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

4224 lines
1.1 MiB
Raw Blame History

/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
var sE=Object.create,pd=Object.defineProperty,iE=Object.getPrototypeOf,oE=Object.prototype.hasOwnProperty,lE=Object.getOwnPropertyNames,uE=Object.getOwnPropertyDescriptor;var zw=e=>pd(e,"__esModule",{value:!0});var cE=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),dd=(e,t)=>{zw(e);for(var n in t)pd(e,n,{get:t[n],enumerable:!0})},pE=(e,t,n)=>{if(zw(e),t&&typeof t=="object"||typeof t=="function")for(let a of lE(t))!oE.call(e,a)&&a!=="default"&&pd(e,a,{get:()=>t[a],enumerable:!(n=uE(t,a))||n.enumerable});return e},dE=e=>e&&e.__esModule?e:pE(pd(e!=null?sE(iE(e)):{},"default",{value:e,enumerable:!0}),e);var aC=cE((Lre,nC)=>{dd(Lre,{isNodejs:()=>zre});function zre(){return typeof global=="object"&&!0&&typeof nC!="undefined"&&typeof process!="undefined"&&!!process.version}});var Bg={};dd(Bg,{Abs:()=>Co,Acos:()=>_o,Acosh:()=>Eo,AdadeltaOptimizer:()=>Lh,AdagradOptimizer:()=>zh,AdamOptimizer:()=>Bh,AdamaxOptimizer:()=>Wh,Add:()=>Wr,AddN:()=>_s,All:()=>xd,Any:()=>vd,ArgMax:()=>Es,ArgMin:()=>lc,Asin:()=>Ao,Asinh:()=>Fo,Atan:()=>$o,Atan2:()=>Mo,Atanh:()=>Do,AvgPool:()=>As,AvgPool3D:()=>uc,AvgPool3DGrad:()=>kd,AvgPoolGrad:()=>wd,BackendWasm:()=>Z2,BatchMatMul:()=>Fs,BatchToSpaceND:()=>cc,Bincount:()=>Id,BroadcastTo:()=>a0,Callback:()=>ZI,CallbackList:()=>Yk,Cast:()=>$s,Ceil:()=>Ro,ClipByValue:()=>Vr,Complex:()=>Nd,ComplexAbs:()=>pc,Concat:()=>Po,Conv2D:()=>Ds,Conv2DBackpropFilter:()=>Td,Conv2DBackpropInput:()=>Ms,Conv3D:()=>dc,Conv3DBackpropFilterV2:()=>Sd,Conv3DBackpropInputV2:()=>Cd,Cos:()=>Rs,Cosh:()=>Oo,CropAndResize:()=>Lo,Cumsum:()=>Ps,CustomCallback:()=>Zk,DataStorage:()=>fd,DenseBincount:()=>_d,DepthToSpace:()=>zo,DepthwiseConv2dNative:()=>Os,DepthwiseConv2dNativeBackpropFilter:()=>Ed,DepthwiseConv2dNativeBackpropInput:()=>Ad,Diag:()=>Fd,Dilation2D:()=>hc,Dilation2DBackpropFilter:()=>Dd,Dilation2DBackpropInput:()=>$d,ENV:()=>Gg,EarlyStopping:()=>eN,Elu:()=>Bo,EluGrad:()=>Md,Environment:()=>e0,Equal:()=>Vo,Erf:()=>Wo,Exp:()=>zs,ExpandDims:()=>Uo,Expm1:()=>Go,FFT:()=>Rd,Fill:()=>mc,FlipLeftRight:()=>Ho,Floor:()=>Bs,FloorDiv:()=>Ws,FromPixels:()=>Xd,FusedBatchNorm:()=>Vs,FusedConv2D:()=>xi,FusedDepthwiseConv2D:()=>vi,GatherNd:()=>qo,GatherV2:()=>jo,GraphModel:()=>EN,Greater:()=>Ko,GreaterEqual:()=>Us,History:()=>Jk,IFFT:()=>Pd,Identity:()=>Xo,Imag:()=>Od,InputSpec:()=>Jt,IsFinite:()=>Yo,IsInf:()=>Jo,IsNan:()=>Zo,KernelBackend:()=>sc,LRN:()=>yc,LRNGrad:()=>zd,LayerVariable:()=>Hk,LayersModel:()=>Nr,LeakyRelu:()=>Gs,Less:()=>Qo,LessEqual:()=>el,LinSpace:()=>Ld,Log:()=>Hs,Log1p:()=>tl,LogSoftmax:()=>r0,LogicalAnd:()=>nl,LogicalNot:()=>fc,LogicalOr:()=>gc,Max:()=>js,MaxPool:()=>Ks,MaxPool3D:()=>bc,MaxPool3DGrad:()=>Wd,MaxPoolGrad:()=>Bd,MaxPoolWithArgmax:()=>Vd,Maximum:()=>qs,Mean:()=>Xs,Min:()=>Ys,Minimum:()=>Js,MirrorPad:()=>xc,Mod:()=>al,MomentumOptimizer:()=>Vh,Multinomial:()=>Ud,Multiply:()=>Zs,Neg:()=>rl,NonMaxSuppressionV3:()=>il,NonMaxSuppressionV4:()=>ol,NonMaxSuppressionV5:()=>ll,NotEqual:()=>sl,OP_SCOPE_SUFFIX:()=>f0,OneHot:()=>Qs,OnesLike:()=>ul,Optimizer:()=>wr,Pack:()=>cl,PadV2:()=>ei,Pool:()=>QE,Pow:()=>ti,Prelu:()=>ni,Prod:()=>pl,RMSPropOptimizer:()=>Uh,RNN:()=>nr,Range:()=>vc,Rank:()=>Zg,Real:()=>Gd,RealDiv:()=>Ls,Reciprocal:()=>dl,Reduction:()=>hn,Relu:()=>ai,Relu6:()=>si,Reshape:()=>hl,ResizeBilinear:()=>ri,ResizeBilinearGrad:()=>jd,ResizeNearestNeighbor:()=>wc,ResizeNearestNeighborGrad:()=>Hd,Reverse:()=>ii,RotateWithOffset:()=>Cl,Round:()=>oi,Rsqrt:()=>li,SGDOptimizer:()=>Yc,ScatterNd:()=>ml,Select:()=>fl,Selu:()=>gl,Sequential:()=>Jl,Sigmoid:()=>ci,Sign:()=>xl,Sin:()=>ui,Sinh:()=>bl,Slice:()=>yl,Softmax:()=>hi,Softplus:()=>vl,SpaceToBatchND:()=>kc,SparseToDense:()=>qd,SplitV:()=>wl,Sqrt:()=>pi,Square:()=>Ic,SquaredDifference:()=>mi,Step:()=>Gr,StridedSlice:()=>kl,Sub:()=>fi,Sum:()=>di,SymbolicTensor:()=>Da,Tan:()=>Il,Tanh:()=>gi,Tensor:()=>z,TensorBuffer:()=>zt,Tile:()=>Ur,TopK:()=>Nl,Transpose:()=>yi,Unique:()=>Kd,Unpack:()=>Tl,UnsortedSegmentSum:()=>Nc,Variable:()=>Hr,ZerosLike:()=>Sl,_FusedMatMul:()=>bi,abs:()=>Bt,acos:()=>wy,acosh:()=>ky,add:()=>Z,addN:()=>J0,addStrict:()=>R1,all:()=>uh,any:()=>Mc,argMax:()=>Rc,argMin:()=>Iy,asin:()=>Ny,asinh:()=>Ty,atan:()=>Sy,atan2:()=>Cy,atanh:()=>_y,avgPool:()=>Yn,avgPool3d:()=>Fy,backend:()=>Y0,backend_util:()=>E,basicLSTMCell:()=>_$,batchNorm:()=>gr,batchNorm2d:()=>t1,batchNorm3d:()=>n1,batchNorm4d:()=>a1,batchToSpaceND:()=>Oc,bincount:()=>r1,booleanMaskAsync:()=>$R,broadcastTo:()=>Lc,browser:()=>Ni,buffer:()=>Le,callbacks:()=>O4,cast:()=>pe,ceil:()=>$y,clipByValue:()=>Xt,clone:()=>Xr,complex:()=>jr,concat:()=>Qe,concat1d:()=>s1,concat2d:()=>i1,concat3d:()=>o1,concat4d:()=>l1,constraints:()=>bk,conv1d:()=>ph,conv2d:()=>Ft,conv2dTranspose:()=>dh,conv3d:()=>My,conv3dTranspose:()=>Y$,copyRegisteredKernels:()=>nA,cos:()=>zc,cosh:()=>hh,cosineWindow:()=>sb,cumsum:()=>mh,customGrad:()=>Ka,data:()=>FN,denseBincount:()=>c1,deprecationWarn:()=>Rt,depthToSpace:()=>Ry,depthwiseConv2d:()=>yr,deregisterOp:()=>z4,device_util:()=>th,diag:()=>rD,dilation2d:()=>Py,disableDeprecationWarnings:()=>BF,dispose:()=>Fe,disposeVariables:()=>WF,div:()=>we,divNoNan:()=>Oy,divStrict:()=>P1,dot:()=>p1,dropout:()=>q1,elu:()=>Ol,enableDebugMode:()=>zF,enableProdMode:()=>LF,enclosingPowerOfTwo:()=>K1,engine:()=>Jr,env:()=>te,equal:()=>br,equalStrict:()=>E1,erf:()=>Ly,exp:()=>dn,expandDims:()=>Jn,expm1:()=>zy,eye:()=>By,fft:()=>qc,fill:()=>kn,findBackend:()=>KF,findBackendFactory:()=>XF,floor:()=>Ll,floorDiv:()=>lh,fused:()=>ns,gather:()=>Ci,gatherND:()=>j1,gather_util:()=>my,getBackend:()=>jF,getGradient:()=>Kg,getKernel:()=>qg,getKernelsForBackend:()=>Yd,grad:()=>DD,grads:()=>MD,greater:()=>Zn,greaterEqual:()=>xr,greaterEqualStrict:()=>A1,greaterStrict:()=>F1,ifft:()=>Ul,imag:()=>fh,image:()=>Ja,inTopKAsync:()=>rP,initializers:()=>Tk,input:()=>Lk,io:()=>qt,irfft:()=>Ah,isFinite:()=>d1,isInf:()=>h1,isNaN:()=>m1,keep:()=>Kt,kernel_impls:()=>Za,layers:()=>Ok,leakyRelu:()=>Bc,less:()=>Wc,lessEqual:()=>Qr,lessEqualStrict:()=>$1,lessStrict:()=>D1,linalg:()=>ik,linspace:()=>f1,loadGraphModel:()=>zV,loadLayersModel:()=>a4,localResponseNormalization:()=>Wy,log:()=>Dn,log1p:()=>gh,logSigmoid:()=>y1,logSoftmax:()=>bh,logSumExp:()=>Gy,logicalAnd:()=>ha,logicalNot:()=>Vc,logicalOr:()=>xh,logicalXor:()=>w1,losses:()=>xO,matMul:()=>ze,math:()=>F0,max:()=>Qn,maxPool:()=>$t,maxPool3d:()=>Hy,maxPoolWithArgmax:()=>k1,maximum:()=>Sa,maximumStrict:()=>O1,mean:()=>Ct,memory:()=>ih,metrics:()=>XI,min:()=>Bl,minimum:()=>Ei,minimumStrict:()=>L1,mirrorPad:()=>jy,mod:()=>vh,modStrict:()=>z1,model:()=>t4,models:()=>YI,moments:()=>wh,movingAverage:()=>YR,mul:()=>L,mulStrict:()=>B1,multiRNNCell:()=>uM,multinomial:()=>I1,neg:()=>St,nextFrame:()=>Gh,norm:()=>Dh,notEqual:()=>es,notEqualStrict:()=>M1,oneHot:()=>Dl,ones:()=>Xa,onesLike:()=>Mn,op:()=>R,outerProduct:()=>mM,pad:()=>ea,pad1d:()=>yM,pad2d:()=>xM,pad3d:()=>wM,pad4d:()=>IM,pool:()=>N1,pow:()=>Ya,powStrict:()=>W1,prelu:()=>Gc,print:()=>T0,prod:()=>kh,profile:()=>VF,rand:()=>$M,randomGamma:()=>PM,randomNormal:()=>T1,randomUniform:()=>Wl,range:()=>Ih,ready:()=>HF,real:()=>Hc,reciprocal:()=>Xy,registerBackend:()=>oh,registerCallbackConstructor:()=>r4,registerGradient:()=>s0,registerKernel:()=>Sc,registerOp:()=>L4,regularizers:()=>JI,relu:()=>Ye,relu6:()=>Nh,removeBackend:()=>qF,reshape:()=>q,reverse:()=>Rn,reverse1d:()=>HM,reverse2d:()=>qM,reverse3d:()=>XM,reverse4d:()=>JM,rfft:()=>Kc,round:()=>Yy,rsqrt:()=>Th,scalar:()=>he,scatterND:()=>H1,scatter_util:()=>fy,selu:()=>Sh,separableConv2d:()=>Ai,sequential:()=>n4,serialization:()=>re,setBackend:()=>GF,setPlatform:()=>YF,setWasmPath:()=>_re,setWasmPaths:()=>Ere,setdiff1dAsync:()=>S1,sigmoid:()=>da,sign:()=>Jy,signal:()=>bO,sin:()=>Ch,sinh:()=>_h,slice:()=>We,slice1d:()=>Eh,slice2d:()=>Zy,slice3d:()=>Vl,slice4d:()=>jc,slice_util:()=>pn,softmax:()=>Ca,softplus:()=>zl,spaceToBatchND:()=>Uc,sparseToDense:()=>rb,spectral:()=>yO,split:()=>Pn,sqrt:()=>nn,square:()=>ut,squaredDifference:()=>Xc,squaredDifferenceStrict:()=>V1,squeeze:()=>ts,stack:()=>Dt,step:()=>Gl,stridedSlice:()=>Qy,sub:()=>ge,subStrict:()=>U1,sum:()=>Ce,sumOutType:()=>eh,tan:()=>eb,tanh:()=>Pl,tensor:()=>Kn,tensor1d:()=>tt,tensor2d:()=>_a,tensor3d:()=>rh,tensor4d:()=>Ea,tensor5d:()=>IR,tensor6d:()=>NR,tensor_util:()=>Ta,test_util:()=>j0,tidy:()=>D,tile:()=>qa,time:()=>UF,topk:()=>tb,train:()=>$i,transpose:()=>Ue,truncatedNormal:()=>Fh,unique:()=>$h,unregisterGradient:()=>tA,unregisterKernel:()=>eA,unsortedSegmentSum:()=>nb,unstack:()=>ht,upcastType:()=>pa,util:()=>k,valueAndGrad:()=>RD,valueAndGrads:()=>PD,variable:()=>C1,variableGrads:()=>g1,version:()=>Oee,version_converter:()=>AN,version_core:()=>X0,version_layers:()=>mm,version_wasm:()=>Are,where:()=>wn,whereAsync:()=>ab,zeros:()=>bt,zerosLike:()=>Ge});var hE=Object.create,hd=Object.defineProperty,mE=Object.getPrototypeOf,fE=Object.prototype.hasOwnProperty,gE=Object.getOwnPropertyNames,yE=Object.getOwnPropertyDescriptor,Bw=e=>hd(e,"__esModule",{value:!0}),cn=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),Oe=(e,t)=>{Bw(e);for(var n in t)hd(e,n,{get:t[n],enumerable:!0})},bE=(e,t,n)=>{if(Bw(e),t&&typeof t=="object"||typeof t=="function")for(let a of gE(t))!fE.call(e,a)&&a!=="default"&&hd(e,a,{get:()=>t[a],enumerable:!(n=yE(t,a))||n.enumerable});return e},No=e=>e&&e.__esModule?e:bE(hd(e!=null?hE(mE(e)):{},"default",{value:e,enumerable:!0}),e),xE=cn(()=>{}),vE=cn((e,t)=>{(function(n,a,r){function s(c){var u=this,p=l();u.next=function(){var d=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=d-(u.c=d|0)},u.c=1,u.s0=p(" "),u.s1=p(" "),u.s2=p(" "),u.s0-=p(c),u.s0<0&&(u.s0+=1),u.s1-=p(c),u.s1<0&&(u.s1+=1),u.s2-=p(c),u.s2<0&&(u.s2+=1),p=null}function i(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function o(c,u){var p=new s(c),d=u&&u.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,d&&(typeof d=="object"&&i(d,p),h.state=function(){return i(p,{})}),h}function l(){var c=4022871197,u=function(p){p=p.toString();for(var d=0;d<p.length;d++){c+=p.charCodeAt(d);var h=.02519603282416938*c;c=h>>>0,h-=c,h*=c,c=h>>>0,h-=c,c+=h*4294967296}return(c>>>0)*23283064365386963e-26};return u}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)}),wE=cn((e,t)=>{(function(n,a,r){function s(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var d=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^d^d>>>8},l===(l|0)?c.x=l:u+=l;for(var p=0;p<u.length+64;p++)c.x^=u.charCodeAt(p)|0,c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function o(l,c){var u=new s(l),p=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var h=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,p&&(typeof p=="object"&&i(p,u),d.state=function(){return i(u,{})}),d}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)}),kE=cn((e,t)=>{(function(n,a,r){function s(l){var c=this,u="";c.next=function(){var d=c.x^c.x>>>2;return c.x=c.y,c.y=c.z,c.z=c.w,c.w=c.v,(c.d=c.d+362437|0)+(c.v=c.v^c.v<<4^(d^d<<1))|0},c.x=0,c.y=0,c.z=0,c.w=0,c.v=0,l===(l|0)?c.x=l:u+=l;for(var p=0;p<u.length+64;p++)c.x^=u.charCodeAt(p)|0,p==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c.v=l.v,c.d=l.d,c}function o(l,c){var u=new s(l),p=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var h=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,p&&(typeof p=="object"&&i(p,u),d.state=function(){return i(u,{})}),d}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)}),IE=cn((e,t)=>{(function(n,a,r){function s(l){var c=this;c.next=function(){var p=c.x,d=c.i,h,m,f;return h=p[d],h^=h>>>7,m=h^h<<24,h=p[d+1&7],m^=h^h>>>10,h=p[d+3&7],m^=h^h>>>3,h=p[d+4&7],m^=h^h<<7,h=p[d+7&7],h=h^h<<13,m^=h^h<<9,p[d]=m,c.i=d+1&7,m};function u(p,d){var h,m,f=[];if(d===(d|0))m=f[0]=d;else for(d=""+d,h=0;h<d.length;++h)f[h&7]=f[h&7]<<15^d.charCodeAt(h)+f[h+1&7]<<13;for(;f.length<8;)f.push(0);for(h=0;h<8&&f[h]===0;++h);for(h==8?m=f[7]=-1:m=f[h],p.x=f,p.i=0,h=256;h>0;--h)p.next()}u(c,l)}function i(l,c){return c.x=l.x.slice(),c.i=l.i,c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),p=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var h=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,p&&(p.x&&i(p,u),d.state=function(){return i(u,{})}),d}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)}),NE=cn((e,t)=>{(function(n,a,r){function s(l){var c=this;c.next=function(){var p=c.w,d=c.X,h=c.i,m,f;return c.w=p=p+1640531527|0,f=d[h+34&127],m=d[h=h+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=d[h]=f^m,c.i=h,f+(p^p>>>16)|0};function u(p,d){var h,m,f,g,y,b=[],v=128;for(d===(d|0)?(m=d,d=null):(d=d+"\0",m=0,v=Math.max(v,d.length)),f=0,g=-32;g<v;++g)d&&(m^=d.charCodeAt((g+32)%d.length)),g===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,g>=0&&(y=y+1640531527|0,h=b[g&127]^=m+y,f=h==0?f+1:0);for(f>=128&&(b[(d&&d.length||0)&127]=-1),f=127,g=4*128;g>0;--g)m=b[f+34&127],h=b[f=f+1&127],m^=m<<13,h^=h<<17,m^=m>>>15,h^=h>>>12,b[f]=m^h;p.w=y,p.X=b,p.i=f}u(c,l)}function i(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),p=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var h=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,p&&(p.X&&i(p,u),d.state=function(){return i(u,{})}),d}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)}),TE=cn((e,t)=>{(function(n,a,r){function s(l){var c=this,u="";c.next=function(){var d=c.b,h=c.c,m=c.d,f=c.a;return d=d<<25^d>>>7^h,h=h-m|0,m=m<<24^m>>>8^f,f=f-d|0,c.b=d=d<<20^d>>>12^h,c.c=h=h-m|0,c.d=m<<16^h>>>16^f,c.a=f-d|0},c.a=0,c.b=0,c.c=2654435769|0,c.d=1367130551,l===Math.floor(l)?(c.a=l/4294967296|0,c.b=l|0):u+=l;for(var p=0;p<u.length+20;p++)c.b^=u.charCodeAt(p)|0,c.next()}function i(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function o(l,c){var u=new s(l),p=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var h=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(h+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,p&&(typeof p=="object"&&i(p,u),d.state=function(){return i(u,{})}),d}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)}),SE=cn(()=>{}),CE=cn((e,t)=>{(function(n,a){var r=this,s=256,i=6,o=52,l="random",c=a.pow(s,i),u=a.pow(2,o),p=u*2,d=s-1,h;function m(N,T,C){var $=[];T=T==!0?{entropy:!0}:T||{};var F=b(y(T.entropy?[N,x(n)]:N==null?v():N,3),$),O=new f($),W=function(){for(var V=O.g(i),H=c,K=0;V<u;)V=(V+K)*s,H*=s,K=O.g(1);for(;V>=p;)V/=2,H/=2,K>>>=1;return(V+K)/H};return W.int32=function(){return O.g(4)|0},W.quick=function(){return O.g(4)/4294967296},W.double=W,b(x(O.S),n),(T.pass||C||function(V,H,K,j){return j&&(j.S&&g(j,O),V.state=function(){return g(O,{})}),K?(a[l]=V,H):V})(W,F,"global"in T?T.global:this==a,T.state)}a["seed"+l]=m;function f(N){var T,C=N.length,$=this,F=0,O=$.i=$.j=0,W=$.S=[];for(C||(N=[C++]);F<s;)W[F]=F++;for(F=0;F<s;F++)W[F]=W[O=d&O+N[F%C]+(T=W[F])],W[O]=T;($.g=function(V){for(var H,K=0,j=$.i,Y=$.j,J=$.S;V--;)H=J[j=d&j+1],K=K*s+J[d&(J[j]=J[Y=d&Y+H])+(J[Y]=H)];return $.i=j,$.j=Y,K})(s)}function g(N,T){return T.i=N.i,T.j=N.j,T.S=N.S.slice(),T}function y(N,T){var C=[],$=typeof N,F;if(T&&$=="object")for(F in N)try{C.push(y(N[F],T-1))}catch(O){}return C.length?C:$=="string"?N:N+"\0"}function b(N,T){for(var C=N+"",$,F=0;F<C.length;)T[d&F]=d&($^=T[d&F]*19)+C.charCodeAt(F++);return x(T)}function v(){try{var N;return h&&(N=h.randomBytes)?N=N(s):(N=new Uint8Array(s),(r.crypto||r.msCrypto).getRandomValues(N)),x(N)}catch($){var T=r.navigator,C=T&&T.plugins;return[+new Date,r,C,r.screen,x(n)]}}function x(N){return String.fromCharCode.apply(0,N)}if(b(a.random(),n),typeof t=="object"&&t.exports){t.exports=m;try{h=SE()}catch(N){}}else typeof define=="function"&&define.amd&&define(function(){return m})})([],Math)}),md=cn((e,t)=>{var n=vE(),a=wE(),r=kE(),s=IE(),i=NE(),o=TE(),l=CE();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),_E=cn(()=>{}),rc=cn(()=>{}),EE=cn(()=>{}),AE=cn(()=>{}),FE=cn((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 ee.buffer!=Ze&&Fn(ee.buffer),En}function i(){return ee.buffer!=Ze&&Fn(ee.buffer),tn}function o(){return ee.buffer!=Ze&&Fn(ee.buffer),vn}function l(){return ee.buffer!=Ze&&Fn(ee.buffer),un}function c(){return ee.buffer!=Ze&&Fn(ee.buffer),Va}var u=typeof r!="undefined"?r:{},p={},d;for(d in u)u.hasOwnProperty(d)&&(p[d]=u[d]);var h=[],m="./this.program",f=function(w,S){throw S},g=!1,y=!1,b=!1,v=!1;g=typeof window=="object",y=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",v=!g&&!b&&!y;var x=u.ENVIRONMENT_IS_PTHREAD||!1;x&&(Ze=u.buffer,oa=u.DYNAMIC_BASE,Na=u.DYNAMICTOP_PTR);var N="";function T(w){return u.locateFile?u.locateFile(w,N):N+w}var C,$,F,O,W,V;if(b){y?N=rc().dirname(N)+"/":N=__dirname+"/",C=function(w,S){return W||(W=require("fs")),V||(V=rc()),w=V.normalize(w),W.readFileSync(w,S?null:"utf8")},F=function(w){var S=C(w,!0);return S.buffer||(S=new Uint8Array(S)),be(S.buffer),S},process.argv.length>1&&(m=process.argv[1].replace(/\\/g,"/")),h=process.argv.slice(2),process.on("uncaughtException",function(w){if(!(w instanceof Ow))throw w}),process.on("unhandledRejection",pr),f=function(w){process.exit(w)},u.inspect=function(){return"[Emscripten Module object]"};var H;try{H=EE()}catch(w){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),w}Worker=H.Worker}else v?(typeof read!="undefined"&&(C=function(w){return read(w)}),F=function(w){var S;return typeof readbuffer=="function"?new Uint8Array(readbuffer(w)):(S=read(w,"binary"),be(typeof S=="object"),S)},typeof scriptArgs!="undefined"?h=scriptArgs:typeof arguments!="undefined"&&(h=arguments),typeof quit=="function"&&(f=function(w){quit(w)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(g||y)&&(y?N=self.location.href:document.currentScript&&(N=document.currentScript.src),typeof a!="undefined"&&a&&(N=a),N.indexOf("blob:")!==0?N=N.substr(0,N.lastIndexOf("/")+1):N="",b?(C=function(w,S){return W||(W=require("fs")),V||(V=rc()),w=V.normalize(w),W.readFileSync(w,S?null:"utf8")},F=function(w){var S=C(w,!0);return S.buffer||(S=new Uint8Array(S)),be(S.buffer),S}):(C=function(w){var S=new XMLHttpRequest;return S.open("GET",w,!1),S.send(null),S.responseText},y&&(F=function(w){var S=new XMLHttpRequest;return S.open("GET",w,!1),S.responseType="arraybuffer",S.send(null),new Uint8Array(S.response)}),$=function(w,S,M){var G=new XMLHttpRequest;G.open("GET",w,!0),G.responseType="arraybuffer",G.onload=function(){if(G.status==200||G.status==0&&G.response){S(G.response);return}M()},G.onerror=M,G.send(null)}),O=function(w){document.title=w});b&&typeof performance=="undefined"&&(performance=AE().performance);var K=u.print||console.log.bind(console),j=u.printErr||console.warn.bind(console);for(d in p)p.hasOwnProperty(d)&&(u[d]=p[d]);p=null,u.arguments&&(h=u.arguments),u.thisProgram&&(m=u.thisProgram),u.quit&&(f=u.quit);var Y=Atomics.load,J=Atomics.store,ne=Atomics.compareExchange,Q;u.wasmBinary&&(Q=u.wasmBinary);var ie;u.noExitRuntime&&(ie=u.noExitRuntime),typeof WebAssembly!="object"&&j("no native wasm support detected");var ee,le=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),se,ce=0,de=0,fe=!1,xe=0;function be(w,S){w||pr("Assertion failed: "+S)}function Se(w){var S=u["_"+w];return be(S,"Cannot call unknown function "+w+", make sure it is exported"),S}function _e(w,S,M,G,me){var ue={string:function(qn){var Lr=0;if(qn!=null&&qn!==0){var ac=(qn.length<<2)+1;Lr=vo(ac),lt(qn,Lr,ac)}return Lr},array:function(qn){var Lr=vo(qn.length);return pt(qn,Lr),Lr}};function oe(qn){return S==="string"?Ve(qn):S==="boolean"?Boolean(qn):qn}var ke=Se(w),et=[],Ot=0;if(G)for(var on=0;on<G.length;on++){var ko=ue[M[on]];ko?(Ot===0&&(Ot=ec()),et[on]=ko(G[on])):et[on]=G[on]}var nc=ke.apply(null,et);return nc=oe(nc),Ot!==0&&wo(Ot),nc}function Me(w,S,M,G){M=M||[];var me=M.every(function(oe){return oe==="number"}),ue=S!=="string";return ue&&me&&!G?Se(w):function(){return _e(w,S,M,arguments,G)}}function Ke(w,S,M){for(var G=S+M,me="";!(S>=G);){var ue=w[S++];if(!ue)return me;if(!(ue&128)){me+=String.fromCharCode(ue);continue}var oe=w[S++]&63;if((ue&224)==192){me+=String.fromCharCode((ue&31)<<6|oe);continue}var ke=w[S++]&63;if((ue&240)==224?ue=(ue&15)<<12|oe<<6|ke:ue=(ue&7)<<18|oe<<12|ke<<6|w[S++]&63,ue<65536)me+=String.fromCharCode(ue);else{var et=ue-65536;me+=String.fromCharCode(55296|et>>10,56320|et&1023)}}return me}function Ve(w,S){return w?Ke(i(),w,S):""}function st(w,S,M,G){if(!(G>0))return 0;for(var me=M,ue=M+G-1,oe=0;oe<w.length;++oe){var ke=w.charCodeAt(oe);if(ke>=55296&&ke<=57343){var et=w.charCodeAt(++oe);ke=65536+((ke&1023)<<10)|et&1023}if(ke<=127){if(M>=ue)break;S[M++]=ke}else if(ke<=2047){if(M+1>=ue)break;S[M++]=192|ke>>6,S[M++]=128|ke&63}else if(ke<=65535){if(M+2>=ue)break;S[M++]=224|ke>>12,S[M++]=128|ke>>6&63,S[M++]=128|ke&63}else{if(M+3>=ue)break;S[M++]=240|ke>>18,S[M++]=128|ke>>12&63,S[M++]=128|ke>>6&63,S[M++]=128|ke&63}}return S[M]=0,M-me}function lt(w,S,M){return st(w,i(),S,M)}function He(w){for(var S=0,M=0;M<w.length;++M){var G=w.charCodeAt(M);G>=55296&&G<=57343&&(G=65536+((G&1023)<<10)|w.charCodeAt(++M)&1023),G<=127?++S:G<=2047?S+=2:G<=65535?S+=3:S+=4}return S}function pt(w,S){s().set(w,S)}var dt=65536;function Hn(w,S){return w%S>0&&(w+=S-w%S),w}var Ze,En,tn,An,sa,vn,un,ia,Va;function Fn(w){Ze=w,u.HEAP8=En=new Int8Array(w),u.HEAP16=An=new Int16Array(w),u.HEAP32=vn=new Int32Array(w),u.HEAPU8=tn=new Uint8Array(w),u.HEAPU16=sa=new Uint16Array(w),u.HEAPU32=un=new Uint32Array(w),u.HEAPF32=ia=new Float32Array(w),u.HEAPF64=Va=new Float64Array(w)}var co=5256464,zu=co,Ia=13584,oa=5256464,Na=12656,po=u.INITIAL_MEMORY||16777216;if(x)ee=u.wasmMemory,Ze=u.buffer;else if(u.wasmMemory)ee=u.wasmMemory;else if(ee=new WebAssembly.Memory({initial:po/dt,maximum:2147483648/dt,shared:!0}),!(ee.buffer instanceof SharedArrayBuffer))throw j("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");ee&&(Ze=ee.buffer),po=Ze.byteLength,Fn(Ze),x||(o()[Na>>2]=oa);function ho(w){for(;w.length>0;){var S=w.shift();if(typeof S=="function"){S(u);continue}var M=S.func;typeof M=="number"?S.arg===void 0?u.dynCall_v(M):u.dynCall_vi(M,S.arg):M(S.arg===void 0?null:S.arg)}}var xs=[],Bu=[],Ff=[],Wu=[],Gp=[],Vu=!1;x&&(Vu=!0);function la(){if(!x){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)Mf(u.preRun.shift());ho(xs)}}function Hp(){Vu=!0,ho(Bu)}function $f(){x||ho(Ff)}function Df(){if(!x){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)vs(u.postRun.shift());ho(Gp)}}function Mf(w){xs.unshift(w)}function vs(w){Gp.unshift(w)}var mo=Math.ceil,Rf=Math.floor,cr=0,Uu=null,ws=null;function Pf(w){be(!x,"addRunDependency cannot be used in a pthread worker"),cr++,u.monitorRunDependencies&&u.monitorRunDependencies(cr)}function Of(w){if(cr--,u.monitorRunDependencies&&u.monitorRunDependencies(cr),cr==0&&(Uu!==null&&(clearInterval(Uu),Uu=null),ws)){var S=ws;ws=null,S()}}u.preloadedImages={},u.preloadedAudios={};function pr(w){throw u.onAbort&&u.onAbort(w),x&&console.error("Pthread aborting at "+new Error().stack),w+="",K(w),j(w),fe=!0,xe=1,w="abort("+w+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(w)}function Gu(w,S){return String.prototype.startsWith?w.startsWith(S):w.indexOf(S)===0}var Lf="data:application/octet-stream;base64,";function jp(w){return Gu(w,Lf)}var zf="file://";function qp(w){return Gu(w,zf)}var ua="tfjs-backend-wasm-threaded-simd.wasm";jp(ua)||(ua=T(ua));function Kp(){try{if(Q)return new Uint8Array(Q);if(F)return F(ua);throw"both async and sync fetching of the wasm failed"}catch(w){pr(w)}}function Bf(){return!Q&&(g||y)&&typeof fetch=="function"&&!qp(ua)?fetch(ua,{credentials:"same-origin"}).then(function(w){if(!w.ok)throw"failed to load wasm binary file at '"+ua+"'";return w.arrayBuffer()}).catch(function(){return Kp()}):new Promise(function(w,S){w(Kp())})}function Wf(){var w={a:$g};function S(oe,ke){var et=oe.exports;if(u.asm=et,se=ke,!x){var Ot=ye.unusedWorkers.length;ye.unusedWorkers.forEach(function(on){ye.loadWasmModuleToWorker(on,function(){--Ot||Of("wasm-instantiate")})})}}x||Pf("wasm-instantiate");function M(oe){S(oe.instance,oe.module)}function G(oe){return Bf().then(function(ke){return WebAssembly.instantiate(ke,w)}).then(oe,function(ke){j("failed to asynchronously prepare wasm: "+ke),pr(ke)})}function me(){if(!Q&&typeof WebAssembly.instantiateStreaming=="function"&&!jp(ua)&&!qp(ua)&&typeof fetch=="function")fetch(ua,{credentials:"same-origin"}).then(function(oe){var ke=WebAssembly.instantiateStreaming(oe,w);return ke.then(M,function(et){j("wasm streaming compile failed: "+et),j("falling back to ArrayBuffer instantiation"),G(M)})});else return G(M)}if(u.instantiateWasm)try{var ue=u.instantiateWasm(w,S);return ue}catch(oe){return j("Module.instantiateWasm callback failed with error: "+oe),!1}return me(),{}}var Vf={};function Uf(){ye.initRuntime()}x||Bu.push({func:function(){qu()}});var Xp=0,Yp=0,Jp=0;function fo(w,S,M){w=w|0,S=S|0,M=M|0,Xp=w,Jp=S,Yp=M}u.__register_pthread_ptr=fo;var Hu={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},go=13568;function yo(w,S){if(w<=0||w>s().length||w&!0||S<0)return-28;if(S==0)return 0;S>=2147483647&&(S=Infinity);var M=Atomics.load(o(),go>>2),G=0;if(M==w){var me=Atomics.compareExchange(o(),go>>2,M,0);if(me==M&&(--S,G=1,S<=0))return 1}var ue=Atomics.notify(o(),w>>2,S);if(ue>=0)return ue+G;throw"Atomics.notify returned an unexpected value "+ue}u._emscripten_futex_wake=yo;function Gf(w){if(x)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!w)throw"Internal Error! Null pthread_ptr in _kill_thread!";o()[w+12>>2]=0;var S=ye.pthreads[w];S.worker.terminate(),ye.freeThreadData(S),ye.runningWorkers.splice(ye.runningWorkers.indexOf(S.worker),1),S.worker.pthread=void 0}function Hf(w){if(x)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!w)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var S=ye.pthreads[w];S.worker.postMessage({cmd:"cancel"})}function jf(w){if(x)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!w)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";o()[w+12>>2]=0;var S=ye.pthreads[w];if(S){var M=S.worker;ye.returnWorkerToPool(M)}}var ye={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){fo(ye.mainThreadBlock,!y,1),Dw(ye.mainThreadBlock)},initMainThreadBlock:function(){for(var w=8,S=0;S<w;++S)ye.allocateUnusedWorker();ye.mainThreadBlock=12816;for(var S=0;S<232/4;++S)l()[ye.mainThreadBlock/4+S]=0;o()[ye.mainThreadBlock+12>>2]=ye.mainThreadBlock;var M=ye.mainThreadBlock+156;o()[M>>2]=M;for(var G=13056,S=0;S<128;++S)l()[G/4+S]=0;Atomics.store(l(),ye.mainThreadBlock+104>>2,G),Atomics.store(l(),ye.mainThreadBlock+40>>2,ye.mainThreadBlock),Atomics.store(l(),ye.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(ye.exitHandlers!==null){for(;ye.exitHandlers.length>0;)ye.exitHandlers.pop()();ye.exitHandlers=null}x&&ce&&$w()},threadExit:function(w){var S=Ua();S&&(Atomics.store(l(),S+4>>2,w),Atomics.store(l(),S+0>>2,1),Atomics.store(l(),S+60>>2,1),Atomics.store(l(),S+64>>2,0),ye.runExitHandlers(),yo(S+0,2147483647),fo(0,0,0),ce=0,x&&postMessage({cmd:"exit"}))},threadCancel:function(){ye.runExitHandlers(),Atomics.store(l(),ce+4>>2,-1),Atomics.store(l(),ce+0>>2,1),yo(ce+0,2147483647),ce=de=0,fo(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var w in ye.pthreads){var S=ye.pthreads[w];S&&S.worker&&ye.returnWorkerToPool(S.worker)}ye.pthreads={};for(var M=0;M<ye.unusedWorkers.length;++M){var G=ye.unusedWorkers[M];G.terminate()}ye.unusedWorkers=[];for(var M=0;M<ye.runningWorkers.length;++M){var G=ye.runningWorkers[M],S=G.pthread;ye.freeThreadData(S),G.terminate()}ye.runningWorkers=[]},freeThreadData:function(w){if(w){if(w.threadInfoStruct){var S=o()[w.threadInfoStruct+104>>2];o()[w.threadInfoStruct+104>>2]=0,Qu(S),Qu(w.threadInfoStruct)}w.threadInfoStruct=0,w.allocatedOwnStack&&w.stackBase&&Qu(w.stackBase),w.stackBase=0,w.worker&&(w.worker.pthread=null)}},returnWorkerToPool:function(w){delete ye.pthreads[w.pthread.thread],ye.unusedWorkers.push(w),ye.runningWorkers.splice(ye.runningWorkers.indexOf(w),1),ye.freeThreadData(w.pthread),w.pthread=void 0},receiveObjectTransfer:function(w){},loadWasmModuleToWorker:function(w,S){w.onmessage=function(M){var G=M.data,me=G.cmd;if(w.pthread&&(ye.currentProxiedOperationCallerThread=w.pthread.threadInfoStruct),G.targetThread&&G.targetThread!=Ua()){var ue=ye.pthreads[G.targetThread];ue?ue.worker.postMessage(M.data,G.transferList):console.error('Internal error! Worker sent a message "'+me+'" to target pthread '+G.targetThread+", but that thread no longer exists!"),ye.currentProxiedOperationCallerThread=void 0;return}if(me==="processQueuedMainThreadWork")Rg();else if(me==="spawnThread")ad(M.data);else if(me==="cleanupThread")jf(G.thread);else if(me==="killThread")Gf(G.thread);else if(me==="cancelThread")Hf(G.thread);else if(me==="loaded")w.loaded=!0,S&&S(w),w.runPthread&&(w.runPthread(),delete w.runPthread);else if(me==="print")K("Thread "+G.threadId+": "+G.text);else if(me==="printErr")j("Thread "+G.threadId+": "+G.text);else if(me==="alert")alert("Thread "+G.threadId+": "+G.text);else if(me==="exit"){var oe=w.pthread&&Atomics.load(l(),w.pthread.thread+68>>2);oe&&ye.returnWorkerToPool(w)}else me==="cancelDone"?ye.returnWorkerToPool(w):me==="objectTransfer"?ye.receiveObjectTransfer(M.data):M.data.target==="setimmediate"?w.postMessage(M.data):j("worker sent an unknown command "+me);ye.currentProxiedOperationCallerThread=void 0},w.onerror=function(M){j("pthread sent an error! "+M.filename+":"+M.lineno+": "+M.message)},b&&(w.on("message",function(M){w.onmessage({data:M})}),w.on("error",function(M){w.onerror(M)}),w.on("exit",function(M){console.log("worker exited - TODO: update the worker queue?")})),w.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||a,wasmMemory:ee,wasmModule:se,DYNAMIC_BASE:oa,DYNAMICTOP_PTR:Na})},allocateUnusedWorker:function(){var w=T("tfjs-backend-wasm-threaded-simd.worker.js");ye.unusedWorkers.push(new Worker(w))},getNewWorker:function(){return ye.unusedWorkers.length==0&&(ye.allocateUnusedWorker(),ye.loadWasmModuleToWorker(ye.unusedWorkers[0])),ye.unusedWorkers.length>0?ye.unusedWorkers.pop():null},busySpinWait:function(w){for(var S=performance.now()+w;performance.now()<S;);}};function qf(w,S){co=zu=w,Ia=S,wo(w)}u.establishStackSpace=qf;function Kf(){return ie}u.getNoExitRuntime=Kf;function Xf(w,S,M,G){pr("Assertion failed: "+Ve(w)+", at: "+[S?Ve(S):"unknown filename",M,G?Ve(G):"unknown function"])}function Yf(w,S){var M=_main(w,S)}var ks;b?ks=function(){var w=process.hrtime();return w[0]*1e3+w[1]/1e6}:x?ks=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?ks=dateNow:ks=function(){return performance.now()};function Jf(w){return o()[Ew()>>2]=w,w}function Zf(w,S){if(x)return Rr(1,1,w,S);Wu.unshift({func:w,arg:S})}function Qf(w,S){if(w==S)postMessage({cmd:"processQueuedMainThreadWork"});else if(x)postMessage({targetThread:w,cmd:"processThreadQueue"});else{var M=ye.pthreads[w],G=M&&M.worker;if(!G)return;G.postMessage({cmd:"processThreadQueue"})}return 1}function eg(){pr()}function tg(w,S){w=w|0,S=S|0}function ng(w,S,M){if(w<=0||w>s().length||w&!0)return-28;if(y){var G=Atomics.wait(o(),w>>2,S,M);if(G==="timed-out")return-73;if(G==="not-equal")return-6;if(G==="ok")return 0;throw"Atomics.wait returned an unexpected value "+G}else{var me=Atomics.load(o(),w>>2);if(S!=me)return-6;var ue=performance.now(),oe=ue+M;Atomics.store(o(),go>>2,w);for(var ke=w;w==ke;){if(ue=performance.now(),ue>oe)return-73;Rg(),w=Atomics.load(o(),go>>2)}return 0}}function ag(){return Jp|0}function rg(){return Yp|0}function sg(w,S,M){i().copyWithin(w,S,S+M)}function ig(){return navigator.hardwareConcurrency}function Rr(w,S){for(var M=arguments.length-2,G=ec(),me=vo(M*8),ue=me>>3,oe=0;oe<M;oe++)c()[ue+oe]=arguments[2+oe];var ke=Rw(w,M,me,S);return wo(G),ke}var Is=[];function bo(w,S){bo.array||(bo.array=[]);var M=bo.array;M.length=0;for(var G;G=i()[w++];)G===100||G===102?(S=S+7&~7,M.push(c()[S>>3]),S+=8):(S=S+3&~3,M.push(o()[S>>2]),S+=4);return M}function og(w,S,M){Is.length=S;for(var G=M>>3,me=0;me<S;me++)Is[me]=c()[G+me];var ue=w<0,oe=ue?Vf[-w-1]:Fg[w];if(ue){var ke=Is[1],et=Is[2],Ot=bo(ke,et);return oe.apply(null,Ot)}return oe.apply(null,Is)}function lg(){return i().length}function ug(w){try{return ee.grow(w-Ze.byteLength+65535>>>16),Fn(ee.buffer),1}catch(S){}}function cg(w){w=w>>>0;var S=lg();if(w<=S)return!1;var M=65536,G=2147483648;if(w>G)return!1;for(var me=16777216,ue=1;ue<=4;ue*=2){var oe=S*(1+.2/ue);oe=Math.min(oe,w+100663296);var ke=Math.min(G,Hn(Math.max(me,w,oe),M)),et=ug(ke);if(et)return!0}return!1}var Pe={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var w=Pe.eventHandlers.length-1;w>=0;--w)Pe._removeHandler(w);Pe.eventHandlers=[],Pe.deferredCalls=[]},registerRemoveEventListeners:function(){Pe.removeEventListenersRegistered||(Wu.push(Pe.removeAllEventListeners),Pe.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(w,S,M){function G(oe,ke){if(oe.length!=ke.length)return!1;for(var et in oe)if(oe[et]!=ke[et])return!1;return!0}for(var me in Pe.deferredCalls){var ue=Pe.deferredCalls[me];if(ue.targetFunction==w&&G(ue.argsList,M))return}Pe.deferredCalls.push({targetFunction:w,precedence:S,argsList:M}),Pe.deferredCalls.sort(function(oe,ke){return oe.precedence<ke.precedence})},removeDeferredCalls:function(w){for(var S=0;S<Pe.deferredCalls.length;++S)Pe.deferredCalls[S].targetFunction==w&&(Pe.deferredCalls.splice(S,1),--S)},canPerformEventHandlerRequests:function(){return Pe.inEventHandler&&Pe.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Pe.canPerformEventHandlerRequests())for(var w=0;w<Pe.deferredCalls.length;++w){var S=Pe.deferredCalls[w];Pe.deferredCalls.splice(w,1),--w,S.targetFunction.apply(null,S.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(w,S){for(var M=0;M<Pe.eventHandlers.length;++M)Pe.eventHandlers[M].target==w&&(!S||S==Pe.eventHandlers[M].eventTypeString)&&Pe._removeHandler(M--)},_removeHandler:function(w){var S=Pe.eventHandlers[w];S.target.removeEventListener(S.eventTypeString,S.eventListenerFunc,S.useCapture),Pe.eventHandlers.splice(w,1)},registerOrRemoveHandler:function(w){var S=function(G){++Pe.inEventHandler,Pe.currentEventHandler=w,Pe.runDeferredCalls(),w.handlerFunc(G),Pe.runDeferredCalls(),--Pe.inEventHandler};if(w.callbackfunc)w.eventListenerFunc=S,w.target.addEventListener(w.eventTypeString,S,w.useCapture),Pe.eventHandlers.push(w),Pe.registerRemoveEventListeners();else for(var M=0;M<Pe.eventHandlers.length;++M)Pe.eventHandlers[M].target==w.target&&Pe.eventHandlers[M].eventTypeString==w.eventTypeString&&Pe._removeHandler(M--)},queueEventHandlerOnThread_iiii:function(w,S,M,G,me){var ue=ec(),oe=vo(12);o()[oe>>2]=M,o()[oe+4>>2]=G,o()[oe+8>>2]=me,Pg(w,637534208,S,G,oe),wo(ue)},getTargetThreadForEventCallback:function(w){switch(w){case 1:return 0;case 2:return ye.currentProxiedOperationCallerThread;default:return w}},getNodeNameForTarget:function(w){return w?w==window?"#window":w==screen?"#screen":w&&w.nodeName?w.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function pg(w){var S=He(w)+1,M=Zu(S);return lt(w,M,S),M}function dg(w,S,M,G){var me=ec(),ue=vo(12),oe=0;S&&(oe=pg(S)),o()[ue>>2]=oe,o()[ue+4>>2]=M,o()[ue+8>>2]=G,Pg(w,657457152,0,oe,ue),wo(me)}function hg(w,S,M,G){S=S?Ve(S):"",dg(w,S,M,G)}function mg(w){return w>2?Ve(w):w}var fg=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function gg(w){w=mg(w);var S=fg[w]||(typeof document!="undefined"?document.querySelector(w):void 0);return S}function ju(w){return gg(w)}function Zp(w,S,M){var G=ju(w);if(!G)return-4;if(G.canvasSharedPtr&&(o()[G.canvasSharedPtr>>2]=S,o()[G.canvasSharedPtr+4>>2]=M),G.offscreenCanvas||!G.controlTransferredOffscreen){G.offscreenCanvas&&(G=G.offscreenCanvas);var me=!1;if(G.GLctxObject&&G.GLctxObject.GLctx){var ue=G.GLctxObject.GLctx.getParameter(2978);me=ue[0]===0&&ue[1]===0&&ue[2]===G.width&&ue[3]===G.height}G.width=S,G.height=M,me&&G.GLctxObject.GLctx.viewport(0,0,S,M)}else if(G.canvasSharedPtr){var oe=o()[G.canvasSharedPtr+8>>2];return hg(oe,w,S,M),1}else return-4;return 0}function Qp(w,S,M){return x?Rr(2,1,w,S,M):Zp(w,S,M)}function yg(w,S,M){var G=ju(w);return G?Zp(w,S,M):Qp(w,S,M)}function bg(w){w=w|0}function xg(w,S){w=w|0,S=S|0}function vg(w){var S=w.getExtension("ANGLE_instanced_arrays");if(S)return w.vertexAttribDivisor=function(M,G){S.vertexAttribDivisorANGLE(M,G)},w.drawArraysInstanced=function(M,G,me,ue){S.drawArraysInstancedANGLE(M,G,me,ue)},w.drawElementsInstanced=function(M,G,me,ue,oe){S.drawElementsInstancedANGLE(M,G,me,ue,oe)},1}function wg(w){var S=w.getExtension("OES_vertex_array_object");if(S)return w.createVertexArray=function(){return S.createVertexArrayOES()},w.deleteVertexArray=function(M){S.deleteVertexArrayOES(M)},w.bindVertexArray=function(M){S.bindVertexArrayOES(M)},w.isVertexArray=function(M){return S.isVertexArrayOES(M)},1}function kg(w){var S=w.getExtension("WEBGL_draw_buffers");if(S)return w.drawBuffers=function(M,G){S.drawBuffersWEBGL(M,G)},1}var Be={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var w=new Float32Array(Be.MINI_TEMP_BUFFER_SIZE),S=0;S<Be.MINI_TEMP_BUFFER_SIZE;S++)Be.miniTempBufferFloatViews[S]=w.subarray(0,S+1);for(var M=new Int32Array(Be.MINI_TEMP_BUFFER_SIZE),S=0;S<Be.MINI_TEMP_BUFFER_SIZE;S++)Be.miniTempBufferIntViews[S]=M.subarray(0,S+1)},recordError:function(w){Be.lastError||(Be.lastError=w)},getNewId:function(w){for(var S=Be.counter++,M=w.length;M<S;M++)w[M]=null;return S},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(w,S,M,G){for(var me="",ue=0;ue<S;++ue){var oe=G?o()[G+ue*4>>2]:-1;me+=Ve(o()[M+ue*4>>2],oe<0?void 0:oe)}return me},createContext:function(w,S){var M=w.getContext("webgl",S);if(!M)return 0;var G=Be.registerContext(M,S);return G},registerContext:function(w,S){var M=Zu(8);o()[M+4>>2]=Ua();var G={handle:M,attributes:S,version:S.majorVersion,GLctx:w};return w.canvas&&(w.canvas.GLctxObject=G),Be.contexts[M]=G,(typeof S.enableExtensionsByDefault=="undefined"||S.enableExtensionsByDefault)&&Be.initExtensions(G),M},makeContextCurrent:function(w){return Be.currentContext=Be.contexts[w],u.ctx=Pr=Be.currentContext&&Be.currentContext.GLctx,!(w&&!Pr)},getContext:function(w){return Be.contexts[w]},deleteContext:function(w){Be.currentContext===Be.contexts[w]&&(Be.currentContext=null),typeof Pe=="object"&&Pe.removeAllHandlersOnTarget(Be.contexts[w].GLctx.canvas),Be.contexts[w]&&Be.contexts[w].GLctx.canvas&&(Be.contexts[w].GLctx.canvas.GLctxObject=void 0),Qu(Be.contexts[w].handle),Be.contexts[w]=null},initExtensions:function(w){if(w||(w=Be.currentContext),!w.initExtensionsDone){w.initExtensionsDone=!0;var S=w.GLctx;vg(S),wg(S),kg(S),S.disjointTimerQueryExt=S.getExtension("EXT_disjoint_timer_query");var M=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],G=S.getSupportedExtensions()||[];G.forEach(function(me){M.indexOf(me)!=-1&&S.getExtension(me)})}},populateUniformTable:function(w){for(var S=Be.programs[w],M=Be.programInfos[w]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},G=M.uniforms,me=Pr.getProgramParameter(S,35718),ue=0;ue<me;++ue){var oe=Pr.getActiveUniform(S,ue),ke=oe.name;M.maxUniformLength=Math.max(M.maxUniformLength,ke.length+1),ke.slice(-1)=="]"&&(ke=ke.slice(0,ke.lastIndexOf("[")));var et=Pr.getUniformLocation(S,ke);if(et){var Ot=Be.getNewId(Be.uniforms);G[ke]=[oe.size,Ot],Be.uniforms[Ot]=et;for(var on=1;on<oe.size;++on){var ko=ke+"["+on+"]";et=Pr.getUniformLocation(S,ko),Ot=Be.getNewId(Be.uniforms),Be.uniforms[Ot]=et}}}}},Ig=["default","low-power","high-performance"];function Ng(w,S){var M={},G=S>>2;M.alpha=!!o()[G+(0>>2)],M.depth=!!o()[G+(4>>2)],M.stencil=!!o()[G+(8>>2)],M.antialias=!!o()[G+(12>>2)],M.premultipliedAlpha=!!o()[G+(16>>2)],M.preserveDrawingBuffer=!!o()[G+(20>>2)];var me=o()[G+(24>>2)];M.powerPreference=Ig[me],M.failIfMajorPerformanceCaveat=!!o()[G+(28>>2)],M.majorVersion=o()[G+(32>>2)],M.minorVersion=o()[G+(36>>2)],M.enableExtensionsByDefault=o()[G+(40>>2)],M.explicitSwapControl=o()[G+(44>>2)],M.proxyContextToMainThread=o()[G+(48>>2)],M.renderViaOffscreenBackBuffer=o()[G+(52>>2)];var ue=ju(w);if(!ue)return-4;if(M.explicitSwapControl)return-1;var oe=Be.createContext(ue,M);return oe}function Tg(w,S){return Ng(w,S)}var Ns={splitPath:function(w){var S=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return S.exec(w).slice(1)},normalizeArray:function(w,S){for(var M=0,G=w.length-1;G>=0;G--){var me=w[G];me==="."?w.splice(G,1):me===".."?(w.splice(G,1),M++):M&&(w.splice(G,1),M--)}if(S)for(;M;M--)w.unshift("..");return w},normalize:function(w){var S=w.charAt(0)==="/",M=w.substr(-1)==="/";return w=Ns.normalizeArray(w.split("/").filter(function(G){return!!G}),!S).join("/"),!w&&!S&&(w="."),w&&M&&(w+="/"),(S?"/":"")+w},dirname:function(w){var S=Ns.splitPath(w),M=S[0],G=S[1];return!M&&!G?".":(G&&(G=G.substr(0,G.length-1)),M+G)},basename:function(w){if(w==="/")return"/";var S=w.lastIndexOf("/");return S===-1?w:w.substr(S+1)},extname:function(w){return Ns.splitPath(w)[3]},join:function(){var w=Array.prototype.slice.call(arguments,0);return Ns.normalize(w.join("/"))},join2:function(w,S){return Ns.normalize(w+"/"+S)}},xo={mappings:{},buffers:[null,[],[]],printChar:function(w,S){var M=xo.buffers[w];S===0||S===10?((w===1?K:j)(Ke(M,0)),M.length=0):M.push(S)},varargs:void 0,get:function(){xo.varargs+=4;var w=o()[xo.varargs-4>>2];return w},getStr:function(w){var S=Ve(w);return S},get64:function(w,S){return w}};function ed(w){return x?Rr(3,1,w):0}function td(w,S,M,G,me){if(x)return Rr(4,1,w,S,M,G,me)}function nd(w,S,M,G){if(x)return Rr(5,1,w,S,M,G);for(var me=0,ue=0;ue<M;ue++){for(var oe=o()[S+ue*8>>2],ke=o()[S+(ue*8+4)>>2],et=0;et<ke;et++)xo.printChar(w,i()[oe+et]);me+=ke}return o()[G>>2]=me,0}function Sg(w){var S=ye.exitHandlers.pop();w&&S()}function Cg(w,S){ye.exitHandlers===null&&(ye.exitHandlers=[]),ye.exitHandlers.push(function(){Pw(w,S)})}function ad(w){if(x)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var S=ye.getNewWorker();if(S.pthread!==void 0)throw"Internal error!";if(!w.pthread_ptr)throw"Internal error, no pthread ptr!";ye.runningWorkers.push(S);for(var M=Zu(128*4),G=0;G<128;++G)o()[M+G*4>>2]=0;var me=w.stackBase+w.stackSize,ue=ye.pthreads[w.pthread_ptr]={worker:S,stackBase:w.stackBase,stackSize:w.stackSize,allocatedOwnStack:w.allocatedOwnStack,thread:w.pthread_ptr,threadInfoStruct:w.pthread_ptr},oe=ue.threadInfoStruct>>2;Atomics.store(l(),oe+(0>>2),0),Atomics.store(l(),oe+(4>>2),0),Atomics.store(l(),oe+(8>>2),0),Atomics.store(l(),oe+(68>>2),w.detached),Atomics.store(l(),oe+(104>>2),M),Atomics.store(l(),oe+(48>>2),0),Atomics.store(l(),oe+(40>>2),ue.threadInfoStruct),Atomics.store(l(),oe+(44>>2),42),Atomics.store(l(),oe+(108>>2),w.stackSize),Atomics.store(l(),oe+(84>>2),w.stackSize),Atomics.store(l(),oe+(80>>2),me),Atomics.store(l(),oe+(108+8>>2),me),Atomics.store(l(),oe+(108+12>>2),w.detached),Atomics.store(l(),oe+(108+20>>2),w.schedPolicy),Atomics.store(l(),oe+(108+24>>2),w.schedPrio);var ke=Aw(),et=ke+40;Atomics.store(l(),oe+(176>>2),et),S.pthread=ue;var Ot={cmd:"run",start_routine:w.startRoutine,arg:w.arg,threadInfoStruct:w.pthread_ptr,selfThreadId:w.pthread_ptr,parentThreadId:w.parent_pthread_ptr,stackBase:w.stackBase,stackSize:w.stackSize};S.runPthread=function(){Ot.time=performance.now(),S.postMessage(Ot,w.transferList)},S.loaded&&(S.runPthread(),delete S.runPthread)}function _g(w,S,M){if(!S&&!M)return Hu.EINVAL;if(!w)return j("pthread_getschedparam called with a null thread pointer!"),Hu.ESRCH;var G=o()[w+12>>2];if(G!==w)return j("pthread_getschedparam attempted on thread "+w+", which does not point to a valid thread, or does not exist anymore!"),Hu.ESRCH;var me=Atomics.load(l(),w+108+20>>2),ue=Atomics.load(l(),w+108+24>>2);return S&&(o()[S>>2]=me),M&&(o()[M>>2]=ue),0}function Ua(){return Xp|0}u._pthread_self=Ua;function Eg(w,S,M,G){if(typeof SharedArrayBuffer=="undefined")return j("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!w)return j("pthread_create called with a null thread pointer!"),28;var me=[],ue=0;if(x&&(me.length===0||ue))return Mw(687865856,w,S,M,G);if(ue)return ue;var oe=0,ke=0,et=0,Ot=0,on=0;if(S){oe=o()[S>>2],oe+=81920,ke=o()[S+8>>2],et=o()[S+12>>2]!==0;var ko=o()[S+16>>2]===0;if(ko){var nc=o()[S+20>>2],qn=o()[S+24>>2],Lr=ye.currentProxiedOperationCallerThread?ye.currentProxiedOperationCallerThread:Ua();_g(Lr,S+20,S+24),Ot=o()[S+20>>2],on=o()[S+24>>2],o()[S+20>>2]=nc,o()[S+24>>2]=qn}else Ot=o()[S+20>>2],on=o()[S+24>>2]}else oe=2097152;var ac=ke==0;ac?ke=Fw(16,oe):(ke-=oe,be(ke>0));for(var Io=Zu(232),Lg=0;Lg<232>>2;++Lg)l()[(Io>>2)+Lg]=0;o()[w>>2]=Io,o()[Io+12>>2]=Io;var Lw=Io+156;o()[Lw>>2]=Lw;var zg={stackBase:ke,stackSize:oe,allocatedOwnStack:ac,schedPolicy:Ot,schedPrio:on,detached:et,startRoutine:M,pthread_ptr:Io,parent_pthread_ptr:Ua(),arg:G,transferList:me};return x?(zg.cmd="spawnThread",postMessage(zg,me)):ad(zg),0}function Ag(w){return w=+w,w>=0?+Rf(w+.5):+mo(w-.5)}function rd(w){if(x)return Rr(6,1,w);switch(w){case 30:return 16384;case 85:var S=2147483648;return S/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 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79: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: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 Jf(28),-1}x?ye.initWorker():ye.initMainThreadBlock();var Pr;Be.init();var Fg=[null,Zf,Qp,ed,td,nd,rd],$g={e:Xf,r:Yf,w:Qf,a:eg,l:tg,d:ng,c:yo,h:ks,g:ag,x:rg,q:sg,B:ig,t:og,A:cg,u:yg,k:bg,s:xg,v:Tg,m:ed,o:td,i:nd,p:Uf,memory:ee||u.wasmMemory,y:Sg,z:Cg,j:Eg,b:Ua,f:Ag,n:rd,table:le},sd=Wf();u.asm=sd;var qu=u.___wasm_call_ctors=function(){return(qu=u.___wasm_call_ctors=u.asm.C).apply(null,arguments)},Ku=u._init=function(){return(Ku=u._init=u.asm.D).apply(null,arguments)},id=u._register_tensor=function(){return(id=u._register_tensor=u.asm.E).apply(null,arguments)},Ts=u._dispose_data=function(){return(Ts=u._dispose_data=u.asm.F).apply(null,arguments)},Xu=u._dispose=function(){return(Xu=u._dispose=u.asm.G).apply(null,arguments)},Dg=u._Abs=function(){return(Dg=u._Abs=u.asm.H).apply(null,arguments)},Mg=u._Add=function(){return(Mg=u._Add=u.asm.I).apply(null,arguments)},Yu=u._AddN=function(){return(Yu=u._AddN=u.asm.J).apply(null,arguments)},od=u._ArgMax=function(){return(od=u._ArgMax=u.asm.K).apply(null,arguments)},ld=u._AvgPool=function(){return(ld=u._AvgPool=u.asm.L).apply(null,arguments)},U=u._BatchMatMul=function(){return(U=u._BatchMatMul=u.asm.M).apply(null,arguments)},ae=u._ClipByValue=function(){return(ae=u._ClipByValue=u.asm.N).apply(null,arguments)},Ne=u._Conv2D=function(){return(Ne=u._Conv2D=u.asm.O).apply(null,arguments)},Ee=u._Conv2DBackpropInput=function(){return(Ee=u._Conv2DBackpropInput=u.asm.P).apply(null,arguments)},at=u._Cos=function(){return(at=u._Cos=u.asm.Q).apply(null,arguments)},Et=u._CropAndResize=function(){return(Et=u._CropAndResize=u.asm.R).apply(null,arguments)},Xe=u._Cumsum=function(){return(Xe=u._Cumsum=u.asm.S).apply(null,arguments)},je=u._DepthToSpace=function(){return(je=u._DepthToSpace=u.asm.T).apply(null,arguments)},Ht=u._DepthwiseConv2dNative=function(){return(Ht=u._DepthwiseConv2dNative=u.asm.U).apply(null,arguments)},dr=u._Equal=function(){return(dr=u._Equal=u.asm.V).apply(null,arguments)},hr=u._Exp=function(){return(hr=u._Exp=u.asm.W).apply(null,arguments)},ud=u._FlipLeftRight=function(){return(ud=u._FlipLeftRight=u.asm.X).apply(null,arguments)},Ju=u._Floor=function(){return(Ju=u._Floor=u.asm.Y).apply(null,arguments)},jn=u._FloorDiv=function(){return(jn=u._FloorDiv=u.asm.Z).apply(null,arguments)},Or=u._FusedBatchNorm=function(){return(Or=u._FusedBatchNorm=u.asm._).apply(null,arguments)},cd=u._FusedConv2D=function(){return(cd=u._FusedConv2D=u.asm.$).apply(null,arguments)},HC=u._FusedDepthwiseConv2D=function(){return(HC=u._FusedDepthwiseConv2D=u.asm.aa).apply(null,arguments)},jC=u._Gather=function(){return(jC=u._Gather=u.asm.ba).apply(null,arguments)},qC=u._GatherNd=function(){return(qC=u._GatherNd=u.asm.ca).apply(null,arguments)},KC=u._Greater=function(){return(KC=u._Greater=u.asm.da).apply(null,arguments)},XC=u._GreaterEqual=function(){return(XC=u._GreaterEqual=u.asm.ea).apply(null,arguments)},YC=u._LeakyRelu=function(){return(YC=u._LeakyRelu=u.asm.fa).apply(null,arguments)},JC=u._Less=function(){return(JC=u._Less=u.asm.ga).apply(null,arguments)},ZC=u._LessEqual=function(){return(ZC=u._LessEqual=u.asm.ha).apply(null,arguments)},QC=u._Log=function(){return(QC=u._Log=u.asm.ia).apply(null,arguments)},e_=u._LogicalAnd=function(){return(e_=u._LogicalAnd=u.asm.ja).apply(null,arguments)},t_=u._Max=function(){return(t_=u._Max=u.asm.ka).apply(null,arguments)},n_=u._MaxPool=function(){return(n_=u._MaxPool=u.asm.la).apply(null,arguments)},a_=u._Maximum=function(){return(a_=u._Maximum=u.asm.ma).apply(null,arguments)},r_=u._Mean=function(){return(r_=u._Mean=u.asm.na).apply(null,arguments)},s_=u._Min=function(){return(s_=u._Min=u.asm.oa).apply(null,arguments)},i_=u._Minimum=function(){return(i_=u._Minimum=u.asm.pa).apply(null,arguments)},o_=u._Multiply=function(){return(o_=u._Multiply=u.asm.qa).apply(null,arguments)},l_=u._Neg=function(){return(l_=u._Neg=u.asm.ra).apply(null,arguments)},u_=u._NonMaxSuppressionV3=function(){return(u_=u._NonMaxSuppressionV3=u.asm.sa).apply(null,arguments)},c_=u._NonMaxSuppressionV4=function(){return(c_=u._NonMaxSuppressionV4=u.asm.ta).apply(null,arguments)},p_=u._NonMaxSuppressionV5=function(){return(p_=u._NonMaxSuppressionV5=u.asm.ua).apply(null,arguments)},d_=u._NotEqual=function(){return(d_=u._NotEqual=u.asm.va).apply(null,arguments)},h_=u._OneHot=function(){return(h_=u._OneHot=u.asm.wa).apply(null,arguments)},m_=u._PadV2=function(){return(m_=u._PadV2=u.asm.xa).apply(null,arguments)},f_=u._Pow=function(){return(f_=u._Pow=u.asm.ya).apply(null,arguments)},g_=u._Prelu=function(){return(g_=u._Prelu=u.asm.za).apply(null,arguments)},y_=u._Prod=function(){return(y_=u._Prod=u.asm.Aa).apply(null,arguments)},b_=u._RealDiv=function(){return(b_=u._RealDiv=u.asm.Ba).apply(null,arguments)},x_=u._Relu=function(){return(x_=u._Relu=u.asm.Ca).apply(null,arguments)},v_=u._Relu6=function(){return(v_=u._Relu6=u.asm.Da).apply(null,arguments)},w_=u._ResizeBilinear=function(){return(w_=u._ResizeBilinear=u.asm.Ea).apply(null,arguments)},k_=u._Reverse=function(){return(k_=u._Reverse=u.asm.Fa).apply(null,arguments)},I_=u._RotateWithOffset=function(){return(I_=u._RotateWithOffset=u.asm.Ga).apply(null,arguments)},N_=u._Round=function(){return(N_=u._Round=u.asm.Ha).apply(null,arguments)},T_=u._Rsqrt=function(){return(T_=u._Rsqrt=u.asm.Ia).apply(null,arguments)},S_=u._ScatterNd=function(){return(S_=u._ScatterNd=u.asm.Ja).apply(null,arguments)},C_=u._SelectV2=function(){return(C_=u._SelectV2=u.asm.Ka).apply(null,arguments)},__=u._Sigmoid=function(){return(__=u._Sigmoid=u.asm.La).apply(null,arguments)},E_=u._Sin=function(){return(E_=u._Sin=u.asm.Ma).apply(null,arguments)},A_=u._Softmax=function(){return(A_=u._Softmax=u.asm.Na).apply(null,arguments)},F_=u._Sqrt=function(){return(F_=u._Sqrt=u.asm.Oa).apply(null,arguments)},$_=u._Square=function(){return($_=u._Square=u.asm.Pa).apply(null,arguments)},D_=u._SquaredDifference=function(){return(D_=u._SquaredDifference=u.asm.Qa).apply(null,arguments)},M_=u._Step=function(){return(M_=u._Step=u.asm.Ra).apply(null,arguments)},R_=u._StridedSlice=function(){return(R_=u._StridedSlice=u.asm.Sa).apply(null,arguments)},P_=u._Sub=function(){return(P_=u._Sub=u.asm.Ta).apply(null,arguments)},O_=u._Sum=function(){return(O_=u._Sum=u.asm.Ua).apply(null,arguments)},L_=u._Tanh=function(){return(L_=u._Tanh=u.asm.Va).apply(null,arguments)},z_=u._Tile=function(){return(z_=u._Tile=u.asm.Wa).apply(null,arguments)},B_=u._TopK=function(){return(B_=u._TopK=u.asm.Xa).apply(null,arguments)},W_=u._Transpose=function(){return(W_=u._Transpose=u.asm.Ya).apply(null,arguments)},V_=u.__FusedMatMul=function(){return(V_=u.__FusedMatMul=u.asm.Za).apply(null,arguments)},Zu=u._malloc=function(){return(Zu=u._malloc=u.asm._a).apply(null,arguments)},Qu=u._free=function(){return(Qu=u._free=u.asm.$a).apply(null,arguments)},Ew=u.___errno_location=function(){return(Ew=u.___errno_location=u.asm.ab).apply(null,arguments)},Aw=u._emscripten_get_global_libc=function(){return(Aw=u._emscripten_get_global_libc=u.asm.bb).apply(null,arguments)},U_=u.___em_js__initPthreadsJS=function(){return(U_=u.___em_js__initPthreadsJS=u.asm.cb).apply(null,arguments)},Fw=u._memalign=function(){return(Fw=u._memalign=u.asm.db).apply(null,arguments)},$w=u.___pthread_tsd_run_dtors=function(){return($w=u.___pthread_tsd_run_dtors=u.asm.eb).apply(null,arguments)},Rg=u._emscripten_main_thread_process_queued_calls=function(){return(Rg=u._emscripten_main_thread_process_queued_calls=u.asm.fb).apply(null,arguments)},G_=u._emscripten_current_thread_process_queued_calls=function(){return(G_=u._emscripten_current_thread_process_queued_calls=u.asm.gb).apply(null,arguments)},Dw=u._emscripten_register_main_browser_thread_id=function(){return(Dw=u._emscripten_register_main_browser_thread_id=u.asm.hb).apply(null,arguments)},H_=u._emscripten_main_browser_thread_id=function(){return(H_=u._emscripten_main_browser_thread_id=u.asm.ib).apply(null,arguments)},j_=u._emscripten_async_run_in_main_thread=function(){return(j_=u._emscripten_async_run_in_main_thread=u.asm.jb).apply(null,arguments)},q_=u._emscripten_sync_run_in_main_thread=function(){return(q_=u._emscripten_sync_run_in_main_thread=u.asm.kb).apply(null,arguments)},K_=u._emscripten_sync_run_in_main_thread_0=function(){return(K_=u._emscripten_sync_run_in_main_thread_0=u.asm.lb).apply(null,arguments)},X_=u._emscripten_sync_run_in_main_thread_1=function(){return(X_=u._emscripten_sync_run_in_main_thread_1=u.asm.mb).apply(null,arguments)},Y_=u._emscripten_sync_run_in_main_thread_2=function(){return(Y_=u._emscripten_sync_run_in_main_thread_2=u.asm.nb).apply(null,arguments)},J_=u._emscripten_sync_run_in_main_thread_xprintf_varargs=function(){return(J_=u._emscripten_sync_run_in_main_thread_xprintf_varargs=u.asm.ob).apply(null,arguments)},Z_=u._emscripten_sync_run_in_main_thread_3=function(){return(Z_=u._emscripten_sync_run_in_main_thread_3=u.asm.pb).apply(null,arguments)},Mw=u._emscripten_sync_run_in_main_thread_4=function(){return(Mw=u._emscripten_sync_run_in_main_thread_4=u.asm.qb).apply(null,arguments)},Q_=u._emscripten_sync_run_in_main_thread_5=function(){return(Q_=u._emscripten_sync_run_in_main_thread_5=u.asm.rb).apply(null,arguments)},eE=u._emscripten_sync_run_in_main_thread_6=function(){return(eE=u._emscripten_sync_run_in_main_thread_6=u.asm.sb).apply(null,arguments)},tE=u._emscripten_sync_run_in_main_thread_7=function(){return(tE=u._emscripten_sync_run_in_main_thread_7=u.asm.tb).apply(null,arguments)},Rw=u._emscripten_run_in_main_runtime_thread_js=function(){return(Rw=u._emscripten_run_in_main_runtime_thread_js=u.asm.ub).apply(null,arguments)},Pg=u._emscripten_async_queue_on_thread_=function(){return(Pg=u._emscripten_async_queue_on_thread_=u.asm.vb).apply(null,arguments)},nE=u._emscripten_tls_init=function(){return(nE=u._emscripten_tls_init=u.asm.wb).apply(null,arguments)},ec=u.stackSave=function(){return(ec=u.stackSave=u.asm.xb).apply(null,arguments)},vo=u.stackAlloc=function(){return(vo=u.stackAlloc=u.asm.yb).apply(null,arguments)},wo=u.stackRestore=function(){return(wo=u.stackRestore=u.asm.zb).apply(null,arguments)},Pw=u.dynCall_vi=function(){return(Pw=u.dynCall_vi=u.asm.Ab).apply(null,arguments)},aE=u.dynCall_v=function(){return(aE=u.dynCall_v=u.asm.Bb).apply(null,arguments)},rE=u.dynCall_ii=function(){return(rE=u.dynCall_ii=u.asm.Cb).apply(null,arguments)};u.asm=sd,u.cwrap=Me,u.PThread=ye,u.PThread=ye,u._pthread_self=Ua,u.wasmMemory=ee,u.ExitStatus=Ow;var tc;u.then=function(w){if(tc)w(u);else{var S=u.onRuntimeInitialized;u.onRuntimeInitialized=function(){S&&S(),w(u)}}return u};function Ow(w){this.name="ExitStatus",this.message="Program terminated with exit("+w+")",this.status=w}ws=function w(){tc||Og(),tc||(ws=w)};function Og(w){if(w=w||h,cr>0||(la(),cr>0))return;function S(){tc||(tc=!0,u.calledRun=!0,!fe&&(Hp(),$f(),u.onRuntimeInitialized&&u.onRuntimeInitialized(),Df()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),S()},1)):S()}if(u.run=Og,u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return x||(ie=!0),x||Og(),r}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}),$E=cn((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;for(o in s)s.hasOwnProperty(o)&&(i[o]=s[o]);var l=[],c="./this.program",u=function(U,ae){throw ae},p=!1,d=!1,h=!1,m=!1;p=typeof window=="object",d=typeof importScripts=="function",h=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",m=!p&&!h&&!d;var f="";function g(U){return s.locateFile?s.locateFile(U,f):f+U}var y,b,v,x,N,T;h?(d?f=rc().dirname(f)+"/":f=__dirname+"/",y=function(U,ae){return N||(N=require("fs")),T||(T=rc()),U=T.normalize(U),N.readFileSync(U,ae?null:"utf8")},v=function(U){var ae=y(U,!0);return ae.buffer||(ae=new Uint8Array(ae)),j(ae.buffer),ae},process.argv.length>1&&(c=process.argv[1].replace(/\\/g,"/")),l=process.argv.slice(2),process.on("uncaughtException",function(U){if(!(U instanceof Xu))throw U}),process.on("unhandledRejection",xs),u=function(U){process.exit(U)},s.inspect=function(){return"[Emscripten Module object]"}):m?(typeof read!="undefined"&&(y=function(U){return read(U)}),v=function(U){var ae;return typeof readbuffer=="function"?new Uint8Array(readbuffer(U)):(ae=read(U,"binary"),j(typeof ae=="object"),ae)},typeof scriptArgs!="undefined"?l=scriptArgs:typeof arguments!="undefined"&&(l=arguments),typeof quit=="function"&&(u=function(U){quit(U)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(p||d)&&(d?f=self.location.href:document.currentScript&&(f=document.currentScript.src),a&&(f=a),f.indexOf("blob:")!==0?f=f.substr(0,f.lastIndexOf("/")+1):f="",y=function(U){var ae=new XMLHttpRequest;return ae.open("GET",U,!1),ae.send(null),ae.responseText},d&&(v=function(U){var ae=new XMLHttpRequest;return ae.open("GET",U,!1),ae.responseType="arraybuffer",ae.send(null),new Uint8Array(ae.response)}),b=function(U,ae,Ne){var Ee=new XMLHttpRequest;Ee.open("GET",U,!0),Ee.responseType="arraybuffer",Ee.onload=function(){if(Ee.status==200||Ee.status==0&&Ee.response){ae(Ee.response);return}Ne()},Ee.onerror=Ne,Ee.send(null)},x=function(U){document.title=U});var C=s.print||console.log.bind(console),$=s.printErr||console.warn.bind(console);for(o in i)i.hasOwnProperty(o)&&(s[o]=i[o]);i=null,s.arguments&&(l=s.arguments),s.thisProgram&&(c=s.thisProgram),s.quit&&(u=s.quit);var F;s.wasmBinary&&(F=s.wasmBinary);var O;s.noExitRuntime&&(O=s.noExitRuntime),typeof WebAssembly!="object"&&$("no native wasm support detected");var W,V=new WebAssembly.Table({initial:151,maximum:151+0,element:"anyfunc"}),H=!1,K=0;function j(U,ae){U||xs("Assertion failed: "+ae)}function Y(U){var ae=s["_"+U];return j(ae,"Cannot call unknown function "+U+", make sure it is exported"),ae}function J(U,ae,Ne,Ee,at){var Et={string:function(jn){var Or=0;if(jn!=null&&jn!==0){var cd=(jn.length<<2)+1;Or=Ku(cd),se(jn,Or,cd)}return Or},array:function(jn){var Or=Ku(jn.length);return ce(jn,Or),Or}};function Xe(jn){return ae==="string"?ee(jn):ae==="boolean"?Boolean(jn):jn}var je=Y(U),Ht=[],dr=0;if(Ee)for(var hr=0;hr<Ee.length;hr++){var ud=Et[Ne[hr]];ud?(dr===0&&(dr=qu()),Ht[hr]=ud(Ee[hr])):Ht[hr]=Ee[hr]}var Ju=je.apply(null,Ht);return Ju=Xe(Ju),dr!==0&&id(dr),Ju}function ne(U,ae,Ne,Ee){Ne=Ne||[];var at=Ne.every(function(Xe){return Xe==="number"}),Et=ae!=="string";return Et&&at&&!Ee?Y(U):function(){return J(U,ae,Ne,arguments,Ee)}}var Q=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function ie(U,ae,Ne){for(var Ee=ae+Ne,at=ae;U[at]&&!(at>=Ee);)++at;if(at-ae>16&&U.subarray&&Q)return Q.decode(U.subarray(ae,at));for(var Et="";ae<at;){var Xe=U[ae++];if(!(Xe&128)){Et+=String.fromCharCode(Xe);continue}var je=U[ae++]&63;if((Xe&224)==192){Et+=String.fromCharCode((Xe&31)<<6|je);continue}var Ht=U[ae++]&63;if((Xe&240)==224?Xe=(Xe&15)<<12|je<<6|Ht:Xe=(Xe&7)<<18|je<<12|Ht<<6|U[ae++]&63,Xe<65536)Et+=String.fromCharCode(Xe);else{var dr=Xe-65536;Et+=String.fromCharCode(55296|dr>>10,56320|dr&1023)}}return Et}function ee(U,ae){return U?ie(xe,U,ae):""}function le(U,ae,Ne,Ee){if(!(Ee>0))return 0;for(var at=Ne,Et=Ne+Ee-1,Xe=0;Xe<U.length;++Xe){var je=U.charCodeAt(Xe);if(je>=55296&&je<=57343){var Ht=U.charCodeAt(++Xe);je=65536+((je&1023)<<10)|Ht&1023}if(je<=127){if(Ne>=Et)break;ae[Ne++]=je}else if(je<=2047){if(Ne+1>=Et)break;ae[Ne++]=192|je>>6,ae[Ne++]=128|je&63}else if(je<=65535){if(Ne+2>=Et)break;ae[Ne++]=224|je>>12,ae[Ne++]=128|je>>6&63,ae[Ne++]=128|je&63}else{if(Ne+3>=Et)break;ae[Ne++]=240|je>>18,ae[Ne++]=128|je>>12&63,ae[Ne++]=128|je>>6&63,ae[Ne++]=128|je&63}}return ae[Ne]=0,Ne-at}function se(U,ae,Ne){return le(U,xe,ae,Ne)}function ce(U,ae){fe.set(U,ae)}var de,fe,xe,be,Se,_e,Me,Ke,Ve;function st(U){de=U,s.HEAP8=fe=new Int8Array(U),s.HEAP16=be=new Int16Array(U),s.HEAP32=_e=new Int32Array(U),s.HEAPU8=xe=new Uint8Array(U),s.HEAPU16=Se=new Uint16Array(U),s.HEAPU32=Me=new Uint32Array(U),s.HEAPF32=Ke=new Float32Array(U),s.HEAPF64=Ve=new Float64Array(U)}var lt=s.INITIAL_MEMORY||16777216;function He(U){for(;U.length>0;){var ae=U.shift();if(typeof ae=="function"){ae(s);continue}var Ne=ae.func;typeof Ne=="number"?ae.arg===void 0?s.dynCall_v(Ne):s.dynCall_vi(Ne,ae.arg):Ne(ae.arg===void 0?null:ae.arg)}}var pt=[],dt=[],Hn=[],Ze=[],En=!1,tn=!1;function An(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Va(s.preRun.shift());He(pt)}function sa(){En=!0,He(dt)}function vn(){He(Hn)}function un(){tn=!0}function ia(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)Fn(s.postRun.shift());He(Ze)}function Va(U){pt.unshift(U)}function Fn(U){Ze.unshift(U)}var co=Math.ceil,zu=Math.floor,Ia=0,oa=null,Na=null;function po(U){Ia++,s.monitorRunDependencies&&s.monitorRunDependencies(Ia)}function ho(U){if(Ia--,s.monitorRunDependencies&&s.monitorRunDependencies(Ia),Ia==0&&(oa!==null&&(clearInterval(oa),oa=null),Na)){var ae=Na;Na=null,ae()}}s.preloadedImages={},s.preloadedAudios={};function xs(U){throw s.onAbort&&s.onAbort(U),U+="",C(U),$(U),H=!0,K=1,U="abort("+U+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(U)}function Bu(U,ae){return String.prototype.startsWith?U.startsWith(ae):U.indexOf(ae)===0}var Ff="data:application/octet-stream;base64,";function Wu(U){return Bu(U,Ff)}var Gp="file://";function Vu(U){return Bu(U,Gp)}var la="tfjs-backend-wasm.wasm";Wu(la)||(la=g(la));function Hp(){try{if(F)return new Uint8Array(F);if(v)return v(la);throw"both async and sync fetching of the wasm failed"}catch(U){xs(U)}}function $f(){return!F&&(p||d)&&typeof fetch=="function"&&!Vu(la)?fetch(la,{credentials:"same-origin"}).then(function(U){if(!U.ok)throw"failed to load wasm binary file at '"+la+"'";return U.arrayBuffer()}).catch(function(){return Hp()}):new Promise(function(U,ae){U(Hp())})}function Df(){var U={env:pr,wasi_snapshot_preview1:pr};function ae(Xe,je){var Ht=Xe.exports;s.asm=Ht,W=Ht.memory,st(W.buffer),ho("wasm-instantiate")}po("wasm-instantiate");function Ne(Xe){ae(Xe.instance)}function Ee(Xe){return $f().then(function(je){return WebAssembly.instantiate(je,U)}).then(Xe,function(je){$("failed to asynchronously prepare wasm: "+je),xs(je)})}function at(){if(!F&&typeof WebAssembly.instantiateStreaming=="function"&&!Wu(la)&&!Vu(la)&&typeof fetch=="function")fetch(la,{credentials:"same-origin"}).then(function(Xe){var je=WebAssembly.instantiateStreaming(Xe,U);return je.then(Ne,function(Ht){$("wasm streaming compile failed: "+Ht),$("falling back to ArrayBuffer instantiation"),Ee(Ne)})});else return Ee(Ne)}if(s.instantiateWasm)try{var Et=s.instantiateWasm(U,ae);return Et}catch(Xe){return $("Module.instantiateWasm callback failed with error: "+Xe),!1}return at(),{}}dt.push();function Mf(U){st(W.buffer)}var vs={splitPath:function(U){var ae=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return ae.exec(U).slice(1)},normalizeArray:function(U,ae){for(var Ne=0,Ee=U.length-1;Ee>=0;Ee--){var at=U[Ee];at==="."