,E,_),$}var T5e={kernelName:al,backendName:"wasm",setupFunc:I5e,kernelFunc:S5e},N5e=!1,C5e=Gn(il,N5e,"bool"),E5e=Hn(Eo);function mb(e){let{inputs:t,attrs:n,backend:r}=e,{input:s}=t,{dim:a}=n,o=s.shape.length,i=s.shape.slice(),l=a;return a<0&&(k.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),As({inputs:{x:s},backend:r,attrs:{shape:i}})}var $5e={kernelName:Mc,backendName:"wasm",kernelFunc:mb};function _5e(e){let{attrs:{shape:t,value:n,dtype:r},backend:s}=e,a=s.makeOutput(t,r);return s.typedArrayFromHeap(a).fill(n),a}var R5e={kernelName:Qp,backendName:"wasm",kernelFunc:_5e},R9;function D5e(e){R9=e.wasm.cwrap(Oc,null,["number","number","number","number","number","number"])}function F5e(e){let{inputs:t,backend:n}=e,{image:r}=t,s=n.makeOutput(r.shape,r.dtype),a=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,[i,l,u,c]=r.shape;return R9(a,i,l,u,c,o),s}var M5e={kernelName:Oc,backendName:"wasm",kernelFunc:F5e,setupFunc:D5e},O5e=Hn($o),P5e=!1,z5e=Gn(ul,P5e),D9;function L5e(e){D9=e.wasm.cwrap(cl,null,["number","number","number","number","number","number","number"])}function B5e(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:s}=r,{x:a,mean:o,variance:i,offset:l,scale:u}=n,c=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(k.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return D9(c,d,h,p,f,s,g),m}var W5e={kernelName:cl,backendName:"wasm",setupFunc:L5e,kernelFunc:B5e},F9;function V5e(e){F9=e.wasm.cwrap(Bl,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 U5e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=n,m=R.computeConv2DInfo(s.shape,a.shape,l,c,u,h),g=gh[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(s.dataId).id,A=r.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let ne=r.dataIdMap.get(o.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${x})`);b=ne.id}let v=m.filterHeight,w=m.filterWidth,S=m.padInfo.top,I=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,$=m.dilationHeight,_=m.dilationWidth,N=m.strideHeight,P=m.strideWidth,B=m.inChannels,j=m.padInfo.type==="SAME"?1:0,X=m.batchSize,Y=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let oe=r.makeOutput(m.outShape,"float32"),se=r.dataIdMap.get(oe.dataId).id,ie=i==null?0:r.dataIdMap.get(i.dataId).id;return F9(y,X,Y,ee,A,v,w,b,S,I,E,F,j,$,_,N,P,B,x,g,ie,f||0,se),oe}var H5e={kernelName:Bl,backendName:"wasm",setupFunc:V5e,kernelFunc:U5e},M9;function G5e(e){M9=e.wasm.cwrap(Wl,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 j5e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=n,m=R.computeConv2DInfo(s.shape,a.shape,l,c,u,h,!0),g=gh[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(s.dataId).id,A=r.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let ne=r.dataIdMap.get(o.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${x})`);b=ne.id}let v=m.filterHeight,w=m.filterWidth,S=m.padInfo.top,I=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,$=m.dilationHeight,_=m.dilationWidth,N=m.strideHeight,P=m.strideWidth,B=m.inChannels,j=m.padInfo.type==="SAME"?1:0,X=m.batchSize,Y=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let oe=r.makeOutput(m.outShape,"float32"),se=r.dataIdMap.get(oe.dataId).id,ie=i==null?0:r.dataIdMap.get(i.dataId).id;return M9(y,X,Y,ee,A,v,w,b,S,I,E,F,j,$,_,N,P,B,x,g,ie,f||0,se),oe}var q5e={kernelName:Wl,backendName:"wasm",setupFunc:G5e,kernelFunc:j5e},O9;function K5e(e){O9=e.wasm.cwrap(zc,null,["number","number","number","number","number","number","array","number"])}function X5e(e){let{backend:t,inputs:n}=e,{params:r,indices:s}=n,[a,o,i,l]=N6.prepareAndValidate(r,s),u=t.makeOutput(a,r.dtype);if(o===0)return u;let c=s.shape,d=c[c.length-1],p=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),y=t.dataIdMap.get(u.dataId).id;return O9(p,tr[r.dtype],m,o,d,i,g,y),u}var Z5e={kernelName:zc,backendName:"wasm",setupFunc:K5e,kernelFunc:X5e},P9;function Y5e(e){P9=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function J5e(e){let{backend:t,inputs:n,attrs:r}=e,{x:s,indices:a}=n,{axis:o,batchDims:i}=r,l=k.parseAxisParam(o,s.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(s,a,l,i),c=As({inputs:{x:s},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),d=k.sizeFromShape(a.shape),h=As({inputs:{x:a},attrs:{shape:[u.batchSize,d/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize],f=t.makeOutput(p,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let m=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(h.dataId).id,b=t.dataIdMap.get(f.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(c.shape)).buffer),w=new Uint8Array(new Int32Array(k.computeStrides(p)).buffer);return P9(y,tr[s.dtype],v,m,x,u.batchSize,w,b),t.disposeData(c.dataId),t.disposeData(h.dataId),f.shape=u.outputShape,f}var Q5e={kernelName:Pc,backendName:"wasm",setupFunc:Y5e,kernelFunc:J5e},ebe=!1,tbe=Gn(dl,ebe,"bool"),nbe=!1,rbe=Gn(_o,nbe,"bool"),z9;function sbe(e){z9=e.wasm.cwrap(pl,null,["number","number","number"])}function abe(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,s=r.dataIdMap.get(t.dataId).id,a=r.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let o=r.dataIdMap.get(a.dataId).id;z9(s,n,o)}return a}var obe={kernelName:pl,backendName:"wasm",setupFunc:sbe,kernelFunc:abe},ibe=!1,lbe=Gn(fl,ibe,"bool"),ube=!1,cbe=Gn(ml,ube,"bool"),dbe=Hn(Ro),hbe=!1,pbe=Gn(Uc,hbe,"bool"),L9;function fbe(e){L9=e.wasm.cwrap(gl,null,["number, number, number"])}function mbe(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:s,keepDims:a}=r,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=to(o,s,t);if(p){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;R.assertAxesAreInnerMostDims("max",d,f);let[m,g]=R.computeOutAndReduceShapes(u.shape,d),y=k.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;L9(l,y,x)}if(p&&t.disposeData(c.dataId),a){let x=R.expandShapeToKeepDim(A.shape,h);A.shape=x}return A}var gbe={kernelName:gl,backendName:"wasm",setupFunc:fbe,kernelFunc:mbe},ybe=!1,Abe=Gn(Do,ybe),B9;function xbe(e){B9=e.wasm.cwrap(yl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bbe(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=R.computePool2DInfo(s.shape,o,i,1,l,u),d=c.filterHeight,h=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,A=c.dilationWidth,x=c.strideHeight,b=c.strideWidth,v=c.inChannels,w=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=r.makeOutput(c.outShape,"float32"),I=r.dataIdMap.get(S.dataId).id;return B9(a,s.shape[0],s.shape[1],s.shape[2],d,h,p,f,m,g,y,A,x,b,v,w,I),S}var vbe={kernelName:yl,backendName:"wasm",setupFunc:xbe,kernelFunc:bbe},W9;function wbe(e){W9=e.wasm.cwrap(Al,null,["number, number, number"])}function kbe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=to(o,s,t),f=d;if(p){let b=t.dataIdMap.get(c.dataId).id;b!==i&&(u=c,l=b,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=R.computeOutAndReduceShapes(u.shape,f),y=k.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=t0({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(m,"float32");if(k.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;W9(l,y,b)}if(p&&t.disposeData(c.dataId),a){let b=R.expandShapeToKeepDim(x.shape,h);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var Ibe={kernelName:Al,backendName:"wasm",setupFunc:wbe,kernelFunc:kbe},V9;function Sbe(e){V9=e.wasm.cwrap(xl,null,["number, number, number"])}function Tbe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=to(o,s,t);if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x)}let f=u.shape.length;R.assertAxesAreInnerMostDims("min",d,f);let[m,g]=R.computeOutAndReduceShapes(u.shape,d),y=k.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;V9(l,y,x)}if(p&&t.disposeData(c.dataId),a){let x=R.expandShapeToKeepDim(A.shape,h);A.shape=x}return A}var Nbe={kernelName:xl,backendName:"wasm",setupFunc:Sbe,kernelFunc:Tbe},Cbe=!1,Ebe=Gn(Fo,Cbe),gb;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(gb||(gb={}));var U9;function $be(e){U9=e.wasm.cwrap(bl,null,["number","array","number","number","array","array","number","number"])}function _be(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:s}}=e,a=r.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=r.map(f=>f[0]),d=r.map(f=>f[1]),h=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(d).buffer);return U9(o,u,t.shape.length,tr[t.dtype],h,p,gb[s],l),i}var Rbe={kernelName:bl,backendName:"wasm",kernelFunc:_be,setupFunc:$be},Dbe=!0,Fbe=Gn(Mo,Dbe),Mbe=Hn(Gc);function yb(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],s=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:s,pSelectedScores:a,pValidOutputs:o}}var H9;function Obe(e){H9=e.wasm.cwrap(jc,"number",["number","number","number","number","number"])}function Pbe(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:s,maxOutputSize:a,scoreThreshold:o}=r,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,d=H9(u,c,a,s,o),{pSelectedIndices:h,selectedSize:p,pSelectedScores:f,pValidOutputs:m}=yb(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([p],"int32",h)}var zbe={kernelName:jc,backendName:"wasm",setupFunc:Obe,kernelFunc:Pbe},G9;function Lbe(e){G9=e.wasm.cwrap(qc,"number",["number","number","number","number","number","bool"])}function Bbe(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:s,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,h=G9(c,d,a,s,o,i),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=yb(t,h);t.wasm._free(m);let y=t.makeOutput([f],"int32",p),A=t.makeOutput([],"int32",g);return[y,A]}var Wbe={kernelName:qc,backendName:"wasm",setupFunc:Lbe,kernelFunc:Bbe},j9;function Vbe(e){j9=e.wasm.cwrap(Kc,"number",["number","number","number","number","number","number"])}function Ube(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:s,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,h=j9(c,d,a,s,o,i),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=yb(t,h);t.wasm._free(g);let y=t.makeOutput([f],"int32",p),A=t.makeOutput([f],"float32",m);return[y,A]}var Hbe={kernelName:Kc,backendName:"wasm",setupFunc:Vbe,kernelFunc:Ube},Gbe=!1,jbe=Gn(vl,Gbe,"bool"),q9;function qbe(e){q9=e.wasm.cwrap(wl,null,["number","number","number","number","number"])}function Kbe(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r,l=n.makeOutput([...s.shape,a],"int32"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(s.dataId).id;return q9(d,a,o,i,u),l}var Xbe={kernelName:wl,backendName:"wasm",setupFunc:qbe,kernelFunc:Kbe};function Zbe(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var Ybe={kernelName:Xc,backendName:"wasm",kernelFunc:Zbe};function Jbe(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return mb({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{k.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),k.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=mb({inputs:{input:c},backend:n,attrs:{dim:s}});return i.push(d),d}),u=S9({inputs:l,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeData(c.dataId)),u}var Qbe={kernelName:Zc,backendName:"wasm",kernelFunc:Jbe},K9;function e3e(e){K9=e.wasm.cwrap(kl,null,["number","array","number","number","array","array","number","number"])}function t3e(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:s}}=e,a=r.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=r.map(f=>f[0]),d=r.map(f=>f[1]),h=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(d).buffer);return K9(o,u,t.shape.length,tr[t.dtype],h,p,s,l),i}var n3e={kernelName:kl,backendName:"wasm",kernelFunc:t3e,setupFunc:e3e},r3e=!1,s3e=Gn(Il,r3e),X9;function a3e(e){X9=e.wasm.cwrap(Sl,null,["number","number","number"])}function o3e(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,i=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(i.dataId).id;return X9(a,o,l),i}var i3e={kernelName:Sl,backendName:"wasm",setupFunc:a3e,kernelFunc:o3e},Z9;function l3e(e){Z9=e.wasm.cwrap(Yc,null,["number","number","number","number"])}function u3e(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=to(o,s,t),f=d;if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=R.computeOutAndReduceShapes(u.shape,f),y=k.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;Z9(l,y,tr[A.dtype],x)}if(p&&t.disposeData(c.dataId),a){let x=R.expandShapeToKeepDim(A.shape,h);A.shape=x}return A}var c3e={kernelName:Yc,backendName:"wasm",setupFunc:l3e,kernelFunc:u3e},d3e=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=lE(r,s,a,o),l=t.makeOutput([i.length],o);return t.typedArrayFromHeap(l).set(i),l},h3e={kernelName:sf,backendName:"wasm",kernelFunc:d3e},p3e=!0,f3e=Gn(ol,p3e),m3e=Hn(Tl),g3e=Hn(Cl),Y9;function y3e(e){Y9=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","number","number"])}function A3e(e){let{backend:t,inputs:n,attrs:r}=e,{images:s}=n,{alignCorners:a,halfPixelCenters:o,size:i}=r,[l,u]=i,[c,d,h,p]=s.shape,f=[c,l,u,p],m=t.dataIdMap.get(s.dataId),g;m.dtype!=="float32"&&(g=t0({backend:t,inputs:{x:s},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(k.sizeFromShape(s.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return Y9(y,c,d,h,p,l,u,a?1:0,o?1:0,x),g!=null&&t.disposeData(g.dataId),A}var x3e={kernelName:Nl,backendName:"wasm",setupFunc:y3e,kernelFunc:A3e},J9;function b3e(e){J9=e.wasm.cwrap(El,null,["number","array","number","array","number","number"])}function v3e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=k.parseAxisParam(a,s.shape);if(s.shape.length===0)return Qm({inputs:{x:s},backend:n});let i=n.makeOutput(s.shape,s.dtype),l=n.dataIdMap.get(s.dataId).id,u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(o).buffer),d=new Uint8Array(new Int32Array(s.shape).buffer);J9(l,c,o.length,d,s.shape.length,u);let h=As({inputs:{x:i},attrs:{shape:s.shape},backend:n});return n.disposeData(i.dataId),h}var w3e={kernelName:El,backendName:"wasm",kernelFunc:v3e,setupFunc:b3e},Q9;function k3e(e){Q9=e.wasm.cwrap(pd,null,["number","number","number","number","number","number","number","number","array","number","number"])}function I3e(e){let{inputs:t,backend:n,attrs:r}=e,{image:s}=t,{radians:a,fillValue:o,center:i}=r,l=n.makeOutput(s.shape,s.dtype),u=n.dataIdMap.get(s.dataId).id,c=n.dataIdMap.get(l.dataId).id,[d,h,p,f]=s.shape,[m,g]=R.getImageCenter(i,h,p),y=o===0,A=255,x=typeof o=="number"?[o,o,o,y?0:A]:[...o,A],b=new Uint8Array(new Int32Array(x).buffer);return Q9(u,d,h,p,f,a,m,g,b,x.length,c),l}var S3e={kernelName:pd,backendName:"wasm",kernelFunc:I3e,setupFunc:k3e},T3e=Hn($l),N3e=Hn(Oo),e$;function C3e(e){e$=e.wasm.cwrap(ed,null,["number","number","number","number","number","number","array","number","number"])}function E3e(e){let{backend:t,inputs:n,attrs:r}=e,{indices:s,updates:a}=n,{shape:o}=r,i=t.makeOutput(o,a.dtype);if(k.sizeFromShape(o)===0)return i;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:h}=E6.calculateShapes(a,s,o),f=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(d).buffer),A=t.dataIdMap.get(i.dataId).id;return e$(f,g,tr[a.dtype],l,u,c,y,h,A),i}var $3e={kernelName:ed,backendName:"wasm",setupFunc:C3e,kernelFunc:E3e},t$;function _3e(e){t$=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function R3e(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(s.dataId).id,l=n.dataIdMap.get(a.dataId).id,u=n.makeOutput(s.shape,s.dtype),c=n.dataIdMap.get(u.dataId).id,d=r.shape.length,h=s.shape.length,p=d===0||d>1||h===1?1:k.sizeFromShape(s.shape.slice(1));return t$(o,i,l,p,c),u}var D3e={kernelName:td,backendName:"wasm",kernelFunc:R3e,setupFunc:_3e},n$;function F3e(e){n$=e.wasm.cwrap(Rl,null,["number","number"])}function M3e(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(s.dataId).id;return k.sizeFromShape(s.shape)===0||n$(r,a),s}var O3e={kernelName:"Sigmoid",backendName:"wasm",setupFunc:F3e,kernelFunc:M3e},P3e=Hn(_l);function n0(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:s}=e,[a,o]=En.parseSliceParams(t,n,r),i=En.isSliceContinous(t.shape,a,o),l=s.readSync(t.dataId),u=s.makeOutput(o,t.dtype),c=k.computeStrides(t.shape),d=s.dataIdMap.get(u.dataId);if(i){let f=En.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=l.slice(f,f+k.sizeFromShape(o)):s.typedArrayFromHeap(u).set(l.subarray(f,f+k.sizeFromShape(o))),u}if(t.dtype==="string"){let f=ob(l,a,o,t.shape,t.dtype);return d.stringBytes=f,u}let h=s.typedArrayFromHeap(u),p=t.shape.length;if(p===2)z3e(l,c[0],h,a,o);else if(p===3)L3e(l,c[0],c[1],h,a,o);else if(p===4)B3e(l,c[0],c[1],c[2],h,a,o);else{let f=ob(l,a,o,t.shape,t.dtype);h.set(f)}return u}function z3e(e,t,n,r,s){let a=0,o=r[0],i=r[1],l=o+s[0];for(let u=o;u<l;u++){let c=u*t+i;n.set(e.subarray(c,c+s[1]),a),a+=s[1]}}function L3e(e,t,n,r,s,a){let o=0,i=s[0],l=s[1],u=s[2],c=i+a[0],d=l+a[1];for(let h=i;h<c;h++)for(let p=l;p<d;p++){let f=h*t+p*n+u;r.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function B3e(e,t,n,r,s,a,o){let i=0,l=a[0],u=a[1],c=a[2],d=l+o[0],h=u+o[1],p=c+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=u;g<h;g++)for(let y=c;y<p;y++){let A=m*t+g*n+y*r+f;s.set(e.subarray(A,A+o[3]),i),i+=o[3]}}var W3e={kernelName:rd,backendName:"wasm",kernelFunc:n0},r$;function V3e(e){r$=e.wasm.cwrap(Ml,null,["number","number","number","number"])}function U3e(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,s=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[r],l=k.sizeFromShape(n.shape)/i;return k.sizeFromShape(a.shape)===0||r$(s,o,i,l),a}var H3e={kernelName:Ml,backendName:"wasm",setupFunc:V3e,kernelFunc:U3e};function G3e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=n,i=k.parseAxisParam(o,s.shape)[0],l=R.prepareSplitSize(s,a,i),u=new Array(s.shape.length).fill(0),c=s.shape.slice();return l.map(d=>{let h=[...c];h[i]=d;let p=n0({inputs:{x:s},attrs:{begin:u,size:h},backend:r});return u[i]+=d,p})}var j3e={kernelName:id,backendName:"wasm",kernelFunc:G3e},q3e=Hn(Dl),K3e=Hn(lf),X3e=!0,Z3e=Gn(Po,X3e),s$;function Y3e(e){s$=e.wasm.cwrap(Bo,null,["number","number","number"])}function J3e(e){let{backend:t,inputs:n,attrs:r}=e,{alpha:s}=r,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),l=t.dataIdMap.get(i.dataId).id;return s$(o,s,l),i}var Q3e={kernelName:Bo,backendName:"wasm",setupFunc:Y3e,kernelFunc:J3e},a$;function eve(e){a$=e.wasm.cwrap(ld,null,["number","array","number","array","array","array","array","array","number","number"])}function tve(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{begin:a,end:o,strides:i}=r;i==null&&(i=new Array(a.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=r,p=R.slice_util.maskToAxes(c);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&h!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=s.shape.length-a.length,m=R.slice_util.maskToAxes(d),g=s.shape.slice();m.forEach($=>{a[$]=0,o[$]=1,g.splice($,0,1)});let y=As({inputs:{x:s},attrs:{shape:g},backend:t}),{begin:A,end:x,strides:b}=R.slice_util.getNormalizedAxes(y.shape,p,f,a,o,i,l,u,c);a=A,o=x,i=b;let v=R.slice_util.maskToAxes(h);v.forEach($=>{o[$]=a[$]+1,i[$]=1});let w=R.slice_util.computeOutShape(a,o,i),S=w.filter(($,_)=>v.indexOf(_)===-1);if(i.every($=>$===1)){let $=n0({inputs:{x:y},attrs:{begin:a,size:w},backend:t});t.disposeData(y.dataId);let _=As({inputs:{x:$},attrs:{shape:S},backend:t});return t.disposeData($.dataId),_}let E=t.makeOutput(S,"float32");if(!S.some($=>$===0)){let $=t.dataIdMap.get(y.dataId).id,_=new Uint8Array(new Int32Array(k.computeStrides(y.shape)).buffer),N=new Uint8Array(new Int32Array(a).buffer),P=new Uint8Array(new Int32Array(o).buffer),B=new Uint8Array(new Int32Array(i).buffer),j=new Uint8Array(new Int32Array(S).buffer),X=new Uint8Array(new Int32Array(k.computeStrides(S)).buffer),Y=t.dataIdMap.get(E.dataId).id;a$($,_,y.shape.length,N,P,B,j,X,S.length,Y)}t.disposeData(y.dataId);let F=As({inputs:{x:E},attrs:{shape:S},backend:t});return t.disposeData(E.dataId),F}var nve={kernelName:ld,backendName:"wasm",setupFunc:eve,kernelFunc:tve},rve=!0,sve=Gn(zo,rve),o$;function ave(e){o$=e.wasm.cwrap(Fl,null,["number, number, number"])}function ove(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=to(o,s,t),f=d;if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,g]=R.computeOutAndReduceShapes(u.shape,f),y=k.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;o$(l,y,x)}if(p&&t.disposeData(c.dataId),a){let x=R.expandShapeToKeepDim(A.shape,h);A.shape=x}return A}var ive={kernelName:Fl,backendName:"wasm",setupFunc:ave,kernelFunc:ove},lve=Hn(Ol),uve=Hn(Pl),i$;function cve(e){i$=e.wasm.cwrap(Lo,null,["number","array","number","array","number","number"])}function dve(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,a=n.dataIdMap.get(s.dataId).id,{reps:o}=r,i=new Array(s.shape.length);for(let h=0;h<i.length;h++)i[h]=s.shape[h]*o[h];let l=new Uint8Array(new Int32Array(s.shape).buffer),u=new Uint8Array(new Int32Array(i).buffer),c=n.makeOutput(i,s.dtype),d=n.dataIdMap.get(c.dataId).id;return i$(a,l,s.shape.length,u,i.length,tr[c.dtype],d),c}var hve={kernelName:Lo,backendName:"wasm",setupFunc:cve,kernelFunc:dve},l$;function pve(e){l$=e.wasm.cwrap(ud,null,["number","array","number","number","number","bool","number","number"])}var fve=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{k:s,sorted:a}=n,o=t.dataIdMap.get(r.dataId).id,i=new Uint8Array(new Int32Array(r.shape).buffer),l=r.shape.slice();l[l.length-1]=s;let u=t.makeOutput(l,r.dtype),c=t.dataIdMap.get(u.dataId).id,d=t.makeOutput(l,"int32"),h=t.dataIdMap.get(d.dataId).id;return l$(o,i,r.shape.length,tr[r.dtype],s,a,c,h),[u,d]},mve={kernelName:ud,backendName:"wasm",setupFunc:pve,kernelFunc:fve},u$;function gve(e){u$=e.wasm.cwrap(cd,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function yve(e){let{backend:t,inputs:n,attrs:r}=e,{image:s,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=r,[c,d,h,p]=s.shape,[f,m]=u!=null?u:[d,h],g=[c,f,m,p],y=new Uint8Array(new Int32Array(k.computeStrides(s.shape)).buffer),A=t.makeOutput(g,s.dtype),x=t.dataIdMap.get(A.dataId).id,v=t.dataIdMap.get(s.dataId).id,S=t.dataIdMap.get(a.dataId).id,I=o==="nearest"?1:2,E;switch(i){case"constant":E=1;break;case"reflect":E=2;break;case"wrap":E=3;break;case"nearest":E=4;break;default:E=1;break}return u$(v,S,a.shape[0]>1,c,f,m,p,h,d,y,s.shape.length-1,I,E,l,x),A}var Ave={kernelName:cd,backendName:"wasm",setupFunc:gve,kernelFunc:yve};function xve(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s.shape[a],i=s.shape.length,l=new Array(i-1),u=0;for(let p=0;p<i;p++)p!==a&&(l[u++]=s.shape[p]);let c=new Array(o),d=new Array(i).fill(0),h=s.shape.slice();h[a]=1;for(let p=0;p<c.length;p++)d[a]=p,c[p]=n0({inputs:{x:s},attrs:{begin:d,size:h},backend:n});return c.map(({dataId:p,dtype:f})=>({dataId:p,dtype:f,shape:l}))}var bve={kernelName:dd,backendName:"wasm",kernelFunc:xve};function vve(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var wve={kernelName:hd,backendName:"wasm",kernelFunc:vve},kve=[Exe,_xe,Fxe,Vxe,Gxe,Kxe,Yxe,t5e,n5e,r5e,o5e,i5e,c5e,p5e,f5e,y5e,b5e,k5e,T5e,C5e,E5e,$5e,R5e,M5e,O5e,z5e,Cxe,W5e,H5e,q5e,Z5e,Q5e,tbe,rbe,Mxe,obe,lbe,cbe,dbe,pbe,gbe,Abe,vbe,Ibe,Nbe,Ebe,Rbe,Fbe,Mbe,zbe,Wbe,Hbe,jbe,Xbe,Ybe,Qbe,n3e,s3e,i3e,c3e,h3e,f3e,m3e,g3e,Jxe,x3e,w3e,S3e,N3e,T3e,$3e,D3e,O3e,P3e,W3e,H3e,j3e,q3e,K3e,Z3e,Q3e,nve,sve,ive,lve,uve,hve,mve,Ave,Lxe,bve,wve];for(let e of kve)iA(e);var Ab=ae();Ab.