/* Human library homepage: author: ' */ var Human=(()=>{var mI=Object.defineProperty;var bm=e=>{if(typeof require!="undefined")return require(e);throw new Error('Dynamic require of "'+e+'" is not supported')};var ga=(e,t)=>{for(var n in t)mI(e,n,{get:t[n],enumerable:!0})};var u5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var on=(e,t,n)=>(u5(e,t,"read from private field"),n?n.call(e):t.get(e)),Jn=(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)},xa=(e,t,n,a)=>(u5(e,t,"write to private field"),a?a.call(e,n):t.set(e,n),n);var Doe={};ga(Doe,{Human:()=>N9,default:()=>N9});function vt(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`Human: ModelPath Error: ${r} Expecting JSON file`);return r}function de(...e){let t=new Date,n=`${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 zn(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,a)=>(Object.keys(a||{}).forEach(r=>{let s=n[r],i=a[r];Array.isArray(s)&&Array.isArray(i)?n[r]=s.concat(...i):t(s)&&t(i)?n[r]=zn(s,i):n[r]=i}),n),{})}var d5={backend:"webgl",modelBasePath:"../models/",wasmPath:"../node_modules/@tensorflow/tfjs-backend-wasm/dist//",debug:!0,async:!0,warmup:"full",cacheSensitivity:.75,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:!1,maxDetected:10,skipFrames:21,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:31,minConfidence:.1},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetected:1,minConfidence:.2},hand:{enabled:!0,rotation:!1,skipFrames:32,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:41}};function p5(){let e,t;if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);if(n&&n[0]){let a=n[0].match(/\(([^()]+)\)/g);e=a?a[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 ${process.version}`);return{platform:e,agent:t}}var tp={};ga(tp,{Abs:()=>oo,Acos:()=>lo,Acosh:()=>uo,AdadeltaOptimizer:()=>ih,AdagradOptimizer:()=>oh,AdamOptimizer:()=>lh,AdamaxOptimizer:()=>uh,Add:()=>Mr,AddN:()=>hs,All:()=>po,Any:()=>co,ArgMax:()=>fs,ArgMin:()=>Nu,Asin:()=>ho,Asinh:()=>fo,Atan:()=>mo,Atan2:()=>Ao,Atanh:()=>yo,AvgPool:()=>ms,AvgPool3D:()=>Tu,AvgPool3DGrad:()=>Wp,AvgPoolGrad:()=>Lp,BackendWasm:()=>_6,BatchMatMul:()=>ys,BatchToSpaceND:()=>Eu,Bincount:()=>Bp,BroadcastTo:()=>ab,Callback:()=>C8,CallbackList:()=>v4,Cast:()=>As,Ceil:()=>gs,ClipByValue:()=>Fr,Complex:()=>Vp,ComplexAbs:()=>Cu,Concat:()=>go,Conv2D:()=>xs,Conv2DBackpropFilter:()=>jp,Conv2DBackpropInput:()=>bs,Conv3D:()=>Ru,Conv3DBackpropFilterV2:()=>Up,Conv3DBackpropInputV2:()=>Hp,Cos:()=>vs,Cosh:()=>xo,CropAndResize:()=>bo,Cumsum:()=>ws,CustomCallback:()=>k4,DataStorage:()=>zp,DenseBincount:()=>Gp,DepthToSpace:()=>vo,DepthwiseConv2dNative:()=>ks,DepthwiseConv2dNativeBackpropFilter:()=>qp,DepthwiseConv2dNativeBackpropInput:()=>Xp,Diag:()=>Kp,Dilation2D:()=>Mu,Dilation2DBackpropFilter:()=>Yp,Dilation2DBackpropInput:()=>Zp,ENV:()=>ua,EarlyStopping:()=>M8,Einsum:()=>Jp,Elu:()=>wo,EluGrad:()=>Qp,Environment:()=>tb,Equal:()=>Io,Erf:()=>ko,Exp:()=>Ss,ExpandDims:()=>So,Expm1:()=>No,FFT:()=>ec,Fill:()=>Fu,FlipLeftRight:()=>To,Floor:()=>Ns,FloorDiv:()=>Ts,FromPixels:()=>yc,FusedBatchNorm:()=>Es,FusedConv2D:()=>li,FusedDepthwiseConv2D:()=>ui,GPGPUContext:()=>Nh,GatherNd:()=>Co,GatherV2:()=>Eo,GraphModel:()=>ok,Greater:()=>Ro,GreaterEqual:()=>Cs,History:()=>w4,IFFT:()=>tc,Identity:()=>Rs,Imag:()=>nc,InputSpec:()=>zt,IsFinite:()=>Mo,IsInf:()=>Fo,IsNan:()=>$o,KernelBackend:()=>ku,LRN:()=>zu,LRNGrad:()=>rc,LayerVariable:()=>y4,LayersModel:()=>xr,LeakyRelu:()=>Ms,Less:()=>Do,LessEqual:()=>zo,LinSpace:()=>ac,Log:()=>Fs,Log1p:()=>Oo,LogSoftmax:()=>rb,LogicalAnd:()=>_o,LogicalNot:()=>$u,LogicalOr:()=>Du,MathBackendCPU:()=>hh,MathBackendWebGL:()=>jl,Max:()=>$s,MaxPool:()=>zs,MaxPool3D:()=>Ou,MaxPool3DGrad:()=>ic,MaxPoolGrad:()=>sc,MaxPoolWithArgmax:()=>oc,Maximum:()=>Ds,Mean:()=>Os,Min:()=>_s,Minimum:()=>Ps,MirrorPad:()=>Ls,Mod:()=>Po,MomentumOptimizer:()=>dh,Multinomial:()=>lc,Multiply:()=>Ws,Neg:()=>Lo,NonMaxSuppressionV3:()=>Bo,NonMaxSuppressionV4:()=>Vo,NonMaxSuppressionV5:()=>jo,NotEqual:()=>Wo,OP_SCOPE_SUFFIX:()=>mb,OneHot:()=>Bs,OnesLike:()=>Uo,Optimizer:()=>mr,Pack:()=>Ho,PadV2:()=>Vs,Pool:()=>yS,Pow:()=>js,Prelu:()=>Us,Prod:()=>Go,RMSPropOptimizer:()=>ph,RNN:()=>Qa,Range:()=>_u,Rank:()=>zm,Real:()=>uc,RealDiv:()=>Is,Reciprocal:()=>qo,Reduction:()=>cn,Relu:()=>Hs,Relu6:()=>qs,Reshape:()=>Xo,ResizeBilinear:()=>Gs,ResizeBilinearGrad:()=>pc,ResizeNearestNeighbor:()=>Pu,ResizeNearestNeighborGrad:()=>dc,Reverse:()=>Xs,RotateWithOffset:()=>ll,Round:()=>Ks,Rsqrt:()=>Zs,SGDOptimizer:()=>fd,ScatterNd:()=>Ko,Select:()=>Zo,Selu:()=>Yo,Sequential:()=>Jl,Sigmoid:()=>Js,Sign:()=>el,Sin:()=>Ys,Sinh:()=>Qo,Slice:()=>Jo,Softmax:()=>ti,Softplus:()=>tl,SpaceToBatchND:()=>Lu,SparseFillEmptyRows:()=>cc,SparseReshape:()=>hc,SparseToDense:()=>fc,SplitV:()=>nl,Sqrt:()=>Qs,Square:()=>Wu,SquaredDifference:()=>ni,Step:()=>Dr,StridedSlice:()=>al,Sub:()=>ai,Sum:()=>ei,SymbolicTensor:()=>Ca,Tan:()=>ri,Tanh:()=>si,Tensor:()=>Le,TensorBuffer:()=>Pt,Tile:()=>$r,TopK:()=>rl,Transform:()=>sl,Transpose:()=>ii,Unique:()=>mc,Unpack:()=>il,UnsortedSegmentSum:()=>Bu,Variable:()=>Xu,ZerosLike:()=>ol,_FusedMatMul:()=>oi,abs:()=>Lt,acos:()=>ly,acosh:()=>uy,add:()=>se,addN:()=>Ec,all:()=>Cc,any:()=>Qu,argMax:()=>yi,argMin:()=>dy,asin:()=>py,asinh:()=>cy,atan:()=>hy,atan2:()=>fy,atanh:()=>my,avgPool:()=>td,avgPool3d:()=>gy,backend:()=>Zb,backend_util:()=>R,basicLSTMCell:()=>KT,batchNorm:()=>xi,batchNorm2d:()=>e3,batchNorm3d:()=>t3,batchNorm4d:()=>n3,batchToSpaceND:()=>nd,bincount:()=>xy,booleanMaskAsync:()=>tM,broadcastTo:()=>xl,browser:()=>fi,buffer:()=>We,callbacks:()=>cse,cast:()=>me,ceil:()=>by,clipByValue:()=>Nn,clone:()=>Wa,complex:()=>zr,concat:()=>lt,concat1d:()=>a3,concat2d:()=>bl,concat3d:()=>r3,concat4d:()=>s3,constraints:()=>X6,conv1d:()=>Mc,conv2d:()=>pr,conv2dTranspose:()=>Fc,conv3d:()=>wy,conv3dTranspose:()=>o3,copyRegisteredKernels:()=>xS,cos:()=>ad,cosh:()=>$c,cosineWindow:()=>Ky,cumsum:()=>Dc,customGrad:()=>Va,data:()=>lk,denseBincount:()=>l3,deprecationWarn:()=>iy,depthToSpace:()=>ky,depthwiseConv2d:()=>vl,deregisterOp:()=>fse,device_util:()=>Zu,diag:()=>IE,dilation2d:()=>Iy,disableDeprecationWarnings:()=>lT,dispose:()=>Ne,disposeVariables:()=>uT,div:()=>fe,divNoNan:()=>Sy,dot:()=>u3,dropout:()=>R3,einsum:()=>d3,elu:()=>wl,enableDebugMode:()=>oT,enableProdMode:()=>iT,enclosingPowerOfTwo:()=>M3,engine:()=>dr,env:()=>J,equal:()=>Wr,erf:()=>Ny,exp:()=>ea,expandDims:()=>dn,expm1:()=>Ty,eye:()=>Ey,fft:()=>cd,fill:()=>kl,findBackend:()=>oy,findBackendFactory:()=>yT,floor:()=>Il,floorDiv:()=>Tc,forceHalfFloat:()=>Xv,fused:()=>Ur,gather:()=>bi,gatherND:()=>C3,gather_util:()=>Qm,getBackend:()=>fT,getGradient:()=>Fm,getKernel:()=>Ac,getKernelsForBackend:()=>dl,gpgpu_util:()=>gv,grad:()=>eC,grads:()=>tC,greater:()=>On,greaterEqual:()=>Vr,ifft:()=>Cl,imag:()=>zc,image:()=>Ve,inTopKAsync:()=>cM,initializers:()=>t4,input:()=>G4,io:()=>In,irfft:()=>Yc,isFinite:()=>p3,isInf:()=>c3,isNaN:()=>Cy,keep:()=>Gt,kernel_impls:()=>Ga,layers:()=>h4,leakyRelu:()=>rd,less:()=>Oc,lessEqual:()=>jr,linalg:()=>j3,linspace:()=>h3,loadGraphModel:()=>gt,loadLayersModel:()=>wae,localResponseNormalization:()=>Ry,log:()=>_n,log1p:()=>_c,logSigmoid:()=>m3,logSoftmax:()=>Lc,logSumExp:()=>$y,logicalAnd:()=>ca,logicalNot:()=>sd,logicalOr:()=>Wc,logicalXor:()=>x3,losses:()=>OF,matMul:()=>Be,math:()=>Mb,max:()=>Tn,maxPool:()=>id,maxPool3d:()=>Dy,maxPoolWithArgmax:()=>b3,maximum:()=>ja,mean:()=>St,memory:()=>Nc,meshgrid:()=>kC,metrics:()=>N8,min:()=>Sl,minimum:()=>Nl,mirrorPad:()=>zy,mod:()=>Oy,model:()=>bae,models:()=>T8,moments:()=>Bc,movingAverage:()=>rM,mul:()=>L,multiRNNCell:()=>MC,multinomial:()=>v3,neg:()=>It,nextFrame:()=>ch,norm:()=>th,notEqual:()=>ki,oneHot:()=>ml,ones:()=>Pn,onesLike:()=>Ln,op:()=>_,outerProduct:()=>OC,pad:()=>cr,pad1d:()=>LC,pad2d:()=>BC,pad3d:()=>jC,pad4d:()=>HC,pool:()=>w3,pow:()=>hr,prelu:()=>ld,print:()=>Sb,prod:()=>Vc,profile:()=>dT,rand:()=>eR,randomGamma:()=>rR,randomNormal:()=>k3,randomUniform:()=>Tl,range:()=>El,ready:()=>hT,real:()=>ud,reciprocal:()=>Ly,registerBackend:()=>Al,registerCallbackConstructor:()=>kae,registerGradient:()=>sb,registerKernel:()=>di,registerOp:()=>hse,regularizers:()=>E8,relu:()=>Ua,relu6:()=>jc,removeBackend:()=>mT,reshape:()=>H,reverse:()=>Wn,reverse1d:()=>hR,reverse2d:()=>mR,reverse3d:()=>AR,reverse4d:()=>xR,rfft:()=>hd,round:()=>Uc,rsqrt:()=>Hc,scalar:()=>we,scatterND:()=>E3,scatter_util:()=>ey,selu:()=>Gc,separableConv2d:()=>Wy,sequential:()=>vae,serialization:()=>ae,setBackend:()=>cT,setPlatform:()=>AT,setWasmPath:()=>NQ,setWasmPaths:()=>TQ,setWebGLContext:()=>xh,setdiff1dAsync:()=>I3,shared:()=>e1,sigmoid:()=>Sn,sign:()=>By,signal:()=>zF,sin:()=>qc,sinh:()=>Xc,slice:()=>Re,slice1d:()=>Kc,slice2d:()=>Vy,slice3d:()=>Zc,slice4d:()=>dd,slice_util:()=>un,softmax:()=>pd,softplus:()=>vi,spaceToBatchND:()=>od,sparse:()=>U3,sparseToDense:()=>Xy,spectral:()=>DF,split:()=>qt,sqrt:()=>en,square:()=>ot,squaredDifference:()=>Jc,squeeze:()=>Ha,stack:()=>pn,step:()=>Rl,stridedSlice:()=>jy,sub:()=>ye,sum:()=>Ie,sumOutType:()=>vc,tan:()=>Uy,tanh:()=>gi,tensor:()=>pa,tensor1d:()=>Mt,tensor2d:()=>wa,tensor3d:()=>Ic,tensor4d:()=>HR,tensor5d:()=>GR,tensor6d:()=>qR,tensor_util:()=>ba,test_util:()=>qb,tidy:()=>B,tile:()=>Br,time:()=>pT,topk:()=>Hy,train:()=>Si,transpose:()=>Ye,truncatedNormal:()=>Qc,unique:()=>eh,unregisterGradient:()=>gS,unregisterKernel:()=>AS,unsortedSegmentSum:()=>Gy,unstack:()=>ha,upcastType:()=>da,util:()=>k,valueAndGrad:()=>nC,valueAndGrads:()=>aC,variable:()=>S3,variableGrads:()=>f3,version:()=>eoe,version_converter:()=>fie,version_core:()=>sT,version_cpu:()=>I7,version_layers:()=>yA,version_wasm:()=>L6,version_webgl:()=>qv,webgl:()=>kB,webgl_util:()=>G7,where:()=>rn,whereAsync:()=>qy,zeros:()=>$t,zerosLike:()=>He});var yI=Object.create,Dp=Object.defineProperty,AI=Object.getOwnPropertyDescriptor,gI=Object.getOwnPropertyNames,xI=Object.getPrototypeOf,bI=Object.prototype.hasOwnProperty,vI=e=>Dp(e,"__esModule",{value:!0}),ao=e=>{if(typeof bm!="undefined")return bm(e);throw new Error('Dynamic require of "'+e+'" is not supported')},wt=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Fe=(e,t)=>{for(var n in t)Dp(e,n,{get:t[n],enumerable:!0})},wI=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let a of gI(t))!bI.call(e,a)&&a!=="default"&&Dp(e,a,{get:()=>t[a],enumerable:!(n=AI(t,a))||n.enumerable});return e},ro=e=>wI(vI(Dp(e!=null?yI(xI(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),kI=wt(()=>{}),II=wt((e,t)=>{(function(n,a,r){function s(u){var d=this,p=l();d.next=function(){var c=2091639*d.s0+d.c*23283064365386963e-26;return d.s0=d.s1,d.s1=d.s2,d.s2=c-(d.c=c|0)},d.c=1,d.s0=p(" "),d.s1=p(" "),d.s2=p(" "),d.s0-=p(u),d.s0<0&&(d.s0+=1),d.s1-=p(u),d.s1<0&&(d.s1+=1),d.s2-=p(u),d.s2<0&&(d.s2+=1),p=null}function i(u,d){return d.c=u.c,d.s0=u.s0,d.s1=u.s1,d.s2=u.s2,d}function o(u,d){var p=new s(u),c=d&&d.state,h=p.next;return h.int32=function(){return p.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,c&&(typeof c=="object"&&i(c,p),h.state=function(){return i(p,{})}),h}function l(){var u=4022871197,d=function(p){p=p.toString();for(var c=0;c>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return d}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),SI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var c=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^c^c>>>8},l===(l|0)?u.x=l:d+=l;for(var p=0;p>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),NI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(c^c<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:d+=l;for(var p=0;p>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var d=new s(l),p=u&&u.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),TI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.x,c=u.i,h,m,f;return h=p[c],h^=h>>>7,m=h^h<<24,h=p[c+1&7],m^=h^h>>>10,h=p[c+3&7],m^=h^h>>>3,h=p[c+4&7],m^=h^h<<7,h=p[c+7&7],h=h^h<<13,m^=h^h<<9,p[c]=m,u.i=c+1&7,m};function d(p,c){var h,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,h=0;h0;--h)p.next()}d(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.x&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),EI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.w,c=u.X,h=u.i,m,f;return u.w=p=p+1640531527|0,f=c[h+34&127],m=c[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[h]=f^m,u.i=h,f+(p^p>>>16)|0};function d(p,c){var h,m,f,y,A,g=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,y=-32;y>>15,m^=m<<4,m^=m>>>13,y>=0&&(A=A+1640531527|0,h=g[y&127]^=m+A,f=h==0?f+1:0);for(f>=128&&(g[(c&&c.length||0)&127]=-1),f=127,y=4*128;y>0;--y)m=g[f+34&127],h=g[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,g[f]=m^h;p.w=A,p.X=g,p.i=f}d(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.X&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),CI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.b,h=u.c,m=u.d,f=u.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,u.b=c=c<<20^c>>>12^h,u.c=h=h-m|0,u.d=m<<16^h>>>16^f,u.a=f-c|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):d+=l;for(var p=0;p>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),c5=wt(()=>{}),RI=wt((e,t)=>{(function(n,a){var r=this,s=256,i=6,o=52,l="random",u=a.pow(s,i),d=a.pow(2,o),p=d*2,c=s-1,h;function m(b,v,S){var T=[];v=v==!0?{entropy:!0}:v||{};var C=g(A(v.entropy?[b,w(n)]:b==null?x():b,3),T),$=new f(T),O=function(){for(var P=$.g(i),j=u,D=0;P=p;)P/=2,j/=2,D>>>=1;return(P+D)/j};return O.int32=function(){return $.g(4)|0},O.quick=function(){return $.g(4)/4294967296},O.double=O,g(w($.S),n),(v.pass||S||function(P,j,D,U){return U&&(U.S&&y(U,$),P.state=function(){return y($,{})}),D?(a[l]=P,j):P})(O,C,"global"in v?v.global:this==a,v.state)}a["seed"+l]=m;function f(b){var v,S=b.length,T=this,C=0,$=T.i=T.j=0,O=T.S=[];for(S||(b=[S++]);C{var n=II(),a=SI(),r=NI(),s=TI(),i=EI(),o=CI(),l=RI();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),wu=wt(()=>{}),MI=wt(()=>{}),FI=wt(()=>{}),$I=wt((e,t)=>{var n=function(){var a=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(a=a||__filename),function(r){r=r||{};function s(){return Q.buffer!=je&&Jt(Q.buffer),xn}function i(){return Q.buffer!=je&&Jt(Q.buffer),bt}function o(){return Q.buffer!=je&&Jt(Q.buffer),bn}function l(){return Q.buffer!=je&&Jt(Q.buffer),Zn}function u(){return Q.buffer!=je&&Jt(Q.buffer),sn}var d=typeof r!="undefined"?r:{},p,c;d.ready=new Promise(function(N,E){p=N,c=E});var h={},m;for(m in d)d.hasOwnProperty(m)&&(h[m]=d[m]);var f=[],y="./this.program",A=function(N,E){throw E},g=!1,x=!1,w=!1,b=!1;g=typeof window=="object",x=typeof importScripts=="function",w=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",b=!g&&!w&&!x;var v=d.ENVIRONMENT_IS_PTHREAD||!1;v&&(je=d.buffer);var S="";function T(N){return d.locateFile?d.locateFile(N,S):S+N}var C,$,O,P,j,D;if(w){x?S=wu().dirname(S)+"/":S=__dirname+"/",C=function(N,E){return j||(j=ao("fs")),D||(D=wu()),N=D.normalize(N),j.readFileSync(N,E?null:"utf8")},O=function(N){var E=C(N,!0);return E.buffer||(E=new Uint8Array(E)),he(E.buffer),E},process.argv.length>1&&(y=process.argv[1].replace(/\\/g,"/")),f=process.argv.slice(2),process.on("uncaughtException",function(N){if(!(N instanceof vu))throw N}),process.on("unhandledRejection",sr),A=function(N){process.exit(N)},d.inspect=function(){return"[Emscripten Module object]"};var U;try{U=MI()}catch(N){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),N}global.Worker=U.Worker}else b?(typeof read!="undefined"&&(C=function(N){return read(N)}),O=function(N){var E;return typeof readbuffer=="function"?new Uint8Array(readbuffer(N)):(E=read(N,"binary"),he(typeof E=="object"),E)},typeof scriptArgs!="undefined"?f=scriptArgs:typeof arguments!="undefined"&&(f=arguments),typeof quit=="function"&&(A=function(N){quit(N)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(g||x)&&(x?S=self.location.href:typeof document!="undefined"&&document.currentScript&&(S=document.currentScript.src),typeof a!="undefined"&&a&&(S=a),S.indexOf("blob:")!==0?S=S.substr(0,S.lastIndexOf("/")+1):S="",w?(C=function(N,E){return j||(j=ao("fs")),D||(D=wu()),N=D.normalize(N),j.readFileSync(N,E?null:"utf8")},O=function(N){var E=C(N,!0);return E.buffer||(E=new Uint8Array(E)),he(E.buffer),E}):(C=function(N){var E=new XMLHttpRequest;return E.open("GET",N,!1),E.send(null),E.responseText},x&&(O=function(N){var E=new XMLHttpRequest;return E.open("GET",N,!1),E.responseType="arraybuffer",E.send(null),new Uint8Array(E.response)}),$=function(N,E,W){var q=new XMLHttpRequest;q.open("GET",N,!0),q.responseType="arraybuffer",q.onload=function(){if(q.status==200||q.status==0&&q.response){E(q.response);return}W()},q.onerror=W,q.send(null)}),P=function(N){document.title=N});w&&typeof performance=="undefined"&&(global.performance=FI().performance);var X=d.print||console.log.bind(console),G=d.printErr||console.warn.bind(console);for(m in h)h.hasOwnProperty(m)&&(d[m]=h[m]);h=null,d.arguments&&(f=d.arguments),d.thisProgram&&(y=d.thisProgram),d.quit&&(A=d.quit);var ee=Atomics.load,Y=Atomics.store,re=Atomics.compareExchange,ne;d.wasmBinary&&(ne=d.wasmBinary);var ie=d.noExitRuntime||!0;typeof WebAssembly!="object"&&sr("no native wasm support detected");var Q,pe,oe=!1,ge;function he(N,E){N||sr("Assertion failed: "+E)}function Se(N){var E=d["_"+N];return he(E,"Cannot call unknown function "+N+", make sure it is exported"),E}function Te(N,E,W,q,ce){var le={string:function(kn){var no=0;if(kn!=null&&kn!==0){var l5=(kn.length<<2)+1;no=Qi(l5),et(kn,no,l5)}return no},array:function(kn){var no=Qi(kn.length);return Ke(kn,no),no}};function ue(kn){return E==="string"?De(kn):E==="boolean"?Boolean(kn):kn}var be=Se(N),tt=[],Ut=0;if(q)for(var _t=0;_t=q);){var le=N[E++];if(!le)return ce;if(!(le&128)){ce+=String.fromCharCode(le);continue}var ue=N[E++]&63;if((le&224)==192){ce+=String.fromCharCode((le&31)<<6|ue);continue}var be=N[E++]&63;if((le&240)==224?le=(le&15)<<12|ue<<6|be:le=(le&7)<<18|ue<<12|be<<6|N[E++]&63,le<65536)ce+=String.fromCharCode(le);else{var tt=le-65536;ce+=String.fromCharCode(55296|tt>>10,56320|tt&1023)}}return ce}function De(N,E){return N?Oe(i(),N,E):""}function Qe(N,E,W,q){if(!(q>0))return 0;for(var ce=W,le=W+q-1,ue=0;ue=55296&&be<=57343){var tt=N.charCodeAt(++ue);be=65536+((be&1023)<<10)|tt&1023}if(be<=127){if(W>=le)break;E[W++]=be}else if(be<=2047){if(W+1>=le)break;E[W++]=192|be>>6,E[W++]=128|be&63}else if(be<=65535){if(W+2>=le)break;E[W++]=224|be>>12,E[W++]=128|be>>6&63,E[W++]=128|be&63}else{if(W+3>=le)break;E[W++]=240|be>>18,E[W++]=128|be>>12&63,E[W++]=128|be>>6&63,E[W++]=128|be&63}}return E[W]=0,W-ce}function et(N,E,W){return Qe(N,i(),E,W)}function it(N){for(var E=0,W=0;W=55296&&q<=57343&&(q=65536+((q&1023)<<10)|N.charCodeAt(++W)&1023),q<=127?++E:q<=2047?E+=2:q<=65535?E+=3:E+=4}return E}function Ke(N,E){s().set(N,E)}function pt(N,E){return N%E>0&&(N+=E-N%E),N}var je,xn,bt,Kn,Yt,bn,Zn,Dn,sn;function Jt(N){je=N,d.HEAP8=xn=new Int8Array(N),d.HEAP16=Kn=new Int16Array(N),d.HEAP32=bn=new Int32Array(N),d.HEAPU8=bt=new Uint8Array(N),d.HEAPU16=Yt=new Uint16Array(N),d.HEAPU32=Zn=new Uint32Array(N),d.HEAPF32=Dn=new Float32Array(N),d.HEAPF64=sn=new Float64Array(N)}var Oa=d.INITIAL_MEMORY||16777216;if(v)Q=d.wasmMemory,je=d.buffer;else if(d.wasmMemory)Q=d.wasmMemory;else if(Q=new WebAssembly.Memory({initial:Oa/65536,maximum:2147483648/65536,shared:!0}),!(Q.buffer instanceof SharedArrayBuffer))throw G("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"),w&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Q&&(je=Q.buffer),Oa=je.byteLength,Jt(je);var sa,ia=[],wr=[],ar=[],kr=[],Gi=[],_a=!1,cp=!1;v||wr.push({func:function(){Tp()}});function K0(){if(!v){if(d.preRun)for(typeof d.preRun=="function"&&(d.preRun=[d.preRun]);d.preRun.length;)fp(d.preRun.shift());Xi(ia)}}function pu(){_a=!0,!v&&Xi(wr)}function Z0(){v||Xi(ar)}function hp(){v||(cp=!0)}function vn(){if(!v){if(d.postRun)for(typeof d.postRun=="function"&&(d.postRun=[d.postRun]);d.postRun.length;)Y0(d.postRun.shift());Xi(Gi)}}function fp(N){ia.unshift(N)}function Y0(N){Gi.unshift(N)}var rr=0,Ir=null,ls=null;function J0(N){he(!v,"addRunDependency cannot be used in a pthread worker"),rr++,d.monitorRunDependencies&&d.monitorRunDependencies(rr)}function Q0(N){if(rr--,d.monitorRunDependencies&&d.monitorRunDependencies(rr),rr==0&&(Ir!==null&&(clearInterval(Ir),Ir=null),ls)){var E=ls;ls=null,E()}}d.preloadedImages={},d.preloadedAudios={};function sr(N){d.onAbort&&d.onAbort(N),v&&console.error("Pthread aborting at "+new Error().stack),N+="",G(N),oe=!0,ge=1,N="abort("+N+"). Build with -s ASSERTIONS=1 for more info.";var E=new WebAssembly.RuntimeError(N);throw c(E),E}function mp(N,E){return String.prototype.startsWith?N.startsWith(E):N.indexOf(E)===0}var qi="data:application/octet-stream;base64,";function yp(N){return mp(N,qi)}var ef="file://";function Ap(N){return mp(N,ef)}var wn="tfjs-backend-wasm-threaded-simd.wasm";yp(wn)||(wn=T(wn));function gp(N){try{if(N==wn&&ne)return new Uint8Array(ne);if(O)return O(N);throw"both async and sync fetching of the wasm failed"}catch(E){sr(E)}}function tf(){if(!ne&&(g||x)){if(typeof fetch=="function"&&!Ap(wn))return fetch(wn,{credentials:"same-origin"}).then(function(N){if(!N.ok)throw"failed to load wasm binary file at '"+wn+"'";return N.arrayBuffer()}).catch(function(){return gp(wn)});if($)return new Promise(function(N,E){$(wn,function(W){N(new Uint8Array(W))},E)})}return Promise.resolve().then(function(){return gp(wn)})}function nf(){var N={a:qf};function E(ue,be){var tt=ue.exports;if(d.asm=tt,sa=d.asm.F,pe=be,!v){var Ut=ke.unusedWorkers.length;ke.unusedWorkers.forEach(function(_t){ke.loadWasmModuleToWorker(_t,function(){--Ut||Q0("wasm-instantiate")})})}}v||J0("wasm-instantiate");function W(ue){E(ue.instance,ue.module)}function q(ue){return tf().then(function(be){return WebAssembly.instantiate(be,N)}).then(ue,function(be){G("failed to asynchronously prepare wasm: "+be),sr(be)})}function ce(){return!ne&&typeof WebAssembly.instantiateStreaming=="function"&&!yp(wn)&&!Ap(wn)&&typeof fetch=="function"?fetch(wn,{credentials:"same-origin"}).then(function(ue){var be=WebAssembly.instantiateStreaming(ue,N);return be.then(W,function(tt){return G("wasm streaming compile failed: "+tt),G("falling back to ArrayBuffer instantiation"),q(W)})}):q(W)}if(d.instantiateWasm)try{var le=d.instantiateWasm(N,E);return le}catch(ue){return G("Module.instantiateWasm callback failed with error: "+ue),!1}return ce().catch(c),{}}var af={9816:function(){throw"Canceled!"},9834:function(N,E){setTimeout(function(){n5(N,E)},0)}};function xp(){ke.initRuntime()}function Xi(N){for(;N.length>0;){var E=N.shift();if(typeof E=="function"){E(d);continue}var W=E.func;typeof W=="number"?E.arg===void 0?sa.get(W)():sa.get(W)(E.arg):W(E.arg===void 0?null:E.arg)}}function cu(N,E){if(N<=0||N>s().length||N&!0||E<0)return-28;if(E==0)return 0;E>=2147483647&&(E=Infinity);var W=Atomics.load(o(),eo>>2),q=0;if(W==N){var ce=Atomics.compareExchange(o(),eo>>2,W,0);if(ce==W&&(--E,q=1,E<=0))return 1}var le=Atomics.notify(o(),N>>2,E);if(le>=0)return le+q;throw"Atomics.notify returned an unexpected value "+le}d._emscripten_futex_wake=cu;function rf(N){if(v)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in killThread!";o()[N+12>>2]=0;var E=ke.pthreads[N];E.worker.terminate(),ke.freeThreadData(E),ke.runningWorkers.splice(ke.runningWorkers.indexOf(E.worker),1),E.worker.pthread=void 0}function sf(N){if(v)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in cancelThread!";var E=ke.pthreads[N];E.worker.postMessage({cmd:"cancel"})}function of(N){if(v)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in cleanupThread!";var E=ke.pthreads[N];if(E){o()[N+12>>2]=0;var W=E.worker;ke.returnWorkerToPool(W)}}var ke={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var N=Math.min(4,Math.max(1,(navigator.hardwareConcurrency||1)/2)),E=0;E>2]=N;var W=N+152;o()[W>>2]=W;for(var q=ds(512),E=0;E<128;++E)l()[q/4+E]=0;Atomics.store(l(),N+100>>2,q),Atomics.store(l(),N+40>>2,N),gm(N,!x,1),t5(N)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;ke.threadExitHandlers.length>0;)ke.threadExitHandlers.pop()();v&&Yi()&&e5()},runExitHandlersAndDeinitThread:function(N,E){Atomics.store(l(),N+56>>2,1),Atomics.store(l(),N+60>>2,0),ke.runExitHandlers(),Atomics.store(l(),N+4>>2,E),Atomics.store(l(),N+0>>2,1),cu(N+0,2147483647),gm(0,0,0)},threadExit:function(N){var E=Yi();E&&(ke.runExitHandlersAndDeinitThread(E,N),v&&postMessage({cmd:"exit"}))},threadCancel:function(){ke.runExitHandlersAndDeinitThread(Yi(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var N in ke.pthreads){var E=ke.pthreads[N];E&&E.worker&&ke.returnWorkerToPool(E.worker)}ke.pthreads={};for(var W=0;W>2];o()[N.threadInfoStruct+100>>2]=0,xu(E),xu(N.threadInfoStruct)}N.threadInfoStruct=0,N.allocatedOwnStack&&N.stackBase&&xu(N.stackBase),N.stackBase=0,N.worker&&(N.worker.pthread=null)}},returnWorkerToPool:function(N){ke.runWithoutMainThreadQueuedCalls(function(){delete ke.pthreads[N.pthread.threadInfoStruct],ke.unusedWorkers.push(N),ke.runningWorkers.splice(ke.runningWorkers.indexOf(N),1),ke.freeThreadData(N.pthread),N.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(N){o()[o5>>2]=0;try{N()}finally{o()[o5>>2]=1}},receiveObjectTransfer:function(N){},loadWasmModuleToWorker:function(N,E){N.onmessage=function(W){var q=W.data,ce=q.cmd;if(N.pthread&&(ke.currentProxiedOperationCallerThread=N.pthread.threadInfoStruct),q.targetThread&&q.targetThread!=Yi()){var le=ke.pthreads[q.targetThread];le?le.worker.postMessage(W.data,q.transferList):console.error('Internal error! Worker sent a message "'+ce+'" to target pthread '+q.targetThread+", but that thread no longer exists!"),ke.currentProxiedOperationCallerThread=void 0;return}if(ce==="processQueuedMainThreadWork")ym();else if(ce==="spawnThread")Sp(W.data);else if(ce==="cleanupThread")of(q.thread);else if(ce==="killThread")rf(q.thread);else if(ce==="cancelThread")sf(q.thread);else if(ce==="loaded")N.loaded=!0,E&&E(N),N.runPthread&&(N.runPthread(),delete N.runPthread);else if(ce==="print")X("Thread "+q.threadId+": "+q.text);else if(ce==="printErr")G("Thread "+q.threadId+": "+q.text);else if(ce==="alert")alert("Thread "+q.threadId+": "+q.text);else if(ce==="exit"){var ue=N.pthread&&Atomics.load(l(),N.pthread.threadInfoStruct+64>>2);ue&&ke.returnWorkerToPool(N)}else if(ce==="exitProcess")try{fI(q.returnCode)}catch(be){if(be instanceof vu)return;throw be}else ce==="cancelDone"?ke.returnWorkerToPool(N):ce==="objectTransfer"?ke.receiveObjectTransfer(W.data):W.data.target==="setimmediate"?N.postMessage(W.data):G("worker sent an unknown command "+ce);ke.currentProxiedOperationCallerThread=void 0},N.onerror=function(W){G("pthread sent an error! "+W.filename+":"+W.lineno+": "+W.message)},w&&(N.on("message",function(W){N.onmessage({data:W})}),N.on("error",function(W){N.onerror(W)}),N.on("exit",function(W){})),N.postMessage({cmd:"load",urlOrBlob:d.mainScriptUrlOrBlob||a,wasmMemory:Q,wasmModule:pe})},allocateUnusedWorker:function(){var N=T("tfjs-backend-wasm-threaded-simd.worker.js");ke.unusedWorkers.push(new Worker(N))},getNewWorker:function(){return ke.unusedWorkers.length==0&&(ke.allocateUnusedWorker(),ke.loadWasmModuleToWorker(ke.unusedWorkers[0])),ke.unusedWorkers.length>0?ke.unusedWorkers.pop():null},busySpinWait:function(N){for(var E=performance.now()+N;performance.now()>2]=N,N}function ff(N,E){if(v)return Sr(1,1,N,E)}function mf(N,E){if(N==E)postMessage({cmd:"processQueuedMainThreadWork"});else if(v)postMessage({targetThread:N,cmd:"processThreadQueue"});else{var W=ke.pthreads[N],q=W&&W.worker;if(!q)return;q.postMessage({cmd:"processThreadQueue"})}return 1}function yf(){sr()}function Af(N,E,W){var q=wf(E,W);return af[N].apply(null,q)}function gf(N,E){}function xf(N,E,W){if(N<=0||N>s().length||N&!0)return-28;if(g){if(Atomics.load(o(),N>>2)!=E)return-6;for(var q=performance.now(),ce=q+W,le=Atomics.exchange(o(),eo>>2,N);;){if(q=performance.now(),q>ce)return le=Atomics.exchange(o(),eo>>2,0),-73;if(le=Atomics.exchange(o(),eo>>2,0),le==0)break;if(ym(),Atomics.load(o(),N>>2)!=E)return-6;le=Atomics.exchange(o(),eo>>2,N)}return 0}else{var ue=Atomics.wait(o(),N>>2,E,W);if(ue==="timed-out")return-73;if(ue==="not-equal")return-6;if(ue==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ue}}function bf(N,E,W){i().copyWithin(N,E,E+W)}function vf(){return w?ao("os").cpus().length:navigator.hardwareConcurrency}function Sr(N,E){for(var W=arguments.length-2,q=bu(),ce=W,le=Qi(ce*8),ue=le>>3,be=0;be>=2;W=i()[N++];){var q=W<105;q&&E&1&&E++,fu.push(q?u()[E++>>1]:o()[E]),++E}return fu}function kf(N,E,W){hu.length=E;for(var q=W>>3,ce=0;ce>>16),Jt(Q.buffer),1}catch(E){}}function Nf(N){var E=If();if(N<=E)return!1;var W=2147483648;if(N>W)return!1;for(var q=1;q<=4;q*=2){var ce=E*(1+.2/q);ce=Math.min(ce,N+100663296);var le=Math.min(W,pt(Math.max(N,ce),65536)),ue=Sf(le);if(ue)return!0}return!1}var Pe={inEventHandler:0,removeAllEventListeners:function(){for(var N=Pe.eventHandlers.length-1;N>=0;--N)Pe._removeHandler(N);Pe.eventHandlers=[],Pe.deferredCalls=[]},registerRemoveEventListeners:function(){Pe.removeEventListenersRegistered||(kr.push(Pe.removeAllEventListeners),Pe.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(N,E,W){function q(ue,be){if(ue.length!=be.length)return!1;for(var tt in ue)if(ue[tt]!=be[tt])return!1;return!0}for(var ce in Pe.deferredCalls){var le=Pe.deferredCalls[ce];if(le.targetFunction==N&&q(le.argsList,W))return}Pe.deferredCalls.push({targetFunction:N,precedence:E,argsList:W}),Pe.deferredCalls.sort(function(ue,be){return ue.precedence>2]=W,o()[ue+4>>2]=q,o()[ue+8>>2]=ce,Am(0,N,637534208,E,q,ue),Ji(le)},getTargetThreadForEventCallback:function(N){switch(N){case 1:return 0;case 2:return ke.currentProxiedOperationCallerThread;default:return N}},getNodeNameForTarget:function(N){return N?N==window?"#window":N==screen?"#screen":N&&N.nodeName?N.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function Tf(N){var E=it(N)+1,W=ds(E);return et(N,W,E),W}function Ef(N,E,W,q){var ce=bu(),le=Qi(12),ue=0;E&&(ue=Tf(E)),o()[le>>2]=ue,o()[le+4>>2]=W,o()[le+8>>2]=q,Am(0,N,657457152,0,ue,le),Ji(ce)}function Cf(N,E,W,q){E=E?De(E):"",Ef(N,E,W,q)}function Rf(N){return N>2?De(N):N}var Mf=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function Ff(N){N=Rf(N);var E=Mf[N]||(typeof document!="undefined"?document.querySelector(N):void 0);return E}function mu(N){return Ff(N)}function bp(N,E,W){var q=mu(N);if(!q)return-4;if(q.canvasSharedPtr&&(o()[q.canvasSharedPtr>>2]=E,o()[q.canvasSharedPtr+4>>2]=W),q.offscreenCanvas||!q.controlTransferredOffscreen){q.offscreenCanvas&&(q=q.offscreenCanvas);var ce=!1;if(q.GLctxObject&&q.GLctxObject.GLctx){var le=q.GLctxObject.GLctx.getParameter(2978);ce=le[0]===0&&le[1]===0&&le[2]===q.width&&le[3]===q.height}q.width=E,q.height=W,ce&&q.GLctxObject.GLctx.viewport(0,0,E,W)}else if(q.canvasSharedPtr){var ue=o()[q.canvasSharedPtr+8>>2];return Cf(ue,N,E,W),1}else return-4;return 0}function vp(N,E,W){return v?Sr(2,1,N,E,W):bp(N,E,W)}function $f(N,E,W){var q=mu(N);return q?bp(N,E,W):vp(N,E,W)}function Df(N){}function zf(N,E){}function Of(N){var E=N.getExtension("ANGLE_instanced_arrays");if(E)return N.vertexAttribDivisor=function(W,q){E.vertexAttribDivisorANGLE(W,q)},N.drawArraysInstanced=function(W,q,ce,le){E.drawArraysInstancedANGLE(W,q,ce,le)},N.drawElementsInstanced=function(W,q,ce,le,ue){E.drawElementsInstancedANGLE(W,q,ce,le,ue)},1}function _f(N){var E=N.getExtension("OES_vertex_array_object");if(E)return N.createVertexArray=function(){return E.createVertexArrayOES()},N.deleteVertexArray=function(W){E.deleteVertexArrayOES(W)},N.bindVertexArray=function(W){E.bindVertexArrayOES(W)},N.isVertexArray=function(W){return E.isVertexArrayOES(W)},1}function Pf(N){var E=N.getExtension("WEBGL_draw_buffers");if(E)return N.drawBuffers=function(W,q){E.drawBuffersWEBGL(W,q)},1}function Lf(N){return!!(N.multiDrawWebgl=N.getExtension("WEBGL_multi_draw"))}var Je={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(N){Je.lastError||(Je.lastError=N)},getNewId:function(N){for(var E=Je.counter++,W=N.length;W>2]:-1;ce+=De(o()[W+le*4>>2],ue<0?void 0:ue)}return ce},createContext:function(N,E){var W=N.getContext("webgl",E);if(!W)return 0;var q=Je.registerContext(W,E);return q},registerContext:function(N,E){var W=ds(8);o()[W+4>>2]=Yi();var q={handle:W,attributes:E,version:E.majorVersion,GLctx:N};return N.canvas&&(N.canvas.GLctxObject=q),Je.contexts[W]=q,(typeof E.enableExtensionsByDefault=="undefined"||E.enableExtensionsByDefault)&&Je.initExtensions(q),W},makeContextCurrent:function(N){return Je.currentContext=Je.contexts[N],d.ctx=Nr=Je.currentContext&&Je.currentContext.GLctx,!(N&&!Nr)},getContext:function(N){return Je.contexts[N]},deleteContext:function(N){Je.currentContext===Je.contexts[N]&&(Je.currentContext=null),typeof Pe=="object"&&Pe.removeAllHandlersOnTarget(Je.contexts[N].GLctx.canvas),Je.contexts[N]&&Je.contexts[N].GLctx.canvas&&(Je.contexts[N].GLctx.canvas.GLctxObject=void 0),xu(Je.contexts[N].handle),Je.contexts[N]=null},initExtensions:function(N){if(N||(N=Je.currentContext),!N.initExtensionsDone){N.initExtensionsDone=!0;var E=N.GLctx;Of(E),_f(E),Pf(E),E.disjointTimerQueryExt=E.getExtension("EXT_disjoint_timer_query"),Lf(E);var W=E.getSupportedExtensions()||[];W.forEach(function(q){q.indexOf("lose_context")<0&&q.indexOf("debug")<0&&E.getExtension(q)})}},populateUniformTable:function(N){for(var E=Je.programs[N],W=Je.programInfos[N]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},q=W.uniforms,ce=Nr.getProgramParameter(E,35718),le=0;le>2,q=o()[W+(24>>2)],ce={alpha:!!o()[W+(0>>2)],depth:!!o()[W+(4>>2)],stencil:!!o()[W+(8>>2)],antialias:!!o()[W+(12>>2)],premultipliedAlpha:!!o()[W+(16>>2)],preserveDrawingBuffer:!!o()[W+(20>>2)],powerPreference:Wf[q],failIfMajorPerformanceCaveat:!!o()[W+(28>>2)],majorVersion:o()[W+(32>>2)],minorVersion:o()[W+(36>>2)],enableExtensionsByDefault:o()[W+(40>>2)],explicitSwapControl:o()[W+(44>>2)],proxyContextToMainThread:o()[W+(48>>2)],renderViaOffscreenBackBuffer:o()[W+(52>>2)]},le=mu(N);if(!le||ce.explicitSwapControl)return 0;var ue=Je.createContext(le,ce);return ue}function Vf(N,E){return Bf(N,E)}var Ki={mappings:{},buffers:[null,[],[]],printChar:function(N,E){var W=Ki.buffers[N];E===0||E===10?((N===1?X:G)(Oe(W,0)),W.length=0):W.push(E)},varargs:void 0,get:function(){Ki.varargs+=4;var N=o()[Ki.varargs-4>>2];return N},getStr:function(N){var E=De(N);return E},get64:function(N,E){return N}};function wp(N){return v?Sr(3,1,N):0}function kp(N,E,W,q,ce){if(v)return Sr(4,1,N,E,W,q,ce)}function Ip(N,E,W,q){if(v)return Sr(5,1,N,E,W,q);for(var ce=0,le=0;le>2],be=o()[E+(le*8+4)>>2],tt=0;tt>2]=ce,0}function jf(N){var E=ke.threadExitHandlers.pop();N&&E()}function Uf(N,E){ke.threadExitHandlers.push(function(){sa.get(N)(E)})}function Sp(N){if(v)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var E=ke.getNewWorker();if(E.pthread!==void 0)throw"Internal error!";if(!N.pthread_ptr)throw"Internal error, no pthread ptr!";ke.runningWorkers.push(E);for(var W=ds(128*4),q=0;q<128;++q)o()[W+q*4>>2]=0;var ce=N.stackBase+N.stackSize,le=ke.pthreads[N.pthread_ptr]={worker:E,stackBase:N.stackBase,stackSize:N.stackSize,allocatedOwnStack:N.allocatedOwnStack,threadInfoStruct:N.pthread_ptr},ue=le.threadInfoStruct>>2;Atomics.store(l(),ue+(64>>2),N.detached),Atomics.store(l(),ue+(100>>2),W),Atomics.store(l(),ue+(40>>2),le.threadInfoStruct),Atomics.store(l(),ue+(80>>2),N.stackSize),Atomics.store(l(),ue+(76>>2),ce),Atomics.store(l(),ue+(104>>2),N.stackSize),Atomics.store(l(),ue+(104+8>>2),ce),Atomics.store(l(),ue+(104+12>>2),N.detached);var be=Qg(),tt=be+40;Atomics.store(l(),ue+(172>>2),tt),E.pthread=le;var Ut={cmd:"run",start_routine:N.startRoutine,arg:N.arg,threadInfoStruct:N.pthread_ptr,stackBase:N.stackBase,stackSize:N.stackSize};E.runPthread=function(){Ut.time=performance.now(),E.postMessage(Ut,N.transferList)},E.loaded&&(E.runPthread(),delete E.runPthread)}function Hf(N,E,W,q){if(typeof SharedArrayBuffer=="undefined")return G("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!N)return G("pthread_create called with a null thread pointer!"),28;var ce=[],le=0;if(v&&(ce.length===0||le))return a5(687865856,N,E,W,q);if(le)return le;var ue=0,be=0,tt=0;E&&E!=-1?(ue=o()[E>>2],ue+=81920,be=o()[E+8>>2],tt=o()[E+12>>2]!==0):ue=2097152;var Ut=be==0;Ut?be=i5(16,ue):(be-=ue,he(be>0));for(var _t=ds(228),Er=0;Er<228>>2;++Er)l()[(_t>>2)+Er]=0;o()[N>>2]=_t,o()[_t+12>>2]=_t;var to=_t+152;o()[to>>2]=to;var kn={stackBase:be,stackSize:ue,allocatedOwnStack:Ut,detached:tt,startRoutine:W,pthread_ptr:_t,arg:q,transferList:ce};return v?(kn.cmd="spawnThread",postMessage(kn,ce)):Sp(kn),0}function Np(N){if(v)return Sr(6,1,N);switch(N){case 30:return 16384;case 85:var E=2147483648;return E/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 hf(28),-1}v||ke.initMainThreadBlock();var Nr,Gf=[null,ff,vp,wp,kp,Ip,Np],qf={e:pf,r:cf,x:mf,b:yf,y:Af,j:gf,c:xf,d:cu,f:us,p:bf,z:vf,u:kf,q:Nf,v:$f,i:Df,t:zf,w:Vf,m:wp,n:kp,g:Ip,o:xp,a:Q||d.wasmMemory,k:jf,l:Uf,h:Hf,s:Np},Yg=nf(),Tp=d.___wasm_call_ctors=function(){return(Tp=d.___wasm_call_ctors=d.asm.A).apply(null,arguments)},Xf=d._init=function(){return(Xf=d._init=d.asm.B).apply(null,arguments)},Kf=d._register_tensor=function(){return(Kf=d._register_tensor=d.asm.C).apply(null,arguments)},Zf=d._dispose_data=function(){return(Zf=d._dispose_data=d.asm.D).apply(null,arguments)},Yf=d._dispose=function(){return(Yf=d._dispose=d.asm.E).apply(null,arguments)},Jf=d._Abs=function(){return(Jf=d._Abs=d.asm.G).apply(null,arguments)},Qf=d._Add=function(){return(Qf=d._Add=d.asm.H).apply(null,arguments)},em=d._AddN=function(){return(em=d._AddN=d.asm.I).apply(null,arguments)},tm=d._All=function(){return(tm=d._All=d.asm.J).apply(null,arguments)},nm=d._Any=function(){return(nm=d._Any=d.asm.K).apply(null,arguments)},am=d._ArgMax=function(){return(am=d._ArgMax=d.asm.L).apply(null,arguments)},rm=d._AvgPool=function(){return(rm=d._AvgPool=d.asm.M).apply(null,arguments)},sm=d._BatchMatMul=function(){return(sm=d._BatchMatMul=d.asm.N).apply(null,arguments)},im=d._Ceil=function(){return(im=d._Ceil=d.asm.O).apply(null,arguments)},om=d._ClipByValue=function(){return(om=d._ClipByValue=d.asm.P).apply(null,arguments)},lm=d._Conv2D=function(){return(lm=d._Conv2D=d.asm.Q).apply(null,arguments)},um=d._Conv2DBackpropInput=function(){return(um=d._Conv2DBackpropInput=d.asm.R).apply(null,arguments)},dm=d._Cos=function(){return(dm=d._Cos=d.asm.S).apply(null,arguments)},pm=d._CropAndResize=function(){return(pm=d._CropAndResize=d.asm.T).apply(null,arguments)},cm=d._Cumsum=function(){return(cm=d._Cumsum=d.asm.U).apply(null,arguments)},hm=d._DepthToSpace=function(){return(hm=d._DepthToSpace=d.asm.V).apply(null,arguments)},fm=d._DepthwiseConv2dNative=function(){return(fm=d._DepthwiseConv2dNative=d.asm.W).apply(null,arguments)},Ep=d._Equal=function(){return(Ep=d._Equal=d.asm.X).apply(null,arguments)},Cp=d._Exp=function(){return(Cp=d._Exp=d.asm.Y).apply(null,arguments)},Rp=d._FlipLeftRight=function(){return(Rp=d._FlipLeftRight=d.asm.Z).apply(null,arguments)},yu=d._Floor=function(){return(yu=d._Floor=d.asm._).apply(null,arguments)},Zi=d._FloorDiv=function(){return(Zi=d._FloorDiv=d.asm.$).apply(null,arguments)},mm=d._FusedBatchNorm=function(){return(mm=d._FusedBatchNorm=d.asm.aa).apply(null,arguments)},Au=d._FusedConv2D=function(){return(Au=d._FusedConv2D=d.asm.ba).apply(null,arguments)},K=d._FusedDepthwiseConv2D=function(){return(K=d._FusedDepthwiseConv2D=d.asm.ca).apply(null,arguments)},te=d._Gather=function(){return(te=d._Gather=d.asm.da).apply(null,arguments)},Ee=d._GatherNd=function(){return(Ee=d._GatherNd=d.asm.ea).apply(null,arguments)},Ze=d._Greater=function(){return(Ze=d._Greater=d.asm.fa).apply(null,arguments)},Tt=d._GreaterEqual=function(){return(Tt=d._GreaterEqual=d.asm.ga).apply(null,arguments)},mt=d._LeakyRelu=function(){return(mt=d._LeakyRelu=d.asm.ha).apply(null,arguments)},Ue=d._Less=function(){return(Ue=d._Less=d.asm.ia).apply(null,arguments)},Ge=d._LessEqual=function(){return(Ge=d._LessEqual=d.asm.ja).apply(null,arguments)},Qt=d._Log=function(){return(Qt=d._Log=d.asm.ka).apply(null,arguments)},ir=d._LogicalAnd=function(){return(ir=d._LogicalAnd=d.asm.la).apply(null,arguments)},or=d._Max=function(){return(or=d._Max=d.asm.ma).apply(null,arguments)},Mp=d._MaxPool=function(){return(Mp=d._MaxPool=d.asm.na).apply(null,arguments)},gu=d._Maximum=function(){return(gu=d._Maximum=d.asm.oa).apply(null,arguments)},Yn=d._Mean=function(){return(Yn=d._Mean=d.asm.pa).apply(null,arguments)},Tr=d._Min=function(){return(Tr=d._Min=d.asm.qa).apply(null,arguments)},Fp=d._Minimum=function(){return(Fp=d._Minimum=d.asm.ra).apply(null,arguments)},T9=d._MirrorPad=function(){return(T9=d._MirrorPad=d.asm.sa).apply(null,arguments)},E9=d._Multiply=function(){return(E9=d._Multiply=d.asm.ta).apply(null,arguments)},C9=d._Neg=function(){return(C9=d._Neg=d.asm.ua).apply(null,arguments)},R9=d._NonMaxSuppressionV3=function(){return(R9=d._NonMaxSuppressionV3=d.asm.va).apply(null,arguments)},M9=d._NonMaxSuppressionV4=function(){return(M9=d._NonMaxSuppressionV4=d.asm.wa).apply(null,arguments)},F9=d._NonMaxSuppressionV5=function(){return(F9=d._NonMaxSuppressionV5=d.asm.xa).apply(null,arguments)},$9=d._NotEqual=function(){return($9=d._NotEqual=d.asm.ya).apply(null,arguments)},D9=d._OneHot=function(){return(D9=d._OneHot=d.asm.za).apply(null,arguments)},z9=d._PadV2=function(){return(z9=d._PadV2=d.asm.Aa).apply(null,arguments)},O9=d._Pow=function(){return(O9=d._Pow=d.asm.Ba).apply(null,arguments)},_9=d._Prelu=function(){return(_9=d._Prelu=d.asm.Ca).apply(null,arguments)},P9=d._Prod=function(){return(P9=d._Prod=d.asm.Da).apply(null,arguments)},L9=d._RealDiv=function(){return(L9=d._RealDiv=d.asm.Ea).apply(null,arguments)},W9=d._Relu=function(){return(W9=d._Relu=d.asm.Fa).apply(null,arguments)},B9=d._Relu6=function(){return(B9=d._Relu6=d.asm.Ga).apply(null,arguments)},V9=d._ResizeBilinear=function(){return(V9=d._ResizeBilinear=d.asm.Ha).apply(null,arguments)},j9=d._Reverse=function(){return(j9=d._Reverse=d.asm.Ia).apply(null,arguments)},U9=d._RotateWithOffset=function(){return(U9=d._RotateWithOffset=d.asm.Ja).apply(null,arguments)},H9=d._Round=function(){return(H9=d._Round=d.asm.Ka).apply(null,arguments)},G9=d._Rsqrt=function(){return(G9=d._Rsqrt=d.asm.La).apply(null,arguments)},q9=d._ScatterNd=function(){return(q9=d._ScatterNd=d.asm.Ma).apply(null,arguments)},X9=d._SelectV2=function(){return(X9=d._SelectV2=d.asm.Na).apply(null,arguments)},K9=d._Sigmoid=function(){return(K9=d._Sigmoid=d.asm.Oa).apply(null,arguments)},Z9=d._Sin=function(){return(Z9=d._Sin=d.asm.Pa).apply(null,arguments)},Y9=d._Softmax=function(){return(Y9=d._Softmax=d.asm.Qa).apply(null,arguments)},J9=d._Sqrt=function(){return(J9=d._Sqrt=d.asm.Ra).apply(null,arguments)},Q9=d._Square=function(){return(Q9=d._Square=d.asm.Sa).apply(null,arguments)},eI=d._SquaredDifference=function(){return(eI=d._SquaredDifference=d.asm.Ta).apply(null,arguments)},tI=d._Step=function(){return(tI=d._Step=d.asm.Ua).apply(null,arguments)},nI=d._StridedSlice=function(){return(nI=d._StridedSlice=d.asm.Va).apply(null,arguments)},aI=d._Sub=function(){return(aI=d._Sub=d.asm.Wa).apply(null,arguments)},rI=d._Sum=function(){return(rI=d._Sum=d.asm.Xa).apply(null,arguments)},sI=d._Tan=function(){return(sI=d._Tan=d.asm.Ya).apply(null,arguments)},iI=d._Tanh=function(){return(iI=d._Tanh=d.asm.Za).apply(null,arguments)},oI=d._Tile=function(){return(oI=d._Tile=d.asm._a).apply(null,arguments)},lI=d._TopK=function(){return(lI=d._TopK=d.asm.$a).apply(null,arguments)},uI=d._Transform=function(){return(uI=d._Transform=d.asm.ab).apply(null,arguments)},dI=d._Transpose=function(){return(dI=d._Transpose=d.asm.bb).apply(null,arguments)},pI=d.__FusedMatMul=function(){return(pI=d.__FusedMatMul=d.asm.cb).apply(null,arguments)},ds=d._malloc=function(){return(ds=d._malloc=d.asm.db).apply(null,arguments)},xu=d._free=function(){return(xu=d._free=d.asm.eb).apply(null,arguments)},Jg=d.___errno_location=function(){return(Jg=d.___errno_location=d.asm.fb).apply(null,arguments)},Qg=d._emscripten_get_global_libc=function(){return(Qg=d._emscripten_get_global_libc=d.asm.gb).apply(null,arguments)},Yi=d._pthread_self=function(){return(Yi=d._pthread_self=d.asm.hb).apply(null,arguments)},e5=d.___pthread_tsd_run_dtors=function(){return(e5=d.___pthread_tsd_run_dtors=d.asm.ib).apply(null,arguments)},ym=d._emscripten_main_thread_process_queued_calls=function(){return(ym=d._emscripten_main_thread_process_queued_calls=d.asm.jb).apply(null,arguments)},cI=d._emscripten_current_thread_process_queued_calls=function(){return(cI=d._emscripten_current_thread_process_queued_calls=d.asm.kb).apply(null,arguments)},t5=d._emscripten_register_main_browser_thread_id=function(){return(t5=d._emscripten_register_main_browser_thread_id=d.asm.lb).apply(null,arguments)},n5=d.__emscripten_do_dispatch_to_thread=function(){return(n5=d.__emscripten_do_dispatch_to_thread=d.asm.mb).apply(null,arguments)},a5=d._emscripten_sync_run_in_main_thread_4=function(){return(a5=d._emscripten_sync_run_in_main_thread_4=d.asm.nb).apply(null,arguments)},r5=d._emscripten_run_in_main_runtime_thread_js=function(){return(r5=d._emscripten_run_in_main_runtime_thread_js=d.asm.ob).apply(null,arguments)},Am=d.__emscripten_call_on_thread=function(){return(Am=d.__emscripten_call_on_thread=d.asm.pb).apply(null,arguments)},hI=d._emscripten_tls_init=function(){return(hI=d._emscripten_tls_init=d.asm.qb).apply(null,arguments)},gm=d.__emscripten_thread_init=function(){return(gm=d.__emscripten_thread_init=d.asm.rb).apply(null,arguments)},bu=d.stackSave=function(){return(bu=d.stackSave=d.asm.sb).apply(null,arguments)},Ji=d.stackRestore=function(){return(Ji=d.stackRestore=d.asm.tb).apply(null,arguments)},Qi=d.stackAlloc=function(){return(Qi=d.stackAlloc=d.asm.ub).apply(null,arguments)},s5=d._emscripten_stack_set_limits=function(){return(s5=d._emscripten_stack_set_limits=d.asm.vb).apply(null,arguments)},i5=d._memalign=function(){return(i5=d._memalign=d.asm.wb).apply(null,arguments)},o5=d.__emscripten_allow_main_runtime_queued_calls=9808,eo=d.__emscripten_main_thread_futex=11432;d.cwrap=$e,d.PThread=ke,d.PThread=ke,d.wasmMemory=Q,d.ExitStatus=vu;var $p;function vu(N){this.name="ExitStatus",this.message="Program terminated with exit("+N+")",this.status=N}ls=function N(){$p||xm(),$p||(ls=N)};function xm(N){if(N=N||f,rr>0)return;if(v){p(d),pu(),postMessage({cmd:"loaded"});return}if(K0(),rr>0)return;function E(){$p||($p=!0,d.calledRun=!0,!oe&&(pu(),Z0(),p(d),d.onRuntimeInitialized&&d.onRuntimeInitialized(),vn()))}d.setStatus?(d.setStatus("Running..."),setTimeout(function(){setTimeout(function(){d.setStatus("")},1),E()},1)):E()}d.run=xm;function fI(N,E){if(!(E&&ie&&N===0)){if(!E&&v)throw postMessage({cmd:"exitProcess",returnCode:N}),new vu(N);ie||(ke.terminateAllThreads(),ge=N,hp(),d.onExit&&d.onExit(N),oe=!0),A(N,new vu(N))}}if(d.preInit)for(typeof d.preInit=="function"&&(d.preInit=[d.preInit]);d.preInit.length>0;)d.preInit.pop()();return v&&(ie=!1,ke.initWorker()),xm(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}),DI=wt((e,t)=>{var n=function(){var a=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(a=a||__filename),function(r){r=r||{};var s=typeof r!="undefined"?r:{},i,o;s.ready=new Promise(function(K,te){i=K,o=te});var l={},u;for(u in s)s.hasOwnProperty(u)&&(l[u]=s[u]);var d=[],p="./this.program",c=function(K,te){throw te},h=!1,m=!1,f=!1,y=!1;h=typeof window=="object",m=typeof importScripts=="function",f=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",y=!h&&!f&&!m;var A="";function g(K){return s.locateFile?s.locateFile(K,A):A+K}var x,w,b,v,S,T;f?(m?A=wu().dirname(A)+"/":A=__dirname+"/",x=function(K,te){return S||(S=ao("fs")),T||(T=wu()),K=T.normalize(K),S.readFileSync(K,te?null:"utf8")},b=function(K){var te=x(K,!0);return te.buffer||(te=new Uint8Array(te)),X(te.buffer),te},process.argv.length>1&&(p=process.argv[1].replace(/\\/g,"/")),d=process.argv.slice(2),process.on("uncaughtException",function(K){if(!(K instanceof mm))throw K}),process.on("unhandledRejection",_a),c=function(K){process.exit(K)},s.inspect=function(){return"[Emscripten Module object]"}):y?(typeof read!="undefined"&&(x=function(K){return read(K)}),b=function(K){var te;return typeof readbuffer=="function"?new Uint8Array(readbuffer(K)):(te=read(K,"binary"),X(typeof te=="object"),te)},typeof scriptArgs!="undefined"?d=scriptArgs:typeof arguments!="undefined"&&(d=arguments),typeof quit=="function"&&(c=function(K){quit(K)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(h||m)&&(m?A=self.location.href:typeof document!="undefined"&&document.currentScript&&(A=document.currentScript.src),a&&(A=a),A.indexOf("blob:")!==0?A=A.substr(0,A.lastIndexOf("/")+1):A="",x=function(K){var te=new XMLHttpRequest;return te.open("GET",K,!1),te.send(null),te.responseText},m&&(b=function(K){var te=new XMLHttpRequest;return te.open("GET",K,!1),te.responseType="arraybuffer",te.send(null),new Uint8Array(te.response)}),w=function(K,te,Ee){var Ze=new XMLHttpRequest;Ze.open("GET",K,!0),Ze.responseType="arraybuffer",Ze.onload=function(){if(Ze.status==200||Ze.status==0&&Ze.response){te(Ze.response);return}Ee()},Ze.onerror=Ee,Ze.send(null)},v=function(K){document.title=K});var C=s.print||console.log.bind(console),$=s.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(s[u]=l[u]);l=null,s.arguments&&(d=s.arguments),s.thisProgram&&(p=s.thisProgram),s.quit&&(c=s.quit);var O;s.wasmBinary&&(O=s.wasmBinary);var P=s.noExitRuntime||!0;typeof WebAssembly!="object"&&_a("no native wasm support detected");var j,D=!1,U;function X(K,te){K||_a("Assertion failed: "+te)}function G(K){var te=s["_"+K];return X(te,"Cannot call unknown function "+K+", make sure it is exported"),te}function ee(K,te,Ee,Ze,Tt){var mt={string:function(Yn){var Tr=0;if(Yn!=null&&Yn!==0){var Fp=(Yn.length<<2)+1;Tr=yu(Fp),pe(Yn,Tr,Fp)}return Tr},array:function(Yn){var Tr=yu(Yn.length);return oe(Yn,Tr),Tr}};function Ue(Yn){return te==="string"?ie(Yn):te==="boolean"?Boolean(Yn):Yn}var Ge=G(K),Qt=[],ir=0;if(Ze)for(var or=0;or=Ze);)++Tt;if(Tt-te>16&&K.subarray&&re)return re.decode(K.subarray(te,Tt));for(var mt="";te>10,56320|ir&1023)}}return mt}function ie(K,te){return K?ne(Te,K,te):""}function Q(K,te,Ee,Ze){if(!(Ze>0))return 0;for(var Tt=Ee,mt=Ee+Ze-1,Ue=0;Ue=55296&&Ge<=57343){var Qt=K.charCodeAt(++Ue);Ge=65536+((Ge&1023)<<10)|Qt&1023}if(Ge<=127){if(Ee>=mt)break;te[Ee++]=Ge}else if(Ge<=2047){if(Ee+1>=mt)break;te[Ee++]=192|Ge>>6,te[Ee++]=128|Ge&63}else if(Ge<=65535){if(Ee+2>=mt)break;te[Ee++]=224|Ge>>12,te[Ee++]=128|Ge>>6&63,te[Ee++]=128|Ge&63}else{if(Ee+3>=mt)break;te[Ee++]=240|Ge>>18,te[Ee++]=128|Ge>>12&63,te[Ee++]=128|Ge>>6&63,te[Ee++]=128|Ge&63}}return te[Ee]=0,Ee-Tt}function pe(K,te,Ee){return Q(K,Te,te,Ee)}function oe(K,te){Se.set(K,te)}function ge(K,te){return K%te>0&&(K+=te-K%te),K}var he,Se,Te,$e,Oe,De,Qe,et,it;function Ke(K){he=K,s.HEAP8=Se=new Int8Array(K),s.HEAP16=$e=new Int16Array(K),s.HEAP32=De=new Int32Array(K),s.HEAPU8=Te=new Uint8Array(K),s.HEAPU16=Oe=new Uint16Array(K),s.HEAPU32=Qe=new Uint32Array(K),s.HEAPF32=et=new Float32Array(K),s.HEAPF64=it=new Float64Array(K)}var pt=s.INITIAL_MEMORY||16777216,je,xn=[],bt=[],Kn=[],Yt=[],bn=!1;bt.push({func:function(){xp()}});function Zn(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Oa(s.preRun.shift());Ir(xn)}function Dn(){bn=!0,Ir(bt)}function sn(){Ir(Kn)}function Jt(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)sa(s.postRun.shift());Ir(Yt)}function Oa(K){xn.unshift(K)}function sa(K){Yt.unshift(K)}var ia=0,wr=null,ar=null;function kr(K){ia++,s.monitorRunDependencies&&s.monitorRunDependencies(ia)}function Gi(K){if(ia--,s.monitorRunDependencies&&s.monitorRunDependencies(ia),ia==0&&(wr!==null&&(clearInterval(wr),wr=null),ar)){var te=ar;ar=null,te()}}s.preloadedImages={},s.preloadedAudios={};function _a(K){s.onAbort&&s.onAbort(K),K+="",$(K),D=!0,U=1,K="abort("+K+"). Build with -s ASSERTIONS=1 for more info.";var te=new WebAssembly.RuntimeError(K);throw o(te),te}function cp(K,te){return String.prototype.startsWith?K.startsWith(te):K.indexOf(te)===0}var K0="data:application/octet-stream;base64,";function pu(K){return cp(K,K0)}var Z0="file://";function hp(K){return cp(K,Z0)}var vn="tfjs-backend-wasm.wasm";pu(vn)||(vn=g(vn));function fp(K){try{if(K==vn&&O)return new Uint8Array(O);if(b)return b(K);throw"both async and sync fetching of the wasm failed"}catch(te){_a(te)}}function Y0(){if(!O&&(h||m)){if(typeof fetch=="function"&&!hp(vn))return fetch(vn,{credentials:"same-origin"}).then(function(K){if(!K.ok)throw"failed to load wasm binary file at '"+vn+"'";return K.arrayBuffer()}).catch(function(){return fp(vn)});if(w)return new Promise(function(K,te){w(vn,function(Ee){K(new Uint8Array(Ee))},te)})}return Promise.resolve().then(function(){return fp(vn)})}function rr(){var K={a:nf};function te(Ue,Ge){var Qt=Ue.exports;s.asm=Qt,j=s.asm.i,Ke(j.buffer),je=s.asm.o,Gi("wasm-instantiate")}kr("wasm-instantiate");function Ee(Ue){te(Ue.instance)}function Ze(Ue){return Y0().then(function(Ge){return WebAssembly.instantiate(Ge,K)}).then(Ue,function(Ge){$("failed to asynchronously prepare wasm: "+Ge),_a(Ge)})}function Tt(){return!O&&typeof WebAssembly.instantiateStreaming=="function"&&!pu(vn)&&!hp(vn)&&typeof fetch=="function"?fetch(vn,{credentials:"same-origin"}).then(function(Ue){var Ge=WebAssembly.instantiateStreaming(Ue,K);return Ge.then(Ee,function(Qt){return $("wasm streaming compile failed: "+Qt),$("falling back to ArrayBuffer instantiation"),Ze(Ee)})}):Ze(Ee)}if(s.instantiateWasm)try{var mt=s.instantiateWasm(K,te);return mt}catch(Ue){return $("Module.instantiateWasm callback failed with error: "+Ue),!1}return Tt().catch(o),{}}function Ir(K){for(;K.length>0;){var te=K.shift();if(typeof te=="function"){te(s);continue}var Ee=te.func;typeof Ee=="number"?te.arg===void 0?je.get(Ee)():je.get(Ee)(te.arg):Ee(te.arg===void 0?null:te.arg)}}function ls(){_a()}function J0(K,te,Ee){Te.copyWithin(K,te,te+Ee)}function Q0(){return Te.length}function sr(K){try{return j.grow(K-he.byteLength+65535>>>16),Ke(j.buffer),1}catch(te){}}function mp(K){var te=Q0(),Ee=2147483648;if(K>Ee)return!1;for(var Ze=1;Ze<=4;Ze*=2){var Tt=te*(1+.2/Ze);Tt=Math.min(Tt,K+100663296);var mt=Math.min(Ee,ge(Math.max(K,Tt),65536)),Ue=sr(mt);if(Ue)return!0}return!1}var qi={mappings:{},buffers:[null,[],[]],printChar:function(K,te){var Ee=qi.buffers[K];te===0||te===10?((K===1?C:$)(ne(Ee,0)),Ee.length=0):Ee.push(te)},varargs:void 0,get:function(){qi.varargs+=4;var K=De[qi.varargs-4>>2];return K},getStr:function(K){var te=ie(K);return te},get64:function(K,te){return K}};function yp(K){return 0}function ef(K,te,Ee,Ze,Tt){}function Ap(K,te,Ee,Ze){for(var Tt=0,mt=0;mt>2],Ge=De[te+(mt*8+4)>>2],Qt=0;Qt>2]=Tt,0}function wn(){return 6}function gp(K){return De[Ep()>>2]=K,K}function tf(K){switch(K){case 30:return 16384;case 85:var te=2147483648;return te/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 gp(28),-1}var nf={a:ls,d:J0,e:mp,f:yp,c:ef,b:Ap,g:wn,h:tf},af=rr(),xp=s.___wasm_call_ctors=function(){return(xp=s.___wasm_call_ctors=s.asm.j).apply(null,arguments)},Xi=s._init=function(){return(Xi=s._init=s.asm.k).apply(null,arguments)},cu=s._register_tensor=function(){return(cu=s._register_tensor=s.asm.l).apply(null,arguments)},rf=s._dispose_data=function(){return(rf=s._dispose_data=s.asm.m).apply(null,arguments)},sf=s._dispose=function(){return(sf=s._dispose=s.asm.n).apply(null,arguments)},of=s._Abs=function(){return(of=s._Abs=s.asm.p).apply(null,arguments)},ke=s._Add=function(){return(ke=s._Add=s.asm.q).apply(null,arguments)},lf=s._AddN=function(){return(lf=s._AddN=s.asm.r).apply(null,arguments)},uf=s._All=function(){return(uf=s._All=s.asm.s).apply(null,arguments)},df=s._Any=function(){return(df=s._Any=s.asm.t).apply(null,arguments)},pf=s._ArgMax=function(){return(pf=s._ArgMax=s.asm.u).apply(null,arguments)},cf=s._AvgPool=function(){return(cf=s._AvgPool=s.asm.v).apply(null,arguments)},us=s._BatchMatMul=function(){return(us=s._BatchMatMul=s.asm.w).apply(null,arguments)},hf=s._Ceil=function(){return(hf=s._Ceil=s.asm.x).apply(null,arguments)},ff=s._ClipByValue=function(){return(ff=s._ClipByValue=s.asm.y).apply(null,arguments)},mf=s._Conv2D=function(){return(mf=s._Conv2D=s.asm.z).apply(null,arguments)},yf=s._Conv2DBackpropInput=function(){return(yf=s._Conv2DBackpropInput=s.asm.A).apply(null,arguments)},Af=s._Cos=function(){return(Af=s._Cos=s.asm.B).apply(null,arguments)},gf=s._CropAndResize=function(){return(gf=s._CropAndResize=s.asm.C).apply(null,arguments)},xf=s._Cumsum=function(){return(xf=s._Cumsum=s.asm.D).apply(null,arguments)},bf=s._DepthToSpace=function(){return(bf=s._DepthToSpace=s.asm.E).apply(null,arguments)},vf=s._DepthwiseConv2dNative=function(){return(vf=s._DepthwiseConv2dNative=s.asm.F).apply(null,arguments)},Sr=s._Equal=function(){return(Sr=s._Equal=s.asm.G).apply(null,arguments)},hu=s._Exp=function(){return(hu=s._Exp=s.asm.H).apply(null,arguments)},fu=s._FlipLeftRight=function(){return(fu=s._FlipLeftRight=s.asm.I).apply(null,arguments)},wf=s._Floor=function(){return(wf=s._Floor=s.asm.J).apply(null,arguments)},kf=s._FloorDiv=function(){return(kf=s._FloorDiv=s.asm.K).apply(null,arguments)},If=s._FusedBatchNorm=function(){return(If=s._FusedBatchNorm=s.asm.L).apply(null,arguments)},Sf=s._FusedConv2D=function(){return(Sf=s._FusedConv2D=s.asm.M).apply(null,arguments)},Nf=s._FusedDepthwiseConv2D=function(){return(Nf=s._FusedDepthwiseConv2D=s.asm.N).apply(null,arguments)},Pe=s._Gather=function(){return(Pe=s._Gather=s.asm.O).apply(null,arguments)},Tf=s._GatherNd=function(){return(Tf=s._GatherNd=s.asm.P).apply(null,arguments)},Ef=s._Greater=function(){return(Ef=s._Greater=s.asm.Q).apply(null,arguments)},Cf=s._GreaterEqual=function(){return(Cf=s._GreaterEqual=s.asm.R).apply(null,arguments)},Rf=s._LeakyRelu=function(){return(Rf=s._LeakyRelu=s.asm.S).apply(null,arguments)},Mf=s._Less=function(){return(Mf=s._Less=s.asm.T).apply(null,arguments)},Ff=s._LessEqual=function(){return(Ff=s._LessEqual=s.asm.U).apply(null,arguments)},mu=s._Log=function(){return(mu=s._Log=s.asm.V).apply(null,arguments)},bp=s._LogicalAnd=function(){return(bp=s._LogicalAnd=s.asm.W).apply(null,arguments)},vp=s._Max=function(){return(vp=s._Max=s.asm.X).apply(null,arguments)},$f=s._MaxPool=function(){return($f=s._MaxPool=s.asm.Y).apply(null,arguments)},Df=s._Maximum=function(){return(Df=s._Maximum=s.asm.Z).apply(null,arguments)},zf=s._Mean=function(){return(zf=s._Mean=s.asm._).apply(null,arguments)},Of=s._Min=function(){return(Of=s._Min=s.asm.$).apply(null,arguments)},_f=s._Minimum=function(){return(_f=s._Minimum=s.asm.aa).apply(null,arguments)},Pf=s._MirrorPad=function(){return(Pf=s._MirrorPad=s.asm.ba).apply(null,arguments)},Lf=s._Multiply=function(){return(Lf=s._Multiply=s.asm.ca).apply(null,arguments)},Je=s._Neg=function(){return(Je=s._Neg=s.asm.da).apply(null,arguments)},Wf=s._NonMaxSuppressionV3=function(){return(Wf=s._NonMaxSuppressionV3=s.asm.ea).apply(null,arguments)},Bf=s._NonMaxSuppressionV4=function(){return(Bf=s._NonMaxSuppressionV4=s.asm.fa).apply(null,arguments)},Vf=s._NonMaxSuppressionV5=function(){return(Vf=s._NonMaxSuppressionV5=s.asm.ga).apply(null,arguments)},Ki=s._NotEqual=function(){return(Ki=s._NotEqual=s.asm.ha).apply(null,arguments)},wp=s._OneHot=function(){return(wp=s._OneHot=s.asm.ia).apply(null,arguments)},kp=s._PadV2=function(){return(kp=s._PadV2=s.asm.ja).apply(null,arguments)},Ip=s._Pow=function(){return(Ip=s._Pow=s.asm.ka).apply(null,arguments)},jf=s._Prelu=function(){return(jf=s._Prelu=s.asm.la).apply(null,arguments)},Uf=s._Prod=function(){return(Uf=s._Prod=s.asm.ma).apply(null,arguments)},Sp=s._RealDiv=function(){return(Sp=s._RealDiv=s.asm.na).apply(null,arguments)},Hf=s._Relu=function(){return(Hf=s._Relu=s.asm.oa).apply(null,arguments)},Np=s._Relu6=function(){return(Np=s._Relu6=s.asm.pa).apply(null,arguments)},Nr=s._ResizeBilinear=function(){return(Nr=s._ResizeBilinear=s.asm.qa).apply(null,arguments)},Gf=s._Reverse=function(){return(Gf=s._Reverse=s.asm.ra).apply(null,arguments)},qf=s._RotateWithOffset=function(){return(qf=s._RotateWithOffset=s.asm.sa).apply(null,arguments)},Yg=s._Round=function(){return(Yg=s._Round=s.asm.ta).apply(null,arguments)},Tp=s._Rsqrt=function(){return(Tp=s._Rsqrt=s.asm.ua).apply(null,arguments)},Xf=s._ScatterNd=function(){return(Xf=s._ScatterNd=s.asm.va).apply(null,arguments)},Kf=s._SelectV2=function(){return(Kf=s._SelectV2=s.asm.wa).apply(null,arguments)},Zf=s._Sigmoid=function(){return(Zf=s._Sigmoid=s.asm.xa).apply(null,arguments)},Yf=s._Sin=function(){return(Yf=s._Sin=s.asm.ya).apply(null,arguments)},Jf=s._Softmax=function(){return(Jf=s._Softmax=s.asm.za).apply(null,arguments)},Qf=s._Sqrt=function(){return(Qf=s._Sqrt=s.asm.Aa).apply(null,arguments)},em=s._Square=function(){return(em=s._Square=s.asm.Ba).apply(null,arguments)},tm=s._SquaredDifference=function(){return(tm=s._SquaredDifference=s.asm.Ca).apply(null,arguments)},nm=s._Step=function(){return(nm=s._Step=s.asm.Da).apply(null,arguments)},am=s._StridedSlice=function(){return(am=s._StridedSlice=s.asm.Ea).apply(null,arguments)},rm=s._Sub=function(){return(rm=s._Sub=s.asm.Fa).apply(null,arguments)},sm=s._Sum=function(){return(sm=s._Sum=s.asm.Ga).apply(null,arguments)},im=s._Tan=function(){return(im=s._Tan=s.asm.Ha).apply(null,arguments)},om=s._Tanh=function(){return(om=s._Tanh=s.asm.Ia).apply(null,arguments)},lm=s._Tile=function(){return(lm=s._Tile=s.asm.Ja).apply(null,arguments)},um=s._TopK=function(){return(um=s._TopK=s.asm.Ka).apply(null,arguments)},dm=s._Transform=function(){return(dm=s._Transform=s.asm.La).apply(null,arguments)},pm=s._Transpose=function(){return(pm=s._Transpose=s.asm.Ma).apply(null,arguments)},cm=s.__FusedMatMul=function(){return(cm=s.__FusedMatMul=s.asm.Na).apply(null,arguments)},hm=s._malloc=function(){return(hm=s._malloc=s.asm.Oa).apply(null,arguments)},fm=s._free=function(){return(fm=s._free=s.asm.Pa).apply(null,arguments)},Ep=s.___errno_location=function(){return(Ep=s.___errno_location=s.asm.Qa).apply(null,arguments)},Cp=s.stackSave=function(){return(Cp=s.stackSave=s.asm.Ra).apply(null,arguments)},Rp=s.stackRestore=function(){return(Rp=s.stackRestore=s.asm.Sa).apply(null,arguments)},yu=s.stackAlloc=function(){return(yu=s.stackAlloc=s.asm.Ta).apply(null,arguments)};s.cwrap=Y;var Zi;function mm(K){this.name="ExitStatus",this.message="Program terminated with exit("+K+")",this.status=K}ar=function K(){Zi||Au(),Zi||(ar=K)};function Au(K){if(K=K||d,ia>0||(Zn(),ia>0))return;function te(){Zi||(Zi=!0,s.calledRun=!0,!D&&(Dn(),sn(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Jt()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),te()},1)):te()}if(s.run=Au,s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();return Au(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}),zI=wt((e,t)=>{(function(n,a,r){function s(u){var d=this,p=l();d.next=function(){var c=2091639*d.s0+d.c*23283064365386963e-26;return d.s0=d.s1,d.s1=d.s2,d.s2=c-(d.c=c|0)},d.c=1,d.s0=p(" "),d.s1=p(" "),d.s2=p(" "),d.s0-=p(u),d.s0<0&&(d.s0+=1),d.s1-=p(u),d.s1<0&&(d.s1+=1),d.s2-=p(u),d.s2<0&&(d.s2+=1),p=null}function i(u,d){return d.c=u.c,d.s0=u.s0,d.s1=u.s1,d.s2=u.s2,d}function o(u,d){var p=new s(u),c=d&&d.state,h=p.next;return h.int32=function(){return p.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,c&&(typeof c=="object"&&i(c,p),h.state=function(){return i(p,{})}),h}function l(){var u=4022871197,d=function(p){p=String(p);for(var c=0;c>>0,h-=u,h*=u,u=h>>>0,h-=u,u+=h*4294967296}return(u>>>0)*23283064365386963e-26};return d}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),OI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var c=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^c^c>>>8},l===(l|0)?u.x=l:d+=l;for(var p=0;p>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),_I=wt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(c^c<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:d+=l;for(var p=0;p>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var d=new s(l),p=u&&u.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),PI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.x,c=u.i,h,m,f;return h=p[c],h^=h>>>7,m=h^h<<24,h=p[c+1&7],m^=h^h>>>10,h=p[c+3&7],m^=h^h>>>3,h=p[c+4&7],m^=h^h<<7,h=p[c+7&7],h=h^h<<13,m^=h^h<<9,p[c]=m,u.i=c+1&7,m};function d(p,c){var h,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,h=0;h0;--h)p.next()}d(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.x&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),LI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this;u.next=function(){var p=u.w,c=u.X,h=u.i,m,f;return u.w=p=p+1640531527|0,f=c[h+34&127],m=c[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[h]=f^m,u.i=h,f+(p^p>>>16)|0};function d(p,c){var h,m,f,y,A,g=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,y=-32;y>>15,m^=m<<4,m^=m>>>13,y>=0&&(A=A+1640531527|0,h=g[y&127]^=m+A,f=h==0?f+1:0);for(f>=128&&(g[(c&&c.length||0)&127]=-1),f=127,y=4*128;y>0;--y)m=g[f+34&127],h=g[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,g[f]=m^h;p.w=A,p.X=g,p.i=f}d(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var d=new s(l),p=u&&u.state,c=function(){return(d.next()>>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(p.X&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),WI=wt((e,t)=>{(function(n,a,r){function s(l){var u=this,d="";u.next=function(){var c=u.b,h=u.c,m=u.d,f=u.a;return c=c<<25^c>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-c|0,u.b=c=c<<20^c>>>12^h,u.c=h=h-m|0,u.d=m<<16^h>>>16^f,u.a=f-c|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):d+=l;for(var p=0;p>>0)/4294967296};return c.double=function(){do var h=d.next()>>>11,m=(d.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},c.int32=d.next,c.quick=c,p&&(typeof p=="object"&&i(p,d),c.state=function(){return i(d,{})}),c}a&&a.exports?a.exports=o:r&&r.amd?r(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),BI=wt((e,t)=>{(function(n,a,r){var s=256,i=6,o=52,l="random",u=r.pow(s,i),d=r.pow(2,o),p=d*2,c=s-1,h;function m(b,v,S){var T=[];v=v==!0?{entropy:!0}:v||{};var C=g(A(v.entropy?[b,w(a)]:b==null?x():b,3),T),$=new f(T),O=function(){for(var P=$.g(i),j=u,D=0;P=p;)P/=2,j/=2,D>>>=1;return(P+D)/j};return O.int32=function(){return $.g(4)|0},O.quick=function(){return $.g(4)/4294967296},O.double=O,g(w($.S),a),(v.pass||S||function(P,j,D,U){return U&&(U.S&&y(U,$),P.state=function(){return y($,{})}),D?(r[l]=P,j):P})(O,C,"global"in v?v.global:this==r,v.state)}function f(b){var v,S=b.length,T=this,C=0,$=T.i=T.j=0,O=T.S=[];for(S||(b=[S++]);C{var n=zI(),a=OI(),r=_I(),s=PI(),i=LI(),o=WI(),l=BI();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),VI=wt(()=>{}),vm={};Fe(vm,{bin:()=>S5,browser:()=>M5,default:()=>jI,dependencies:()=>R5,description:()=>A5,devDependencies:()=>E5,jsdelivr:()=>v5,license:()=>T5,main:()=>x5,miniprogram:()=>I5,module:()=>b5,name:()=>m5,private:()=>g5,repository:()=>N5,scripts:()=>C5,types:()=>k5,unpkg:()=>w5,version:()=>y5});var m5="@tensorflow/tfjs",y5="3.6.0",A5="An open-source machine learning framework.",g5=!1,x5="dist/tf.node.js",b5="dist/index.js",v5="dist/tf.min.js",w5="dist/tf.min.js",k5="dist/index.d.ts",I5="dist/miniprogram",S5={"tfjs-custom-module":"dist/tools/custom_module/cli.js"},N5={type:"git",url:"https://github.com/tensorflow/tfjs.git"},T5="Apache-2.0",E5={"@babel/core":"^7.9.0","@babel/polyfill":"^7.10.4","@babel/preset-env":"^7.9.5","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@types/argparse":"^1.0.38","@types/jasmine":"2.8.7","@types/node":"~10.17.50","@types/shelljs":"^0.8.4","@types/yargs":"^15.0.7","clang-format":"~1.2.2",commander:"~2.14.1",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.2","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.5.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-babel":"^4.4.0","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~4.2.2",shelljs:"~0.8.1","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"1.0.0-pre.50"},C5={build:"tsc && yarn build-cli && yarn bundle","build-ci":"tsc && yarn build-cli && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-converter":"cd ../tfjs-converter && yarn && yarn build","build-converter-ci":"cd ../tfjs-converter && yarn && yarn build-ci","build-data":"cd ../tfjs-data && yarn && yarn build","build-data-ci":"cd ../tfjs-data && yarn && yarn build-ci","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-converter && yarn build-data && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-converter-ci && yarn build-data-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-cli":"tsc --project ./tools/custom_module/tsconfig.json && chmod +x ./dist/tools/custom_module/cli.js","run-custom-build":"ts-node -s ./tools/custom_module/cli.ts","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && yarn build && karma start","test-dev":"karma start","test-tools":"ts-node --project ./tools/custom_module/tsconfig.json run_tools_tests.ts","test-ci":"./scripts/test-ci.sh"},R5={"@tensorflow/tfjs-backend-cpu":"3.6.0","@tensorflow/tfjs-backend-webgl":"3.6.0","@tensorflow/tfjs-converter":"3.6.0","@tensorflow/tfjs-core":"3.6.0","@tensorflow/tfjs-data":"3.6.0","@tensorflow/tfjs-layers":"3.6.0",argparse:"^1.0.10",chalk:"^4.1.0","core-js":"3","regenerator-runtime":"^0.13.5",yargs:"^16.0.3"},M5={"node-fetch":!1,util:!1,crypto:!1},jI={name:m5,version:y5,description:A5,private:g5,main:x5,module:b5,jsdelivr:v5,unpkg:w5,types:k5,miniprogram:I5,bin:S5,repository:N5,license:T5,devDependencies:E5,scripts:C5,dependencies:R5,browser:M5},wm={};Fe(wm,{browser:()=>K5,default:()=>UI,dependencies:()=>X5,description:()=>D5,devDependencies:()=>G5,engines:()=>j5,jsdelivr:()=>_5,"jsnext:main":()=>W5,license:()=>H5,main:()=>O5,miniprogram:()=>V5,module:()=>B5,name:()=>F5,private:()=>z5,repository:()=>U5,scripts:()=>q5,sideEffects:()=>Z5,types:()=>L5,unpkg:()=>P5,version:()=>$5});var F5="@tensorflow/tfjs-core",$5="3.6.0",D5="Hardware-accelerated JavaScript library for machine intelligence",z5=!1,O5="dist/tf-core.node.js",_5="dist/tf-core.min.js",P5="dist/tf-core.min.js",L5="dist/index.d.ts",W5="dist/index.js",B5="dist/index.js",V5="dist/miniprogram",j5={yarn:">= 1.3.2"},U5={type:"git",url:"https://github.com/tensorflow/tfjs-core.git"},H5="Apache-2.0",G5={"@bazel/bazelisk":"^1.3.0","@bazel/typescript":"^0.27.8","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"link:../tfjs-backend-cpu","@types/jasmine":"~3.0.0","@types/node":"~9.6.0","@types/node-fetch":"~2.1.2","clang-format":"~1.2.4",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"6.3.2","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~3.1.0","karma-jasmine":"~4.0.1","karma-typescript":"~5.5.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~5.3.0","rollup-plugin-visualizer":"~3.3.2",shelljs:"~0.8.3","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"~1.0.0-pre.21",yargs:"~13.2.2"},q5={"build-ci":"./scripts/enumerate-tests.js --ci && tsc && yarn bundle-ci && yarn build-test-snippets",build:"node ./scripts/enumerate-tests.js && tsc && yarn bundle",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-npm":"./scripts/build-npm.sh","build-deps":"yarn build && yarn build-cpu-backend","build-cpu-backend":"cd ../tfjs-backend-cpu && yarn && yarn build","build-cpu-backend-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build:bazel":"bazelisk build //...","build-test-snippets":"yarn tsc --project ./scripts/test_snippets/tsconfig.json","format-all":"clang-format -i -style=Google --glob=src/**/*.ts","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build && rollup -c && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-webworker":"karma start --worker","run-browserstack":"karma start --browserstack","test-bundle-size":"./scripts/test-bundle-size.js","test-node":"rimraf dist/ && yarn build-deps && yarn build && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-dev":"tsc && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-ci":"ts-node --transpile-only -P tsconfig.test.json dist/test_node.js","test-async-backends":"rimraf dist/ && yarn build && ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-async-backends-ci":"ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-snippets":"yarn build && yarn build-cpu-backend && ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts","test-snippets-ci":"ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts"},X5={"@types/offscreencanvas":"~2019.3.0","@types/seedrandom":"2.4.27","@types/webgl-ext":"0.0.30","node-fetch":"~2.6.1",seedrandom:"2.4.3"},K5={"node-fetch":!1,util:!1,crypto:!1,worker_threads:!1},Z5=["./dist/index.js","./dist/engine.js","./dist/tensor.js","./dist/base_side_effects.js","./dist/flags.js","./dist/platforms/*.js","./dist/register_all_gradients.js","./dist/public/chained_ops/*.js","./dist/io/*.js"],UI={name:F5,version:$5,description:D5,private:z5,main:O5,jsdelivr:_5,unpkg:P5,types:L5,"jsnext:main":W5,module:B5,miniprogram:V5,engines:j5,repository:U5,license:H5,devDependencies:G5,scripts:q5,dependencies:X5,browser:K5,sideEffects:Z5},km={};Fe(km,{browser:()=>hx,default:()=>HI,dependencies:()=>cx,description:()=>Q5,devDependencies:()=>ux,jsdelivr:()=>nx,"jsnext:main":()=>sx,license:()=>lx,main:()=>tx,miniprogram:()=>ox,module:()=>ix,name:()=>Y5,peerDependencies:()=>px,private:()=>ex,scripts:()=>dx,types:()=>rx,unpkg:()=>ax,version:()=>J5});var Y5="@tensorflow/tfjs-data",J5="3.6.0",Q5="TensorFlow Data API in JavaScript",ex=!1,tx="dist/tf-data.node.js",nx="dist/tf-data.min.js",ax="dist/tf-data.min.js",rx="dist/index.d.ts",sx="dist/index.js",ix="dist/index.js",ox="dist/miniprogram",lx="Apache-2.0",ux={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.6.0","@tensorflow/tfjs-core":"3.6.0","@tensorflow/tfjs-layers":"3.6.0","@types/jasmine":"~2.5.53","@types/seedrandom":"^2.4.27","@types/utf8":"~2.1.6","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",nyc:"^15.1.0",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~7.0.0",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"^1.0.0-pre.50"},dx={build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-backend-cpu-ci","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-dev":"tsc && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-browsers":"karma start --browsers='Chrome,Firefox'","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/test_node.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts",coverage:"yarn nyc yarn ts-node --transpile-only -P tsconfig.test.json src/test_node.ts",lint:"tslint -p . -t verbose"},px={"@tensorflow/tfjs-core":"3.6.0",seedrandom:"~2.4.3"},cx={"@types/node-fetch":"^2.1.2","node-fetch":"~2.6.1"},hx={fs:!1,"node-fetch":!1,string_decoder:!1,crypto:!1},HI={name:Y5,version:J5,description:Q5,private:ex,main:tx,jsdelivr:nx,unpkg:ax,types:rx,"jsnext:main":sx,module:ix,miniprogram:ox,license:lx,devDependencies:ux,scripts:dx,peerDependencies:px,dependencies:cx,browser:hx},Im={};Fe(Im,{default:()=>GI,description:()=>yx,devDependencies:()=>Nx,jsdelivr:()=>kx,"jsnext:main":()=>vx,license:()=>Ax,main:()=>xx,miniprogram:()=>Sx,module:()=>wx,name:()=>fx,peerDependencies:()=>Ex,private:()=>gx,scripts:()=>Tx,types:()=>bx,unpkg:()=>Ix,version:()=>mx});var fx="@tensorflow/tfjs-layers",mx="3.6.0",yx="TensorFlow layers API in JavaScript",Ax="Apache-2.0 AND MIT",gx=!1,xx="dist/tf-layers.node.js",bx="dist/index.d.ts",vx="dist/index.js",wx="dist/index.js",kx="dist/tf-layers.min.js",Ix="dist/tf-layers.min.js",Sx="dist/miniprogram",Nx={"@babel/polyfill":"^7.8.7","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.6.0","@tensorflow/tfjs-backend-webgl":"3.6.0","@tensorflow/tfjs-core":"3.6.0","@types/jasmine":"~2.5.53","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},Tx={prep:"yarn install && yarn build-ci",build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-npm":"./scripts/build-npm.sh",format:"./tools/clang_format_ts.