?U.splice(Ee,1):at===".."?(U.splice(Ee,1),Ne++):Ne&&(U.splice(Ee,1),Ne--)}if(ae)for(;Ne;Ne--)U.unshift("..");return U},normalize:function(U){var ae=U.charAt(0)==="/",Ne=U.substr(-1)==="/";return U=vs.normalizeArray(U.split("/").filter(function(Ee){return!!Ee}),!ae).join("/"),!U&&!ae&&(U="."),U&&Ne&&(U+="/"),(ae?"/":"")+U},dirname:function(U){var ae=vs.splitPath(U),Ne=ae[0],Ee=ae[1];return!Ne&&!Ee?".":(Ee&&(Ee=Ee.substr(0,Ee.length-1)),Ne+Ee)},basename:function(U){if(U==="/")return"/";var ae=U.lastIndexOf("/");return ae===-1?U:U.substr(ae+1)},extname:function(U){return vs.splitPath(U)[3]},join:function(){var U=Array.prototype.slice.call(arguments,0);return vs.normalize(U.join("/"))},join2:function(U,ae){return vs.normalize(U+"/"+ae)}},mo={mappings:{},buffers:[null,[],[]],printChar:function(U,ae){var Ne=mo.buffers[U];ae===0||ae===10?((U===1?C:$)(ie(Ne,0)),Ne.length=0):Ne.push(ae)},varargs:void 0,get:function(){mo.varargs+=4;var U=_e[mo.varargs-4>>2];return U},getStr:function(U){var ae=ee(U);return ae},get64:function(U,ae){return U}};function Rf(U){return 0}function cr(U,ae,Ne,Ee,at){}function Uu(U,ae,Ne,Ee){for(var at=0,Et=0;Et<Ne;Et++){for(var Xe=_e[ae+Et*8>>2],je=_e[ae+(Et*8+4)>>2],Ht=0;Ht<je;Ht++)mo.printChar(U,xe[Xe+Ht]);at+=je}return _e[Ee>>2]=at,0}function ws(U){od(U)}function Pf(U){ws(U)}function Of(U){return U=+U,U>=0?+zu(U+.5):+co(U-.5)}var pr={emscripten_notify_memory_growth:Mf,fd_close:Rf,fd_seek:cr,fd_write:Uu,proc_exit:Pf,roundf:Of},Gu=Df();s.asm=Gu;var Lf=s._init=function(){return(Lf=s._init=s.asm.init).apply(null,arguments)},jp=s._register_tensor=function(){return(jp=s._register_tensor=s.asm.register_tensor).apply(null,arguments)},zf=s._dispose_data=function(){return(zf=s._dispose_data=s.asm.dispose_data).apply(null,arguments)},qp=s._dispose=function(){return(qp=s._dispose=s.asm.dispose).apply(null,arguments)},ua=s._Abs=function(){return(ua=s._Abs=s.asm.Abs).apply(null,arguments)},Kp=s._Add=function(){return(Kp=s._Add=s.asm.Add).apply(null,arguments)},Bf=s._AddN=function(){return(Bf=s._AddN=s.asm.AddN).apply(null,arguments)},Wf=s._ArgMax=function(){return(Wf=s._ArgMax=s.asm.ArgMax).apply(null,arguments)},Vf=s._AvgPool=function(){return(Vf=s._AvgPool=s.asm.AvgPool).apply(null,arguments)},Uf=s._BatchMatMul=function(){return(Uf=s._BatchMatMul=s.asm.BatchMatMul).apply(null,arguments)},Xp=s._ClipByValue=function(){return(Xp=s._ClipByValue=s.asm.ClipByValue).apply(null,arguments)},Yp=s._Conv2D=function(){return(Yp=s._Conv2D=s.asm.Conv2D).apply(null,arguments)},Jp=s._Conv2DBackpropInput=function(){return(Jp=s._Conv2DBackpropInput=s.asm.Conv2DBackpropInput).apply(null,arguments)},fo=s._Cos=function(){return(fo=s._Cos=s.asm.Cos).apply(null,arguments)},Hu=s._CropAndResize=function(){return(Hu=s._CropAndResize=s.asm.CropAndResize).apply(null,arguments)},go=s._Cumsum=function(){return(go=s._Cumsum=s.asm.Cumsum).apply(null,arguments)},yo=s._DepthToSpace=function(){return(yo=s._DepthToSpace=s.asm.DepthToSpace).apply(null,arguments)},Gf=s._DepthwiseConv2dNative=function(){return(Gf=s._DepthwiseConv2dNative=s.asm.DepthwiseConv2dNative).apply(null,arguments)},Hf=s._Equal=function(){return(Hf=s._Equal=s.asm.Equal).apply(null,arguments)},jf=s._Exp=function(){return(jf=s._Exp=s.asm.Exp).apply(null,arguments)},ye=s._FlipLeftRight=function(){return(ye=s._FlipLeftRight=s.asm.FlipLeftRight).apply(null,arguments)},qf=s._Floor=function(){return(qf=s._Floor=s.asm.Floor).apply(null,arguments)},Kf=s._FloorDiv=function(){return(Kf=s._FloorDiv=s.asm.FloorDiv).apply(null,arguments)},Xf=s._FusedBatchNorm=function(){return(Xf=s._FusedBatchNorm=s.asm.FusedBatchNorm).apply(null,arguments)},Yf=s._FusedConv2D=function(){return(Yf=s._FusedConv2D=s.asm.FusedConv2D).apply(null,arguments)},ks=s._FusedDepthwiseConv2D=function(){return(ks=s._FusedDepthwiseConv2D=s.asm.FusedDepthwiseConv2D).apply(null,arguments)},Jf=s._Gather=function(){return(Jf=s._Gather=s.asm.Gather).apply(null,arguments)},Zf=s._GatherNd=function(){return(Zf=s._GatherNd=s.asm.GatherNd).apply(null,arguments)},Qf=s._Greater=function(){return(Qf=s._Greater=s.asm.Greater).apply(null,arguments)},eg=s._GreaterEqual=function(){return(eg=s._GreaterEqual=s.asm.GreaterEqual).apply(null,arguments)},tg=s._LeakyRelu=function(){return(tg=s._LeakyRelu=s.asm.LeakyRelu).apply(null,arguments)},ng=s._Less=function(){return(ng=s._Less=s.asm.Less).apply(null,arguments)},ag=s._LessEqual=function(){return(ag=s._LessEqual=s.asm.LessEqual).apply(null,arguments)},rg=s._Log=function(){return(rg=s._Log=s.asm.Log).apply(null,arguments)},sg=s._LogicalAnd=function(){return(sg=s._LogicalAnd=s.asm.LogicalAnd).apply(null,arguments)},ig=s._Max=function(){return(ig=s._Max=s.asm.Max).apply(null,arguments)},Rr=s._MaxPool=function(){return(Rr=s._MaxPool=s.asm.MaxPool).apply(null,arguments)},Is=s._Maximum=function(){return(Is=s._Maximum=s.asm.Maximum).apply(null,arguments)},bo=s._Mean=function(){return(bo=s._Mean=s.asm.Mean).apply(null,arguments)},og=s._Min=function(){return(og=s._Min=s.asm.Min).apply(null,arguments)},lg=s._Minimum=function(){return(lg=s._Minimum=s.asm.Minimum).apply(null,arguments)},ug=s._Multiply=function(){return(ug=s._Multiply=s.asm.Multiply).apply(null,arguments)},cg=s._Neg=function(){return(cg=s._Neg=s.asm.Neg).apply(null,arguments)},Pe=s._NonMaxSuppressionV3=function(){return(Pe=s._NonMaxSuppressionV3=s.asm.NonMaxSuppressionV3).apply(null,arguments)},pg=s._NonMaxSuppressionV4=function(){return(pg=s._NonMaxSuppressionV4=s.asm.NonMaxSuppressionV4).apply(null,arguments)},dg=s._NonMaxSuppressionV5=function(){return(dg=s._NonMaxSuppressionV5=s.asm.NonMaxSuppressionV5).apply(null,arguments)},hg=s._NotEqual=function(){return(hg=s._NotEqual=s.asm.NotEqual).apply(null,arguments)},mg=s._OneHot=function(){return(mg=s._OneHot=s.asm.OneHot).apply(null,arguments)},fg=s._PadV2=function(){return(fg=s._PadV2=s.asm.PadV2).apply(null,arguments)},gg=s._Pow=function(){return(gg=s._Pow=s.asm.Pow).apply(null,arguments)},ju=s._Prelu=function(){return(ju=s._Prelu=s.asm.Prelu).apply(null,arguments)},Zp=s._Prod=function(){return(Zp=s._Prod=s.asm.Prod).apply(null,arguments)},Qp=s._RealDiv=function(){return(Qp=s._RealDiv=s.asm.RealDiv).apply(null,arguments)},yg=s._Relu=function(){return(yg=s._Relu=s.asm.Relu).apply(null,arguments)},bg=s._Relu6=function(){return(bg=s._Relu6=s.asm.Relu6).apply(null,arguments)},xg=s._ResizeBilinear=function(){return(xg=s._ResizeBilinear=s.asm.ResizeBilinear).apply(null,arguments)},vg=s._Reverse=function(){return(vg=s._Reverse=s.asm.Reverse).apply(null,arguments)},wg=s._RotateWithOffset=function(){return(wg=s._RotateWithOffset=s.asm.RotateWithOffset).apply(null,arguments)},kg=s._Round=function(){return(kg=s._Round=s.asm.Round).apply(null,arguments)},Be=s._Rsqrt=function(){return(Be=s._Rsqrt=s.asm.Rsqrt).apply(null,arguments)},Ig=s._ScatterNd=function(){return(Ig=s._ScatterNd=s.asm.ScatterNd).apply(null,arguments)},Ng=s._SelectV2=function(){return(Ng=s._SelectV2=s.asm.SelectV2).apply(null,arguments)},Tg=s._Sigmoid=function(){return(Tg=s._Sigmoid=s.asm.Sigmoid).apply(null,arguments)},Ns=s._Sin=function(){return(Ns=s._Sin=s.asm.Sin).apply(null,arguments)},xo=s._Softmax=function(){return(xo=s._Softmax=s.asm.Softmax).apply(null,arguments)},ed=s._Sqrt=function(){return(ed=s._Sqrt=s.asm.Sqrt).apply(null,arguments)},td=s._Square=function(){return(td=s._Square=s.asm.Square).apply(null,arguments)},nd=s._SquaredDifference=function(){return(nd=s._SquaredDifference=s.asm.SquaredDifference).apply(null,arguments)},Sg=s._Step=function(){return(Sg=s._Step=s.asm.Step).apply(null,arguments)},Cg=s._StridedSlice=function(){return(Cg=s._StridedSlice=s.asm.StridedSlice).apply(null,arguments)},ad=s._Sub=function(){return(ad=s._Sub=s.asm.Sub).apply(null,arguments)},_g=s._Sum=function(){return(_g=s._Sum=s.asm.Sum).apply(null,arguments)},Ua=s._Tanh=function(){return(Ua=s._Tanh=s.asm.Tanh).apply(null,arguments)},Eg=s._Tile=function(){return(Eg=s._Tile=s.asm.Tile).apply(null,arguments)},Ag=s._TopK=function(){return(Ag=s._TopK=s.asm.TopK).apply(null,arguments)},rd=s._Transpose=function(){return(rd=s._Transpose=s.asm.Transpose).apply(null,arguments)},Pr=s.__FusedMatMul=function(){return(Pr=s.__FusedMatMul=s.asm._FusedMatMul).apply(null,arguments)},Fg=s._malloc=function(){return(Fg=s._malloc=s.asm.malloc).apply(null,arguments)},$g=s._free=function(){return($g=s._free=s.asm.free).apply(null,arguments)},sd=s.__start=function(){return(sd=s.__start=s.asm._start).apply(null,arguments)},qu=s.stackSave=function(){return(qu=s.stackSave=s.asm.stackSave).apply(null,arguments)},Ku=s.stackAlloc=function(){return(Ku=s.stackAlloc=s.asm.stackAlloc).apply(null,arguments)},id=s.stackRestore=function(){return(id=s.stackRestore=s.asm.stackRestore).apply(null,arguments)};s.asm=Gu,s.cwrap=ne;var Ts;s.then=function(U){if(Ts)U(s);else{var ae=s.onRuntimeInitialized;s.onRuntimeInitialized=function(){ae&&ae(),U(s)}}return s};function Xu(U){this.name="ExitStatus",this.message="Program terminated with exit("+U+")",this.status=U}var Dg=!1;Na=function U(){Ts||Yu(),Ts||(Na=U)};function Mg(U){var ae=s.__start;try{ae();var Ne=0;od(Ne,!0)}catch(at){if(at instanceof Xu)return;if(at=="unwind"){O=!0;return}else{var Ee=at;at&&typeof at=="object"&&at.stack&&(Ee=[at,at.stack]),$("exception thrown: "+Ee),u(1,at)}}finally{Dg=!0}}function Yu(U){if(U=U||l,Ia>0||(An(),Ia>0))return;function ae(){Ts||(Ts=!0,s.calledRun=!0,!H&&(sa(),vn(),s.onRuntimeInitialized&&s.onRuntimeInitialized(),ld&&Mg(U),ia()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ae()},1)):ae()}s.run=Yu;function od(U,ae){ae&&O&&U===0||(O||(H=!0,K=U,un(),s.onExit&&s.onExit(U)),u(U,new Xu(U)))}if(s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();var ld=!0;return s.noInitialRun&&(ld=!1),O=!0,Yu(),r}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}),DE=1e-7,ME=1e-4,fd=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},sc=class{decComplexRef(e){}time(e){return X("time")}read(e){return X("read")}readSync(e){return X("readSync")}numDataIds(){return X("numDataIds")}disposeData(e){return X("disposeData")}write(e,t,n){return X("write")}move(e,t,n,a){return X("move")}memory(){return X("memory")}floatPrecision(){return X("floatPrecision")}epsilon(){return this.floatPrecision()===32?DE:ME}batchMatMul(e,t,n,a){return X("batchMatMul")}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:a,bias:r,activation:s,preluActivationWeights:i}){return X("fusedBatchMatMul")}slice(e,t,n){return X("slice")}stridedSlice(e,t,n,a){return X("stridedSlice")}unstack(e,t){return X("unstack")}reverse(e,t){return X("reverse")}concat(e,t){return X("concat")}neg(e){return X("neg")}add(e,t){return X("add")}addN(e){return X("addN")}subtract(e,t){return X("subtract")}multiply(e,t){return X("multiply")}realDivide(e,t){return X("realDivide")}floorDiv(e,t){return X("floorDiv")}sum(e,t){return X("sum")}prod(e,t){return X("prod")}unsortedSegmentSum(e,t,n){return X("unsortedSegmentSum")}argMin(e,t){return X("argMin")}argMax(e,t){return X("argMax")}equal(e,t){return X("equal")}notEqual(e,t){return X("notEqual")}less(e,t){return X("less")}lessEqual(e,t){return X("lessEqual")}greater(e,t){return X("greater")}greaterEqual(e,t){return X("greaterEqual")}logicalNot(e){return X("logicalNot")}logicalAnd(e,t){return X("logicalAnd")}logicalOr(e,t){return X("logicalOr")}where(e){return X("where")}select(e,t,n){return X("select")}topk(e,t,n){return X("topk")}min(e,t){return X("min")}minimum(e,t){return X("minimum")}mod(e,t){return X("mod")}max(e,t){return X("max")}maximum(e,t){return X("maximum")}all(e,t){return X("all")}any(e,t){return X("any")}squaredDifference(e,t){return X("squaredDifference")}ceil(e){return X("ceil")}floor(e){return X("floor")}round(e){return X("round")}sign(e){return X("sign")}isNaN(e){return X("isNaN")}isInf(e){return X("isInf")}isFinite(e){return X("isFinite")}pow(e,t){return X("pow")}exp(e){return X("exp")}expm1(e){return X("expm1")}softmax(e,t){return X("softmax")}log(e){return X("log")}log1p(e){return X("log1p")}sqrt(e){return X("sqrt")}rsqrt(e){return X("rsqrt")}square(e){return X("square")}reciprocal(e){return X("reciprocal")}relu(e){return X("relu")}relu6(e){return X("relu6")}prelu(e,t){return X("prelu")}elu(e){return X("elu")}eluDer(e,t){return X("eluDer")}selu(e){return X("selu")}int(e){return X("int")}clip(e,t,n){return X("clip")}abs(e){return X("abs")}complexAbs(e){return X("complexAbs")}sigmoid(e){return X("sigmoid")}softplus(e){return X("softplus")}sin(e){return X("sin")}cos(e){return X("cos")}tan(e){return X("tan")}asin(e){return X("asin")}acos(e){return X("acos")}atan(e){return X("atan")}atan2(e,t){return X("atan2")}sinh(e){return X("sinh")}cosh(e){return X("cosh")}tanh(e){return X("tanh")}asinh(e){return X("asinh")}acosh(e){return X("acosh")}atanh(e){return X("atanh")}erf(e){return X("erf")}step(e,t){return X("step")}fusedConv2d({input:e,filter:t,convInfo:n,bias:a,activation:r,preluActivationWeights:s}){return X("fusedConv2d")}conv2d(e,t,n){return X("conv2d")}conv2dDerInput(e,t,n){return X("conv2dDerInput")}conv2dDerFilter(e,t,n){return X("conv2dDerFilter")}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:a,activation:r,preluActivationWeights:s}){return X("fusedDepthwiseConv2D")}depthwiseConv2D(e,t,n){return X("depthwiseConv2D")}depthwiseConv2DDerInput(e,t,n){return X("depthwiseConv2DDerInput")}depthwiseConv2DDerFilter(e,t,n){return X("depthwiseConv2DDerFilter")}conv3d(e,t,n){return X("conv3d")}conv3dDerInput(e,t,n){return X("conv3dDerInput")}conv3dDerFilter(e,t,n){return X("conv3dDerFilter")}maxPool(e,t){return X("maxPool")}maxPoolBackprop(e,t,n,a){return X("maxPoolBackprop")}avgPool(e,t){return X("avgPool")}avgPoolBackprop(e,t,n){return X("avgPoolBackprop")}avgPool3d(e,t){return X("avgPool3d")}avgPool3dBackprop(e,t,n){return X("avgPool3dBackprop")}maxPool3d(e,t){return X("maxPool3d")}maxPool3dBackprop(e,t,n,a){return X("maxPool3dBackprop")}reshape(e,t){return X("reshape")}cast(e,t){return X("cast")}tile(e,t){return X("tile")}pad(e,t,n){return X("pad")}transpose(e,t){return X("transpose")}gather(e,t,n,a=0){return X("gather")}gatherND(e,t){return X("gatherND")}scatterND(e,t,n){return X("scatterND")}batchToSpaceND(e,t,n){return X("batchToSpaceND")}spaceToBatchND(e,t,n){return X("spaceToBatchND")}resizeBilinear(e,t,n,a,r){return X("resizeBilinear")}resizeBilinearBackprop(e,t,n){return X("resizeBilinearBackprop")}resizeNearestNeighbor(e,t,n,a,r){return X("resizeNearestNeighbor")}resizeNearestNeighborBackprop(e,t,n){return X("resizeNearestNeighborBackprop")}batchNorm(e,t,n,a,r,s){return X("batchNorm")}localResponseNormalization4D(e,t,n,a,r){return X("localResponseNormalization4D")}LRNGrad(e,t,n,a,r,s,i){return X("LRNGrad")}multinomial(e,t,n,a){return X("multinomial")}oneHot(e,t,n,a){return X("oneHot")}cumsum(e,t,n,a){return X("cumsum")}nonMaxSuppression(e,t,n,a,r){return X("nonMaxSuppression")}fft(e){return X("fft")}ifft(e){return X("ifft")}complex(e,t){return X("complex")}real(e){return X("real")}imag(e){return X("imag")}cropAndResize(e,t,n,a,r,s){return X("cropAndResize")}depthToSpace(e,t,n){return X("depthToSpace")}split(e,t,n){return X("split")}sparseToDense(e,t,n,a){return X("sparseToDense")}diag(e){return X("diag")}fill(e,t,n){return X("fill")}onesLike(e){return X("onesLike")}zerosLike(e){return X("zerosLike")}linspace(e,t,n){return X("linspace")}dispose(){return X("dispose")}};function X(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 Ww(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 ic(e,t,n){return Math.max(e,Math.min(t,n))}function RE(e){return e%2==0?e:e+1}function PE(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function OE(e,t){let n=Math.random();return t*n+(1-n)*e}function LE(e,t){let n=0;for(let a=0;a<e.length;a++){let r=Number(e[a])-Number(t[a]);n+=r*r}return n}function A(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function rt(e,t,n=""){A(mr(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function Ss(e){A(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)||ln(e)&&!n)for(let a=0;a<e.length;++a)Cs(e[a],t,n);else t.push(e);return t}function Lt(e){if(e.length===0)return 1;let t=e[0];for(let n=1;n<e.length;n++)t*=e[n];return t}function zE(e){return e.length===0}function mr(e,t){if(e===t)return!0;if(e==null||t==null||e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function jt(e){return e%1==0}function BE(e){if(Math.tanh!=null)return Math.tanh(e);if(e===Infinity)return 1;if(e===-Infinity)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function WE(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function VE(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return Ww(t),t}function oc(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function UE(e,t=a=>0,n){return new Promise((a,r)=>{let s=0,i=()=>{if(e()){a();return}s++;let o=t(s);if(n!=null&&s>=n){r();return}setTimeout(i,o)};i()})}function GE(e,t){let n=1,a=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)n*=e[s];else if(e[s]===-1){if(a!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${a} and dim ${s}`);a=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(a===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let r=e.slice();return r[a]=t/n,r}function ca(e,t){let n=t.length;return e=e==null?t.map((a,r)=>r):[].concat(e),A(e.every(a=>a>=-n&&a<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),A(e.every(a=>jt(a)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(a=>a<0?n+a:a)}function Vw(e,t){let n=[],a=[],r=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||r?null:ca(t,e).sort(),i=0;for(let o=0;o<e.length;++o){if(s!=null){if(s[i]===o&&e[o]!==1)throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);(s[i]==null||s[i]>o)&&e[o]===1&&(n.push(e[o]),a.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),a.push(o))}return{newShape:n,keptDims:a}}function Uw(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 Gw(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 Hw(e,t){for(let n=0;n<e.length;n++){let a=e[n];if(isNaN(a)||!isFinite(a))throw Error(`A tensor of type ${t} being uploaded contains ${a}.`)}}function jw(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function qw(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function ln(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function Kw(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function Xw(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function zr(e){return typeof e=="string"||e instanceof String}function Yw(e){return typeof e=="boolean"}function Jw(e){return typeof e=="number"}function gd(e){return Array.isArray(e)?gd(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Jw(e)?"float32":zr(e)?"string":Yw(e)?"bool":"float32"}function Br(e){return!!(e&&e.constructor&&e.call&&e.apply)}function yd(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function To(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let a=t-3;a>=0;--a)n[a]=n[a+1]*e[a+1];return n}function Zw(e,t,n){let a=new Array;if(t.length===1){let r=t[0];for(let s=0;s<r;s++)a[s]=n[e+s]}else{let r=t[0],s=t.slice(1),i=s.reduce((o,l)=>o*l);for(let o=0;o<r;o++)a[o]=Zw(e+o*i,s,n)}return a}function So(e,t){if(e.length===0)return t[0];let n=e.reduce((a,r)=>a*r);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}.`);return Zw(0,e,t)}function Wg(e,t){let n=bd(e,t);for(let a=0;a<n.length;a++)n[a]=1;return n}function bd(e,t){if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool")return new Uint8Array(e);throw new Error(`Unknown data type ${t}`)}function HE(e,t){let n=e.reduce((a,r)=>a*r,1);if(t==null||t==="float32")return So(e,new Float32Array(n));if(t==="int32")return So(e,new Int32Array(n));if(t==="bool")return So(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function Vg(e){e.forEach(t=>{A(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function jE(e,t,n){if(t===0)return 0;if(t===1)return e[0];let a=e[e.length-1];for(let r=0;r<e.length-1;++r)a+=n[r]*e[r];return a}function qE(e,t,n){if(t===0)return[];if(t===1)return[e];let a=new Array(t);for(let r=0;r<a.length-1;++r)a[r]=Math.floor(e/n[r]),e-=a[r]*n[r];return a[a.length-1]=e,a}function Ug(e){return e&&e.then&&typeof e.then=="function"}var Qw="tfjsflags",e0=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let a=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${a}.`),this.set(e,a)}}async getAsync(e){return e in this.flags?this.flags[e]:(this.flags[e]=await this.evaluateFlag(e),this.flags[e])}get(e){if(e in this.flags)return this.flags[e];let t=this.evaluateFlag(e);if(Ug(t))throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[e]=t,this.flags[e]}getNumber(e){return this.get(e)}getBool(e){return this.get(e)}getFlags(){return this.flags}get features(){return this.flags}set(e,t){if(this.flagRegistry[e]==null)throw new Error(`Cannot set flag ${e} as it has not been registered.`);this.flags[e]=t,this.flagRegistry[e].setHook!=null&&this.flagRegistry[e].setHook(t)}evaluateFlag(e){if(this.flagRegistry[e]==null)throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);return this.flagRegistry[e].evaluationFn()}setFlags(e){this.flags=Object.assign({},e)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;let e=KE(this.global.location.search);Qw in e&&e[Qw].split(",").forEach(t=>{let[n,a]=t.split(":");this.urlFlags[n]=XE(n,a)})}};function KE(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...a)=>(YE(t,a[0],a[1]),a.join("="))),t}function YE(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function XE(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 ${e}.`)}function te(){return Gg}var Gg=null;function JE(e){Gg=e}var Hg;function t0(){if(Hg==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");Hg=e}return Hg}function ZE(){let e=t0();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function n0(e,t){let n=ZE();if(n.has(e))return n.get(e);{let a=t();return n.set(e,a),n.get(e)}}var Co="Abs",_o="Acos",Eo="Acosh",Wr="Add",_s="AddN",xd="All",vd="Any",Es="ArgMax",lc="ArgMin",Ao="Asin",Fo="Asinh",$o="Atan",Do="Atanh",Mo="Atan2",As="AvgPool",wd="AvgPoolGrad",uc="AvgPool3D",kd="AvgPool3DGrad",Fs="BatchMatMul",cc="BatchToSpaceND",Id="Bincount",a0="BroadcastTo",$s="Cast",Ro="Ceil",Vr="ClipByValue",Nd="Complex",pc="ComplexAbs",Po="Concat",Ds="Conv2D",Td="Conv2DBackpropFilter",Ms="Conv2DBackpropInput",dc="Conv3D",Sd="Conv3DBackpropFilterV2",Cd="Conv3DBackpropInputV2",Rs="Cos",Oo="Cosh",Ps="Cumsum",Lo="CropAndResize",_d="DenseBincount",zo="DepthToSpace",Os="DepthwiseConv2dNative",Ed="DepthwiseConv2dNativeBackpropFilter",Ad="DepthwiseConv2dNativeBackpropInput",Fd="Diag",hc="Dilation2D",$d="Dilation2DBackpropInput",Dd="Dilation2DBackpropFilter",Ls="RealDiv",Bo="Elu",Md="EluGrad",Wo="Erf",Vo="Equal",zs="Exp",Uo="ExpandDims",Go="Expm1",Rd="FFT",mc="Fill",Ho="FlipLeftRight",Bs="Floor",Ws="FloorDiv",Vs="FusedBatchNorm",jo="GatherV2",qo="GatherNd",Ko="Greater",Us="GreaterEqual",Xo="Identity",Pd="IFFT",Od="Imag",Yo="IsFinite",Jo="IsInf",Zo="IsNan",Gs="LeakyRelu",Qo="Less",el="LessEqual",Ld="LinSpace",Hs="Log",tl="Log1p",nl="LogicalAnd",fc="LogicalNot",gc="LogicalOr",r0="LogSoftmax",yc="LRN",zd="LRNGrad",js="Max",qs="Maximum",Ks="MaxPool",Bd="MaxPoolGrad",bc="MaxPool3D",Wd="MaxPool3DGrad",Vd="MaxPoolWithArgmax",Xs="Mean",Ys="Min",Js="Minimum",xc="MirrorPad",al="Mod",Ud="Multinomial",Zs="Multiply",rl="Neg",sl="NotEqual",il="NonMaxSuppressionV3",ol="NonMaxSuppressionV4",ll="NonMaxSuppressionV5",ul="OnesLike",Qs="OneHot",cl="Pack",ei="PadV2",QE="Pool",ti="Pow",ni="Prelu",pl="Prod",vc="Range",Gd="Real",dl="Reciprocal",ai="Relu",hl="Reshape",wc="ResizeNearestNeighbor",Hd="ResizeNearestNeighborGrad",ri="ResizeBilinear",jd="ResizeBilinearGrad",si="Relu6",ii="Reverse",oi="Round",li="Rsqrt",ml="ScatterNd",fl="Select",gl="Selu",yl="Slice",ui="Sin",bl="Sinh",xl="Sign",ci="Sigmoid",vl="Softplus",pi="Sqrt",di="Sum",kc="SpaceToBatchND",wl="SplitV",hi="Softmax",mi="SquaredDifference",Ic="Square",fi="Sub",qd="SparseToDense",kl="StridedSlice",Il="Tan",gi="Tanh",Ur="Tile",Nl="TopK",yi="Transpose",Kd="Unique",Tl="Unpack",Nc="UnsortedSegmentSum",Sl="ZerosLike",Gr="Step",Xd="FromPixels",Cl="RotateWithOffset",bi="_FusedMatMul",xi="FusedConv2D",vi="FusedDepthwiseConv2D",_l=n0("kernelRegistry",()=>new Map),Tc=n0("gradRegistry",()=>new Map);function qg(e,t){let n=jg(e,t);return _l.get(n)}function Kg(e){return Tc.get(e)}function Yd(e){let t=_l.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 Sc(e){let{kernelName:t,backendName:n}=e,a=jg(t,n);_l.has(a)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),_l.set(a,e)}function s0(e){let{kernelName:t}=e;Tc.has(t)&&te().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Tc.set(t,e)}function eA(e,t){let n=jg(e,t);if(!_l.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);_l.delete(n)}function tA(e){if(!Tc.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Tc.delete(e)}function nA(e,t){Yd(e).forEach(n=>{let a=Object.assign({},n,{backendName:t});Sc(a)})}function jg(e,t){return`${t}_${e}`}var k={};Oe(k,{arraysEqual:()=>mr,assert:()=>A,assertNonNegativeIntegerDimensions:()=>Vg,assertNonNull:()=>Ss,assertShapesMatch:()=>rt,bytesFromStringArray:()=>Xw,bytesPerElement:()=>Kw,checkConversionForErrors:()=>Hw,clamp:()=>ic,computeStrides:()=>To,createScalarValue:()=>aA,createShuffledIndices:()=>VE,decodeString:()=>Zd,distSquared:()=>LE,encodeString:()=>Cc,fetch:()=>rA,flatten:()=>Cs,getArrayFromDType:()=>Gw,getTypedArrayFromDType:()=>Uw,hasEncodingLoss:()=>qw,indexToLoc:()=>qE,inferDtype:()=>gd,inferFromImplicitShape:()=>GE,isBoolean:()=>Yw,isFunction:()=>Br,isInt:()=>jt,isNumber:()=>Jw,isPromise:()=>Ug,isScalarShape:()=>zE,isString:()=>zr,isTypedArray:()=>ln,isValidDtype:()=>jw,locToIndex:()=>jE,makeOnesTypedArray:()=>Wg,makeZerosNestedTypedArray:()=>HE,makeZerosTypedArray:()=>bd,nearestDivisor:()=>yd,nearestLargerEven:()=>RE,now:()=>Xg,parseAxisParam:()=>ca,randUniform:()=>OE,repeatedTry:()=>UE,rightPad:()=>oc,shuffle:()=>Ww,sizeFromShape:()=>Lt,sizeToSquarishShape:()=>WE,squeezeShape:()=>Vw,sum:()=>PE,tanh:()=>BE,toNestedArray:()=>So,toTypedArray:()=>Jd});function aA(e,t){return t==="string"?Cc(e):Jd([e],t)}function sA(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function Jd(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=Cs(e)),te().getBool("DEBUG")&&Hw(e,t),sA(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){let n=new Uint8Array(e.length);for(let a=0;a<n.length;++a)Math.round(e[a])!==0&&(n[a]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Xg(){return te().platform.now()}function rA(e,t){return te().platform.fetch(e,t)}function Cc(e,t="utf-8"){return t=t||"utf-8",te().platform.encode(e,t)}function Zd(e,t="utf-8"){return t=t||"utf-8",te().platform.decode(e,t)}var lA=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new oA)}profileKernel(e,t,n){let a,r=()=>{a=n()},s=this.backendTimer.time(r);if(te().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<a.length;i++){let o=a[i];o.data().then(l=>{iA(l,o.dtype,e)})}return{kernelName:e,outputs:a,inputs:t,timeMs:s.then(i=>i.kernelMs),extraInfo:s.then(i=>i.getExtraProfileInfo!=null?i.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 iA(e,t,n){if(t!=="float32")return!1;for(let a=0;a<e.length;a++){let r=e[a];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}var oA=class{logKernelProfile(e,t,n,a,r,s){let i=typeof a=="number"?oc(`${a}ms`,9):a.error,o=oc(e,25),l=t.rank,c=t.size,u=oc(t.shape.toString(),14),p="";for(let d in r){let h=r[d];if(h!=null){let m=h.shape||t.shape,f=m.length;p+=`${d}: ${f}D ${f>0?m:""} `}}console.log(`%c${o} %c${i} %c${l}D ${u} %c${c} %c${p} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function uA(e,t,n){let a={},r={};for(let l=0;l<t.length;l++)a[t[l].id]=!0;for(let l=0;l<e.length;l++){let c=e[l],u=c.inputs;for(let p in u){let d=u[p],h=!1;for(let m=0;m<t.length;m++)if(a[d.id]){c.outputs.forEach(f=>a[f.id]=!0),h=!0,r[c.id]=!0;break}if(h)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let c=e[l],u=c.inputs;for(let p=0;p<c.outputs.length;p++)if(s[c.outputs[p].id]){for(let d in u)s[u[d].id]=!0,i[c.id]=!0;break}}let o=[];for(let l=0;l<e.length;l++){let c=e[l];if(r[c.id]&&i[c.id]){let u={};for(let d in c.inputs){let h=c.inputs[d];a[h.id]&&(u[d]=h)}let p=Object.assign({},c);p.inputs=u,p.outputs=c.outputs,o.push(p)}}return o}function cA(e,t,n,a){for(let r=t.length-1;r>=0;r--){let s=t[r],i=[];if(s.outputs.forEach(l=>{let c=e[l.id];c!=null?i.push(c):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 c=n(()=>o[l]());if(c.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${c.dtype}'`);let u=s.inputs[l];if(!mr(c.shape,u.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${c.shape}', which does not match the shape of the input '${u.shape}'`);if(e[u.id]==null)e[u.id]=c;else{let p=e[u.id];e[u.id]=a(p,c),p.dispose()}}}}var i0=20,_c=3,Yg=7;function dA(e,t,n,a){let r=To(t),s=pA(e,t,n,r),i=t.length,o=Qd(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(c=>" "+c).join(`
`)),l.join(`
`)}function pA(e,t,n,a){let r=Lt(t),s=a[a.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Ac(e):e;if(o>1)for(let c=0;c<r/s;c++){let u=c*s;for(let p=0;p<s;p++)i[p]=Math.max(i[p],Ec(l[u+p],0,n).length)}return i}function Ec(e,t,n){let a;return Array.isArray(e)?a=`${parseFloat(e[0].toFixed(Yg))} + ${parseFloat(e[1].toFixed(Yg))}j`:zr(e)?a=`'${e}'`:n==="bool"?a=o0(e):a=parseFloat(e.toFixed(Yg)).toString(),oc(a,t)}function o0(e){return e===0?"false":"true"}function Qd(e,t,n,a,r,s=!0){let i=n==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(n==="complex64"){let f=Ac(e);return[Ec(f[0],0,n)]}return n==="bool"?[o0(e[0])]:[e[0].toString()]}if(l===1){if(o>i0){let g=_c*i,y=Array.from(e.slice(0,g)),b=Array.from(e.slice((o-_c)*i,o*i));return n==="complex64"&&(y=Ac(y),b=Ac(b)),["["+y.map((v,x)=>Ec(v,r[x],n)).join(", ")+", ..., "+b.map((v,x)=>Ec(v,r[o-_c+x],n)).join(", ")+"]"]}let f=n==="complex64"?Ac(e):Array.from(e);return["["+f.map((g,y)=>Ec(g,r[y],n)).join(", ")+"]"]}let c=t.slice(1),u=a.slice(1),p=a[0]*i,d=[];if(o>i0){for(let f=0;f<_c;f++){let g=f*p,y=g+p;d.push(...Qd(e.slice(g,y),c,n,u,r,!1))}d.push("...");for(let f=o-_c;f<o;f++){let g=f*p,y=g+p;d.push(...Qd(e.slice(g,y),c,n,u,r,f===o-1))}}else for(let f=0;f<o;f++){let g=f*p,y=g+p;d.push(...Qd(e.slice(g,y),c,n,u,r,f===o-1))}let h=l===2?",":"";d[0]="["+d[0]+h;for(let f=1;f<d.length-1;f++)d[f]=" "+d[f]+h;let m=`,
`;for(let f=2;f<l;f++)m+=`
`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":m),d}function Ac(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var zt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Lt(e),n!=null){let a=n.length;A(a===this.size,()=>`Length of values '${a}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Gw(t,this.size),this.strides=To(e)}set(e,...t){t.length===0&&(t=[0]),A(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let a of e){if(a<0||a>=this.shape[t]){let r=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let a=0;a<e.length-1;++a)n+=this.strides[a]*e[a];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Ga().makeTensor(this.values,this.shape,this.dtype)}},Ga=null,El=null,hA=null;function mA(e){Ga=e}function fA(e){El=e}function gA(e){hA=e}var z=class{constructor(e,t,n,a){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Lt(e),this.strides=To(e),this.dataId=n,this.id=a,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return El.buffer(this.shape,this.dtype,e)}bufferSync(){return El.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return So(this.shape,e)}arraySync(){return So(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Ga().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Zd(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=Ga().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Zd(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 Ga().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Ga().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return El.print(this,e)}clone(){return this.throwIfDisposed(),El.clone(this)}toString(e=!1){let t=this.dataSync();return dA(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),El.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ga().makeVariable(this,e,t,n)}};Object.defineProperty(z,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});var Hr=class extends z{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(!mr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ga().disposeTensor(this),this.dataId=e.dataId,Ga().incRef(this,null)}dispose(){Ga().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Hr,Symbol.hasInstance,{value:e=>e instanceof z&&e.assign!=null&&e.assign instanceof Function});var Ta={};Oe(Ta,{assertTypesMatch:()=>l0,getTensorsInContainer:()=>Jg,isTensorInList:()=>yA,makeTypesMatch:()=>Tt});var Zg;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Zg||(Zg={}));var Qg;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Qg||(Qg={}));var ey;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(ey||(ey={}));var ty;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(ty||(ty={}));var ny;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(ny||(ny={}));var bA={float32:ty,int32:Qg,bool:ey,complex64:ny};function pa(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return bA[e][t]}function eh(e){return pa(e,"int32")}function Tt(e,t){if(e.dtype===t.dtype)return[e,t];let n=pa(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function l0(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function yA(e,t){return t.some(n=>n.id===e.id)}function Jg(e){let t=[],n=new Set;return u0(e,t,n),t}function u0(e,t,n){if(e==null)return;if(e instanceof z){t.push(e);return}if(!xA(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),u0(s,t,n))}}function xA(e){return Array.isArray(e)||typeof e=="object"}var c0=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Fc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new c0}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new lA(this.backendInstance),!0}setupRegisteredKernels(){Yd(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Yd(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 sc)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t);a.disposeData(t),n.backend=e,e.move(t,r,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Fc.nextTensorId++}nextVariableId(){return Fc.nextVariableId++}clone(e){let t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return P.runKernelFunc(c=>c.cast(s,i),o,null,$s,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n,a,r){let s=null,i=null;return this.runKernelFunc(s,t,i,e,n,a,r)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e,t,n,a,r,s,i){let o,l=[],c=this.isTapeOn();a==null&&(a=this.state.activeScope!=null?this.state.activeScope.name:"");let u=this.state.numBytes,p=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let d;this.backendName==null&&this.backend;let h=qg(a,this.backendName),m;if(h!=null)d=()=>{let g=this.backend.numDataIds();m=h.kernelFunc({inputs:t,attrs:r,backend:this.backend});let y=Array.isArray(m)?m:[m];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(a,g,y);let b=y.map(v=>{if(v.rank!=null)return v;let{dataId:x,shape:N,dtype:T}=v;return this.makeTensorFromDataId(x,N,T)});if(c){let v=this.getTensorsForGradient(a,t,b);if(v==null){i==null&&(i=[]);let x=b.filter((N,T)=>i[T]);v=(s||[]).slice().concat(x)}l=this.saveTensorsForBackwardMode(v)}return b};else{if(e==null)throw new Error(`Error running ${a}: Neither modular kernel nor forward func passed`);let g=y=>{!c||(l=y.map(b=>this.keep(this.clone(b))))};d=()=>{let y=this.backend.numDataIds();m=this.tidy(()=>e(this.backend,g));let b=Array.isArray(m)?m:[m];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(a,y,b),b}}let f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?o=d():(f=this.profiler.profileKernel(a,t,()=>d()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),o=f.outputs)}),c&&this.addTapeNode(a,t,o,n,l,r),this.state.profiling&&this.state.activeProfile.kernels.push({name:a,bytesAdded:this.state.numBytes-u,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-p,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(g=>t[g]!=null?t[g].shape:null),outputShapes:o.map(g=>g.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(m)?o:o[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Kg(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(A(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,c)=>s[c]);return i.concat(o)}return null}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"&&zr(e[0])&&(r=e.map(o=>Cc(o)));let s=a.write(r,t,n),i=new z(t,n,s,this.nextTensorId());if(this.incRef(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=Xw(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new z(t,n,e,this.nextTensorId());return this.incRef(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 Hr(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}incRef(e,t){let n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let a=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(a=e.size*Kw(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:a,refCount:0}),this.state.numBytes+=a}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Hr||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}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=Kg(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let p=n[u],d=bd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return c}),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=Jg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(A(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));A(r instanceof z,()=>"The result y returned by f() must be a tensor.");let s=uA(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?vA(r.shape):n,cA(i,s,l=>this.tidy(l),wA);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return A(Br(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{A(t.every(r=>r instanceof z),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};return t.forEach((r,s)=>{a[s]=r}),this.runKernelFunc((r,s)=>(n=e(...t,s),A(n.value instanceof z,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(Br(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a,(r,s)=>{let i=n.gradFunc(r,s),o=Array.isArray(i)?i:[i];A(o.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(...)."),A(o.every(c=>c instanceof z),()=>"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 l={};return o.forEach((c,u)=>{l[u]=()=>c}),l})}}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=Xg(),n=await this.backend.time(e);return n.wallMs=Xg()-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 c0;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}};Fc.nextTensorId=0;Fc.nextVariableId=0;function vA(e){let t=Wg(Lt(e),"float32");return P.makeTensor(t,e,"float32")}function p0(){let e=t0();if(e._tfengine==null){let t=new e0(e);e._tfengine=new Fc(t)}return JE(e._tfengine.ENV),mA(()=>e._tfengine),e._tfengine}var P=p0();function wA(e,t){let n={a:e,b:t};return P.runKernel(Wr,n)}var th={};Oe(th,{isBrowser:()=>d0,isMobile:()=>kA});function IA(){return typeof navigator!="undefined"&&navigator!=null}function kA(){if(IA()){let e=navigator.userAgent||navigator.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(e)||/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(e.substr(0,4))}return!1}function d0(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Ha=te();Ha.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.")});Ha.registerFlag("IS_BROWSER",()=>d0());Ha.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Ha.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Ha.registerFlag("PROD",()=>!1);Ha.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Ha.getBool("DEBUG"));Ha.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Ha.registerFlag("IS_TEST",()=>!1);Ha.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function ja(e,t){let n=e;if(ln(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let a=[];for(;Array.isArray(n)||ln(n)&&t!=="string";)a.push(n.length),n=n[0];return Array.isArray(e)&&te().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&h0(e,a,[]),a}function h0(e,t,n){if(n=n||[],!Array.isArray(e)&&!ln(e)){A(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}A(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),A(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let a=t.slice(1);for(let r=0;r<e.length;++r)h0(e[r],a,n.concat(r))}function m0(e,t,n,a){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${a}' must be ${e} tensor, but got ${t} tensor`)}}function _(e,t,n,a="numeric"){if(e instanceof z)return m0(a,e.dtype,t,n),e;let r=gd(e);if(r!=="string"&&["bool","int32","float32"].indexOf(a)>=0&&(r=a),m0(a,r,t,n),e==null||!ln(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=ja(e,r);!ln(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?Jd(e,r):Cs(e,[],!0);return P.makeTensor(i,s,r)}function $c(e,t,n,a="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((r,s)=>_(r,`${t}[${s}]`,n,a))}var f0="__op";function R(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+f0;let r=(...s)=>{P.startScope(n);try{let i=a(...s);return Ug(i)&&console.error("Cannot return a Promise inside of tidy."),P.endScope(i),i}catch(i){throw P.endScope(null),i}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function NA(e,t){let n=_(e,"real","complex"),a=_(t,"imag","complex");rt(n.shape,a.shape,`real and imag shapes, ${n.shape} and ${a.shape}, must match in call to tf.complex().`);let r={real:n,imag:a};return P.runKernel(Nd,r)}var jr=R({complex_:NA});function qr(e,t,n,a){if(a==null&&(a=gd(e)),a==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!ln(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Vg(t);let r=Lt(t),s=Lt(n);A(r===s,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==Lt(t.slice(i)):!0;A(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!ln(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=a!=="string"?Jd(e,a):Cs(e,[],!0),P.makeTensor(e,t,a)}function Kn(e,t,n){let a=ja(e,n);return qr(e,t,a,n)}var ay={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},nh=4;async function SA(e,t){let n=[],a=[],r=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<r.length;++i){let o=r[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let c={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let u=new Promise(async p=>{let d=await l.bytes(),h=d.reduce((g,y)=>g+y.length,0)+nh*d.length,m=new Uint8Array(h),f=0;for(let g=0;g<d.length;g++){let y=d[g],b=new Uint8Array(new Uint32Array([y.length]).buffer);m.set(b,f),f+=nh,m.set(y,f),f+=y.length}p(m)});a.push(u)}else a.push(l.data());t!=null&&(c.group=t),n.push(c)}let s=await Promise.all(a);return{data:TA(s),specs:n}}function g0(e,t){let n={},a,r=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,c=Lt(l),u;if("quantization"in s){let p=s.quantization;if(p.dtype==="uint8"||p.dtype==="uint16"){if(!("min"in p&&"scale"in p))throw new Error(`Weight ${s.name} with quantization ${p.dtype} doesn't have corresponding metadata min and scale.`)}else if(p.dtype==="float16"){if(o!=="float32")throw new Error(`Weight ${s.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${o}.`)}else throw new Error(`Weight ${s.name} has unknown quantization dtype ${p.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let d=ay[p.dtype],h=e.slice(r,r+c*d),m=p.dtype==="uint8"?new Uint8Array(h):new Uint16Array(h);if(o==="float32")if(p.dtype==="uint8"||p.dtype==="uint16"){u=new Float32Array(m.length);for(let f=0;f<m.length;f++){let g=m[f];u[f]=g*p.scale+p.min}}else if(p.dtype==="float16")a===void 0&&(a=CA()),u=a(m);else throw new Error(`Unsupported quantization type ${p.dtype} for weight type float32.`);else if(o==="int32"){if(p.dtype!=="uint8"&&p.dtype!=="uint16")throw new Error(`Unsupported quantization type ${p.dtype} for weight type int32.`);u=new Int32Array(m.length);for(let f=0;f<m.length;f++){let g=m[f];u[f]=Math.round(g*p.scale+p.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=c*d}else if(o==="string"){let p=Lt(s.shape);u=[];for(let d=0;d<p;d++){let h=new Uint32Array(e.slice(r,r+nh))[0];r+=nh;let m=new Uint8Array(e.slice(r,r+h));u.push(m),r+=h}}else{let p=ay[o],d=e.slice(r,r+c*p);if(o==="float32")u=new Float32Array(d);else if(o==="int32")u=new Int32Array(d);else if(o==="bool")u=new Uint8Array(d);else if(o==="complex64"){u=new Float32Array(d);let h=new Float32Array(u.length/2),m=new Float32Array(u.length/2);for(let y=0;y<h.length;y++)h[y]=u[y*2],m[y]=u[y*2+1];let f=Kn(h,l,"float32"),g=Kn(m,l,"float32");n[i]=jr(f,g),f.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=c*p}o!=="complex64"&&(n[i]=Kn(u,l,o))}return n}function TA(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(s=>{if(t+=s.byteLength,n.push(s.byteLength===s.buffer.byteLength?s:new s.constructor(s)),!(s instanceof Float32Array||s instanceof Int32Array||s instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`)});let a=new Uint8Array(t),r=0;return n.forEach(s=>{a.set(new Uint8Array(s.buffer),r),r+=s.byteLength}),a.buffer}var ry=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function y0(e){return ry?Buffer.byteLength(e):new Blob([e]).size}function _A(e){if(ry)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let a=0,r=t.length;a<r;a++)n+=String.fromCharCode(t[a]);return btoa(n)}function EA(e){if(ry){let a=Buffer.from(e,"base64");return a.buffer.slice(a.byteOffset,a.byteOffset+a.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let a=0;a<t.length;++a)n.set([t.charCodeAt(a)],a);return n.buffer}function sy(e){if(e.length===1)return e[0];let t=0;e.forEach(r=>{t+=r.byteLength});let n=new Uint8Array(t),a=0;return e.forEach(r=>{n.set(new Uint8Array(r),a),a+=r.byteLength}),n.buffer}function b0(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 Dc(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:y0(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:y0(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function AA(){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 FA(){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 $A(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function CA(){let e=AA(),t=FA(),n=$A();return a=>{let r=new ArrayBuffer(4*a.length),s=new Uint32Array(r);for(let i=0;i<a.length;i++){let o=a[i],l=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(r)}}var At=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return At.instance==null&&(At.instance=new At),At.instance}static registerSaveRouter(e){At.getInstance().saveRouters.push(e)}static registerLoadRouter(e){At.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return At.getHandlers(e,"save")}static getLoadHandlers(e,t){return At.getHandlers(e,"load",t)}static getHandlers(e,t,n){let a=[];return(t==="load"?At.getInstance().loadRouters:At.getInstance().saveRouters).forEach(r=>{let s=r(e,n);s!==null&&a.push(s)}),a}},DA=e=>At.registerSaveRouter(e),MA=e=>At.registerLoadRouter(e),RA=e=>At.getSaveHandlers(e),PA=(e,t)=>At.getLoadHandlers(e,t),iy="tensorflowjs",oy=1,wi="models_store",Kr="model_info_store";function x0(){if(!te().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 ly(e){let t=e.result;t.createObjectStore(wi,{keyPath:"modelPath"}),t.createObjectStore(Kr,{keyPath:"modelPath"})}var ki=class{constructor(e){if(this.indexedDB=x0(),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(iy,oy);r.onupgradeneeded=()=>ly(r),r.onsuccess=()=>{let s=r.result;if(t==null){let i=s.transaction(wi,"readonly"),o=i.objectStore(wi).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),a(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),a(o.error)),i.oncomplete=()=>s.close()}else{let i=Dc(t),o=s.transaction(Kr,"readwrite"),l=o.objectStore(Kr),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),u;c.onsuccess=()=>{u=s.transaction(wi,"readwrite");let p=u.objectStore(wi).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});p.onsuccess=()=>n({modelArtifactsInfo:i}),p.onerror=d=>{l=o.objectStore(Kr);let h=l.delete(this.modelPath);h.onsuccess=()=>(s.close(),a(p.error)),h.onerror=m=>(s.close(),a(p.error))}},c.onerror=p=>(s.close(),a(c.error)),o.oncomplete=()=>{u==null?s.close():u.oncomplete=()=>s.close()}}},r.onerror=s=>a(r.error)})}};ki.URL_SCHEME="indexeddb://";var v0=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ki.URL_SCHEME)?OA(e.slice(ki.URL_SCHEME.length)):null;At.registerSaveRouter(v0);At.registerLoadRouter(v0);function OA(e){return new ki(e)}function LA(e){return e.startsWith(ki.URL_SCHEME)?e.slice(ki.URL_SCHEME.length):e}var zA=class{constructor(){this.indexedDB=x0()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(iy,oy);n.onupgradeneeded=()=>ly(n),n.onsuccess=()=>{let a=n.result,r=a.transaction(Kr,"readonly"),s=r.objectStore(Kr).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(a.close(),t(s.error)),r.oncomplete=()=>a.close()},n.onerror=a=>t(n.error)})}async removeModel(e){return e=LA(e),new Promise((t,n)=>{let a=this.indexedDB.open(iy,oy);a.onupgradeneeded=()=>ly(a),a.onsuccess=()=>{let r=a.result,s=r.transaction(Kr,"readwrite"),i=s.objectStore(Kr),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=i.delete(e),u=()=>{l=r.transaction(wi,"readwrite");let p=l.objectStore(wi).delete(e);p.onsuccess=()=>t(o.result.modelArtifactsInfo),p.onerror=d=>n(o.error)};c.onsuccess=u,c.onerror=p=>(u(),r.close(),n(o.error))}},o.onerror=c=>(r.close(),n(o.error)),s.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},a.onerror=r=>n(a.error)})}},fr="/",Al="tensorflowjs_models",w0="info",BA="model_topology",WA="weight_specs",VA="weight_data",UA="model_metadata";function k0(e){return{info:[Al,e,w0].join(fr),topology:[Al,e,BA].join(fr),weightSpecs:[Al,e,WA].join(fr),weightData:[Al,e,VA].join(fr),modelMetadata:[Al,e,UA].join(fr)}}function GA(e){let t=e.split(fr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(fr)}function HA(e){return e.startsWith(Ii.URL_SCHEME)?e.slice(Ii.URL_SCHEME.length):e}var Ii=class{constructor(e){if(!te().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=k0(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),a=Dc(e);try{this.LS.setItem(this.keys.info,JSON.stringify(a)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,_A(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:a}}catch(r){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=EA(s),t}};Ii.URL_SCHEME="localstorage://";var I0=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Ii.URL_SCHEME)?jA(e.slice(Ii.URL_SCHEME.length)):null;At.registerSaveRouter(I0);At.registerLoadRouter(I0);function jA(e){return new Ii(e)}var qA=class{constructor(){A(te().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),A(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Al+fr,n=fr+w0;for(let a=0;a<this.LS.length;++a){let r=this.LS.key(a);if(r.startsWith(t)&&r.endsWith(n)){let s=GA(r);e[s]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=HA(e);let t=k0(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}},Fl="://",Xn=class{constructor(){this.managers={}}static getInstance(){return Xn.instance==null&&(Xn.instance=new Xn),Xn.instance}static registerManager(e,t){A(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Fl)&&(e=e.slice(0,e.indexOf(Fl))),A(e.length>0,()=>"scheme must not be an empty string.");let n=Xn.getInstance();A(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 ah(e){if(e.indexOf(Fl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Xn.getSchemes().join(",")}`);return{scheme:e.split(Fl)[0],path:e.split(Fl)[1]}}async function N0(e,t,n=!1){A(e!==t,()=>`Old path and new path are the same: '${e}'`);let a=At.getLoadHandlers(e);A(a.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),A(a.length<2,()=>`Copying failed because more than one (${a.length}) load handlers for source URL ${e}.`);let r=a[0],s=At.getSaveHandlers(t);A(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),A(s.length<2,()=>`Copying failed because more than one (${a.length}) save handlers for destination URL ${t}.`);let i=s[0],o=ah(e).scheme,l=ah(e).path,c=o===ah(e).scheme,u=await r.load();n&&c&&await Xn.getManager(o).removeModel(l);let p=await i.save(u);return n&&!c&&await Xn.getManager(o).removeModel(l),p.modelArtifactsInfo}async function KA(){let e=Xn.getSchemes(),t={};for(let n of e){let a=await Xn.getManager(n).listModels();for(let r in a){let s=n+Fl+r;t[s]=a[r]}}return t}async function XA(e){let t=ah(e);return Xn.getManager(t.scheme).removeModel(t.path)}async function YA(e,t){return N0(e,t,!1)}async function JA(e,t){return N0(e,t,!0)}var ZA=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(te().get("IS_BROWSER")){te().setPlatform("browser",new ZA);try{Xn.registerManager(Ii.URL_SCHEME,new qA)}catch(e){}try{Xn.registerManager(ki.URL_SCHEME,new zA)}catch(e){}}var QA={importFetch:()=>xE()},uy,eF=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return te().global.fetch!=null?te().global.fetch(e,t):(uy==null&&(uy=QA.importFetch()),uy(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)}};te().get("IS_NODE")&&te().setPlatform("node",new eF);function Le(e,t="float32",n){return t=t||"float32",Vg(e),new zt(e,t,n)}function tF(e,t){let n=_(e,"x","cast");if(!jw(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let a={x:n},r={dtype:t};return P.runKernel($s,a,r)}var pe=R({cast_:tF});function nF(e){let t={x:_(e,"x","clone","string_or_numeric")};return P.runKernel(Xo,t)}var Xr=R({clone_:nF});function T0(e,t=!1){console.log(e.toString(t))}p0();var aF={buffer:Le,cast:pe,clone:Xr,print:T0};fA(aF);var qt={};Oe(qt,{browserFiles:()=>rF,browserHTTPRequest:()=>iF,concatenateArrayBuffers:()=>sy,copyModel:()=>YA,decodeWeights:()=>g0,encodeWeights:()=>SA,fromMemory:()=>oF,getLoadHandlers:()=>PA,getModelArtifactsInfoForJSON:()=>Dc,getSaveHandlers:()=>RA,http:()=>py,isHTTPScheme:()=>cy,listModels:()=>KA,loadWeights:()=>sF,moveModel:()=>JA,registerLoadRouter:()=>MA,registerSaveRouter:()=>DA,removeModel:()=>XA,weightsLoaderFactory:()=>S0,withSaveHandler:()=>lF});var uF="model",cF=".json",pF=".weights.bin";function C0(e){return new Promise(t=>setTimeout(t)).then(e)}var $l=class{constructor(e){if(!te().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith($l.URL_SCHEME)&&(e=e.slice($l.URL_SCHEME.length)),(e==null||e.length===0)&&(e=uF),this.modelTopologyFileName=e+cF,this.weightDataFileName=e+pF}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],a={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer);let r=window.URL.createObjectURL(new Blob([JSON.stringify(a)],{type:"application/json"})),s=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(s.download=this.modelTopologyFileName,s.href=r,await C0(()=>s.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await C0(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Dc(e)}}}};$l.URL_SCHEME="downloads://";var dF=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 ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(l,t)}catch(h){a(h);return}let u=[],p=[],d=[];l.forEach(h=>{h.paths.forEach(m=>{p.push(m),d.push(null)}),u.push(...h.weights)}),l.forEach(h=>{h.paths.forEach(m=>{let f=new FileReader;f.onload=g=>{let y=g.target.result,b=p.indexOf(m);if(d[b]=y,d.indexOf(null)===-1){let v={modelTopology:o,weightSpecs:u,weightData:sy(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(v.signature=i.signature),i.userDefinedMetadata!=null&&(v.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(v.modelInitializer=i.modelInitializer),n(v)}},f.onerror=g=>a(`Failed to weights data from file of path '${m}'.`),f.readAsArrayBuffer(c[m])})})},r.onerror=s=>a(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],a=t.map(s=>b0(s.name)),r={};for(let s of e)s.paths.forEach(i=>{let o=b0(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${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 (${t.length}).`);return r}},mF=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith($l.URL_SCHEME)?hF(e.slice($l.URL_SCHEME.length)):null;At.registerSaveRouter(mF);function hF(e="model"){return new $l(e)}function rF(e){return new dF(e)}function _0(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(c=>{let u=n+ ++r/e.length*(a-n);return t(u),c}),l);function i(l){A(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,c){A(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),A(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),A(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(s))}async function E0(e,t){t==null&&(t={});let n=t.fetchFunc==null?te().platform.fetch:t.fetchFunc,a=e.map(c=>n(c,t.requestInit,{isBinary:!0})),r=0,s=.5,i=(t.onProgress==null?await Promise.all(a):await _0(a,t.onProgress,r,s)).map(c=>c.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await _0(i,t.onProgress,o,l)}async function sF(e,t="",n,a){return S0(r=>E0(r,{requestInit:a}))(e,t,n)}function S0(e){return async(t,n="",a)=>{let r=t.map(()=>!1),s={},i=a!=null?a.map(()=>!1):[],o=[];if(t.forEach((h,m)=>{let f=0;h.weights.forEach(g=>{let y="quantization"in g?g.quantization.dtype:g.dtype,b=ay[y]*Lt(g.shape),v=()=>{r[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:g,groupOffset:f,sizeBytes:b})};a!=null?a.forEach((x,N)=>{x===g.name&&(v(),i[N]=!0)}):v(),o.push(g.name),f+=b})}),!i.every(h=>h)){let h=a.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}.