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));Ab.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Ab.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var c$=Ks(NR()),Ive='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',Sve=Ks(CR()),d$=class extends Bp{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new my(this,za())}write(e,t,n){let r={id:this.dataIdNextNumber++};return this.move(r,e,t,n,1),r}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}move(e,t,n,r,s){let a=this.dataIdNextNumber++;if(r==="string"){let u=t;this.dataIdMap.set(e,{id:a,stringBytes:u,shape:n,dtype:r,memoryOffset:null,refCount:s});return}let o=k.sizeFromShape(n),i=o*k.bytesPerElement(r),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:r,refCount:s}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:r,stringBytes:s}=this.dataIdMap.get(e);if(n==="string")return s;let a=this.wasm.HEAPU8.slice(t,t+k.sizeFromShape(r)*k.bytesPerElement(n));return Cve(a.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let r;if(n==null)r=this.write(null,e,t);else{let s=this.dataIdNextNumber++;r={id:s},this.dataIdMap.set(r,{id:s,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=k.sizeFromShape(e);this.wasm.tfjs.registerTensor(s,a,n)}return{dataId:r,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let r=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(n),a=k.sizeFromShape(e);switch(t){case"float32":return new Float32Array(r,s,a);case"int32":return new Int32Array(r,s,a);case"bool":return new Uint8Array(r,s,a);default:throw new Error(`Unknown dtype ${t}`)}}};function Tve(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(s=>{WebAssembly.instantiate(s,t).then(a=>{n(a.instance,a.module)})})}),{})}function h$(e,t,n){if(r0!=null)return r0;let r="tfjs-backend-wasm.wasm";return e&&t?r="tfjs-backend-wasm-threaded-simd.wasm":e&&(r="tfjs-backend-wasm-simd.wasm"),Ah!=null&&Ah[r]!=null?Ah[r]:n+r}async function Nve(){let[e,t]=await Promise.all([ae().getAsync("WASM_HAS_SIMD_SUPPORT"),ae().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,r)=>{let s={};s.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let u=Ive,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?h$(e,t,yh!=null?yh:l):l+i},xb&&(s.instantiateWasm=Tve(h$(e,t,yh!=null?yh:"")));let a=!1;s.onAbort=()=>{if(a||xh)return;xh=!0,r({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let o;t&&e&&r0==null?(s.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+c$.default.toString()],{type:"text/javascript"}),o=(0,c$.default)(s)):o=(0,Sve.default)(s),o.then(i=>{a=!0,xh=!1;let l=null;i.tfjs={init:i.cwrap("init",null,[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",l,["number"]),dispose:i.cwrap("dispose",l,[])},n({wasm:i})})})}function Cve(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 Eve=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],r0=null,yh=null,Ah={},xh=!1,xb=!1;function $ve(e,t=!1){if(U6("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),xh)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");r0=e,xb=t}function _ve(e,t=!1){if(xh)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")yh=e;else{Ah=e;let n=Eve.filter(r=>Ah[r]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}xb=t}var Rve="3.7.0",Dve=2;_A("wasm",async()=>{let{wasm:e}=await Nve();return new d$(e)},Dve);var Fve={tfjs:ER,"tfjs-core":$R,"tfjs-data":_R,"tfjs-layers":RR,"tfjs-converter":DR,"tfjs-backend-cpu":FR,"tfjs-backend-webgl":MR,"tfjs-backend-wasm":OR};var nr={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,extensions:[],webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Mve(){let e=nr.gl;!e||(nr.extensions=e.getSupportedExtensions())}function p$(){if(!K2(nr.name)){try{nr.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(nr.width,nr.height):document.createElement("canvas")}catch(e){me("error: cannot create canvas:",e);return}try{nr.gl=nr.canvas.getContext("webgl2",nr.webGLattr)}catch(e){me("error: cannot get WebGL2 context:",e);return}try{Dm(2,nr.gl)}catch(e){me("error: cannot set WebGL2 context:",e);return}try{let e=new Bm(nr.gl);X2(nr.name,()=>new dh(e),nr.priority)}catch(e){me("error: cannot register WebGL backend:",e);return}try{Li("webgl").forEach(t=>{let n={...t,backendName:nr.name};sp(n)})}catch(e){me("error: cannot update WebGL backend registration:",e);return}try{Sr.set("WEBGL_VERSION",2)}catch(e){me("error: cannot set WebGL backend flags:",e);return}Mve(),me("backend registered:",nr.name)}}function f$(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function vh(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Tu(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Nu(e,t,n){let r=t.shape[1],s=t.shape[2],a=[[e.startPoint[1]/r,e.startPoint[0]/s,e.endPoint[1]/r,e.endPoint[0]/s]];return Ye.cropAndResize(t,a,[0],n)}function s0(e,t=1.5){let n=Tu(e),r=vh(e),s=[t*r[0]/2,t*r[1]/2],a=[n[0]-s[0],n[1]-s[1]],o=[n[0]+s[0],n[1]+s[1]];return{startPoint:a,endPoint:o,landmarks:e.landmarks}}function a0(e){let t=Tu(e),n=vh(e),s=Math.max(...n)/2,a=[Math.round(t[0]-s),Math.round(t[1]-s)],o=[Math.round(t[0]+s),Math.round(t[1]+s)];return{startPoint:a,endPoint:o,landmarks:e.landmarks}}function bb(e){let t=e.map(a=>a[0]),n=e.map(a=>a[1]),r=[Math.min(...t),Math.min(...n)],s=[Math.max(...t),Math.max(...n)];return{startPoint:r,endPoint:s,landmarks:e}}var m$=e=>({startPoint:Ze(e,[0,0],[-1,2]),endPoint:Ze(e,[0,2],[-1,2])});var o0=[[1,0,0],[0,1,0],[0,0,1]];function Ove(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function vb(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Ove(n)}function g$(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function no(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Pve(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function y$(e,t){let n=[],r=e.length;for(let s=0;s<r;s++){n.push([]);for(let a=0;a<r;a++)n[s].push(no(e[s],Pve(t,a)))}return n}function i0(e,t){let n=Math.cos(e),r=Math.sin(e),s=[[n,-r,0],[r,n,0],[0,0,1]],a=g$(t[0],t[1]),o=y$(a,s),i=g$(-t[0],-t[1]);return y$(o,i)}function A$(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-no(t[0],n),-no(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function x$(e,t){return[no(e,t[0]),no(e,t[1])]}function b$(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r<t.strides.length;r++){let s=t.strides[r],a=Math.floor((e+s-1)/s),o=Math.floor((e+s-1)/s),i=t.anchors[r];for(let l=0;l<a;l++){let u=s*(l+.5);for(let c=0;c<o;c++){let d=s*(c+.5);for(let h=0;h<i;h++)n.push([d,u])}}}return n}var v$=6;function zve(e,t,n){let r=Ze(e,[0,1],[-1,2]),s=Me(r,t),a=Ze(e,[0,3],[-1,2]),o=Qe(a,n),i=Qe(s,n),l=Qe(o,2),u=He(i,l),c=Me(i,l),d=fe(u,n),h=fe(c,n);return lc([d,h],1)}var w$=class{constructor(t,n){this.model=t,this.anchorsData=b$(t.inputs[0].shape[1]),this.anchors=ra(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,s]=Ue(()=>{let u=Ye.resizeBilinear(t,[this.inputSize,this.inputSize]).div(127.5).sub(.5),c=this.model.execute(u),d;if(Array.isArray(c)){let m=c.sort((x,b)=>x.size-b.size),g=an([m[0],m[2]],2),y=an([m[1],m[3]],2);d=an([y,g],1).squeeze(0)}else d=Zn(c);let h=zve(d,this.anchors,[this.inputSize,this.inputSize]),p=Ze(d,[0,0],[-1,1]),f=Ts(p).squeeze().dataSync();return[d,h,f]}),a=await Ye.nonMaxSuppressionAsync(r,s,this.config.face.detector.maxDetected,this.config.face.detector.iouThreshold,this.config.face.detector.minConfidence),o=a.arraySync();a.dispose();let i=[];for(let l=0;l<o.length;l++){let u=s[o[l]];if(u>this.config.face.detector.minConfidence){let c=Ze(r,[o[l],0],[1,-1]),d=m$(c);c.dispose();let h=this.anchorsData[o[l]],p=Ue(()=>Ze(n,[o[l],v$-1],[1,-1]).squeeze().reshape([v$,-1]));i.push({box:d,landmarks:p,anchor:h,confidence:u})}}return n.dispose(),r.dispose(),{boxes:i,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function k$(e){let t=await Et($t(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new w$(t,e);return!t||!t.modelUrl?me("load model failed:",e.face.detector.modelPath):e.debug&&me("load model:",t.modelUrl),n}var Bs={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],rightEyeIris:[473,474,475,476,477],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],leftEyeIris:[468,469,470,471,472],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]},wb=[{key:"EyeUpper0",indices:[9,10,11,12,13,14,15]},{key:"EyeUpper1",indices:[25,26,27,28,29,30,31]},{key:"EyeUpper2",indices:[41,42,43,44,45,46,47]},{key:"EyeLower0",indices:[0,1,2,3,4,5,6,7,8]},{key:"EyeLower1",indices:[16,17,18,19,20,21,22,23,24]},{key:"EyeLower2",indices:[32,33,34,35,36,37,38,39,40]},{key:"EyeLower3",indices:[54,55,56,57,58,59,60,61,62]}],wh=[[.499976992607117,.652534008026123],[.500025987625122,.547487020492554],[.499974012374878,.602371990680695],[.482113003730774,.471979022026062],[.500150978565216,.527155995368958],[.499909996986389,.498252987861633],[.499523013830185,.40106201171875],[.289712011814117,.380764007568359],[.499954998493195,.312398016452789],[.499987006187439,.269918978214264],[.500023007392883,.107050001621246],[.500023007392883,.666234016418457],[.5000159740448,.679224014282227],[.500023007392883,.692348003387451],[.499976992607117,.695277988910675],[.499976992607117,.70593398809433],[.499976992607117,.719385027885437],[.499976992607117,.737019002437592],[.499967992305756,.781370997428894],[.499816000461578,.562981009483337],[.473773002624512,.573909997940063],[.104906998574734,.254140973091125],[.365929991006851,.409575998783112],[.338757991790771,.41302502155304],[.311120003461838,.409460008144379],[.274657994508743,.389131009578705],[.393361985683441,.403706014156342],[.345234006643295,.344011008739471],[.370094001293182,.346076011657715],[.319321990013123,.347265005111694],[.297903001308441,.353591024875641],[.24779200553894,.410809993743896],[.396889001131058,.842755019664764],[.280097991228104,.375599980354309],[.106310002505779,.399955987930298],[.2099249958992,.391353011131287],[.355807989835739,.534406006336212],[.471751004457474,.65040397644043],[.474155008792877,.680191993713379],[.439785003662109,.657229006290436],[.414617002010345,.66654098033905],[.450374007225037,.680860996246338],[.428770989179611,.682690978050232],[.374971002340317,.727805018424988],[.486716985702515,.547628998756409],[.485300987958908,.527395009994507],[.257764995098114,.314490020275116],[.401223003864288,.455172002315521],[.429818987846375,.548614978790283],[.421351999044418,.533740997314453],[.276895999908447,.532056987285614],[.483370006084442,.499586999416351],[.33721199631691,.282882988452911],[.296391993761063,.293242990970612],[.169294998049736,.193813979625702],[.447580009698868,.302609980106354],[.392390012741089,.353887975215912],[.354490011930466,.696784019470215],[.067304998636246,.730105042457581],[.442739009857178,.572826027870178],[.457098007202148,.584792017936707],[.381974011659622,.694710969924927],[.392388999462128,.694203019142151],[.277076005935669,.271932005882263],[.422551989555359,.563233017921448],[.385919004678726,.281364023685455],[.383103013038635,.255840003490448],[.331431001424789,.119714021682739],[.229923993349075,.232002973556519],[.364500999450684,.189113974571228],[.229622006416321,.299540996551514],[.173287004232407,.278747975826263],[.472878992557526,.666198015213013],[.446828007698059,.668527007102966],[.422762006521225,.673889994621277],[.445307999849319,.580065965652466],[.388103008270264,.693961024284363],[.403039008378983,.706539988517761],[.403629004955292,.693953037261963],[.460041999816895,.557139039039612],[.431158006191254,.692366003990173],[.452181994915009,.692366003990173],[.475387006998062,.692366003990173],[.465828001499176,.779190003871918],[.472328990697861,.736225962638855],[.473087012767792,.717857003211975],[.473122000694275,.704625964164734],[.473033010959625,.695277988910675],[.427942007780075,.695277988910675],[.426479011774063,.703539967536926],[.423162013292313,.711845993995667],[.4183090031147,.720062971115112],[.390094995498657,.639572978019714],[.013953999616206,.560034036636353],[.499913990497589,.58014702796936],[.413199990987778,.69539999961853],[.409626007080078,.701822996139526],[.468080013990402,.601534962654114],[.422728985548019,.585985004901886],[.463079988956451,.593783974647522],[.37211999297142,.47341400384903],[.334562003612518,.496073007583618],[.411671012639999,.546965003013611],[.242175996303558,.14767599105835],[.290776997804642,.201445996761322],[.327338010072708,.256527006626129],[.399509996175766,.748921036720276],[.441727995872498,.261676013469696],[.429764986038208,.187834024429321],[.412198007106781,.108901023864746],[.288955003023148,.398952007293701],[.218936994671822,.435410976409912],[.41278201341629,.398970007896423],[.257135003805161,.355440020561218],[.427684992551804,.437960982322693],[.448339998722076,.536936044692993],[.178560003638268,.45755398273468],[.247308000922203,.457193970680237],[.286267012357712,.467674970626831],[.332827985286713,.460712015628815],[.368755996227264,.447206974029541],[.398963987827301,.432654976844788],[.476410001516342,.405806005001068],[.189241006970406,.523923993110657],[.228962004184723,.348950982093811],[.490725994110107,.562400996685028],[.404670000076294,.485132992267609],[.019469000399113,.401564002037048],[.426243007183075,.420431017875671],[.396993011236191,.548797011375427],[.266469985246658,.376977026462555],[.439121007919312,.51895797252655],[.032313998788595,.644356966018677],[.419054001569748,.387154996395111],[.462783008813858,.505746960639954],[.238978996872902,.779744982719421],[.198220998048782,.831938028335571],[.107550002634525,.540755033493042],[.183610007166862,.740257024765015],[.134409993886948,.333683013916016],[.385764002799988,.883153975009918],[.490967005491257,.579378008842468],[.382384985685349,.508572995662689],[.174399003386497,.397670984268188],[.318785011768341,.39623498916626],[.343364000320435,.400596976280212],[.396100014448166,.710216999053955],[.187885001301765,.588537991046906],[.430987000465393,.944064974784851],[.318993002176285,.898285031318665],[.266247987747192,.869701027870178],[.500023007392883,.190576016902924],[.499976992607117,.954452991485596],[.366169989109039,.398822009563446],[.393207013607025,.39553701877594],[.410373002290726,.391080021858215],[.194993004202843,.342101991176605],[.388664990663528,.362284004688263],[.365961998701096,.355970978736877],[.343364000320435,.355356991291046],[.318785011768341,.35834002494812],[.301414996385574,.363156020641327],[.058132998645306,.319076001644135],[.301414996385574,.387449026107788],[.499987989664078,.618434011936188],[.415838003158569,.624195992946625],[.445681989192963,.566076993942261],[.465844005346298,.620640993118286],[.49992299079895,.351523995399475],[.288718998432159,.819945991039276],[.335278987884521,.852819979190826],[.440512001514435,.902418971061707],[.128294005990028,.791940987110138],[.408771991729736,.373893976211548],[.455606997013092,.451801002025604],[.499877005815506,.908990025520325],[.375436991453171,.924192011356354],[.11421000212431,.615022003650665],[.448662012815475,.695277988910675],[.4480200111866,.704632043838501],[.447111994028091,.715808033943176],[.444831997156143,.730794012546539],[.430011987686157,.766808986663818],[.406787008047104,.685672998428345],[.400738000869751,.681069016456604],[.392399996519089,.677703022956848],[.367855995893478,.663918972015381],[.247923001646996,.601333022117615],[.452769994735718,.420849978923798],[.43639200925827,.359887003898621],[.416164010763168,.368713974952698],[.413385987281799,.692366003990173],[.228018000721931,.683571994304657],[.468268007040024,.352671027183533],[.411361992359161,.804327011108398],[.499989002943039,.469825029373169],[.479153990745544,.442654013633728],[.499974012374878,.439637005329132],[.432112008333206,.493588984012604],[.499886006116867,.866917014122009],[.49991300702095,.821729004383087],[.456548988819122,.819200992584229],[.344549000263214,.745438992977142],[.37890899181366,.574010014533997],[.374292999505997,.780184984207153],[.319687992334366,.570737957954407],[.357154995203018,.604269981384277],[.295284003019333,.621580958366394],[.447750002145767,.862477004528046],[.410986006259918,.508723020553589],[.31395098567009,.775308012962341],[.354128003120422,.812552988529205],[.324548006057739,.703992962837219],[.189096003770828,.646299958229065],[.279776990413666,.71465802192688],[.1338230073452,.682700991630554],[.336768001317978,.644733011722565],[.429883986711502,.466521978378296],[.455527991056442,.548622965812683],[.437114000320435,.558896005153656],[.467287987470627,.529924988746643],[.414712011814117,.335219979286194],[.37704598903656,.322777986526489],[.344107985496521,.320150971412659],[.312875986099243,.32233202457428],[.283526003360748,.333190023899078],[.241245999932289,.382785975933075],[.102986000478268,.468762993812561],[.267612010240555,.424560010433197],[.297879010438919,.433175981044769],[.333433985710144,.433878004550934],[.366427004337311,.426115989685059],[.396012008190155,.416696012020111],[.420121014118195,.41022801399231],[.007561000064015,.480777025222778],[.432949006557465,.569517970085144],[.458638995885849,.479089021682739],[.473466008901596,.545744001865387],[.476087987422943,.563830018043518],[.468472003936768,.555056989192963],[.433990985155106,.582361996173859],[.483518004417419,.562983989715576],[.482482999563217,.57784903049469],[.42645001411438,.389798998832703],[.438998997211456,.39649498462677],[.450067013502121,.400434017181396],[.289712011814117,.368252992630005],[.276670008897781,.363372981548309],[.517862021923065,.471948027610779],[.710287988185883,.380764007568359],[.526226997375488,.573909997940063],[.895093023777008,.254140973091125],[.634069979190826,.409575998783112],[.661242008209229,.41302502155304],[.688880026340485,.409460008144379],[.725341975688934,.389131009578705],[.606630027294159,.40370500087738],[.654766023159027,.344011008739471],[.629905998706818,.346076011657715],[.680678009986877,.347265005111694],[.702096998691559,.353591024875641],[.75221198797226,.410804986953735],[.602918028831482,.842862963676453],[.719901978969574,.375599980354309],[.893692970275879,.399959981441498],[.790081977844238,.391354024410248],[.643998026847839,.534487962722778],[.528249025344849,.65040397644043],[.525849997997284,.680191040039062],[.560214996337891,.657229006290436],[.585384011268616,.66654098033905],[.549625992774963,.680860996246338],[.57122802734375,.682691991329193],[.624852001667023,.72809898853302],[.513050019741058,.547281980514526],[.51509702205658,.527251958847046],[.742246985435486,.314507007598877],[.598631024360657,.454979002475739],[.570338010787964,.548575043678284],[.578631997108459,.533622980117798],[.723087012767792,.532054007053375],[.516445994377136,.499638974666595],[.662801027297974,.282917976379395],[.70362401008606,.293271005153656],[.830704987049103,.193813979625702],[.552385985851288,.302568018436432],[.607609987258911,.353887975215912],[.645429015159607,.696707010269165],[.932694971561432,.730105042457581],[.557260990142822,.572826027870178],[.542901992797852,.584792017936707],[.6180260181427,.694710969924927],[.607590973377228,.694203019142151],[.722943007946014,.271963000297546],[.577413976192474,.563166975975037],[.614082992076874,.281386971473694],[.616907000541687,.255886018276215],[.668509006500244,.119913995265961],[.770092010498047,.232020974159241],[.635536015033722,.189248979091644],[.77039098739624,.299556016921997],[.826722025871277,.278755009174347],[.527121007442474,.666198015213013],[.553171992301941,.668527007102966],[.577238023281097,.673889994621277],[.554691970348358,.580065965652466],[.611896991729736,.693961024284363],[.59696102142334,.706539988517761],[.596370995044708,.693953037261963],[.539958000183105,.557139039039612],[.568841993808746,.692366003990173],[.547818005084991,.692366003990173],[.52461302280426,.692366003990173],[.534089982509613,.779141008853912],[.527670979499817,.736225962638855],[.526912987232208,.717857003211975],[.526877999305725,.704625964164734],[.526966989040375,.695277988910675],[.572058022022247,.695277988910675],[.573521018028259,.703539967536926],[.57683801651001,.711845993995667],[.581691026687622,.720062971115112],[.609944999217987,.639909982681274],[.986046016216278,.560034036636353],[.5867999792099,.69539999961853],[.590372025966644,.701822996139526],[.531915009021759,.601536989212036],[.577268004417419,.585934996604919],[.536915004253387,.593786001205444],[.627542972564697,.473352015018463],[.665585994720459,.495950996875763],[.588353991508484,.546862006187439],[.757824003696442,.14767599105835],[.709249973297119,.201507985591888],[.672684013843536,.256581008434296],[.600408971309662,.74900496006012],[.55826598405838,.261672019958496],[.570303976535797,.187870979309082],[.588165998458862,.109044015407562],[.711045026779175,.398952007293701],[.781069993972778,.435405015945435],[.587247014045715,.398931980133057],[.742869973182678,.355445981025696],[.572156012058258,.437651991844177],[.55186802148819,.536570012569427],[.821442008018494,.457556009292603],[.752701997756958,.457181990146637],[.71375697851181,.467626988887787],[.66711300611496,.460672974586487],[.631101012229919,.447153985500336],[.6008620262146,.432473003864288],[.523481011390686,.405627012252808],[.810747981071472,.523926019668579],[.771045982837677,.348959028720856],[.509127020835876,.562718033790588],[.595292985439301,.485023975372314],[.980530977249146,.401564002037048],[.573499977588654,.420000016689301],[.602994978427887,.548687994480133],[.733529984951019,.376977026462555],[.560611009597778,.519016981124878],[.967685997486115,.644356966018677],[.580985009670258,.387160003185272],[.537728011608124,.505385041236877],[.760966002941132,.779752969741821],[.801778972148895,.831938028335571],[.892440974712372,.54076099395752],[.816350996494293,.740260004997253],[.865594983100891,.333687007427216],[.614073991775513,.883246004581451],[.508952975273132,.579437971115112],[.617941975593567,.508316040039062],[.825608015060425,.397674977779388],[.681214988231659,.39623498916626],[.656635999679565,.400596976280212],[.603900015354156,.710216999053955],[.81208598613739,.588539004325867],[.56801301240921,.944564998149872],[.681007981300354,.898285031318665],[.733752012252808,.869701027870178],[.633830010890961,.398822009563446],[.606792986392975,.39553701877594],[.589659988880157,.391062021255493],[.805015981197357,.342108011245728],[.611334979534149,.362284004688263],[.634037971496582,.355970978736877],[.656635999679565,.355356991291046],[.681214988231659,.35834002494812],[.698584973812103,.363156020641327],[.941866993904114,.319076001