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts","run-browserstack":"karma start --browsers='bs_chrome_mac' --singleRun --reporters='dots,karma-typescript'",lint:"tslint -p . -t verbose"},Ex={"@tensorflow/tfjs-core":"3.6.0"},GI={name:fx,version:mx,description:yx,license:Ax,private:gx,main:xx,types:bx,"jsnext:main":vx,module:wx,jsdelivr:kx,unpkg:Ix,miniprogram:Sx,devDependencies:Nx,scripts:Tx,peerDependencies:Ex},Sm={};Fe(Sm,{default:()=>qI,description:()=>Mx,devDependencies:()=>Vx,jsdelivr:()=>_x,"jsnext:main":()=>$x,license:()=>Wx,main:()=>Fx,miniprogram:()=>Px,module:()=>Dx,name:()=>Cx,peerDependencies:()=>Bx,repository:()=>Lx,scripts:()=>jx,types:()=>zx,unpkg:()=>Ox,version:()=>Rx});var Cx="@tensorflow/tfjs-converter",Rx="3.6.0",Mx="Tensorflow model converter for javascript",Fx="dist/tf-converter.node.js",$x="dist/index.js",Dx="dist/index.js",zx="dist/index.d.ts",Ox="dist/tf-converter.min.js",_x="dist/tf-converter.min.js",Px="dist/miniprogram",Lx={type:"git",url:"https://github.com/tensorflow/tfjs-converter.git"},Wx="Apache-2.0",Bx={"@tensorflow/tfjs-core":"3.6.0"},Vx={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-replace":"^2.3.3","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.6.0","@tensorflow/tfjs-core":"3.6.0","@types/argparse":"^1.0.38","@types/deep-equal":"^1.0.1","@types/jasmine":"~2.8.6","@types/long":"~3.0.32","@types/node-fetch":"1.6.9",ajv:"~6.3.0",argparse:"^1.0.10","babel-core":"~6.26.3","babel-plugin-external-helpers":"~6.22.0","babel-preset-env":"~1.7.0","clang-format":"~1.2.2",copyfiles:"~1.2.0","deep-equal":"^1.0.1","jasmine-core":"~3.5.0","node-fetch":"~2.6.1",opn:"~5.1.0",protobufjs:"~6.8.6",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-morph":"^7.1.3","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"~0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},jx={build:"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle","build-ci":"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && yarn gen-json --test && yarn gen-kernel2ops && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts","test-dev":"tsc && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts",lint:"tslint -p . -t verbose","make-version":"sh -c ./scripts/make-version","gen-doc":"ts-node -s ./scripts/gen_doc.ts","gen-json":"ts-node -s ./scripts/gen_json.ts","model-summary":"ts-node -s ./tools/model_summary.ts",pb2json:"ts-node -s ./tools/pb2json_converter.ts","build-pip-package":"yarn gen-json --test && cd python && ./build-pip-package.sh --test /tmp/tfjs-pips","run-python-tests":"yarn gen-json --test && cd python && ./run-python-tests.sh","gen-kernel2ops":"ts-node -s scripts/kernels_to_ops.ts --out metadata/kernel2op.json"},qI={name:Cx,version:Rx,description:Mx,main:Fx,"jsnext:main":$x,module:Dx,types:zx,unpkg:Ox,jsdelivr:_x,miniprogram:Px,repository:Lx,license:Wx,peerDependencies:Bx,devDependencies:Vx,scripts:jx},XI=1e-7,KI=1e-4,zp=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},ku=class{refCount(e){return oa("refCount")}incRef(e){return oa("incRef")}timerAvailable(){return!0}time(e){return oa("time")}read(e){return oa("read")}readSync(e){return oa("readSync")}numDataIds(){return oa("numDataIds")}disposeData(e,t){return oa("disposeData")}write(e,t,n){return oa("write")}move(e,t,n,a,r){return oa("move")}memory(){return oa("memory")}floatPrecision(){return oa("floatPrecision")}epsilon(){return this.floatPrecision()===32?XI:KI}dispose(){return oa("dispose")}};function oa(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 Ux(e){let t=e.length,n=0,a=0;for(;t>0;)a=Math.random()*t|0,t--,n=e[t],e[t]=e[a],e[a]=n}function ZI(e,t){if(e.length!==t.length)throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);let n=e.length,a,r,s=0;for(;n>0;)s=Math.random()*n|0,n--,a=e[n],r=t[n],e[n]=e[s],t[n]=t[s],e[s]=a,t[s]=r}function Iu(e,t,n){return Math.max(e,Math.min(t,n))}function YI(e){return e%2==0?e:e+1}function JI(e){let t=0;for(let n=0;nn+` Shapes ${e} and ${t} must match`)}function ps(e){F(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function cs(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||an(e)&&!n)for(let a=0;a0,n){return new Promise((a,r)=>{let s=0,i=()=>{if(e()){a();return}s++;let o=t(s);if(n!=null&&s>=n){r();return}setTimeout(i,o)};i()})}function iS(e,t){let n=1,a=-1;for(let s=0;s=0)n*=e[s];else if(e[s]===-1){if(a!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${a} and dim ${s}`);a=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(a===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let r=e.slice();return r[a]=t/n,r}function la(e,t){let n=t.length;return e=e==null?t.map((a,r)=>r):[].concat(e),F(e.every(a=>a>=-n&&a`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),F(e.every(a=>Ht(a)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(a=>a<0?n+a:a)}function Hx(e,t){let n=[],a=[],r=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||r?null:la(t,e).sort(),i=0;for(let o=0;oo)&&e[o]===1&&(n.push(e[o]),a.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),a.push(o))}return{newShape:n,keptDims:a}}function Gx(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 qx(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 ${e}`);return n}function Xx(e,t){for(let n=0;nt+=n.length),t}function Cr(e){return typeof e=="string"||e instanceof String}function Yx(e){return typeof e=="boolean"}function Jx(e){return typeof e=="number"}function Op(e){return Array.isArray(e)?Op(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Jx(e)?"float32":Cr(e)?"string":Yx(e)?"bool":"float32"}function Rr(e){return!!(e&&e.constructor&&e.call&&e.apply)}function _p(e,t){for(let n=t;n=0;--a)n[a]=n[a+1]*e[a+1];return n}function Qx(e,t,n,a=!1){let r=new Array;if(t.length===1){let s=t[0]*(a?2:1);for(let i=0;il*u)*(a?2:1);for(let l=0;lr*s)*(n?2:1);if(a===0)return[];if(a!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return Qx(0,e,t,n)}function Tm(e,t){let n=Pp(e,t);for(let a=0;aa*r,1);if(t==null||t==="float32")return io(e,new Float32Array(n));if(t==="int32")return io(e,new Int32Array(n));if(t==="bool")return io(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function Em(e){e.forEach(t=>{F(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function uS(e,t,n){if(t===0)return 0;if(t===1)return e[0];let a=e[e.length-1];for(let r=0;r{let[n,a]=t.split(":");this.urlFlags[n]=hS(n,a)})}};function pS(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...a)=>(cS(t,a[0],a[1]),a.join("="))),t}function cS(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function hS(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 ${e}.`)}function J(){return ua}var ua=null;function fS(e){ua=e}var Rm;function nb(){if(Rm==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");Rm=e}return Rm}function mS(){let e=nb();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function Mm(e,t){let n=mS();if(n.has(e))return n.get(e);{let a=t();return n.set(e,a),n.get(e)}}var oo="Abs",lo="Acos",uo="Acosh",Mr="Add",hs="AddN",po="All",co="Any",fs="ArgMax",Nu="ArgMin",ho="Asin",fo="Asinh",mo="Atan",yo="Atanh",Ao="Atan2",ms="AvgPool",Lp="AvgPoolGrad",Tu="AvgPool3D",Wp="AvgPool3DGrad",ys="BatchMatMul",Eu="BatchToSpaceND",Bp="Bincount",ab="BroadcastTo",As="Cast",gs="Ceil",Fr="ClipByValue",Vp="Complex",Cu="ComplexAbs",go="Concat",xs="Conv2D",jp="Conv2DBackpropFilter",bs="Conv2DBackpropInput",Ru="Conv3D",Up="Conv3DBackpropFilterV2",Hp="Conv3DBackpropInputV2",vs="Cos",xo="Cosh",ws="Cumsum",bo="CropAndResize",Gp="DenseBincount",vo="DepthToSpace",ks="DepthwiseConv2dNative",qp="DepthwiseConv2dNativeBackpropFilter",Xp="DepthwiseConv2dNativeBackpropInput",Kp="Diag",Mu="Dilation2D",Zp="Dilation2DBackpropInput",Yp="Dilation2DBackpropFilter",Is="RealDiv",Jp="Einsum",wo="Elu",Qp="EluGrad",ko="Erf",Io="Equal",Ss="Exp",So="ExpandDims",No="Expm1",ec="FFT",Fu="Fill",To="FlipLeftRight",Ns="Floor",Ts="FloorDiv",Es="FusedBatchNorm",Eo="GatherV2",Co="GatherNd",Ro="Greater",Cs="GreaterEqual",Rs="Identity",tc="IFFT",nc="Imag",Mo="IsFinite",Fo="IsInf",$o="IsNan",Ms="LeakyRelu",Do="Less",zo="LessEqual",ac="LinSpace",Fs="Log",Oo="Log1p",_o="LogicalAnd",$u="LogicalNot",Du="LogicalOr",rb="LogSoftmax",zu="LRN",rc="LRNGrad",$s="Max",Ds="Maximum",zs="MaxPool",sc="MaxPoolGrad",Ou="MaxPool3D",ic="MaxPool3DGrad",oc="MaxPoolWithArgmax",Os="Mean",_s="Min",Ps="Minimum",Ls="MirrorPad",Po="Mod",lc="Multinomial",Ws="Multiply",Lo="Neg",Wo="NotEqual",Bo="NonMaxSuppressionV3",Vo="NonMaxSuppressionV4",jo="NonMaxSuppressionV5",Uo="OnesLike",Bs="OneHot",Ho="Pack",Vs="PadV2",yS="Pool",js="Pow",Us="Prelu",Go="Prod",_u="Range",uc="Real",qo="Reciprocal",Hs="Relu",Xo="Reshape",Pu="ResizeNearestNeighbor",dc="ResizeNearestNeighborGrad",Gs="ResizeBilinear",pc="ResizeBilinearGrad",qs="Relu6",Xs="Reverse",Ks="Round",Zs="Rsqrt",Ko="ScatterNd",Zo="Select",Yo="Selu",Jo="Slice",Ys="Sin",Qo="Sinh",el="Sign",Js="Sigmoid",tl="Softplus",Qs="Sqrt",ei="Sum",Lu="SpaceToBatchND",nl="SplitV",ti="Softmax",cc="SparseFillEmptyRows",hc="SparseReshape",fc="SparseToDense",ni="SquaredDifference",Wu="Square",al="StridedSlice",ai="Sub",ri="Tan",si="Tanh",$r="Tile",rl="TopK",sl="Transform",ii="Transpose",mc="Unique",il="Unpack",Bu="UnsortedSegmentSum",ol="ZerosLike",Dr="Step",yc="FromPixels",ll="RotateWithOffset",oi="_FusedMatMul",li="FusedConv2D",ui="FusedDepthwiseConv2D",ul=Mm("kernelRegistry",()=>new Map),Vu=Mm("gradRegistry",()=>new Map);function Ac(e,t){let n=$m(e,t);return ul.get(n)}function Fm(e){return Vu.get(e)}function dl(e){let t=ul.entries(),n=[];for(;;){let{done:a,value:r}=t.next();if(a)break;let[s,i]=r,[o]=s.split("_");o===e&&n.push(i)}return n}function di(e){let{kernelName:t,backendName:n}=e,a=$m(t,n);ul.has(a)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),ul.set(a,e)}function sb(e){let{kernelName:t}=e;Vu.has(t)&&J().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Vu.set(t,e)}function AS(e,t){let n=$m(e,t);if(!ul.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);ul.delete(n)}function gS(e){if(!Vu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Vu.delete(e)}function xS(e,t){dl(e).forEach(n=>{let a=Object.assign({},n,{backendName:t});di(a)})}function $m(e,t){return`${t}_${e}`}var k={};Fe(k,{arraysEqual:()=>lr,assert:()=>F,assertNonNegativeIntegerDimensions:()=>Em,assertNonNull:()=>ps,assertShapesMatch:()=>ln,bytesFromStringArray:()=>Zx,bytesPerElement:()=>Nm,checkConversionForErrors:()=>Xx,clamp:()=>Iu,computeStrides:()=>so,createScalarValue:()=>bS,createShuffledIndices:()=>rS,decodeString:()=>xc,distSquared:()=>eS,encodeString:()=>Uu,fetch:()=>wS,flatten:()=>cs,getArrayFromDType:()=>qx,getTypedArrayFromDType:()=>Gx,hasEncodingLoss:()=>oS,indexToLoc:()=>dS,inferDtype:()=>Op,inferFromImplicitShape:()=>iS,isBoolean:()=>Yx,isFunction:()=>Rr,isInt:()=>Ht,isNumber:()=>Jx,isPromise:()=>Cm,isScalarShape:()=>tS,isString:()=>Cr,isTypedArray:()=>an,isValidDtype:()=>Kx,locToIndex:()=>uS,makeOnesTypedArray:()=>Tm,makeZerosNestedTypedArray:()=>lS,makeZerosTypedArray:()=>Pp,nearestDivisor:()=>_p,nearestLargerEven:()=>YI,now:()=>ju,parseAxisParam:()=>la,randUniform:()=>QI,repeatedTry:()=>sS,rightPad:()=>Su,shuffle:()=>Ux,shuffleCombo:()=>ZI,sizeFromShape:()=>Rt,sizeToSquarishShape:()=>aS,squeezeShape:()=>Hx,sum:()=>JI,tanh:()=>nS,toNestedArray:()=>io,toTypedArray:()=>gc});function bS(e,t){return t==="string"?Uu(e):gc([e],t)}function vS(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function gc(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=cs(e)),J().getBool("DEBUG")&&Xx(e,t),vS(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){let n=new Uint8Array(e.length);for(let a=0;a{a=n()},s,i=ju();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(r);else{r();for(let o of a)o.dataSync();s=Promise.resolve({kernelMs:ju()-i})}if(J().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o{IS(u,l.dtype,e)})}return{kernelName:e,outputs:a,inputs:t,timeMs:s.then(o=>o.kernelMs),extraInfo:s.then(o=>o.getExtraProfileInfo!=null?o.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:a,inputs:r,extraInfo:s}=e;n.forEach(i=>{Promise.all([i.data(),a,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],r,o[2])})})}};function IS(e,t,n){if(t!=="float32")return!1;for(let a=0;a0?m:""} `}}console.log(`%c${o} %c${i} %c${l}D ${d} %c${u} %c${p} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function NS(e,t,n){let a={},r={};for(let l=0;la[f.id]=!0),h=!0,r[u.id]=!0;break}if(h)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let u=e[l],d=u.inputs;for(let p=0;p=0;r--){let s=t[r],i=[];if(s.outputs.forEach(l=>{let u=e[l.id];u!=null?i.push(u):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let o=s.gradient(i);for(let l in s.inputs){if(!(l in o))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(o)}.`);let u=n(()=>o[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let d=s.inputs[l];if(!lr(u.shape,d.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${d.shape}'`);if(e[d.id]==null)e[d.id]=u;else{let p=e[d.id];e[d.id]=a(p,u),p.dispose()}}}}var ib=20,Hu=3,Dm=7;function ES(e,t,n,a){let r=so(t),s=CS(e,t,n,r),i=t.length,o=bc(e,t,n,r,s),l=["Tensor"];return a&&(l.push(` dtype: ${n}`),l.push(` rank: ${i}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(o.map(u=>" "+u).join(` `)),l.join(` `)}function CS(e,t,n,a){let r=Rt(t),s=a[a.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?qu(e):e;if(o>1)for(let u=0;uib){let y=Hu*i,A=Array.from(e.slice(0,y)),g=Array.from(e.slice((o-Hu)*i,o*i));return n==="complex64"&&(A=qu(A),g=qu(g)),["["+A.map((x,w)=>Gu(x,r[w],n)).join(", ")+", ..., "+g.map((x,w)=>Gu(x,r[o-Hu+w],n)).join(", ")+"]"]}let f=n==="complex64"?qu(e):Array.from(e);return["["+f.map((y,A)=>Gu(y,r[A],n)).join(", ")+"]"]}let u=t.slice(1),d=a.slice(1),p=a[0]*i,c=[];if(o>ib){for(let f=0;f`Length of values '${a}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||qx(t,this.size),this.strides=so(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let a of e){if(a<0||a>=this.shape[t]){let r=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let a=0;axc(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=Pa().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>xc(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 Pa().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Pa().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return pl.print(this,e)}clone(){return this.throwIfDisposed(),pl.clone(this)}toString(e=!1){let t=this.dataSync();return ES(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),pl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Pa().makeVariable(this,e,t,n)}};Object.defineProperty(Le,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return Mm("Tensor",()=>Le)}Z();var Xu=class extends Le{constructor(e,t,n,a){super(e.shape,e.dtype,e.dataId,a);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!lr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Pa().disposeTensor(this),this.dataId=e.dataId,Pa().incRef(this,null)}dispose(){Pa().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Xu,Symbol.hasInstance,{value:e=>e instanceof Le&&e.assign!=null&&e.assign instanceof Function});var ba={};Fe(ba,{assertTypesMatch:()=>lb,getTensorsInContainer:()=>Wm,isTensorInList:()=>zS,makeTypesMatch:()=>kt});var zm;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(zm||(zm={}));var Om;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Om||(Om={}));var _m;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(_m||(_m={}));var Pm;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Pm||(Pm={}));var Lm;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Lm||(Lm={}));var DS={float32:Pm,int32:Om,bool:_m,complex64:Lm};function da(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return DS[e][t]}function vc(e){return da(e,"int32")}function kt(e,t){if(e.dtype===t.dtype)return[e,t];let n=da(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function lb(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function zS(e,t){return t.some(n=>n.id===e.id)}function Wm(e){let t=[],n=new Set;return ub(e,t,n),t}function ub(e,t,n){if(e==null)return;if(e instanceof Le){t.push(e);return}if(!OS(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),ub(s,t,n))}}function OS(e){return Array.isArray(e)||typeof e=="object"}function Bm(e){return e.kernelName!=null}var db=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()}},Ku=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new db}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.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){dl(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof ku)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a(athis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Ku.nextTensorId++}nextVariableId(){return Ku.nextVariableId++}clone(e){let t=z.runKernel(Rs,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return z.runKernel(As,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(Ac(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Bm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Bm(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let y=Ac(h,this.backendName);F(y!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let A=this.backend.numDataIds();o=y.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,g);let x=g.map(w=>{if(w.rank!=null)return w;let{dataId:b,shape:v,dtype:S}=w;return this.makeTensorFromDataId(b,v,S)});if(a){let w=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(w)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(y=>this.keep(this.clone(y))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let y=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,y),y}}let{inputs:u,attrs:d}=e,p=Bm(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,u,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Fm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Cr(e[0])&&(r=e.map(o=>Uu(o)));let s=a.write(r,t,n),i=new Le(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=Zx(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Le(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Xu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Nm(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 Xu||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*Nm(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=Fm(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let p=n[d],c=Pp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return u}),a(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Wm(e),n=new Set(t.map(r=>r.id));for(let r=0;r{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof Le,()=>"The result y returned by f() must be a tensor.");let s=NS(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=n==null?_S(r.shape):n,TS(i,s,l=>this.tidy(l),PS);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return F(Rr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof Le),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};t.forEach((i,o)=>{a[o]=i});let r=(i,o)=>(n=e(...t,o),F(n.value instanceof Le,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(Rr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),u=Array.isArray(l)?l:[l];F(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(...)."),F(u.every(p=>p instanceof Le),()=>"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 d={};return u.forEach((p,c)=>{d[c]=()=>p}),d};return this.runKernelFunc({forwardFunc:r,backwardsFunc:s,inputs:a})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}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 db;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}};Ku.nextTensorId=0;Ku.nextVariableId=0;function _S(e){let t=Tm(Rt(e),"float32");return z.makeTensor(t,e,"float32")}function pb(){let e=nb();if(e._tfengine==null){let t=new tb(e);e._tfengine=new Ku(t)}return fS(e._tfengine.ENV),MS(()=>e._tfengine),e._tfengine}var z=pb();function PS(e,t){let n={a:e,b:t};return z.runKernel(Mr,n)}var Zu={};Fe(Zu,{isBrowser:()=>cb,isMobile:()=>WS});function LS(){return typeof navigator!="undefined"&&navigator!=null}function WS(e){if(e||LS()){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 cb(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var va=J();va.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.")});va.registerFlag("IS_BROWSER",()=>cb());va.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");va.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));va.registerFlag("PROD",()=>!1);va.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>va.getBool("DEBUG"));va.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);va.registerFlag("IS_TEST",()=>!1);va.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);va.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function La(e,t){let n=e;if(an(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let a=[];for(;Array.isArray(n)||an(n)&&t!=="string";)a.push(n.length),n=n[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&hb(e,a,[]),a}function hb(e,t,n){if(n=n||[],!Array.isArray(e)&&!an(e)){F(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}F(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),F(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let a=t.slice(1);for(let r=0;r=0&&(r=a),fb(a,r,t,n),e==null||!an(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=La(e,r);!an(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?gc(e,r):cs(e,[],!0);return z.makeTensor(i,s,r)}function Yu(e,t,n,a="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((r,s)=>M(r,`${t}[${s}]`,n,a))}var mb="__op";function _(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0],a=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+mb;let r=(...s)=>{z.startScope(n);try{let i=a(...s);return Cm(i)&&console.error("Cannot return a Promise inside of tidy."),z.endScope(i),i}catch(i){throw z.endScope(null),i}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function BS(e,t){let n=M(e,"real","complex"),a=M(t,"imag","complex");ln(n.shape,a.shape,`real and imag shapes, ${n.shape} and ${a.shape}, must match in call to tf.complex().`);let r={real:n,imag:a};return z.runKernel(Vp,r)}var zr=_({complex_:BS});function Or(e,t,n,a){if(a==null&&(a=Op(e)),a==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!an(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Em(t);let r=Rt(t),s=Rt(n);F(r===s,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${s}`);for(let i=0;i`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!an(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=a!=="string"?gc(e,a):cs(e,[],!0),z.makeTensor(e,t,a)}function pa(e,t,n){let a=La(e,n);return Or(e,t,a,n)}var Vm={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},wc=4;async function VS(e,t){let n=[],a=[],r=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i{let c=await l.bytes(),h=c.reduce((y,A)=>y+A.length,0)+wc*c.length,m=new Uint8Array(h),f=0;for(let y=0;y{if(t+=s.byteLength,n.push(s.byteLength===s.buffer.byteLength?s:new s.constructor(s)),!(s instanceof Float32Array||s instanceof Int32Array||s instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`)});let a=new Uint8Array(t),r=0;return n.forEach(s=>{a.set(new Uint8Array(s.buffer),r),r+=s.byteLength}),a.buffer}var jm=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function Ab(e){return jm?Buffer.byteLength(e):new Blob([e]).size}function US(e){if(jm)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let a=0,r=t.length;a{t+=r.byteLength});let n=new Uint8Array(t),a=0;return e.forEach(r=>{n.set(new Uint8Array(r),a),a+=r.byteLength}),n.buffer}function gb(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 Ju(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:Ab(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:Ab(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function GS(){let e=n=>{let a=n<<13,r=0;for(;(a&8388608)==0;)r-=8388608,a<<=1;return a&=~8388608,r+=947912704,a|r},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function qS(){let e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}function XS(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function KS(){let e=GS(),t=qS(),n=XS();return a=>{let r=new ArrayBuffer(4*a.length),s=new Uint32Array(r);for(let i=0;i>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(r)}}var Et=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Et.instance==null&&(Et.instance=new Et),Et.instance}static registerSaveRouter(e){Et.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Et.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Et.getHandlers(e,"save")}static getLoadHandlers(e,t){return Et.getHandlers(e,"load",t)}static getHandlers(e,t,n){let a=[];return(t==="load"?Et.getInstance().loadRouters:Et.getInstance().saveRouters).forEach(r=>{let s=r(e,n);s!==null&&a.push(s)}),a}},ZS=e=>Et.registerSaveRouter(e),YS=e=>Et.registerLoadRouter(e),JS=e=>Et.getSaveHandlers(e),QS=(e,t)=>Et.getLoadHandlers(e,t),Hm="tensorflowjs",Gm=1,pi="models_store",_r="model_info_store";function xb(){if(!J().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 qm(e){let t=e.result;t.createObjectStore(pi,{keyPath:"modelPath"}),t.createObjectStore(_r,{keyPath:"modelPath"})}var ci=class{constructor(e){if(this.indexedDB=xb(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,a)=>{let r=this.indexedDB.open(Hm,Gm);r.onupgradeneeded=()=>qm(r),r.onsuccess=()=>{let s=r.result;if(t==null){let i=s.transaction(pi,"readonly"),o=i.objectStore(pi).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),a(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),a(o.error)),i.oncomplete=()=>s.close()}else{let i=Ju(t),o=s.transaction(_r,"readwrite"),l=o.objectStore(_r),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),d;u.onsuccess=()=>{d=s.transaction(pi,"readwrite");let p=d.objectStore(pi).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});p.onsuccess=()=>n({modelArtifactsInfo:i}),p.onerror=c=>{l=o.objectStore(_r);let h=l.delete(this.modelPath);h.onsuccess=()=>(s.close(),a(p.error)),h.onerror=m=>(s.close(),a(p.error))}},u.onerror=p=>(s.close(),a(u.error)),o.oncomplete=()=>{d==null?s.close():d.oncomplete=()=>s.close()}}},r.onerror=s=>a(r.error)})}};ci.URL_SCHEME="indexeddb://";var bb=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ci.URL_SCHEME)?eN(e.slice(ci.URL_SCHEME.length)):null;Et.registerSaveRouter(bb);Et.registerLoadRouter(bb);function eN(e){return new ci(e)}function tN(e){return e.startsWith(ci.URL_SCHEME)?e.slice(ci.URL_SCHEME.length):e}var nN=class{constructor(){this.indexedDB=xb()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(Hm,Gm);n.onupgradeneeded=()=>qm(n),n.onsuccess=()=>{let a=n.result,r=a.transaction(_r,"readonly"),s=r.objectStore(_r).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(a.close(),t(s.error)),r.oncomplete=()=>a.close()},n.onerror=a=>t(n.error)})}async removeModel(e){return e=tN(e),new Promise((t,n)=>{let a=this.indexedDB.open(Hm,Gm);a.onupgradeneeded=()=>qm(a),a.onsuccess=()=>{let r=a.result,s=r.transaction(_r,"readwrite"),i=s.objectStore(_r),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=i.delete(e),d=()=>{l=r.transaction(pi,"readwrite");let p=l.objectStore(pi).delete(e);p.onsuccess=()=>t(o.result.modelArtifactsInfo),p.onerror=c=>n(o.error)};u.onsuccess=d,u.onerror=p=>(d(),r.close(),n(o.error))}},o.onerror=u=>(r.close(),n(o.error)),s.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},a.onerror=r=>n(a.error)})}},ur="/",cl="tensorflowjs_models",vb="info",aN="model_topology",rN="weight_specs",sN="weight_data",iN="model_metadata";function wb(e){return{info:[cl,e,vb].join(ur),topology:[cl,e,aN].join(ur),weightSpecs:[cl,e,rN].join(ur),weightData:[cl,e,sN].join(ur),modelMetadata:[cl,e,iN].join(ur)}}function oN(e){let t=e.split(ur);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(ur)}function lN(e){return e.startsWith(hi.URL_SCHEME)?e.slice(hi.URL_SCHEME.length):e}var hi=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=wb(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),a=Ju(e);try{this.LS.setItem(this.keys.info,JSON.stringify(a)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,US(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:a}}catch(r){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${a.modelTopologyBytes}, weightSpecsBytes=${a.weightSpecsBytes}, weightDataBytes=${a.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;let a=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(a==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=a;let r=this.LS.getItem(this.keys.modelMetadata);if(r!=null){let i=JSON.parse(r);t.format=i.format,t.generatedBy=i.generatedBy,t.convertedBy=i.convertedBy,i.signature!=null&&(t.signature=i.signature),i.userDefinedMetadata!=null&&(t.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(t.modelInitializer=i.modelInitializer)}let s=this.LS.getItem(this.keys.weightData);if(s==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=HS(s),t}};hi.URL_SCHEME="localstorage://";var kb=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(hi.URL_SCHEME)?uN(e.slice(hi.URL_SCHEME.length)):null;Et.registerSaveRouter(kb);Et.registerLoadRouter(kb);function uN(e){return new hi(e)}var dN=class{constructor(){F(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),F(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=cl+ur,n=ur+vb;for(let a=0;a"scheme must not be undefined or null."),e.endsWith(hl)&&(e=e.slice(0,e.indexOf(hl))),F(e.length>0,()=>"scheme must not be an empty string.");let n=Qn.getInstance();F(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${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 kc(e){if(e.indexOf(hl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Qn.getSchemes().join(",")}`);return{scheme:e.split(hl)[0],path:e.split(hl)[1]}}async function Ib(e,t,n=!1){F(e!==t,()=>`Old path and new path are the same: '${e}'`);let a=Et.getLoadHandlers(e);F(a.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),F(a.length<2,()=>`Copying failed because more than one (${a.length}) load handlers for source URL ${e}.`);let r=a[0],s=Et.getSaveHandlers(t);F(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),F(s.length<2,()=>`Copying failed because more than one (${a.length}) save handlers for destination URL ${t}.`);let i=s[0],o=kc(e).scheme,l=kc(e).path,u=o===kc(e).scheme,d=await r.load();n&&u&&await Qn.getManager(o).removeModel(l);let p=await i.save(d);return n&&!u&&await Qn.getManager(o).removeModel(l),p.modelArtifactsInfo}async function pN(){let e=Qn.getSchemes(),t={};for(let n of e){let a=await Qn.getManager(n).listModels();for(let r in a){let s=n+hl+r;t[s]=a[r]}}return t}async function cN(e){let t=kc(e);return Qn.getManager(t.scheme).removeModel(t.path)}async function hN(e,t){return Ib(e,t,!1)}async function fN(e,t){return Ib(e,t,!0)}var mN=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(J().get("IS_BROWSER")){J().setPlatform("browser",new mN);try{Qn.registerManager(hi.URL_SCHEME,new dN)}catch(e){}try{Qn.registerManager(ci.URL_SCHEME,new nN)}catch(e){}}var yN={importFetch:()=>kI()},Xm,AN=class{constructor(){this.util=ao("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return J().global.fetch!=null?J().global.fetch(e,t):(Xm==null&&(Xm=yN.importFetch()),Xm(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)}};J().get("IS_NODE")&&J().setPlatform("node",new AN);function We(e,t="float32",n){return t=t||"float32",Em(e),new Pt(e,t,n)}function gN(e,t){let n=M(e,"x","cast");if(!Kx(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let a={x:n},r={dtype:t};return z.runKernel(As,a,r)}var me=_({cast_:gN});function xN(e){let t={x:M(e,"x","clone","string_or_numeric")};return z.runKernel(Rs,t)}var Wa=_({clone_:xN});function Sb(e,t=!1){console.log(e.toString(t))}pb();var bN={buffer:We,cast:me,clone:Wa,print:Sb};FS(bN);var In={};Fe(In,{browserFiles:()=>TN,browserHTTPRequest:()=>FN,concatenateArrayBuffers:()=>Um,copyModel:()=>hN,decodeWeights:()=>yb,encodeWeights:()=>VS,fromMemory:()=>DN,getLoadHandlers:()=>QS,getModelArtifactsInfoForJSON:()=>Ju,getSaveHandlers:()=>JS,http:()=>Ym,isHTTPScheme:()=>Zm,listModels:()=>pN,loadWeights:()=>EN,moveModel:()=>fN,registerLoadRouter:()=>YS,registerSaveRouter:()=>ZS,removeModel:()=>cN,weightsLoaderFactory:()=>Cb,withSaveHandler:()=>zN});var vN="model",wN=".json",kN=".weights.bin";function Nb(e){return new Promise(t=>setTimeout(t)).then(e)}var fl=class{constructor(e){if(!J().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(fl.URL_SCHEME)&&(e=e.slice(fl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=vN),this.modelTopologyFileName=e+wN,this.weightDataFileName=e+kN}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],a={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer);let r=window.URL.createObjectURL(new Blob([JSON.stringify(a)],{type:"application/json"})),s=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(s.download=this.modelTopologyFileName,s.href=r,await Nb(()=>s.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await Nb(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ju(e)}}}};fl.URL_SCHEME="downloads://";var IN=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,a)=>{let r=new FileReader;r.onload=s=>{let i=JSON.parse(s.target.result),o=i.modelTopology;if(o==null){a(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:o});let l=i.weightsManifest;if(l==null){a(new Error(`weightManifest field is missing from file ${e.name}`));return}let u;try{u=this.checkManifestAndWeightFiles(l,t)}catch(h){a(h);return}let d=[],p=[],c=[];l.forEach(h=>{h.paths.forEach(m=>{p.push(m),c.push(null)}),d.push(...h.weights)}),l.forEach(h=>{h.paths.forEach(m=>{let f=new FileReader;f.onload=y=>{let A=y.target.result,g=p.indexOf(m);if(c[g]=A,c.indexOf(null)===-1){let x={modelTopology:o,weightSpecs:d,weightData:Um(c),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(x.signature=i.signature),i.userDefinedMetadata!=null&&(x.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(x.modelInitializer=i.modelInitializer),n(x)}},f.onerror=y=>a(`Failed to weights data from file of path '${m}'.`),f.readAsArrayBuffer(u[m])})})},r.onerror=s=>a(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],a=t.map(s=>gb(s.name)),r={};for(let s of e)s.paths.forEach(i=>{let o=gb(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),a.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);r[i]=t[a.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return r}},SN=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(fl.URL_SCHEME)?NN(e.slice(fl.URL_SCHEME.length)):null;Et.registerSaveRouter(SN);function NN(e="model"){return new fl(e)}function TN(e){return new IN(e)}function Tb(e,t,n,a){i(e),n=n==null?0:n,a=a==null?1:a,o(n,a);let r=0,s=l=>(l.then(u=>{let d=n+ ++r/e.length*(a-n);return t(d),u}),l);function i(l){F(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,u){F(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),F(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),F(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(s))}async function Eb(e,t){t==null&&(t={});let n=t.fetchFunc==null?J().platform.fetch:t.fetchFunc,a=e.map(u=>n(u,t.requestInit,{isBinary:!0})),r=0,s=.5,i=(t.onProgress==null?await Promise.all(a):await Tb(a,t.onProgress,r,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await Tb(i,t.onProgress,o,l)}async function EN(e,t="",n,a){return Cb(r=>Eb(r,{requestInit:a}))(e,t,n)}function Cb(e){return async(t,n="",a)=>{let r=t.map(()=>!1),s={},i=a!=null?a.map(()=>!1):[],o=[];if(t.forEach((h,m)=>{let f=0;h.weights.forEach(y=>{let A="quantization"in y?y.quantization.dtype:y.dtype,g=Vm[A]*Rt(y.shape),x=()=>{r[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:y,groupOffset:f,sizeBytes:g})};a!=null?a.forEach((w,b)=>{w===y.name&&(x(),i[b]=!0)}):x(),o.push(y.name),f+=g})}),!i.every(h=>h)){let h=a.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}. Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=r.reduce((h,m,f)=>(m&&h.push(f),h),[]),u=[];l.forEach(h=>{t[h].paths.forEach(m=>{let f=n+(n.endsWith("/")?"":"/")+m;u.push(f)})});let d=await e(u),p={},c=0;return l.forEach(h=>{let m=t[h].paths.length,f=0;for(let x=0;x{let w=y.slice(x.groupOffset,x.groupOffset+x.sizeBytes),b=yb(w,[x.manifestEntry]);for(let v in b)p[v]=b[v]}),c+=m}),p}}var CN="application/octet-stream",RN="application/json",Km=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?(F(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=J().platform.fetch,F(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&F(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],a={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(a)],{type:RN}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:CN}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Ju(e),responses:[r]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`)}async load(){let e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(h){let m=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?m+=" 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.":m+=" Please make sure the server is serving valid JSON for this request.",new Error(m)}let n=t.modelTopology,a=t.weightsManifest,r=t.generatedBy,s=t.convertedBy,i=t.format,o=t.signature,l=t.userDefinedMetadata;if(n==null&&a==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let u,d;a!=null&&([u,d]=await this.loadWeights(a));let p={modelTopology:n,weightSpecs:u,weightData:d,generatedBy:r,convertedBy:s,format:i};o!=null&&(p.signature=o),l!=null&&(p.userDefinedMetadata=l);let c=t.modelInitializer;return c&&(p.modelInitializer=c),p}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,a]=MN(t),r=this.weightPathPrefix||n,s=[];for(let u of e)s.push(...u.weights);let i=[],o=[];for(let u of e)for(let d of u.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(d)):i.push(r+d+a);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await Eb(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,Um(l)]}};Km.URL_SCHEME_REGEX=/^https?:\/\//;function MN(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),a=e.substring(0,t),r=n>t?e.substring(n):"";return[a+"/",r]}function Zm(e){return e.match(Km.URL_SCHEME_REGEX)!=null}var Rb=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(a=>Zm(a)):n=Zm(e),n)return Ym(e,t)}return null};Et.registerSaveRouter(Rb);Et.registerLoadRouter(Rb);function Ym(e,t){return new Km(e,t)}function FN(e,t){return Ym(e,t)}var Jm=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},$N=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function DN(e,t,n,a){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new Jm(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 Jm({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 Jm({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:a}))}function zN(e){return new $N(e)}var Mb={};Fe(Mb,{confusionMatrix:()=>WN});function ON(e,t,n=!1,a=!1){let r=M(e,"a","matMul"),s=M(t,"b","matMul");[r,s]=kt(r,s);let i={a:r,b:s},o={transposeA:n,transposeB:a};return z.runKernel(ys,i,o)}var Be=_({matMul_:ON});function _N(e,t,n=1,a=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let r={indices:M(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:a};return z.runKernel(Bs,r,s)}var ml=_({oneHot_:_N});function PN(e,t){let n=M(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{F(s>=0&&s`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let a={x:n},r={perm:t};return z.runKernel(ii,a,r)}var Ye=_({transpose_:PN});function LN(e,t,n){let a=M(e,"labels","confusionMatrix"),r=M(t,"predictions","confusionMatrix");F(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),F(a.rank===1,()=>`Expected the rank of labels to be 1, but got ${a.rank}`),F(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),F(a.shape[0]===r.shape[0],()=>`Mismatch in the number of examples: ${a.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`),F(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=ml(me(a,"int32"),n),i=ml(me(r,"int32"),n),o=Ye(s),l=Be(o,i);return me(l,"int32")}var WN=_({confusionMatrix_:LN}),fi={};Fe(fi,{fromPixels:()=>qN,fromPixelsAsync:()=>HN,toPixels:()=>GN});function Ic(e,t,n){if(ps(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let a=La(e,n);if(a.length!==3&&a.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Or(e,t,a,n)}var yl;function Fb(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,a=!1,r=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)a=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)r=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)s=!0;else if(e.getContext!=null)i=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)o=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(r){let c=2;if(r&&e.readyState element.")}if(Ac(yc,z.backendName)!=null){let c={pixels:e},h={numChannels:t};return z.runKernel(yc,c,h)}let[l,u]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;i?d=e.getContext("2d").getImageData(0,0,l,u).data:a||n?d=e.data:(s||r||o)&&(yl==null&&(yl=document.createElement("canvas").getContext("2d")),yl.canvas.width=l,yl.canvas.height=u,yl.drawImage(e,0,0,l,u),d=yl.getImageData(0,0,l,u).data);let p;if(t===4)p=new Int32Array(d);else{let c=l*u;p=new Int32Array(c*t);for(let h=0;h4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let i=await n.data(),o=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(r*a*4);for(let u=0;u1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${h}.`)}else if(n.dtype==="int32"&&(h<0||h>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${h}.`);s===1?(d[0]=h*o,d[1]=h*o,d[2]=h*o):d[c]=h*o}let p=u*4;l[p+0]=Math.round(d[0]),l[p+1]=Math.round(d[1]),l[p+2]=Math.round(d[2]),l[p+3]=Math.round(d[3])}if(t!=null){t.width=r,t.height=a;let u=t.getContext("2d"),d=new ImageData(l,r,a);u.putImageData(d,0,0)}return n!==e&&n.dispose(),l}var qN=_({fromPixels_:Fb}),Qm={};Fe(Qm,{prepareAndValidate:()=>$b});function $b(e,t){let n=e.shape.length,a=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(a<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${a}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[a-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[a-1]} vs. ${n}`);if(Rt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let r=t.shape,s=r[r.length-1],i=1;for(let p=0;pp/u),1].slice(0,s);return[l,i,u,d]}var ey={};Fe(ey,{calculateShapes:()=>Db,validateInput:()=>ny,validateUpdateShape:()=>ty});function ty(e,t,n){let a=t.rank>1?t.shape[t.rank-1]:1,r=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${a}, and batchDim: ${r}.`;if(n.rank1?t.shape[a-1]:1,s=n.length,i=1;for(let p=r;pXN,computeFlatOffset:()=>ZN,computeOutShape:()=>zb,getNormalizedAxes:()=>Lb,isSliceContinous:()=>KN,maskToAxes:()=>Sc,parseSliceParams:()=>Hb,sliceInfo:()=>YN,startForAxis:()=>jb,startIndicesWithElidedDims:()=>Wb,stopForAxis:()=>Ub,stopIndicesWithElidedDims:()=>Bb,stridesForAxis:()=>Vb,stridesWithElidedDims:()=>Ob});function XN(e,t,n){let a=e.shape.length;F(a===t.length,()=>`Error in slice${a}D: Length of begin ${t} must match the rank of the array (${a}).`),F(a===n.length,()=>`Error in slice${a}D: Length of size ${n} must match the rank of the array (${a}).`);for(let r=0;r`Error in slice${a}D: begin[${r}] + size[${r}] (${t[r]+n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`)}function Sc(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function zb(e,t,n){let a=[];for(let r=0;r0){let h=t[0],m=n+1;d=Wb(i,h,m,a,e),p=Bb(o,h,m,r,e),c=Ob(s,h,m,e)}else for(let h=0;h-1)s[o]=0;else{let l=_b(t,n,o),u=a[l];e&1<-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=_b(t,n,o),u=a[l];e&1<0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),i=Iu(0,i,l-1),i}function Ub(e,t,n,a,r,s){let i=t[r],o=n[r]||1;(e&1<0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),o>0?i=Iu(0,i,l):i=Iu(-1,i,l-1),i}function KN(e,t,n){let a=n.length;for(let r=0;r1){a=r;break}for(let r=a+1;r0||n[r]!==e[r])return!1;return!0}function ZN(e,t){let n=e.length>0?e[e.length-1]:1;for(let a=0;a{F(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(r).fill(-1):typeof n=="number"?s=[n,...new Array(r-1).fill(-1)]:n.lengthi>=0?i:(F(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-a[o])),[a,s]}function YN(e,t,n,a,r,s,i,o,l){let u=t.slice(),d=n.slice(),p=a;a==null&&(p=new Array(u.length));let c=Sc(i);if(c.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let h=e.length-u.length,m=Sc(o),f=e.slice();m.forEach(v=>{u[v]=0,d[v]=1,f.splice(v,0,1)});let{begin:y,end:A,strides:g}=Lb(f,c,h,u,d,p,r,s,i);u=y,d=A,p=g;let x=Sc(l);x.forEach(v=>{d[v]=u[v]+1,p[v]=1});let w=zb(u,d,p),b=w.filter((v,S)=>x.indexOf(S)===-1);return{nonStrided:p.every(v=>v===1),$begin:u,$end:d,$strides:p,size:w,newShape:f,outShape:b}}var ae={};Fe(ae,{Serializable:()=>Gb,SerializationMap:()=>mi,registerClass:()=>Pr});var Gb=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},mi=class{constructor(){this.classNameMap={}}static getMap(){return mi.instance==null&&(mi.instance=new mi),mi.instance}static register(e){mi.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Pr(e){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),mi.register(e)}var qb={};Fe(qb,{TEST_EPSILON_FLOAT16:()=>Xb,encodeStrings:()=>Kb,expectArrayBuffersEqual:()=>rT,expectArraysClose:()=>QN,expectArraysEqual:()=>tT,expectNumbersClose:()=>nT,expectPromiseToFail:()=>eT,expectValuesInRange:()=>aT,testEpsilon:()=>ay});var JN=.001,Xb=.1;function QN(e,t,n){return n==null&&(n=ay()),ry(e,t,(a,r)=>sy(a,r,n))}function ay(){return z.backend.floatPrecision()===32?JN:Xb}function ry(e,t,n){let a=!0;if((an(e)||an(t))&&(a=!1),an(e)&&an(t)&&(a=!0),a){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=La(e),o=La(t);if(!lr(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let r=an(e)?e:cs(e),s=an(t)?t:cs(t);if(r.length!==s.length)throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${s.length}. Actual: ${r}. Expected: ${s}.`);for(let i=0;it.fail(),()=>t())}function tT(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Cr(e)||Cr(e[0])||Cr(t)||Cr(t[0])?ry(e,n,(a,r)=>a==r):ry(e,t,(a,r)=>sy(a,r,0))}function nT(e,t,n){if(n==null&&(n=ay()),!sy(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function sy(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function aT(e,t,n){for(let a=0;an)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function rT(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function Kb(e){for(let t=0;tt.dispose())}function Gt(e){return z.keep(e)}function pT(e){return z.time(e)}function cT(e){return z.setBackend(e)}function hT(){return z.ready()}function fT(){return z.backendName}function mT(e){z.removeBackend(e)}function oy(e){return z.findBackend(e)}function yT(e){return z.findBackendFactory(e)}function Al(e,t,n=1){return z.registerBackend(e,t,n)}function Zb(){return z.backend}function AT(e,t){J().setPlatform(e,t)}function gT(e,t){let n=M(e,"a","add"),a=M(t,"b","add");[n,a]=kt(n,a);let r={a:n,b:a};return z.runKernel(Mr,r)}var se=_({add_:gT});function xT(e,t){let n=M(e,"a","floorDiv"),a=M(t,"b","floorDiv");[n,a]=kt(n,a);let r={a:n,b:a};return z.runKernel(Ts,r)}var Tc=_({floorDiv_:xT});function bT(e,t){let n=M(e,"a","div"),a=M(t,"b","div");if([n,a]=kt(n,a),n.dtype==="int32"&&a.dtype==="int32")return Tc(n,a);let r={a:n,b:a},s={};return z.runKernel(Is,r,s)}var fe=_({div_:bT});function vT(e,t){let n=M(e,"a","mul"),a=M(t,"b","mul");[n,a]=kt(n,a);let r={a:n,b:a};return z.runKernel(Ws,r)}var L=_({mul_:vT});function wT(e){let t=M(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return z.runKernel(Cu,n)}else{let n={x:t};return z.runKernel(oo,n)}}var Lt=_({abs_:wT});function kT(e){let t={x:M(e,"x","acos")};return z.runKernel(lo,t)}var ly=_({acos_:kT});function IT(e){let t={x:M(e,"x","acosh")};return z.runKernel(uo,t)}var uy=_({acosh_:IT});function ST(e){F(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),F(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((r,s)=>M(r,`tensors${s}`,"addN")),n=t[0];t.forEach(r=>{if(r.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(r=>{if(!lr(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let a=t;return z.runKernel(hs,a)}var Ec=_({addN_:ST});function NT(e,t=null,n=!1){let a={x:M(e,"x","all","bool")},r={axis:t,keepDims:n};return z.runKernel(po,a,r)}var Cc=_({all_:NT});function TT(e,t=null,n=!1){let a={x:M(e,"x","any","bool")},r={axis:t,keepDims:n};return z.runKernel(co,a,r)}var Qu=_({any_:TT});function ET(e,t=0){let n={x:M(e,"x","argMax")},a={axis:t};return z.runKernel(fs,n,a)}var yi=_({argMax_:ET});function CT(e,t=0){let n={x:M(e,"x","argMin")},a={axis:t};return z.runKernel(Nu,n,a)}var dy=_({argMin_:CT});function RT(e){let t={x:M(e,"x","asin")};return z.runKernel(ho,t)}var py=_({asin_:RT});function MT(e){let t={x:M(e,"x","asinh")};return z.runKernel(fo,t)}var cy=_({asinh_:MT});function FT(e){let t={x:M(e,"x","atan")};return z.runKernel(mo,t)}var hy=_({atan_:FT});function $T(e,t){let n=M(e,"a","atan2"),a=M(t,"b","atan2");[n,a]=kt(n,a);let r={a:n,b:a};return z.runKernel(Ao,r)}var fy=_({atan2_:$T});function DT(e){let t={x:M(e,"x","atanh")};return z.runKernel(yo,t)}var my=_({atanh_:DT});function zT(e,t,n,a,r="NHWC",s){let i=e[3],o=[...t,i],l=Qb(r);return ed(e,o,n,s,a,null,null,l)}function Yb(e,t,n,a,r,s,i="channelsLast"){let[o,l]=Rc(t),u;if(i==="channelsLast")u=[o,l,e[3],e[3]];else if(i==="channelsFirst")u=[o,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return ed(e,u,n,a,r,s,!1,i)}function OT(e,t,n,a,r,s,i="NDHWC"){let[o,l,u]=Ay(t),d,p;if(i==="NDHWC")p="channelsLast",d=[o,l,u,e[4],e[4]];else if(i==="NCDHW")p="channelsFirst",d=[o,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return Jb(e,d,n,a,r,!1,p,s)}function ed(e,t,n,a,r,s,i=!1,o="channelsLast"){let[l,u,d,p]=[-1,-1,-1,-1];if(o==="channelsLast")[l,u,d,p]=e;else if(o==="channelsFirst")[l,p,u,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[c,h,,m]=t,[f,y]=Rc(n),[A,g]=Rc(a),x=gl(c,A),w=gl(h,g),{padInfo:b,outHeight:v,outWidth:S}=LT(r,u,d,f,y,x,w,s,o),T=i?m*p:m,C;return o==="channelsFirst"?C=[l,T,v,S]:o==="channelsLast"&&(C=[l,v,S,T]),{batchSize:l,dataFormat:o,inHeight:u,inWidth:d,inChannels:p,outHeight:v,outWidth:S,outChannels:T,padInfo:b,strideHeight:f,strideWidth:y,filterHeight:c,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:w,dilationHeight:A,dilationWidth:g,inShape:e,outShape:C,filterShape:t}}function Jb(e,t,n,a,r,s=!1,i="channelsLast",o){let[l,u,d,p,c]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,u,d,p,c]=e;else if(i==="channelsFirst")[l,c,u,d,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,m,f,,y]=t,[A,g,x]=Ay(n),[w,b,v]=Ay(a),S=gl(h,w),T=gl(m,b),C=gl(f,v),{padInfo:$,outDepth:O,outHeight:P,outWidth:j}=WT(r,u,d,p,A,g,x,S,T,C,o),D=s?y*c:y,U;return i==="channelsFirst"?U=[l,D,O,P,j]:i==="channelsLast"&&(U=[l,O,P,j,D]),{batchSize:l,dataFormat:i,inDepth:u,inHeight:d,inWidth:p,inChannels:c,outDepth:O,outHeight:P,outWidth:j,outChannels:D,padInfo:$,strideDepth:A,strideHeight:g,strideWidth:x,filterDepth:h,filterHeight:m,filterWidth:f,effectiveFilterDepth:S,effectiveFilterHeight:T,effectiveFilterWidth:C,dilationDepth:w,dilationHeight:b,dilationWidth:v,inShape:e,outShape:U,filterShape:t}}function _T(e,t,n,a,r){a==null&&(a=yy(e,t,n));let s=e[0],i=e[1],o=Ai((s-t+2*a)/n+1,r),l=Ai((i-t+2*a)/n+1,r);return[o,l]}function PT(e,t,n,a,r,s){r==null&&(r=yy(e,t,a));let i=e[0],o=e[1],l=e[2],u=Ai((i-t+2*r)/a+1,s),d=Ai((o-t+2*r)/a+1,s),p=Ai((l-t+2*r)/a+1,s);return[u,d,p,n]}function yy(e,t,n,a=1){let r=gl(t,a);return Math.floor((e[0]*(n-1)-n+r)/2)}function Rc(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Ay(e){return typeof e=="number"?[e,e,e]:e}function gl(e,t){return t<=1?e:e+(e-1)*(t-1)}function LT(e,t,n,a,r,s,i,o,l){let u,d,p;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let c=_T([t,n],s,a,e,o);d=c[0],p=c[1]}else if(e==="same"){d=Math.ceil(t/a),p=Math.ceil(n/r);let c=Math.max(0,(d-1)*a+s-t),h=Math.max(0,(p-1)*r+i-n),m=Math.floor(c/2),f=c-m,y=Math.floor(h/2),A=h-y;u={top:m,bottom:f,left:y,right:A,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},d=Math.ceil((t-s+1)/a),p=Math.ceil((n-i+1)/r);else if(typeof e=="object"){let c=l==="channelsLast"?e[1][0]:e[2][0],h=l==="channelsLast"?e[1][1]:e[2][1],m=l==="channelsLast"?e[2][0]:e[3][0],f=l==="channelsLast"?e[2][1]:e[3][1];u={top:c,bottom:h,left:m,right:f,type:c===0&&h===0&&m===0&&f===0?"VALID":"EXPLICIT"},d=Ai((t-s+c+h)/a+1,o),p=Ai((n-i+m+f)/r+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:d,outWidth:p}}function WT(e,t,n,a,r,s,i,o,l,u,d){let p,c,h,m;if(typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let f=PT([t,n,a,1],o,1,r,e,d);c=f[0],h=f[1],m=f[2]}else if(e==="same"){c=Math.ceil(t/r),h=Math.ceil(n/s),m=Math.ceil(a/i);let f=(c-1)*r+o-t,y=(h-1)*s+l-n,A=(m-1)*i+u-a,g=Math.floor(f/2),x=f-g,w=Math.floor(y/2),b=y-w,v=Math.floor(A/2),S=A-v;p={top:w,bottom:b,left:v,right:S,front:g,back:x,type:"SAME"}}else if(e==="valid")p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},c=Math.ceil((t-o+1)/r),h=Math.ceil((n-l+1)/s),m=Math.ceil((a-u+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:c,outHeight:h,outWidth:m}}function Ai(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 Lr(e){let[t,n,a]=Rc(e);return t===1&&n===1&&a===1}function Ba(e,t){return Lr(e)||Lr(t)}function Qb(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function BT(e,t){let n={x:M(e,"x","reshape","string_or_numeric")},a={shape:t};return z.runKernel(Xo,n,a)}var H=_({reshape_:BT});function VT(e,t,n,a,r){let s=M(e,"x","avgPool","float32"),i=1;F(Ba(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),r!=null&&F(Ht(a),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=z.runKernel(ms,u,d);return p=me(p,s.dtype),l?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var td=_({avgPool_:VT});function jT(e,t,n,a,r,s="NDHWC"){let i=M(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),r!=null&&F(Ht(a),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=z.runKernel(Tu,u,d);return p=me(p,o.dtype),l?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var gy=_({avgPool3d_:jT});function UT(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=Yu(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${s.dtype}. `)}),n.length===1)return Wa(n[0]);let a=n,r={axis:t};return z.runKernel(go,a,r)}var lt=_({concat_:UT});function HT(e){let t={x:M(e,"x","sigmoid")};return z.runKernel(Js,t)}var Sn=_({sigmoid_:HT});function GT(e,t,n){let a=M(e,"x","slice","string_or_numeric");if(a.rank===0)throw new Error("Slicing scalar is not possible");let r={x:a},s={begin:t,size:n};return z.runKernel(Jo,r,s)}var Re=_({slice_:GT});function qT(e){let t={x:M(e,"x","tanh")};return z.runKernel(si,t)}var gi=_({tanh_:qT});function XT(e,t,n,a,r,s){let i=M(e,"forgetBias","basicLSTMCell"),o=M(t,"lstmKernel","basicLSTMCell"),l=M(n,"lstmBias","basicLSTMCell"),u=M(a,"data","basicLSTMCell"),d=M(r,"c","basicLSTMCell"),p=M(s,"h","basicLSTMCell"),c=lt([u,p],1),h=Be(c,o),m=se(h,l),f=m.shape[0],y=m.shape[1]/4,A=[f,y],g=Re(m,[0,0],A),x=Re(m,[0,y],A),w=Re(m,[0,y*2],A),b=Re(m,[0,y*3],A),v=se(L(Sn(g),gi(x)),L(d,Sn(se(i,w)))),S=L(gi(v),Sn(b));return[v,S]}var KT=_({basicLSTMCell_:XT});function ZT(e,t,n){let a=M(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);F(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(a.shape[0]%r==0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return z.runKernel(Eu,s,i)}var nd=_({batchToSpaceND_:ZT});function YT(e){let t;return e.rank===0||e.rank===1?t=H(e,[1,1,1,e.size]):e.rank===2?t=H(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function JT(e,t,n,a,r,s){s==null&&(s=.001);let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;a!=null&&(d=M(a,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(d==null||o.rank===d.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:YT(i),scale:u,offset:d,mean:o,variance:l},c={varianceEpsilon:s},h=z.runKernel(Es,p,c);return H(h,i.shape)}var xi=_({batchNorm_:JT});function QT(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===2||d.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),xi(i,o,l,d,u,s)}var e3=_({batchNorm2d_:QT});function eE(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),xi(i,o,l,d,u,s)}var t3=_({batchNorm3d_:eE});function tE(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),u;r!=null&&(u=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),xi(i,o,l,d,u,s)}var n3=_({batchNorm4d_:tE});function nE(e,t,n){let a=M(e,"x","bincount"),r=M(t,"weights","bincount");F(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return z.runKernel(Bp,s,i)}var xy=_({bincount_:nE});function aE(e,t){let n=M(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.lengthn.rank){let l=n.shape.slice();for(;l.length=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return Wa(n);let i={x:n},o={reps:s};return z.runKernel($r,i,o)}var xl=_({broadcastTo_:aE});function rE(e){let t={x:M(e,"x","ceil")};return z.runKernel(gs,t)}var by=_({ceil_:rE});function sE(e,t,n){let a=M(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return z.runKernel(Fr,r,s)}var Nn=_({clipByValue_:sE});function iE(e){return lt(e,0)}var a3=_({concat1d_:iE});function oE(e,t){return lt(e,t)}var bl=_({concat2d_:oE});function lE(e,t){return lt(e,t)}var r3=_({concat3d_:lE});function uE(e,t){return lt(e,t)}var s3=_({concat4d_:uE});function dE(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","conv2d"),l=M(t,"filter","conv2d"),u=o,d=!1;o.rank===3&&(d=!0,u=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(Ht(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p=r==="NHWC"?u.shape[3]:u.shape[1];F(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),F(Ba(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let c={x:u,filter:l},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=z.runKernel(xs,c,h);return d?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var pr=_({conv2d_:dE});function pE(e,t,n,a,r="NWC",s=1,i){let o=M(e,"x","conv1d"),l=M(t,"filter","conv1d"),u=o,d=!1;o.rank===2&&(d=!0,u=H(o,[1,o.shape[0],o.shape[1]])),F(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(Ht(a),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),F(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(Ba(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=H(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=pr(c,p,[1,n],a,"NHWC",[1,s],i);return d?H(h,[h.shape[2],h.shape[3]]):H(h,[h.shape[0],h.shape[2],h.shape[3]])}var Mc=_({conv1d_:pE});function cE(e,t,n,a,r,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let d=s==="NHWC"?o[3]:o[1],p=s==="NHWC"?l.shape[3]:l.shape[1];F(d===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${n.shape[2]}.`),F(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(Ht(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let c={dy:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=z.runKernel(bs,c,h);return u?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var vy=_({conv2DBackpropInput_:cE});function hE(e,t,n,a,r,s){let i=M(e,"x","conv2dTranspose"),o=M(t,"filter","conv2dTranspose");return vy(n,i,o,a,r,"NHWC",s)}var Fc=_({conv2dTranspose_:hE});function fE(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=M(e,"x","conv3d"),o=M(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(Ba(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let d={x:l,filter:o},p={strides:n,pad:a,dataFormat:r,dilations:s},c=z.runKernel(Ru,d,p);return u?H(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var wy=_({conv3d_:fE});function mE(e,t,n,a,r){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],u=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let d={dy:i,filter:n},p={pad:r,strides:a,inputShape:s},c=z.runKernel(Hp,d,p);return o?H(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var i3=_({conv3DBackpropInput_:mE});function yE(e,t,n,a,r){let s=M(e,"x","conv3dTranspose"),i=M(t,"filter","conv3dTranspose");return i3(n,s,i,a,r)}var o3=_({conv3dTranspose_:yE});function AE(e){let t={x:M(e,"x","cos")};return z.runKernel(vs,t)}var ad=_({cos_:AE});function gE(e){let t={x:M(e,"x","cosh")};return z.runKernel(xo,t)}var $c=_({cosh_:gE});function xE(e,t=0,n=!1,a=!1){let r={x:M(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return z.runKernel(ws,r,s)}var Dc=_({cumsum_:xE});function bE(e,t,n,a=!1){let r=M(e,"x","denseBincount"),s=M(t,"weights","denseBincount");F(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),F(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:n,binaryOutput:a};return z.runKernel(Gp,i,o)}var l3=_({denseBincount_:bE});function vE(e,t,n="NHWC"){let a=M(e,"x","depthToSpace"),r=n==="NHWC"?a.shape[1]:a.shape[2],s=n==="NHWC"?a.shape[2]:a.shape[3],i=n==="NHWC"?a.shape[3]:a.shape[1];F(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying ${r} and ${t} for depthToSpace with input shape ${a.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying ${s} and ${t} for depthToSpace with input shape ${a.shape}`),F(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${a.shape}`);let o={x:a},l={blockSize:t,dataFormat:n};return z.runKernel(vo,o,l)}var ky=_({depthToSpace_:vE});function wE(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","depthwiseConv2d"),l=M(t,"filter","depthwiseConv2d"),u=o,d=!1;o.rank===3&&(d=!0,u=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(Ht(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:u,filter:l},c={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=z.runKernel(ks,p,c);return d?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var vl=_({depthwiseConv2d_:wE});function kE(e){let t={x:M(e,"x","diag")};return z.runKernel(Kp,t)}var IE=_({diag_:kE});function SE(e,t,n,a,r=[1,1],s="NHWC"){let i=M(e,"x","dilation2d"),o=M(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,u=!1;i.rank===3&&(l=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let d={x:l,filter:o},p={strides:n,pad:a,dilations:r},c=z.runKernel(Mu,d,p);return u?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Iy=_({dilation2d_:SE});function NE(e,t){let n=e.length,a=[];for(let r=0;r1&&i===1&&a.unshift(s)}return a}function Wt(e,t){let n=[];for(let a=0;a1)&&n.unshift(s)}return n}function ct(e,t){let n=[],a=Math.max(e.length,t.length);for(let r=0;r`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${a.rank}.`);let r=n.rank===1?n.size:n.shape[1],s=a.rank===1?a.size:a.shape[0];if(F(r===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${s}.`),n.rank===1&&a.rank===1){let i=H(n,[1,-1]),o=H(a,[-1,1]),l=Be(i,o);return H(l,[])}else if(n.rank===1&&a.rank===2){let i=H(n,[1,-1]),o=H(a,[a.shape[0],a.shape[1]]),l=Be(i,o);return H(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=H(a,[-1,1]),o=Be(n,i);return H(o,[o.size])}else{let i=H(a,[a.shape[0],a.shape[1]]);return Be(n,i)}}var u3=_({dot_:ME});function FE(e,...t){let n=t.map((r,s)=>M(r,`tensors${s}`,"einsum")),a={equation:e};return z.runKernel(Jp,n,a)}var d3=_({einsum_:FE});function $E(e){let t={x:M(e,"x","elu")};return z.runKernel(wo,t)}var wl=_({elu_:$E});function DE(e){let t=M(e,"x","erf");F(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return z.runKernel(ko,n)}var Ny=_({erf_:DE});function zE(e){let t={x:M(e,"x","exp")};return z.runKernel(Ss,t)}var ea=_({exp_:zE});function OE(e,t=0){let n=M(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let a={input:n},r={dim:t};return z.runKernel(So,a,r)}var dn=_({expandDims_:OE});function _E(e){let t={x:M(e,"x","expm1")};return z.runKernel(No,t)}var Ty=_({expm1_:_E});function PE(e,t){let n=M(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let a={x:n},r={reps:t};return z.runKernel($r,a,r)}var Br=_({tile_:PE});function LE(e,t,n,a="float32"){t==null&&(t=e);let r=We([e,t],a),s=e<=t?e:t;for(let o=0;o`Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${s.rank}.`),F(Ht(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=H(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},u={depthRadius:t,bias:n,alpha:a,beta:r},d=z.runKernel(zu,l,u);return o?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Ry=_({localResponseNormalization_:YE});function JE(e){let t={x:M(e,"x","log")};return z.runKernel(Fs,t)}var _n=_({log_:JE});function QE(e){let t={x:M(e,"x","log1p")};return z.runKernel(Oo,t)}var _c=_({log1p_:QE});function eC(e){return F(Rr(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=M(t,"x","tf.grad","string_or_numeric"),r=n!=null?M(n,"dy","tf.grad"):null;return z.tidy(()=>{let{value:s,grads:i}=z.gradients(()=>e(a),[a],r);return r!=null&&ln(s.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Pc(i),i[0]})}}function tC(e){return F(Rr(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=Yu(t,"args","tf.grads","string_or_numeric"),r=n!=null?M(n,"dy","tf.grads"):null;return z.tidy(()=>{let{value:s,grads:i}=z.gradients(()=>e(...a),a,r);return r!=null&&ln(s.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Pc(i),i})}}function nC(e){return F(Rr(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof Le,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(n==null||n instanceof Le,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:a,value:r}=z.gradients(()=>e(t),[t],n);return Pc(a),{grad:a[0],value:r}}}function aC(e){return F(Rr(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(r=>r instanceof Le),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(n==null||n instanceof Le,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let a=z.gradients(()=>e(...t),t,n);return n!=null&&ln(a.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Pc(a.grads),a}}function f3(e,t){F(Rr(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(u=>u instanceof Xu),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in z.registeredVariables)t.push(z.registeredVariables[u])}let a=n?t.filter(u=>!u.trainable):null,r=t.length;t=t.filter(u=>u.trainable),F(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=z.gradients(e,t,null,s);F(o.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),F(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((u,d)=>{o[d]!=null&&(l[u.name]=o[d])}),a!=null&&a.forEach(u=>l[u.name]=null),{value:i,grads:l}}function Va(e){return z.customGrad(e)}function Pc(e){if(e.filter(t=>t==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.`)}function rC(e){let t={x:M(e,"x","neg")};return z.runKernel(Lo,t)}var It=_({neg_:rC});function sC(e){let t={x:M(e,"x","softplus")};return z.runKernel(tl,t)}var vi=_({softplus_:sC});function iC(e){let t=M(e,"x","logSigmoid");return Va(n=>({value:It(vi(It(n))),gradFunc:a=>L(a,Sn(It(n)))}))(t)}var m3=_({logSigmoid_:iC});function oC(e,t=null,n=!1){let a={x:M(e,"x","max")},r={reductionIndices:t,keepDims:n};return z.runKernel($s,a,r)}var Tn=_({max_:oC});function lC(e,t){let n=M(e,"a","sub"),a=M(t,"b","sub");[n,a]=kt(n,a);let r={a:n,b:a};return z.runKernel(ai,r)}var ye=_({sub_:lC});function uC(e,t=null,n=!1){let a=M(e,"x","sum");a.dtype==="bool"&&(a=me(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return z.runKernel(ei,r,s)}var Ie=_({sum_:uC});function dC(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 ${t}`);return Va((a,r)=>{let s=!0,i=Tn(a,t,!0),o=ye(a,i),l=ye(me(o,"float32"),_n(Ie(ea(o),t,s)));return r([l]),{value:l,gradFunc:(u,d)=>{let[p]=d,c=!0,h=ea(p);return ye(u,L(Ie(u,t,c),h))}}})(n)}var Lc=_({logSoftmax_:dC});function My(e,t){for(let n=0;ne[s]);return[n,r]}function wi(e,t){let n=t.map(a=>1);return y3(e,n,t)}function pC(e,t,n){F(My(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function g3(e,t){if(My(e,t))return null;let n=[];for(let a=0;an.push(a)),n}function Fy(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function cC(e,t){let n=[];for(let a=t-e;a`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),F(Ba(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&F(Ht(a),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=z.runKernel(zs,u,d);return l?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var id=_({maxPool_:gC});function xC(e,t=[1,1,1],n,a,r,s="NDHWC"){let i=M(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),r!=null&&F(Ht(a),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let u={x:o},d={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=z.runKernel(Ou,u,d);return l?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Dy=_({maxPool3d_:xC});function bC(e,t,n,a,r=!1){let s={x:M(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:a,includeBatchInIndex:r},o=z.runKernel(oc,s,i);return{result:o[0],indexes:o[1]}}var b3=_({maxPoolWithArgmax_:bC});function vC(e,t){let n=M(e,"a","maximum"),a=M(t,"b","maximum");[n,a]=kt(n,a),n.dtype==="bool"&&(n=me(n,"int32"),a=me(a,"int32")),ct(n.shape,a.shape);let r={a:n,b:a};return z.runKernel(Ds,r)}var ja=_({maximum_:vC});function wC(e,t=null,n=!1){let a={x:M(e,"x","mean")},r={axis:t,keepDims:n};return z.runKernel(Os,a,r)}var St=_({mean_:wC});function $t(e,t="float32"){if(t==="complex64"){let a=$t(e,"float32"),r=$t(e,"float32");return zr(a,r)}let n=Pp(Rt(e),t);return z.makeTensor(n,e,t)}function Pn(e,t="float32"){if(t==="complex64"){let a=Pn(e,"float32"),r=$t(e,"float32");return zr(a,r)}let n=Tm(Rt(e),t);return z.makeTensor(n,e,t)}function kC(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let a=M(e,"x","meshgrid",e instanceof Le?e.dtype:"float32");if(t===void 0)return[a];let r=M(t,"y","meshgrid",t instanceof Le?t.dtype:"float32"),s=Rt(a.shape),i=Rt(r.shape);return n==="xy"?(a=H(a,[1,-1]),r=H(r,[-1,1]),[Be(Pn([i,1],a.dtype),a),Be(r,Pn([1,s],r.dtype))]):(a=H(a,[-1,1]),r=H(r,[1,-1]),[Be(a,Pn([1,i],a.dtype)),Be(Pn([s,1],r.dtype),r)])}function IC(e,t=null,n=!1){let a={x:M(e,"x","min")},r={axis:t,keepDims:n};return z.runKernel(_s,a,r)}var Sl=_({min_:IC});function SC(e,t){let n=M(e,"a","minimum"),a=M(t,"b","minimum");[n,a]=kt(n,a),n.dtype==="bool"&&(n=me(n,"int32"),a=me(a,"int32")),ct(n.shape,a.shape);let r={a:n,b:a};return z.runKernel(Ps,r)}var Nl=_({minimum_:SC});function NC(e,t,n){F(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let a=M(e,"x","mirrorPad");if(a.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");F(t.length===a.rank,()=>`Padding doesn't match input. Must be ${a.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let o=0;o"Invalid number of paddings. Must be length of 2 each."),F(t[o][0]>=0&&t[o][0]<=a.shape[o]-r&&t[o][1]>=0&&t[o][1]<=a.shape[o]-r,()=>`Padding in dimension ${o} cannot be greater than or equal to ${a.shape[o]-r} or less than 0 for input of shape ${a.shape}`);let s={paddings:t,mode:n},i={x:a};return z.runKernel(Ls,i,s)}var zy=_({mirrorPad_:NC});function TC(e,t){let n=M(e,"a","mod"),a=M(t,"b","mod");[n,a]=kt(n,a);let r={a:n,b:a};return z.runKernel(Po,r)}var Oy=_({mod_:TC});function EC(e){let t=M(e,"x","square"),n={};return z.runKernel("Square",{x:t},n)}var ot=_({square_:EC});function CC(e,t=null,n=!1){e=M(e,"x","moments");let a=la(t,e.shape),r=St(e,a,n),s=r.shape;n||(s=wi(r.shape,a));let i=ot(ye(me(e,"float32"),H(r,s))),o=St(i,a,n);return{mean:r,variance:o}}var Bc=_({moments_:CC});function RC(e,t,n,a){let r=M(t,"data","multiRNNCell"),s=Yu(n,"c","multiRNNCell"),i=Yu(a,"h","multiRNNCell"),o=r,l=[];for(let p=0;p2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);n=n||Math.random();let o={logits:i===1?H(r,[1,-1]):r},l={numSamples:t,seed:n,normalized:a},u=z.runKernel(lc,o,l);return i===1?H(u,[u.size]):u}var v3=_({multinomial_:FC});function $C(e,t){let n=M(e,"a","notEqual"),a=M(t,"b","notEqual");[n,a]=kt(n,a),ct(n.shape,a.shape);let r={a:n,b:a};return z.runKernel(Wo,r)}var ki=_({notEqual_:$C});function DC(e){let t={x:M(e,"x","onesLike")};return z.runKernel(Uo,t)}var Ln=_({onesLike_:DC});function zC(e,t){let n=M(e,"v1","outerProduct"),a=M(t,"v2","outerProduct");F(n.rank===1&&a.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${a.rank}.`);let r=H(n,[-1,1]),s=H(a,[1,-1]);return Be(r,s)}var OC=_({outerProduct_:zC});function _C(e,t,n=0){let a=M(e,"x","pad");if(a.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let r={paddings:t,constantValue:n},s={x:a};return z.runKernel(Vs,s,r)}var cr=_({pad_:_C});function PC(e,t,n=0){return F(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),cr(e,[t],n)}var LC=_({pad1d_:PC});function WC(e,t,n=0){return F(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),cr(e,t,n)}var BC=_({pad2d_:WC});function VC(e,t,n=0){return F(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."),cr(e,t,n)}var jC=_({pad3d_:VC});function UC(e,t,n=0){return F(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."),cr(e,t,n)}var HC=_({pad4d_:UC});function GC(e,t,n){let a=M(e,"x","spaceToBatchND");F(a.rank>=1+t.length,()=>`input rank ${a.rank} should be > than [blockShape] ${t.length}`),F(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),F(a.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]==0:i,!0),()=>`input spatial dimensions ${a.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let r={x:a},s={blockShape:t,paddings:n};return z.runKernel(Lu,r,s)}var od=_({spaceToBatchND_:GC});function qC(e,t,n,a,r,s){r==null&&(r=[1,1]),s==null&&(s=1),a===0&&(a="valid");let i=M(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2]])),F(Ba(s,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${r}'`);let u=Yb(o.shape,t,s,r,a),d=[u.dilationHeight,u.dilationWidth],p;a==="same"?p=KC([u.filterHeight,u.filterWidth],d):p=[[0,0],[0,0]];let c=d[0]===1&&d[1]===1,[h,m]=XC([u.inHeight,u.inWidth],d,p),f=c?a:"valid",y=c?o:od(o,d,h),A=(n==="avg"?()=>td(y,t,s,f):()=>id(y,t,s,f))(),g=c?A:nd(A,d,m);return l?H(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function XC(e,t,n){let a=n.map(d=>d[0]),r=n.map(d=>d[1]),s=e.concat(a,r),i=t.map((d,p)=>(d-s[p]%d)%d),o=r.map((d,p)=>d+i[p]),l=t.map((d,p)=>[a[p],o[p]]),u=t.map((d,p)=>[0,i[p]]);return[l,u]}function KC(e,t){let n=e.map((s,i)=>s+(s-1)*(t[i]-1)).map(s=>s-1),a=n.map(s=>Math.floor(s/2)),r=n.map((s,i)=>s-a[i]);return n.map((s,i)=>[a[i],r[i]])}var w3=_({pool_:qC});function ZC(e,t){let n=M(e,"base","pow"),a=M(t,"exp","pow");[n,a]=kt(n,a);let r={a:n,b:a};return z.runKernel(js,r)}var hr=_({pow_:ZC});function YC(e,t){let n=M(e,"x","prelu"),a=M(t,"alpha","prelu"),r={x:n,alpha:a};return z.runKernel(Us,r)}var ld=_({prelu_:YC});function JC(e,t=null,n=!1){let a=M(e,"x","prod");a.dtype==="bool"&&(a=me(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return z.runKernel(Go,r,s)}var Vc=_({prod_:JC});function QC(e,t,n){let a=Rt(e),r=null;if(n==null||n==="float32")r=new Float32Array(a);else if(n==="int32")r=new Int32Array(a);else if(n==="bool")r=new Uint8Array(a);else throw new Error(`Unknown data type ${n}`);for(let s=0;s=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*a*i,t=this.mean+this.stdDev*r*i,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},tR=class{constructor(e,t,n,a){this.alpha=e,this.beta=1/t,this.dtype=n;let r=a||Math.random();this.randu=_y.alea(r.toString()),this.randn=new Py(0,1,n,!1,this.randu()),e<1?this.d=e+2/3:this.d=e-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let e,t,n,a,r,s;for(;;){do a=this.randn.nextValue(),s=1+this.c*a;while(s<=0);if(s*=s*s,e=a*a,t=1-.331*e*e,n=.5*e+this.d*(1-s+Math.log(s)),r=this.randu(),rthis.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,a==null&&(a=Math.random()),typeof a=="number"&&(a=a.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=_y.alea(a)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function aR(e,t,n=1,a="float32",r){if(n==null&&(n=1),a==null&&(a="float32"),a!=="float32"&&a!=="int32")throw new Error(`Unsupported data type ${a}`);let s=new tR(t,n,a,r),i=We(e,a);for(let o=0;o`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Wn(t,0)}var hR=_({reverse1d_:cR});function fR(e,t){let n=M(e,"x","reverse");return F(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Wn(n,t)}var mR=_({reverse2d_:fR});function yR(e,t){let n=M(e,"x","reverse");return F(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Wn(n,t)}var AR=_({reverse3d_:yR});function gR(e,t){let n=M(e,"x","reverse");return F(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Wn(n,t)}var xR=_({reverse4d_:gR});function bR(e){let t={x:M(e,"x","round")};return z.runKernel(Ks,t)}var Uc=_({round_:bR});function vR(e){let t={x:M(e,"x","rsqrt")};return z.runKernel(Zs,t)}var Hc=_({rsqrt_:vR});function we(e,t){if((an(e)&&t!=="string"||Array.isArray(e))&&t!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(t==="string"&&an(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Or(e,[],[],t)}function wR(e){let t={x:M(e,"x","selu")};return z.runKernel(Yo,t)}var Gc=_({selu_:wR});function kR(e,t,n,a,r,s=[1,1],i="NHWC"){let o=M(e,"x","separableConv2d"),l=M(t,"depthwiseFilter","separableConv2d"),u=M(n,"pointwiseFilter","separableConv2d"),d=o,p=!1;if(o.rank===3&&(p=!0,d=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");F(d.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${d.rank}.`),F(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),F(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),F(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),F(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let c=l.shape[2],h=l.shape[3];F(u.shape[2]===c*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${c*h}, but got ${u.shape[2]}.`);let m=vl(d,l,a,r,i,s),f=pr(m,u,1,"valid",i);return p?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Wy=_({separableConv2d_:kR});async function IR(e,t){let n=M(e,"x","setdiff1d"),a=M(t,"y","setdiff1d");F(n.dtype===a.