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=r.reduce((h,m,f)=>(m&&h.push(f),h),[]),c=[];l.forEach(h=>{t[h].paths.forEach(m=>{let f=n+(n.endsWith("/")?"":"/")+m;c.push(f)})});let u=await e(c),p={},d=0;return l.forEach(h=>{let m=t[h].paths.length,f=0;for(let v=0;v<m;v++)f+=u[d+v].byteLength;let g=new ArrayBuffer(f),y=new Uint8Array(g),b=0;for(let v=0;v<m;v++){let x=new Uint8Array(u[d+v]);y.set(x,b),b+=x.byteLength}s[h].forEach(v=>{let x=g.slice(v.groupOffset,v.groupOffset+v.sizeBytes),N=g0(x,[v.manifestEntry]);for(let T in N)p[T]=N[T]}),d+=m}),p}}var fF="application/octet-stream",gF="application/json",dy=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?(A(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=te().platform.fetch,A(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&A(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:gF}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:fF}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Dc(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 c,u;a!=null&&([c,u]=await this.loadWeights(a));let p={modelTopology:n,weightSpecs:c,weightData:u,generatedBy:r,convertedBy:s,format:i};o!=null&&(p.signature=o),l!=null&&(p.userDefinedMetadata=l);let d=t.modelInitializer;return d&&(p.modelInitializer=d),p}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,a]=yF(t),r=this.weightPathPrefix||n,s=[];for(let c of e)s.push(...c.weights);let i=[],o=[];for(let c of e)for(let u of c.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(u)):i.push(r+u+a);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await E0(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,sy(l)]}};dy.URL_SCHEME_REGEX=/^https?:\/\//;function yF(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),a=e.substring(0,t),r=n>t?e.substring(n):"";return[a+"/",r]}function cy(e){return e.match(dy.URL_SCHEME_REGEX)!=null}var A0=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(a=>cy(a)):n=cy(e),n)return py(e,t)}return null};At.registerSaveRouter(A0);At.registerLoadRouter(A0);function py(e,t){return new dy(e,t)}function iF(e,t){return py(e,t)}var hy=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},bF=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function oF(e,t,n,a){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new hy(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 hy({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 hy({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:a}))}function lF(e){return new bF(e)}var F0={};Oe(F0,{confusionMatrix:()=>xF});function vF(e,t,n=!1,a=!1){let r=_(e,"a","matMul"),s=_(t,"b","matMul");[r,s]=Tt(r,s);let i={a:r,b:s},o={transposeA:n,transposeB:a};return P.runKernel(Fs,i,o)}var ze=R({matMul_:vF});function wF(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:_(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:a};return P.runKernel(Qs,r,s)}var Dl=R({oneHot_:wF});function kF(e,t){let n=_(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),A(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{A(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let a={x:n},r={perm:t};return P.runKernel(yi,a,r)}var Ue=R({transpose_:kF});function IF(e,t,n){let a=_(e,"labels","confusionMatrix"),r=_(t,"predictions","confusionMatrix");A(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),A(a.rank===1,()=>`Expected the rank of labels to be 1, but got ${a.rank}`),A(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),A(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.`),A(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=Dl(pe(a,"int32"),n),i=Dl(pe(r,"int32"),n),o=Ue(s),l=ze(o,i);return pe(l,"int32")}var xF=R({confusionMatrix_:IF}),Ni={};Oe(Ni,{fromPixels:()=>TF,toPixels:()=>NF});function rh(e,t,n){if(Ss(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let a=ja(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 qr(e,t,a,n)}var Ml;function SF(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 d=2;if(r&&e.readyState<d)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(qg(Xd,P.backendName)!=null){let d={pixels:e},h={numChannels:t};return P.runKernel(Xd,d,h)}let[l,c]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],u;i?u=e.getContext("2d").getImageData(0,0,l,c).data:a||n?u=e.data:(s||r||o)&&(Ml==null&&(Ml=document.createElement("canvas").getContext("2d")),Ml.canvas.width=l,Ml.canvas.height=c,Ml.drawImage(e,0,0,l,c),u=Ml.getImageData(0,0,l,c).data);let p;if(t===4)p=new Int32Array(u);else{let d=l*c;p=new Int32Array(d*t);for(let h=0;h<d;h++)for(let m=0;m<t;++m)p[h*t+m]=u[h*4+m]}return rh(p,[c,l,t],"int32")}async function NF(e,t){let n=_(e,"img","toPixels");if(!(e instanceof z)){let c=n;n=pe(c,"int32"),c.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[a,r]=n.shape.slice(0,2),s=n.rank===2?1:n.shape[2];if(s>4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let i=await n.data(),o=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(r*a*4);for(let c=0;c<a*r;++c){let u=[0,0,0,255];for(let d=0;d<s;d++){let h=i[c*s+d];if(n.dtype==="float32"){if(h<0||h>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${h}.`)}else if(n.dtype==="int32"&&(h<0||h>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${h}.`);s===1?(u[0]=h*o,u[1]=h*o,u[2]=h*o):u[d]=h*o}let p=c*4;l[p+0]=Math.round(u[0]),l[p+1]=Math.round(u[1]),l[p+2]=Math.round(u[2]),l[p+3]=Math.round(u[3])}if(t!=null){t.width=r,t.height=a;let c=t.getContext("2d"),u=new ImageData(l,r,a);c.putImageData(u,0,0)}return n!==e&&n.dispose(),l}var TF=R({fromPixels_:SF}),my={};Oe(my,{prepareAndValidate:()=>$0});function $0(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(Lt(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;p<r.length-1;++p)i*=r[p];let o=e.shape,l=r.slice();l.pop();let c=1;for(let p=s;p<n;++p)c*=o[p],l.push(o[p]);let u=[...To(e.shape).map(p=>p/c),1].slice(0,s);return[l,i,c,u]}var fy={};Oe(fy,{calculateShapes:()=>D0,validateInput:()=>yy,validateUpdateShape:()=>gy});function gy(e,t,n){let a=t.rank>1?t.shape[t.rank-1]:1,r=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${a}, and batchDim: ${r}.`;if(n.rank<r)throw new Error(s+` update.rank < ${r}. `);if(e.length<a+(n.rank-r))throw new Error(s+` Output shape length < ${a+(n.rank-r)}`);if(n.rank!==r+e.length-a)throw new Error(s+` update.rank != ${r+e.length-a}`);for(let i=0;i<r;++i)if(n.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<n.rank-r;++i)if(n.shape[i+r]!==e[i+a])throw new Error(s+` updates.shape[${i+r}] (${n.shape[i+r]}) != shape[${i+r}] (${e[i+r]})`)}function yy(e,t,n){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(e.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(n.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${n}`);if(n.length===0){if(t.size===0)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(e.size===0)throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`)}gy(n,t,e)}function D0(e,t,n){let a=t.shape.length,r=a>1?t.shape[a-1]:1,s=n.length,i=1;for(let p=r;p<s;++p)i*=n[p];let o=r<1?1:r,l=Lt(t.shape)/o,c=[...To(n.slice(0,r)),1],u=Lt(n);return{sliceRank:r,numUpdates:l,sliceSize:i,strides:c,outputSize:u}}var pn={};Oe(pn,{assertParamsValid:()=>CF,computeFlatOffset:()=>EF,computeOutShape:()=>M0,getNormalizedAxes:()=>P0,isSliceContinous:()=>_F,maskToAxes:()=>sh,parseSliceParams:()=>V0,sliceInfo:()=>AF,startForAxis:()=>B0,startIndicesWithElidedDims:()=>O0,stopForAxis:()=>W0,stopIndicesWithElidedDims:()=>L0,stridesForAxis:()=>z0,stridesWithElidedDims:()=>R0});function CF(e,t,n){let a=e.shape.length;A(a===t.length,()=>`Error in slice${a}D: Length of begin ${t} must match the rank of the array (${a}).`),A(a===n.length,()=>`Error in slice${a}D: Length of size ${n} must match the rank of the array (${a}).`);for(let r=0;r<a;++r)A(t[r]+n[r]<=e.shape[r],()=>`Error in slice${a}D: begin[${r}] + size[${r}] (${t[r]+n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`)}function sh(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function M0(e,t,n){let a=[];for(let r=0;r<e.length;r++)a[r]=Math.ceil((t[r]-e[r])/n[r]);return a}function R0(e,t,n,a){let r=[...e];for(let s=r.length;s<a.length;s++)r.push(1);for(let s=0;s<n;s++)s===0?r[t]=1:(r.splice(t,0,1),r.pop());return r}function U0(e,t,n){return n<=e?n:n-(t-1)}function G0(e,t){let n=[];for(let a=0;a<e;a++)n.push(t+a);return n}function P0(e,t,n,a,r,s,i,o,l){let c=e.length,u=new Array(c),p=new Array(c),d=new Array(c);if(t.length&&n>0){let h=t[0],m=n+1;u=O0(i,h,m,a,e),p=L0(o,h,m,r,e),d=R0(s,h,m,e)}else for(let h=0;h<c;h++)u[h]=B0(i,a,s,e,h,l),p[h]=W0(o,r,s,e,h,l),d[h]=z0(s,h,l);return{begin:u,end:p,strides:d}}function O0(e,t,n,a,r){let s=[...r],i=G0(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=U0(t,n,o),c=a[l];e&1<<l&&(c=0),s[o]=c}return s}function L0(e,t,n,a,r){let s=[...r],i=G0(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=U0(t,n,o),c=a[l];e&1<<l&&(c=Number.MAX_SAFE_INTEGER),s[o]=c}for(let o=0;o<s.length;o++){let l=r[o];s[o]<0&&(s[o]+=l),s[o]=ic(0,s[o],r[o])}return s}function z0(e,t,n){let a=e[t];return(n&1<<t||a==null)&&(a=1),a}function B0(e,t,n,a,r,s){let i=t[r],o=n[r]||1;(e&1<<r||s&1<<r||i==null)&&(o>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),i=ic(0,i,l-1),i}function W0(e,t,n,a,r,s){let i=t[r],o=n[r]||1;(e&1<<r||s&1<<r||i==null)&&(o>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),o>0?i=ic(0,i,l):i=ic(-1,i,l-1),i}function _F(e,t,n){let a=n.length;for(let r=0;r<n.length;r++)if(n[r]>1){a=r;break}for(let r=a+1;r<n.length;r++)if(t[r]>0||n[r]!==e[r])return!1;return!0}function EF(e,t){let n=e.length>0?e[e.length-1]:1;for(let a=0;a<e.length-1;a++)n+=e[a]*t[a];return n}function V0(e,t,n){let a,r=e.shape.length;typeof t=="number"?a=[t,...new Array(r-1).fill(0)]:t.length<r?a=t.concat(new Array(r-t.length).fill(0)):a=t.slice(),a.forEach(i=>{A(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(r).fill(-1):typeof n=="number"?s=[n,...new Array(r-1).fill(-1)]:n.length<r?s=n.concat(new Array(r-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(A(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-a[o])),[a,s]}function AF(e,t,n,a,r,s,i,o,l){let c=t.slice(),u=n.slice(),p=a;a==null&&(p=new Array(c.length));let d=sh(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let h=e.length-c.length,m=sh(o),f=e.slice();m.forEach(T=>{c[T]=0,u[T]=1,f.splice(T,0,1)});let{begin:g,end:y,strides:b}=P0(f,d,h,c,u,p,r,s,i);c=g,u=y,p=b;let v=sh(l);v.forEach(T=>{u[T]=c[T]+1,p[T]=1});let x=M0(c,u,p),N=x.filter((T,C)=>v.indexOf(C)===-1);return{nonStrided:p.every(T=>T===1),$begin:c,$end:u,$strides:p,size:x,newShape:f,outShape:N}}var re={};Oe(re,{Serializable:()=>H0,SerializationMap:()=>Ti,registerClass:()=>Yr});var H0=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Ti=class{constructor(){this.classNameMap={}}static getMap(){return Ti.instance==null&&(Ti.instance=new Ti),Ti.instance}static register(e){Ti.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Yr(e){A(e.className!=null,()=>"Class being registered does not have the static className property defined."),A(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),A(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Ti.register(e)}var j0={};Oe(j0,{TEST_EPSILON_FLOAT16:()=>q0,encodeStrings:()=>K0,expectArrayBuffersEqual:()=>PF,expectArraysClose:()=>FF,expectArraysEqual:()=>DF,expectNumbersClose:()=>MF,expectPromiseToFail:()=>$F,expectValuesInRange:()=>RF,testEpsilon:()=>by});var OF=.001,q0=.1;function FF(e,t,n){return n==null&&(n=by()),xy(e,t,(a,r)=>vy(a,r,n))}function by(){return P.backend.floatPrecision()===32?OF:q0}function xy(e,t,n){let a=!0;if((ln(e)||ln(t))&&(a=!1),ln(e)&&ln(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: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=ja(e),o=ja(t);if(!mr(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let r=ln(e)?e:Cs(e),s=ln(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: ${s}.`);for(let i=0;i<s.length;++i){let o=r[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
Actual: ${r}.
Expected: ${s}.`)}}function $F(e,t){e().then(()=>t.fail(),()=>t())}function DF(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return zr(e)||zr(e[0])||zr(t)||zr(t[0])?xy(e,n,(a,r)=>a==r):xy(e,t,(a,r)=>vy(a,r,0))}function MF(e,t,n){if(n==null&&(n=by()),!vy(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function vy(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function RF(e,t,n){for(let a=0;a<e.length;a++)if(e[a]<t||e[a]>n)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function PF(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function K0(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?K0(n):e[t]=Cc(n)}return e}var X0="2.8.5";function LF(){te().set("PROD",!0)}function zF(){te().set("DEBUG",!0)}function BF(){te().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Rt(e){te().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}gA(Rt);function WF(){P.disposeVariables()}function Jr(){return P}function ih(){return P.memory()}function VF(e){return P.profile(e)}function D(e,t){return P.tidy(e,t)}function Fe(e){Jg(e).forEach(t=>t.dispose())}function Kt(e){return P.keep(e)}function UF(e){return P.time(e)}function GF(e){return P.setBackend(e)}function HF(){return P.ready()}function jF(){return P.backendName}function qF(e){P.removeBackend(e)}function KF(e){return P.findBackend(e)}function XF(e){return P.findBackendFactory(e)}function oh(e,t,n=1){return P.registerBackend(e,t,n)}function Y0(){return P.backend}function YF(e,t){te().setPlatform(e,t)}function JF(e,t){let n=_(e,"a","add"),a=_(t,"b","add");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Wr,r)}var Z=R({add_:JF});function ZF(e,t){let n=_(e,"a","floorDiv"),a=_(t,"b","floorDiv");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Ws,r)}var lh=R({floorDiv_:ZF});function QF(e,t){let n=_(e,"a","div"),a=_(t,"b","div");if([n,a]=Tt(n,a),n.dtype==="int32"&&a.dtype==="int32")return lh(n,a);let r={a:n,b:a},s={};return P.runKernel(Ls,r,s)}var we=R({div_:QF});function e$(e,t){let n=_(e,"a","mul"),a=_(t,"b","mul");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Zs,r)}var L=R({mul_:e$});function t$(e){let t=_(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return P.runKernel(pc,n)}else{let n={x:t};return P.runKernel(Co,n)}}var Bt=R({abs_:t$});function n$(e){let t={x:_(e,"x","acos")};return P.runKernel(_o,t)}var wy=R({acos_:n$});function a$(e){let t={x:_(e,"x","acosh")};return P.runKernel(Eo,t)}var ky=R({acosh_:a$});function r$(e){A(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),A(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((r,s)=>_(r,`tensors${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(!mr(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let a=t;return P.runKernel(_s,a)}var J0=R({addN_:r$});function s$(e,t=null,n=!1){let a={x:_(e,"x","all","bool")},r={axis:t,keepDims:n};return P.runKernel(xd,a,r)}var uh=R({all_:s$});function i$(e,t=null,n=!1){let a={x:_(e,"x","any","bool")},r={axis:t,keepDims:n};return P.runKernel(vd,a,r)}var Mc=R({any_:i$});function o$(e,t=0){let n={x:_(e,"x","argMax")},a={axis:t};return P.runKernel(Es,n,a)}var Rc=R({argMax_:o$});function l$(e,t=0){let n={x:_(e,"x","argMin")},a={axis:t};return P.runKernel(lc,n,a)}var Iy=R({argMin_:l$});function u$(e){let t={x:_(e,"x","asin")};return P.runKernel(Ao,t)}var Ny=R({asin_:u$});function c$(e){let t={x:_(e,"x","asinh")};return P.runKernel(Fo,t)}var Ty=R({asinh_:c$});function p$(e){let t={x:_(e,"x","atan")};return P.runKernel($o,t)}var Sy=R({atan_:p$});function d$(e,t){let n=_(e,"a","atan2"),a=_(t,"b","atan2");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Mo,r)}var Cy=R({atan2_:d$});function h$(e){let t={x:_(e,"x","atanh")};return P.runKernel(Do,t)}var _y=R({atanh_:h$});function m$(e,t,n,a,r="NHWC",s){let i=e[3],o=[...t,i],l=Z0(r);return Pc(e,o,n,s,a,null,null,l)}function Q0(e,t,n,a,r,s,i="channelsLast"){let[o,l]=ch(t),c;if(i==="channelsLast")c=[o,l,e[3],e[3]];else if(i==="channelsFirst")c=[o,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return Pc(e,c,n,a,r,s,!1,i)}function f$(e,t,n,a,r,s,i="NDHWC"){let[o,l,c]=Ey(t),u,p;if(i==="NDHWC")p="channelsLast",u=[o,l,c,e[4],e[4]];else if(i==="NCDHW")p="channelsFirst",u=[o,l,c,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return e1(e,u,n,a,r,!1,p,s)}function Pc(e,t,n,a,r,s,i=!1,o="channelsLast"){let[l,c,u,p]=[-1,-1,-1,-1];if(o==="channelsLast")[l,c,u,p]=e;else if(o==="channelsFirst")[l,p,c,u]=e;else throw new Error(`Unknown dataFormat ${o}`);let[d,h,,m]=t,[f,g]=ch(n),[y,b]=ch(a),v=Rl(d,y),x=Rl(h,b),{padInfo:N,outHeight:T,outWidth:C}=g$(r,c,u,f,g,v,x,s,o),$=i?m*p:m,F;return o==="channelsFirst"?F=[l,$,T,C]:o==="channelsLast"&&(F=[l,T,C,$]),{batchSize:l,dataFormat:o,inHeight:c,inWidth:u,inChannels:p,outHeight:T,outWidth:C,outChannels:$,padInfo:N,strideHeight:f,strideWidth:g,filterHeight:d,filterWidth:h,effectiveFilterHeight:v,effectiveFilterWidth:x,dilationHeight:y,dilationWidth:b,inShape:e,outShape:F,filterShape:t}}function e1(e,t,n,a,r,s=!1,i="channelsLast",o){let[l,c,u,p,d]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,c,u,p,d]=e;else if(i==="channelsFirst")[l,d,c,u,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,m,f,,g]=t,[y,b,v]=Ey(n),[x,N,T]=Ey(a),C=Rl(h,x),$=Rl(m,N),F=Rl(f,T),{padInfo:O,outDepth:W,outHeight:V,outWidth:H}=y$(r,c,u,p,y,b,v,C,$,F,o),K=s?g*d:g,j;return i==="channelsFirst"?j=[l,K,W,V,H]:i==="channelsLast"&&(j=[l,W,V,H,K]),{batchSize:l,dataFormat:i,inDepth:c,inHeight:u,inWidth:p,inChannels:d,outDepth:W,outHeight:V,outWidth:H,outChannels:K,padInfo:O,strideDepth:y,strideHeight:b,strideWidth:v,filterDepth:h,filterHeight:m,filterWidth:f,effectiveFilterDepth:C,effectiveFilterHeight:$,effectiveFilterWidth:F,dilationDepth:x,dilationHeight:N,dilationWidth:T,inShape:e,outShape:j,filterShape:t}}function b$(e,t,n,a,r){a==null&&(a=Ay(e,t,n));let s=e[0],i=e[1],o=Si((s-t+2*a)/n+1,r),l=Si((i-t+2*a)/n+1,r);return[o,l]}function x$(e,t,n,a,r,s){r==null&&(r=Ay(e,t,a));let i=e[0],o=e[1],l=e[2],c=Si((i-t+2*r)/a+1,s),u=Si((o-t+2*r)/a+1,s),p=Si((l-t+2*r)/a+1,s);return[c,u,p,n]}function Ay(e,t,n,a=1){let r=Rl(t,a);return Math.floor((e[0]*(n-1)-n+r)/2)}function ch(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Ey(e){return typeof e=="number"?[e,e,e]:e}function Rl(e,t){return t<=1?e:e+(e-1)*(t-1)}function g$(e,t,n,a,r,s,i,o,l){let c,u,p;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let d=b$([t,n],s,a,e,o);u=d[0],p=d[1]}else if(e==="same"){u=Math.ceil(t/a),p=Math.ceil(n/r);let d=Math.max(0,(u-1)*a+s-t),h=Math.max(0,(p-1)*r+i-n),m=Math.floor(d/2),f=d-m,g=Math.floor(h/2),y=h-g;c={top:m,bottom:f,left:g,right:y,type:"SAME"}}else if(e==="valid")c={top:0,bottom:0,left:0,right:0,type:"VALID"},u=Math.ceil((t-s+1)/a),p=Math.ceil((n-i+1)/r);else if(typeof e=="object"){let d=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];c={top:d,bottom:h,left:m,right:f,type:d===0&&h===0&&m===0&&f===0?"VALID":"EXPLICIT"},u=Si((t-s+d+h)/a+1,o),p=Si((n-i+m+f)/r+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:p}}function y$(e,t,n,a,r,s,i,o,l,c,u){let p,d,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=x$([t,n,a,1],o,1,r,e,u);d=f[0],h=f[1],m=f[2]}else if(e==="same"){d=Math.ceil(t/r),h=Math.ceil(n/s),m=Math.ceil(a/i);let f=(d-1)*r+o-t,g=(h-1)*s+l-n,y=(m-1)*i+c-a,b=Math.floor(f/2),v=f-b,x=Math.floor(g/2),N=g-x,T=Math.floor(y/2),C=y-T;p={top:x,bottom:N,left:T,right:C,front:b,back:v,type:"SAME"}}else if(e==="valid")p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/r),h=Math.ceil((n-l+1)/s),m=Math.ceil((a-c+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:d,outHeight:h,outWidth:m}}function Si(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 Zr(e){let[t,n,a]=ch(e);return t===1&&n===1&&a===1}function $n(e,t){return Zr(e)||Zr(t)}function Z0(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function v$(e,t){let n={x:_(e,"x","reshape","string_or_numeric")},a={shape:t};return P.runKernel(hl,n,a)}var q=R({reshape_:v$});function w$(e,t,n,a,r){let s=_(e,"x","avgPool","float32"),i=1;A($n(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=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),A(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),r!=null&&A(jt(a),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let c={x:o},u={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=P.runKernel(As,c,u);return p=pe(p,s.dtype),l?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Yn=R({avgPool_:w$});function k$(e,t,n,a,r,s="NDHWC",i){i==null?i=[1,1,1]:Rt("dilations is deprecated, this field will be gone in v3.0.0.");let o=_(e,"x","avgPool3d","float32"),l=o,c=!1;o.rank===4&&(c=!0,l=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),A(l.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${l.rank}.`),A(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),A($n(n,i),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&A(jt(a),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let u={x:l},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s,dilations:i},d=P.runKernel(uc,u,p);return d=pe(d,l.dtype),c?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Fy=R({avgPool3d_:k$});function I$(e,t=0){A(e.length>=1,()=>"Pass at least one tensor to concat");let n=$c(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 ${s.dtype}. `)}),n.length===1)return Xr(n[0]);let a=n,r={axis:t};return P.runKernel(Po,a,r)}var Qe=R({concat_:I$});function N$(e){let t={x:_(e,"x","sigmoid")};return P.runKernel(ci,t)}var da=R({sigmoid_:N$});function T$(e,t,n){let a=_(e,"x","slice","string_or_numeric");if(a.rank===0)throw new Error("Slicing scalar is not possible");let r={x:a},s={begin:t,size:n};return P.runKernel(yl,r,s)}var We=R({slice_:T$});function S$(e){let t={x:_(e,"x","tanh")};return P.runKernel(gi,t)}var Pl=R({tanh_:S$});function C$(e,t,n,a,r,s){let i=_(e,"forgetBias","basicLSTMCell"),o=_(t,"lstmKernel","basicLSTMCell"),l=_(n,"lstmBias","basicLSTMCell"),c=_(a,"data","basicLSTMCell"),u=_(r,"c","basicLSTMCell"),p=_(s,"h","basicLSTMCell"),d=Qe([c,p],1),h=ze(d,o),m=Z(h,l),f=m.shape[0],g=m.shape[1]/4,y=[f,g],b=We(m,[0,0],y),v=We(m,[0,g],y),x=We(m,[0,g*2],y),N=We(m,[0,g*3],y),T=Z(L(da(b),Pl(v)),L(u,da(Z(i,x)))),C=L(Pl(T),da(N));return[T,C]}var _$=R({basicLSTMCell_:C$});function E$(e,t,n){let a=_(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);A(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),A(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),A(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(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return P.runKernel(cc,s,i)}var Oc=R({batchToSpaceND_:E$});function A$(e){let t;return e.rank===0||e.rank===1?t=q(e,[1,1,1,e.size]):e.rank===2?t=q(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function F$(e,t,n,a,r,s){s==null&&(s=.001);let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;a!=null&&(u=_(a,"offset","batchNorm")),A(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),A(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),A(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:A$(i),scale:c,offset:u,mean:o,variance:l},d={varianceEpsilon:s},h=P.runKernel(Vs,p,d);return q(h,i.shape)}var gr=R({batchNorm_:F$});function $$(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;return a!=null&&(u=_(a,"offset","batchNorm")),A(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),A(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),A(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&A(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&A(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),gr(i,o,l,u,c,s)}var t1=R({batchNorm2d_:$$});function D$(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;return a!=null&&(u=_(a,"offset","batchNorm")),A(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),A(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&A(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),gr(i,o,l,u,c,s)}var n1=R({batchNorm3d_:D$});function M$(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;return a!=null&&(u=_(a,"offset","batchNorm")),A(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),A(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&A(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),gr(i,o,l,u,c,s)}var a1=R({batchNorm4d_:M$});function R$(e,t,n){let a=_(e,"x","bincount"),r=_(t,"weights","bincount");A(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),A(n>=0,()=>`size must be non-negative, but got ${n}.`),A(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: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return P.runKernel(Id,s,i)}var r1=R({bincount_:R$});function P$(e,t){let n=_(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=q(n,l)}let r=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return Xr(n);let i={x:n},o={reps:s};return P.runKernel(Ur,i,o)}var Lc=R({broadcastTo_:P$});function O$(e){let t={x:_(e,"x","ceil")};return P.runKernel(Ro,t)}var $y=R({ceil_:O$});function L$(e,t,n){let a=_(e,"x","clipByValue");A(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return P.runKernel(Vr,r,s)}var Xt=R({clipByValue_:L$});function z$(e){return Qe(e,0)}var s1=R({concat1d_:z$});function B$(e,t){return Qe(e,t)}var i1=R({concat2d_:B$});function W$(e,t){return Qe(e,t)}var o1=R({concat3d_:W$});function V$(e,t){return Qe(e,t)}var l1=R({concat4d_:V$});function U$(e,t,n,a,r="NHWC",s=[1,1],i){let o=_(e,"x","conv2d"),l=_(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),A(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&A(jt(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p=r==="NHWC"?c.shape[3]:c.shape[1];A(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),A($n(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:c,filter:l},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=P.runKernel(Ds,d,h);return u?q(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Ft=R({conv2d_:U$});function G$(e,t,n,a,r="NWC",s=1,i){let o=_(e,"x","conv1d"),l=_(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1]])),A(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),A(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&A(jt(a),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),A(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),A($n(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),A(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=q(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=q(c,[c.shape[0],1,c.shape[1],c.shape[2]]),h=Ft(d,p,[1,n],a,"NHWC",[1,s],i);return u?q(h,[h.shape[2],h.shape[3]]):q(h,[h.shape[0],h.shape[2],h.shape[3]])}var ph=R({conv1d_:G$});function H$(e,t,n,a,r,s="NHWC",i){A(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),A(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),A(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),A(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],p=s==="NHWC"?l.shape[3]:l.shape[1];A(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),A(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),i!=null&&A(jt(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let d={dy:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=P.runKernel(Ms,d,h);return c?q(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Dy=R({conv2DBackpropInput_:H$});function j$(e,t,n,a,r,s){let i=_(e,"x","conv2dTranspose"),o=_(t,"filter","conv2dTranspose");return Dy(n,i,o,a,r,"NHWC",s)}var dh=R({conv2dTranspose_:j$});function q$(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=_(e,"x","conv3d"),o=_(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),A(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),A(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),A(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),A($n(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),A(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:o},p={strides:n,pad:a,dataFormat:r,dilations:s},d=P.runKernel(dc,u,p);return c?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var My=R({conv3d_:q$});function K$(e,t,n,a,r){A(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=q(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],c=i.shape[4];A(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),A(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),A(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),A(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),A(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},p={pad:r,strides:a,inputShape:s},d=P.runKernel(Cd,u,p);return o?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var u1=R({conv3DBackpropInput_:K$});function X$(e,t,n,a,r){let s=_(e,"x","conv3dTranspose"),i=_(t,"filter","conv3dTranspose");return u1(n,s,i,a,r)}var Y$=R({conv3dTranspose_:X$});function J$(e){let t={x:_(e,"x","cos")};return P.runKernel(Rs,t)}var zc=R({cos_:J$});function Z$(e){let t={x:_(e,"x","cosh")};return P.runKernel(Oo,t)}var hh=R({cosh_:Z$});function Q$(e,t=0,n=!1,a=!1){let r={x:_(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return P.runKernel(Ps,r,s)}var mh=R({cumsum_:Q$});function eD(e,t,n,a=!1){let r=_(e,"x","denseBincount"),s=_(t,"weights","denseBincount");A(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),A(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),A(n>=0,()=>`size must be non-negative, but got ${n}.`),A(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 P.runKernel(_d,i,o)}var c1=R({denseBincount_:eD});function tD(e,t,n="NHWC"){let a=_(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];A(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${r} and ${t} for depthToSpace with input shape
${a.shape}`),A(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${s} and ${t} for depthToSpace with input shape
${a.shape}`),A(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 P.runKernel(zo,o,l)}var Ry=R({depthToSpace_:tD});function nD(e,t,n,a,r="NHWC",s=[1,1],i){let o=_(e,"x","depthwiseConv2d"),l=_(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),A(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),A(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),A(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&A(jt(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:c,filter:l},d={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=P.runKernel(Os,p,d);return u?q(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var yr=R({depthwiseConv2d_:nD});function aD(e){let t={x:_(e,"x","diag")};return P.runKernel(Fd,t)}var rD=R({diag_:aD});function sD(e,t,n,a,r=[1,1],s="NHWC"){let i=_(e,"x","dilation2d"),o=_(t,"filter","dilation2d");A(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),A(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),A(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},p={strides:n,pad:a,dilations:r},d=P.runKernel(hc,u,p);return c?q(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Py=R({dilation2d_:sD});function iD(e,t){let n=e.length,a=[];for(let r=0;r<n;r++){let s=n-1-r,i=e[s]||1;(t[t.length-1-r]||1)>1&&i===1&&a.unshift(s)}return a}function Wt(e,t){let n=[];for(let a=0;a<t.length;a++){let r=e[e.length-a-1],s=t.length-a-1,i=t[s];(r==null||r===1&&i>1)&&n.unshift(s)}return n}function wt(e,t){let n=[],a=Math.max(e.length,t.length);for(let r=0;r<a;r++){let s=e[e.length-r-1];s==null&&(s=1);let i=t[t.length-r-1];if(i==null&&(i=1),s===1)n.unshift(i);else if(i===1)n.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else n.unshift(s)}return n}function oD(e,t){let n=_(e,"a","equal"),a=_(t,"b","equal");[n,a]=Tt(n,a),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Vo,r)}var br=R({equal_:oD});function lD(e,t,n){let a=_(t,"a","where"),r=_(n,"b","where"),s=_(e,"condition","where","bool"),i=wt(a.shape,r.shape),o=Lc(a,i),l=Lc(r,i);s.rank===1&&A(s.shape[0]===a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&rt(s.shape,l.shape,"Error in where: ");let c={condition:s,t:o,e:l};return P.runKernel(fl,c)}var wn=R({where_:lD});function uD(e){let t={x:_(e,"x","zerosLike")};return P.runKernel(Sl,t)}var Ge=R({zerosLike_:uD});function cD(e,t){let n=_(e,"a","div"),a=_(t,"b","div");[n,a]=Tt(n,a);let r=we(n,a),s=Ge(r),i=br(a,s);return wn(i,s,r)}var Oy=R({divNoNan_:cD});function pD(e,t){let n=_(e,"t1","dot"),a=_(t,"t2","dot");A((n.rank===1||n.rank===2)&&(a.rank===1||a.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${a.rank}.`);let r=n.rank===1?n.size:n.shape[1],s=a.rank===1?a.size:a.shape[0];if(A(r===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${s}.`),n.rank===1&&a.rank===1){let i=q(n,[1,-1]),o=q(a,[-1,1]),l=ze(i,o);return q(l,[])}else if(n.rank===1&&a.rank===2){let i=q(n,[1,-1]),o=q(a,[a.shape[0],a.shape[1]]),l=ze(i,o);return q(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=q(a,[-1,1]),o=ze(n,i);return q(o,[o.size])}else{let i=q(a,[a.shape[0],a.shape[1]]);return ze(n,i)}}var p1=R({dot_:pD});function dD(e){let t={x:_(e,"x","elu")};return P.runKernel(Bo,t)}var Ol=R({elu_:dD});function hD(e){let t=_(e,"x","erf");A(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=pe(t,"float32"));let n={x:t};return P.runKernel(Wo,n)}var Ly=R({erf_:hD});function mD(e){let t={x:_(e,"x","exp")};return P.runKernel(zs,t)}var dn=R({exp_:mD});function fD(e,t=0){let n=_(e,"x","expandDims","string_or_numeric");A(t<=n.rank,()=>"Axis must be <= rank of the tensor");let a={input:n},r={dim:t};return P.runKernel(Uo,a,r)}var Jn=R({expandDims_:fD});function gD(e){let t={x:_(e,"x","expm1")};return P.runKernel(Go,t)}var zy=R({expm1_:gD});function yD(e,t){let n=_(e,"x","tile","string_or_numeric");A(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 P.runKernel(Ur,a,r)}var qa=R({tile_:yD});function bD(e,t,n,a="float32"){t==null&&(t=e);let r=Le([e,t],a),s=e<=t?e:t;for(let o=0;o<s;++o)r.set(1,o,o);let i=q(r.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return qa(Jn(i,0),[n[0],1,1]);if(n.length===2)return qa(Jn(Jn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return qa(Jn(Jn(Jn(i,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var By=R({eye_:bD});function kn(e,t,n){let a={shape:e,value:t,dtype:n};return P.runKernel(mc,{},a)}function xD(e){let t={x:_(e,"x","floor")};return P.runKernel(Bs,t)}var Ll=R({floor_:xD});function vD(e,t,n=0,a=0){let r=_(e,"x","gather"),s=_(t,"indices","gather","int32"),i={x:r,indices:s},o={axis:n,batchDims:a};return P.runKernel(jo,i,o)}var Ci=R({gather_:vD});function wD(e,t){let n=_(e,"a","greater"),a=_(t,"b","greater");[n,a]=Tt(n,a),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Ko,r)}var Zn=R({greater_:wD});function kD(e,t){let n=_(e,"a","greaterEqual"),a=_(t,"b","greaterEqual");[n,a]=Tt(n,a),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Us,r)}var xr=R({greaterEqual_:kD});function ID(e){let t={input:_(e,"input","imag")};return P.runKernel(Od,t)}var fh=R({imag_:ID});function ND(e){let t={x:_(e,"x","isFinite")};return P.runKernel(Yo,t)}var d1=R({isFinite_:ND});function TD(e){let t={x:_(e,"x","isInf")};return P.runKernel(Jo,t)}var h1=R({isInf_:TD});function SD(e){let t={x:_(e,"x","isNaN")};return P.runKernel(Zo,t)}var m1=R({isNaN_:SD});function CD(e,t=.2){let n={x:_(e,"x","leakyRelu")},a={alpha:t};return P.runKernel(Gs,n,a)}var Bc=R({leakyRelu_:CD});function _D(e,t){let n=_(e,"a","less"),a=_(t,"b","less");[n,a]=Tt(n,a),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Qo,r)}var Wc=R({less_:_D});function ED(e,t){let n=_(e,"a","lessEqual"),a=_(t,"b","lessEqual");[n,a]=Tt(n,a),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(el,r)}var Qr=R({lessEqual_:ED});function f1(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let a={start:e,stop:t,num:n};return P.runKernel(Ld,{},a)}function AD(e,t=5,n=1,a=1,r=.5){let s=_(e,"x","localResponseNormalization");A(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${s.rank}.`),A(jt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=q(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},c={depthRadius:t,bias:n,alpha:a,beta:r},u=P.runKernel(yc,l,c);return o?q(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Wy=R({localResponseNormalization_:AD});function FD(e){let t={x:_(e,"x","log")};return P.runKernel(Hs,t)}var Dn=R({log_:FD});function $D(e){let t={x:_(e,"x","log1p")};return P.runKernel(tl,t)}var gh=R({log1p_:$D});function DD(e){return A(Br(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=_(t,"x","tf.grad","string_or_numeric"),r=n!=null?_(n,"dy","tf.grad"):null;return P.tidy(()=>{let{value:s,grads:i}=P.gradients(()=>e(a),[a],r);return r!=null&&rt(s.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),yh(i),i[0]})}}function MD(e){return A(Br(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{A(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=$c(t,"args","tf.grads","string_or_numeric"),r=n!=null?_(n,"dy","tf.grads"):null;return P.tidy(()=>{let{value:s,grads:i}=P.gradients(()=>e(...a),a,r);return r!=null&&rt(s.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),yh(i),i})}}function RD(e){return A(Br(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{A(t instanceof z,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),A(n==null||n instanceof z,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:a,value:r}=P.gradients(()=>e(t),[t],n);return yh(a),{grad:a[0],value:r}}}function PD(e){return A(Br(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{A(Array.isArray(t)&&t.every(r=>r instanceof z),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),A(n==null||n instanceof z,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let a=P.gradients(()=>e(...t),t,n);return n!=null&&rt(a.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),yh(a.grads),a}}function g1(e,t){A(Br(e),()=>"The f passed in variableGrads(f) must be a function"),A(t==null||Array.isArray(t)&&t.every(c=>c instanceof Hr),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in P.registeredVariables)t.push(P.registeredVariables[c])}let a=n?t.filter(c=>!c.trainable):null,r=t.length;t=t.filter(c=>c.trainable),A(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=P.gradients(e,t,null,s);A(o.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),A(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((c,u)=>{o[u]!=null&&(l[c.name]=o[u])}),a!=null&&a.forEach(c=>l[c.name]=null),{value:i,grads:l}}function Ka(e){return P.customGrad(e)}function yh(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 OD(e){let t={x:_(e,"x","neg")};return P.runKernel(rl,t)}var St=R({neg_:OD});function LD(e){let t={x:_(e,"x","softplus")};return P.runKernel(vl,t)}var zl=R({softplus_:LD});function zD(e){let t=_(e,"x","logSigmoid");return Ka(n=>({value:St(zl(St(n))),gradFunc:a=>L(a,da(St(n)))}))(t)}var y1=R({logSigmoid_:zD});function BD(e,t=null,n=!1){let a={x:_(e,"x","max")},r={reductionIndices:t,keepDims:n};return P.runKernel(js,a,r)}var Qn=R({max_:BD});function WD(e,t){let n=_(e,"a","sub"),a=_(t,"b","sub");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(fi,r)}var ge=R({sub_:WD});function VD(e,t=null,n=!1){let a=_(e,"x","sum");a.dtype==="bool"&&(a=pe(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return P.runKernel(di,r,s)}var Ce=R({sum_:VD});function UD(e,t=-1){let n=_(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 ${t}`);return Ka((a,r)=>{let s=!0,i=Qn(a,t,!0),o=ge(a,i),l=ge(pe(o,"float32"),Dn(Ce(dn(o),t,s)));return r([l]),{value:l,gradFunc:(c,u)=>{let[p]=u,d=!0,h=dn(p);return ge(c,L(Ce(c,t,d),h))}}})(n)}var bh=R({logSoftmax_:UD});function Vy(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function b1(e,t,n){let a=e.length+t.length,r=[],s=0,i=0;for(let o=0;o<a;o++)n.indexOf(o)===-1?r.push(e[s++]):r.push(t[i++]);return r}function x1(e,t){let n=[],a=e.length;for(let s=0;s<a;s++)t.indexOf(s)===-1&&n.push(e[s]);let r=t.map(s=>e[s]);return[n,r]}function _i(e,t){let n=t.map(a=>1);return b1(e,n,t)}function GD(e,t,n){A(Vy(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function v1(e,t){if(Vy(e,t))return null;let n=[];for(let a=0;a<t;++a)e.indexOf(a)===-1&&n.push(a);return e.forEach(a=>n.push(a)),n}function Uy(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function HD(e,t){let n=[];for(let a=t-e;a<t;++a)n.push(a);return n}function jD(e,t=null,n=!1){let a=_(e,"x","logSumExp"),r=ca(t,a.shape),s=Qn(a,r,!0),i=ge(a,s),o=dn(i),l=Ce(o,r),c=Dn(l),u=Z(q(s,c.shape),c);if(n){let p=_i(u.shape,r);return q(u,p)}return u}var Gy=R({logSumExp_:jD});function qD(e,t){let n=_(e,"a","logicalAnd","bool"),a=_(t,"b","logicalAnd","bool");wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(nl,r)}var ha=R({logicalAnd_:qD});function KD(e){let t={x:_(e,"x","logicalNot","bool")};return P.runKernel(fc,t)}var Vc=R({logicalNot_:KD});function XD(e,t){let n=_(e,"a","logicalOr","bool"),a=_(t,"b","logicalOr","bool");wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(gc,r)}var xh=R({logicalOr_:XD});function YD(e,t){let n=_(e,"a","logicalXor","bool"),a=_(t,"b","logicalXor","bool");return wt(n.shape,a.shape),ha(xh(e,t),Vc(ha(e,t)))}var w1=R({logicalXor_:YD});function JD(e,t,n,a,r){let s=_(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),A(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),A($n(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&A(jt(a),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let c={x:o},u={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=P.runKernel(Ks,c,u);return l?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var $t=R({maxPool_:JD});function ZD(e,t=[1,1,1],n,a,r,s="NDHWC",i){i==null?i=[1,1,1]:Rt("dilations is deprecated, this field will be gone in v3.0.0.");let o=_(e,"x","maxPool3d"),l=o,c=!1;o.rank===4&&(c=!0,l=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),A(l.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${l.rank}.`),A(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),A($n(n,i),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&A(jt(a),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let u={x:l},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s,dilations:i},d=P.runKernel(bc,u,p);return c?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Hy=R({maxPool3d_:ZD});function QD(e,t,n,a,r=!1){let s={x:_(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:a,includeBatchInIndex:r},o=P.runKernel(Vd,s,i);return{result:o[0],indexes:o[1]}}var k1=R({maxPoolWithArgmax_:QD});function eM(e,t){let n=_(e,"a","maximum"),a=_(t,"b","maximum");[n,a]=Tt(n,a),n.dtype==="bool"&&(n=pe(n,"int32"),a=pe(a,"int32")),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(qs,r)}var Sa=R({maximum_:eM});function tM(e,t=null,n=!1){let a={x:_(e,"x","mean")},r={axis:t,keepDims:n};return P.runKernel(Xs,a,r)}var Ct=R({mean_:tM});function nM(e,t=null,n=!1){let a={x:_(e,"x","min")},r={axis:t,keepDims:n};return P.runKernel(Ys,a,r)}var Bl=R({min_:nM});function aM(e,t){let n=_(e,"a","minimum"),a=_(t,"b","minimum");[n,a]=Tt(n,a),n.dtype==="bool"&&(n=pe(n,"int32"),a=pe(a,"int32")),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Js,r)}var Ei=R({minimum_:aM});function rM(e,t,n){A(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let a=_(e,"x","mirrorPad");if(a.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");A(t.length===a.rank,()=>`Padding doesn't match input. Must be ${a.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let o=0;o<a.rank;o++)A(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),A(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 ${a.shape}`);let s={paddings:t,mode:n},i={x:a};return P.runKernel(xc,i,s)}var jy=R({mirrorPad_:rM});function sM(e,t){let n=_(e,"a","mod"),a=_(t,"b","mod");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(al,r)}var vh=R({mod_:sM});function iM(e){let t=_(e,"x","square"),n={};return P.runKernel("Square",{x:t},n)}var ut=R({square_:iM});function oM(e,t=null,n=!1){e=_(e,"x","moments");let a=ca(t,e.shape),r=Ct(e,a,n),s=r.shape;n||(s=_i(r.shape,a));let i=ut(ge(pe(e,"float32"),q(r,s))),o=Ct(i,a,n);return{mean:r,variance:o}}var wh=R({moments_:oM});function lM(e,t,n,a){let r=_(t,"data","multiRNNCell"),s=$c(n,"c","multiRNNCell"),i=$c(a,"h","multiRNNCell"),o=r,l=[];for(let p=0;p<e.length;p++){let d=e[p](o,s[p],i[p]);l.push(d[0]),l.push(d[1]),o=d[1]}let c=[],u=[];for(let p=0;p<l.length;p+=2)c.push(l[p]),u.push(l[p+1]);return[c,u]}var uM=R({multiRNNCell_:lM});function cM(e,t,n,a=!1){let r=_(e,"logits","multinomial"),s=r.size,i=r.rank;if(s<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);if(i>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);n=n||Math.random();let o={logits:i===1?q(r,[1,-1]):r},l={numSamples:t,seed:n,normalized:a},c=P.runKernel(Ud,o,l);return i===1?q(c,[c.size]):c}var I1=R({multinomial_:cM});function pM(e,t){let n=_(e,"a","notEqual"),a=_(t,"b","notEqual");[n,a]=Tt(n,a),wt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(sl,r)}var es=R({notEqual_:pM});function bt(e,t="float32"){if(t==="complex64"){let a=bt(e,"float32"),r=bt(e,"float32");return jr(a,r)}let n=bd(Lt(e),t);return P.makeTensor(n,e,t)}function Xa(e,t="float32"){if(t==="complex64"){let a=Xa(e,"float32"),r=bt(e,"float32");return jr(a,r)}let n=Wg(Lt(e),t);return P.makeTensor(n,e,t)}function dM(e){let t={x:_(e,"x","onesLike")};return P.runKernel(ul,t)}var Mn=R({onesLike_:dM});function hM(e,t){let n=_(e,"v1","outerProduct"),a=_(t,"v2","outerProduct");A(n.rank===1&&a.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${a.rank}.`);let r=q(n,[-1,1]),s=q(a,[1,-1]);return ze(r,s)}var mM=R({outerProduct_:hM});function fM(e,t,n=0){let a=_(e,"x","pad");if(a.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let r={paddings:t,constantValue:n},s={x:a};return P.runKernel(ei,s,r)}var ea=R({pad_:fM});function gM(e,t,n=0){return A(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),ea(e,[t],n)}var yM=R({pad1d_:gM});function bM(e,t,n=0){return A(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ea(e,t,n)}var xM=R({pad2d_:bM});function vM(e,t,n=0){return A(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."),ea(e,t,n)}var wM=R({pad3d_:vM});function kM(e,t,n=0){return A(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."),ea(e,t,n)}var IM=R({pad4d_:kM});function NM(e,t,n){let a=_(e,"x","spaceToBatchND");A(a.rank>=1+t.length,()=>`input rank ${a.rank} should be > than [blockShape] ${t.length}`),A(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),A(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 ${t.toString()}`);let r={x:a},s={blockShape:t,paddings:n};return P.runKernel(kc,r,s)}var Uc=R({spaceToBatchND_:NM});function CM(e,t,n,a,r,s){r==null&&(r=[1,1]),s==null&&(s=1),a===0&&(a="valid");let i=_(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2]])),A($n(s,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${r}'`);let c=Q0(o.shape,t,s,r,a),u=[c.dilationHeight,c.dilationWidth],p;a==="same"?p=SM([c.filterHeight,c.filterWidth],u):p=[[0,0],[0,0]];let d=u[0]===1&&u[1]===1,[h,m]=TM([c.inHeight,c.inWidth],u,p),f=d?a:"valid",g=d?o:Uc(o,u,h),y=(n==="avg"?()=>Yn(g,t,s,f):()=>$t(g,t,s,f))(),b=d?y:Oc(y,u,m);return l?q(b,[b.shape[1],b.shape[2],b.shape[3]]):b}function TM(e,t,n){let a=n.map(u=>u[0]),r=n.map(u=>u[1]),s=e.concat(a,r),i=t.map((u,p)=>(u-s[p]%u)%u),o=r.map((u,p)=>u+i[p]),l=t.map((u,p)=>[a[p],o[p]]),c=t.map((u,p)=>[0,i[p]]);return[l,c]}function SM(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 N1=R({pool_:CM});function _M(e,t){let n=_(e,"base","pow"),a=_(t,"exp","pow");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(ti,r)}var Ya=R({pow_:_M});function EM(e,t){let n=_(e,"x","prelu"),a=_(t,"alpha","prelu"),r={x:n,alpha:a};return P.runKernel(ni,r)}var Gc=R({prelu_:EM});function AM(e,t=null,n=!1){let a=_(e,"x","prod");a.dtype==="bool"&&(a=pe(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return P.runKernel(pl,r,s)}var kh=R({prod_:AM});function FM(e,t,n){let a=Lt(e),r=null;if(n==null||n==="float32")r=new Float32Array(a);else if(n==="int32")r=new Int32Array(a);else if(n==="bool")r=new Uint8Array(a);else throw new Error(`Unknown data type ${n}`);for(let s=0;s<a;s++)r[s]=t();return P.makeTensor(r,e,n)}var $M=R({rand_:FM}),qy=No(md()),Ky=class{constructor(e,t,n,a,r){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=a,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=r||Math.random();this.random=qy.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let a=this.nextVal;return this.nextVal=NaN,a}let e,t,n=!1;for(;!n;){let a,r,s;do a=2*this.random()-1,r=2*this.random()-1,s=a*a+r*r;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*a*i,t=this.mean+this.stdDev*r*i,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},DM=class{constructor(e,t,n,a){this.alpha=e,this.beta=1/t,this.dtype=n;let r=a||Math.random();this.randu=qy.alea(r.toString()),this.randn=new Ky(0,1,n,!1,this.randu()),e<1?this.d=e+2/3:this.d=e-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let e,t,n,a,r,s;for(;;){do a=this.randn.nextValue(),s=1+this.c*a;while(s<=0);if(s*=s*s,e=a*a,t=1-.331*e*e,n=.5*e+this.d*(1-s+Math.log(s)),r=this.randu(),r<t||Math.log(r)<n)break}return s=1/this.beta*this.d*s,this.alpha<1&&(s*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(s)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},MM=class{constructor(e=0,t=1,n,a){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,a==null&&(a=Math.random()),typeof a=="number"&&(a=a.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=qy.alea(a)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function RM(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 DM(t,n,a,r),i=Le(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var PM=R({randomGamma_:RM});function OM(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error(`Unsupported data type ${a}`);let s=new Ky(t,n,a,!1,r),i=Le(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var T1=R({randomNormal_:OM});function LM(e,t=0,n=1,a="float32",r){let s=Le(e,a),i=new MM(t,n,null,r);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var Wl=R({randomUniform_:LM});function Ih(e,t,n=1,a="float32"){if(n===0)throw new Error("Cannot have a step of zero");let r={start:e,stop:t,step:n,dtype:a};return P.runKernel(vc,{},r)}function zM(e){let t={input:_(e,"input","real")};return P.runKernel(Gd,t)}var Hc=R({real_:zM});function BM(e){let t={x:_(e,"x","reciprocal")};return P.runKernel(dl,t)}var Xy=R({reciprocal_:BM});function WM(e){let t={x:_(e,"x","relu")};return P.runKernel(ai,t)}var Ye=R({relu_:WM});function VM(e){let t={x:_(e,"x","relu6")};return P.runKernel(si,t)}var Nh=R({relu6_:VM});function UM(e,t){let n={x:_(e,"x","reverse")},a={dims:t};return P.runKernel(ii,n,a)}var Rn=R({reverse_:UM});function GM(e){let t=_(e,"x","reverse");return A(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Rn(t,0)}var HM=R({reverse1d_:GM});function jM(e,t){let n=_(e,"x","reverse");return A(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Rn(n,t)}var qM=R({reverse2d_:jM});function KM(e,t){let n=_(e,"x","reverse");return A(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Rn(n,t)}var XM=R({reverse3d_:KM});function YM(e,t){let n=_(e,"x","reverse");return A(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Rn(n,t)}var JM=R({reverse4d_:YM});function ZM(e){let t={x:_(e,"x","round")};return P.runKernel(oi,t)}var Yy=R({round_:ZM});function QM(e){let t={x:_(e,"x","rsqrt")};return P.runKernel(li,t)}var Th=R({rsqrt_:QM});function he(e,t){if((ln(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"&&ln(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return qr(e,[],[],t)}function eR(e){let t={x:_(e,"x","selu")};return P.runKernel(gl,t)}var Sh=R({selu_:eR});function tR(e,t,n,a,r,s=[1,1],i="NHWC"){let o=_(e,"x","separableConv2d"),l=_(t,"depthwiseFilter","separableConv2d"),c=_(n,"pointwiseFilter","separableConv2d"),u=o,p=!1;if(o.rank===3&&(p=!0,u=q(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");A(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),A(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),A(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${c.shape[0]}.`),A(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${c.shape[1]}.`);let d=l.shape[2],h=l.shape[3];A(c.shape[2]===d*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*h}, but got ${c.shape[2]}.`);let m=yr(u,l,a,r,i,s),f=Ft(m,c,1,"valid",i);return p?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Ai=R({separableConv2d_:tR});async function nR(e,t){let n=_(e,"x","setdiff1d"),a=_(t,"y","setdiff1d");A(n.dtype===a.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${a.dtype}).`),A(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),A(a.rank===1,()=>`y should be 1D tensor, but got y (${a.shape}).`);let r=await n.data(),s=await a.data(),i=new Set(s),o=0;for(let u=0;u<r.length;u++)i.has(r[u])||o++;let l=new zt([o],n.dtype),c=new zt([o],"int32");for(let u=0,p=0;u<r.length;u++)i.has(r[u])||(l.values[p]=r[u],c.values[p]=u,p++);return[l.toTensor(),c.toTensor()]}var S1=nR;function aR(e){let t={x:_(e,"x","sign")};return P.runKernel(xl,t)}var Jy=R({sign_:aR});function rR(e){let t={x:_(e,"x","sin")};return P.runKernel(ui,t)}var Ch=R({sin_:rR});function sR(e){let t={x:_(e,"x","sinh")};return P.runKernel(bl,t)}var _h=R({sinh_:sR});function iR(e,t,n){let a=_(e,"x","slice1d");return A(a.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${a.rank} tensor`),We(a,[t],[n])}var Eh=R({slice1d_:iR});function oR(e,t,n){let a=_(e,"x","slice2d");return A(a.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${a.rank} tensor`),We(a,t,n)}var Zy=R({slice2d_:oR});function lR(e,t,n){let a=_(e,"x","slice3d");return A(a.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${a.rank} tensor`),We(a,t,n)}var Vl=R({slice3d_:lR});function uR(e,t,n){let a=_(e,"x","slice4d");return A(a.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${a.rank} tensor`),We(a,t,n)}var jc=R({slice4d_:uR});function cR(e,t=-1){let n=_(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 P.runKernel(hi,a,r)}var Ca=R({softmax_:cR});function pR(e){A(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return P.runKernel(Rd,t)}var qc=R({fft_:pR});function dR(e){A(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return P.runKernel(Pd,t)}var Ul=R({ifft_:dR});function hR(e){let t=e.shape[e.shape.length-1],n=e.size/t,a;if(t<=2){let r=q(e,[n,t]);a=Ul(r)}else{let r=[n,2*(t-1)],s=q(Hc(e),[n,t]),i=q(fh(e),[n,t]),o=Rn(We(s,[0,1],[n,t-2]),1),l=L(Rn(We(i,[0,1],[n,t-2]),1),he(-1)),c=Qe([s,o],1),u=Qe([i,l],1),p=q(jr(c,u),[r[0],r[1]]);a=Ul(p)}if(a=Hc(a),e.rank===3&&e.shape[0]!==0){let r=a,s=e.shape[0];a=q(a,[s,a.shape[0]/s,a.shape[1]]),r.dispose()}return a}var Ah=R({irfft_:hR});function mR(e,t,n=0){let a={x:_(e,"x","split")},r={numOrSizeSplits:t,axis:n};return P.runKernel(wl,a,r)}var Pn=R({split_:mR});function fR(e,t){A(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],a=e.size/n,r;if(t!=null&&t<n){let m=e.shape.map(g=>0),f=e.shape.map(g=>g);f[e.shape.length-1]=t,r=We(e,m,f),n=t}else if(t!=null&&t>n){let m=e.shape.map(f=>f);m[e.shape.length-1]=t-n,r=Qe([e,bt(m)],e.shape.length-1),n=t}else r=e;let s=Ge(r),i=q(jr(r,s),[a,n]),o=qc(i),l=Math.floor(n/2)+1,c=Hc(o),u=fh(o),p=Pn(c,[l,n-l],c.shape.length-1),d=Pn(u,[l,n-l],u.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,q(jr(p[0],d[0]),h)}var Kc=R({rfft_:fR});function gR(e){let t={x:_(e,"x","sqrt")};return P.runKernel(pi,t)}var nn=R({sqrt_:gR});function yR(e,t){let n=_(e,"a","squaredDifference"),a=_(t,"b","squaredDifference");[n,a]=Tt(n,a),wt(n.shape,a.shape);let r={a:n,b:a},s={};return P.runKernel(mi,r,s)}var Xc=R({squaredDifference_:yR});function bR(e,t){let n=_(e,"x","squeeze");return q(n,Vw(n.shape,t).newShape)}var ts=R({squeeze_:bR});function xR(e,t=0){let n=$c(e,"tensors","stack","string_or_numeric");A(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&A(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let a=n,r={axis:t};return P.runKernel(cl,a,r)}var Dt=R({stack_:xR});function vR(e,t=0){let n={x:_(e,"x","step")},a={alpha:t};return P.runKernel(Gr,n,a)}var Gl=R({step_:vR});function wR(e,t,n,a,r=0,s=0,i=0,o=0,l=0){let c={x:_(e,"x","stridedSlice")},u={begin:t,end:n,strides:a,beginMask:r,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return P.runKernel(kl,c,u)}var Qy=R({stridedSlice_:wR});function kR(e){let t={x:_(e,"x","tan")};return P.runKernel(Il,t)}var eb=R({tan_:kR});function tt(e,t){Ss(e);let n=ja(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return qr(e,null,n,t)}function _a(e,t,n){if(Ss(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let a=ja(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 qr(e,t,a,n)}function Ea(e,t,n){if(Ss(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let a=ja(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 qr(e,t,a,n)}function IR(e,t,n){if(Ss(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let a=ja(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 qr(e,t,a,n)}function NR(e,t,n){if(Ss(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let a=ja(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,qr(e,t,a,n)}function TR(e,t=1,n=!0){let a=_(e,"x","topk");if(a.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let r=a.shape[a.shape.length-1];if(t>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]=P.runKernel(Nl,s,i);return{values:o,indices:l}}var tb=R({topk_:TR});function SR(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new Ky(t,n,a,!0,r),i=Le(e,a);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Fh=R({truncatedNormal_:SR});function CR(e,t=0){let n=_(e,"x","unique","string_or_numeric");A(n.rank>0,()=>"The input tensor must be at least 1D");let a={x:n},r={axis:t},[s,i]=P.runKernel(Kd,a,r);return{values:s,indices:i}}var $h=R({unique_:CR});function _R(e,t,n){let a=_(e,"x","unsortedSegmentSum"),r=_(t,"segmentIds","unsortedSegmentSum","int32");A(jt(n),()=>"numSegments must be of dtype int");let s={x:a,segmentIds:r},i={numSegments:n};return P.runKernel(Nc,s,i)}var nb=R({unsortedSegmentSum_:_R});function ER(e,t=0){let n=_(e,"x","unstack","string_or_numeric");A(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let a={value:n},r={axis:t};return P.runKernel(Tl,a,r)}var ht=R({unstack_:ER});function C1(e,t=!0,n,a){return P.makeVariable(e,t,n,a)}function _1(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let a=Le(e,"int32"),r=Le([n.length,e.length],"int32");for(let s=0;s<n.length;s++){let i=a.indexToLoc(n[s]),o=s*e.length;r.values.set(i,o)}return r.toTensor()}async function AR(e){let t=_(e,"condition","whereAsync","bool"),n=await t.data(),a=_1(t.shape,n);return e!==t&&t.dispose(),a}var ab=AR;async function FR(e,t,n){let a=_(e,"tensor","boolMask"),r=_(t,"mask","boolMask","bool"),s=n==null?0:n,i=r.rank,o=a.shape;A(i>0,()=>"mask cannot be scalar"),rt(o.slice(s,s+i),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let f=s;f<s+i;f++)l*=o[f];let c=o.slice(0,s).concat([l],o.slice(s+i)),u=q(a,c),p=q(r,[-1]),d=await ab(p),h=ts(d,[1]),m=Ci(u,h,s);return e!==a&&a.dispose(),t!==r&&r.dispose(),h.dispose(),u.dispose(),p.dispose(),d.dispose(),m}var $R=FR;function DR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","notEqualStrict"),a=_(t,"b","notEqualStrict");return rt(n.shape,a.shape,"Error in notEqualStrict: "),es(n,a)}function MR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","lessStrict"),a=_(t,"b","lessStrict");return rt(n.shape,a.shape,"Error in lessStrict: "),Wc(n,a)}function RR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","equalStrict"),a=_(t,"b","equalStrict");return rt(n.shape,a.shape,"Error in equalStrict: "),br(n,a)}function PR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","lessEqualStrict"),a=_(t,"b","lessEqualStrict");return rt(n.shape,a.shape,"Error in lessEqualStrict: "),Qr(n,a)}function OR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","greaterStrict"),a=_(t,"b","greaterStrict");return rt(n.shape,a.shape,"Error in greaterStrict: "),Zn(n,a)}function LR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","greaterEqualStrict"),a=_(t,"b","greaterEqualStrict");return rt(n.shape,a.shape,"Error in greaterEqualStrict: "),xr(n,a)}var E1=R({equalStrict_:RR}),A1=R({greaterEqualStrict_:LR}),F1=R({greaterStrict_:OR}),$1=R({lessEqualStrict_:PR}),D1=R({lessStrict_:MR}),M1=R({notEqualStrict_:DR});function zR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","addStrict"),a=_(t,"b","addStrict");return rt(n.shape,a.shape,"Error in addStrict: "),Z(n,a)}function BR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","subStrict"),a=_(t,"b","subStrict");return rt(n.shape,a.shape,"Error in subStrict: "),ge(n,a)}function WR(e,t){return Rt("strict variants of ops have been deprecated and will be removed in future"),rt(e.shape,t.shape,"Error in powStrict: "),Ya(e,t)}function VR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","mul"),a=_(t,"b","mul");return rt(n.shape,a.shape,"Error in multiplyStrict: "),L(n,a)}function UR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","div"),a=_(t,"b","div");return rt(n.shape,a.shape,"Error in divideStrict: "),we(n,a)}function GR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","modStrict"),a=_(t,"b","modStrict");return rt(n.shape,a.shape,"Error in modStrict: "),vh(n,a)}function HR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","minimumStrict"),a=_(t,"b","minimumStrict");return rt(n.shape,a.shape,"Error in minimumStrict: "),Ei(n,a)}function jR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","maximumStrict"),a=_(t,"b","maximumStrict");return rt(n.shape,a.shape,"Error in maximumStrict: "),Sa(n,a)}function qR(e,t){Rt("strict variants of ops have been deprecated and will be removed in future");let n=_(e,"a","squaredDifferenceStrict"),a=_(t,"b","squaredDifferenceStrict");return rt(n.shape,a.shape,"Error in squaredDifferenceStrict: "),Xc(n,a)}var R1=R({addStrict_:zR}),P1=R({divStrict_:UR}),O1=R({maximumStrict_:jR}),L1=R({minimumStrict_:HR}),z1=R({modStrict_:GR}),B1=R({mulStrict_:VR}),W1=R({powStrict_:WR}),V1=R({squaredDifferenceStrict_:qR}),U1=R({subStrict_:BR});function KR(e,t="euclidean",n=null,a=!1){e=_(e,"x","norm");let r=G1(e,t,n),s=r.shape;if(a){let i=ca(n,e.shape);s=_i(r.shape,i)}return q(r,s)}function G1(e,t,n=null){if(e.rank===0)return Bt(e);if(e.rank!==1&&n===null)return G1(q(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Ce(Bt(e),n);if(t===Infinity)return Qn(Bt(e),n);if(t===-Infinity)return Bl(Bt(e),n);if(t==="euclidean"||t===2)return nn(Ce(Ya(Bt(e),he(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Qn(Ce(Bt(e),n[0]),n[1]-1);if(t===Infinity)return Qn(Ce(Bt(e),n[1]),n[0]);if(t===-Infinity)return Bl(Ce(Bt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return nn(Ce(ut(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Dh=R({norm_:KR});function XR(e,t,n,a,r=!0){let s=_(e,"v","movingAverage"),i=_(t,"x","movingAverage"),o=_(n,"decay","movingAverage");l0(s,i),A(mr(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=he(1),c=ge(l,o),u=L(ge(i,s),c);if(r){A(a!=null,()=>"When using zeroDebias: true, step is required.");let p=_(a,"step","movingAverage");u=we(u,ge(l,Ya(o,p)))}return Z(s,u)}var YR=R({movingAverage_:XR});function JR(e,t,n){let a=_(e,"indices","scatterND","int32"),r=_(t,"updates","scatterND");yy(r,a,n);let s={indices:a,updates:r},i={shape:n};return P.runKernel(ml,s,i)}var H1=R({scatterND_:JR});function ZR(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 QR(e,t,n,a=0){let r=_(e,"sparseIndices","sparseToDense","int32"),s=_(t,"sparseValues","sparseToDense"),i=_(a,"defaultValue","sparseToDense",s.dtype);ZR(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:n};return P.runKernel(qd,o,l)}var rb=R({sparseToDense_:QR});function eP(e,t){let n=_(t,"indices","gatherND","int32"),a={params:_(e,"x","gatherND"),indices:n};return P.runKernel(qo,a)}var j1=R({gatherND_:eP});function tP(e,t){if(t==null)return e.shape.slice();if(mr(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let a=0;a<e.shape.length;a++)t[a]==null&&e.shape[a]!=null?n.push(e.shape[a]):n.push(t[a]);return n}return t}function nP(e,t,n,a){let r=_(e,"x","dropout");if(A(r.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`),A(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof z?r.clone():r;let s=tP(r,n),i=1-t,o=we(Ll(Z(Wl(s,0,1,"float32",a),i)),i);return L(r,o)}var q1=R({dropout_:nP});function K1(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function sb(e,t,n){let a=1-e%2,r=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+a-1);r[s]=t-n*Math.cos(i)}return tt(r,"float32")}async function aP(e,t,n=1){let a=_(e,"predictions","inTopK"),r=_(t,"targets","inTopK");A(a.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${a.rank}`),A(a.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${a.rank} and targets rank ${r.rank}`),rt(a.shape.slice(0,a.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=a.shape[a.shape.length-1];A(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await a.data(),o=await r.data(),[l,c]=[i.length/s,s],u=Uw("bool",l);for(let p=0;p<l;p++){let d=p*c,h=i.subarray(d,d+c),m=[];for(let f=0;f<h.length;f++)m.push({value:h[f],index:f});m.sort((f,g)=>g.value-f.value),u[p]=0;for(let f=0;f<n;f++)if(m[f].index===o[p]){u[p]=1;break}}return e!==a&&a.dispose(),t!==r&&r.dispose(),Kn(u,r.shape,"bool")}var rP=aP,ns={};Oe(ns,{conv2d:()=>sP,depthwiseConv2d:()=>iP,matMul:()=>oP});function lP(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]])),A(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),A(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),A(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let c=s==="NHWC"?o.shape[3]:o.shape[1],u=s==="NHWC"?l.shape[3]:l.shape[1];A(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),A(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),i!=null&&A(jt(r),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let p={x:o,dy:l},d={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return P.runKernel(Td,p,d)}var ib=R({conv2DBackpropFilter_:lP});function Mh(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return L(e,Gl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Rh(e,t){let n=t,a=Wt(e.shape,t.shape);return a.length>0&&(n=Ce(n,a)),q(n,e.shape)}function Ph(e,t,n,a){if(t==="linear")return e;if(t==="relu")return Ye(e);if(t==="elu")return Ol(e);if(t==="relu6")return Nh(e);if(t==="prelu")return Gc(e,n);if(t==="leakyrelu")return Bc(e,a);throw new Error(`Unknown fused activation ${t}.`)}var Oh=(e,t)=>!(e>0)||t==="linear";function uP({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Oh(P.state.gradientDepth,l)===!1){let N=Ft(e,t,n,a,r,s,i);return o!=null&&(N=Z(N,o)),Ph(N,l,c,u)}let p=_(e,"x","conv2d"),d=_(t,"filter","conv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=q(p,[1,p.shape[0],p.shape[1],p.shape[2]])),A(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),A(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&A(jt(a),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),A(h.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`),A($n(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),A(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let f=Pc(h.shape,d.shape,n,s,a,i),g;o!=null&&(g=_(o,"bias","fused conv2d"),[g]=Tt(g,p),wt(f.outShape,g.shape));let y;c!=null&&(y=_(c,"prelu weights","fused conv2d"));let b=(N,T)=>{let[C,$,F,O]=T,W=Mh(N,F,l);A(Zr(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let V=Dy($.shape,W,C,n,a),H=ib($,W,C.shape,n,a),K=[V,H];if(O!=null){let j=Rh(O,W);K.push(j)}return K},v={x:h,filter:d,bias:g,preluActivationWeights:y},x={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Ka((N,T,C)=>{let $=P.runKernel(xi,v,x);return C([T,N,$]),m&&($=q($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,d):Ka((N,T,C,$)=>{let F=P.runKernel(xi,v,x);return $([T,N,F,C]),m&&(F=q(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(h,d,g)}var sP=R({fusedConv2d_:uP});function cP(e,t,n,a,r,s=[1,1],i){let o=e;e.rank===3&&(o=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={x:o,dy:l},u={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return P.runKernel(Ed,c,u)}var X1=R({depthwiseConv2dNativeBackpropFilter_:cP});function pP(e,t,n,a,r,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:o,filter:n},u={strides:a,pad:r,dimRoundingMode:i,dilations:s,inputShape:e},p=P.runKernel(Ad,c,u);return l?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Y1=R({depthwiseConv2dNativeBackpropInput_:pP});function dP({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(Oh(P.state.gradientDepth,l)===!1){let N=yr(e,t,n,a,r,s,i);return o!=null&&(N=Z(N,o)),Ph(N,l,c,u)}let p=_(e,"x","depthwiseConv2d"),d=_(t,"filter","depthwiseConv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=q(p,[1,p.shape[0],p.shape[1],p.shape[2]])),A(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),A(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),A(h.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),A($n(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&A(jt(a),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${a}.`);let f=Pc(h.shape,d.shape,n,s,a,i,!0),g;o!