644135],[.698584973812103,.387449026107788],[.584177017211914,.624107003211975],[.554318010807037,.566076993942261],[.534153997898102,.62064003944397],[.711217999458313,.819975018501282],[.664629995822906,.852871000766754],[.559099972248077,.902631998062134],[.871706008911133,.791940987110138],[.591234028339386,.373893976211548],[.544341027736664,.451583981513977],[.624562978744507,.924192011356354],[.88577002286911,.615028977394104],[.551338016986847,.695277988910675],[.551980018615723,.704632043838501],[.552887976169586,.715808033943176],[.555167973041534,.730794012546539],[.569944024085999,.767035007476807],[.593203008174896,.685675978660583],[.599261999130249,.681069016456604],[.607599973678589,.677703022956848],[.631937980651855,.663500010967255],[.752032995223999,.601315021514893],[.547226011753082,.420395016670227],[.563543975353241,.359827995300293],[.583841025829315,.368713974952698],[.586614012718201,.692366003990173],[.771915018558502,.683578014373779],[.531597018241882,.352482974529266],[.588370978832245,.804440975189209],[.52079701423645,.442565023899078],[.567984998226166,.493479013442993],[.543282985687256,.819254994392395],[.655317008495331,.745514988899231],[.621008992195129,.574018001556396],[.625559985637665,.78031200170517],[.680198013782501,.570719003677368],[.64276397228241,.604337990283966],[.704662978649139,.621529996395111],[.552012026309967,.862591981887817],[.589071989059448,.508637011051178],[.685944974422455,.775357007980347],[.645735025405884,.812640011310577],[.675342977046967,.703978002071381],[.810858011245728,.646304965019226],[.72012197971344,.714666962623596],[.866151988506317,.682704985141754],[.663187026977539,.644596993923187],[.570082008838654,.466325998306274],[.544561982154846,.548375964164734],[.562758982181549,.558784961700439],[.531987011432648,.530140042304993],[.585271000862122,.335177004337311],[.622952997684479,.32277899980545],[.655896008014679,.320163011550903],[.687132000923157,.322345972061157],[.716481983661652,.333200991153717],[.758756995201111,.382786989212036],[.897013008594513,.468769013881683],[.732392013072968,.424547016620636],[.70211398601532,.433162987232208],[.66652500629425,.433866024017334],[.633504986763,.426087975502014],[.603875994682312,.416586995124817],[.579657971858978,.409945011138916],[.992439985275269,.480777025222778],[.567192018032074,.569419980049133],[.54136598110199,.478899002075195],[.526564002037048,.546118021011353],[.523913025856018,.563830018043518],[.531529009342194,.555056989192963],[.566035985946655,.582329034805298],[.51631098985672,.563053965568542],[.5174720287323,.577877044677734],[.573594987392426,.389806985855103],[.560697972774506,.395331978797913],[.549755990505219,.399751007556915],[.710287988185883,.368252992630005],[.723330020904541,.363372981548309]],vi=[127,34,139,11,0,37,232,231,120,72,37,39,128,121,47,232,121,128,104,69,67,175,171,148,157,154,155,118,50,101,73,39,40,9,151,108,48,115,131,194,204,211,74,40,185,80,42,183,40,92,186,230,229,118,202,212,214,83,18,17,76,61,146,160,29,30,56,157,173,106,204,194,135,214,192,203,165,98,21,71,68,51,45,4,144,24,23,77,146,91,205,50,187,201,200,18,91,106,182,90,91,181,85,84,17,206,203,36,148,171,140,92,40,39,193,189,244,159,158,28,247,246,161,236,3,196,54,68,104,193,168,8,117,228,31,189,193,55,98,97,99,126,47,100,166,79,218,155,154,26,209,49,131,135,136,150,47,126,217,223,52,53,45,51,134,211,170,140,67,69,108,43,106,91,230,119,120,226,130,247,63,53,52,238,20,242,46,70,156,78,62,96,46,53,63,143,34,227,173,155,133,123,117,111,44,125,19,236,134,51,216,206,205,154,153,22,39,37,167,200,201,208,36,142,100,57,212,202,20,60,99,28,158,157,35,226,113,160,159,27,204,202,210,113,225,46,43,202,204,62,76,77,137,123,116,41,38,72,203,129,142,64,98,240,49,102,64,41,73,74,212,216,207,42,74,184,169,170,211,170,149,176,105,66,69,122,6,168,123,147,187,96,77,90,65,55,107,89,90,180,101,100,120,63,105,104,93,137,227,15,86,85,129,102,49,14,87,86,55,8,9,100,47,121,145,23,22,88,89,179,6,122,196,88,95,96,138,172,136,215,58,172,115,48,219,42,80,81,195,3,51,43,146,61,171,175,199,81,82,38,53,46,225,144,163,110,246,33,7,52,65,66,229,228,117,34,127,234,107,108,69,109,108,151,48,64,235,62,78,191,129,209,126,111,35,143,163,161,246,117,123,50,222,65,52,19,125,141,221,55,65,3,195,197,25,7,33,220,237,44,70,71,139,122,193,245,247,130,33,71,21,162,153,158,159,170,169,150,188,174,196,216,186,92,144,160,161,2,97,167,141,125,241,164,167,37,72,38,12,145,159,160,38,82,13,63,68,71,226,35,111,158,153,154,101,50,205,206,92,165,209,198,217,165,167,97,220,115,218,133,112,243,239,238,241,214,135,169,190,173,133,171,208,32,125,44,237,86,87,178,85,86,179,84,85,180,83,84,181,201,83,182,137,93,132,76,62,183,61,76,184,57,61,185,212,57,186,214,207,187,34,143,156,79,239,237,123,137,177,44,1,4,201,194,32,64,102,129,213,215,138,59,166,219,242,99,97,2,94,141,75,59,235,24,110,228,25,130,226,23,24,229,22,23,230,26,22,231,112,26,232,189,190,243,221,56,190,28,56,221,27,28,222,29,27,223,30,29,224,247,30,225,238,79,20,166,59,75,60,75,240,147,177,215,20,79,166,187,147,213,112,233,244,233,128,245,128,114,188,114,217,174,131,115,220,217,198,236,198,131,134,177,132,58,143,35,124,110,163,7,228,110,25,356,389,368,11,302,267,452,350,349,302,303,269,357,343,277,452,453,357,333,332,297,175,152,377,384,398,382,347,348,330,303,304,270,9,336,337,278,279,360,418,262,431,304,408,409,310,415,407,270,409,410,450,348,347,422,430,434,313,314,17,306,307,375,387,388,260,286,414,398,335,406,418,364,367,416,423,358,327,251,284,298,281,5,4,373,374,253,307,320,321,425,427,411,421,313,18,321,405,406,320,404,405,315,16,17,426,425,266,377,400,369,322,391,269,417,465,464,386,257,258,466,260,388,456,399,419,284,332,333,417,285,8,346,340,261,413,441,285,327,460,328,355,371,329,392,439,438,382,341,256,429,420,360,364,394,379,277,343,437,443,444,283,275,440,363,431,262,369,297,338,337,273,375,321,450,451,349,446,342,467,293,334,282,458,461,462,276,353,383,308,324,325,276,300,293,372,345,447,382,398,362,352,345,340,274,1,19,456,248,281,436,427,425,381,256,252,269,391,393,200,199,428,266,330,329,287,273,422,250,462,328,258,286,384,265,353,342,387,259,257,424,431,430,342,353,276,273,335,424,292,325,307,366,447,345,271,303,302,423,266,371,294,455,460,279,278,294,271,272,304,432,434,427,272,407,408,394,430,431,395,369,400,334,333,299,351,417,168,352,280,411,325,319,320,295,296,336,319,403,404,330,348,349,293,298,333,323,454,447,15,16,315,358,429,279,14,15,316,285,336,9,329,349,350,374,380,252,318,402,403,6,197,419,318,319,325,367,364,365,435,367,397,344,438,439,272,271,311,195,5,281,273,287,291,396,428,199,311,271,268,283,444,445,373,254,339,263,466,249,282,334,296,449,347,346,264,447,454,336,296,299,338,10,151,278,439,455,292,407,415,358,371,355,340,345,372,390,249,466,346,347,280,442,443,282,19,94,370,441,442,295,248,419,197,263,255,359,440,275,274,300,383,368,351,412,465,263,467,466,301,368,389,380,374,386,395,378,379,412,351,419,436,426,322,373,390,388,2,164,393,370,462,461,164,0,267,302,11,12,374,373,387,268,12,13,293,300,301,446,261,340,385,384,381,330,266,425,426,423,391,429,355,437,391,327,326,440,457,438,341,382,362,459,457,461,434,430,394,414,463,362,396,369,262,354,461,457,316,403,402,315,404,403,314,405,404,313,406,405,421,418,406,366,401,361,306,408,407,291,409,408,287,410,409,432,436,410,434,416,411,264,368,383,309,438,457,352,376,401,274,275,4,421,428,262,294,327,358,433,416,367,289,455,439,462,370,326,2,326,370,305,460,455,254,449,448,255,261,446,253,450,449,252,451,450,256,452,451,341,453,452,413,464,463,441,413,414,258,442,441,257,443,442,259,444,443,260,445,444,467,342,445,459,458,250,289,392,290,290,328,460,376,433,435,250,290,392,411,416,433,341,463,464,453,464,465,357,465,412,343,412,399,360,363,440,437,399,456,420,456,363,401,435,288,372,383,353,339,255,249,448,261,255,133,243,190,133,155,112,33,246,247,33,130,25,398,384,286,362,398,414,362,463,341,263,359,467,263,249,255,466,467,260,75,60,166,238,239,79,162,127,139,72,11,37,121,232,120,73,72,39,114,128,47,233,232,128,103,104,67,152,175,148,173,157,155,119,118,101,74,73,40,107,9,108,49,48,131,32,194,211,184,74,185,191,80,183,185,40,186,119,230,118,210,202,214,84,83,17,77,76,146,161,160,30,190,56,173,182,106,194,138,135,192,129,203,98,54,21,68,5,51,4,145,144,23,90,77,91,207,205,187,83,201,18,181,91,182,180,90,181,16,85,17,205,206,36,176,148,140,165,92,39,245,193,244,27,159,28,30,247,161,174,236,196,103,54,104,55,193,8,111,117,31,221,189,55,240,98,99,142,126,100,219,166,218,112,155,26,198,209,131,169,135,150,114,47,217,224,223,53,220,45,134,32,211,140,109,67,108,146,43,91,231,230,120,113,226,247,105,63,52,241,238,242,124,46,156,95,78,96,70,46,63,116,143,227,116,123,111,1,44,19,3,236,51,207,216,205,26,154,22,165,39,167,199,200,208,101,36,100,43,57,202,242,20,99,56,28,157,124,35,113,29,160,27,211,204,210,124,113,46,106,43,204,96,62,77,227,137,116,73,41,72,36,203,142,235,64,240,48,49,64,42,41,74,214,212,207,183,42,184,210,169,211,140,170,176,104,105,69,193,122,168,50,123,187,89,96,90,66,65,107,179,89,180,119,101,120,68,63,104,234,93,227,16,15,85,209,129,49,15,14,86,107,55,9,120,100,121,153,145,22,178,88,179,197,6,196,89,88,96,135,138,136,138,215,172,218,115,219,41,42,81,5,195,51,57,43,61,208,171,199,41,81,38,224,53,225,24,144,110,105,52,66,118,229,117,227,34,234,66,107,69,10,109,151,219,48,235,183,62,191,142,129,126,116,111,143,7,163,246,118,117,50,223,222,52,94,19,141,222,221,65,196,3,197,45,220,44,156,70,139,188,122,245,139,71,162,145,153,159,149,170,150,122,188,196,206,216,92,163,144,161,164,2,167,242,141,241,0,164,37,11,72,12,144,145,160,12,38,13,70,63,71,31,226,111,157,158,154,36,101,205,203,206,165,126,209,217,98,165,97,237,220,218,237,239,241,210,214,169,140,171,32,241,125,237,179,86,178,180,85,179,181,84,180,182,83,181,194,201,182,177,137,132,184,76,183,185,61,184,186,57,185,216,212,186,192,214,187,139,34,156,218,79,237,147,123,177,45,44,4,208,201,32,98,64,129,192,213,138,235,59,219,141,242,97,97,2,141,240,75,235,229,24,228,31,25,226,230,23,229,231,22,230,232,26,231,233,112,232,244,189,243,189,221,190,222,28,221,223,27,222,224,29,223,225,30,224,113,247,225,99,60,240,213,147,215,60,20,166,192,187,213,243,112,244,244,233,245,245,128,188,188,114,174,134,131,220,174,217,236,236,198,134,215,177,58,156,143,124,25,110,7,31,228,25,264,356,368,0,11,267,451,452,349,267,302,269,350,357,277,350,452,357,299,333,297,396,175,377,381,384,382,280,347,330,269,303,270,151,9,337,344,278,360,424,418,431,270,304,409,272,310,407,322,270,410,449,450,347,432,422,434,18,313,17,291,306,375,259,387,260,424,335,418,434,364,416,391,423,327,301,251,298,275,281,4,254,373,253,375,307,321,280,425,411,200,421,18,335,321,406,321,320,405,314,315,17,423,426,266,396,377,369,270,322,269,413,417,464,385,386,258,248,456,419,298,284,333,168,417,8,448,346,261,417,413,285,326,327,328,277,355,329,309,392,438,381,382,256,279,429,360,365,364,379,355,277,437,282,443,283,281,275,363,395,431,369,299,297,337,335,273,321,348,450,349,359,446,467,283,293,282,250,458,462,300,276,383,292,308,325,283,276,293,264,372,447,346,352,340,354,274,19,363,456,281,426,436,425,380,381,252,267,269,393,421,200,428,371,266,329,432,287,422,290,250,328,385,258,384,446,265,342,386,387,257,422,424,430,445,342,276,422,273,424,306,292,307,352,366,345,268,271,302,358,423,371,327,294,460,331,279,294,303,271,304,436,432,427,304,272,408,395,394,431,378,395,400,296,334,299,6,351,168,376,352,411,307,325,320,285,295,336,320,319,404,329,330,349,334,293,333,366,323,447,316,15,315,331,358,279,317,14,316,8,285,9,277,329,350,253,374,252,319,318,403,351,6,419,324,318,325,397,367,365,288,435,397,278,344,439,310,272,311,248,195,281,375,273,291,175,396,199,312,311,268,276,283,445,390,373,339,295,282,296,448,449,346,356,264,454,337,336,299,337,338,151,294,278,455,308,292,415,429,358,355,265,340,372,388,390,466,352,346,280,295,442,282,354,19,370,285,441,295,195,248,197,457,440,274,301,300,368,417,351,465,251,301,389,385,380,386,394,395,379,399,412,419,410,436,322,387,373,388,326,2,393,354,370,461,393,164,267,268,302,12,386,374,387,312,268,13,298,293,301,265,446,340,380,385,381,280,330,425,322,426,391,420,429,437,393,391,326,344,440,438,458,459,461,364,434,394,428,396,262,274,354,457,317,316,402,316,315,403,315,314,404,314,313,405,313,421,406,323,366,361,292,306,407,306,291,408,291,287,409,287,432,410,427,434,411,372,264,383,459,309,457,366,352,401,1,274,4,418,421,262,331,294,358,435,433,367,392,289,439,328,462,326,94,2,370,289,305,455,339,254,448,359,255,446,254,253,449,253,252,450,252,256,451,256,341,452,414,413,463,286,441,414,286,258,441,258,257,442,257,259,443,259,260,444,260,467,445,309,459,250,305,289,290,305,290,460,401,376,435,309,250,392,376,411,433,453,341,464,357,453,465,343,357,412,437,343,399,344,360,440,420,437,456,360,420,363,361,401,288,265,372,353,390,339,249,339,448,255];var Lve=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Bve=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Wve=[33,133,362,263,1,78,308],b7e=Lve.map(e=>wh[e]),v7e=Bve.map(e=>wh[e]),w7e=Wve.map(e=>wh[e]);var kb=Bs.leftEyeLower0,Ib=Bs.rightEyeLower0,Cu={leftBounds:[kb[0],kb[kb.length-1]],rightBounds:[Ib[0],Ib[Ib.length-1]]},l0={count:468,mouth:13,symmetryLine:[13,Bs.midwayBetweenEyes[0]]},I$={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Eu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function u0(e,t,n,r){for(let s=0;s<wb.length;s++){let{key:a,indices:o}=wb[s],i=Bs[`${n}${a}`];if(!r||r.includes(a))for(let l=0;l<o.length;l++){let u=o[l];e[i[l]]=[t[u][0],t[u][1],(t[u][2]+e[i[l]][2])/2]}}}var Sb=class{constructor(t,n,r){var s,a;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,s){let a=vh({startPoint:n.startPoint,endPoint:n.endPoint}),o=t.map(d=>[a[0]/this.meshSize*(d[0]-this.meshSize/2),a[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),i=r!==0?i0(r,[0,0]):o0,l=r!==0?o.map(d=>[...x$(d,i),d[2]]):o,u=r!==0?A$(s):o0,c=[...Tu({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(d=>[Math.round(d[0]+no(c,u[0])),Math.round(d[1]+no(c,u[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(t){let n=t[Cu.leftBounds[0]][2],r=t[Cu.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,s,a=!1){let o=a0(s0(bb([t[r],t[s]]),this.irisEnlarge)),i=vh(o),l=Ye.cropAndResize(n,[[o.startPoint[1]/this.meshSize,o.startPoint[0]/this.meshSize,o.endPoint[1]/this.meshSize,o.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return a&&Sr.flags.IS_BROWSER&&(l=Ye.flipLeftRight(l)),{box:o,boxSize:i,crop:l}}getEyeCoords(t,n,r,s=!1){let a=[];for(let o=0;o<Eu.numCoordinates;o++){let i=t[o*3],l=t[o*3+1],u=t[o*3+2];a.push([(s?1-i/this.irisSize:i/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],u])}return{rawCoords:a,iris:a.slice(Eu.index)}}getAdjustedIrisCoords(t,n,r){let s=t[Bs[`${r}EyeUpper0`][Eu.upperCenter]][2],a=t[Bs[`${r}EyeLower0`][Eu.lowerCenter]][2],o=(s+a)/2;return n.map((i,l)=>{let u=o;return l===2?u=s:l===4&&(u=a),[i[0],i[1],u]})}async predict(t,n){let r=!1,s;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(s=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||s&&s.boxes&&(!n.face.mesh.enabled||s.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let o of s.boxes)this.storedBoxes.push({startPoint:o.box.startPoint.dataSync(),endPoint:o.box.endPoint.dataSync(),landmarks:o.landmarks.arraySync(),confidence:o.confidence});this.storedBoxes.length>0&&(r=!0)}if(r){if(!s||!s.boxes||s.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let o=0;o<this.storedBoxes.length;o++){let i=f$({startPoint:this.storedBoxes[o].startPoint,endPoint:this.storedBoxes[o].endPoint},s.scaleFactor),l=s0(i),u=a0(l),c=this.storedBoxes[o].landmarks,d=this.storedBoxes[o].confidence;this.storedBoxes[o]={...u,confidence:d,landmarks:c}}}s&&s.boxes&&s.boxes.forEach(o=>{o.box.startPoint.dispose(),o.box.endPoint.dispose(),o.landmarks.dispose()});let a=Ue(()=>this.storedBoxes.map((o,i)=>{let l,u=0,c;if(n.face.detector.rotation&&n.face.mesh.enabled&&Sr.flags.IS_BROWSER){let[x,b]=o.landmarks.length>=l0.count?l0.symmetryLine:I$.symmetryLine;u=vb(o.landmarks[x],o.landmarks[b]);let v=Tu({startPoint:o.startPoint,endPoint:o.endPoint}),w=[v[0]/t.shape[2],v[1]/t.shape[1]],S=Ye.rotateWithOffset(t,u,0,w);c=i0(-u,v),n.face.mesh.enabled?l=Nu({startPoint:o.startPoint,endPoint:o.endPoint},S,[this.meshSize,this.meshSize]).div(255):l=Nu({startPoint:o.startPoint,endPoint:o.endPoint},S,[this.boxSize,this.boxSize]).div(255)}else{c=o0;let x=t.clone();n.face.mesh.enabled?l=Nu({startPoint:o.startPoint,endPoint:o.endPoint},x,[this.meshSize,this.meshSize]).div(255):l=Nu({startPoint:o.startPoint,endPoint:o.endPoint},x,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:o,faceConfidence:null,boxConfidence:o.confidence,confidence:o.confidence,image:l};let[,d,h]=this.meshDetector.execute(l),p=d.dataSync()[0];if(p<n.face.detector.minConfidence)return this.storedBoxes[i].confidence=p,null;let m=ue(h,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:x,boxSize:b,crop:v}=this.getEyeBox(m,l,Cu.leftBounds[0],Cu.leftBounds[1],!0),{box:w,boxSize:S,crop:I}=this.getEyeBox(m,l,Cu.rightBounds[0],Cu.rightBounds[1]),F=this.irisModel.predict(an([v,I])).dataSync(),$=F.slice(0,Eu.numCoordinates*3),{rawCoords:_,iris:N}=this.getEyeCoords($,x,b,!0),P=F.slice(Eu.numCoordinates*3),{rawCoords:B,iris:j}=this.getEyeCoords(P,w,S),X=this.getLeftToRightEyeDepthDifference(m);Math.abs(X)<30?(u0(m,_,"left",null),u0(m,B,"right",null)):X<1?u0(m,_,"left",["EyeUpper0","EyeLower0"]):u0(m,B,"right",["EyeUpper0","EyeLower0"]);let Y=this.getAdjustedIrisCoords(m,N,"left"),ee=this.getAdjustedIrisCoords(m,j,"right");m=m.concat(Y).concat(ee)}let g=this.transformRawCoords(m,o,u,c),y=o.confidence;if(o=s0(bb(g),1.5),o.confidence=y,n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&Sr.flags.IS_BROWSER){let[x,b]=o.landmarks.length>=l0.count?l0.symmetryLine:I$.symmetryLine;u=vb(o.landmarks[x],o.landmarks[b]);let v=Tu({startPoint:o.startPoint,endPoint:o.endPoint}),w=[v[0]/t.shape[2],v[1]/t.shape[1]],S=Ye.rotateWithOffset(t.toFloat(),u,0,w);c=i0(-u,v),l=Nu({startPoint:o.startPoint,endPoint:o.endPoint},S,[this.meshSize,this.meshSize]).div(255)}let A={mesh:g,box:o,faceConfidence:p,boxConfidence:o.confidence,image:l};return this.storedBoxes[i]={...a0(o),confidence:o.confidence,faceConfidence:p},A}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(o=>o.confidence>n.face.detector.minConfidence)),this.detectedFaces=a.length,a}};var Zt=[null,null,null],Tb;async function S$(e,t){let n=await Tb.predict(e,t),r=[],s=0;for(let a of n||[]){if(!a||a.isDisposedInternal)continue;let o=a.mesh.map(c=>[c[0]/(e.shape[2]||0),c[1]/(e.shape[1]||0),c[2]/Tb.meshSize]),i={};if(a.mesh&&a.mesh.length>0)for(let c of Object.keys(Bs))i[c]=Bs[c].map(d=>a.mesh[d]);let l=a.box?[Math.trunc(Math.max(0,a.box.startPoint[0])),Math.trunc(Math.max(0,a.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,a.box.endPoint[0])-Math.max(0,a.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,a.box.endPoint[1])-Math.max(0,a.box.startPoint[1]))]:[0,0,0,0],u=a.box?[a.box.startPoint[0]/(e.shape[2]||0),a.box.startPoint[1]/(e.shape[1]||0),(a.box.endPoint[0]-a.box.startPoint[0])/(e.shape[2]||0),(a.box.endPoint[1]-a.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];r.push({id:s++,score:Math.round(100*a.faceConfidence||100*a.boxConfidence||0)/100,boxScore:Math.round(100*a.boxConfidence)/100,faceScore:Math.round(100*a.faceConfidence)/100,box:l,boxRaw:u,mesh:a.mesh,meshRaw:o,annotations:i,image:a.image,tensor:a.image}),a.coords&&a.coords.dispose()}return r}async function Nb(e){return!Zt[0]&&e.face.enabled||!Zt[1]&&e.face.mesh.enabled||!Zt[2]&&e.face.iris.enabled?(Zt=await Promise.all([!Zt[0]&&e.face.enabled?k$(e):null,!Zt[1]&&e.face.mesh.enabled?Et($t(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Zt[2]&&e.face.iris.enabled?Et($t(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Zt[1]||!Zt[1].modelUrl?me("load model failed:",e.face.mesh.modelPath):e.debug&&me("load model:",Zt[1].modelUrl)),e.face.iris.enabled&&(!Zt[2]||!Zt[2].modelUrl?me("load model failed:",e.face.iris.modelPath):e.debug&&me("load model:",Zt[2].modelUrl))):e.debug&&(Zt[0]&&me("cached model:",Zt[0].model.modelUrl),Zt[1]&&me("cached model:",Zt[1].modelUrl),Zt[2]&&me("cached model:",Zt[2].modelUrl)),Tb=new Sb(Zt[0],Zt[1],Zt[2]),Zt}var T$=vi,N$=wh;var xs,c0=[],C$=0,Cb=Number.MAX_SAFE_INTEGER;async function Eb(e){let t=$t(e.modelBasePath,e.face.description.modelPath);return xs?e.debug&&me("cached model:",t):(xs=await Et(t),xs?e.debug&&me("load model:",t):me("load model failed:",e.face.description.modelPath)),xs}function $b(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let r=5*e.map((a,o)=>Math.abs(e[o]-t[o])**n).reduce((a,o)=>a+o,0)**(1/n);return Math.max(0,100-r)/100}function E$(e,t,n=0){let r={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return r;for(let s of t)if(s.embedding&&s.name){let a=$b(e,s.embedding);a>n&&a>r.similarity&&(r={...s,similarity:a})}return r}function _b(e){return Ue(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Tt))return null;let r=[[.05,.15,.85,.85]];return xs.inputs[0].shape?(n.shape.length===3?Ye.cropAndResize(ea(n,0),r,[0],[xs.inputs[0].shape[2],xs.inputs[0].shape[1]]):Ye.cropAndResize(n,r,[0],[xs.inputs[0].shape[2],xs.inputs[0].shape[1]])).mul(255):null})}async function Rb(e,t,n,r){var s,a;return xs?Cb<t.face.description.skipFrames&&t.skipFrame&&C$===r&&((s=c0[n])==null?void 0:s.age)&&((a=c0[n])==null?void 0:a.age)>0?(Cb++,c0[n]):(Cb=0,new Promise(async o=>{let i=_b(e),l,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};t.face.description.enabled&&(l=await xs.predict(i)),Ve(i),l&&(Ue(()=>{let c=l.find(m=>m.shape[1]===1).dataSync(),d=Math.trunc(200*Math.abs(c[0]-.5))/100;d>t.face.description.minConfidence&&(u.gender=c[0]<=.5?"female":"male",u.genderScore=Math.min(.99,d));let h=l.find(m=>m.shape[1]===100).argMax(1).dataSync()[0],p=l.find(m=>m.shape[1]===100).dataSync();u.age=Math.round(p[h-1]>p[h+1]?10*h-100*p[h-1]:10*h+100*p[h+1])/10;let f=l.find(m=>m.shape[1]===1024);u.descriptor=[...f.dataSync()]}),l.forEach(c=>Ve(c))),c0[n]=u,C$=r,o(u)})):null}var Vve=["angry","disgust","fear","happy","sad","surprise","neutral"],bs,d0=[],$$=0,Db=Number.MAX_SAFE_INTEGER,Fb=[.2989,.587,.114];async function Mb(e){return bs?e.debug&&me("cached model:",bs.modelUrl):(bs=await Et($t(e.modelBasePath,e.face.emotion.modelPath)),!bs||!bs.modelUrl?me("load model failed:",e.face.emotion.modelPath):e.debug&&me("load model:",bs.modelUrl)),bs}async function Ob(e,t,n,r){return bs?Db<t.face.emotion.skipFrames&&t.skipFrame&&$$===r&&d0[n]&&d0[n].length>0?