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${a.dtype}).`),F(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),F(a.rank===1,()=>`y should be 1D tensor, but got y (${a.shape}).`);let r=await n.data(),s=await a.data(),i=new Set(s),o=0;for(let d=0;d`slice1d expects a rank-1 tensor, but got a rank-${a.rank} tensor`),Re(a,[t],[n])}var Kc=_({slice1d_:ER});function CR(e,t,n){let a=M(e,"x","slice2d");return F(a.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var Vy=_({slice2d_:CR});function RR(e,t,n){let a=M(e,"x","slice3d");return F(a.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var Zc=_({slice3d_:RR});function MR(e,t,n){let a=M(e,"x","slice4d");return F(a.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${a.rank} tensor`),Re(a,t,n)}var dd=_({slice4d_:MR});function FR(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 a={logits:n},r={dim:t};return z.runKernel(ti,a,r)}var pd=_({softmax_:FR});function $R(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return z.runKernel(ec,t)}var cd=_({fft_:$R});function DR(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return z.runKernel(tc,t)}var Cl=_({ifft_:DR});function zR(e){let t=e.shape[e.shape.length-1],n=e.size/t,a;if(t<=2){let r=H(e,[n,t]);a=Cl(r)}else{let r=[n,2*(t-1)],s=H(ud(e),[n,t]),i=H(zc(e),[n,t]),o=Wn(Re(s,[0,1],[n,t-2]),1),l=L(Wn(Re(i,[0,1],[n,t-2]),1),we(-1)),u=lt([s,o],1),d=lt([i,l],1),p=H(zr(u,d),[r[0],r[1]]);a=Cl(p)}if(a=ud(a),e.rank===3&&e.shape[0]!==0){let r=a,s=e.shape[0];a=H(a,[s,a.shape[0]/s,a.shape[1]]),r.dispose()}return a}var Yc=_({irfft_:zR});function OR(e,t,n=0){let a={x:M(e,"x","split")},r={numOrSizeSplits:t,axis:n};return z.runKernel(nl,a,r)}var qt=_({split_:OR});function _R(e,t){F(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],a=e.size/n,r;if(t!=null&&t0),f=e.shape.map(y=>y);f[e.shape.length-1]=t,r=Re(e,m,f),n=t}else if(t!=null&&t>n){let m=e.shape.map(f=>f);m[e.shape.length-1]=t-n,r=lt([e,$t(m)],e.shape.length-1),n=t}else r=e;let s=He(r),i=H(zr(r,s),[a,n]),o=cd(i),l=Math.floor(n/2)+1,u=ud(o),d=zc(o),p=qt(u,[l,n-l],u.shape.length-1),c=qt(d,[l,n-l],d.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,H(zr(p[0],c[0]),h)}var hd=_({rfft_:_R});function PR(e){let t={x:M(e,"x","sqrt")};return z.runKernel(Qs,t)}var en=_({sqrt_:PR});function LR(e,t){let n=M(e,"a","squaredDifference"),a=M(t,"b","squaredDifference");[n,a]=kt(n,a),ct(n.shape,a.shape);let r={a:n,b:a},s={};return z.runKernel(ni,r,s)}var Jc=_({squaredDifference_:LR});function WR(e,t){let n=M(e,"x","squeeze");return H(n,Hx(n.shape,t).newShape)}var Ha=_({squeeze_:WR});function BR(e,t=0){let n=Yu(e,"tensors","stack","string_or_numeric");F(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&F(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let a=n,r={axis:t};return z.runKernel(Ho,a,r)}var pn=_({stack_:BR});function VR(e,t=0){let n={x:M(e,"x","step")},a={alpha:t};return z.runKernel(Dr,n,a)}var Rl=_({step_:VR});function jR(e,t,n,a,r=0,s=0,i=0,o=0,l=0){let u={x:M(e,"x","stridedSlice")},d={begin:t,end:n,strides:a,beginMask:r,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return z.runKernel(al,u,d)}var jy=_({stridedSlice_:jR});function UR(e){let t={x:M(e,"x","tan")};return z.runKernel(ri,t)}var Uy=_({tan_:UR});function Mt(e,t){ps(e);let n=La(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Or(e,null,n,t)}function wa(e,t,n){if(ps(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let a=La(e,n);if(a.length!==2&&a.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Or(e,t,a,n)}function HR(e,t,n){if(ps(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let a=La(e,n);if(a.length!==4&&a.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Or(e,t,a,n)}function GR(e,t,n){if(ps(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let a=La(e,n);if(a.length!==5&&a.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Or(e,t,a,n)}function qR(e,t,n){if(ps(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let a=La(e,n);if(a.length!==6&&a.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(a.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||a,Or(e,t,a,n)}function XR(e,t=1,n=!0){let a=M(e,"x","topk");if(a.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let r=a.shape[a.shape.length-1];if(t>r)throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);let s={x:a},i={k:t,sorted:n},[o,l]=z.runKernel(rl,s,i);return{values:o,indices:l}}var Hy=_({topk_:XR});function KR(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new Py(t,n,a,!0,r),i=We(e,a);for(let o=0;o0,()=>"The input tensor must be at least 1D");let a={x:n},r={axis:t},[s,i]=z.runKernel(mc,a,r);return{values:s,indices:i}}var eh=_({unique_:ZR});function YR(e,t,n){let a=M(e,"x","unsortedSegmentSum"),r=M(t,"segmentIds","unsortedSegmentSum","int32");F(Ht(n),()=>"numSegments must be of dtype int");let s={x:a,segmentIds:r},i={numSegments:n};return z.runKernel(Bu,s,i)}var Gy=_({unsortedSegmentSum_:YR});function JR(e,t=0){let n=M(e,"x","unstack","string_or_numeric");F(t>=-n.shape.length&&t`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let a={value:n},r={axis:t};return z.runKernel(il,a,r)}var ha=_({unstack_:JR});function S3(e,t=!0,n,a){return z.makeVariable(e,t,n,a)}function N3(e,t){let n=[];for(let s=0;s0,()=>"mask cannot be scalar"),ln(o.slice(s,s+i),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let f=s;f"Shape mismatch in v and x");let l=we(1),u=ye(l,o),d=L(ye(i,s),u);if(r){F(a!=null,()=>"When using zeroDebias: true, step is required.");let p=M(a,"step","movingAverage");d=fe(d,ye(l,hr(o,p)))}return se(s,d)}var rM=_({movingAverage_:aM});function sM(e,t,n){let a=M(e,"indices","scatterND","int32"),r=M(t,"updates","scatterND");ny(r,a,n);let s={indices:a,updates:r},i={shape:n};return z.runKernel(Ko,s,i)}var E3=_({scatterND_:sM});function iM(e,t,n,a){if(e.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);let r=e.rank>0?e.shape[0]:1,s=e.rank>1?e.shape[1]:1;if(n.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===r))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);if(t.dtype!==a.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function oM(e,t,n,a=0){let r=M(e,"sparseIndices","sparseToDense","int32"),s=M(t,"sparseValues","sparseToDense"),i=M(a,"defaultValue","sparseToDense",s.dtype);iM(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:n};return z.runKernel(fc,o,l)}var Xy=_({sparseToDense_:oM});function lM(e,t){let n=M(t,"indices","gatherND","int32"),a={params:M(e,"x","gatherND"),indices:n};return z.runKernel(Co,a)}var C3=_({gatherND_:lM});function uM(e,t){if(t==null)return e.shape.slice();if(lr(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let a=0;a`x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`),F(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Le?r.clone():r;let s=uM(r,n),i=1-t,o=fe(Il(se(Tl(s,0,1,"float32",a),i)),i);return L(r,o)}var R3=_({dropout_:dM});function M3(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function Ky(e,t,n){let a=1-e%2,r=new Float32Array(e);for(let s=0;s1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${a.rank}`),F(a.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${a.rank} and targets rank ${r.rank}`),ln(a.shape.slice(0,a.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=a.shape[a.shape.length-1];F(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await a.data(),o=await r.data(),[l,u]=[i.length/s,s],d=Gx("bool",l);for(let p=0;py.value-f.value),d[p]=0;for(let f=0;fmM,depthwiseConv2d:()=>xM,matMul:()=>vM});function hM(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]])),F(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),F(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),F(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],d=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),F(d===n[3],()=>`Error in conv2dDerFilter: depth of dy (${d}) must match output depth for filter (${n[3]}).`),i!=null&&F(Ht(r),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let p={x:o,dy:l},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return z.runKernel(jp,p,c)}var Zy=_({conv2DBackpropFilter_:hM});function nh(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return L(e,Rl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function ah(e,t){let n=t,a=Wt(e.shape,t.shape);return a.length>0&&(n=Ie(n,a)),H(n,e.shape)}function rh(e,t,n,a){if(t==="linear")return e;if(t==="relu")return Ua(e);if(t==="elu")return wl(e);if(t==="relu6")return jc(e);if(t==="prelu")return ld(e,n);if(t==="leakyrelu")return rd(e,a);if(t==="sigmoid")return Sn(e);throw new Error(`Unknown fused activation ${t}.`)}var sh=(e,t)=>!(e>0)||t==="linear";function fM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(l=l||"linear",sh(z.state.gradientDepth,l)===!1){let b=pr(e,t,n,a,r,s,i);return o!=null&&(b=se(b,o)),rh(b,l,u,d)}let p=M(e,"x","conv2d"),c=M(t,"filter","conv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=H(p,[1,p.shape[0],p.shape[1],p.shape[2]])),F(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),F(c.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${c.rank}.`),i!=null&&F(Ht(a),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),F(h.shape[3]===c.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${c.shape[2]}.`),F(Ba(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let f=ed(h.shape,c.shape,n,s,a,i),y;o!=null&&(y=M(o,"bias","fused conv2d"),[y]=kt(y,p),ct(f.outShape,y.shape));let A;u!=null&&(A=M(u,"prelu weights","fused conv2d"));let g=(b,v)=>{let[S,T,C,$]=v,O=nh(b,C,l);F(Lr(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let P=vy(T.shape,O,S,n,a),j=Zy(T,O,S.shape,n,a),D=[P,j];if($!=null){let U=ah($,O);D.push(U)}return D},x={x:h,filter:c,bias:y,preluActivationWeights:A},w={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:d};return o==null?Va((b,v,S)=>{let T=z.runKernel(li,x,w);return S([v,b,T]),m&&(T=H(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(h,c):Va((b,v,S,T)=>{let C=z.runKernel(li,x,w);return T([v,b,C,S]),m&&(C=H(C,[C.shape[1],C.shape[2],C.shape[3]])),{value:C,gradFunc:g}})(h,c,y)}var mM=_({fusedConv2d_:fM});function yM(e,t,n,a,r,s=[1,1],i){let o=e;e.rank===3&&(o=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},d={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return z.runKernel(qp,u,d)}var F3=_({depthwiseConv2dNativeBackpropFilter_:yM});function AM(e,t,n,a,r,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},d={strides:a,pad:r,dimRoundingMode:i,dilations:s,inputShape:e},p=z.runKernel(Xp,u,d);return l?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var $3=_({depthwiseConv2dNativeBackpropInput_:AM});function gM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(sh(z.state.gradientDepth,l)===!1){let b=vl(e,t,n,a,r,s,i);return o!=null&&(b=se(b,o)),rh(b,l,u,d)}let p=M(e,"x","depthwiseConv2d"),c=M(t,"filter","depthwiseConv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=H(p,[1,p.shape[0],p.shape[1],p.shape[2]])),F(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),F(c.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${c.rank}.`),F(h.shape[3]===c.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),s==null&&(s=[1,1]),F(Ba(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&F(Ht(a),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${a}.`);let f=ed(h.shape,c.shape,n,s,a,i,!0),y;o!=null&&(y=M(o,"bias","fused conv2d"),[y]=kt(y,p),ct(f.outShape,y.shape));let A;u!=null&&(A=M(u,"prelu weights","fused depthwiseConv2d"));let g=(b,v)=>{F(Lr(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[S,T,C,$]=v,O=nh(b,C,l),P=$3(T.shape,O,S,n,a,s,i),j=F3(T,O,S.shape,n,a,s,i);if($!=null){let D=ah(y,O);return[P,j,D]}return[P,j]},x={x:h,filter:c,bias:y,preluActivationWeights:A},w={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:d};return o==null?Va((b,v,S)=>{let T=z.runKernel(ui,x,w);return S([v,b,T]),m&&(T=H(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(h,c):Va((b,v,S,T)=>{let C=z.runKernel(ui,x,w);return T([v,b,C,S]),m&&(C=H(C,[C.shape[1],C.shape[2],C.shape[3]])),{value:C,gradFunc:g}})(h,c,y)}var xM=_({fusedDepthwiseConv2d_:gM});function bM({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(sh(z.state.gradientDepth,s)===!1){let $=Be(e,t,n,a);return r!=null&&($=se($,r)),rh($,s,i,o)}let l=M(e,"a","fused matMul"),u=M(t,"b","fused matMul");[l,u]=kt(l,u);let d=n?l.shape[l.rank-2]:l.shape[l.rank-1],p=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),y=Rt(m),A=Rt(f);F(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 ${u.rank}.`),F(lr(m,f),()=>`Error in fused matMul: outer dimensions (${m}) and (${f}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),F(d===p,()=>`Error in fused matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let g=l.shape.slice(0,-2).concat([c,h]),x=n?H(l,[y,d,c]):H(l,[y,c,d]),w=a?H(u,[A,h,p]):H(u,[A,p,h]),b;r!=null&&(b=M(r,"bias","fused matMul"),[b]=kt(b,l),ct(g,b.shape));let v;i!=null&&(v=M(i,"prelu weights","fused matMul"));let S=($,O)=>{let[P,j,D,U]=O,X=nh(H($,D.shape),D,s),G,ee;if(!n&&!a?(G=Be(X,j,!1,!0),ee=Be(P,X,!0,!1)):!n&&a?(G=Be(X,j,!1,!1),ee=Be(X,P,!0,!1)):n&&!a?(G=Be(j,X,!1,!0),ee=Be(P,X,!1,!1)):(G=Be(j,X,!0,!0),ee=Be(X,P,!0,!0)),r!=null){let Y=ah(U,X);return[G,ee,Y]}else return[G,ee]},T={a:x,b:w,bias:b,preluActivationWeights:v},C={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?Va(($,O,P)=>{let j=z.runKernel(oi,T,C);return P([$,O,j]),{value:H(j,g),gradFunc:S}})(x,w):Va(($,O,P,j)=>{let D=z.runKernel(oi,T,C);return j([$,O,D,P]),{value:H(D,g),gradFunc:S}})(x,w,b)}var vM=_({fusedMatMul_:bM});function wM(e){return Ky(e,.54,.46)}var kM=_({hammingWindow_:wM});function IM(e){return Ky(e,.5,.5)}var D3=_({hannWindow_:IM});function SM(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Re(e,s,t)),s+=n;if(a)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),F(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),F(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${a.length}.`),F(a[0]>=1&&a[1]>=1,()=>`cropSize must be atleast [1,1], but was ${a}`),F(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let d={image:i,boxes:o,boxInd:l},p={method:r,extrapolationValue:s,cropSize:a};return z.runKernel(bo,d,p)}var CM=_({cropAndResize_:EM});function RM(e){let t=M(e,"image","flipLeftRight","float32");F(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return z.runKernel(To,n,{})}var MM=_({flipLeftRight_:RM});function FM(e,t,n=0,a=.5){let r=M(e,"image","rotateWithOffset","float32");F(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return z.runKernel(ll,s,i)}var $M=_({rotateWithOffset_:FM});function Ml(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),F(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),F(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),F(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),F(t.rank===1,()=>"scores must be a 1D tensor"),F(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),F(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s}}function DM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=M(e,"boxes","nonMaxSuppression"),i=M(t,"scores","nonMaxSuppression"),o=Ml(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:a,scoreThreshold:r};return z.runKernel(Bo,{boxes:s,scores:i},l)}var zM=_({nonMaxSuppression_:DM});function OM(e,t,n){let a=_M(e,t,n),r=a<0?-(a+1):a;e.splice(r,0,t)}function _M(e,t,n){return LM(e,t,n||PM)}function PM(e,t){return e>t?1:e>>1);let o=n(t,e[s]);o>0?a=s+1:(r=s,i=!o)}return i?a:-a-1}function O3(e,t,n,a,r){return Yy(e,t,n,a,r,0)}function _3(e,t,n,a,r,s){return Yy(e,t,n,a,r,0,!1,s,!0)}function P3(e,t,n,a,r,s){return Yy(e,t,n,a,r,s,!0)}function Yy(e,t,n,a,r,s,i=!1,o=!1,l=!1){let u=[];for(let y=0;yr&&u.push({score:t[y],boxIndex:y,suppressBeginIndex:0});u.sort(L3);let d=s>0?-.5/s:0,p=[],c=[];for(;p.length0;){let y=u.pop(),{score:A,boxIndex:g,suppressBeginIndex:x}=y;if(A=x;--b){let v=WM(e,g,p[b]);if(v>=a){w=!0;break}if(y.score=y.score*BM(a,d,v),y.score<=r)break}y.suppressBeginIndex=p.length,w||(y.score===A?(p.push(g),c.push(y.score)):y.score>r&&OM(u,y,L3))}let h=p.length,m=n-h;o&&m>0&&(p.push(...new Array(m).fill(0)),c.push(...new Array(m).fill(0)));let f={selectedIndices:p};return i&&(f.selectedScores=c),l&&(f.validOutputs=h),f}function WM(e,t,n){let a=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),s=Math.min(a[0],a[2]),i=Math.min(a[1],a[3]),o=Math.max(a[0],a[2]),l=Math.max(a[1],a[3]),u=Math.min(r[0],r[2]),d=Math.min(r[1],r[3]),p=Math.max(r[0],r[2]),c=Math.max(r[1],r[3]),h=(o-s)*(l-i),m=(p-u)*(c-d);if(h<=0||m<=0)return 0;let f=Math.max(s,u),y=Math.max(i,d),A=Math.min(o,p),g=Math.min(l,c),x=Math.max(A-f,0)*Math.max(g-y,0);return x/(h+m-x)}function BM(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function L3(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function VM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=M(e,"boxes","nonMaxSuppressionAsync"),i=M(t,"scores","nonMaxSuppressionAsync"),o=Ml(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],d=l[1],{selectedIndices:p}=O3(u,d,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),Mt(p,"int32")}var jM=VM;function UM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),l=Ml(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},d={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},p=z.runKernel(jo,u,d);return{selectedIndices:p[0],selectedScores:p[1]}}var HM=_({nonMaxSuppressionWithScore_:UM});async function GM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),l=Ml(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),d=u[0],p=u[1],{selectedIndices:c,selectedScores:h}=P3(d,p,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Mt(c,"int32"),selectedScores:Mt(h)}}var qM=GM;function XM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=M(e,"boxes","nonMaxSuppression"),o=M(t,"scores","nonMaxSuppression"),l=Ml(i,o,n,a,r,null),u=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,c={boxes:i,scores:o},h={maxOutputSize:u,iouThreshold:d,scoreThreshold:p,padToMaxOutputSize:s},m=z.runKernel(Vo,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var KM=_({nonMaxSuppressionPadded_:XM});async function ZM(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=M(e,"boxes","nonMaxSuppressionAsync"),o=M(t,"scores","nonMaxSuppressionAsync"),l=Ml(i,o,n,a,r,null),u=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,[c,h]=await Promise.all([i.data(),o.data()]),{selectedIndices:m,validOutputs:f}=_3(c,h,u,d,p,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Mt(m,"int32"),validOutputs:we(f,"int32")}}var YM=ZM;function JM(e,t,n=!1,a=!1){let r=M(e,"images","resizeBilinear");F(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),F(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),F(a===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=H(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},u=z.runKernel(Gs,o,l);return i?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var W3=_({resizeBilinear_:JM});function QM(e,t,n=!1,a=!1){let r=M(e,"images","resizeNearestNeighbor");F(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),F(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),F(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),F(a===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=H(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},u=z.runKernel(Pu,o,l);return i?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var B3=_({resizeNearestNeighbor_:QM});function eF(e,t="binary",n=!1,a=.5){let r=M(e,"image","threshold"),s=.2989,i=.587,o=.114,l=r.shape[0]*r.shape[1],u=L(Mt([a]),255),d,p,c,h;if(F(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),F(r.shape[2]===3||r.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`),F(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),F(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[d,p,c]=qt(r,[1,1,1],-1);let f=L(d,s),y=L(p,i),A=L(c,o);h=se(se(f,y),A)}else h=e;if(t==="otsu"){let f=xy(me(Uc(h),"int32"),pa([]),256);u=tF(f,l)}let m=n?jr(h,u):On(h,u);return me(L(m,255),"int32")}function tF(e,t){let n=Mt([-1]),a=Mt([0]),r=Mt([0]),s,i,o,l,u,d;for(let p=0;p`Error in transform: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&(o.shape[0]===i.shape[0]||o.shape[0]===1)&&o.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),F(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},u={interpolation:n,fillMode:a,fillValue:r,outputShape:s};return z.runKernel(sl,l,u)}var rF=_({transform_:aF});function sF(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=M(e,"a","bandPart");F(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=H(El(0,s,1,"int32"),[-1,1]),l=El(0,i,1,"int32"),u=ye(o,l),d=ca(jr(u,we(+t,"int32")),Vr(u,we(-n,"int32"))),p=$t([s,i],a.dtype);return H(pn(ha(H(a,[-1,s,i])).map(c=>rn(d,c,p))),r)}var iF=_({bandPart_:sF});function oF(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${r})`)}else t=!0,e=qt(e,e.shape[0],0).map(r=>Ha(r,[0]));F(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],a=e;for(let r=0;r{let s=a[r];if(r>0)for(let i=0;i=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return V3(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),a=ha(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(l=>{let[u,d]=V3(l,t);r.push(u),s.push(d)});let i=H(pn(r,0),e.shape),o=H(pn(s,0),e.shape);return[i,o]}}function V3(e,t=!1){return z.tidy(()=>{F(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],a=e.shape[1],r=Ey(n),s=Wa(e),i=wa([[1]],[1,1]),o=Wa(i),l=n>=a?a:n;for(let u=0;u{let h=Re(s,[u,u],[n-u,1]),m=th(h),f=Re(s,[u,u],[1,1]),y=rn(On(f,0),wa([[-1]]),wa([[1]])),A=ye(f,L(y,m)),g=fe(h,A);g.shape[0]===1?o=Wa(i):o=lt([i,Re(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let x=It(fe(Be(y,A),m)),w=Re(s,[u,0],[n-u,a]),b=L(x,o),v=Ye(o);if(u===0)s=ye(w,Be(b,Be(v,w)));else{let C=ye(w,Be(b,Be(v,w)));s=lt([Re(s,[0,0],[u,a]),C],0)}let S=Ye(b),T=Re(r,[0,u],[n,r.shape[1]-u]);if(u===0)r=ye(T,Be(Be(T,o),S));else{let C=ye(T,Be(Be(T,o),S));r=lt([Re(r,[0,0],[n,u]),C],1)}return[o,s,r]}),Ne([d,p,c])}return!t&&n>a&&(r=Re(r,[0,0],[n,a]),s=Re(s,[0,0],[a,a])),[r,s]})}var dF=_({qr_:uF}),cn;(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"})(cn||(cn={}));function pF(e,t,n=cn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=M(t,"weights","computeWeightedLoss"));let s=r==null?a:L(a,r);if(n===cn.NONE)return s;if(n===cn.SUM)return Ie(s);if(n===cn.MEAN){if(r==null)return St(s);{let i=a.size/r.size,o=fe(Ie(s),Ie(r));return i>1?fe(o,we(i)):o}}if(n===cn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return fe(Ie(s),we(a.size));{let i=L(r,Pn(a.shape)),o=me(Ie(ki(i,we(0))),"float32");return fe(Ie(s),o)}}throw Error(`Unknown reduction: ${n}`)}var fr=_({computeWeightedLoss_:pF});function cF(e,t,n,a=cn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","absoluteDifference"),s=M(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=M(n,"weights","absoluteDifference")),ln(r.shape,s.shape,"Error in absoluteDifference: ");let o=Lt(ye(r,s));return fr(o,i,a)}var hF=_({absoluteDifference_:cF});function fF(e,t,n,a,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","cosineDistance"),i=M(t,"predictions","cosineDistance"),o=null;a!=null&&(o=M(a,"weights","cosineDistance")),ln(s.shape,i.shape,"Error in cosineDistance: ");let l=we(1),u=ye(l,Ie(L(s,i),n,!0));return fr(u,o,r)}var mF=_({cosineDistance_:fF});function yF(e,t,n,a=cn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),i=null;n!=null&&(i=M(n,"weights","hingeLoss")),ln(r.shape,s.shape,"Error in hingeLoss: ");let o=we(1);r=ye(L(we(2),r),o);let l=Ua(ye(o,L(r,s)));return fr(l,i,a)}var AF=_({hingeLoss_:yF});function gF(e,t,n,a=1,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","huberLoss"),i=M(t,"predictions","huberLoss"),o=null;n!=null&&(o=M(n,"weights","huberLoss")),ln(s.shape,i.shape,"Error in huberLoss: ");let l=we(a),u=Lt(ye(i,s)),d=Nl(u,l),p=ye(u,d),c=se(L(we(.5),ot(d)),L(l,p));return fr(c,o,r)}var xF=_({huberLoss_:gF});function bF(e,t,n,a=1e-7,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","logLoss"),i=M(t,"predictions","logLoss"),o=null;n!=null&&(o=M(n,"weights","logLoss")),ln(s.shape,i.shape,"Error in logLoss: ");let l=we(1),u=we(a),d=It(L(s,_n(se(i,u)))),p=L(ye(l,s),_n(se(ye(l,i),u))),c=ye(d,p);return fr(c,o,r)}var vF=_({logLoss_:bF});function wF(e,t,n,a=cn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=M(n,"weights","meanSquaredError")),ln(r.shape,s.shape,"Error in meanSquaredError: ");let o=Jc(r,s);return fr(o,i,a)}var kF=_({meanSquaredError_:wF});function IF(e,t){let n=M(e,"labels","sigmoidCrossEntropyWithLogits"),a=M(t,"logits","sigmoidCrossEntropyWithLogits");ln(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ua(a),s=L(a,n),i=_c(ea(It(Lt(a))));return se(ye(r,s),i)}function SF(e,t,n,a=0,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"multiClassLabels","sigmoidCrossEntropy"),i=M(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","sigmoidCrossEntropy")),ln(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let u=we(a),d=we(1),p=we(.5);s=se(L(s,ye(d,u)),L(p,u))}let l=IF(s,i);return fr(l,o,r)}var NF=_({sigmoidCrossEntropy_:SF});function TF(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 ${n}`);return Va((a,r,s)=>{let i=$y(r,[n],!0),o=ye(me(r,"float32"),i);s([a,o]);let l=It(L(o,a));return{value:Ie(l,[n]),gradFunc:(u,d)=>{let[p,c]=d,h=wi(u.shape,[n]);return[L(H(u,h),ye(me(p,"float32"),ea(c))),L(H(u,h),ye(ea(c),me(p,"float32")))]}}})(e,t)}function EF(e,t,n,a=0,r=cn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"onehotLabels","softmaxCrossEntropy"),i=M(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","softmaxCrossEntropy")),ln(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let u=we(a),d=we(1),p=we(s.shape[1]);s=se(L(s,ye(d,u)),fe(u,p))}let l=TF(s,i);return fr(l,o,r)}var CF=_({softmaxCrossEntropy_:EF});function RF(e,t,n,a){let r=M(e,"indices","sparseFillEmptyRows"),s=M(t,"values","sparseFillEmptyRows"),i=M(n,"denseShape","sparseFillEmptyRows"),o=M(a,"defaultValue","sparseFillEmptyRows",s.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(o.rank!==0)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);let l={indices:r,values:s,denseShape:i,defaultValue:o},u=z.runKernel(cc,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var MF=_({sparseFillEmptyRows_:RF});function FF(e,t,n){let a=M(e,"inputIndices","sparseReshape"),r=M(t,"inputShape","sparseReshape"),s=M(n,"newShape","sparseReshape");if(a.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape ${a.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:a,inputShape:r,newShape:s},o=z.runKernel(hc,i);return{outputIndices:o[0],outputShape:o[1]}}var $F=_({sparseReshape_:FF}),DF={fft:cd,ifft:Cl,rfft:hd,irfft:Yc},zF={hammingWindow:kM,hannWindow:D3,frame:z3,stft:TM},Ve={flipLeftRight:MM,resizeNearestNeighbor:B3,resizeBilinear:W3,rotateWithOffset:$M,cropAndResize:CM,nonMaxSuppression:zM,nonMaxSuppressionAsync:jM,nonMaxSuppressionWithScore:HM,nonMaxSuppressionWithScoreAsync:qM,nonMaxSuppressionPadded:KM,nonMaxSuppressionPaddedAsync:YM,threshold:nF,transform:rF},j3={bandPart:iF,gramSchmidt:lF,qr:dF},OF={absoluteDifference:hF,computeWeightedLoss:fr,cosineDistance:mF,hingeLoss:AF,huberLoss:xF,logLoss:vF,meanSquaredError:kF,sigmoidCrossEntropy:NF,softmaxCrossEntropy:CF},U3={sparseFillEmptyRows:MF,sparseReshape:$F},mr=class extends Gb{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Ne(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return f3(e,t)}dispose(){this.iterations_!=null&&Ne(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:we(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(mr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var ih=class extends mr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=z.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:B(()=>He(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:B(()=>He(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;B(()=>{let l=se(L(i,this.rho),L(ot(s),1-this.rho)),u=L(fe(en(se(o,this.epsilon)),en(se(i,this.epsilon))),s),d=se(L(o,this.rho),L(ot(u),1-this.rho));i.assign(l),o.assign(d);let p=se(L(u,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ne(this.accumulatedGrads.map(e=>e.variable)),Ne(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ih.className="Adadelta";Pr(ih);var oh=class extends mr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=z.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:B(()=>kl(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;B(()=>{let i=se(s,ot(r));s.assign(i);let o=se(L(fe(r,en(se(i,z.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ne(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)}};oh.className="Adagrad";Pr(oh);var lh=class extends mr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),a==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=ye(1,this.accBeta1),a=ye(1,this.accBeta2);t.forEach((r,s)=>{let i=z.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:B(()=>He(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:B(()=>He(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,p=se(L(u,this.beta1),L(l,1-this.beta1)),c=se(L(d,this.beta2),L(ot(l),1-this.beta2)),h=fe(p,n),m=fe(c,a);u.assign(p),d.assign(c);let f=se(L(fe(h,se(en(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ne(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),B(()=>{this.accBeta1.assign(hr(this.beta1,this.iterations_+1)),this.accBeta2.assign(hr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};lh.className="Adam";Pr(lh);var uh=class extends mr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),a==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=ye(1,this.accBeta1),a=fe(-this.learningRate,se(L(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=z.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:He(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:He(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,p=se(L(u,this.beta1),L(l,1-this.beta1)),c=L(d,this.beta2),h=Lt(l),m=ja(c,h);u.assign(p),d.assign(m);let f=se(L(fe(a,n),fe(p,se(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ne(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)}};uh.className="Adamax";Pr(uh);var fd=class extends mr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=z.registeredVariables[t];B(()=>{let s=se(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Gt(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};fd.className="SGD";Pr(fd);var dh=class extends fd{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=we(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=z.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:B(()=>He(a).variable(i))}}let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&B(()=>{let i,o=se(L(this.m,r),s);this.useNesterov?i=se(L(this.c,se(s,L(o,this.m))),a):i=se(L(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ne(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};dh.className="Momentum";Pr(dh);var ph=class extends mr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=z.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=z.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:B(()=>He(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:B(()=>He(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:B(()=>He(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;B(()=>{let l=se(L(i,this.decay),L(ot(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,d=se(L(u,this.decay),L(s,1-this.decay)),p=fe(L(s,this.learningRate),en(ye(l,se(ot(d),this.epsilon)))),c=se(L(o,this.momentum),p);i.assign(l),u.assign(d),o.assign(c);let h=ye(a,c);a.assign(h)}else{let u=se(L(i,this.decay),L(ot(s),1-this.decay)),d=se(L(o,this.momentum),fe(L(s,this.learningRate),en(se(u,this.epsilon))));i.assign(u),o.assign(d);let p=ye(a,d);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ne(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};ph.className="RMSProp";Pr(ph);var Ii=class{static sgd(e){return new fd(e)}static momentum(e,t,n=!1){return new dh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new ph(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new lh(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new ih(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new uh(e,t,n,a,r)}static adagrad(e,t=.1){return new oh(e,t)}},Si={sgd:Ii.sgd,momentum:Ii.momentum,adadelta:Ii.adadelta,adagrad:Ii.adagrad,rmsprop:Ii.rmsprop,adamax:Ii.adamax,adam:Ii.adam},_F=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function ch(){return new Promise(e=>_F(()=>e()))}var R={};Fe(R,{ERF_A1:()=>XF,ERF_A2:()=>KF,ERF_A3:()=>ZF,ERF_A4:()=>YF,ERF_A5:()=>JF,ERF_P:()=>qF,PARALLELIZE_THRESHOLD:()=>Jy,SELU_SCALE:()=>G3,SELU_SCALEALPHA:()=>H3,applyActivation:()=>rh,assertAndGetBroadcastShape:()=>ct,assertAxesAreInnerMostDims:()=>pC,assertParamsConsistent:()=>PF,assignToTypedArray:()=>i$,axesAreInnerMostDims:()=>My,calculateShapes:()=>Db,checkEinsumDimSizes:()=>c$,combineLocations:()=>y3,complexWithEvenIndex:()=>a$,complexWithOddIndex:()=>r$,computeConv2DInfo:()=>ed,computeConv3DInfo:()=>Jb,computeDefaultPad:()=>yy,computeDilation2DInfo:()=>zT,computeOptimalWindowSize:()=>WF,computeOutAndReduceShapes:()=>A3,computeOutShape:()=>LF,computePool2DInfo:()=>Yb,computePool3DInfo:()=>OT,convertConv2DDataFormat:()=>Qb,decodeEinsumEquation:()=>d$,eitherStridesOrDilationsAreOne:()=>Ba,expandShapeToKeepDim:()=>wi,exponent:()=>l$,exponents:()=>o$,fromStringArrayToUint8:()=>v$,fromUint8ToStringArray:()=>b$,getAxesPermutation:()=>g3,getBroadcastDims:()=>NE,getComplexWithIndex:()=>s$,getEinsumComputePath:()=>h$,getEinsumPermutation:()=>p$,getFusedBiasGradient:()=>ah,getFusedDyActivation:()=>nh,getImageCenter:()=>BF,getInnerMostAxes:()=>cC,getPermuted:()=>jF,getReductionAxes:()=>Wt,getReshaped:()=>VF,getReshapedPermuted:()=>UF,getSliceBeginCoords:()=>HF,getSliceSize:()=>GF,getUndoAxesPermutation:()=>Fy,isIdentityPermutation:()=>f$,log:()=>e$,mergeRealAndImagArrays:()=>t$,prepareAndValidate:()=>$b,prepareSplitSize:()=>y$,segment_util:()=>K3,shouldFuse:()=>sh,slice_util:()=>un,splitRealAndImagArrays:()=>n$,tupleValuesAreOne:()=>Lr,upcastType:()=>da,validateInput:()=>ny,validateUpdateShape:()=>ty,warn:()=>QF});function PF(e,t){let n=e[0].length;e.forEach((r,s)=>{F(r.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let a=e[0];e.forEach((r,s)=>{for(let i=0;i`Error in concat${n}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${a}) along the non-concatenated axis ${s}.`)})}function LF(e,t){let n=e[0].slice();for(let a=1;a=t*2+1||i%2==1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function UF(e,t,n,a=!0){let r=[];a?r.push(e[0]/n):r.push(e[0]*n);for(let s=1;s/g,q3=",",X3="...";function d$(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(u$,"").length)/Qy.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${Qy}").`);let[a,r]=e.split(Qy);F(a.indexOf(X3)===-1,()=>`The ellipsis notation ("${X3}") is not supported yet.`);let s=a.split(q3),i=s.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let o=[];for(let c=0;cm.indexOf(h)!==-1))throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);o.indexOf(h)===-1&&o.push(h)}for(let c=0;cr!==-1),{permutationIndices:n,expandDims:a}}function c$(e,t,n){let a=new Array(e);for(let r=0;r`Expected dimension ${a[t[r][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function h$(e,t){let n=e,a=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let i=0;it===n)}function m$(e,t){let n=[];for(let a=0;a"Number of splits must evenly divide the axis."),a=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);F(r<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}F(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),a=t}return a}var K3={};Fe(K3,{collectGatherOpShapeInfo:()=>x$,computeOutShape:()=>g$,segOpComputeOptimalWindowSize:()=>A$});function A$(e,t){let n=!1,a;for(e<=Jy?(a=e,n=!0):a=_p(e,Math.floor(Math.sqrt(e)));!n;)a>t||a===e?n=!0:a=_p(e,a+1);return a}function g$(e,t,n){let a=[],r=e.length;for(let s=0;sr))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${a}`);if(a<0&&(a+=r),a>s)throw new Error(`batchDims (${a}) must be less than rank(x) ( ${s}).`);if(nxc(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function v$(e){return e.map(t=>Uu(t))}var Ga={};Fe(Ga,{nonMaxSuppressionV3Impl:()=>O3,nonMaxSuppressionV4Impl:()=>_3,nonMaxSuppressionV5Impl:()=>P3,whereImpl:()=>N3});function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var w$=Ga.whereImpl,hh=class extends ku{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new zp(this,dr())}nextDataId(){return hh.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().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 a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,a,r){this.data.set(e,{values:t,dtype:a,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return R.mergeRealAndImagArrays(a,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}makeOutput(e,t,n){let a=this.write(e,t,n);return dr().makeTensorFromDataId(a,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){ve([e],"where");let t=this.readSync(e.dataId);return w$(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};hh.nextDataId=0;var e1={};Fe(e1,{addImpl:()=>Y3,bincountImpl:()=>n1,bincountReduceImpl:()=>J3,ceilImpl:()=>Q3,concatImpl:()=>a1,expImpl:()=>e7,expm1Impl:()=>n7,floorImpl:()=>a7,gatherV2Impl:()=>r7,greaterImpl:()=>s7,lessImpl:()=>i7,linSpaceImpl:()=>o7,logImpl:()=>l7,maxImpl:()=>u7,maximumImpl:()=>d7,minimumImpl:()=>p7,multiplyImpl:()=>r1,negImpl:()=>c7,notEqualImpl:()=>h7,prodImpl:()=>f7,rangeImpl:()=>i1,rsqrtImpl:()=>m7,simpleAbsImpl:()=>Z3,sliceImpl:()=>yh,sparseFillEmptyRowsImpl:()=>y7,sparseReshapeImpl:()=>A7,squaredDifferenceImpl:()=>g7,stridedSliceImpl:()=>x7,subImpl:()=>b7,tileImpl:()=>v7,topKImpl:()=>w7,transposeImpl:()=>s1,uniqueImpl:()=>k7});function Z3(e){let t=new Float32Array(e.length);for(let n=0;n{let{x:t}=e.inputs,n=e.backend;ve(t,"abs");let a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=Z3(r),n.makeOutput(a,t.shape,"float32")},I$={kernelName:oo,backendName:"cpu",kernelFunc:k$};function Dt(e){return(t,n,a,r,s)=>{let i=R.assertAndGetBroadcastShape(t,n),o=i.length,l=k.computeStrides(i),u=k.sizeFromShape(i),d=k.getTypedArrayFromDType(s,u),p=t.length,c=n.length,h=k.computeStrides(t),m=k.computeStrides(n),f=R.getBroadcastDims(t,i),y=R.getBroadcastDims(n,i);if(f.length+y.length===0)for(let A=0;Ax[S]=0);let w=k.locToIndex(x,p,h),b=g.slice(-c);y.forEach(S=>b[S]=0);let v=k.locToIndex(b,c,m);d[A]=e(a[w],r[v])}return[d,i]}}function Bn(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=n.makeTensorInfo(a.shape,"complex64"),l=n.data.get(o.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(a.shape,"float32",s),imag:n.makeTensorInfo(r.shape,"float32",i)},o}var S$={kernelName:Vp,backendName:"cpu",kernelFunc:Bn};function fh(e,t,n="float32"){if(n==="complex64"){let r=fh(e,t,"float32"),s=fh(e,t,"float32");return Bn({inputs:{real:r,imag:s},backend:e})}let a=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,a)}function qa(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var N$={kernelName:Rs,backendName:"cpu",kernelFunc:qa};function Ni(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.real,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var T$={kernelName:uc,backendName:"cpu",kernelFunc:Ni};function Hr(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return qa({inputs:{x:r},backend:n});let i=fh(n,r.shape,r.dtype),o=Hr({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Bn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Ni({inputs:{input:r},backend:n}),o=Hr({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=qa({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(r.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(r.shape,"int32",o)}if(s==="bool"){let i=n.data.get(r.dataId).values,o=k.toTypedArray([0],r.dtype),[l,u]=Dt((d,p)=>d!==p?1:0)(r.shape,[],i,o,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var E$={kernelName:As,backendName:"cpu",kernelFunc:Hr};function Xt(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;ve([i,o],e);let u=l.data.get(i.dataId).values,d=l.data.get(o.dataId).values,p=a||i.dtype,[c,h]=t(i.shape,o.shape,u,d,p);return l.makeTensorInfo(h,p,c)}:({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let u=Hr({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),d=l.data.get(u.dataId),p=d.complexTensorInfos.real,c=d.complexTensorInfos.imag,h=l.data.get(p.dataId).values,m=l.data.get(c.dataId).values,f=Hr({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),y=l.data.get(f.dataId),A=y.complexTensorInfos.real,g=y.complexTensorInfos.imag,x=l.data.get(A.dataId).values,w=l.data.get(g.dataId).values,[b,v,S]=n(i.shape,o.shape,h,m,x,w),T=l.makeTensorInfo(S,"float32",b),C=l.makeTensorInfo(S,"float32",v),$=Bn({inputs:{real:T,imag:C},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(f),l.disposeIntermediateTensorInfo(T),l.disposeIntermediateTensorInfo(C),$}else{let u=l.data.get(i.dataId).values,d=l.data.get(o.dataId).values,p=a||i.dtype,[c,h]=t(i.shape,o.shape,u,d,p);return l.makeTensorInfo(h,p,c)}}}function t1(e){return(t,n,a,r,s,i)=>{let o=R.assertAndGetBroadcastShape(t,n),l=k.sizeFromShape(o),u=o.length,d=k.computeStrides(o),p=k.getTypedArrayFromDType("float32",l),c=k.getTypedArrayFromDType("float32",l),h=R.getBroadcastDims(t,o),m=R.getBroadcastDims(n,o),f=R.mergeRealAndImagArrays(a,r),y=R.mergeRealAndImagArrays(s,i),A=t.length,g=k.computeStrides(t),x=n.length,w=k.computeStrides(n);if(h.length+m.length===0)for(let b=0;bS[P]=0);let T=k.locToIndex(S,A,g),C=v.slice(-x);m.forEach(P=>C[P]=0);let $=k.locToIndex(C,x,w),O=e(f[T*2],f[T*2+1],y[$*2],y[$*2+1]);p[b]=O.real,c[b]=O.imag}return[p,c,o]}}var Y3=Dt((e,t)=>e+t),C$=t1((e,t,n,a)=>({real:e+n,imag:t+a})),md=Xt(Mr,Y3,C$),R$={kernelName:Mr,backendName:"cpu",kernelFunc:md};function n1(e,t,n,a,r){let s=k.sizeFromShape(a),i=k.makeZerosTypedArray(r,n);for(let o=0;o=r||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function J3(e,t,n,a=!1){let r=e.shape[0],s=e.shape[1],i=We([r,n],t.dtype);for(let o=0;o=n||(a?i.set(1,o,u):t.size>0?i.set(i.get(o,u)+t.get(o,l),o,u):i.set(i.get(o,u)+1,o,u))}return i}function Fl(e){return(t,n,a)=>{let r=k.getTypedArrayFromDType(n,t.length);for(let s=0;s{let{x:i}=a;if(ve(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=k.sizeFromShape(i.shape),d=n||i.dtype,p=k.getArrayFromDType(d,u);for(let c=0;c{let{x:i}=a;if(ve(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=n||i.dtype,d=t(l,u,r);return o.makeTensorInfo(i.shape,u,d)}}var Q3=Fl(e=>Math.ceil(e)),M$=$l(gs,Q3),F$={kernelName:gs,backendName:"cpu",kernelFunc:M$};function a1(e,t,n,a){let r=k.getArrayFromDType(n,k.sizeFromShape(t));if(a&&n!=="string"){let s=0;e.forEach(i=>{let o=k.sizeFromShape(i.shape);r.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?R.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let u=0;uMath.exp(e)),t7=$l(Ss,e7),$$={kernelName:Ss,backendName:"cpu",kernelFunc:t7},n7=Fl(e=>Math.expm1(e)),D$=$l(No,n7),z$={kernelName:No,backendName:"cpu",kernelFunc:D$},a7=Fl(e=>Math.floor(e)),O$=$l(Ns,a7),_$={kernelName:Ns,backendName:"cpu",kernelFunc:O$};function r7(e,t,n){let a=We(n,e.dtype);for(let r=0;re>t?1:0),P$=Xt(Ro,s7,null,"bool"),L$={kernelName:Ro,backendName:"cpu",kernelFunc:P$},i7=Dt((e,t)=>eMath.log(e)),V$=$l(Fs,l7),j$={kernelName:Fs,backendName:"cpu",kernelFunc:V$};function u7(e,t,n,a){let r=k.getTypedArrayFromDType(a,k.sizeFromShape(n));for(let s=0;so&&(o=u)}r[s]=o}return r}var d7=Dt((e,t)=>Math.max(e,t)),U$=Xt(Ds,d7),H$={kernelName:Ds,backendName:"cpu",kernelFunc:U$},p7=Dt((e,t)=>Math.min(e,t)),G$=Xt(Ps,p7),q$={kernelName:Ps,backendName:"cpu",kernelFunc:G$},r1=Dt((e,t)=>e*t),X$=t1((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),mh=Xt(Ws,r1,X$),K$={kernelName:Ws,backendName:"cpu",kernelFunc:mh};function c7(e,t,n){let a=k.createScalarValue(-1,n);return r1([],t,a,e,n)}function Z$(e){let{inputs:t,backend:n}=e,{x:a}=t;ve(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=c7(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var Y$={kernelName:Lo,backendName:"cpu",kernelFunc:Z$},h7=Dt((e,t)=>e!==t?1:0),J$=Xt(Wo,h7,null,"bool"),Q$={kernelName:Wo,backendName:"cpu",kernelFunc:J$};function s1(e,t,n,a,r){let s=t.length,i=k.sizeFromShape(t),o=k.computeStrides(t),l=k.computeStrides(r),u=k.getTypedArrayFromDType(n,k.sizeFromShape(r));for(let d=0;dn.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(A,y,m)}var nD={kernelName:Go,backendName:"cpu",kernelFunc:tD};function i1(e,t,n,a){let r=e===t,s=e1;if(r||s||i)return k.makeZerosTypedArray(0,a);let o=Math.abs(Math.ceil((t-e)/n)),l=k.makeZerosTypedArray(o,a);t1/Math.sqrt(e)),aD=$l(Zs,m7),rD={kernelName:Zs,backendName:"cpu",kernelFunc:aD};function yh(e,t,n,a,r){let s=un.isSliceContinous(a,t,n),i=k.sizeFromShape(n),o=k.computeStrides(a);if(s){let p=un.computeFlatOffset(t,o);return r==="string"?e.slice(p,p+i):e.subarray(p,p+i)}let l=r==="string"?R.fromUint8ToStringArray(e):e,u=We(a,r,l),d=We(n,r);for(let p=0;pm+t[f]);d.set(u.get(...h),...c)}return r==="string"?R.fromStringArrayToUint8(d.values):d.values}function Ti(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;ve(r,"slice");let[o,l]=un.parseSliceParams(r,s,i);un.assertParamsValid(r,o,l);let u=n.data.get(r.dataId).values,d=yh(u,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}var sD={kernelName:Jo,backendName:"cpu",kernelFunc:Ti};function y7(e,t,n,a,r,s,i){let o=t[0],l=s[0],u=new Array(l),d=new Array(o),p=t[1];if(l===0){if(o!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but indices.shape[0] = ${o}`);let y=k.getArrayFromDType(n,0),A=k.getArrayFromDType(r,0);return[y,[0,p],A,u,d]}let c=!0,h=0,m=new Array(l).fill(0);for(let y=0;y=l)throw new Error(`indices(${y}, 0) is invalid: ${A} >= ${l}`);++m[A],c=c&&A>=h,h=A}let f=!0;for(let y=0;y0&&(m[y]+=m[y-1])}if(f&&c){let y=e,A=a;for(let g=0;g0){h[c-1]=1;for(let y=c-2;y>=0;--y)h[y]=h[y+1]*a[y+1]}let m=[];if(o>0){m[o-1]=1;for(let y=o-2;y>=0;--y)m[y]=m[y+1]*l[y+1]}let f=k.getArrayFromDType(n,i*o);for(let y=0;y{let n=e-t;return n*n}),iD=Xt(ni,g7),oD={kernelName:ni,backendName:"cpu",kernelFunc:iD};function x7(e,t,n,a){let r=We(e,t.dtype);for(let s=0;se-t),lD=t1((e,t,n,a)=>({real:e-n,imag:t-a})),o1=Xt(ai,b7,lD),uD={kernelName:ai,backendName:"cpu",kernelFunc:o1};function v7(e,t){let n=new Array(e.rank);for(let r=0;rx.value-g.value);let f=p*a,y=l.subarray(f,f+a),A=u.subarray(f,f+a);for(let g=0;g{for(let y=0;ynew hh,1);var S7=rt(wo,e=>e>=0?e:Math.exp(e)-1),dD={kernelName:wo,backendName:"cpu",kernelFunc:S7};function N7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;ve([r],"leakyRelu");let i=k.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,l=k.getTypedArrayFromDType("float32",i);for(let u=0;ue<0?t*e:e);function T7(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;ve([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=cD(a.shape,r.shape,s,i,a.dtype);return n.makeTensorInfo(l,a.dtype,o)}var hD={kernelName:Us,backendName:"cpu",kernelFunc:T7},E7=rt(Hs,e=>Math.max(0,e)),fD={kernelName:Hs,backendName:"cpu",kernelFunc:E7},C7=rt(qs,e=>Math.min(Math.max(0,e),6)),mD={kernelName:qs,backendName:"cpu",kernelFunc:C7},R7=rt(Js,e=>1/(1+Math.exp(-e))),yD={kernelName:Js,backendName:"cpu",kernelFunc:R7};function l1(e,t,n,a,r){if(n==="linear")return qa({inputs:{x:t},backend:e});if(n==="relu")return E7({inputs:{x:t},backend:e});if(n==="elu")return S7({inputs:{x:t},backend:e});if(n==="relu6")return C7({inputs:{x:t},backend:e});if(n==="prelu")return T7({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return N7({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return R7({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function ht(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let u=n.data.get(r.dataId);if(u.complexTensorInfos!=null){let d=u.complexTensorInfos.real,p=u.complexTensorInfos.imag;d.shape=o,p.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var AD={kernelName:Xo,backendName:"cpu",kernelFunc:ht};function M7(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;ve([r,s],"matMul");let l=r.shape.length,u=s.shape.length,d=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),y=k.sizeFromShape(m),A=k.sizeFromShape(f),g=y===A||y===1||A===1;k.assert(l>=2&&u>=2&&g,()=>`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 (${f}).`);let x=(y>A?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([c,h]);k.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let w=i?[y,d,c]:[y,c,d],b=o?[A,h,p]:[A,p,h],v=ht({inputs:{x:r},backend:n,attrs:{shape:w}}),S=ht({inputs:{x:s},backend:n,attrs:{shape:b}}),T=i?v.shape[1]:v.shape[2],C=i?v.shape[2]:v.shape[1],$=o?S.shape[1]:S.shape[2],O=Math.max(y,A),P=n.data.get(v.dataId).values,j=n.data.get(S.dataId).values,D=k.computeStrides(v.shape),U=k.computeStrides(S.shape),[X,G,ee]=i?[D[0],1,D[1]]:[D[0],D[1],1],[Y,re,ne]=o?[1,U[1],U[0]]:[U[1],1,U[0]],ie=C*$,Q=We([O,C,$],v.dtype),pe=Q.values,oe=n.blockSize;for(let ge=0;geMath.acos(e)),wD={kernelName:lo,backendName:"cpu",kernelFunc:vD},kD=rt(uo,e=>Math.acosh(e)),ID={kernelName:uo,backendName:"cpu",kernelFunc:kD};function SD(e){let{inputs:t,backend:n}=e,a=t;ve(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=We(a[0].shape,a[0].dtype),i=s.values;for(let o=0;og&&(g=b,x=w)}h[y]=x}return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(d,"int32",h)}var FD={kernelName:fs,backendName:"cpu",kernelFunc:MD};function $D(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ve(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ta({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,p]=R.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let y=0;yn.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(d,"int32",h)}var DD={kernelName:Nu,backendName:"cpu",kernelFunc:$D},zD=rt(ho,e=>Math.asin(e)),OD={kernelName:ho,backendName:"cpu",kernelFunc:zD},_D=rt(fo,e=>Math.asinh(e)),PD={kernelName:fo,backendName:"cpu",kernelFunc:_D},LD=rt(mo,e=>Math.atan(e)),WD={kernelName:mo,backendName:"cpu",kernelFunc:LD},BD=Dt((e,t)=>Math.atan2(e,t)),VD=Xt(Ao,BD),jD={kernelName:Ao,backendName:"cpu",kernelFunc:VD},UD=rt(yo,e=>Math.atanh(e)),HD={kernelName:yo,backendName:"cpu",kernelFunc:UD};function u1(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,d=r.effectiveFilterHeight,p=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=We(r.outShape,n),y=f.values,A=r.outShape[1]*r.outShape[2]*r.outShape[3],g=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let w=0;wG?G=oe:s==="avg"&&(ee+=oe,Y++)}if(isNaN(G))break}let re=P+j*x+S;y[re]=s==="avg"?ee/Y:G}}}return f}function F7(e,t,n,a,r=!1,s=!1){let i=We(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,d=a.dilationWidth,p=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=We(t,n,e);for(let y=0;y$&&($=X,r?O=s?((y*a.inHeight+P)*a.inWidth+D)*a.inChannels+A:(P*a.inWidth+D)*a.inChannels+A:O=j*c+U)}}i.set(O,y,g,v,A)}}return i}function $7(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,d=r.dilationHeight,p=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,y=r.padInfo.top,A=r.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(r.outShape,n),w=x.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],v=r.outShape[2]*r.outShape[3]*r.outShape[4],S=r.outShape[3]*r.outShape[4],T=r.outShape[4];for(let C=0;CSe?Se=je:s==="avg"&&(Te+=je,$e++),isNaN(Se))break}if(isNaN(Se))break}if(isNaN(Se))break}let Oe=he+P;w[Oe]=s==="avg"?Te/$e:Se}}}}return x}function GD(e,t){let n=We(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,d=t.effectiveFilterHeight,p=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=j&&(j=ne,D=X*d*p+ee*d+re)}}}n.set(D,f,A,b,C,y)}}}return n}function qD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ve(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l),p;if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))p=qa({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=u1(c,r.shape,r.dtype,h,d,"avg");p=n.makeTensorInfo(d.outShape,r.dtype,m.values)}return p}var XD={kernelName:ms,backendName:"cpu",kernelFunc:qD};function KD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;ve(r,"avgPool3d");let d=R.computePool3DInfo(r.shape,s,i,1,o,l,u),p=n.data.get(r.dataId).values,c=$7(p,r.shape,r.dtype,k.computeStrides(r.shape),d,"avg");return n.makeTensorInfo(c.shape,"float32",c.values)}var ZD={kernelName:Tu,backendName:"cpu",kernelFunc:KD};function YD(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;ve([r,s],"avgPool3DGrad");let d=R.computePool3DInfo(s.shape,i,o,1,l,u),p=d.strideDepth,c=d.strideHeight,h=d.strideWidth,m=d.filterDepth,f=d.filterHeight,y=d.filterWidth,A=d.dilationDepth,g=d.dilationHeight,x=d.dilationWidth,w=d.effectiveFilterDepth,b=d.effectiveFilterHeight,v=d.effectiveFilterWidth,S=w-1-d.padInfo.front,T=v-1-d.padInfo.left,C=b-1-d.padInfo.top,$=We(s.shape,"float32"),O=1/(m*f*y),P=n.bufferSync(r);for(let j=0;j=d.outDepth||Math.floor(Q)!==Q))for(let pe=0;pe=d.outHeight||Math.floor(oe)!==oe))for(let ge=0;ge=d.outWidth||Math.floor(he)!==he||(ne+=P.get(j,Q,oe,he,D))}}}$.set(ne*O,j,U,X,G,D)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var JD={kernelName:Wp,backendName:"cpu",kernelFunc:YD};function QD(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ve([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=R.computePool2DInfo(i.shape,o,l,1,u),p=d.strideHeight,c=d.strideWidth,h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,y=d.dilationWidth,A=d.effectiveFilterHeight,g=d.effectiveFilterWidth,x=g-1-d.padInfo.left,w=A-1-d.padInfo.top,b=We(i.shape,"float32"),v=1/(h*m),S=n.data.get(r.dataId).values,T=We(r.shape,"float32",S);for(let C=0;C=d.outHeight||Math.floor(G)!==G))for(let ee=0;ee=d.outWidth||Math.floor(Y)!==Y||(U+=T.get(C,G,Y,$))}}b.set(U*v,C,O,P,$)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var ez={kernelName:Lp,backendName:"cpu",kernelFunc:QD};function tz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ve([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=a;u==null&&(u=.001);let d=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,c=n.data.get(l.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(d.length),y=m.length,A=h.length,g=c.length,x=p.length,w=0,b=0,v=0,S=0;for(let T=0;T=y&&(w=0),b>=x&&(b=0),v>=A&&(v=0),S>=g&&(S=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var nz={kernelName:Es,backendName:"cpu",kernelFunc:tz};function az(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ve([r],"batchToSpaceND");let o=s.reduce((A,g)=>A*g),l=R.getReshaped(r.shape,s,o),u=R.getPermuted(l.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=ht({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ta({inputs:{x:h},backend:n,attrs:{perm:u}}),f=ht({inputs:{x:m},backend:n,attrs:{shape:d}}),y=Ti({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),y}var rz={kernelName:Eu,backendName:"cpu",kernelFunc:az};function sz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=n1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var iz={kernelName:Bp,backendName:"cpu",kernelFunc:sz},oz=rt(Fr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return qa({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(R.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(w=>Ni({inputs:{input:w},backend:n})),y=o.map(w=>Dl({inputs:{input:w},backend:n})),A=zl({inputs:f,backend:n,attrs:{axis:s}}),g=zl({inputs:y,backend:n,attrs:{axis:s}}),x=Bn({inputs:{real:A,imag:g},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),y.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(g),x}let u=o.map(f=>{let y=k.sizeFromShape(f.shape.slice(s));return ht({inputs:{x:f},backend:n,attrs:{shape:[-1,y]}})}),d=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=R.computeOutShape(u.map(f=>f.shape),1);let p=u[0].shape[0]===1,c=a1(d,i,t[0].dtype,p),h=R.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var cz={kernelName:go,backendName:"cpu",kernelFunc:zl};function D7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a;ve([r,s],"conv2d");let p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,p),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,y=c.dilationWidth,A=c.padInfo.left,g=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new Pt(c.outShape,r.dtype),b=k.computeStrides(r.shape),v=k.computeStrides(s.shape),S=b[0],T=x?b[1]:b[2],C=x?b[2]:1,$=x?1:b[1],O=w.strides[0],P=x?w.strides[1]:w.strides[2],j=x?w.strides[2]:1,D=x?1:w.strides[1],U=n.data.get(r.dataId).values,X=n.data.get(s.dataId).values,G=w.values;for(let ee=0;ee=c.inHeight)continue;let ge=pe*v[0],he=Y+oe*T;for(let Se=0;Se=c.inWidth)continue;let Qe=ge+Oe*v[1],et=he+De*C,it=Qe;for(let Ke=0;Ke=u.inDepth)continue;let ee=X*C[0],Y=O+G*T[1];for(let re=0;re=u.inHeight)continue;let oe=ee+Q*C[1],ge=Y+pe*T[2];for(let he=0;he=u.inWidth)continue;let De=oe+$e*C[2],Qe=ge+Oe*u.inChannels,et=De;for(let it=0;itMath.cos(e)),Sz={kernelName:vs,backendName:"cpu",kernelFunc:Iz},Nz=rt(xo,e=>Math.cosh(e)),Tz={kernelName:xo,backendName:"cpu",kernelFunc:Nz};function Ez(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[d,p,c,h]=r.shape,m=s.shape[0],[f,y]=o,A=We([m,f,y,h],"float32"),g=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,b=k.computeStrides(r.shape),v=k.computeStrides(A.shape);for(let S=0;S=d)continue;let D=f>1?(O-C)*(p-1)/(f-1):0,U=y>1?(P-$)*(c-1)/(y-1):0;for(let X=0;X1?C*(p-1)+X*D:.5*(C+O)*(p-1);if(G<0||G>p-1){for(let ee=0;ee1?$*(c-1)+ne*U:.5*($+P)*(c-1);if(ie<0||ie>c-1){for(let ge=0;ge1?$*(c-1)+ee*U:.5*($+P)*(c-1);if(Y<0||Y>c-1){for(let ie=0;ieA+m-g-1:(A,g)=>A+g;for(let A=0;A`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],d=r.shape[3],p=l*s,c=u*s,h=d/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*p*c*h),y=0;for(let A=0;A`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=R.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:y,dilationWidth:A,padInfo:g}=h,x=g.left,w=g.top,b=h.outChannels/h.inChannels,v=new Pt(h.outShape,r.dtype),S=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=v.values;for(let $=0;$=h.inHeight)continue;let ee=X*p[0],Y=O+G*d[1];for(let re=0;re=h.inWidth)continue;let oe=ee+Q*p[1],ge=Y+pe*h.inChannels,he=ne,Se=oe;for(let Te=0;Te{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,d=a.shape.length,p=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:y,outHeight:A,outWidth:g,padInfo:x,strideHeight:w,strideWidth:b,filterHeight:v,filterWidth:S,dilationHeight:T,dilationWidth:C,outShape:$}=R.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),O=k.sizeFromShape($),P=$.length,j=k.getArrayFromDType(a.dtype,O);for(let D=0;D=0&&Q=0&&oere&&(re=Se)}}}let ne=k.locToIndex([D,U,G,Y],P,k.computeStrides($));j[ne]=re}}}return{dataId:l.write(k.toTypedArray(j,a.dtype),$,a.dtype),shape:$,dtype:a.dtype}}},Uz={kernelName:Yp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,d=k.toNestedArray(a.shape,u.data.get(a.dataId).values),p=k.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:y,outWidth:A,padInfo:g,strideHeight:x,strideWidth:w,filterHeight:b,filterWidth:v,dilationHeight:S,dilationWidth:T,outShape:C}=R.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===C.length,()=>`Error in ${Yp}, dy must have the same rank as output ${C.length}, but got ${s.rank}`);let $=k.toNestedArray(C,u.data.get(s.dataId).values),O=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let P=0;P=0&&ie=0&&peee&&(ee=oe,Y=ne,re=Q)}}}O[Y][re][G]+=$[P][j][U][G]}}}return{dataId:u.write(k.toTypedArray(O,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Hz={kernelName:Zp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,d=k.toNestedArray(a.shape,u.data.get(a.dataId).values),p=k.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:y,outWidth:A,padInfo:g,strideHeight:x,strideWidth:w,filterHeight:b,filterWidth:v,dilationHeight:S,dilationWidth:T,outShape:C}=R.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===C.length,()=>`Error in ${Zp}, dy must have the same rank as output ${C.length}, but got ${s.rank}`);let $=k.toNestedArray(C,u.data.get(s.dataId).values),O=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let P=0;P=0&&ie=0&&peee&&(ee=oe,Y=ie,re=pe)}}}O[P][Y][re][G]+=$[P][j][U][G]}}}return{dataId:u.write(k.toTypedArray(O,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function yd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"sum");let o;r.dtype==="bool"?o=Hr({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=qa({inputs:{x:r},backend:n});let l=o.shape.length,u=k.parseAxisParam(s,o.shape),d=R.getAxesPermutation(u,l),p=u,c=o;d!=null&&(c=ta({inputs:{x:o},backend:n,attrs:{perm:d}}),p=R.getInnerMostAxes(p.length,l)),R.assertAxesAreInnerMostDims("sum",p,c.shape.length);let[h,m]=R.computeOutAndReduceShapes(c.shape,p),f=R.upcastType(c.dtype,"int32"),y=fh(n,h,f),A=k.sizeFromShape(m),g=n.data.get(y.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=yd({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Xz={kernelName:Jp,backendName:"cpu",kernelFunc:qz};function Kz(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ve([a,r],"eluGrad");let s=new Float32Array(k.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var Zz={kernelName:Qp,backendName:"cpu",kernelFunc:Kz},Yz=Dt((e,t)=>e===t?1:0),O7=Xt(Io,Yz,null,"bool"),Jz={kernelName:Io,backendName:"cpu",kernelFunc:O7},Qz=R.ERF_P,eO=R.ERF_A1,tO=R.ERF_A2,nO=R.ERF_A3,aO=R.ERF_A4,rO=R.ERF_A5,sO=rt(ko,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+Qz*n);return t*(1-((((rO*a+aO)*a+nO)*a+tO)*a+eO)*a*Math.exp(-n*n))}),iO={kernelName:ko,backendName:"cpu",kernelFunc:sO};function Ah(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ht({inputs:{x:r},backend:n,attrs:{shape:o}})}var oO={kernelName:So,backendName:"cpu",kernelFunc:Ah},lO=Dt((e,t)=>e/t),d1=Xt(Is,lO),p1={kernelName:Is,backendName:"cpu",kernelFunc:d1};function _7(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],d=k.sizeFromShape(u),p=k.getTypedArrayFromDType("float32",d),c=k.getTypedArrayFromDType("float32",d);for(let y=0;y{let{image:a}=e,r=n,s=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,d=r.data.get(a.dataId).values;for(let p=0;p=0&&xMath.floor(e/t)),gO=Xt(Ts,AO,null,"int32"),xO={kernelName:Ts,backendName:"cpu",kernelFunc:gO};function bO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=D7({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c}});if(i){let y=f;f=md({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(y)}if(h){let y=f;f=l1(n,f,h,o,m),n.disposeIntermediateTensorInfo(y)}return f}var vO={kernelName:li,backendName:"cpu",kernelFunc:bO};function wO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=z7({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c}});if(i){let y=f;f=md({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(y)}if(h){let y=f;f=l1(n,f,h,o,m),n.disposeIntermediateTensorInfo(y)}return f}var kO={kernelName:ui,backendName:"cpu",kernelFunc:wO};function IO(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=k.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,u,d,p]=R.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let c=We([u,d],a.dtype),h=n.data.get(r.dataId).values,m=n.data.get(a.dataId).values;for(let f=0;f=s/d)throw new Error(`Invalid indices: ${y} does not index into ${a.shape}`);for(let g=0;ge>=t?1:0),CO=Xt(Cs,EO,null,"bool"),RO={kernelName:Cs,backendName:"cpu",kernelFunc:CO};function MO(e){let{inputs:t,backend:n}=e,{input:a}=t,r=k.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=ht({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=_7(o,!0,n),u=ht({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var FO={kernelName:tc,backendName:"cpu",kernelFunc:MO},$O=rt(Mo,e=>Number.isFinite(e)?1:0,"bool"),DO={kernelName:Mo,backendName:"cpu",kernelFunc:$O},zO=rt(Fo,e=>Math.abs(e)===Infinity?1:0,"bool"),OO={kernelName:Fo,backendName:"cpu",kernelFunc:zO},_O=rt($o,e=>Number.isNaN(e)?1:0,"bool"),PO={kernelName:$o,backendName:"cpu",kernelFunc:_O},LO=Dt((e,t)=>e<=t?1:0),WO=Xt(zo,LO,null,"bool"),BO={kernelName:zo,backendName:"cpu",kernelFunc:WO};function VO(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=o7(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var jO={kernelName:ac,backendName:"cpu",kernelFunc:VO},UO=rt(Oo,e=>Math.log1p(e)),HO={kernelName:Oo,backendName:"cpu",kernelFunc:UO},GO=Dt((e,t)=>e&&t),qO=Xt(_o,GO,null,"bool"),XO={kernelName:_o,backendName:"cpu",kernelFunc:qO},KO=rt($u,e=>e?0:1,"bool"),ZO={kernelName:$u,backendName:"cpu",kernelFunc:KO},YO=Dt((e,t)=>e||t),JO=Xt(Du,YO,null,"bool"),QO={kernelName:Du,backendName:"cpu",kernelFunc:JO};function e_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ve(r,"LRN");let u=r.shape[3],d=u-1,p=n.data.get(r.dataId).values,c=k.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let y=f%u,A=f-y+Math.max(0,y-s),g=f-y+Math.min(y+s,d),x=0;for(;A<=g;A++){let w=p[A];x+=w*w}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l),p;if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))p=qa({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=u1(c,r.shape,r.dtype,h,d,"max");p=n.makeTensorInfo(d.outShape,r.dtype,m.values)}return p}var i_={kernelName:zs,backendName:"cpu",kernelFunc:s_};function o_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;ve(r,"maxPool3d");let d=R.computePool3DInfo(r.shape,s,i,1,o,l,u),p=n.data.get(r.dataId).values,c=$7(p,r.shape,r.dtype,k.computeStrides(r.shape),d,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var l_={kernelName:Ou,backendName:"cpu",kernelFunc:o_};function u_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;ve([r,s],"maxPool3DGrad");let d=R.computePool3DInfo(s.shape,i,o,1,l,u),p=n.bufferSync(s),c=GD(p,d),h=d.strideDepth,m=d.strideHeight,f=d.strideWidth,y=d.dilationDepth,A=d.dilationHeight,g=d.dilationWidth,x=d.effectiveFilterDepth,w=d.effectiveFilterHeight,b=d.effectiveFilterWidth,v=x-1-d.padInfo.front,S=b-1-d.padInfo.left,T=w-1-d.padInfo.top,C=We(s.shape,"float32"),$=n.bufferSync(r);for(let O=0;O=d.outDepth||Math.floor(ne)!==ne))for(let ie=0;ie=d.outHeight||Math.floor(Q)!==Q))for(let pe=0;pe=d.outWidth||Math.floor(oe)!==oe)continue;let ge=x*w*b-1-c.get(O,ne,Q,oe,P),he=re*w*b+ie*b+pe,Se=ge===he?1:0;Se!==0&&(Y+=$.get(O,ne,Q,oe,P)*Se)}}}C.set(Y,O,j,D,U,P)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var d_={kernelName:ic,backendName:"cpu",kernelFunc:u_};function p_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ve([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=R.computePool2DInfo(o.shape,l,u,1,d,p),h=n.data.get(o.dataId).values,m=We(c.outShape,o.dtype,F7(h,o.shape,o.dtype,c).values),f=c.strideHeight,y=c.strideWidth,A=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,w=c.effectiveFilterWidth,b=w-1-c.padInfo.left,v=x-1-c.padInfo.top,S=We(o.shape,"float32"),T=n.data.get(r.dataId).values,C=We(r.shape,"float32",T);for(let $=0;$=c.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y=c.outWidth||Math.floor(re)!==re)continue;let ne=x*w-1-m.get($,ee,re,O),ie=G*w+Y,Q=ne===ie?1:0;Q!==0&&(X+=C.get($,ee,re,O)*Q)}}S.set(X,$,P,j,O)}return n.makeTensorInfo(S.shape,S.dtype,S.values)}var c_={kernelName:sc,backendName:"cpu",kernelFunc:p_};function h_(e,t,n,a,r){let s=k.computeStrides(t),i=u1(e,t,n,s,r,"max"),o=F7(e,t,n,r,!0,a);return[i.values,o.values]}var f_={kernelName:oc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ve(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,d=R.computePool2DInfo(a.shape,r,s,[1,1],i),[p,c]=h_(u,a.shape,a.dtype,o,d),h=l.write(p,d.outShape,a.dtype),m=l.write(c,d.outShape,a.dtype);return[{dataId:h,shape:d.outShape,dtype:a.dtype},{dataId:m,shape:d.outShape,dtype:"int32"}]}};function m_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),l=R.computeOutAndReduceShapes(r.shape,o)[1],u=k.sizeFromShape(l),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([u]));d.push(p);let c=Hr({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(c);let h=d1({inputs:{a:c,b:p},backend:n});d.push(h);let m=yd({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var y_={kernelName:Os,backendName:"cpu",kernelFunc:m_};function A_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"min");let o=k.parseAxisParam(s,r.shape),l=o,u=R.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=ta({inputs:{x:r},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,r.shape.length)),R.assertAxesAreInnerMostDims("min",l,d.shape.length);let[p,c]=R.computeOutAndReduceShapes(d.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let A=0;Ag[0]+r.shape[x]+g[1]),l=s.map(g=>g[0]),u=s.map((g,x)=>g[0]+r.shape[x]),d=i==="reflect"?0:1,p=n.data.get(r.dataId).values,c=r.shape.length,h=k.computeStrides(r.shape),m=k.sizeFromShape(o),f=o.length,y=k.computeStrides(o),A=k.getTypedArrayFromDType(r.dtype,m);for(let g=0;g=u[b]&&(x[b]=(u[b]-1)*2-x[b]+d);x=x.map((b,v)=>b-l[v]);let w=k.locToIndex(x,c,h);A[g]=p[w]}return{dataId:n.write(A,o,r.dtype),shape:o,dtype:r.dtype}}var b_={kernelName:Ls,backendName:"cpu",kernelFunc:x_},v_=Dt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),w_=Xt(Po,v_),k_={kernelName:Po,backendName:"cpu",kernelFunc:w_},I_=ro(h5());function L7(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=k.parseAxisParam([o],r.shape),u=P7({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),d=R.expandShapeToKeepDim(u.shape,l),p=ht({inputs:{x:u},backend:n,attrs:{shape:d}}),c=o1({inputs:{a:r,b:p},backend:n}),h=t7({inputs:{x:c},backend:n}),m=yd({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=ht({inputs:{x:m},backend:n,attrs:{shape:d}}),y=d1({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),y}var S_={kernelName:ti,backendName:"cpu",kernelFunc:L7};function N_(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;ve(r,"multinomial");let l=o?r:L7({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],d=l.shape[1],p=n.data.get(l.dataId).values,c=[u,s],h=k.makeZerosTypedArray(k.sizeFromShape(c),"int32");for(let m=0;m=0&&d[p]{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let p=Ah({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=zl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var B_={kernelName:Ho,backendName:"cpu",kernelFunc:B7};function V_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ve(r,"pad");let o=s.map((A,g)=>A[0]+r.shape[g]+A[1]),l=s.map(A=>A[0]),u=n.data.get(r.dataId).values,d=k.sizeFromShape(r.shape),p=r.shape.length,c=k.computeStrides(r.shape),h=k.sizeFromShape(o),m=o.length,f=k.computeStrides(o),y=k.getTypedArrayFromDType(r.dtype,h);i!==0&&y.fill(i);for(let A=0;Aw+l[b]),x=k.locToIndex(g,m,f);y[x]=u[A]}return{dataId:n.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var V7={kernelName:Vs,backendName:"cpu",kernelFunc:V_},j_=Dt((e,t)=>Math.pow(e,t)),U_=Xt(js,j_),H_={kernelName:js,backendName:"cpu",kernelFunc:U_};function G_(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=i1(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var q_={kernelName:_u,backendName:"cpu",kernelFunc:G_},X_=rt(qo,e=>1/e),K_={kernelName:qo,backendName:"cpu",kernelFunc:X_};function Z_(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ve(r,"resizeBilinear");let l=k.computeStrides(r.shape),[u,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,y=new Float32Array(k.sizeFromShape([p,u,d,m])),A=[s&&u>1?c-1:c,s&&d>1?h-1:h],g=[s&&u>1?u-1:u,s&&d>1?d-1:d],x=0,w=A[0]/g[0],b=A[1]/g[1];for(let v=0;v1?u-1:u,i&&h>1?d-1:d],y=[i&&c>1?c-1:c,i&&h>1?h-1:h],A=f[0]/y[0],g=f[1]/y[1],x=n.data.get(s.dataId).values,w=0;for(let b=0;b1?c-1:c,s&&d>1?h-1:h],g=[s&&u>1?u-1:u,s&&d>1?d-1:d],x=A[0]/g[0],w=A[1]/g[1],b=0;for(let v=0;v1?d-1:d,i&&m>1?p-1:p],g=[i&&h>1?h-1:h,i&&m>1?m-1:m],x=A[0]/g[0],w=A[1]/g[1],b=1/x,v=1/w,S=Math.ceil(b)*2+2,T=Math.ceil(v)*2+2;for(let C=0;C=h)continue;let Q=$+ie*l[1],pe=ie*x,oe=Math.min(d-1,i?Math.round(pe):Math.floor(pe));if(O===oe)for(let ge=0;ge=m)continue;let Se=Q+he*l[2],Te=he*w,$e=Math.min(p-1,i?Math.round(Te):Math.floor(Te));U===$e&&(re+=y[Se+Y])}}f[X+Y]=re}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var aP={kernelName:dc,backendName:"cpu",kernelFunc:nP};function rP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;ve(r,"reverse");let i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return qa({inputs:{x:r},backend:n});let l=new Pt(r.shape,r.dtype),u=n.bufferSync(r);for(let d=0;dc[h]=r.shape[h]-1-c[h]),l.set(u.get(...c),...p)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var sP={kernelName:Xs,backendName:"cpu",kernelFunc:rP},iP={kernelName:ll,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[u,d,p,c]=a.shape,[h,m]=R.getImageCenter(i,d,p),f=255,y=Math.sin(r),A=Math.cos(r),g=o.data.get(a.dataId).values;for(let x=0;x=0&&j=0&&D{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),lP={kernelName:Ks,backendName:"cpu",kernelFunc:oP};function j7(e,t,n,a,r,s,i,o,l,u){let d=[a/r,r],p=e.values,c=t.values;if(a===0)return We(n,t.dtype);let h=We(d,t.dtype);h.values.fill(l);for(let m=0;m=a/r)throw new Error(`Invalid indices: ${f} does not index into ${n}`);for(let A=0;A1||r.shape.length===1?1:k.sizeFromShape(r.shape.slice(1));for(let m=0;me>=0?fP*e:hP*(Math.exp(e)-1)),yP={kernelName:Yo,backendName:"cpu",kernelFunc:mP},AP=rt(el,e=>e<0?-1:e>0?1:0),gP={kernelName:el,backendName:"cpu",kernelFunc:AP},xP=rt(Ys,e=>Math.sin(e)),bP={kernelName:Ys,backendName:"cpu",kernelFunc:xP},vP=rt(Qo,e=>Math.sinh(e)),wP={kernelName:Qo,backendName:"cpu",kernelFunc:vP},kP=11920928955078125e-23,U7=Math.log(kP)+2,IP=rt(tl,e=>{let t=e>-U7,n=eNumber(y)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var CP={kernelName:cc,backendName:"cpu",kernelFunc:EP};function RP(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.data.get(r.dataId).values),o=n.data.get(a.dataId).values,l=Array.from(n.data.get(s.dataId).values),[u,d,p]=A7(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var MP={kernelName:hc,backendName:"cpu",kernelFunc:RP};function FP(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:d,strides:p,outputSize:c}=R.calculateShapes(s,r,o),h=!1,m=n.bufferSync(r),f=n.bufferSync(s),y=n.data.get(i.dataId).values[0],A=j7(m,f,o,c,d,u,l,p,y,h);return n.makeTensorInfo(o,A.dtype,A.values)}var $P={kernelName:fc,backendName:"cpu",kernelFunc:FP};function DP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=R.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),d=r.shape.slice();return l.map(p=>{let c=[...d];c[o]=p;let h=Ti({inputs:{x:r},backend:n,attrs:{begin:u,size:c}});return u[o]+=p,h})}var zP={kernelName:nl,backendName:"cpu",kernelFunc:DP},OP=rt(Qs,e=>Math.sqrt(e)),_P={kernelName:Qs,backendName:"cpu",kernelFunc:OP},PP={kernelName:Wu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;ve(n,"square");let r=a.data.get(n.dataId).values,s=new Float32Array(r.length);for(let i=0;i{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),WP={kernelName:Dr,backendName:"cpu",kernelFunc:LP};function BP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a;ve(r,"stridedSlice");let{nonStrided:h,$begin:m,$strides:f,size:y,newShape:A,outShape:g}=un.sliceInfo(r.shape,s,i,o,l,u,d,p,c),x=ht({inputs:{x:r},backend:n,attrs:{shape:A}}),w;if(h){let v=Ti({inputs:{x},backend:n,attrs:{begin:m,size:y}});w=ht({inputs:{x:v},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(v)}else if(g.some(v=>v===0))w=n.makeTensorInfo(g,r.dtype,[]);else{let v=n.bufferSync(x),S=x7(g,v,f,m);w=n.makeTensorInfo(S.shape,S.dtype,S.values)}let b=ht({inputs:{x:w},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),b}var VP={kernelName:al,backendName:"cpu",kernelFunc:BP},jP=rt(ri,e=>Math.tan(e)),UP={kernelName:ri,backendName:"cpu",kernelFunc:jP},HP=rt(si,e=>Math.tanh(e)),GP={kernelName:si,backendName:"cpu",kernelFunc:HP};function qP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;ve(r,"tile");let i=v7(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var XP={kernelName:$r,backendName:"cpu",kernelFunc:qP};function KP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;ve(r,"topk");let o=n.data.get(r.dataId).values,[l,u]=w7(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var ZP={kernelName:rl,backendName:"cpu",kernelFunc:KP};function YP(e){let{inputs:t,attrs:n,backend:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],y=[d,m,f,h],A=k.computeStrides(r.shape),g=A[0],x=A[1],w=A[2],b=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(y));b.fill(l);let v=a.data.get(r.dataId).values,S=a.data.get(s.dataId).values;for(let T=0;Tt-1)if(t<=1)n=0;else{let a=2*t;n-=a*Math.trunc(n/a),n>=t&&(n=a-n-1)}return k.clamp(0,n,t-1)}function eL(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let a=t-1;n+=t*(Math.trunc(-n/a)+1)}else if(n>t-1)if(t<=1)n=0;else{let a=t-1;n-=t*Math.trunc(n/a)}return k.clamp(0,n,t-1)}function tL(e,t){return e}function nL(e,t){return k.clamp(0,e,t-1)}function Ad(e,t,n,a,r,s,i,o,l,u,d){let p=i*a+o*r+l*s+u;return 0<=o&&on.disposeIntermediateTensorInfo(m)),h}var dL={kernelName:Bu,backendName:"cpu",kernelFunc:uL},pL=[bD,I$,wD,ID,R$,ND,ED,RD,FD,DD,OD,PD,WD,jD,HD,XD,ZD,JD,ez,gD,nz,rz,iz,E$,F$,lz,S$,dz,cz,mz,Az,hz,vz,kz,xz,Sz,Tz,Cz,Mz,$z,zz,Oz,Pz,Wz,Vz,jz,Hz,Uz,p1,Xz,dD,Zz,Jz,iO,$$,oO,z$,hO,fO,yO,_$,xO,vO,kO,SO,TO,L$,RO,N$,FO,pz,DO,OO,PO,pD,B$,BO,jO,j$,HO,XO,ZO,QO,t_,a_,H$,i_,l_,d_,c_,f_,r_,y_,g_,q$,b_,k_,T_,K$,Y$,R_,$_,O_,Q$,P_,W_,B_,V7,H_,hD,nD,q_,T$,K_,fD,mD,AD,Y_,Q_,tP,aP,sP,iP,lP,rD,dP,cP,yP,yD,gP,bP,wP,sD,S_,SP,TP,CP,MP,$P,zP,_P,PP,oD,WP,VP,uD,Gz,UP,GP,XP,ZP,eD,JP,iL,lL,dL,L_];for(let e of pL)di(e);var G7={};Fe(G7,{assertNotComplex:()=>_l,bindCanvasToFramebuffer:()=>kL,bindColorTextureToFramebuffer:()=>vh,bindTextureToProgramUniformSampler:()=>ov,bindTextureUnit:()=>rv,bindVertexBufferToProgramAttribute:()=>y1,callAndCheck:()=>xe,canBeRepresented:()=>q7,createFragmentShader:()=>Z7,createFramebuffer:()=>av,createProgram:()=>Y7,createStaticIndexBuffer:()=>ev,createStaticVertexBuffer:()=>Q7,createTexture:()=>tv,createVertexShader:()=>K7,getBatchDim:()=>Ci,getExtensionOrThrow:()=>vd,getFramebufferErrorMessage:()=>lv,getMaxTexturesInShader:()=>cv,getNumChannels:()=>vL,getProgramUniformLocation:()=>iv,getProgramUniformLocationOrThrow:()=>sv,getRowsCols:()=>Ri,getShapeAs3D:()=>wh,getTextureShapeFromLogicalShape:()=>dv,getWebGLDisjointQueryTimerVersion:()=>hv,getWebGLErrorMessage:()=>X7,getWebGLMaxTextureSize:()=>pv,hasExtension:()=>aa,isCapableOfRenderingToFloatTexture:()=>fv,isDownloadFloatTextureEnabled:()=>mv,isReshapeFree:()=>kd,isWebGLFenceEnabled:()=>yv,isWebGLVersionEnabled:()=>g1,linkProgram:()=>J7,resetMaxTextureSize:()=>IL,resetMaxTexturesInShader:()=>SL,unbindColorTextureFromFramebuffer:()=>A1,unbindTextureUnit:()=>wL,validateFramebuffer:()=>wd,validateProgram:()=>bh,validateTextureSize:()=>nv});var Ei={},f1={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function xh(e,t){Ei[e]=t}function Xa(e){if(!