=null&&(g=_(o,"bias","fused conv2d"),[g]=Tt(g,p),wt(f.outShape,g.shape));let y;c!=null&&(y=_(c,"prelu weights","fused depthwiseConv2d"));let b=(N,T)=>{A(Zr(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[C,$,F,O]=T,W=Mh(N,F,l),V=Y1($.shape,W,C,n,a,s,i),H=X1($,W,C.shape,n,a,s,i);if(O!=null){let K=Rh(g,W);return[V,H,K]}return[V,H]},v={x:h,filter:d,bias:g,preluActivationWeights:y},x={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Ka((N,T,C)=>{let $=P.runKernel(vi,v,x);return C([T,N,$]),m&&($=q($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,d):Ka((N,T,C,$)=>{let F=P.runKernel(vi,v,x);return $([T,N,F,C]),m&&(F=q(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(h,d,g)}var iP=R({fusedDepthwiseConv2d_:dP});function hP({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Oh(P.state.gradientDepth,s)===!1){let O=ze(e,t,n,a);return r!=null&&(O=Z(O,r)),Ph(O,s,i,o)}let l=_(e,"a","fused matMul"),c=_(t,"b","fused matMul");[l,c]=Tt(l,c);let u=n?l.shape[l.rank-2]:l.shape[l.rank-1],p=a?c.shape[c.rank-1]:c.shape[c.rank-2],d=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?c.shape[c.rank-2]:c.shape[c.rank-1],m=l.shape.slice(0,-2),f=c.shape.slice(0,-2),g=Lt(m),y=Lt(f);A(l.rank>=2&&c.rank>=2&&l.rank===c.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${c.rank}.`),A(mr(m,f),()=>`Error in fused matMul: outer dimensions (${m}) and (${f}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),A(u===p,()=>`Error in fused matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=l.shape.slice(0,-2).concat([d,h]),v=n?q(l,[g,u,d]):q(l,[g,d,u]),x=a?q(c,[y,h,p]):q(c,[y,p,h]),N;r!=null&&(N=_(r,"bias","fused matMul"),[N]=Tt(N,l),wt(b,N.shape));let T;i!=null&&(T=_(i,"prelu weights","fused matMul"));let C=(O,W)=>{let[V,H,K,j]=W,Y=Mh(q(O,K.shape),K,s),J,ne;if(!n&&!a?(J=ze(Y,H,!1,!0),ne=ze(V,Y,!0,!1)):!n&&a?(J=ze(Y,H,!1,!1),ne=ze(Y,V,!0,!1)):n&&!a?(J=ze(H,Y,!1,!0),ne=ze(V,Y,!1,!1)):(J=ze(H,Y,!0,!0),ne=ze(Y,V,!0,!0)),r!=null){let Q=Rh(j,Y);return[J,ne,Q]}else return[J,ne]},$={a:v,b:x,bias:N,preluActivationWeights:T},F={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?Ka((O,W,V)=>{let H=P.runKernel(bi,$,F);return V([O,W,H]),{value:q(H,b),gradFunc:C}})(v,x):Ka((O,W,V,H)=>{let K=P.runKernel(bi,$,F);return H([O,W,K,V]),{value:q(K,b),gradFunc:C}})(v,x,N)}var oP=R({fusedMatMul_:hP});function mP(e){return sb(e,.54,.46)}var fP=R({hammingWindow_:mP});function gP(e){return sb(e,.5,.5)}var J1=R({hannWindow_:gP});function yP(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(We(e,s,t)),s+=n;if(a)for(;s<e.size;){let o=s+t-e.size,l=Qe([We(e,s,t-o),kn([o],r)]);i.push(l),s+=n}return i.length===0?_a([],[0,t]):q(Qe(i),[i.length,t])}var Z1=R({frame_:yP});function bP(e,t,n,a,r=J1){a==null&&(a=K1(t));let s=Z1(e,t,n),i=L(s,r(t)),o=[];for(let l=0;l<s.shape[0];l++)o.push(Kc(We(i,[l,0],[1,t]),a));return Qe(o)}var xP=R({stft_:bP});function vP(e,t,n,a,r="bilinear",s=0){let i=_(e,"image","cropAndResize"),o=_(t,"boxes","cropAndResize","float32"),l=_(n,"boxInd","cropAndResize","int32"),c=o.shape[0];A(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),A(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${o.shape}.`),A(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${o.shape}.`),A(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${a.length}.`),A(a[0]>=1&&a[1]>=1,()=>`cropSize must be atleast [1,1], but was ${a}`),A(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let u={image:i,boxes:o,boxInd:l},p={method:r,extrapolationValue:s,cropSize:a};return P.runKernel(Lo,u,p)}var wP=R({cropAndResize_:vP});function kP(e){let t=_(e,"image","flipLeftRight","float32");A(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return P.runKernel(Ho,n,{})}var IP=R({flipLeftRight_:kP});function NP(e,t,n=0,a=.5){let r=_(e,"image","rotateWithOffset","float32");A(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return P.runKernel(Cl,s,i)}var TP=R({rotateWithOffset_:NP});function Hl(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),A(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),A(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),A(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),A(t.rank===1,()=>"scores must be a 1D tensor"),A(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),A(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s}}function SP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=_(e,"boxes","nonMaxSuppression"),i=_(t,"scores","nonMaxSuppression"),o=Hl(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:a,scoreThreshold:r};return P.runKernel(il,{boxes:s,scores:i},l)}var CP=R({nonMaxSuppression_:SP});function EP(e,t,n){let a=_P(e,t,n),r=a<0?-(a+1):a;e.splice(r,0,t)}function _P(e,t,n){return FP(e,t,n||AP)}function AP(e,t){return e>t?1:e<t?-1:0}function FP(e,t,n){let a=0,r=e.length,s=0,i=!1;for(;a<r;){s=a+(r-a>>>1);let o=n(t,e[s]);o>0?a=s+1:(r=s,i=!o)}return i?a:-a-1}function Q1(e,t,n,a,r){return ob(e,t,n,a,r,0)}function ek(e,t,n,a,r,s){return ob(e,t,n,a,r,0,!1,s,!0)}function tk(e,t,n,a,r,s){return ob(e,t,n,a,r,s,!0)}function ob(e,t,n,a,r,s,i=!1,o=!1,l=!1){let c=[];for(let g=0;g<t.length;g++)t[g]>r&&c.push({score:t[g],boxIndex:g,suppressBeginIndex:0});c.sort(nk);let u=s>0?-.5/s:0,p=[],d=[];for(;p.length<n&&c.length>0;){let g=c.pop(),{score:y,boxIndex:b,suppressBeginIndex:v}=g;if(y<r)break;let x=!1;for(let N=p.length-1;N>=v;--N){let T=$P(e,b,p[N]);if(T>=a){x=!0;break}if(g.score=g.score*DP(a,u,T),g.score<=r)break}g.suppressBeginIndex=p.length,x||(g.score===y?(p.push(b),d.push(g.score)):g.score>r&&EP(c,g,nk))}let h=p.length,m=n-h;o&&m>0&&(p.push(...new Array(m).fill(0)),d.push(...new Array(m).fill(0)));let f={selectedIndices:p};return i&&(f.selectedScores=d),l&&(f.validOutputs=h),f}function $P(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]),c=Math.min(r[0],r[2]),u=Math.min(r[1],r[3]),p=Math.max(r[0],r[2]),d=Math.max(r[1],r[3]),h=(o-s)*(l-i),m=(p-c)*(d-u);if(h<=0||m<=0)return 0;let f=Math.max(s,c),g=Math.max(i,u),y=Math.min(o,p),b=Math.min(l,d),v=Math.max(y-f,0)*Math.max(b-g,0);return v/(h+m-v)}function DP(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function nk(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function MP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=_(e,"boxes","nonMaxSuppressionAsync"),i=_(t,"scores","nonMaxSuppressionAsync"),o=Hl(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),c=l[0],u=l[1],{selectedIndices:p}=Q1(c,u,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),tt(p,"int32")}var RP=MP;function PP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=_(e,"boxes","nonMaxSuppression"),o=_(t,"scores","nonMaxSuppression"),l=Hl(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let c={boxes:i,scores:o},u={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},p=P.runKernel(ll,c,u);return{selectedIndices:p[0],selectedScores:p[1]}}var OP=R({nonMaxSuppressionWithScore_:PP});async function LP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=_(e,"boxes","nonMaxSuppressionAsync"),o=_(t,"scores","nonMaxSuppressionAsync"),l=Hl(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let c=await Promise.all([i.data(),o.data()]),u=c[0],p=c[1],{selectedIndices:d,selectedScores:h}=tk(u,p,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:tt(d,"int32"),selectedScores:tt(h)}}var zP=LP;function BP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=_(e,"boxes","nonMaxSuppression"),o=_(t,"scores","nonMaxSuppression"),l=Hl(i,o,n,a,r,null),c=l.maxOutputSize,u=l.iouThreshold,p=l.scoreThreshold,d={boxes:i,scores:o},h={maxOutputSize:c,iouThreshold:u,scoreThreshold:p,padToMaxOutputSize:s},m=P.runKernel(ol,d,h);return{selectedIndices:m[0],validOutputs:m[1]}}var WP=R({nonMaxSuppressionPadded_:BP});async function VP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=_(e,"boxes","nonMaxSuppressionAsync"),o=_(t,"scores","nonMaxSuppressionAsync"),l=Hl(i,o,n,a,r,null),c=l.maxOutputSize,u=l.iouThreshold,p=l.scoreThreshold,[d,h]=await Promise.all([i.data(),o.data()]),{selectedIndices:m,validOutputs:f}=ek(d,h,c,u,p,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:tt(m,"int32"),validOutputs:he(f,"int32")}}var UP=VP;function GP(e,t,n=!1,a=!1){let r=_(e,"images","resizeBilinear");A(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),A(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),A(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=q(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},c=P.runKernel(ri,o,l);return i?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var ak=R({resizeBilinear_:GP});function HP(e,t,n=!1,a=!1){let r=_(e,"images","resizeNearestNeighbor");A(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),A(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),A(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),A(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=q(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},c=P.runKernel(wc,o,l);return i?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var rk=R({resizeNearestNeighbor_:HP});function jP(e,t,n){A(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),A(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=_(e,"a","bandPart");A(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 (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=q(Ih(0,s,1,"int32"),[-1,1]),l=Ih(0,i,1,"int32"),c=ge(o,l),u=ha(Qr(c,he(+t,"int32")),xr(c,he(-n,"int32"))),p=bt([s,i],a.dtype);return q(Dt(ht(q(a,[-1,s,i])).map(d=>wn(u,d,p))),r)}var qP=R({bandPart_:jP});function KP(e){let t;if(Array.isArray(e)){t=!1,A(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s<e.length;++s)A(e[s].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${r})`)}else t=!0,e=Pn(e,e.shape[0],0).map(r=>ts(r,[0]));A(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],a=e;for(let r=0;r<e.length;++r)n.push(P.tidy(()=>{let s=a[r];if(r>0)for(let i=0;i<r;++i){let o=L(Ce(L(n[i],s)),n[i]);s=ge(s,o)}return we(s,Dh(s,"euclidean"))}));return t?Dt(n,0):n}var XP=R({gramSchmidt_:KP});function YP(e,t=!1){if(A(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return sk(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),a=ht(q(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(l=>{let[c,u]=sk(l,t);r.push(c),s.push(u)});let i=q(Dt(r,0),e.shape),o=q(Dt(s,0),e.shape);return[i,o]}}function sk(e,t=!1){return P.tidy(()=>{A(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],a=e.shape[1],r=By(n),s=Xr(e),i=_a([[1]],[1,1]),o=Xr(i),l=n>=a?a:n;for(let c=0;c<l;++c){let u=s,p=o,d=r;[o,s,r]=P.tidy(()=>{let h=We(s,[c,c],[n-c,1]),m=Dh(h),f=We(s,[c,c],[1,1]),g=wn(Zn(f,0),_a([[-1]]),_a([[1]])),y=ge(f,L(g,m)),b=we(h,y);b.shape[0]===1?o=Xr(i):o=Qe([i,We(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let v=St(we(ze(g,y),m)),x=We(s,[c,0],[n-c,a]),N=L(v,o),T=Ue(o);if(c===0)s=ge(x,ze(N,ze(T,x)));else{let F=ge(x,ze(N,ze(T,x)));s=Qe([We(s,[0,0],[c,a]),F],0)}let C=Ue(N),$=We(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=ge($,ze(ze($,o),C));else{let F=ge($,ze(ze($,o),C));r=Qe([We(r,[0,0],[n,c]),F],1)}return[o,s,r]}),Fe([u,p,d])}return!t&&n>a&&(r=We(r,[0,0],[n,a]),s=We(s,[0,0],[a,a])),[r,s]})}var JP=R({qr_:YP}),hn;(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"})(hn||(hn={}));function ZP(e,t,n=hn.SUM_BY_NONZERO_WEIGHTS){let a=_(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=_(t,"weights","computeWeightedLoss"));let s=r==null?a:L(a,r);if(n===hn.NONE)return s;if(n===hn.SUM)return Ce(s);if(n===hn.MEAN){if(r==null)return Ct(s);{let i=a.size/r.size,o=we(Ce(s),Ce(r));return i>1?we(o,he(i)):o}}if(n===hn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return we(Ce(s),he(a.size));{let i=L(r,Xa(a.shape)),o=pe(Ce(es(i,he(0))),"float32");return we(Ce(s),o)}}throw Error(`Unknown reduction: ${n}`)}var vr=R({computeWeightedLoss_:ZP});function QP(e,t,n,a=hn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","absoluteDifference"),s=_(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=_(n,"weights","absoluteDifference")),rt(r.shape,s.shape,"Error in absoluteDifference: ");let o=Bt(ge(r,s));return vr(o,i,a)}var eO=R({absoluteDifference_:QP});function tO(e,t,n,a,r=hn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"labels","cosineDistance"),i=_(t,"predictions","cosineDistance"),o=null;a!=null&&(o=_(a,"weights","cosineDistance")),rt(s.shape,i.shape,"Error in cosineDistance: ");let l=he(1),c=ge(l,Ce(L(s,i),n,!0));return vr(c,o,r)}var nO=R({cosineDistance_:tO});function aO(e,t,n,a=hn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","hingeLoss"),s=_(t,"predictions","hingeLoss"),i=null;n!=null&&(i=_(n,"weights","hingeLoss")),rt(r.shape,s.shape,"Error in hingeLoss: ");let o=he(1);r=ge(L(he(2),r),o);let l=Ye(ge(o,L(r,s)));return vr(l,i,a)}var rO=R({hingeLoss_:aO});function sO(e,t,n,a=1,r=hn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"labels","huberLoss"),i=_(t,"predictions","huberLoss"),o=null;n!=null&&(o=_(n,"weights","huberLoss")),rt(s.shape,i.shape,"Error in huberLoss: ");let l=he(a),c=Bt(ge(i,s)),u=Ei(c,l),p=ge(c,u),d=Z(L(he(.5),ut(u)),L(l,p));return vr(d,o,r)}var iO=R({huberLoss_:sO});function oO(e,t,n,a=1e-7,r=hn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"labels","logLoss"),i=_(t,"predictions","logLoss"),o=null;n!=null&&(o=_(n,"weights","logLoss")),rt(s.shape,i.shape,"Error in logLoss: ");let l=he(1),c=he(a),u=St(L(s,Dn(Z(i,c)))),p=L(ge(l,s),Dn(Z(ge(l,i),c))),d=ge(u,p);return vr(d,o,r)}var lO=R({logLoss_:oO});function uO(e,t,n,a=hn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","meanSquaredError"),s=_(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=_(n,"weights","meanSquaredError")),rt(r.shape,s.shape,"Error in meanSquaredError: ");let o=Xc(r,s);return vr(o,i,a)}var cO=R({meanSquaredError_:uO});function pO(e,t){let n=_(e,"labels","sigmoidCrossEntropyWithLogits"),a=_(t,"logits","sigmoidCrossEntropyWithLogits");rt(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ye(a),s=L(a,n),i=gh(dn(St(Bt(a))));return Z(ge(r,s),i)}function dO(e,t,n,a=0,r=hn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"multiClassLabels","sigmoidCrossEntropy"),i=_(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=_(n,"weights","sigmoidCrossEntropy")),rt(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let c=he(a),u=he(1),p=he(.5);s=Z(L(s,ge(u,c)),L(p,c))}let l=pO(s,i);return vr(l,o,r)}var hO=R({sigmoidCrossEntropy_:dO});function mO(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 Ka((a,r,s)=>{let i=Gy(r,[n],!0),o=ge(pe(r,"float32"),i);s([a,o]);let l=St(L(o,a));return{value:Ce(l,[n]),gradFunc:(c,u)=>{let[p,d]=u,h=_i(c.shape,[n]);return[L(q(c,h),ge(pe(p,"float32"),dn(d))),L(q(c,h),ge(dn(d),pe(p,"float32")))]}}})(e,t)}function fO(e,t,n,a=0,r=hn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"onehotLabels","softmaxCrossEntropy"),i=_(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=_(n,"weights","softmaxCrossEntropy")),rt(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let c=he(a),u=he(1),p=he(s.shape[1]);s=Z(L(s,ge(u,c)),we(c,p))}let l=mO(s,i);return vr(l,o,r)}var gO=R({softmaxCrossEntropy_:fO}),yO={fft:qc,ifft:Ul,rfft:Kc,irfft:Ah},bO={hammingWindow:fP,hannWindow:J1,frame:Z1,stft:xP},Ja={flipLeftRight:IP,resizeNearestNeighbor:rk,resizeBilinear:ak,rotateWithOffset:TP,cropAndResize:wP,nonMaxSuppression:CP,nonMaxSuppressionAsync:RP,nonMaxSuppressionWithScore:OP,nonMaxSuppressionWithScoreAsync:zP,nonMaxSuppressionPadded:WP,nonMaxSuppressionPaddedAsync:UP},ik={bandPart:qP,gramSchmidt:XP,qr:JP},xO={absoluteDifference:eO,computeWeightedLoss:vr,cosineDistance:nO,hingeLoss:rO,huberLoss:iO,logLoss:lO,meanSquaredError:cO,sigmoidCrossEntropy:hO,softmaxCrossEntropy:gO},wr=class extends H0{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 Fe(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 g1(e,t)}dispose(){this.iterations_!=null&&Fe(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:he(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Lh=class extends wr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;D(()=>{let l=Z(L(i,this.rho),L(ut(s),1-this.rho)),c=L(we(nn(Z(o,this.epsilon)),nn(Z(i,this.epsilon))),s),u=Z(L(o,this.rho),L(ut(c),1-this.rho));i.assign(l),o.assign(u);let p=Z(L(c,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Fe(this.accumulatedGrads.map(e=>e.variable)),Fe(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Lh.className="Adadelta";Yr(Lh);var zh=class extends wr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:D(()=>kn(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;D(()=>{let i=Z(s,ut(r));s.assign(i);let o=Z(L(we(r,nn(Z(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Fe(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)}};zh.className="Adagrad";Yr(zh);var Bh=class extends wr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],D(()=>{this.accBeta1=he(t).variable(),this.accBeta2=he(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);D(()=>{let n=ge(1,this.accBeta1),a=ge(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:D(()=>Ge(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:D(()=>Ge(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,p=Z(L(c,this.beta1),L(l,1-this.beta1)),d=Z(L(u,this.beta2),L(ut(l),1-this.beta2)),h=we(p,n),m=we(d,a);c.assign(p),u.assign(d);let f=Z(L(we(h,Z(nn(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&&Fe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Fe(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),D(()=>{this.accBeta1.assign(Ya(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ya(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)}};Bh.className="Adam";Yr(Bh);var Wh=class extends wr{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=[],D(()=>{this.iteration=he(0).variable(),this.accBeta1=he(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);D(()=>{let n=ge(1,this.accBeta1),a=we(-this.learningRate,Z(L(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ge(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ge(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,p=Z(L(c,this.beta1),L(l,1-this.beta1)),d=L(u,this.beta2),h=Bt(l),m=Sa(d,h);c.assign(p),u.assign(m);let f=Z(L(we(a,n),we(p,Z(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(Z(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Fe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Fe(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)}};Wh.className="Adamax";Yr(Wh);var Yc=class extends wr{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=P.registeredVariables[t];D(()=>{let s=Z(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Kt(he(-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)}};Yc.className="SGD";Yr(Yc);var Vh=class extends Yc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=he(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:D(()=>Ge(a).variable(i))}}let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&D(()=>{let i,o=Z(L(this.m,r),s);this.useNesterov?i=Z(L(this.c,Z(s,L(o,this.m))),a):i=Z(L(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Fe(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Vh.className="Momentum";Yr(Vh);var Uh=class extends wr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=P.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:D(()=>Ge(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;D(()=>{let l=Z(L(i,this.decay),L(ut(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=Z(L(c,this.decay),L(s,1-this.decay)),p=we(L(s,this.learningRate),nn(ge(l,Z(ut(u),this.epsilon)))),d=Z(L(o,this.momentum),p);i.assign(l),c.assign(u),o.assign(d);let h=ge(a,d);a.assign(h)}else{let c=Z(L(i,this.decay),L(ut(s),1-this.decay)),u=Z(L(o,this.momentum),we(L(s,this.learningRate),nn(Z(c,this.epsilon))));i.assign(c),o.assign(u);let p=ge(a,u);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Fe(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Fe(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Fe(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)}};Uh.className="RMSProp";Yr(Uh);var Fi=class{static sgd(e){return new Yc(e)}static momentum(e,t,n=!1){return new Vh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new Uh(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new Bh(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new Lh(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new Wh(e,t,n,a,r)}static adagrad(e,t=.1){return new zh(e,t)}},$i={sgd:Fi.sgd,momentum:Fi.momentum,adadelta:Fi.adadelta,adagrad:Fi.adagrad,rmsprop:Fi.rmsprop,adamax:Fi.adamax,adam:Fi.adam},vO=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Gh(){return new Promise(e=>vO(()=>e()))}var E={};Oe(E,{ERF_A1:()=>FO,ERF_A2:()=>$O,ERF_A3:()=>DO,ERF_A4:()=>MO,ERF_A5:()=>RO,ERF_P:()=>AO,PARALLELIZE_THRESHOLD:()=>lb,SELU_SCALE:()=>lk,SELU_SCALEALPHA:()=>ok,applyActivation:()=>Ph,assertAndGetBroadcastShape:()=>wt,assertAxesAreInnerMostDims:()=>GD,assertParamsConsistent:()=>wO,assignToTypedArray:()=>UO,axesAreInnerMostDims:()=>Vy,calculateShapes:()=>D0,castTensor:()=>qO,combineLocations:()=>b1,complexWithEvenIndex:()=>BO,complexWithOddIndex:()=>WO,computeConv2DInfo:()=>Pc,computeConv3DInfo:()=>e1,computeDefaultPad:()=>Ay,computeDilation2DInfo:()=>m$,computeOptimalWindowSize:()=>IO,computeOutAndReduceShapes:()=>x1,computeOutShape:()=>kO,computePool2DInfo:()=>Q0,computePool3DInfo:()=>f$,convertConv2DDataFormat:()=>Z0,eitherStridesOrDilationsAreOne:()=>$n,expandShapeToKeepDim:()=>_i,exponent:()=>HO,exponents:()=>GO,fromStringArrayToUint8:()=>YO,fromUint8ToStringArray:()=>XO,getAxesPermutation:()=>v1,getBroadcastDims:()=>iD,getComplexWithIndex:()=>VO,getFusedBiasGradient:()=>Rh,getFusedDyActivation:()=>Mh,getImageCenter:()=>NO,getInnerMostAxes:()=>HD,getPermuted:()=>SO,getReductionAxes:()=>Wt,getReshaped:()=>TO,getReshapedPermuted:()=>CO,getSliceBeginCoords:()=>_O,getSliceSize:()=>EO,getUndoAxesPermutation:()=>Uy,log:()=>OO,mergeRealAndImagArrays:()=>LO,prepareAndValidate:()=>$0,prepareSplitSize:()=>jO,reshapeTensor:()=>KO,segment_util:()=>uk,shouldFuse:()=>Oh,slice_util:()=>pn,splitRealAndImagArrays:()=>zO,tupleValuesAreOne:()=>Zr,upcastType:()=>pa,validateInput:()=>yy,validateUpdateShape:()=>gy,warn:()=>PO});function wO(e,t){let n=e[0].length;e.forEach((r,s)=>{A(r.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),A(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let a=e[0];e.forEach((r,s)=>{for(let i=0;i<n;i++)A(i===t||r[i]===a[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${a}) along the non-concatenated axis ${s}.`)})}function kO(e,t){let n=e[0].slice();for(let a=1;a<e.length;a++)n[t]+=e[a][t];return n}var lb=30;function IO(e){return e<=lb?e:yd(e,Math.floor(Math.sqrt(e)))}function NO(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function TO(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(s+1))}return r}function SO(e,t,n=!0){let a=[];if(n){a.push(t);for(let r=t+1;r<e;++r)r<=2*t?(a.push(r),a.push(r-(t+1))):a.push(r)}else{let r=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function CO(e,t,n,a=!0){let r=[];a?r.push(e[0]/n):r.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?a?r.push(t[s-1]*e[s]):r.push(e[s]/t[s-1]):r.push(e[s]);return r}function _O(e,t){let n=[0];for(let a=0;a<t;++a)n.push(e[a][0]);return n}function EO(e,t,n){let a=e.slice(0,1);for(let r=0;r<n;++r)a.push(e[r+1]-t[r][0]-t[r][1]);return a}var ok=1.7580993408473768,lk=1.0507009873554805,AO=.3275911,FO=.254829592,$O=-.284496736,DO=1.421413741,MO=-1.453152027,RO=1.061405429;function PO(...e){te().getBool("IS_TEST")||console.warn(...e)}function OO(...e){te().getBool("IS_TEST")||console.log(...e)}function LO(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let a=0;a<n.length;a+=2)n[a]=e[a/2],n[a+1]=t[a/2];return n}function zO(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let a=0;a<e.length;a+=2)t[a/2]=e[a],n[a/2]=e[a+1];return{real:t,imag:n}}function BO(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function WO(e){let t=Math.floor(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function VO(e,t){let n=e[t*2],a=e[t*2+1];return{real:n,imag:a}}function UO(e,t,n,a){e[a*2]=t,e[a*2+1]=n}function GO(e,t){let n=new Float32Array(e/2),a=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let s=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(s),a[r]=Math.sin(s)}return{real:n,imag:a}}function HO(e,t,n){let a=(n?2:-2)*Math.PI*(e/t),r=Math.cos(a),s=Math.sin(a);return{real:r,imag:s}}function jO(e,t,n=0){let a=[];if(typeof t=="number")A(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),a=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);A(r<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}A(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),a=t}return a}var uk={};Oe(uk,{collectGatherOpShapeInfo:()=>QO,computeOutShape:()=>ZO,segOpComputeOptimalWindowSize:()=>JO});function JO(e,t){let n=!1,a;for(e<=lb?(a=e,n=!0):a=yd(e,Math.floor(Math.sqrt(e)));!n;)a>t||a===e?n=!0:a=yd(e,a+1);return a}function ZO(e,t,n){let a=[],r=e.length;for(let s=0;s<r;s++)s!==t?a.push(e[s]):a.push(n);return a}function QO(e,t,n,a){let r=t.shape.length,s=e.shape.length;if(a!==0&&(a<-r||a>r))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${a}`);if(a<0&&(a+=r),a>s)throw new Error(`batchDims (${a}) must be less than rank(x) (
${s}).`);if(n<a)throw new Error(`batchDims (${a}) must be less than or equal to axis (${n}).`);for(let p=0;p<a;++p)if(e.shape[p]!==t.shape[p])throw new Error(`x.shape[${p}]: ${e.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);let i=e.shape[n],o=[],l=1,c=1,u=1;for(let p=0;p<a;++p)o.push(e.shape[p]),l*=e.shape[p];for(let p=a;p<n;p++)o.push(e.shape[p]),c*=e.shape[p];for(let p=a;p<r;p++)o.push(t.shape[p]);for(let p=n+1;p<s;p++)o.push(e.shape[p]),u*=e.shape[p];return{batchSize:l,sliceSize:u,outerSize:c,dimSize:i,outputShape:o}}function qO(e,t,n){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();let a=bt(e.shape),r=pe(e,"float32"),s=n.complex(r,a);return a.dispose(),r.dispose(),s}if(!qw(e.dtype,t))return P.makeTensorFromDataId(e.dataId,e.shape,t);if(e.dtype==="complex64"){let a=n.real(e),r=pe(a,t);return a.dispose(),r}if(t==="int32")return n.int(e);if(t==="bool"){let a=he(0,e.dtype),r=n.notEqual(e,a);return a.dispose(),r}else throw new Error(`Error in Cast: failed to cast ${e.dtype} to ${t}`)}function KO(e,t){return P.makeTensorFromDataId(e.dataId,t,e.dtype)}function XO(e){try{return e.map(t=>Zd(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function YO(e){return e.map(t=>Cc(t))}var Za={};Oe(Za,{nonMaxSuppressionV3Impl:()=>Q1,nonMaxSuppressionV4Impl:()=>ek,nonMaxSuppressionV5Impl:()=>tk,whereImpl:()=>_1});var ck={kernelName:Co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Gl(pe(n,"float32"),-1))}}},eL={kernelName:_o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ut(pe(n,"float32")),r=nn(ge(he(1),a));return St(we(e,r))}}}},tL={kernelName:Eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=nn(ge(ut(pe(n,"float32")),1));return we(e,a)}}}},nL={kernelName:Wr,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=wt(n.shape,a.shape);return{a:()=>{let s=e,i=Wt(n.shape,r);return i.length>0&&(s=Ce(s,i)),q(s,n.shape)},b:()=>{let s=e,i=Wt(a.shape,r);return i.length>0&&(s=Ce(s,i)),q(s,a.shape)}}}},aL={kernelName:_s,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((a,r)=>{n[r]=()=>e.clone()}),n}},rL={kernelName:Es,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ge(n)}}},sL={kernelName:lc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ge(n)}}},iL={kernelName:Ao,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,nn(ge(he(1),ut(pe(n,"float32")))))}}},oL={kernelName:Fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=nn(Z(he(1),ut(pe(n,"float32"))));return we(e,a)}}}},lL={kernelName:Mo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=wt(n.shape,a.shape);return{a:()=>{let s=Z(ut(n),ut(a)),i=L(e,we(a,s)),o=Wt(n.shape,r);return o.length>0&&(i=Ce(i,o)),q(i,n.shape)},b:()=>{let s=Z(ut(n),ut(a)),i=St(L(e,we(n,s))),o=Wt(a.shape,r);return o.length>0&&(i=Ce(i,o)),q(i,a.shape)}}}},uL={kernelName:$o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,Z(ut(pe(n,"float32")),1))}}},cL={kernelName:Do,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,ge(he(1),ut(pe(n,"float32"))))}}};function pL(e,t,n,a,r=[1,1,1],s,i){let o=_(e,"dy","avgPool3dGrad"),l=_(t,"input","avgPool3dGrad"),c=o,u=l,p=!1;l.rank===4&&(p=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),u=q(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]])),A(c.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),A(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),A($n(a,r),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`),i!=null&&A(jt(s),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let d={dy:c,input:u},h={filterSize:n,strides:a,dilations:r,pad:s,dimRoundingMode:i},m=P.runKernel(kd,d,h);return p?q(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var dL=R({avgPool3dGrad_:pL}),hL={kernelName:uc,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,dilations:i,pad:o,dimRoundingMode:l}=n,c=i==null?[1,1,1]:i;return{x:()=>dL(e,a,r,s,c,o,l)}}};function mL(e,t,n,a,r){let s=_(e,"dy","avgPoolGrad"),i=_(t,"input","avgPoolGrad");A(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,c=!1;i.rank===3&&(c=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),A(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),A(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let u={dy:l,input:o},p={filterSize:n,strides:a,pad:r},d=P.runKernel(wd,u,p);return c?q(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var fL=R({avgPoolGrad_:mL}),gL={kernelName:As,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i}=n;return{x:()=>fL(e,a,r,s,i)}}},yL={kernelName:Fs,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[a,r]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>ze(e,r,!1,!0),b:()=>ze(a,e,!0,!1)}:!s&&i?{a:()=>ze(e,r,!1,!1),b:()=>ze(e,a,!0,!1)}:s&&!i?{a:()=>ze(r,e,!1,!0),b:()=>ze(a,e,!1,!1)}:{a:()=>ze(r,e,!0,!0),b:()=>ze(e,a,!0,!0)}}},bL={kernelName:cc,gradFunc:(e,t,n)=>{let{blockShape:a,crops:r}=n;return{x:()=>Uc(e,a,r)}}},xL={kernelName:a0,gradFunc:(e,t,n)=>{let a=n,r=a.inputShape,s=a.shape,i=Array.from(s);for(let l=r.length-1;l>=0;l--)if(r[l]===s[l])i[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l<i.length;l++)i[l]>1&&o.push(l);return{x:()=>Ce(e,o,!0)}}},vL={kernelName:$s,gradFunc:e=>({x:()=>e.clone()})},wL={kernelName:Ro,gradFunc:e=>({x:()=>Ge(e)})},kL={kernelName:Vr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{clipValueMin:r,clipValueMax:s}=n;return{x:()=>wn(ha(xr(a,r),Qr(a,s)),e,Ge(e))}}},IL={kernelName:pc,inputsToSave:["x"],gradFunc:ck.gradFunc},NL={kernelName:Po,saveAllInputs:!0,gradFunc:(e,t,n)=>{let a=t.map(o=>o.shape),{axis:r}=n,s=ca(r,t[0].shape)[0],i=a.map(o=>o[s]);return Pn(e,i,s).map(o=>()=>o)}},TL={kernelName:Ds,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return A(Zr(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>Dy(a.shape,e,r,i,o,l),filter:()=>ib(a,e,r.shape,i,o,l)}}},SL={kernelName:Ms,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>Ft(e,r,s,i,o,1,l),filter:()=>ib(e,a,r.shape,s,i,o,l)}}};function CL(e,t,n,a,r){let s=e;e.rank===4&&(s=q(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=q(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),A(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),A(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),A(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),A(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),A(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 P.runKernel(Sd,o,l)}var _L=R({conv3DBackpropFilter_:CL}),EL={kernelName:dc,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s}=n;A(Zr(a),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let[i,o]=t;return{x:()=>u1(i.shape,e,o,r,s),filter:()=>_L(i,e,o.shape,r,s)}}},AL={kernelName:Rs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(St(Ch(pe(n,"float32"))),e)}}},FL={kernelName:Oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(_h(pe(n,"float32")),e)}}},$L={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r,exclusive:s,reverse:i}=n;return{x:()=>{let o=v1([r],a.rank),l=mh(e,r,s,!i);return o!=null&&(l=Ue(l,o)),l}}}},DL={kernelName:Os,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s,dimRoundingMode:i}=n,o=a==null?[1,1]:a;A(Zr(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,c]=t;return A(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),A(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),A(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),A($n(r,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${o}'.`),i!=null&&A(jt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>Y1(l.shape,e,c,r,s,a,i),filter:()=>X1(l,e,c.shape,r,s,a,i)}}},ML={kernelName:hc,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,s={x:a,filter:r,dy:e},i={x:a,filter:r,dy:e};return{x:()=>P.runKernel($d,s,n),filter:()=>P.runKernel(Dd,i,n)}}},RL={kernelName:Bo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,a={dy:e,y:n};return{x:()=>P.runKernel(Md,a)}}},PL={kernelName:Wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=L(dn(St(ut(n))),2/Math.sqrt(Math.PI));return{x:()=>L(e,a)}}},OL={kernelName:zs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,n)}}},LL={kernelName:Uo,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>q(e,n.shape)}}},zL={kernelName:Go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,dn(n))}}},BL={kernelName:Bs,gradFunc:e=>({x:()=>Ge(e)})},WL={kernelName:Ws,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=wt(n.shape,a.shape);return{a:()=>{let s=we(e,pe(a,"float32")),i=Wt(n.shape,r);return i.length>0?q(Ce(s,i),n.shape):s},b:()=>{let s=L(e,pe(n,"float32")),i=Wt(a.shape,r);i.length>0&&(s=q(Ce(s,i),a.shape));let o=ut(a);return St(we(s,pe(o,"float32")))}}}},VL={kernelName:Vs,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:a}=n,[r,s,i,o]=t,l=o==null?he(1):o,c=Wt(s.shape,r.shape),u=[];if(s.rank===1){for(let f=0;f<r.shape.length-1;++f)u.push(r.shape[f]);u.push(1)}let p=ge(r,s),d=L(e,l),h=Th(Z(i,he(a))),m=L(L(L(h,h),h),he(-.5));return{x:()=>s.rank===1?q(L(L(e,qa(q(h,[1,1,1,s.shape[0]]),u)),l),r.shape):q(L(L(e,h),l),r.shape),mean:()=>{let f=L(L(h,he(-1)),d);return s.rank===1&&(f=Ce(f,c)),q(f,s.shape)},variance:()=>{let f=L(L(m,p),d);return s.rank===1&&(f=Ce(f,c)),q(f,s.shape)},scale:()=>{let f=L(p,h),g=L(e,f);return s.rank===1&&(g=Ce(g,c)),q(g,s.shape)},offset:()=>{let f=e;return s.rank===1&&(f=Ce(f,c)),q(f,s.shape)}}}},UL={kernelName:jo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[a,r]=t,{axis:s}=n,i=ca(s,a.shape)[0];return{x:()=>{let o=a.shape,l=r.size,c=o.slice(0,i),u=c.length,p=o.slice(s,o.length).slice(1),d=p.length,h=pk(0,u),m=pk(u+1,u+1+d),f=dk([c,[l],p]),g=q(e,f),y=q(r,[l]),b=dk([[u],h,m]),v=Ue(g,b),x=nb(v,y,a.shape[i]),N=Uy(b);return x=Ue(x,N),x},indices:()=>r}}};function pk(e,t){let n=[];for(let a=e;a<t;++a)n.push(a);return n}function dk(e){let t=[];for(let n=0;n<e.length;++n)for(let a=0;a<e[n].length;++a)t.push(e[n][a]);return t}var GL={kernelName:Us,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>Ge(n),b:()=>Ge(a)}}},HL={kernelName:Xo,gradFunc:e=>({x:()=>pe(e,"float32")})},jL={kernelName:Yo,gradFunc:e=>({x:()=>Ge(e)})},qL={kernelName:Jo,gradFunc:e=>({x:()=>Ge(e)})},KL={kernelName:Zo,gradFunc:e=>({x:()=>Ge(e)})},XL={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{alpha:r}=n,s=Zn(a,0);return{x:()=>wn(s,e,L(e,r))}}},YL={kernelName:tl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,Z(n,1))}}},JL={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,pe(n,"float32"))}}},ZL={kernelName:r0,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n;return{logits:()=>{let s=!0,i=dn(a);return ge(e,L(Ce(e,r,s),i))}}}};function QL(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 P.runKernel(zd,o,l)}var e3=R({localResponseNormalizationBackprop_:QL}),t3={kernelName:yc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>e3(a,r,e,s,i,o,l)}}};function hk(e,t,n,a){return t.rank<n.rank&&(t=q(t,_i(t.shape,a))),e.rank<n.rank&&(e=q(e,_i(e.shape,a))),{x:()=>L(e,pe(br(n,t),e.dtype))}}var mk={kernelName:js,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{reductionIndices:r}=a,s=t[0],i=t[1],o=ca(r,s.shape),l=hk(e,i,s,o);return{x:()=>l.x()}}},n3={kernelName:qs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>L(e,pe(xr(n,a),"float32")),b:()=>L(e,pe(Wc(n,a),"float32"))}}};function a3(e,t,n,a,r,s=[1,1,1],i,o){let l=_(e,"dy","maxPool3dGrad"),c=_(t,"input","maxPool3dGrad"),u=_(n,"output","maxPool3dGrad"),p=l,d=c,h=u,m=!1;c.rank===4&&(m=!0,p=q(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),d=q(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]]),h=q(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),A(p.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${p.rank}.`),A(d.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${d.rank}.`),A(h.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${h.rank}.`),A($n(r,s),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${r} and dilations '${s}'`),o!=null&&A(jt(i),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${i}.`);let f={dy:p,input:d,output:h},g={filterSize:a,strides:r,dilations:s,pad:i,dimRoundingMode:o},y=P.runKernel(Wd,f,g);return m?q(y,[y.shape[1],y.shape[2],y.shape[3],y.shape[4]]):y}var r3=R({maxPool3dGrad_:a3}),s3={kernelName:bc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,dilations:o,pad:l,dimRoundingMode:c}=n,u=o==null?[1,1,1]:o;return{x:()=>r3(e,a,r,s,i,u,l,c)}}};function i3(e,t,n,a,r,s,i){let o=_(e,"dy","maxPoolGrad"),l=_(t,"input","maxPoolGrad"),c=_(n,"output","maxPoolGrad");A(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),A(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),A(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&A(jt(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let u={dy:o,input:l,output:c},p={filterSize:a,strides:r,pad:s,dimRoundingMode:i};return P.runKernel(Bd,u,p)}var o3=R({maxPoolGrad_:i3}),l3={kernelName:Ks,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>o3(e,a,r,s,i,o)}}},u3={kernelName:Xs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=ca(r,a.shape),i=x1(a.shape,s)[1],o=Lt(i);return{x:()=>{let l=a.shape.slice();s.forEach(u=>{l[u]=1});let c=q(e,l);return we(L(c,Xa(a.shape,"float32")),o)}}}},c3={kernelName:Ys,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{axis:r}=a,[s,i]=t,o=ca(r,s.shape),l=hk(e,i,s,o);return{x:()=>l.x()}}},p3={kernelName:Js,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>L(e,pe(Qr(n,a),"float32")),b:()=>L(e,pe(Zn(n,a),"float32"))}}},d3={kernelName:xc,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>We(e,s,a.shape)}}},h3={kernelName:al,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=wt(n.shape,a.shape);return{a:()=>{let s=Wt(n.shape,r);return s.length>0?q(Ce(e,s),n.shape):e},b:()=>{let s=L(e,St(Ll(we(n,a)))),i=Wt(a.shape,r);return i.length>0?q(Ce(s,i),a.shape):s}}}},m3={kernelName:Zs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=wt(n.shape,a.shape);return{a:()=>{let s=L(e,pe(a,"float32")),i=Wt(n.shape,r);return i.length>0?q(Ce(s,i),n.shape):s},b:()=>{let s=L(e,pe(n,"float32")),i=Wt(a.shape,r);return i.length>0?q(Ce(s,i),a.shape):s}}}},f3={kernelName:rl,gradFunc:e=>({x:()=>St(e)})},g3={kernelName:Qs,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>bt(n.shape,"float32")}}},y3={kernelName:ul,gradFunc:e=>({x:()=>Ge(e)})},b3={kernelName:cl,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:a}=n;return ht(e,a).map(r=>()=>r)}},fk={kernelName:ei,inputsToSave:["x"],gradFunc:(e,t,n)=>{let a=t[0],{paddings:r}=n,s=r.map(i=>i[0]);return{x:()=>We(e,s,a.shape)}}},x3={kernelName:ti,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,a,r]=t,s=n,i=a,o=wt(s.shape,i.shape);return{a:()=>{let l=pe(i,"float32"),c=L(e,L(l,Ya(s,ge(l,he(1))))),u=Wt(s.shape,o);return u.length>0&&(c=Ce(c,u)),q(c,s.shape)},b:()=>{let l=Zn(s,0),c=wn(l,Dn(s),Ge(s)),u=L(e,L(r,c)),p=Wt(i.shape,o);return p.length>0&&(u=Ce(u,p)),q(u,i.shape)}}}},v3={kernelName:ni,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,a]=t,r=Zn(n,0);return{x:()=>wn(r,e,L(e,a)),alpha:()=>{let s=wn(r,Ge(e),L(e,n)),i=Wt(a.shape,e.shape);return i.length>0&&(s=Ce(s,i)),q(s,a.shape)}}}},w3={kernelName:Ls,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=wt(n.shape,a.shape);return{a:()=>{let s=we(e,pe(a,"float32")),i=Wt(n.shape,r);return i.length>0?q(Ce(s,i),n.shape):s},b:()=>{let s=L(e,pe(n,"float32")),i=Wt(a.shape,r);i.length>0&&(s=q(Ce(s,i),a.shape));let o=ut(a);return St(we(s,pe(o,"float32")))}}}},k3={kernelName:dl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,St(ut(n)))}}},I3={kernelName:si,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=L(Qr(n,6),Gl(n));return{x:()=>L(e,pe(a,"float32"))}}},N3={kernelName:ai,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,pe(Gl(n),"float32"))}}},T3={kernelName:hl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>q(e,n.shape)}}},S3={kernelName:ri,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(jd,r,n)}}},C3={kernelName:wc,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(Hd,r,n)}}},_3={kernelName:ii,gradFunc:(e,t,n)=>{let{dims:a}=n,r=ca(a,e.shape);return{x:()=>Rn(e,r)}}},E3={kernelName:oi,gradFunc:e=>({x:()=>Ge(e)})},A3={kernelName:li,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>St(we(e,L(Ya(n,1.5),2)))}}},F3={kernelName:fl,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>pe(Ge(n),"float32"),t:()=>L(e,pe(n,e.dtype)),e:()=>L(e,pe(Vc(n),e.dtype))}}},$3={kernelName:gl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=Zn(n,he(0)),r=he(ok),s=he(lk),i=L(e,s),o=L(L(e,r),dn(pe(n,"float32")));return wn(a,i,o)}}}},D3={kernelName:ci,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(n,ge(he(1),n)))}}},M3={kernelName:xl,gradFunc:e=>({x:()=>Ge(e)})},R3={kernelName:ui,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(zc(pe(n,"float32")),e)}}},P3={kernelName:bl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(hh(pe(n,"float32")),e)}}},O3={kernelName:yl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{begin:r,size:s}=n,i=a.shape,[o,l]=V0(a,r,s),c=[];for(let u=0;u<e.rank;u++)c.push([o[u],i[u]-o[u]-l[u]]);return{x:()=>ea(e,c)}}},L3={kernelName:hi,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{dim:r}=n,s=!0,i=L(e,a);return{logits:()=>ge(i,L(Ce(i,[r],s),a))}}},z3={kernelName:vl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,da(n))}}},gk={kernelName:kc,gradFunc:(e,t,n)=>{let{blockShape:a,paddings:r}=n;return{x:()=>Oc(e,a,r)}}},yk={kernelName:wl,gradFunc:(e,t,n)=>{let{axis:a}=n;return{x:()=>Qe(e,a)}}},B3={kernelName:pi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,L(nn(pe(n,"float32")),2))}}},W3={kernelName:Ic,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(pe(n,"float32"),2))}}},V3={kernelName:mi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=he(2);return{a:()=>L(e,L(r,ge(n,a))),b:()=>L(e,L(r,ge(a,n)))}}},U3={kernelName:Gr,gradFunc:e=>({x:()=>Ge(e)})},G3={kernelName:fi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=wt(n.shape,a.shape);return{a:()=>{let s=e,i=Wt(n.shape,r);return i.length>0&&(s=Ce(s,i)),q(s,n.shape)},b:()=>{let s=e,i=Wt(a.shape,r);return i.length>0&&(s=Ce(s,i)),q(St(s),a.shape)}}}},H3={kernelName:di,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,r=a.shape.slice(),{axis:s}=n;ca(s,a.shape).forEach(l=>{r[l]=1});let i=q(e,r),o=L(i,Xa(a.shape,"float32"));return{x:()=>o}}},j3={kernelName:Il,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>we(e,ut(zc(n)))}}},q3={kernelName:gi,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(ge(he(1),ut(n)),e)}}},K3={kernelName:Ur,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{reps:r}=n;return{x:()=>{let s=Ge(a);if(a.rank===1)for(let i=0;i<r[0];++i)s=Z(s,We(e,[i*a.shape[0]],[a.shape[0]]));else if(a.rank===2)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)s=Z(s,We(e,[i*a.shape[0],o*a.shape[1]],[a.shape[0],a.shape[1]]));else if(a.rank===3)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let l=0;l<r[2];++l)s=Z(s,We(e,[i*a.shape[0],o*a.shape[1],l*a.shape[2]],[a.shape[0],a.shape[1],a.shape[2]]));else if(a.rank===4)for(let i=0;i<r[0];++i)for(let o=0;o<r[1];++o)for(let l=0;l<r[2];++l)for(let c=0;c<r[3];++c)s=Z(s,We(e,[i*a.shape[0],o*a.shape[1],l*a.shape[2],c*a.shape[3]],[a.shape[0],a.shape[1],a.shape[2],a.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${a.rank} tensors yet.`);return s}}}},X3={kernelName:yi,gradFunc:(e,t,n)=>{let a=n,{perm:r}=a,s=Uy(r);return{x:()=>Ue(e,s)}}},Y3={kernelName:Tl,gradFunc:(e,t,n)=>{let a=n,{axis:r}=a;return{value:()=>Dt(e,r)}}},Z3={kernelName:Nc,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>J3(e,n)}}};function J3(e,t){let n=Sa(t,Ge(t)),a=Ci(e,n),r=xr(t,he(0,"int32")),s=a.rank-r.rank;for(let o=0;o<s;++o)r=Jn(r,o+1);r=ha(r,Xa(a.shape,"bool"));let i=Ge(a);return wn(r,a,i)}var Q3={kernelName:Sl,gradFunc:e=>({x:()=>Ge(e)})},ez=[ck,eL,tL,nL,aL,rL,sL,iL,oL,lL,uL,cL,hL,gL,yL,bL,xL,vL,wL,kL,IL,NL,SL,TL,EL,AL,FL,$L,DL,ML,w3,RL,PL,OL,LL,zL,WL,BL,VL,UL,GL,HL,jL,qL,KL,XL,YL,JL,ZL,t3,mk,mk,n3,s3,l3,u3,c3,p3,d3,h3,m3,f3,g3,y3,b3,fk,fk,x3,v3,k3,I3,N3,T3,S3,C3,_3,E3,A3,F3,$3,D3,M3,R3,P3,O3,L3,z3,gk,gk,yk,yk,B3,V3,W3,U3,G3,H3,j3,q3,K3,X3,Y3,Z3,Q3];for(let e of ez)s0(e);z.prototype.abs=function(){return this.throwIfDisposed(),Bt(this)};z.prototype.acos=function(){return this.throwIfDisposed(),wy(this)};z.prototype.acosh=function(){return this.throwIfDisposed(),ky(this)};z.prototype.addStrict=function(e){return this.throwIfDisposed(),R1(this,e)};z.prototype.add=function(e){return this.throwIfDisposed(),Z(this,e)};z.prototype.all=function(e,t){return this.throwIfDisposed(),uh(this,e,t)};z.prototype.any=function(e,t){return this.throwIfDisposed(),Mc(this,e,t)};z.prototype.argMax=function(e){return this.throwIfDisposed(),Rc(this,e)};z.prototype.argMin=function(e){return this.throwIfDisposed(),Iy(this,e)};z.prototype.asScalar=function(){return this.throwIfDisposed(),A(this.size===1,()=>"The array must have only 1 element."),q(this,[])};z.prototype.asType=function(e){return this.throwIfDisposed(),pe(this,e)};z.prototype.as1D=function(){return this.throwIfDisposed(),q(this,[this.size])};z.prototype.as2D=function(e,t){return this.throwIfDisposed(),q(this,[e,t])};z.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),q(this,[e,t,n])};z.prototype.as4D=function(e,t,n,a){return this.throwIfDisposed(),q(this,[e,t,n,a])};z.prototype.as5D=function(e,t,n,a,r){return this.throwIfDisposed(),q(this,[e,t,n,a,r])};z.prototype.asin=function(){return this.throwIfDisposed(),Ny(this)};z.prototype.asinh=function(){return this.throwIfDisposed(),Ty(this)};z.prototype.atan=function(){return this.throwIfDisposed(),Sy(this)};z.prototype.atan2=function(e){return this.throwIfDisposed(),Cy(this,e)};z.prototype.atanh=function(){return this.throwIfDisposed(),_y(this)};z.prototype.avgPool=function(e,t,n,a){return this.throwIfDisposed(),Yn(this,e,t,n,a)};z.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Oc(this,e,t)};z.prototype.batchNorm=function(e,t,n,a,r){return this.throwIfDisposed(),gr(this,e,t,n,a,r)};z.prototype.broadcastTo=function(e){return this.throwIfDisposed(),Lc(this,e)};z.prototype.cast=function(e){return this.throwIfDisposed(),pe(this,e)};z.prototype.ceil=function(){return this.throwIfDisposed(),$y(this)};z.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),Xt(this,e,t)};z.prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof z&&(e=[e]),Qe([this,...e],t)};z.prototype.conv1d=function(e,t,n,a,r,s){return this.throwIfDisposed(),ph(this,e,t,n,a,r,s)};z.prototype.conv2dTranspose=function(e,t,n,a,r){return this.throwIfDisposed(),dh(this,e,t,n,a,r)};z.prototype.conv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Ft(this,e,t,n,a,r,s)};z.prototype.cos=function(){return this.throwIfDisposed(),zc(this)};z.prototype.cosh=function(){return this.throwIfDisposed(),hh(this)};z.prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),mh(this,e,t,n)};z.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),Ry(this,e,t)};z.prototype.depthwiseConv2D=function(e,t,n,a,r,s){return Rt("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),yr(this,e,t,n,a,r,s)};z.prototype.depthwiseConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),yr(this,e,t,n,a,r,s)};z.prototype.dilation2d=function(e,t,n,a,r){return this.throwIfDisposed(),Py(this,e,t,n,a,r)};z.prototype.divNoNan=function(e){return this.throwIfDisposed(),Oy(this,e)};z.prototype.divStrict=function(e){return this.throwIfDisposed(),P1(this,e)};z.prototype.div=function(e){return this.throwIfDisposed(),we(this,e)};z.prototype.dot=function(e){return this.throwIfDisposed(),p1(this,e)};z.prototype.elu=function(){return this.throwIfDisposed(),Ol(this)};z.prototype.equalStrict=function(e){return this.throwIfDisposed(),E1(this,e)};z.prototype.equal=function(e){return this.throwIfDisposed(),br(this,e)};z.prototype.erf=function(){return this.throwIfDisposed(),Ly(this)};z.prototype.exp=function(){return this.throwIfDisposed(),dn(this)};z.prototype.expandDims=function(e){return this.throwIfDisposed(),Jn(this,e)};z.prototype.expm1=function(){return this.throwIfDisposed(),zy(this)};z.prototype.fft=function(){return this.throwIfDisposed(),qc(this)};z.prototype.flatten=function(){return this.throwIfDisposed(),q(this,[this.size])};z.prototype.floor=function(){return this.throwIfDisposed(),Ll(this)};z.prototype.floorDiv=function(e){return this.throwIfDisposed(),lh(this,e)};z.prototype.gather=function(e,t){return this.throwIfDisposed(),Ci(this,e,t)};z.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),A1(this,e)};z.prototype.greaterEqual=function(e){return this.throwIfDisposed(),xr(this,e)};z.prototype.greaterStrict=function(e){return this.throwIfDisposed(),F1(this,e)};z.prototype.greater=function(e){return this.throwIfDisposed(),Zn(this,e)};z.prototype.ifft=function(){return this.throwIfDisposed(),Ul(this)};z.prototype.irfft=function(){return this.throwIfDisposed(),Ah(this)};z.prototype.isFinite=function(){return this.throwIfDisposed(),d1(this)};z.prototype.isInf=function(){return this.throwIfDisposed(),h1(this)};z.prototype.isNaN=function(){return this.throwIfDisposed(),m1(this)};z.prototype.leakyRelu=function(e){return this.throwIfDisposed(),Bc(this,e)};z.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),$1(this,e)};z.prototype.lessEqual=function(e){return this.throwIfDisposed(),Qr(this,e)};z.prototype.lessStrict=function(e){return this.throwIfDisposed(),D1(this,e)};z.prototype.less=function(e){return this.throwIfDisposed(),Wc(this,e)};z.prototype.localResponseNormalization=function(e,t,n,a){return this.throwIfDisposed(),Wy(this,e,t,n,a)};z.prototype.logSigmoid=function(){return this.throwIfDisposed(),y1(this)};z.prototype.logSoftmax=function(e){return this.throwIfDisposed(),bh(this,e)};z.prototype.logSumExp=function(e,t){return this.throwIfDisposed(),Gy(this,e,t)};z.prototype.log=function(){return this.throwIfDisposed(),Dn(this)};z.prototype.log1p=function(){return this.throwIfDisposed(),gh(this)};z.prototype.logicalAnd=function(e){return this.throwIfDisposed(),ha(this,e)};z.prototype.logicalNot=function(){return this.throwIfDisposed(),Vc(this)};z.prototype.logicalOr=function(e){return this.throwIfDisposed(),xh(this,e)};z.prototype.logicalXor=function(e){return this.throwIfDisposed(),w1(this,e)};z.prototype.matMul=function(e,t,n){return this.throwIfDisposed(),ze(this,e,t,n)};z.prototype.maxPool=function(e,t,n,a){return this.throwIfDisposed(),$t(this,e,t,n,a)};z.prototype.max=function(e,t){return this.throwIfDisposed(),Qn(this,e,t)};z.prototype.maximumStrict=function(e){return this.throwIfDisposed(),O1(this,e)};z.prototype.maximum=function(e){return this.throwIfDisposed(),Sa(this,e)};z.prototype.mean=function(e,t){return this.throwIfDisposed(),Ct(this,e,t)};z.prototype.min=function(e,t){return this.throwIfDisposed(),Bl(this,e,t)};z.prototype.minimumStrict=function(e){return this.throwIfDisposed(),L1(this,e)};z.prototype.minimum=function(e){return this.throwIfDisposed(),Ei(this,e)};z.prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),jy(this,e,t)};z.prototype.modStrict=function(e){return this.throwIfDisposed(),z1(this,e)};z.prototype.mod=function(e){return this.throwIfDisposed(),vh(this,e)};z.prototype.mulStrict=function(e){return this.throwIfDisposed(),B1(this,e)};z.prototype.mul=function(e){return this.throwIfDisposed(),L(this,e)};z.prototype.neg=function(){return this.throwIfDisposed(),St(this)};z.prototype.norm=function(e,t,n){return this.throwIfDisposed(),Dh(this,e,t,n)};z.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),M1(this,e)};z.prototype.notEqual=function(e){return this.throwIfDisposed(),es(this,e)};z.prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),Dl(this,e,t,n)};z.prototype.onesLike=function(){return this.throwIfDisposed(),Mn(this)};z.prototype.pad=function(e,t){return this.throwIfDisposed(),ea(this,e,t)};z.prototype.pool=function(e,t,n,a,r){return this.throwIfDisposed(),N1(this,e,t,n,a,r)};z.prototype.powStrict=function(e){return this.throwIfDisposed(),W1(this,e)};z.prototype.pow=function(e){return this.throwIfDisposed(),Ya(this,e)};z.prototype.prelu=function(e){return this.throwIfDisposed(),Gc(this,e)};z.prototype.prod=function(e,t){return this.throwIfDisposed(),kh(this,e,t)};z.prototype.reciprocal=function(){return this.throwIfDisposed(),Xy(this)};z.prototype.relu=function(){return this.throwIfDisposed(),Ye(this)};z.prototype.relu6=function(){return this.throwIfDisposed(),Nh(this)};z.prototype.reshapeAs=function(e){return this.throwIfDisposed(),q(this,e.shape)};z.prototype.reshape=function(e){return this.throwIfDisposed(),q(this,e)};z.prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),ak(this,e,t,n)};z.prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),rk(this,e,t,n)};z.prototype.reverse=function(e){return this.throwIfDisposed(),Rn(this,e)};z.prototype.rfft=function(){return this.throwIfDisposed(),Kc(this)};z.prototype.round=function(){return this.throwIfDisposed(),Yy(this)};z.prototype.rsqrt=function(){return this.throwIfDisposed(),Th(this)};z.prototype.selu=function(){return this.throwIfDisposed(),Sh(this)};z.prototype.separableConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),Ai(this,e,t,n,a,r,s)};z.prototype.sigmoid=function(){return this.throwIfDisposed(),da(this)};z.prototype.sign=function(){return this.throwIfDisposed(),Jy(this)};z.prototype.sin=function(){return this.throwIfDisposed(),Ch(this)};z.prototype.sinh=function(){return this.throwIfDisposed(),_h(this)};z.prototype.slice=function(e,t){return this.throwIfDisposed(),We(this,e,t)};z.prototype.softmax=function(e){return this.throwIfDisposed(),Ca(this,e)};z.prototype.softplus=function(){return this.throwIfDisposed(),zl(this)};z.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Uc(this,e,t)};z.prototype.split=function(e,t){return this.throwIfDisposed(),Pn(this,e,t)};z.prototype.sqrt=function(){return this.throwIfDisposed(),nn(this)};z.prototype.square=function(){return this.throwIfDisposed(),ut(this)};z.prototype.squaredDifference=function(e){return this.throwIfDisposed(),Xc(this,e)};z.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),V1(this,e)};z.prototype.squeeze=function(e){return this.throwIfDisposed(),ts(this,e)};z.prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof z?[this,e]:[this,...e];return Dt(n,t)};z.prototype.step=function(e){return this.throwIfDisposed(),Gl(this,e)};z.prototype.stridedSlice=function(e,t,n,a,r,s,i,o){return this.throwIfDisposed(),Qy(this,e,t,n,a,r,s,i,o)};z.prototype.subStrict=function(e){return this.throwIfDisposed(),U1(this,e)};z.prototype.sub=function(e){return this.throwIfDisposed(),ge(this,e)};z.prototype.sum=function(e,t){return this.throwIfDisposed(),Ce(this,e,t)};z.prototype.tan=function(){return this.throwIfDisposed(),eb(this)};z.prototype.tanh=function(){return this.throwIfDisposed(),Pl(this)};z.prototype.tile=function(e){return this.throwIfDisposed(),qa(this,e)};z.prototype.toBool=function(){return this.throwIfDisposed(),pe(this,"bool")};z.prototype.toFloat=function(){return this.throwIfDisposed(),pe(this,"float32")};z.prototype.toInt=function(){return this.throwIfDisposed(),pe(this,"int32")};z.prototype.topk=function(e,t){return this.throwIfDisposed(),tb(this,e,t)};z.prototype.transpose=function(e){return this.throwIfDisposed(),Ue(this,e)};z.prototype.unique=function(e){return this.throwIfDisposed(),$h(this,e)};z.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),nb(this,e,t)};z.prototype.unstack=function(e){return this.throwIfDisposed(),ht(this,e)};z.prototype.where=function(e,t){return this.throwIfDisposed(),wn(e,this,t)};z.prototype.zerosLike=function(){return this.throwIfDisposed(),Ge(this)};var bk={};Oe(bk,{maxNorm:()=>tz,minMaxNorm:()=>rz,nonNeg:()=>az,unitNorm:()=>nz});var ub;function Vt(){return ub==null&&(ub=Y0().epsilon()),ub}function Aa(){return"channelsLast"}var kr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,kr.prototype)}},Fa=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Fa.prototype)}},B=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,B.prototype)}},$e=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,$e.prototype)}},xk=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,xk.prototype)}},sz=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,sz.prototype)}};function Di(e,t){if(Array.isArray(e)){let n=[];for(let a=0;a<t;a++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function Qa(e,t){if(!e)throw new xk(t)}function vk(e,t){let n=0;for(let a of e)a===t&&n++;return n}function In(e){return e.length===1?e[0]:e}function xt(e){return Array.isArray(e)?e:[e]}function Ir(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function Mi(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var ma={};function cb(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function pb(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>pb(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:pb(a))}}}function Jc(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 ma)i=ma[s];else if(i=t[s],i==null)throw new B(`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 ${a} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new B(`${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 ma?[o,l]=ma.className:i in t&&([o,l]=t[i]),o==null)throw new B(`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 c={};for(let h of Object.keys(ma))c[h]=ma[h];for(let h of Object.keys(n))c[h]=n[h];let u=s.config;u.customObjects=c;let p=Object.assign({},ma);for(let h of Object.keys(n))ma[h]=n[h];pb(s.config);let d=l(o,s.config,n,r);return ma=Object.assign({},p),d}else{let c=Object.assign({},ma);for(let p of Object.keys(n))ma[p]=n[p];let u=new o(s.config);return ma=Object.assign({},c),u}}}function iz(e,t){return e<t?-1:e>t?1:0}function Hh(e,t){return-1*iz(e,t)}function as(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function oz(e){if(e==null)throw new B(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Ri(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new B(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function db(e,t,n=0,a=Infinity){return Qa(n>=0),Qa(a>=n),Array.isArray(e)&&e.length>=n&&e.length<=a&&e.every(r=>typeof r===t)}function Yt(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,a)=>Yt(n,`element ${a+1} of ${t}`))):k.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${wk(e)}.`)}function wk(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>wk(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function lz(e,t){let n=k.now(),a;return(...r)=>{let s=k.now();return s-n<t||(n=s,a=e(...r)),a}}function kk(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function hb(e,t){return D(()=>nn(Ce(L(e,e),t,!0)))}var Zc=class extends re.Serializable{getConfig(){return{}}},mb=class extends Zc{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 D(()=>{let t=hb(e,this.axis),n=Xt(t,0,this.maxValue);return L(e,we(n,Z(Vt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};mb.className="MaxNorm";re.registerClass(mb);var fb=class extends Zc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return D(()=>we(e,Z(Vt(),hb(e,this.axis))))}getConfig(){return{axis:this.axis}}};fb.className="UnitNorm";re.registerClass(fb);var gb=class extends Zc{apply(e){return Ye(e)}};gb.className="NonNeg";re.registerClass(gb);var yb=class extends Zc{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 D(()=>{let t=hb(e,this.axis),n=Z(L(this.rate,Xt(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,we(n,Z(Vt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};yb.className="MinMaxNorm";re.registerClass(yb);var Ik={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ut(e){return cb(e)}function Nk(e,t={}){return Jc(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function Gt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in Ik?Ik[e]:e,config:{}};return Nk(t)}else return e instanceof Zc?e:Nk(e)}function tz(e){return new mb(e)}function nz(e){return new fb(e)}function az(){return new gb}function rz(e){return new yb(e)}var Tk={};Oe(Tk,{constant:()=>pz,glorotNormal:()=>bz,glorotUniform:()=>yz,heNormal:()=>xz,heUniform:()=>vz,identity:()=>fz,leCunNormal:()=>wz,leCunUniform:()=>kz,ones:()=>cz,orthogonal:()=>Iz,randomNormal:()=>hz,randomUniform:()=>dz,truncatedNormal:()=>mz,varianceScaling:()=>gz,zeros:()=>uz});var Nz=["channelsFirst","channelsLast"],Tz=["nearest","bilinear"],Sz=["valid","same","causal"],Cz=["max","avg"],_z=["sum","mul","concat","ave"],jl=new Map;function Mt(e){Ri(Nz,"DataFormat",e)}function Ez(e){Ri(Tz,"InterpolationFormat",e)}function ta(e){Ri(Sz,"PaddingMode",e)}function Sk(e){Ri(Cz,"PoolMode",e)}var Qc=[],Ck="/";function Pi(e,t){Qc.push(e);try{let n=t();return Qc.pop(),n}catch(n){throw Qc.pop(),n}}function Az(){return Qc.length===0?"":Qc.join(Ck)+Ck}function Ek(e){if(!_k(e))throw new Error("Not a valid tensor name: '"+e+"'");return Az()+e}function Ak(e){if(!_k(e))throw new Error("Not a valid tensor name: '"+e+"'");jl.has(e)||jl.set(e,0);let t=jl.get(e);if(jl.set(e,jl.get(e)+1),t>0){let n=`${e}_${t}`;return jl.set(n,1),n}else return e}var Fz=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function _k(e){return!!e.match(Fz)}function $z(e){return e===parseInt(e.toString(),10)}function rs(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let a=1;for(let r=t;r<n;++r)a*=e[r];return a}function Fk(e){return e=Array.isArray(e)?new Float32Array(e):e,tt(e)}function ql(e){return Bl(Fk(e)).dataSync()[0]}function ss(e){return Qn(Fk(e)).dataSync()[0]}function $a(e,t){if(t<e)throw new B(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let a=e;a<t;++a)n.push(a);return n}function ep(e,t){return e.asType(t)}function tp(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function Dz(e,t){return D(()=>{if(e.shape.length!