(Db++,d0[n]):(Db=0,new Promise(async s=>{let a=Ye.resizeBilinear(e,[bs.inputs[0].shape[2],bs.inputs[0].shape[1]],!1),[o,i,l]=ta(a,3,3);a.dispose();let u=fe(o,Fb[0]),c=fe(i,Fb[1]),d=fe(l,Fb[2]);o.dispose(),i.dispose(),l.dispose();let h=Z2([u,c,d]);u.dispose(),c.dispose(),d.dispose();let p=Ue(()=>h.sub(.5).mul(2));h.dispose();let f=[];if(t.face.emotion.enabled){let m=await bs.predict(p),g=m.dataSync();Ve(m);for(let y=0;y<g.length;y++)g[y]>t.face.emotion.minConfidence&&f.push({score:Math.min(.99,Math.trunc(100*g[y])/100),emotion:Vve[y]});f.sort((y,A)=>A.score-y.score)}p.dispose(),d0[n]=f,$$=r,s(f)})):null}var kh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_$=kh.length,Ih=kh.reduce((e,t,n)=>(e[t]=n,e),{}),Uve=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Hve=Uve.map(([e,t])=>[Ih[e],Ih[t]]),R$=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function D$(e){let t=e.reduce(({maxX:n,maxY:r,minX:s,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(r,i),minX:Math.min(s,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function F$(e,[t,n],[r,s]){let a=t/r,o=n/s,i=(u,c)=>({id:c,score:u.score,boxRaw:[u.box[0]/s,u.box[1]/r,u.box[2]/s,u.box[3]/r],box:[Math.trunc(u.box[0]*o),Math.trunc(u.box[1]*a),Math.trunc(u.box[2]*o),Math.trunc(u.box[3]*a)],keypoints:u.keypoints.map(({score:d,part:h,position:p})=>({score:d,part:h,position:[Math.trunc(p.x*o),Math.trunc(p.y*a)],positionRaw:[p.x/r,p.y/r]}))});return e.map((u,c)=>i(u,c))}var Pb=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function zb(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+_$)}}function Lb(e,t,n){let{heatmapY:r,heatmapX:s,id:a}=e,{y:o,x:i}=zb(r,s,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function Bb(e,t,n){return e<t?t:e>n?n:e}function M$(e,t,n,r){let s=n-e,a=r-t;return s*s+a*a}function Wb(e,t){return{x:e.x+t.x,y:e.y+t.y}}var h0=1,$u=16,Gve=50**2;function O$(e,t,n,r,s,a,o=2){let i=y=>({y:a.get(y.y,y.x,e),x:a.get(y.y,y.x,a.shape[2]/2+e)}),l=(y,A,x)=>({y:Bb(Math.round(y.y/$u),0,A-1),x:Bb(Math.round(y.x/$u),0,x-1)}),[u,c]=r.shape,d=l(t.position,u,c),h=i(d),f=Wb(t.position,h);for(let y=0;y<o;y++){let A=l(f,u,c),x=zb(A.y,A.x,n,s);f=Wb({x:A.x*$u,y:A.y*$u},{x:x.x,y:x.y})}let m=l(f,u,c),g=r.get(m.y,m.x,n);return{position:f,part:kh[n],score:g}}function jve(e,t,n,r,s){let a=R$.map(([h,p])=>[Ih[h],Ih[p]]),o=a.map(([,h])=>h),i=a.map(([h])=>h),l=t.shape[2],u=o.length,c=new Array(l),d=Lb(e.part,$u,n);c[e.part.id]={score:e.score,part:kh[e.part.id],position:d};for(let h=u-1;h>=0;--h){let p=o[h],f=i[h];c[p]&&!c[f]&&(c[f]=O$(h,c[p],f,t,n,s))}for(let h=0;h<u;++h){let p=i[h],f=o[h];c[p]&&!c[f]&&(c[f]=O$(h,c[p],f,t,n,r))}return c}function qve(e,t,n,r,s){let[a,o]=s.shape,i=!0,l=Math.max(n-h0,0),u=Math.min(n+h0+1,a);for(let c=l;c<u;++c){let d=Math.max(r-h0,0),h=Math.min(r+h0+1,o);for(let p=d;p<h;++p)if(s.get(c,p,e)>t){i=!1;break}if(!i)break}return i}function Kve(e,t){let[n,r,s]=t.shape,a=new Pb(n*r*s,({score:o})=>o);for(let o=0;o<n;++o)for(let i=0;i<r;++i)for(let l=0;l<s;++l){let u=t.get(o,i,l);u<e||qve(l,u,o,i,t)&&a.enqueue({score:u,part:{heatmapY:o,heatmapX:i,id:l}})}return a}function P$(e,{x:t,y:n},r){return e.some(({keypoints:s})=>{var o;let a=(o=s[r])==null?void 0:o.position;return a?M$(n,t,a.y,a.x)<=Gve:!1})}function Xve(e,t){return t.reduce((r,{position:s,score:a},o)=>(P$(e,s,o)||(r+=a),r),0)/t.length}function z$(e,t,n,r,s,a){let o=[],i=Kve(a,t);for(;o.length<s&&!i.empty();){let l=i.dequeue(),u=Lb(l.part,$u,e);if(P$(o,u,l.part.id))continue;let c=jve(l,t,e,n,r);c=c.filter(p=>p.score>a);let d=Xve(o,c),h=D$(c);d>a&&o.push({keypoints:c,box:h,score:Math.round(100*d)/100})}return o}var yr,Zve=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"];async function Vb(e,t){let n=Ue(()=>{if(!yr.inputs[0].shape)return[];let i=Ye.resizeBilinear(e,[yr.inputs[0].shape[2],yr.inputs[0].shape[1]]).toFloat().div(127.5).sub(1),u=yr.execute(i,Zve).map(c=>Zn(c,[0]));return u[1]=u[1].sigmoid(),u}),r=await Promise.all(n.map(o=>o.buffer()));for(let o of n)o.dispose();let s=await z$(r[0],r[1],r[2],r[3],t.body.maxDetected,t.body.minConfidence);return yr.inputs[0].shape?F$(s,[e.shape[1],e.shape[2]],[yr.inputs[0].shape[2],yr.inputs[0].shape[1]]):[]}async function Ub(e){return yr?e.debug&&me("cached model:",yr.modelUrl):(yr=await Et($t(e.modelBasePath,e.body.modelPath)),!yr||!yr.modelUrl?me("load model failed:",e.body.modelPath):e.debug&&me("load model:",yr.modelUrl)),yr}function p0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Sh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function L$(e,t,n){let r=t.shape[1],s=t.shape[2],a=[[e.startPoint[1]/r,e.startPoint[0]/s,e.endPoint[1]/r,e.endPoint[0]/s]];return Ye.cropAndResize(t,a,[0],n)}function B$(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:s,confidence:e.confidence}}function f0(e,t=1.5){let n=Sh(e),r=p0(e),s=[t*r[0]/2,t*r[1]/2],a=[n[0]-s[0],n[1]-s[1]],o=[n[0]+s[0],n[1]+s[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function m0(e){let t=Sh(e),n=p0(e),s=Math.max(...n)/2,a=[t[0]-s,t[1]-s],o=[t[0]+s,t[1]+s];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}var W$=[{x:.015625,y:.015625},{x:.015625,y:.015625},{x:.046875,y:.015625},{x:.046875,y:.015625},{x:.078125,y:.015625},{x:.078125,y:.015625},{x:.109375,y:.015625},{x:.109375,y:.015625},{x:.140625,y:.015625},{x:.140625,y:.015625},{x:.171875,y:.015625},{x:.171875,y:.015625},{x:.203125,y:.015625},{x:.203125,y:.015625},{x:.234375,y:.015625},{x:.234375,y:.015625},{x:.265625,y:.015625},{x:.265625,y:.015625},{x:.296875,y:.015625},{x:.296875,y:.015625},{x:.328125,y:.015625},{x:.328125,y:.015625},{x:.359375,y:.015625},{x:.359375,y:.015625},{x:.390625,y:.015625},{x:.390625,y:.015625},{x:.421875,y:.015625},{x:.421875,y:.015625},{x:.453125,y:.015625},{x:.453125,y:.015625},{x:.484375,y:.015625},{x:.484375,y:.015625},{x:.515625,y:.015625},{x:.515625,y:.015625},{x:.546875,y:.015625},{x:.546875,y:.015625},{x:.578125,y:.015625},{x:.578125,y:.015625},{x:.609375,y:.015625},{x:.609375,y:.015625},{x:.640625,y:.015625},{x:.640625,y:.015625},{x:.671875,y:.015625},{x:.671875,y:.015625},{x:.703125,y:.015625},{x:.703125,y:.015625},{x:.734375,y:.015625},{x:.734375,y:.015625},{x:.765625,y:.015625},{x:.765625,y:.015625},{x:.796875,y:.015625},{x:.796875,y:.015625},{x:.828125,y:.015625},{x:.828125,y:.015625},{x:.859375,y:.015625},{x:.859375,y:.015625},{x:.890625,y:.015625},{x:.890625,y:.015625},{x:.921875,y:.015625},{x:.921875,y:.015625},{x:.953125,y:.015625},{x:.953125,y:.015625},{x:.984375,y:.015625},{x:.984375,y:.015625},{x:.015625,y:.046875},{x:.015625,y:.046875},{x:.046875,y:.046875},{x:.046875,y:.046875},{x:.078125,y:.046875},{x:.078125,y:.046875},{x:.109375,y:.046875},{x:.109375,y:.046875},{x:.140625,y:.046875},{x:.140625,y:.046875},{x:.171875,y:.046875},{x:.171875,y:.046875},{x:.203125,y:.046875},{x:.203125,y:.046875},{x:.234375,y:.046875},{x:.234375,y:.046875},{x:.265625,y:.046875},{x:.265625,y:.046875},{x:.296875,y:.046875},{x:.296875,y:.046875},{x:.328125,y:.046875},{x:.328125,y:.046875},{x:.359375,y:.046875},{x:.359375,y:.046875},{x:.390625,y:.046875},{x:.390625,y:.046875},{x:.421875,y:.046875},{x:.421875,y:.046875},{x:.453125,y:.046875},{x:.453125,y:.046875},{x:.484375,y:.046875},{x:.484375,y:.046875},{x:.515625,y:.046875},{x:.515625,y:.046875},{x:.546875,y:.046875},{x:.546875,y:.046875},{x:.578125,y:.046875},{x:.578125,y:.046875},{x:.609375,y:.046875},{x:.609375,y:.046875},{x:.640625,y:.046875},{x:.640625,y:.046875},{x:.671875,y:.046875},{x:.671875,y:.046875},{x:.703125,y:.046875},{x:.703125,y:.046875},{x:.734375,y:.046875},{x:.734375,y:.046875},{x:.765625,y:.046875},{x:.765625,y:.046875},{x:.796875,y:.046875},{x:.796875,y:.046875},{x:.828125,y:.046875},{x:.828125,y:.046875},{x:.859375,y:.046875},{x:.859375,y:.046875},{x:.890625,y:.046875},{x:.890625,y:.046875},{x:.921875,y:.046875},{x:.921875,y:.046875},{x:.953125,y:.046875},{x:.953125,y:.046875},{x:.984375,y:.046875},{x:.984375,y:.046875},{x:.015625,y:.078125},{x:.015625,y:.078125},{x:.046875,y:.078125},{x:.046875,y:.078125},{x:.078125,y:.078125},{x:.078125,y:.078125},{x:.109375,y:.078125},{x:.109375,y:.078125},{x:.140625,y:.078125},{x:.140625,y:.078125},{x:.171875,y:.078125},{x:.171875,y:.078125},{x:.203125,y:.078125},{x:.203125,y:.078125},{x:.234375,y:.078125},{x:.234375,y:.078125},{x:.265625,y:.078125},{x:.265625,y:.078125},{x:.296875,y:.078125},{x:.296875,y:.078125},{x:.328125,y:.078125},{x:.328125,y:.078125},{x:.359375,y:.078125},{x:.359375,y:.078125},{x:.390625,y:.078125},{x:.390625,y:.078125},{x:.421875,y:.078125},{x:.421875,y:.078125},{x:.453125,y:.078125},{x:.453125,y:.078125},{x:.484375,y:.078125},{x:.484375,y:.078125},{x:.515625,y:.078125},{x:.515625,y:.078125},{x:.546875,y:.078125},{x:.546875,y:.078125},{x:.578125,y:.078125},{x:.578125,y:.078125},{x:.609375,y:.078125},{x:.609375,y:.078125},{x:.640625,y:.078125},{x:.640625,y:.078125},{x:.671875,y:.078125},{x:.671875,y:.078125},{x:.703125,y:.078125},{x:.703125,y:.078125},{x:.734375,y:.078125},{x:.734375,y:.078125},{x:.765625,y:.078125},{x:.765625,y:.078125},{x:.796875,y:.078125},{x:.796875,y:.078125},{x:.828125,y:.078125},{x:.828125,y:.078125},{x:.859375,y:.078125},{x:.859375,y:.078125},{x:.890625,y:.078125},{x:.890625,y:.078125},{x:.921875,y:.078125},{x:.921875,y:.078125},{x:.953125,y:.078125},{x:.953125,y:.078125},{x:.984375,y:.078125},{x:.984375,y:.078125},{x:.015625,y:.109375},{x:.015625,y:.109375},{x:.046875,y:.109375},{x:.046875,y:.109375},{x:.078125,y:.109375},{x:.078125,y:.109375},{x:.109375,y:.109375},{x:.109375,y:.109375},{x:.140625,y:.109375},{x:.140625,y:.109375},{x:.171875,y:.109375},{x:.171875,y:.109375},{x:.203125,y:.109375},{x:.203125,y:.109375},{x:.234375,y:.109375},{x:.234375,y:.109375},{x:.265625,y:.109375},{x:.265625,y:.109375},{x:.296875,y:.109375},{x:.296875,y:.109375},{x:.328125,y:.109375},{x:.328125,y:.109375},{x:.359375,y:.109375},{x:.359375,y:.109375},{x:.390625,y:.109375},{x:.390625,y:.109375},{x:.421875,y:.109375},{x:.421875,y:.109375},{x:.453125,y:.109375},{x:.453125,y:.109375},{x:.484375,y:.109375},{x:.484375,y:.109375},{x:.515625,y:.109375},{x:.515625,y:.109375},{x:.546875,y:.109375},{x:.546875,y:.109375},{x:.578125,y:.109375},{x:.578125,y:.109375},{x:.609375,y:.109375},{x:.609375,y:.109375},{x:.640625,y:.109375},{x:.640625,y:.109375},{x:.671875,y:.109375},{x:.671875,y:.109375},{x:.703125,y:.109375},{x:.703125,y:.109375},{x:.734375,y:.109375},{x:.734375,y:.109375},{x:.765625,y:.109375},{x:.765625,y:.109375},{x:.796875,y:.109375},{x:.796875,y:.109375},{x:.828125,y:.109375},{x:.828125,y:.109375},{x:.859375,y:.109375},{x:.859375,y:.109375},{x:.890625,y:.109375},{x:.890625,y:.109375},{x:.921875,y:.109375},{x:.921875,y:.109375},{x:.953125,y:.109375},{x:.953125,y:.109375},{x:.984375,y:.109375},{x:.984375,y:.109375},{x:.015625,y:.140625},{x:.015625,y:.140625},{x:.046875,y:.140625},{x:.046875,y:.140625},{x:.078125,y:.140625},{x:.078125,y:.140625},{x:.109375,y:.140625},{x:.109375,y:.140625},{x:.140625,y:.140625},{x:.140625,y:.140625},{x:.171875,y:.140625},{x:.171875,y:.140625},{x:.203125,y:.140625},{x:.203125,y:.140625},{x:.234375,y:.140625},{x:.234375,y:.140625},{x:.265625,y:.140625},{x:.265625,y:.140625},{x:.296875,y:.140625},{x:.296875,y:.140625},{x:.328125,y:.140625},{x:.328125,y:.140625},{x:.359375,y:.140625},{x:.359375,y:.140625},{x:.390625,y:.140625},{x:.390625,y:.140625},{x:.421875,y:.140625},{x:.421875,y:.140625},{x:.453125,y:.140625},{x:.453125,y:.140625},{x:.484375,y:.140625},{x:.484375,y:.140625},{x:.515625,y:.140625},{x:.515625,y:.140625},{x:.546875,y:.140625},{x:.546875,y:.140625},{x:.578125,y:.140625},{x:.578125,y:.140625},{x:.609375,y:.140625},{x:.609375,y:.140625},{x:.640625,y:.140625},{x:.640625,y:.140625},{x:.671875,y:.140625},{x:.671875,y:.140625},{x:.703125,y:.140625},{x:.703125,y:.140625},{x:.734375,y:.140625},{x:.734375,y:.140625},{x:.765625,y:.140625},{x:.765625,y:.140625},{x:.796875,y:.140625},{x:.796875,y:.140625},{x:.828125,y:.140625},{x:.828125,y:.140625},{x:.859375,y:.140625},{x:.859375,y:.140625},{x:.890625,y:.140625},{x:.890625,y:.140625},{x:.921875,y:.140625},{x:.921875,y:.140625},{x:.953125,y:.140625},{x:.953125,y:.140625},{x:.984375,y:.140625},{x:.984375,y:.140625},{x:.015625,y:.171875},{x:.015625,y:.171875},{x:.046875,y:.171875},{x:.046875,y:.171875},{x:.078125,y:.171875},{x:.078125,y:.171875},{x:.109375,y:.171875},{x:.109375,y:.171875},{x:.140625,y:.171875},{x:.140625,y:.171875},{x:.171875,y:.171875},{x:.171875,y:.171875},{x:.203125,y:.171875},{x:.203125,y:.171875},{x:.234375,y:.171875},{x:.234375,y:.171875},{x:.265625,y:.171875},{x:.265625,y:.171875},{x:.296875,y:.171875},{x:.296875,y:.171875},{x:.328125,y:.171875},{x:.328125,y:.171875},{x:.359375,y:.171875},{x:.359375,y:.171875},{x:.390625,y:.171875},{x:.390625,y:.171875},{x:.421875,y:.171875},{x:.421875,y:.171875},{x:.453125,y:.171875},{x:.453125,y:.171875},{x:.484375,y:.171875},{x:.484375,y:.171875},{x:.515625,y:.171875},{x:.515625,y:.171875},{x:.546875,y:.171875},{x:.546875,y:.171875},{x:.578125,y:.171875},{x:.578125,y:.171875},{x:.609375,y:.171875},{x:.609375,y:.171875},{x:.640625,y:.171875},{x:.640625,y:.171875},{x:.671875,y:.171875},{x:.671875,y:.171875},{x:.703125,y:.171875},{x:.703125,y:.171875},{x:.734375,y:.171875},{x:.734375,y:.171875},{x:.765625,y:.171875},{x:.765625,y:.171875},{x:.796875,y:.171875},{x:.796875,y:.171875},{x:.828125,y:.171875},{x:.828125,y:.171875},{x:.859375,y:.171875},{x:.859375,y:.171875},{x:.890625,y:.171875},{x:.890625,y:.171875},{x:.921875,y:.171875},{x:.921875,y:.171875},{x:.953125,y:.171875},{x:.953125,y:.171875},{x:.984375,y:.171875},{x:.984375,y:.171875},{x:.015625,y:.203125},{x:.015625,y:.203125},{x:.046875,y:.203125},{x:.046875,y:.203125},{x:.078125,y:.203125},{x:.078125,y:.203125},{x:.109375,y:.203125},{x:.109375,y:.203125},{x:.140625,y:.203125},{x:.140625,y:.203125},{x:.171875,y:.203125},{x:.171875,y:.203125},{x:.203125,y:.203125},{x:.203125,y:.203125},{x:.234375,y:.203125},{x:.234375,y:.203125},{x:.265625,y:.203125},{x:.265625,y:.203125},{x:.296875,y:.203125},{x:.296875,y:.203125},{x:.328125,y:.203125},{x:.328125,y:.203125},{x:.359375,y:.203125},{x:.359375,y:.203125},{x:.390625,y:.203125},{x:.390625,y:.203125},{x:.421875,y:.203125},{x:.421875,y:.203125},{x:.453125,y:.203125},{x:.453125,y:.203125},{x:.484375,y:.203125},{x:.484375,y:.203125},{x:.515625,y:.203125},{x:.515625,y:.203125},{x:.546875,y:.203125},{x:.546875,y:.203125},{x:.578125,y:.203125},{x:.578125,y:.203125},{x:.609375,y:.203125},{x:.609375,y:.203125},{x:.640625,y:.203125},{x:.640625,y:.203125},{x:.671875,y:.203125},{x:.671875,y:.203125},{x:.703125,y:.203125},{x:.703125,y:.203125},{x:.734375,y:.203125},{x:.734375,y:.203125},{x:.765625,y:.203125},{x:.765625,y:.203125},{x:.796875,y:.203125},{x:.796875,y:.203125},{x:.828125,y:.203125},{x:.828125,y:.203125},{x:.859375,y:.203125},{x:.859375,y:.203125},{x:.890625,y:.203125},{x:.890625,y:.203125},{x:.921875,y:.203125},{x:.921875,y:.203125},{x:.953125,y:.203125},{x:.953125,y:.203125},{x:.984375,y:.203125},{x:.984375,y:.203125},{x:.015625,y:.234375},{x:.015625,y:.234375},{x:.046875,y:.234375},{x:.046875,y:.234375},{x:.078125,y:.234375},{x:.078125,y:.234375},{x:.109375,y:.234375},{x:.109375,y:.234375},{x:.140625,y:.234375},{x:.140625,y:.234375},{x:.171875,y:.234375},{x:.171875,y:.234375},{x:.203125,y:.234375},{x:.203125,y:.234375},{x:.234375,y:.234375},{x:.234375,y:.234375},{x:.265625,y:.234375},{x:.265625,y:.234375},{x:.296875,y:.234375},{x:.296875,y:.234375},{x:.328125,y:.234375},{x:.328125,y:.234375},{x:.359375,y:.234375},{x:.359375,y:.234375},{x:.390625,y:.234375},{x:.390625,y:.234375},{x:.421875,y:.234375},{x:.421875,y:.234375},{x:.453125,y:.234375},{x:.453125,y:.234375},{x:.484375,y:.234375},{x:.484375,y:.234375},{x:.515625,y:.234375},{x:.515625,y:.234375},{x:.546875,y:.234375},{x:.546875,y:.234375},{x:.578125,y:.234375},{x:.578125,y:.234375},{x:.609375,y:.234375},{x:.609375,y:.234375},{x:.640625,y:.234375},{x:.640625,y:.234375},{x:.671875,y:.234375},{x:.671875,y:.234375},{x:.703125,y:.234375},{x:.703125,y:.234375},{x:.734375,y:.234375},{x:.734375,y:.234375},{x:.765625,y:.234375},{x:.765625,y:.234375},{x:.796875,y:.234375},{x:.796875,y:.234375},{x:.828125,y:.234375},{x:.828125,y:.234375},{x:.859375,y:.234375},{x:.859375,y:.234375},{x:.890625,y:.234375},{x:.890625,y:.234375},{x:.921875,y:.234375},{x:.921875,y:.234375},{x:.953125,y:.234375},{x:.953125,y:.234375},{x:.984375,y:.234375},{x:.984375,y:.234375},{x:.015625,y:.265625},{x:.015625,y:.265625},{x:.046875,y:.265625},{x:.046875,y:.265625},{x:.078125,y:.265625},{x:.078125,y:.265625},{x:.109375,y:.265625},{x:.109375,y:.265625},{x:.140625,y:.265625},{x:.140625,y:.265625},{x:.171875,y:.265625},{x:.171875,y:.265625},{x:.203125,y:.265625},{x:.203125,y:.265625},{x:.234375,y:.265625},{x:.234375,y:.265625},{x:.265625,y:.265625},{x:.265625,y:.265625},{x:.296875,y:.265625},{x:.296875,y:.265625},{x:.328125,y:.265625},{x:.328125,y:.265625},{x:.359375,y:.265625},{x:.359375,y:.265625},{x:.390625,y:.265625},{x:.390625,y:.265625},{x:.421875,y:.265625},{x:.421875,y:.265625},{x:.453125,y:.265625},{x:.453125,y:.265625},{x:.484375,y:.265625},{x:.484375,y:.265625},{x:.515625,y:.265625},{x:.515625,y:.265625},{x:.546875,y:.265625},{x:.546875,y:.265625},{x:.578125,y:.265625},{x:.578125,y:.265625},{x:.609375,y:.265625},{x:.609375,y:.265625},{x:.640625,y:.265625},{x:.640625,y:.265625},{x:.671875,y:.265625},{x:.671875,y:.265625},{x:.703125,y:.265625},{x:.703125,y:.265625},{x:.734375,y:.265625},{x:.734375,y:.265625},{x:.765625,y:.265625},{x:.765625,y:.265625},{x:.796875,y:.265625},{x:.796875,y:.265625},{x:.828125,y:.265625},{x:.828125,y:.265625},{x:.859375,y:.265625},{x:.859375,y:.265625},{x:.890625,y:.265625},{x:.890625,y:.265625},{x:.921875,y:.265625},{x:.921875,y:.265625},{x:.953125,y:.265625},{x:.953125,y:.265625},{x:.984375,y:.265625},{x:.984375,y:.265625},{x:.015625,y:.296875},{x:.015625,y:.296875},{x:.046875,y:.296875},{x:.046875,y:.296875},{x:.078125,y:.296875},{x:.078125,y:.296875},{x:.109375,y:.296875},{x:.109375,y:.296875},{x:.140625,y:.296875},{x:.140625,y:.296875},{x:.171875,y:.296875},{x:.171875,y:.296875},{x:.203125,y:.296875},{x:.203125,y:.296875},{x:.234375,y:.296875},{x:.234375,y:.296875},{x:.265625,y:.296875},{x:.265625,y:.296875},{x:.296875,y:.296875},{x:.296875,y:.296875},{x:.328125,y:.296875},{x:.328125,y:.296875},{x:.359375,y:.296875},{x:.359375,y:.296875},{x:.390625,y:.296875},{x:.390625,y:.296875},{x:.421875,y:.296875},{x:.421875,y:.296875},{x:.453125,y:.296875},{x:.453125,y:.296875},{x:.484375,y:.296875},{x:.484375,y:.296875},{x:.515625,y:.296875},{x:.515625,y:.296875},{x:.546875,y:.296875},{x:.546875,y:.296875},{x:.578125,y:.296875},{x:.578125,y:.296875},{x:.609375,y:.296875},{x:.609375,y:.296875},{x:.640625,y:.296875},{x:.640625,y:.296875},{x:.671875,y:.296875},{x:.671875,y:.296875},{x:.703125,y:.296875},{x:.703125,y:.296875},{x:.734375,y:.296875},{x:.734375,y:.296875},{x:.765625,y:.296875},{x:.765625,y:.296875},{x:.796875,y:.296875},{x:.796875,y:.296875},{x:.828125,y:.296875},{x:.828125,y:.296875},{x:.859375,y:.296875},{x:.859375,y:.296875},{x:.890625,y:.296875},{x:.890625,y:.296875},{x:.921875,y:.296875},{x:.921875,y:.296875},{x:.953125,y:.296875},{x:.953125,y:.296875},{x:.984375,y:.296875},{x:.984375,y:.296875},{x:.015625,y:.328125},{x:.015625,y:.328125},{x:.046875,y:.328125},{x:.046875,y:.328125},{x:.078125,y:.328125},{x:.078125,y:.328125},{x:.109375,y:.328125},{x:.109375,y:.328125},{x:.140625,y:.328125},{x:.140625,y:.328125},{x:.171875,y:.328125},{x:.171875,y:.328125},{x:.203125,y:.328125},{x:.203125,y:.328125},{x:.234375,y:.328125},{x:.234375,y:.328125},{x:.265625,y:.328125},{x:.265625,y:.328125},{x:.296875,y:.328125},{x:.296875,y:.328125},{x:.328125,y:.328125},{x:.328125,y:.328125},{x:.359375,y:.328125},{x:.359375,y:.328125},{x:.390625,y:.328125},{x:.390625,y:.328125},{x:.421875,y:.328125},{x:.421875,y:.328125},{x:.453125,y:.328125},{x:.453125,y:.328125},{x:.484375,y:.328125},{x:.484375,y:.328125},{x:.515625,y:.328125},{x:.515625,y:.328125},{x:.546875,y:.328125},{x:.546875,y:.328125},{x:.578125,y:.328125},{x:.578125,y:.328125},{x:.609375,y:.328125},{x:.609375,y:.328125},{x:.640625,y:.328125},{x:.640625,y:.328125},{x:.671875,y:.328125},{x:.671875,y:.328125},{x:.703125,y:.328125},{x:.703125,y:.328125},{x:.734375,y:.328125},{x:.734375,y:.328125},{x:.765625,y:.328125},{x:.765625,y:.328125},{x:.796875,y:.328125},{x:.796875,y:.328125},{x:.828125,y:.328125},{x:.828125,y:.328125},{x:.859375,y:.328125},{x:.859375,y:.328125},{x:.890625,y:.328125},{x:.890625,y:.328125},{x:.921875,y:.328125},{x:.921875,y:.328125},{x:.953125,y:.328125},{x:.953125,y:.328125},{x:.984375,y:.328125},{x:.984375,y:.328125},{x:.015625,y:.359375},{x:.015625,y:.359375},{x:.046875,y:.359375},{x:.046875,y:.359375},{x:.078125,y:.359375},{x:.078125,y:.359375},{x:.109375,y:.359375},{x:.109375,y:.359375},{x:.140625,y:.359375},{x:.140625,y:.359375},{x:.171875,y:.359375},{x:.171875,y:.359375},{x:.203125,y:.359375},{x:.203125,y:.359375},{x:.234375,y:.359375},{x:.234375,y:.359375},{x:.265625,y:.359375},{x:.265625,y:.359375},{x:.296875,y:.359375},{x:.296875,y:.359375},{x:.328125,y:.359375},{x:.328125,y:.359375},{x:.359375,y:.359375},{x:.359375,y:.359375},{x:.390625,y:.359375},{x:.390625,y:.359375},{x:.421875,y:.359375},{x:.421875,y:.359375},{x:.453125,y:.359375},{x:.453125,y:.359375},{x:.484375,y:.359375},{x:.484375,y:.359375},{x:.515625,y:.359375},{x:.515625,y:.359375},{x:.546875,y:.359375},{x:.546875,y:.359375},{x:.578125,y:.359375},{x:.578125,y:.359375},{x:.609375,y:.359375},{x:.609375,y:.359375},{x:.640625,y:.359375},{x:.640625,y:.359375},{x:.671875,y:.359375},{x:.671875,y:.359375},{x:.703125,y:.359375},{x:.703125,y:.359375},{x:.734375,y:.359375},{x:.734375,y:.359375},{x:.765625,y:.359375},{x:.765625,y:.359375},{x:.796875,y:.359375},{x:.796875,y:.359375},{x:.828125,y:.359375},{x:.828125,y:.359375},{x:.859375,y:.359375},{x:.859375,y:.359375},{x:.890625,y:.359375},{x:.890625,y:.359375},{x:.921875,y:.359375},{x:.921875,y:.359375},{x:.953125,y:.359375},{x:.953125,y:.359375},{x:.984375,y:.359375},{x:.984375,y:.359375},{x:.015625,y:.390625},{x:.015625,y:.390625},{x:.046875,y:.390625},{x:.046875,y:.390625},{x:.078125,y:.390625},{x:.078125,y:.390625},{x:.109375,y:.390625},{x:.109375,y:.390625},{x:.140625,y:.390625},{x:.140625,y:.390625},{x:.171875,y:.390625},{x:.171875,y:.390625},{x:.203125,y:.390625},{x:.203125,y:.390625},{x:.234375,y:.390625},{x:.234375,y:.390625},{x:.265625,y:.390625},{x:.265625,y:.390625},{x:.296875,y:.390625},{x:.296875,y:.390625},{x:.328125,y:.390625},{x:.328125,y:.390625},{x:.359375,y:.390625},{x:.359375,y:.390625},{x:.390625,y:.390625},{x:.390625,y:.390625},{x:.421875,y:.390625},{x:.421875,y:.390625},{x:.453125,y:.390625},{x:.453125,y:.390625},{x:.484375,y:.390625},{x:.484375,y:.390625},{x:.515625,y:.390625},{x:.515625,y:.390625},{x:.546875,y:.390625},{x:.546875,y:.390625},{x:.578125,y:.390625},{x:.578125,y:.390625},{x:.609375,y:.390625},{x:.609375,y:.390625},{x:.640625,y:.390625},{x:.640625,y:.390625},{x:.671875,y:.390625},{x:.671875,y:.390625},{x:.703125,y:.390625},{x:.703125,y:.390625},{x:.734375,y:.390625},{x:.734375,y:.390