(e in Ei)){let n=hL(e);if(n!==null)Ei[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Ei[e];return t.isContextLost()?(delete Ei[e],Xa(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),Ei[e])}function cL(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 hL(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=cL(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Ei[e]},!1),e===1?t.getContext("webgl",f1)||t.getContext("experimental-webgl",f1):t.getContext("webgl2",f1)}var gd;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(gd||(gd={}));var na;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(na||(na={}));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 xd(e,t){return[t,e]}function fL(e,t){return e*t}function bd(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Ol(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function mL(e,t){let[n,a]=Ol(e,t);return n*a*4}function m1(e,t){let n=e,a,r,s,i,o,l,u,d,p,c;return J().getNumber("WEBGL_VERSION")===2?(a=n.R32F,r=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,u=4,d=1,p=n.HALF_FLOAT,c=n.FLOAT):(a=e.RGBA,r=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,u=4,d=4,p=t!=null?t.HALF_FLOAT_OES:null,c=e.FLOAT),l=e.RGBA,{internalFormatFloat:a,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:d,textureTypeHalfFloat:p,textureTypeFloat:c}}function xe(e,t){let n=t();return J().getBool("DEBUG")&&yL(e),n}function yL(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+X7(e,t))}var AL=596e-10,gL=65504;function q7(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||ALe.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function K7(e,t){let n=yr(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(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 Z7(e,t){let n=yr(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw bL(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var xL=/ERROR: [0-9]+:([0-9]+):/g;function bL(e,t){let n=xL.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let a=+n[1],r=e.split(` `),s=r.length.toString().length+2,i=r.map((p,c)=>k.rightPad((c+1).toString(),s)+p),o=0;for(let p=0;pe.createProgram(),"Unable to create WebGLProgram.")}function J7(e,t){if(xe(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function bh(e,t){if(xe(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function Q7(e,t){let n=yr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function ev(e,t){let n=yr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function vL(){return J().getNumber("WEBGL_VERSION")===2?1:4}function tv(e){return yr(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function nv(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${e}x${t}]`;throw new Error("Requested texture size "+a+" is invalid.")}if(e>n||t>n){let a=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+a+" greater than WebGL maximum on this browser / GPU "+r+".")}}function av(e){return yr(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function y1(e,t,n,a,r,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),xe(e,()=>e.vertexAttribPointer(o,r,e.FLOAT,!1,s,i)),xe(e,()=>e.enableVertexAttribArray(o)),!0)}function rv(e,t,n){uv(e,n),xe(e,()=>e.activeTexture(e.TEXTURE0+n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function wL(e,t){uv(e,t),xe(e,()=>e.activeTexture(e.TEXTURE0+t)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function sv(e,t,n){return yr(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function iv(e,t,n){return e.getUniformLocation(t,n)}function ov(e,t,n,a){xe(e,()=>rv(e,t,a)),xe(e,()=>e.uniform1i(n,a))}function kL(e){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),xe(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function vh(e,t,n){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function A1(e,t){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function wd(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+lv(e,t))}function lv(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 ${t}`}}function yr(e,t,n){let a=xe(e,()=>t());if(a==null)throw new Error(n);return a}function uv(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(an){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function Ci(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function Ri(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 wh(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Ci(e),...Ri(e)]),t}function dv(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,s)=>s>=e.length-2?k.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=k.squeezeShape(e).newShape);let a=k.sizeFromShape(e);if(e.length<=1&&a<=n)return[1,a];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=Ci(e),s=2,i=2;return e.length&&([s,i]=Ri(e)),a=r*(s/2)*(i/2),k.sizeToSquarishShape(a).map(o=>o*2)}return k.sizeToSquarishShape(a)}function kh(e){return e%2==0}function kd(e,t){if(e=e.slice(-2),t=t.slice(-2),k.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],a=t.slice(-1)[0];if(n===a||kh(n)&&kh(a)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&kh(e[0])&&kh(t[0])}var Ih,Sh;function pv(e){if(Ih==null){let t=Xa(e);Ih=t.getParameter(t.MAX_TEXTURE_SIZE)}return Ih}function IL(){Ih=null}function SL(){Sh=null}function cv(e){if(Sh==null){let t=Xa(e);Sh=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Sh)}function hv(e){if(e===0)return 0;let t,n=Xa(e);return aa(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:aa(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function aa(e,t){return e.getExtension(t)!=null}function g1(e){try{if(Xa(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function fv(e){if(e===0)return!1;let t=Xa(e);if(e===1){if(!aa(t,"OES_texture_float"))return!1}else if(!aa(t,"EXT_color_buffer_float"))return!1;return x1(t)}function mv(e){if(e===0)return!1;let t=Xa(e);if(e===1){if(!aa(t,"OES_texture_float")||!aa(t,"WEBGL_color_buffer_float"))return!1}else{if(aa(t,"EXT_color_buffer_float"))return x1(t);let n="EXT_color_buffer_half_float";if(aa(t,n)){let a=t.getExtension(n);return NL(t,a)}return!1}return x1(t)}function x1(e){let t=m1(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function NL(e,t){let n=m1(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function yv(e){return e!==2?!1:Xa(e).fenceSync!=null}function _l(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 Me=J();Me.registerFlag("HAS_WEBGL",()=>Me.getNumber("WEBGL_VERSION")>0);Me.registerFlag("WEBGL_VERSION",()=>g1(2)?2:g1(1)?1:0);Me.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Me.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Me.get("WEBGL_VERSION")===2);Me.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Me.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Me.registerFlag("WEBGL_PACK",()=>Me.getBool("HAS_WEBGL"));Me.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_CLIP",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_REDUCE",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_LAZILY_UNPACK",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_CONV_IM2COL",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>pv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>cv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Me.getNumber("WEBGL_VERSION");return e===0?0:hv(e)});Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Me.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Zu.isMobile());Me.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>fv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Me.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Me.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Me.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>mv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_FENCE_API_ENABLED",()=>yv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Me.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Me.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}.`)});Me.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Zu.isMobile()&&Me.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}.`)});function hn(){let e,t,n,a,r,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=` bool isnan_custom(float val) { 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",a="varying",r="texture2D",s="gl_FragColor",i="",o=` #define isnan(value) isnan_custom(value) bool isnan_custom(float val) { return (val > 0. || val < 1. || val == 0.) ? false : true; } bvec4 isnan_custom(vec4 val) { return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w)); } `,l=` uniform float INFINITY; bool isinf(float val) { return abs(val) == INFINITY; } bvec4 isinf(vec4 val) { return equal(abs(val), vec4(INFINITY)); } `,u=` int round(float value) { return int(floor(value + 0.5)); } ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } `),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Mi(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function b1(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 Av=` 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; } `,TL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gd.DENSE;let t=bd(e),n=hn();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${Mi(["r","c","d"],e)} return ivec3(r, c, d); } void main() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${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; } `}},EL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=gd.DENSE;let t=bd(e),n=hn();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${Mi(["r","c","d"],e)} return ivec3(r, c, d); } void main() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${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; } `}},CL=class{constructor(e){this.variableNames=["A"],this.outTexUsage=na.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=` ${Av} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } `}},RL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=na.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=` ${Av} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } `}},ML=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=hn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=` ${b1(e)} void main() { ivec3 coords = getOutputCoords(); int flatIndex = getFlatIndex(coords); int offset = imod(flatIndex, 4); flatIndex = idiv(flatIndex, 4, 1.); int r = flatIndex / ${s}; int c = imod(flatIndex, ${s}); vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0); vec4 values = ${a.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]; } ${a.output} = vec4(${i}, 0., 0., 0.); } `}},FL=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=hn(),[r,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let d=l*2+u;i+=` 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 / ${s}; c = imod(flatIndex, ${s}); uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0); values = ${a.texture2D}(A, uv); if(offset == 0) { result[${d}] = values[0]; } else if(offset == 1) { result[${d}] = values[1]; } else if(offset == 2) { result[${d}] = values[2]; } else { result[${d}] = values[3]; } } } `}this.userCode=` ${b1(e)} void main() { ivec3 coords = getOutputCoords(); vec4 result = vec4(0.); int flatIndex, r, c, offset; ivec3 localCoords; vec2 uv; vec4 values; ${i} ${a.output} = ${o}; } `}},gv={};Fe(gv,{bindVertexProgramAttributeStreams:()=>Tv,createBufferFromOutputTexture:()=>Rv,createFloat16MatrixTexture:()=>kv,createFloat16PackedMatrixTexture:()=>Nv,createFloat32MatrixTexture:()=>wv,createIndexBuffer:()=>vv,createPackedMatrixTexture:()=>Sv,createUnsignedBytesMatrixTexture:()=>Iv,createVertexBuffer:()=>bv,createVertexShader:()=>xv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Fv,downloadFloat32MatrixFromBuffer:()=>Mv,downloadMatrixFromPackedOutputTexture:()=>Dv,downloadPackedMatrixFromBuffer:()=>$v,getInternalFormatForFloat16MatrixTexture:()=>w1,getInternalFormatForFloat16PackedMatrixTexture:()=>S1,getInternalFormatForFloat32MatrixTexture:()=>v1,getInternalFormatForPackedMatrixTexture:()=>I1,getInternalFormatForUnsignedBytesMatrixTexture:()=>k1,uploadDenseMatrixToTexture:()=>Ev,uploadPixelDataToTexture:()=>Cv});function xv(e){let t=hn(),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 K7(e,n)}function bv(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 Q7(e,t)}function vv(e){let t=new Uint16Array([0,1,2,2,1,3]);return ev(e,t)}function Id(e,t,n,a,r,s){nv(t,n);let i=tv(e),o=e.TEXTURE_2D;return xe(e,()=>e.bindTexture(o,i)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),xe(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function v1(e){return e.internalFormatFloat}function wv(e,t,n,a){let[r,s]=xd(t,n);return Id(e,r,s,v1(a),a.textureFormatFloat,e.FLOAT)}function w1(e){return e.internalFormatHalfFloat}function kv(e,t,n,a){let[r,s]=xd(t,n);return Id(e,r,s,w1(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function k1(e){return e.downloadTextureFormat}function Iv(e,t,n,a){let[r,s]=xd(t,n);return Id(e,r,s,k1(a),e.RGBA,e.UNSIGNED_BYTE)}function I1(e){return e.internalFormatPackedFloat}function Sv(e,t,n,a){let[r,s]=Ol(t,n);return Id(e,r,s,I1(a),e.RGBA,e.FLOAT)}function S1(e){return e.internalFormatPackedHalfFloat}function Nv(e,t,n,a){let[r,s]=Ol(t,n);return Id(e,r,s,S1(a),e.RGBA,a.textureTypeHalfFloat)}function Tv(e,t,n){let a=0,r=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),y1(e,t,"clipSpacePos",n,3,s,a)&&y1(e,t,"uv",n,2,s,r)}function Ev(e,t,n,a,r,s){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Cv(e,t,n){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Rv(e,t,n,a){let r=e.createBuffer();xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return xe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function Mv(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function Fv(e,t,n,a){let[r,s]=xd(t,n),i=4,o=new Uint8Array(fL(t*n,i));return xe(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function $v(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(mL(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Dv(e,t,n){let a=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var Nh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,xh(t,e)):this.gl=Xa(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=vd(this.gl,r),aa(this.gl,s))this.textureHalfFloatExtension=vd(this.gl,s);else if(J().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),aa(this.gl,a))this.colorBufferHalfFloatExtension=vd(this.gl,a);else if(J().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",aa(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(aa(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=bv(this.gl),this.indexBuffer=vv(this.gl),this.framebuffer=av(this.gl),this.textureConfig=m1(this.gl,this.textureHalfFloatExtension)}get debug(){return J().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;xe(e,()=>e.finish()),xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.deleteFramebuffer(this.framebuffer)),xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),xe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),wv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),kv(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Iv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Cv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),Ev(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Nv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Sv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(A1(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Fv(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return $v(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Mv(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=Rv(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Dv(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Z7(t,e);this.vertexShader==null&&(this.vertexShader=xv(t));let a=Y7(t);return xe(t,()=>t.attachShader(a,this.vertexShader)),xe(t,()=>t.attachShader(a,n)),J7(t,a),this.debug&&bh(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Tv(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&bh(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?sv(this.gl,e,t):iv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),xe(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(),ov(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Ol(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&bh(this.gl,this.program),wd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),xe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),xe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=vd(this.gl,J().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(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().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,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=$L(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(),vh(this.gl,e,this.framebuffer),this.debug&&wd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(vh(this.gl,this.outputTexture,this.framebuffer),this.debug&&wd(this.gl)):A1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;vh(a,e,this.framebuffer),this.debug&&wd(a),this.outputTexture=e,xe(a,()=>a.viewport(0,0,t,n)),xe(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function $L(e){let t=0;for(;t{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(` `),i=e.map(h=>zL(h,t,a)).join(` `),o=t.texShape,l=hn(),u=PL(l),d,p,c=BL(l);return t.isPacked?(d=OL(t.logicalShape,o),p=WL(l)):(d=_L(t.logicalShape,o),p=LL(l)),a&&(c+=HL),[c,u,p,s,d,i,n].join(` `)}function Pl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return aW(e);case 1:return sW(e);case 2:return oW(e);case 3:return uW(e);case 4:return pW(e);case 5:return cW(e);case 6:return hW(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Ov(e){switch(e.shapeInfo.logicalShape.length){case 0:return nW(e);case 1:return rW(e);case 2:return iW(e);case 3:return lW(e);default:return dW(e)}}function zL(e,t,n=!1){let a="";n?a+=Ov(e):a+=Pl(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=fW(e,t):a+=mW(e,t)),a}function OL(e,t){switch(e.length){case 0:return _v();case 1:return GL(e,t);case 2:return eW(e,t);case 3:return XL(e,t);default:return ZL(e,t)}}function _L(e,t){switch(e.length){case 0:return _v();case 1:return qL(e,t);case 2:return tW(e,t);case 3:return KL(e,t);case 4:return YL(e,t);case 5:return JL(e,t);case 6:return QL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function PL(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } `}function LL(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } `}function WL(e){return` void setOutput(vec4 val) { ${e.output} = val; } `}function BL(e){return`${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); } ${VL} ${jL} ${UL} `}var VL=` 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); } `,jL=` 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); } `,UL=` 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); } `,HL=` 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 _v(){return` int getOutputCoords() { return 0; } `}function GL(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 qL(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 * ${t[1]} + resTexRC.y; } `}function XL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]})); int index = resTexRC.x * ${n[1]} + resTexRC.y; int b = index / ${r}; index -= b * ${r}; int r = 2 * (index / ${a}); int c = imod(index, ${a}) * 2; return ivec3(b, r, c); } `}function KL(e,t){let n=Mi(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); int index = resTexRC.x * ${t[1]} + resTexRC.y; ${n} return ivec3(r, c, d); } `}function ZL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l=1?d="coords = 0;":d=o.map(y=>`coords.${p[y+u]} = 0;`).join(` `);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((y,A)=>`coords.${p[A+u]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` return vec4(outputValue.x, outputValue.x, 0., 0.); `:h=` return vec4(outputValue.x); `;else if(o.length){let y=s-2,A=s-1;o.indexOf(y)>-1&&o.indexOf(A)>-1?h="return vec4(outputValue.x);":o.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(A)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return` vec4 ${r}() { ${l} coords = getOutputCoords(); ${d} vec4 outputValue = get${a}(${c}); ${h} } `}function mW(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return` float ${r}() { return sampleTexture(${n}, resultUV); } `;let u=ut(l),d=zv(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&d.length>=1?c="coords = 0;":c=d.map(f=>`coords.${h[f+p]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,y)=>`coords.${h[y+p]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${c} return get${a}(${m}); } `}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Wl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Bl(e,t){return t.map(n=>e[n]).join(", ")}function yW(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=DL(s,o,r,t.packedInputs),u=e.createProgram(l),d=null,p=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(u,"INFINITY",!1));let c={};for(let h=0;h{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function AW(e,t,n,a,r){Pv(t.inShapeInfos,n),Pv([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`];if(d!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(d,o.uniformValues[0]);else{let c=o.uniformValues;c instanceof Float32Array||(c=new Float32Array(c)),e.gl.uniform1fv(d,c)}return}o.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,d,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function gW(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:xW,bincountImpl:Lv,bincountReduceImpl:bW,ceilImpl:vW,concatImpl:wW,expImpl:kW,expm1Impl:IW,floorImpl:SW,gatherV2Impl:NW,greaterImpl:TW,lessImpl:EW,linSpaceImpl:CW,logImpl:RW,maxImpl:MW,maximumImpl:FW,minimumImpl:$W,multiplyImpl:DW,negImpl:zW,prodImpl:OW,rangeImpl:_W,rsqrtImpl:PW,simpleAbsImpl:Wv,sliceImpl:LW,sparseFillEmptyRowsImpl:WW,sparseReshapeImpl:BW,stridedSliceImpl:VW,subImpl:jW,tileImpl:UW,topKImpl:HW,transposeImpl:N1,uniqueImpl:GW}=e1;function Bv(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function fn(e,t){return t===1?[e]:Bv(e,t)}function qW(e,t){if(e===1)return"rc";let n="";for(let a=0;a ${t[0]}`;let a="";for(let r=e-2;r= ${t[r]}`,r= ${t}; bool rEdge = rp1 >= ${n}; `}function JW(e,t){let n=e.length,a=KW(n,t);return n===1?`getA(rc), rc + 1 >= ${e[0]} ? 0. : getA(rc + 1), 0, 0`:`getA(${a[0]}), cEdge ? 0. : getA(${a[1]}), rEdge ? 0. : getA(${a[2]}), rEdge || cEdge ? 0. : getA(${a[3]})`}var Vv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` ${r} ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); result[${a}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${a>0?"}":""} `}this.userCode=` ${QW(t)} ${b1(e)} void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0.); ivec3 thisRC; int rows = ${e[1]}; int cols = ${e[2]}; ${n} setOutput(result); } `}};function QW(e){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { ${Mi(["r","c","d"],e)} return ivec3(r, c, d); } `}var eB=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=Uv(t,n),r=Hv(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=jv(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===tn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===tn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===tn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===tn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===tn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=Uv(n,a),s=Hv(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=jv(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});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 tB(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function jv(e,t,n,a,r){let s=nB(t,a),i;if(r){let[l,u]=Ol(e[0],e[1]);i=l*u}else{let[l,u]=xd(e[0],e[1]);i=l*u}let o=tB(n,s);return i*o}function nB(e,t){switch(e){case tn.PACKED_2X2_FLOAT32:return I1(t);case tn.PACKED_2X2_FLOAT16:return S1(t);case tn.UNPACKED_FLOAT32:return v1(t);case tn.UNPACKED_FLOAT16:return w1(t);case tn.PACKED_4X1_UNSIGNED_BYTE:return k1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function aB(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?tn.PACKED_2X2_FLOAT32:tn.UNPACKED_FLOAT32:e?tn.PACKED_2X2_FLOAT16:tn.UNPACKED_FLOAT16}function Uv(e,t){if(e===na.UPLOAD)return tn.PACKED_2X2_FLOAT32;if(e===na.RENDER||e==null)return aB(t);if(e===na.DOWNLOAD||e===na.PIXELS)return tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Hv(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Gr=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); } `}},ka="if (isnan(x)) return x;",rB="return x;",Gv="return abs(x);",sB="return (x >= 0.0) ? x : (exp(x) - 1.0);",iB=ka+` return (x < 0.0) ? 0.0 : x; `,oB=ka+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Th="return x;",lB="return 1.0 / (1.0 + exp(-1.0 * x));",uB="return x;",dB=` 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; `,pB=` 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; `,cB=` 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; `,hB="return 1.0 / (1.0 + exp(-1.0 * x));",Vl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}},fB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=fn("rc",t),a=ut(t),r=qW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${a} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${i})); } `}},mB=Ga.whereImpl,yB=1e-7,AB=1e-4,T1={};function gB(e){return e in T1||(T1[e]={}),T1[e]}var xB=128,bB=600;function vB(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*bB/1024/1024}var jl=class extends ku{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Xa(J().getNumber("WEBGL_VERSION"));this.binaryCache=gB(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Nh(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 eB(this.gpgpu),this.numMBBeforeWarning=vB(),this.texData=new zp(this,dr())}nextDataId(){return jl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:na.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:na.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new Vl(i,Th):p=new Gr(i,Th);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=k.now());let d;if(a==="complex64"){let p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);d=R.mergeRealAndImagArrays(p,c)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Vl(a,Th):h=new Gr(a,Th);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...bd(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];d=R.mergeRealAndImagArrays(m,f)}else if(l==null)d=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}u!=null&&this.disposeIntermediateTensorInfo(u);let p=this.convertAndCacheOnCPU(e,d),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&dr().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=xB){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return dr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new fB(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new XW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ci(e.shape),...Ri(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Ci(t),...Ri(t)],s=new Vv(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=wh(a),i;n?i=new EL(s):i=new TL(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===gd.DENSE){let f=bd(e.outputShape);i.texShape=f.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(f.dataId);if(y.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=f.shape)}else if(!!y.isPacked!=!!e.packedInputs)f=y.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),y=this.texData.get(f.dataId);else if(y.isPacked&&!kd(y.shape,f.shape)){let A=f,g=f.shape;f.shape=y.shape,f=this.packedReshape(f,g),o.push(f),y=this.texData.get(f.dataId),A.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:y,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},d=gW(e,l,u),p=this.getAndSaveBinary(d,()=>yW(this.gpgpu,e,l,u)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),AW(this.gpgpu,p,l,u,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?yB:AB}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=k.now());let d=t.texShape;if(d==null&&(d=dv(n,o),t.texShape=d),r!=null){let p=wh(n),c,h=d[1],m=d[0],f=r instanceof Uint8Array;o?([h,m]=Ol(d[0],d[1]),c=new FL(p,[m,h],f)):c=new ML(p,[m,h],f);let y=this.makeTensorInfo([m,h],a);f?this.texData.get(y.dataId).usage=na.PIXELS:this.texData.get(y.dataId).usage=na.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let A=!0,g=this.runWebGLProgram(c,[y],a,null,A),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-u)}else{let p=this.acquireTexture(d,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=wB(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};jl.nextDataId=0;function wB(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew jl,2);var kB={forceHalfFloat:Xv},Kv=` if (isnan(a)) return a; if (isnan(b)) return b; `,Ul=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)); } `}},Eh=` 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; `,Sd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` ${ut(r)} coords = getOutputCoords(); `,r===1)s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=fn("coords",r);s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= ${this.outputShape[r-2]}; bool nextColOutOfBounds = (${i[r-1]} + 1) >= ${this.outputShape[r-1]}; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `}this.userCode=` vec4 binaryOperation(vec4 a, vec4 b) { ${e} } void main() { vec4 a = getAAtOutCoords(); vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); ${s} setOutput(result); } `}};function Vn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var IB={kernelName:Rs,backendName:"webgl",kernelFunc:Vn};function qr(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Vn({inputs:{x:a},backend:n}),l=Vn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var SB={kernelName:Vp,backendName:"webgl",kernelFunc:qr},Zv="return (a < 0.) ? b * a : a;",Yv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function NB(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(Yv,r.shape,i.shape):new Ul(Zv,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var TB={kernelName:Ms,backendName:"webgl",kernelFunc:NB},Jv="return (a < 0.) ? b * a : a;",Qv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function EB(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(Qv,a.shape,r.shape):new Ul(Jv,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var CB={kernelName:Us,backendName:"webgl",kernelFunc:EB},ew="if (isnan(x)) return x;",RB=` if (isnan(a)) return a; if (isnan(b)) return b; `,MB=` 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 Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new Vl(i.shape,t):d=new Gr(i.shape,e),o.runWebGLProgram(d,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(a&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[y,A]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,b]=x,v={dataId:w.dataId,dtype:w.dtype,shape:l.shape},S={dataId:b.dataId,dtype:b.dtype,shape:u.shape},T=new Ul(e,l.shape,u.shape);return d.runWebGLProgram(T,[v,S],da(w.dtype,b.dtype))}),g=qr({inputs:{real:y,imag:A},backend:d});return d.disposeIntermediateTensorInfo(y),d.disposeIntermediateTensorInfo(A),g}let p=s||da(l.dtype,u.dtype);if(d.shouldExecuteOnCPU([l,u])&&r!=null){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[y,A]=r(l.shape,u.shape,m.values,f.values,p),g=d.makeTensorInfo(A,p),x=d.texData.get(g.dataId);return x.values=y,g}let c=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Sd(t,l.shape,u.shape,n):h=new Ul(e,l.shape,u.shape),d.runWebGLProgram(h,[l,u],p)}}function Ch(e,t=!1){if(e==="linear")return t?uB:rB;if(e==="relu")return t?pB:iB;if(e==="elu")return t?dB:sB;if(e==="relu6")return t?cB:oB;if(e==="prelu")return t?Qv:Jv;if(e==="leakyrelu")return t?Yv:Zv;if(e==="sigmoid")return t?hB:lB;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var tw=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=a?e[1]:e[2],d=Math.ceil(u/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",y="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:f=`vec4 activation(vec4 x) { ${i} }`,y="result = activation(result);");let A=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(r.dataId);return d.isPacked&&!kd(r.shape,l)&&!(d.texture!==null&&kd(d.shape,l))?$B(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var DB={kernelName:Xo,backendName:"webgl",kernelFunc:Ae},sw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${k.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";r%n>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } `),this.userCode=` const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { ${u} return getX(batch, inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${n}; float sumValue = 0.0; for (int i = 0; i < ${i}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${l} } int inIdx = inOffset + ${i}; if (${o===1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); ${l} } else if (${o===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); ${l} } else if (${o===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); ${l} } setOutput(sumValue); } `}},zB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,d=n%4,p=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); prodValue *= tmp[0] * tmp[1]; } else { minMaxValue = ${o}(values, minMaxValue); } `,c="vec4";t==="all"?(i="1.0",p=` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); `,c="bvec4"):t==="any"&&(i="0.0",p=` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); `,c="bvec4");let h="";r%n>0&&(h=` if (inIdx < 0 || inIdx >= ${r}) { return initializationValue; } `),this.userCode=` const float initializationValue = ${i}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { ${h} return getX(batch, inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${n}; vec4 minMaxValue = vec4(${i}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; for (int i = 0; i < ${u}; i += 4) { int inIdx = inOffset + i; ${c} values = ${c}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${p} } int inIdx = inOffset + ${u}; if (${d===1}) { ${c} values = ${c}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${p} } else if (${d===2}) { ${c} values = ${c}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${p} } else if (${d===3}) { ${c} values = ${c}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${p} } setOutput(${l}); } `}};function OB(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function $i(e,t,n,a){let r=OB(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ut(this.rank),r=Bv("rc",this.rank),s=new Array(this.rank);for(let u=0;u=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${y}).`);let w=(A>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[A,p,h]:[A,h,p],v=a?[g,m,c]:[g,c,m],S=Ae({inputs:{x:e},backend:r,attrs:{shape:b}}),T=Ae({inputs:{x:t},backend:r,attrs:{shape:v}}),C=[S,T],$=Math.max(A,g),O=n?S.shape[1]:S.shape[2],P=s!=null,j=i!=null,D=l==="leakyrelu",U=l!=null?Ch(l,!0):null,X=P||j||D||U!=null,G;if((h===1||m===1)&&O>iw&&X===!1){let Y=S,re=T;n&&(Y=mn({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),C.push(Y)),a&&(re=mn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),C.push(re));let ne=m!==1,ie=m===1,Q=Y;ne&&(Q=Ae({inputs:{x:Y},backend:r,attrs:{shape:[$,O,1]}}),C.push(Q));let pe=m===1?2:1,oe=re;ie&&(oe=Ae({inputs:{x:re},backend:r,attrs:{shape:[$,1,O]}}),C.push(oe));let ge=E1({inputs:{a:Q,b:oe},backend:r});G=Mh({inputs:{x:ge},backend:r,attrs:{axis:pe,keepDims:!0}}),C.push(ge)}else{let Y=da(e.dtype,t.dtype),re=new tw(b,v,[$,h,m],n,a,P,U,j,D),ne=[S,T];if(s!=null&&ne.push(s),j&&ne.push(i),D){let ie=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));ne.push(ie),C.push(ie)}G=r.runWebGLProgram(re,ne,Y)}let ee=Ae({inputs:{x:G},backend:r,attrs:{shape:w}});C.push(G);for(let Y of C)r.disposeIntermediateTensorInfo(Y);return ee}function jB(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=a;return Fh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}var UB={kernelName:oi,backendName:"webgl",kernelFunc:jB},ow="return abs(x);";function HB(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=Wv(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vl(a.shape,ow):r=new Gr(a.shape,ow),n.runWebGLProgram(r,[a],a.dtype)}var GB={kernelName:oo,backendName:"webgl",kernelFunc:HB},qB=ka+` if (abs(x) > 1.) { return NAN; } return acos(x); `,XB=Xe({opSnippet:qB}),KB={kernelName:lo,backendName:"webgl",kernelFunc:XB},ZB=ka+` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`,YB=Xe({opSnippet:ZB}),JB={kernelName:uo,backendName:"webgl",kernelFunc:YB},lw="return a + b;",QB=nn({opSnippet:lw,packedOpSnippet:lw,supportsComplex:!0,cpuKernelImpl:xW}),eV={kernelName:Mr,backendName:"webgl",kernelFunc:QB},tV=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} float result = ${a}; setOutput(result); } `}},nV=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} vec4 result = ${a}; setOutput(result); } `}};function $h(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Vn({inputs:{x:a[0]},backend:n});if(a.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=$h({inputs:a.slice(0,o),backend:n}),u=$h({inputs:a.slice(o),backend:n});return $h({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>da(o,l)),s=a.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new nV(a[0].shape,s):new tV(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var aV={kernelName:hs,backendName:"webgl",kernelFunc:$h};function rV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=r;d!=null&&(p=mn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("all",u,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,u),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),y=$i(f,f.dtype,"all",n),A;if(i){let g=R.expandShapeToKeepDim(c,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),d!=null&&n.disposeIntermediateTensorInfo(p),A}var sV={kernelName:po,backendName:"webgl",kernelFunc:rV};function iV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=r;d!=null&&(p=mn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("any",u,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,u),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),y=$i(f,f.dtype,"any",n),A;if(i){let g=R.expandShapeToKeepDim(c,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),d!=null&&n.disposeIntermediateTensorInfo(p),A}var oV={kernelName:co,backendName:"webgl",kernelFunc:iV},lV=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${a}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${a}; i++) { int inIdx = ${o}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}},uV=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ut(o),u=fn("coords",o),d,p;if(s===1){p=o+1;let S=ut(p);d=` ${S} sourceLocR = ${S}(${u.join()}, 0); ++${u[o-1]}; ${S} sourceLocG = ${S}(${u.join()}, 0); ++${u[o-2]}; ${S} sourceLocA = ${S}(${u.join()}, 0); --${u[o-1]}; ${S} sourceLocB = ${S}(${u.join()}, 0); --${u[o-2]};`}else p=o,d=` ${l} sourceLocR = coords; ++${u[o-1]}; ${l} sourceLocG = coords; ++${u[o-2]}; ${l} sourceLocA = coords; --${u[o-1]}; ${l} sourceLocB = coords; --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],m=c.map(S=>"int "+S),f=fn("sourceLocR",p-1).concat("inIdx.r"),y=fn("sourceLocG",p-1).concat("inIdx.g"),A=fn("sourceLocB",p-1).concat("inIdx.b"),g=fn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",w=a?"":` inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), getBestIndicesAChannel(${y.join()}), getBestIndicesAChannel(${A.join()}), getBestIndicesAChannel(${g.join()})));`,b=`vec4( getAChannel(${f.join()}), hasNextCol ? getAChannel(${y.join()}) : 0., hasNextRow ? getAChannel(${A.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,v=a?"":` float getBestIndicesAChannel(${m.join()}) { return getChannel(getBestIndicesA(${c.join()}), vec2(${c.slice(-2).join()})); }`;this.userCode=` float getAChannel(${m.join()}) { return getChannel(getA(${c.join()}), vec2(${c.slice(-2).join()})); } ${v} void main() { ${l} coords = getOutputCoords(); bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; bool hasNextRow = ${u[o-2]} < ${i[o-2]-1}; ${d} ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h}, sourceLocB${h}, sourceLocA${h}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${b}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${w} vec4 candidate = ${b}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${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 uw(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new lV(o,n,a==null),u=[t];a!=null&&u.push(a);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let p=uw(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function dw(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=R.computeOptimalWindowSize(s),o=new uV(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=dw(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}return u}function pw(e,t,n,a){let r=[n];if(R.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let d=uw(e,u,a);s.push(d);let p=Ae({inputs:{x:d},backend:e,attrs:{shape:i}});return s.forEach(c=>e.disposeIntermediateTensorInfo(c)),p}return dw(e,t,a)}function dV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=mn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=pw(n,l,i[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var pV={kernelName:fs,backendName:"webgl",kernelFunc:dV};function cV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=mn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=pw(n,l,i[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var hV={kernelName:Nu,backendName:"webgl",kernelFunc:cV},fV=ka+` if (abs(x) > 1.) { return NAN; } return asin(x); `,mV=Xe({opSnippet:fV}),yV={kernelName:ho,backendName:"webgl",kernelFunc:mV},AV=ka+"return log(x + sqrt(x * x + 1.0));",gV=Xe({opSnippet:AV}),xV={kernelName:fo,backendName:"webgl",kernelFunc:gV},bV=ka+` return atan(x); `,vV=Xe({opSnippet:bV}),wV={kernelName:mo,backendName:"webgl",kernelFunc:vV},kV=RB+` return atan(a, b); `,IV=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+MB+` return result; `,SV=nn({opSnippet:kV,packedOpSnippet:IV}),NV={kernelName:Ao,backendName:"webgl",kernelFunc:SV},TV=ka+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,EV=Xe({opSnippet:TV}),CV={kernelName:yo,backendName:"webgl",kernelFunc:EV},Nd=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(m||(A="-1.0 / 1e-20"),n){let S=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${d}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; 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 = ${a?r?f:y:`wR * ${p} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let w=Math.floor(s/4)*4,b=s%4,v=` if (${m}) { avgValue += dot(values, ones); } else { minMaxValue = ${g}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); const float initializationValue = ${A}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${A}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${d}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${w}; 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) ); ${v} } int xC = xCCorner + ${w}; if (${b===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${v} } else if (${b===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), initializationValue, initializationValue ); ${v} } else if (${b===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), initializationValue ); ${v} } } setOutput(${x}); } `}},C1=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,y=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${y}, ${A}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${c}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${d}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${p}) { 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 ${C} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} + wR * ${m} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let w="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let v=Math.floor(s/4)*4,S=s%4,T=` if (${g}) { avgValue += dot(values, ones); } else { minMaxValue = ${w}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${y}, ${A}); const float initializationValue = ${x}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${x}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${c}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${d}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${v}; wC += 4) { int xC = xCCorner + wC * ${p}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${p}, ch), getValue(batch, xD, xR, xC + 2 * ${p}, ch), getValue(batch, xD, xR, xC + 3 * ${p}, ch) ); ${T} } int xC = xCCorner + ${v}; if (${S===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${T} } else if (${S===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${p}, ch), initializationValue, initializationValue ); ${T} } else if (${S===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${p}, ch), getValue(batch, xD, xR, xC + 2 * ${p}, ch), initializationValue ); ${T} } } setOutput(${b}); } } `}};function RV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;_l(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Vn({inputs:{x:r},backend:n});let p=new Nd(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var MV={kernelName:ms,backendName:"webgl",kernelFunc:RV};function FV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new C1(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var $V={kernelName:Tu,backendName:"webgl",kernelFunc:FV},DV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,p=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${d}); const float avgMultiplier = float(${p}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${o}; wR += ${s}) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${l}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},zV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,y=1/(t*n*a);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${y}); 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 < ${d}; wD += ${o}) { float dyD = float(dyDCorner + wD) / ${r}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${p}; wR += ${l}) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${c}; wC += ${u}) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function OV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,u,d),h=new zV(c);return n.runWebGLProgram(h,[r],i.dtype)}var _V={kernelName:Wp,backendName:"webgl",kernelFunc:OV};function PV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;_l([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=R.computePool2DInfo(i.shape,o,l,1,u),p=new DV(d);return n.runWebGLProgram(p,[r],i.dtype)}var LV={kernelName:Lp,backendName:"webgl",kernelFunc:PV};function WV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Fh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var BV={kernelName:ys,backendName:"webgl",kernelFunc:WV},VV=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${o}; float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}},jV=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${s})); setOutput((x - mean) * inv + offset); } `}},UV=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],d=null;i!=null&&(d=i.shape,u.push(i));let p=null;o!=null&&(p=o.shape,u.push(o));let c=J().getBool("WEBGL_PACK_NORMALIZATION")?new jV(a.shape,r.shape,s.shape,d,p,l):new VV(a.shape,r.shape,s.shape,d,p,l);return t.runWebGLProgram(c,u,u[0].dtype)},HV={kernelName:Es,backendName:"webgl",kernelFunc:UV},GV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,a=qV(this.rank),r,s=e.map((i,o)=>`sourceLoc.${R1[o]} = start[${o}] + coords.${R1[o]};`);r=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` ${n} void main() { ${r} setOutput(getSource(${a})); } `}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)}}},R1=["x","y","z","w","u","v"];function qV(e){if(e===1)return"sourceLoc";if(e<=6)return R1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var XV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=fn("coords",this.rank),a=fn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${a[this.rank-1]}; result.y = ${s}; --${a[this.rank-1]}; } `,o=this.rank===1?"":` --${n[this.rank-1]}; if (++${n[this.rank-2]} < ${e[this.rank-2]}) { ++${a[this.rank-2]}; result.z = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${a[this.rank-1]}; result.w = ${s}; } } `,l=this.rank<=4?`sourceLoc = coords + ${t}(${e.map((u,d)=>`start[${d}]`).join()});`:e.map((u,d)=>`${a[d]} = ${n[d]} + start[${d}];`).join(` `);this.userCode=` uniform int start[${this.rank}]; void main() { ${t} coords = getOutputCoords(); ${t} sourceLoc; ${l} vec4 result = vec4(0.); ${i} ${o} setOutput(result); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${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 KV(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Td(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=un.parseSliceParams(r,s,i);if(un.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=LW(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),d=un.isSliceContinous(r.shape,o,l);if(u||!d){let p=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XV(l):new GV(l),c=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),KV(r,o,l,n)}var ZV={kernelName:Jo,backendName:"webgl",kernelFunc:Td},YV=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=R.getReshaped(r.shape,s,o),u=R.getPermuted(l.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=[],m=Ae({inputs:{x:r},backend:n,attrs:{shape:l}}),f=mn({inputs:{x:m},backend:n,attrs:{perm:u}}),y=Ae({inputs:{x:f},backend:n,attrs:{shape:d}}),A=Td({inputs:{x:y},backend:n,attrs:{begin:p,size:c}});return h.push(m),h.push(f),h.push(y),h.forEach(g=>n.disposeIntermediateTensorInfo(g)),A},JV={kernelName:Eu,backendName:"webgl",kernelFunc:YV};function QV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=Lv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var ej={kernelName:Bp,backendName:"webgl",kernelFunc:QV},tj="return float(a != b);",cw=nn({opSnippet:tj,dtype:"bool"}),nj={kernelName:Wo,backendName:"webgl",kernelFunc:cw};function Ed(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Vn({inputs:{x:r.complexTensorInfos.real},backend:n})}var aj={kernelName:uc,backendName:"webgl",kernelFunc:Ed},rj="return float(int(x));";function sj(e,t){let n=new Gr(e.shape,rj),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function M1(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Vn({inputs:{x:r},backend:n});let i=$t(r.shape),o=M1({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=qr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Ed({inputs:{input:r},backend:n}),o=M1({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Vn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return sj(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=cw({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var ij={kernelName:As,backendName:"webgl",kernelFunc:M1},hw="return ceil(x);",oj=Xe({opSnippet:hw,packedOpSnippet:hw,cpuKernelImpl:vW}),lj={kernelName:gs,backendName:"webgl",kernelFunc:oj},uj=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,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},dj=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,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function pj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;J().getBool("WEBGL_PACK_CLIP")?o=new dj(r.shape):o=new uj(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var cj={kernelName:Fr,backendName:"webgl",kernelFunc:pj},hj=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 fw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function fj(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new hj(a.shape),i=[fw(a,r.complexTensorInfos.real),fw(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var mj={kernelName:Cu,backendName:"webgl",kernelFunc:fj},yj=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { return getChannel( getT${m}(${Dh(i,l,f)}), vec2(${Dh(u,l,f)})); }`}let c=o.length,h=o[o.length-1];p+=` return getChannel( getT${c}(${Dh(i,l,h)}), vec2(${Dh(u,l,h)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${p} } void main() { ${r} coords = getOutputCoords(); vec4 result = vec4(getValue(${s}), 0., 0., 0.); ${s[a-1]} = ${s[a-1]} + 1; if (${s[a-1]} < ${n[a-1]}) { result.g = getValue(${s}); } ${s[a-2]} = ${s[a-2]} + 1; if (${s[a-2]} < ${n[a-2]}) { result.a = getValue(${s}); } ${s[a-1]} = ${s[a-1]} - 1; if (${s[a-2]} < ${n[a-2]} && ${s[a-1]} < ${n[a-1]}) { result.b = getValue(${s}); } setOutput(result); } `}};function Dh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function zh(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Vn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var gj={kernelName:nc,backendName:"webgl",kernelFunc:zh};function Hl(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(f=>Ed({inputs:{input:f},backend:n})),p=e.map(f=>zh({inputs:{input:f},backend:n})),c=Hl(d,t,n),h=Hl(p,t,n),m=qr({inputs:{real:c,imag:h},backend:n});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),p.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(A=>{let g=k.sizeFromShape(A.shape.slice(t));return Ae({inputs:{x:A},backend:n,attrs:{shape:[-1,g]}})}),p=d.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),c=R.computeOutShape(d.map(A=>A.shape),1),h=d[0].shape[0]===1,m=wW(p,c,a,h),f=R.computeOutShape(e.map(A=>A.shape),t),y=n.makeTensorInfo(f,a,m);return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),p=Hl(e.slice(0,d),t,n),c=Hl(e.slice(d),t,n),h=Hl([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new Aj(e.map(p=>p.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=xj(e,t,n),o=new yj(s.map(d=>d.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let u=Ae({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),u}function xj(e,t,n){let a=R.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ae({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function mw(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=R.computeOutShape(t.map(u=>u.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>k.sizeFromShape(u.shape)>0);if(o.length===1)return Vn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return R.assertParamsConsistent(l,s),Hl(o,s,n)}var bj={kernelName:go,backendName:"webgl",kernelFunc:mw},yw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",y=f?1:2,A=f?2:3,g=f?3:1,x="",w="";n&&(a?x=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?x=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:x=` float activation(float x) { ${n} } `,w="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${x} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${g}]; ivec2 xRCCorner = ivec2(coords[${y}], coords[${A}]) * 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 < ${p}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${c}; wC++) { int xC = xCCorner + wC * ${d}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${f}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${m===1}) { if (${f}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); } else { dotProd += getX(batch, ${h}, xR, xC) * getW(wR, wC, ${h}, d2); } } else if (${m===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); if (${f}) { vec2 xValues = vec2( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${m===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); if (${f}) { vec3 xValues = vec3( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1), getX(batch, xR, xC, ${h} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC), getX(batch, ${h} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${b} ${w} setOutput(result); } `}},vj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${a}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${d}; wF++) { int xF = xFCorner + wF * ${o}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${p}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${c}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${m===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); } else if (${m===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${m===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1), getX(batch, xF, xR, xC, ${h} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2), getW(wF, wR, wC, ${h} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}},wj=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:d,dataFormat:p}=n,{left:c,top:h}=o,m=r*a,f=hn(),y=p==="channelsLast",A=y?0:1,g=y?1:2,x="";for(let w=0;w<=1;w++)for(let b=0;b<=1;b++)x+=` blockIndex = rc.y + ${b}; pos = rc.x + ${w}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${l})) * ${i} - ${h}; d0 = offsetY + ${d} * (pos / ${m}); if(d0 < ${t[A]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${c}.); d1 = offsetX + ${u} * (int(mod(float(pos), ${m}.) / ${r}.)); if(d1 < ${t[g]} && d1 >= 0) { ch = int(mod(float(pos), ${r}.)); if (${y}) { innerDims = vec2(d1, ch); result[${w*2+b}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${w*2+b}] = 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} ${f.output} = result; } `}};function Aw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),d=n.inChannels,p=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,y,A=[],g=(p===1||c===1)&&d>iw,x=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let w=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],b=Ae({inputs:{x:e},backend:a,attrs:{shape:[1,w,n.inChannels]}}),v=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Fh({a:b,b:v,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});y=Ae({inputs:{x:S},backend:a,attrs:{shape:n.outShape}}),A.push(b),A.push(v),A.push(S)}else{let w=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),b={dataId:e.dataId,shape:[1,w,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,k.assert(kd(u.shape,b.shape),()=>`packed reshape ${u.shape} to ${b.shape} isn't free`);let S=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(S);let T=Fh({a:b,b:S,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);k.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,y=Vn({inputs:{x:T},backend:a}),y.shape=n.outShape,A.push(T)}for(let w of A)a.disposeIntermediateTensorInfo(w);return y}function gw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*d,y=c*p,A=[f,y],g=!0,x=!1,w=[],b=Ae({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),v=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});w.push(b),w.push(v);let S=new wj(A,b.shape,n),T=a.runWebGLProgram(S,[b],"float32"),C=Ae({inputs:{x:T},backend:a,attrs:{shape:[1,A[0],A[1]]}});w.push(T),w.push(C);let $=r!=null,O=s!=null,P=o==="leakyrelu",j=o?Ch(o,!0):null,D=new tw(C.shape,v.shape,[1,y,n.outChannels],g,x,$,j,O,P),U=[C,v];if(r&&U.push(r),O&&U.push(s),P){let Y=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));U.push(Y),w.push(Y)}let X=a.runWebGLProgram(D,U,"float32"),G=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],ee=Ae({inputs:{x:X},backend:a,attrs:{shape:G}});w.push(X);for(let Y of w)a.disposeIntermediateTensorInfo(Y);return ee}function kj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=Aw({x:r,filter:s,convInfo:c,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=gw({x:r,filter:s,convInfo:c,backend:n});else{let f=new yw(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=Ae({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var Ij={kernelName:xs,backendName:"webgl",kernelFunc:kj},Sj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int d2 = coords.w; // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${s}) { float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } else { float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},Nj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${d}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${s}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } else { float xValue = getDy(batch, d2, idyR, idyC); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}},Tj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; int wR = coords.y; int wC = coords.z; int d1 = coords.w; int d2 = coords.u; float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { int xF = wF + yF * ${t} - ${r}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${n} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${a} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},Ej=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${r}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${n} - 1 - wR; for (int wC = 0; wC < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${a} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function Cj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,d,i,1,o,u,!1,p),h=new Sj(c);return n.runWebGLProgram(h,[r,s],"float32")}var Rj={kernelName:jp,backendName:"webgl",kernelFunc:Cj};function Mj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(u),c=R.computeConv2DInfo(i,s.shape,o,1,l,d,!1,p),h=new Nj(c);return n.runWebGLProgram(h,[r,s],"float32")}var Fj={kernelName:bs,backendName:"webgl",kernelFunc:Mj};function $j(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=R.computeConv3DInfo(r.shape,s.shape,i,l,o),d=new vj(u);return n.runWebGLProgram(d,[r,s],"float32")}var Dj={kernelName:Ru,backendName:"webgl",kernelFunc:$j};function zj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=R.computeConv3DInfo(r.shape,l,i,1,o),d=new Tj(u);return n.runWebGLProgram(d,[r,s],"float32")}var Oj={kernelName:Up,backendName:"webgl",kernelFunc:zj};function _j(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=R.computeConv3DInfo(l,s.shape,o,1,i),d=new Ej(u);return n.runWebGLProgram(d,[r,s],"float32")}var Pj={kernelName:Hp,backendName:"webgl",kernelFunc:_j},Lj=ew+` return cos(x); `,Wj=Xe({opSnippet:Lj}),Bj={kernelName:vs,backendName:"webgl",kernelFunc:Wj},Vj=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,jj=Xe({opSnippet:Vj}),Uj={kernelName:xo,backendName:"webgl",kernelFunc:jj},Hj=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,p]=n;this.outputShape=[u,d,p,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,y,A]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[g,x,w]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${g}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${s}) { return; } float height_scale = ${y}; float width_scale = ${x}; float in_y = ${A}; if( in_y < 0.0 || in_y > ${h} ) { setOutput(float(${r})); return; } float in_x = ${w}; if( in_x < 0.0 || in_x > ${m} ) { setOutput(float(${r})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${c} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},Gj=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,d=new Hj(r.shape,s.shape,o,l,u);return n.runWebGLProgram(d,[r,s,i],"float32")},qj={kernelName:bo,backendName:"webgl",kernelFunc:Gj},xw=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${bw(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${ut(a)} coords = getOutputCoords(); int end = ${vw(a,"coords")}; float val = ${r}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; ${vw(a,"coords")} = idx; val += getX(${bw(a,"coords")}); } setOutput(val); } `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function bw(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 ${e} is not yet supported`)}function vw(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Xj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=R.getAxesPermutation([s],l),d=r;u!=null&&(d=mn({inputs:{x:r},backend:n,attrs:{perm:u}}));let p=R.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Vn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new xw(d.shape,!1,o),y=f.getCustomSetupFunc(m),A=h;h=n.runWebGLProgram(f,[h],h.dtype,y),n.disposeIntermediateTensorInfo(A)}if(i){let m=new xw(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=R.getUndoAxesPermutation(u),f=mn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var Kj={kernelName:ws,backendName:"webgl",kernelFunc:Xj};function Zj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=Lv(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=bW(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Yj={kernelName:Gp,backendName:"webgl",kernelFunc:Zj},Jj=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 Qj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=u*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new Jj(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var eU={kernelName:vo,backendName:"webgl",kernelFunc:Qj},ww=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,d=e.strideWidth,p=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,y="",A="";n&&(a?y=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?y=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:y=` float activation(float x) { ${n} } `,A="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${y} const ivec2 strides = ivec2(${u}, ${d}); const ivec2 pads = ivec2(${o}, ${l}); void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${f}; int q = d2 - d1 * ${f}; 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 < ${h}; wR++) { int xR = xRCorner + wR * ${p}; if (xR < 0 || xR >= ${s}) { continue; } for (int wC = 0; wC < ${m}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${i}) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${g} ${A} setOutput(result); } `}},kw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,d=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,y=f,A=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b=0 && xR < ${i}) { `;for(let v=0;v<(y+1)/2;v++){let S=v*2,T=S*h;if(A+=` xC = xCCorner + ${T}; `,p===1){if(S= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } `,h===1&&T>0?A+=` xC${S} = vec4(xTexelC${T-2}.zw, xTexelC${T}.xy); `:A+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < ${o}) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { previous.zw = vec2(0.0); } xC${S} = vec4(previous.zw, xTexelC${T}.xy); } else { xC${S} = vec4(0.0, 0.0, xTexelC${T}.xy); } `):A+=` if (xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xC, d1); if (xC + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } xC${S} = xTexelC${T}; `,T+1= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${T+2}.zw = vec2(0.0); } xTexelC${T+2}Ready = 1; } `,h>1&&(A+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xCOffset, d1); xTexelC${T}Ready = 1; } `),A+=` xC${S+1} = vec4(xTexelC${T}.zw, xTexelC${T+2}.xy); `):C===1?A+=` xC${S+1} = xTexelC${T}; `:A+=` xCOffset = xC + ${C}; if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= ${o}) { xTexelC${T+2}.zw = vec2(0.0); } xTexelC${T+2}Ready = 1; } xC${S+1} = xTexelC${T+2}; `}}else T= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= ${o}) { xTexelC${T+2}.zw = vec2(0.0); } xTexelC${T+2}Ready = 1; } xC${S} = vec4(xTexelC${T}.zw, xTexelC${T+2}.zw); `,T+1= 0 && xCOffset < ${o}) { final = getX(batch, xR, xCOffset, d1); } xC${S+1} = vec4(xTexelC${T+2}.xy, final.xy); `)):(A+=` if(xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) { xTexelC${T} = getX(batch, xR, xC, d1); if (xC + 1 >= ${o}) { xTexelC${T}.zw = vec2(0.0); } xTexelC${T}Ready = 1; } xCOffset = xC + ${p}; if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) { xTexelC${T+2} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= ${o}) { xTexelC${T+2}.zw = vec2(0.); } xTexelC${T+2}Ready = 1; } xC${S} = vec4( xTexelC${T}.xy, xTexelC${T+2}.xy); `,T+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=R.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),c;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new kw(p):c=new ww(p),n.runWebGLProgram(c,[r,s],"float32")}var nU={kernelName:ks,backendName:"webgl",kernelFunc:tU},aU=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; int d2 = d1 * ${s} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},rU=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = coords.yz - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${o}; dm++) { int d2 = d1 * ${o} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function sU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a,p=R.computeConv2DInfo(r.shape,d,i,o,l,u,!0),c=new aU(p);return n.runWebGLProgram(c,[r,s],"float32")}var iU={kernelName:qp,backendName:"webgl",kernelFunc:sU};function oU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=a,p=R.computeConv2DInfo(d,s.shape,i,o,l,u,!0),c=new rU(p);return n.runWebGLProgram(c,[r,s],"float32")}var lU={kernelName:Xp,backendName:"webgl",kernelFunc:oU},uU=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function dU(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=Ae({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new uU(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var pU={kernelName:Kp,backendName:"webgl",kernelFunc:dU},cU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:p}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${d}, ${p}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${o}; w++) { int wIn = wBeg + w * ${u}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function hU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=R.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),d,p=new cU(u);d=n.runWebGLProgram(p,[r,s],"float32");let c=Ae({inputs:{x:d},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(d),c}var fU={kernelName:Mu,backendName:"webgl",kernelFunc:hU};function mU(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=R.decodeEinsumEquation(r,s.length);R.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=R.getEinsumComputePath(o,l),p=d.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=Mh({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var yU={kernelName:Jp,backendName:"webgl",kernelFunc:mU},AU="return (x >= 0.0) ? x : (exp(x) - 1.0);",gU=` 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; `,xU=Xe({opSnippet:AU,packedOpSnippet:gU}),bU={kernelName:wo,backendName:"webgl",kernelFunc:xU},vU="return (b >= 1.0) ? a : a * (b + 1.0);",wU=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,kU=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(wU,a.shape,r.shape):new Ul(vU,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},IU={kernelName:Qp,backendName:"webgl",kernelFunc:kU},SU=` return vec4(equal(a, b)); `,NU="return float(a == b);",TU=nn({opSnippet:NU,packedOpSnippet:SU,dtype:"bool"}),EU={kernelName:Io,backendName:"webgl",kernelFunc:TU},CU=` // 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 = ${R.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,RU=Xe({opSnippet:CU}),MU={kernelName:ko,backendName:"webgl",kernelFunc:RU},Iw="return exp(x);",Sw=Xe({opSnippet:Iw,packedOpSnippet:Iw,cpuKernelImpl:kW}),FU={kernelName:Ss,backendName:"webgl",kernelFunc:Sw};function F1(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:a,attrs:{shape:o}})}var $U={kernelName:So,backendName:"webgl",kernelFunc:F1},Nw="return exp(x) - 1.0;",DU=Xe({opSnippet:Nw,packedOpSnippet:Nw,cpuKernelImpl:IW}),zU={kernelName:No,backendName:"webgl",kernelFunc:DU},Tw=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${a}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${a}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); float expI = sin(x); float real = getReal(batch, i); float imag = getImag(batch, i); result += unaryOpComplex(real, expR, imag, expI) / ${s}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function Ew(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new Tw("real",l,t),d=new Tw("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=qr({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function OU(e){let{inputs:t,backend:n}=e,{input:a}=t;return Ew(a,!1,n)}var _U={kernelName:ec,backendName:"webgl",kernelFunc:OU},PU=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 $1(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new PU(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var LU={kernelName:Fu,backendName:"webgl",kernelFunc:$1},WU=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 < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}},BU={kernelName:To,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new WU(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},Cw="return floor(x);",VU=Xe({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:SW}),jU={kernelName:Ns,backendName:"webgl",kernelFunc:VU},UU=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); if (ib != 0) { // Windows (D3D) wants guaranteed non-zero int division at compile-time. return float(idiv(ia, ib, s)); } else { return NAN; } `,HU=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); ivec4 result = ivec4(0); vec4 s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { result[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { result[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { result[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); `,GU=nn({opSnippet:UU,packedOpSnippet:HU,dtype:"int32"}),qU={kernelName:Ts,backendName:"webgl",kernelFunc:GU},XU=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},KU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } result[row * 2 + col] = floor(value * 255.0 + 0.5); } } ${t.output} = result; } `}},ZU={kernelName:yc,backendName:"webgl",kernelFunc:YU},Gl;function YU(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[u,l],p=[u,l,s];(o||i)&&(Gl==null&&(Gl=document.createElement("canvas").getContext("2d")),Gl.canvas.width=l,Gl.canvas.height=u,Gl.drawImage(r,0,0,l,u),r=Gl.canvas);let c=n.makeTensorInfo(d,"int32");n.texData.get(c.dataId).usage=na.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=J().getBool("WEBGL_PACK")?new KU(p):new XU(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function JU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=R.convertConv2DDataFormat(d),y=R.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!1,f),A,g=[];if(y.filterHeight===1&&y.filterWidth===1&&y.dilationHeight===1&&y.dilationWidth===1&&y.strideHeight===1&&y.strideWidth===1&&(y.padInfo.type==="SAME"||y.padInfo.type==="VALID"))A=Aw({x:r,filter:s,convInfo:y,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=gw({x:r,filter:s,convInfo:y,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let w=i!=null,b=o!=null,v=h==="leakyrelu",S=h?Ch(h,!1):null,T=new yw(y,w,S,b,v),C=[r,s];if(i&&C.push(i),o&&C.push(o),v){let $=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));C.push($),g.push($)}A=n.runWebGLProgram(T,C,"float32")}let x=Ae({inputs:{x:A},backend:n,attrs:{shape:y.outShape}});return g.push(A),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var QU={kernelName:li,backendName:"webgl",kernelFunc:JU};function eH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(R.