==2)throw new B(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=tp(e,1);return bb(n,[1,t,1])})}function Mz(e){let t=[rs(e.shape)];return e.reshape(t)}function Rz(e){if(e.rank<=1)throw new B(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],rs(e.shape,1)];return e.reshape(t)}function Oi(e,t,n){return D(()=>{switch(e.rank){case 1:return Eh(e,t,n);case 2:return Zy(e,[t,0],[n,e.shape[1]]);case 3:return Vl(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return jc(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return We(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return We(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 B(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function xb(e,t,n){return D(()=>{switch(e.rank){case 1:return Eh(e,t,n);case 2:return Zy(e,[0,t],[e.shape[0],n]);case 3:return Vl(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return jc(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new B(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function jh(e,t,n,a){return D(()=>{switch(e.rank){case 1:return Eh(e,t,n);case 2:switch(a){case 1:return Oi(e,t,n);case 2:return xb(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${a}`)}case 3:switch(a){case 1:return Oi(e,t,n);case 2:return Vl(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return xb(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${a}`)}case 4:switch(a){case 1:return Oi(e,t,n);case 2:return jc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return jc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return xb(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${a}`)}default:throw new B(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function vb(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),Qe(e,t)}function $k(e,t){switch(e.rank){case 1:return s1([e,t]);case 2:return i1([e,t],0);case 3:return o1([e,t],0);case 4:return l1([e,t],0);default:throw new B(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function bb(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new B(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return qa(e,t)}function qh(e,t=0,n=1,a,r){return T1(e,t,n,a,r)}function er(e,t,n,a){if(e.rank<2||t.rank<2)throw new $e(`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 $e(`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 ns.matMul({a:e,b:t,transposeA:r,transposeB:s,bias:a?wb(e.rank,a,Aa()):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(),c=[...i,o],u=Array.from({length:t.rank},(m,f)=>f===0?t.rank-2:f<=t.rank-2?f-1:f);t=t.transpose(u).reshape([l,-1]);let p=[...r,...c],d=!1,h=!1;return ns.matMul({a:e,b:t,transposeA:d,transposeB:h,bias:a?wb(e.rank,a,Aa()):null,activation:n}).reshape(p)}}function Dk(e,t,n){return D(()=>(Array.isArray(t)?t=tt(t,"int32"):t=t.toInt(),Ci(e,t,n)))}function np(e){return L(e,e)}function wb(e,t,n){let a=t.shape;if(t.rank!==1&&t.rank!==e)throw new B(`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 B(`Unsupported input rank by biasAdd: ${t.rank}`)}function tr(e,t,n){return D(()=>(n==null&&(n=Aa()),Mt(n),e.add(wb(e.rank,t,n))))}function Pz(e,t=1){if(t!==1)throw new $e(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Ol(e)}function Oz(e){return D(()=>we(e,Bt(e).add(1)))}function Mk(e,t,n,a){return D(()=>q1(e,t,n,a))}function Lz(e){return D(()=>{let t=Z(.5,L(.2,e));return Xt(t,0,1)})}function ap(e,t,n=!1){return n?e():t()}var zz=["fanIn","fanOut","fanAvg"],Bz=["normal","uniform","truncatedNormal"];function Wz(e){Ri(zz,"FanMode",e)}function Vz(e){Ri(Bz,"Distribution",e)}var fa=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},kb=class extends fa{apply(e,t){return bt(e,t)}};kb.className="Zeros";re.registerClass(kb);var Kh=class extends fa{apply(e,t){return Xa(e,t)}};Kh.className="Ones";re.registerClass(Kh);var Ib=class extends fa{constructor(e){super();if(typeof e!="object")throw new B(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new B(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return D(()=>L(he(this.value),Xa(e,t)))}getConfig(){return{value:this.value}}};Ib.className="Constant";re.registerClass(Ib);var Nb=class extends fa{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 Wl(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};Nb.className="RandomUniform";re.registerClass(Nb);var Tb=class extends fa{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 $e(`randomNormal does not support dType ${t}.`);return qh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Tb.className="RandomNormal";re.registerClass(Tb);var Sb=class extends fa{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 $e(`truncatedNormal does not support dType ${t}.`);return Fh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Sb.className="TruncatedNormal";re.registerClass(Sb);var Cb=class extends fa{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return D(()=>{if(e.length!==2||e[0]!==e[1])throw new B("Identity matrix initializer can only be used for 2D square matrices.");return L(this.gain,By(e[0]))})}getConfig(){return{gain:this.gain}}};Cb.className="Identity";re.registerClass(Cb);function Uz(e,t="channelsLast"){let n,a;if(Mt(t),e.length===2)n=e[0],a=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=rs(e,2);n=e[1]*r,a=e[0]*r}else if(t==="channelsLast"){let r=rs(e,0,e.length-2);n=e[e.length-2]*r,a=e[e.length-1]*r}}else{let r=rs(e);n=Math.sqrt(r),a=Math.sqrt(r)}return[n,a]}var Nn=class extends fa{constructor(e){super();if(e.scale<0)throw new B(`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,Wz(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,Vz(this.distribution),this.seed=e.seed}apply(e,t){let n=Uz(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 $e(`${this.getClassName()} does not support dType ${t}.`);return Fh(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return Wl(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Nn.className="VarianceScaling";re.registerClass(Nn);var Xh=class extends Nn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Nn.className}};Xh.className="GlorotUniform";re.registerClass(Xh);var Yh=class extends Nn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Nn.className}};Yh.className="GlorotNormal";re.registerClass(Yh);var Jh=class extends Nn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Nn.className}};Jh.className="HeNormal";re.registerClass(Jh);var Zh=class extends Nn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Nn.className}};Zh.className="HeUniform";re.registerClass(Zh);var Qh=class extends Nn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Nn.className}};Qh.className="LeCunNormal";re.registerClass(Qh);var em=class extends Nn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Nn.className}};em.className="LeCunNormal";re.registerClass(em);var _b=class extends fa{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 $e("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return D(()=>{if(e.length<2)throw new $e("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,a=qh(n,0,1,"float32"),r=ik.gramSchmidt(a);return e[0]>e[1]&&(r=r.transpose()),L(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};_b.className="Orthogonal";re.registerClass(_b);var Rk={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 Pk(e,t={}){return Jc(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function _t(e){return cb(e)}function kt(e){if(typeof e=="string"){let t=e in Rk?Rk[e]:e;if(t==="GlorotNormal")return new Yh;if(t==="GlorotUniform")return new Xh;if(t==="HeNormal")return new Jh;if(t==="HeUniform")return new Zh;if(t==="LeCunNormal")return new Qh;if(t==="LeCunUniform")return new em;{let n={};return n.className=t,n.config={},Pk(n)}}else return e instanceof fa?e:Pk(e)}function uz(){return new kb}function cz(){return new Kh}function pz(e){return new Ib(e)}function dz(e){return new Nb(e)}function hz(e){return new Tb(e)}function mz(e){return new Sb(e)}function fz(e){return new Cb(e)}function gz(e){return new Nn(e)}function yz(e){return new Xh(e)}function bz(e){return new Yh(e)}function xz(e){return new Jh(e)}function vz(e){return new Zh(e)}function wz(e){return new Qh(e)}function kz(e){return new em(e)}function Iz(e){return new _b(e)}var Ok={};Oe(Ok,{Layer:()=>qe,RNN:()=>nr,RNNCell:()=>rp,activation:()=>sB,add:()=>mB,alphaDropout:()=>JB,average:()=>fB,averagePooling1d:()=>Eb,averagePooling2d:()=>Ab,averagePooling3d:()=>Fb,avgPool1d:()=>NB,avgPool2d:()=>SB,avgPool3d:()=>_B,avgPooling1d:()=>TB,avgPooling2d:()=>CB,avgPooling3d:()=>EB,batchNormalization:()=>wB,bidirectional:()=>UB,concatenate:()=>gB,conv1d:()=>Jz,conv2d:()=>Zz,conv2dTranspose:()=>Qz,conv3d:()=>eB,convLstm2d:()=>zB,convLstm2dCell:()=>BB,cropping2D:()=>nB,dense:()=>iB,depthwiseConv2d:()=>rB,dot:()=>vB,dropout:()=>oB,elu:()=>Hz,embedding:()=>hB,flatten:()=>uB,gaussianDropout:()=>YB,gaussianNoise:()=>XB,globalAveragePooling1d:()=>AB,globalAveragePooling2d:()=>FB,globalMaxPool1d:()=>HB,globalMaxPool2d:()=>jB,globalMaxPooling1d:()=>zk,globalMaxPooling2d:()=>Bk,gru:()=>DB,gruCell:()=>MB,input:()=>Lk,inputLayer:()=>Gz,layerNormalization:()=>kB,leakyReLU:()=>qz,lstm:()=>RB,lstmCell:()=>PB,masking:()=>ZB,maxPool1d:()=>qB,maxPool2d:()=>KB,maxPooling1d:()=>Wk,maxPooling2d:()=>Vk,maxPooling3d:()=>$B,maximum:()=>yB,minimum:()=>bB,multiply:()=>xB,permute:()=>dB,prelu:()=>Kz,reLU:()=>jz,repeatVector:()=>cB,reshape:()=>pB,rnn:()=>WB,separableConv2d:()=>tB,simpleRNN:()=>OB,simpleRNNCell:()=>LB,softmax:()=>Xz,spatialDropout1d:()=>lB,stackedRNNCells:()=>VB,thresholdedReLU:()=>Yz,timeDistributed:()=>GB,upSampling2d:()=>aB,zeroPadding2d:()=>IB});var QB=0;function Uk(){return QB++}var tm={};function nm(e=""){return e in tm||(tm[e]=0),tm[e]+=1,e+tm[e].toString()}function $b(e){return Array.isArray(e)&&Array.isArray(e[0])}function am(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Re(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new B(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function mt(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new B(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function rm(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((a,r)=>a*r);return t}var Gk="Variable",Hk=class{constructor(e,t="float32",n=Gk,a=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=Uk(),n=n==null?Gk:n,this.originalName=Ek(n),this.name=Ak(this.originalName),this.trainable_=a,this.constraint=r,this.val=C1(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),eW(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 eW(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function Db(e){return e.map(t=>t.read())}function Mb(e){e.forEach(t=>{t[0].write(t[1])})}var Jt=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||{}}},Da=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=Uk(),s!=null&&(this.originalName=Ek(s),this.name=Ak(this.originalName)),this.rank=t.length}},tW=0,sm=class{constructor(e,t){this.callArgs=t,this.id=tW++,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}}},nW=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=nW++,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=Ir(n)+"_"+nm(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 Fa(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new B(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return In(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return In(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new kr(`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 kr(`Layer ${this.name} is not connected, no input to return.`);return In(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new kr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new kr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return In(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=xt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=xt(this.inputSpec);if(e.length!==t.length)throw new B(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let a=e[n],r=t[n];if(r==null)continue;let s=a.rank;if(r.ndim!=null&&s!==r.ndim)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${s}`);if(r.maxNDim!=null&&s>r.maxNDim)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${s}`);if(r.minNDim!=null&&s<r.minNDim)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${s}.`);if(r.dtype!=null&&a.dtype!==r.dtype)throw new B(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${a.dtype}.`);if(r.axes){let i=a.shape;for(let o in r.axes){let l=Number(o),c=r.axes[o],u=l>=0?i[l]:i[i.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],l=a.shape[i];if(o!=null&&l!=null&&o!==l)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${a.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=xt(e),a=!0;for(let s of n)if(!(s instanceof Da)){a=!1;break}let r=!0;for(let s of n)if(s instanceof Da){r=!1;break}if(a===r)throw new B("Arguments to apply() must be all SymbolicTensors or all Tensors");return Pi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of xt(e))s.push(i.shape);this.build(In(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=xt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=In(o),this.activityRegularizer!=null)throw new $e("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=aW(e),i=this.computeOutputShape(s),o,l=rW(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((c,u)=>new Da(l,c,this,xt(e),t,this.name,u)):o=new Da(l,i,this,xt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new $e("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 kr(`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 kr(`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 Fa(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return rm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Db(e?this.trainableWeights:this.weights)}setWeights(e){D(()=>{let t=this.weights;if(t.length!==e.length)throw new B(`You called setWeights(weights) on layer "${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=Db(t);for(let r=0;r<a.length;++r){let s=a[r],i=t[r],o=e[r];if(!k.arraysEqual(s.shape,o.shape))throw new B(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Mb(n)})}addWeight(e,t,n,a,r,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new B(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(a=kt("zeros"));let o=a.apply(t,n),l=new Hk(o,n,e,s,i);return o.dispose(),r!=null&&this.addLoss(()=>r.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=xt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=xt(e);t=xt(t),n=xt(n),a=xt(a),r=am(r),s=am(s);let l=[],c=[],u=[];for(let p of o)l.push(p.sourceLayer),c.push(p.nodeIndex),u.push(p.tensorIndex);new sm({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function aW(e){e=xt(e);let t=[];for(let n of e)t.push(n.shape);return In(t)}function rW(e){return"float32"}function jk(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let a=t.inboundNodes[n];if(a.inboundLayers.length===0)return a.inputTensors;{let r=[];for(let s=0;s<a.inboundLayers.length;s++){let i=a.inputTensors[s],o=a.inboundLayers[s],l=a.nodeIndices[s],c=jk(i,o,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Kl=class extends qe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:nm("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new B("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new B("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new B("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let a=new Da(this.dtype,this.batchInputShape,this,[],{},this.name);a.nodeIndex=0,a.tensorIndex=0,new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[a],outputTensors:[a],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new B(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};Kl.className="InputLayer";re.registerClass(Kl);function qk(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new B("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new Kl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function is(e){if(e==null)return;let t=[],n=[],a=[];for(let r in e){let s=e[r];if(typeof s!="number"){let i=s;t.push(i.data()),n.push(r),a.push(i)}}if(t.length>0){let r=await Promise.all(t);for(let s=0;s<r.length;++s)e[n[s]]=r[s][0];Fe(a)}}function Kk(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var Xk;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(Xk||(Xk={}));var sW=125,Xl=class{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}},Yk=class{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(let t of this.callbacks)t.setParams(e)}setModel(e){for(let t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainEnd(e)}},iW=class extends Xl{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let n=t.size==null?0:t.size;this.seen+=n;for(let a in t){let r=t[a];if(typeof r=="number")this.totals.hasOwnProperty(a)||(this.totals[a]=0),this.totals[a]=this.totals[a]+r*n;else{let s;a in this.totals?s=this.totals[a]:this.totals[a]=0;let i=D(()=>Z(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:D(()=>{let a=L(we(1,this.seen),this.totals[n]);t[n]=a,this.totals[n].dispose(),Kt(t[n])}))}},Jk=class extends Xl{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let r in this.history){let s=this.history[r];for(let i=0;i<s.length;++i)if(typeof s[i]!="number"){let o=s[i];e.push(o.data()),t.push(r),n.push(i)}}let a=await Promise.all(e);for(let r=0;r<a.length;++r)this.history[t[r]][n[r]].dispose(),this.history[t[r]][n[r]]=a[r][0]}},Zk=class extends Xl{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=sW),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");k.isNumber(this.yieldEvery)&&(this.maybeWait=lz(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let a=[];this.yield!=null&&(await is(n),a.push(this.yield(e,t,n))),a.push(Gh()),await Promise.all(a)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await is(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await is(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(Gh()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await is(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await is(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(Gh()):k.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await is(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await is(e),await this.trainEnd(e))}};function Qk(e,t){return e==null&&(e={}),e instanceof Xl?[e]:Array.isArray(e)&&e[0]instanceof Xl?e:xt(e).map(n=>new Zk(n,t))}var ga=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}`),ga.checkForDuplicate(t),ga.constructors[e]==null&&(ga.constructors[e]=[]),ga.constructors[e].push(t)}static checkForDuplicate(e){for(let t in ga.constructors)ga.constructors[+t].forEach(n=>{if(n===e)throw new B("Duplicate callback constructor.")})}static clear(){ga.constructors={}}static createCallbacks(e){let t=[];for(let n in ga.constructors){let a=+n;e>=a&&t.push(...ga.constructors[a])}return t.map(n=>new n)}};ga.constructors={};function eI(e,t,n,a,r,s,i,o,l){let c=new Jk,u=[new iW,...ga.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let p=new Yk(u);return p.setParams({epochs:n,initialEpoch:a,samples:r,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:p,history:c}}function Ma(e,t={},n=!1){return Jc(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function im(e,t){return D(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Ce(np(e),t,!0),a=kn(n.shape,Vt()),r=nn(Sa(n,a));return we(e,r)})}function Li(e,t){return D(()=>Ct(np(ge(t,e)),-1))}function om(e,t){return D(()=>Ct(Bt(ge(t,e)),-1))}function Yl(e,t){return D(()=>{let n=ge(e,t),a=Xt(Bt(e),Vt(),Number.MAX_VALUE),r=Bt(we(n,a));return L(100,Ct(r,-1))})}function oW(e,t){return D(()=>{let n=Xt(t,Vt(),Number.MAX_VALUE),a=Dn(Z(1,n)),r=Xt(e,Vt(),Number.MAX_VALUE),s=Dn(Z(1,r));return Ct(np(ge(a,s)),-1)})}function lW(e,t){return D(()=>{let n=Sa(0,ge(1,L(e,t)));return Ct(np(n),-1)})}function uW(e,t){return D(()=>{let n=Sa(0,ge(1,L(e,t)));return Ct(n,-1)})}function cW(e,t){return D(()=>{let n=Ce(L(e,t),-1),a=Qn(L(ge(1,e),t),-1);return Sa(0,Z(1,ge(a,n)))})}function pW(e,t){return D(()=>{let n=Math.log(2),a=ge(t,e),r=ge(Z(a,zl(L(-2,a))),n);return Ct(r,-1)})}function sp(e,t,n=!1){return D(()=>{if(n)t=Ca(t);else{let a=Ce(t,t.shape.length-1,!0);t=we(t,a)}return t=Xt(t,Vt(),1-Vt()),St(Ce(L(e.toFloat(),Dn(t)),t.shape.length-1))})}function lm(e,t,n=!1){return D(()=>{let a=Ll(Mz(e)).toInt();t=Xt(t,Vt(),1-Vt());let r=t.shape,s=Dl(a,r[r.length-1]).reshape(r);return sp(s,t,n)})}function dW(e,t){if(!k.arraysEqual(e.shape,t.shape))throw new B(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return D(()=>{let n=t.relu(),a=t.abs().neg();return n.sub(t.mul(e)).add(a.exp().log1p())})}function um(e,t){return D(()=>{let n;return n=Xt(t,Vt(),1-Vt()),n=Dn(we(n,ge(1,n))),Ct(dW(e,n),-1)})}function hW(e,t){return D(()=>{let n=Xt(e,Vt(),1),a=Xt(t,Vt(),1);return Ce(L(e,Dn(we(n,a))),-1)})}function mW(e,t){return D(()=>{let n=Dn(Z(Vt(),t));return Ct(ge(t,L(e,n)),-1)})}function Rb(e,t){return D(()=>{let n=im(e,-1),a=im(t,-1),r=L(n,a);return St(Ce(r,-1))})}var cm={meanSquaredError:Li,meanAbsoluteError:om,meanAbsolutePercentageError:Yl,meanSquaredLogarithmicError:oW,squaredHinge:lW,hinge:uW,categoricalHinge:cW,logcosh:pW,categoricalCrossentropy:sp,sparseCategoricalCrossentropy:lm,binaryCrossentropy:um,kullbackLeiblerDivergence:hW,poisson:mW,cosineProximity:Rb};function Pb(e){if(typeof e=="string"){if(e in cm)return cm[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 B(t)}else return e}function Ob(e,t){return D(()=>{let n=L(.5,Mn(t)),a=ep(Zn(t,n),e.dtype);return Ct(br(e,a),-1)})}function Lb(e,t){return D(()=>ep(br(Rc(e,-1),Rc(t,-1)),"float32"))}function tI(e,t){return D(()=>ha(e.equal(1),t.equal(1)).sum().cast("float32"))}function fW(e,t){return D(()=>ha(e.equal(1),t.equal(0)).sum().cast("float32"))}function gW(e,t){return D(()=>ha(e.equal(0),t.equal(1)).sum().cast("float32"))}function nI(e,t){return D(()=>{let n=tI(e,t),a=gW(e,t),r=n.add(a);return wn(Zn(r,0),n.div(r),0).cast("float32")})}function yW(e,t){return D(()=>{let n=tI(e,t),a=fW(e,t),r=n.add(a);return wn(Zn(r,0),n.div(r),0).cast("float32")})}function aI(e,t){return um(e,t)}function rI(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)),br(e,t).asType("float32")}var bW=Li,xW=Li,vW=om,wW=om,kW=Yl,IW=Yl,zb=sp,NW=Rb,sI=lm,pm={binaryAccuracy:Ob,categoricalAccuracy:Lb,precision:nI,categoricalCrossentropy:zb,sparseCategoricalCrossentropy:sI,mse:bW,MSE:xW,mae:vW,MAE:wW,mape:kW,MAPE:IW,cosine:NW};function TW(e){if(typeof e=="string"&&e in pm)return pm[e];if(typeof e!="string"&&e!=null)return e;throw new B(`Unknown metric ${e}`)}function dm(e){if(Qa(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(cm))if(cm[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(pm))if(pm[n]===e){t=n;break}return t!==void 0?t:e.name}}function SW(e){let t={Adagrad:()=>$i.adagrad(.01),Adadelta:()=>$i.adadelta(1,.95,Vt()),Adam:()=>$i.adam(.001,.9,.999,Vt()),Adamax:()=>$i.adamax(.002,.9,.999,Vt(),0),RMSProp:()=>$i.rmsprop(.001,.9,0,Vt()),SGD:()=>$i.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 B(`Unknown Optimizer ${e}`)}var iI=1*1024*1024;function oI(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!Bb(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let a=JSON.stringify(e);a.length>iI&&console.warn(`User-defined metadata of model "${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 <= ${iI}.`)}}function Bb(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"||!Bb(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!Bb(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function FW(e,t,n,a=console.log){let r=_W(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(u=>Math.floor(t*u)));let i;if(!r){s.push("Receives inputs"),i=[];for(let u in e.nodesByDepth)i.push(...e.nodesByDepth[u])}a("_".repeat(t)),hm(s,n,a),a("=".repeat(t));let o=e.layers;for(let u=0;u<o.length;++u)r?EW(o[u],n,a):AW(o[u],n,i,a),a((u===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=CW(e),c=rm(e.nonTrainableWeights);a(`Total params: ${l+c}`),a(`Trainable params: ${l}`),a(`Non-trainable params: ${c}`),a("_".repeat(t))}function CW(e){let t;return e.collectedTrainableWeights!=null?t=rm(e.collectedTrainableWeights):t=rm(e.trainableWeights),t}function _W(e){let t=!0,n=[],a=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}a.push(...r)}if(t)for(let r of e.layers){let s=!1;for(let i of r.inboundNodes)if(a.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function hm(e,t,n=console.log){let a="";for(let r=0;r<e.length;++r)r>0&&(a=a.slice(0,a.length-1)+" "),a+=e[r],a=a.slice(0,t[r]),a+=" ".repeat(t[r]-a.length);n(a)}function EW(e,t,n){let a;try{a=JSON.stringify(e.outputShape)}catch(o){a="multiple"}let r=e.name,s=e.getClassName(),i=[`${r} (${s})`,a,e.countParams().toString()];hm(i,t,n)}function AW(e,t,n,a){let r;try{r=JSON.stringify(e.outputShape)}catch(u){r="multiple"}let s=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let p=0;p<u.inboundLayers.length;++p){let d=u.inboundLayers[p].name,h=u.nodeIndices[p],m=u.tensorIndices[p];s.push(`${d}[${h}][${m}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],c=[`${i} (${o})`,r,e.countParams().toString(),l];hm(c,t,a);for(let u=1;u<s.length;++u)hm(["","","",s[u]],t,a)}function lI(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function ip(e,t){if(e===null)return null;if(typeof e=="string")return Mi(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],a=e.length;for(let r=0;r<a;++r){let s=e[r];lI(t,r,s)?n.push(s):n.push(ip(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a];if(a==="name"&&typeof r=="string")n[a]=r;else{let s=Mi(a);n[s]=ip(r,s)}}return n}}function Wb(e,t){if(e==null)return null;if(typeof e=="string")return Ir(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],a=e.length;for(let r=0;r<a;++r){let s=e[r];lI(t,r,s)?n.push(s):n.push(Wb(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a],s=Ir(a);(a==="name"||a==="className")&&typeof r=="string"?n[s]=r:n[s]=Wb(r,a)}return n}}var mm="2.8.5";function $W(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return pe(t,e.dtype)}catch(n){throw new B(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var zi=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof zi)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=$W(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new B(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof Da){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Da){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Fe(this.id2Mask)}},Vb={},uI={};function op(e,t,n,a){let r=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(m=>m.name),l=[],c=t.names();for(let m of o)c.indexOf(m)!==-1?l.push(t.getValue(m)):l.push(null);a!=null&&(a.maxNumTensors=-Infinity,a.minNumTensors=Infinity);let u=o.join(",")+"|"+t.names().join(","),p,d;if(Vb[u]==null){let m=DW(i,t);p=m.sorted,d=m.recipientCounts,Vb[u]=p,uI[u]=d}p=Vb[u],d={},r||Object.assign(d,uI[u]);let h=new zi(t);for(let m=0;m<p.length;++m){if(a!=null){let F=ih().numTensors;F>a.maxNumTensors&&(a.maxNumTensors=F),F<a.minNumTensors&&(a.minNumTensors=F)}let f=p[m],g=f.sourceLayer;if(g instanceof Kl)continue;let y=[],b=[],v=[],x=!1;for(let F of f.inputs){let O=h.getValue(F),W=h.getMask(F);y.push(O),b.push(W),W!=null&&(x=!0),r||(d[F.name]--,d[F.name]===0&&!t.hasKey(F)&&o.indexOf(F.name)===-1&&!O.isDisposed&&F.sourceLayer.stateful!==!0&&v.push(O))}x&&(n=n||{},n.mask=b[0]);let N=xt(g.apply(y,n)),T=null;g.supportsMasking&&(T=g.computeMask(y,b));let C=MW(f),$=Array.isArray(C)?C:[C];for(let F=0;F<$.length;++F){h.hasKey($[F])||h.add($[F],N[F],Array.isArray(T)?T[0]:T);let O=o.indexOf($[F].name);O!==-1&&(l[O]=N[F])}r||Fe(v)}return h.disposeMasks(),s?l:l[0]}function DW(e,t){k.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],a={};if(e.length===1){let r=cI(e[0],t);n=r.sorted,a=r.recipientMap}else{let r=new Set;for(let s of e){let{sorted:i,recipientMap:o}=cI(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(c=>a[l].add(c))}}return{sorted:n,recipientCounts:RW(a)}}function RW(e){let t={};for(let n in e)t[n]=e[n].size;return t}function cI(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 c of o.inputs)r[c.name]==null&&(r[c.name]=new Set),r[c.name].add(o.name),!n.has(c.name)&&s.push(c)}}return{sorted:a,recipientMap:r}}function MW(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let a=0;a<e.sourceLayer.inboundNodes.length;++a)for(let r of e.sourceLayer.inboundNodes[a].outputTensors)if(r.id===e.id){n=a;break}t=e.sourceLayer.getOutputAt(n)}return t}var ar=class extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=nm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],as(this.inputs).length!==this.inputs.length)throw new B(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);as(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;Qa(v===0,"input layer has >1 nodes"),Qa(x===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof Kl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,b,v,x,N,T)=>{(x==null||N==null||T==null)&&(x=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let C=x.inboundNodes[N];if(v.indexOf(C)!==-1)throw new Fa(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(ar.nodeKey(x,N)),x.id in s||(s[x.id]=Object.keys(s).length),v.indexOf(C)===-1&&v.push(C);let $=C.inboundLayers.length;for(let F=0;F<$;F++){let O=C.inputTensors[F],W=C.inboundLayers[F],V=C.nodeIndices[F],H=C.tensorIndices[F];o(O,b,v,W,V,H)}for(b.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);i.push(C)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],v=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,v),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let x=0;x<y.inboundLayers.length;x++){let N=y.inboundLayers[x],T=y.nodeIndices[x],C=N.inboundNodes[T],$=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,$),n[C.id]=C}}let p={};for(let y in t){let b=t[y];b in p||(p[b]=[]),p[b].push(n[y])}let d={};for(let y in a){let b=a[y];b in d||(d[b]=[]),d[b].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(Hh);this.layers=[];for(let y of h){let b=d[y];b.sort((v,x)=>{let N=s[v.id],T=s[x.id];return N<T?-1:N>T?1:0});for(let v of b)v instanceof ar&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(Hh);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let v=b.outboundLayer;if(v!=null){for(let x of b.inputTensors)if(m.indexOf(x)===-1)throw new Fa(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${f}`);for(let x of b.outputTensors)m.push(x);f.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(v=>v===y).length;if(b!==1)throw new Fa(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new B("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 B(`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 B(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new B(`${s.length} of ${a} weights are not set: ${s}`)}Mb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${mm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Wb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=xt(e);let n=new zi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return op(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=xt(e);let n;return t==null?n=Di(null,e.length):n=xt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=am(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Hh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,v=n[b];u.push(v)}let p=c.computeOutputShape(In(u)),d=am(p),h=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${h}_${m}`;n[f]=d[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Qa(o in n),r.push(n[o])}return In(r)}runInternalGraph(e,t){t==null&&(t=Di(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Hh);for(let o of a){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,p=c.inputTensors,d=c.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,g,y,b;if(c.callArgs!=null&&(m=c.callArgs),h.length===1){let[v,x]=h[0];m.mask==null&&(m.mask=x),y=xt(u.call(v,m)),b=xt(u.computeMask(v,x)),f=[v],g=[x]}else f=h.map(v=>v[0]),g=h.map(v=>v[1]),m.mask==null&&(m.mask=g),y=xt(u.call(f,m)),b=xt(u.computeMask(f,g));if(u.activityRegularizer)throw new $e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],N=y[v],T=b[v];n[x.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Qa(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),r.push(l),s.push(c)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof ar?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=ar.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return D(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=ar.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],d=ar.nodeKey(s,u),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],v=ar.nodeKey(g,y),x=t[v];x==null&&(x=0),m.push([g.name,x,b,h])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ar.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,c,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ar.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let v of g){let x=v[0],N=v[1],T=v[2];if(b=v[3]==null?{}:v[3],!(x in r)){i(f,g);return}let C=r[x];if(C.inboundNodes.length<=N){i(f,g);return}let $=C.inboundNodes[N];y.push($.outputTensors[T])}y.length>0&&f.apply(In(y),b)}function l(f){let g=f.name,y=Ma(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!oz(s);)for(let f of u){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];Qa(g in r);let v=r[g].inboundNodes[y].outputTensors;p.push(v[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];Qa(g in r);let v=r[g].inboundNodes[y].outputTensors;d.push(v[b])}return new e({inputs:p,outputs:d,name:c})}get stateful(){if(this._stateful)throw new B("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(){D(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function PW(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: ${JSON.stringify(e)}`)}function pI(e,t){return PW(e,t,"classWeight")}async function dI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());Fe(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])}),tt(i,"float32")}else return null}function OW(e,t){return L(e,t)}var LW=32;function mI(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=hI("input",e.inputNames,n),i=hI("output",e.outputNames,a),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)k.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function hI(e,t,n){if(n instanceof z)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 B(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function zW(e){if(e.length===3)throw new $e("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function WW(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(fI(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=zW(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=Qk(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=eI(u,p,n.epochs,null,null,BW(t,n),null,r,c);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await d.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let v=await f.next();if(a&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(v.value!=null){let{xs:x,ys:N}=mI(e,v.value),T={};T.batch=b,T.size=x[0].shape[0],await d.onBatchBegin(b,T);let C=[];if(n.classWeight!=null){let O=pI(n.classWeight,e.outputNames);for(let W=0;W<O.length;++W)C.push(await dI(N[W],null,O[W]))}let $=x.concat(N).concat(C),F=o($);Fe($);for(let O=0;O<l.length;++O){let W=l[O],V=F[O];T[W]=V,Kt(V)}await d.onBatchEnd(b,T),Kk(T),b++,y++}if(a?y>=n.batchesPerEpoch:v.done){if(r){let x;fI(n.validationData)?x=xt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):x=xt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?LW:n.validationBatchSize,verbose:0}));for(let N=0;N<e.metricsNames.length;++N)g[`val_${e.metricsNames[N]}`]=x[N]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(m,g),m++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function BW(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function fI(e){return typeof e.iterator=="function"}function VW(e){return typeof e.next=="function"}async function UW(e,t,n){n=n||{};let a=n.batches!=null,r=e.testFunction,s=[];if(n.verbose>0)throw new $e("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=VW(t)?t:await t.iterator(),o=0,l=0;for(;a?l<n.batches:!0;){let c=await i.next();if(s=D(()=>{if(c.value){let{xs:u,ys:p}=mI(e,c.value),d=u.concat(p),h=D(()=>r(d));if(Fe(d),l===0)for(let f=0;f<h.length;++f)s.push(he(0));let m=d[0].shape[0];for(let f=0;f<h.length;++f){let g=h[f],y=s[f];s[f]=D(()=>Z(s[f],L(m,g))),l>0&&Fe(y)}Fe(h),o+=m,++l}return s}),c.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 c=0;c<s.length;++c){let u=s[c];s[c]=we(s[c],o),Fe(u)}return In(s)}function Ub(e){k.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function lp(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(a=>Oi(a,t,n-t)):Oi(e,t,n-t)}function Gb(e,t){return D(()=>e==null?null:Array.isArray(e)?e.map(n=>Gb(n,t)):Dk(e,t.dtype==="int32"?t:t.toInt()))}function Hb(e,t){let n=[],a=0,r=null;for(;a<e;)r=a+t,r>=e&&(r=e),n.push([a,r]),a=r;return n}async function GW(e,t,n,a,r,s,i,o,l,c,u,p,d,h,m){r==null&&(r=32),s==null&&(s=1),u==null&&(u=!0),d==null&&(d=0);let f=!1;if(l!=null&&c!=null&&(f=!0),m!=null&&(f=!0,h==null))throw new B("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,r,h,"steps_per_epoch"),y;g!=null&&(y=$a(0,g)),i==null&&(i=1);let{callbackList:b,history:v}=eI(o,i,s,d,g,h,r,f,p);b.setModel(e),e.history=v,await b.onTrainBegin(),e.stopTraining_=!1;for(let x=d;x<s;++x){await b.onEpochBegin(x);let N={};if(h!=null)throw new $e("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new $e("batch shuffling is not implemneted yet");u&&k.shuffle(y);let T=tt(y),C=Hb(g,r);for(let $=0;$<C.length;++$){let F={};if(await b.onBatchBegin($,F),D(()=>{let O=C[$][0],W=C[$][1],V=Oi(T,O,W-O);F.batch=$,F.size=W-O;let H=Gb(n,V),K=t(H);for(let j=0;j<a.length;++j){let Y=a[j],J=K[j];F[Y]=J,Kt(J)}if($===C.length-1&&f){let j=e.testLoop(l,c,r);for(let Y=0;Y<a.length;++Y){let J=a[Y],ne=j[Y];Kt(ne),N["val_"+J]=ne}}}),await b.onBatchEnd($,F),Kk(F),e.stopTraining_)break}T.dispose()}if(await b.onEpochEnd(x,N),e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}async function HW(e,t,n,a={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let r,s,i,o,l,c,u;try{let p=a.batchSize==null?32:a.batchSize;Ub(p);let d=!1,h=await e.standardizeUserData(t,n,a.sampleWeight,a.classWeight,d,p);r=h[0],s=h[1],u=h[2];let m=!1,f;if(a.validationData!=null&&a.validationData.length>0){if(m=!0,a.validationData.length===2)i=a.validationData[0],o=a.validationData[1];else throw a.validationData.length===3?new $e("validationData including sample weights is not supported yet."):new B(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${a.validationData} is invalid.`);let T=!0,C=await e.standardizeUserData(i,o,null,null,T,p);l=C[0],c=C[1],f=l.concat(c)}else if(a.validationSplit!=null&&a.validationSplit>0&&a.validationSplit<1){m=!0;let T=Math.floor(r[0].shape[0]*(1-a.validationSplit)),C=r[0].shape[0];l=lp(r,T,C),r=lp(r,0,T),c=lp(s,T,C),s=lp(s,0,T),f=l.concat(c)}else a.validationSteps!=null&&(m=!0);let g=r.concat(s).concat(u);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),b=e.getDedupedMetricsNames(),v,x;m?(e.makeTestFunction(),v=e.testFunction,x=b.slice().concat(b.map(T=>"val_"+T))):(v=null,f=[],x=b.slice());let N=Qk(a.callbacks,a.yieldEvery);return await GW(e,y,g,b,p,a.epochs,a.verbose,N,v,f,a.shuffle,x,a.initialEpoch,null,null)}finally{e.isTraining=!1,Bi(r,t),Bi(s,n),Bi(l,i),Bi(c,o),u!=null&&Fe(u)}}function gI(e){let t=[];e instanceof z&&(e=[e]);for(let n=0;n<e.length;++n){let a=e[n];if(a.rank===1)t.push(tp(a,1));else{if(a.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(a)}}return t}function Bi(e,t){if(e==null)return;let n=[];if(t instanceof z)n.push(t.id);else if(Array.isArray(t))t.forEach(r=>n.push(r.id));else if(t!=null)for(let r in t){let s=t[r];n.push(s.id)}let a=[];if(e instanceof z)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 jW(e){return e instanceof z}function jb(e){return Array.isArray(e)}function yI(e){return!jW(e)&&!jb(e)}function bI(e,t,n,a=!0,r=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(jb(e)&&e.length>0)i=!0;else if(yI(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new B(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(yI(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new B(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(jb(e)){if(e=e,e.length!==t.length)throw new B(`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 B(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=gI(s),n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new B(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);for(let l=0;l<n[i].length;++l){if(l===0&&!a)continue;let c=o.shape[l],u=n[i][l];if(u!=null&&u>=0&&c!==u)throw new B(`Error when checking ${r}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function qW(e,t,n){let a=as(e.map(s=>s.shape[0]));a.sort();let r=as(t.map(s=>s.shape[0]));if(r.sort(),a.length>1)throw new B(`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 B(`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 B(`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 KW(e,t,n){let a=[Li,um,sp];for(let r=0;r<e.length;++r){let s=e[r],i=t[r],o=n[r];if(i!=null){if(i===sp&&s.shape[s.shape.length-1]===1)throw new B(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(a.indexOf(i)!==-1){let l=s.shape.slice(1),c=o.slice(1);for(let u=0;u<l.length;++u){let p=l[u],d=c[u];if(d!=null&&p!==d)throw new B(`A target Tensor with shape ${s.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function xI(e,t,n,a=!0,r=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new B(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);s=e}else{if(t.length>1)throw new B(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new B(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);for(let l=0;l<n[i].length;++l){if(l===0&&!a)continue;let c=o.shape[l],u=n[i][l];if(u!=null&&u!==c)throw new B(`Error when checking ${r}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function XW(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>[]);let n;if(typeof e=="string"||typeof e=="function")n=[e];else if(Array.isArray(e)||typeof e=="object")n=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(n))return t.map(a=>n);{let a=[];for(let r of t){let s=n.hasOwnProperty(r)?n[r]:[];Array.isArray(s)||(s=[s]),a.push(s)}return a}}var YW="layers-model",Nr=class extends ar{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new B("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");FW(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=SW(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof wr))throw new B("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new B(`Unknown entry in loss dictionary: "${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(Pb(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new B(`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=>Pb(s))}else{let s=Pb(e.loss);this.outputs.forEach(i=>{t.push(s)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let s=0;s<this.outputs.length;++s){let i=this.internalOutputShapes[s],o=this.outputNames[s];this.feedOutputNames.push(o),this.feedOutputShapes.push(i),this.feedLossFns.push(this.lossFunctions[s])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],Pi("loss",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=this.lossFunctions[s];this.outputs.length>1&&(this.metricsTensors.push([i,s]),this.metricsNames.push(this.outputNames[s]+"_loss"))}});let a=XW(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])};Pi("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=a[s];(o=>{let l="",c,u,p;for(let d of o){if(typeof d=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(d)!==-1){let m=this.internalOutputShapes[s];m[m.length-1]===1||this.lossFunctions[s]===um?["accuracy","acc"].indexOf(d)!==-1?u=Ob:["crossentropy","ce"].indexOf(d)!==-1&&(u=aI):this.lossFunctions[s]===lm?["accuracy","acc"].indexOf(d)!==-1?u=rI:["crossentropy","ce"].indexOf(d)!==-1&&(u=sI):["accuracy","acc"].indexOf(d)!==-1?u=Lb:["crossentropy","ce"].indexOf(d)!==-1&&(u=zb);let f;["accuracy","acc"].indexOf(d)!==-1?f="acc":["crossentropy","ce"].indexOf(d)!==-1&&(f="ce"),p=u,c=l+f}else p=TW(d),c=l+dm(d);let h;Pi(c,()=>{h=p}),r(s,c,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;Ub(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 In(l)}finally{Bi(s[0],e),Bi(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),UW(this,e,t)}checkNumSamples(e,t,n,a="steps"){let r;if(n!=null){if(r=null,t!=null)throw new B(`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 B(`Either the input data should have a defined shape, or ${a} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new B("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),a=n?t:[t],r=this.retrieveSymbolicTensors(a),s=new zi;if(e instanceof z&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new B(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;o<this.inputs.length;++o)s.add(this.inputs[o],e[o])}else for(let o of this.inputs){let l=e[o.name];if(l==null)throw new B(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=op(r,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=Di(null,e.length),n=e.length;for(let a of this.layers){let r=Array.isArray(a.output)?a.output:[a.output],s=r.map(i=>i.name);for(let i=0;i<e.length;++i){let o=s.indexOf(e[i]);if(o!==-1&&(t[i]=r[o],n--),n===0)break}if(n===0)break}if(n>0){let a=[];throw t.forEach((r,s)=>{r==null&&a.push(e[s])}),new B(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(a)}`)}return t}predictLoop(e,t=32,n=!1){return D(()=>{let a=this.checkNumSamples(e);if(n)throw new $e("Verbose predictLoop() is not implemented yet.");let r=Hb(a,t),s=this.outputs.map(i=>[]);for(let i=0;i<r.length;++i)D(()=>{let o=r[i][0],l=r[i][1],c=lp(e,o,l),u=[];if(Array.isArray(c))for(let d=0;d<c.length;++d)u.push({key:this.inputs[d],value:c[d]});else u.push({key:this.inputs[0],value:c});let p=new zi(u);return op(this.outputs,p)}).forEach((o,l)=>s[l].push(o));return In(s.map(i=>Qe(i,0)))})}predict(e,t={}){let n=gI(e);xI(n,this.inputNames,this.feedInputShapes,!1);try{let a=t.batchSize==null?32:t.batchSize;return Ub(a),this.predictLoop(n,a)}finally{Bi(n,e)}}predictOnBatch(e){xI(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 Fa("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===lm?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=bI(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=bI(t,this.feedOutputNames,r,!1,"target"),qW(e,t,null),KW(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&a!=null&&a>0&&e[0].shape[0]%a!=0)throw new B(`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 c=pI(a,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await dI(o[u],null,c[u]))}return[i,o,l]}testLoop(e,t,n,a=0,r){return D(()=>{let s=this.checkNumSamples(t,n,r,"steps"),i=[];if(a>0)throw new $e("Verbose mode is not implemented yet.");if(r!=null)throw new $e("steps mode in testLoop() is not implemented yet");{let o=Hb(s,n),l=tt($a(0,s));for(let c=0;c<o.length;++c){let u=o[c][0],p=o[c][1],d=Oi(l,u,p-u),h=Gb(t,d),m=e(h);if(c===0)for(let f=0;f<m.length;++f)i.push(he(0));for(let f=0;f<m.length;++f){let g=m[f];i[f]=Z(i[f],L(p-u,g))}}for(let c=0;c<i.length;++c)i[c]=we(i[c],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let a=e[n],r=a;vk(e,a)>1&&(r+=`_${vk(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 c=[];for(let h=0;h<this.inputs.length;++h)c.push({key:this.inputs[h],value:n[h]});let u=new zi(c),p=op(this.outputs,u,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=OW(m,r[h]));let f=Ct(m);t.push(f),h===0?d=m:d=Z(d,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Ct(f(a[g],p[g]))}Kt(m),s.push(m)}return d=Ct(d),this.calculateLosses().forEach(h=>{d=Z(d,h)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>D(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new zi(s),o=op(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Ct(c(r[l],o[l]));l===0?n=u:n=Z(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],p=Ct(c(r[u],o[u]));t.push(p)}return t})}async fit(e,t,n={}){return HW(this,e,t,n)}async fitDataset(e,t){return WW(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Fe(s),In(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=ih().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-ih().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ir(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ir(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]=Ir(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[Ir(dm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ir(dm(e)));{let e={};for(let t in this.metrics)e[t]=Ir(dm(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=ip(e.optimizer_config),n=Ma(t),a;if(typeof e.loss=="string")a=Mi(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Mi(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Mi(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Mi(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Mi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=qt.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await qt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:YW,generatedBy:`TensorFlow.js tfjs-layers v${mm}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await qt.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=qt.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;oI(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){oI(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Nr.className="Model";re.registerClass(Nr);var vI=class extends Nr{};vI.className="Functional";re.registerClass(vI);async function JW(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=ip(n),r=Ma(a,t);if(e.weightsManifest!=null){let s=await qt.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Fe(s)}return r}async function QW(e,t){if(t==null&&(t={}),typeof e=="string"){let n=qt.getLoadHandlers(e,t);if(n.length===0)n.push(qt.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return ZW(e,void 0,t)}async function ZW(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("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=Ma(ip(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 B("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=e4(a.weightData,a.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Fe(c),Fe(u.map(p=>p.tensor))}return o}function e4(e,t){let n=qt.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var Jl=class extends Nr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:nm("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 B(`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 Nr,n;if(t){if(n=e,n.outputs.length!==1)throw new B("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 B("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 B("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=qk({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 B(`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 B("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=jk(this.outputs[0])}this.inboundNodes=[],new sm({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(mt(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 Nr({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 Fa("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 Fa("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 Fa("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 Fa("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 B("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 $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ma(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("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 B("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";re.registerClass(Jl);function t4(e){return new Nr(e)}function n4(e){return new Jl(e)}function a4(e,t){return t==null&&(t={}),QW(e,t)}function Lk(e){return qk(e)}function r4(e,t){ga.registerCallbackConstructor(e,t)}var On=class extends re.Serializable{getConfig(){return{}}},wI=class extends On{apply(e,t=1){return Pz(e,t)}};wI.className="elu";re.registerClass(wI);var kI=class extends On{apply(e){return Sh(e)}};kI.className="selu";re.registerClass(kI);var II=class extends On{apply(e){return Ye(e)}};II.className="relu";re.registerClass(II);var NI=class extends On{apply(e){return D(()=>Ei(6,Ye(e)))}};NI.className="relu6";re.registerClass(NI);var TI=class extends On{apply(e){return e}};TI.className="linear";re.registerClass(TI);var SI=class extends On{apply(e){return da(e)}};SI.className="sigmoid";re.registerClass(SI);var CI=class extends On{apply(e){return Lz(e)}};CI.className="hardSigmoid";re.registerClass(CI);var _I=class extends On{apply(e){return zl(e)}};_I.className="softplus";re.registerClass(_I);var EI=class extends On{apply(e){return Oz(e)}};EI.className="softsign";re.registerClass(EI);var AI=class extends On{apply(e){return Pl(e)}};AI.className="tanh";re.registerClass(AI);var qb=class extends On{apply(e,t=-1){return Ca(e,t)}};qb.className="softmax";re.registerClass(qb);var FI=class extends On{apply(e,t=-1){return bh(e,t)}};FI.className="logSoftmax";re.registerClass(FI);var $I=class extends On{apply(e,t=1){return D(()=>da(e.mul(t)).mul(e))}};$I.className="swish";re.registerClass($I);function os(e){return e.getClassName()}function Kb(e,t={}){return Jc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function ls(e){if(e==null){let t={};return t.className="linear",t.config={},Kb(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Kb(t)}else return e instanceof On?e:Kb(e)}function Xb(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 DI=class extends re.Serializable{},up=class extends DI{constructor(e){super();Xb(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 D(()=>{let t=bt([1]);return this.hasL1&&(t=Z(t,Ce(L(this.l1,Bt(e))))),this.hasL2&&(t=Z(t,Ce(L(this.l2,np(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};up.className="L1L2";re.registerClass(up);function s4(e){return Xb(e),new up({l1:e!=null?e.l1:null,l2:0})}function i4(e){return Xb(e),new up({l2:e!=null?e.l2:null,l1:0})}var MI={l1l2:"L1L2"};function ft(e){return cb(e)}function RI(e,t={}){return Jc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function It(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in MI?MI[e]:e,config:{}};return RI(t)}else return e instanceof DI?e:RI(e)}var Yb=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Re(e);let n=Ye(e);return this.maxValue!=null&&(n=Xt(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Yb.className="ReLU";re.registerClass(Yb);var Jb=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=Re(e);return Bc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Jb.className="LeakyReLU";re.registerClass(Jb);var Zb=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=kt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=It(e.alphaRegularizer),this.alphaConstraint=Gt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new Jt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Re(e),Gc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:_t(this.alphaInitializer),alphaRegularizer:ft(this.alphaRegularizer),alphaConstraint:Ut(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Zb.className="PReLU";re.registerClass(Zb);var Qb=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new $e(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Re(e);return Ol(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="ELU";re.registerClass(Qb);var ex=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Re(e);return n.mul(ep(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};ex.className="ThresholdedReLU";re.registerClass(ex);var tx=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new qb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Re(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};tx.className="Softmax";re.registerClass(tx);function Zl(e,t,n){if(typeof e=="number")return Di(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!$z(r))throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Ra(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function fm(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+ss([n-t,0]);else if(a==="same")e=e*t;else throw new B(`Unsupport padding mode: ${a}.`);return e}function nx(e,t){return D(()=>(Mt(t),t==="channelsFirst"?Ue(e,[0,2,3,1]):e))}function PI(e,t){return D(()=>(Mt(t),t==="channelsFirst"?Ue(e,[0,2,3,4,1]):e))}function o4(e,t,n,a=1,r="valid",s,i=1){return D(()=>{if(s==null&&(s=Aa()),Mt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ue(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=ph(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=tr(o,n)),o})}function OI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=Aa()),Mt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=nx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ns.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function l4(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=Aa()),Mt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=PI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=My(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=tr(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var ax=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ax.verifyArgs(t),this.rank=e,Yt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ta(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Mt(this.dataFormat),this.activation=ls(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=kt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Gt(t.biasConstraint),this.biasRegularizer=It(t.biasRegularizer),this.activityRegularizer=It(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`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 B(`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 B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Qa("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!db(e.kernelSize,"number",1,3))throw new B(`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:os(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},cp=class extends ax{constructor(e,t){super(e,t);this.kernel=null,cp.verifyArgs(t),this.filters=t.filters,Yt(this.filters,"filters"),this.kernelInitializer=kt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Gt(t.kernelConstraint),this.kernelRegularizer=It(t.kernelRegularizer)}build(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`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 D(()=>{e=Re(e);let n,a=this.bias==null?null:this.bias.read(),r=kk(this.activation.getClassName());if(r!=null&&this.rank===2)n=OI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=o4(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=OI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=l4(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=mt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Ra(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:_t(this.kernelInitializer),kernelRegularizer:ft(this.kernelRegularizer),kernelConstraint:Ut(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},pp=class extends cp{constructor(e){super(2,e);pp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!db(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};pp.className="Conv2D";re.registerClass(pp);var gm=class extends cp{constructor(e){super(3,e);gm.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 B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};gm.className="Conv3D";re.registerClass(gm);var rx=class extends pp{constructor(e){super(e);if(this.inputSpec=[new Jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Re(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=fm(o,p,c,this.padding),m=fm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let g=dh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ue(g,[0,3,1,2])),this.bias!=null&&(g=tr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(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]=fm(t[a],o,s,this.padding),t[r]=fm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};rx.className="Conv2DTranspose";re.registerClass(rx);var LI=class extends cp{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 B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("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 B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=kt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=It(t.depthwiseRegularizer),this.depthwiseConstraint=Gt(t.depthwiseConstraint),this.pointwiseInitializer=kt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=It(t.pointwiseRegularizer),this.pointwiseConstraint=Gt(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Jt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{e=Re(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),n=Ai(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=tr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(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=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};LI.className="SeparableConv";var sx=class extends LI{constructor(e){super(2,e)}};sx.className="SeparableConv2D";re.registerClass(sx);var ym=class extends cp{constructor(e){super(1,e);ym.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"&&!db(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};ym.className="Conv1D";re.registerClass(ym);var ix=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 D(()=>{if(e=Re(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}};ix.className="Cropping2D";re.registerClass(ix);var ox=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,Mt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Ez(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 D(()=>{let n=Re(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(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 Ue(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}};ox.className="UpSampling2D";re.registerClass(ox);function u4(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=Aa()),Mt(r);let i=nx(e,r);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=yr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var lx=class extends ax{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=kt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Gt(e.depthwiseConstraint),this.depthwiseRegularizer=It(e.depthwiseRegularizer)}build(e){if(e=mt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 D(()=>{e=Re(e);let n=u4(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=tr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=mt(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=Ra(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ra(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=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};lx.className="DepthwiseConv2D";re.registerClass(lx);function zI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("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 BI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat($a(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Jn(r,-1)),r=Ue(r,c)),a&&(t=Rn(t,0),r!=null&&(r=Rn(r,0)));let u=[],p,d=n,h=t.shape[0],m=ht(t),f;r!