625},{x:.765625,y:.390625},{x:.765625,y:.390625},{x:.796875,y:.390625},{x:.796875,y:.390625},{x:.828125,y:.390625},{x:.828125,y:.390625},{x:.859375,y:.390625},{x:.859375,y:.390625},{x:.890625,y:.390625},{x:.890625,y:.390625},{x:.921875,y:.390625},{x:.921875,y:.390625},{x:.953125,y:.390625},{x:.953125,y:.390625},{x:.984375,y:.390625},{x:.984375,y:.390625},{x:.015625,y:.421875},{x:.015625,y:.421875},{x:.046875,y:.421875},{x:.046875,y:.421875},{x:.078125,y:.421875},{x:.078125,y:.421875},{x:.109375,y:.421875},{x:.109375,y:.421875},{x:.140625,y:.421875},{x:.140625,y:.421875},{x:.171875,y:.421875},{x:.171875,y:.421875},{x:.203125,y:.421875},{x:.203125,y:.421875},{x:.234375,y:.421875},{x:.234375,y:.421875},{x:.265625,y:.421875},{x:.265625,y:.421875},{x:.296875,y:.421875},{x:.296875,y:.421875},{x:.328125,y:.421875},{x:.328125,y:.421875},{x:.359375,y:.421875},{x:.359375,y:.421875},{x:.390625,y:.421875},{x:.390625,y:.421875},{x:.421875,y:.421875},{x:.421875,y:.421875},{x:.453125,y:.421875},{x:.453125,y:.421875},{x:.484375,y:.421875},{x:.484375,y:.421875},{x:.515625,y:.421875},{x:.515625,y:.421875},{x:.546875,y:.421875},{x:.546875,y:.421875},{x:.578125,y:.421875},{x:.578125,y:.421875},{x:.609375,y:.421875},{x:.609375,y:.421875},{x:.640625,y:.421875},{x:.640625,y:.421875},{x:.671875,y:.421875},{x:.671875,y:.421875},{x:.703125,y:.421875},{x:.703125,y:.421875},{x:.734375,y:.421875},{x:.734375,y:.421875},{x:.765625,y:.421875},{x:.765625,y:.421875},{x:.796875,y:.421875},{x:.796875,y:.421875},{x:.828125,y:.421875},{x:.828125,y:.421875},{x:.859375,y:.421875},{x:.859375,y:.421875},{x:.890625,y:.421875},{x:.890625,y:.421875},{x:.921875,y:.421875},{x:.921875,y:.421875},{x:.953125,y:.421875},{x:.953125,y:.421875},{x:.984375,y:.421875},{x:.984375,y:.421875},{x:.015625,y:.453125},{x:.015625,y:.453125},{x:.046875,y:.453125},{x:.046875,y:.453125},{x:.078125,y:.453125},{x:.078125,y:.453125},{x:.109375,y:.453125},{x:.109375,y:.453125},{x:.140625,y:.453125},{x:.140625,y:.453125},{x:.171875,y:.453125},{x:.171875,y:.453125},{x:.203125,y:.453125},{x:.203125,y:.453125},{x:.234375,y:.453125},{x:.234375,y:.453125},{x:.265625,y:.453125},{x:.265625,y:.453125},{x:.296875,y:.453125},{x:.296875,y:.453125},{x:.328125,y:.453125},{x:.328125,y:.453125},{x:.359375,y:.453125},{x:.359375,y:.453125},{x:.390625,y:.453125},{x:.390625,y:.453125},{x:.421875,y:.453125},{x:.421875,y:.453125},{x:.453125,y:.453125},{x:.453125,y:.453125},{x:.484375,y:.453125},{x:.484375,y:.453125},{x:.515625,y:.453125},{x:.515625,y:.453125},{x:.546875,y:.453125},{x:.546875,y:.453125},{x:.578125,y:.453125},{x:.578125,y:.453125},{x:.609375,y:.453125},{x:.609375,y:.453125},{x:.640625,y:.453125},{x:.640625,y:.453125},{x:.671875,y:.453125},{x:.671875,y:.453125},{x:.703125,y:.453125},{x:.703125,y:.453125},{x:.734375,y:.453125},{x:.734375,y:.453125},{x:.765625,y:.453125},{x:.765625,y:.453125},{x:.796875,y:.453125},{x:.796875,y:.453125},{x:.828125,y:.453125},{x:.828125,y:.453125},{x:.859375,y:.453125},{x:.859375,y:.453125},{x:.890625,y:.453125},{x:.890625,y:.453125},{x:.921875,y:.453125},{x:.921875,y:.453125},{x:.953125,y:.453125},{x:.953125,y:.453125},{x:.984375,y:.453125},{x:.984375,y:.453125},{x:.015625,y:.484375},{x:.015625,y:.484375},{x:.046875,y:.484375},{x:.046875,y:.484375},{x:.078125,y:.484375},{x:.078125,y:.484375},{x:.109375,y:.484375},{x:.109375,y:.484375},{x:.140625,y:.484375},{x:.140625,y:.484375},{x:.171875,y:.484375},{x:.171875,y:.484375},{x:.203125,y:.484375},{x:.203125,y:.484375},{x:.234375,y:.484375},{x:.234375,y:.484375},{x:.265625,y:.484375},{x:.265625,y:.484375},{x:.296875,y:.484375},{x:.296875,y:.484375},{x:.328125,y:.484375},{x:.328125,y:.484375},{x:.359375,y:.484375},{x:.359375,y:.484375},{x:.390625,y:.484375},{x:.390625,y:.484375},{x:.421875,y:.484375},{x:.421875,y:.484375},{x:.453125,y:.484375},{x:.453125,y:.484375},{x:.484375,y:.484375},{x:.484375,y:.484375},{x:.515625,y:.484375},{x:.515625,y:.484375},{x:.546875,y:.484375},{x:.546875,y:.484375},{x:.578125,y:.484375},{x:.578125,y:.484375},{x:.609375,y:.484375},{x:.609375,y:.484375},{x:.640625,y:.484375},{x:.640625,y:.484375},{x:.671875,y:.484375},{x:.671875,y:.484375},{x:.703125,y:.484375},{x:.703125,y:.484375},{x:.734375,y:.484375},{x:.734375,y:.484375},{x:.765625,y:.484375},{x:.765625,y:.484375},{x:.796875,y:.484375},{x:.796875,y:.484375},{x:.828125,y:.484375},{x:.828125,y:.484375},{x:.859375,y:.484375},{x:.859375,y:.484375},{x:.890625,y:.484375},{x:.890625,y:.484375},{x:.921875,y:.484375},{x:.921875,y:.484375},{x:.953125,y:.484375},{x:.953125,y:.484375},{x:.984375,y:.484375},{x:.984375,y:.484375},{x:.015625,y:.515625},{x:.015625,y:.515625},{x:.046875,y:.515625},{x:.046875,y:.515625},{x:.078125,y:.515625},{x:.078125,y:.515625},{x:.109375,y:.515625},{x:.109375,y:.515625},{x:.140625,y:.515625},{x:.140625,y:.515625},{x:.171875,y:.515625},{x:.171875,y:.515625},{x:.203125,y:.515625},{x:.203125,y:.515625},{x:.234375,y:.515625},{x:.234375,y:.515625},{x:.265625,y:.515625},{x:.265625,y:.515625},{x:.296875,y:.515625},{x:.296875,y:.515625},{x:.328125,y:.515625},{x:.328125,y:.515625},{x:.359375,y:.515625},{x:.359375,y:.515625},{x:.390625,y:.515625},{x:.390625,y:.515625},{x:.421875,y:.515625},{x:.421875,y:.515625},{x:.453125,y:.515625},{x:.453125,y:.515625},{x:.484375,y:.515625},{x:.484375,y:.515625},{x:.515625,y:.515625},{x:.515625,y:.515625},{x:.546875,y:.515625},{x:.546875,y:.515625},{x:.578125,y:.515625},{x:.578125,y:.515625},{x:.609375,y:.515625},{x:.609375,y:.515625},{x:.640625,y:.515625},{x:.640625,y:.515625},{x:.671875,y:.515625},{x:.671875,y:.515625},{x:.703125,y:.515625},{x:.703125,y:.515625},{x:.734375,y:.515625},{x:.734375,y:.515625},{x:.765625,y:.515625},{x:.765625,y:.515625},{x:.796875,y:.515625},{x:.796875,y:.515625},{x:.828125,y:.515625},{x:.828125,y:.515625},{x:.859375,y:.515625},{x:.859375,y:.515625},{x:.890625,y:.515625},{x:.890625,y:.515625},{x:.921875,y:.515625},{x:.921875,y:.515625},{x:.953125,y:.515625},{x:.953125,y:.515625},{x:.984375,y:.515625},{x:.984375,y:.515625},{x:.015625,y:.546875},{x:.015625,y:.546875},{x:.046875,y:.546875},{x:.046875,y:.546875},{x:.078125,y:.546875},{x:.078125,y:.546875},{x:.109375,y:.546875},{x:.109375,y:.546875},{x:.140625,y:.546875},{x:.140625,y:.546875},{x:.171875,y:.546875},{x:.171875,y:.546875},{x:.203125,y:.546875},{x:.203125,y:.546875},{x:.234375,y:.546875},{x:.234375,y:.546875},{x:.265625,y:.546875},{x:.265625,y:.546875},{x:.296875,y:.546875},{x:.296875,y:.546875},{x:.328125,y:.546875},{x:.328125,y:.546875},{x:.359375,y:.546875},{x:.359375,y:.546875},{x:.390625,y:.546875},{x:.390625,y:.546875},{x:.421875,y:.546875},{x:.421875,y:.546875},{x:.453125,y:.546875},{x:.453125,y:.546875},{x:.484375,y:.546875},{x:.484375,y:.546875},{x:.515625,y:.546875},{x:.515625,y:.546875},{x:.546875,y:.546875},{x:.546875,y:.546875},{x:.578125,y:.546875},{x:.578125,y:.546875},{x:.609375,y:.546875},{x:.609375,y:.546875},{x:.640625,y:.546875},{x:.640625,y:.546875},{x:.671875,y:.546875},{x:.671875,y:.546875},{x:.703125,y:.546875},{x:.703125,y:.546875},{x:.734375,y:.546875},{x:.734375,y:.546875},{x:.765625,y:.546875},{x:.765625,y:.546875},{x:.796875,y:.546875},{x:.796875,y:.546875},{x:.828125,y:.546875},{x:.828125,y:.546875},{x:.859375,y:.546875},{x:.859375,y:.546875},{x:.890625,y:.546875},{x:.890625,y:.546875},{x:.921875,y:.546875},{x:.921875,y:.546875},{x:.953125,y:.546875},{x:.953125,y:.546875},{x:.984375,y:.546875},{x:.984375,y:.546875},{x:.015625,y:.578125},{x:.015625,y:.578125},{x:.046875,y:.578125},{x:.046875,y:.578125},{x:.078125,y:.578125},{x:.078125,y:.578125},{x:.109375,y:.578125},{x:.109375,y:.578125},{x:.140625,y:.578125},{x:.140625,y:.578125},{x:.171875,y:.578125},{x:.171875,y:.578125},{x:.203125,y:.578125},{x:.203125,y:.578125},{x:.234375,y:.578125},{x:.234375,y:.578125},{x:.265625,y:.578125},{x:.265625,y:.578125},{x:.296875,y:.578125},{x:.296875,y:.578125},{x:.328125,y:.578125},{x:.328125,y:.578125},{x:.359375,y:.578125},{x:.359375,y:.578125},{x:.390625,y:.578125},{x:.390625,y:.578125},{x:.421875,y:.578125},{x:.421875,y:.578125},{x:.453125,y:.578125},{x:.453125,y:.578125},{x:.484375,y:.578125},{x:.484375,y:.578125},{x:.515625,y:.578125},{x:.515625,y:.578125},{x:.546875,y:.578125},{x:.546875,y:.578125},{x:.578125,y:.578125},{x:.578125,y:.578125},{x:.609375,y:.578125},{x:.609375,y:.578125},{x:.640625,y:.578125},{x:.640625,y:.578125},{x:.671875,y:.578125},{x:.671875,y:.578125},{x:.703125,y:.578125},{x:.703125,y:.578125},{x:.734375,y:.578125},{x:.734375,y:.578125},{x:.765625,y:.578125},{x:.765625,y:.578125},{x:.796875,y:.578125},{x:.796875,y:.578125},{x:.828125,y:.578125},{x:.828125,y:.578125},{x:.859375,y:.578125},{x:.859375,y:.578125},{x:.890625,y:.578125},{x:.890625,y:.578125},{x:.921875,y:.578125},{x:.921875,y:.578125},{x:.953125,y:.578125},{x:.953125,y:.578125},{x:.984375,y:.578125},{x:.984375,y:.578125},{x:.015625,y:.609375},{x:.015625,y:.609375},{x:.046875,y:.609375},{x:.046875,y:.609375},{x:.078125,y:.609375},{x:.078125,y:.609375},{x:.109375,y:.609375},{x:.109375,y:.609375},{x:.140625,y:.609375},{x:.140625,y:.609375},{x:.171875,y:.609375},{x:.171875,y:.609375},{x:.203125,y:.609375},{x:.203125,y:.609375},{x:.234375,y:.609375},{x:.234375,y:.609375},{x:.265625,y:.609375},{x:.265625,y:.609375},{x:.296875,y:.609375},{x:.296875,y:.609375},{x:.328125,y:.609375},{x:.328125,y:.609375},{x:.359375,y:.609375},{x:.359375,y:.609375},{x:.390625,y:.609375},{x:.390625,y:.609375},{x:.421875,y:.609375},{x:.421875,y:.609375},{x:.453125,y:.609375},{x:.453125,y:.609375},{x:.484375,y:.609375},{x:.484375,y:.609375},{x:.515625,y:.609375},{x:.515625,y:.609375},{x:.546875,y:.609375},{x:.546875,y:.609375},{x:.578125,y:.609375},{x:.578125,y:.609375},{x:.609375,y:.609375},{x:.609375,y:.609375},{x:.640625,y:.609375},{x:.640625,y:.609375},{x:.671875,y:.609375},{x:.671875,y:.609375},{x:.703125,y:.609375},{x:.703125,y:.609375},{x:.734375,y:.609375},{x:.734375,y:.609375},{x:.765625,y:.609375},{x:.765625,y:.609375},{x:.796875,y:.609375},{x:.796875,y:.609375},{x:.828125,y:.609375},{x:.828125,y:.609375},{x:.859375,y:.609375},{x:.859375,y:.609375},{x:.890625,y:.609375},{x:.890625,y:.609375},{x:.921875,y:.609375},{x:.921875,y:.609375},{x:.953125,y:.609375},{x:.953125,y:.609375},{x:.984375,y:.609375},{x:.984375,y:.609375},{x:.015625,y:.640625},{x:.015625,y:.640625},{x:.046875,y:.640625},{x:.046875,y:.640625},{x:.078125,y:.640625},{x:.078125,y:.640625},{x:.109375,y:.640625},{x:.109375,y:.640625},{x:.140625,y:.640625},{x:.140625,y:.640625},{x:.171875,y:.640625},{x:.171875,y:.640625},{x:.203125,y:.640625},{x:.203125,y:.640625},{x:.234375,y:.640625},{x:.234375,y:.640625},{x:.265625,y:.640625},{x:.265625,y:.640625},{x:.296875,y:.640625},{x:.296875,y:.640625},{x:.328125,y:.640625},{x:.328125,y:.640625},{x:.359375,y:.640625},{x:.359375,y:.640625},{x:.390625,y:.640625},{x:.390625,y:.640625},{x:.421875,y:.640625},{x:.421875,y:.640625},{x:.453125,y:.640625},{x:.453125,y:.640625},{x:.484375,y:.640625},{x:.484375,y:.640625},{x:.515625,y:.640625},{x:.515625,y:.640625},{x:.546875,y:.640625},{x:.546875,y:.640625},{x:.578125,y:.640625},{x:.578125,y:.640625},{x:.609375,y:.640625},{x:.609375,y:.640625},{x:.640625,y:.640625},{x:.640625,y:.640625},{x:.671875,y:.640625},{x:.671875,y:.640625},{x:.703125,y:.640625},{x:.703125,y:.640625},{x:.734375,y:.640625},{x:.734375,y:.640625},{x:.765625,y:.640625},{x:.765625,y:.640625},{x:.796875,y:.640625},{x:.796875,y:.640625},{x:.828125,y:.640625},{x:.828125,y:.640625},{x:.859375,y:.640625},{x:.859375,y:.640625},{x:.890625,y:.640625},{x:.890625,y:.640625},{x:.921875,y:.640625},{x:.921875,y:.640625},{x:.953125,y:.640625},{x:.953125,y:.640625},{x:.984375,y:.640625},{x:.984375,y:.640625},{x:.015625,y:.671875},{x:.015625,y:.671875},{x:.046875,y:.671875},{x:.046875,y:.671875},{x:.078125,y:.671875},{x:.078125,y:.671875},{x:.109375,y:.671875},{x:.109375,y:.671875},{x:.140625,y:.671875},{x:.140625,y:.671875},{x:.171875,y:.671875},{x:.171875,y:.671875},{x:.203125,y:.671875},{x:.203125,y:.671875},{x:.234375,y:.671875},{x:.234375,y:.671875},{x:.265625,y:.671875},{x:.265625,y:.671875},{x:.296875,y:.671875},{x:.296875,y:.671875},{x:.328125,y:.671875},{x:.328125,y:.671875},{x:.359375,y:.671875},{x:.359375,y:.671875},{x:.390625,y:.671875},{x:.390625,y:.671875},{x:.421875,y:.671875},{x:.421875,y:.671875},{x:.453125,y:.671875},{x:.453125,y:.671875},{x:.484375,y:.671875},{x:.484375,y:.671875},{x:.515625,y:.671875},{x:.515625,y:.671875},{x:.546875,y:.671875},{x:.546875,y:.671875},{x:.578125,y:.671875},{x:.578125,y:.671875},{x:.609375,y:.671875},{x:.609375,y:.671875},{x:.640625,y:.671875},{x:.640625,y:.671875},{x:.671875,y:.671875},{x:.671875,y:.671875},{x:.703125,y:.671875},{x:.703125,y:.671875},{x:.734375,y:.671875},{x:.734375,y:.671875},{x:.765625,y:.671875},{x:.765625,y:.671875},{x:.796875,y:.671875},{x:.796875,y:.671875},{x:.828125,y:.671875},{x:.828125,y:.671875},{x:.859375,y:.671875},{x:.859375,y:.671875},{x:.890625,y:.671875},{x:.890625,y:.671875},{x:.921875,y:.671875},{x:.921875,y:.671875},{x:.953125,y:.671875},{x:.953125,y:.671875},{x:.984375,y:.671875},{x:.984375,y:.671875},{x:.015625,y:.703125},{x:.015625,y:.703125},{x:.046875,y:.703125},{x:.046875,y:.703125},{x:.078125,y:.703125},{x:.078125,y:.703125},{x:.109375,y:.703125},{x:.109375,y:.703125},{x:.140625,y:.703125},{x:.140625,y:.703125},{x:.171875,y:.703125},{x:.171875,y:.703125},{x:.203125,y:.703125},{x:.203125,y:.703125},{x:.234375,y:.703125},{x:.234375,y:.703125},{x:.265625,y:.703125},{x:.265625,y:.703125},{x:.296875,y:.703125},{x:.296875,y:.703125},{x:.328125,y:.703125},{x:.328125,y:.703125},{x:.359375,y:.703125},{x:.359375,y:.703125},{x:.390625,y:.703125},{x:.390625,y:.703125},{x:.421875,y:.703125},{x:.421875,y:.703125},{x:.453125,y:.703125},{x:.453125,y:.703125},{x:.484375,y:.703125},{x:.484375,y:.703125},{x:.515625,y:.703125},{x:.515625,y:.703125},{x:.546875,y:.703125},{x:.546875,y:.703125},{x:.578125,y:.703125},{x:.578125,y:.703125},{x:.609375,y:.703125},{x:.609375,y:.703125},{x:.640625,y:.703125},{x:.640625,y:.703125},{x:.671875,y:.703125},{x:.671875,y:.703125},{x:.703125,y:.703125},{x:.703125,y:.703125},{x:.734375,y:.703125},{x:.734375,y:.703125},{x:.765625,y:.703125},{x:.765625,y:.703125},{x:.796875,y:.703125},{x:.796875,y:.703125},{x:.828125,y:.703125},{x:.828125,y:.703125},{x:.859375,y:.703125},{x:.859375,y:.703125},{x:.890625,y:.703125},{x:.890625,y:.703125},{x:.921875,y:.703125},{x:.921875,y:.703125},{x:.953125,y:.703125},{x:.953125,y:.703125},{x:.984375,y:.703125},{x:.984375,y:.703125},{x:.015625,y:.734375},{x:.015625,y:.734375},{x:.046875,y:.734375},{x:.046875,y:.734375},{x:.078125,y:.734375},{x:.078125,y:.734375},{x:.109375,y:.734375},{x:.109375,y:.734375},{x:.140625,y:.734375},{x:.140625,y:.734375},{x:.171875,y:.734375},{x:.171875,y:.734375},{x:.203125,y:.734375},{x:.203125,y:.734375},{x:.234375,y:.734375},{x:.234375,y:.734375},{x:.265625,y:.734375},{x:.265625,y:.734375},{x:.296875,y:.734375},{x:.296875,y:.734375},{x:.328125,y:.734375},{x:.328125,y:.734375},{x:.359375,y:.734375},{x:.359375,y:.734375},{x:.390625,y:.734375},{x:.390625,y:.734375},{x:.421875,y:.734375},{x:.421875,y:.734375},{x:.453125,y:.734375},{x:.453125,y:.734375},{x:.484375,y:.734375},{x:.484375,y:.734375},{x:.515625,y:.734375},{x:.515625,y:.734375},{x:.546875,y:.734375},{x:.546875,y:.734375},{x:.578125,y:.734375},{x:.578125,y:.734375},{x:.609375,y:.734375},{x:.609375,y:.734375},{x:.640625,y:.734375},{x:.640625,y:.734375},{x:.671875,y:.734375},{x:.671875,y:.734375},{x:.703125,y:.734375},{x:.703125,y:.734375},{x:.734375,y:.734375},{x:.734375,y:.734375},{x:.765625,y:.734375},{x:.765625,y:.734375},{x:.796875,y:.734375},{x:.796875,y:.734375},{x:.828125,y:.734375},{x:.828125,y:.734375},{x:.859375,y:.734375},{x:.859375,y:.734375},{x:.890625,y:.734375},{x:.890625,y:.734375},{x:.921875,y:.734375},{x:.921875,y:.734375},{x:.953125,y:.734375},{x:.953125,y:.734375},{x:.984375,y:.734375},{x:.984375,y:.734375},{x:.015625,y:.765625},{x:.015625,y:.765625},{x:.046875,y:.765625},{x:.046875,y:.765625},{x:.078125,y:.765625},{x:.078125,y:.765625},{x:.109375,y:.765625},{x:.109375,y:.765625},{x:.140625,y:.765625},{x:.140625,y:.765625},{x:.171875,y:.765625},{x:.171875,y:.765625},{x:.203125,y:.765625},{x:.203125,y:.765625},{x:.234375,y:.765625},{x:.234375,y:.765625},{x:.265625,y:.765625},{x:.265625,y:.765625},{x:.296875,y:.765625},{x:.296875,y:.765625},{x:.328125,y:.765625},{x:.328125,y:.765625},{x:.359375,y:.765625},{x:.359375,y:.765625},{x:.390625,y:.765625},{x:.390625,y:.765625},{x:.421875,y:.765625},{x:.421875,y:.765625},{x:.453125,y:.765625},{x:.453125,y:.765625},{x:.484375,y:.765625},{x:.484375,y:.765625},{x:.515625,y:.765625},{x:.515625,y:.765625},{x:.546875,y:.765625},{x:.546875,y:.765625},{x:.578125,y:.765625},{x:.578125,y:.765625},{x:.609375,y:.765625},{x:.609375,y:.765625},{x:.640625,y:.765625},{x:.640625,y:.765625},{x:.671875,y:.765625},{x:.671875,y:.765625},{x:.703125,y:.765625},{x:.703125,y:.765625},{x:.734375,y:.765625},{x:.734375,y:.765625},{x:.765625,y:.765625},{x:.765625,y:.765625},{x:.796875,y:.765625},{x:.796875,y:.765625},{x:.828125,y:.765625},{x:.828125,y:.765625},{x:.859375,y:.765625},{x:.859375,y:.765625},{x:.890625,y:.765625},{x:.890625,y:.765625},{x:.921875,y:.765625},{x:.921875,y:.765625},{x:.953125,y:.765625},{x:.953125,y:.765625},{x:.984375,y:.765625},{x:.984375,y:.765625},{x:.015625,y:.796875},{x:.015625,y:.796875},{x:.046875,y:.796875},{x:.046875,y:.796875},{x:.078125,y:.796875},{x:.078125,y:.796875},{x:.109375,y:.796875},{x:.109375,y:.796875},{x:.140625,y:.796875},{x:.140625,y:.796875},{x:.171875,y:.796875},{x:.171875,y:.796875},{x:.203125,y:.796875},{x:.203125,y:.796875},{x:.234375,y:.796875},{x:.234375,y:.796875},{x:.265625,y:.796875},{x:.265625,y:.796875},{x:.296875,y:.796875},{x:.296875,y:.796875},{x:.328125,y:.796875},{x:.328125,y:.796875},{x:.359375,y:.796875},{x:.359375,y:.796875},{x:.390625,y:.796875},{x:.390625,y:.796875},{x:.421875,y:.796875},{x:.421875,y:.796875},{x:.453125,y:.796875},{x:.453125,y:.796875},{x:.484375,y:.796875},{x:.484375,y:.796875},{x:.515625,y:.796875},{x:.515625,y:.796875},{x:.546875,y:.796875},{x:.546875,y:.796875},{x:.578125,y:.796875},{x:.578125,y:.796875},{x:.609375,y:.796875},{x:.609375,y:.796875},{x:.640625,y:.796875},{x:.640625,y:.796875},{x:.671875,y:.796875},{x:.671875,y:.796875},{x:.703125,y:.796875},{x:.703125,y:.796875},{x:.734375,y:.796875},{x:.734375,y:.796875},{x:.765625,y:.796875},{x:.765625,y:.796875},{x:.796875,y:.796875},{x:.796875,y:.796875},{x:.828125,y:.796875},{x:.828125,y:.796875},{x:.859375,y:.796875},{x:.859375,y:.796875},{x:.890625,y:.796875},{x:.890625,y:.796875},{x:.921875,y:.796875},{x:.921875,y:.796875},{x:.953125,y:.796875},{x:.953125,y:.796875},{x:.984375,y:.796875},{x:.984375,y:.796875},{x:.015625,y:.828125},{x:.015625,y:.828125},{x:.046875,y:.828125},{x:.046875,y:.828125},{x:.078125,y:.828125},{x:.078125,y:.828125},{x:.109375,y:.828125},{x:.109375,y:.828125},{x:.140625,y:.828125},{x:.140625,y:.828125},{x:.171875,y:.828125},{x:.171875,y:.828125},{x:.203125,y:.828125},{x:.203125,y:.828125},{x:.234375,y:.828125},{x:.234375,y:.828125},{x:.265625,y:.828125},{x:.265625,y:.828125},{x:.296875,y:.828125},{x:.296875,y:.828125},{x:.328125,y:.828125},{x:.328125,y:.828125},{x:.359375,y:.828125},{x:.359375,y:.828125},{x:.390625,y:.828125},{x:.390625,y:.828125},{x:.421875,y:.828125},{x:.421875,y:.828125},{x:.453125,y:.828125},{x:.453125,y:.828125},{x:.484375,y:.828125},{x:.484375,y:.828125},{x:.515625,y:.828125},{x:.515625,y:.828125},{x:.546875,y:.828125},{x:.546875,y:.828125},{x:.578125,y:.828125},{x:.578125,y:.828125},{x:.609375,y:.828125},{x:.609375,y:.828125},{x:.640625,y:.828125},{x:.640625,y:.828125},{x:.671875,y:.828125},{x:.671875,y:.828125},{x:.703125,y:.828125},{x:.703125,y:.828125},{x:.734375,y:.828125},{x:.734375,y:.828125},{x:.765625,y:.828125},{x:.765625,y:.828125},{x:.796875,y:.828125},{x:.796875,y:.828125},{x:.828125,y:.828125},{x:.828125,y:.828125},{x:.859375,y:.828125},{x:.859375,y:.828125},{x:.890625,y:.828125},{x:.890625,y:.828125},{x:.921875,y:.828125},{x:.921875,y:.828125},{x:.953125,y:.828125},{x:.953125,y:.828125},{x:.984375,y:.828125},{x:.984375,y:.828125},{x:.015625,y:.859375},{x:.015625,y:.859375},{x:.046875,y:.859375},{x:.046875,y:.859375},{x:.078125,y:.859375},{x:.078125,y:.859375},{x:.109375,y:.859375},{x:.109375,y:.859375},{x:.140625,y:.859375},{x:.140625,y:.859375},{x:.171875,y:.859375},{x:.171875,y:.859375},{x:.203125,y:.859375},{x:.203125,y:.859375},{x:.234375,y:.859375},{x:.234375,y:.859375},{x:.265625,y:.859375},{x:.265625,y:.859375},{x:.296875,y:.859375},{x:.296875,y:.859375},{x:.328125,y:.859375},{x:.328125,y:.859375},{x:.359375,y:.859375},{x:.359375,y:.859375},{x:.390625,y:.859375},{x:.390625,y:.859375},{x:.421875,y:.859375},{x:.421875,y:.859375},{x:.453125,y:.859375},{x:.453125,y:.859375},{x:.484375,y:.859375},{x:.484375,y:.859375},{x:.515625,y:.859375},{x:.515625,y:.859375},{x:.546875,y:.859375},{x:.546875,y:.859375},{x:.578125,y:.859375},{x:.578125,y:.859375},{x:.609375,y:.859375},{x:.609375,y:.859375},{x:.640625,y:.859375},{x:.640625,y:.859375},{x:.671875,y:.859375},{x:.671875,y:.859375},{x:.703125,y:.859375},{x:.703125,y:.859375},{x:.734375,y:.859375},{x:.734375,y:.859375},{x:.765625,y:.859375},{x:.765625,y:.859375},{x:.796875,y:.859375},{x:.796875,y:.859375},{x:.828125,y:.859375},{x:.828125,y:.859375},{x:.859375,y:.859375},{x:.859375,y:.859375},{x:.890625,y:.859375},{x:.890625,y:.859375},{x:.921875,y:.859375},{x:.921875,y:.859375},{x:.953125,y:.859375},{x:.953125,y:.859375},{x:.984375,y:.859375},{x:.984375,y:.859375},{x:.015625,y:.890625},{x:.015625,y:.890625},{x:.046875,y:.890625},{x:.046875,y:.890625},{x:.078125,y:.890625},{x:.078125,y:.890625},{x:.109375,y:.890625},{x:.109375,y:.890625},{x:.140625,y:.890625},{x:.140625,y:.890625},{x:.171875,y:.890625},{x:.171875,y:.890625},{x:.203125,y:.890625},{x:.203125,y:.890625},{x:.234375,y:.890625},{x:.234375,y:.890625},{x:.265625,y:.890625},{x:.265625,y:.890625},{x:.296875,y:.890625},{x:.296875,y:.890625},{x:.328125,y:.890625},{x:.328125,y:.890625},{x:.359375,y:.890625},{x:.359375,y:.890625},{x:.390625,y:.890625},{x:.390625,y:.890625},{x:.421875,y:.890625},{x:.421875,y:.890625},{x:.453125,y:.890625},{x:.453125,y:.890625},{x:.484375,y:.890625},{x:.484375,y:.890625},{x:.515625,y:.890625},{x:.515625,y:.890625},{x:.546875,y:.890625},{x:.546875,y:.890625},{x:.578125,y:.890625},{x:.578125,y:.890625},{x:.609375,y:.890625},{x:.609375,y:.890625},{x:.640625,y:.890625},{x:.640625,y:.890625},{x:.671875,y:.890625},{x:.671875,y:.890625},{x:.703125,y:.890625},{x:.703125,y:.890625},{x:.734375,y:.890625},{x:.734375,y:.890625},{x:.765625,y:.890625},{x:.765625,y:.890625},{x:.796875,y:.890625},{x:.796875,y:.890625},{x:.828125,y:.890625},{x:.828125,y:.890625},{x:.859375,y:.890625},{x:.859375,y:.890625},{x:.890625,y:.890625},{x:.890625