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let y=R.computeConv2DInfo(r.shape,s.shape,l,f,u,p,!0),A=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,g=c?Ch(c,A):null,x=[r,s],w=i!=null,b=o!=null,v=c==="leakyrelu";if(w&&x.push(i),b&&x.push(o),v){let C=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push(C),m.push(C)}let S;A?S=new kw(y,w,g,b,v):S=new ww(y,w,g,b,v);let T=n.runWebGLProgram(S,x,"float32");return m.forEach(C=>n.disposeIntermediateTensorInfo(C)),T}var tH={kernelName:ui,backendName:"webgl",kernelFunc:eH},nH=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ut(t.length),r=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${a} strides = ${a}(${this.strides}); void main() { ${r} coords = getOutputCoords(); int flattenIndex = 0; for (int j = 0; j < ${this.sliceDim}; j++) { int index = round(getIndices(coords[0], j)); flattenIndex += index * ${s}; } setOutput(getX(flattenIndex, coords[1])); } `}};function aH(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,u,d]=R.prepareAndValidate(a,r),p=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/u,u]}}),h=new nH(i,d,[l,u]),m=n.runWebGLProgram(h,[c,p],c.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),f}var rH={kernelName:Co,backendName:"webgl",kernelFunc:aH},sH=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=iH(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${a})); } `}};function iH(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;rn.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(u.outputShape,w.dtype,w.values)}let f=new sH(c.shape,m),y=n.runWebGLProgram(f,[c,h],c.dtype);p.push(y);let A=Ae({inputs:{x:y},backend:n,attrs:{shape:u.outputShape}});return p.forEach(g=>n.disposeIntermediateTensorInfo(g)),A}var lH={kernelName:Eo,backendName:"webgl",kernelFunc:oH},uH="return float(a > b);",dH=` return vec4(greaterThan(a, b)); `,pH=nn({opSnippet:uH,packedOpSnippet:dH,cpuKernelImpl:TW,dtype:"bool"}),cH={kernelName:Ro,backendName:"webgl",kernelFunc:pH},hH="return float(a >= b);",fH=` return vec4(greaterThanEqual(a, b)); `,mH=nn({opSnippet:hH,packedOpSnippet:fH,dtype:"bool"}),yH={kernelName:Cs,backendName:"webgl",kernelFunc:mH};function AH(e){let{inputs:t,backend:n}=e,{input:a}=t;return Ew(a,!0,n)}var gH={kernelName:tc,backendName:"webgl",kernelFunc:AH},xH="return float(!isnan(x) && !isinf(x));",bH=Xe({opSnippet:xH,dtype:"bool"}),vH={kernelName:Mo,backendName:"webgl",kernelFunc:bH},wH="return float(isinf(x));",kH=Xe({opSnippet:wH,dtype:"bool"}),IH={kernelName:Fo,backendName:"webgl",kernelFunc:kH},SH="return float(isnan(x));",NH=Xe({opSnippet:SH,dtype:"bool"}),TH={kernelName:$o,backendName:"webgl",kernelFunc:NH},EH="return float(a < b);",CH=` return vec4(lessThan(a, b)); `,RH=nn({opSnippet:EH,packedOpSnippet:CH,cpuKernelImpl:EW,dtype:"bool"}),MH={kernelName:Do,backendName:"webgl",kernelFunc:RH},FH="return float(a <= b);",$H=` return vec4(lessThanEqual(a, b)); `,DH=nn({opSnippet:FH,packedOpSnippet:$H,dtype:"bool"}),zH={kernelName:zo,backendName:"webgl",kernelFunc:DH};function OH(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=CW(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var _H={kernelName:ac,backendName:"webgl",kernelFunc:OH},PH=`if (x < 0.0) return NAN; return log(x);`,LH=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; result.g = isNaN.g == 1.0 ? NAN : result.g; result.b = isNaN.b == 1.0 ? NAN : result.b; result.a = isNaN.a == 1.0 ? NAN : result.a; return result; `,WH=Xe({opSnippet:PH,packedOpSnippet:LH,cpuKernelImpl:RW}),BH={kernelName:Fs,backendName:"webgl",kernelFunc:WH},VH="return log(1.0 + x);",jH=Xe({opSnippet:VH}),UH={kernelName:Oo,backendName:"webgl",kernelFunc:jH},HH="return float(a >= 1.0 && b >= 1.0);",GH=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,qH=nn({opSnippet:HH,packedOpSnippet:GH,dtype:"bool"}),XH={kernelName:_o,backendName:"webgl",kernelFunc:qH},KH="return float(!(x >= 1.0));",ZH=Xe({opSnippet:KH}),YH={kernelName:$u,backendName:"webgl",kernelFunc:ZH},JH="return float(a >= 1.0 || b >= 1.0);",QH=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,eG=nn({opSnippet:JH,packedOpSnippet:QH,dtype:"bool"}),tG={kernelName:Du,backendName:"webgl",kernelFunc:eG},nG=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${s}; j <= ${s}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${i}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${o}; setOutput(val); } `}},aG=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; int r = coords.y; int c = coords.z; int d = coords.w; bool hasNextCol = d < ${this.outputShape[3]}; bool hasNextRow = c < ${this.outputShape[2]}; vec4 sum = vec4(0.); vec4 xFragAtOutputCoords = getX(b, r, c, d); vec4 xAtOutputCoords = vec4( getChannel(xFragAtOutputCoords, vec2(c, d)), hasNextCol ? getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${s}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); if(hasNextRow){ cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); } } ivec2 depth = ivec2(d, d + 1); for (int j = - ${s}; j <= ${s}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; if(depthInRange || depthPlusOneInRange){ vec4 z = vec4(0.); vec4 xFragAtCurrentDepth; z.xz = cache.xy; if(depthPlusOneInRange && hasNextCol){ xFragAtCurrentDepth = idx.y != d ? getX(b, r, c, idx.y) : xFragAtOutputCoords; z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); if(hasNextRow){ z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); } } cache.xy = z.yw; sum += z * z; } } vec4 result = xAtOutputCoords * ${o}; setOutput(result); } `}},rG=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new aG(r.shape,s,i,o,l):new nG(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},sG={kernelName:zu,backendName:"webgl",kernelFunc:rG},iG=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { int depthBegin = int(max(0.0, float(d - ${t}))); int depthEnd = int(min(float(${this.depth}), float(d + ${t} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${a}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${a}) * float(${r}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}},oG=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=a,p=new iG(r.shape,o,l,u,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},lG={kernelName:rc,backendName:"webgl",kernelFunc:oG};function uG(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=$i(i,e.dtype,"max",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function Rw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let g=n.texData.get(h.dataId).values,x=new Array(o);for(let v=0;v`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Vn({inputs:{x:r},backend:n});let p=new Nd(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var yG={kernelName:zs,backendName:"webgl",kernelFunc:mG};function AG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new C1(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var gG={kernelName:Ou,backendName:"webgl",kernelFunc:AG},xG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${r}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${t}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${s}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},bG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${d}, ${p}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${o}; wD += ${r}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${l}; wR += ${s}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${u}; wC += ${i}) { float dyC = float(dyCCorner + wC) / ${a}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${h} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${l} * ${u} + wR * ${u} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function vG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,u,d),h=new C1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new bG(c),y=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var wG={kernelName:ic,backendName:"webgl",kernelFunc:vG};function kG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;_l([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=R.computePool2DInfo(o.shape,l,u,1,d,p),h=!0,m=new Nd(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),y=new xG(c),A=n.runWebGLProgram(y,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),A}var IG={kernelName:sc,backendName:"webgl",kernelFunc:kG};function SG(e,t,n,a){let r=new Nd(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Nd(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var NG={kernelName:oc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];k.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=R.computePool2DInfo(a.shape,r,s,u,i),[p,c]=SG(a,o,d,l);return[p,c]}};function TG(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=$i(i,"float32","mean",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var EG={kernelName:Os,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),u=l,d=R.getAxesPermutation(u,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let S=0;Su[0]+e[d]+u[1]);let a=e.length,r=ut(a),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` int start = ${s}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${l}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${l}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${r} start = ${r}(${s}); ${r} end = ${r}(${i}); void main() { ${r} outC = getOutputCoords(); for (int i = 0; i < ${a}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${l}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; } } ${r} coords = outC - start; setOutput(getX(${o})); } `}},OG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ut(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=fn("rc",a),l=fn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${p}; } else if (source >= end) { source = (end - 1) * 2 - source + ${p}; } source -= start; `;c=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${d}); ${o[a-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${d}); } `}else{let h=` ${r} source = rc; ${r} lt = ${r}(lessThan(source, start)); ${r} gte = ${r}(greaterThanEqual(source, end)); ${r} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${p}) + gte * ((end - 1) * 2 - source + ${p}); source -= start; `;c=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${d}); ${o[a-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${d}); } rc = outputLoc; ${o[a-2]} += 1; if(${o[a-2]} < ${this.outputShape[a-2]}) { ${h} result[2] = getChannel(getX(${l.join()}), ${d}); ${o[a-1]} += 1; if(${u}) { ${h} result[3] = getChannel(getX(${l.join()}), ${d}); } } `}this.userCode=` const ${r} start = ${r}(${s}); const ${r} end = ${r}(${i}); void main() { ${r} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${c} setOutput(result); } `}},_G=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new OG(a.shape,r,s):new zG(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},PG={kernelName:Ls,backendName:"webgl",kernelFunc:_G},LG=`if (b == 0.0) return NAN; return mod(a, b);`,WG=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+Eh+` return result; `,BG=nn({opSnippet:LG,packedOpSnippet:WG}),VG={kernelName:Po,backendName:"webgl",kernelFunc:BG},jG=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)}}},UG=` if (a == b) { return 1.0; }; return a / b;`,HG=` // 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; `,Mw=nn({opSnippet:UG,packedOpSnippet:HG,checkOutOfBounds:!0}),GG={kernelName:Is,backendName:"webgl",kernelFunc:Mw},Fw="return a - b;",$w=nn({opSnippet:Fw,packedOpSnippet:Fw,supportsComplex:!0,cpuKernelImpl:jW}),qG={kernelName:ai,backendName:"webgl",kernelFunc:$w};function Dw(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=Rw({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),d=$w({inputs:{a:r,b:u},backend:n}),p=Sw({inputs:{x:d},backend:n}),c=Mh({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Ae({inputs:{x:c},backend:n,attrs:{shape:l}}),m=Mw({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var XG={kernelName:ti,backendName:"webgl",kernelFunc:Dw};function KG(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:Dw({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],d=l.shape[1],p=new jG(u,d,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var ZG={kernelName:lc,backendName:"webgl",kernelFunc:KG},zw="return -x;";function YG(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=zW(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vl(a.shape,zw):r=new Gr(a.shape,zw),n.runWebGLProgram(r,[a],a.dtype)}var JG={kernelName:Lo,backendName:"webgl",kernelFunc:YG},QG=Ga.nonMaxSuppressionV3Impl;function eq(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=QG(u,d,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var tq={kernelName:Bo,backendName:"webgl",kernelFunc:eq},nq=Ga.nonMaxSuppressionV4Impl;function aq(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=nq(d,p,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var rq={kernelName:Vo,backendName:"webgl",kernelFunc:aq},sq=Ga.nonMaxSuppressionV5Impl;function iq(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:y,selectedScores:A}=sq(d,p,c,h,m,f);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var oq={kernelName:jo,backendName:"webgl",kernelFunc:iq},lq=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${a}), float(${n}), float(index == coords.y))); } `}},uq=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),u=new lq(l,s,i,o),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let c=[...r.shape,s],h=Ae({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},dq={kernelName:Bs,backendName:"webgl",kernelFunc:uq};function Oh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Ed({inputs:{input:a},backend:n}),s=Oh({inputs:{x:r},backend:n}),i=zh({inputs:{input:a},backend:n}),o=Oh({inputs:{x:i},backend:n}),l=qr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $1({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var pq={kernelName:ol,backendName:"webgl",kernelFunc:Oh};function Ow(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Ed({inputs:{input:a},backend:n}),s=Ow({inputs:{x:r},backend:n}),i=zh({inputs:{input:a},backend:n}),o=Oh({inputs:{x:i},backend:n}),l=qr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $1({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var cq={kernelName:Uo,backendName:"webgl",kernelFunc:Ow};function hq(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return F1({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let p=F1({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=mw({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var fq={kernelName:Ho,backendName:"webgl",kernelFunc:hq},mq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ut(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; uniform float value; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${r} start = ${r}(${s}); ${r} end = ${r}(${i}); uniform float value; void main() { ${r} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${r} coords = outC - start; setOutput(getX(${o})); } } `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},yq=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ut(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=fn("rc",a),l=fn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; if(${u}) { `,a===1?"":`} rc = outputLoc; ${o[a-2]} += 1; if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1; if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},_w=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yq(r.shape,s,i):new mq(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},Aq={kernelName:Vs,backendName:"webgl",kernelFunc:_w},gq=` 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); `,xq=` // 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)); `+Eh+` return result; `,bq=nn({opSnippet:gq,packedOpSnippet:xq}),vq={kernelName:js,backendName:"webgl",kernelFunc:bq};function wq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=k.parseAxisParam(s,r.shape),d=u,p=R.getAxesPermutation(d,o),c=r;p!=null&&(c=mn({inputs:{x:r},backend:n,attrs:{perm:p}}),d=R.getInnerMostAxes(d.length,o),l.push(c)),R.assertAxesAreInnerMostDims("prod",d,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:y,outDtype:A}=OW(c.shape,c.dtype,m,d);h=n.makeTensorInfo(y,A,f)}else{let[m,f]=R.computeOutAndReduceShapes(c.shape,d),y=k.sizeFromShape(f),A=Ae({inputs:{x:c},backend:n,attrs:{shape:[-1,y]}}),g=vc(r.dtype),x=$i(A,g,"prod",n);h=Ae({inputs:{x},backend:n,attrs:{shape:m}}),l.push(A),l.push(x)}if(i){l.push(h);let m=R.expandShapeToKeepDim(h.shape,u);h=Ae({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var kq={kernelName:Go,backendName:"webgl",kernelFunc:wq},Pw=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=_W(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Iq={kernelName:_u,backendName:"webgl",kernelFunc:Pw},Sq="return 1.0 / x;",Nq=Xe({opSnippet:Sq}),Tq={kernelName:qo,backendName:"webgl",kernelFunc:Nq},Eq=ka+` return (x < 0.0) ? 0.0 : x; `,Cq=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,Rq=Xe({opSnippet:Eq,packedOpSnippet:Cq}),Mq={kernelName:Hs,backendName:"webgl",kernelFunc:Rq},Fq=ka+` return (x < 0.0) ? 0.0 : min(6.0, x); `,$q=` 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; `,Dq=Xe({opSnippet:Fq,packedOpSnippet:$q}),zq={kernelName:qs,backendName:"webgl",kernelFunc:Dq},Oq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/d[0]}, ${u[1]/d[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${p}; // 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); } `}},_q=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/d[0]}, ${u[1]/d[1]}, ${u[1]/d[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${p}; // 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 Pq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new _q(r.shape,l,u,s,i):new Oq(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],"float32")}var Lq={kernelName:Gs,backendName:"webgl",kernelFunc:Pq},Wq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${d}); const float invHeightScale = float(${p}); const float invWidthScale = float(${c}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${s}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function Bq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Wq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Vq={kernelName:pc,backendName:"webgl",kernelFunc:Bq},jq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/d[0]}, ${u[1]/d[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${c}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}},Uq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/d[0]}, ${u[1]/d[1]}, ${u[1]/d[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${c}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function Hq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Uq(r.shape,l,u,s,i):new jq(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var Gq={kernelName:Pu,backendName:"webgl",kernelFunc:Hq},qq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${d}); const float invHeightScale = float(${p}); const float invWidthScale = float(${c}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${s}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float sourceFracRow = float(${o[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${o[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${a}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Xq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new qq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Kq={kernelName:dc,backendName:"webgl",kernelFunc:Xq},Zq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ut(n);this.userCode=` void main() { ${s} coords = getOutputCoords(); setOutput(getX(${r})); } `}},Yq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=fn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${r}){ result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), ${e[0]} - (rc + 1) - 1); } setOutput(result); } `:this.userCode=` void main() { ${i} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${o(a.slice())}; if(${r}){ result.g = ${l(a.slice())}; } if(${s}) { result.b = ${u(a.slice())}; if(${r}) { result.a = ${d(a.slice())}; } } setOutput(result); } `;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function d(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((A,g)=>c(g,h)),f=m.join(","),y=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${y}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Jq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Vn({inputs:{x:r},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Yq(r.shape,o):new Zq(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Qq={kernelName:Xs,backendName:"webgl",kernelFunc:Jq},eX=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` 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])); ${r} if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},tX={kernelName:ll,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new eX(a.shape,s),[u,d]=R.getImageCenter(i,a.shape[1],a.shape[2]),p=l.getCustomSetupFunc(u,d,Math.sin(r),Math.cos(r));return o.runWebGLProgram(l,[a],a.dtype,p)}},nX=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,aX=Xe({opSnippet:nX}),rX={kernelName:Ks,backendName:"webgl",kernelFunc:aX},sX="return inversesqrt(x);",iX=Xe({opSnippet:sX,cpuKernelImpl:PW}),oX={kernelName:Zs,backendName:"webgl",kernelFunc:iX},Lw=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let d=`getIndices(${u})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${r}); void main() { ${l} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { int index = round(${d}); flattenedIndex += index * ${h}; } if (flattenedIndex == coords[0]) { sum += ${c}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function lX(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:p}=R.calculateShapes(s,r,i),c=[p/u,u];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),y=new Lw(l,o,h.shape.length,m.shape.length,d,c),A=n.runWebGLProgram(y,[m,h,f],m.dtype),g=Ae({inputs:{x:A},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(f),g}var uX={kernelName:Ko,backendName:"webgl",kernelFunc:lX},dX=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 1.0) { setOutput(getA(${r})); } else { setOutput(getB(${r})); } } `}};function pX(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new dX(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],da(r.dtype,s.dtype))}var cX={kernelName:Zo,backendName:"webgl",kernelFunc:pX},hX=` // 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); `,fX=Xe({opSnippet:hX}),mX={kernelName:Yo,backendName:"webgl",kernelFunc:fX},yX="return 1.0 / (1.0 + exp(-1.0 * x));",AX=Xe({opSnippet:yX}),gX={kernelName:Js,backendName:"webgl",kernelFunc:AX},xX=` if (isnan(x)) { return 0.0; } return sign(x); `,bX=Xe({opSnippet:xX}),vX={kernelName:el,backendName:"webgl",kernelFunc:bX},wX=ew+` return sin(x); `,kX=Xe({opSnippet:wX}),IX={kernelName:Ys,backendName:"webgl",kernelFunc:kX},SX=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,NX=Xe({opSnippet:SX}),TX={kernelName:Qo,backendName:"webgl",kernelFunc:NX},EX=` 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; `,CX=Xe({opSnippet:EX}),RX={kernelName:tl,backendName:"webgl",kernelFunc:CX},MX=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,g)=>A*g),l=[[0,0]];l.push(...i);for(let A=1+s.length;An.disposeIntermediateTensorInfo(A)),y},FX={kernelName:Lu,backendName:"webgl",kernelFunc:MX};function $X(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw: ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,c,h,m,f]=WW(o,a.shape,a.dtype,l,r.dtype,u,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(y=>Number(y)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var DX={kernelName:cc,backendName:"webgl",kernelFunc:$X};function zX(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,d,p]=BW(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var OX={kernelName:hc,backendName:"webgl",kernelFunc:zX};function _X(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:d,outputSize:p}=R.calculateShapes(s,r,o),c=!1,h=new Lw(u,l,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var PX={kernelName:fc,backendName:"webgl",kernelFunc:_X};function LX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=R.prepareSplitSize(r,s,o),u=r.shape.length,d=new Array(u).fill(0),p=r.shape.slice();return l.map(c=>{let h=[...p];h[o]=c;let m=Td({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var WX={kernelName:nl,backendName:"webgl",kernelFunc:LX},BX="return sqrt(x);",VX=Xe({opSnippet:BX}),jX={kernelName:Qs,backendName:"webgl",kernelFunc:VX},UX="return x * x;",HX=Xe({opSnippet:UX}),GX={kernelName:Wu,backendName:"webgl",kernelFunc:HX},Ww="return (a - b) * (a - b);",qX=nn({opSnippet:Ww,packedOpSnippet:Ww}),XX={kernelName:ni,backendName:"webgl",kernelFunc:qX};function KX({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=ka+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new Gr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var ZX={kernelName:Dr,backendName:"webgl",kernelFunc:KX},YX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ut(n.length),s=ut(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); void main() { ${s} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function JX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:y,newShape:A,outShape:g}=un.sliceInfo(r.shape,s,i,o,l,u,d,p,c),x=Ae({inputs:{x:r},backend:n,attrs:{shape:A}}),w;if(h){let v=Td({inputs:{x},backend:n,attrs:{begin:m,size:y}});w=Ae({inputs:{x:v},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(v)}else if(g.some(v=>v===0))w=n.makeTensorInfo(g,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let v=n.texData.get(x.dataId).values,S=We(x.shape,x.dtype,v),T=VW(g,S,f,m);w=n.makeTensorInfo(g,x.dtype,T.values)}else{let v=new YX(m,f,g);w=n.runWebGLProgram(v,[x],x.dtype)}let b=Ae({inputs:{x:w},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),b}var QX={kernelName:al,backendName:"webgl",kernelFunc:JX},eK="return tan(x);",tK=Xe({opSnippet:eK}),nK={kernelName:ri,backendName:"webgl",kernelFunc:tK},aK=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,rK=Xe({opSnippet:aK}),sK={kernelName:si,backendName:"webgl",kernelFunc:rK},iK=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,u=We(r.shape,r.dtype,l),d=UW(u,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new iK(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var lK={kernelName:$r,backendName:"webgl",kernelFunc:Bw};function uK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,u]=HW(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var dK={kernelName:rl,backendName:"webgl",kernelFunc:uK},pK=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${o} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${o} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; } } float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${r}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${r}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${i} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function cK(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],y=[d,m,f,h],A=new pK(p,c,i,o,l,y);return n.runWebGLProgram(A,[r,s],"float32")}var hK={kernelName:sl,backendName:"webgl",kernelFunc:cK};function fK(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;_l(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=GW(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var mK={kernelName:mc,backendName:"webgl",kernelFunc:fK};function yK(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),d=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var AK={kernelName:il,backendName:"webgl",kernelFunc:yK},gK=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,d=n%4,p=` sumValue += dot(values, segFilter); `,c="";r%n>0&&(c=` if (inIdx < 0 || inIdx >= ${r}) { return initializationValue; } `);let h="";r%n>0&&(h=` if (inIdx < 0 || inIdx >= ${r}) { return -1.0; } `),this.userCode=` const float initializationValue = ${o}; float getValue(int batch, int inIdx) { ${c} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${h} return getSegmentIds(inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( ${s})) * float(${n})); int currentSeg = int(mod(float(outIdx), float(${s}))); float sumValue = 0.0; for (int i = 0; i < ${u}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); ${p} } int inIdx = inOffset + ${u}; if (${d===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 ); ${p} } else if (${d===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 ); ${p} } else if (${d===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 ); ${p} } setOutput(${l}); } `}};function xK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,d=R.getAxesPermutation([u],o),p=r;d!=null&&(p=mn({inputs:{x:r},backend:n,attrs:{perm:d}}),l.push(p),u=R.getInnerMostAxes(1,o)[0]);let c=R.segment_util.computeOutShape(p.shape,u,i),h=k.sizeFromShape([p.shape[u]]),m=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=vc(r.dtype),y=(w,b,v,S,T)=>{let C=w.shape[0],$=w.shape[1],O=R.segment_util.segOpComputeOptimalWindowSize($,T),P={windowSize:O,inSize:$,batchSize:C,numSegments:T},j=new gK(P,b),D=n.compileAndRun(j,[w,v],S);if(l.push(D),D.shape[1]===T)return D;let U=Pw({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=Bw({inputs:{x:U},backend:n,attrs:{reps:[$/O]}});return l.push(U),l.push(X),y(D,b,X,S,T)},A=y(m,"unsortedSegmentSum",s,f,i),g=Ae({inputs:{x:A},backend:n,attrs:{shape:c}}),x=g;if(d!=null){l.push(g);let w=R.getUndoAxesPermutation(d);x=mn({inputs:{x},backend:n,attrs:{perm:w}})}return l.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var bK={kernelName:Bu,backendName:"webgl",kernelFunc:xK},vK=[sG,lG,UB,GB,KB,JB,eV,aV,sV,oV,pV,hV,yV,xV,NV,wV,CV,$V,MV,_V,LV,BV,HV,JV,ej,ij,lj,cj,mj,SB,bj,Rj,Fj,Ij,Oj,Pj,Dj,Bj,Uj,qj,Kj,Yj,eU,iU,lU,nU,pU,fU,yU,bU,IU,EU,MU,FU,$U,zU,_U,LU,BU,jU,qU,ZU,QU,tH,rH,lH,cH,yH,IB,gH,gj,vH,IH,TH,TB,MH,zH,_H,UH,BH,XH,YH,tG,dG,gG,yG,wG,IG,NG,fG,EG,RG,DG,PG,VG,ZG,FB,JG,tq,rq,oq,nj,dq,cq,fq,Aq,vq,CB,kq,Iq,aj,GG,Tq,zq,Mq,DB,Lq,Vq,Gq,Kq,Qq,tX,rX,oX,uX,cX,mX,gX,vX,IX,TX,ZV,XG,RX,FX,DX,OX,PX,WX,jX,GX,XX,ZX,QX,qG,BB,nK,sK,lK,dK,hK,VB,mK,AK,bK,pq];for(let e of vK)di(e);var En;(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"})(En||(En={}));var Cd;(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"})(Cd||(Cd={}));var Vw;function wK(e){Vw=e.wasm.cwrap(oi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function kK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let T=n.dataIdMap.get(i.dataId);if(T.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${T.shape.length}.`);m=T.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,y=Cd[d];if(y==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],g=u?s.shape[1]:s.shape[2],x=r.shape[0],w=n.makeOutput([x,A,g],r.dtype),b=n.dataIdMap.get(w.dataId).id,v=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return Vw(c,v,r.shape.length,h,S,s.shape.length,l,u,y,m,f,p||0,b),w}var IK={kernelName:oi,backendName:"wasm",setupFunc:wK,kernelFunc:kK};function yn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var SK=yn(oo);function An(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,p=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(d.dataId).id,h=n!=null?n:u.dtype,m=R.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let y=new Uint8Array(new Int32Array(u.shape).buffer),A=new Uint8Array(new Int32Array(d.shape).buffer),g=o.dataIdMap.get(f.dataId).id,x=()=>a(p,y,u.shape.length,c,A,d.shape.length,En[u.dtype],g);if(t&&u.dtype==="float32")return x(),f;let w=R.getBroadcastDims(u.shape,m),b=R.getBroadcastDims(d.shape,m),v=w.every((T,C)=>T===C),S=b.every((T,C)=>T===C);if(v&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var NK=!0,TK=An(Mr,NK),jw;function EK(e){jw=e.wasm.cwrap(hs,null,["array","number","number","number"])}function CK(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return jw(s,r.length,En[a.dtype],i),a}var RK={kernelName:hs,backendName:"wasm",setupFunc:EK,kernelFunc:CK};function _h(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var MK={kernelName:Rs,backendName:"wasm",kernelFunc:_h},Uw;function FK(e){Uw=e.wasm.cwrap(ii,null,["number","array","number","number","number","array","number"])}function Ph(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=DK(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var zK={kernelName:ii,backendName:"wasm",kernelFunc:Ph,setupFunc:FK};function Xr(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=R.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let d=new Array(r);for(let c=0;c`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var XK={kernelName:Xo,backendName:"wasm",kernelFunc:Ia},Kw;function KK(e){Kw=e.wasm.cwrap(ys,null,["number","array","number","number","array","number","number","number","number"])}function ZK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,d=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),y=k.sizeFromShape(m),A=k.sizeFromShape(f),g=y===A||y===1||A===1;k.assert(l>=2&&u>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(y>A?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([c,h]);k.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let w=i?[y,d,c]:[y,c,d],b=o?[A,h,p]:[A,p,h],v=Ia({inputs:{x:r},backend:n,attrs:{shape:w}}),S=Ia({inputs:{x:s},backend:n,attrs:{shape:b}}),T=n.dataIdMap.get(v.dataId).id,C=n.dataIdMap.get(S.dataId).id,$=i?v.shape[2]:v.shape[1],O=o?S.shape[1]:S.shape[2],P=Math.max(y,A),j=n.makeOutput([P,$,O],v.dtype),D=n.dataIdMap.get(j.dataId).id,U=new Uint8Array(new Int32Array(v.shape).buffer),X=new Uint8Array(new Int32Array(S.shape).buffer);return Kw(T,U,v.shape.length,C,X,S.shape.length,i,o,D),n.disposeData(v.dataId),n.disposeData(S.dataId),j.shape=x,j}var YK={kernelName:ys,backendName:"wasm",setupFunc:KK,kernelFunc:ZK};function Lh(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var JK={kernelName:As,backendName:"wasm",kernelFunc:Lh},QK=yn(gs),Zw;function eZ(e){Zw=e.wasm.cwrap(Fr,null,["number","number","number","number"])}function tZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return Zw(o,s,i,u),l}var nZ={kernelName:Fr,backendName:"wasm",setupFunc:eZ,kernelFunc:tZ};function Yw(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=R.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return _h({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(R.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let w=k.sizeFromShape(x.shape.slice(a));return Ia({inputs:{x},backend:n,attrs:{shape:[-1,w]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=R.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,y=a1(m,r,t[0].dtype,f),A=R.computeOutShape(s.map(x=>x.shape),a);i.shape=A;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=R.fromStringArrayToUint8(y),h.forEach(x=>n.disposeData(x.dataId)),i}let l=k.sizeFromShape(s[0].shape.slice(0,a)),u=0,d=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return u+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(i);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=R.getAxesPermutation([s],l),d=r;u!==null&&(d=Ph({inputs:{x:r},attrs:{perm:u},backend:n}));let p=R.getInnerMostAxes(1,l)[0];R.assertAxesAreInnerMostDims("cumsum",[p],l);let c=n.makeOutput(d.shape,d.dtype),h=d.shape[p],m=n.dataIdMap.get(d.dataId).id,f=n.dataIdMap.get(c.dataId).id;t6(m,i?1:0,o?1:0,h,f,En[r.dtype]);let y=c;if(u!==null){let A=R.getUndoAxesPermutation(u);y=Ph({inputs:{x:c},attrs:{perm:A},backend:n}),n.disposeData(d.dataId),n.disposeData(c.dataId)}return y}var yZ={kernelName:ws,backendName:"wasm",setupFunc:fZ,kernelFunc:mZ},n6;function AZ(e){n6=e.wasm.cwrap(vo,null,["number","number","number","array","number","array","array","number","number"])}function gZ(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=u*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=t.makeOutput(m,"float32"),y=t.dataIdMap.get(r.dataId).id,A=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),g=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return n6(y,s,i==="NHWC"?1:0,A,r.shape.length-1,g,x,m.length,w),f}var xZ={kernelName:vo,backendName:"wasm",setupFunc:AZ,kernelFunc:gZ},a6;function bZ(e){a6=e.wasm.cwrap(ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vZ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:p}=n,c=u==null?[1,1]:u,h=R.computeConv2DInfo(r.shape,s.shape,l,c,d,p,!0),m=h.filterHeight,f=h.filterWidth,y=h.padInfo.top,A=h.padInfo.right,g=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,b=h.dilationWidth,v=h.strideHeight,S=h.strideWidth,T=h.inChannels,C=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let O=a.makeOutput(h.outShape,"float32"),P=a.dataIdMap.get(O.dataId).id;return a6(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,y,A,g,x,$,w,b,v,S,T,C,P),O}var wZ={kernelName:ks,backendName:"wasm",setupFunc:bZ,kernelFunc:vZ},kZ=!1,IZ=An(Io,kZ,"bool"),SZ=yn(Ss);function z1(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ia({inputs:{x:r},backend:a,attrs:{shape:o}})}var NZ={kernelName:So,backendName:"wasm",kernelFunc:z1};function TZ(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var EZ={kernelName:Fu,backendName:"wasm",kernelFunc:TZ},r6;function CZ(e){r6=e.wasm.cwrap(To,null,["number","number","number","number","number","number"])}function RZ(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,d]=a.shape;return r6(s,o,l,u,d,i),r}var MZ={kernelName:To,backendName:"wasm",kernelFunc:RZ,setupFunc:CZ},FZ=yn(Ns),$Z=!1,DZ=An(Ts,$Z),s6;function zZ(e){s6=e.wasm.cwrap(Es,null,["number","number","number","number","number","number","number"])}function OZ(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,d=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let y=t.dataIdMap.get(f.dataId).id;return s6(d,p,c,h,m,r,y),f}var _Z={kernelName:Es,backendName:"wasm",setupFunc:zZ,kernelFunc:OZ},i6;function PZ(e){i6=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function LZ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=R.computeConv2DInfo(r.shape,s.shape,l,d,u,c),y=Cd[h];if(y==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=a.dataIdMap.get(r.dataId).id,g=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let Q=a.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);w=Q.id}let b=f.filterHeight,v=f.filterWidth,S=f.padInfo.top,T=f.padInfo.right,C=f.padInfo.bottom,$=f.padInfo.left,O=f.dilationHeight,P=f.dilationWidth,j=f.strideHeight,D=f.strideWidth,U=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return i6(A,G,ee,Y,g,b,v,w,S,T,C,$,X,O,P,j,D,U,x,y,ie,m||0,ne),re}var WZ={kernelName:li,backendName:"wasm",setupFunc:PZ,kernelFunc:LZ},o6;function BZ(e){o6=e.wasm.cwrap(ui,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 VZ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=R.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!0),y=Cd[h];if(y==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=a.dataIdMap.get(r.dataId).id,g=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let Q=a.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);w=Q.id}let b=f.filterHeight,v=f.filterWidth,S=f.padInfo.top,T=f.padInfo.right,C=f.padInfo.bottom,$=f.padInfo.left,O=f.dilationHeight,P=f.dilationWidth,j=f.strideHeight,D=f.strideWidth,U=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return o6(A,G,ee,Y,g,b,v,w,S,T,C,$,X,O,P,j,D,U,x,y,ie,m||0,ne),re}var jZ={kernelName:ui,backendName:"wasm",setupFunc:BZ,kernelFunc:VZ},l6;function UZ(e){l6=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","array","number"])}function HZ(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Qm.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let d=r.shape,p=d[d.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return l6(c,En[a.dtype],h,i,p,o,m,f),u}var GZ={kernelName:Co,backendName:"wasm",setupFunc:UZ,kernelFunc:HZ},u6;function qZ(e){u6=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function XZ(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=Ia({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),c=Ia({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=d.shape.length-1,y=t.dataIdMap.get(d.dataId).id,A=t.dataIdMap.get(c.dataId).id,g=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(k.computeStrides(d.shape)).buffer),w=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return u6(y,En[r.dtype],x,f,A,u.batchSize,w,g),t.disposeData(d.dataId),t.disposeData(c.dataId),m.shape=u.outputShape,m}var KZ={kernelName:Eo,backendName:"wasm",setupFunc:qZ,kernelFunc:XZ},ZZ=!1,YZ=An(Ro,ZZ,"bool"),JZ=!1,QZ=An(Cs,JZ,"bool"),d6;function eY(e){d6=e.wasm.cwrap(Ms,null,["number","number","number"])}function tY(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;d6(r,n,i)}return s}var nY={kernelName:Ms,backendName:"wasm",setupFunc:eY,kernelFunc:tY},aY=!1,rY=An(Do,aY,"bool"),sY=!1,iY=An(zo,sY,"bool"),oY=yn(Fs),lY=!1,uY=An(_o,lY,"bool"),p6;function dY(e){p6=e.wasm.cwrap($s,null,["number, number, number"])}function pY(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:c}=Xr(i,r,t);if(c){let g=t.dataIdMap.get(u.dataId).id;l=u,o=g}let h=l.shape.length;R.assertAxesAreInnerMostDims("max",d,h);let[m,f]=R.computeOutAndReduceShapes(l.shape,d),y=k.sizeFromShape(f),A=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(A.dataId).id;p6(o,y,g)}if(c&&t.disposeData(u.dataId),s){let g=R.expandShapeToKeepDim(A.shape,p);A.shape=g}return A}var cY={kernelName:$s,backendName:"wasm",setupFunc:dY,kernelFunc:pY},hY=!1,fY=An(Ds,hY),c6;function mY(e){c6=e.wasm.cwrap(zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yY(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,d=R.computePool2DInfo(r.shape,i,o,1,l,u),p=d.filterHeight,c=d.filterWidth,h=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,y=d.padInfo.left,A=d.dilationHeight,g=d.dilationWidth,x=d.strideHeight,w=d.strideWidth,b=d.inChannels,v=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(d.outShape,"float32"),T=a.dataIdMap.get(S.dataId).id;return c6(s,r.shape[0],r.shape[1],r.shape[2],p,c,h,m,f,y,A,g,x,w,b,v,T),S}var AY={kernelName:zs,backendName:"wasm",setupFunc:mY,kernelFunc:yY},h6;function gY(e){h6=e.wasm.cwrap(Os,null,["number, number, number"])}function xY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Xr(i,r,t),m=p;if(h){let w=t.dataIdMap.get(d.dataId).id;w!==o&&(u=d,l=w,m=R.getInnerMostAxes(m.length,u.shape.length))}R.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,y]=R.computeOutAndReduceShapes(u.shape,m),A=k.sizeFromShape(y),g=u;u.dtype!=="float32"&&(g=Lh({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let x=t.makeOutput(f,"float32");if(k.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(x.dataId).id;h6(l,A,w)}if(h&&t.disposeData(d.dataId),s){let w=R.expandShapeToKeepDim(x.shape,c);x.shape=w}return u.dtype!=="float32"&&t.disposeData(g.dataId),x}var bY={kernelName:Os,backendName:"wasm",setupFunc:gY,kernelFunc:xY},f6;function vY(e){f6=e.wasm.cwrap(_s,null,["number, number, number"])}function wY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Xr(i,r,t);if(h){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let m=u.shape.length;R.assertAxesAreInnerMostDims("min",p,m);let[f,y]=R.computeOutAndReduceShapes(u.shape,p),A=k.sizeFromShape(y),g=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;f6(l,A,x)}if(h&&t.disposeData(d.dataId),s){let x=R.expandShapeToKeepDim(g.shape,c);g.shape=x}return g}var kY={kernelName:_s,backendName:"wasm",setupFunc:vY,kernelFunc:wY},IY=!1,SY=An(Ps,IY),O1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(O1||(O1={}));var m6;function NY(e){m6=e.wasm.cwrap(Ls,null,["number","array","number","number","array","array","number","number"])}function TY(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),p=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(d).buffer),h=new Uint8Array(new Int32Array(p).buffer);return m6(i,u,t.shape.length,En[t.dtype],c,h,O1[r],l),o}var EY={kernelName:Ls,backendName:"wasm",kernelFunc:TY,setupFunc:NY},CY=!0,RY=An(Ws,CY),MY=yn(Lo);function _1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var y6;function FY(e){y6=e.wasm.cwrap(Bo,"number",["number","number","number","number","number"])}function $Y(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,p=y6(u,d,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=_1(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var DY={kernelName:Bo,backendName:"wasm",setupFunc:FY,kernelFunc:$Y},A6;function zY(e){A6=e.wasm.cwrap(Vo,"number",["number","number","number","number","number","bool"])}function OY(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,c=A6(d,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:y}=_1(t,c);t.wasm._free(f);let A=t.makeOutput([m],"int32",h),g=t.makeOutput([],"int32",y);return[A,g]}var _Y={kernelName:Vo,backendName:"wasm",setupFunc:zY,kernelFunc:OY},g6;function PY(e){g6=e.wasm.cwrap(jo,"number",["number","number","number","number","number","number"])}function LY(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,c=g6(d,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:y}=_1(t,c);t.wasm._free(y);let A=t.makeOutput([m],"int32",h),g=t.makeOutput([m],"float32",f);return[A,g]}var WY={kernelName:jo,backendName:"wasm",setupFunc:PY,kernelFunc:LY},BY=!1,VY=An(Wo,BY,"bool"),x6;function jY(e){x6=e.wasm.cwrap(Bs,null,["number","number","number","number","number"])}function UY(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return x6(d,s,i,o,u),l}var HY={kernelName:Bs,backendName:"wasm",setupFunc:jY,kernelFunc:UY};function GY(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var qY={kernelName:Uo,backendName:"wasm",kernelFunc:GY};function XY(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return z1({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let p=z1({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=Yw({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeData(d.dataId)),u}var KY={kernelName:Ho,backendName:"wasm",kernelFunc:XY},b6;function ZY(e){b6=e.wasm.cwrap(Vs,null,["number","array","number","number","array","array","number","number"])}function YY(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),p=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(d).buffer),h=new Uint8Array(new Int32Array(p).buffer);return b6(i,u,t.shape.length,En[t.dtype],c,h,r,l),o}var JY={kernelName:Vs,backendName:"wasm",kernelFunc:YY,setupFunc:ZY},QY=!1,eJ=An(js,QY),v6;function tJ(e){v6=e.wasm.cwrap(Us,null,["number","number","number"])}function nJ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return v6(s,i,l),o}var aJ={kernelName:Us,backendName:"wasm",setupFunc:tJ,kernelFunc:nJ},w6;function rJ(e){w6=e.wasm.cwrap(Go,null,["number","number","number","number"])}function sJ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Xr(i,r,t),m=p;if(h){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,m=R.getInnerMostAxes(m.length,u.shape.length))}R.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,y]=R.computeOutAndReduceShapes(u.shape,m),A=k.sizeFromShape(y),g=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;w6(l,A,En[g.dtype],x)}if(h&&t.disposeData(d.dataId),s){let x=R.expandShapeToKeepDim(g.shape,c);g.shape=x}return g}var iJ={kernelName:Go,backendName:"wasm",setupFunc:rJ,kernelFunc:sJ},oJ=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=i1(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},lJ={kernelName:_u,backendName:"wasm",kernelFunc:oJ},uJ=!0,dJ=An(Is,uJ),pJ=yn(Hs),cJ=yn(qs),k6;function hJ(e){k6=e.wasm.cwrap(Gs,null,["number","number","number","number","number","number","number","number","number","number"])}function fJ(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[d,p,c,h]=r.shape,m=[d,l,u,h],f=t.dataIdMap.get(r.dataId),y;f.dtype!=="float32"&&(y=Lh({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(y.dataId));let A=f.id,g=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return g;let x=t.dataIdMap.get(g.dataId).id;return k6(A,d,p,c,h,l,u,s?1:0,i?1:0,x),y!=null&&t.disposeData(y.dataId),g}var mJ={kernelName:Gs,backendName:"wasm",setupFunc:hJ,kernelFunc:fJ},I6;function yJ(e){I6=e.wasm.cwrap(Xs,null,["number","array","number","array","number","number"])}function AJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return _h({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);I6(l,d,i.length,p,r.shape.length,u);let c=Ia({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var gJ={kernelName:Xs,backendName:"wasm",kernelFunc:AJ,setupFunc:yJ},S6;function xJ(e){S6=e.wasm.cwrap(ll,null,["number","number","number","number","number","number","number","number","array","number","number"])}function bJ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,d=n.dataIdMap.get(l.dataId).id,[p,c,h,m]=r.shape,[f,y]=R.getImageCenter(o,c,h),A=i===0,g=255,x=typeof i=="number"?[i,i,i,A?0:g]:[...i,g],w=new Uint8Array(new Int32Array(x).buffer);return S6(u,p,c,h,m,s,f,y,w,x.length,d),l}var vJ={kernelName:ll,backendName:"wasm",kernelFunc:bJ,setupFunc:xJ},wJ=yn(Ks),kJ=yn(Zs),N6;function IJ(e){N6=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number","array","number","number"])}function SJ(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:d,strides:p,outputSize:c}=ey.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),y=t.dataIdMap.get(o.dataId).id;return N6(h,m,En[s.dtype],l,u,d,f,c,y),o}var NJ={kernelName:Ko,backendName:"wasm",setupFunc:IJ,kernelFunc:SJ},T6;function TJ(e){T6=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function EJ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(r.shape,r.dtype),d=n.dataIdMap.get(u.dataId).id,p=a.shape.length,c=r.shape.length,h=p===0||p>1||c===1?1:k.sizeFromShape(r.shape.slice(1));return T6(i,o,l,h,d),u}var CJ={kernelName:Zo,backendName:"wasm",kernelFunc:EJ,setupFunc:TJ},E6;function RJ(e){E6=e.wasm.cwrap(Js,null,["number","number"])}function MJ(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return k.sizeFromShape(r.shape)===0||E6(a,s),r}var FJ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:RJ,kernelFunc:MJ},$J=yn(Ys);function Wh(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=un.parseSliceParams(t,n,a),o=un.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),d=k.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(o){let m=un.computeFlatOffset(s,d);return t.dtype==="string"?p.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+k.sizeFromShape(i))),u}if(t.dtype==="string"){let m=yh(l,s,i,t.shape,t.dtype);return p.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)DJ(l,d[0],c,s,i);else if(h===3)zJ(l,d[0],d[1],c,s,i);else if(h===4)OJ(l,d[0],d[1],d[2],c,s,i);else{let m=yh(l,s,i,t.shape,t.dtype);c.set(m)}return u}function DJ(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u{let c=[...d];c[o]=p;let h=Wh({inputs:{x:r},attrs:{begin:u,size:c},backend:a});return u[o]+=p,h})}var VJ={kernelName:nl,backendName:"wasm",kernelFunc:BJ},jJ=yn(Qs),UJ=yn(Wu),HJ=!0,GJ=An(ni,HJ),R6;function qJ(e){R6=e.wasm.cwrap(Dr,null,["number","number","number"])}function XJ(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return R6(i,r,l),o}var KJ={kernelName:Dr,backendName:"wasm",setupFunc:qJ,kernelFunc:XJ},M6;function ZJ(e){M6=e.wasm.cwrap(al,null,["number","array","number","array","array","array","array","array","number","number"])}function YJ(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,h=R.slice_util.maskToAxes(d);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(d!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(d!==0&&c!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let m=r.shape.length-s.length,f=R.slice_util.maskToAxes(p),y=r.shape.slice();f.forEach($=>{s[$]=0,i[$]=1,y.splice($,0,1)});let A=Ia({inputs:{x:r},attrs:{shape:y},backend:t}),{begin:g,end:x,strides:w}=R.slice_util.getNormalizedAxes(A.shape,h,m,s,i,o,l,u,d);s=g,i=x,o=w;let b=R.slice_util.maskToAxes(c);b.forEach($=>{i[$]=s[$]+1,o[$]=1});let v=R.slice_util.computeOutShape(s,i,o),S=v.filter(($,O)=>b.indexOf(O)===-1);if(o.every($=>$===1)){let $=Wh({inputs:{x:A},attrs:{begin:s,size:v},backend:t});t.disposeData(A.dataId);let O=Ia({inputs:{x:$},attrs:{shape:S},backend:t});return t.disposeData($.dataId),O}let T=t.makeOutput(S,"float32");if(!S.some($=>$===0)){let $=t.dataIdMap.get(A.dataId).id,O=new Uint8Array(new Int32Array(k.computeStrides(A.shape)).buffer),P=new Uint8Array(new Int32Array(s).buffer),j=new Uint8Array(new Int32Array(i).buffer),D=new Uint8Array(new Int32Array(o).buffer),U=new Uint8Array(new Int32Array(S).buffer),X=new Uint8Array(new Int32Array(k.computeStrides(S)).buffer),G=t.dataIdMap.get(T.dataId).id;M6($,O,A.shape.length,P,j,D,U,X,S.length,G)}t.disposeData(A.dataId);let C=Ia({inputs:{x:T},attrs:{shape:S},backend:t});return t.disposeData(T.dataId),C}var JJ={kernelName:al,backendName:"wasm",setupFunc:ZJ,kernelFunc:YJ},QJ=!0,eQ=An(ai,QJ),F6;function tQ(e){F6=e.wasm.cwrap(ei,null,["number, number, number"])}function nQ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Xr(i,r,t),m=p;if(h){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,m=R.getInnerMostAxes(m.length,u.shape.length))}R.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,y]=R.computeOutAndReduceShapes(u.shape,m),A=k.sizeFromShape(y),g=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;F6(l,A,x)}if(h&&t.disposeData(d.dataId),s){let x=R.expandShapeToKeepDim(g.shape,c);g.shape=x}return g}var aQ={kernelName:ei,backendName:"wasm",setupFunc:tQ,kernelFunc:nQ},rQ=yn(ri),sQ=yn(si),$6;function iQ(e){$6=e.wasm.cwrap($r,null,["number","array","number","array","number","number"])}function oQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let c=0;c{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),d=t.dataIdMap.get(u.dataId).id,p=t.makeOutput(l,"int32"),c=t.dataIdMap.get(p.dataId).id;return D6(i,o,a.shape.length,En[a.dtype],r,s,d,c),[u,p]},pQ={kernelName:rl,backendName:"wasm",setupFunc:uQ,kernelFunc:dQ},z6;function cQ(e){z6=e.wasm.cwrap(sl,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function hQ(e){let{backend:t,inputs:n,attrs:a}=e,{image:r,transforms:s}=n,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],y=[d,m,f,h],A=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),g=t.makeOutput(y,r.dtype),x=t.dataIdMap.get(g.dataId).id,w=t.dataIdMap.get(r.dataId).id,b=t.dataIdMap.get(s.dataId).id,v=i==="nearest"?1:2,S;switch(o){case"constant":S=1;break;case"reflect":S=2;break;case"wrap":S=3;break;case"nearest":S=4;break;default:S=1;break}return z6(w,b,s.shape[0]>1,d,m,f,h,c,p,A,r.shape.length-1,v,S,l,x),g}var fQ={kernelName:sl,backendName:"wasm",setupFunc:cQ,kernelFunc:hQ};function mQ(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h({dataId:h,dtype:m,shape:l}))}var yQ={kernelName:il,backendName:"wasm",kernelFunc:mQ};function AQ(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var gQ={kernelName:ol,backendName:"wasm",kernelFunc:AQ},xQ=[SK,TK,RK,PK,BK,UK,qK,YK,JK,QK,nZ,aZ,iZ,uZ,dZ,hZ,yZ,xZ,wZ,IZ,SZ,NZ,EZ,MZ,FZ,DZ,IK,_Z,WZ,jZ,GZ,KZ,YZ,QZ,MK,nY,rY,iY,oY,uY,cY,fY,AY,bY,kY,SY,EY,RY,MY,DY,_Y,WY,VY,HY,qY,KY,JY,eJ,aJ,iJ,lJ,dJ,pJ,cJ,XK,mJ,gJ,vJ,kJ,wJ,NJ,CJ,FJ,$J,_J,WJ,VJ,jJ,UJ,GJ,KJ,JJ,eQ,aQ,rQ,sQ,lQ,pQ,fQ,zK,yQ,gQ];for(let e of xQ)di(e);var P1=J();P1.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));P1.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(P1.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var O6=ro($I()),bQ='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()}}}}',vQ=ro(DI()),_6=class extends ku{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new zp(this,dr())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=k.sizeFromShape(n),o=i*k.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:a,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let s=this.wasm.HEAPU8.slice(t,t+k.sizeFromShape(a)*k.bytesPerElement(n));return IQ(s.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 a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=k.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=k.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function wQ(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance,s.module)})})}),{})}function P6(e,t,n){if(Bh!=null)return Bh;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),Md!=null&&Md[a]!=null?Md[a]:n+a}async function kQ(){let[e,t]=await Promise.all([J().getAsync("WASM_HAS_SIMD_SUPPORT"),J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=bQ,d=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(d)}return o.endsWith(".wasm")?P6(e,t,Rd!=null?Rd:l):l+o},L1&&(r.instantiateWasm=wQ(P6(e,t,Rd!=null?Rd:"")));let s=!1;r.onAbort=()=>{s||Fd||(Fd=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&Bh==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+O6.default.toString()],{type:"text/javascript"}),i=(0,O6.default)(r)):i=(0,vQ.default)(r),i.then(o=>{s=!0,Fd=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function IQ(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 SQ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Bh=null,Rd=null,Md={},Fd=!1,L1=!1;function NQ(e,t=!1){if(iy("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Fd)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Bh=e,L1=t}function TQ(e,t=!1){if(Fd)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")Rd=e;else{Md=e;let n=SQ.filter(a=>Md[a]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}L1=t}var L6="3.6.0",EQ=2;Al("wasm",async()=>{let{wasm:e}=await kQ();return new _6(e)},EQ);Z().prototype.abs=function(){return this.throwIfDisposed(),Lt(this)};Z().prototype.acos=function(){return this.throwIfDisposed(),ly(this)};Z().prototype.acosh=function(){return this.throwIfDisposed(),uy(this)};Z().prototype.add=function(e){return this.throwIfDisposed(),se(this,e)};Z().prototype.all=function(e,t){return this.throwIfDisposed(),Cc(this,e,t)};Z().prototype.any=function(e,t){return this.throwIfDisposed(),Qu(this,e,t)};Z().prototype.argMax=function(e){return this.throwIfDisposed(),yi(this,e)};Z().prototype.argMin=function(e){return this.throwIfDisposed(),dy(this,e)};Z().prototype.asScalar=function(){return this.throwIfDisposed(),F(this.size===1,()=>"The array must have only 1 element."),H(this,[])};Z().prototype.asType=function(e){return this.throwIfDisposed(),me(this,e)};Z().prototype.as1D=function(){return this.throwIfDisposed(),H(this,[this.size])};Z().prototype.as2D=function(e,t){return this.throwIfDisposed(),H(this,[e,t])};Z().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),H(this,[e,t,n])};Z().prototype.as4D=function(e,t,n,a){return this.throwIfDisposed(),H(this,[e,t,n,a])};Z().prototype.as5D=function(e,t,n,a,r){return this.throwIfDisposed(),H(this,[e,t,n,a,r])};Z().prototype.asin=function(){return this.throwIfDisposed(),py(this)};Z().prototype.asinh=function(){return this.throwIfDisposed(),cy(this)};Z().prototype.atan=function(){return this.throwIfDisposed(),hy(this)};Z().prototype.atan2=function(e){return this.throwIfDisposed(),fy(this,e)};Z().prototype.atanh=function(){return this.throwIfDisposed(),my(this)};Z().prototype.avgPool=function(e,t,n,a){return this.throwIfDisposed(),td(this,e,t,n,a)};Z().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),nd(this,e,t)};Z().prototype.batchNorm=function(e,t,n,a,r){return this.throwIfDisposed(),xi(this,e,t,n,a,r)};Z().prototype.broadcastTo=function(e){return this.throwIfDisposed(),xl(this,e)};Z().prototype.cast=function(e){return this.throwIfDisposed(),me(this,e)};Z().prototype.ceil=function(){return this.throwIfDisposed(),by(this)};Z().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),Nn(this,e,t)};Z().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Le&&(e=[e]),lt([this,...e],t)};Z().prototype.conv1d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Mc(this,e,t,n,a,r,s)};Z().prototype.conv2dTranspose=function(e,t,n,a,r){return this.throwIfDisposed(),Fc(this,e,t,n,a,r)};Z().prototype.conv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),pr(this,e,t,n,a,r,s)};Z().prototype.cos=function(){return this.throwIfDisposed(),ad(this)};Z().prototype.cosh=function(){return this.throwIfDisposed(),$c(this)};Z().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),Dc(this,e,t,n)};Z().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),ky(this,e,t)};Z().prototype.depthwiseConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),vl(this,e,t,n,a,r,s)};Z().prototype.dilation2d=function(e,t,n,a,r){return this.throwIfDisposed(),Iy(this,e,t,n,a,r)};Z().prototype.divNoNan=function(e){return this.throwIfDisposed(),Sy(this,e)};Z().prototype.div=function(e){return this.throwIfDisposed(),fe(this,e)};Z().prototype.dot=function(e){return this.throwIfDisposed(),u3(this,e)};Z().prototype.elu=function(){return this.throwIfDisposed(),wl(this)};Z().prototype.equal=function(e){return this.throwIfDisposed(),Wr(this,e)};Z().prototype.erf=function(){return this.throwIfDisposed(),Ny(this)};Z().prototype.exp=function(){return this.throwIfDisposed(),ea(this)};Z().prototype.expandDims=function(e){return this.throwIfDisposed(),dn(this,e)};Z().prototype.expm1=function(){return this.throwIfDisposed(),Ty(this)};Z().prototype.fft=function(){return this.throwIfDisposed(),cd(this)};Z().prototype.flatten=function(){return this.throwIfDisposed(),H(this,[this.size])};Z().prototype.floor=function(){return this.throwIfDisposed(),Il(this)};Z().prototype.floorDiv=function(e){return this.throwIfDisposed(),Tc(this,e)};Z().prototype.gather=function(e,t){return this.throwIfDisposed(),bi(this,e,t)};Z().prototype.greaterEqual=function(e){return this.throwIfDisposed(),Vr(this,e)};Z().prototype.greater=function(e){return this.throwIfDisposed(),On(this,e)};Z().prototype.ifft=function(){return this.throwIfDisposed(),Cl(this)};Z().prototype.irfft=function(){return this.throwIfDisposed(),Yc(this)};Z().prototype.isFinite=function(){return this.throwIfDisposed(),p3(this)};Z().prototype.isInf=function(){return this.throwIfDisposed(),c3(this)};Z().prototype.isNaN=function(){return this.throwIfDisposed(),Cy(this)};Z().prototype.leakyRelu=function(e){return this.throwIfDisposed(),rd(this,e)};Z().prototype.lessEqual=function(e){return this.throwIfDisposed(),jr(this,e)};Z().prototype.less=function(e){return this.throwIfDisposed(),Oc(this,e)};Z().prototype.localResponseNormalization=function(e,t,n,a){return this.throwIfDisposed(),Ry(this,e,t,n,a)};Z().prototype.logSigmoid=function(){return this.throwIfDisposed(),m3(this)};Z().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Lc(this,e)};Z().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),$y(this,e,t)};Z().prototype.log=function(){return this.throwIfDisposed(),_n(this)};Z().prototype.log1p=function(){return this.throwIfDisposed(),_c(this)};Z().prototype.logicalAnd=function(e){return this.throwIfDisposed(),ca(this,e)};Z().prototype.logicalNot=function(){return this.throwIfDisposed(),sd(this)};Z().prototype.logicalOr=function(e){return this.throwIfDisposed(),Wc(this,e)};Z().prototype.logicalXor=function(e){return this.throwIfDisposed(),x3(this,e)};Z().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Be(this,e,t,n)};Z().prototype.maxPool=function(e,t,n,a){return this.throwIfDisposed(),id(this,e,t,n,a)};Z().prototype.max=function(e,t){return this.throwIfDisposed(),Tn(this,e,t)};Z().prototype.maximum=function(e){return this.throwIfDisposed(),ja(this,e)};Z().prototype.mean=function(e,t){return this.throwIfDisposed(),St(this,e,t)};Z().prototype.min=function(e,t){return this.throwIfDisposed(),Sl(this,e,t)};Z().prototype.minimum=function(e){return this.throwIfDisposed(),Nl(this,e)};Z().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),zy(this,e,t)};Z().prototype.mod=function(e){return this.throwIfDisposed(),Oy(this,e)};Z().prototype.mul=function(e){return this.throwIfDisposed(),L(this,e)};Z().prototype.neg=function(){return this.throwIfDisposed(),It(this)};Z().prototype.norm=function(e,t,n){return this.throwIfDisposed(),th(this,e,t,n)};Z().prototype.notEqual=function(e){return this.throwIfDisposed(),ki(this,e)};Z().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),ml(this,e,t,n)};Z().prototype.onesLike=function(){return this.throwIfDisposed(),Ln(this)};Z().prototype.pad=function(e,t){return this.throwIfDisposed(),cr(this,e,t)};Z().prototype.pool=function(e,t,n,a,r){return this.throwIfDisposed(),w3(this,e,t,n,a,r)};Z().prototype.pow=function(e){return this.throwIfDisposed(),hr(this,e)};Z().prototype.prelu=function(e){return this.throwIfDisposed(),ld(this,e)};Z().prototype.prod=function(e,t){return this.throwIfDisposed(),Vc(this,e,t)};Z().prototype.reciprocal=function(){return this.throwIfDisposed(),Ly(this)};Z().prototype.relu=function(){return this.throwIfDisposed(),Ua(this)};Z().prototype.relu6=function(){return this.throwIfDisposed(),jc(this)};Z().prototype.reshapeAs=function(e){return this.throwIfDisposed(),H(this,e.shape)};Z().prototype.reshape=function(e){return this.throwIfDisposed(),H(this,e)};Z().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),W3(this,e,t,n)};Z().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),B3(this,e,t,n)};Z().prototype.reverse=function(e){return this.throwIfDisposed(),Wn(this,e)};Z().prototype.rfft=function(){return this.throwIfDisposed(),hd(this)};Z().prototype.round=function(){return this.throwIfDisposed(),Uc(this)};Z().prototype.rsqrt=function(){return this.throwIfDisposed(),Hc(this)};Z().prototype.selu=function(){return this.throwIfDisposed(),Gc(this)};Z().prototype.separableConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Wy(this,e,t,n,a,r,s)};Z().prototype.sigmoid=function(){return this.throwIfDisposed(),Sn(this)};Z().prototype.sign=function(){return this.throwIfDisposed(),By(this)};Z().prototype.sin=function(){return this.throwIfDisposed(),qc(this)};Z().prototype.sinh=function(){return this.throwIfDisposed(),Xc(this)};Z().prototype.slice=function(e,t){return this.throwIfDisposed(),Re(this,e,t)};Z().prototype.softmax=function(e){return this.throwIfDisposed(),pd(this,e)};Z().prototype.softplus=function(){return this.throwIfDisposed(),vi(this)};Z().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),od(this,e,t)};Z().prototype.split=function(e,t){return this.throwIfDisposed(),qt(this,e,t)};Z().prototype.sqrt=function(){return this.throwIfDisposed(),en(this)};Z().prototype.square=function(){return this.throwIfDisposed(),ot(this)};Z().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Jc(this,e)};Z().prototype.squeeze=function(e){return this.throwIfDisposed(),Ha(this,e)};Z().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Le?[this,e]:[this,...e];return pn(n,t)};Z().prototype.step=function(e){return this.throwIfDisposed(),Rl(this,e)};Z().prototype.stridedSlice=function(e,t,n,a,r,s,i,o){return this.throwIfDisposed(),jy(this,e,t,n,a,r,s,i,o)};Z().prototype.sub=function(e){return this.throwIfDisposed(),ye(this,e)};Z().prototype.sum=function(e,t){return this.throwIfDisposed(),Ie(this,e,t)};Z().prototype.tan=function(){return this.throwIfDisposed(),Uy(this)};Z().prototype.tanh=function(){return this.throwIfDisposed(),gi(this)};Z().prototype.tile=function(e){return this.throwIfDisposed(),Br(this,e)};Z().prototype.toBool=function(){return this.throwIfDisposed(),me(this,"bool")};Z().prototype.toFloat=function(){return this.throwIfDisposed(),me(this,"float32")};Z().prototype.toInt=function(){return this.throwIfDisposed(),me(this,"int32")};Z().prototype.topk=function(e,t){return this.throwIfDisposed(),Hy(this,e,t)};Z().prototype.transpose=function(e){return this.throwIfDisposed(),Ye(this,e)};Z().prototype.unique=function(e){return this.throwIfDisposed(),eh(this,e)};Z().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),Gy(this,e,t)};Z().prototype.unstack=function(e){return this.throwIfDisposed(),ha(this,e)};Z().prototype.where=function(e,t){return this.throwIfDisposed(),rn(e,this,t)};Z().prototype.zerosLike=function(){return this.throwIfDisposed(),He(this)};var W6={kernelName:oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Rl(me(n,"float32"),-1))}}},CQ={kernelName:lo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ot(me(n,"float32")),r=en(ye(we(1),a));return It(fe(e,r))}}}},RQ={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=en(ye(ot(me(n,"float32")),1));return fe(e,a)}}}},MQ={kernelName:Mr,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ct(n.shape,a.shape);return{a:()=>{let s=e,i=Wt(n.shape,r);return i.length>0&&(s=Ie(s,i)),H(s,n.shape)},b:()=>{let s=e,i=Wt(a.shape,r);return i.length>0&&(s=Ie(s,i)),H(s,a.shape)}}}},FQ={kernelName:hs,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((a,r)=>{n[r]=()=>e.clone()}),n}},$Q={kernelName:fs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},DQ={kernelName:Nu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},zQ={kernelName:ho,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,en(ye(we(1),ot(me(n,"float32")))))}}},OQ={kernelName:fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=en(se(we(1),ot(me(n,"float32"))));return fe(e,a)}}}},_Q={kernelName:Ao,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ct(n.shape,a.shape);return{a:()=>{let s=se(ot(n),ot(a)),i=L(e,fe(a,s)),o=Wt(n.shape,r);return o.length>0&&(i=Ie(i,o)),H(i,n.shape)},b:()=>{let s=se(ot(n),ot(a)),i=It(L(e,fe(n,s))),o=Wt(a.shape,r);return o.length>0&&(i=Ie(i,o)),H(i,a.shape)}}}},PQ={kernelName:mo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,se(ot(me(n,"float32")),1))}}},LQ={kernelName:yo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ye(we(1),ot(me(n,"float32"))))}}};function WQ(e,t,n,a,r,s){let i=M(e,"dy","avgPool3dGrad"),o=M(t,"input","avgPool3dGrad"),l=i,u=o,d=!1;o.rank===4&&(d=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),u=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),F(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),F(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),s!=null&&F(Ht(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);let p={dy:l,input:u},c={filterSize:n,strides:a,pad:r,dimRoundingMode:s},h=z.runKernel(Wp,p,c);return d?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var BQ=_({avgPool3dGrad_:WQ}),VQ={kernelName:Tu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>BQ(e,a,r,s,i,o)}}};function jQ(e,t,n,a,r){let s=M(e,"dy","avgPoolGrad"),i=M(t,"input","avgPoolGrad");F(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,u=!1;i.rank===3&&(u=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),F(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let d={dy:l,input:o},p={filterSize:n,strides:a,pad:r},c=z.runKernel(Lp,d,p);return u?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var UQ=_({avgPoolGrad_:jQ}),HQ={kernelName:ms,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i}=n;return{x:()=>UQ(e,a,r,s,i)}}},GQ={kernelName:ys,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[a,r]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>Be(e,r,!1,!0),b:()=>Be(a,e,!0,!1)}:!s&&i?{a:()=>Be(e,r,!1,!1),b:()=>Be(e,a,!0,!1)}:s&&!i?{a:()=>Be(r,e,!1,!0),b:()=>Be(a,e,!1,!1)}:{a:()=>Be(r,e,!0,!0),b:()=>Be(e,a,!0,!0)}}},qQ={kernelName:Eu,gradFunc:(e,t,n)=>{let{blockShape:a,crops:r}=n;return{x:()=>od(e,a,r)}}},XQ={kernelName:ab,gradFunc:(e,t,n)=>{let a=n,r=a.inputShape,s=a.shape,i=Array.from(s);for(let l=r.length-1;l>=0;l--)if(r[l]===s[l])i[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l1&&o.push(l);return{x:()=>Ie(e,o,!0)}}},KQ={kernelName:As,gradFunc:e=>({x:()=>e.clone()})},ZQ={kernelName:gs,gradFunc:e=>({x:()=>He(e)})},YQ={kernelName:Fr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{clipValueMin:r,clipValueMax:s}=n;return{x:()=>rn(ca(Vr(a,r),jr(a,s)),e,He(e))}}},JQ={kernelName:Cu,inputsToSave:["x"],gradFunc:W6.gradFunc},QQ={kernelName:go,saveAllInputs:!0,gradFunc:(e,t,n)=>{let a=t.map(o=>o.shape),{axis:r}=n,s=la(r,t[0].shape)[0],i=a.map(o=>o[s]);return qt(e,i,s).map(o=>()=>o)}},eee={kernelName:xs,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return F(Lr(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>vy(a.shape,e,r,i,o,l),filter:()=>Zy(a,e,r.shape,i,o,l)}}},tee={kernelName:bs,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>pr(e,r,s,i,o,1,l),filter:()=>Zy(e,a,r.shape,s,i,o,l)}}};function nee(e,t,n,a,r){let s=e;e.rank===4&&(s=H(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),F(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),F(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),F(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),F(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),F(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:a,pad:r,filterShape:n};return z.runKernel(Up,o,l)}var aee=_({conv3DBackpropFilter_:nee}),ree={kernelName:Ru,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s}=n;F(Lr(a),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let[i,o]=t;return{x:()=>i3(i.shape,e,o,r,s),filter:()=>aee(i,e,o.shape,r,s)}}},see={kernelName:vs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(It(qc(me(n,"float32"))),e)}}},iee={kernelName:xo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(Xc(me(n,"float32")),e)}}},oee={kernelName:ws,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r,exclusive:s,reverse:i}=n;return{x:()=>{let o=g3([r],a.rank),l=Dc(e,r,s,!i);return o!=null&&(l=Ye(l,o)),l}}}},lee={kernelName:ks,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s,dimRoundingMode:i}=n,o=a==null?[1,1]:a;F(Lr(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,u]=t;return F(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),F(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),F(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),F(Ba(r,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${o}'.`),i!=null&&F(Ht(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>$3(l.shape,e,u,r,s,a,i),filter:()=>F3(l,e,u.shape,r,s,a,i)}}},uee={kernelName:Mu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,s={x:a,filter:r,dy:e},i={x:a,filter:r,dy:e};return{x:()=>z.runKernel(Zp,s,n),filter:()=>z.runKernel(Yp,i,n)}}},dee={kernelName:wo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,a={dy:e,y:n};return{x:()=>z.runKernel(Qp,a)}}},pee={kernelName:ko,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=L(ea(It(ot(n))),2/Math.sqrt(Math.PI));return{x:()=>L(e,a)}}},cee={kernelName:Ss,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,n)}}},hee={kernelName:So,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>H(e,n.shape)}}},fee={kernelName:No,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,ea(n))}}},mee={kernelName:Ns,gradFunc:e=>({x:()=>He(e)})},yee={kernelName:Ts,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ct(n.shape,a.shape);return{a:()=>{let s=fe(e,me(a,"float32")),i=Wt(n.shape,r);return i.length>0?H(Ie(s,i),n.shape):s},b:()=>{let s=L(e,me(n,"float32")),i=Wt(a.shape,r);i.length>0&&(s=H(Ie(s,i),a.shape));let o=ot(a);return It(fe(s,me(o,"float32")))}}}},Aee={kernelName:Es,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:a}=n,[r,s,i,o]=t,l=o==null?we(1):o,u=Wt(s.shape,r.shape),d=[];if(s.rank===1){for(let f=0;fs.rank===1?H(L(L(e,Br(H(h,[1,1,1,s.shape[0]]),d)),l),r.shape):H(L(L(e,h),l),r.shape),mean:()=>{let f=L(L(h,we(-1)),c);return s.rank===1&&(f=Ie(f,u)),H(f,s.shape)},variance:()=>{let f=L(L(m,p),c);return s.rank===1&&(f=Ie(f,u)),H(f,s.shape)},scale:()=>{let f=L(p,h),y=L(e,f);return s.rank===1&&(y=Ie(y,u)),H(y,s.shape)},offset:()=>{let f=e;return s.rank===1&&(f=Ie(f,u)),H(f,s.shape)}}}},gee={kernelName:Eo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[a,r]=t,{axis:s}=n,i=la(s,a.shape)[0];return{x:()=>{let o=a.shape,l=r.size,u=o.slice(0,i),d=u.length,p=o.slice(s,o.length).slice(1),c=p.length,h=B6(0,d),m=B6(d+1,d+1+c),f=V6([u,[l],p]),y=H(e,f),A=H(r,[l]),g=V6([[d],h,m]),x=Ye(y,g),w=Gy(x,A,a.shape[i]),b=Fy(g);return w=Ye(w,b),w},indices:()=>r}}};function B6(e,t){let n=[];for(let a=e;a{let[n,a]=t;return{a:()=>He(n),b:()=>He(a)}}},bee={kernelName:Rs,gradFunc:e=>({x:()=>me(e,"float32")})},vee={kernelName:Mo,gradFunc:e=>({x:()=>He(e)})},wee={kernelName:Fo,gradFunc:e=>({x:()=>He(e)})},kee={kernelName:$o,gradFunc:e=>({x:()=>He(e)})},Iee={kernelName:Ms,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{alpha:r}=n,s=On(a,0);return{x:()=>rn(s,e,L(e,r))}}},See={kernelName:Oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,se(n,1))}}},Nee={kernelName:Fs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,me(n,"float32"))}}},Tee={kernelName:rb,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n;return{logits:()=>{let s=!0,i=ea(a);return ye(e,L(Ie(e,r,s),i))}}}};function Eee(e,t,n,a=5,r=1,s=1,i=.5){let o={x:e,y:t,dy:n},l={depthRadius:a,bias:r,alpha:s,beta:i};return z.runKernel(rc,o,l)}var Cee=_({localResponseNormalizationBackprop_:Eee}),Ree={kernelName:zu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>Cee(a,r,e,s,i,o,l)}}};function j6(e,t,n,a){return t.rankL(e,me(Wr(n,t),e.dtype))}}var U6={kernelName:$s,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{reductionIndices:r}=a,s=t[0],i=t[1],o=la(r,s.shape),l=j6(e,i,s,o);return{x:()=>l.x()}}},Mee={kernelName:Ds,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>L(e,me(Vr(n,a),"float32")),b:()=>L(e,me(Oc(n,a),"float32"))}}};function Fee(e,t,n,a,r,s,i){let o=M(e,"dy","maxPool3dGrad"),l=M(t,"input","maxPool3dGrad"),u=M(n,"output","maxPool3dGrad"),d=o,p=l,c=u,h=!1;l.rank===4&&(h=!0,d=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),p=H(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),c=H(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),F(d.