=null&&(f=ht(r));for(let y=0;y<h;++y){let b=m[y],v=D(()=>e(b,d));if(r==null)p=v[0],d=v[1];else{let x=D(()=>{let N=f[y],T=Mn(N).sub(N),C=v[0].mul(N).add(d[0].mul(T)),$=d.map((F,O)=>v[1][O].mul(N).add(F.mul(T)));return{output:C,newStates:$}});p=x.output,d=x.newStates}o&&u.push(p)}let g;return o&&(g=Dt(u,1)),[p,g,d]})}var nr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new bm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("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 Jt({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 $a(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){$b(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 D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");$b(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Jt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Jt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("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=>bt([n,a])):this.states_=[bt([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>bt([n,a])):this.states_[0]=bt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`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()):Fe(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new B(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Kt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=zI(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 Jt({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 Da){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Re(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 B(`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=BI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=bt(e.shape);return t=Ce(t,[1,2]),t=tp(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?bb(t,[1,n]):t):this.cell.stateSize>1?[bb(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()===nr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ma(a,n);return new e(Object.assign(t,{cell:r}))}};nr.className="RNN";re.registerClass(nr);var rp=class extends qe{},xm=class extends rp{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,Yt(this.units,"units"),this.activation=ls(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=It(e.kernelRegularizer),this.recurrentRegularizer=It(e.recurrentRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=ql([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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 D(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>Mn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>Mn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=er(L(e,s),this.kernel.read()):r=er(e,this.kernel.read()),this.bias!=null&&(r=tr(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,er(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:os(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};xm.className="SimpleRNNCell";re.registerClass(xm);var ux=class extends nr{constructor(e){e.cell=new xm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(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)}};ux.className="SimpleRNN";re.registerClass(ux);var vm=class extends rp{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 B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Yt(this.units,"units"),this.activation=ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=It(e.kernelRegularizer),this.recurrentRegularizer=It(e.recurrentRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=ql([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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 D(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>Mn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>Mn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=er(e,this.kernel.read());this.useBias&&(c=tr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,d]=Pn(u,[2*this.units,this.units],u.rank-1),h=er(a,p),[m,f,g]=Pn(c,3,c.rank-1),[y,b]=Pn(h,2,h.rank-1);i=this.recurrentActivation.apply(Z(m,y)),o=this.recurrentActivation.apply(Z(f,b));let v=er(L(o,a),d);l=this.activation.apply(Z(g,v));let x=Z(L(i,a),L(Z(1,St(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:os(this.activation),recurrentActivation:os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};vm.className="GRUCell";re.registerClass(vm);var cx=class extends nr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new vm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(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)}};cx.className="GRU";re.registerClass(cx);var dp=class extends rp{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,Yt(this.units,"units"),this.activation=ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=It(e.kernelRegularizer),this.recurrentRegularizer=It(e.recurrentRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=ql([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,ss([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=mt(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 fa{apply(i,o){let l=r.apply([s]),c=new Kh().apply([s]),u=r.apply([s*2]);return $k($k(l,c),u)}},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 D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>Mn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>Mn(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let p=er(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,er(a,this.recurrentKernel.read())),this.useBias&&(p=tr(p,this.bias.read()));let[d,h,m,f]=Pn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),c=Z(L(l,r),L(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:os(this.activation),recurrentActivation:os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};dp.className="LSTMCell";re.registerClass(dp);var px=class extends nr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new dp(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(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)}};px.className="LSTM";re.registerClass(px);var bm=class extends rp{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 D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){$b(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Pi(`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(Ma(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 Db(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Mb(t)}};bm.className="StackedRNNCells";re.registerClass(bm);function us(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>Mk(t(),n),i=()=>ap(s,t,a);return!r||r<=1?Kt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Kt(o.clone()))}var c4=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},WI=class extends nr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Jt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("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 D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=bt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new kr("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 B("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(()=>bt(r)):this.states_=[bt(r)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>bt(r)):this.states_[0]=bt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`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()):Fe(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Kt(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],c=e[o?4:3],u=Ra(l,a[0],r,s[0],i[0]),p=Ra(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};WI.className="ConvRNN2D";var wm=class extends dp{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,Yt(this.filters,"filters"),this.kernelSize=Zl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Yt(o,"kernelSize")),this.strides=Zl(a||1,2,"strides"),this.strides.forEach(o=>Yt(o,"strides")),this.padding=r||"valid",ta(this.padding),this.dataFormat=s||"channelsLast",Mt(this.dataFormat),this.dilationRate=Zl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Yt(o,"dilationRate"))}build(e){var t;e=mt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`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,c=this.filters;o=new(t=class extends fa{apply(u,p){let d=l.apply([c]),h=Xa([c]),m=l.apply([c*2]);return vb([d,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 D(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>Mn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Q,ie,ee)=>!ie||!ie[ee]?Q:L(ie[ee],Q),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>Mn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[v,x,N,T]=Pn(this.kernel.read(),i,b),[C,$,F,O]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,v,C,this.padding),u=this.inputConv(u,x,$,this.padding),p=this.inputConv(p,N,F,this.padding),d=this.inputConv(d,T,O,this.padding);let[W,V,H,K]=Pn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,W),f=this.recurrentConv(f,V),g=this.recurrentConv(g,H),y=this.recurrentConv(y,K);let j=this.recurrentActivation.apply(Z(c,m)),Y=this.recurrentActivation.apply(Z(u,f)),J=Z(L(Y,s),L(j,this.activation.apply(Z(p,g)))),ne=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(J));return[ne,ne,J]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=c4(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=Ft(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?tr(r,n,this.dataFormat):r}recurrentConv(e,t){return Ft(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};wm.className="ConvLSTM2DCell";re.registerClass(wm);var dx=class extends WI{constructor(e){let t=new wm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};dx.className="ConvLSTM2D";re.registerClass(dx);var km=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.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return ap(()=>Mk(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()}};km.className="Dropout";re.registerClass(km);var hx=class extends km{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};hx.className="SpatialDropout1D";re.registerClass(hx);var mx=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,Yt(this.units,"units"),this.activation=ls(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Gt(e.kernelConstraint),this.biasConstraint=Gt(e.biasConstraint),this.kernelRegularizer=It(e.kernelRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.activityRegularizer=It(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=mt(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=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e),a=kk(this.activation.getClassName()),r;return a!=null?r=er(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=er(n,this.kernel.read()),this.bias!=null&&(r=tr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:os(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};mx.className="Dense";re.registerClass(mx);var fx=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(e);for(let t of e.slice(1))if(t==null)throw new B(`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],rs(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return Rz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};fx.className="Flatten";re.registerClass(fx);var gx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=ls(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.activation.apply(n)})}getConfig(){let e={activation:os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};gx.className="Activation";re.registerClass(gx);var yx=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 D(()=>(e=Re(e),Dz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};yx.className="RepeatVector";re.registerClass(yx);var bx=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else r*=l}let i=rs(e);if(s!==null){if(r===0||i%r!=0)throw new B(n);a[s]=i/r}else if(i!==r)throw new B(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(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}};bx.className="Reshape";re.registerClass(bx);var xx=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 ${e.dims} instead.`);let t=$a(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 Jt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ue(Re(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};xx.className="Permute";re.registerClass(xx);var vx=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=Re(e),a=-1;return Mc(es(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e),a=-1,r=!0,s=Mc(es(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};vx.className="Masking";re.registerClass(vx);var wx=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(xt(e.inputLength))}this.inputDim=e.inputDim,Yt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Yt(this.outputDim,"outputDim"),this.embeddingsInitializer=kt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=It(e.embeddingsRegularizer),this.activityRegularizer=It(e.activityRegularizer),this.embeddingsConstraint=Gt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return D(()=>this.maskZero?(e=Re(e),es(e,Ge(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=xt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);return n.dtype!=="int32"&&(n=ep(n,"int32")),Dk(this.embeddings.read(),n.as1D()).reshape(mt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_t(this.embeddingsInitializer),embeddingsRegularizer:ft(this.embeddingsRegularizer),activityRegularizer:ft(this.activityRegularizer),embeddingsConstraint:Ut(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};wx.className="Embedding";re.registerClass(wx);var Wi=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=as(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&as(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ss(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=tp(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(rs(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=$a(1,l).concat([0]);n.push(Ue(o,c)),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,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ue(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat($a(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=as(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Jn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=ha(n,t[a]);return n})}},kx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return t})}};kx.className="Add";re.registerClass(kx);var Ix=class extends Wi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};Ix.className="Multiply";re.registerClass(Ix);var Nx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return L(1/e.length,t)})}};Nx.className="Average";re.registerClass(Nx);var Tx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Sa(t,e[n]);return t})}};Tx.className="Maximum";re.registerClass(Tx);var Sx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ei(t,e[n]);return t})}};Sx.className="Minimum";re.registerClass(Sx);var Cx=class extends Wi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>vb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("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 B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Mn(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(Jn(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return uh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cx.className="Concatenate";re.registerClass(Cx);function hp(e,t){for(;e<0;)e+=t;return e}function p4(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("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 $e("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 D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var _x=class extends Wi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("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 B(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`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)=>hp(r,e[s].shape.length)):a=[hp(this.axes,t.shape.length),hp(this.axes,n.shape.length)],this.normalize&&(t=im(t,a[0]),n=im(n,a[1])),p4(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[hp(this.axes,e.length),hp(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 $e("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}};_x.className="Dot";re.registerClass(_x);var Ex=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 D(()=>{this.invokeCallHook(e,t);let n=Re(e);return ap(()=>qh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Ex.className="GaussianNoise";re.registerClass(Ex);var Ax=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 D(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.rate>0&&this.rate<1?ap(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(qh(n.shape,1,a))},()=>n,t.training||!1):n})}};Ax.className="GaussianDropout";re.registerClass(Ax);var Fx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Re(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 D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return ap(()=>{let a=Re(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=xr(Wl(n),this.rate);o=ep(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Re(e),t.training||!1)}return e})}};Fx.className="AlphaDropout";re.registerClass(Fx);function mp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=t1(e,t,n,a,r,s);else if(e.rank===3)i=n1(e,t,n,a,r,s);else if(e.rank===4)i=a1(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function d4(e,t,n,a,r=.001){return D(()=>{let s=wh(e,a),i=s.mean,o=s.variance;return[mp(e,i,o,n,t,r),i,o]})}function h4(e,t,n,a,r=.001){return D(()=>{let s=wh(e,a),i=s.mean,o=s.variance,l=[];for(let h of $a(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[mp(e,c,u,d,p,r),i,o]})}function m4(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),$a(0,e.rank-1))?d4(e,t,n,a,r):h4(e,t,n,a,r)}var $x=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=kt(e.betaInitializer||"zeros"),this.gammaInitializer=kt(e.gammaInitializer||"ones"),this.movingMeanInitializer=kt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=kt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Gt(e.betaConstraint),this.gammaConstraint=Gt(e.gammaConstraint),this.betaRegularizer=It(e.betaRegularizer),this.gammaRegularizer=It(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Jt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Re(e),r=a.shape,s=r.length,i=$a(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 c=i.slice();c.sort();let u=!k.arraysEqual(c,$a(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,v=this.scale?this.gamma.read().reshape(l):null;return mp(a,g,y,b,v,this.epsilon)}else return mp(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,m]=m4(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let v=1-b,x=g.read(),N=x.sub(y).mul(v);g.write(x.sub(N))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:ft(this.betaRegularizer),gammaRegularizer:ft(this.gammaRegularizer),betaConstraint:Ut(this.betaConstraint),gammaConstraint:Ut(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$x.className="BatchNormalization";re.registerClass($x);var Dx=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=kt(e.betaInitializer||"zeros"),this.gammaInitializer=kt(e.gammaInitializer||"ones"),this.betaRegularizer=It(e.betaRegularizer),this.gammaRegularizer=It(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==as(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=Re(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=wh(n,this.axis,s),l=Di(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),mp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),betaRegularizer:ft(this.betaRegularizer),gammaRegularizer:ft(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="LayerNormalization";re.registerClass(Dx);function f4(e,t,n){return D(()=>{if(e.rank!==4)throw new B(`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 B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Aa()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ea(e,a)})}var Mx=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Aa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){e=mt(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 D(()=>f4(Re(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="ZeroPadding2D";re.registerClass(Mx);function Im(e,t,n,a,r,s){return D(()=>{Mt(r),Sk(s),ta(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Aa()),s==null&&(s="max"),e=nx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=$t(e,t,n,o):i=Yn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function VI(e,t,n,a,r,s){return D(()=>{Mt(r),Sk(s),ta(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Aa()),s==null&&(s="max"),e=PI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Hy(e,t,n,o):i=Fy(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var UI=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 B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Yt(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 B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ta(this.padding),this.inputSpec=[new Jt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=Ra(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=tp(Re(e),2);let n=this.poolingFunction(Re(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ts(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Rx=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),Im(e,t,n,a,r,"max")}};Rx.className="MaxPooling1D";re.registerClass(Rx);var Px=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),Im(e,t,n,a,r,"avg")}};Px.className="AveragePooling1D";re.registerClass(Px);var GI=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 B(`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];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),ta(this.padding),this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(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 D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Re(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}},Ox=class extends GI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),Im(e,t,n,a,r,"max")}};Ox.className="MaxPooling2D";re.registerClass(Ox);var Lx=class extends GI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),Im(e,t,n,a,r,"avg")}};Lx.className="AveragePooling2D";re.registerClass(Lx);var HI=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 B(`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];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),ta(this.padding),this.inputSpec=[new Jt({ndim:5})]}computeOutputShape(e){e=mt(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=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(n,this.poolSize[1],this.padding,this.strides[1]),a=Ra(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 D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Re(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}},zx=class extends HI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),VI(e,t,n,a,r,"max")}};zx.className="MaxPooling3D";re.registerClass(zx);var Bx=class extends HI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),VI(e,t,n,a,r,"avg")}};Bx.className="AveragePooling3D";re.registerClass(Bx);var jI=class extends qe{constructor(e){super(e);this.inputSpec=[new Jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Wx=class extends jI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return Ct(n,1)})}};Wx.className="GlobalAveragePooling1D";re.registerClass(Wx);var Vx=class extends jI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return Qn(n,1)})}};Vx.className="GlobalMaxPooling1D";re.registerClass(Vx);var qI=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ux=class extends qI{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};Ux.className="GlobalAveragePooling2D";re.registerClass(Ux);var Gx=class extends qI{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?Qn(n,[1,2]):Qn(n,[2,3])})}};Gx.className="GlobalMaxPooling2D";re.registerClass(Gx);var KI=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=Ma(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},Hx=class extends KI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new B(`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=mt(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 D(()=>(e=Re(e),BI((n,a)=>[Re(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Hx.className="TimeDistributed";re.registerClass(Hx);function g4(e){Ri(_z,"BidirectionalMergeMode",e)}var y4="concat",jx=class extends KI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ma(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ma(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?y4:e.mergeMode,g4(this.mergeMode),e.weights)throw new $e("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()):In(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=zI(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 B("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 c=n.map(u=>new Jt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(a!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Da;for(let l of s)if(l instanceof Da!==o)throw new B("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),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return D(()=>{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=Rn(r,1));let i;return this.mergeMode==="concat"?i=vb([a,r]):this.mergeMode==="sum"?i=Z(a,r):this.mergeMode==="ave"?i=L(.5,Z(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){Pi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Pi(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=Ma(t.layer);if(delete t.layer,t.numConstants!=null)throw new $e("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};jx.className="Bidirectional";re.registerClass(jx);function Gz(e){return new Kl(e)}function Hz(e){return new Qb(e)}function jz(e){return new Yb(e)}function qz(e){return new Jb(e)}function Kz(e){return new Zb(e)}function Xz(e){return new tx(e)}function Yz(e){return new ex(e)}function Jz(e){return new ym(e)}function Zz(e){return new pp(e)}function Qz(e){return new rx(e)}function eB(e){return new gm(e)}function tB(e){return new sx(e)}function nB(e){return new ix(e)}function aB(e){return new ox(e)}function rB(e){return new lx(e)}function sB(e){return new gx(e)}function iB(e){return new mx(e)}function oB(e){return new km(e)}function lB(e){return new hx(e)}function uB(e){return new fx(e)}function cB(e){return new yx(e)}function pB(e){return new bx(e)}function dB(e){return new xx(e)}function hB(e){return new wx(e)}function mB(e){return new kx(e)}function fB(e){return new Nx(e)}function gB(e){return new Cx(e)}function yB(e){return new Tx(e)}function bB(e){return new Sx(e)}function xB(e){return new Ix(e)}function vB(e){return new _x(e)}function wB(e){return new $x(e)}function kB(e){return new Dx(e)}function IB(e){return new Mx(e)}function Eb(e){return new Px(e)}function NB(e){return Eb(e)}function TB(e){return Eb(e)}function Ab(e){return new Lx(e)}function SB(e){return Ab(e)}function CB(e){return Ab(e)}function Fb(e){return new Bx(e)}function _B(e){return Fb(e)}function EB(e){return Fb(e)}function AB(e){return new Wx(e)}function FB(e){return new Ux(e)}function zk(e){return new Vx(e)}function Bk(e){return new Gx(e)}function Wk(e){return new Rx(e)}function Vk(e){return new Ox(e)}function $B(e){return new zx(e)}function DB(e){return new cx(e)}function MB(e){return new vm(e)}function RB(e){return new px(e)}function PB(e){return new dp(e)}function OB(e){return new ux(e)}function LB(e){return new xm(e)}function zB(e){return new dx(e)}function BB(e){return new wm(e)}function WB(e){return new nr(e)}function VB(e){return new bm(e)}function UB(e){return new jx(e)}function GB(e){return new Hx(e)}var HB=zk,jB=Bk,qB=Wk,KB=Vk;function XB(e){return new Ex(e)}function YB(e){return new Ax(e)}function JB(e){return new Fx(e)}function ZB(e){return new vx(e)}var XI={};Oe(XI,{MAPE:()=>_4,MSE:()=>F4,binaryAccuracy:()=>b4,binaryCrossentropy:()=>x4,categoricalAccuracy:()=>w4,categoricalCrossentropy:()=>k4,cosineProximity:()=>T4,mape:()=>E4,meanAbsoluteError:()=>S4,meanAbsolutePercentageError:()=>C4,meanSquaredError:()=>A4,mse:()=>$4,precision:()=>I4,recall:()=>N4,sparseCategoricalAccuracy:()=>v4});function b4(e,t){return Ob(e,t)}function x4(e,t){return aI(e,t)}function v4(e,t){return rI(e,t)}function w4(e,t){return Lb(e,t)}function k4(e,t){return zb(e,t)}function I4(e,t){return nI(e,t)}function N4(e,t){return yW(e,t)}function T4(e,t){return Rb(e,t)}function S4(e,t){return om(e,t)}function C4(e,t){return Yl(e,t)}function _4(e,t){return Yl(e,t)}function E4(e,t){return Yl(e,t)}function A4(e,t){return Li(e,t)}function F4(e,t){return Li(e,t)}function $4(e,t){return Li(e,t)}var YI={};Oe(YI,{modelFromJSON:()=>JW});var JI={};Oe(JI,{l1:()=>M4,l1l2:()=>D4,l2:()=>R4});function D4(e){return new up(e)}function M4(e){return s4(e)}function R4(e){return i4(e)}var ZI=class extends Xl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof Nr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function Nm(e,t){return e<t}function QI(e,t){return e>t}var eN=class extends ZI{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new $e("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=Nm:this.mode==="max"?this.monitorFunc=QI:this.monitor.indexOf("acc")!==-1?this.monitorFunc=QI:this.monitorFunc=Nm,this.monitorFunc===Nm&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===Nm?Infinity:-Infinity}async onEpochEnd(e,t){await is(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 P4(e){return new eN(e)}var O4={earlyStopping:P4},Pa;(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"})(Pa||(Pa={}));var tN;(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={}))})(tN||(tN={}));var qx={};function L4(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};qx[e]=n}function nN(e){return qx[e]}function z4(e){delete qx[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 Tn(t.inputNames[s.inputIndexStart],n,a,r);if(s.type==="tensors")return t.inputNames.slice(o,l).map(p=>Tn(p,n,a,r));let c=Tn(t.inputNames.slice(o)[0],n,a,r),u=c.dataSync();return s.type==="number"?u[0]:k.toNestedArray(c.shape,u)}let i=t.attrParams[e];return i&&i.value}function Tn(e,t,n,a){let[r,s]=Ln(e);if(a!=null){let o=a.getHashTableHandleByName(r);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[Tm(r,o)]);return i!==void 0?t[Tm(r,i)][s]:void 0}function B4(e,t,n){return t[Tm(e,n.currentContextId)]}function Tr(e,t){let[n,a]=Ln(e);return[Tm(n,t&&t.currentContextId),a]}function Tm(e,t){return t?`${e}-${t}`:e}function Ln(e){let t=e.split(":");return t.length===1?[e,0]:[t[0],Number(t[t.length-1])]}function Sm(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 Sr(e){return e.kept?e:Xr(e)}var aN={};Oe(aN,{json:()=>W4});var W4=[{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}]}],rN={};Oe(rN,{json:()=>V4});var V4=[{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}]}],sN={};Oe(sN,{json:()=>U4});var U4=[{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"}]}],iN={};Oe(iN,{json:()=>G4});var G4=[{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"}]}],oN={};Oe(oN,{json:()=>H4});var H4=[{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"}]}],lN={};Oe(lN,{json:()=>j4});var j4=[{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}]}],uN={};Oe(uN,{json:()=>q4});var q4=[{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"}]}],cN={};Oe(cN,{json:()=>K4});var K4=[{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"}]}],pN={};Oe(pN,{json:()=>X4});var X4=[{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}]}],dN={};Oe(dN,{json:()=>Y4});var Y4=[{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"}]}],hN={};Oe(hN,{json:()=>J4});var J4=[{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}]}],mN={};Oe(mN,{json:()=>Z4});var Z4=[{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}]}],fN={};Oe(fN,{json:()=>Q4});var Q4=[{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}]}],gN={};Oe(gN,{json:()=>eV});var eV=[{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"}]}],yN={};Oe(yN,{json:()=>tV});var tV=[{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}]}],bN={};Oe(bN,{json:()=>nV});var nV=[{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}]}],xN={};Oe(xN,{json:()=>aV});var aV=[{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:[]}],wN=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[aN,rN,sN,iN,oN,lN,uN,hN,dN,cN,mN,fN,gN,yN,bN,xN,pN],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=[],c={},u={};t!=null&&(c=this.mapSignatureEntries(t.inputs),u=this.mapSignatureEntries(t.outputs));let p=Object.keys(i);p.forEach(m=>{let f=i[m];f.inputNames.forEach(g=>{let[y]=Tr(g);f.inputs.push(i[y]),i[y].children.push(f)})}),Object.keys(u).length===0?p.forEach(m=>{let f=i[m];f.children.length===0&&l.push(f)}):Object.keys(u).forEach(m=>{let[f]=Tr(m),g=i[f];g!=null&&(g.signatureKey=u[m],l.push(g))}),Object.keys(c).length>0?Object.keys(c).forEach(m=>{let[f]=Tr(m),g=i[f];g&&(g.signatureKey=c[m],o.push(g))}):o=a;let d={};e.library!=null&&e.library.function!=null&&(d=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:d};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=nN(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=Kx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Kx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":i=nv(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=nv(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":i=Yx(e.attr,r.tfName,r.defaultValue||0),i===void 0&&!!r.tfDeprecatedName&&(i=Yx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":i=tv(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=tv(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":i=Xx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Xx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":i=rv(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=rv(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":i=ev(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=ev(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":i=av(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=av(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":i=Zx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Zx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":i=Qx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Qx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":i=vN(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=vN(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((c,u)=>(c[u.name]=this.mapNode(u),u.op==="Const"&&a.push(c[u.name]),c),{}));let s=[],i=[];e.signature.inputArg.forEach(c=>{let[u]=Tr(c.name),p={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Jx(c.type),type:"dtype"}},children:[]};p.signatureKey=c.name,s.push(p),r[u]=p}),Object.keys(r).forEach(c=>{let u=r[c];u.inputNames.forEach(p=>{let[d]=Tr(p);u.inputs.push(r[d]),r[d].children.push(u)})});let o=e.ret;e.signature.outputArg.forEach(c=>{let[u,p]=Tr(o[c.name]),d=r[u];d!=null&&(d.defaultOutput=p,i.push(d))});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 rV(e){let t=te().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 kN(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):rV(e);return t?n:n.toLowerCase()}function Kx(e,t,n,a=!1){let r=e[t];return r!=null?kN(r.s,a):n}function Xx(e,t,n){let a=e[t];return a?a.b:n}function Yx(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 Jx(e){switch(typeof e=="string"&&(e=Pa[e]),e){case Pa.DT_FLOAT:return"float32";case Pa.DT_INT32:case Pa.DT_INT64:case Pa.DT_INT8:case Pa.DT_UINT8:return"int32";case Pa.DT_BOOL:return"bool";case Pa.DT_DOUBLE:return"float32";case Pa.DT_STRING:return"string";default:return null}}function vN(e,t,n){let a=e[t];return a&&a.func?a.func.name:n}function Zx(e,t,n){let a=e[t];return a&&a.type?Jx(a.type):n}function Qx(e,t,n){let a=e[t];return a&&a.list&&a.list.type?a.list.type.map(r=>Jx(r)):n}function IN(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function ev(e,t,n){let a=e[t];return a&&a.shape?IN(a.shape):n}function tv(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 nv(e,t,n,a=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(s=>kN(s,a)):n}function av(e,t,n){let a=e[t];return a&&a.list&&a.list.shape?a.list.shape.map(r=>IN(r)):n}function rv(e,t,n){let a=e[t];return a&&a.list&&a.list.b?a.list.b:n}var sV=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 Tn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Tn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Yx(this.node.rawAttrs,e,t);if(n.s!=null)return Kx(this.node.rawAttrs,e,t);if(n.b!=null)return Xx(this.node.rawAttrs,e,t);if(n.shape!=null)return ev(this.node.rawAttrs,e,t);if(n.type!=null)return Zx(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return tv(this.node.rawAttrs,e,t);if(n.list.s!=null)return nv(this.node.rawAttrs,e,t);if(n.list.shape!=null)return av(this.node.rawAttrs,e,t);if(n.list.b!=null)return rv(this.node.rawAttrs,e,t);if(n.list.type!=null)return Qx(this.node.rawAttrs,e,t)}return t}},iV=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[Z(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[J0(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[vh(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[we(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[Oy(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[lh(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[ge(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[Ei(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[Sa(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[Ya(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[Xc(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},oV=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Bt(I("x",e,t,n))];case"Acos":return[wy(I("x",e,t,n))];case"Acosh":return[ky(I("x",e,t,n))];case"Asin":return[Ny(I("x",e,t,n))];case"Asinh":return[Ty(I("x",e,t,n))];case"Atan":return[Sy(I("x",e,t,n))];case"Atan2":return[Cy(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[_y(I("x",e,t,n))];case"Ceil":return[$y(I("x",e,t,n))];case"Complex":return[jr(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[zc(I("x",e,t,n))];case"Cosh":return[hh(I("x",e,t,n))];case"Elu":return[Ol(I("x",e,t,n))];case"Erf":return[Ly(I("x",e,t,n))];case"Exp":return[dn(I("x",e,t,n))];case"Expm1":return[zy(I("x",e,t,n))];case"Floor":return[Ll(I("x",e,t,n))];case"Log":return[Dn(I("x",e,t,n))];case"Log1p":return[gh(I("x",e,t,n))];case"Imag":return[fh(I("x",e,t,n))];case"Neg":return[St(I("x",e,t,n))];case"Reciprocal":return[Xy(I("x",e,t,n))];case"Real":return[Hc(I("x",e,t,n))];case"Relu":return[Ye(I("x",e,t,n))];case"Round":return[Yy(I("x",e,t,n))];case"Selu":return[Sh(I("x",e,t,n))];case"Sigmoid":return[da(I("x",e,t,n))];case"Sin":return[Ch(I("x",e,t,n))];case"Sign":return[Jy(I("x",e,t,n))];case"Sinh":return[_h(I("x",e,t,n))];case"Softplus":return[zl(I("x",e,t,n))];case"Sqrt":return[nn(I("x",e,t,n))];case"Square":return[ut(I("x",e,t,n))];case"Tanh":return[Pl(I("x",e,t,n))];case"Tan":return[eb(I("x",e,t,n))];case"ClipByValue":return[Xt(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Nh(I("x",e,t,n))];case"Rsqrt":return[Th(Tn(e.inputNames[0],t,n))];case"Prod":return[kh(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[Bc(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[Gc(I("x",e,t,n),I("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ya(e,t,n=""){k.assert(lV(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function lV(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==-1&&t[n]!==-1&&e[n]!==t[n])return!1;return!0}var uV=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=he(0),Kt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),ya(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Kt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let a=0;a<this.size();a++)e.push(a)}if(e.length===0)return Kn([],[0].concat(this.elementShape));let n=this.readMany(e);return ya(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Dt(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Kn([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return ya(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Qe(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,ht(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];D(()=>{t=q(t,[1,n,r]);for(let o=0;o<e.length;++o){let l=o===0?0:a[o-1],c=[0,l,0],u=[1,e[o],r];s[o]=q(We(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},fp=class{constructor(e,t,n,a=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);ya(t,r.shape,"TensorList shape mismatch: "),Kt(r)}),this.idTensor=he(0),this.maxNumElements=a,Kt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new fp([...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.`);return ya(e,this.elementShape,"TensorList shape mismatch: "),D(()=>{let a=this.tensors.map(r=>q(r,e));return Dt(a,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=this.tensors.pop();return ya(n.shape,e,"TensorList shape mismatch: "),q(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ya(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Kt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return ya(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}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.`);ya(this.elementShape,t.shape,"TensorList shape mismatch: "),Kt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return ya(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?Kn([],[0].concat(this.elementShape)):D(()=>{let a=e.map(r=>q(this.tensors[r],n));return Dt(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return ya(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?Kn([],[0].concat(this.elementShape)):D(()=>{let n=this.tensors.map(a=>q(a,t));return Qe(n,0)})}};function cV(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);ya(r,t,"TensorList shape mismatch: ");let s=ht(e);return new fp(s,t,a)}function pV(e,t,n){return new fp([],e,t,n)}function dV(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 fp([],n,e.dtype,a),i=ht(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function hV(e,t,n){let a=0,r=t.map(l=>(a+=l,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=a===0?0:e.size/a,i=D(()=>{let l=[];e=q(e,[1,a,s]);for(let c=0;c<t.length;++c){let u=c===0?0:r[c-1],p=[0,u,0],d=[1,t[c],s];l[c]=q(We(e,p,d),n)}return e.dispose(),l}),o=new fp([],n,e.dtype,t.length);for(let l=0;l<i.length;l++)o.setItem(l,i[l]);return o}var mV=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let p=c.map(h=>h.id);u.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return c}case"LoopCond":{let a=I("pred",e,t,n);return[Sr(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Sr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>Tn(r,t,n)!==void 0);if(a){let r=Tn(a,t,n);return[Sr(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Sr(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Sr(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Sr(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),c=I("name",e,t,n),u=new uV(c,r,a,s,l,i,o);return n.addTensorArray(u),[u.idTensor,he(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[he(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=dV(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=pV(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=cV(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=hV(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function NN(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 c=I("strides",e,t,n),u=Sm(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[h,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:p,dilations:d,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var fV=(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[ph(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=Sm(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Ft(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:c,leakyreluAlpha:u}=NN(e,t,n);return[ns.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:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=NN(e,t,n);return[ns.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:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let a=I("outputShape",e,t,n),r=I("strides",e,t,n),s=Sm(e,t,n);return[dh(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=Sm(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[yr(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[My(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[Yn(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[$t(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}=k1(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[Fy(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[Hy(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],c=s[2];return[Py(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},gV=(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[kn(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[f1(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[I1(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[Dl(a,r,s,i)]}case"Ones":return[Xa(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Mn(I("x",e,t,n))];case"RandomUniform":return[Wl(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[Ih(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[Fh(a,r,s,I("dtype",e,t,n),i)]}case"Zeros":return[bt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[Ge(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function sv(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 yV=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=sv(e,t,n),c=await Ja.nonMaxSuppressionWithScoreAsync(a,r,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=sv(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Ja.nonMaxSuppressionPaddedAsync(a,r,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=sv(e,t,n);return[await Ja.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=pe(I("condition",e,t,n),"bool"),r=[await ab(a)];return a.dispose(),r}case"ListDiff":return S1(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},bV=(e,t,n)=>{switch(e.op){case"TopKV2":{let a=I("x",e,t,n),r=I("k",e,t,n),s=I("sorted",e,t,n),i=tb(a,r,s);return[i.values,i.indices]}case"Unique":{let a=I("x",e,t,n),r=$h(a);return[r.values,r.indices]}case"UniqueV2":{let a=I("x",e,t,n),r=I("axis",e,t,n),s=$h(a,r);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},xV=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let a=I("default",e,t,n);return[Tn(e.name,t,n)||a];case"Placeholder":return[Tn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",e,t,n);return[Sr(c)]}case"IdentityN":return I("x",e,t,n).map(c=>Sr(c));case"Snapshot":let r=I("x",e,t,n);return[Sr(r)];case"Shape":return[tt(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(c=>tt(c.shape));case"Size":return[he(I("x",e,t,n).size,"int32")];case"Rank":return[he(I("x",e,t,n).rank,"int32")];case"NoOp":return[he(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 c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},vV=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=he(0),this.tensorMap=new Map,Kt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),D(()=>{let a=ht(t),r=n.length,s=a.length;k.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i<r;i++){let o=n[i],l=a[i];Kt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return D(()=>{let a=[];for(let r=0;r<n.length;r++){let s=n[r],i=this.findWithDefault(s,t);a.push(i)}return Dt(a)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},wV=async(e,t,n,a)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new vV(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)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},kV=(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[Ja.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[Ja.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[Ja.cropAndResize(a,r,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},IV=(e,t,n)=>{switch(e.op){case"Equal":return[br(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[es(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Zn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[xr(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Wc(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[Qr(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[ha(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Vc(I("a",e,t,n))];case"LogicalOr":return[xh(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[wn(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`)}},NV=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[ze(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Transpose":return[Ue(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[c,u]=I("args",e,t,n);return[ns.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:c,activation:r,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},TV=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[gr(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[gr(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[Wy(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[Ca(I("x",e,t,n))];case"LogSoftmax":return[bh(I("x",e,t,n))];case"SparseToDense":return[rb(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`)}},SV=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Qn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ct(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Bl(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ce(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[uh(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Mc(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[Rc(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[Iy(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[kh(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[mh(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[r1(a,r,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),l=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[c1(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},CV=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let a=I("n",e,t,n),r=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,a),[Qe(s,r)]}case"Gather":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[Ci(a,pe(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[Ci(s,pe(i,"int32"),a,r)]}case"Reverse":{let a=I("dims",e,t,n),r=[];for(let i=0;i<a.length;i++)a[i]&&r.push(i);let s=I("x",e,t,n);return[Rn(s,r)]}case"ReverseV2":{let a=I("axis",e,t,n),r=I("x",e,t,n);return[Rn(r,a)]}case"Slice":{let a=I("begin",e,t,n),r=I("size",e,t,n);return[We(I("x",e,t,n),a,r)]}case"StridedSlice":{let a=I("begin",e,t,n),r=I("end",e,t,n),s=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),c=I("newAxisMask",e,t,n),u=I("shrinkAxisMask",e,t,n),p=I("x",e,t,n);return[Qy(p,a,r,s,i,o,l,c,u)]}case"Pack":return D(()=>{let a=I("axis",e,t,n),r=I("tensors",e,t,n),s=r[0].shape,i=ts(r[0]).shape,o=r.map(l=>{let c=k.arraysEqual(l.shape,s);if(!c&&!k.arraysEqual(ts(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:q(l,s)});return[Dt(o,a)]});case"Unpack":{let a=I("axis",e,t,n),r=I("tensor",e,t,n);return ht(r,a)}case"Tile":{let a=I("reps",e,t,n);return[qa(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 Pn(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[H1(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[j1(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[rb(a,s,r,s.dtype===i.dtype?i:pe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_V=(e,t,n)=>{switch(e.op){case"FFT":return[qc(I("x",e,t,n))];case"IFFT":return[Ul(I("x",e,t,n))];case"RFFT":return[Kc(I("x",e,t,n))];case"IRFFT":return[Ah(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},EV=(e,t,n)=>{switch(e.op){case"Cast":return[pe(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let a=I("axis",e,t,n);return[Jn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[ts(I("x",e,t,n),a)]}case"Reshape":return[q(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[jy(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ea(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[Uc(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Oc(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[Ry(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Lc(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function TN(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return D(()=>iV(s,i,o));case"basic_math":return D(()=>oV(s,i,o));case"control":return mV(s,i,o);case"convolution":return D(()=>fV(s,i,o));case"creation":return D(()=>gV(s,i,o));case"dynamic":return yV(s,i,o);case"evaluation":return D(()=>bV(s,i,o));case"image":return D(()=>kV(s,i,o));case"graph":return D(()=>xV(s,i,o));case"logical":return D(()=>IV(s,i,o));case"matrices":return D(()=>NV(s,i,o));case"normalization":return D(()=>TV(s,i,o));case"reduction":return D(()=>SV(s,i,o));case"slice_join":return D(()=>CV(s,i,o));case"spectral":return D(()=>_V(s,i,o));case"transformation":return D(()=>EV(s,i,o));case"hash_table":return wV(s,i,o,a);case"custom":let l=nN(s.op);if(l&&l.customExecutor)return l.customExecutor(new sV(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 SN=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function _N(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Ln(d)[0]),u=[];a!=null&&(u=a.map(d=>Ln(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((CN(d)||AV(d)||FV(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.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 $V(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>Ln(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return c}var DV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],MV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],RV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function CN(e){return DV.indexOf(e.op)>=0}function AV(e){return MV.indexOf(e.op)>=0}function FV(e){return RV.indexOf(e.op)>=0}var iv=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 iv(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}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=_N(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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 $V(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(u=>this.graph.nodes[Ln(u)[0]]),r=t.map(u=>Ln(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return D(()=>{let u=new SN(this.weightMap,l,c,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Ln(m),y=[];y[g]=e[m],p[f]=y});let d=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=TN(f,p,u,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);p[f.name]=g,this.checkTensorForDisposal(f.name,f,p,u,d,r,h)}}return this.parent==null&&u.dispose(d),t.map(m=>Tn(m,p,u))})}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=B4(o.name,n,a);l!=null&&l.forEach(c=>{if(c&&!r.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.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 SN(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>Tn(p,i,s)),l=o.map(p=>p.id),c=Object.keys(e).map(p=>e[p].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(p=>{i[p].forEach(d=>{d&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),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(b=>this.graph.nodes[Ln(b)[0]]),i=n.map(b=>Ln(b)[0]),o=i.map(b=>this.graph.nodes[b]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:p}=_N(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{let[v,x]=Ln(b),N=[];N[x]=e[b],h[v]=N});let m={},f=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let b=this.processStack(s,d,t,h,g,f,i,m,l);await Promise.all(b)}u==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(b=>!CN(b)&&!Tn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw u!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let p="";if(u.node.op==="Enter"&&I("isConstant",u.node,a,n)&&([p]=Tr(u.node.name,n)),a[u.node.name]==null){let d=TN(u.node,a,n,this._resourceManager);p||([p]=Tr(u.node.name,n));let h=n.currentContext;k.isPromise(d)?c.push(d.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l),m))):(a[p]=d,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l))}else this.processChildNodes(u.node,t,n,a,r,l)}return c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Tr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Tn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Tn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Ln(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Ln(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Ln(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},PV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},OV="?tfjs-format=file",LV="model.json",EN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new PV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=qt.browserHTTPRequest(e,this.loadOptions);else{let t=qt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(qt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=qt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new iv(wN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=wN.Instance.transformGraph(e.modelInitializer);this.initializer=new iv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=qt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof z)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function zV(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${LV}${OV}`);let n=new EN(e,t);return await n.load(),n}var AN="2.8.5",FN={};Oe(FN,{CSVDataset:()=>DN,Dataset:()=>Ql,FileDataSource:()=>MN,TextLineDataset:()=>$N,URLDataSource:()=>RN,array:()=>BV,csv:()=>VV,func:()=>UV,generator:()=>GV,microphone:()=>jV,version_data:()=>PN,webcam:()=>HV,zip:()=>WV});var qV=No(md()),KV=No(md());function XV(e,t){return Cm(e,t)}function Cm(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=Cm(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 YV(e,t=LN){return ON(e,t)}function ON(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(c=>c[i]),l=ON(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 LN(e){return e===null?null:eu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function zN(e,t){let n=new Map;Cm(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 Cm(e,t,n)}function eu(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof z))}function ZV(e){return e==null||JV(e)||Array.isArray(e)||typeof e=="object"&&e instanceof z||k.isTypedArray(e)}function JV(e){return e===null||typeof e!="object"&&typeof e!="function"}function eU(e){return XV(e,QV)}function QV(e){return e instanceof z?{value:e.clone(),recurse:!1}:eu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var BN=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}},ov=class extends BN{constructor(){super(ov.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};ov.INITIAL_CAPACITY=32;function WN(e){return new tU(e)}function lv(e){return new nU(e)}function aU(e,t){return new VN(e,t)}function sU(e,t=cs.FAIL){return new rU(e,t)}var Zt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new dU(this,e)}filter(e){return new cU(this,e)}map(e){return new pU(this,e)}mapAsync(e){return new UN(this,e)}serialMapAsync(e){return new UN(this,e).serial()}flatmap(e){return new hU(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new uU(this,e,t)}columnMajorBatch(e,t=!0,n=LN){return this.rowMajorBatch(e,t).map(a=>YV(a,n))}concatenate(e,t){return new VN(WN([this,e]),t)}take(e){return e<0||e==null?this:new lU(this,e)}skip(e){return e<0||e==null?this:new oU(this,e)}prefetch(e){return new GN(this,e)}shuffle(e,t){return new mU(this,e,t)}serial(){return new iU(this)}},tU=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:eU(e),done:!1}}},nU=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: ${e.message}`,e}}},iU=class extends Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},oU=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`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Fe(e.value)}return this.upstream.next()}},lU=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},uU=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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},cU=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Fe(e.value)}}},pU=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ta.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ta.getTensorsInContainer(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},dU=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},UN=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ta.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ta.getTensorsInContainer(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},uv=class extends Zt{constructor(){super();this.outputQueue=new ov,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},hU=class extends uv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ta.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ta.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return!0}},VN=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}},cs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(cs||(cs={}));var rU=class extends Zt{constructor(e,t=cs.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 zN(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case cs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case cs.SHORTEST:return{value:null,done:!0};case cs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},GN=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new BN(t)}summary(){return`${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()}},mU=class extends GN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=KV.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}}},Ql=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),zn(async()=>(await n.iterator()).columnMajorBatch(e,t,fU),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,zn(async()=>(await t.iterator()).filter(a=>D(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return zn(async()=>(await t.iterator()).map(n=>D(()=>e(n))),this.size)}mapAsync(e){let t=this;return zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,zn(async()=>{let a=lv(async()=>({value:await t.iterator(),done:!1}));return aU(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,zn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=qV.alea(t||k.now().toString());return zn(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,zn(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()}};Ql.MAX_BUFFER_SIZE=1e4;function zn(e,t=null){return new class extends Ql{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function BV(e){return zn(async()=>WN(e),e.length)}function WV(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<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return zn(async()=>{let n=await zN(e,a=>{if(a instanceof Ql)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 sU(n,cs.SHORTEST)},t)}function fU(e){if(e===null)return null;let t=e[0];return ZV(t)?{value:gU(e),recurse:!1}:{value:null,recurse:!0}}function gU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof z?Dt(e):Kn(e)}var $N=class extends Ql{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))}},_m='"',gp=Symbol("out"),HN=Symbol("field"),Em=Symbol("quote"),cv=Symbol("quoteafterquote"),jN=Symbol("quoteinquote"),DN=class extends Ql{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 $N(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=gp;for(let i=0;i<r;i++)switch(s){case gp:switch(e.charAt(i)){case _m:a=i+1,s=Em;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=gp;break;default:s=HN,a=i;break}break;case HN:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=gp,a=i+1;break;default:}break;case Em:switch(e.charAt(i)){case _m:s=cv;break;default:}break;case cv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=gp,a=i+1;break;case _m:s=Em;break;default:s=jN;break}break;case jN:switch(e.charAt(i)){case _m:s=Em;break;default:}break;default:}if(s===cv?