,y:.890625},{x:.921875,y:.890625},{x:.921875,y:.890625},{x:.953125,y:.890625},{x:.953125,y:.890625},{x:.984375,y:.890625},{x:.984375,y:.890625},{x:.015625,y:.921875},{x:.015625,y:.921875},{x:.046875,y:.921875},{x:.046875,y:.921875},{x:.078125,y:.921875},{x:.078125,y:.921875},{x:.109375,y:.921875},{x:.109375,y:.921875},{x:.140625,y:.921875},{x:.140625,y:.921875},{x:.171875,y:.921875},{x:.171875,y:.921875},{x:.203125,y:.921875},{x:.203125,y:.921875},{x:.234375,y:.921875},{x:.234375,y:.921875},{x:.265625,y:.921875},{x:.265625,y:.921875},{x:.296875,y:.921875},{x:.296875,y:.921875},{x:.328125,y:.921875},{x:.328125,y:.921875},{x:.359375,y:.921875},{x:.359375,y:.921875},{x:.390625,y:.921875},{x:.390625,y:.921875},{x:.421875,y:.921875},{x:.421875,y:.921875},{x:.453125,y:.921875},{x:.453125,y:.921875},{x:.484375,y:.921875},{x:.484375,y:.921875},{x:.515625,y:.921875},{x:.515625,y:.921875},{x:.546875,y:.921875},{x:.546875,y:.921875},{x:.578125,y:.921875},{x:.578125,y:.921875},{x:.609375,y:.921875},{x:.609375,y:.921875},{x:.640625,y:.921875},{x:.640625,y:.921875},{x:.671875,y:.921875},{x:.671875,y:.921875},{x:.703125,y:.921875},{x:.703125,y:.921875},{x:.734375,y:.921875},{x:.734375,y:.921875},{x:.765625,y:.921875},{x:.765625,y:.921875},{x:.796875,y:.921875},{x:.796875,y:.921875},{x:.828125,y:.921875},{x:.828125,y:.921875},{x:.859375,y:.921875},{x:.859375,y:.921875},{x:.890625,y:.921875},{x:.890625,y:.921875},{x:.921875,y:.921875},{x:.921875,y:.921875},{x:.953125,y:.921875},{x:.953125,y:.921875},{x:.984375,y:.921875},{x:.984375,y:.921875},{x:.015625,y:.953125},{x:.015625,y:.953125},{x:.046875,y:.953125},{x:.046875,y:.953125},{x:.078125,y:.953125},{x:.078125,y:.953125},{x:.109375,y:.953125},{x:.109375,y:.953125},{x:.140625,y:.953125},{x:.140625,y:.953125},{x:.171875,y:.953125},{x:.171875,y:.953125},{x:.203125,y:.953125},{x:.203125,y:.953125},{x:.234375,y:.953125},{x:.234375,y:.953125},{x:.265625,y:.953125},{x:.265625,y:.953125},{x:.296875,y:.953125},{x:.296875,y:.953125},{x:.328125,y:.953125},{x:.328125,y:.953125},{x:.359375,y:.953125},{x:.359375,y:.953125},{x:.390625,y:.953125},{x:.390625,y:.953125},{x:.421875,y:.953125},{x:.421875,y:.953125},{x:.453125,y:.953125},{x:.453125,y:.953125},{x:.484375,y:.953125},{x:.484375,y:.953125},{x:.515625,y:.953125},{x:.515625,y:.953125},{x:.546875,y:.953125},{x:.546875,y:.953125},{x:.578125,y:.953125},{x:.578125,y:.953125},{x:.609375,y:.953125},{x:.609375,y:.953125},{x:.640625,y:.953125},{x:.640625,y:.953125},{x:.671875,y:.953125},{x:.671875,y:.953125},{x:.703125,y:.953125},{x:.703125,y:.953125},{x:.734375,y:.953125},{x:.734375,y:.953125},{x:.765625,y:.953125},{x:.765625,y:.953125},{x:.796875,y:.953125},{x:.796875,y:.953125},{x:.828125,y:.953125},{x:.828125,y:.953125},{x:.859375,y:.953125},{x:.859375,y:.953125},{x:.890625,y:.953125},{x:.890625,y:.953125},{x:.921875,y:.953125},{x:.921875,y:.953125},{x:.953125,y:.953125},{x:.953125,y:.953125},{x:.984375,y:.953125},{x:.984375,y:.953125},{x:.015625,y:.984375},{x:.015625,y:.984375},{x:.046875,y:.984375},{x:.046875,y:.984375},{x:.078125,y:.984375},{x:.078125,y:.984375},{x:.109375,y:.984375},{x:.109375,y:.984375},{x:.140625,y:.984375},{x:.140625,y:.984375},{x:.171875,y:.984375},{x:.171875,y:.984375},{x:.203125,y:.984375},{x:.203125,y:.984375},{x:.234375,y:.984375},{x:.234375,y:.984375},{x:.265625,y:.984375},{x:.265625,y:.984375},{x:.296875,y:.984375},{x:.296875,y:.984375},{x:.328125,y:.984375},{x:.328125,y:.984375},{x:.359375,y:.984375},{x:.359375,y:.984375},{x:.390625,y:.984375},{x:.390625,y:.984375},{x:.421875,y:.984375},{x:.421875,y:.984375},{x:.453125,y:.984375},{x:.453125,y:.984375},{x:.484375,y:.984375},{x:.484375,y:.984375},{x:.515625,y:.984375},{x:.515625,y:.984375},{x:.546875,y:.984375},{x:.546875,y:.984375},{x:.578125,y:.984375},{x:.578125,y:.984375},{x:.609375,y:.984375},{x:.609375,y:.984375},{x:.640625,y:.984375},{x:.640625,y:.984375},{x:.671875,y:.984375},{x:.671875,y:.984375},{x:.703125,y:.984375},{x:.703125,y:.984375},{x:.734375,y:.984375},{x:.734375,y:.984375},{x:.765625,y:.984375},{x:.765625,y:.984375},{x:.796875,y:.984375},{x:.796875,y:.984375},{x:.828125,y:.984375},{x:.828125,y:.984375},{x:.859375,y:.984375},{x:.859375,y:.984375},{x:.890625,y:.984375},{x:.890625,y:.984375},{x:.921875,y:.984375},{x:.921875,y:.984375},{x:.953125,y:.984375},{x:.953125,y:.984375},{x:.984375,y:.984375},{x:.984375,y:.984375},{x:.03125,y:.03125},{x:.03125,y:.03125},{x:.09375,y:.03125},{x:.09375,y:.03125},{x:.15625,y:.03125},{x:.15625,y:.03125},{x:.21875,y:.03125},{x:.21875,y:.03125},{x:.28125,y:.03125},{x:.28125,y:.03125},{x:.34375,y:.03125},{x:.34375,y:.03125},{x:.40625,y:.03125},{x:.40625,y:.03125},{x:.46875,y:.03125},{x:.46875,y:.03125},{x:.53125,y:.03125},{x:.53125,y:.03125},{x:.59375,y:.03125},{x:.59375,y:.03125},{x:.65625,y:.03125},{x:.65625,y:.03125},{x:.71875,y:.03125},{x:.71875,y:.03125},{x:.78125,y:.03125},{x:.78125,y:.03125},{x:.84375,y:.03125},{x:.84375,y:.03125},{x:.90625,y:.03125},{x:.90625,y:.03125},{x:.96875,y:.03125},{x:.96875,y:.03125},{x:.03125,y:.09375},{x:.03125,y:.09375},{x:.09375,y:.09375},{x:.09375,y:.09375},{x:.15625,y:.09375},{x:.15625,y:.09375},{x:.21875,y:.09375},{x:.21875,y:.09375},{x:.28125,y:.09375},{x:.28125,y:.09375},{x:.34375,y:.09375},{x:.34375,y:.09375},{x:.40625,y:.09375},{x:.40625,y:.09375},{x:.46875,y:.09375},{x:.46875,y:.09375},{x:.53125,y:.09375},{x:.53125,y:.09375},{x:.59375,y:.09375},{x:.59375,y:.09375},{x:.65625,y:.09375},{x:.65625,y:.09375},{x:.71875,y:.09375},{x:.71875,y:.09375},{x:.78125,y:.09375},{x:.78125,y:.09375},{x:.84375,y:.09375},{x:.84375,y:.09375},{x:.90625,y:.09375},{x:.90625,y:.09375},{x:.96875,y:.09375},{x:.96875,y:.09375},{x:.03125,y:.15625},{x:.03125,y:.15625},{x:.09375,y:.15625},{x:.09375,y:.15625},{x:.15625,y:.15625},{x:.15625,y:.15625},{x:.21875,y:.15625},{x:.21875,y:.15625},{x:.28125,y:.15625},{x:.28125,y:.15625},{x:.34375,y:.15625},{x:.34375,y:.15625},{x:.40625,y:.15625},{x:.40625,y:.15625},{x:.46875,y:.15625},{x:.46875,y:.15625},{x:.53125,y:.15625},{x:.53125,y:.15625},{x:.59375,y:.15625},{x:.59375,y:.15625},{x:.65625,y:.15625},{x:.65625,y:.15625},{x:.71875,y:.15625},{x:.71875,y:.15625},{x:.78125,y:.15625},{x:.78125,y:.15625},{x:.84375,y:.15625},{x:.84375,y:.15625},{x:.90625,y:.15625},{x:.90625,y:.15625},{x:.96875,y:.15625},{x:.96875,y:.15625},{x:.03125,y:.21875},{x:.03125,y:.21875},{x:.09375,y:.21875},{x:.09375,y:.21875},{x:.15625,y:.21875},{x:.15625,y:.21875},{x:.21875,y:.21875},{x:.21875,y:.21875},{x:.28125,y:.21875},{x:.28125,y:.21875},{x:.34375,y:.21875},{x:.34375,y:.21875},{x:.40625,y:.21875},{x:.40625,y:.21875},{x:.46875,y:.21875},{x:.46875,y:.21875},{x:.53125,y:.21875},{x:.53125,y:.21875},{x:.59375,y:.21875},{x:.59375,y:.21875},{x:.65625,y:.21875},{x:.65625,y:.21875},{x:.71875,y:.21875},{x:.71875,y:.21875},{x:.78125,y:.21875},{x:.78125,y:.21875},{x:.84375,y:.21875},{x:.84375,y:.21875},{x:.90625,y:.21875},{x:.90625,y:.21875},{x:.96875,y:.21875},{x:.96875,y:.21875},{x:.03125,y:.28125},{x:.03125,y:.28125},{x:.09375,y:.28125},{x:.09375,y:.28125},{x:.15625,y:.28125},{x:.15625,y:.28125},{x:.21875,y:.28125},{x:.21875,y:.28125},{x:.28125,y:.28125},{x:.28125,y:.28125},{x:.34375,y:.28125},{x:.34375,y:.28125},{x:.40625,y:.28125},{x:.40625,y:.28125},{x:.46875,y:.28125},{x:.46875,y:.28125},{x:.53125,y:.28125},{x:.53125,y:.28125},{x:.59375,y:.28125},{x:.59375,y:.28125},{x:.65625,y:.28125},{x:.65625,y:.28125},{x:.71875,y:.28125},{x:.71875,y:.28125},{x:.78125,y:.28125},{x:.78125,y:.28125},{x:.84375,y:.28125},{x:.84375,y:.28125},{x:.90625,y:.28125},{x:.90625,y:.28125},{x:.96875,y:.28125},{x:.96875,y:.28125},{x:.03125,y:.34375},{x:.03125,y:.34375},{x:.09375,y:.34375},{x:.09375,y:.34375},{x:.15625,y:.34375},{x:.15625,y:.34375},{x:.21875,y:.34375},{x:.21875,y:.34375},{x:.28125,y:.34375},{x:.28125,y:.34375},{x:.34375,y:.34375},{x:.34375,y:.34375},{x:.40625,y:.34375},{x:.40625,y:.34375},{x:.46875,y:.34375},{x:.46875,y:.34375},{x:.53125,y:.34375},{x:.53125,y:.34375},{x:.59375,y:.34375},{x:.59375,y:.34375},{x:.65625,y:.34375},{x:.65625,y:.34375},{x:.71875,y:.34375},{x:.71875,y:.34375},{x:.78125,y:.34375},{x:.78125,y:.34375},{x:.84375,y:.34375},{x:.84375,y:.34375},{x:.90625,y:.34375},{x:.90625,y:.34375},{x:.96875,y:.34375},{x:.96875,y:.34375},{x:.03125,y:.40625},{x:.03125,y:.40625},{x:.09375,y:.40625},{x:.09375,y:.40625},{x:.15625,y:.40625},{x:.15625,y:.40625},{x:.21875,y:.40625},{x:.21875,y:.40625},{x:.28125,y:.40625},{x:.28125,y:.40625},{x:.34375,y:.40625},{x:.34375,y:.40625},{x:.40625,y:.40625},{x:.40625,y:.40625},{x:.46875,y:.40625},{x:.46875,y:.40625},{x:.53125,y:.40625},{x:.53125,y:.40625},{x:.59375,y:.40625},{x:.59375,y:.40625},{x:.65625,y:.40625},{x:.65625,y:.40625},{x:.71875,y:.40625},{x:.71875,y:.40625},{x:.78125,y:.40625},{x:.78125,y:.40625},{x:.84375,y:.40625},{x:.84375,y:.40625},{x:.90625,y:.40625},{x:.90625,y:.40625},{x:.96875,y:.40625},{x:.96875,y:.40625},{x:.03125,y:.46875},{x:.03125,y:.46875},{x:.09375,y:.46875},{x:.09375,y:.46875},{x:.15625,y:.46875},{x:.15625,y:.46875},{x:.21875,y:.46875},{x:.21875,y:.46875},{x:.28125,y:.46875},{x:.28125,y:.46875},{x:.34375,y:.46875},{x:.34375,y:.46875},{x:.40625,y:.46875},{x:.40625,y:.46875},{x:.46875,y:.46875},{x:.46875,y:.46875},{x:.53125,y:.46875},{x:.53125,y:.46875},{x:.59375,y:.46875},{x:.59375,y:.46875},{x:.65625,y:.46875},{x:.65625,y:.46875},{x:.71875,y:.46875},{x:.71875,y:.46875},{x:.78125,y:.46875},{x:.78125,y:.46875},{x:.84375,y:.46875},{x:.84375,y:.46875},{x:.90625,y:.46875},{x:.90625,y:.46875},{x:.96875,y:.46875},{x:.96875,y:.46875},{x:.03125,y:.53125},{x:.03125,y:.53125},{x:.09375,y:.53125},{x:.09375,y:.53125},{x:.15625,y:.53125},{x:.15625,y:.53125},{x:.21875,y:.53125},{x:.21875,y:.53125},{x:.28125,y:.53125},{x:.28125,y:.53125},{x:.34375,y:.53125},{x:.34375,y:.53125},{x:.40625,y:.53125},{x:.40625,y:.53125},{x:.46875,y:.53125},{x:.46875,y:.53125},{x:.53125,y:.53125},{x:.53125,y:.53125},{x:.59375,y:.53125},{x:.59375,y:.53125},{x:.65625,y:.53125},{x:.65625,y:.53125},{x:.71875,y:.53125},{x:.71875,y:.53125},{x:.78125,y:.53125},{x:.78125,y:.53125},{x:.84375,y:.53125},{x:.84375,y:.53125},{x:.90625,y:.53125},{x:.90625,y:.53125},{x:.96875,y:.53125},{x:.96875,y:.53125},{x:.03125,y:.59375},{x:.03125,y:.59375},{x:.09375,y:.59375},{x:.09375,y:.59375},{x:.15625,y:.59375},{x:.15625,y:.59375},{x:.21875,y:.59375},{x:.21875,y:.59375},{x:.28125,y:.59375},{x:.28125,y:.59375},{x:.34375,y:.59375},{x:.34375,y:.59375},{x:.40625,y:.59375},{x:.40625,y:.59375},{x:.46875,y:.59375},{x:.46875,y:.59375},{x:.53125,y:.59375},{x:.53125,y:.59375},{x:.59375,y:.59375},{x:.59375,y:.59375},{x:.65625,y:.59375},{x:.65625,y:.59375},{x:.71875,y:.59375},{x:.71875,y:.59375},{x:.78125,y:.59375},{x:.78125,y:.59375},{x:.84375,y:.59375},{x:.84375,y:.59375},{x:.90625,y:.59375},{x:.90625,y:.59375},{x:.96875,y:.59375},{x:.96875,y:.59375},{x:.03125,y:.65625},{x:.03125,y:.65625},{x:.09375,y:.65625},{x:.09375,y:.65625},{x:.15625,y:.65625},{x:.15625,y:.65625},{x:.21875,y:.65625},{x:.21875,y:.65625},{x:.28125,y:.65625},{x:.28125,y:.65625},{x:.34375,y:.65625},{x:.34375,y:.65625},{x:.40625,y:.65625},{x:.40625,y:.65625},{x:.46875,y:.65625},{x:.46875,y:.65625},{x:.53125,y:.65625},{x:.53125,y:.65625},{x:.59375,y:.65625},{x:.59375,y:.65625},{x:.65625,y:.65625},{x:.65625,y:.65625},{x:.71875,y:.65625},{x:.71875,y:.65625},{x:.78125,y:.65625},{x:.78125,y:.65625},{x:.84375,y:.65625},{x:.84375,y:.65625},{x:.90625,y:.65625},{x:.90625,y:.65625},{x:.96875,y:.65625},{x:.96875,y:.65625},{x:.03125,y:.71875},{x:.03125,y:.71875},{x:.09375,y:.71875},{x:.09375,y:.71875},{x:.15625,y:.71875},{x:.15625,y:.71875},{x:.21875,y:.71875},{x:.21875,y:.71875},{x:.28125,y:.71875},{x:.28125,y:.71875},{x:.34375,y:.71875},{x:.34375,y:.71875},{x:.40625,y:.71875},{x:.40625,y:.71875},{x:.46875,y:.71875},{x:.46875,y:.71875},{x:.53125,y:.71875},{x:.53125,y:.71875},{x:.59375,y:.71875},{x:.59375,y:.71875},{x:.65625,y:.71875},{x:.65625,y:.71875},{x:.71875,y:.71875},{x:.71875,y:.71875},{x:.78125,y:.71875},{x:.78125,y:.71875},{x:.84375,y:.71875},{x:.84375,y:.71875},{x:.90625,y:.71875},{x:.90625,y:.71875},{x:.96875,y:.71875},{x:.96875,y:.71875},{x:.03125,y:.78125},{x:.03125,y:.78125},{x:.09375,y:.78125},{x:.09375,y:.78125},{x:.15625,y:.78125},{x:.15625,y:.78125},{x:.21875,y:.78125},{x:.21875,y:.78125},{x:.28125,y:.78125},{x:.28125,y:.78125},{x:.34375,y:.78125},{x:.34375,y:.78125},{x:.40625,y:.78125},{x:.40625,y:.78125},{x:.46875,y:.78125},{x:.46875,y:.78125},{x:.53125,y:.78125},{x:.53125,y:.78125},{x:.59375,y:.78125},{x:.59375,y:.78125},{x:.65625,y:.78125},{x:.65625,y:.78125},{x:.71875,y:.78125},{x:.71875,y:.78125},{x:.78125,y:.78125},{x:.78125,y:.78125},{x:.84375,y:.78125},{x:.84375,y:.78125},{x:.90625,y:.78125},{x:.90625,y:.78125},{x:.96875,y:.78125},{x:.96875,y:.78125},{x:.03125,y:.84375},{x:.03125,y:.84375},{x:.09375,y:.84375},{x:.09375,y:.84375},{x:.15625,y:.84375},{x:.15625,y:.84375},{x:.21875,y:.84375},{x:.21875,y:.84375},{x:.28125,y:.84375},{x:.28125,y:.84375},{x:.34375,y:.84375},{x:.34375,y:.84375},{x:.40625,y:.84375},{x:.40625,y:.84375},{x:.46875,y:.84375},{x:.46875,y:.84375},{x:.53125,y:.84375},{x:.53125,y:.84375},{x:.59375,y:.84375},{x:.59375,y:.84375},{x:.65625,y:.84375},{x:.65625,y:.84375},{x:.71875,y:.84375},{x:.71875,y:.84375},{x:.78125,y:.84375},{x:.78125,y:.84375},{x:.84375,y:.84375},{x:.84375,y:.84375},{x:.90625,y:.84375},{x:.90625,y:.84375},{x:.96875,y:.84375},{x:.96875,y:.84375},{x:.03125,y:.90625},{x:.03125,y:.90625},{x:.09375,y:.90625},{x:.09375,y:.90625},{x:.15625,y:.90625},{x:.15625,y:.90625},{x:.21875,y:.90625},{x:.21875,y:.90625},{x:.28125,y:.90625},{x:.28125,y:.90625},{x:.34375,y:.90625},{x:.34375,y:.90625},{x:.40625,y:.90625},{x:.40625,y:.90625},{x:.46875,y:.90625},{x:.46875,y:.90625},{x:.53125,y:.90625},{x:.53125,y:.90625},{x:.59375,y:.90625},{x:.59375,y:.90625},{x:.65625,y:.90625},{x:.65625,y:.90625},{x:.71875,y:.90625},{x:.71875,y:.90625},{x:.78125,y:.90625},{x:.78125,y:.90625},{x:.84375,y:.90625},{x:.84375,y:.90625},{x:.90625,y:.90625},{x:.90625,y:.90625},{x:.96875,y:.90625},{x:.96875,y:.90625},{x:.03125,y:.96875},{x:.03125,y:.96875},{x:.09375,y:.96875},{x:.09375,y:.96875},{x:.15625,y:.96875},{x:.15625,y:.96875},{x:.21875,y:.96875},{x:.21875,y:.96875},{x:.28125,y:.96875},{x:.28125,y:.96875},{x:.34375,y:.96875},{x:.34375,y:.96875},{x:.40625,y:.96875},{x:.40625,y:.96875},{x:.46875,y:.96875},{x:.46875,y:.96875},{x:.53125,y:.96875},{x:.53125,y:.96875},{x:.59375,y:.96875},{x:.59375,y:.96875},{x:.65625,y:.96875},{x:.65625,y:.96875},{x:.71875,y:.96875},{x:.71875,y:.96875},{x:.78125,y:.96875},{x:.78125,y:.96875},{x:.84375,y:.96875},{x:.84375,y:.96875},{x:.90625,y:.96875},{x:.90625,y:.96875},{x:.96875,y:.96875},{x:.96875,y:.96875},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375}];var Hb=class{constructor(t){var n;this.model=t,this.anchors=W$.map(r=>[r.x,r.y]),this.anchorsTensor=ra(this.anchors),this.inputSize=(n=this.model)==null?void 0:n.inputs[0].shape[2],this.inputSizeTensor=ur([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=ur([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){return Ue(()=>{let n=Ze(t,[0,0],[-1,2]),r=Ze(t,[0,2],[-1,2]),s=Me(Qe(n,this.inputSizeTensor),this.anchorsTensor),a=Qe(r,this.doubleInputSizeTensor),o=fe(He(s,a),this.inputSizeTensor),i=fe(Me(s,a),this.inputSizeTensor);return lc([o,i],1)})}normalizeLandmarks(t,n){return Ue(()=>{let r=Me(Qe(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return fe(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),s=Zn(r);r.dispose();let a=Ue(()=>Ts(Ze(s,[0,0],[-1,1])).squeeze()),o=a.dataSync(),i=Ze(s,[0,1],[-1,4]),l=this.normalizeBoxes(i);i.dispose();let u=await Ye.nonMaxSuppressionAsync(l,o,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),c=u.arraySync();a.dispose(),u.dispose();let d=[];for(let h of c)if(o[h]>=n.hand.minConfidence){let p=Ze(l,[h,0],[1,-1]),f=Ze(s,[h,5],[1,14]),m=Ue(()=>this.normalizeLandmarks(f,h).reshape([-1,2]));f.dispose(),d.push({box:p,palmLandmarks:m,confidence:o[h]})}return s.dispose(),l.dispose(),d}async estimateHandBounds(t,n){let r=t.shape[1],s=t.shape[2],a=Ue(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),o=await this.getBoxes(a,n);a.dispose();let i=[];if(!o||o.length===0)return i;for(let l of o){let u=l.box.dataSync(),c=u.slice(0,2),d=u.slice(2,4),h=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),i.push(B$({startPoint:c,endPoint:d,palmLandmarks:h,confidence:l.confidence},[s/this.inputSize,r/this.inputSize]))}return i}};function Yve(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function V$(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Yve(n)}var U$=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ro(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Jve(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function H$(e,t){let n=[],r=e.length;for(let s=0;s<r;s++){n.push([]);for(let a=0;a<r;a++)n[s].push(ro(e[s],Jve(t,a)))}return n}function Gb(e,t){let n=Math.cos(e),r=Math.sin(e),s=[[n,-r,0],[r,n,0],[0,0,1]],a=U$(t[0],t[1]),o=H$(a,s),i=U$(-t[0],-t[1]);return H$(o,i)}function G$(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-ro(t[0],n),-ro(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function jb(e,t){return[ro(e,t[0]),ro(e,t[1])]}var Qve=5,j$=1.65,q$=[0,5,9,13,17,1,2],ewe=0,twe=2,qb=class{constructor(t,n){var r;this.handDetector=t,this.handPoseModel=n,this.inputSize=(r=this.handPoseModel)==null?void 0:r.inputs[0].shape[2],this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),r=t.map(o=>o[1]),s=[Math.min(...n),Math.min(...r)],a=[Math.max(...n),Math.max(...r)];return{startPoint:s,endPoint:a}}getBoxForPalmLandmarks(t,n){let r=t.map(a=>jb([...a,1],n)),s=this.calculateLandmarksBoundingBox(r);return f0(m0(s),Qve)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=f0(m0(n),j$);r.palmLandmarks=[];for(let s=0;s<q$.length;s++)r.palmLandmarks.push(t[q$[s]].slice(0,2));return r}transformRawCoords(t,n,r,s){let a=p0(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(p=>[o[0]*(p[0]-this.inputSize/2),o[1]*(p[1]-this.inputSize/2),o[2]*p[2]]),l=Gb(r,[0,0]),u=i.map(p=>[...jb(p,l),p[2]]),c=G$(s),d=[...Sh(n),1],h=[ro(d,c[0]),ro(d,c[1])];return u.map(p=>[Math.trunc(p[0]+h[0]),Math.trunc(p[1]+h[1]),Math.trunc(p[2])])}async estimateHands(t,n){let r=!1,s;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(s=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(r=!0));let a=[];for(let o=0;o<this.storedBoxes.length;o++){let i=this.storedBoxes[o];if(!!i)if(n.hand.landmarks){let l=n.hand.rotation?V$(i.palmLandmarks[ewe],i.palmLandmarks[twe]):0,u=Sh(i),c=[u[0]/t.shape[2],u[1]/t.shape[1]],d=n.hand.rotation&&Sr.flags.IS_BROWSER?Ye.rotateWithOffset(t,l,0,c):t.clone(),h=Gb(-l,u),p=r?this.getBoxForPalmLandmarks(i.palmLandmarks,h):i,f=L$(p,d,[this.inputSize,this.inputSize]),m=f.div(255);f.dispose(),d.dispose();let[g,y]=await this.handPoseModel.predict(m);m.dispose();let A=g.dataSync()[0];if(g.dispose(),A>=n.hand.minConfidence){let x=ue(y,[-1,3]),b=x.arraySync();y.dispose(),x.dispose();let v=this.transformRawCoords(b,p,l,h),w=this.getBoxForHandLandmarks(v);this.storedBoxes[o]={...w,confidence:A};let S={landmarks:v,confidence:A,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};a.push(S)}else this.storedBoxes[o]=null;y.dispose()}else{let l=f0(m0(i),j$),u={confidence:i.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};a.push(u)}}return this.storedBoxes=this.storedBoxes.filter(o=>o!==null),this.detectedHands=a.length,a}};var K$={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},so,ao,X$;async function Kb(e,t){let n=await X$.estimateHands(e,t);if(!n)return[];let r=[];for(let s=0;s<n.length;s++){let a={};if(n[s].landmarks)for(let u of Object.keys(K$))a[u]=K$[u].map(c=>n[s].landmarks[c]);let o=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]<i[0]&&(i[0]=u[0]),u[1]<i[1]&&(i[1]=u[1]),u[0]>i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],l=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];r.push({id:s,score:Math.round(100*n[s].confidence)/100,box:i,boxRaw:l,keypoints:o,annotations:a})}return r}async function Xb(e){!so||!ao?([so,ao]=await Promise.all([e.hand.enabled?Et($t(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?Et($t(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!so||!so.modelUrl?me("load model failed:",e.hand.detector.modelPath):e.debug&&me("load model:",so.modelUrl),!ao||!ao.modelUrl?me("load model failed:",e.hand.skeleton.modelPath):e.debug&&me("load model:",ao.modelUrl))):(e.debug&&me("cached model:",so.modelUrl),e.debug&&me("cached model:",ao.modelUrl));let t=new Hb(so);return X$=new qb(t,ao),[so,ao]}var Z$=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],Y$=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var rr;async function g0(e){return rr?e.debug&&me("cached model:",rr.modelUrl):(rr=await Et($t(e.modelBasePath,e.body.modelPath)),rr.width=parseInt(rr.signature.inputs["input_1:0"].tensorShape.dim[2].size),rr.height=parseInt(rr.signature.inputs["input_1:0"].tensorShape.dim[1].size),!rr||!rr.modelUrl?me("load model failed:",e.body.modelPath):e.debug&&me("load model:",rr.modelUrl)),rr}async function Zb(e,t){var m;if(!rr)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},r=Ye.resizeBilinear(e,[rr.width,rr.height],!1),s=Qe(r,[255]);r.dispose();let a=await rr.predict(s),o=((m=a.find(g=>g.size===195||g.size===155))==null?void 0:m.dataSync())||[];a.forEach(g=>g.dispose()),s.dispose();let i=[],l=(o==null?void 0:o.length)===195?Z$:Y$,u=5;for(let g=0;g<o.length/u;g++)i.push({id:g,part:l[g],position:[Math.trunc(n.width*o[u*g+0]/255),Math.trunc(n.height*o[u*g+1]/255),Math.trunc(o[u*g+2])+0],positionRaw:[o[u*g+0]/255,o[u*g+1]/255,o[u*g+2]+0],score:(100-Math.trunc(100/(1+Math.exp(o[u*g+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(o[u*g+4]))))/100});let c=i.map(g=>g.position[0]),d=i.map(g=>g.position[1]),h=[Math.min(...c),Math.min(...d),Math.max(...c)-Math.min(...c),Math.max(...d)-Math.min(...c)],p=[0,0,0,0],f=i.reduce((g,y)=>y.score>g?y.score:g,0);return[{id:0,score:f,box:h,boxRaw:p,keypoints:i}]}var sr,Ws=[],Yb=[0,0,0,0],Jb=[0,0,0,0],y0=0,Qb=Number.MAX_SAFE_INTEGER,nwe=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function J$(e){return sr?e.debug&&me("cached model:",sr.modelUrl):(sr=await Et($t(e.modelBasePath,e.body.modelPath)),!sr||!sr.modelUrl?me("load model failed:",e.body.modelPath):e.debug&&me("load model:",sr.modelUrl)),sr}function rwe(e,t){let[n,r]=e.shape;return Ue(()=>{let s=(i,l)=>He(i,fe(Qe(i,ut(l,"int32")),ut(l,"int32"))),a=ue(e,[r*n]),o=_a(a,0).dataSync()[0];if(o>t){let i=Y2(a,0),l=s(i,n).dataSync()[0],u=Qe(i,ut(n,"int32")).dataSync()[0];return[l,u,o]}return[0,0,o]})}async function e3(e,t){return Qb<t.body.skipFrames&&t.skipFrame&&Object.keys(Ws).length>0?