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${d.rank}.`),F(p.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`),F(c.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${c.rank}.`),i!=null&&F(Ht(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let m={dy:d,input:p,output:c},f={filterSize:a,strides:r,pad:s,dimRoundingMode:i},y=z.runKernel(ic,m,f);return h?H(y,[y.shape[1],y.shape[2],y.shape[3],y.shape[4]]):y}var $ee=_({maxPool3dGrad_:Fee}),Dee={kernelName:Ou,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>$ee(e,a,r,s,i,o,l)}}};function zee(e,t,n,a,r,s,i){let o=M(e,"dy","maxPoolGrad"),l=M(t,"input","maxPoolGrad"),u=M(n,"output","maxPoolGrad");F(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),F(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),F(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&F(Ht(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let d={dy:o,input:l,output:u},p={filterSize:a,strides:r,pad:s,dimRoundingMode:i};return z.runKernel(sc,d,p)}var Oee=_({maxPoolGrad_:zee}),_ee={kernelName:zs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>Oee(e,a,r,s,i,o)}}},Pee={kernelName:Os,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=la(r,a.shape),i=A3(a.shape,s)[1],o=Rt(i);return{x:()=>{let l=a.shape.slice();s.forEach(d=>{l[d]=1});let u=H(e,l);return fe(L(u,Pn(a.shape,"float32")),o)}}}},Lee={kernelName:_s,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{axis:r}=a,[s,i]=t,o=la(r,s.shape),l=j6(e,i,s,o);return{x:()=>l.x()}}},Wee={kernelName:Ps,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>L(e,me(jr(n,a),"float32")),b:()=>L(e,me(On(n,a),"float32"))}}},Bee={kernelName:Ls,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Re(e,s,a.shape)}}},Vee={kernelName:Po,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ct(n.shape,a.shape);return{a:()=>{let s=Wt(n.shape,r);return s.length>0?H(Ie(e,s),n.shape):e},b:()=>{let s=L(e,It(Il(fe(n,a)))),i=Wt(a.shape,r);return i.length>0?H(Ie(s,i),a.shape):s}}}},jee={kernelName:Ws,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ct(n.shape,a.shape);return{a:()=>{let s=L(e,me(a,"float32")),i=Wt(n.shape,r);return i.length>0?H(Ie(s,i),n.shape):s},b:()=>{let s=L(e,me(n,"float32")),i=Wt(a.shape,r);return i.length>0?H(Ie(s,i),a.shape):s}}}},Uee={kernelName:Lo,gradFunc:e=>({x:()=>It(e)})},Hee={kernelName:Bs,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>$t(n.shape,"float32")}}},Gee={kernelName:Uo,gradFunc:e=>({x:()=>He(e)})},qee={kernelName:Ho,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:a}=n;return ha(e,a).map(r=>()=>r)}},H6={kernelName:Vs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>Re(e,s,a.shape)}}},Xee={kernelName:js,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,a,r]=t,s=n,i=a,o=ct(s.shape,i.shape);return{a:()=>{let l=me(i,"float32"),u=L(e,L(l,hr(s,ye(l,we(1))))),d=Wt(s.shape,o);return d.length>0&&(u=Ie(u,d)),H(u,s.shape)},b:()=>{let l=On(s,0),u=rn(l,_n(s),He(s)),d=L(e,L(r,u)),p=Wt(i.shape,o);return p.length>0&&(d=Ie(d,p)),H(d,i.shape)}}}},Kee={kernelName:Us,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,a]=t,r=On(n,0);return{x:()=>rn(r,e,L(e,a)),alpha:()=>{let s=rn(r,He(e),L(e,n)),i=Wt(a.shape,e.shape);return i.length>0&&(s=Ie(s,i)),H(s,a.shape)}}}},Zee={kernelName:Is,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ct(n.shape,a.shape);return{a:()=>{let s=fe(e,me(a,"float32")),i=Wt(n.shape,r);return i.length>0?H(Ie(s,i),n.shape):s},b:()=>{let s=L(e,me(n,"float32")),i=Wt(a.shape,r);i.length>0&&(s=H(Ie(s,i),a.shape));let o=ot(a);return It(fe(s,me(o,"float32")))}}}},Yee={kernelName:qo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,It(ot(n)))}}},Jee={kernelName:qs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=L(jr(n,6),Rl(n));return{x:()=>L(e,me(a,"float32"))}}},Qee={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,me(Rl(n),"float32"))}}},ete={kernelName:Xo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>H(e,n.shape)}}},tte={kernelName:Gs,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>z.runKernel(pc,r,n)}}},nte={kernelName:Pu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>z.runKernel(dc,r,n)}}},ate={kernelName:Xs,gradFunc:(e,t,n)=>{let{dims:a}=n,r=la(a,e.shape);return{x:()=>Wn(e,r)}}},rte={kernelName:Ks,gradFunc:e=>({x:()=>He(e)})},ste={kernelName:Zs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>It(fe(e,L(hr(n,1.5),2)))}}},ite={kernelName:Zo,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>me(He(n),"float32"),t:()=>L(e,me(n,e.dtype)),e:()=>L(e,me(sd(n),e.dtype))}}},ote={kernelName:Yo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=On(n,we(0)),r=we(H3),s=we(G3),i=L(e,s),o=L(L(e,r),ea(me(n,"float32")));return rn(a,i,o)}}}},lte={kernelName:Js,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(n,ye(we(1),n)))}}},ute={kernelName:el,gradFunc:e=>({x:()=>He(e)})},dte={kernelName:Ys,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(ad(me(n,"float32")),e)}}},pte={kernelName:Qo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L($c(me(n,"float32")),e)}}},cte={kernelName:Jo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{begin:r,size:s}=n,i=a.shape,[o,l]=Hb(a,r,s),u=[];for(let d=0;dcr(e,u)}}},hte={kernelName:ti,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{dim:r}=n,s=!0,i=L(e,a);return{logits:()=>ye(i,L(Ie(i,[r],s),a))}}},fte={kernelName:tl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Sn(n))}}},G6={kernelName:Lu,gradFunc:(e,t,n)=>{let{blockShape:a,paddings:r}=n;return{x:()=>nd(e,a,r)}}},q6={kernelName:nl,gradFunc:(e,t,n)=>{let{axis:a}=n;return{x:()=>lt(e,a)}}},mte={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,L(en(me(n,"float32")),2))}}},yte={kernelName:Wu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(me(n,"float32"),2))}}},Ate={kernelName:ni,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=we(2);return{a:()=>L(e,L(r,ye(n,a))),b:()=>L(e,L(r,ye(a,n)))}}},gte={kernelName:Dr,gradFunc:e=>({x:()=>He(e)})},xte={kernelName:ai,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=ct(n.shape,a.shape);return{a:()=>{let s=e,i=Wt(n.shape,r);return i.length>0&&(s=Ie(s,i)),H(s,n.shape)},b:()=>{let s=e,i=Wt(a.shape,r);return i.length>0&&(s=Ie(s,i)),H(It(s),a.shape)}}}},bte={kernelName:ei,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,r=a.shape.slice(),{axis:s}=n;la(s,a.shape).forEach(l=>{r[l]=1});let i=H(e,r),o=L(i,Pn(a.shape,"float32"));return{x:()=>o}}},vte={kernelName:ri,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ot(ad(n)))}}},wte={kernelName:si,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(ye(we(1),ot(n)),e)}}},kte={kernelName:$r,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{reps:r}=n;return{x:()=>{let s=He(a);if(a.rank===1)for(let i=0;i{let a=n,{perm:r}=a,s=Fy(r);return{x:()=>Ye(e,s)}}},Ste={kernelName:il,gradFunc:(e,t,n)=>{let a=n,{axis:r}=a;return{value:()=>pn(e,r)}}},Nte={kernelName:Bu,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Tte(e,n)}}};function Tte(e,t){let n=ja(t,He(t)),a=bi(e,n),r=Vr(t,we(0,"int32")),s=a.rank-r.rank;for(let o=0;o({x:()=>He(e)})},Cte=[W6,CQ,RQ,MQ,FQ,$Q,DQ,zQ,OQ,_Q,PQ,LQ,VQ,HQ,GQ,qQ,XQ,KQ,ZQ,YQ,JQ,QQ,tee,eee,ree,see,iee,oee,lee,uee,Zee,dee,pee,cee,hee,fee,yee,mee,Aee,gee,xee,bee,vee,wee,kee,Iee,See,Nee,Tee,Ree,U6,U6,Mee,Dee,_ee,Pee,Lee,Wee,Bee,Vee,jee,Uee,Hee,Gee,qee,H6,H6,Xee,Kee,Yee,Jee,Qee,ete,tte,nte,ate,rte,ste,ite,ote,lte,ute,dte,pte,cte,hte,fte,G6,G6,q6,q6,mte,Ate,yte,gte,xte,bte,vte,wte,kte,Ite,Ste,Nte,Ete];for(let e of Cte)sb(e);var X6={};Fe(X6,{maxNorm:()=>$te,minMaxNorm:()=>Ote,nonNeg:()=>zte,unitNorm:()=>Dte});var W1;function Bt(){return W1==null&&(W1=Zb().epsilon()),W1}function Sa(){return"channelsLast"}var Ar=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Ar.prototype)}},Na=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Na.prototype)}},V=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,V.prototype)}},ze=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,ze.prototype)}},K6=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,K6.prototype)}};function Di(e,t){if(Array.isArray(e)){let n=[];for(let a=0;an.toUpperCase())}var fa={};function B1(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function V1(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>V1(t));else{let t=Object.keys(e);for(let n of t){let a=e[n];a!=null&&typeof a=="object"&&(!Array.isArray(a)&&a.type==="ndarray"&&typeof a.value=="number"?e[n]=a.value:V1(a))}}}function $d(e,t={},n={},a="object",r=!1){if(typeof e=="string"){let s=e,i;if(s in n)i=n[s];else if(s in fa)i=fa[s];else if(i=t[s],i==null)throw new V(`Unknown ${a}: ${e}. This may be due to one of the following reasons: 1. The ${a} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. 2. The custom ${a} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new V(`${a}: Improper config format: ${JSON.stringify(s)}. 'className' and 'config' must set.`);let i=s.className,o,l;if(i in n?[o,l]=n[i]:i in fa?[o,l]=fa.className:i in t&&([o,l]=t[i]),o==null)throw new V(`Unknown ${a}: ${i}. This may be due to one of the following reasons: 1. The ${a} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. 2. The custom ${a} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let h of Object.keys(fa))u[h]=fa[h];for(let h of Object.keys(n))u[h]=n[h];let d=s.config;d.customObjects=u;let p=Object.assign({},fa);for(let h of Object.keys(n))fa[h]=n[h];V1(s.config);let c=l(o,s.config,n,r);return fa=Object.assign({},p),c}else{let u=Object.assign({},fa);for(let p of Object.keys(n))fa[p]=n[p];let d=new o(s.config);return fa=Object.assign({},u),d}}}function Rte(e,t){return et?1:0}function Vh(e,t){return-1*Rte(e,t)}function Kr(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function Mte(e){if(e==null)throw new V(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Oi(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new V(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function j1(e,t,n=0,a=Infinity){return Ka(n>=0),Ka(a>=n),Array.isArray(e)&&e.length>=n&&e.length<=a&&e.every(r=>typeof r===t)}function Kt(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,a)=>Kt(n,`element ${a+1} of ${t}`))):k.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${Y6(e)}.`)}function Y6(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>Y6(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function Fte(e,t){let n=k.now(),a;return(...r)=>{let s=k.now();return s-nen(Ie(L(e,e),t,!0)))}var Dd=class extends ae.Serializable{getConfig(){return{}}},H1=class extends Dd{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 B(()=>{let t=U1(e,this.axis),n=Nn(t,0,this.maxValue);return L(e,fe(n,se(Bt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};H1.className="MaxNorm";ae.registerClass(H1);var G1=class extends Dd{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return B(()=>fe(e,se(Bt(),U1(e,this.axis))))}getConfig(){return{axis:this.axis}}};G1.className="UnitNorm";ae.registerClass(G1);var q1=class extends Dd{apply(e){return Ua(e)}};q1.className="NonNeg";ae.registerClass(q1);var X1=class extends Dd{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 B(()=>{let t=U1(e,this.axis),n=se(L(this.rate,Nn(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,fe(n,se(Bt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};X1.className="MinMaxNorm";ae.registerClass(X1);var Q6={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Vt(e){return B1(e)}function e4(e,t={}){return $d(e,ae.SerializationMap.getMap().classNameMap,t,"constraint")}function jt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in Q6?Q6[e]:e,config:{}};return e4(t)}else return e instanceof Dd?e:e4(e)}function $te(e){return new H1(e)}function Dte(e){return new G1(e)}function zte(){return new q1}function Ote(e){return new X1(e)}var t4={};Fe(t4,{constant:()=>sne,glorotNormal:()=>cne,glorotUniform:()=>pne,heNormal:()=>hne,heUniform:()=>fne,identity:()=>une,leCunNormal:()=>mne,leCunUniform:()=>yne,ones:()=>rne,orthogonal:()=>Ane,randomNormal:()=>one,randomUniform:()=>ine,truncatedNormal:()=>lne,varianceScaling:()=>dne,zeros:()=>ane});var _te=["channelsFirst","channelsLast"],Pte=["nearest","bilinear"],Lte=["valid","same","causal"],Wte=["max","avg"],Bte=["sum","mul","concat","ave"],ql=new Map;function Ft(e){Oi(_te,"DataFormat",e)}function Vte(e){Oi(Pte,"InterpolationFormat",e)}function ra(e){Oi(Lte,"PaddingMode",e)}function n4(e){Oi(Wte,"PoolMode",e)}var zd=[],a4="/";function _i(e,t){zd.push(e);try{let n=t();return zd.pop(),n}catch(n){throw zd.pop(),n}}function jte(){return zd.length===0?"":zd.join(a4)+a4}function r4(e){if(!i4(e))throw new Error("Not a valid tensor name: '"+e+"'");return jte()+e}function s4(e){if(!i4(e))throw new Error("Not a valid tensor name: '"+e+"'");ql.has(e)||ql.set(e,0);let t=ql.get(e);if(ql.set(e,ql.get(e)+1),t>0){let n=`${e}_${t}`;return ql.set(n,1),n}else return e}var Ute=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function i4(e){return!!e.match(Ute)}function Hte(e){return e===parseInt(e.toString(),10)}function Zr(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let a=1;for(let r=t;r{if(e.shape.length!==2)throw new V(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=_d(e,1);return Y1(n,[1,t,1])})}function qte(e){let t=[Zr(e.shape)];return e.reshape(t)}function Xte(e){if(e.rank<=1)throw new V(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Zr(e.shape,1)];return e.reshape(t)}function Pi(e,t,n){return B(()=>{switch(e.rank){case 1:return Kc(e,t,n);case 2:return Vy(e,[t,0],[n,e.shape[1]]);case 3:return Zc(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return dd(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Re(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Re(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new V(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function K1(e,t,n){return B(()=>{switch(e.rank){case 1:return Kc(e,t,n);case 2:return Vy(e,[0,t],[e.shape[0],n]);case 3:return Zc(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return dd(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new V(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function jh(e,t,n,a){return B(()=>{switch(e.rank){case 1:return Kc(e,t,n);case 2:switch(a){case 1:return Pi(e,t,n);case 2:return K1(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${a}`)}case 3:switch(a){case 1:return Pi(e,t,n);case 2:return Zc(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return K1(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${a}`)}case 4:switch(a){case 1:return Pi(e,t,n);case 2:return dd(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return dd(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return K1(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${a}`)}default:throw new V(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Z1(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),lt(e,t)}function l4(e,t){switch(e.rank){case 1:return a3([e,t]);case 2:return bl([e,t],0);case 3:return r3([e,t],0);case 4:return s3([e,t],0);default:throw new V(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function Y1(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new V(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Br(e,t)}function Uh(e,t=0,n=1,a,r){return k3(e,t,n,a,r)}function Za(e,t,n,a){if(e.rank<2||t.rank<2)throw new ze(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let r=e.shape.slice(-1)[0],s=t.shape.slice(-2)[0];if(r!==s)throw new ze(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let r=!1,s=!1;return Ur.matMul({a:e,b:t,transposeA:r,transposeB:s,bias:a?J1(e.rank,a,Sa()):null,activation:n})}else{let r=e.shape.slice(),s=r.pop();e=e.reshape([-1,s]);let i=t.shape.slice(),o=i.pop(),l=i.pop(),u=[...i,o],d=Array.from({length:t.rank},(m,f)=>f===0?t.rank-2:f<=t.rank-2?f-1:f);t=t.transpose(d).reshape([l,-1]);let p=[...r,...u],c=!1,h=!1;return Ur.matMul({a:e,b:t,transposeA:c,transposeB:h,bias:a?J1(e.rank,a,Sa()):null,activation:n}).reshape(p)}}function u4(e,t,n){return B(()=>(Array.isArray(t)?t=Mt(t,"int32"):t=t.toInt(),bi(e,t,n)))}function Pd(e){return L(e,e)}function J1(e,t,n){let a=t.shape;if(t.rank!==1&&t.rank!==e)throw new V(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return a.length===1?t.reshape([1,a[0],1,1,1]):t.reshape([1,a[3],a[0],a[1],a[2]]);if(n==="channelsLast")return a.length===1?t.reshape([1,1,1,1,a[0]]):t.reshape([1].concat(a))}else if(e===4){if(n==="channelsFirst")return a.length===1?t.reshape([1,a[0],1,1]):t.reshape([1,a[2],a[0],a[1]]);if(n==="channelsLast")return a.length===1?t.reshape([1,1,1,a[0]]):t.reshape([1].concat(a))}else if(e===3){if(n==="channelsFirst")return a.length===1?t.reshape([1,a[0],1]):t.reshape([1,a[1],a[0]]);if(n==="channelsLast")return a.length===1?t.reshape([1,1,a[0]]):t.reshape([1].concat(a))}else if(e<3)return t;throw new V(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ea(e,t,n){return B(()=>(n==null&&(n=Sa()),Ft(n),e.add(J1(e.rank,t,n))))}function Kte(e,t=1){if(t!==1)throw new ze(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return wl(e)}function Zte(e){return B(()=>fe(e,Lt(e).add(1)))}function d4(e,t,n,a){return B(()=>R3(e,t,n,a))}function Yte(e){return B(()=>{let t=se(.5,L(.2,e));return Nn(t,0,1)})}function Ld(e,t,n=!1){return n?e():t()}var Jte=["fanIn","fanOut","fanAvg"],Qte=["normal","uniform","truncatedNormal"];function ene(e){Oi(Jte,"FanMode",e)}function tne(e){Oi(Qte,"Distribution",e)}var ma=class extends ae.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Q1=class extends ma{apply(e,t){return $t(e,t)}};Q1.className="Zeros";ae.registerClass(Q1);var Hh=class extends ma{apply(e,t){return Pn(e,t)}};Hh.className="Ones";ae.registerClass(Hh);var eA=class extends ma{constructor(e){super();if(typeof e!="object")throw new V(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new V(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return B(()=>L(we(this.value),Pn(e,t)))}getConfig(){return{value:this.value}}};eA.className="Constant";ae.registerClass(eA);var tA=class extends ma{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 Tl(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};tA.className="RandomUniform";ae.registerClass(tA);var nA=class extends ma{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 ze(`randomNormal does not support dType ${t}.`);return Uh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};nA.className="RandomNormal";ae.registerClass(nA);var aA=class extends ma{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 ze(`truncatedNormal does not support dType ${t}.`);return Qc(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};aA.className="TruncatedNormal";ae.registerClass(aA);var rA=class extends ma{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return B(()=>{if(e.length!==2||e[0]!==e[1])throw new V("Identity matrix initializer can only be used for 2D square matrices.");return L(this.gain,Ey(e[0]))})}getConfig(){return{gain:this.gain}}};rA.className="Identity";ae.registerClass(rA);function nne(e,t="channelsLast"){let n,a;if(Ft(t),e.length===2)n=e[0],a=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=Zr(e,2);n=e[1]*r,a=e[0]*r}else if(t==="channelsLast"){let r=Zr(e,0,e.length-2);n=e[e.length-2]*r,a=e[e.length-1]*r}}else{let r=Zr(e);n=Math.sqrt(r),a=Math.sqrt(r)}return[n,a]}var Rn=class extends ma{constructor(e){super();if(e.scale<0)throw new V(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,ene(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,tne(this.distribution),this.seed=e.seed}apply(e,t){let n=nne(e),a=n[0],r=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,a):this.mode==="fanOut"?s/=Math.max(1,r):s/=Math.max(1,(a+r)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ze(`${this.getClassName()} does not support dType ${t}.`);return Qc(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return Tl(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Rn.className="VarianceScaling";ae.registerClass(Rn);var Gh=class extends Rn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};Gh.className="GlorotUniform";ae.registerClass(Gh);var qh=class extends Rn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};qh.className="GlorotNormal";ae.registerClass(qh);var Xh=class extends Rn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};Xh.className="HeNormal";ae.registerClass(Xh);var Kh=class extends Rn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};Kh.className="HeUniform";ae.registerClass(Kh);var Zh=class extends Rn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};Zh.className="LeCunNormal";ae.registerClass(Zh);var Yh=class extends Rn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};Yh.className="LeCunNormal";ae.registerClass(Yh);var sA=class extends ma{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new ze("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return B(()=>{if(e.length<2)throw new ze("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,a=Uh(n,0,1,"float32"),r=j3.gramSchmidt(a);return e[0]>e[1]&&(r=r.transpose()),L(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};sA.className="Orthogonal";ae.registerClass(sA);var p4={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 c4(e,t={}){return $d(e,ae.SerializationMap.getMap().classNameMap,t,"initializer")}function Nt(e){return B1(e)}function yt(e){if(typeof e=="string"){let t=e in p4?p4[e]:e;if(t==="GlorotNormal")return new qh;if(t==="GlorotUniform")return new Gh;if(t==="HeNormal")return new Xh;if(t==="HeUniform")return new Kh;if(t==="LeCunNormal")return new Zh;if(t==="LeCunUniform")return new Yh;{let n={};return n.className=t,n.config={},c4(n)}}else return e instanceof ma?e:c4(e)}function ane(){return new Q1}function rne(){return new Hh}function sne(e){return new eA(e)}function ine(e){return new tA(e)}function one(e){return new nA(e)}function lne(e){return new aA(e)}function une(e){return new rA(e)}function dne(e){return new Rn(e)}function pne(e){return new Gh(e)}function cne(e){return new qh(e)}function hne(e){return new Xh(e)}function fne(e){return new Kh(e)}function mne(e){return new Zh(e)}function yne(e){return new Yh(e)}function Ane(e){return new sA(e)}var h4={};Fe(h4,{Layer:()=>qe,RNN:()=>Qa,RNNCell:()=>Xd,activation:()=>Qae,add:()=>lre,alphaDropout:()=>Hre,average:()=>ure,averagePooling1d:()=>k2,averagePooling2d:()=>I2,averagePooling3d:()=>S2,avgPool1d:()=>gre,avgPool2d:()=>bre,avgPool3d:()=>wre,avgPooling1d:()=>xre,avgPooling2d:()=>vre,avgPooling3d:()=>kre,batchNormalization:()=>mre,bidirectional:()=>_re,concatenate:()=>dre,conv1d:()=>Uae,conv2d:()=>Hae,conv2dTranspose:()=>Gae,conv3d:()=>qae,conv3dTranspose:()=>Xae,convLstm2d:()=>$re,convLstm2dCell:()=>Dre,cropping2D:()=>Zae,dense:()=>ere,depthwiseConv2d:()=>Jae,dot:()=>fre,dropout:()=>tre,elu:()=>Pae,embedding:()=>ore,flatten:()=>are,gaussianDropout:()=>Ure,gaussianNoise:()=>jre,globalAveragePooling1d:()=>Ire,globalAveragePooling2d:()=>Sre,globalMaxPool1d:()=>Lre,globalMaxPool2d:()=>Wre,globalMaxPooling1d:()=>w8,globalMaxPooling2d:()=>k8,gru:()=>Tre,gruCell:()=>Ere,input:()=>G4,inputLayer:()=>_ae,layerNormalization:()=>yre,leakyReLU:()=>Wae,lstm:()=>Cre,lstmCell:()=>Rre,masking:()=>Gre,maxPool1d:()=>Bre,maxPool2d:()=>Vre,maxPooling1d:()=>I8,maxPooling2d:()=>S8,maxPooling3d:()=>Nre,maximum:()=>pre,minimum:()=>cre,multiply:()=>hre,permute:()=>ire,prelu:()=>Bae,reLU:()=>Lae,repeatVector:()=>rre,reshape:()=>sre,rnn:()=>zre,separableConv2d:()=>Kae,simpleRNN:()=>Mre,simpleRNNCell:()=>Fre,softmax:()=>Vae,spatialDropout1d:()=>nre,stackedRNNCells:()=>Ore,thresholdedReLU:()=>jae,timeDistributed:()=>Pre,upSampling2d:()=>Yae,zeroPadding2d:()=>Are});var gne=0;function f4(){return gne++}var Jh={};function Qh(e=""){return e in Jh||(Jh[e]=0),Jh[e]+=1,e+Jh[e].toString()}function iA(e){return Array.isArray(e)&&Array.isArray(e[0])}function e0(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function _e(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new V(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function st(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new V(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function t0(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((a,r)=>a*r);return t}var m4="Variable",y4=class{constructor(e,t="float32",n=m4,a=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=f4(),n=n==null?m4:n,this.originalName=r4(n),this.name=s4(this.originalName),this.trainable_=a,this.constraint=r,this.val=S3(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),xne(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 xne(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function oA(e){return e.map(t=>t.read())}function lA(e){e.forEach(t=>{t[0].write(t[1])})}var zt=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||{}}},Ca=class{constructor(e,t,n,a,r,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=a,this.callArgs=r,this.outputTensorIndex=i,this.id=f4(),s!=null&&(this.originalName=r4(s),this.name=s4(this.originalName)),this.rank=t.length}},bne=0,n0=class{constructor(e,t){this.callArgs=t,this.id=bne++,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}}},vne=0,qe=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=vne++,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=gr(n)+"_"+Qh(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let a=e.dtype;a==null&&(a=e.inputDType),a==null&&(a="float32"),this.dtype=a}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Na(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new V(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Cn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Cn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Ar(`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 Ar(`Layer ${this.name} is not connected, no input to return.`);return Cn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Ar(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Ar(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Cn(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=ft(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=ft(this.inputSpec);if(e.length!==t.length)throw new V(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;nr.maxNDim)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${s}`);if(r.minNDim!=null&&s=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of ft(e))s.push(i.shape);this.build(Cn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=ft(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Cn(o),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=wne(e),i=this.computeOutputShape(s),o,l=kne(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new Ca(l,u,this,ft(e),t,this.name,d)):o=new Ca(l,i,this,ft(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,a)=>{n!=null&&e[a]!=null&&e[a]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Ar(`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 Ar(`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 Na(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return t0(this.weights)}build(e){this.built=!0}getWeights(e=!1){return oA(e?this.trainableWeights:this.weights)}setWeights(e){B(()=>{let t=this.weights;if(t.length!==e.length)throw new V(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],a=oA(t);for(let r=0;rr.apply(l.read())),s==null&&(s=!0),s?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=ft(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=ft(e);t=ft(t),n=ft(n),a=ft(a),r=e0(r),s=e0(s);let l=[],u=[],d=[];for(let p of o)l.push(p.sourceLayer),u.push(p.nodeIndex),d.push(p.tensorIndex);new n0({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let p=0;pe.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 wne(e){e=ft(e);let t=[];for(let n of e)t.push(n.shape);return Cn(t)}function kne(e){return"float32"}function A4(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let a=t.inboundNodes[n];if(a.inboundLayers.length===0)return a.inputTensors;{let r=[];for(let s=0;s0){let r=await Promise.all(t);for(let s=0;sse(this.totals[a],L(r,n)));this.totals[a]=i,s!=null&&s.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:B(()=>{let a=L(fe(1,this.seen),this.totals[n]);t[n]=a,this.totals[n].dispose(),Gt(t[n])}))}},w4=class extends Zl{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let r in this.history){let s=this.history[r];for(let i=0;inew k4(n,t))}var ya=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}`),ya.checkForDuplicate(t),ya.constructors[e]==null&&(ya.constructors[e]=[]),ya.constructors[e].push(t)}static checkForDuplicate(e){for(let t in ya.constructors)ya.constructors[+t].forEach(n=>{if(n===e)throw new V("Duplicate callback constructor.")})}static clear(){ya.constructors={}}static createCallbacks(e){let t=[];for(let n in ya.constructors){let a=+n;e>=a&&t.push(...ya.constructors[a])}return t.map(n=>new n)}};ya.constructors={};function S4(e,t,n,a,r,s,i,o,l){let u=new w4,d=[new Sne,...ya.createCallbacks(t)];e!=null&&d.push(...e),d.push(u);let p=new v4(d);return p.setParams({epochs:n,initialEpoch:a,samples:r,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:p,history:u}}function Ra(e,t={},n=!1){return $d(e,ae.SerializationMap.getMap().classNameMap,t,"layer",n)}function a0(e,t){return B(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Ie(Pd(e),t,!0),a=kl(n.shape,Bt()),r=en(ja(n,a));return fe(e,r)})}function Li(e,t){return B(()=>St(Pd(ye(t,e)),-1))}function r0(e,t){return B(()=>St(Lt(ye(t,e)),-1))}function Yl(e,t){return B(()=>{let n=ye(e,t),a=Nn(Lt(e),Bt(),Number.MAX_VALUE),r=Lt(fe(n,a));return L(100,St(r,-1))})}function Nne(e,t){return B(()=>{let n=Nn(t,Bt(),Number.MAX_VALUE),a=_n(se(1,n)),r=Nn(e,Bt(),Number.MAX_VALUE),s=_n(se(1,r));return St(Pd(ye(a,s)),-1)})}function Tne(e,t){return B(()=>{let n=ja(0,ye(1,L(e,t)));return St(Pd(n),-1)})}function Ene(e,t){return B(()=>{let n=ja(0,ye(1,L(e,t)));return St(n,-1)})}function Cne(e,t){return B(()=>{let n=Ie(L(e,t),-1),a=Tn(L(ye(1,e),t),-1);return ja(0,se(1,ye(a,n)))})}function Rne(e,t){return B(()=>{let n=Math.log(2),a=ye(t,e),r=ye(se(a,vi(L(-2,a))),n);return St(r,-1)})}function Wd(e,t,n=!1){return B(()=>{if(n)t=pd(t);else{let a=Ie(t,t.shape.length-1,!0);t=fe(t,a)}return t=Nn(t,Bt(),1-Bt()),It(Ie(L(e.toFloat(),_n(t)),t.shape.length-1))})}function s0(e,t,n=!1){return B(()=>{let a=Il(qte(e)).toInt();t=Nn(t,Bt(),1-Bt());let r=t.shape,s=ml(a,r[r.length-1]).reshape(r);return Wd(s,t,n)})}function Mne(e,t){if(!k.arraysEqual(e.shape,t.shape))throw new V(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return B(()=>{let n=t.relu(),a=t.abs().neg();return n.sub(t.mul(e)).add(a.exp().log1p())})}function i0(e,t){return B(()=>{let n;return n=Nn(t,Bt(),1-Bt()),n=_n(fe(n,ye(1,n))),St(Mne(e,n),-1)})}function Fne(e,t){return B(()=>{let n=Nn(e,Bt(),1),a=Nn(t,Bt(),1);return Ie(L(e,_n(fe(n,a))),-1)})}function $ne(e,t){return B(()=>{let n=_n(se(Bt(),t));return St(ye(t,L(e,n)),-1)})}function uA(e,t){return B(()=>{let n=a0(e,-1),a=a0(t,-1),r=L(n,a);return It(Ie(r,-1))})}var o0={meanSquaredError:Li,meanAbsoluteError:r0,meanAbsolutePercentageError:Yl,meanSquaredLogarithmicError:Nne,squaredHinge:Tne,hinge:Ene,categoricalHinge:Cne,logcosh:Rne,categoricalCrossentropy:Wd,sparseCategoricalCrossentropy:s0,binaryCrossentropy:i0,kullbackLeiblerDivergence:Fne,poisson:$ne,cosineProximity:uA};function dA(e){if(typeof e=="string"){if(e in o0)return o0[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 V(t)}else return e}function pA(e,t){return B(()=>{let n=L(.5,Ln(t)),a=Od(On(t,n),e.dtype);return St(Wr(e,a),-1)})}function cA(e,t){return B(()=>Od(Wr(yi(e,-1),yi(t,-1)),"float32"))}function N4(e,t){return B(()=>ca(e.equal(1),t.equal(1)).sum().cast("float32"))}function Dne(e,t){return B(()=>ca(e.equal(1),t.equal(0)).sum().cast("float32"))}function zne(e,t){return B(()=>ca(e.equal(0),t.equal(1)).sum().cast("float32"))}function T4(e,t){return B(()=>{let n=N4(e,t),a=zne(e,t),r=n.add(a);return rn(On(r,0),n.div(r),0).cast("float32")})}function One(e,t){return B(()=>{let n=N4(e,t),a=Dne(e,t),r=n.add(a);return rn(On(r,0),n.div(r),0).cast("float32")})}function E4(e,t){return i0(e,t)}function C4(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)),Wr(e,t).asType("float32")}var _ne=Li,Pne=Li,Lne=r0,Wne=r0,Bne=Yl,Vne=Yl,hA=Wd,jne=uA,R4=s0,l0={binaryAccuracy:pA,categoricalAccuracy:cA,precision:T4,categoricalCrossentropy:hA,sparseCategoricalCrossentropy:R4,mse:_ne,MSE:Pne,mae:Lne,MAE:Wne,mape:Bne,MAPE:Vne,cosine:jne};function Une(e){if(typeof e=="string"&&e in l0)return l0[e];if(typeof e!="string"&&e!=null)return e;throw new V(`Unknown metric ${e}`)}function u0(e){if(Ka(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(o0))if(o0[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(l0))if(l0[n]===e){t=n;break}return t!==void 0?t:e.name}}function Hne(e){let t={Adagrad:()=>Si.adagrad(.01),Adadelta:()=>Si.adadelta(1,.95,Bt()),Adam:()=>Si.adam(.001,.9,.999,Bt()),Adamax:()=>Si.adamax(.002,.9,.999,Bt(),0),RMSProp:()=>Si.rmsprop(.001,.9,0,Bt()),SGD:()=>Si.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 V(`Unknown Optimizer ${e}`)}var M4=1*1024*1024;function F4(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!fA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let a=JSON.stringify(e);a.length>M4&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${a.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${M4}.`)}}function fA(e){if(e===null)return!0;if(typeof e=="object")if(Object.getPrototypeOf(e)===Object.prototype){let t=Object.keys(e);for(let n of t)if(typeof n!="string"||!fA(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!fA(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function Gne(e,t,n,a=console.log){let r=Xne(e),s=["Layer (type)","Output shape","Param #"];r?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(d=>Math.floor(t*d)));let i;if(!r){s.push("Receives inputs"),i=[];for(let d in e.nodesByDepth)i.push(...e.nodesByDepth[d])}a("_".repeat(t)),d0(s,n,a),a("=".repeat(t));let o=e.layers;for(let d=0;d1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}a.push(...r)}if(t)for(let r of e.layers){let s=!1;for(let i of r.inboundNodes)if(a.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function d0(e,t,n=console.log){let a="";for(let r=0;r0&&(a=a.slice(0,a.length-1)+" "),a+=e[r],a=a.slice(0,t[r]),a+=" ".repeat(t[r]-a.length);n(a)}function Kne(e,t,n){let a;try{a=JSON.stringify(e.outputShape)}catch(o){a="multiple"}let r=e.name,s=e.getClassName(),i=[`${r} (${s})`,a,e.countParams().toString()];d0(i,t,n)}function Zne(e,t,n,a){let r;try{r=JSON.stringify(e.outputShape)}catch(d){r="multiple"}let s=[];for(let d of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(d)===-1))for(let p=0;pm.name),l=[],u=t.names();for(let m of o)u.indexOf(m)!==-1?l.push(t.getValue(m)):l.push(null);a!=null&&(a.maxNumTensors=-Infinity,a.minNumTensors=Infinity);let d=o.join(",")+"|"+t.names().join(","),p,c;if(AA[d]==null){let m=Jne(i,t);p=m.sorted,c=m.recipientCounts,AA[d]=p,D4[d]=c}p=AA[d],c={},r||Object.assign(c,D4[d]);let h=new Wi(t);for(let m=0;ma.maxNumTensors&&(a.maxNumTensors=C),C0,()=>"Expected at least one fetch, got none");let n=[],a={};if(e.length===1){let r=z4(e[0],t);n=r.sorted,a=r.recipientMap}else{let r=new Set;for(let s of e){let{sorted:i,recipientMap:o}=z4(s,t);for(let l of i)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in o)a[l]==null&&(a[l]=new Set),o[l].forEach(u=>a[l].add(u))}}return{sorted:n,recipientCounts:Qne(a)}}function Qne(e){let t={};for(let n in e)t[n]=e[n].size;return t}function z4(e,t){let n=new Set,a=[],r={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),a.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)r[u.name]==null&&(r[u.name]=new Set),r[u.name].add(o.name),!n.has(u.name)&&s.push(u)}}return{sorted:a,recipientMap:r}}function eae(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let a=0;aA.name)}`);Kr(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(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let g=A.sourceLayer,x=A.nodeIndex,w=A.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(w)}for(let A of this.inputs){let g=A.sourceLayer,x=A.nodeIndex,w=A.tensorIndex;Ka(x===0,"input layer has >1 nodes"),Ka(w===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(w)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;AA.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},a={},r={},s={},i=[],o=(A,g,x,w,b,v)=>{(w==null||b==null||v==null)&&(w=A.sourceLayer,b=A.nodeIndex,v=A.tensorIndex);let S=w.inboundNodes[b];if(x.indexOf(S)!==-1)throw new Na(`The tensor ${A.name} at layer "${w.name}" is part of a cycle.`);if(g.indexOf(S)!==-1)return;this.containerNodes.add(Ya.nodeKey(w,b)),w.id in s||(s[w.id]=Object.keys(s).length),x.indexOf(S)===-1&&x.push(S);let T=S.inboundLayers.length;for(let C=0;C=0;)x.splice(x.indexOf(S),1);i.push(S)},l=[],u=[];for(let A of this.outputs)o(A,l,u);let d=i.slice().reverse();for(let A of d){n[A.id]=A,A.id in t||(t[A.id]=0);let g=t[A.id],x=a[A.outboundLayer.id]==null?0:a[A.outboundLayer.id];g=Math.max(g,x),a[A.outboundLayer.id]=g,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=g;for(let w=0;wparseInt(A,10)).sort(Vh);this.layers=[];for(let A of h){let g=c[A];g.sort((x,w)=>{let b=s[x.id],v=s[w.id];return bv?1:0});for(let x of g)x instanceof Ya&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(p).map(A=>parseInt(A,10)).sort(Vh);let m=this.inputs.slice(),f=[];for(let A of h)for(let g of p[A]){let x=g.outboundLayer;if(x!=null){for(let w of g.inputTensors)if(m.indexOf(w)===-1)throw new Na(`Graph disconnected: cannot obtain value for tensor ${w} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let w of g.outputTensors)m.push(w);f.push(x.name)}}this.nodesByDepth=p;let y=this.layers.map(A=>A.name);for(let A of y){let g=y.filter(x=>x===A).length;if(g!==1)throw new Na(`The name "${A}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(y))}this.outboundNodes=[],this.inboundNodes=[],new n0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 V("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new V(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new V(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new V(`${s.length} of ${a} weights are not set: ${s}`)}lA(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${yA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=mA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return B(()=>{e=ft(e);let n=new Wi;for(let a=0;a{e=ft(e);let n;return t==null?n=Di(null,e.length):n=ft(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=e0(e);if(t.length!==this.inputLayers.length)throw new V(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;iparseInt(i,10)).sort(Vh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let d=[];for(let m=0;mparseInt(o,10)).sort(Vh);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,p=u.inputTensors,c=u.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,y,A,g;if(u.callArgs!=null&&(m=u.callArgs),h.length===1){let[x,w]=h[0];m.mask==null&&(m.mask=w),A=ft(d.call(x,m)),g=ft(d.computeMask(x,w)),f=[x],y=[w]}else f=h.map(x=>x[0]),y=h.map(x=>x[1]),m.mask==null&&(m.mask=y),A=ft(d.call(f,m)),g=ft(d.computeMask(f,y));if(d.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x{let e=[];for(let t of this.layers)for(let n=0;n0){let m=[];for(let f=0;f0&&f.apply(Cn(A),g)}function l(f){let y=f.name,A=Ra(f,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(a),r[y]=A,f.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new V(`Corrupted configuration, expected array for nodeData: ${g}`);i(A,g)})}let u=t.name,d=t.layers;for(let f of d)l(f);for(;!Mte(s);)for(let f of d){let y=r[f.name];if(y.name in s){let A=s[y.name];delete s[y.name];for(let g of A)o(y,g)}}let p=[],c=[],h=t.inputLayers;for(let f of h){let y=f[0],A=f[1],g=f[2];Ka(y in r);let x=r[y].inboundNodes[A].outputTensors;p.push(x[g])}let m=t.outputLayers;for(let f of m){let y=f[0],A=f[1],g=f[2];Ka(y in r);let x=r[y].inboundNodes[A].outputTensors;c.push(x[g])}return new e({inputs:p,outputs:c,name:u})}get stateful(){if(this._stateful)throw new V("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(){B(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function tae(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function O4(e,t){return tae(e,t,"classWeight")}async function _4(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=B(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),s=Array.from(await r.data());Ne(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Mt(i,"float32")}else return null}function nae(e,t){return L(e,t)}var aae=32;function P4(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=L4("input",e.inputNames,n),i=L4("output",e.outputNames,a),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function L4(e,t,n){if(n instanceof Le)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new V(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function rae(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function sae(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(W4(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 y=rae(n.validationData);s=y.xs,i=y.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(y=>"val_"+y)):u=l.slice();let d=I4(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=S4(d,p,n.epochs,null,null,iae(t,n),null,r,u);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m=n.batchesPerEpoch:x.done){if(r){let w;W4(n.validationData)?w=ft(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):w=ft(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?aae:n.validationBatchSize,verbose:0}));for(let b=0;b0)throw new ze("Verbose mode is not implemented yet.");k.assert(!a||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=oae(t)?t:await t.iterator(),o=0,l=0;for(;a?l{if(u.value){let{xs:d,ys:p}=P4(e,u.value),c=d.concat(p),h=B(()=>r(c));if(Ne(c),l===0)for(let f=0;fse(s[f],L(m,y))),l>0&&Ne(A)}Ne(h),o+=m,++l}return s}),u.done){a&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let u=0;u0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function jd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(a=>Pi(a,t,n-t)):Pi(e,t,n-t)}function xA(e,t){return B(()=>e==null?null:Array.isArray(e)?e.map(n=>xA(n,t)):u4(e,t.dtype==="int32"?t:t.toInt()))}function bA(e,t){let n=[],a=0,r=null;for(;a=e&&(r=e),n.push([a,r]),a=r;return n}async function uae(e,t,n,a,r,s,i,o,l,u,d,p,c,h,m){r==null&&(r=32),s==null&&(s=1),d==null&&(d=!0),c==null&&(c=0);let f=!1;if(l!=null&&u!=null&&(f=!0),m!=null&&(f=!0,h==null))throw new V("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let y=e.checkNumSamples(n,r,h,"steps_per_epoch"),A;y!=null&&(A=Ta(0,y)),i==null&&(i=1);let{callbackList:g,history:x}=S4(o,i,s,c,y,h,r,f,p);g.setModel(e),e.history=x,await g.onTrainBegin(),e.stopTraining_=!1;for(let w=c;w{let $=S[T][0],O=S[T][1],P=Pi(v,$,O-$);C.batch=T,C.size=O-$;let j=xA(n,P),D=t(j);for(let U=0;U0){if(m=!0,a.validationData.length===2)i=a.validationData[0],o=a.validationData[1];else throw a.validationData.length===3?new ze("validationData including sample weights is not supported yet."):new V(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${a.validationData} is invalid.`);let v=!0,S=await e.standardizeUserData(i,o,null,null,v,p);l=S[0],u=S[1],f=l.concat(u)}else if(a.validationSplit!=null&&a.validationSplit>0&&a.validationSplit<1){m=!0;let v=Math.floor(r[0].shape[0]*(1-a.validationSplit)),S=r[0].shape[0];l=jd(r,v,S),r=jd(r,0,v),u=jd(s,v,S),s=jd(s,0,v),f=l.concat(u)}else a.validationSteps!=null&&(m=!0);let y=r.concat(s).concat(d);e.checkTrainableWeightsConsistency();let A=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),x,w;m?(e.makeTestFunction(),x=e.testFunction,w=g.slice().concat(g.map(v=>"val_"+v))):(x=null,f=[],w=g.slice());let b=I4(a.callbacks,a.yieldEvery);return await uae(e,A,y,g,p,a.epochs,a.verbose,b,x,f,a.shuffle,w,a.initialEpoch,null,null)}finally{e.isTraining=!1,Bi(r,t),Bi(s,n),Bi(l,i),Bi(u,o),d!=null&&Ne(d)}}function B4(e){let t=[];e instanceof Le&&(e=[e]);for(let n=0;nn.push(r.id));else if(t!=null)for(let r in t){let s=t[r];n.push(s.id)}let a=[];if(e instanceof Le)n.indexOf(e.id)===-1&&a.push(e);else if(Array.isArray(e))e.forEach(r=>{n.indexOf(r.id)===-1&&a.push(r)});else if(e!=null)for(let r in e){let s=e[r];n.indexOf(s.id)===-1&&a.push(s)}a.forEach(r=>{r.isDisposed||r.dispose()})}function pae(e){return e instanceof Le}function vA(e){return Array.isArray(e)}function V4(e){return!pae(e)&&!vA(e)}function j4(e,t,n,a=!0,r=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(vA(e)&&e.length>0)i=!0;else if(V4(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new V(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(V4(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new V(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(vA(e)){if(e=e,e.length!==t.length)throw new V(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);s=e}else{if(e=e,t.length>1)throw new V(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=B4(s),n!=null)for(let i=0;i=0&&u!==d)throw new V(`Error when checking ${r}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function cae(e,t,n){let a=Kr(e.map(s=>s.shape[0]));a.sort();let r=Kr(t.map(s=>s.shape[0]));if(r.sort(),a.length>1)throw new V(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(r.length>1)throw new V(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(a.length>0&&r.length>0&&!k.arraysEqual(a,r))throw new V(`Input Tensors should have the same number of samples as target Tensors. Found ${a[0]} input sample(s) and ${r[0]} target sample(s).`)}function hae(e,t,n){let a=[Li,i0,Wd];for(let r=0;r1)throw new V(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(n!=null)for(let i=0;i[]);let n;if(typeof e=="string"||typeof e=="function")n=[e];else if(Array.isArray(e)||typeof e=="object")n=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(n))return t.map(a=>n);{let a=[];for(let r of t){let s=n.hasOwnProperty(r)?n[r]:[];Array.isArray(s)||(s=[s]),a.push(s)}return a}}var mae="layers-model",xr=class extends Ya{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new V("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");Gne(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=Hne(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof mr))throw new V("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new V(`Unknown entry in loss dictionary: "${s}". Only expected the following keys: ${this.outputNames}`);for(let s of this.outputNames)e.loss[s]==null&&console.warn(`Output "${s}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${s} during training`),t.push(dA(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new V(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(s=>dA(s))}else{let s=dA(e.loss);this.outputs.forEach(i=>{t.push(s)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let s=0;s{for(let s=0;s1&&(this.metricsTensors.push([i,s]),this.metricsNames.push(this.outputNames[s]+"_loss"))}});let a=fae(e.metrics,this.outputNames),r=(s,i,o)=>{this.outputNames.length>1&&(i=this.outputNames[s]+"_"+i),this.metricsNames.push(i),this.metricsTensors.push([o,s])};_i("metric",()=>{for(let s=0;s{let l="",u,d,p;for(let c of o){if(typeof c=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(c)!==-1){let m=this.internalOutputShapes[s];m[m.length-1]===1||this.lossFunctions[s]===i0?["accuracy","acc"].indexOf(c)!==-1?d=pA:["crossentropy","ce"].indexOf(c)!==-1&&(d=E4):this.lossFunctions[s]===s0?["accuracy","acc"].indexOf(c)!==-1?d=C4:["crossentropy","ce"].indexOf(c)!==-1&&(d=R4):["accuracy","acc"].indexOf(c)!==-1?d=cA:["crossentropy","ce"].indexOf(c)!==-1&&(d=hA);let f;["accuracy","acc"].indexOf(c)!==-1?f="acc":["crossentropy","ce"].indexOf(c)!==-1&&(f="ce"),p=d,u=l+f}else p=Une(c),u=l+u0(c);let h;_i(u,()=>{h=p}),r(s,u,h)}})(i)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let a=n.batchSize==null?32:n.batchSize;gA(a);let r=!0,s=this.standardizeUserDataXY(e,t,r,a);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,l=this.testLoop(o,i,a,n.verbose,n.steps);return Cn(l)}finally{Bi(s[0],e),Bi(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),lae(this,e,t)}checkNumSamples(e,t,n,a="steps"){let r;if(n!=null){if(r=null,t!=null)throw new V(`If ${a} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?r=e[0].shape[0]:r=e.shape[0];else throw new V(`Either the input data should have a defined shape, or ${a} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new V("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),a=n?t:[t],r=this.retrieveSymbolicTensors(a),s=new Wi;if(e instanceof Le&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new V(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;oi.name);for(let i=0;i0){let a=[];throw t.forEach((r,s)=>{r==null&&a.push(e[s])}),new V(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(a)}`)}return t}predictLoop(e,t=32,n=!1){return B(()=>{let a=this.checkNumSamples(e);if(n)throw new ze("Verbose predictLoop() is not implemented yet.");let r=bA(a,t),s=this.outputs.map(i=>[]);for(let i=0;i{let o=r[i][0],l=r[i][1],u=jd(e,o,l),d=[];if(Array.isArray(u))for(let c=0;cs[l].push(o));return Cn(s.map(i=>lt(i,0)))})}predict(e,t={}){let n=B4(e);U4(n,this.inputNames,this.feedInputShapes,!1);try{let a=t.batchSize==null?32:t.batchSize;return gA(a),this.predictLoop(n,a)}finally{Bi(n,e)}}predictOnBatch(e){U4(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,a){if(this.optimizer_==null)throw new Na("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let s=0;s0&&e[0].shape[0]%a!=0)throw new V(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${a}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,a,r=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,r,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(a!=null){let u=O4(a,this.outputNames);l=[];for(let d=0;d{let s=this.checkNumSamples(t,n,r,"steps"),i=[];if(a>0)throw new ze("Verbose mode is not implemented yet.");if(r!=null)throw new ze("steps mode in testLoop() is not implemented yet");{let o=bA(s,n),l=Mt(Ta(0,s));for(let u=0;u1&&(r+=`_${Z6(e.slice(0,n),a)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let h=0;h1&&h{c=se(c,h)}),c},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>B(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;lgr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=gr(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[gr(u0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>gr(u0(e)));{let e={};for(let t in this.metrics)e[t]=gr(u0(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=Bd(e.optimizer_config),n=Ra(t),a;if(typeof e.loss=="string")a=zi(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>zi(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=zi(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>zi(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=zi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=In.getSaveHandlers(e);if(i.length===0)throw new V(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new V(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new V("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await In.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:mae,generatedBy:`TensorFlow.js tfjs-layers v${yA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await In.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=In.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;F4(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){F4(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};xr.className="Model";ae.registerClass(xr);var H4=class extends xr{};H4.className="Functional";ae.registerClass(H4);async function yae(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=Bd(n),r=Ra(a,t);if(e.weightsManifest!=null){let s=await In.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Ne(s)}return r}async function Aae(e,t){if(t==null&&(t={}),typeof e=="string"){let n=In.getLoadHandlers(e,t);if(n.length===0)n.push(In.browserHTTPRequest(e,t));else if(n.length>1)throw new V(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return gae(e,void 0,t)}async function gae(e,t,n){if(n==null&&(n={}),e.load==null)throw new V("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=Ra(Bd(r),t,i),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new V("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=xae(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),Ne(u),Ne(d.map(p=>p.tensor))}return o}function xae(e,t){let n=In.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var Jl=class extends xr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Qh("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new V(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Jl||e instanceof xr,n;if(t){if(n=e,n.outputs.length!==1)throw new V("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 V("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 V("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=g4({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new V(`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 V("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=A4(this.outputs[0])}this.inboundNodes=[],new n0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Di(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new xr({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 Na("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 Na("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 Na("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 Na("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");r=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Jl))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ra(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("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 V("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}}};Jl.className="Sequential";ae.registerClass(Jl);function bae(e){return new xr(e)}function vae(e){return new Jl(e)}function wae(e,t){return t==null&&(t={}),Aae(e,t)}function G4(e){return g4(e)}function kae(e,t){ya.registerCallbackConstructor(e,t)}var Mn=class extends ae.Serializable{getConfig(){return{}}},q4=class extends Mn{apply(e,t=1){return Kte(e,t)}};q4.className="elu";ae.registerClass(q4);var X4=class extends Mn{apply(e){return Gc(e)}};X4.className="selu";ae.registerClass(X4);var K4=class extends Mn{apply(e){return Ua(e)}};K4.className="relu";ae.registerClass(K4);var Z4=class extends Mn{apply(e){return B(()=>Nl(6,Ua(e)))}};Z4.className="relu6";ae.registerClass(Z4);var Y4=class extends Mn{apply(e){return e}};Y4.className="linear";ae.registerClass(Y4);var J4=class extends Mn{apply(e){return Sn(e)}};J4.className="sigmoid";ae.registerClass(J4);var Q4=class extends Mn{apply(e){return Yte(e)}};Q4.className="hardSigmoid";ae.registerClass(Q4);var e8=class extends Mn{apply(e){return vi(e)}};e8.className="softplus";ae.registerClass(e8);var t8=class extends Mn{apply(e){return Zte(e)}};t8.className="softsign";ae.registerClass(t8);var n8=class extends Mn{apply(e){return gi(e)}};n8.className="tanh";ae.registerClass(n8);var wA=class extends Mn{apply(e,t=-1){return pd(e,t)}};wA.className="softmax";ae.registerClass(wA);var a8=class extends Mn{apply(e,t=-1){return Lc(e,t)}};a8.className="logSoftmax";ae.registerClass(a8);var r8=class extends Mn{apply(e,t=1){return B(()=>Sn(e.mul(t)).mul(e))}};r8.className="swish";ae.registerClass(r8);var s8=class extends Mn{apply(e){return B(()=>L(e,gi(vi(e))))}};s8.className="mish";ae.registerClass(s8);function Qr(e){return e.getClassName()}function kA(e,t={}){return $d(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function es(e){if(e==null){let t={};return t.className="linear",t.config={},kA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},kA(t)}else return e instanceof Mn?e:kA(e)}function IA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var i8=class extends ae.Serializable{},Ud=class extends i8{constructor(e){super();IA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=$t([1]);return this.hasL1&&(t=se(t,Ie(L(this.l1,Lt(e))))),this.hasL2&&(t=se(t,Ie(L(this.l2,Pd(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ud.className="L1L2";ae.registerClass(Ud);function Iae(e){return IA(e),new Ud({l1:e!=null?e.l1:null,l2:0})}function Sae(e){return IA(e),new Ud({l2:e!=null?e.l2:null,l1:0})}var o8={l1l2:"L1L2"};function dt(e){return B1(e)}function l8(e,t={}){return $d(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function At(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in o8?o8[e]:e,config:{}};return l8(t)}else return e instanceof i8?e:l8(e)}var SA=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=_e(e);let n=Ua(e);return this.maxValue!=null&&(n=Nn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};SA.className="ReLU";ae.registerClass(SA);var NA=class extends qe{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=_e(e);return rd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};NA.className="LeakyReLU";ae.registerClass(NA);var TA=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=yt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=At(e.alphaRegularizer),this.alphaConstraint=jt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Ft(t),t==="channelsFirst"?Ye(e,[0,2,3,1]):e))}function u8(e,t){return B(()=>(Ft(t),t==="channelsFirst"?Ye(e,[0,2,3,4,1]):e))}function Nae(e,t,n,a=1,r="valid",s,i=1){return B(()=>{if(s==null&&(s=Sa()),Ft(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ye(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Mc(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ea(o,n)),o})}function d8(e,t,n,a=[1,1],r="valid",s,i,o=null){return B(()=>{if(s==null&&(s=Sa()),Ft(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=MA(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ur.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ye(l,[0,3,1,2])),l})}function Tae(e,t,n,a=[1,1,1],r="valid",s,i){return B(()=>{if(s==null&&(s=Sa()),Ft(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=u8(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=wy(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ea(o,n)),s==="channelsFirst"&&(o=Ye(o,[0,4,1,2,3])),o})}var FA=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",FA.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ql(t.kernelSize,e,"kernelSize"),this.strides=Ql(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ra(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=jt(t.biasConstraint),this.biasRegularizer=At(t.biasRegularizer),this.activityRegularizer=At(t.activityRegularizer),this.dilationRate=Ql(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ka("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!j1(e.kernelSize,"number",1,3))throw new V(`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:Qr(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hd=class extends FA{constructor(e,t){super(e,t);this.kernel=null,Hd.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=jt(t.kernelConstraint),this.kernelRegularizer=At(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return B(()=>{e=_e(e);let n,a=this.bias==null?null:this.bias.read(),r=J6(this.activation.getClassName());if(r!=null&&this.rank===2)n=d8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=Nae(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=d8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Tae(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Gd=class extends Hd{constructor(e){super(2,e);Gd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!j1(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Gd.className="Conv2D";ae.registerClass(Gd);var qd=class extends Hd{constructor(e){super(3,e);qd.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};qd.className="Conv3D";ae.registerClass(qd);var $A=class extends Gd{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=_e(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],d=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=Ja(o,p,u,this.padding),m=Ja(l,c,d,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,1]));let y=Fc(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=Ye(y,[0,3,1,2])),this.bias!=null&&(y=Ea(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=Ja(t[a],o,s,this.padding),t[r]=Ja(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};$A.className="Conv2DTranspose";ae.registerClass($A);var DA=class extends qd{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=_e(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],d=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],y=this.strides[2],A=Ja(l,m,p,this.padding),g=Ja(u,f,c,this.padding),x=Ja(d,y,h,this.padding),w=[r,A,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,4,1]));let b=o3(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Ye(b,[0,4,1,2,3])),this.bias!==null&&(b=Ea(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=Ja(t[a],u,i,this.padding),t[r]=Ja(t[r],d,o,this.padding),t[s]=Ja(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};DA.className="Conv3DTranspose";ae.registerClass(DA);var p8=class extends Hd{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 V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`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=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=At(t.depthwiseRegularizer),this.depthwiseConstraint=jt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=At(t.pointwiseRegularizer),this.pointwiseConstraint=jt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length{e=_e(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ye(e,[0,2,3,1])),n=Wy(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ea(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ye(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=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseConstraint),e.pointwiseConstraint=Vt(this.pointwiseConstraint),e}};p8.className="SeparableConv";var zA=class extends p8{constructor(e){super(2,e)}};zA.className="SeparableConv2D";ae.registerClass(zA);var p0=class extends Hd{constructor(e){super(1,e);p0.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"&&!j1(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};p0.className="Conv1D";ae.registerClass(p0);var OA=class extends qe{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 B(()=>{if(e=_e(e),this.dataFormat==="channelsLast"){let n=jh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return jh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=jh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return jh(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}};OA.className="Cropping2D";ae.registerClass(OA);var _A=class extends qe{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,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Vte(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 B(()=>{let n=_e(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ye(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ye(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};_A.className="UpSampling2D";ae.registerClass(_A);function Eae(e,t,n=[1,1],a="valid",r,s){return B(()=>{r==null&&(r=Sa()),Ft(r);let i=MA(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=vl(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ye(i,[0,3,1,2])),i})}var PA=class extends FA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=jt(e.depthwiseConstraint),this.depthwiseRegularizer=At(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=_e(e);let n=Eae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ea(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ma(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ma(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseRegularizer),e}};PA.className="DepthwiseConv2D";ae.registerClass(PA);function c8(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function h8(e,t,n,a=!1,r,s,i=!1,o=!1){return B(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ta(2,l));if(t=Ye(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=dn(r,-1)),r=Ye(r,u)),a&&(t=Wn(t,0),r!=null&&(r=Wn(r,0)));let d=[],p,c=n,h=t.shape[0],m=ha(t),f;r!=null&&(f=ha(r));for(let A=0;Ae(g,c));if(r==null)p=x[0],c=x[1];else{let w=B(()=>{let b=f[A],v=Ln(b).sub(b),S=x[0].mul(b).add(c[0].mul(v)),T=c.map((C,$)=>x[1][$].mul(b).add(C.mul(v)));return{output:S,newStates:T}});p=w.output,c=w.newStates}o&&d.push(p)}let y;return o&&(y=pn(d,1)),[p,y,c]})}var Qa=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new f0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ta(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){iA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new Ar("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Ne(this.states_),this.keptStates!=null&&(Ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`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()):Ne(this.states_);for(let a=0;aGt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=c8(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ca){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=_e(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=h8((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?u:l;return this.returnState?[p].concat(d):p})}getInitialState(e){return B(()=>{let t=$t(e.shape);return t=Ie(t,[1,2]),t=_d(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Y1(t,[1,n]):t):this.cell.stateSize>1?[Y1(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()===Qa.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ra(a,n);return new e(Object.assign(t,{cell:r}))}};Qa.className="RNN";ae.registerClass(Qa);var Xd=class extends qe{},c0=class extends Xd{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,Kt(this.units,"units"),this.activation=es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Xl([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xl([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0Ln(e),rate:this.dropout,training:a})),0Ln(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Za(L(e,s),this.kernel.read()):r=Za(e,this.kernel.read()),this.bias!=null&&(r=Ea(r,this.bias.read())),i!=null&&(n=L(n,i));let o=se(r,Za(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qr(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};c0.className="SimpleRNNCell";ae.registerClass(c0);var LA=class extends Qa{constructor(e){e.cell=new c0(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};LA.className="SimpleRNN";ae.registerClass(LA);var h0=class extends Xd{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 V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Xl([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xl([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0Ln(e),rate:this.dropout,training:n,count:3})),0Ln(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};WA.className="GRU";ae.registerClass(WA);var Kd=class extends Xd{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,Kt(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Xl([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xl([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ma{apply(i,o){let l=r.apply([s]),u=new Hh().apply([s]),d=r.apply([s*2]);return l4(l4(l,u),d)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0Ln(e),rate:this.dropout,training:n,count:4})),0Ln(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0{this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};BA.className="LSTM";ae.registerClass(BA);var f0=class extends Xd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return B(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{_i(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ra(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return oA(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;sd4(t(),n),i=()=>Ld(s,t,a);return!r||r<=1?Gt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Gt(o.clone()))}var Cae=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return B(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=$t(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new Ar("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Ne(this.states_),this.keptStates!=null&&(Ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`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()):Ne(this.states_);for(let s=0;sGt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=Ma(l,a[0],r,s[0],i[0]),p=Ma(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};f8.className="ConvRNN2D";var m0=class extends Kd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Ql(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Ql(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",ra(this.padding),this.dataFormat=s||"channelsLast",Ft(this.dataFormat),this.dilationRate=Ql(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Kt(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends ma{apply(d,p){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return Z1([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0Ln(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,re,ne)=>!re||!re[ne]?Y:L(re[ne],Y),u=l(a,o,0),d=l(a,o,1),p=l(a,o,2),c=l(a,o,3);0Ln(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),y=l(r,h,2),A=l(r,h,3),g=3,[x,w,b,v]=qt(this.kernel.read(),i,g),[S,T,C,$]=this.useBias?qt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),d=this.inputConv(d,w,T,this.padding),p=this.inputConv(p,b,C,this.padding),c=this.inputConv(c,v,$,this.padding);let[O,P,j,D]=qt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,O),f=this.recurrentConv(f,P),y=this.recurrentConv(y,j),A=this.recurrentConv(A,D);let U=this.recurrentActivation.apply(se(u,m)),X=this.recurrentActivation.apply(se(d,f)),G=se(L(X,s),L(U,this.activation.apply(se(p,y)))),ee=L(this.recurrentActivation.apply(se(c,A)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Cae(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=pr(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ea(r,n,this.dataFormat):r}recurrentConv(e,t){return pr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};m0.className="ConvLSTM2DCell";ae.registerClass(m0);var VA=class extends f8{constructor(e){let t=new m0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};VA.className="ConvLSTM2D";ae.registerClass(VA);var y0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=_e(e);if(0d4(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};y0.className="Dropout";ae.registerClass(y0);var jA=class extends y0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};jA.className="SpatialDropout1D";ae.registerClass(jA);var UA=class extends qe{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,Kt(this.units,"units"),this.activation=es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=jt(e.kernelConstraint),this.biasConstraint=jt(e.biasConstraint),this.kernelRegularizer=At(e.kernelRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=_e(e),a=J6(this.activation.getClassName()),r;return a!=null?r=Za(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Za(n,this.kernel.read()),this.bias!=null&&(r=Ea(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Qr(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};UA.className="Dense";ae.registerClass(UA);var HA=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new V(`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],Zr(e,1)]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=_e(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=_e(e);return this.activation.apply(n)})}getConfig(){let e={activation:Qr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};GA.className="Activation";ae.registerClass(GA);var qA=class extends qe{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 B(()=>(e=_e(e),Gte(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};qA.className="RepeatVector";ae.registerClass(qA);var XA=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=_e(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};XA.className="Reshape";ae.registerClass(XA);var KA=class extends qe{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=Ta(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 zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ye(_e(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Permute";ae.registerClass(KA);var ZA=class extends qe{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=_e(e),a=-1;return Qu(ki(n,this.maskValue),a)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=_e(e),a=-1,r=!0,s=Qu(ki(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};ZA.className="Masking";ae.registerClass(ZA);var YA=class extends qe{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(ft(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=At(e.embeddingsRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.embeddingsConstraint=jt(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 B(()=>this.maskZero?(e=_e(e),ki(e,He(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a{this.invokeCallHook(e,t);let n=_e(e);return n.dtype!