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},qN=class extends Zt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(te().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new qN(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-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),Kn(n,t)}},KN=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=tt([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=_a([s,r,o,i],[1,4])}else this.cropBox=_a([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(te().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 KN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ni.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=e.toFloat().expandDims(0),n;n=Ja.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return n.reshape(a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},XN=class{},YN=class extends Zt{split(e){return new yU(this,e)}},yU=class extends YN{constructor(e,t){super();this.upstream=e,this.impl=new bU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},bU=class extends uv{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},vU=class extends Zt{decodeUTF8(){return new xU(this)}},xU=class extends YN{constructor(e){super();this.upstream=e,this.impl=new wU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wU=class extends uv{constructor(e){super();if(this.upstream=e,te().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=_E();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return te().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},JN=class extends vU{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(te().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function IU(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=kU(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new JN(s,t)}else throw new Error(r.statusText)}var kU=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function ZN(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var MN=class extends XN{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(ZN(this.input)&&te().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new JN(this.input,this.options)}},RN=class extends XN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return ZN(this.url)?new MN(this.url,this.fileOptions).iterator():IU(this.url,this.fileOptions)}};function VV(e,t={}){return new DN(new RN(e),t)}function UV(e){let t=lv(e);return zn(async()=>t)}function GV(e){return zn(async()=>{let t=await e();return lv(()=>t.next())})}async function HV(e,t){return KN.create(e,t)}async function jV(e){return qN.create(e)}var PN="2.8.5";function Ie(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var NU=Za.whereImpl,TU=class extends sc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new fd(this,Jr())}write(e,t,n){this.firstUse&&(this.firstUse=!1,te().get("IS_NODE")&&E.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let a={};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}}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){this.data.set(e,{values:t,dtype:a,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return E.mergeRealAndImagArrays(a,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}makeOutput(e,t,n){let a=this.write(e,t,n);return Jr().makeTensorFromDataId(a,t,n,this)}disposeData(e){if(this.data.has(e)){let{complexTensorInfos:t}=this.data.get(e);t!=null&&(this.disposeData(t.real.dataId),this.disposeData(t.imag.dataId)),this.data.delete(e)}}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.data.has(t)){let n=this.data.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}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){Ie([e],"where");let t=this.readSync(e.dataId);return NU(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},QN={};Oe(QN,{addImpl:()=>tT,bincountImpl:()=>pv,bincountReduceImpl:()=>nT,ceilImpl:()=>aT,concatImpl:()=>dv,expImpl:()=>rT,expm1Impl:()=>sT,floorImpl:()=>iT,gatherV2Impl:()=>oT,greaterImpl:()=>lT,lessImpl:()=>uT,linSpaceImpl:()=>cT,logImpl:()=>pT,maxImpl:()=>dT,maximumImpl:()=>hT,minimumImpl:()=>mT,multiplyImpl:()=>hv,negImpl:()=>fT,notEqualImpl:()=>gT,prodImpl:()=>yT,rangeImpl:()=>fv,rsqrtImpl:()=>bT,simpleAbsImpl:()=>eT,sliceImpl:()=>Am,squaredDifferenceImpl:()=>xT,stridedSliceImpl:()=>vT,subImpl:()=>wT,tileImpl:()=>kT,topKImpl:()=>IT,transposeImpl:()=>mv,uniqueImpl:()=>NT});function eT(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var SU=e=>{let{x:t}=e.inputs,n=e.backend;Ie(t,"abs");let a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=eT(r),n.makeOutput(a,t.shape,"float32")},CU={kernelName:Co,backendName:"cpu",kernelFunc:SU};function Pt(e){return(t,n,a,r,s)=>{let i=E.assertAndGetBroadcastShape(t,n),o=i.length,l=k.computeStrides(i),c=k.sizeFromShape(i),u=k.getTypedArrayFromDType(s,c),p=t.length,d=n.length,h=k.computeStrides(t),m=k.computeStrides(n),f=E.getBroadcastDims(t,i),g=E.getBroadcastDims(n,i);if(f.length+g.length===0)for(let y=0;y<u.length;++y)u[y]=e(a[y%a.length],r[y%r.length]);else for(let y=0;y<u.length;++y){let b=k.indexToLoc(y,o,l),v=b.slice(-p);f.forEach(C=>v[C]=0);let x=k.locToIndex(v,p,h),N=b.slice(-d);g.forEach(C=>N[C]=0);let T=k.locToIndex(N,d,m);u[y]=e(a[x],r[T])}return[u,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 _U={kernelName:Nd,backendName:"cpu",kernelFunc:Bn};function Fm(e,t,n="float32"){if(n==="complex64"){let r=Fm(e,t,"float32"),s=Fm(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 rr(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 EU={kernelName:Xo,backendName:"cpu",kernelFunc:rr};function Vi(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 AU={kernelName:Gd,backendName:"cpu",kernelFunc:Vi};function ps(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return rr({inputs:{x:r},backend:n});let i=Fm(n,r.shape,r.dtype),o=ps({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=Vi({inputs:{input:r},backend:n}),o=ps({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=rr({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,c]=Pt((u,p)=>u!==p?1:0)(r.shape,[],i,o,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var FU={kernelName:$s,backendName:"cpu",kernelFunc:ps};function Qt(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;Ie([i,o],e);let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,p=a||i.dtype,[d,h]=t(i.shape,o.shape,c,u,p);return l.makeTensorInfo(h,p,d)}:({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let c=ps({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),u=l.data.get(c.dataId),p=u.complexTensorInfos.real,d=u.complexTensorInfos.imag,h=l.data.get(p.dataId).values,m=l.data.get(d.dataId).values,f=ps({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(f.dataId),y=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,v=l.data.get(y.dataId).values,x=l.data.get(b.dataId).values,[N,T,C]=n(i.shape,o.shape,h,m,v,x),$=l.makeTensorInfo(C,"float32",N),F=l.makeTensorInfo(C,"float32",T),O=Bn({inputs:{real:$,imag:F},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(f),l.disposeIntermediateTensorInfo($),l.disposeIntermediateTensorInfo(F),O}else{let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,p=a||i.dtype,[d,h]=t(i.shape,o.shape,c,u,p);return l.makeTensorInfo(h,p,d)}}}function gv(e){return(t,n,a,r,s,i)=>{let o=E.assertAndGetBroadcastShape(t,n),l=k.sizeFromShape(o),c=o.length,u=k.computeStrides(o),p=k.getTypedArrayFromDType("float32",l),d=k.getTypedArrayFromDType("float32",l),h=E.getBroadcastDims(t,o),m=E.getBroadcastDims(n,o),f=E.mergeRealAndImagArrays(a,r),g=E.mergeRealAndImagArrays(s,i),y=t.length,b=k.computeStrides(t),v=n.length,x=k.computeStrides(n);if(h.length+m.length===0)for(let N=0;N<p.length;N++){let T=N%f.length,C=N%g.length,$=e(f[T*2],f[T*2+1],g[C*2],g[C*2+1]);p[N]=$.real,d[N]=$.imag}else for(let N=0;N<p.length;N++){let T=k.indexToLoc(N,c,u),C=T.slice(-y);h.forEach(V=>C[V]=0);let $=k.locToIndex(C,y,b),F=T.slice(-v);m.forEach(V=>F[V]=0);let O=k.locToIndex(F,v,x),W=e(f[$*2],f[$*2+1],g[O*2],g[O*2+1]);p[N]=W.real,d[N]=W.imag}return[p,d,o]}}var tT=Pt((e,t)=>e+t),$U=gv((e,t,n,a)=>({real:e+n,imag:t+a})),yp=Qt(Wr,tT,$U),DU={kernelName:Wr,backendName:"cpu",kernelFunc:yp};function pv(e,t,n,a,r){let s=k.sizeFromShape(a),i=k.makeZerosTypedArray(r,n);for(let o=0;o<e.length;o++){let l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=r||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function nT(e,t,n,a=!1){let r=e.shape[0],s=e.shape[1],i=Le([r,n],t.dtype);for(let o=0;o<r;o++)for(let l=0;l<s;l++){let c=e.get(o,l);if(c<0)throw new Error("Input x must be non-negative!");c>=n||(a?i.set(1,o,c):t.size>0?i.set(i.get(o,c)+t.get(o,l),o,c):i.set(i.get(o,c)+1,o,c))}return i}function tu(e){return(t,n,a)=>{let r=k.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)r[s]=e(t[s],a);return r}}function ot(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(Ie(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,c=k.sizeFromShape(i.shape),u=n||i.dtype,p=k.getArrayFromDType(u,c);for(let d=0;d<c;++d)p[d]=t(l[d],r);return o.makeTensorInfo(i.shape,u,p)}}function nu(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(Ie(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,c=n||i.dtype,u=t(l,c,r);return o.makeTensorInfo(i.shape,c,u)}}var aT=tu(e=>Math.ceil(e)),MU=nu(Ro,aT),RU={kernelName:Ro,backendName:"cpu",kernelFunc:MU};function dv(e,t,n,a){let r=k.getArrayFromDType(n,k.sizeFromShape(t));if(a&&n!=="string"){let s=0;e.forEach(i=>{let o=k.sizeFromShape(i.shape);r.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?E.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let c=0;c<i.shape[0];++c){let u=c*t[1]+s;for(let p=0;p<i.shape[1];++p)r[u+p]=o[l++]}s+=i.shape[1]})}return r}var rT=tu(e=>Math.exp(e)),TT=nu(zs,rT),PU={kernelName:zs,backendName:"cpu",kernelFunc:TT},sT=tu(e=>Math.expm1(e)),OU=nu(Go,sT),LU={kernelName:Go,backendName:"cpu",kernelFunc:OU},iT=tu(e=>Math.floor(e)),zU=nu(Bs,iT),BU={kernelName:Bs,backendName:"cpu",kernelFunc:zU};function oT(e,t,n){let a=Le(n,e.dtype);for(let r=0;r<a.size;++r){let s=a.indexToLoc(r).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let c=e.locToIndex(s);a.values[r]=e.values[c]}return a}var lT=Pt((e,t)=>e>t?1:0),WU=Qt(Ko,lT,null,"bool"),VU={kernelName:Ko,backendName:"cpu",kernelFunc:WU},uT=Pt((e,t)=>e<t?1:0),UU=Qt(Qo,uT,null,"bool"),GU={kernelName:Qo,backendName:"cpu",kernelFunc:UU};function cT(e,t,n){let a=(t-e)/(n-1),r=k.makeZerosTypedArray(n,"float32");r[0]=e;for(let s=1;s<r.length;s++)r[s]=r[s-1]+a;return r}var pT=tu(e=>Math.log(e)),HU=nu(Hs,pT),jU={kernelName:Hs,backendName:"cpu",kernelFunc:HU};function dT(e,t,n,a){let r=k.getTypedArrayFromDType(a,k.sizeFromShape(n));for(let s=0;s<r.length;++s){let i=s*t,o=e[i];for(let l=0;l<t;++l){let c=e[i+l];c>o&&(o=c)}r[s]=o}return r}var hT=Pt((e,t)=>Math.max(e,t)),qU=Qt(qs,hT),KU={kernelName:qs,backendName:"cpu",kernelFunc:qU},mT=Pt((e,t)=>Math.min(e,t)),XU=Qt(Js,mT),YU={kernelName:Js,backendName:"cpu",kernelFunc:XU},hv=Pt((e,t)=>e*t),JU=gv((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),yv=Qt(Zs,hv,JU),ZU={kernelName:Zs,backendName:"cpu",kernelFunc:yv};function fT(e,t,n){let a=k.createScalarValue(-1,n);return hv([],t,a,e,n)}function QU(e){let{inputs:t,backend:n}=e,{x:a}=t;Ie(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=fT(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var eG={kernelName:rl,backendName:"cpu",kernelFunc:QU},gT=Pt((e,t)=>e!==t?1:0),tG=Qt(sl,gT,null,"bool"),nG={kernelName:sl,backendName:"cpu",kernelFunc:tG};function mv(e,t,n,a,r){let s=t.length,i=k.sizeFromShape(t),o=k.computeStrides(t),l=k.computeStrides(r),c=k.getTypedArrayFromDType(n,k.sizeFromShape(r));for(let u=0;u<i;++u){let p=k.indexToLoc(u,s,o),d=new Array(p.length);for(let m=0;m<d.length;m++)d[m]=p[a[m]];let h=k.locToIndex(d,s,l);c[h]=e[u]}return c}function ba(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{perm:s}=n;Ie(r,"transpose");let i=r.shape.length,o=new Array(i);for(let u=0;u<o.length;u++)o[u]=r.shape[s[u]];let l=a.data.get(r.dataId).values,c=mv(l,r.shape,r.dtype,s,o);return{dataId:a.write(c,o,r.dtype),shape:o,dtype:r.dtype}}var aG={kernelName:yi,backendName:"cpu",kernelFunc:ba};function yT(e,t,n,a){let[r,s]=E.computeOutAndReduceShapes(e,a),i=pa(t,"int32"),o=k.makeZerosTypedArray(k.sizeFromShape(r),i),l=k.sizeFromShape(s);for(let c=0;c<o.length;++c){let u=c*l,p=1;for(let d=0;d<l;++d)p*=n[u+d];o[c]=p}return{outVals:o,outShape:r,outDtype:i}}function rG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Ie(r,"prod");let o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=E.getAxesPermutation(l,o),u=l,p=r,d=[];c!=null&&(p=ba({inputs:{x:r},backend:n,attrs:{perm:c}}),d.push(p),u=E.getInnerMostAxes(u.length,o));let h=n.data.get(p.dataId).values,{outVals:m,outShape:f,outDtype:g}=yT(p.shape,p.dtype,h,u),y=f;return i&&(y=E.expandShapeToKeepDim(f,l)),d.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(y,g,m)}var sG={kernelName:pl,backendName:"cpu",kernelFunc:rG};function fv(e,t,n,a){let r=e===t,s=e<t&&n<0,i=t<e&&n>1;if(r||s||i)return k.makeZerosTypedArray(0,a);let o=Math.abs(Math.ceil((t-e)/n)),l=k.makeZerosTypedArray(o,a);t<e&&n===1&&(n=-1),l[0]=e;for(let c=1;c<l.length;c++)l[c]=l[c-1]+n;return l}var bT=tu(e=>1/Math.sqrt(e)),iG=nu(li,bT),oG={kernelName:li,backendName:"cpu",kernelFunc:iG};function Am(e,t,n,a,r){let s=pn.isSliceContinous(a,t,n),i=k.sizeFromShape(n),o=k.computeStrides(a);if(s){let p=pn.computeFlatOffset(t,o);return r==="string"?e.slice(p,p+i):e.subarray(p,p+i)}let l=r==="string"?E.fromUint8ToStringArray(e):e,c=Le(a,r,l),u=Le(n,r);for(let p=0;p<u.size;++p){let d=u.indexToLoc(p),h=d.map((m,f)=>m+t[f]);u.set(c.get(...h),...d)}return r==="string"?E.fromStringArrayToUint8(u.values):u.values}function Ui(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;Ie(r,"slice");let[o,l]=pn.parseSliceParams(r,s,i);pn.assertParamsValid(r,o,l);let c=n.data.get(r.dataId).values,u=Am(c,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}var lG={kernelName:yl,backendName:"cpu",kernelFunc:Ui},xT=Pt((e,t)=>{let n=e-t;return n*n}),uG=Qt(mi,xT),cG={kernelName:mi,backendName:"cpu",kernelFunc:uG};function vT(e,t,n,a){let r=Le(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*n[l]+a[l];r.set(t.get(...o),...i)}return r}var wT=Pt((e,t)=>e-t),pG=gv((e,t,n,a)=>({real:e-n,imag:t-a})),bv=Qt(fi,wT,pG),dG={kernelName:fi,backendName:"cpu",kernelFunc:bv};function kT(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let a=Le(n,e.dtype);for(let r=0;r<a.values.length;++r){let s=a.indexToLoc(r),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}function IT(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],l=k.getTypedArrayFromDType(n,i*a),c=k.getTypedArrayFromDType("int32",i*a);for(let p=0;p<i;p++){let d=p*o,h=e.subarray(d,d+o),m=[];for(let b=0;b<h.length;b++)m.push({value:h[b],index:b});m.sort((b,v)=>v.value-b.value);let f=p*a,g=l.subarray(f,f+a),y=c.subarray(f,f+a);for(let b=0;b<a;b++)g[b]=m[b].value,y[b]=m[b].index}let u=t.slice();return u[u.length-1]=a,[Le(u,n,l),Le(u,"int32",c)]}function NT(e,t,n,a){let r=k.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let m=0;m<r;m++)s[0]*=n[m];s[1]=n[r];for(let m=r+1;m<n.length;m++)s[2]*=n[m];let i={},o=new Int32Array(n[r]),l=new zt(s,a,e),c=[],u=s[0]===1&&s[2]===1;for(let m=0;m<n[r];m++){let f;if(u)f=e[m].toString();else{let g=[];for(let y=0;y<s[0];y++)for(let b=0;b<s[2];b++)g.push(l.get(y,m,b));f=g.join(",")}if(i[f]!==void 0)o[m]=i[f];else{let g=Object.keys(i).length;i[f]=g,o[m]=g,c.push(m)}}let p=s.slice();p[1]=Object.keys(i).length;let d=new zt(p,a);c.forEach((m,f)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)d.set(l.get(g,m,y),g,f,y)});let h=n.slice();return h[r]=p[1],{outputValues:d.values,outputShape:h,indices:o}}var hG="2.8.5";oh("cpu",()=>new TU,1);var ST=ot(Bo,e=>e>=0?e:Math.exp(e)-1),mG={kernelName:Bo,backendName:"cpu",kernelFunc:ST};function CT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;Ie([r],"leakyRelu");let i=k.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,l=k.getTypedArrayFromDType("float32",i);for(let c=0;c<o.length;c++)l[c]=o[c]<0?s*o[c]:o[c];return n.makeTensorInfo(r.shape,"float32",l)}var fG={kernelName:Gs,backendName:"cpu",kernelFunc:CT},gG=Pt((e,t)=>e<0?t*e:e);function _T(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;Ie([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=gG(a.shape,r.shape,s,i,a.dtype);return n.makeTensorInfo(l,a.dtype,o)}var yG={kernelName:ni,backendName:"cpu",kernelFunc:_T},ET=ot(ai,e=>Math.max(0,e)),bG={kernelName:ai,backendName:"cpu",kernelFunc:ET},AT=ot(si,e=>Math.min(Math.max(0,e),6)),xG={kernelName:si,backendName:"cpu",kernelFunc:AT};function xv(e,t,n,a,r){if(n==="linear")return rr({inputs:{x:t},backend:e});if(n==="relu")return ET({inputs:{x:t},backend:e});if(n==="elu")return ST({inputs:{x:t},backend:e});if(n==="relu6")return AT({inputs:{x:t},backend:e});if(n==="prelu")return _T({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return CT({inputs:{x:t},backend:e,attrs:{alpha:r}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Nt(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let c=n.data.get(r.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,p=c.complexTensorInfos.imag;u.shape=o,p.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var vG={kernelName:hl,backendName:"cpu",kernelFunc:Nt};function FT(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;Ie([r,s],"matMul");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let v=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);k.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,u,d]:[g,d,u],N=o?[y,h,p]:[y,p,h],T=Nt({inputs:{x:r},backend:n,attrs:{shape:x}}),C=Nt({inputs:{x:s},backend:n,attrs:{shape:N}}),$=i?T.shape[1]:T.shape[2],F=i?T.shape[2]:T.shape[1],O=o?C.shape[1]:C.shape[2],W=Math.max(g,y),V=n.data.get(T.dataId).values,H=n.data.get(C.dataId).values,K=k.computeStrides(T.shape),j=k.computeStrides(C.shape),[Y,J,ne]=i?[K[0],1,K[1]]:[K[0],K[1],1],[Q,ie,ee]=o?[1,j[1],j[0]]:[j[1],1,j[0]],le=F*O,se=Le([W,F,O],T.dtype),ce=se.values,de=n.blockSize;for(let fe=0;fe<W;fe++)for(let xe=0;xe<F;xe+=de)for(let be=0;be<O;be+=de)for(let Se=0;Se<$;Se+=de){let _e=Math.min(xe+de,F),Me=Math.min(be+de,O),Ke=Math.min(Se+de,$);for(let Ve=xe;Ve<_e;Ve++)for(let st=be;st<Me;st++){let lt=0;for(let He=Se;He<Ke;He++){let pt=Math.min(fe,g-1)*Y,dt=Math.min(fe,y-1)*ee,Hn=V[pt+Ve*J+He*ne],Ze=H[He*Q+st*ie+dt];lt+=Hn*Ze}ce[fe*le+(Ve*O+st)]+=lt}}return n.disposeIntermediateTensorInfo(T),n.disposeIntermediateTensorInfo(C),n.makeTensorInfo(v,se.dtype,se.values)}var wG={kernelName:Fs,backendName:"cpu",kernelFunc:FT};function kG(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a,d,h,m,f=[];d=FT({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:c},backend:n}),i&&(h=yp({inputs:{a:d,b:i},backend:n}),f.push(d),d=h),u&&(m=xv(n,d,u,o,p),f.push(d),d=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return d}var IG={kernelName:bi,backendName:"cpu",kernelFunc:kG},NG=ot(_o,e=>Math.acos(e)),TG={kernelName:_o,backendName:"cpu",kernelFunc:NG},SG=ot(Eo,e=>Math.acosh(e)),CG={kernelName:Eo,backendName:"cpu",kernelFunc:SG};function _G(e){let{inputs:t,backend:n}=e,a=t;Ie(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Le(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var EG={kernelName:_s,backendName:"cpu",kernelFunc:_G};function AG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Ie(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ba({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("all",l,u.shape.length);let[p,d]=E.computeOutAndReduceShapes(u.shape,l),h=k.sizeFromShape(d),m=k.makeZerosTypedArray(k.sizeFromShape(p),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,v=f[b];for(let x=0;x<h;++x){let N=f[b+x];v=v&&N}m[y]=v}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let y=E.expandShapeToKeepDim(p,o),b=Nt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var FG={kernelName:xd,backendName:"cpu",kernelFunc:AG};function $G(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Ie(r,"any");let o=k.parseAxisParam(s,r.shape),l=o,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ba({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("any",l,u.shape.length);let[p,d]=E.computeOutAndReduceShapes(u.shape,l),h=k.sizeFromShape(d),m=k.makeZerosTypedArray(k.sizeFromShape(p),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,v=f[b];for(let x=0;x<h;++x){let N=f[b+x];v=v||N}m[y]=v}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let y=E.expandShapeToKeepDim(p,o),b=Nt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var DG={kernelName:vd,backendName:"cpu",kernelFunc:$G};function MG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;Ie(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=ba({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],E.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,p]=E.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),h=k.makeZerosTypedArray(d,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],v=0;for(let x=0;x<m;++x){let N=f[y+x];N>b&&(b=N,v=x)}h[g]=v}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var RG={kernelName:Es,backendName:"cpu",kernelFunc:MG};function PG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;Ie(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=ba({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],E.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,p]=E.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),h=k.makeZerosTypedArray(d,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],v=0;for(let x=0;x<m;++x){let N=f[y+x];N<b&&(b=N,v=x)}h[g]=v}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var OG={kernelName:lc,backendName:"cpu",kernelFunc:PG},LG=ot(Ao,e=>Math.asin(e)),zG={kernelName:Ao,backendName:"cpu",kernelFunc:LG},BG=ot(Fo,e=>Math.asinh(e)),WG={kernelName:Fo,backendName:"cpu",kernelFunc:BG},VG=ot($o,e=>Math.atan(e)),UG={kernelName:$o,backendName:"cpu",kernelFunc:VG},GG=Pt((e,t)=>Math.atan2(e,t)),HG=Qt(Mo,GG),jG={kernelName:Mo,backendName:"cpu",kernelFunc:HG},qG=ot(Do,e=>Math.atanh(e)),KG={kernelName:Do,backendName:"cpu",kernelFunc:qG};function vv(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,u=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Le(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let N=x*y,T=x*a[0];for(let C=0;C<r.inChannels;++C)for(let $=0;$<r.outHeight;++$){let F=$*i-d,O=Math.max(0,F),W=Math.min(r.inHeight,u+F),V=N+$*b;for(let H=0;H<r.outWidth;++H){let K=H*o-h,j=Math.max(0,K),Y=Math.min(r.inWidth,p+K),J=m,ne=0,Q=0;for(let ee=O;ee<W;ee+=l){let le=T+ee*a[1];for(let se=j;se<Y;se+=c){let ce=le+se*a[2],de=e[ce+C];s==="max"&&de>J?J=de:s==="avg"&&(ne+=de,Q++)}if(isNaN(J))break}let ie=V+H*v+C;g[ie]=s==="avg"?ne/Q:J}}}return f}function $T(e,t,n,a,r=!1,s=!1){let i=Le(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,c=a.dilationHeight,u=a.dilationWidth,p=a.effectiveFilterHeight,d=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=Le(t,n,e);for(let g=0;g<a.batchSize;++g)for(let y=0;y<a.inChannels;++y)for(let b=0;b<a.outHeight;++b){let v=b*o-h,x=v;for(;x<0;)x+=c;let N=Math.min(a.inHeight,p+v);for(let T=0;T<a.outWidth;++T){let C=T*l-m,$=C;for(;$<0;)$+=u;let F=Math.min(a.inWidth,d+C),O=Number.NEGATIVE_INFINITY,W=-1;for(let V=x;V<N;V+=c){let H=V-v;for(let K=$;K<F;K+=u){let j=K-C,Y=f.get(g,V,K,y);Y>O&&(O=Y,r?W=s?((g*a.inHeight+V)*a.inWidth+K)*a.inChannels+y:(V*a.inWidth+K)*a.inChannels+y:W=H*d+j)}}i.set(W,g,b,T,y)}}return i}function DT(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,c=r.dilationDepth,u=r.dilationHeight,p=r.dilationWidth,d=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=Le(r.outShape,n),x=v.values,N=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],$=r.outShape[4];for(let F=0;F<r.batchSize;++F){let O=F*N,W=F*a[0];for(let V=0;V<r.inChannels;++V)for(let H=0;H<r.outDepth;++H){let K=H*i-f,j=K;for(;j<0;)j+=c;let Y=Math.min(r.inDepth,d+K),J=O+H*T;for(let ne=0;ne<r.outHeight;++ne){let Q=ne*o-g,ie=Q;for(;ie<0;)ie+=u;let ee=Math.min(r.inHeight,h+Q),le=J+ne*C;for(let se=0;se<r.outWidth;++se){let ce=se*l-y,de=ce;for(;de<0;)de+=p;let fe=Math.min(r.inWidth,m+ce),xe=le+se*$,be=b,Se=0,_e=0;for(let Ke=j;Ke<Y;Ke+=c){let Ve=W+Ke*a[1];for(let st=ie;st<ee;st+=u){let lt=Ve+st*a[2];for(let He=de;He<fe;He+=p){let pt=lt+He*a[3],dt=e[pt+V];if(s==="max"&&dt>be?be=dt:s==="avg"&&(Se+=dt,_e++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let Me=xe+V;x[Me]=s==="avg"?Se/_e:be}}}}return v}function XG(e,t){let n=Le(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-d,v=b;for(;v<0;)v+=i;let x=Math.min(t.inDepth,c+b);for(let N=0;N<t.outHeight;++N){let T=N*r-h,C=T;for(;C<0;)C+=o;let $=Math.min(t.inHeight,u+T);for(let F=0;F<t.outWidth;++F){let O=F*s-m,W=O;for(;W<0;)W+=l;let V=Math.min(t.inWidth,p+O),H=Number.NEGATIVE_INFINITY,K=-1;for(let j=v;j<x;j+=i){let Y=j-b;for(let J=C;J<$;J+=o){let ne=J-T;for(let Q=W;Q<V;Q+=l){let ie=Q-O,ee=e.get(f,j,J,Q,g);ee>=H&&(H=ee,K=Y*u*p+ne*u+ie)}}}n.set(K,f,y,N,F,g)}}}return n}function YG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Ie(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,s,i,c,o,l),p;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))p=rr({inputs:{x:r},backend:n});else{let d=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=vv(d,r.shape,r.dtype,h,u,"avg");p=n.makeTensorInfo(u.outShape,r.dtype,m.values)}return p}var JG={kernelName:As,backendName:"cpu",kernelFunc:YG};function ZG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c,dilations:u}=a;Ie(r,"avgPool3d");let p=u;p==null&&(p=[1,1,1]);let d=E.computePool3DInfo(r.shape,s,i,p,o,l,c),h=n.data.get(r.dataId).values,m=DT(h,r.shape,r.dtype,k.computeStrides(r.shape),d,"avg");return n.makeTensorInfo(m.shape,"float32",m.values)}var QG={kernelName:uc,backendName:"cpu",kernelFunc:ZG};function eH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dilations:c,dimRoundingMode:u}=a;Ie([r,s],"avgPool3DGrad");let p=E.computePool3DInfo(s.shape,i,o,c,l,u),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=p.dilationDepth,v=p.dilationHeight,x=p.dilationWidth,N=p.effectiveFilterDepth,T=p.effectiveFilterHeight,C=p.effectiveFilterWidth,$=N-1-p.padInfo.front,F=C-1-p.padInfo.left,O=T-1-p.padInfo.top,W=Le(s.shape,"float32"),V=1/(f*g*y),H=n.bufferSync(r);for(let K=0;K<p.batchSize;++K)for(let j=0;j<p.inChannels;++j)for(let Y=0;Y<p.inDepth;++Y)for(let J=0;J<p.inHeight;++J)for(let ne=0;ne<p.inWidth;++ne){let Q=Y-$,ie=J-O,ee=ne-F,le=0;for(let se=0;se<N;se+=b){let ce=(Q+se)/d;if(!(ce<0||ce>=p.outDepth||Math.floor(ce)!==ce))for(let de=0;de<T;de+=v){let fe=(ie+de)/h;if(!(fe<0||fe>=p.outHeight||Math.floor(fe)!==fe))for(let xe=0;xe<C;xe+=x){let be=(ee+xe)/m;be<0||be>=p.outWidth||Math.floor(be)!==be||(le+=H.get(K,ce,fe,be,j))}}}W.set(le*V,K,Y,J,ne,j)}return n.makeTensorInfo(W.shape,W.dtype,W.values)}var tH={kernelName:kd,backendName:"cpu",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=E.computePool2DInfo(i.shape,o,l,1,c),p=u.strideHeight,d=u.strideWidth,h=u.filterHeight,m=u.filterWidth,f=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,b=u.effectiveFilterWidth,v=b-1-u.padInfo.left,x=y-1-u.padInfo.top,N=Le(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,$=Le(r.shape,"float32",C);for(let F=0;F<u.batchSize;++F)for(let O=0;O<u.inChannels;++O)for(let W=0;W<u.inHeight;++W)for(let V=0;V<u.inWidth;++V){let H=W-x,K=V-v,j=0;for(let Y=0;Y<y;Y+=f){let J=(H+Y)/p;if(!(J<0||J>=u.outHeight||Math.floor(J)!==J))for(let ne=0;ne<b;ne+=g){let Q=(K+ne)/d;Q<0||Q>=u.outWidth||Math.floor(Q)!==Q||(j+=$.get(F,J,Q,O))}}N.set(j*T,F,W,V,O)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var aH={kernelName:wd,backendName:"cpu",kernelFunc:nH};function rH(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."),Ie([r,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=a;c==null&&(c=.001);let u=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,d=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(u.length),g=m.length,y=h.length,b=d.length,v=p.length,x=0,N=0,T=0,C=0;for(let $=0;$<u.length;++$)f[$]=m[x++]+(u[$]-p[N++])*h[T++]/Math.sqrt(d[C++]+c),x>=g&&(x=0),N>=v&&(N=0),T>=y&&(T=0),C>=b&&(C=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var sH={kernelName:Vs,backendName:"cpu",kernelFunc:rH};function iH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;Ie([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=E.getReshaped(r.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(r.shape,s,o),p=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),h=Nt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ba({inputs:{x:h},backend:n,attrs:{perm:c}}),f=Nt({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Ui({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var oH={kernelName:cc,backendName:"cpu",kernelFunc:iH};function lH(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,c=pv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var uH={kernelName:Id,backendName:"cpu",kernelFunc:lH},cH=ot(Vr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),pH={kernelName:Vr,backendName:"cpu",kernelFunc:cH},dH=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 c=0;c<o.length;c++){let u=o[c],p=l[c];a[c]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},hH={kernelName:pc,backendName:"cpu",kernelFunc:dH};function au(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var mH={kernelName:Od,backendName:"cpu",kernelFunc:au};function ru(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=E.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return rr({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(E.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(x=>Vi({inputs:{input:x},backend:n})),g=o.map(x=>au({inputs:{input:x},backend:n})),y=ru({inputs:f,backend:n,attrs:{axis:s}}),b=ru({inputs:g,backend:n,attrs:{axis:s}}),v=Bn({inputs:{real:y,imag:b},backend:n});return f.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),v}let c=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return Nt({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=E.computeOutShape(c.map(f=>f.shape),1);let p=c[0].shape[0]===1,d=dv(u,i,t[0].dtype,p),h=E.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,d);return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var fH={kernelName:Po,backendName:"cpu",kernelFunc:ru};function MT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a;Ie([r,s],"conv2d");let p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,b=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new zt(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),C=N[0],$=v?N[1]:N[2],F=v?N[2]:1,O=v?1:N[1],W=x.strides[0],V=v?x.strides[1]:x.strides[2],H=v?x.strides[2]:1,K=v?1:x.strides[1],j=n.data.get(r.dataId).values,Y=n.data.get(s.dataId).values,J=x.values;for(let ne=0;ne<d.batchSize;++ne){let Q=ne*C,ie=ne*W;for(let ee=0;ee<d.outHeight;++ee){let le=ie+ee*V,se=ee*d.strideHeight-b;for(let ce=0;ce<h;++ce){let de=se+ce*f;if(de<0||de>=d.inHeight)continue;let fe=ce*T[0],xe=Q+de*$;for(let be=0;be<d.outWidth;++be){let Se=le+be*H,_e=be*d.strideWidth-y;for(let Me=0;Me<m;++Me){let Ke=_e+Me*g;if(Ke<0||Ke>=d.inWidth)continue;let Ve=fe+Me*T[1],st=xe+Ke*F,lt=Ve;for(let He=0;He<d.inChannels;++He){let pt=j[st+He*O];for(let dt=0;dt<d.outChannels;++dt)J[Se+dt*K]+=pt*Y[lt+dt];lt+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,J)}var gH={kernelName:Ds,backendName:"cpu",kernelFunc:MT};function yH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a;Ie([r,s],"conv2dBackpropFilter");let p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),{strideHeight:h,strideWidth:m,filterHeight:f,filterWidth:g}=d,y=d.dataFormat==="channelsLast",b=new zt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=new zt(r.shape,r.dtype,N),$=new zt(s.shape,s.dtype,T);for(let F=0;F<f;++F){let O=Math.max(0,Math.ceil((x-F)/h)),W=Math.min(d.outHeight,(d.inHeight+x-F)/h);for(let V=0;V<g;++V){let H=Math.max(0,Math.ceil((v-V)/m)),K=Math.min(d.outWidth,(d.inWidth+v-V)/m);for(let j=0;j<d.inChannels;++j)for(let Y=0;Y<d.outChannels;++Y){let J=0;for(let ne=0;ne<d.batchSize;++ne)for(let Q=O;Q<W;++Q){let ie=F+Q*h-x;for(let ee=H;ee<K;++ee){let le=V+ee*m-v;y?J+=C.get(ne,ie,le,j)*$.get(ne,Q,ee,Y):J+=C.get(ne,j,ie,le)*$.get(ne,Y,Q,ee)}}b.set(J,F,V,j,Y)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var bH={kernelName:Td,backendName:"cpu",kernelFunc:yH};function xH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a;Ie([r,s],"conv2dBackpropInput");let p=k.computeStrides(s.shape),d=k.computeStrides(r.shape),h=E.convertConv2DDataFormat(c),m=E.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),f=new zt(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,b=n.data.get(s.dataId).values,[v,x,N]=p,{batchSize:T,filterHeight:C,filterWidth:$,inChannels:F,inHeight:O,inWidth:W,outChannels:V,outHeight:H,outWidth:K,strideHeight:j,strideWidth:Y}=m;h=m.dataFormat;let J=C-1-m.padInfo.top,ne=$-1-m.padInfo.left,Q=h==="channelsLast",ie=f.strides[0],ee=Q?f.strides[1]:f.strides[2],le=Q?f.strides[2]:1,se=Q?1:f.strides[1],ce=d[0],de=Q?d[1]:d[2],fe=Q?d[2]:1,xe=Q?1:d[1];for(let be=0;be<T;++be)for(let Se=0;Se<F;++Se)for(let _e=0;_e<O;++_e){let Me=_e-J,Ke=Math.max(0,Math.ceil(Me/j)),Ve=Math.min(H,(C+Me)/j);for(let st=0;st<W;++st){let lt=st-ne,He=Math.max(0,Math.ceil(lt/Y)),pt=Math.min(K,($+lt)/Y),dt=0;for(let Ze=Ke;Ze<Ve;++Ze){let En=Ze*j-Me;for(let tn=He;tn<pt;++tn){let An=tn*Y-lt,sa=ce*be+de*Ze+fe*tn,vn=v*(C-1-En)+x*($-1-An)+N*Se;for(let un=0;un<V;++un){let ia=y[sa+xe*un],Va=b[vn+un];dt+=ia*Va}}}let Hn=ie*be+ee*_e+le*st+se*Se;g[Hn]=dt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var vH={kernelName:Ms,backendName:"cpu",kernelFunc:xH};function wH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;Ie([r,s],"conv3d");let c=E.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,v=g.top,x=new zt(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=x.values,$=k.computeStrides(r.shape),F=k.computeStrides(s.shape);for(let O=0;O<c.batchSize;++O){let W=O*$[0],V=O*x.strides[0];for(let H=0;H<c.outDepth;++H){let K=V+H*x.strides[1],j=H*c.strideDepth-y;for(let Y=0;Y<u;++Y){let J=j+Y*h;if(J<0||J>=c.inDepth)continue;let ne=Y*F[0],Q=W+J*$[1];for(let ie=0;ie<c.outHeight;++ie){let ee=K+ie*x.strides[2],le=ie*c.strideHeight-v;for(let se=0;se<p;++se){let ce=le+se*m;if(ce<0||ce>=c.inHeight)continue;let de=ne+se*F[1],fe=Q+ce*$[2];for(let xe=0;xe<c.outWidth;++xe){let be=ee+xe*c.outChannels,Se=xe*c.strideWidth-b;for(let _e=0;_e<d;++_e){let Me=Se+_e*f;if(Me<0||Me>=c.inWidth)continue;let Ke=de+_e*F[2],Ve=fe+Me*c.inChannels,st=Ke;for(let lt=0;lt<c.inChannels;++lt){let He=N[Ve+lt];for(let pt=0;pt<c.outChannels;++pt)C[be+pt]+=He*T[st+pt];st+=c.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var kH={kernelName:dc,backendName:"cpu",kernelFunc:wH};function IH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;Ie([r,s],"conv3dBackpropFilterV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=E.computeConv3DInfo(r.shape,l,i,1,o),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=new zt(p.filterShape,"float32"),v=b.values,[x,N,T,C]=b.strides,$=n.data.get(s.dataId).values,[F,O,W,V]=u,H=n.data.get(r.dataId).values,[K,j,Y,J]=c,ne=p.padInfo.front,Q=p.padInfo.left,ie=p.padInfo.top;for(let ee=0;ee<f;++ee){let le=Math.max(0,Math.ceil((ne-ee)/d)),se=Math.min(p.outDepth,(p.inDepth+ne-ee)/d),ce=ee*x;for(let de=0;de<g;++de){let fe=Math.max(0,Math.ceil((ie-de)/h)),xe=Math.min(p.outHeight,(p.inHeight+ie-de)/h),be=de*N+ce;for(let Se=0;Se<y;++Se){let _e=Math.max(0,Math.ceil((Q-Se)/m)),Me=Math.min(p.outWidth,(p.inWidth+Q-Se)/m),Ke=Se*T+be;for(let Ve=0;Ve<p.inChannels;++Ve){let st=Ve*C+Ke;for(let lt=0;lt<p.outChannels;++lt){let He=0;for(let pt=0;pt<p.batchSize;++pt){let dt=pt*K,Hn=pt*F;for(let Ze=le;Ze<se;++Ze){let En=(ee+Ze*d-ne)*j+dt,tn=Ze*O+Hn;for(let An=fe;An<xe;++An){let sa=(de+An*h-ie)*Y+En,vn=An*W+tn;for(let un=_e;un<Me;++un){let ia=(Se+un*m-Q)*J+sa,Va=un*V+vn;He+=H[ia+Ve]*$[Va+lt]}}}}v[st+lt]=He}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var NH={kernelName:Sd,backendName:"cpu",kernelFunc:IH};function TH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;Ie([r],"conv3dBackpropInputV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=E.computeConv3DInfo(l,s.shape,o,1,i),d=new zt(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[v,x,N,T]=c,C=n.data.get(s.dataId).values,[$,F,O,W]=u,{batchSize:V,filterDepth:H,filterHeight:K,filterWidth:j,inChannels:Y,inDepth:J,inHeight:ne,inWidth:Q,outChannels:ie,outDepth:ee,outHeight:le,outWidth:se,strideDepth:ce,strideHeight:de,strideWidth:fe}=p,xe=H-1-p.padInfo.front,be=K-1-p.padInfo.top,Se=j-1-p.padInfo.left;for(let _e=0;_e<V;++_e)for(let Me=0;Me<Y;++Me)for(let Ke=0;Ke<J;++Ke){let Ve=Ke-xe,st=Math.max(0,Math.ceil(Ve/ce)),lt=Math.min(ee,(H+Ve)/ce);for(let He=0;He<ne;++He){let pt=He-be,dt=Math.max(0,Math.ceil(pt/de)),Hn=Math.min(le,(K+pt)/de);for(let Ze=0;Ze<Q;++Ze){let En=Ze-Se,tn=Math.max(0,Math.ceil(En/fe)),An=Math.min(se,(j+En)/fe),sa=0;for(let vn=st;vn<lt;++vn){let un=vn*ce-Ve;for(let ia=dt;ia<Hn;++ia){let Va=ia*de-pt;for(let Fn=tn;Fn<An;++Fn){let co=Fn*fe-En,zu=v*_e+x*vn+N*ia+T*Fn,Ia=$*(H-1-un)+F*(K-1-Va)+O*(j-1-co)+W*Me;for(let oa=0;oa<ie;++oa){let Na=b[zu+oa],po=C[Ia+oa];sa+=Na*po}}}}h[m*_e+f*Ke+g*He+y*Ze+Me]=sa}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var SH={kernelName:Cd,backendName:"cpu",kernelFunc:TH},CH=ot(Rs,e=>Math.cos(e)),_H={kernelName:Rs,backendName:"cpu",kernelFunc:CH},EH=ot(Oo,e=>Math.cosh(e)),AH={kernelName:Oo,backendName:"cpu",kernelFunc:EH};function FH(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,[u,p,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,N=k.computeStrides(r.shape),T=k.computeStrides(y.shape);for(let C=0;C<m;C++){let $=C*4,F=b[$],O=b[$+1],W=b[$+2],V=b[$+3],H=v[C];if(H>=u)continue;let K=f>1?(W-F)*(p-1)/(f-1):0,j=g>1?(V-O)*(d-1)/(g-1):0;for(let Y=0;Y<f;Y++){let J=f>1?F*(p-1)+Y*K:.5*(F+W)*(p-1);if(J<0||J>p-1){for(let ne=0;ne<g;ne++)for(let Q=0;Q<h;Q++){let ie=Q+ne*T[2]+Y*T[1]+C*T[0];y.values[ie]=c}continue}if(l==="bilinear"){let ne=Math.floor(J),Q=Math.ceil(J),ie=J-ne;for(let ee=0;ee<g;ee++){let le=g>1?O*(d-1)+ee*j:.5*(O+V)*(d-1);if(le<0||le>d-1){for(let fe=0;fe<h;fe++){let xe=fe+ee*T[2]+Y*T[1]+C*T[0];y.values[xe]=c}continue}let se=Math.floor(le),ce=Math.ceil(le),de=le-se;for(let fe=0;fe<h;fe++){let xe=fe+se*N[2]+ne*N[1]+H*N[0],be=x[xe];xe=fe+ce*N[2]+ne*N[1]+H*N[0];let Se=x[xe];xe=fe+se*N[2]+Q*N[1]+H*N[0];let _e=x[xe];xe=fe+ce*N[2]+Q*N[1]+H*N[0];let Me=x[xe],Ke=be+(Se-be)*de,Ve=_e+(Me-_e)*de;xe=fe+ee*T[2]+Y*T[1]+C*T[0],y.values[xe]=Ke+(Ve-Ke)*ie}}}else for(let ne=0;ne<g;++ne){let Q=g>1?O*(d-1)+ne*j:.5*(O+V)*(d-1);if(Q<0||Q>d-1){for(let le=0;le<h;le++){let se=le+ne*T[2]+Y*T[1]+C*T[0];y.values[se]=c}continue}let ie=Math.round(Q),ee=Math.round(J);for(let le=0;le<h;le++){let se=le+ie*N[2]+ee*N[1]+H*N[0],ce=le+ne*T[2]+Y*T[1]+C*T[0];y.values[ce]=x[se]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var $H={kernelName:Lo,backendName:"cpu",kernelFunc:FH};function DH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;Ie(r,"cumsum");let l=E.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=ba({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=E.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=pa(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let v=f(y,b);if(b===0)d[v]=i?0:h[v];else{let x=f(y,b-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=E.getUndoAxesPermutation(l),b=ba({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var MH={kernelName:Ps,backendName:"cpu",kernelFunc:DH};function RH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=pv(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=nT(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var PH={kernelName:_d,backendName:"cpu",kernelFunc:RH};function OH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],p=l*s,d=c*s,h=u/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*p*d*h),g=0;for(let y=0;y<o;++y)for(let b=0;b<p;++b){let v=Math.floor(b/s),x=b%s;for(let N=0;N<d;++N){let T=Math.floor(N/s),C=N%s,$=(x*s+C)*h;for(let F=0;F<h;++F){let O=F+$+u*(T+c*(v+l*y));f[g++]=m[O]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,f)}var LH={kernelName:zo,backendName:"cpu",kernelFunc:OH};function RT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a;Ie([r,s],"depthwiseConv2DNative");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),k.assert(E.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=E.computeConv2DInfo(r.shape,s.shape,i,d,o,c,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,v=b.left,x=b.top,N=h.outChannels/h.inChannels,T=new zt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,$=n.data.get(s.dataId).values,F=T.values;for(let O=0;O<h.batchSize;++O){let W=O*u[0],V=O*T.strides[0];for(let H=0;H<h.outHeight;++H){let K=V+H*T.strides[1],j=H*h.strideHeight-v;for(let Y=0;Y<m;++Y){let J=j+Y*g;if(J<0||J>=h.inHeight)continue;let ne=Y*p[0],Q=W+J*u[1];for(let ie=0;ie<h.outWidth;++ie){let ee=K+ie*T.strides[2],le=ie*h.strideWidth-x;for(let se=0;se<f;++se){let ce=le+se*y;if(ce<0||ce>=h.inWidth)continue;let de=ne+se*p[1],fe=Q+ce*h.inChannels,xe=ee,be=de;for(let Se=0;Se<h.inChannels;++Se){let _e=C[fe+Se];for(let Me=0;Me<N;++Me)F[xe+Me]+=_e*$[be+Me];xe+=N,be+=N}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var zH={kernelName:Os,backendName:"cpu",kernelFunc:RT};function BH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a;Ie([r,s],"depthwiseConv2dNativeBackpropFilter");let p=E.computeConv2DInfo(r.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new zt(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,N=new zt(r.shape,r.dtype,x),T=n.data.get(s.dataId).values,C=new zt(s.shape,s.dtype,T);for(let $=0;$<m;++$){let F=Math.max(0,Math.ceil((b-$)/d)),O=Math.min(p.outHeight,(p.inHeight+b-$)/d);for(let W=0;W<f;++W){let V=Math.max(0,Math.ceil((y-W)/h)),H=Math.min(p.outWidth,(p.inWidth+y-W)/h);for(let K=0;K<p.outChannels;++K){let j=Math.trunc(K/v),Y=K%v,J=0;for(let ne=0;ne<p.batchSize;++ne)for(let Q=F;Q<O;++Q){let ie=$+Q*d-b;for(let ee=V;ee<H;++ee){let le=W+ee*h-y;J+=N.get(ne,ie,le,j)*C.get(ne,Q,ee,K)}}g.set(J,$,W,j,Y)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var WH={kernelName:Ed,backendName:"cpu",kernelFunc:BH};function VH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a;Ie([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new zt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,v=n.data.get(r.dataId).values,[x,N,T]=p,C=n.data.get(s.dataId).values,[$,F,O]=d,{batchSize:W,filterHeight:V,filterWidth:H,inChannels:K,inHeight:j,inWidth:Y,outChannels:J,outHeight:ne,outWidth:Q,strideHeight:ie,strideWidth:ee}=h,le=V-1-h.padInfo.top,se=H-1-h.padInfo.left,ce=J/K;for(let de=0;de<W;++de)for(let fe=0;fe<K;++fe)for(let xe=0;xe<j;++xe){let be=xe-le,Se=Math.max(0,Math.ceil(be/ie)),_e=Math.min(ne,(V+be)/ie);for(let Me=0;Me<Y;++Me){let Ke=Me-se,Ve=Math.max(0,Math.ceil(Ke/ee)),st=Math.min(Q,(H+Ke)/ee),lt=0;for(let He=Se;He<_e;++He){let pt=He*ie-be;for(let dt=Ve;dt<st;++dt){let Hn=dt*ee-Ke,Ze=x*de+N*He+T*dt,En=$*(V-1-pt)+F*(H-1-Hn)+O*fe;for(let tn=0;tn<ce;++tn){let An=fe*ce+tn,sa=v[Ze+An],vn=C[En+tn];lt+=sa*vn}}}f[g*de+y*xe+b*Me+fe]=lt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var UH={kernelName:Ad,backendName:"cpu",kernelFunc:VH};function GH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Le([r,r],a.dtype),o=i.values;for(let c=0;c<s.length;c++)o[c*r+c]=s[c];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var HH={kernelName:Fd,backendName:"cpu",kernelFunc:GH},jH={kernelName:hc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(a.dataId).values,u=a.shape.length,p=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:v,strideHeight:x,strideWidth:N,filterHeight:T,filterWidth:C,dilationHeight:$,dilationWidth:F,outShape:O}=E.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),W=k.sizeFromShape(O),V=O.length,H=k.getArrayFromDType(a.dtype,W);for(let K=0;K<h;++K)for(let j=0;j<y;++j){let Y=j*x-v.top;for(let J=0;J<b;++J){let ne=J*N-v.left;for(let Q=0;Q<g;++Q){let ie=Number.MIN_SAFE_INTEGER;for(let le=0;le<T;++le){let se=Y+le*$;if(se>=0&&se<m)for(let ce=0;ce<C;++ce){let de=ne+ce*F;if(de>=0&&de<f){let fe=k.locToIndex([K,se,de,Q],u,k.computeStrides(a.shape)),xe=k.locToIndex([le,ce,Q],d,k.computeStrides(r.shape)),be=c[fe]+p[xe];be>ie&&(ie=be)}}}let ee=k.locToIndex([K,j,J,Q],V,k.computeStrides(O));H[ee]=ie}}}return{dataId:l.write(k.toTypedArray(H,a.dtype),O,a.dtype),shape:O,dtype:a.dtype}}},qH={kernelName:Dd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(a.shape,c.data.get(a.dataId).values),p=k.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:v,strideWidth:x,filterHeight:N,filterWidth:T,dilationHeight:C,dilationWidth:$,outShape:F}=E.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===F.length,()=>`Error in ${Dd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let O=k.toNestedArray(F,c.data.get(s.dataId).values),W=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let V=0;V<d;++V)for(let H=0;H<g;++H){let K=H*v-b.top;for(let j=0;j<y;++j){let Y=j*x-b.left;for(let J=0;J<f;++J){let ne=Number.MIN_SAFE_INTEGER,Q=0,ie=0;for(let ee=0;ee<N;++ee){let le=K+ee*C;if(le>=0&&le<h)for(let se=0;se<T;++se){let ce=Y+se*$;if(ce>=0&&ce<m){let de=u[V][le][ce][J]+p[ee][se][J];de>ne&&(ne=de,Q=ee,ie=se)}}}W[Q][ie][J]+=O[V][H][j][J]}}}return{dataId:c.write(k.toTypedArray(W,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},KH={kernelName:$d,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(a.shape,c.data.get(a.dataId).values),p=k.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:v,strideWidth:x,filterHeight:N,filterWidth:T,dilationHeight:C,dilationWidth:$,outShape:F}=E.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===F.length,()=>`Error in ${$d}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let O=k.toNestedArray(F,c.data.get(s.dataId).values),W=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let V=0;V<d;++V)for(let H=0;H<g;++H){let K=H*v-b.top;for(let j=0;j<y;++j){let Y=j*x-b.left;for(let J=0;J<f;++J){let ne=Number.MIN_SAFE_INTEGER,Q=K<0?0:K,ie=Y<0?0:Y;for(let ee=0;ee<N;++ee){let le=K+ee*C;if(le>=0&&le<h)for(let se=0;se<T;++se){let ce=Y+se*$;if(ce>=0&&ce<m){let de=u[V][le][ce][J]+p[ee][se][J];de>ne&&(ne=de,Q=le,ie=ce)}}}W[V][Q][ie][J]+=O[V][H][j][J]}}}return{dataId:c.write(k.toTypedArray(W,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function XH(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;Ie([a,r],"eluGrad");let s=new Float32Array(k.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",s)}var YH={kernelName:Md,backendName:"cpu",kernelFunc:XH},JH=Pt((e,t)=>e===t?1:0),PT=Qt(Vo,JH,null,"bool"),ZH={kernelName:Vo,backendName:"cpu",kernelFunc:PT},QH=E.ERF_P,e6=E.ERF_A1,t6=E.ERF_A2,n6=E.ERF_A3,a6=E.ERF_A4,r6=E.ERF_A5,s6=ot(Wo,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+QH*n);return t*(1-((((r6*a+a6)*a+n6)*a+t6)*a+e6)*a*Math.exp(-n*n))}),i6={kernelName:Wo,backendName:"cpu",kernelFunc:s6};function $m(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Nt({inputs:{x:r},backend:n,attrs:{shape:o}})}var o6={kernelName:Uo,backendName:"cpu",kernelFunc:$m},l6=Pt((e,t)=>e/t),wv=Qt(Ls,l6),kv={kernelName:Ls,backendName:"cpu",kernelFunc:wv};function OT(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,c=[r,s],u=k.sizeFromShape(c),p=k.getTypedArrayFromDType("float32",u),d=k.getTypedArrayFromDType("float32",u);for(let g=0;g<r;g++){let y=Ui({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,s]}}),b=Ui({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,s]}}),v=Bn({inputs:{real:y,imag:b},backend:n}),{real:x,imag:N}=u6(v,t,n),T=E.mergeRealAndImagArrays(x,N);for(let C=0;C<s;C++){let $=E.getComplexWithIndex(T,C);p[g*s+C]=$.real,d[g*s+C]=$.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(v)}let h=n.makeTensorInfo(c,"float32",p),m=n.makeTensorInfo(c,"float32",d),f=Bn({inputs:{real:h,imag:m},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}function u6(e,t,n){let a=k.sizeFromShape(e.shape),r=n.data.get(e.dataId),s=n.data.get(r.complexTensorInfos.real.dataId).values,i=n.data.get(r.complexTensorInfos.imag.dataId).values;if(c6(a)){let o=Iv(s,i,a,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),p=n.makeTensorInfo([],"float32",k.createScalarValue(a,"float32")),d=rr({inputs:{x:p},backend:n}),h=kv.kernelFunc({inputs:{a:c,b:p},backend:n}),m=kv.kernelFunc({inputs:{a:u,b:d},backend:n}),f=n.data.get(h.dataId).values,g=n.data.get(m.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),{real:f,imag:g}}return o}else{let o=E.mergeRealAndImagArrays(s,i),l=p6(o,a,t);return E.splitRealAndImagArrays(l)}}function c6(e){return(e&e-1)==0}function Iv(e,t,n,a,r){if(n===1)return{real:e,imag:t};let s=E.mergeRealAndImagArrays(e,t),i=n/2,o=E.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],p=r.makeTensorInfo(u,"float32",l),d=r.makeTensorInfo(u,"float32",c),h=Bn({inputs:{real:p,imag:d},backend:r}),m=E.complexWithOddIndex(s),f=m.real,g=m.imag,y=[f.length],b=r.makeTensorInfo(y,"float32",f),v=r.makeTensorInfo(y,"float32",g),x=Bn({inputs:{real:b,imag:v},backend:r}),N=Iv(l,c,i,a,r),T=N.real,C=N.imag,$=[T.length],F=r.makeTensorInfo($,"float32",T),O=r.makeTensorInfo($,"float32",C),W=Bn({inputs:{real:F,imag:O},backend:r}),V=Iv(f,g,i,a,r),H=V.real,K=V.imag,j=[H.length],Y=r.makeTensorInfo(j,"float32",H),J=r.makeTensorInfo(j,"float32",K),ne=Bn({inputs:{real:Y,imag:J},backend:r}),Q=E.exponents(n,a),ie=[Q.real.length],ee=r.makeTensorInfo(ie,"float32",Q.real),le=r.makeTensorInfo(ie,"float32",Q.imag),se=Bn({inputs:{real:ee,imag:le},backend:r}),ce=yv({inputs:{a:se,b:ne},backend:r}),de=yp({inputs:{a:W,b:ce},backend:r}),fe=bv({inputs:{a:W,b:ce},backend:r}),xe=Vi({inputs:{input:de},backend:r}),be=Vi({inputs:{input:fe},backend:r}),Se=au({inputs:{input:de},backend:r}),_e=au({inputs:{input:fe},backend:r}),Me=ru({inputs:[xe,be],backend:r,attrs:{axis:0}}),Ke=ru({inputs:[Se,_e],backend:r,attrs:{axis:0}}),Ve=r.data.get(Me.dataId).values,st=r.data.get(Ke.dataId).values;return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(F),r.disposeIntermediateTensorInfo(O),r.disposeIntermediateTensorInfo(W),r.disposeIntermediateTensorInfo(Y),r.disposeIntermediateTensorInfo(J),r.disposeIntermediateTensorInfo(ne),r.disposeIntermediateTensorInfo(ee),r.disposeIntermediateTensorInfo(le),r.disposeIntermediateTensorInfo(se),r.disposeIntermediateTensorInfo(ce),r.disposeIntermediateTensorInfo(de),r.disposeIntermediateTensorInfo(fe),r.disposeIntermediateTensorInfo(xe),r.disposeIntermediateTensorInfo(Se),r.disposeIntermediateTensorInfo(be),r.disposeIntermediateTensorInfo(_e),r.disposeIntermediateTensorInfo(Me),r.disposeIntermediateTensorInfo(Ke),{real:Ve,imag:st}}function p6(e,t,n){let a=new Float32Array(t*2);for(let r=0;r<t;r++){let s=0,i=0;for(let o=0;o<t;o++){let l=E.exponent(r*o,t,n),c=E.getComplexWithIndex(e,o);s+=c.real*l.real-c.imag*l.imag,i+=c.real*l.imag+c.imag*l.real}n&&(s/=t,i/=t),E.assignToTypedArray(a,s,i,r)}return a}function d6(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=Nt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=OT(o,!1,n),c=Nt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var h6={kernelName:Rd,backendName:"cpu",kernelFunc:d6};function Nv(e){let{backend:t,attrs:n}=e,{shape:a,value:r,dtype:s}=n,i=s||k.inferDtype(r),o=k.getArrayFromDType(i,k.sizeFromShape(a));return m6(o,r,i),t.makeTensorInfo(a,i,o)}var f6={kernelName:mc,backendName:"cpu",kernelFunc:Nv};function m6(e,t,n){e.fill(t)}var g6={kernelName:Ho,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,r=n,s=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[i,o,l,c]=a.shape,u=r.data.get(a.dataId).values;for(let p=0;p<i;p++){let d=p*l*o*c;for(let h=0;h<o;h++){let m=h*(l*c);for(let f=0;f<l;f++){let g=f*c;for(let y=0;y<c;y++){let b=[i,h,f,y][2],v=Math.round(l-b),x=d+m+g+y,N=u[x];if(v>=0&&v<l){let T=v*c,C=d+m+T+y;N=u[C]}s[x]=N}}}}return{dataId:r.write(s,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},y6=Pt((e,t)=>Math.floor(e/t)),b6=Qt(Ws,y6,null,"int32"),x6={kernelName:Ws,backendName:"cpu",kernelFunc:b6};function v6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=MT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d}});if(i){let g=f;f=yp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=xv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var w6={kernelName:xi,backendName:"cpu",kernelFunc:v6};function k6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=RT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d}});if(i){let g=f;f=yp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=xv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var I6={kernelName:vi,backendName:"cpu",kernelFunc:k6};function N6(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,c,u,p]=E.prepareAndValidate(a,r);if(c===0)return n.makeTensorInfo(l,a.dtype,[]);let d=Le([c,u],a.dtype),h=n.data.get(r.dataId).values,m=n.data.get(a.dataId).values;for(let f=0;f<c;f++){let g=[],y=0;for(let b=0;b<o;b++){let v=h[f*o+b];y+=v*p[b],g.push(v)}if(y<0||y>=s/u)throw new Error(`Invalid indices: ${g} does not index into ${a.shape}`);for(let b=0;b<u;b++)d.values[f*u+b]=m[y*u+b]}return n.makeTensorInfo(l,d.dtype,d.values)}var T6={kernelName:qo,backendName:"cpu",kernelFunc:N6};function S6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;Ie([r,s],"gatherV2");let l=o;o==null&&(l=0);let c=k.sizeFromShape(s.shape),u=k.parseAxisParam(i,r.shape)[0],p=E.segment_util.collectGatherOpShapeInfo(r,s,u,l),d=Nt({inputs:{x:r},backend:n,attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]}}),h=Nt({inputs:{x:s},backend:n,attrs:{shape:[p.batchSize,c/p.batchSize]}}),m=[p.batchSize,p.outerSize,c/p.batchSize,p.sliceSize],f=n.bufferSync(h),g=n.bufferSync(d),y=oT(g,f,m);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(p.outputShape,y.dtype,y.values)}var C6={kernelName:jo,backendName:"cpu",kernelFunc:S6},_6=Pt((e,t)=>e>=t?1:0),E6=Qt(Us,_6,null,"bool"),A6={kernelName:Us,backendName:"cpu",kernelFunc:E6};function F6(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=Nt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=OT(o,!0,n),c=Nt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var $6={kernelName:Pd,backendName:"cpu",kernelFunc:F6},D6=ot(Yo,e=>Number.isFinite(e)?1:0,"bool"),M6={kernelName:Yo,backendName:"cpu",kernelFunc:D6},R6=ot(Jo,e=>Math.abs(e)===Infinity?1:0,"bool"),P6={kernelName:Jo,backendName:"cpu",kernelFunc:R6},O6=ot(Zo,e=>Number.isNaN(e)?1:0,"bool"),L6={kernelName:Zo,backendName:"cpu",kernelFunc:O6},z6=Pt((e,t)=>e<=t?1:0),B6=Qt(el,z6,null,"bool"),W6={kernelName:el,backendName:"cpu",kernelFunc:B6};function V6(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=cT(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var U6={kernelName:Ld,backendName:"cpu",kernelFunc:V6},G6=ot(tl,e=>Math.log1p(e)),H6={kernelName:tl,backendName:"cpu",kernelFunc:G6},j6=Pt((e,t)=>e&&t),q6=Qt(nl,j6,null,"bool"),K6={kernelName:nl,backendName:"cpu",kernelFunc:q6},X6=ot(fc,e=>e?0:1,"bool"),Y6={kernelName:fc,backendName:"cpu",kernelFunc:X6},J6=Pt((e,t)=>e||t),Z6=Qt(gc,J6,null,"bool"),Q6={kernelName:gc,backendName:"cpu",kernelFunc:Z6};function ej(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;Ie(r,"LRN");let c=r.shape[3],u=c-1,p=n.data.get(r.dataId).values,d=k.sizeFromShape(r.shape),h=new Float32Array(d);function m(f){let g=f%c,y=f-g+Math.max(0,g-s),b=f-g+Math.min(g+s,u),v=0;for(;y<=b;y++){let x=p[y];v+=x*x}return v}for(let f=0;f<d;f++){let g=m(f),y=p[f]*Math.pow(i+o*g,-l);h[f]=y}return n.makeTensorInfo(r.shape,r.dtype,h)}var tj={kernelName:yc,backendName:"cpu",kernelFunc:ej};function nj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a;Ie(i,"LRNGrad");let p=k.sizeFromShape(i.shape),d=i.shape[3],h=n.data.get(i.dataId).values,m=n.data.get(r.dataId).values,f=n.data.get(s.dataId).values,g=new Float32Array(p),y=p;for(let b=0;b<y;b++){let v=b%d,x=b-v+Math.max(0,v-o),N=b-v+Math.min(d,v+o+1),T=0;for(let C=x;C<N;C++)T+=Math.pow(m[C],2);T=c*T+l;for(let C=x;C<N;C++){let $=-2*c*u*m[C]*f[b]/T;b===C&&($+=Math.pow(T,-u)),$*=h[b],g[C]+=$}}return n.makeTensorInfo(i.shape,r.dtype,g)}var aj={kernelName:zd,backendName:"cpu",kernelFunc:nj};function LT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=n,l=r.shape,c=l.length,u=k.parseAxisParam(s,l),p=u,d=E.getAxesPermutation(p,c),h=o.data.get(r.dataId).values;if(d!=null){let x=new Array(c);for(let N=0;N<x.length;N++)x[N]=l[d[N]];h=mv(h,l,r.dtype,d,x),p=E.getInnerMostAxes(p.length,c),l=x}Ie(r,"max"),E.assertAxesAreInnerMostDims("max",p,c);let[m,f]=E.computeOutAndReduceShapes(l,p),g=k.sizeFromShape(f),y=dT(h,g,m,r.dtype),b=o.write(y,m,r.dtype),v=m;return i&&(v=E.expandShapeToKeepDim(m,u)),{dataId:b,shape:v,dtype:r.dtype}}var rj={kernelName:js,backendName:"cpu",kernelFunc:LT};function sj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Ie(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,s,i,c,o,l),p;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))p=rr({inputs:{x:r},backend:n});else{let d=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=vv(d,r.shape,r.dtype,h,u,"max");p=n.makeTensorInfo(u.outShape,r.dtype,m.values)}return p}var ij={kernelName:Ks,backendName:"cpu",kernelFunc:sj};function oj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c,dilations:u}=a;Ie(r,"maxPool3d");let p=u;p==null&&(p=[1,1,1]);let d=E.computePool3DInfo(r.shape,s,i,p,o,l,c),h=n.data.get(r.dataId).values,m=DT(h,r.shape,r.dtype,k.computeStrides(r.shape),d,"max");return n.makeTensorInfo(m.shape,"float32",m.values)}var lj={kernelName:bc,backendName:"cpu",kernelFunc:oj};function uj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dilations:c,dimRoundingMode:u}=a;Ie([r,s],"maxPool3DGrad");let p=E.computePool3DInfo(s.shape,i,o,c,l,u),d=n.bufferSync(s),h=XG(d,p),m=p.strideDepth,f=p.strideHeight,g=p.strideWidth,y=p.dilationDepth,b=p.dilationHeight,v=p.dilationWidth,x=p.effectiveFilterDepth,N=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=x-1-p.padInfo.front,$=T-1-p.padInfo.left,F=N-1-p.padInfo.top,O=Le(s.shape,"float32"),W=n.bufferSync(r);for(let V=0;V<p.batchSize;++V)for(let H=0;H<p.inChannels;++H)for(let K=0;K<p.inDepth;++K)for(let j=0;j<p.inHeight;++j)for(let Y=0;Y<p.inWidth;++Y){let J=K-C,ne=j-F,Q=Y-$,ie=0;for(let ee=0;ee<x;ee+=y){let le=(J+ee)/m;if(!(le<0||le>=p.outDepth||Math.floor(le)!==le))for(let se=0;se<N;se+=b){let ce=(ne+se)/f;if(!(ce<0||ce>=p.outHeight||Math.floor(ce)!==ce))for(let de=0;de<T;de+=v){let fe=(Q+de)/g;if(fe<0||fe>=p.outWidth||Math.floor(fe)!==fe)continue;let xe=x*N*T-1-h.get(V,le,ce,fe,H),be=ee*N*T+se*T+de,Se=xe===be?1:0;Se!==0&&(ie+=W.get(V,le,ce,fe,H)*Se)}}}O.set(ie,V,K,j,Y,H)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var cj={kernelName:Wd,backendName:"cpu",kernelFunc:uj};function pj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Ie([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=E.computePool2DInfo(o.shape,l,c,1,u,p),h=n.data.get(o.dataId).values,m=Le(d.outShape,o.dtype,$T(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,b=d.dilationWidth,v=d.effectiveFilterHeight,x=d.effectiveFilterWidth,N=x-1-d.padInfo.left,T=v-1-d.padInfo.top,C=Le(o.shape,"float32"),$=n.data.get(r.dataId).values,F=Le(r.shape,"float32",$);for(let O=0;O<d.batchSize;++O)for(let W=0;W<d.inChannels;++W)for(let V=0;V<d.inHeight;++V)for(let H=0;H<d.inWidth;++H){let K=V-T,j=H-N,Y=0;for(let J=0;J<v;J+=y){let ne=(K+J)/f;if(!(ne<0||ne>=d.outHeight||Math.floor(ne)!==ne))for(let Q=0;Q<x;Q+=b){let ie=(j+Q)/g;if(ie<0||ie>=d.outWidth||Math.floor(ie)!==ie)continue;let ee=v*x-1-m.get(O,ne,ie,W),le=J*x+Q,se=ee===le?1:0;se!==0&&(Y+=F.get(O,ne,ie,W)*se)}}C.set(Y,O,V,H,W)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var dj={kernelName:Bd,backendName:"cpu",kernelFunc:pj};function hj(e,t,n,a,r){let s=k.computeStrides(t),i=vv(e,t,n,s,r,"max"),o=$T(e,t,n,r,!0,a);return[i.values,o.values]}var mj={kernelName:Vd,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;Ie(a,"MaxPoolWithArgmax");let c=l.data.get(a.dataId).values,u=E.computePool2DInfo(a.shape,r,s,[1,1],i),[p,d]=hj(c,a.shape,a.dtype,o,u),h=l.write(p,u.outShape,a.dtype),m=l.write(d,u.outShape,a.dtype);return[{dataId:h,shape:u.outShape,dtype:a.dtype},{dataId:m,shape:u.outShape,dtype:"int32"}]}};function Dm(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Ie(r,"sum");let o;r.dtype==="bool"?o=ps({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=rr({inputs:{x:r},backend:n});let l=o.shape.length,c=k.parseAxisParam(s,o.shape),u=E.getAxesPermutation(c,l),p=c,d=o;u!=null&&(d=ba({inputs:{x:o},backend:n,attrs:{perm:u}}),p=E.getInnerMostAxes(p.length,l)),E.assertAxesAreInnerMostDims("sum",p,d.shape.length);let[h,m]=E.computeOutAndReduceShapes(d.shape,p),f=E.upcastType(d.dtype,"int32"),g=Fm(n,h,f),y=k.sizeFromShape(m),b=n.data.get(g.dataId).values,v=n.data.get(d.dataId).values;for(let x=0;x<b.length;++x){let N=x*y,T=0;for(let C=0;C<y;++C)T+=v[N+C];b[x]=T}if(i){let x=E.expandShapeToKeepDim(g.shape,c),N=g;g=Nt({inputs:{x:g},backend:n,attrs:{shape:x}}),n.disposeIntermediateTensorInfo(N)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),g}var fj={kernelName:di,backendName:"cpu",kernelFunc:Dm};function gj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),l=E.computeOutAndReduceShapes(r.shape,o)[1],c=k.sizeFromShape(l),u=[],p=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(p);let d=ps({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});u.push(d);let h=wv({inputs:{a:d,b:p},backend:n});u.push(h);let m=Dm({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var yj={kernelName:Xs,backendName:"cpu",kernelFunc:gj};function bj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Ie(r,"min");let o=k.parseAxisParam(s,r.shape),l=o,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ba({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",l,u.shape.length);let[p,d]=E.computeOutAndReduceShapes(u.shape,l),h=k.sizeFromShape(d),m=k.makeZerosTypedArray(k.sizeFromShape(p),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,v=f[b];for(let x=0;x<h;++x){let N=f[b+x];N<v&&(v=N)}m[y]=v}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let y=E.expandShapeToKeepDim(p,o),b=Nt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var xj={kernelName:Ys,backendName:"cpu",kernelFunc:bj};function vj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,mode:i}=a;Ie(r,"mirrorPad");let o=s.map((b,v)=>b[0]+r.shape[v]+b[1]),l=s.map(b=>b[0]),c=s.map((b,v)=>b[0]+r.shape[v]),u=i==="reflect"?0:1,p=n.data.get(r.dataId).values,d=r.shape.length,h=k.computeStrides(r.shape),m=k.sizeFromShape(o),f=o.length,g=k.computeStrides(o),y=k.getTypedArrayFromDType(r.dtype,m);for(let b=0;b<m;b++){let v=k.indexToLoc(b,f,g);for(let N=0;N<f;N++)v[N]<l[N]?v[N]=l[N]*2-v[N]-u:v[N]>=c[N]&&(v[N]=(c[N]-1)*2-v[N]+u);v=v.map((N,T)=>N-l[T]);let x=k.locToIndex(v,d,h);y[b]=p[x]}return{dataId:n.