(Qb++,[{id:0,score:y0,box:Yb,boxRaw:Jb,keypoints:Ws}]):(Qb=0,new Promise(async n=>{let r=Ue(()=>{if(!sr.inputs[0].shape)return null;let u=Ye.resizeBilinear(e,[sr.inputs[0].shape[2],sr.inputs[0].shape[1]],!1);return fe(u,2).sub(1)}),s;if(t.body.enabled&&(s=await sr.predict(r)),r.dispose(),s){Ws.length=0;let u=s.squeeze();Ve(s);let c=u.unstack(2);Ve(u);for(let d=0;d<c.length;d++){let[h,p,f]=rwe(c[d],t.body.minConfidence);y0>t.body.minConfidence&&Ws.push({score:Math.round(100*f)/100,part:nwe[d],positionRaw:[h/sr.inputs[0].shape[2],p/sr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*h/sr.inputs[0].shape[2]),Math.round(e.shape[1]*p/sr.inputs[0].shape[1])]})}c.forEach(d=>Ve(d))}y0=Ws.reduce((u,c)=>c.score>u?c.score:u,0);let a=Ws.map(u=>u.position[0]),o=Ws.map(u=>u.position[1]);Yb=[Math.min(...a),Math.min(...o),Math.max(...a)-Math.min(...a),Math.max(...o)-Math.min(...o)];let i=Ws.map(u=>u.positionRaw[0]),l=Ws.map(u=>u.positionRaw[1]);Jb=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)],n([{id:0,score:y0,box:Yb,boxRaw:Jb,keypoints:Ws}])}))}var vs,Vs=[],t3=[0,0,0,0],n3=[0,0,0,0],_u=0,r3=Number.MAX_SAFE_INTEGER,swe=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function s3(e){return vs?e.debug&&me("cached model:",vs.modelUrl):(vs=await Et($t(e.modelBasePath,e.body.modelPath)),!vs||!vs.modelUrl?me("load model failed:",e.body.modelPath):e.debug&&me("load model:",vs.modelUrl)),vs}async function a3(e,t){return r3<t.body.skipFrames&&t.skipFrame&&Object.keys(Vs).length>0?(r3++,[{id:0,score:_u,box:t3,boxRaw:n3,keypoints:Vs}]):(r3=0,new Promise(async n=>{let r=Ue(()=>{if(!vs.inputs[0].shape)return null;let u=Ye.resizeBilinear(e,[vs.inputs[0].shape[2],vs.inputs[0].shape[1]],!1);return Pt(u,"int32")}),s;if(t.body.enabled&&(s=await vs.predict(r)),r.dispose(),s){Vs.length=0;let u=s.arraySync();Ve(s);let c=u[0][0];for(let d=0;d<c.length;d++)_u=c[d][2],_u>t.body.minConfidence&&Vs.push({score:Math.round(100*_u)/100,part:swe[d],positionRaw:[c[d][1],c[d][0]],position:[Math.round((e.shape[2]||0)*c[d][1]),Math.round((e.shape[1]||0)*c[d][0])]})}_u=Vs.reduce((u,c)=>c.score>u?c.score:u,0);let a=Vs.map(u=>u.position[0]),o=Vs.map(u=>u.position[1]);t3=[Math.min(...a),Math.min(...o),Math.max(...a)-Math.min(...a),Math.max(...o)-Math.min(...o)];let i=Vs.map(u=>u.positionRaw[0]),l=Vs.map(u=>u.positionRaw[1]);n3=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)],n([{id:0,score:_u,box:t3,boxRaw:n3,keypoints:Vs}])}))}var Ru=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var Ar,o3=[],i3=Number.MAX_SAFE_INTEGER,A0=2.5;async function l3(e){if(Ar)e.debug&&me("cached model:",Ar.modelUrl);else{Ar=await Et($t(e.modelBasePath,e.object.modelPath));let t=Object.values(Ar.modelSignature.inputs);if(Ar.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Ar.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Ar||!Ar.modelUrl?me("load model failed:",e.object.modelPath):e.debug&&me("load model:",Ar.modelUrl)}return Ar}async function awe(e,t,n,r){let s=0,a=[];for(let u of[1,2,4])Ue(()=>{var g,y;let c=u*13,d=(g=e.find(A=>A.shape[1]===c**2&&A.shape[2]===Ru.length))==null?void 0:g.squeeze(),h=(y=e.find(A=>A.shape[1]===c**2&&A.shape[2]<Ru.length))==null?void 0:y.squeeze(),f=h.reshape([-1,4,h.shape[1]/4]).argMax(2).arraySync(),m=d.arraySync();for(let A=0;A<d.shape[0];A++)for(let x=0;x<d.shape[1];x++){let b=m[A][x];if(b>r.object.minConfidence&&x!==61){let v=(.5+Math.trunc(A%c))/c,w=(.5+Math.trunc(A/c))/c,S=f[A].map(B=>B*(c/u/t)),[I,E]=[v-A0/u*S[0],w-A0/u*S[1]],[F,$]=[v+A0/u*S[2]-I,w+A0/u*S[3]-E],_=[I,E,F,$];_=_.map(B=>Math.max(0,Math.min(B,1)));let N=[_[0]*n[0],_[1]*n[1],_[2]*n[0],_[3]*n[1]],P={id:s++,score:Math.round(100*b)/100,class:x+1,label:Ru[x].label,box:N.map(B=>Math.trunc(B)),boxRaw:_};a.push(P)}}});e.forEach(u=>Ve(u));let o=a.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),i=a.map(u=>u.score),l=[];if(o&&o.length>0){let u=await Ye.nonMaxSuppressionAsync(o,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);l=u.dataSync(),Ve(u)}return a=a.filter((u,c)=>l.includes(c)).sort((u,c)=>c.score-u.score),a}async function u3(e,t){return i3<t.object.skipFrames&&t.skipFrame&&o3.length>0?(i3++,o3):(i3=0,new Promise(async n=>{let r=[e.shape[2],e.shape[1]],s=Ye.resizeBilinear(e,[Ar.inputSize,Ar.inputSize],!1),a=s.div(255),o=a.transpose([0,3,1,2]);a.dispose(),s.dispose();let i;t.object.enabled&&(i=await Ar.predict(o)),o.dispose();let l=await awe(i,Ar.inputSize,r,t);o3=l,n(l)}))}var xr,c3=[],d3=Number.MAX_SAFE_INTEGER;async function h3(e){if(xr)e.debug&&me("cached model:",xr.modelUrl);else{xr=await Et($t(e.modelBasePath,e.object.modelPath));let t=Object.values(xr.modelSignature.inputs);if(xr.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!xr.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!xr||!xr.modelUrl?me("load model failed:",e.object.modelPath):e.debug&&me("load model:",xr.modelUrl)}return xr}async function owe(e,t,n,r){if(!e)return[];let s=[],a=e.arraySync(),o=Zn(e);e.dispose();let i=ta(o,6,1);o.dispose();let u=So([i[1],i[0],i[3],i[2]],1).squeeze(),c=i[4].squeeze(),d=i[5].squeeze();i.forEach(m=>m.dispose());let h=await Ye.nonMaxSuppressionAsync(u,c,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);u.dispose(),c.dispose(),d.dispose();let p=h.dataSync();h.dispose();let f=0;for(let m of p){let g=Math.trunc(100*a[0][m][4])/100,y=a[0][m][5],A=Ru[y].label,[x,b]=[a[0][m][0]/t,a[0][m][1]/t],v=[x,b,a[0][m][2]/t-x,a[0][m][3]/t-b],w=[Math.trunc(v[0]*n[0]),Math.trunc(v[1]*n[1]),Math.trunc(v[2]*n[0]),Math.trunc(v[3]*n[1])];s.push({id:f++,score:g,class:y,label:A,box:w,boxRaw:v})}return s}async function p3(e,t){return d3<t.object.skipFrames&&t.skipFrame&&c3.length>0?(d3++,c3):(d3=0,new Promise(async n=>{let r=[e.shape[2],e.shape[1]],s=Ye.resizeBilinear(e,[xr.inputSize,xr.inputSize]),a=t.object.enabled?xr.execute(s,["tower_0/detections"]):null;s.dispose();let o=await owe(a,xr.inputSize,r,t);c3=o,n(o)}))}function iwe(e,t,n){let r=function(i,l,u){let c=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");i.replace(c,(d,h)=>(u[h]=0,d))},s=function(i,l){let u=e.createShader(l);if(e.shaderSource(u,i),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let a=s(t,e.VERTEX_SHADER),o=s(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,a),e.attachShader(this.id,o),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),r(t,"attribute",this.attribute);for(let i in this.attribute)this.attribute[i]=e.getAttribLocation(this.id,i);r(t,"uniform",this.uniform),r(n,"uniform",this.uniform);for(let i in this.uniform)this.uniform[i]=e.getUniformLocation(this.id,i)}function Q$(e){e||(e={});let t=0,n=null,r=!1,s=-1,a=[null,null],o=[],i=-1,l=-1,u=null,c=null,d={},h=e.canvas||document.createElement("canvas"),p={},f={INTERMEDIATE:1},m=h.getContext("webgl");if(!m)throw new Error("Filter: getContext() failed");this.addFilter=function(v){let w=Array.prototype.slice.call(arguments,1),S=d[v];o.push({func:S,args:w})},this.reset=function(){o=[]};let g=function(v,w){if(!(v===i&&w===l)){if(h.width=v,i=v,h.height=w,l=w,!u){let S=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);u=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,u),m.bufferData(m.ARRAY_BUFFER,S,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,i,l),a=[null,null]}},y=function(v,w){let S=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,S);let I=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,I);let E=m.createTexture();return m.bindTexture(m.TEXTURE_2D,E),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,v,w,0,m.RGBA,m.UNSIGNED_BYTE,null),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.framebufferTexture2D(m.FRAMEBUFFER,m.COLOR_ATTACHMENT0,m.TEXTURE_2D,E,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:S,texture:E}},A=function(v){return a[v]=a[v]||y(i,l),a[v]},x=function(v=null){var E,F;let w=null,S=null,I=!1;t===0?w=n:w=(E=A(s))==null?void 0:E.texture,t++,r&&!(v&f.INTERMEDIATE)?(S=null,I=t%2==0):(s=(s+1)%2,S=(F=A(s))==null?void 0:F.fbo),m.bindTexture(m.TEXTURE_2D,w),m.bindFramebuffer(m.FRAMEBUFFER,S),m.uniform1f(c.uniform.flipY,I?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(v){if(g(v.width,v.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,v),o.length===0)return x(),h;for(let w=0;w<o.length;w++){r=w===o.length-1;let S=o[w];S.func.apply(this,S.args||[])}return h};let b=function(v){if(p[v])return c=p[v],m.useProgram(c.id),c;let w={};w.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
`),w.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`),c=new iwe(m,w.VERTEX_IDENTITY,v);let S=Float32Array.BYTES_PER_ELEMENT,I=4*S;return m.enableVertexAttribArray(c.attribute.pos),m.vertexAttribPointer(c.attribute.pos,2,m.FLOAT,!1,I,0*S),m.enableVertexAttribArray(c.attribute.uv),m.vertexAttribPointer(c.attribute.uv,2,m.FLOAT,!1,I,2*S),p[v]=c,c};d.colorMatrix=function(v){let w=new Float32Array(v);w[4]/=255,w[9]/=255,w[14]/=255,w[19]/=255;let S=w[18]===1&&w[3]===0&&w[8]===0&&w[13]===0&&w[15]===0&&w[16]===0&&w[17]===0&&w[19]===0?d.colorMatrix.SHADER.WITHOUT_ALPHA:d.colorMatrix.SHADER.WITH_ALPHA,I=b(S);m.uniform1fv(I.uniform.m,w),x()},d.colorMatrix.SHADER={},d.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
`),d.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
`),d.brightness=function(v){let w=(v||0)+1;d.colorMatrix([w,0,0,0,0,0,w,0,0,0,0,0,w,0,0,0,0,0,1,0])},d.saturation=function(v){let w=(v||0)*2/3+1,S=(w-1)*-.5;d.colorMatrix([w,S,S,0,0,S,w,S,0,0,S,S,w,0,0,0,0,0,1,0])},d.desaturate=function(){d.saturation(-1)},d.contrast=function(v){let w=(v||0)+1,S=-128*(w-1);d.colorMatrix([w,0,0,0,S,0,w,0,0,S,0,0,w,0,S,0,0,0,1,0])},d.negative=function(){d.contrast(-2)},d.hue=function(v){v=(v||0)/180*Math.PI;let w=Math.cos(v),S=Math.sin(v),I=.213,E=.715,F=.072;d.colorMatrix([I+w*(1-I)+S*-I,E+w*-E+S*-E,F+w*-F+S*(1-F),0,0,I+w*-I+S*.143,E+w*(1-E)+S*.14,F+w*-F+S*-.283,0,0,I+w*-I+S*-(1-I),E+w*-E+S*E,F+w*(1-F)+S*F,0,0,0,0,0,1,0])},d.desaturateLuminance=function(){d.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},d.sepia=function(){d.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},d.brownie=function(){d.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},d.vintagePinhole=function(){d.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},d.kodachrome=function(){d.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},d.technicolor=function(){d.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},d.polaroid=function(){d.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},d.shiftToBGR=function(){d.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},d.convolution=function(v){let w=new Float32Array(v),S=1/i,I=1/l,E=b(d.convolution.SHADER);m.uniform1fv(E.uniform.m,w),m.uniform2f(E.uniform.px,S,I),x()},d.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
`),d.detectEdges=function(){d.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},d.sobelX=function(){d.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},d.sobelY=function(){d.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},d.sharpen=function(v){let w=v||1;d.convolution.call(this,[0,-1*w,0,-1*w,1+4*w,-1*w,0,-1*w,0])},d.emboss=function(v){let w=v||1;d.convolution.call(this,[-2*w,-1*w,0,-1*w,1,1*w,0,1*w,2*w])},d.blur=function(v){let w=v/7/i,S=v/7/l,I=b(d.blur.SHADER);m.uniform2f(I.uniform.px,0,S),x(f.INTERMEDIATE),m.uniform2f(I.uniform.px,w,0),x()},d.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;","}"].join(`
`),d.pixelate=function(v){let w=v/i,S=v/l,I=b(d.pixelate.SHADER);m.uniform2f(I.uniform.size,w,S),x()},d.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
`)}var x0=2048,Oe,Bt,rn;function wi(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof Tt)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof Tt)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Js(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let s=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!s||!a)return{tensor:null,canvas:Oe};let o=s,i=a;if(o>x0&&(o=x0,i=o*a/s),i>x0&&(i=x0,o=i*s/a),t.filter.width>0?o=t.filter.width:t.filter.height>0&&(o=s*(t.filter.height/a)),t.filter.height>0?i=t.filter.height:t.filter.width>0&&(i=a*(t.filter.width/s)),!o||!i)throw new Error("Human: Input cannot determine dimension");(!Oe||(Oe==null?void 0:Oe.width)!==o||(Oe==null?void 0:Oe.height)!==i)&&(Oe=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(o,i):document.createElement("canvas"),(Oe==null?void 0:Oe.width)!==o&&(Oe.width=o),(Oe==null?void 0:Oe.height)!==i&&(Oe.height=i));let l=Oe.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(s,0),l.scale(-1,1),l.drawImage(e,0,0,s,a,0,0,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,s,a,0,0,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),t.filter.enabled){if((!rn||!Bt||Oe.width!==Bt.width||(Oe==null?void 0:Oe.height)!==(Bt==null?void 0:Bt.height))&&(Bt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height):document.createElement("canvas"),(Bt==null?void 0:Bt.width)!==(Oe==null?void 0:Oe.width)&&(Bt.width=Oe==null?void 0:Oe.width),(Bt==null?void 0:Bt.height)!==(Oe==null?void 0:Oe.height)&&(Bt.height=Oe==null?void 0:Oe.height),rn=Sr.flags.IS_BROWSER?new Q$({canvas:Bt}):null),!rn)return{tensor:null,canvas:Oe};rn.reset(),rn.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&rn.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&rn.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&rn.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&rn.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&rn.addFilter("hue",t.filter.hue),t.filter.negative&&rn.addFilter("negative"),t.filter.sepia&&rn.addFilter("sepia"),t.filter.vintage&&rn.addFilter("brownie"),t.filter.sepia&&rn.addFilter("sepia"),t.filter.kodachrome&&rn.addFilter("kodachrome"),t.filter.technicolor&&rn.addFilter("technicolor"),t.filter.polaroid&&rn.addFilter("polaroid"),t.filter.pixelate!==0&&rn.addFilter("pixelate",t.filter.pixelate),rn.apply(Oe)}else Bt=Oe,rn&&(rn=null);let u;if(Bt.data){let c=[Bt.height,Bt.width,3];u=mp(Bt.data,c,"int32")}else if(Bt instanceof ImageData)u=Hr?Hr.fromPixels(Bt):null;else if(t.backend==="webgl"||t.backend==="humangl"){let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(o,i):document.createElement("canvas");c.width=o,c.height=i;let d=c.getContext("2d");d==null||d.drawImage(Bt,0,0),u=Hr?Hr.fromPixels(c):null}else{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(o,i):document.createElement("canvas");c.width=o,c.height=i;let d=c.getContext("2d");d==null||d.drawImage(Bt,0,0);let h=d==null?void 0:d.getImageData(0,0,o,i);u=Hr?Hr.fromPixels(h):null}if(u){let c=u.toFloat();n=c.expandDims(0),u.dispose(),c.dispose()}}let r=t.filter.return?Bt:null;return{tensor:n,canvas:r}}var Wr,f3=!1;async function b0(e){return Wr?e.debug&&me("cached model:",Wr.modelUrl):(Wr=await Et($t(e.modelBasePath,e.segmentation.modelPath)),!Wr||!Wr.modelUrl?me("load model failed:",e.segmentation.modelPath):e.debug&&me("load model:",Wr.modelUrl)),Wr}async function m3(e){var f,m;let t=((f=e.tensor)==null?void 0:f.shape[1])||0,n=((m=e.tensor)==null?void 0:m.shape[2])||0;if(!e.tensor||!Wr||!Wr.inputs[0].shape)return null;let r=Ye.resizeBilinear(e.tensor,[Wr.inputs[0].shape[1],Wr.inputs[0].shape[2]],!1),s=r.div(255),a=Wr.predict(s);Ve(r),Ve(s);let o=Zn(a,0),i;if(o.shape[2]===2){let g=o.softmax(),[y,A]=fc(g,2),x=A.expandDims(2),b=x.expandDims(0);Ve(g),Ve(y),Ve(A);let v=Ye.cropAndResize(b,[[0,0,.5,.5]],[0],[t,n]);i=v.squeeze(0),Ve(v),Ve(x),Ve(b)}else i=Ye.resizeBilinear(o,[t,n]);if(typeof document=="undefined")return i.dataSync();let l=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");l.width=t,l.height=n,Hr&&await Hr.toPixels(i,l),Ve(i),Ve(o),Ve(a);let u=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");u.width=t,u.height=n;let c=u.getContext("2d");c.filter="blur(8px",await c.drawImage(l,0,0);let d=c.getImageData(0,0,t,n).data,h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");h.width=t,h.height=n;let p=h.getContext("2d");return e.canvas&&await p.drawImage(e.canvas,0,0),p.globalCompositeOperation="darken",p.filter="blur(8px)",await p.drawImage(l,0,0),p.globalCompositeOperation="source-over",p.filter="none",e.canvas=h,d}async function e_(e,t,n){var a;if(f3)return null;f3=!0,Wr||await b0(n);let r=wi(e,n),s=await m3(r);if(Ve(r.tensor),t&&s){let o=wi(t,n),i=o.canvas;Ve(o.tensor);let l=r.canvas,u=(a=l.getContext("2d"))==null?void 0:a.getImageData(0,0,l.width,l.height).data,c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(l.width,l.height):document.createElement("canvas");c.width=l.width,c.height=l.height;let d=c.getContext("2d");d.globalCompositeOperation="copy",d.drawImage(i,0,0,c.width,c.height);let h=d.getImageData(0,0,c.width,c.height);for(let p=0;p<c.width*c.height;p++)h.data[4*p+0]=(255-s[4*p+0])/255*h.data[4*p+0]+s[4*p+0]/255*u[4*p+0],h.data[4*p+1]=(255-s[4*p+1])/255*h.data[4*p+1]+s[4*p+1]/255*u[4*p+1],h.data[4*p+2]=(255-s[4*p+2])/255*h.data[4*p+2]+s[4*p+2]/255*u[4*p+2],h.data[4*p+3]=(255-s[4*p+3])/255*h.data[4*p+3]+s[4*p+3]/255*u[4*p+3];d.putImageData(h,0,0),r.canvas=c}return f3=!1,r.canvas}async function t_(e){e.config.async?[e.models.face,e.models.emotion,e.models.handpose,e.models.posenet,e.models.blazepose,e.models.efficientpose,e.models.movenet,e.models.nanodet,e.models.centernet,e.models.faceres,e.models.segmentation]=await Promise.all([e.models.face||(e.config.face.enabled?Nb(e.config):null),e.models.emotion||(e.config.face.enabled&&e.config.face.emotion.enabled?Mb(e.config):null),e.models.handpose||(e.config.hand.enabled?Xb(e.config):null),e.models.posenet||(e.config.body.enabled&&e.config.body.modelPath.includes("posenet")?Ub(e.config):null),e.models.blazepose||(e.config.body.enabled&&e.config.body.modelPath.includes("blazepose")?g0(e.config):null),e.models.efficientpose||(e.config.body.enabled&&e.config.body.modelPath.includes("efficientpose")?J$(e.config):null),e.models.movenet||(e.config.body.enabled&&e.config.body.modelPath.includes("movenet")?s3(e.config):null),e.models.nanodet||(e.config.object.enabled&&e.config.object.modelPath.includes("nanodet")?l3(e.config):null),e.models.centernet||(e.config.object.enabled&&e.config.object.modelPath.includes("centernet")?h3(e.config):null),e.models.faceres||(e.config.face.enabled&&e.config.face.description.enabled?Eb(e.config):null),e.models.segmentation||(e.config.segmentation.enabled?b0(e.config):null)]):(e.config.face.enabled&&!e.models.face&&(e.models.face=await Nb(e.config)),e.config.face.enabled&&e.config.face.emotion.enabled&&!e.models.emotion&&(e.models.emotion=await Mb(e.config)),e.config.hand.enabled&&!e.models.handpose&&(e.models.handpose=await Xb(e.config)),e.config.body.enabled&&!e.models.posenet&&e.config.body.modelPath.includes("posenet")&&(e.models.posenet=await Ub(e.config)),e.config.body.enabled&&!e.models.blazepose&&e.config.body.modelPath.includes("blazepose")&&(e.models.blazepose=await g0(e.config)),e.config.body.enabled&&!e.models.efficientpose&&e.config.body.modelPath.includes("efficientpose")&&(e.models.efficientpose=await g0(e.config)),e.config.body.enabled&&!e.models.movenet&&e.config.body.modelPath.includes("movenet")&&(e.models.movenet=await s3(e.config)),e.config.object.enabled&&!e.models.nanodet&&e.config.object.modelPath.includes("nanodet")&&(e.models.nanodet=await l3(e.config)),e.config.object.enabled&&!e.models.centernet&&e.config.object.modelPath.includes("centernet")&&(e.models.centernet=await h3(e.config)),e.config.face.enabled&&e.config.face.description.enabled&&!e.models.faceres&&(e.models.faceres=await Eb(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=await b0(e.config)))}var lwe=e=>{let t=(d,h)=>Math.atan2(d[1]-h[1],d[0]-h[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],r=1,s=e.mesh[33][2]>e.mesh[263][2],a=s?e.mesh[473]:e.mesh[468],o=s?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=s?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],r*(a[1]-o[1])/i[1]-n[1]],u=Math.sqrt(l[0]**2+l[1]**2);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},uwe=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},r=(g,y)=>{let A=g[0]-y[0],x=g[1]-y[1],b=g[2]-y[2];return[A,x,b]},s=(g,y)=>{let A=g[1]*y[2]-g[2]*y[1],x=g[2]*y[0]-g[0]*y[2],b=g[0]*y[1]-g[1]*y[0];return[A,x,b]},a=g=>{let[y,A,x,b,v,w,S,I,E]=g,F,$,_;return b<1?b>-1?(_=Math.asin(b),$=Math.atan2(-S,y),F=Math.atan2(-w,v)):(_=-Math.PI/2,$=-Math.atan2(I,E),F=0):(_=Math.PI/2,$=Math.atan2(I,E),F=0),{pitch:2*-F,yaw:2*-$,roll:2*-_}},o=g=>{let y=(x,b,v,w)=>Math.atan2(w-b,v-x);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),c=n(r(u[1],u[0])),d=n(r(u[3],u[2])),h=n(s(d,c));d=s(c,h);let p=[d[0],d[1],d[2],c[0],c[1],c[2],h[0],h[1],h[2]],f=a(p),m=i.length===478?lwe(e):{bearing:0,strength:0};return{angle:f,matrix:p,gaze:m}},g3=async(e,t)=>{var c,d,h,p,f,m;let n,r,s,a,o,i,l=[];e.state="run:face",n=at();let u=await S$(t,e.config);if(e.performance.face=Math.trunc(at()-n),!t.shape||t.shape.length!==4)return[];if(!u)return[];for(let g=0;g<u.length;g++){if(e.analyze("Get Face"),!u[g].image||u[g].image.isDisposedInternal){me("Face object is disposed:",u[g].image);continue}let y=uwe(u[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?a=e.config.face.emotion.enabled?Ob(u[g].image||ts([]),e.config,g,u.length):{}:(e.state="run:emotion",n=at(),a=e.config.face.emotion.enabled?await Ob(u[g].image||ts([]),e.config,g,u.length):{},e.performance.emotion=Math.trunc(at()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?i=e.config.face.description.enabled?Rb(u[g].image||ts([]),e.config,g,u.length):[]:(e.state="run:description",n=at(),i=e.config.face.description.enabled?await Rb(u[g].image||ts([]),e.config,g,u.length):[],e.performance.embedding=Math.trunc(at()-n)),e.analyze("End Description:"),e.config.async&&([r,s,a,o,i]=await Promise.all([r,s,a,o,i])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((d=(c=u[g])==null?void 0:c.annotations)==null?void 0:d.leftEyeIris)&&((p=(h=u[g])==null?void 0:h.annotations)==null?void 0:p.rightEyeIris)&&(delete u[g].annotations.leftEyeIris,delete u[g].annotations.rightEyeIris);let A=((f=u[g].annotations)==null?void 0:f.leftEyeIris)&&((m=u[g].annotations)==null?void 0:m.rightEyeIris)?Math.max(Math.abs(u[g].annotations.leftEyeIris[3][0]-u[g].annotations.leftEyeIris[1][0]),Math.abs(u[g].annotations.rightEyeIris[4][1]-u[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0;l.push({...u[g],id:g,age:i.age,gender:i.gender,genderScore:i.genderScore,embedding:i.descriptor,emotion:a,iris:A!