=="int32"&&(n=Od(n,"int32")),u4(this.embeddings.read(),n.as1D()).reshape(st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:Vt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};YA.className="Embedding";ae.registerClass(YA);var Vi=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&Kr(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=Yr(a);for(let s of e){let i=s.rank;for(let o=0;o1){let u=Ta(1,l).concat([0]);n.push(Ye(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=Ye(s.reshape([-1,u]),[1,0]).reshape(d)}else if(i>1){let o=[i-1].concat(Ta(0,i-1));s=Ye(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:dn(a,0));let n=t[0];for(let a=1;a{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return B(()=>Z1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new V("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new V("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new V(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return B(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s3||t.shape.length>3)throw new ze("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 ze("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return B(()=>{let i;if(a>r){i=a-r;let l=[];for(let u=0;ua){i=r-a;let l=[];for(let u=0;u0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let d=l;d"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 ze("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new V(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>Zd(r,e[s].shape.length)):a=[Zd(this.axes,t.shape.length),Zd(this.axes,n.shape.length)],this.normalize&&(t=a0(t,a[0]),n=a0(n,a[1])),Rae(t,n,a)}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 ze("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};r2.className="Dot";ae.registerClass(r2);var s2=class extends qe{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 B(()=>{this.invokeCallHook(e,t);let n=_e(e);return Ld(()=>Uh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};s2.className="GaussianNoise";ae.registerClass(s2);var i2=class extends qe{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 B(()=>{this.invokeCallHook(e,t);let n=_e(e);return this.rate>0&&this.rate<1?Ld(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(Uh(n.shape,1,a))},()=>n,t.training||!1):n})}};i2.className="GaussianDropout";ae.registerClass(i2);var o2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||_e(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 B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ld(()=>{let a=_e(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Vr(Tl(n),this.rate);o=Od(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>_e(e),t.training||!1)}return e})}};o2.className="AlphaDropout";ae.registerClass(o2);function Yd(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=e3(e,t,n,a,r,s);else if(e.rank===3)i=t3(e,t,n,a,r,s);else if(e.rank===4)i=n3(e,t,n,a,r,s);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Mae(e,t,n,a,r=.001){return B(()=>{let s=Bc(e,a),i=s.mean,o=s.variance;return[Yd(e,i,o,n,t,r),i,o]})}function Fae(e,t,n,a,r=.001){return B(()=>{let s=Bc(e,a),i=s.mean,o=s.variance,l=[];for(let h of Ta(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=i.reshape(l),d=o.reshape(l),p=t==null?null:t.reshape(l),c=n==null?null:n.reshape(l);return[Yd(e,u,d,c,p,r),i,o]})}function $ae(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Ta(0,e.rank-1))?Mae(e,t,n,a,r):Fae(e,t,n,a,r)}var l2=class extends qe{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=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=jt(e.betaConstraint),this.gammaConstraint=jt(e.gammaConstraint),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training,a=_e(e),r=a.shape,s=r.length,i=Ta(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Di(1,s);l[o]=r[o];let u=i.slice();u.sort();let d=!k.arraysEqual(u,Ta(0,s).slice(0,s-1)),p=()=>{if(d){let y=this.movingMean.read().reshape(l),A=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return Yd(a,y,A,g,x,this.epsilon)}else return Yd(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[c,h,m]=$ae(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(y,A,g)=>{B(()=>{let x=1-g,w=y.read(),b=w.sub(A).mul(x);y.write(w.sub(b))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:Vt(this.betaConstraint),gammaConstraint:Vt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};l2.className="BatchNormalization";ae.registerClass(l2);var u2=class extends qe{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=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Kr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=_e(e),a=n.shape,r=a.length;return B(()=>{let s=!0,{mean:i,variance:o}=Bc(n,this.axis,s),l=Di(1,r);for(let m of this.axis)l[m]=a[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,d=u(this.gamma.read()),p=u(this.beta.read()),c=[],h=[];for(let m=0;m{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Sa()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],cr(e,a)})}var d2=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Sa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return B(()=>Dae(_e(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};d2.className="ZeroPadding2D";ae.registerClass(d2);function A0(e,t,n,a,r,s){return B(()=>{Ft(r),n4(s),ra(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Sa()),s==null&&(s="max"),e=MA(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=id(e,t,n,o):i=td(e,t,n,o),r==="channelsFirst"&&(i=Ye(i,[0,3,1,2])),i})}function m8(e,t,n,a,r,s){return B(()=>{Ft(r),n4(s),ra(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Sa()),s==null&&(s="max"),e=u8(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Dy(e,t,n,o):i=gy(e,t,n,o),r==="channelsFirst"&&(i=Ye(i,[0,4,1,2,3])),i})}var y8=class extends qe{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 V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ra(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=st(e);let t=Ma(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return B(()=>{this.invokeCallHook(e,t),e=_d(_e(e),2);let n=this.poolingFunction(_e(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ha(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},p2=class extends y8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ra(a),A0(e,t,n,a,r,"max")}};p2.className="MaxPooling1D";ae.registerClass(p2);var c2=class extends y8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ra(a),A0(e,t,n,a,r,"avg")}};c2.className="AveragePooling1D";ae.registerClass(c2);var A8=class extends qe{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 V(`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];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ra(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ma(t,this.poolSize[0],this.padding,this.strides[0]),n=Ma(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 B(()=>(this.invokeCallHook(e,t),this.poolingFunction(_e(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}},h2=class extends A8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ra(a),A0(e,t,n,a,r,"max")}};h2.className="MaxPooling2D";ae.registerClass(h2);var f2=class extends A8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ra(a),A0(e,t,n,a,r,"avg")}};f2.className="AveragePooling2D";ae.registerClass(f2);var g8=class extends qe{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 V(`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];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ra(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ma(t,this.poolSize[0],this.padding,this.strides[0]),n=Ma(n,this.poolSize[1],this.padding,this.strides[1]),a=Ma(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(_e(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}},m2=class extends g8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ra(a),m8(e,t,n,a,r,"max")}};m2.className="MaxPooling3D";ae.registerClass(m2);var y2=class extends g8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ra(a),m8(e,t,n,a,r,"avg")}};y2.className="AveragePooling3D";ae.registerClass(y2);var x8=class extends qe{constructor(e){super(e);this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},A2=class extends x8{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=_e(e);return St(n,1)})}};A2.className="GlobalAveragePooling1D";ae.registerClass(A2);var g2=class extends x8{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=_e(e);return Tn(n,1)})}};g2.className="GlobalMaxPooling1D";ae.registerClass(g2);var b8=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},x2=class extends b8{call(e,t){return B(()=>{let n=_e(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};x2.className="GlobalAveragePooling2D";ae.registerClass(x2);var b2=class extends b8{call(e,t){return B(()=>{let n=_e(e);return this.dataFormat==="channelsLast"?Tn(n,[1,2]):Tn(n,[2,3])})}};b2.className="GlobalMaxPooling2D";ae.registerClass(b2);var v8=class extends qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=Ra(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},v2=class extends v8{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new V(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return B(()=>(e=_e(e),h8((n,a)=>[_e(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};v2.className="TimeDistributed";ae.registerClass(v2);function zae(e){Oi(Bte,"BidirectionalMergeMode",e)}var Oae="concat",w2=class extends v8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ra(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ra(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Oae:e.mergeMode,zae(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Cn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=c8(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(d=>new zt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ca;for(let l of s)if(l instanceof Ca!==o)throw new V("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=d,p}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Wn(r,1));let i;return this.mergeMode==="concat"?i=Z1([a,r]):this.mergeMode==="sum"?i=se(a,r):this.mergeMode==="ave"?i=L(.5,se(a,r)):this.mergeMode==="mul"?i=L(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){_i(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),_i(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ra(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};w2.className="Bidirectional";ae.registerClass(w2);function _ae(e){return new Kl(e)}function Pae(e){return new EA(e)}function Lae(e){return new SA(e)}function Wae(e){return new NA(e)}function Bae(e){return new TA(e)}function Vae(e){return new RA(e)}function jae(e){return new CA(e)}function Uae(e){return new p0(e)}function Hae(e){return new Gd(e)}function Gae(e){return new $A(e)}function qae(e){return new qd(e)}function Xae(e){return new DA(e)}function Kae(e){return new zA(e)}function Zae(e){return new OA(e)}function Yae(e){return new _A(e)}function Jae(e){return new PA(e)}function Qae(e){return new GA(e)}function ere(e){return new UA(e)}function tre(e){return new y0(e)}function nre(e){return new jA(e)}function are(e){return new HA(e)}function rre(e){return new qA(e)}function sre(e){return new XA(e)}function ire(e){return new KA(e)}function ore(e){return new YA(e)}function lre(e){return new JA(e)}function ure(e){return new e2(e)}function dre(e){return new a2(e)}function pre(e){return new t2(e)}function cre(e){return new n2(e)}function hre(e){return new QA(e)}function fre(e){return new r2(e)}function mre(e){return new l2(e)}function yre(e){return new u2(e)}function Are(e){return new d2(e)}function k2(e){return new c2(e)}function gre(e){return k2(e)}function xre(e){return k2(e)}function I2(e){return new f2(e)}function bre(e){return I2(e)}function vre(e){return I2(e)}function S2(e){return new y2(e)}function wre(e){return S2(e)}function kre(e){return S2(e)}function Ire(e){return new A2(e)}function Sre(e){return new x2(e)}function w8(e){return new g2(e)}function k8(e){return new b2(e)}function I8(e){return new p2(e)}function S8(e){return new h2(e)}function Nre(e){return new m2(e)}function Tre(e){return new WA(e)}function Ere(e){return new h0(e)}function Cre(e){return new BA(e)}function Rre(e){return new Kd(e)}function Mre(e){return new LA(e)}function Fre(e){return new c0(e)}function $re(e){return new VA(e)}function Dre(e){return new m0(e)}function zre(e){return new Qa(e)}function Ore(e){return new f0(e)}function _re(e){return new w2(e)}function Pre(e){return new v2(e)}var Lre=w8,Wre=k8,Bre=I8,Vre=S8;function jre(e){return new s2(e)}function Ure(e){return new i2(e)}function Hre(e){return new o2(e)}function Gre(e){return new ZA(e)}var N8={};Fe(N8,{MAPE:()=>ase,MSE:()=>ise,binaryAccuracy:()=>qre,binaryCrossentropy:()=>Xre,categoricalAccuracy:()=>Zre,categoricalCrossentropy:()=>Yre,cosineProximity:()=>ese,mape:()=>rse,meanAbsoluteError:()=>tse,meanAbsolutePercentageError:()=>nse,meanSquaredError:()=>sse,mse:()=>ose,precision:()=>Jre,recall:()=>Qre,sparseCategoricalAccuracy:()=>Kre});function qre(e,t){return pA(e,t)}function Xre(e,t){return E4(e,t)}function Kre(e,t){return C4(e,t)}function Zre(e,t){return cA(e,t)}function Yre(e,t){return hA(e,t)}function Jre(e,t){return T4(e,t)}function Qre(e,t){return One(e,t)}function ese(e,t){return uA(e,t)}function tse(e,t){return r0(e,t)}function nse(e,t){return Yl(e,t)}function ase(e,t){return Yl(e,t)}function rse(e,t){return Yl(e,t)}function sse(e,t){return Li(e,t)}function ise(e,t){return Li(e,t)}function ose(e,t){return Li(e,t)}var T8={};Fe(T8,{modelFromJSON:()=>yae});var E8={};Fe(E8,{l1:()=>use,l1l2:()=>lse,l2:()=>dse});function lse(e){return new Ud(e)}function use(e){return Iae(e)}function dse(e){return Sae(e)}var C8=class extends Zl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof xr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function g0(e,t){return et}var M8=class extends C8{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new ze("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=g0:this.mode==="max"?this.monitorFunc=R8:this.monitor.indexOf("acc")!==-1?this.monitorFunc=R8:this.monitorFunc=g0,this.monitorFunc===g0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===g0?Infinity:-Infinity}async onEpochEnd(e,t){await Jr(t);let n=this.getMonitorValue(t);n!=null&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=e,this.model.stopTraining=!0)))}async onTrainEnd(e){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(e){e==null&&(e={});let t=e[this.monitor];return t==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`),t}};function pse(e){return new M8(e)}var cse={earlyStopping:pse},Fa;(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"})(Fa||(Fa={}));var F8;(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={}))})(F8||(F8={}));var N2={};function hse(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};N2[e]=n}function $8(e){return N2[e]}function fse(e){delete N2[e]}function I(e,t,n,a,r){let s=t.inputParams[e];if(s&&s.inputIndexStart!==void 0){let o=s.inputIndexStart,l=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?o+1:s.inputIndexEnd;if(s.type==="tensor")return gn(t.inputNames[s.inputIndexStart],n,a,r);if(s.type==="tensors")return t.inputNames.slice(o,l).map(p=>gn(p,n,a,r));let u=gn(t.inputNames.slice(o)[0],n,a,r),d=u.dataSync();return s.type==="number"?d[0]:k.toNestedArray(u.shape,d)}let i=t.attrParams[e];return i&&i.value}function gn(e,t,n,a){let[r,s]=jn(e);if(a!=null){let o=a.getHashTableHandleByName(r);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[x0(r,o)]);return i!==void 0?t[x0(r,i)][s]:void 0}function mse(e,t,n){return t[x0(e,n.currentContextId)]}function br(e,t){let[n,a]=jn(e);return[x0(n,t&&t.currentContextId),a]}function x0(e,t){return t?`${e}-${t}`:e}function jn(e){let t=e.split(":");return t.length===1?[e,0]:[t[0],Number(t[t.length-1])]}function b0(e,t,n){let a=I("pad",e,t,n);if(a==="explicit"){a=I("explicitPaddings",e,t,n);let r=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)r[s][0]=a[s*2],r[s][1]=a[s*2+1];return r}return a}function vr(e){return e.kept?e:Wa(e)}var D8={};Fe(D8,{json:()=>yse});var yse=[{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}]}],z8={};Fe(z8,{json:()=>Ase});var Ase=[{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}]}],O8={};Fe(O8,{json:()=>gse});var gse=[{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"}]}],_8={};Fe(_8,{json:()=>xse});var xse=[{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"}]}],P8={};Fe(P8,{json:()=>bse});var bse=[{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"}]}],L8={};Fe(L8,{json:()=>vse});var vse=[{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}]}],W8={};Fe(W8,{json:()=>wse});var wse=[{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"}]}],B8={};Fe(B8,{json:()=>kse});var kse=[{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"}]}],V8={};Fe(V8,{json:()=>Ise});var Ise=[{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"}]}],j8={};Fe(j8,{json:()=>Sse});var Sse=[{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"}]}],U8={};Fe(U8,{json:()=>Nse});var Nse=[{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}]}],H8={};Fe(H8,{json:()=>Tse});var Tse=[{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"}]}],G8={};Fe(G8,{json:()=>Ese});var Ese=[{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}]}],q8={};Fe(q8,{json:()=>Cse});var Cse=[{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"}]}],X8={};Fe(X8,{json:()=>Rse});var Rse=[{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}]}],K8={};Fe(K8,{json:()=>Mse});var Mse=[{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}]}],Z8={};Fe(Z8,{json:()=>Fse});var Fse=[{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:[]}],Y8=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[D8,z8,O8,_8,P8,L8,W8,U8,j8,B8,H8,G8,q8,X8,K8,Z8,V8],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,a)=>(n[a.tfOpName]=a,n),{})}transformGraph(e,t={}){let n=e.node,a=[],r=[],s=[],i=n.reduce((m,f)=>(m[f.name]=this.mapNode(f),f.op.startsWith("Placeholder")?a.push(m[f.name]):f.op==="Const"?r.push(m[f.name]):(f.input==null||f.input.length===0)&&s.push(m[f.name]),m),{}),o=[],l=[],u={},d={};t!=null&&(u=this.mapSignatureEntries(t.inputs),d=this.mapSignatureEntries(t.outputs));let p=Object.keys(i);p.forEach(m=>{let f=i[m];f.inputNames.forEach(y=>{let[A]=br(y);f.inputs.push(i[A]),i[A].children.push(f)})}),Object.keys(d).length===0?p.forEach(m=>{let f=i[m];f.children.length===0&&l.push(f)}):Object.keys(d).forEach(m=>{let[f]=br(m),y=i[f];y!=null&&(y.signatureKey=d[m],l.push(y))}),Object.keys(u).length>0?Object.keys(u).forEach(m=>{let[f]=br(m),y=i[f];y&&(y.signatureKey=u[m],o.push(y))}):o=a;let c={};e.library!=null&&e.library.function!=null&&(c=e.library.function.reduce((m,f)=>(m[f.signature.name]=this.mapFunction(f),m),{}));let h={nodes:i,inputs:o,outputs:l,weights:r,placeholders:a,signature:t,functions:c};return s.length>0&&(h.initNodes=s),h}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=$8(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(a=>a.startsWith("^")?a.substr(1):a),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((a,r)=>(a[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},a),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((a,r)=>{let s=r.type,i;switch(r.type){case"string":i=T2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=T2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":i=z2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=z2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":i=C2(e.attr,r.tfName,r.defaultValue||0),i===void 0&&!!r.tfDeprecatedName&&(i=C2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":i=D2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=D2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":i=E2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=E2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":i=_2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=_2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":i=$2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=$2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":i=O2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=O2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":i=M2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=M2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":i=F2(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=F2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":i=Q8(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Q8(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return a[r.name]={value:i,type:s},a},{})),n}mapFunction(e){let t=e.nodeDef,n=[],a=[],r={};t!=null&&(r=t.reduce((u,d)=>(u[d.name]=this.mapNode(d),d.op==="Const"&&a.push(u[d.name]),u),{}));let s=[],i=[];e.signature.inputArg.forEach(u=>{let[d]=br(u.name),p={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:R2(u.type),type:"dtype"}},children:[]};p.signatureKey=u.name,s.push(p),r[d]=p}),Object.keys(r).forEach(u=>{let d=r[u];d.inputNames.forEach(p=>{let[c]=br(p);d.inputs.push(r[c]),r[c].children.push(d)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[d,p]=br(o[u.name]),c=r[d];c!=null&&(c.defaultOutput=p,i.push(c))});let l=this.mapArgsToSignature(e);return{nodes:r,inputs:s,outputs:i,weights:a,placeholders:n,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function $se(e){let t=J().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function J8(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):$se(e);return t?n:n.toLowerCase()}function T2(e,t,n,a=!1){let r=e[t];return r!=null?J8(r.s,a):n}function E2(e,t,n){let a=e[t];return a?a.b:n}function C2(e,t,n){let a=e[t]||{},r=a.i!=null?a.i:a.f!=null?a.f:n;return typeof r=="number"?r:parseInt(r,10)}function R2(e){switch(typeof e=="string"&&(e=Fa[e]),e){case Fa.DT_FLOAT:return"float32";case Fa.DT_INT32:case Fa.DT_INT64:case Fa.DT_INT8:case Fa.DT_UINT8:return"int32";case Fa.DT_BOOL:return"bool";case Fa.DT_DOUBLE:return"float32";case Fa.DT_STRING:return"string";default:return null}}function Q8(e,t,n){let a=e[t];return a&&a.func?a.func.name:n}function M2(e,t,n){let a=e[t];return a&&a.type?R2(a.type):n}function F2(e,t,n){let a=e[t];return a&&a.list&&a.list.type?a.list.type.map(r=>R2(r)):n}function ek(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function $2(e,t,n){let a=e[t];return a&&a.shape?ek(a.shape):n}function D2(e,t,n){let a=e[t];return a?((a.list.f&&a.list.f.length?a.list.f:a.list.i)||[]).map(r=>typeof r=="number"?r:parseInt(r,10)):n}function z2(e,t,n,a=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(s=>J8(s,a)):n}function O2(e,t,n){let a=e[t];return a&&a.list&&a.list.shape?a.list.shape.map(r=>ek(r)):n}function _2(e,t,n){let a=e[t];return a&&a.list&&a.list.b?a.list.b:n}var Dse=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(a=>this.getInput(a)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((a,r)=>(a[r]=this.getAttr(r),a),{}))}getInput(e){return gn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return gn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return C2(this.node.rawAttrs,e,t);if(n.s!=null)return T2(this.node.rawAttrs,e,t);if(n.b!=null)return E2(this.node.rawAttrs,e,t);if(n.shape!=null)return $2(this.node.rawAttrs,e,t);if(n.type!=null)return M2(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return D2(this.node.rawAttrs,e,t);if(n.list.s!=null)return z2(this.node.rawAttrs,e,t);if(n.list.shape!=null)return O2(this.node.rawAttrs,e,t);if(n.list.b!=null)return _2(this.node.rawAttrs,e,t);if(n.list.type!=null)return F2(this.node.rawAttrs,e,t)}return t}},zse=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[se(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[Ec(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[Oy(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[L(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[fe(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[Sy(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[Tc(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[ye(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[Nl(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[ja(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[hr(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[Jc(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ose=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Lt(I("x",e,t,n))];case"Acos":return[ly(I("x",e,t,n))];case"Acosh":return[uy(I("x",e,t,n))];case"Asin":return[py(I("x",e,t,n))];case"Asinh":return[cy(I("x",e,t,n))];case"Atan":return[hy(I("x",e,t,n))];case"Atan2":return[fy(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[my(I("x",e,t,n))];case"Ceil":return[by(I("x",e,t,n))];case"Complex":return[zr(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[ad(I("x",e,t,n))];case"Cosh":return[$c(I("x",e,t,n))];case"Elu":return[wl(I("x",e,t,n))];case"Erf":return[Ny(I("x",e,t,n))];case"Exp":return[ea(I("x",e,t,n))];case"Expm1":return[Ty(I("x",e,t,n))];case"Floor":return[Il(I("x",e,t,n))];case"Log":return[_n(I("x",e,t,n))];case"Log1p":return[_c(I("x",e,t,n))];case"Imag":return[zc(I("x",e,t,n))];case"Neg":return[It(I("x",e,t,n))];case"Reciprocal":return[Ly(I("x",e,t,n))];case"Real":return[ud(I("x",e,t,n))];case"Relu":return[Ua(I("x",e,t,n))];case"Round":return[Uc(I("x",e,t,n))];case"Selu":return[Gc(I("x",e,t,n))];case"Sigmoid":return[Sn(I("x",e,t,n))];case"Sin":return[qc(I("x",e,t,n))];case"Sign":return[By(I("x",e,t,n))];case"Sinh":return[Xc(I("x",e,t,n))];case"Softplus":return[vi(I("x",e,t,n))];case"Sqrt":return[en(I("x",e,t,n))];case"Square":return[ot(I("x",e,t,n))];case"Tanh":return[gi(I("x",e,t,n))];case"Tan":return[Uy(I("x",e,t,n))];case"ClipByValue":return[Nn(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[jc(I("x",e,t,n))];case"Rsqrt":return[Hc(gn(e.inputNames[0],t,n))];case"Prod":return[Vc(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[rd(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[ld(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[Cy(gn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Aa(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){k.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let a=0;an+` Shapes ${e} and ${t} must match`)}}}function tk(e){return!(typeof e=="number"||e.some(t=>t<0))}function Jd(e,t,n){let a=P2(e,n),r=!tk(a);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${a}`);if(r&&t.forEach(s=>{a=P2(s.shape,a)}),!tk(a))throw new Error(`Non-fully-defined elementShape: ${a}`);return a}function P2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let a=0;a=0&&s>=0&&r!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[a]=r>=0?r:s}return n}var _se=class{constructor(e,t,n,a,r,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=a,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=we(0),Gt(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),Aa(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,Gt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let a=0;a=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,ha(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];B(()=>{t=H(t,[1,n,r]);for(let o=0;o{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);Aa(t,r.shape,"TensorList shape mismatch: "),Gt(r)}),this.idTensor=we(0),this.maxNumElements=a,Gt(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.`);Aa(e,this.elementShape,"TensorList shape mismatch: ");let a=Jd(this.elementShape,this.tensors,e);return B(()=>{let r=this.tensors.map(s=>H(s,a));return pn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Jd(this.elementShape,this.tensors,e),a=this.tensors.pop();return Aa(a.shape,e,"TensorList shape mismatch: "),H(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Aa(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Gt(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.`);Aa(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=Jd(this.elementShape,this.tensors,t);return H(this.tensors[e],a)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Aa(this.elementShape,t.shape,"TensorList shape mismatch: "),Gt(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}`);Aa(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=Jd(this.elementShape,this.tensors,n);return e.length===0?pa([],[0].concat(a)):B(()=>{let r=e.map(s=>H(this.tensors[s],a));return pn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Aa(this.elementShape,t,"TensorList shape mismatch: ");let n=Jd(this.elementShape,this.tensors,t);return this.size()===0?pa([],[0].concat(n)):B(()=>{let a=this.tensors.map(r=>H(r,n));return lt(a,0)})}};function Pse(e,t,n){let a=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);Aa(r,t,"TensorList shape mismatch: ");let s=ha(e);return new Qd(s,t,a)}function Lse(e,t,n){return new Qd([],e,t,n)}function Wse(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new Qd([],n,e.dtype,a),i=ha(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function Bse(e,t,n){let a=0,r=t.map(d=>(a+=d,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=P2(s,n),o=a===0?0:e.size/a,l=B(()=>{let d=[];e=H(e,[1,a,o]);for(let p=0;p{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(d=>d.id),l=await i[0].data();i.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&d.dispose()});let u=s;for(;l[0];){let d=u;u=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let p=u.map(h=>h.id);d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let a=I("pred",e,t,n);return[vr(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=vr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>gn(r,t,n)!==void 0);if(a){let r=gn(a,t,n);return[vr(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[vr(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[vr(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[vr(a)]}case"TensorArrayV3":{let a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),u=I("name",e,t,n),d=new _se(u,r,a,s,l,i,o);return n.addTensorArray(d),[d.idTensor,we(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[we(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=I("indices",e,t,n),r=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=Wse(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=Lse(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=Pse(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(a.id).popBack(r,s)]}case"TensorListSplit":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=Bse(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function nk(e,t,n){let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=a==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let u=I("strides",e,t,n),d=b0(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),c=I("dilations",e,t,n),[h,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:u,pad:d,dataFormat:p,dilations:c,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var jse=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Mc(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=b0(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[pr(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=nk(e,t,n);return[Ur.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=nk(e,t,n);return[Ur.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let a=I("outputShape",e,t,n),r=I("strides",e,t,n),s=b0(e,t,n);return[Fc(I("x",e,t,n),I("filter",e,t,n),a,[r[1],r[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let a=I("strides",e,t,n),r=b0(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[vl(I("input",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,i,[s[1],s[2]])]}case"Conv3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[wy(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2],a[3]],r,s,[i[1],i[2],i[3]])]}case"AvgPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[td(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[id(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPoolWithArgmax":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=b3(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r,i);return[o,l]}case"AvgPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[gy(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"MaxPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Dy(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"Dilation2D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dilations",e,t,n),i=a[1],o=a[2],l=s[1],u=s[2];return[Iy(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Use=(e,t,n)=>{switch(e.op){case"Fill":{let a=I("shape",e,t,n),r=I("dtype",e,t,n),s=I("value",e,t,n);return[kl(a,s,r)]}case"LinSpace":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("num",e,t,n);return[h3(a,r,s)]}case"Multinomial":{let a=I("logits",e,t,n),r=I("numSamples",e,t,n),s=I("seed",e,t,n);return[v3(a,r,s)]}case"OneHot":{let a=I("indices",e,t,n),r=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[ml(a,r,s,i)]}case"Ones":return[Pn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Ln(I("x",e,t,n))];case"RandomUniform":return[Tl(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("step",e,t,n);return[El(a,r,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let a=I("shape",e,t,n),r=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[Qc(a,r,s,I("dtype",e,t,n),i)]}case"Zeros":return[$t(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[He(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function L2(e,t,n){let a=I("boxes",e,t,n),r=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var Hse=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=L2(e,t,n),u=await Ve.nonMaxSuppressionWithScoreAsync(a,r,s,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=L2(e,t,n),l=I("padToMaxOutputSize",e,t,n),u=await Ve.nonMaxSuppressionPaddedAsync(a,r,s,i,o,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=L2(e,t,n);return[await Ve.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=me(I("condition",e,t,n),"bool"),r=[await qy(a)];return a.dispose(),r}case"ListDiff":return I3(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},Gse=(e,t,n)=>{switch(e.op){case"TopKV2":{let a=I("x",e,t,n),r=I("k",e,t,n),s=I("sorted",e,t,n),i=Hy(a,r,s);return[i.values,i.indices]}case"Unique":{let a=I("x",e,t,n),r=eh(a);return[r.values,r.indices]}case"UniqueV2":{let a=I("x",e,t,n),r=I("axis",e,t,n),s=eh(a,r);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},qse=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let a=I("default",e,t,n);return[gn(e.name,t,n)||a];case"Placeholder":return[gn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=I("x",e,t,n);return[vr(u)]}case"IdentityN":return I("x",e,t,n).map(u=>vr(u));case"Snapshot":let r=I("x",e,t,n);return[vr(r)];case"Shape":return[Mt(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(u=>Mt(u.shape));case"Size":return[we(I("x",e,t,n).size,"int32")];case"Rank":return[we(I("x",e,t,n).rank,"int32")];case"NoOp":return[we(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;ue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return we(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),B(()=>{let a=ha(t),r=n.length,s=a.length;k.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let a=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new Xse(r,s);return a.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=I("tableHandle",e,t,n,a),s=I("keys",e,t,n),i=I("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=I("tableHandle",e,t,n,a),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await a.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=I("tableHandle",e,t,n,a);return[a.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Zse=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let a=I("images",e,t,n),r=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Ve.resizeBilinear(a,[r[0],r[1]],s,i)]}case"ResizeNearestNeighbor":{let a=I("images",e,t,n),r=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Ve.resizeNearestNeighbor(a,[r[0],r[1]],s,i)]}case"CropAndResize":{let a=I("image",e,t,n),r=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[Ve.cropAndResize(a,r,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yse=(e,t,n)=>{switch(e.op){case"Equal":return[Wr(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[ki(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[On(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[Vr(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Oc(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[jr(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[ca(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[sd(I("a",e,t,n))];case"LogicalOr":return[Wc(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[rn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Jse=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Be(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[d3(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Ye(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,d]=I("args",e,t,n);return[Ur.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:u,activation:r,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Qse=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[xi(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[xi(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[Ry(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[pd(I("x",e,t,n))];case"LogSoftmax":return[Lc(I("x",e,t,n))];case"SparseToDense":return[Xy(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},eie=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Tn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[St(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Sl(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ie(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Cc(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Qu(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[yi(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[dy(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Vc(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[Dc(I("x",e,t,n),i,o,l)]}case"Bincount":let a=I("x",e,t,n),r=I("weights",e,t,n),s=I("size",e,t,n);return[xy(a,r,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),l=I("size",e,t,n),u=I("binaryOutput",e,t,n);return[l3(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},tie=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let a=I("n",e,t,n),r=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,a),[lt(s,r)]}case"Gather":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[bi(a,me(r,"int32"),0)]}case"GatherV2":{let a=I("axis",e,t,n),r=I("batchDims",e,t,n),s=I("x",e,t,n),i=I("indices",e,t,n);return[bi(s,me(i,"int32"),a,r)]}case"Reverse":{let a=I("dims",e,t,n),r=[];for(let i=0;i{let a=I("axis",e,t,n),r=I("tensors",e,t,n),s=r[0].shape,i=Ha(r[0]).shape,o=r.map(l=>{let u=k.arraysEqual(l.shape,s);if(!u&&!k.arraysEqual(Ha(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:H(l,s)});return[pn(o,a)]});case"Unpack":{let a=I("axis",e,t,n),r=I("tensor",e,t,n);return ha(r,a)}case"Tile":{let a=I("reps",e,t,n);return[Br(I("x",e,t,n),a)]}case"Split":case"SplitV":{let a=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return qt(s,r,a)}case"ScatterNd":{let a=I("indices",e,t,n),r=I("values",e,t,n),s=I("shape",e,t,n);return[E3(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[C3(a,r)]}case"SparseToDense":{let a=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[Xy(a,s,r,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},nie=(e,t,n)=>{switch(e.op){case"SparseReshape":{let{outputIndices:a,outputShape:r}=U3.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[a,r]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},aie=(e,t,n)=>{switch(e.op){case"FFT":return[cd(I("x",e,t,n))];case"IFFT":return[Cl(I("x",e,t,n))];case"RFFT":return[hd(I("x",e,t,n))];case"IRFFT":return[Yc(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},rie=(e,t,n)=>{switch(e.op){case"Cast":return[me(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let a=I("axis",e,t,n);return[dn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[Ha(I("x",e,t,n),a)]}case"Reshape":return[H(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[zy(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[cr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let a=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[od(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[nd(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[ky(I("x",e,t,n),a,r)]}case"BroadcastTo":return[xl(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ak(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return B(()=>zse(s,i,o));case"basic_math":return B(()=>Ose(s,i,o));case"control":return Vse(s,i,o);case"convolution":return B(()=>jse(s,i,o));case"creation":return B(()=>Use(s,i,o));case"dynamic":return Hse(s,i,o);case"evaluation":return B(()=>Gse(s,i,o));case"image":return B(()=>Zse(s,i,o));case"graph":return B(()=>qse(s,i,o));case"logical":return B(()=>Yse(s,i,o));case"matrices":return B(()=>Jse(s,i,o));case"normalization":return B(()=>Qse(s,i,o));case"reduction":return B(()=>eie(s,i,o));case"slice_join":return B(()=>tie(s,i,o));case"sparse":return B(()=>nie(s,i,o));case"spectral":return B(()=>aie(s,i,o));case"transformation":return B(()=>rie(s,i,o));case"hash_table":return Kse(s,i,o,a);case"custom":let l=$8(s.op);if(l&&l.customExecutor)return l.customExecutor(new Dse(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var rk=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function sk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>jn(c)[0]),d=[];a!=null&&(d=a.map(c=>jn(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((ik(c)||uie(c)||die(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&d.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function sie(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(d=>jn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{a.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{a.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{a.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>l.has(c.name))&&s.push(p)})}return u}var iie=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],oie=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],lie=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function ik(e){return iie.indexOf(e.op)>=0}function uie(e){return oie.indexOf(e.op)>=0}function die(e){return lie.indexOf(e.op)>=0}var W2=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 W2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=sk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return sie(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[jn(d)[0]]),r=t.map(d=>jn(d)[0]),s=r.map(d=>this.graph.nodes[d]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return B(()=>{let d=new rk(this.weightMap,l,u,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,y]=jn(m),A=[];A[y]=e[m],p[f]=A});let c=this.getFrozenTensorIds(p),h={};for(let m=0;mgn(m,p,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=mse(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let d=i[u.id];d===1?(u.dispose(),delete i[u.id]):d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new rk(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>gn(p,i,s)),l=o.map(p=>p.id),u=Object.keys(e).map(p=>e[p].id),d=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(p=>{i[p].forEach(c=>{c&&!c.kept&&!c.isDisposed&&!d.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(d),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(g=>this.graph.nodes[jn(g)[0]]),i=n.map(g=>jn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:p}=sk(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[x,w]=jn(g),b=[];b[w]=e[g],h[x]=b});let m={},f=this.getFrozenTensorIds(h),y={};for(;c.length>0;){let g=this.processStack(s,c,t,h,y,f,i,m,l);await Promise.all(g)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=o.filter(g=>!ik(g)&&!gn(g.name,h,t)).map(g=>g.name);if(A.length>0){let g="";throw d!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${g}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();n.currentContext=d.contexts;let p="";if(d.node.op==="Enter"&&I("isConstant",d.node,a,n)&&([p]=br(d.node.name,n)),a[d.node.name]==null){let c=ak(d.node,a,n,this._resourceManager);p||([p]=br(d.node.name,n));let h=n.currentContext;k.isPromise(c)?u.push(c.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l),m))):(a[p]=c,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l))}else this.processChildNodes(d.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=br(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!gn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!gn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=jn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=jn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=jn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},pie=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},cie="?tfjs-format=file",hie="model.json",ok=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new pie}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=In.browserHTTPRequest(e,this.loadOptions);else{let t=In.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(In.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=In.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new W2(Y8.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Y8.Instance.transformGraph(e.modelInitializer);this.initializer=new W2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=In.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Le)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function gt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${hie}${cie}`);let n=new ok(e,t);return await n.load(),n}var fie="3.6.0",lk={};Fe(lk,{CSVDataset:()=>bk,Dataset:()=>tu,FileDataSource:()=>Tk,TextLineDataset:()=>Ak,URLDataSource:()=>Ek,array:()=>_ie,csv:()=>Xie,func:()=>Kie,generator:()=>Zie,microphone:()=>Jie,version_data:()=>Qie,webcam:()=>Yie,zip:()=>Pie});var mie=ro(f5()),yie=ro(f5());function Aie(e,t){return v0(e,t)}function v0(e,t,n=new Map,a=new Set){if(e==null)return null;if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(eu(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=v0(o,t,n,a);s[i]=l}return a.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function gie(e,t=dk){return uk(e,t)}function uk(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(eu(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=uk(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function dk(e){return e===null?null:eu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function pk(e,t){let n=new Map;v0(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return v0(e,t,n)}function eu(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Le))}function xie(e){return e==null||bie(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Le||k.isTypedArray(e)}function bie(e){return e===null||typeof e!="object"&&typeof e!="function"}function vie(e){return Aie(e,wie)}function wie(e){return e instanceof Le?{value:e.clone(),recurse:!1}:eu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var ck=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}},B2=class extends ck{constructor(){super(B2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;at===!0)}rowMajorBatch(e,t=!0){return new Rie(this,e,t)}columnMajorBatch(e,t=!0,n=dk){return this.rowMajorBatch(e,t).map(a=>gie(a,n))}concatenate(e,t){return new mk(hk([this,e]),t)}take(e){return e<0||e==null?this:new Cie(this,e)}skip(e){return e<0||e==null?this:new Eie(this,e)}prefetch(e){return new yk(this,e)}shuffle(e,t){return new Oie(this,e,t)}serial(){return new Tie(this)}},Sie=class extends Zt{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:vie(e),done:!1}}},Nie=class extends Zt{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: ${e.message}`,e}}},Tie=class extends Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Eie=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Rie=class extends Zt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Mie=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ne(e.value)}}},Fie=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ba.getTensorsInContainer(e.value),n=this.transform(e.value),a=ba.getTensorsInContainer(n);for(let r of t)ba.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},$ie=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},fk=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ba.getTensorsInContainer(e.value),n=await this.transform(e.value),a=ba.getTensorsInContainer(n);for(let r of t)ba.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},j2=class extends Zt{constructor(){super();this.outputQueue=new B2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Die=class extends j2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ba.getTensorsInContainer(e.value),n=this.transform(e.value),a=ba.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)ba.isTensorInList(r,a)||r.dispose();return!0}},mk=class extends Zt{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}},ns;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ns||(ns={}));var zie=class extends Zt{constructor(e,t=ns.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof Zt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await pk(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ns.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ns.SHORTEST:return{value:null,done:!0};case ns.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},yk=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new ck(t)}summary(){return`${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()}},Oie=class extends yk{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=yie.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}}},tu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Un(async()=>(await n.iterator()).columnMajorBatch(e,t,Lie),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Un(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Un(async()=>(await t.iterator()).filter(a=>B(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Un(async()=>(await t.iterator()).map(n=>B(()=>e(n))),this.size)}mapAsync(e){let t=this;return Un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Un(async()=>{let a=V2(async()=>({value:await t.iterator(),done:!1}));return kie(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=mie.alea(t||k.now().toString());return Un(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Un(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()}};tu.MAX_BUFFER_SIZE=1e4;function Un(e,t=null){return new class extends tu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function _ie(e){return Un(async()=>hk(e),e.length)}function Pie(e){if(!eu(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{let n=await pk(e,a=>{if(a instanceof tu)return{value:a.iterator(),recurse:!1};if(eu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Iie(n,ns.SHORTEST)},t)}function Lie(e){if(e===null)return null;let t=e[0];return xie(t)?{value:Wie(e),recurse:!1}:{value:null,recurse:!0}}function Wie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Le?pn(e):pa(e)}var Ak=class extends tu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` `).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},w0='"',ep=Symbol("out"),gk=Symbol("field"),k0=Symbol("quote"),U2=Symbol("quoteafterquote"),xk=Symbol("quoteinquote"),bk=class extends tu{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 Ak(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!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(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new vk(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),pa(n,t)}},wk=class extends Zt{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=Mt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=wa([s,r,o,i],[1,4])}else this.cropBox=wa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().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 wk(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=fi.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=dn(me(e,"float32"),0),n;n=Ve.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return H(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},kk=class{},Ik=class extends Zt{split(e){return new Bie(this,e)}},Bie=class extends Ik{constructor(e,t){super();this.upstream=e,this.impl=new Vie(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Vie=class extends j2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},jie=class extends Zt{decodeUTF8(){return new Uie(this)}},Uie=class extends Ik{constructor(e){super();this.upstream=e,this.impl=new Hie(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Hie=class extends j2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=VI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Sk=class extends jie{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function Gie(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=qie(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new Sk(s,t)}else throw new Error(r.statusText)}var qie=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 Nk(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var Tk=class extends kk{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Nk(this.input)&&J().get("IS_NODE")){let e=ao("fs");this.input=e.readFileSync(this.input.substr(7))}return new Sk(this.input,this.options)}},Ek=class extends kk{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Nk(this.url)?new Tk(this.url,this.fileOptions).iterator():Gie(this.url,this.fileOptions)}};function Xie(e,t={}){return new bk(new Ek(e),t)}function Kie(e){let t=V2(e);return Un(async()=>t)}function Zie(e){return Un(async()=>{let t=await e();return V2(()=>t.next())})}async function Yie(e,t){return wk.create(e,t)}async function Jie(e){return vk.create(e)}var Qie="3.6.0",eoe={tfjs:(vm==null?void 0:vm.version)||void 0,"tfjs-core":(wm==null?void 0:wm.version)||void 0,"tfjs-data":(km==null?void 0:km.version)||void 0,"tfjs-layers":(Im==null?void 0:Im.version)||void 0,"tfjs-converter":(Sm==null?void 0:Sm.version)||void 0,"tfjs-backend-cpu":I7||void 0,"tfjs-backend-webgl":qv||void 0,"tfjs-backend-wasm":L6||void 0};var Hn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Ck(){if(!oy(Hn.name)){de("backend registration:",Hn.name);try{Hn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Hn.width,Hn.height):document.createElement("canvas")}catch(e){de("error: cannot create canvas:",e);return}try{Hn.gl=Hn.canvas.getContext("webgl2",Hn.webGLattr)}catch(e){de("error: cannot get WebGL2 context:",e);return}try{xh(2,Hn.gl)}catch(e){de("error: cannot set WebGL2 context:",e);return}try{let e=new Nh(Hn.gl);Al(Hn.name,()=>new jl(e),Hn.priority)}catch(e){de("error: cannot register WebGL backend:",e);return}try{dl("webgl").forEach(t=>{let n={...t,backendName:Hn.name};di(n)})}catch(e){de("error: cannot update WebGL backend registration:",e);return}try{ua.set("WEBGL_VERSION",2)}catch(e){de("error: cannot set WebGL backend flags:",e);return}de("backend registered:",Hn.name)}}var eg={};ga(eg,{load:()=>Q2,predict:()=>J2,triangulation:()=>Bk,uvmap:()=>Vk});function Rk(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:a}}function np(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function nu(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function au(e,t,n){let a=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/r,e.endPoint[1]/a,e.endPoint[0]/r]];return Ve.cropAndResize(t,s,[0],n)}function I0(e,t=1.5){let n=nu(e),a=np(e),r=[t*a[0]/2,t*a[1]/2],s=[n[0]-r[0],n[1]-r[1]],i=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function S0(e){let t=nu(e),n=np(e),r=Math.max(...n)/2,s=[Math.round(t[0]-r),Math.round(t[1]-r)],i=[Math.round(t[0]+r),Math.round(t[1]+r)];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function H2(e){let t=e.map(s=>s[0]),n=e.map(s=>s[1]),a=[Math.min(...t),Math.min(...n)],r=[Math.max(...t),Math.max(...n)];return{startPoint:a,endPoint:r,landmarks:e}}var Mk=e=>({startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});var N0=[[1,0,0],[0,1,0],[0,0,1]];function toe(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function G2(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return toe(n)}function Fk(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function as(e,t){let n=0;for(let a=0;a{let u=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),d=this.model.execute(u),p;if(Array.isArray(d)){let f=d.sort((x,w)=>x.size-w.size),y=lt([f[0],f[2]],2),A=lt([f[1],f[3]],2);p=lt([A,y],1).squeeze(0)}else p=d.squeeze();let c=aoe(p,this.anchors,[this.inputSize,this.inputSize]),h=Re(p,[0,0],[-1,1]),m=Sn(h).squeeze().dataSync();return[p,c,m]}),s=await Ve.nonMaxSuppressionAsync(a,r,this.config.face.detector.maxDetected,this.config.face.detector.iouThreshold,this.config.face.detector.minConfidence),i=s.arraySync();s.dispose();let o=[];for(let l=0;lthis.config.face.detector.minConfidence){let d=Re(a,[i[l],0],[1,-1]),p=Mk(d);d.dispose();let c=this.anchorsData[i[l]],h=B(()=>Re(n,[i[l],_k-1],[1,-1]).squeeze().reshape([_k,-1]));o.push({box:p,landmarks:h,anchor:c,confidence:u})}}return n.dispose(),a.dispose(),{boxes:o,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function Lk(e){let t=await gt(vt(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new Pk(t,e);return!t||!t.modelUrl?de("load model failed:",e.face.detector.modelPath):e.debug&&de("load model:",t.modelUrl),n}var er={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]},q2=[{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]}],ap=[[.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]],ji=[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 roe=[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],soe=[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],ioe=[33,133,362,263,1,78,308],Zoe=roe.map(e=>ap[e]),Yoe=soe.map(e=>ap[e]),Joe=ioe.map(e=>ap[e]);var X2=er.leftEyeLower0,K2=er.rightEyeLower0,ru={leftBounds:[X2[0],X2[X2.length-1]],rightBounds:[K2[0],K2[K2.length-1]]},E0={count:468,mouth:13,symmetryLine:[13,er.midwayBetweenEyes[0]]},Wk={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},su={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function C0(e,t,n,a){for(let r=0;r[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?T0(a,[0,0]):N0,l=a!==0?i.map(p=>[...zk(p,o),p[2]]):i,u=a!==0?Dk(r):N0,d=[...nu({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(p=>[Math.round(p[0]+as(d,u[0])),Math.round(p[1]+as(d,u[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[ru.leftBounds[0]][2],a=t[ru.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=S0(I0(H2([t[a],t[r]]),this.irisEnlarge)),o=np(i),l=Ve.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&ua.flags.IS_BROWSER&&(l=Ve.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i{let u=i;return l===2?u=r:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks.arraySync(),confidence:i.confidence});this.storedBoxes.length>0&&(a=!0)}if(a){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=B(()=>this.storedBoxes.map((i,o)=>{let l,u=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&ua.flags.IS_BROWSER){let[x,w]=i.landmarks.length>=E0.count?E0.symmetryLine:Wk.symmetryLine;u=G2(i.landmarks[x],i.landmarks[w]);let b=nu({startPoint:i.startPoint,endPoint:i.endPoint}),v=[b[0]/t.shape[2],b[1]/t.shape[1]],S=Ve.rotateWithOffset(t,u,0,v);d=T0(-u,b),n.face.mesh.enabled?l=au({startPoint:i.startPoint,endPoint:i.endPoint},S,[this.meshSize,this.meshSize]).div(255):l=au({startPoint:i.startPoint,endPoint:i.endPoint},S,[this.boxSize,this.boxSize]).div(255)}else{d=N0;let x=t.clone();n.face.mesh.enabled?l=au({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):l=au({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:l};let[,p,c]=this.meshDetector.execute(l),h=p.dataSync()[0];if(h=E0.count?E0.symmetryLine:Wk.symmetryLine;u=G2(i.landmarks[x],i.landmarks[w]);let b=nu({startPoint:i.startPoint,endPoint:i.endPoint}),v=[b[0]/t.shape[2],b[1]/t.shape[1]],S=Ve.rotateWithOffset(t.toFloat(),u,0,v);d=T0(-u,b),l=au({startPoint:i.startPoint,endPoint:i.endPoint},S,[this.meshSize,this.meshSize]).div(255)}let g={mesh:y,box:i,faceConfidence:h,boxConfidence:i.confidence,image:l};return this.storedBoxes[o]={...S0(i),confidence:i.confidence,faceConfidence:h},g}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Ct=[null,null,null],Y2;async function J2(e,t){let n=await Y2.predict(e,t),a=[];for(let r of n||[]){if(!r||r.isDisposedInternal)continue;let s=r.mesh.map(u=>[u[0]/e.shape[2],u[1]/e.shape[1],u[2]/Y2.meshSize]),i={};if(r.mesh&&r.mesh.length>0)for(let u of Object.keys(er))i[u]=er[u].map(d=>r.mesh[d]);let o=r.box?[Math.max(0,r.box.startPoint[0]),Math.max(0,r.box.startPoint[1]),Math.min(e.shape[2],r.box.endPoint[0])-Math.max(0,r.box.startPoint[0]),Math.min(e.shape[1],r.box.endPoint[1])-Math.max(0,r.box.startPoint[1])]:0,l=r.box?[r.box.startPoint[0]/e.shape[2],r.box.startPoint[1]/e.shape[1],(r.box.endPoint[0]-r.box.startPoint[0])/e.shape[2],(r.box.endPoint[1]-r.box.startPoint[1])/e.shape[1]]:[];a.push({confidence:Math.round(100*r.faceConfidence||100*r.boxConfidence||0)/100,boxConfidence:Math.round(100*r.boxConfidence)/100,faceConfidence:Math.round(100*r.faceConfidence)/100,box:o,boxRaw:l,mesh:r.mesh,meshRaw:s,annotations:i,image:r.image}),r.coords&&r.coords.dispose()}return a}async function Q2(e){return!Ct[0]&&e.face.enabled||!Ct[1]&&e.face.mesh.enabled||!Ct[2]&&e.face.iris.enabled?(Ct=await Promise.all([!Ct[0]&&e.face.enabled?Lk(e):null,!Ct[1]&&e.face.mesh.enabled?gt(vt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Ct[2]&&e.face.iris.enabled?gt(vt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Ct[1]||!Ct[1].modelUrl?de("load model failed:",e.face.mesh.modelPath):e.debug&&de("load model:",Ct[1].modelUrl)),e.face.iris.enabled&&(!Ct[2]||!Ct[2].modelUrl?de("load model failed:",e.face.iris.modelPath):e.debug&&de("load model:",Ct[2].modelUrl))):e.debug&&(Ct[0]&&de("cached model:",Ct[0].model.modelUrl),Ct[1]&&de("cached model:",Ct[1].modelUrl),Ct[2]&&de("cached model:",Ct[2].modelUrl)),Y2=new Z2(Ct[0],Ct[1],Ct[2]),Ct}var Bk=ji,Vk=ap;var rg={};ga(rg,{load:()=>ag,predict:()=>M0});var ooe=["angry","disgust","fear","happy","sad","surprise","neutral"],$a,R0=[],jk=0,tg=Number.MAX_SAFE_INTEGER,ng=[.2989,.587,.114];async function ag(e){return $a?e.debug&&de("cached model:",$a.modelUrl):($a=await gt(vt(e.modelBasePath,e.face.emotion.modelPath)),!$a||!$a.modelUrl?de("load model failed:",e.face.emotion.modelPath):e.debug&&de("load model:",$a.modelUrl)),$a}async function M0(e,t,n,a){return $a?tg0?(tg++,R0[n]):(tg=0,new Promise(async r=>{let s=Ve.resizeBilinear(e,[$a.inputs[0].shape[2],$a.inputs[0].shape[1]],!1),[i,o,l]=qt(s,3,3);s.dispose();let u=L(i,ng[0]),d=L(o,ng[1]),p=L(l,ng[2]);i.dispose(),o.dispose(),l.dispose();let c=Ec([u,d,p]);u.dispose(),d.dispose(),p.dispose();let h=B(()=>c.sub(.5).mul(2));c.dispose();let m=[];if(t.face.emotion.enabled){let f=await $a.predict(h),y=f.dataSync();Ne(f);for(let A=0;At.face.emotion.minConfidence&&m.push({score:Math.min(.99,Math.trunc(100*y[A])/100),emotion:ooe[A]});m.sort((A,g)=>g.score-A.score)}h.dispose(),R0[n]=m,jk=a,r(m)})):null}var ug={};ga(ug,{enhance:()=>lg,load:()=>ig,match:()=>Hk,predict:()=>$0,similarity:()=>og});var Da,F0=[],Uk=0,sg=Number.MAX_SAFE_INTEGER;async function ig(e){let t=vt(e.modelBasePath,e.face.description.modelPath);return Da?e.debug&&de("cached model:",t):(Da=await gt(t),Da?e.debug&&de("load model:",t):de("load model failed:",e.face.description.modelPath)),Da}function og(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 a=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-a)/100}function Hk(e,t,n=0){let a={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return a;for(let r of t)if(r.embedding&&r.name){let s=og(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function lg(e){return B(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Le))return null;let a=[[.05,.15,.85,.85]];return Da.inputs[0].shape?(n.shape.length===3?Ve.cropAndResize(dn(n,0),a,[0],[Da.inputs[0].shape[2],Da.inputs[0].shape[1]]):Ve.cropAndResize(n,a,[0],[Da.inputs[0].shape[2],Da.inputs[0].shape[1]])).mul(255):null})}async function $0(e,t,n,a){var r,s;return Da?sg0?(sg++,F0):(sg=0,new Promise(async i=>{let o=lg(e),l,u={age:0,gender:"unknown",genderConfidence:0,descriptor:[]};t.face.description.enabled&&(l=await Da.predict(o)),Ne(o),l&&(B(()=>{let d=l.find(f=>f.shape[1]===1).dataSync(),p=Math.trunc(200*Math.abs(d[0]-.5))/100;p>t.face.description.minConfidence&&(u.gender=d[0]<=.5?"female":"male",u.genderConfidence=Math.min(.99,p));let c=l.find(f=>f.shape[1]===100).argMax(1).dataSync()[0],h=l.find(f=>f.shape[1]===100).dataSync();u.age=Math.round(h[c-1]>h[c+1]?10*c-100*h[c-1]:10*c+100*h[c+1])/10;let m=l.find(f=>f.shape[1]===1024);u.descriptor=[...m.dataSync()]}),l.forEach(d=>Ne(d))),F0[n]=u,Uk=a,i(u)})):null}var loe=e=>{let t=(p,c)=>Math.atan2(p[1]-c[1],p[0]-c[0]),n=[0,0],a=5,r=e[33][2]>e[263][2],s=r?e[473]:e[468],i=r?[(e[133][0]+e[33][0])/2,(e[133][1]+e[33][1])/2]:[(e[263][0]+e[362][0])/2,(e[263][1]+e[362][1])/2],o=r?[e[133][0]-e[33][0],e[23][1]-e[27][1]]:[e[263][0]-e[362][0],e[253][1]-e[257][1]],l=[(i[0]-s[0])/o[0]-n[0],a*(s[1]-i[1])/o[1]-n[1]],u=Math.sqrt(l[0]**2+l[1]**2);return{angle:t([0,0],l),strength:u}},uoe=(e,t)=>{let n=y=>{let A=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=A,y[1]/=A,y[2]/=A,y},a=(y,A)=>{let g=y[0]-A[0],x=y[1]-A[1],w=y[2]-A[2];return[g,x,w]},r=(y,A)=>{let g=y[1]*A[2]-y[2]*A[1],x=y[2]*A[0]-y[0]*A[2],w=y[0]*A[1]-y[1]*A[0];return[g,x,w]},s=y=>{let[A,g,x,w,b,v,S,T,C]=y,$,O,P;return w<1?w>-1?(P=Math.asin(w),O=Math.atan2(-S,A),$=Math.atan2(-v,b)):(P=-Math.PI/2,O=-Math.atan2(T,C),$=0):(P=Math.PI/2,O=Math.atan2(T,C),$=0),{pitch:2*-$,yaw:2*-O,roll:2*-P}},i=y=>{let A=(x,w,b,v)=>Math.atan2(v-w,b-x);return{pitch:A(y[10][1],y[10][2],y[152][1],y[152][2]),yaw:A(y[33][0],y[33][2],y[263][0],y[263][2]),roll:A(y[33][0],y[33][1],y[263][0],y[263][1])}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{angle:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[o[10],o[152],o[234],o[454]].map(y=>[y[0]*t[0]/l,y[1]*t[1]/l,y[2]]),d=n(a(u[1],u[0])),p=n(a(u[3],u[2])),c=n(r(p,d));p=r(d,c);let h=[p[0],p[1],p[2],d[0],d[1],d[2],c[0],c[1],c[2]],m=s(h),f=o.length===478?loe(o):{angle:0,strength:0};return{angle:m,matrix:h,gaze:f}},dg=async(e,t)=>{var d,p,c,h,m,f,y,A;let n,a,r,s,i,o,l=[];e.state="run:face",n=at();let u=await J2(t,e.config);if(e.perf.face=Math.trunc(at()-n),!u)return[];for(let g=0;gAg,predict:()=>yg});var rp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Gk=rp.length,sp=rp.reduce((e,t,n)=>(e[t]=n,e),{}),doe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],poe=doe.map(([e,t])=>[sp[e],sp[t]]),qk=[["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 Xk(e){let t=e.reduce(({maxX:n,maxY:a,minX:r,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(n,i),maxY:Math.max(a,o),minX:Math.min(r,i),minY:Math.min(s,o)}),{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 Kk(e,[t,n],[a,r]){let s=t/a,i=n/r,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/a,u.box[2]/r,u.box[3]/a],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:p,part:c,position:h})=>({score:p,part:c,position:{x:Math.trunc(h.x*i),y:Math.trunc(h.y*s)}}))});return e.map((u,d)=>o(u,d))}var pg=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(nn?n:e}function Zk(e,t,n,a){let r=n-e,s=a-t;return r*r+s*s}function mg(e,t){return{x:e.x+t.x,y:e.y+t.y}}var D0=1,iu=16,coe=50**2;function Yk(e,t,n,a,r,s,i=2){let o=A=>({y:s.get(A.y,A.x,e),x:s.get(A.y,A.x,s.shape[2]/2+e)}),l=(A,g,x)=>({y:fg(Math.round(A.y/iu),0,g-1),x:fg(Math.round(A.x/iu),0,x-1)}),[u,d]=a.shape,p=l(t.position,u,d),c=o(p),m=mg(t.position,c);for(let A=0;A[sp[c],sp[h]]),i=s.map(([,c])=>c),o=s.map(([c])=>c),l=t.shape[2],u=i.length,d=new Array(l),p=hg(e.part,iu,n);d[e.part.id]={score:e.score,part:rp[e.part.id],position:p};for(let c=u-1;c>=0;--c){let h=i[c],m=o[c];d[h]&&!d[m]&&(d[m]=Yk(c,d[h],m,t,n,r))}for(let c=0;ct){o=!1;break}if(!o)break}return o}function moe(e,t){let[n,a,r]=t.shape,s=new pg(n*a*r,({score:i})=>i);for(let i=0;i{var i;let s=(i=r[a])==null?void 0:i.position;return s?Zk(n,t,s.y,s.x)<=coe:!1})}function yoe(e,t){return t.reduce((a,{position:r,score:s},i)=>(Jk(e,r,i)||(a+=s),a),0)/t.length}function Qk(e,t,n,a,r,s){let i=[],o=moe(s,t);for(;i.lengthh.score>s);let p=yoe(i,d),c=Xk(d);p>s&&i.push({keypoints:d,box:c,score:Math.round(100*p)/100})}return i}var Gn,Aoe=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"];async function yg(e,t){let n=B(()=>{if(!Gn.inputs[0].shape)return[];let o=e.resizeBilinear([Gn.inputs[0].shape[2],Gn.inputs[0].shape[1]]).toFloat().div(127.5).sub(1),u=Gn.execute(o,Aoe).map(d=>d.squeeze([0]));return u[1]=u[1].sigmoid(),u}),a=await Promise.all(n.map(i=>i.buffer()));for(let i of n)i.dispose();let r=await Qk(a[0],a[1],a[2],a[3],t.body.maxDetected,t.body.minConfidence);return Gn.inputs[0].shape?Kk(r,[e.shape[1],e.shape[2]],[Gn.inputs[0].shape[2],Gn.inputs[0].shape[1]]):[]}async function Ag(e){return Gn?e.debug&&de("cached model:",Gn.modelUrl):(Gn=await gt(vt(e.modelBasePath,e.body.modelPath)),!Gn||!Gn.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",Gn.modelUrl)),Gn}var Sg={};ga(Sg,{load:()=>Ig,predict:()=>kg});function z0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function ip(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function e9(e,t,n){let a=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/r,e.endPoint[1]/a,e.endPoint[0]/r]];return Ve.cropAndResize(t,s,[0],n)}function t9(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:a,palmLandmarks:r,confidence:e.confidence}}function O0(e,t=1.5){let n=ip(e),a=z0(e),r=[t*a[0]/2,t*a[1]/2],s=[n[0]-r[0],n[1]-r[1]],i=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function _0(e){let t=ip(e),n=z0(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var n9=[{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 xg=class{constructor(t){var n;this.model=t,this.anchors=n9.map(a=>[a.x,a.y]),this.anchorsTensor=wa(this.anchors),this.inputSize=(n=this.model)==null?void 0:n.inputs[0].shape[2],this.inputSizeTensor=Mt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Mt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){return B(()=>{let n=Re(t,[0,0],[-1,2]),a=Re(t,[0,2],[-1,2]),r=se(fe(n,this.inputSizeTensor),this.anchorsTensor),s=fe(a,this.doubleInputSizeTensor),i=L(ye(r,s),this.inputSizeTensor),o=L(se(r,s),this.inputSizeTensor);return bl([i,o],1)})}normalizeLandmarks(t,n){return B(()=>{let a=se(fe(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return L(a,this.inputSizeTensor)})}async getBoxes(t,n){let a=this.model.predict(t),r=a.squeeze();a.dispose();let s=B(()=>Sn(Re(r,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Re(r,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let u=await Ve.nonMaxSuppressionAsync(l,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=u.arraySync();s.dispose(),u.dispose();let p=[];for(let c of d)if(i[c]>=n.hand.minConfidence){let h=Re(l,[c,0],[1,-1]),m=Re(r,[c,5],[1,14]),f=B(()=>this.normalizeLandmarks(m,c).reshape([-1,2]));m.dispose(),p.push({box:h,palmLandmarks:f,confidence:i[c]})}return r.dispose(),l.dispose(),p}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=B(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let u=l.box.dataSync(),d=u.slice(0,2),p=u.slice(2,4),c=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(t9({startPoint:d,endPoint:p,palmLandmarks:c,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function goe(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function a9(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return goe(n)}var r9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function rs(e,t){let n=0;for(let a=0;ai[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>vg([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return O0(_0(r),boe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=O0(_0(n),o9);a.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=bg(a,[0,0]),u=o.map(h=>[...vg(h,l),h[2]]),d=i9(r),p=[...ip(n),1],c=[rs(p,d[0]),rs(p,d[1])];return u.map(h=>[h[0]+c[0],h[1]+c[1],h[2]])}async estimateHands(t,n){let a=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(a=!0));let s=[];for(let i=0;i=n.hand.minConfidence){let x=H(A,[-1,3]),w=x.arraySync();A.dispose(),x.dispose();let b=this.transformRawCoords(w,h,l,c),v=this.getBoxForHandLandmarks(b);this.storedBoxes[i]={...v,confidence:g};let S={landmarks:b,confidence:g,box:{topLeft:v.startPoint,bottomRight:v.endPoint}};s.push(S)}else this.storedBoxes[i]=null;A.dispose()}else{let l=O0(_0(o),o9),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}};var u9={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]},ss,is,d9;async function kg(e,t){let n=await d9.estimateHands(e,t);if(!n)return[];let a=[];for(let r=0;rn[r].landmarks[d]);let i=n[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let u of i)u[0]o[2]&&(o[2]=u[0]),u[1]>o[3]&&(o[3]=u[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/e.shape[2],o[1]/e.shape[1],o[2]/e.shape[2],o[3]/e.shape[1]]}else o=n[r].box?[Math.max(0,n[r].box.topLeft[0]),Math.max(0,n[r].box.topLeft[1]),Math.min(e.shape[2],n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0]),Math.min(e.shape[1],n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1])]:[0,0,0,0],l=[n[r].box.topLeft[0]/e.shape[2],n[r].box.topLeft[1]/e.shape[1],(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/e.shape[2],(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/e.shape[1]];a.push({id:r,confidence:Math.round(100*n[r].confidence)/100,box:o,boxRaw:l,landmarks:i,annotations:s})}return a}async function Ig(e){!ss||!is?([ss,is]=await Promise.all([e.hand.enabled?gt(vt(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?gt(vt(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!ss||!ss.modelUrl?de("load model failed:",e.hand.detector.modelPath):e.debug&&de("load model:",ss.modelUrl),!is||!is.modelUrl?de("load model failed:",e.hand.skeleton.modelPath):e.debug&&de("load model:",is.modelUrl))):(e.debug&&de("cached model:",ss.modelUrl),e.debug&&de("cached model:",is.modelUrl));let t=new xg(ss);return d9=new wg(t,is),[ss,is]}var Tg={};ga(Tg,{load:()=>P0,predict:()=>Ng});var p9=["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"],c9=["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 Fn;async function P0(e){return Fn?e.debug&&de("cached model:",Fn.modelUrl):(Fn=await gt(vt(e.modelBasePath,e.body.modelPath)),Fn.width=parseInt(Fn.signature.inputs["input_1:0"].tensorShape.dim[2].size),Fn.height=parseInt(Fn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!Fn||!Fn.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",Fn.modelUrl)),Fn}async function Ng(e,t){var f;if(!Fn)return[];if(!t.body.enabled)return[];let n={width:e.shape[2],height:e.shape[1]},a=Ve.resizeBilinear(e,[Fn.width,Fn.height],!1),r=fe(a,[255]);a.dispose();let s=await Fn.predict(r),i=((f=s.find(y=>y.size===195||y.size===155))==null?void 0:f.dataSync())||[];s.forEach(y=>y.dispose()),r.dispose();let o=[],l=(i==null?void 0:i.length)===195?p9:c9,u=5;for(let y=0;yy.position.x),p=o.map(y=>y.position.y),c=[Math.min(...d),Math.min(...p),Math.max(...d)-Math.min(...d),Math.max(...p)-Math.min(...d)],h=[0,0,0,0],m=o.reduce((y,A)=>A.score>y?A.score:y,0);return[{id:0,score:m,box:c,boxRaw:h,keypoints:o}]}var $n,tr=[],Eg=[0,0,0,0],Cg=[0,0,0,0],L0=0,Rg=Number.MAX_SAFE_INTEGER,koe=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function h9(e){return $n?e.debug&&de("cached model:",$n.modelUrl):($n=await gt(vt(e.modelBasePath,e.body.modelPath)),!$n||!$n.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",$n.modelUrl)),$n}function Ioe(e,t){let[n,a]=e.shape;return B(()=>{let r=(o,l)=>ye(o,L(fe(o,we(l,"int32")),we(l,"int32"))),s=H(e,[a*n]),i=Tn(s,0).dataSync()[0];if(i>t){let o=yi(s,0),l=r(o,n).dataSync()[0],u=fe(o,we(n,"int32")).dataSync()[0];return[l,u,i]}return[0,0,i]})}async function Mg(e,t){return Rg0?(Rg++,[{id:0,score:L0,box:Eg,boxRaw:Cg,keypoints:tr}]):(Rg=0,new Promise(async n=>{let a=B(()=>{if(!$n.inputs[0].shape)return null;let u=Ve.resizeBilinear(e,[$n.inputs[0].shape[2],$n.inputs[0].shape[1]],!1);return L(u,2).sub(1)}),r;if(t.body.enabled&&(r=await $n.predict(a)),a.dispose(),r){tr.length=0;let u=r.squeeze();Ne(r);let d=u.unstack(2);Ne(u);for(let p=0;pt.body.minConfidence&&tr.push({score:Math.round(100*m)/100,part:koe[p],positionRaw:{x:c/$n.inputs[0].shape[2],y:h/$n.inputs[0].shape[1]},position:{x:Math.round(e.shape[2]*c/$n.inputs[0].shape[2]),y:Math.round(e.shape[1]*h/$n.inputs[0].shape[1])}})}d.forEach(p=>Ne(p))}L0=tr.reduce((u,d)=>d.score>u?d.score:u,0);let s=tr.map(u=>u.position.x),i=tr.map(u=>u.position.y);Eg=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=tr.map(u=>u.positionRaw.x),l=tr.map(u=>u.positionRaw.y);Cg=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:L0,box:Eg,boxRaw:Cg,keypoints:tr}])}))}var za,nr=[],Fg=[0,0,0,0],$g=[0,0,0,0],ou=0,Dg=Number.MAX_SAFE_INTEGER,Soe=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function zg(e){return za?e.debug&&de("cached model:",za.modelUrl):(za=await gt(vt(e.modelBasePath,e.body.modelPath)),!za||!za.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",za.modelUrl)),za}async function Og(e,t){return Dg0?(Dg++,[{id:0,score:ou,box:Fg,boxRaw:$g,keypoints:nr}]):(Dg=0,new Promise(async n=>{let a=B(()=>{if(!za.inputs[0].shape)return null;let u=Ve.resizeBilinear(e,[za.inputs[0].shape[2],za.inputs[0].shape[1]],!1);return me(u,"int32")}),r;if(t.body.enabled&&(r=await za.predict(a)),a.dispose(),r){nr.length=0;let u=r.arraySync();Ne(r);let d=u[0][0];for(let p=0;pt.body.minConfidence&&nr.push({score:Math.round(100*ou)/100,part:Soe[p],positionRaw:{x:d[p][1],y:d[p][0]},position:{x:Math.round(e.shape[2]*d[p][1]),y:Math.round(e.shape[1]*d[p][0])}})}ou=nr.reduce((u,d)=>d.score>u?d.score:u,0);let s=nr.map(u=>u.position.x),i=nr.map(u=>u.position.y);Fg=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=nr.map(u=>u.positionRaw.x),l=nr.map(u=>u.positionRaw.y);$g=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:ou,box:Fg,boxRaw:$g,keypoints:nr}])}))}var Bg={};ga(Bg,{load:()=>Lg,predict:()=>Wg});var lu=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var qn,_g=[],Pg=Number.MAX_SAFE_INTEGER,W0=2.5;async function Lg(e){if(qn)e.debug&&de("cached model:",qn.modelUrl);else{qn=await gt(vt(e.modelBasePath,e.object.modelPath));let t=Object.values(qn.modelSignature.inputs);if(qn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!qn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!qn||!qn.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",qn.modelUrl)}return qn}async function Noe(e,t,n,a){let r=0,s=[];for(let u of[1,2,4])B(()=>{var y,A;let d=u*13,p=(y=e.find(g=>g.shape[1]===d**2&&g.shape[2]===lu.length))==null?void 0:y.squeeze(),c=(A=e.find(g=>g.shape[1]===d**2&&g.shape[2]a.object.minConfidence&&x!==61){let b=(.5+Math.trunc(g%d))/d,v=(.5+Math.trunc(g/d))/d,S=m[g].map(U=>U*(d/u/t)),[T,C]=[b-W0/u*S[0],v-W0/u*S[1]],[$,O]=[b+W0/u*S[2]-T,v+W0/u*S[3]-C],P=[T,C,$,O];P=P.map(U=>Math.max(0,Math.min(U,1)));let j=[P[0]*n[0],P[1]*n[1],P[2]*n[0],P[3]*n[1]],D={id:r++,strideSize:u,score:Math.round(100*w)/100,class:x+1,label:lu[x].label,center:[Math.trunc(n[0]*b),Math.trunc(n[1]*v)],centerRaw:[b,v],box:j.map(U=>Math.trunc(U)),boxRaw:P};s.push(D)}}});e.forEach(u=>Ne(u));let i=s.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),o=s.map(u=>u.score),l=[];if(i&&i.length>0){let u=await Ve.nonMaxSuppressionAsync(i,o,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);l=u.dataSync(),Ne(u)}return s=s.filter((u,d)=>l.includes(d)).sort((u,d)=>d.score-u.score),s}async function Wg(e,t){return Pg0?(Pg++,_g):(Pg=0,new Promise(async n=>{let a=[e.shape[2],e.shape[1]],r=Ve.resizeBilinear(e,[qn.inputSize,qn.inputSize],!1),s=r.div(255),i=s.transpose([0,3,1,2]);s.dispose(),r.dispose();let o;t.object.enabled&&(o=await qn.predict(i)),i.dispose();let l=await Noe(o,qn.inputSize,a,t);_g=l,n(l)}))}var Gg={};ga(Gg,{load:()=>Ug,predict:()=>Hg});var Xn,Vg=[],jg=Number.MAX_SAFE_INTEGER;async function Ug(e){if(Xn)e.debug&&de("cached model:",Xn.modelUrl);else{Xn=await gt(vt(e.modelBasePath,e.object.modelPath));let t=Object.values(Xn.modelSignature.inputs);if(Xn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Xn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Xn||!Xn.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",Xn.modelUrl)}return Xn}async function Toe(e,t,n,a){let r=[],s=e.arraySync(),i=Ha(e);e.dispose();let o=qt(i,6,1);i.dispose();let u=pn([o[1],o[0],o[3],o[2]],1).squeeze(),d=o[4].squeeze(),p=o[5].squeeze();o.forEach(f=>f.dispose());let c=await Ve.nonMaxSuppressionAsync(u,d,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);u.dispose(),d.dispose(),p.dispose();let h=c.dataSync();c.dispose();let m=0;for(let f of h){let y=s[0][f][4],A=s[0][f][5],g=lu[A].label,x=[s[0][f][0]/t,s[0][f][1]/t,s[0][f][2]/t,s[0][f][3]/t],w=[Math.trunc(x[0]*n[0]),Math.trunc(x[1]*n[1]),Math.trunc(x[2]*n[0]),Math.trunc(x[3]*n[1])];r.push({id:m++,score:y,class:A,label:g,box:w,boxRaw:x})}return r}async function Hg(e,t){return jg0?(jg++,Vg):(jg=0,new Promise(async n=>{let a=[e.shape[2],e.shape[1]],r=Ve.resizeBilinear(e,[Xn.inputSize,Xn.inputSize],!1),s;t.object.enabled&&(s=Xn.execute(r,"tower_0/detections")),r.dispose();let i=await Toe(s,Xn.inputSize,a,t);Vg=i,n(i)}))}var f9=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&a&&r&&a.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},m9=e=>{if(!e)return[];let t=[];for(let n=0;n0){let a=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(a)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${a<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 i=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]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},y9=e=>{if(!e)return[];let t=[];for(let n=0;n.06||p>.06)&&(u=!1),c>.06&&t.push({iris:n,gesture:"looking right"}),p>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],m=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(m<.01||h<.01||m>.022||h>.022)&&(u=!1),(m<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(m>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},A9=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=a.reduce((i,o)=>i.position[2]i.position[1](u[c]=0,p))},r=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),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 s=r(t,e.VERTEX_SHADER),i=r(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),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),a(t,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=e.getAttribLocation(this.id,o);a(t,"uniform",this.uniform),a(n,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=e.getUniformLocation(this.id,o)}function g9(e){e||(e={});let t=0,n=null,a=!1,r=-1,s=[null,null],i=[],o=-1,l=-1,u=null,d=null,p={},c=e.canvas||document.createElement("canvas"),h={},m={INTERMEDIATE:1},f=c.getContext("webgl");if(!f)throw new Error("Filter: getContext() failed");this.addFilter=function(b){let v=Array.prototype.slice.call(arguments,1),S=p[b];i.push({func:S,args:v})},this.reset=function(){i=[]};let y=function(b,v){if(!(b===o&&v===l)){if(c.width=b,o=b,c.height=v,l=v,!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=f.createBuffer(),f.bindBuffer(f.ARRAY_BUFFER,u),f.bufferData(f.ARRAY_BUFFER,S,f.STATIC_DRAW),f.pixelStorei(f.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}f.viewport(0,0,o,l),s=[null,null]}},A=function(b,v){let S=f.createFramebuffer();f.bindFramebuffer(f.FRAMEBUFFER,S);let T=f.createRenderbuffer();f.bindRenderbuffer(f.RENDERBUFFER,T);let C=f.createTexture();return f.bindTexture(f.TEXTURE_2D,C),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,b,v,0,f.RGBA,f.UNSIGNED_BYTE,null),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.framebufferTexture2D(f.FRAMEBUFFER,f.COLOR_ATTACHMENT0,f.TEXTURE_2D,C,0),f.bindTexture(f.TEXTURE_2D,null),f.bindFramebuffer(f.FRAMEBUFFER,null),{fbo:S,texture:C}},g=function(b){return s[b]=s[b]||A(o,l),s[b]},x=function(b=null){var C,$;let v=null,S=null,T=!1;t===0?v=n:v=(C=g(r))==null?void 0:C.texture,t++,a&&!(b&m.INTERMEDIATE)?(S=null,T=t%2==0):(r=(r+1)%2,S=($=g(r))==null?void 0:$.fbo),f.bindTexture(f.TEXTURE_2D,v),f.bindFramebuffer(f.FRAMEBUFFER,S),f.uniform1f(d.uniform.flipY,T?-1:1),f.drawArrays(f.TRIANGLES,0,6)};this.apply=function(b){if(y(b.width,b.height),t=0,n||(n=f.createTexture()),f.bindTexture(f.TEXTURE_2D,n),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.NEAREST),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.NEAREST),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,f.RGBA,f.UNSIGNED_BYTE,b),i.length===0)return x(),c;for(let v=0;v0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=r,o=s;if(i>B0&&(i=B0,o=i*s/r),o>B0&&(o=B0,i=o*r/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=r*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/r)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Ce||(Ce==null?void 0:Ce.width)!==i||(Ce==null?void 0:Ce.height)!==o)&&(Ce=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ce==null?void 0:Ce.width)!==i&&(Ce.width=i),(Ce==null?void 0:Ce.height)!==o&&(Ce.height=o));let l=Ce.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(r,0),l.scale(-1,1),l.drawImage(e,0,0,r,s,0,0,Ce==null?void 0:Ce.width,Ce==null?void 0:Ce.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,r,s,0,0,Ce==null?void 0:Ce.width,Ce==null?void 0:Ce.height),t.filter.enabled){if((!Ot||!xt||Ce.width!==xt.width||(Ce==null?void 0:Ce.height)!==(xt==null?void 0:xt.height))&&(xt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ce==null?void 0:Ce.width,Ce==null?void 0:Ce.height):document.createElement("canvas"),(xt==null?void 0:xt.width)!==(Ce==null?void 0:Ce.width)&&(xt.width=Ce==null?void 0:Ce.width),(xt==null?void 0:xt.height)!==(Ce==null?void 0:Ce.height)&&(xt.height=Ce==null?void 0:Ce.height),Ot=ua.flags.IS_BROWSER?new g9({canvas:xt}):null),!Ot)return{tensor:null,canvas:Ce};Ot.reset(),Ot.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Ot.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ot.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ot.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Ot.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Ot.addFilter("hue",t.filter.hue),t.filter.negative&&Ot.addFilter("negative"),t.filter.sepia&&Ot.addFilter("sepia"),t.filter.vintage&&Ot.addFilter("brownie"),t.filter.sepia&&Ot.addFilter("sepia"),t.filter.kodachrome&&Ot.addFilter("kodachrome"),t.filter.technicolor&&Ot.addFilter("technicolor"),t.filter.polaroid&&Ot.addFilter("polaroid"),t.filter.pixelate!==0&&Ot.addFilter("pixelate",t.filter.pixelate),Ot.apply(Ce)}else xt=Ce,Ot&&(Ot=null);let u;if(xt.data){let p=[xt.height,xt.width,3];u=Ic(xt.data,p,"int32")}else if(xt instanceof ImageData)u=fi.fromPixels(xt);else if(t.backend==="webgl"||t.backend==="humangl"){let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(xt,0,0),u=fi.fromPixels(p)}else{let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(xt,0,0);let h=c==null?void 0:c.getImageData(0,0,i,o);u=fi.fromPixels(h)}let d=u.toFloat();n=d.expandDims(0),u.dispose(),d.dispose()}let a=t.filter.return?xt:null;return{tensor:n,canvas:a}}var Zg={};ga(Zg,{all:()=>Foe,body:()=>v9,canvas:()=>Moe,face:()=>b9,gesture:()=>x9,hand:()=>w9,object:()=>k9,options:()=>os,person:()=>Coe});var os={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!1,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedFactor:2,bufferedOutput:!1},nt={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function Xg(e,t,n,a=0,r){e.fillStyle=r.useDepth&&a?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function op(e,t,n,a,r,s){if(e.beginPath(),s.useCurves){let i=(t+t+a)/2,o=(n+n+r)/2;e.ellipse(i,o,a/2,r/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+a-s.roundRect,n),e.quadraticCurveTo(t+a,n,t+a,n+s.roundRect),e.lineTo(t+a,n+r-s.roundRect),e.quadraticCurveTo(t+a,n+r,t+a-s.roundRect,n+r),e.lineTo(t+s.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function Kg(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let a of t){let r=a[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function lp(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){Kg(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d,8,2+s*a.lineHeight)),r.fillStyle=a.labelColor,r.fillText(d,6,0+s*a.lineHeight),s+=1}}}async function b9(e,t,n){var s,i,o,l;let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r)for(let u of t){r.font=a.font,r.strokeStyle=a.color,r.fillStyle=a.color,a.drawBoxes&&op(r,u.box[0],u.box[1],u.box[2],u.box[3],a);let d=[];if(d.push(`face confidence: ${Math.trunc(100*u.confidence)}%`),u.genderConfidence&&d.push(`${u.gender||""} ${Math.trunc(100*u.genderConfidence)}% confident`),u.age&&d.push(`age: ${u.age||""}`),u.iris&&d.push(`iris distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let p=u.emotion.map(c=>`${Math.trunc(100*c.score)}% ${c.emotion}`);d.push(p.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.angle.roll&&d.push(`roll: ${Math.trunc(100*u.rotation.angle.roll)/100} yaw:${Math.trunc(100*u.rotation.angle.yaw)/100} pitch:${Math.trunc(100*u.rotation.angle.pitch)/100}`),d.length===0&&d.push("face"),r.fillStyle=a.color;for(let p=d.length-1;p>=0;p--){let c=Math.max(u.box[0],0),h=p*a.lineHeight+u.box[1];a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d[p],c+5,h+16)),r.fillStyle=a.labelColor,r.fillText(d[p],c+4,h+15)}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(a.drawPoints)for(let p of u.mesh)Xg(r,p[0],p[1],p[2],a);if(a.drawPolygons){r.lineWidth=1;for(let p=0;pu.mesh[h]);Kg(r,c,a)}if(u.annotations&&u.annotations.leftEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,c=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,c=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(a.drawGaze&&((i=(s=u.rotation)==null?void 0:s.gaze)==null?void 0:i.strength)&&((l=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:l.angle)){let p=[u.annotations.leftEyeIris[0][0]+Math.cos(u.rotation.gaze.angle)*u.rotation.gaze.strength*u.box[2],u.annotations.leftEyeIris[0][1]-Math.sin(u.rotation.gaze.angle)*u.rotation.gaze.strength*u.box[3]];r.beginPath(),r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.strokeStyle="pink",r.lineTo(p[0],p[1]),r.stroke();let c=[u.annotations.rightEyeIris[0][0]+Math.cos(u.rotation.gaze.angle)*u.rotation.gaze.strength*u.box[2],u.annotations.rightEyeIris[0][1]-Math.sin(u.rotation.gaze.angle)*u.rotation.gaze.strength*u.box[3]];r.beginPath(),r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.strokeStyle="pink",r.lineTo(c[0],c[1]),r.stroke()}}}}}async function v9(e,t,n){var s;let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round";for(let i=0;iu.part==="leftShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position.x,o.position.y]),lp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position.x,o.position.y]),l.length===4&&Kg(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftKnee"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftAnkle"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftHeel"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftFoot"),o&&l.push([o.position.x,o.position.y]),lp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightKnee"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightAnkle"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightHeel"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightFoot"),o&&l.push([o.position.x,o.position.y]),lp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftElbow"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftWrist"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="leftPalm"),o&&l.push([o.position.x,o.position.y]),lp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightElbow"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightWrist"),o&&l.push([o.position.x,o.position.y]),o=t[i].keypoints.find(u=>u.part==="rightPalm"),o&&l.push([o.position.x,o.position.y]),lp(r,l,a)}}}}async function w9(e,t,n){let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes&&(r.strokeStyle=a.color,r.fillStyle=a.color,op(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText("hand",s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),r.stroke()),a.drawPoints&&s.landmarks&&s.landmarks.length>0)for(let i of s.landmarks)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,Xg(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,l)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 0.5)`:a.color,r.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};r.font=a.font,i(s.annotations.indexFinger,"index"),i(s.annotations.middleFinger,"middle"),i(s.annotations.ringFinger,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palmBase,"palm")}if(a.drawPolygons){let i=o=>{if(!!o)for(let l=0;l0?l-1:0][0],o[l>0?l-1:0][1]),r.lineTo(o[l][0],o[l][1]),r.stroke()};r.lineWidth=a.lineWidth,i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function k9(e,t,n){let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,op(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}r.stroke()}}}async function Coe(e,t,n){let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s=0;s((t.bufferedFactor-1)*nt.body[a].box[s]+r)/t.bufferedFactor),nt.body[a].boxRaw=e.body[a].boxRaw.map((r,s)=>((t.bufferedFactor-1)*nt.body[a].boxRaw[s]+r)/t.bufferedFactor),nt.body[a].keypoints=e.body[a].keypoints.map((r,s)=>({score:r.score,part:r.part,position:{x:nt.body[a].keypoints[s]?((t.bufferedFactor-1)*nt.body[a].keypoints[s].position.x+r.position.x)/t.bufferedFactor:r.position.x,y:nt.body[a].keypoints[s]?((t.bufferedFactor-1)*nt.body[a].keypoints[s].position.y+r.position.y)/t.bufferedFactor:r.position.y}}));(!nt.hand||e.hand.length!==nt.hand.length)&&(nt.hand=JSON.parse(JSON.stringify(e.hand)));for(let a=0;a((t.bufferedFactor-1)*nt.hand[a].box[i]+s)/t.bufferedFactor),nt.hand[a].boxRaw=e.hand[a].boxRaw.map((s,i)=>((t.bufferedFactor-1)*nt.hand[a].boxRaw[i]+s)/t.bufferedFactor),nt.hand[a].landmarks=e.hand[a].landmarks.map((s,i)=>s.map((o,l)=>((t.bufferedFactor-1)*nt.hand[a].landmarks[i][l]+o)/t.bufferedFactor));let r=Object.keys(e.hand[a].annotations);for(let s of r)nt.hand[a].annotations[s]=e.hand[a].annotations[s].map((i,o)=>i.map((l,u)=>((t.bufferedFactor-1)*nt.hand[a].annotations[s][o][u]+l)/t.bufferedFactor))}let n=e.persons;(!nt.persons||n.length!==nt.persons.length)&&(nt.persons=JSON.parse(JSON.stringify(n)));for(let a=0;a((t.bufferedFactor-1)*nt.persons[a].box[s]+r)/t.bufferedFactor)}async function Moe(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 Foe(e,t,n){let a=zn(os,n);!t||!e||e instanceof HTMLCanvasElement&&(a.bufferedOutput?Roe(t,a):nt=t,b9(e,t.face,a),v9(e,nt.body,a),w9(e,nt.hand,a),x9(e,t.gesture,a),k9(e,t.object,a))}function I9(e,t,n,a,r){var o,l,u,d,p,c,h,m,f,y,A,g,x,w,b,v;let s=0,i=[];for(let S of e){let T={id:s++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let D of t)S.box[0]>D.box[0]&&S.box[0]D.box[1]&&S.box[1]+S.box[3]T.body.box[0]&&D.box[0]+D.box[2]T.body.box[1]&&D.box[1]+D.box[3]T.body.box[0]&&D.box[1]+D.box[3]>T.body.box[1]&&D.box[1]+D.box[3]{D&&D.length===4&&(C.push(D[0],D[0]+D[2]),$.push(D[1],D[1]+D[3]))};O((A=T.face)==null?void 0:A.box),O((g=T.body)==null?void 0:g.box),O((w=(x=T.hands)==null?void 0:x.left)==null?void 0:w.box),O((v=(b=T.hands)==null?void 0:b.right)==null?void 0:v.box);let P=Math.min(...C),j=Math.min(...$);T.box=[P,j,Math.max(...C)-P,Math.max(...$)-j],r&&r.length===4&&(T.boxRaw=[T.box[0]/r[2],T.box[1]/r[1],T.box[2]/r[2],T.box[3]/r[1]]),i.push(T)}return i}var V0=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob IxwWFiAsICMmJykqKRkfLTAtKDAlKCko/9sAQwEHBwcKCAoTCgoTKBoWGigoKCgoKCgoKCgoKCgo KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgo/8AAEQgBAAEAAwEhAAIRAQMRAf/E AB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAE EQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZH SElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1 tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEB AQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXET IjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFla Y2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A+qaKACigApGOKAML Xp8xlF5A7V4X8RtYs7PzfNImnx8sa8Kp9z3q2tEgp6angWs62ZZ5CTGoJ6DArGNz5p+UrID6EUrF PUlW1EuN0XNW7PQ2L5j3JnoKXN0KijqNP0eYoqXBdgPuuo+ZPeupisWn2Jd4+0r924XgsQOCff3/ AJ1FzRKxDqGii6m3siiQ8F1XGfXI6YNWLfRbiRQMkcZI9fpTDluT2/h6Qy8gDPbtmtG38JeY480Z 5zSLUTZg8M28YwYxjAArXtdPt402qgHbpSaLWhma3o0Uqk7Nx9DWLaaVblgPs6qRyds2M/gRSQp9 zZOni2iWS2hlQ+kjYz9OMGrdjq89vIPPVhj+8M/lQyDq9P1WOYBlMZz1AOD+VdDaTiReOKulK0jO tHmi0WDTlr0TyxRVhT8tJjIX+9SUxHXUV553BRQAVBcPhSBTSuxPY86+IGti0s5I7dsORy9fM3i6 8e8mfDO5P90ZrWWiJicNPpZZtxV/xrW0jQt4DOv6Vk2dEEdTY6BHuB25rpbPSo0QARjP0qTRI17W wA/hFaMWmoQMgflQXYsDS142rU9tpqqenfNA7GgtihxkdKuRW6qMY/GkDZY8sY4Ap4hXbyB+VArk EtuH4wPyrk/EGkOm+a3jw3suRQLc5i38SX9hJ9nnY+XnBUdPyNdFY6pa3KkkAE9l6f8AfJ/pSJT6 GhDmI+Zb4ZRycdv6ium0nUhKFydrelTsNnS2829RnrVgV6NKXNG55lWPLIM81Op+WrZkRMfmNNzT A7GivPO4KKAEY4XNYWt3vkwPg4OK0giJdjw/xrqhm87Zs8tc7pX5A+leSajf6aHYJ50kn4AZpTep rBWRm2Vobm4BXfyehPFdnpmnBFUY5rI2SN63tlToK0YI+KZpFF+3QdavwoKTLtoW0Toaswpk5pCb LCxipAhoIuP2dKevHXoaYDylRyxhlwRQI4nxVoCXWZI1GfpXGtbSWjYPGP73+NIGupt6TqMsLruZ ih4xnP5V09mQ+JLd8gn0xSYJnVaVdkook69K34zuUGunDS3Rx4qOzHVIp4rrOMY3NJQI7GivPO8K KAILt9kZrz3xlebYiu8KCCWb0XvW0NFch6ysfO3jLVjfXLIn+pQkKorl7WxNxIPl71g2dUUdpo+l pBGvHPet23iC8ihFosrxirkHQUFo0IF4FXI1O726CpKLacCrMJoJLYHAPpTwucHpSRJJ5e4AZI9x UqpxzVpCuOC8cUpQUMRnXttuB4rjNdsYyeVwfXpmpGmcvcQyafMCFJjPY10eg34BUg4DcZP8jUO4 HaRq3lLNF+IHet7R7jz7c56rwa2wz9+xhiVeFy/T1PFegeaNPWigDsc0ZrzzvDNIaAM7VpNqdegr xL4l6kywyRhseZ19lrdfAZL4jxYg3Fw20d63tJsdrDI5rm3Z3R0R0Mce1eKnQYAplIkWrMJ45oZS NO3PHbNXIyfpSGWowSOasxLUiZdjFSqtNEMkUemKlAGKsRJjAppFAiORMjmsTVrNZEO4cfSoZSOD 1eJ7WXBUzQZ+7nkfSo7e2Ei+ZaMzxntjBX2NSU1Y6/wxqojiEFzkA8KTXYaUoWRyv3W5rSjpNHPX +BmpSg8V6J5gUUAdhRXnneFFAGHrTfu5PpXzj8S70/aZtxzztXFbv4DKHxHI+H4GZiz9zxXXW8G3 GBXMjvLRXAx0oPGPSmMVeOnWrMTYpFI0bcg1fh54xmgovRcD3qxETSIZcRvzp+/BpEkqsBUqsM9K q4Em4Gkxk0yRGXrVW6i8yFhkg+tJjRxGsWrxllkUMh9eK5uMz6bcebbnfG33kPcVkay2OntPKuo0 nhXI67c8qa7Lw3c+adjcEDGK1paSRhVV4s6A0or0jyRRQ1AHX0V553hRQBz+vNtt5z3xXzX8Qbdm uic5YnOMdK3l8JnTXvlbwpYl+WySOgrp5YfLOOB9O1c62O7qQkc+9RsKChFPWp4DluOlSykaNruH ArUgHShFNF2NT1qxGO3NBmyxGcE1N2560CFzjrUysO9JAPDDjFOVuKoQuSRTWouBkazbCa3cd8cV wF7IISQccHBzUSWpV9C3o1x5b5GAjdQD1rs9DjC3kckbEhqKfxIzn8LOupRXqnkPccBSkUAzraK8 87wooA5rxMSI3HqK8B8bQl9Q8sffY5b/AAraXwkUviNrw9pH2W1ViMMRTdRjw4HpWNtDti9TPc4P FQs2M5qdyyMHLcfjV63HTAoBGtap0wK0YxigpsuRDtVhVYd6GQydVwwIqdRnqKCR23I5pCMUW6gD YNKuetAEise9KTxQBWuFyhrznxNZkXjFeN3I+tTIZg2OqmzmxNF0PO3vXp/g2+hukVl4zyPanTXv JmVR+60dpThXpnlPceopWFAbnV0V553hSGgRynjC5FujOey14Ssp1HxNmTnc+a3kvcIpv37HoEYQ QmMdVHSsnVbYJF5jVk0dsNzlruVIsl2wKxbjWrVHILjg1CRbZJb+ILHPzyhfStODWLQgFJFYd+el UJM27HUIXxhga1Y5lLVLKLkMnoauxnPPrSEx7ShF+Y/n2qrc6xBbhizDAqkK1zJuvG9nbg8ZA681 ly/Ei052RO3uKAsZlx8QGd8xxvt9Aa1NH8dK7AXMcip64zigdkdrZX8F7EJLdwwNXMkrz1qRMRly CK4TxmpidWI49felPYSOMmi80NIoOV6qRzXYeA5SskYPfirpfEjGr8LPWVHyD6U4CvQPL3ZItOYc UDOoNFeed4Uhpks4H4iE/Z5MeleMeGULeLgjds10S+BGdL+Jc9OSBU2Huc5Nc74yvUtrcDBrJnZF 63PJdXvLy/lKWw46bvQVz82jXhkLO5Y+9ZlsYthcRnbIjY9R3q3awTRkEM3WmJI6C0ea3dGRsr1x XY6TqW9FLHnjrUs0izpLK5DDjofSta3ckH09KRUkZuuTvFGdvPauE1Y3U6Mqbssf/rUxHPTaJPK2 ZmJPbBqzY6DCZh5xJC9s9aBJHU6dpemJjfEmfetJtI0+VPkUr/unFOxdiextHs33W07YHQHk11mk Xb3KbZ1xIvcd6LEyWho4Nct41sTPYb16ipexCPPZN+wYGCvH1rrPAEJmvkPoc1VL4kZVvgZ6yFwK cBXoHkkqinFaVyzo80GuE7WJRQSziPiGdthK5HQV4x4J/wBI8WPIewNdEvgRNL42emO/yj1UHNef eNpRczbC+I17DvWT2OqJxc0sMK4TCisy41q0hfEkqj8aixdwTXNOlwvmqD9anS9tXH7uVG+hosO4 /wC0oOhrR0+6G4YNIEzsNEuCxAPNdjZruA4xxUmjINSjURksOlcbqFykbnjFA1sYGoassaknCqO5 rl7rxhGm7yBnBxuJq0rkSlYpw+NLlsfd5P8AerVsvHEqSBHwPVgcgVpyMyVXU3rXxcHYETAk+hru /DWti6ZSTyOKzZqndHaxvvUGq2rQ+dYyqR24qWI8dvbr7LqDxyDAzXpvw6FvIxePGSM06Xxoyr/A zviKFHNegeX1J41zUhXioGbuaSuM6wpCaBHG/EcA6HN/exxXjXw2jL67cv8A3Qa6H8CFR+NnoWpO I4XI44rxLxrqjQzSEsQM1gdSPM9U1uR1YbmWIdXHf2rmpIb67YS28UrRlsLI3c/jW0VZGUpO5pW1 jfLNOjahawzwReYI5cjzMkDavHJ5/SrVv9uhtPtVxCPLBwzxnlT9KGghLU3tKvvPjHzbl7EGuisJ GRxWLOg7nRXJEbDjmvSNK+aFSfSoZr0KutRkphc4NcRrdkVjL9aVio7Hk3iqS8ubhrWzUlsZY9kG cZNc5D4aee5MclzJIFTzHAO0MfatqSOWu7bFS1srDUZEis0vIZoUxPvfcC+4/dx2xjr712XiTwXb WmlQ6hol3cRhoFd4rlg3zY5wR0GelavQwjq7GD4etdVvSnk2wAB+9v8A8mvcfA2kXiRo0/UdcDis ZnTTulqeoWqbUAJqWUb42X1FZlnjfjSwlGrr5S/eNdD4RkvLAAQ4yRyaUZcruVKl7TQ9I0G+mnzH ckFwM8VuIK7ac3KF2eXiKapz5UWYxipNtMyNejNch0jSar3cjR27uoyQCRVRWom9DxTx54gu5fMi lbKdMVjfCZPNlv5v9rFbVHpYqjGzbOn8SzFI9o715L4u0r7arYzk+lYdTqSujy7U/C0u4vHk+WwO xuh9q3J9dgvbdVukMV1EwbDDgn04rZMwlHoZ+orZ6hfQ3RWVnQYCgZAq+8U0ln5NtBsV2yxYcfgK JtW0CnB31LlroVwJ1nQLGDjeP7w+lb0dsFxjrWB0tHS6NuWPJ6A16ToUm63T3Gallr4S7cxiTjrX PaxaF7dlVeSMUhxZ5jd+H7qCa4eF3DSE5x3zXN3Wk6jbyeaiFWUY6ZyPStYS5SalPmVipFbX0E4c W0alvmPHJrag0rVvEE6LdljGpG2NRtQD+tW5XMI0uU9M8NeFo9PiQhecDIIrtrOMIoG3H4VlJm9t C6CB06VPGM1IHLeItGS6uw+ORT7e3jsbQvj7gzUNam0JaWE+HN7NqOqX80n3FO1RXo8YzXdS+BHk 4z+KyzGPapcU2YIv7qQtiuaxvcaWqG4O6FwfSrS1JbPnrxoxkv7qIfejcitj4V2f2exumI+8+aKn xHTT+G5d8Txlm4rjLxMsQwzWT3OiK0Mm6sEkVsAcjFc1d+FEmlGwEDPQVopaEuOpr6f4ZWNAu3tW vHpAj5ZQcUFIWaDjGMVUMQ3cVDBmvbhY7QAV2nh+T/R1yeKhlrY31+b61FcQK6nIoJMi401WblRi qr6PCw5UYq9y+YgOgWzNkRrx3xWjp+nx2v3FQcelAbmko9anQ4GBUNisPHWr1qMrQhS2K11HvmYV hamcxSRZ5xRIqluS/DKAQQXZxyXrvo2FdlL4EeZjH+/ZbjNSZpswLNBrE1Gt7VE4ODVIlnh/j61F j4lmeTGyUbq6LwdEqWbeX0YbhSqfEddP4Bddj4JIrhL5d8h7VjI6oLQqKNzelWre3yc4/ClFjaL6 wqBxxUUxwCKu5BmXRA6c+9ZjP83FSBoQuPs4BrsNBlUW659KmRrDY6G1lyQtW3Hy0lqQ1qVJnAbm oy3b9KYJCqRj3o4zRctIlhjLHmpSuOBRbQOpLGpPFaES7UqkZzKN1KsEc87/AHUUmvPLTVGv72aQ k7WJwKmRrQ3ud74Ltilgz4++2a6iNDXdS0gjyMU71my7GpqTbxSbMki3SViajTTHqkSeR/GeyZmg nQHkEE1S+F+oPPavBL96I4/Cia1udVF+4dVrkW+Fq8+v4tjMDWUkdVJ6WM0cNV+F+MVmjUcZgqnP 1qpNNnkcVRLiZtxIS1UzzIF7mghlxUZpVQdq6nTVdAoAOKzkbQWhvwM6gMM1twOJYx3NOJE11Kt1 H1/pVVlwBkk+9NocXoOQ45FPj+fkUJFF2NSB700v/hTEty5ZpkjvVyUgcCq6GM9zC14/8Se6GcZQ 1574Xs5WkI2HBPHFQ1dm1KSSZ7Rotn9l0+KPHIHNacae1dy0Vjxaj5ptlhVp+2s2CJ9ppCKzuWNx zSFc1SYrHNeNdIGpaYw25ZeRXmvheyk0jVpEdcLJ0q3ZxNKTa0O3vQHg/DNcHrsJDmsmjspnNzNt fFIJ24GazOhC+azDmgZIOOKBsp3J2qSaZodubq58yQ4QAnmhGT3NO18pb7BORmu205LfYpyKVkWp Oxr5gKYWoIZWgfGfloFq1qTPLubnGO1RPtxg4P0oBAkY/hBz6VNDDkZ6AU0W2WSdqkdKr9ZOaGSj VtcLHmnOcgmmYvcz7mBLy3MbdD1q9ouiRK6bUAVeelOC1InPlidSsWMDFOCEdq3uefykqrinYqGy rFvApMVka2DAowKAsMkRXQqwyDXn/iWyitNQ3qPl6itIvRoF8RXinW4tQ6HI6GuW8SIVBPalc6qe 5x9x97r3qruwTjrWZ0ksZ9TUmcDNAmZ9/wAoao63rR0+w22MLPtAzt6mghmfofiB76LdJBJBIp5D d/oa7bSdWLIPnpDi9TM8TeKdas51XTbIyxd3J/pXS+E/EFxqNoFu7do5OmD60maHWrnZyDRkn/69 MlEyOR0xntVoNx+FUgYjPxg4FLCuWDZyKQr2RoRnP0qO+nEFpJITgAUzLqZnhu6+0rknOTXpOmwJ Fbrt5yMmnHYyr6Oxb2ijaKLnPYMClwKQWK3n0hn+lachHOJ9pNNN0apQFzsY10a4v4hXQh0xpieQ MA1XLZNjhK80cT8OdV+3Wl3A7ZZJCw+hrR1qLcjZ/CsbnfHRnFXseHJArOYYbrUs1uPhYbuatqFP ByfSkMq3UIINYkto+87Tx6GkSxfsDbflGD7CtTw/pk4nzITtPIFMFudsukh4Rxz71paTpKwP5jcn 0qTRy0NORMDgVCqewoJTJgAoxjntTiTu7fWmFxAcnn1q3EPl+X8KZMi4gKqB1Peob/Tv7Us5bfeU yOoq4R5nYxqT5I8xieH9J1DTbvyJELRg8ODwa9Ms5mSFV9BWiptbnNVrKdmif7Q1KLg96XIZc5Is pNL5pqeUrmMtZs0jzV08phchaY00zH1p2ZNxjS1g+LdJOt6U9ssmxjyGp2urDjLlaZzng/wUPDqz TSTmWeTrjpVjVk3Rvjr2rnqQ5dDvo1XUd2cTqSNk9OKxXGCeKxZ1DAxHTr2q5C/y8GokUhsz54qu uCxzSQjQ0+FZblR2ro4bZYiMVQ0dBb7Qi5x0qzuG5QOh71LYErDufpSeWrHnimIXbjkUjLkH1Hem gGxryc+tXI19KYmWegq9YLiLJ7mtqS945cS7QsWehqxA9dEjz4krPSxyZqbFFhGxUm6smjRM55Lk HvSvNxXTY57kLT+9MNwKdhXGm5FIbkU7Bca1wMEVhaiuQcVhXWiZ14R6tHGanGBI2OtYkqEHjgVy s9ErEeo6UBsHipKEZs5qpPdRxcbhx70NCSuybTNWihc5brW9Fq6vjMnFSdEIdDRi8RRKygZbHFbu m6nb3RA3gMegNJhOm0jbXGOoxTuCc1Rz3FyoGKawz9KaAVcZqeMgCmIkB4FaUTbYwB6V00Fuzixb 0SFMuDU8Mlbs4UPeXHeiOXkUrDuXYnyKk3cVk0ap6HMxxketSMhrcwRC0dMMZFMQ3yzSeVQAeUaz 9Vj8uPd271nVV4m+GdpnHX67pCeKyLtBtNcR6xlk9RVeWTb3qRnO6trgttyIfm71z7ai8j7/AJmN DNqUVa5Yi1AnjynHuBV+11YJhWWXcP8AZNSzqgmaEerSsf3NtIQP4mGKtRavdRgMIpVI9KjU0a7n R6T43uYQI7qN2Tpkqciu503VVuQGAYZHQjFVc4alPlZrpKGAznpTwxOc9+lWjIlUACnM4XApiLNk nmvnsK0NvpXZRVonmYqV52GsmanhXitTmFkSiJTSAvwrxUxXIrJ7miOfjf1pzNWxkRlqYWpgJupu 6gQbuahvIxPA6eo4pNXVioS5WmefakGhndH4INZs5DJXA10PaTurmLO21uKpSZqGMoXGnRzBiyjd 9Kx5rcQS428fSkjanLoaOliHGZFB56VswW+mtPufcBsGOAfmxz+tFkd8HpoaUx09FAtFY8DO71qb Sms/Nb7RbecG6AEjFLS5c78t+p0djpVs9wsyQiJAdyr1rW+zqjErzSe559Sbk9S3C+MA1bjbgE1S MSXzMVG0vNUI2tPKrAuCMnrVzNd0PhR49W/O2xrHmp4TxVMzQshpIzzQBehqesnuaI5VGzT2bitz FEbNTC1ADS1JupgG6l3UAc14s04yR/aYRll+8BXCtLncDXFWjys9TCz5oW7GddH5qqNzWDOgQnC8 VSuo1kHzAGkPYopEY2+RWxV23Vzj5G/Kg3jWaNazhZuqNXS6TaKhB2c0jR1nJWOlhOxRxU4YkCgx Y0OQatQyDbyaaFYe8uF4NY3iC9ltbVGj43NTIL3h7WzMihjzXVQXYYDdW9Cf2WcOJpfaRZ3g9KsQ mupnCLIabGeaAL0LcVY3cVmzRHIxtUhetzEjZqjLUAIWpN1ArhupwagAfDKQ3Q1594v0c2bm6tx+ 5Y8j+6ayrR5onThp8s7dzkZjuqAAmuBnqC7c0iwgtzSA0rWzjfGRW3ZadDu4AoNYo2rfS4v7orSh 05UA2r0pDbsTm29KRottBNyJ0wpJ9KhD7f6U0ikNWffIFBz60zVUW52ow4UcUN6EPcx44WsbgOmd ua7TT5Bd24KHnFKnLlZFSN4koluLdueRWvp14swweG9DXoxldHlTjYtzGoo25qzEvwtUxas2jRPQ 5CNqkLVsYoYzUzdQA3dSFqBBmnqaBhuqhriCXTpVIzxUz+Fl03aSPI9QTypW2/dz0qKNw3SvOPZR Mqin8VLKRcs3O4Cuk0w/MDjt1NBtHY6O2IIHY1pxgFaETIRwMkjtVSUEk4570MlFW5bap6dKzWm8 1tqH8aY+hp2FvGoGayNevVt7/ap4xzUvYjqTLtvLPcvJxSaVcyWsxTnFZlnT2t15xHmCtOBYwQy4 B9q7cPO+jPPxFO2qLEj5HWo42+aus4HpoX4W4FTF+KlotbHII9SFuK0MUNZqiLUDE3UbqBBupwag Bc1DefPbyD/ZND2KjujyPWlKzuPesRZjHJXms9lMuw3StjnmphKDSLTJ7OfE3JrpbO4GQc9qlnRA 3LO82k5NbFvdADkjBoCSHyXIIIzgVQvdRigT7wzjgUzO1jHknlvG7qnp61etYFQDIpCZoqVijzXn 3iC8EmsOuaCGb/heR/s0ijkVv6fbxy3QMg5xmsnuX0Ldzut3+UYTPWk+2GJSe+M1pFtamcldalmx 1eO4XaThhWnC+TXqR2PHqL3maUJ4qRjxSEjj42qXdxVmaGs1MJoATfSbqBAG5p6mgAzTJTmNvpQU tzzHXY83D/U1zF5FhjgV5r3Pa6FMsV5HWnLe7RhqBRdmTwagN2d2K2rPU1C5LAnPrUs6Iysbdrq6 f3gK0BrUKj/WClY05iM6xLOcQAj3NT29uznfKSzHuadzNu7NSBFjHNSm5VO9IRnajqoWMhTzXFtA bvUfMduSeg702Qz0rS7FbTToQFwzjJqaGTFyfK5PQViyzUuFmuIdgGABya5u/vTaN5cnUHFUmLoZ zyskwlgJweSK6zQdUEwVJeGr0aUrxPLxEfe0OrhPAqVjxWhznGRtUwatDK4jNxURbmkAm6jNABup 6tQAFqhupNtu59qUnZFwV5JHnWsHdIx96w5lz15rzT2uhRmt85xWbcxMnUGmZlB0bdxmrNvFIcfM 350mWjbs7YkDJY/jW5ZWW4jikWkdNp9mqYJFaJdEHHakUULu/VB1rLn1Ld/FgetMGYd/qWSQmSa0 /AemS32pfa7piLeLkg9z6UmQtz0W7uQ2cZx0A9BVzR7cAea6j2rPqX0L99KRat5A6Dk1wOoKZ52a YfMORTYRLujiGWEq6/NWza2yKQVHNdOHerRy4laJo6TTnbbtb8KuM3Fdh5z3OJjbmpt3FaMxAtUZ agBN1GaQBzTwaAAms3VbjERUGsa07RsdeFpuUuY4jUjljWTKK4j02RE4IpJYFk6imQkVl0xWarsO mAEcUi0bNnZBR0rWtoguMCkUi21wI161mXuocEKaYXMS4u+pY/hVCSWSY4HT0pEmlouiSahdpEBl mOceleiwWcNjClvHgJH97Hc1EmVFFi3Czy7mwIl/WtJbjP7uLgd/apQ2VNVvtsBhiPzdK5S4nAuR nqOCaTGi9pcytPlU+XpmumtWII44rah8ZjiNIXRuWeNvvViQ/LXpJWPJbu7nCRvVkNxVsxBmqJmo EPiXca0YLMuOlJsuKuPlsSi5IrNuG8s4HWs5VEkbwoOTKsk+FJY4rC1K53k1xTk5O7PSpwVNWRzt 4cms+WpKICtSLTETQj5q0YeBSGiys23pUguGxQMq3E59ayrm4x3yaAKiRtO2WPHcmhruKFxFajzZ ScA44qRHoXhuMaLpxaUg6hcDLMf4F9KlhuDeXGASIl+8azZslYma68y48m1+7nFW5rtbRNhb5z1p iMKbUg0zuW4A4rPgb7VdKXOMmpA7HRbMS7nUYiUda0lkQOBngVrS+JGdbWLRt2bAx5BqeQ/LXpnj PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,j0=` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA AhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAEDAgQFBgf/xABDEAEAAgECBAMECQIDBgUFAQAA AQIDBBEFEiExE0FRBiJhcRQjMkJSgZGhsWLBJDNyFSVTY3OSNEPR4fAHFjWCokT/xAAYAQEAAwEA AAAAAAAAAAAAAAAAAQIDBP/EACARAQEBAQADAQEBAQEBAAAAAAABAhEDITFBEjJRIhP/2gAMAwEA AhEDEQA/APqYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAKNTq8OkxzfNkisQC8eb1XtRNbzXT4q7eU2nu0MntRq/D8StMccvW29ZmdvgjsTyvZjxOLj +s8WLxn8TFPXs6Oj9oct7c14rkxz22nrB2I49KOdTjelmszfmpMeUxv/AA28OqwZ4icWWtt/SUi4 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAmdo3nsPNe0Pt Fh09Z0+DNWL7+9O/7A3eJcZppsV5raI27esvH6jX5ddM25p79Ilo59VbUZOe2Tm/PeGvfPfT2iKR PLv1+DO678XmW/a97U6TtOyzTbTF538/T9WjTNecm9a7126tqk3rSYxY5ta1plRZqZNXGjyZcPXl mZmsx+qjBrsuO16xM7eXRt04JrdTltk5OWJnfaWf0a2lty5MdZnfzSn+WOHiOutFpjHa9e8bQ2fp +alYy462pk7zXbuxjPesbRS0f6ZZV1ET1tErzXFLHo+A+1ddZf6NrI8PJHa1vN6iJi0bxMTHwfOa zhzd61v1846utwniM6DUdb3nBaNrVmd9vjC/ZVePYirBqMWppz4rxaPgtEAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAItaK1m09ojcHnvarjM8P0vh49+a/eY8ng9D h1fGM1rxjtGPfvbzdbjuTJxHX48cTPNltM/KsS9Dw7S49Jp6UpHaGe2vjz1y9J7LYK13vHWe7bj2 ex1tvM80ekuxW3RnW3Vm6P5jRx8H0+OYmMcb+bapo8GKPdpC6bQwtdHU8JpWkdJ/JweL6e23iU67 d4dubSqyVi9Zi0bwIs68XGp36TtEq7ZJmZmevzdbifCKWtbJinkt6eTgZPFw32t+sRurbWVzxs1y Rv6T8V1NZNPtfq0seTm+Kevr+SZuxXjvaPiV8N4viycto9HseG6+uu08W6Rkj7UPmFck1tE1nlmP Ld3eA8V8HVVi1pjq6Ma/pnqce/ERMTETHaUrKgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAADW19+TQ5p/p2bLS4v04Zmt5VjeQeJ4bjnLqsupv+Ka1+ERLv4reTmcNxcuC vy3l0qdI2hlr66sT02ot0ZV7qqrInruzrVZLGSZ37JjqgYTG0K5lbaFVhDT1Ub456RPweY4hixWi eSdpjvD1eWejz3FNHWYtkpvFo9EIseb3tS3SerOms22rfpPqZKzvvHSYUz70TExG6Gdbs2rljeJ/ Mx5L0vEzPaelnOi98c9J2bFNTFpit47+a+PVUvx9T9nOIfT+GV5p3yY/ds67wvsXqpxau+G09Lx+ r3TqrEAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADV4ljnLw3U0jvO O0fs2lWqyUw6XLkyfYrWZkHldBEV09eveG3Fq1mI3jd4vPrOIaid8G9MP3Y38k6fNrt/rMk9Ou8s tfXXn49rGWInuy8SO/k5Gl1E3rG/fzbOe94wTy99mbRvTrMOOvNfJWsesywniukrG/jU6fF43WYN TmtEeJtEQ06aSmK2+bNtEd+qfSO17unF9Hmvy1y13XWyVmN4tExLxVK8PmNq5NrT58zawam+m/yc 0Xj8NpRYSvQZ7xEOdqI3rPozxayNRXe0ct/ON03jmrKB5nV4q1yTO20Obmv4c+cx8HoeI6WZpNoj q83niYmYscU0r8aJ6T1n49zeJ+Meqm1drb9J+Kd5p136StGVem9l9TbHxLDFp7W7+sS+q1nesT6w +PcAzVjiGHftzQ+v4f8AJpv6On8jH9ZgIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAABp8VrW/C9TW0ztOO3b5Nxp8VmI4bn37TWYB8f1HFtTfUfR9FWJmsdZ9I7MtJxDX5s d8ta1y0xzteaR2277rcuhycP12SceLxMeWNpjttHwlu8I0mfQ1y+D7k5YmJmY36T36Ka43z/AF1t cI1ds+qxVj7/AEej19PCw9HJ4NoK4OIU5Y35YmZdzVTGebVZabx5jJS+Tmns81rNLm1Wrzc9rVw4 Yibbem72mXTTS0w0M3BvEta1bWrM95ie5EanY87wXgNOL6XPfxraXLhra/W28bR/dzYzarBqJxRe bzE7Rt5vWU9n8mPHOGmS0Ypnea1naJb+k9ncNLR7u2y/WcxXO4TOoyUrN6zD0FaW5Y3hu49FiwUi KxCvLMR0hlW0jn6ukWw3iXjOJzbDlneOj3GaN6zDzfFOH+LE7SRGo83XNSZ2lbG2/WfdlvaT2cy6 rNFInlrv1mfJ37cK4PwTTxOoidRm2+/2/KFuyMp47XB4LivXiunrH2b2iH2qn2K/J8x4fGDNxTSZ 9Nh8OviRvTyfT6xtWI+DeXs9MNZubypASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAOZx6/LoOWPvWiHTcf2hiZ0e8fc2mf1E5+vP/AEeuSd7RC2uKtI6QjHfeINTfwtPf Jvty9WPfbt/lucP03gxfJf7d/wBoReYpm97zaNeLb4Ims9Nt94auDjem1Wo5PFi1onylS+1o7l8V bxvtupjDMdNkYtXS1+Stt+m63xImEJ4xjHER2ZxMUjeUTO3VRmydBbjLJqPi08mbeVOXJPq1sl5Q Vbkz9+rRy35rxHqzmZlVEe/Ez5LRlW5iyfR6zffaIjq1OSNZps2a21rZInafSPJhxGMl9LStLRWM lorM/A4dkrWbYfLZC2W/7K6eubX6b4RzT+W76K8b7G6X62cu3Sten59nsm3j+OXz3/0ANGIAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0OIYfpOHPijvNNo+fdvtXJO18k/ /OwPFYbz2ls3jx8VqW6xMdWPEdP9D4lkx/dt79flLLHbkxTPwY6nt2512ORTRzE2x4/dpE7cvkme E4IrW3hRMxO8THRtU1FKWtvtvK2upx22rzRCtXkqzh2jtF7ZbT122b01ndnpuWuP3Z3+Ky20qDVv fauzVy3mejZzNK8dVjqi87KLRLYtXruqvXzkQp7Qoid88R6rcl+WGlW0/Sa22mfhCZOq2x082ix6 jkm822pO8VrPdr4dNObVeDo8XW3uzMbzK+mvxT7szE27cvnu9j7PcNjSaXx8mOIzZevbrEeic5tN +SZnpt8J4fHD9HXHO3PPW0x/DeBtJxx29vaAJQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAKNRim9Z5e89Nl4DzXtVh5babURHrSf7f3ec1+qnDorWrvvt5Pccb0n0zhmWk Rvevv1+cPE2rGTFNZU26PFfxwa5dVkjelI2772nZnX6bbrEUq3o0d678u8wmuDL2ittvVjXdneeK cGv4jpJ6U56+kS7+j118+GLXpakzHaWlp9NNY3tv+bbiYiNoQy1y30uyZJlrWmZnuym6q1iIJnop yW2Te8bdWnnypQqzZOadokiIpSZntWN5lrxki19vNRxrUeBwnNNd+fJEY6/OejXLn3Xe/wDp9wyn E8uo4lqqxblv7lJ26T6vpD5X7G8QycKzeBMbzMRM1/FH/wA/h9QwZ6ajDXLitvWzRgsAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeL45w+dDrZvWv1OWd4+E+j2jX 12jx67TWw5Y6T2nzifU+rZ1y9eHwzDYxxEy18+DJodXfT5o96vafWPVbjyxDn1OOzHudbM0rt2UW iI69mVtRXZq5tREb9VUoy2iIlRbJ0UX1VZ6btTLrI7V6yk62M2oisT1c7JmtkttVMUyZp6x0beDS RWOvdKijDimvWd3G9pNRMfRcNfvZOb9Hpb0itJeP47k/3hgjaZnbaP1XxWW3T0movbNS0W645nbf 0nrMPpXs3xamoxdJiLbe/X1n8Uf3fKsOTw4jbaXo+EarJhtGTHMxeJ6xH7Sti9Zaj6x3HM4NxXFx DS1mtoi8dJrv2l011QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AGjxLhODieOIye7kr9m8d4eM4to9RwjPXFa0ZIvG9bR0fQXmPbDFvTTZPOJmEWS/V8bs9R43NxLL G8eFbePg1bajU5/s0l1ceKLx1hbjwRE9mOpx0y2uRTSZsm3PMw2aaKtIjo6kYo9EXpET0hVLXxYK xC6MZvyx1lFs0RHfaPiCnU12pLyHGNDbUajBekWma2npWN3p8+opa20e9LSyZLxExTlpM+vdOdcZ a9tPS8MyUvFrzWlI6727u1pYxYrbVmb7x+TQx6au3Nqcl7/0rcmW9axGnwZJj1novmxnZXV0fFp4 ZxLBPgTGK8xzXr5fOH0bFlpmxVyY7Rato3iYfNuG2x56Wrqa8s2jz+7Lu8O12bS6jkwzN6THNNI6 tvrN68Y4rxlx1vHa0bskAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAA4XtTTm0OKfTJ/aXdcL2pyRGjwU362yb7fkJz9eTxxyZJjyltRXzUZK7TFtl9Lbwy06YzrHwa+ fJFd/wCVt8m0bQ0eS2qzcm+1K/an+zNZFL5M1pjFXeI72ky48eGnPkvNp27+TPU6nHpMfLXaIjpE erk5dRMxOfN1mPeisfshW1ne1a1577Y6x5R3U0zze31FOWI6ze0byU098kRlzbxM9qrMlPDpyRMR Md5Vt/Ihp5898mWZm1pjftE91uCt7fCI7dWeHDEW3t723l6rslqxWZnasR+SYhFbzhnfxJ2jyeq9 lcGXWZcmW0zWKxHLaI7794eJx5fpfEKabT8t8l5isddo3l9S4VjrwrRUwzSJt3tav3pdOL6Y6dXD j8HFWm+/KsU4NRXPvtWazHquWVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAa+fXYNP9u8b+kdZBsDkZOO135cWOZn4y5Wu4xqctbe9y19Kp4njt6vi+PDm8DFMWybbzPlV 5PiGtz67UxbNbeKTtWIjaIXYpnwuaftT5tXJT3vmi1pMsrU5qIrG1V1a+5DCa7b9GFbRr5J6Wnbt Cu+Wmk0m8956z8ZWZNorbfzcbX5rZslazPux3hUt41NTntktObJ13+zX1bek01r4/HzVm0bxPXy/ +bNfDgjVa2uOY92kdfg6ufJOKvLXtttVVSqbcta2vM7zXtHpLQy5ZtMd+vWd+7Zy3mdJHXra3f0c vUarw7zFY5rT2hH1Lavnrgx81p3U49Pk4nE5L35MO/StfNRXR5tXnrS8W67WvfyiPSPi7uLHFK1p jrtSsbR5Lc4RzsXBaYreP4l45esRD2HD9fnw6evvWvO3Tfr0aGk0U55ra0TFInv6uzgrXFXlx0i0 77RPlC83Yj+JW7oddqr6vHzTTw9/f6dod+L1t9m0T8pcbFSmPHER3892W0zPuz+jSbVvidkcqmfP Sel7bekrI4n4dZnPWIrHeYnZee2Wpy8dEaml4npNZblw5qzb8M9JbYgAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAABEzFYmZnaI7yCXL1XGa0jJXT0571nbee27DiXEprp8nhbxG20W8 5cbD0ikfnKO+urTPvjoZdXqctdsmTaPSvRpWmsdZ6yztfaGplvv3lWW1tyRlz1x0vkn7Vo5atTNe Y0+1o79V2KsZsvX7Ne5mwxnyTNvsx2iGneM/rCdRSuOsTasTt5kRFtpjqmOH4t4nk7estiMNa97R Hwhna0iuKTEdmGWa4672nZtRele1N59Zlq6vLOSsYorEc07qcW65euzRvtXvPZy52naZ7ujr6fXV rWdukREK8+njHgmZmPc67bq6ivVWhxxgxZLztNrT1mZ/SP4VZs0zaOvfp84WUtNsXLvtv3699+rU z7+Jtt5qURqMnPpctaR1rMSw4ZoK57eNk6xHaJRh97Ltt7lo5Z+L1HAPZvVauZ2nFTSzMTzeJEz8 to6xPfvsZntPZ9rXxabmxzefdrv0j1dXh/BcmstW1qxTHHasR3+b0GPhGl+kWmd64dNEVjf73T7X y8vy+Ddx6O3iRakxTH5RXrMw1/lX+3Itw2MFIraN48qRHdZi0cUjmmPen9noox1iO0fNzdXEYrTt stcmd9aX0bJ+HePmiKTitO8TMLZ1cVjrMfqpz6ys4pjfrPRWZ9rXXptUit6zO+23VyaRHEc05L1/ w9J9ys/en1ljqdVbwYw452tlnl3jyjzbmmiMeKtYjpEbLeTXPUU8ee/+qjJpsV5rbkrFqzE1tEbT DpYNbW21Mnu29fKWna0KbqTdjXXjld0cvQ63ltGHNPSfs2n+HUbS9c2s2UASqAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAOVxPWe99HpP8ArmP4b+r1EabT3yT3iOkesvMVtN7za07zad5l XV5GmM9vVfEstvDx0jtaVVMlq+UJ18b5cMRvPeSuK87bUt+i2Z3PtG7zXpjkzXt6R+TXyTMzvM7t ydHqZ+zhv1+Cv/ZuqvPTHMfOYaTMil1a1K2vHSLTELq2v+KWzThGo84rH5rq8JzedqR+ZeI7WnOS 34pYTafWXR/2Pln/AMyrKOCWnvmiPyR6O1y9585lhWJvl557Q6eo4T4dYiMvW3b3UanhldHpJtGX e09unmjsT7eb1l4trI2t0hsZfrdNO0bzy+nzU20/+NmkzO9esz+TZxWis9dttvPv+Tn21jjaW8zn 26bTG3mp1M/Wzv3t0jyWXiKZJmsTERaZhXXDbNl8WaztWenxZLstPp5pau8frDtVrNMM5cfTfpMf 3aunxxbes9d/R09Dp8ebJi09ptFr3jtt2WyrW9wy1Jx132mK+Xq9PotT0iIU19ntLtExa3T47T+q 6nBaYvsZstZ+cT/LeMnUi0TXffo1s2m8Ws2/OIMWk5Jib5L328rS2t94Sh5TV4ppklpW6PT6rh+P NbebTHyas8E081mZy5P2W6OFhjxNTE/hr/LoRO0Kvo9dPqctKzMxEx1la5t3tdnjnMs4noievcrO yZjeFF1OSnNV0OG62cn1GWffj7Mz5w05joovzY7xes7TE7w0xrjPeex6Ua+j1UarBFu1o6Wj0lsN 3JfQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrU5o0+nvlt92P3BxuM6nxNRGCs+7Tv8 2hToxm1r3m9utrTvMsonqyt7XTmcja0u3O6FMfi5t/u0/lzdJM81p9O3zdvHTwsUR5+bfPqOfX1h dqV+3O7bs1+T31oqmI3TEM4rvCdkDGIIhlFd2daboS0NXG2bD6bufxXU1vlmu/u4us/N0+L1tTSx kr9qk7w89j1FNZMV3jxLzvaJ8mer+LSOZqK2xZotbvljfr/89U453rXt9lse081xZtNjx7TGKu0t DHlrevSevaN5Y6+tJ8c7VRNMt63n3ub+6/R54rERMztDYy4a5omclYmfxKcenrjtHLvtPrCnVmdb eFe3JXmjy6eS/DrMuLVYsta9Mdt++6qLxO+0dEc8UmInr18iUfReHcXrqccb9Z27Q61Lb13eJ9nc 1Z35rTvE9avY4bTkpG8xEfB05vYxqybc07R281naGMREdoT5JQqy9mply7Q3bV3iXG1eXw7TWSka c258t7+tpT5/BjT7MfHqndz12Z+M4lMMKyziUJJiN1WSu9fku23RaOgKNJqbaTU1t9yelo+D0cTE xEx1iXmM1Nt3W4PqvFweDaffx9vjDbGvxz+TP66QDRiAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAOJxzU73rp6z296zsZMkYsdr2naKxvLyObNOfNfJbvad1dXkaeOdpvsc2yuZVzfbfqybutwu s5s8R92J3dvJb3tnO4HSMegtmt3nfZvYp8SZl0z45NfSK7onH1bNcfRFqnUKJr0Y7dVtq7prjEsK 0XVpEM6028mW20IHK41aPo3J6zs4ODhdcvPnvExFevNXpMOrxi/PlrTee7PLX6Pwa09uaNlKtHg9 dM3z5d7ReOu02nu0JzZMfblrv5R5uvrcdImZ26T1mYhxs1Os7RH93PZ7axuafNfLitvbaYU3yZYt PXs9NwHhui1HBa5LVicsb81onrEuVqNNSuS8Y67dZ6xPZa59Il9uX41vEitImZme3q2Kxbxora0T Md/ROSa4Ztkj7c9OafL5LuGYubmyX3iu/TfbdSfVnpvZLT/XZK233+Mbbva1xRXyiPk8pwbH4N6T adq5a71n0tD1WDL4tPe6Xr0tDpz8YVnJHWEXYxbqlBedoef4tW0XraO09HdyztSZcbUz43C+ee9b SVMaeOfqq7+jGckQ1Yz7+7v2RN/WXPXZPjci2+2yyJaVMuy+uSJlA2d+pNoVRbeDcSxyTE+TDDlt pdRXLTynrHrDOyiyZeVFnY9TjvXJjres71tG8MnJ4Nqt4tp7T1jrV1nRL1x2cvABKAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAHJ49qfD09cNZ97JPX5PPw2uI6j6Vrsl/ux7tfk1mWr7dOM8iLdm vfebREefRsWldw7SxqNbWbR7lPesrn3Vteo7dYjDpMGCvfbeXQ0uLlxRLRxROfUc34p6fCHYrXlr EejqrjY8uzCYW7MZjdVKqK9VlaxCYrsnYExBMRMJRPZA8/xPHtmpP9W2xx76vhWOInvt/C7ike7N vwzE9kcapGfhlevTaFbFo8RqJ5vy8/RoW09ek0msxHfp3dzNoLzp4zUmZpMbT8HJyYJi20X2n0lh ZY1li/RaidBF4w2mK3jrHaFGp1lN+tptPp5IjBkid5mIp16TKu0abBPv33vPlM7z+iPdFNcWXU5I tkrNce/b1W5db1nTaf3ax9q0fxDW1ebNk2phty1mOu09VOm8W19orEz23j1TwfSeERFuEYMddptW d43dvBn21eKJ75KbW+cf/JcTgMxXTb3nbljz+TpcPmc2uyZO1KRtVtGVdi0bx07qJnllsRO6rNTe N4XVamsy8mnvPwc3R2jPwe8TPbdlxXNOPSZfhWWpwO85OFzv57qrODkzeHntSe8Sn6Rv0a3EZ218 8nXekfr1a0ZLVnqx19dWb6demXybOO7lYMvNMdW9S/VVLo0us7tPHdtUtEwJiZU3jq2Jhham8CVG PNODNTJXvWd3qcWSubFXJWd4tG8PK3pPd1OB6veLaa89Y61/u2xfxh5c/rsgNHOAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAANLimq+i6O0xPv392rdeZ4rq/pOqnlnelOkIt5F8Z7Wj27I2I6sb25YY V1ImY3dbQ08LRc23vZp2j5OJG+XJWle9p2h6HHtbJXFT7OOIpX+7TxT31j5rycdTh+Dpz+XaG/sw w18PHWseULN2trBE9UcrJKBhFU7JAQi0dEomegNDUYovM7x3jb5tO1ZvpbaTLtzRExWfWPJ08kbT Ex5NXWYYyV5omYtHWJieyeDzuizfRs19Jn6TM7Ru1uMcJxZqTkw+5f4ebqa7SV1MR4tdrx2vEfy1 axqsNOTLjnLXytVXi3Xj8+nmsxTLM16d5npPyUzpekTtSK+U7vS6vQ/SYmK1vWPS1HOn2dvvvvE/ tDO5XlcO+LbfHSd/W3o6/BdDOXPTnj3Kz38rS6Wm4FNrRyRzTH3p6RH/AKvR8L4dXSzE3jmtHn5I mbfqLV+m4dbLSsZInHjr3iI6zLpYaxS01rHuxHRHiT9mv6s67Vj1aqL6326MrWiYa+/Q54BxPaGe XRZpj8MquB4+Xg8zPnB7SX30to379GxpK1xcHiKz5IS8xr8PLPixH2bftLTy05o6dHYyVjLhy0t1 izjZa3pMVv3iO/qz1G2L+NbSajbNyW7xLsY8kTDz+fJXFqKZN4iZnafi6WHL0iYlStI7OO+7axW2 crFl7dW9jvE9ULN+J3ZbdFGOy+AYWpEqN7afNXLj+1Wd23KrJVMvCzseh0+auow1yU7WhY4fCdV4 OadPefcvPuz6S7jol649Tl4AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAV581NPhtkvO0R+4NPi2 r8DB4dJ9+/7Q83Po2NTqLanNbLfvPaPSFDHV66sZ5ET0hRknyW2lTtMyouz0c8usx2n7s7vScKwx zc1vu/y85p+maJh6Th+SOWeveXR4/wDLm8v+nX5mUWa9bbrInolmu5jdTNkxYFk2Isr3TuCzeGMz +THdEyDDJO9Ja823rt2XWnya946pGvktDXta0ztWu/ybvLE9dkcoOf4GbJPWK1j49VmLh9JtE33v Mevb9G7WsW8l1ccREISophiJ2jpDYpijbaOjOuOJ8ujOdqxsgVcsUjaETYvbaFFrgu5lVsm0yUtu ryg43H5m+GIj1XcJzePoL4pnrWGtxmfchr8JvfHS1622if3QljzTTLes+qrNjrkiYtCzPMxnm095 YZJ6boS5teB49Tqscza97VtvWvlv8V/FOF34RrIxTM2xXjelp/eHoeA6XnzReY3ivX/0dfivDcfE 9HbDbaLx1pb0lOs+jO7K8Lis3cN+0NKcd9PmthzV5clJ2mF9J9GHHVL108dm1SznYr/Ft0tuhLb8 mNohFbMhLWy0mJ3rPXvDvcO1karBG8/WV6Wj+7kWrvDDBlvpdRGSnbzj1hpjX4z8mOx6UYYstc2O uSk71tG7Ns5AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeXneJ62dVl5KT9VTt8Z9W9xbWclPo+O fft9qfSHEU1pv48ftYST23ZTDC/p0YtlVuvVjMbM5+LCZjYGWGdrTPxiHY4ffaf3cjTxz1v6xMS6 Olty2iXVj/Dk8n+ndrkhnGRo1v8AFdW3RCrZ5uiYsqrboncSu508yjmZRYQt50TfowYTbYGVrKrT uTZjvukQnYhMIGVY2ZxPVWyrHVCWzXpVXkt3TE7Va+W4K7X3jv1auTNy3jdba0RZpamfroQN7Hk3 6wr1GTaN2OOJiu6Mu98NvgDi8Wy74d/yZ8PiPAiO2zU4nb6qIn1bugjfFE/ASp1ke9u15mbbRDZ1 Mb823kx0Ontn1OOkedoJCvT8I03gaKsz9q/WW+isRWsVjtHRKyrhe0XCfpWL6Vgr9fjjrEfeh5fF feH0V5Dj3DPoOo+k4a/U5J6xH3ZZ7z3228evytOk7NvFbo0cdols47bSybt7HbddHVqUs2aW3Qnq xVeu8LILR3SlZw3V/R8nhXn6u0/pLuPMXjeHT4Zruf6jLPvR9mZ8/g1xrvpz+TH7HUAaMAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAABRq9VXSYJyW79qx6yvmdo3l5viGs+maqYrO+OnSvx+KLeLZz2te1rZL2v ed7WneZYWnZl5K72YV1xEyxmeqJljzIEWlVkszvbZp5soN3h2SJz3pP3odCnuWmPRxuERfJrZmtZ mtY96fR28kbX3dXj/wAuTyf6bmK+9YX1s0cNtm3Sd4LFY2K23W1s16StiUJW7bp22RW3RluBuruz mWEgrmCGWyNkoExKE1QlPmsqRDKeyBjaejWy2W3ttDUyz1QKslvehVqKTNosyyTvELabXptIJpaP B39Ia2mz+JGpr51jdZefDx2hzuHZObNq58poJaGtjxJ2+LoaKP8ADRPo5+T3skx5OhpOmC0fBNQ0 5yTbn+bt8A0u9raiY6RHLVwY62mI6zMvaaHBGn0mPHt1iN5+aYVsACBXqMFNTgviyxvW0bSsAeE1 mkvw7V2w5Ote9besJx2er4rw2nEdNNekZa9aW9JeQjnxZLYskTW9Z2mJY7zz26fHrrdpbZsY7NGt mxjvso1b9NmUwpx33XRO4K7VUTE1nmrvEx1bVo2VWiJE/XY4frY1WPlt0y17x6/FuPM0m+HJGTHO 1qu9pNVXVYt46Xj7VfRtnXXL5MfzexsALsgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHM4jxOMFJphmJv529Dq ZLfjDjPEIx450+K3v2+1MeUOHSOWFc3nJkmZnf4yujpVlqunOeFpV2nctLCZUXRM7MJtsWlRkv3Q ky5NmpWt9RnrixVm17TtEQnJabXisRMzPSIew9n+CRoccajURvqLx5/chfOest642OGcIpoOG2w7 ROW9d72+LQvXevyejcPUU5M+SvpLeOataraw2a0dLbLqTtK1G3Es4lVWWUSoldFtmcXUbpidgXzK GEW3TuCUSncnsDFMMLSms9EC6J6FpVzbZE5ALy0809ZbFr9GtfrEoFMzuuwz0Ueey3HbaBLDXe7i tMOfwWnP9I+NZbuttvhs1uBRtXPb4SDm3iIvf57N7Dbl0VrS5+XrltEd+Z1Jx7cNms9N4TURRw3T +PrcO3WszEvZOD7P6aYiMlvu16S7y1QAIAABxOPcLnUY/pWCv1tI96I+9DtgmXl68Biy7/NtUu3+ O8HnFa2s0tfd75KR5fFyMWTdhrPHVnX9R0cd21S3Rzsdm1iuqs256wrmGcT0RYSx5d047X02SMmO esd49YRE9WcdSXhZ2O1p89NRji9J+cei1xMc3wXi+KZj1j1dTTaqmor06WjvWW+ddcu8XK8BZmAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAMMmWmKu952UZ9XFZmuP3revlDTtzWnmvO8q3XGmfHb9ZanV3yxtWeWn7y4es vPNtDqZJ6Ts5mppvdl/XXRMyfGvSNlu/RVvtOzLfoipLT1VTKbSpvfogRkvtDVyZOhkyvQcA4Dzz XV6yvTvTHMfvK+c9U3rkW+zvA/D21urr789cdZ8vi9KDb45rejl8Rry6iJ/FV1HP4vXbBTJEfYt1 +UpiHM295bXsqrO9l8QkZ0lZEqqLeyBZHZLGvZkhIndADKJ3TMoqWQMZ6pjsxll2jsCLSrmU2lFY 36gieyu0LJk3jbsga0wdqzK20QpyztQGprL/AFMrOE05NLkt6qdVWZxNrSe5o9vWBLiUjnzXn0vL q555dHt8HOwV928/1z/LpzXxbYccRvzTB+jucOwxh0dI22mY3ltIrHLWIjyjZKyoAAAAACJiJjaY 3iXleM8InR5J1GniZw2n3oj7s/8Ao9Wi9a3rNbRE1mNpifNFnVs65XhcWTdt47bnFuF24dm8TFEz p7T0/pn0a+HJux1OOrOux08d1ndqY7tillVkzExLOk7yd4YxGwluViJhE45raL0na0dtlWO0+bZr 1TKi+2zptZGTamT3b/tLacvJjiY3XaTWdYxZZ6/dtPm1zrv1z78fPcbwC7EAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABhkyV xUm152iAZWtFazNp2iGhm1Vss8uP3aevnKrNntqLdelI7VRHRnrX/HRjx/tZREVjZXeybW6KbWZt pCZ6S08tN7Nmbb7zCrJtyoS5145bSx5mWafelr3tsKmS/o08uXyhlly7RPV2+AcBnPNdZrK+53pS fP4ytnPVda4y4BwHxOXV6uvu96Unz+MvVxG0bQRG0bR2G0nHLb2gCUDX12LxtFmpHeazt82wT1gH mMN4tWs+rcr2aEV8DU5sM/cvO3yb+O0csLUTSdrLphRE8tlkZI7Atr2ZMazDJVKTYSCawi7Ksq7z 1QERvLK3ZGPrKbyCrbdnMcsbeaa18/RhvvM7oGEwTG0JmYYTIML22a2e28xELM19oURPNO4lOem+ n3ZY5+prVnMc2GYU4/L4A0a15cNf6rz/AC6fC6+NxCPOuOu/5tHJTbHj+F5/l1+BYumXJMd9o3/d MRXYASgAAAAAAABhlxUz4rY8lYtS0bTEvH8R4ffhmo6bzhtPu29Pg9mq1Gnx6rDbFmrzVsizq2df zXkMWTeIbNL7tbXaHLwzUctvexWn3bmPL8WFnHVL326VZ91MfFVjvvVlz79kLrcf2m7j7bNHH3bl J2SirLQoy4t1++7G0dBC/RanxI8PJPv18/WG241+alovSdrV6w6mDNGfFF4/OPSW2b1zeTPL1aAs zAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAVZ9RXBTe3WZ7R6iZOpzZq4ac1p+UermZMl89+a/byj0Ra9815ted59PQ32hlrXXRjH DpCLX6ML5NlNsm/ZRqstfdXzbsZt06sLZNvNB1Za8RDWyZdo7q8udq5Mu/mIMt4md2lmy7JzZuWJ dHgfBL8RvGo1MTXTxPSPx/8AstJ1XWpIs4BwSdbeNVqq/URPu0n73/s9hEREbRG0QUpWlYrWIisR tER5JbSccur2gCUAAAAPM8Sry8Uyz67fwuxbzVPGsE49XGbvF42V4M0TEL33ERnktsxpk3sumK2j admFdPFZ33VS2Mdui2J3UU6LYlFSsN2O5NkCyJ6K7T1TEsbAsxdpReerKkTFGMxvYEz0rsqtbbpC b2VT1QEzuwtbaGUxspuJU3neWdKoiu8rq12gCI92YatLcublnzbEz1aOptyZqTuDHLfxN6R0+t5X qdJhjBp6UiPLeXl9NSMnEKxHa1+bb8nrlvxUAAAAAAAAAAABTqtNj1eC2LLXeto/R43VabJw/VTh ydY+7b1h7ho8V4dXiGlmvbJXrS3xRZ1fGv5rzeHN02bEW3cys3xZJx5ImtqztMS3MeTeGFjqlb2O 8btql3NpbZtYsnSBLeiWfdTjtutid+ghherHS5p0+f3vsX6T8Fkw181d4lMvEWdnHaGnw/UeNh5L T7+PpPxbjdyWcvAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAo1Oprgr63ntAmTqdRqK4K9etp7Q5d7Wy2m953lNrWyWm953mVd77R0 Za1104xxlN9lV8qnJl2a9s3xUXX2ybsJyRDWtl3YWydEC+2VRkzeW6q+T4tbJm+KRdfK1cmWZnlr vNp7RC/R6HU8SycmCk7ed57Q9ZwvgOn4fEXtHi5/O9o7fJaZ6z1uRyOEezVstq6jiEbV71xevzer rWtKxWsRFY6REeSRrJxz22gCUAAAAAANbX6aNVpL0npMRvWfSXlKamsRMVvXm+EvZXjmpaPWHzfL oNRjzXicfWJ8phfPxFejx72x7xMzK+sXiNoiXlq+Pi6fWV/VfTNqfLJl/WTg9Pji8R70LqvMV1Gq j/zcv6yz+lanzzZP1lWpelTET6S81Gp1P/Gyf90s412rjtnyfqql6asREdWM9+jz9eJ6yP8Az7uh odZqMt458tpB1JvEViI3/RhzRt13/R1MNaziiZiJn5K9ZNceKZiIiQcu/WekT+iYrWI3lzdTrs+8 8uW0fJzcur1Np/zsn6g79phVaIeetqNR/wAXJ/3SwnUaj/i5P+6UD0ldonum161h5mNRqP8Ai5P1 lNtRqJjacuT9Qd22WN5aGeZyZd/KHJy59RHbLf8AVq31Gp/4uT9ZEvS8Lr/vSs2npzRtL1z53wK+ oza/HW2XJNd99pmX0Rb8VAAAAAAAAAAAAAAcHj/C5yV+l4I9+v24jzj1cLFk8nu5jeNpeW41wmdL knU6ev1Vp96sfdn/ANFdTrXG+eq1q5F2LLtbZoY8m8d11bbSydErsYsm+zZrO/zcnBm226uhiyRK EtrvCrJDOJTeu8A1MWX6Lqq5N/dnpb5O5ExMbx2cPNTeJb/DM/iYPDtPvY+nzhri/jDy5/W6AuwA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAa2p1UYo5adbz+xbxMlvqJ1OqjDHLXree0ejmzNrWm953tPmTPWbWneZ7yoy5YhjrXXTjH8s75N mtkyxt0VZM2/m175N1V03yTKubMLXVXybeYLLX2VXy7eam+b0bOg4VquJW+rry4/O9uyZOq3UjVm 9r25axMzPaIdvhns1kzbZddM0p5Y47z8/R2+HcF03Doi1a8+Xzvbv+TotJnjDXkt+K8ODHp8cY8N IpSO0RCwF2YAAAAAAAAACvUZYw6fJkntWN3k8dfHz2vLucdz8mkjFE9bz1+UOZosX1UzPm0nqI/W MYo9FlcPNklfFGeH/NshLGun+Cz6PtHZtVZWlRLS+jxPkRpIn7rdoupHTdA5s6SI+7H6Mfo+32Y2 +To3neSIiZ7A0IjPXpXLePlMotGW3272t85datKzHZjbTVnsDj+FG/2Y/RlGP4R+jo20u7H6N1Ql o+H8I/REY957R+jpfReiK6eOYHLtj2tttH6KrY/6Y/R2c+kjeJiFVtLG24hxpw7/AHY/RRkw9O37 O99Hrt1YX0tfOBLjcGp4XF8c+u8fs9c4dcVcGemSI61nd3IneN1orQAAAAAAAAAAAAABFqxes1tE TE9JiUgPKcX4RbRXnNgiZwWnrH4XPi28PdXpW9JraImsxtMS8pxXhF9DecuGJtgmf+1TWW2N/la1 L7N7T5e3Vy6W3hsYcvLbqzbO9jvvCzvDR0+XeO7crO6FmGSvRThy/RtVXJ92elvk2rRvDUzU7pl4 izsd2J3jeBpcNz+Lg5LT7+Pp+Xk3W7js5eAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADs0NTrN96Yp6edkW8Wzm6+LNTq4pvTHO9vOfRoWtt 1mes95YWvs1s2fZldddOczLPLn2ju0MmebT3YZc2/mpm3qqllN1drsbZIhr3yzvtHf4AsvlYYseb V5Yx4KTe0+UQ6nDvZ3UazbJqd8OKeu33peq0eh0+hxcmnxxWPOfOfm0mP+steT/ji8N9mKY9suum L37+HHaPm9DSlaVitKxWsdohI0Y22gAgAAAAAAAAAABXnyRhw3yT92Nwef4xm8bVzET0rPJH5d12 CvLhho3rN9RWs9Z23n5y6O21YhrVYbdGOCfrrLPJRpv863zVS6FS09SvZj3lVZZRdPSqmnSWdrIE ebOkK4ldTsgW1WKqd1oMZhEVZyRAImOjGI6rJ7IiATNd46qL02bHkiaxaoNGY2n4ImPgtyV2n0Vo Gvlx7x2beiyTk08RPevSVUxux00+Fn2n7N+n5rRFb4AAAAAAAAAAAAAAACLVres1tETWekxKQHlu L8InR2nPp43wz3j8P/s5dLveWrFqzW0bxPeJeV4xwmdFec+CJnDM9Y/CrY1xv8qvTZ+WYdbDk5oh 5zHk283U0eo3jaZZ2N5XYjrCnLSJhOK+8d1kxvCqzSwZvousrb7k9LfJ3nB1OLeJdLhufx9LEWn3 6e7LXN9Ofy5/W4AuxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAETaKxMzO0Qi9646Ta07RDmZ9VbPbaOlI7Qi3i+c3TPUaqcu9adKfy0722ZXvFa9 XO1OrjrESxt66ZJmcjPUanlidmhkzTZVfLN5VWvsC2b7R3U3yqrZZtO1esz2h2+F+zWTUcuXXTNM feKR3n5+iZLVbqRzNJo9TxHLyaekz62ntD1fDOA6fQbZL7Zc/wCKY6R8odLBgxabFGPDSKUjyiFj SZkYa3aALKAAAAAAAAAAAAAADQ4pl2pTFH3p3n5Q33E12Tn1eSfKscsLZ+orS00eJqbW+Lfnu1tF XaJnZsz3WpCfsyp00fWSvmPdVYOmSUDd8kR3InoQosy7JmUX7MdwZ17ro7KKT1XRPRAsrO0rYndr 79V1ZBaQiJ6JgCSIJASwrO07MpV2nqBlrv1a1o2bf2qtfLXaQUTO0sb05o3jv3ZXhjS20xEphW5h yeJjjf7UdJWNKLziyRePsz0lux1SgAQAAAAAAAAAAAAAADG9K5KTS8Rato2mJZAPIcU4ZbQZuekT OC3afT4NXFkmlntc2GmoxWx5K71tG0vHa/RX0GpmlutJ61t6wrY2xr8dXS5uesN+tt4ef0eaa223 2dnHk3juyreM81OaFGiy/RtZET9jJ7s/2bdutd2jqKeic3iNTsd8a2h1H0jTVtP2o6W+bZbOO+gA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABje9cdJt adohGTLXFTmvO0fy52bJfU23t0pHaqLeL5xdK9Rnvqb+cUjtCi94xxvK3JetKuHrdZvaa1ljb10y cnIs1Wt3naJc++TmVWvMz1YWybfMGdsm3eWek0mo4jm8PT0mfW3lDf4V7P5tdMZdRviwfvZ6/TaX DpMMYsFIpWPTzXmf+steT8jn8L4Dp+HxF77Zc/4pjpHydYGjC3oAAAAAAAAAAAAAAAAADG9opS1p 7RG7zszN6WtPe0zLua+3Joss/wBOzhzG2OsL5+IrY09dsSyYRijbHEMvOChb7KjF0yS2LQ169Mso S24noyrPVXWejNVKbTuw3T3REdQWU6LYlVvsyiUDPfqupPRr79VuOQX1lZEqoZxIMksd0gT2VT0l bPZVbuCaW8i8bwr32WxbcGnkjaZa9p2ndv5qbw5+aNugLItF6TEtvTX5sMb969HMpfazc0d9stqe vVZDdAQAAAAAAAAAAAAAAAADV1+iprtPOO/2u9bektoB4TJTJpNRbHkja1Z6uto8viVht+0HDvpG H6Tjj6zHHvbecONw7Ltfkmeqmo6Ma69DXbbZTkr1mGWO3RneOaGbZRoM30fVzSelMnT83aef1FZ7 x3h1tBqfpGnjmn369LNc3sc3kzy9bQCzIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAa+q1dNNXr7157VhGp1Xh70x+9f9ocy283m1p5rz3mVbrjXHjt91lz 5c9+fJ1nyjyhdM8lZlOOIiqrUXikd+kMreunnI5XEdX4dZiZcG+XmtNl/F83PeeWWHDOGanieSKY q+5H2rz2hMzWd1Iqx1yajJXHhrNrW6REeb1nCPZumn2z62Ivl7xTyr/6uhwzhGn4Zj2xxzZJ+1kn vLoNJnjHW7TbbsAszAAAAAAAAAAAAAAAAAAAAaPFrbaSK/itEOXt0rDf4xb/ACa/GZacRvaF58Q2 IjasQnzPIhCU92tMbZGzHmotG10C6nZkwpPRmipIllEbMIZIE7solgmJBnCyk9VMM6z1BtVllEqK z0WRILYlluriWcSDJVbusV27gwInaSWM9ECyZ3hqamnSWxFmOSOaqRx725bNnSZNs9J+OynVY+WZ YYr7TE+nVaIr0Ais81Yn1hKAAAAAAAAAAAAAAAAAABExvG09peU4nov9n66L0j6q/WPg9Y1OJaON ZpL0+9HWs/EWzeVz9PbmrEtnyc3h9reHy26TWdnSr2YX6657ijLXpLX0+onSamL/AHJ6W+Tbv2aW ekTv16JzeI1Ox6KJiYiY7Slz+E6jxdN4dp3vj6fl5Og2clnKACAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeQRMxEbzO0Q08uqtkma4ulfO3r8lefUePMxWf cjy9WvlzVxV6T1Z61/x0Y8f7Wc7Ur1lqVy+LqOWJ2hp6rXddon5rOF1tfmz5OkT0qzb8dWbxjp1c biuuilJ5Z6r+IcQrixzEy8zl1E6rNt1tMztFY81sztU1eRucN4ffi2p5esRM72n0h7rS6XFo8FcO CkVpX082nwXh3+z9FWLxHi36328vg6TZyW9ABAAAAAAAAAAAAAAAAAAAAAADj8Unm1tK/hqppHvw y1k8/EMk+m0GOPeafiFpCZYwolnXspvHvLa9mF46gmnZmwozRUiUCBKYYsoBLOFbKAX0llEqqyzi QXRLOJVRLOOwLIljZMEgrlhKyYYTAK5nZPN0RZjugUanHzVlz6xtLq361c+9eXItPpXX0dubTU+E bL2lw2++O1fSW6m/VYAISAAAAAAAAAAAAAAAAAp1GbwcfTreelYEydcuMcRrM/L9nnlsV6wqpi2r tv133mfWVkRyRtEdGFva7MzkYZNoamWN4bV4mYa9qztKIujhVppxGI8r1mJegeZpknBqKZY+7L0t LRekWrO8TG8Ns/HJ5ZypAWZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAADS12fp4VJ6z9qVuq1HgUiI+3bpDl589cOKZmevqprXPTbx477rDJlrhr1nq4+s182tMRP RqaziXiZJrWekNG17ZbxWJ336M5LXRbI3dLTJrs07RMY6fan1dHLrowY+X7MVjt6N3R6Kul0EbWm s7bz8Z+LnabQX43r7Y53php/mXj+Dnv0f1JO1x/8ZxbUzj02O15mfLtD13AvZqnDds+pmMmo26el XX0Wh0/D8EYtNjilY7+s/NstpOOTW7QBKgAAAAAAAAAAAAAAAAAAAAAADG88tLW9I3BwJtz6nNf1 vK/DHVqYJ3pzT5y3MPZeojOWMQylEKpTVjZnDCwkqzYQyRRICATCITAJZQxhMAshnEq4ZQC2srKq qrIBZCWNZZgwswmFloVyCu0dFcx1WyrtCBhv5NTPHXds2U5o3hIz4ffbPt+KHUcTSW5c9Jme0u2v VYAKpAAAAAAAAAAAAAAAAYZctcVOa35R6tLrltN795/YvknNqrfhpPLH92V5isd9mWq6fHjk6rn0 ZxG8KK5Jm/wbVZiYZtqrmkqL023bkxvCiY3lJHNyRG81mHS4Rn5sNsNp64+3yaWaNrzOzHBl+i6q mT7s9J+S+ay8mex6EIneN47SNXKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAImYiJme0JafEs3h6fkidrZOn5eaLeJk7eOdm1Hi2vmtPTry/CHmOJcUvmvOPF1n09Pm 6HF9ZGm01qxO3R5vSY7XwzmzTy47zzTEd7en5Mfvt2/PURWdo3tvPrPlKymbktFqTtMTvHzbOLDG f63JXbFX7FdnoODcDprZpq9TjiMMTvSn4vj8l5fxnrk91saPSa7i2hpOfbTVt5x1m0fLydzR6PDo dPGHBXasd585n1lsRERG0dIF5OOe6tAEqgAAAAAAAAAAAAAAAAAAAAAAADX11+TRZrf0y2Gjxe22 gtH4piP3TPpXKwxtjhuYo9xq442iIblI2pC1RET2ILd9kxCqRjZmwlCSEohIJAQAAJZISDKGUd2M MoBnVbVVCyAWVWeSuqyOwIlXZZKue4MJV2WWYT2QKbKL9YlfdRdIo35b7/Hd3KTzUrPrDh27uxpb c2mpPwX/ABX9XAKpAAAAAAAAAAAAAACekTIp1eTwtJmv+GkyJn1oafeazbfpMzLR4jq/o8b823zX 6XNF8ERCvTcNpxLV5LauvPhx9Irv3lhztdtv8TtaWLicXrt03jzjzb2k1nid56ty3s/w+a7Uwzjn 1raejlarhmbhl/FpbxMO/fzj5p/ixSeXOvTtRfeI280ZI26tfDm3pWe63LaZx7qtGvniJ6tPLvOK fOa9WzbJvTbza02jl3n5SSljscK1MajSxWZ96nSW88xw/VfQ9XMT9nfa3yemid43jtLeXsce88qQ EqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADia3UTm1l4j7OP3Y/u 7Vp2rM+kPJW1PhYcmS0+9MzKm/jbwz31weMzbV8UppazPL9q0/BF4rk1GLDSNqxPWPhCnHmnNrtT qPKteWPm6U6OdHaZvO+SaRNvhv12Ub/q3FhtrNVj0uKOt56z6R5y9zix1w4qY6RtWsREOJ7L6OKa S2rvX6zNM7T6Vh3mmZyOfya7eACzIAAAAAAAAAAAAAAAAAAAAAAAAAAczjVvqMVfW/8AZ03I41bf Lp6/OVs/UVrY47NyOzUxd4bUJpEbb3Z7IiOrKIVSjZhMLJYyhKIgmGUQSDESIEbJEgQmCITEAmGU IiGUAyhZVhDOoM4Wx2VQtqBKuyyWEgqlhKyyuyBVaGtkbNmvk7A15l1eH2300R6TMORPSXT4ZO+O 8fFefEX63gEAAAAAAAAAAAAAAAq1WPxdLlp+Kkx+y1Fvsz8gjhaDauGK8sx07y3OE3m1tT6RaP4c vU6yMNKUx73zT0ilY3l2eF6a+m0kRl/zbzz3+Ez5M8z26fJruW6wzYq5sV8d43raNpZjRzPPaTmx 5b6bJ9rHO3zb2WJ8GWPEscY9bgzxH2t62n19GWW0eHOzHU5XbjXZ1x8WTnz2iZ7S2M1IjH2+LX0V KTqs8zO9ot0j8nUthi1J3UaOFMTfLFo6xMbS9BwHWTqdHOO8+/hnln5eTjYMFo1WTH5VnePzXcIm 2k4zlpPSmXy/hfF5eMfJns69OA2cgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAADG/2LfJ874rW845mubliY7bPoto5qzHrDz0+yePNF41OotaJ7RWNtpV1OtfHqZ715fhu j8adNpcVfeyzE2/vLuanhOu1nEctIxTTFa/+ZPbZ3eHcF0vDbTfFE2yzG03t32+DokynXl9+leDB TTYKYccbUpWIhYCzEAAAAAAAAAAAAAAAAAAAAAAAAAAAAcXjE/4zDH9M/wAu04XF5/3jj/0f3Wz9 RUYmzDWxS2I7FSyjuzY1ZKpRKEygEwiWUIkGIk2QJNhKQhMIhkCYZQxhlAMoZwwZwgWQshVCyATL CWc9ldpBhZXLOVdpQK7NfJPRdaWvknoDVvPvOnwuel4+TlXn3nS4VPvXj4QtEV0wAAAAAAAAAAAA AAAAAVV02CmTxK4qRf8AFFeq0AAAanEsfPpZmO9Ji0NDLfkwdOsulrumiyzHlVzJrz4Ovoy26vB8 cTBa9NffLtMY77Rv8Yegx5ImkKdJoY1HC81Y+3OSbVn0mGGkmbY45u6tnrrTOu2xGO0RxCd+nNVj qKxTV1vH2pjaGtnyzXXYdo96ZmGXEMk15b7/AGZiVerWPTYckZcNbx5wzc7hGbnxXxzPWk7x8pdF 0S9jh1OXgAlUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAcPjEf4/FP9H93ccXjMf4vDP9Mx+62fqKrx+S+GvibEFSsqyYwlVK ZYsmIMoRKYJQIPIEiQ2ATCUQygCGUIhMAyhnDCGUIFkLIV1ZxIMpVWWSrsCuyqyyyq09ECq8tfJK 66jJ2Bp5J6upwn7dv9Lk5J951uE/av8AJaIrqAAAAAAAAAAAAAAAAAAAAAAq1Mc2myxPnWf4cmtu XT9fR0tffk0WSe28bfq5Wbamm3326MtunwfK6PCv/AxPraZ/dz9PO97/AOqf5dHhdZrw7Dv3mOb9 XOxRFM+avpe38mvkPHf/AFWlrKba7Tzt99ZxKkfR7euyNXMTrtPHfa0z+zPiM/UR8Zj+Wbdu8HpN M2bfzrV13M4dO2pyR61dNvj44/J/oAWZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADj8bj63BPzdhyeNx0wz8ZWz9RWri7Nmv VrYu0NmqaRZHZlDGGSiwxZSgCEkCBCQSCQBMJRCYgEsoYx3Z17AlMIhlCBnDOGEM4AlhZZKq4KrK 7LLKrIFN2vdfZReAaObu6/CO9vk5OePR1uEd7fJeIrqAIAAAAAAAAAAAAAAAAAAAAGtxCk5NFliI 3mI32+XVyNTyZOHTee946PQKPoeDffw4777eW/yVs60xv+ZxOnr4Okx1t05KRv8Ao41Z5q3yed5m XY1szXRZ5jvFJ/hxItP0aOSN9q7yrtr4f2tHFM5+KT16Yq/vK/iGSbXw4vO14UcPx5MGfNbPG18m 1oj4THRsTw7VanPXVYpi3gzMcnrvCnG11JOupwuN8+a3pEQ6jT4divjxWnJExa09pbjbM5HHu90A JUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAHM41H1GOf6nTc/jEf4Ws+lls/UX45uGekNujTwdm5RNIthKIZKLDFlsiQIShIC EgCUJ7AmGTGO7IDzZQhMSDJMMYZQgZwzhhDOATuqssmVdgVWVWWyqtCBTeVF19lF+wNLNG7q8I+9 8nLyupwnt+S8RXUAQAAAAAAAAAAAAAAAAAAAAAAItWL1mto3iY2lyrcLyUxzix2ia2nvPeK+jrCL OrTVnxpanhuPPemSs8l6RtE7dJj0ldpNP9GwRSZ3neZmV4cR/Vs4AJQAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANHi1d9H M+kt5ra+vPoskfDdOfqK4mn7Q3aNHBPZu0W0RdDOGFWcKLCJZeTGQQlCQSgASBsCYZQxhlAJTAmA TsmAgGcM4YQyjsgRLC3VnaVcgwsrt3Z2V2QK7tbJ1bN5a9waeWO7p8Knt8nNyebpcK8vkvlFdQBA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAK9RXmwZI+ErEWjesx6wQeZwejeo0cccuW8 elpblJaaRGxVnCuss4ZrMvJEgCAASISCQIBlCYYpieoM0wx8k7gzIRueYM4Z79FcSy3QEsLJmWFp BjaVVpZWlXMoGNmvkXXlr3kGtknu6XCf7OXkl1OEdl8orqgIAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAHmskcmtzV/rls0U62OXiWX4zErcc9GmkRfWVkSqqziWayxCPIANwBIhIJSxS CRG6dwZwlhEs4BluMdzfqgZxLLdXuy3AmVdpZTKuZBjaVVpWWV2QlhZRdfZRcGpl7urwfrzfJy8r rcH61vPyWitdMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHA4nHLxKZ9awnH2ZcY jbW459aq8fZpfiI2IZwrqzhmsz3Ebm4JN0AMhCQSIASndiAziWUSriWcAyRujc80DM3RCfIETLCW UsZEsJYSslXZAwlTddPZTkBp5e7r8Gj6rJPxhx8k9Xa4PG2C8/FaK10QAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAcfjcbZMFvnDWx9m5x2PqcNvS+zSxT7sNPxH62YZQwqzhRZO6UCB KUAJTux3SDIRuAncQAmJZRLBMSgZ7iIAZRKd2DICUSlAljLCYWMLIFVukNfI2bNbIDTyT7zu8Ijb Sz/qcG/2nf4T/wCE/wD2WnxWt4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHL9oL +Hw2cm28VvEuPptfgyVj6yIn0no7/FtJfW8NzYMe3PaPd39d3iMug1WktNc2C9dvPbeP1aZ9xF+v T471tHu2iflK2HkqWmvaZj5Surqc9Ps5bx+alTHqYHm68S1Vf/NmfnC2vGNTXvyT84Ql6A3cSvHM sfaxVn5Ssrxyv3sM/lKB1xza8bwT3pePyWV4tpZ+/MfOEjfGrXiGlt2zV/PotrqcN/s5aT/+wLRj FontMSlAlKEgndO6IAZQljDIEgeQljLCzOVdkCu/SGrkbF56NPNeKxMzMRHxENe0+89DwuNtHHzl 5PJr8NcnLW3Pbf7r1nCZm2gpae8zMrz4i/W6AgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAETETG0xukB4HVaeMHEtRi26RedvkyjBSfX9W77QYvC4xz7dMlYlrU7M929dWJLFc6aPK0q 7YLxPS0S22FlP6q38Zac0yR92s/KVc3tHfFf8tpbcsLRvB/dR/8ALLVnU0r9uL1+dZI1mnmdvGpv 6TOy6ym+Oto2tWJ+cJ/tW+KLK5KW+zes/KU7tG+h01p64qx8Y6NXNo6Y+uPJlp8rLf0rfG7MXtHa 0x8pZxqs9e2a8f8A7Oj7HaTHn0+f6RWM23LETfr6vRW4PoL99NT8ui7F4+vEdXXtnt+fVbXjGsr/ AOZE/OsPS29nuH27YrV+VpeV9pdPXhOtw49NG9Mld55+vXcTPd42I47qo7xSfyWV9oM8d8VJ/VxM d8l46xWF9cV7en6o/qLfxp2I9ob+eCv/AHMo9op89P8A/wBORGmyT5R+qfo2X8P7n9Q/jTsx7RR5 6ef+4/8AuHftg/8A6cWcOSO9J/WEbWr3pY7Efzp2Lcfv5YK/9zWy8d1E/ZpSv5Oba1/+Hb9lc+LP bFt87I7E/wAabWbiurvEx4nL/pjZzc2bJkn372t85ZXx55/BX85lucC0vPxnTxlnnjm32mOiZqUu LJ2p4TwnVavNWaYbRTfre0bQ99pcH0bT0xb78vmtiIiNojaErMwAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAHnfarF7umzRHaZrLjYrdIen9ocPi8JyTt1xzF4eUw23rCm3R4r6bMy wt6kdTaWLdjswmNoZontsCm0K5XWjopnuDC0dGpqG5bs08/daKV672MjbSaif6oh6Z5f2LtvptRX 0tEvUN3Jfo8f7cYve0eX4zV7B5z20xc/C8eSPuZIRficfXlcPaG7ino08HWIbePpLF2NuiyOyrHK 3fZFSwuovHVfaVF4QK5YWTM9UT0EKry6Ps1Tn4zjn8NZn9nOtLseydObiWW34cf918fWfk+PYANn KAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAq1WKM+ly4p+/WYeBxTNd6zG0xO0 vobw3FcP0bi2em20Tbmj5Srr418V9sa2Z7qKyzi07MXUylhaU7yjqhLCeiq3ddaFNxFYW7NLNG8t zya+WO6Va9J7FW66mvwidnrXiPY3Ny8RyUn71Jj9Ht3RPjk19HK9pMHj8D1ER3rHN+jqqtTjjNps uOe16zAifXzfTz7kNyndpYazS9qT0mszDdoxrsi6m8LazMq6zDOsq1ZEyrt1WWlXaUCqyq0rbKbi Fdp6PReyFd8uqv8ACsfy83aXrPZHHto89/xX2/SP/dpj6y8vx6EBq5gAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAB5n2q03LfDqqx39y39npmlxbS/TOG5se29tuavzgWzeV4mtui2 O3RRSY2hdVhqO2MvI36iu9lUsrSrvDHn6spnmSiq5jooyV6tq1VV69RC32byTh43h8otMx+r6I+Z aK/g8TwX7bXh9Mid4iW+fjl8n1ICWb57xLBOm4zqse20Tbmj8+qKdnS9q8PhcTw5tumSm0/OHMxz 0Za+uzx3sX1t0Zxurr1ZxvspWiZYWZbsbT0QK7KLrZVZJFaqt5vbezNOTg9J/FaZeJns93wCvLwb T/GJn92uGHldIBowAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADuAPA67F9H4l qMW20VvO3yRWW97T4fC4rXJHSMtI/WGhVlue3b473K2KzMML4+62tujG9pnozXaOSOVFMnVbmq1t trJRW5E7wwvUxTvCyY6CHOt7moxz6Wh9PxTzYaT61h8x1MbZK/OH0zTf+Fxf6I/htj45vL9WgLMn mvbPFvocGWO9L7fq85p5maw9d7VYvE4JkmPu2if3eW0+PasdFNOnxfF1Y2hlykRsmY+LJ0MZjZXa eq2eyi8oQTO0KLdZWzPRjWu6VaqtHR73g0bcI0sf0Q8Nkq93wqNuFaWP+XDTDDytwBowAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAef9q8HNpcGaI60vtPyl56k9Iew49j8ThGe PwxFv0l4zH2U26fDfTYiyJljvsjf4sm6vJ1hrXjq2MkqLdZEVbgbMx0auGdmzNt6iHN1Ub5af6of TdPG2nxx6Vj+HzaaTm1+nx/iyVj930ysbViPRrj45vL9SAuyc7j1efguqj+jd4/T33rD3HEcPj8O 1GP8WOY/Z4TTT7sKadHhbcsZnaCJ3TPZk6VdrKbTutmP0U2nqgrGOsr8deiuI2X09EqKM1dt3uuG f/jdN/06/wAPE546S9rwud+Gaaf+XH8NMMPK2wGjAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAABrcRp4nDtRWPPHP8PCYusPoWSvNjtX1iYfPuWaXtX8MzCuvjfw32siu8ptXoxi 0wy5t4YulReqmazu2skbquURWFInddM7VYRGyL291KFnCcfj8e0le/Lbmn8n0N4b2Ur4nHLWmPsY 5e5a5+OXyXugBZmiY3iY9Xz7NjnTa3Ph/BeYj5PoTxftFg8Hjk2iOmWkW/Psrr418V5WrWd2faFc V2jdnEMXWxntupmN7NiYU27iWML6dVMVnddjgVqMsdHr+CW5uE6f4Rt+7yuSsTDv+zWXn0WTHP3L /tK+GHl+O0A1c4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA8Dn93W56/wDM t/L3z59qp24jn+OS38lnpr4r7ZxHQ2TEstt3PXUrt27K57rr1VT0BjKnJPRbMqMs7QlV2fYvHvrd VknyrEfu9m8f7FZI8fVU85iJewbT45NfQBKo817W4eulzxHaZrL0rje09ItwqbfhtBVs3leai8RD KLw1sduesL606dWFdsZT1jdhNeq6K9DlhCVUU6s4jZnt1YzAhnM71dH2bycmszY/K1d/0c6OzY4R fwuK4p8rTstn6z8k7HrwGzkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHz3 Vxvr80/8y38voTwGpj/F5/8AqT/JfjTx/WVeyY6FPspc9dZPVXaOq2WEwIUTVRmjo2rNfLHRI3vZ DJycXtX8dZh7t879nsnhcbwz23tt+r6I2nxyb+gCVBzuPY/E4PqI9K7ui19fTxNBnp60n+Aj5/pJ 3jZu1aOnnltMNussdfXbm+l3ZM9URHREdZVXTuT1Nk7boQiOkJw28PU47/htEp5eivJPLMTCZ9Vv x7mJ3iJ9UqNHk8XR4b+tIXuhxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD weqjbWZ4/wCZP8vePCaz/wDIaiP+Zb+UX408f0r9lOxWOifJhXWjfyYWllPRXYQxnrCrJHRd3YZI 6A1NJecHEsN/S0T+76bE7xE+r5dk93LW3pL6ZpMni6PDf8VIn9m2fjm8s9rgFmQxvHNS0esbMiew PnHLyai9fS0w2aNfUTtrs3+uf5bGPqy068fF227KtSsdFlKqNGMV6myyY6sbdIQI8tlOWOi6Jhhk j3RD0vA8nicMx9etZmHRcT2Zyb6XNT8N9/2dt0T449T2AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAHhdfG3E9TH9cvdPEcXjk4zqI/q3L8aeP6xr2TsxpLOekMK6mFo6qpXSrm OqBixvHSVmzC4OfqK7S9/wAByeLwbTW9K7fo8Fqo6Paeyl+fglI/Da0NcMPK7QC7AAB8313TiOf/ AKk/y2MHWrX4jG3E9R/1Lfyv0/aFNOrHxuU7LI7MMayGTVlHWUXhNe6Z6wIUsb9d1m20q7dkDpez N9tRqKT5xEvRvKez9+Xis1/FSYerb5+OTyf6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAB43j9eXjN/jWJ/Z7J5L2mry8Upb8VIF8f6aGOey2eynHvOy7bowrrYSxZSwQJ2YXZ 92N4BoanrEvVexmTm4blr+HJ/aHltRHSXofYm/1Wrp5RaJaYY+X49WA0c4AD51xONuKan/qW/lbp +0MOLRtxbU/9SU4J7KadWPjep2WQrr2WRPRk1TvsndXMpiRCb9FNu0rbTuqvKBscCjfi9PhWZeue V9n434rafTHL1TfPxy+T/QAszAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHmv avHtfTZfnV6VxPajHzcNrf8ABeJFs/XnMcr4no18c+6vr2YadkY2YM57sEDLyY37Mo7MMnYGlqO0 vQ+xNfqNVb1tEfs87qZ2rL0/sVX/AHdnt65P7Q0wx8vx6UBo5wAHz/jUbcX1PT78qtO2vaCnJxjP 8Zif2amnnspp04+OjWejKJ6MKdmcMmyJn4m5ZHzEVPMwtJv0VZLbQDqezcb8RzT6Y/7vUPM+ytZt n1OTyiIh6Ztn45N/6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABocbxeLw nUR5xXm/Rvq8+OMuDJjntaswEeBxT0bNZ6NatZpNqz3rO0rqsdO3PxlaWEMpY+aqWXkryT0ZT2V3 7A0dVPuy9f7G124NM/iyT/Z4zWT7sw957MYfB4Fp4/FE2/WWmGHldcBowAAeM9qKcvFeb8VIly9P 0nq7ntbTbVYL+tJj93CwT76unR4/jo0nozhhTsy3Y1sWljM9Ce7HyQIm3RRlttVbaWrnt0Sh6n2U x8vD8mSfv3/h3XN4Bi8Lg2nj8Uc36y6TeOPXugCUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAPD8RxeBxXUU26Tbmj8+quro+02Lw+I4ssdslNvzhzazvDPbq8d7GW7Dfqz2VzG 0s2qd+iu/Zn5Ksk9BVztX1mI8930zh2LwOHabH+HHWP2fNYp4+vwYvxXiP3fUqxtWIjyjZtj45/L faQFmQADzftfj3w6fJ6WmHmsP23rvaqnNwqLfhvEvIYZ+sV038bo0noy36MK9oZQxrdMyrlnMbMZ QKrS1M07zEestq/RRjr4utwY/wAV4j91p9V18fQdJj8LR4ccfdpEfsuREbREJbuMAAAAAAAAAAAA BAJAAAAEAJEAJQAJQAJEAJQAJQAJEACUJAQlAJEAJQAJQJAAAEAJEAJBAAAJAABAJEJAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABwvanDzaPFmjvjv8A tLztJ3h7HjGHx+FainnFeaPnHV4vFbeIU038VbHeGF+kso7Mb9mTdhKnLK3dRm7SIrHhGPxeP6Sv 9cT/AHfSnz72Zx+J7Q45/BWZ/Z9BbZ+OXyfQBZQABzeP4/E4NqI9Ii36S8Ng/wAx9C4jTxOH6ivr jn+Hz3B/mQi/GvjdCnWNlsdI2V07LIlg6USrt2ZzZXMoFV+zPhGLxeOaavpbm/RVltEN72Yx+Jxm b7dKUmf7L5+s9/HtRA2cqRACRACRACRACUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAACQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQCQQCRACRACRCQBCQBCQB ACRACRACRACRACL1i9LVntMbPATTwdRkxT3pea/u+gPE8Xx+DxrPHlaYt+qNfGvjvtXXsi0dOrKk dEXjZg6VMtbP2bMtXUdpEV0/Y2nNxbNf8OP+727xvsXH+N1U/wBEfy9k3nxyb+gCVQAGOWvNivX1 rMPnGGOXNNfOJ2fSZ6w+dZKeHxDPX8N7R+6L8a+L63KdoZ7q6zvEMpnowdKJ6ywmWUyqvIKM0vQ+ x+D6rU55+9aKx+TzWa36vbezmDwODYenW+95/Nphj5L6dQBo5wAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAEiAAAEoA AAAAAAAAAAAAAEAkEAkRuAkQbgkQAkQAkQAkQAl5T2nx8nEMOT8dNv0l6pwfarHvpcGWPu32/WCr YvK4mOem6b9mGKd4Z3idmFdka0y1c892zfpMtLPaNpEV6D2Kj/Eauf6YeweQ9ieuTVz8K/3evbT4 5NfQBKoAA8FxCvJxrUx/XMvevD8Zry8fz/Haf2RfjTx/6RSOnRMyypHu9kXjowrqVSrvPRnZVl6V kK0775MsUjvadn0nT4ow6bFijtSsVfPuFYvpPGtNTy54mfy6vorXDm8l9pEC7JIgBIgBIgBIgBIg BIgBIhIAgBIhIAgBIgBIIBIAAhIAhIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJAAAAAAAAAAAAAAA AAAAAAAAABAJQkAEAAAAAAAAAAjc3BIjdG4Mkbo5kcwMjdhzHMDPc3V8xzAs3N1fMjmBZubq+Y5g Wbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmTzAz3N2HMnmBlu5ftFTx OEZJ/DMW/d0t2rxKni8N1FPWkiZ9eS08e7Cy8dGGn6UhZaJljXZGnmc3UT3dPP2cnUT78xCIV6j2 H/8A9c/6f7vXPI+w8bU1U+vL/d63du5NfUiDcVSIAS8b7RV5eOb/AIqRL2TyXtNX/e2KfXH/AHlF +NPH/pr4+2xcxx0hFpY11K7R16KM32ZWz3UaidqSgrc9kcPicWyZJjfw6T+727y3sXh2xarN+K0V h6lvPjj3e0ASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJQAAAAAkQAkQAkAAAAAAAAAAAAAAA EgAAAAAAAAAAAAAAAAAAAAAgAAABKDcAN0bgkY8xzAyRux5kcwM9zdXNkTcFm6OZXzMeYFvMibKu ZHMC2bo51U2RuC2bom6rc3BZzom6sBZzI52ADPnOdggFnMc6skFnMc6rc3BbznOp3RzAv50c6nml HMC/nOf4qOY5wX85zqOc5wbHOc7X5znBsc6edr85zg2ec52vzpi4NjmY5bROG+/bllVzsNTk5dLl n0pP8BHmMHWNmzt0aum8obm08vVjfrtnxztR0mXHzTvaZdjVRMTLkZo6yiFen9iZ2pqY/wBP93rN 3kPY+/LfPX1rE/u9XzN3HfqzdO6vmTuIZ7m7Hc3Bnu8t7TR/vHBP9E/y9Pu837SV31umn+if5Rfi /j/01MMb1hjkrtKzBG0bMsmOZY11tOYamr6Und0LUc7XT7u3rJPqL8er9lcPhcFpbzyWm39v7O00 +FYvA4Zpsc94xxu227jv1IAgAAAAAAAAABKAAAASgASgBIgBIgBIgBIhIAAAAAAAAAAAAAAAAAAC UACUJAAAAAAAAAAAABIAAAAAAAAAAAAAAAAAAAAg3AEbomQZbo3YzLGbAz3RNlc3YzcFs2YzdVN2 M2Bdzom6nmNwW86JurTAMuY3REJ2BB1ZRVMVBhsbSsiqeUFXLucq3lTygp5TlXcpygp5TlXcpygp 5TlXcqOUFXKjlXcrGYBXysdlswiYBVMdUTCyY6sZBWxlnMMZgGLGZZSwkDdHMiWO4MuY5mEyjcFn N1OdVzHMC3nTzqeY5gX85zqOZPMC+Lqdbk20eb/RKOZr8QybaK/XvtH7iZ9aGlp2luzT3fg19NHS OjbmPcYX67XH1XSZ9XIzRvMuzrK7zLkZYmYnciunb9lZ5dTk+OP+71cXeP8AZnJ/ip2nf3J/l6iL /Fu5L9bMWZczXi6YuIbEWTzKIuyiwLt3nuO25uI4a/hx7/rLuczg8TicvFLbfdpEK6+NPH/phhjo stLGkctUWnoxrrU3j1cnWTzZq1jzl1clo5Zcu8c+txR63iP3Tn6pv4+g4o5cVI9IiGe7CJ2iE7t3 GyN2O6dwSINwSISAlAAlACRAAlAAlACRACRCQAAAAAAAAAASgASISAAAAAAAAAAAAACQAAAAAAAA AAAAAASAAAAAAAAAAAAAAAAIAAAQCAJljuljsCJlhMs9mOwMJYys5TkBVsjZdyHICrZPKt5E8oK4 qmKrOVOwMIqyirPY2Bjyp2ZbAI2NmSARsbMgEbI2ZAMdjZICNkbMkSCNmOzJEgx2YyzljMAwlhKy WEwCuWErJhhMArlhLOWEgxljMpljIImWMyTKJA3N0IBO5vux3NwZbnMx3NwZczT4jf3MdPW27a3a fJOq1XNP2KdIRfi+J2trSYfcjeF+Wm1OicVeWIiN9kai8xjY12ORqultnI1Ecsujq79XP1FovWYI rTgeq+j8QrWZ+3Mx+r2UXeC0WG2Ti2kiN5mL807eUREvbzbaejefHJv62Iv8WUXa0WTFhVtRdlF2 rz9WUXBtc7jR9dqc2T1ttHyhvZMvJitb0jdq6XHNcNenWVN3028U99WRj6Kb02be3Tq18/SN2Lpc 3UdN9nOmZrqKX/DaJ/d0svvTLRzV3jomK6+Pd1vvWJj0ZczT0mXxNJht60hfFnQ4qu3N1cWTEgs3 Tur5k7gz3N2O5uDM3Y7m4MtxBuCQASIASIASAAAAAAACRCQAAAAAAAAEoSAAAAAAAAAAAlAAlCQA AAAAAAAAAAASAAAAAAAAAAAAIASgAAAEJAQJQCNkbMgGOyOVnsAw5TlZ7GwMOVPKy2NgY7GzIBGx skA2AAAAAAAAAAQkBAEghEskAxYzDPZGwK5hjMLJhjMAqmGEwumrCagomFcw2JqqtUFEsLLrV82F o7gqljKyYYTGwMZRKUSCAQAboJnaN5Bjkneu0d5W4ccViIiOzHFWbTzNumP1Zarr8eeRMbxDW1Mx NO67NbkhzNVnmInqzaOZrL93JyZeV0M1++7S02jvxDWxhxx033tPpC8Z6rrezWjmZyazJG2/u03h 2vFibTHoqvamiwVwY+nLGzV0+SZ1Mx8G0/45tOhzJ5lXMc3UVXRdlF1HP+iYsDPLPPy49/tz1+Te pSIr0ho6ak5Ms5J8o2q6NImOrHV7XX488ypzTtHXo0s9t6zG7c1G1qz6ubeZiZ3UatXJG3yauSO7 cvMTEx5tPLb3prPRMVr0HB8vicNxf0+7+kt+LOJwTJyY/Bnz3tH93X36N58cWvq6LSyiyndMSlC7 mZcymLJiwLosmJVRLKLAtiU7q4lMSCzc3YxJuDMRuAlKAEgAAAlAkAAAAAABKAEgAAAAAJAAAAAA AAAAAAAEgAAAAAAAAAAAAAkAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAhIAAACAAAASgAAAAAAEAAAA hGzJAImGMwzQDDZjNVuyNgUTVhNGxysZqDVmiu1G5NN2M4waM0+DCaN2cbGcQNGaMZq3JxMJxA1J qx2bU4kU09slorWNwa20z02RXHbJbl26QvtFovbHWkxEdJt5y2MOHlr2U1W3jx+1hiw8vSO63lmI XRTaEWmtY6snRHO1VpmJ+DjavpSZl2s8b7y4HFcnh0n0gha5ebJN55KRM2mdoiPN6fh+kpwXh0Wy RHj5Otp/s5Ps1p62y31+em9aTMYt/OfVfxTiPjZ52naI7fBrI5t66xz5+a1rW7yx0eSL6iZjtEOX qNbSletom3lENjh2fbHzbbWt3iVozruc+5ztWubf4M4ybpQ2Oboyrva0Vjza8WdDR4OkXt3n9ldX kaePP9VtYqctYhdvt5oivTeCZ2YOxXk6ubqMfV0b9mrljfqlFcq88k7z2U5axeItDa1OPessuC8P ya7XRWYnwqdbT/ZMilvIu4dpslNdixXja8Y5tt85djZdbDWnGOesRtXFtuw6T27No5Kx2OrKYQlC ExKJgBnEpiyvdlEgsizKLKollFgWxLKJVRLKJBbEp3VxLKJBnuMWQJEbpBIAAAJAAAABIAAAAAAA lAJAAAAAAAAAAAAAASAAAAAAAAAAAAAJAAAABAJABAlAAAAAAAAAAAAAAAAAAAAAAAAIAAAAAAAA AAABAJQAAAAgAABAAI2EoBGyJhkgGPKxmqxAKpownHC+YRMdN5BrTj67R3bOn01o7p01Iv71u89o b9a7LfBTfS1vWI2jf12VfQPSW8KX2mas+NC2iv6xMNfJpMnLtEbuuxtMRCtzF55NR5rPps1N/ctP y6uHreE6nXZ4pak48X3rT06fB7fNeI33cbX6mI32R/MWu7XF116aDSRhxbRERs8f499bkyZeeKae kzE2mdon81/tfxDLGOunwbzlzbx08oaHBvZHJlx48mrvaa94pu04y617576rNGLRRM0397JEd/lu 9Dw/S3x4qxffo6mm4NjwUiKY4iI9Ib1dHFY6QIaNabbrYrLfrpJtaK1rMzPZb/s+05IpP59OyLeJ k7eNfRaOc1ue32I7fGXYpi5Y77M8OGMeOKxHSFsU3Y29deZMzirl6dlVvhLatCjJHeYQv1rXnps1 8k9/VsW6qLVmZIi1rzitlvFKRvaZ2h6TSaenC9FFY+3brM+sqeG8Prp4+kZ+lvuxPkr1mqm95nfp DXM459676a2q1dsV7XietvNno78+CJn1cjX6mOeIm0bR33dfRU5NJjidt9t5afjG/V6JZ7I2QMNh nyo2BhsMuVG3wAhMSbbQRAMolnE+iuGUSCyJZRKuGUSCyJZK4llEgyZMYTuCUsYSCQASISAAAlCQ AAAAAAEoASCASAAAAAAAAAAAAlACRACQAAAAAAAAAEgCEoASCAAAAAAAAAAAAAAAAAAAAAAABAAA AAAAAAAISAIAAAAAAQAAACASgAAAQJAQAAhIDHZhln3do7z0WS18mWsajHjmes7pg3dNi5aRMNqO yvDHTpPRaigHZhN4hHRlaVN59JY3zRENLUavaO+yq0iNVlitJ6vNcR1MVi0zO0era1/Ea0rPvbz5 PM5MWp45qvo2GZrhmfrsnpHpHzTCseEcM/2vrr8Q1Eb4qzy44nziPN63HpYiIiI7LNHoqabBTFii IpSNohuVxrKtWMEejPwY9G1FFmHB4mWJn7MdfnIM9JpIx15to5pbUaas/a6rqViI7MxPxqX0UT1r O3wVzpbR2hviP5i03Y5s6a879FNtHljydhExCv8AMTPJXBnRZbz0iG5ptFjwe/l96zctMVamTJtE yTMibu1VrdTzRMR0j0ed4lr64MVpm0RERvMz5NvX62uOJ69XhOKX1HH9bHDtFvNYnfJeOy0Z2ojX 6jjnEq6fRUmccTvN/J9H0eKcOnx45neaxEbubwHgOHg+milI3vP2resu3Wu0JQmITsmISDHZHKz2 JgFc1RMLJhGwK9iIZ7MZgEdgmAEwyiWCdwWRLKJVxKYsC2JTuriWUSDNlEsIlMAySx3SCRCQSIAS AAACRACQAAAAAAASIASAAAAAAAAAAAAAAACRACRACQASIAAAAAAAAAAAAAAAAAAAAAAAAQCUAAAA AAAAAAIAAAAAAAAQAAAAAACBICBICAAEJAQJQCJcLjuS2ny6fPG/LWdpd1o8T0X07SXx/e7wCdJx Wa0jmneHQpxPDMdZmJfNtZm49weZrh0/j4o7VtSZ2+Uw0/8A7o49k92vBLc/ntFohFW9PqGXimOI 6Tu1L8T3eCx6r2t1O3JwvHjifO99v7t/Bwf2l1PXU6rS6eJ8qUm8x+so5TsekzcSjbvs4mt4rzW5 K2mbT0itesy2cHsvbvqtbmyz5xERWP2jd1tJwrTaONsOKtZ8585+cnDrzmn4Rq+IZObUROHD32n7 Vv8A0ej0uhxaXFGPFSK1j0bkY4jyZRVZVXFGUVWbGwKsk8mObekNrSW3pWf1a2aYjHbm7bNnQ1id PW0TvuDdhJEbQABMsLW2R0ZTMQrvfbz2YWzVhpanUxEd0dWkW5c8R5uXxDX1w4pnfr5Q19XxKuOJ 2neXltVqtVxbV/RdJ715+1bypANfiOu1HENV9C0MTfNeesx2rD1PAeBYuE6aKx72W3W9/WVnBuB4 eF4dqRzZbdb5J72l160WVK02ZxCYhOwI23TsnY2BGxsnYBjsiYZsZBjMMZZSgGEolMsQDdG6NwZ7 piVe6YkFsSziVMWZRILolMSriWUSCyJTuwhMSDMRCQSI3SAlACRCQAAEoAEoASAAAAAAAAACUACR ACQAAAAAAAAAAAAASAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAABAAAAAAAAAAAAACBKAAAAAAAQ JQAAAhICEbJAYTWJ7wx8KvpC0BV4ceieWGewDHlNmWwCNjZICNhIDmcZredBecdpiY69FXCOLW+i UiZidukulmxxlx2paN4mNng+K4+I8Hy2yaTfl37TXetoCPfRxfp1qi3F48ofKMvtvxak8s6LDv61 rZji9rPaLUf5PC+bfttS0q8q3p9W/wBrRMdpUZuKdN99nzvFqPbTVz7nD8OKs+do2/mW3h4D7Xaq ZnPrtNpqz35aRaYOHY9Zk4pNt9rR+rl6zi+OnS+WN57Rv1lXp/YrNaYtruL6zNPnGO3hxP6O5w/2 f0HDuun09Yv55Le9afznqcOvO4tBreMTHu30unnva0bWt8on+70nDuE4OHYYx4Kbesz3tPrMuhGO IjpDOKrK9YVpsyiGUQnYGOyUgI2SlAIEmwMWMs9kTAMJYzDOYRMArmGErZhhMArlHmzmGMwDE3Ts bAbs4swj5pgFkSziVcM4BZEsolXDKAZwyhjCYBkACQhIAAAAAAAJAAAAAAAAAAAAAAAAAAAShIAA AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ 2Q==`;var S9="1.9.4";var uu,up,dp,Ui,Hi,du,U0,pp,H0,G0,q0,X0,N9=class{constructor(t={}){Jn(this,uu,void 0);Jn(this,up,void 0);Jn(this,dp,void 0);Jn(this,Ui,void 0);Jn(this,Hi,void 0);Jn(this,du,void 0);this.analyze=(...t)=>{if(!on(this,up))return;let n=this.tf.engine().state.numTensors,a=on(this,uu);xa(this,uu,n);let r=n-a;r!==0&&de(...t,r)};Jn(this,U0,t=>{if(!on(this,dp))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Le))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Jn(this,pp,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let a=at();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&de("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&&de("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&de("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 r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&de(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&de("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&Ck();try{await this.tf.setBackend(this.config.backend)}catch(r){de("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),ua.set("WEBGL_FORCE_F16_TEXTURES",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),typeof this.config.deallocate!="undefined"&&(de("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&de(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(at()-a)}});Jn(this,H0,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,a=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=a.dataSync(),s=0;for(let l=0;l10*this.config.cacheSensitivity?0:i),o});Jn(this,G0,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(V0);break;case"full":n=await t(j0);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});Jn(this,q0,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+V0;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+j0;break;default:n=null}let r=new Image;r.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(a,a):document.createElement("canvas");s.width=r.naturalWidth,s.height=r.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(r,0,0);let o=await this.detect(s,this.config);t(o)},n?r.src=n:t(null)}));Jn(this,X0,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(V0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(j0)),!n)return null;let a;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),s=r.expandDims(0);this.tf.dispose(r),a=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&de("Warmup tfjs-node not loaded");return a});this.tf=tp,this.draw=Zg,this.version=S9,this.config=zn(d5,t),this.state="idle",xa(this,uu,0),xa(this,up,!1),xa(this,dp,!1),xa(this,Ui,!0),xa(this,du,0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null},this.image=n=>qg(n,this.config),this.classes={facemesh:eg,emotion:rg,faceres:ug,body:this.config.body.modelPath.includes("posenet")?gg:Tg,hand:Sg,nanodet:Bg,centernet:Gg},this.faceTriangulation=Bk,this.faceUVMap=Vk,this.sysinfo=p5(),xa(this,Hi,1)}similarity(t,n){return og(t,n)}enhance(t){return lg(t)}match(t,n,a=0){return Hk(t,n,a)}async load(t={}){this.state="load";let n=at();t&&(this.config=zn(this.config,t)),on(this,Ui)&&(this.config.debug&&de(`version: ${this.version}`),this.config.debug&&de(`tfjs version: ${this.tf.version_core}`),this.config.debug&&de("platform:",this.sysinfo.platform),this.config.debug&&de("agent:",this.sysinfo.agent),await on(this,pp).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&de("configuration:",this.config),this.config.debug&&de("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.movenet,this.models.nanodet,this.models.centernet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?Q2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?ag(this.config):null),this.models.handpose||(this.config.hand.enabled?Ig(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?Ag(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?P0(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?h9(this.config):null),this.models.movenet||(this.config.body.enabled&&this.config.body.modelPath.includes("movenet")?zg(this.config):null),this.models.nanodet||(this.config.object.enabled&&this.config.object.modelPath.includes("nanodet")?Lg(this.config):null),this.models.centernet||(this.config.object.enabled&&this.config.object.modelPath.includes("centernet")?Ug(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?ig(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await Q2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await ag(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await Ig(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await Ag(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await P0(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await P0(this.config)),this.config.body.enabled&&!this.models.movenet&&this.config.body.modelPath.includes("movenet")&&(this.models.movenet=await zg(this.config)),this.config.object.enabled&&!this.models.nanodet&&this.config.object.modelPath.includes("nanodet")&&(this.models.nanodet=await Lg(this.config)),this.config.object.enabled&&!this.models.centernet&&this.config.object.modelPath.includes("centernet")&&(this.models.centernet=await Ug(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await ig(this.config))),on(this,Ui)&&(this.config.debug&&de("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),xa(this,Ui,!1));let a=Math.trunc(at()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async detect(t,n={}){return new Promise(async a=>{this.state="config";let r;this.config=zn(this.config,n),this.state="check";let s=on(this,U0).call(this,t);s&&(de(s,t),a({error:s}));let i=at();await on(this,pp).call(this),await this.load(),r=at();let o=qg(t,this.config);if(!o||!o.tensor){de("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(at()-r),this.analyze("Get Image:"),r=at(),this.config.skipFrame=await on(this,H0).call(this,o.tensor),this.perf.frames||(this.perf.frames=0),this.perf.cached||(this.perf.cached=0),this.perf.frames++,this.config.skipFrame&&this.perf.cached++,this.perf.changed=Math.trunc(at()-r),this.analyze("Check Changed:");let l,u,d,p,c;this.config.async?(l=this.config.face.enabled?dg(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=at(),l=this.config.face.enabled?await dg(this,o.tensor):[],c=Math.trunc(at()-r),c>0&&(this.perf.face=c)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?u=this.config.body.enabled?yg(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?u=this.config.body.enabled?Ng(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?u=this.config.body.enabled?Mg(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(u=this.config.body.enabled?Og(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=at(),this.config.body.modelPath.includes("posenet")?u=this.config.body.enabled?await yg(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?u=this.config.body.enabled?await Ng(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?u=this.config.body.enabled?await Mg(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(u=this.config.body.enabled?await Og(o.tensor,this.config):[]),c=Math.trunc(at()-r),c>0&&(this.perf.body=c)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?kg(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=at(),d=this.config.hand.enabled?await kg(o.tensor,this.config):[],c=Math.trunc(at()-r),c>0&&(this.perf.hand=c)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?Wg(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?Hg(o.tensor,this.config):[]),this.perf.object&&delete this.perf.object):(this.state="run:object",r=at(),this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?await Wg(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?await Hg(o.tensor,this.config):[]),c=Math.trunc(at()-r),c>0&&(this.perf.object=c)),this.analyze("End Object:"),this.config.async&&([l,u,d,p]=await Promise.all([l,u,d,p]));let h=[];this.config.gesture.enabled&&(r=at(),h=[...m9(l),...f9(u),...A9(d),...y9(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(at()-r)),this.perf.total=Math.trunc(at()-i),this.state="idle";let m={face:l,body:u,hand:d,gesture:h,object:p,performance:this.perf,canvas:o.canvas,timestamp:Date.now(),get persons(){var f;return I9(l,u,d,h,(f=o==null?void 0:o.tensor)==null?void 0:f.shape)}};Ne(o.tensor),a(m)})}async warmup(t={}){let n=at();if(t&&(this.config=zn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await on(this,G0).call(this):typeof Image!="undefined"?a=await on(this,q0).call(this):a=await on(this,X0).call(this);let r=at();return this.config.debug&&de("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};uu=new WeakMap,up=new WeakMap,dp=new WeakMap,Ui=new WeakMap,Hi=new WeakMap,du=new WeakMap,U0=new WeakMap,pp=new WeakMap,H0=new WeakMap,G0=new WeakMap,q0=new WeakMap,X0=new WeakMap;return Doe;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the License); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * https://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** @license See the LICENSE file. */