==0?Math.trunc(500/A/11.7)/100:0,rotation:y,tensor:e.config.face.detector.return?Zn(u[g].image):null}),Ve(u[g].image),u[g].image&&delete u[g].image,e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var n_=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=e[n].keypoints.find(l=>l.part==="leftWrist"),s=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&r&&s&&r.position.y<a.position.y&&s.position.y<a.position.y?t.push({body:n,gesture:"i give up"}):a&&r&&r.position.y<a.position.y?t.push({body:n,gesture:"raise left hand"}):a&&s&&s.position.y<a.position.y&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position.y>i.position.y?"left":"right"}`})}return t},r_=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let r=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${r<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},s_=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],s=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(r*s),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),u=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],h=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(h>.06||d>.06)&&(u=!1),h>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let p=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||p<.01||f>.022||p>.022)&&(u=!1),(f<.01||p<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||p>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},a_=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[s,a]of Object.entries(e[n].annotations))s!=="palmBase"&&Array.isArray(a)&&r.push({name:s.toLowerCase(),position:a[0]});if(r&&r.length>0){let s=r.reduce((o,i)=>o.position[2]<i.position[2]?o:i),a=r.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${s.name} forward ${a.name} up`})}}return t};var x3={};e2(x3,{all:()=>hwe,body:()=>l_,canvas:()=>dwe,face:()=>i_,gesture:()=>o_,hand:()=>u_,object:()=>c_,options:()=>oo,person:()=>cwe});var oo={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},v0=e=>Math.round(e*180/Math.PI);function y3(e,t,n,r=0,s){e.fillStyle=s.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:s.color,e.beginPath(),e.arc(t,n,s.pointSize,0,2*Math.PI),e.fill()}function Th(e,t,n,r,s,a){if(e.beginPath(),a.useCurves){let o=(t+t+r)/2,i=(n+n+s)/2;e.ellipse(o,i,r/2,s/2,0,0,2*Math.PI)}else e.lineWidth=a.lineWidth,e.moveTo(t+a.roundRect,n),e.lineTo(t+r-a.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+a.roundRect),e.lineTo(t+r,n+s-a.roundRect),e.quadraticCurveTo(t+r,n+s,t+r-a.roundRect,n+s),e.lineTo(t+a.roundRect,n+s),e.quadraticCurveTo(t,n+s,t,n+s-a.roundRect),e.lineTo(t,n+a.roundRect),e.quadraticCurveTo(t,n,t+a.roundRect,n),e.closePath();e.stroke()}function A3(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let r of t){let s=r[2]||0;e.strokeStyle=n.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:n.color,e.lineTo(r[0],Math.round(r[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Nh(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){A3(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let r=0;r<t.length-2;r++){let s=(t[r][0]+t[r+1][0])/2,a=(t[r][1]+t[r+1][1])/2;e.quadraticCurveTo(t[r][0],t[r][1],s,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function o_(e,t,n){let r=lr(oo,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let s=e.getContext("2d");if(!s)return;s.font=r.font,s.fillStyle=r.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let u=i[1]>0?`#${i[1]}`:"",c=`${i[0]} ${u}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(s.fillStyle=r.shadowColor,s.fillText(c,8,2+a*r.lineHeight)),s.fillStyle=r.labelColor,s.fillText(c,6,0+a*r.lineHeight),a+=1}}}async function i_(e,t,n){var a,o,i,l;let r=lr(oo,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let s=e.getContext("2d");if(!!s)for(let u of t){s.font=r.font,s.strokeStyle=r.color,s.fillStyle=r.color,r.drawBoxes&&Th(s,u.box[0],u.box[1],u.box[2],u.box[3],r);let c=[];if(c.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&c.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&c.push(`age: ${u.age||""}`),u.iris&&c.push(`distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let d=u.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);d.length>3&&(d.length=3),c.push(d.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&c.push(`roll: ${v0(u.rotation.angle.roll)}\xB0 yaw:${v0(u.rotation.angle.yaw)}\xB0 pitch:${v0(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&c.push(`gaze: ${v0(u.rotation.gaze.bearing)}\xB0`)),c.length===0&&c.push("face"),s.fillStyle=r.color;for(let d=c.length-1;d>=0;d--){let h=Math.max(u.box[0],0),p=d*r.lineHeight+u.box[1];r.shadowColor&&r.shadowColor!==""&&(s.fillStyle=r.shadowColor,s.fillText(c[d],h+5,p+16)),s.fillStyle=r.labelColor,s.fillText(c[d],h+4,p+15)}if(s.lineWidth=1,u.mesh&&u.mesh.length>0){if(r.drawPoints)for(let d of u.mesh)y3(s,d[0],d[1],d[2],r);if(r.drawPolygons){s.lineWidth=1;for(let d=0;d<vi.length/3;d++){let h=[vi[d*3+0],vi[d*3+1],vi[d*3+2]].map(p=>u.mesh[p]);A3(s,h,r)}if(u.annotations&&u.annotations.leftEyeIris){s.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,s.beginPath();let d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,h=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;s.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,h,0,0,2*Math.PI),s.stroke(),r.fillPolygons&&(s.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,s.fill())}if(u.annotations&&u.annotations.rightEyeIris){s.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,s.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,h=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;s.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,h,0,0,2*Math.PI),s.stroke(),r.fillPolygons&&(s.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,s.fill())}if(r.drawGaze&&((o=(a=u.rotation)==null?void 0:a.gaze)==null?void 0:o.strength)&&((l=(i=u.rotation)==null?void 0:i.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){s.strokeStyle="pink",s.beginPath();let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];s.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),s.lineTo(d[0],d[1]);let h=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];s.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),s.lineTo(h[0],h[1]),s.stroke()}}}}}async function l_(e,t,n){var a;let r=lr(oo,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let s=e.getContext("2d");if(!!s){s.lineJoin="round";for(let o=0;o<t.length;o++){if(s.strokeStyle=r.color,s.fillStyle=r.color,s.lineWidth=r.lineWidth,s.font=r.font,r.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(Th(s,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(s.fillStyle=r.shadowColor,s.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+r.lineHeight,t[o].box[2])),s.fillStyle=r.labelColor,s.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+r.lineHeight,t[o].box[2]))),r.drawPoints)for(let i=0;i<t[o].keypoints.length;i++)s.fillStyle=r.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:r.color,y3(s,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,r);if(r.drawLabels&&(s.font=r.font,t[o].keypoints))for(let i of t[o].keypoints)s.fillStyle=r.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:r.color,s.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4);if(r.drawPolygons&&t[o].keypoints){let i,l=[];l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),Nh(s,l,r),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),l.length===4&&A3(s,l,r),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftFoot"),i&&l.push([i.position[0],i.position[1]]),Nh(s,l,r),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightFoot"),i&&l.push([i.position[0],i.position[1]]),Nh(s,l,r),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftPalm"),i&&l.push([i.position[0],i.position[1]]),Nh(s,l,r),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightPalm"),i&&l.push([i.position[0],i.position[1]]),Nh(s,l,r)}}}}async function u_(e,t,n){let r=lr(oo,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let s=e.getContext("2d");if(!!s){s.lineJoin="round",s.font=r.font;for(let a of t){if(r.drawBoxes&&(s.strokeStyle=r.color,s.fillStyle=r.color,Th(s,a.box[0],a.box[1],a.box[2],a.box[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(s.fillStyle=r.shadowColor,s.fillText("hand",a.box[0]+3,1+a.box[1]+r.lineHeight,a.box[2])),s.fillStyle=r.labelColor,s.fillText("hand",a.box[0]+2,0+a.box[1]+r.lineHeight,a.box[2])),s.stroke()),r.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)s.fillStyle=r.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:r.color,y3(s,o[0],o[1],0,r);if(r.drawLabels){let o=(i,l)=>{s.fillStyle=r.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:r.color,s.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4)};s.font=r.font,o(a.annotations.indexFinger,"index"),o(a.annotations.middleFinger,"middle"),o(a.annotations.ringFinger,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palmBase,"palm")}if(r.drawPolygons){let o=i=>{if(!!i)for(let l=0;l<i.length;l++)s.beginPath(),s.strokeStyle=r.useDepth?`rgba(${127.5+2*i[l][2]}, ${127.5-2*i[l][2]}, 255, 0.5)`:r.color,s.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),s.lineTo(i[l][0],i[l][1]),s.stroke()};s.lineWidth=r.lineWidth,o(a.annotations.indexFinger),o(a.annotations.middleFinger),o(a.annotations.ringFinger),o(a.annotations.pinky),o(a.annotations.thumb)}}}}async function c_(e,t,n){let r=lr(oo,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let s=e.getContext("2d");if(!!s){s.lineJoin="round",s.font=r.font;for(let a of t)if(r.drawBoxes){if(s.strokeStyle=r.color,s.fillStyle=r.color,Th(s,a.box[0],a.box[1],a.box[2],a.box[3],r),r.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;r.shadowColor&&r.shadowColor!==""&&(s.fillStyle=r.shadowColor,s.fillText(o,a.box[0]+3,1+a.box[1]+r.lineHeight,a.box[2])),s.fillStyle=r.labelColor,s.fillText(o,a.box[0]+2,0+a.box[1]+r.lineHeight,a.box[2])}s.stroke()}}}async function cwe(e,t,n){let r=lr(oo,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let s=e.getContext("2d");if(!!s){s.lineJoin="round",s.font=r.font;for(let a=0;a<t.length;a++)if(r.drawBoxes){if(s.strokeStyle=r.color,s.fillStyle=r.color,Th(s,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],r),r.drawLabels){let o=`person #${a}`;r.shadowColor&&r.shadowColor!==""&&(s.fillStyle=r.shadowColor,s.fillText(o,t[a].box[0]+3,1+t[a].box[1]+r.lineHeight,t[a].box[2])),s.fillStyle=r.labelColor,s.fillText(o,t[a].box[0]+2,0+t[a].box[1]+r.lineHeight,t[a].box[2])}s.stroke()}}}async function dwe(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function hwe(e,t,n){let r=at(),s=lr(oo,n);!t||!e||e instanceof HTMLCanvasElement&&(i_(e,t.face,s),l_(e,t.body,s),u_(e,t.hand,s),c_(e,t.object,s),o_(e,t.gesture,s),t.performance.draw=Math.trunc(at()-r))}function d_(e,t,n,r,s){var i,l,u,c,d,h,p,f,m,g,y,A,x,b,v,w;let a=0,o=[];for(let S of e){let I={id:a++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let P of t)S.box[0]>P.box[0]&&S.box[0]<P.box[0]+P.box[2]&&S.box[1]+S.box[3]>P.box[1]&&S.box[1]+S.box[3]<P.box[1]+P.box[3]&&(I.body=P);if(I.body)for(let P of n)P.box[0]+P.box[2]>I.body.box[0]&&P.box[0]+P.box[2]<I.body.box[0]+I.body.box[2]&&P.box[1]+P.box[3]>I.body.box[1]&&P.box[1]+P.box[3]<I.body.box[1]+I.body.box[3]&&I.hands&&(I.hands.left=P),P.box[0]<I.body.box[0]+I.body.box[2]&&P.box[0]>I.body.box[0]&&P.box[1]+P.box[3]>I.body.box[1]&&P.box[1]+P.box[3]<I.body.box[1]+I.body.box[3]&&I.hands&&(I.hands.right=P);for(let P of r)P.face!==void 0&&P.face===S.id?(i=I.gestures)==null||i.push(P):P.iris!==void 0&&P.iris===S.id?(l=I.gestures)==null||l.push(P):P.body!==void 0&&P.body===((u=I.body)==null?void 0:u.id)?(c=I.gestures)==null||c.push(P):P.hand!==void 0&&P.hand===((h=(d=I.hands)==null?void 0:d.left)==null?void 0:h.id)?(p=I.gestures)==null||p.push(P):P.hand!==void 0&&P.hand===((m=(f=I.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=I.gestures)==null||g.push(P));let E=[],F=[],$=P=>{P&&P.length===4&&(E.push(P[0],P[0]+P[2]),F.push(P[1],P[1]+P[3]))};$((y=I.face)==null?void 0:y.box),$((A=I.body)==null?void 0:A.box),$((b=(x=I.hands)==null?void 0:x.left)==null?void 0:b.box),$((w=(v=I.hands)==null?void 0:v.right)==null?void 0:w.box);let _=Math.min(...E),N=Math.min(...F);I.box=[_,N,Math.max(...E)-_,Math.max(...F)-N],s&&s.length===4&&(I.boxRaw=[I.box[0]/s[2],I.box[1]/s[1],I.box[2]/s[2],I.box[3]/s[1]]),o.push(I)}return o}var Be={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function h_(e){var r,s,a,o,i,l,u,c,d,h,p,f,m,g,y,A,x,b,v,w,S;let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t):1;if(Be.canvas=e.canvas,!Be.body||e.body.length!==Be.body.length)Be.body=JSON.parse(JSON.stringify(e.body));else for(let I=0;I<e.body.length;I++){let E=e.body[I].box.map((_,N)=>((n-1)*Be.body[I].box[N]+_)/n),F=e.body[I].boxRaw.map((_,N)=>((n-1)*Be.body[I].boxRaw[N]+_)/n),$=e.body[I].keypoints.map((_,N)=>({score:_.score,part:_.part,position:[Be.body[I].keypoints[N]?((n-1)*Be.body[I].keypoints[N].position[0]+_.position[0])/n:_.position[0],Be.body[I].keypoints[N]?((n-1)*Be.body[I].keypoints[N].position[1]+_.position[1])/n:_.position[1]],positionRaw:[Be.body[I].keypoints[N]?((n-1)*Be.body[I].keypoints[N].positionRaw[0]+_.positionRaw[0])/n:_.position[0],Be.body[I].keypoints[N]?((n-1)*Be.body[I].keypoints[N].positionRaw[1]+_.positionRaw[1])/n:_.position[1]]}));Be.body[I]={...e.body[I],box:E,boxRaw:F,keypoints:$}}if(!Be.hand||e.hand.length!==Be.hand.length)Be.hand=JSON.parse(JSON.stringify(e.hand));else for(let I=0;I<e.hand.length;I++){let E=e.hand[I].box.map((P,B)=>((n-1)*Be.hand[I].box[B]+P)/n),F=e.hand[I].boxRaw.map((P,B)=>((n-1)*Be.hand[I].boxRaw[B]+P)/n),$=e.hand[I].keypoints.map((P,B)=>P.map((j,X)=>((n-1)*Be.hand[I].keypoints[B][X]+j)/n)),_=Object.keys(e.hand[I].annotations),N={};for(let P of _)N[P]=e.hand[I].annotations[P].map((B,j)=>B.map((X,Y)=>((n-1)*Be.hand[I].annotations[P][j][Y]+X)/n));Be.hand[I]={...e.hand[I],box:E,boxRaw:F,keypoints:$,annotations:N}}if(!Be.face||e.face.length!==Be.face.length)Be.face=JSON.parse(JSON.stringify(e.face));else for(let I=0;I<e.face.length;I++){let E=e.face[I].box.map((_,N)=>((n-1)*Be.face[I].box[N]+_)/n),F=e.face[I].boxRaw.map((_,N)=>((n-1)*Be.face[I].boxRaw[N]+_)/n),$={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(r=e.face[I].rotation)==null?void 0:r.matrix,$.angle={roll:((n-1)*(((a=(s=Be.face[I].rotation)==null?void 0:s.angle)==null?void 0:a.roll)||0)+(((i=(o=e.face[I].rotation)==null?void 0:o.angle)==null?void 0:i.roll)||0))/n,yaw:((n-1)*(((u=(l=Be.face[I].rotation)==null?void 0:l.angle)==null?void 0:u.yaw)||0)+(((d=(c=e.face[I].rotation)==null?void 0:c.angle)==null?void 0:d.yaw)||0))/n,pitch:((n-1)*(((p=(h=Be.face[I].rotation)==null?void 0:h.angle)==null?void 0:p.pitch)||0)+(((m=(f=e.face[I].rotation)==null?void 0:f.angle)==null?void 0:m.pitch)||0))/n},$.gaze={bearing:((n-1)*(((y=(g=Be.face[I].rotation)==null?void 0:g.gaze)==null?void 0:y.bearing)||0)+(((x=(A=e.face[I].rotation)==null?void 0:A.gaze)==null?void 0:x.bearing)||0))/n,strength:((n-1)*(((v=(b=Be.face[I].rotation)==null?void 0:b.gaze)==null?void 0:v.strength)||0)+(((S=(w=e.face[I].rotation)==null?void 0:w.gaze)==null?void 0:S.strength)||0))/n},Be.face[I]={...e.face[I],rotation:$,box:E,boxRaw:F}}if(!Be.object||e.object.length!==Be.object.length)Be.object=JSON.parse(JSON.stringify(e.object));else for(let I=0;I<e.object.length;I++){let E=e.object[I].box.map(($,_)=>((n-1)*Be.object[I].box[_]+$)/n),F=e.object[I].boxRaw.map(($,_)=>((n-1)*Be.object[I].boxRaw[_]+$)/n);Be.object[I]={...e.object[I],box:E,boxRaw:F}}if(e.persons){let I=e.persons;if(!Be.persons||I.length!==Be.persons.length)Be.persons=JSON.parse(JSON.stringify(I));else for(let E=0;E<I.length;E++)Be.persons[E].box=I[E].box.map((F,$)=>((n-1)*Be.persons[E].box[$]+F)/n)}return e.gesture&&(Be.gesture=e.gesture),e.performance&&(Be.performance=e.performance),Be}var w0=`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==`,k0=`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`;var p_="2.0.3";var Du,Ch,Eh,ki,Ii,Fu,I0,$h,S0,T0,N0,C0,f_=class{constructor(t){Ir(this,Du,void 0);Ir(this,Ch,void 0);Ir(this,Eh,void 0);Ir(this,ki,void 0);Ir(this,Ii,void 0);Ir(this,Fu,void 0);this.analyze=(...t)=>{if(!Fn(this,Ch))return;let n=this.tf.engine().state.numTensors,r=Fn(this,Du);Qr(this,Du,n);let s=n-r;s!==0&&me(...t,s)};Ir(this,I0,t=>{if(!Fn(this,Eh))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Tt))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Ir(this,$h,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=at();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&me("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&me("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&me("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let s=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&me(`wasm execution: ${s?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),this.config.debug&&!s&&me("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&p$();try{await this.tf.setBackend(this.config.backend)}catch(s){me("error: cannot set backend:",this.config.backend,s)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(me("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let s=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&me(`gl version:${s.getParameter(s.VERSION)} renderer:${s.getParameter(s.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(at()-r)}});this.next=t=>h_(t||this.result);Ir(this,S0,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,r=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),s=r.dataSync(),a=0;for(let l=0;l<s.length/3;l++)a+=s[3*l+2];r.dispose();let o=100*(Math.max(a,Fn(this,Ii))/Math.min(a,Fn(this,Ii))-1);Qr(this,Ii,a);let i=o<Math.max(this.config.cacheSensitivity,Fn(this,Fu));return Qr(this,Fu,o>10*this.config.cacheSensitivity?0:o),i});Ir(this,T0,async()=>{let t=(s,a="application/octet-stream")=>fetch(`data:${a};base64,${s}`).then(o=>o.blob()),n,r;switch(this.config.warmup){case"face":n=await t(w0);break;case"full":n=await t(k0);break;default:n=null}if(n){let s=await createImageBitmap(n);r=await this.detect(s,this.config),s.close()}return r});Ir(this,N0,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+w0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+k0;break;default:n=null}let s=new Image;s.onload=async()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");a.width=s.naturalWidth,a.height=s.naturalHeight;let o=a.getContext("2d");o==null||o.drawImage(s,0,0);let i=await this.detect(a,this.config);t(i)},n?s.src=n:t(null)}));Ir(this,C0,async()=>{let t=s=>Buffer.from(s,"base64"),n;if(this.config.warmup==="face"&&(n=t(w0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(k0)),!n)return null;let r;if(typeof void 0!="undefined"){let s=(void 0).decodeJpeg(n),a=s.expandDims(0);this.tf.dispose(s),r=await this.detect(a,this.config),this.tf.dispose(a)}else this.config.debug&&me("Warmup tfjs-node not loaded");return r});this.config=lr(F3,t||{}),this.tf=bh,this.draw=x3,this.version=p_,this.state="idle",Qr(this,Du,0),Qr(this,Ch,!1),Qr(this,Eh,!1),Qr(this,ki,!0),Qr(this,Fu,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.image=n=>wi(n,this.config),this.faceTriangulation=T$,this.faceUVMap=N$,this.sysinfo=M3(),Qr(this,Ii,1)}similarity(t,n){return $b(t,n)}segmentation(t,n){return e_(t,n,this.config)}enhance(t){return _b(t)}match(t,n,r=0){return E$(t,n,r)}async load(t){this.state="load";let n=at();t&&(this.config=lr(this.config,t)),Fn(this,ki)&&(this.config.debug&&me(`version: ${this.version}`),this.config.debug&&me(`tfjs version: ${this.tf.version_core}`),this.config.debug&&me("platform:",this.sysinfo.platform),this.config.debug&&me("agent:",this.sysinfo.agent),await Fn(this,$h).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&me("configuration:",this.config),this.config.debug&&me("tf flags:",this.tf.ENV.flags))),await t_(this),Fn(this,ki)&&(this.config.debug&&me("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Qr(this,ki,!1));let r=Math.trunc(at()-n);r>(this.performance.load||0)&&(this.performance.load=r)}async detect(t,n){return new Promise(async r=>{this.state="config";let s,a;this.config=lr(this.config,n),this.state="check";let o=Fn(this,I0).call(this,t);o&&(me(o,t),r({error:o}));let i=at();await Fn(this,$h).call(this),await this.load(),s=at();let l=wi(t,this.config);if(this.performance.image=Math.trunc(at()-s),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",s=at(),await m3(l),a=Math.trunc(at()-s),a>0&&(this.performance.segmentation=a),l.canvas&&(l.tensor.dispose(),l=wi(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){me("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}s=at(),this.config.skipFrame=await Fn(this,S0).call(this,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(at()-s),this.analyze("Check Changed:");let u,c,d,h;this.config.async?(u=this.config.face.enabled?g3(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",s=at(),u=this.config.face.enabled?await g3(this,l.tensor):[],a=Math.trunc(at()-s),a>0&&(this.performance.face=a)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?Vb(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?Zb(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?e3(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?a3(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",s=at(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await Vb(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await Zb(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?await e3(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?await a3(l.tensor,this.config):[]),a=Math.trunc(at()-s),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?Kb(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",s=at(),d=this.config.hand.enabled?await Kb(l.tensor,this.config):[],a=Math.trunc(at()-s),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?u3(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?p3(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",s=at(),this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?await u3(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?await p3(l.tensor,this.config):[]),a=Math.trunc(at()-s),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.config.async&&([u,c,d,h]=await Promise.all([u,c,d,h]));let p=[];this.config.gesture.enabled&&(s=at(),p=[...r_(u),...n_(c),...a_(d),...s_(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(at()-s)),this.performance.total=Math.trunc(at()-i),this.state="idle",this.result={face:u,body:c,hand:d,gesture:p,object:h,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var f;return d_(u,c,d,p,(f=l==null?void 0:l.tensor)==null?void 0:f.shape)}},Ve(l.tensor),r(this.result)})}async warmup(t){let n=at();if(t&&(this.config=lr(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r;typeof createImageBitmap=="function"?r=await Fn(this,T0).call(this):typeof Image!="undefined"?r=await Fn(this,N0).call(this):r=await Fn(this,C0).call(this);let s=at();return this.config.debug&&me("Warmup",this.config.warmup,Math.round(s-n),"ms",r),r}};Du=new WeakMap,Ch=new WeakMap,Eh=new WeakMap,ki=new WeakMap,Ii=new WeakMap,Fu=new WeakMap,I0=new WeakMap,$h=new WeakMap,S0=new WeakMap,T0=new WeakMap,N0=new WeakMap,C0=new WeakMap;return fwe;})();
/**
 * @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 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. */