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var wj={kernelName:xc,backendName:"cpu",kernelFunc:vj},kj=Pt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),Ij=Qt(al,kj),Nj={kernelName:al,backendName:"cpu",kernelFunc:Ij},Tj=No(md());function zT(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=k.parseAxisParam([o],r.shape),c=LT({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=E.expandShapeToKeepDim(c.shape,l),p=Nt({inputs:{x:c},backend:n,attrs:{shape:u}}),d=bv({inputs:{a:r,b:p},backend:n}),h=TT({inputs:{x:d},backend:n}),m=Dm({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=Nt({inputs:{x:m},backend:n,attrs:{shape:u}}),g=wv({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var Sj={kernelName:hi,backendName:"cpu",kernelFunc:zT};function Cj(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;Ie(r,"multinomial");let l=o?r:zT({inputs:{logits:r},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],p=n.data.get(l.dataId).values,d=[c,s],h=k.makeZerosTypedArray(k.sizeFromShape(d),"int32");for(let m=0;m<c;++m){let f=m*u,g=new Float32Array(u-1);g[0]=p[f];for(let v=1;v<g.length;++v)g[v]=g[v-1]+p[f+v];let y=Tj.alea(i.toString()),b=m*s;for(let v=0;v<s;++v){let x=y();h[b+v]=g.length;for(let N=0;N<g.length;N++)if(x<g[N]){h[b+v]=N;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",h)}var _j={kernelName:Ud,backendName:"cpu",kernelFunc:Cj},Ej=Za.nonMaxSuppressionV3Impl;function Aj(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a;Ie(r,"NonMaxSuppression");let c=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:p}=Ej(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Fj={kernelName:il,backendName:"cpu",kernelFunc:Aj},$j=Za.nonMaxSuppressionV4Impl;function Dj(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a;Ie(r,"NonMaxSuppressionPadded");let u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:h}=$j(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Mj={kernelName:ol,backendName:"cpu",kernelFunc:Dj},Rj=Za.nonMaxSuppressionV5Impl;function Pj(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a;Ie(r,"NonMaxSuppressionWithScore");let u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=Rj(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Oj={kernelName:ll,backendName:"cpu",kernelFunc:Pj};function Lj(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;Ie(r,"oneHot");let l=k.sizeFromShape(r.shape),c=new Float32Array(l*s);c.fill(o);let u=n.data.get(r.dataId).values;for(let p=0;p<l;++p)u[p]>=0&&u[p]<s&&(c[p*s+u[p]]=i);return n.makeTensorInfo([...r.shape,s],"int32",c)}var zj={kernelName:Qs,backendName:"cpu",kernelFunc:Lj};function Mm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(a.dtype==="complex64"){let r=Vi({inputs:{input:a},backend:n}),s=Mm({inputs:{x:r},backend:n}),i=au({inputs:{input:a},backend:n}),o=Mm({inputs:{x:i},backend:n}),l=Bn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Nv({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var Bj={kernelName:Sl,backendName:"cpu",kernelFunc:Mm};function BT(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(a.dtype==="complex64"){let r=Vi({inputs:{input:a},backend:n}),s=BT({inputs:{x:r},backend:n}),i=au({inputs:{input:a},backend:n}),o=Mm({inputs:{x:i},backend:n}),l=Bn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Nv({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var Wj={kernelName:ul,backendName:"cpu",kernelFunc:BT};function WT(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return $m({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=$m({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=ru({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Vj={kernelName:cl,backendName:"cpu",kernelFunc:WT};function Uj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;Ie(r,"pad");let o=s.map((y,b)=>y[0]+r.shape[b]+y[1]),l=s.map(y=>y[0]),c=n.data.get(r.dataId).values,u=k.sizeFromShape(r.shape),p=r.shape.length,d=k.computeStrides(r.shape),h=k.sizeFromShape(o),m=o.length,f=k.computeStrides(o),g=k.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let y=0;y<u;y++){let b=k.indexToLoc(y,p,d).map((x,N)=>x+l[N]),v=k.locToIndex(b,m,f);g[v]=c[y]}return{dataId:n.write(g,o,r.dtype),shape:o,dtype:r.dtype}}var VT={kernelName:ei,backendName:"cpu",kernelFunc:Uj},Gj=Pt((e,t)=>Math.pow(e,t)),Hj=Qt(ti,Gj),jj={kernelName:ti,backendName:"cpu",kernelFunc:Hj};function qj(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=fv(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var Kj={kernelName:vc,backendName:"cpu",kernelFunc:qj},Xj=ot(dl,e=>1/e),Yj={kernelName:dl,backendName:"cpu",kernelFunc:Xj};function Jj(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;Ie(r,"resizeBilinear");let l=k.computeStrides(r.shape),[c,u]=o,[p,d,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(k.sizeFromShape([p,c,u,m])),y=[s&&c>1?d-1:d,s&&u>1?h-1:h],b=[s&&c>1?c-1:c,s&&u>1?u-1:u],v=0,x=y[0]/b[0],N=y[1]/b[1];for(let T=0;T<p;T++)for(let C=0;C<c;C++){let $;i?$=x*(C+.5)-.5:$=x*C;let F=Math.max(0,Math.floor($)),O=$-F,W=Math.min(d-1,Math.ceil($)),V=T*l[0]+F*l[1],H=T*l[0]+W*l[1];for(let K=0;K<u;K++){let j;i?j=N*(K+.5)-.5:j=N*K;let Y=Math.max(0,Math.floor(j)),J=j-Y,ne=Math.min(h-1,Math.ceil(j)),Q=V+Y*l[2],ie=H+Y*l[2],ee=V+ne*l[2],le=H+ne*l[2];for(let se=0;se<m;se++){let ce=f[Q+se],de=f[ie+se],fe=f[ee+se],xe=f[le+se],be=ce+(fe-ce)*J,Se=de+(xe-de)*J,_e=be+(Se-be)*O;g[v++]=_e}}}return n.makeTensorInfo([p,c,u,m],"float32",g)}var Zj={kernelName:ri,backendName:"cpu",kernelFunc:Jj};function Qj(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;Ie([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,c,u,p]=r.shape,[,d,h]=s.shape,m=new Float32Array(l*c*u*p),f=[i&&d>1?c-1:c,i&&h>1?u-1:u],g=[i&&d>1?d-1:d,i&&h>1?h-1:h],y=f[0]/g[0],b=f[1]/g[1],v=n.data.get(s.dataId).values,x=0;for(let N=0;N<l;N++){let T=N*o[0];for(let C=0;C<d;C++){let $=C*y,F=Math.floor($),O=Math.min(Math.ceil($),c-1),W=T+F*o[1],V=T+O*o[1],H=$-F,K=1-H;for(let j=0;j<h;j++){let Y=j*b,J=Math.floor(Y),ne=Math.min(Math.ceil(Y),u-1),Q=Y-J,ie=1-Q,ee=W+J*o[2],le=W+ne*o[2],se=V+J*o[2],ce=V+ne*o[2],de=K*ie,fe=K*Q,xe=H*ie,be=H*Q;for(let Se=0;Se<p;Se++){let _e=v[x++];m[ee+Se]+=_e*de,m[le+Se]+=_e*fe,m[se+Se]+=_e*xe,m[ce+Se]+=_e*be}}}}return n.makeTensorInfo([l,u,c,p],"float32",m)}var e5={kernelName:jd,backendName:"cpu",kernelFunc:Qj};function t5(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;Ie(r,"resizeNearestNeighbor");let l=k.computeStrides(r.shape),[c,u]=o,[p,d,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(p*c*u*m),y=[s&&c>1?d-1:d,s&&u>1?h-1:h],b=[s&&c>1?c-1:c,s&&u>1?u-1:u],v=y[0]/b[0],x=y[1]/b[1],N=0;for(let T=0;T<p;T++){let C=T*l[0];for(let $=0;$<c;$++){let F=i?v*($+.5):v*$,O=Math.min(d-1,s?Math.round(F):Math.floor(F));i&&(O=Math.max(0,O));let W=C+O*l[1];for(let V=0;V<u;V++){let H=i?x*(V+.5):x*V,K=Math.min(h-1,s?Math.round(H):Math.floor(H));i&&(K=Math.max(0,K));let j=W+K*l[2];for(let Y=0;Y<m;Y++){let J=f[j+Y];g[N++]=J}}}}return n.makeTensorInfo([p,c,u,m],r.dtype,g)}var n5={kernelName:wc,backendName:"cpu",kernelFunc:t5};function a5(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;Ie([s,r],"resizeNearestNeighborGrad");let o=k.computeStrides(r.shape),l=k.computeStrides(s.shape),[c,u,p,d]=r.shape,[,h,m]=s.shape,f=new Float32Array(c*u*p*d),g=n.data.get(s.dataId).values,y=[i&&h>1?u-1:u,i&&m>1?p-1:p],b=[i&&h>1?h-1:h,i&&m>1?m-1:m],v=y[0]/b[0],x=y[1]/b[1],N=1/v,T=1/x,C=Math.ceil(N)*2+2,$=Math.ceil(T)*2+2;for(let F=0;F<c;F++){let O=F*o[0];for(let W=0;W<u;W++){let V=O+W*o[1],H=Math.floor(W*N),K=Math.floor(H-C/2);for(let j=0;j<p;j++){let Y=V+j*o[2],J=Math.floor(j*T),ne=Math.floor(J-$/2);for(let Q=0;Q<d;Q++){let ie=0;for(let ee=0;ee<C;ee++){let le=ee+K;if(le<0||le>=h)continue;let se=O+le*l[1],ce=le*v,de=Math.min(u-1,i?Math.round(ce):Math.floor(ce));if(W===de)for(let fe=0;fe<$;fe++){let xe=fe+ne;if(xe<0||xe>=m)continue;let be=se+xe*l[2],Se=xe*x,_e=Math.min(p-1,i?Math.round(Se):Math.floor(Se));j===_e&&(ie+=g[be+Q])}}f[Y+Q]=ie}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var r5={kernelName:Hd,backendName:"cpu",kernelFunc:a5};function s5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;Ie(r,"reverse");let i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return rr({inputs:{x:r},backend:n});let l=new zt(r.shape,r.dtype),c=n.bufferSync(r);for(let u=0;u<l.size;u++){let p=l.indexToLoc(u),d=p.slice();o.forEach(h=>d[h]=r.shape[h]-1-d[h]),l.set(c.get(...d),...p)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var i5={kernelName:ii,backendName:"cpu",kernelFunc:s5},o5={kernelName:Cl,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)),[c,u,p,d]=a.shape,[h,m]=E.getImageCenter(i,u,p),f=255,g=Math.sin(r),y=Math.cos(r),b=o.data.get(a.dataId).values;for(let v=0;v<c;v++){let x=v*p*u*d;for(let N=0;N<u;N++){let T=N*(p*d);for(let C=0;C<p;C++){let $=C*d;for(let F=0;F<d;F++){let O=[c,N,C,F],W=O[2],V=O[1],H=(W-h)*y-(V-m)*g,K=(W-h)*g+(V-m)*y;H=Math.round(H+h),K=Math.round(K+m);let j=s;if(typeof s!="number"&&(F===3?j=f:j=s[F]),H>=0&&H<p&&K>=0&&K<u){let J=K*(p*d),ne=H*d,Q=x+J+ne+F;j=b[Q]}let Y=x+T+$+F;l[Y]=j}}}}return{dataId:o.write(l,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},l5=ot(oi,e=>{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),u5={kernelName:oi,backendName:"cpu",kernelFunc:l5};function UT(e,t,n,a,r,s,i,o,l,c){let u=[a/r,r],p=e.values,d=t.values;if(a===0)return Le(n,t.dtype);let h=Le(u,t.dtype);h.values.fill(l);for(let m=0;m<s;m++){let f=[],g=0;for(let y=0;y<i;y++){let b=p[m*i+y];f.push(b),g+=b*o[y]}if(g<0||g>=a/r)throw new Error(`Invalid indices: ${f} does not index into ${n}`);for(let y=0;y<r;y++)c?h.values[g*r+y]+=d[m*r+y]:h.values[g*r+y]=t.rank===0?d[0]:d[m*r+y]}return h}function c5(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=E.calculateShapes(s,r,i),d=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=UT(h,m,i,p,c,l,o,u,0,d);return n.makeTensorInfo(i,f.dtype,f.values)}var p5={kernelName:ml,backendName:"cpu",kernelFunc:c5};function d5(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t;Ie([a,r,s],"select");let i=a.shape.length,o=n.data.get(a.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=pa(r.dtype,s.dtype),p=k.makeZerosTypedArray(k.sizeFromShape(r.shape),u),d=0,h=i===0||i>1||r.shape.length===1?1:k.sizeFromShape(r.shape.slice(1));for(let m=0;m<o.length;m++)for(let f=0;f<h;f++)o[m]===1?p[d++]=l[m]:p[d++]=c[m];return n.makeTensorInfo(r.shape,u,p)}var h5={kernelName:fl,backendName:"cpu",kernelFunc:d5},m5=E.SELU_SCALEALPHA,f5=E.SELU_SCALE,g5=ot(gl,e=>e>=0?f5*e:m5*(Math.exp(e)-1)),y5={kernelName:gl,backendName:"cpu",kernelFunc:g5},b5=ot(ci,e=>1/(1+Math.exp(-e))),x5={kernelName:ci,backendName:"cpu",kernelFunc:b5},v5=ot(xl,e=>e<0?-1:e>0?1:0),w5={kernelName:xl,backendName:"cpu",kernelFunc:v5},k5=ot(ui,e=>Math.sin(e)),I5={kernelName:ui,backendName:"cpu",kernelFunc:k5},N5=ot(bl,e=>Math.sinh(e)),T5={kernelName:bl,backendName:"cpu",kernelFunc:N5},S5=11920928955078125e-23,GT=Math.log(S5)+2,C5=ot(vl,e=>{let t=e>-GT,n=e<GT,a=Math.exp(e),r;return n?r=a:t?r=e:r=Math.log(1+a),r}),_5={kernelName:vl,backendName:"cpu",kernelFunc:C5};function E5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;Ie([r],"spaceToBatchND");let o=k.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<r.shape.length;++g)l.push([0,0]);let c=VT.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=E.getReshaped(c.shape,s,o,!1),p=E.getPermuted(u.length,s.length,!1),d=E.getReshapedPermuted(c.shape,s,o,!1),h=Nt({inputs:{x:c},backend:n,attrs:{shape:u}}),m=ba({inputs:{x:h},backend:n,attrs:{perm:p}}),f=Nt({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}var A5={kernelName:kc,backendName:"cpu",kernelFunc:E5};function F5(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=E.calculateShapes(s,r,o),h=!1,m=n.bufferSync(r),f=n.bufferSync(s),g=n.data.get(i.dataId).values[0],y=UT(m,f,o,d,u,c,l,p,g,h);return n.makeTensorInfo(o,y.dtype,y.values)}var $5={kernelName:qd,backendName:"cpu",kernelFunc:F5};function D5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=E.prepareSplitSize(r,s,o),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(p=>{let d=[...u];d[o]=p;let h=Ui({inputs:{x:r},backend:n,attrs:{begin:c,size:d}});return c[o]+=p,h})}var M5={kernelName:wl,backendName:"cpu",kernelFunc:D5},R5=ot(pi,e=>Math.sqrt(e)),P5={kernelName:pi,backendName:"cpu",kernelFunc:R5},O5={kernelName:Ic,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;Ie(n,"square");let r=a.data.get(n.dataId).values,s=new Float32Array(r.length);for(let i=0;i<r.length;++i){let o=r[i];s[i]=o*o}return{dataId:a.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},L5=ot(Gr,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),z5={kernelName:Gr,backendName:"cpu",kernelFunc:L5};function B5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a;Ie(r,"stridedSlice");let{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:b}=pn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),v=Nt({inputs:{x:r},backend:n,attrs:{shape:y}}),x;if(h){let T=Ui({inputs:{x:v},backend:n,attrs:{begin:m,size:g}});x=Nt({inputs:{x:T},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(T)}else if(b.some(T=>T===0))x=n.makeTensorInfo(b,r.dtype,[]);else{let T=n.bufferSync(v),C=vT(b,T,f,m);x=n.makeTensorInfo(C.shape,C.dtype,C.values)}let N=Nt({inputs:{x},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(v),n.disposeIntermediateTensorInfo(x),N}var W5={kernelName:kl,backendName:"cpu",kernelFunc:B5},V5=ot(Il,e=>Math.tan(e)),U5={kernelName:Il,backendName:"cpu",kernelFunc:V5},G5=ot(gi,e=>Math.tanh(e)),H5={kernelName:gi,backendName:"cpu",kernelFunc:G5};function j5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;Ie(r,"tile");let i=kT(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var q5={kernelName:Ur,backendName:"cpu",kernelFunc:j5};function K5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;Ie(r,"topk");let o=n.data.get(r.dataId).values,[l,c]=IT(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var X5={kernelName:Nl,backendName:"cpu",kernelFunc:K5};function Y5(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Ie(s,"unique");let i=a.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:c}=NT(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([c.length],"int32",c)]}var J5={kernelName:Kd,backendName:"cpu",kernelFunc:Y5};function Z5(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape.length,o=r.shape[s],l=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(l[c++]=r.shape[h]);let u=new Array(i).fill(0),p=r.shape.slice();p[s]=1;let d=new Array(o);for(let h=0;h<d.length;h++){u[s]=h;let m=Ui({inputs:{x:r},backend:n,attrs:{begin:u,size:p}});d[h]=Nt({inputs:{x:m},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(m)}return d}var Q5={kernelName:Tl,backendName:"cpu",kernelFunc:Z5};function eq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a;Ie(r,"unsortedSegmentSum");let o=r.shape.length,l=s.shape.length,c=[],u=[],p=o-l,d=s;for(let m=0;m<p;++m){let f=$m({inputs:{input:d},backend:n,attrs:{dim:m+1}});d=f,u.push(f)}for(let m=0;m<i;++m){let f=k.createScalarValue(m,"int32"),g=n.makeTensorInfo([],"int32",f),y=PT({inputs:{a:g,b:d},backend:n}),b=ps({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),v=yv({inputs:{a:b,b:r},backend:n}),x=Dm({inputs:{x:v},backend:n,attrs:{axis:0,keepDims:!1}});c.push(x),u.push(g),u.push(y),u.push(b),u.push(v),u.push(x)}let h=WT({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var tq={kernelName:Nc,backendName:"cpu",kernelFunc:eq},nq=[IG,CU,TG,CG,DU,EG,FG,DG,RG,OG,zG,WG,UG,jG,KG,JG,QG,tH,aH,wG,sH,oH,uH,FU,RU,pH,_U,hH,fH,bH,vH,gH,NH,SH,kH,_H,AH,$H,MH,PH,LH,zH,WH,UH,HH,jH,KH,qH,kv,mG,YH,ZH,i6,PU,o6,LU,h6,f6,g6,BU,x6,w6,I6,T6,C6,VU,A6,EU,$6,mH,M6,P6,L6,fG,GU,W6,U6,jU,H6,K6,Y6,Q6,tj,aj,KU,ij,lj,cj,dj,mj,rj,yj,xj,YU,wj,Nj,_j,ZU,eG,Fj,Mj,Oj,nG,zj,Wj,Vj,VT,jj,yG,sG,Kj,AU,Yj,bG,xG,vG,Zj,e5,n5,r5,i5,o5,u5,oG,p5,h5,y5,x5,w5,I5,T5,lG,Sj,_5,A5,$5,M5,P5,O5,cG,z5,W5,dG,fj,U5,H5,q5,X5,aG,J5,Q5,tq,Bj];for(let e of nq)Sc(e);var Gi={},Tv={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function aq(e,t){Gi[e]=t}function sr(e){if(!(e in Gi)){let n=rq(e);if(n!==null)Gi[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Gi[e];return t.isContextLost()?(delete Gi[e],sr(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),Gi[e])}function sq(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 rq(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=sq(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Gi[e]},!1),e===1?t.getContext("webgl",Tv)||t.getContext("experimental-webgl",Tv):t.getContext("webgl2",Tv)}var bp;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(bp||(bp={}));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 an;(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"})(an||(an={}));function xp(e,t){return[t,e]}function iq(e,t){return e*t}function vp(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function su(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function oq(e,t){let[n,a]=su(e,t);return n*a*4}function Sv(e,t){let n=e,a,r,s,i,o,l,c,u,p,d;return te().getNumber("WEBGL_VERSION")===2?(a=n.R32F,r=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,c=4,u=1,p=n.HALF_FLOAT,d=n.FLOAT):(a=e.RGBA,r=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,c=4,u=4,p=t!=null?t.HALF_FLOAT_OES:null,d=e.FLOAT),l=e.RGBA,{internalFormatFloat:a,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:c,defaultNumChannels:u,textureTypeHalfFloat:p,textureTypeFloat:d}}function Te(e,t){let n=t();return te().getBool("DEBUG")&&lq(e),n}function lq(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+uq(e,t))}var cq=596e-10,pq=65504;function dq(e){return!!(te().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||cq<Math.abs(e)&&Math.abs(e)<pq)}function uq(e,t){switch(t){case e.NO_ERROR:return"NO_ERROR";case e.INVALID_ENUM:return"INVALID_ENUM";case e.INVALID_VALUE:return"INVALID_VALUE";case e.INVALID_OPERATION:return"INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${t}`}}function Rm(e,t){return Cr(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function hq(e,t){let n=Cr(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(Te(e,()=>e.shaderSource(n,t)),Te(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 fq(e,t){let n=Cr(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(Te(e,()=>e.shaderSource(n,t)),Te(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw mq(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var gq=/ERROR: [0-9]+:([0-9]+):/g;function mq(e,t){let n=gq.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,d)=>k.rightPad((d+1).toString(),s)+p),o=0;for(let p=0;p<i.length;p++)o=Math.max(i[p].length,o);let l=i.slice(0,a-1),c=i.slice(a-1,a),u=i.slice(a);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${k.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
`))}function yq(e){return Cr(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function bq(e,t){if(Te(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function Cv(e,t){if(Te(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function xq(e,t){let n=Cr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Te(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function vq(e,t){let n=Cr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Te(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),Te(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function wq(e){return Cr(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function kq(e,t){let n=te().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${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 Iq(e){return Cr(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function HT(e,t,n,a,r,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),Te(e,()=>e.vertexAttribPointer(o,r,e.FLOAT,!1,s,i)),Te(e,()=>e.enableVertexAttribArray(o)),!0)}function Tq(e,t,n){Nq(e,n),Te(e,()=>e.activeTexture(e.TEXTURE0+n)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function Sq(e,t,n){return Cr(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Cq(e,t,n){return e.getUniformLocation(t,n)}function _q(e,t,n,a){Te(e,()=>Tq(e,t,a)),Te(e,()=>e.uniform1i(n,a))}function _v(e,t,n){Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),Te(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function jT(e,t){Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),Te(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Pm(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+Eq(e,t))}function Eq(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 ${t}`}}function Cr(e,t,n){let a=Te(e,()=>t());if(a==null)throw new Error(n);return a}function Nq(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function iu(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function ou(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 Ev(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[iu(e),...ou(e)]),t}function Aq(e,t=!1){let n=te().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=iu(e),s=2,i=2;return e.length&&([s,i]=ou(e)),a=r*(s/2)*(i/2),k.sizeToSquarishShape(a).map(o=>o*2)}return k.sizeToSquarishShape(a)}function Om(e){return e%2==0}function Lm(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||Om(n)&&Om(a)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Om(e[0])&&Om(t[0])}var Av,Fv;function Fq(e){if(Av==null){let t=sr(e);Av=t.getParameter(t.MAX_TEXTURE_SIZE)}return Av}function $q(e){if(Fv==null){let t=sr(e);Fv=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Fv)}function Dq(e){if(e===0)return 0;let t,n=sr(e);return xa(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:xa(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function xa(e,t){return e.getExtension(t)!=null}function qT(e){try{if(sr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function Mq(e){if(e===0)return!1;let t=sr(e);if(e===1){if(!xa(t,"OES_texture_float"))return!1}else if(!xa(t,"EXT_color_buffer_float"))return!1;return $v(t)}function Pq(e){if(e===0)return!1;let t=sr(e);if(e===1){if(!xa(t,"OES_texture_float")||!xa(t,"WEBGL_color_buffer_float"))return!1}else{if(xa(t,"EXT_color_buffer_float"))return $v(t);let n="EXT_color_buffer_half_float";if(xa(t,n)){let a=t.getExtension(n);return Rq(t,a)}return!1}return $v(t)}function $v(e){let t=Sv(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function Rq(e,t){let n=Sv(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function Oq(e){return e!==2?!1:sr(e).fenceSync!=null}function wp(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ae=te();Ae.registerFlag("HAS_WEBGL",()=>Ae.getNumber("WEBGL_VERSION")>0);Ae.registerFlag("WEBGL_VERSION",()=>qT(2)?2:qT(1)?1:0);Ae.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ae.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ae.get("WEBGL_VERSION")===2);Ae.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ae.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ae.registerFlag("WEBGL_PACK",()=>Ae.getBool("HAS_WEBGL"));Ae.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_CLIP",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ae.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_REDUCE",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_CONV_IM2COL",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>Fq(Ae.getNumber("WEBGL_VERSION")));Ae.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>$q(Ae.getNumber("WEBGL_VERSION")));Ae.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ae.getNumber("WEBGL_VERSION");return e===0?0:Dq(e)});Ae.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ae.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!th.isMobile());Ae.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Mq(Ae.getNumber("WEBGL_VERSION")));Ae.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ae.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ae.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ae.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Pq(Ae.getNumber("WEBGL_VERSION")));Ae.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Oq(Ae.getNumber("WEBGL_VERSION")));Ae.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ae.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ae.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}.`)});function mn(){let e,t,n,a,r,s,i,o,l,c;return te().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="",c=`
#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));
}
`,c=`
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:c}}function Hi(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 Dv(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 KT=`
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;
}
`,Lq=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=bp.DENSE;let t=vp(e),n=mn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Hi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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;
}
`}},zq=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=bp.DENSE;let t=vp(e),n=mn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Hi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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;
}
`}},Bq=class{constructor(e){this.variableNames=["A"],this.outTexUsage=na.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
${KT}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},Wq=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=na.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
${KT}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},Vq=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=mn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${Dv(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.);
}
`}},Uq=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=mn(),[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 c=0;c<=1;c++){let u=l*2+c;i+=`
localCoords = coords;
if(localCoords[2] + ${c} < ${e[2]}) {
localCoords[2] += ${c};
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 = ${a.texture2D}(A, uv);
if(offset == 0) {
result[${u}] = values[0];
} else if(offset == 1) {
result[${u}] = values[1];
} else if(offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${Dv(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${a.output} = ${o};
}
`}};function Gq(e){let t=mn(),n=`${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 hq(e,n)}function Hq(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 xq(e,t)}function jq(e){let t=new Uint16Array([0,1,2,2,1,3]);return vq(e,t)}function kp(e,t,n,a,r,s){kq(t,n);let i=wq(e),o=e.TEXTURE_2D;return Te(e,()=>e.bindTexture(o,i)),Te(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Te(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Te(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function XT(e){return e.internalFormatFloat}function qq(e,t,n,a){let[r,s]=xp(t,n);return kp(e,r,s,XT(a),a.textureFormatFloat,e.FLOAT)}function YT(e){return e.internalFormatHalfFloat}function Kq(e,t,n,a){let[r,s]=xp(t,n);return kp(e,r,s,YT(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function JT(e){return e.downloadTextureFormat}function Xq(e,t,n,a){let[r,s]=xp(t,n);return kp(e,r,s,JT(a),e.RGBA,e.UNSIGNED_BYTE)}function ZT(e){return e.internalFormatPackedFloat}function Yq(e,t,n,a){let[r,s]=su(t,n);return kp(e,r,s,ZT(a),e.RGBA,e.FLOAT)}function QT(e){return e.internalFormatPackedHalfFloat}function Jq(e,t,n,a){let[r,s]=su(t,n);return kp(e,r,s,QT(a),e.RGBA,a.textureTypeHalfFloat)}function Zq(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),HT(e,t,"clipSpacePos",n,3,s,a)&&HT(e,t,"uv",n,2,s,r)}function Qq(e,t,n,a,r,s){Te(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),Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function e8(e,t,n){Te(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function t8(e,t,n,a){let r=e.createBuffer();Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Te(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function n8(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 a8(e,t,n,a){let[r,s]=xp(t,n),i=4,o=new Uint8Array(iq(t*n,i));return Te(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function r8(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(oq(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function s8(e,t,n){let a=new Float32Array(t*n*4);return Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var o8=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=te().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,aq(t,e)):this.gl=sr(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(te().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Rm(this.gl,r),xa(this.gl,s))this.textureHalfFloatExtension=Rm(this.gl,s);else if(te().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),xa(this.gl,a))this.colorBufferHalfFloatExtension=Rm(this.gl,a);else if(te().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",xa(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(xa(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Hq(this.gl),this.indexBuffer=jq(this.gl),this.framebuffer=Iq(this.gl),this.textureConfig=Sv(this.gl,this.textureHalfFloatExtension)}get debug(){return te().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;Te(e,()=>e.finish()),Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Te(e,()=>e.deleteFramebuffer(this.framebuffer)),Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Te(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Te(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),qq(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Kq(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Xq(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),e8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),Qq(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Jq(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Yq(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(jT(this.gl,this.framebuffer),this.outputTexture=null),Te(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>a8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return r8(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return n8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=t8(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(te().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 te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>s8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=fq(t,e),a=Gq(t),r=yq(t);return Te(t,()=>t.attachShader(r,a)),Te(t,()=>t.attachShader(r,n)),bq(t,r),this.debug&&Cv(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=Zq(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Te(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Cv(this.gl,this.program),Te(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Sq(this.gl,e,t):Cq(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Te(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(),_q(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=su(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&&Cv(this.gl,this.program),Pm(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Te(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Te(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Rm(this.gl,te().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(te().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(te().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,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,te().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=i8(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(),_v(this.gl,e,this.framebuffer),this.debug&&Pm(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(_v(this.gl,this.outputTexture,this.framebuffer),this.debug&&Pm(this.gl)):jT(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;_v(a,e,this.framebuffer),this.debug&&Pm(a),this.outputTexture=e,Te(a,()=>a.viewport(0,0,t,n)),Te(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Te(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 i8(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:eS}=E;function g8(e,t,n,a){let r=[];e.forEach(h=>{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=>l8(h,t,a)).join(`
`),o=t.texShape,l=mn(),c=p8(l),u,p,d=m8(l);return t.isPacked?(u=u8(t.logicalShape,o),p=h8(l)):(u=c8(t.logicalShape,o),p=d8(l)),a&&(d+=f8),[d,c,p,s,u,i,n].join(`
`)}function lu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return y8(e);case 1:return b8(e);case 2:return x8(e);case 3:return v8(e);case 4:return w8(e);case 5:return k8(e);case 6:return I8(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function tS(e){switch(e.shapeInfo.logicalShape.length){case 0:return N8(e);case 1:return T8(e);case 2:return S8(e);case 3:return C8(e);default:return _8(e)}}function l8(e,t,n=!1){let a="";n?a+=tS(e):a+=lu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=E8(e,t):a+=A8(e,t)),a}function u8(e,t){switch(e.length){case 0:return nS();case 1:return F8(e,t);case 2:return M8(e,t);case 3:return $8(e,t);default:return D8(e,t)}}function c8(e,t){switch(e.length){case 0:return nS();case 1:return R8(e,t);case 2:return B8(e,t);case 3:return P8(e,t);case 4:return O8(e,t);case 5:return L8(e,t);case 6:return z8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function p8(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function d8(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function h8(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function m8(e){return`${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);
}
${W8}
${V8}
${U8}
`}var W8=`
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);
}
`,V8=`
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);
}
`,U8=`
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);
}
`,f8=`
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 nS(){return`
int getOutputCoords() {
return 0;
}
`}function F8(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 R8(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 $8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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, ${a}) * 2;
return ivec3(b, r, c);
}
`}function P8(e,t){let n=Hi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function D8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+i,o=`b${l}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${r};
index -= b * ${r};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec${e.length}(${o});
}
`}function O8(e,t){let n=Hi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function L8(e,t){let n=Hi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function z8(e,t){let n=Hi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function M8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let a=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec2(r, c);
}
`}function B8(e,t){return k.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function ji(e){return`offset${e}`}function N8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=mn();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function y8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,i]=e.shapeInfo.texShape,o=ji(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function T8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=mn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${r[0]}, ${r[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function b8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${uu(e)}
}
`;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=ji(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
return sampleTexture(${t}, uv);
}
`:r===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function S8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=mn();if(r!=null&&k.arraysEqual(t,r))return`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${a}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function x8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],d=r[1];return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let p=cu(e,o),d=["row","col"];return`
${lu(p)}
float ${a}(int row, int col) {
return ${a}(${pu(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${uu(e)}
}
`;let l=r[0],c=r[1],u=ji(n);return c===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${u};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function C8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=cu(e,p),m=["b","row","col"];return`
${tS(h)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${pu(m,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=mn();return`
vec4 ${a}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${c}, ${l}, b, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function v8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=cu(e,l),f=["row","col","depth"];return`
${lu(m)}
float ${a}(int row, int col, int depth) {
return ${a}(${pu(f,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${r}, ${s}, 1)));
${uu(e)}
}
`;let c=e.shapeInfo.texShape,u=c[0],p=c[1],d=e.shapeInfo.flatOffset;if(p===r&&d==null)return`
float ${a}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;if(p===s&&d==null)return`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;let h=ji(n);return`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r} + col * ${s} + depth + ${h};
vec2 uv = uvFromFlat(${u}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function _8(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),p="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let h=mn();return`
vec4 ${r}(${p}) {
int index = ${d};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${h.texture2D}(${a}, uv);
}
`}function w8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=cu(e,o),f=["row","col","depth","depth2"];return`
${lu(m)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${pu(f,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${r}, 1)));
${uu(e)}
}
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,p=u[0],d=u[1];if(d===i&&c==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(d===r&&c==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let h=ji(n);return`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${r} + depth2;
vec2 uv = uvFromFlat(${p}, ${d}, index + ${h});
return sampleTexture(${n}, uv);
}
`}function k8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let f=cu(e,l),g=["row","col","depth","depth2","depth3"];return`
${lu(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${pu(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${r})) +
depth3;
${uu(e)}
}
`;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&u==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&u==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let m=ji(n);return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${r} + depth3 + ${m};
vec2 uv = uvFromFlat(${d}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function I8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=cu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${lu(g)}
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${a}(${pu(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${uu(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],m=d[1];if(m===u&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let f=ji(n);return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${h}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function uu(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function E8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=eS(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+c]} = 0;`).join(`
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+c]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${a}(${d});
${h}
}
`}function A8(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 c=gt(l),u=eS(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${d}
return get${a}(${m});
}
`}function gt(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 ${e} is not yet supported`)}function cu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function pu(e,t){return t.map(n=>e[n]).join(", ")}function G8(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=g8(s,o,r,t.packedInputs),c=e.createProgram(l),u=null,p=e.getUniformLocation(c,"NAN",!1);te().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;d[m]=e.getUniformLocation(c,m,f),d[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:p}}function aS(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function H8(e,t,n,a,r){aS(t.inShapeInfos,n),aS([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),te().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function j8(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}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:q8,bincountImpl:rS,bincountReduceImpl:K8,ceilImpl:X8,concatImpl:Y8,expImpl:J8,expm1Impl:Z8,floorImpl:Q8,gatherV2Impl:eK,greaterImpl:tK,lessImpl:nK,linSpaceImpl:aK,logImpl:rK,maxImpl:sK,maximumImpl:iK,minimumImpl:oK,multiplyImpl:lK,negImpl:uK,prodImpl:cK,rangeImpl:pK,rsqrtImpl:dK,simpleAbsImpl:sS,sliceImpl:hK,stridedSliceImpl:mK,subImpl:fK,tileImpl:gK,topKImpl:yK,transposeImpl:Mv,uniqueImpl:bK}=QN;function iS(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function fn(e,t){return t===1?[e]:iS(e,t)}function xK(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var IK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=fn("rc",t),a=gt(t),r=vK(t,e,n),s=wK(t,e[e.length-1],e[e.length-2],n),i=kK(e,n);this.userCode=`
void main() {
${a} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
}
}
`}}};function NK(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function vK(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function wK(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
int r = ${r[0]};
int c = ${r[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function kK(e,t){let n=e.length,a=NK(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 oS=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=`
${TK(t)}
${Dv(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}};function TK(e){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${Hi(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var SK=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=uS(t,n),r=cS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=lS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===an.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===an.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===an.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===an.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===an.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=uS(n,a),s=cS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=lS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=te().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 CK(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 lS(e,t,n,a,r){let s=_K(t,a),i;if(r){let[l,c]=su(e[0],e[1]);i=l*c}else{let[l,c]=xp(e[0],e[1]);i=l*c}let o=CK(n,s);return i*o}function _K(e,t){switch(e){case an.PACKED_2X2_FLOAT32:return ZT(t);case an.PACKED_2X2_FLOAT16:return QT(t);case an.UNPACKED_FLOAT32:return XT(t);case an.UNPACKED_FLOAT16:return YT(t);case an.PACKED_4X1_UNSIGNED_BYTE:return JT(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function EK(e){return te().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?an.PACKED_2X2_FLOAT32:an.UNPACKED_FLOAT32:e?an.PACKED_2X2_FLOAT16:an.UNPACKED_FLOAT16}function uS(e,t){if(e===na.UPLOAD)return an.PACKED_2X2_FLOAT32;if(e===na.RENDER||e==null)return EK(t);if(e===na.DOWNLOAD||e===na.PIXELS)return an.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function cS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ds=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Oa="if (isnan(x)) return x;",AK="return x;",pS="return abs(x);",FK="return (x >= 0.0) ? x : (exp(x) - 1.0);",$K=Oa+`
return (x < 0.0) ? 0.0 : x;
`,DK=Oa+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,zm="return x;",MK="return x;",RK=`
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;
`,PK=`
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;
`,OK=`
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;
`,du=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},LK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=fn("rc",t),a=gt(t),r=xK(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${i}));
}
`}},zK=Za.whereImpl,BK=1e-7,WK=1e-4,Rv={};function VK(e){return e in Rv||(Rv[e]={}),Rv[e]}var UK=128,GK=600;function HK(){return te().global.screen==null?1024:te().global.screen.height*te().global.screen.width*window.devicePixelRatio*GK/1024/1024}var qK=class extends sc{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.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!te().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=sr(te().getNumber("WEBGL_VERSION"));this.binaryCache=VK(te().getNumber("WEBGL_VERSION")),this.gpgpu=new o8(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 SK(this.gpgpu),this.numMBBeforeWarning=HK(),this.texData=new fd(this,Jr())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((te().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||te().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={};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:na.UPLOAD,refCount:1,complexParentRefCount:0}),a}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--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,n,a){if(te().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:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}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 du(i,zm):p=new ds(i,zm);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,c;l&&(c=k.now());let u;if(a==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);u=E.mergeRealAndImagArrays(p,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-c),this.convertAndCacheOnCPU(e,u)}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 du(a,zm):h=new ds(a,zm);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(!te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&te().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,c;if(s!=="complex64"&&te().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...vp(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];u=E.mergeRealAndImagArrays(m,f)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}c!=null&&this.disposeIntermediateTensorInfo(c);let p=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),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 Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!dq(n))throw te().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...vp(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=te().getBool("WEBGL_PACK")&&a===!0,i=s?Ev(t):t,o=s?new Wq(i):new Bq(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}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 c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return te().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Jr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=UK){let n=this.getCPUBackend();return!te().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(a=>this.texData.get(a.dataId).texture==null&&k.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return zK(e.shape,t)}packedUnaryOp(e,t,n){let a=new du(e.shape,t);return this.compileAndRun(a,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=sS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(te().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,pS,e.dtype);let t=new ds(e.shape,pS);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return Jr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new LK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new IK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[iu(e.shape),...ou(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[iu(t),...ou(t)],s=new oS(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=Ev(a),i;n?i=new zq(s):i=new Lq(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===bp.DENSE){let m=vp(e.outputShape);i.texShape=m.map(f=>f*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(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let f=this.texData.get(m.dataId);if(f.texture==null){if(!e.packedInputs&&k.sizeFromShape(m.shape)<=te().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:f.values};e.packedInputs&&(f.isPacked=!0,f.shape=m.shape)}else if(!!f.isPacked!=!!e.packedInputs)m=f.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),f=this.texData.get(m.dataId);else if(f.isPacked&&!Lm(f.shape,m.shape)){let g=m,y=m.shape;m.shape=f.shape,m=this.packedReshape(m,y),o.push(m),f=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:f,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=j8(e,l,c),p=this.getAndSaveBinary(u,()=>G8(this.gpgpu,e,l,c)),d=this.activeTimers!=null,h;if(d&&(h=this.startTimer()),H8(this.gpgpu,p,l,c,a),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)})),!te().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,a,r=!1){n=n||t[0].dtype;let s=this.runWebGLProgram(e,t,n,a,r);return Jr().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(te().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=D(()=>{if(!te().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=te().getBool("DEBUG");te().set("DEBUG",!1);let t=this.abs(he(1e-8)).dataSync()[0];if(te().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?BK:WK}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,c;l&&(c=k.now());let u=t.texShape;if(u==null&&(u=Aq(n,o),t.texShape=u),r!=null){let p=Ev(n),d,h=u[1],m=u[0],f=r instanceof Uint8Array;o?([h,m]=su(u[0],u[1]),d=new Uq(p,[m,h],f)):d=new Vq(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=na.PIXELS:this.texData.get(g.dataId).usage=na.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,b=this.runWebGLProgram(d,[g],a,null,y),v=this.texData.get(b.dataId);t.texture=v.texture,t.texShape=v.texShape,t.isPacked=v.isPacked,t.usage=v.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(b.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-c)}else{let p=this.acquireTexture(u,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=jK(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)}};function jK(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var KK="2.8.5";th.isBrowser()&&oh("webgl",()=>new qK,2);var dS=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,hu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Bm=`
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;
`,Ip=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${gt(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 Wn(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 XK={kernelName:Xo,backendName:"webgl",kernelFunc:Wn};function hs(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=Wn({inputs:{x:a},backend:n}),l=n.texData.get(o.dataId);l.complexParentRefCount++;let c=Wn({inputs:{x:r},backend:n}),u=n.texData.get(c.dataId);return u.complexParentRefCount++,i.complexTensorInfos={real:o,imag:c},s}var YK={kernelName:Nd,backendName:"webgl",kernelFunc:hs},hS="return (a < 0.) ? b * a : a;",mS=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function JK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ip(mS,r.shape,i.shape):new hu(hS,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var ZK={kernelName:Gs,backendName:"webgl",kernelFunc:JK},fS="return (a < 0.) ? b * a : a;",gS=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function QK(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ip(gS,a.shape,r.shape):new hu(fS,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var eX={kernelName:ni,backendName:"webgl",kernelFunc:QK},yS="if (isnan(x)) return x;",tX=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,nX=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Je({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=te().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new du(i.shape,t):u=new ds(i.shape,e),o.runWebGLProgram(u,[i],l)}}function rn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(a&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(v=>{let[x,N]=v,T={dataId:x.dataId,dtype:x.dtype,shape:l.shape},C={dataId:N.dataId,dtype:N.dtype,shape:c.shape},$=new hu(e,l.shape,c.shape);return u.runWebGLProgram($,[T,C],pa(x.dtype,N.dtype))}),b=hs({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),b}let p=s||pa(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&r!=null){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=r(l.shape,c.shape,m.values,f.values,p),b=u.makeTensorInfo(y,p),v=u.texData.get(b.dataId);return v.values=g,b}let d=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Ip(t,l.shape,c.shape,n):h=new hu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],p)}}function Wm(e,t=!1){if(e==="linear")return t?MK:AK;if(e==="relu")return t?PK:$K;if(e==="elu")return t?RK:FK;if(e==="relu6")return t?OK:DK;if(e==="prelu")return t?gS:fS;if(e==="leakyrelu")return t?mS:hS;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var bS=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 c=a?e[1]:e[2],u=Math.ceil(c/2),p=a?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?f=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:f=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",v="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${b};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${p});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${m[0]});
result += (${h[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},xS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},vS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},wS="return a * b;";function kS(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=E.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new vS(xS.REAL,a.shape,r.shape),u=new vS(xS.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=hs({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=lK(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ip(wS,a.shape,r.shape):i=new hu(wS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var aX={kernelName:Zs,backendName:"webgl",kernelFunc:kS};function rX(e,t,n){let a=[iu(e.shape),...ou(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[iu(t),...ou(t)],i=new oS(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ve(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!Lm(r.shape,l)&&!(u.texture!==null&&Lm(u.shape,l))?rX(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var sX={kernelName:hl,backendName:"webgl",kernelFunc:ve},IS=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 u=1/t;l=`sumValue += dot(values * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
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) {
${c}
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);
${l}
}
setOutput(sumValue);
}
`}},iX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 c=Math.floor(n/4)*4,u=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);
}
`,d="vec4";t==="all"?(i="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(i="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="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 < ${c}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${u===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${u===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${l});
}
`}};function oX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function qi(e,t,n,a){let r=oX(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:c}=r[i],u,p;n==="mean"?u=i===0?new IS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new IS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new iX({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var uX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=gt(this.rank),r=lX(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function lX(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var cX=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=gt(this.rank),r=iS("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Vm(e,t,n){let a=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cX(e.shape,t):new uX(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function pX(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=E.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=Vm(e,l,a),o=E.getInnerMostAxes(o.length,s)),E.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=E.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=E.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=ve({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=eh(e.dtype),b=qi(g,y,"sum",a),v=ve({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),v}function Pv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return pX(r,s,i,n)}var dX={kernelName:di,backendName:"webgl",kernelFunc:Pv};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Mv(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=Vm(r,s,i);return c}var hX={kernelName:yi,backendName:"webgl",kernelFunc:Sn},NS=1e3;function Um({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),v=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&v,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let x=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],C=ve({inputs:{x:e},backend:r,attrs:{shape:N}}),$=ve({inputs:{x:t},backend:r,attrs:{shape:T}}),F=[C,$],O=Math.max(y,b),W=n?C.shape[1]:C.shape[2],V=s!=null,H=i!=null,K=l==="leakyrelu",j=l!=null?Wm(l,!0):null,Y=V||H||K||j!=null,J;if((h===1||m===1)&&W>NS&&Y===!1){let Q=C,ie=$;n&&(Q=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),F.push(Q)),a&&(ie=Sn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),F.push(ie));let ee=m!==1,le=m===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:r,attrs:{shape:[O,W,1]}}),F.push(se));let ce=m===1?2:1,de=ie;le&&(de=ve({inputs:{x:ie},backend:r,attrs:{shape:[O,1,W]}}),F.push(de));let fe=kS({inputs:{a:se,b:de},backend:r});J=Pv({inputs:{x:fe},backend:r,attrs:{axis:ce,keepDims:!0}}),F.push(fe)}else{let Q=pa(e.dtype,t.dtype),ie=new bS(N,T,[O,h,m],n,a,V,j,H,K),ee=[C,$];if(s!=null&&ee.push(s),H&&ee.push(i),K){let le=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));ee.push(le),F.push(le)}J=r.runWebGLProgram(ie,ee,Q)}let ne=ve({inputs:{x:J},backend:r,attrs:{shape:x}});F.push(J);for(let Q of F)r.disposeIntermediateTensorInfo(Q);return ne}function mX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return Um({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var fX={kernelName:bi,backendName:"webgl",kernelFunc:mX},TS="return abs(x);";function gX(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=sS(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new du(a.shape,TS):r=new ds(a.shape,TS),n.runWebGLProgram(r,[a],a.dtype)}var yX={kernelName:Co,backendName:"webgl",kernelFunc:gX},bX=Oa+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,xX=Je({opSnippet:bX}),vX={kernelName:_o,backendName:"webgl",kernelFunc:xX},wX=Oa+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,kX=Je({opSnippet:wX}),IX={kernelName:Eo,backendName:"webgl",kernelFunc:kX},SS="return a + b;",NX=rn({opSnippet:SS,packedOpSnippet:SS,supportsComplex:!0,cpuKernelImpl:q8}),TX={kernelName:Wr,backendName:"webgl",kernelFunc:NX},SX=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 = ${a};
setOutput(result);
}
`}},CX=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 Gm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Wn({inputs:{x:a[0]},backend:n});if(a.length>te().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Gm({inputs:a.slice(0,o),backend:n}),c=Gm({inputs:a.slice(o),backend:n});return Gm({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=a.map(o=>o.shape),i=te().getBool("WEBGL_PACK")?new CX(a[0].shape,s):new SX(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var _X={kernelName:_s,backendName:"webgl",kernelFunc:Gm};function EX(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),c=l,u=E.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("all",c,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=qi(f,f.dtype,"all",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var AX={kernelName:xd,backendName:"webgl",kernelFunc:EX};function FX(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),c=l,u=E.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("any",c,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=qi(f,f.dtype,"any",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var $X={kernelName:vd,backendName:"webgl",kernelFunc:FX},DX=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 ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},MX=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${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=gt(o),c=fn("coords",o),u,p;if(s===1){p=o+1;let C=gt(p);u=`
${C} sourceLocR = ${C}(${c.join()}, 0);
++${c[o-1]};
${C} sourceLocG = ${C}(${c.join()}, 0);
++${c[o-2]};
${C} sourceLocA = ${C}(${c.join()}, 0);
--${c[o-1]};
${C} sourceLocB = ${C}(${c.join()}, 0);
--${c[o-2]};`}else p=o,u=`
${l} sourceLocR = coords;
++${c[o-1]};
${l} sourceLocG = coords;
++${c[o-2]};
${l} sourceLocA = coords;
--${c[o-1]};
${l} sourceLocB = coords;
--${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],m=d.map(C=>"int "+C),f=fn("sourceLocR",p-1).concat("inIdx.r"),g=fn("sourceLocG",p-1).concat("inIdx.g"),y=fn("sourceLocB",p-1).concat("inIdx.b"),b=fn("sourceLocA",p-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=a?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${b.join()})));`,N=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,T=a?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${T}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${N};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${N};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${v}(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 CS(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=E.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new DX(o,n,a==null),c=[t];a!=null&&c.push(a);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let p=CS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function _S(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=E.computeOptimalWindowSize(s),o=new MX(r,i,n,a==null),l=a==null?[t]:[t,a],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=_S(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function ES(e,t,n,a){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!te().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=E.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=ve({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=CS(e,c,a);s.push(u);let p=ve({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return _S(e,t,a)}function RX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=ES(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var PX={kernelName:Es,backendName:"webgl",kernelFunc:RX};function OX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=ES(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var LX={kernelName:lc,backendName:"webgl",kernelFunc:OX},zX=Oa+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,BX=Je({opSnippet:zX}),WX={kernelName:Ao,backendName:"webgl",kernelFunc:BX},VX=Oa+"return log(x + sqrt(x * x + 1.0));",UX=Je({opSnippet:VX}),GX={kernelName:Fo,backendName:"webgl",kernelFunc:UX},HX=Oa+`
return atan(x);
`,jX=Je({opSnippet:HX}),qX={kernelName:$o,backendName:"webgl",kernelFunc:jX},KX=tX+`
return atan(a, b);
`,XX=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+nX+`
return result;
`,YX=rn({opSnippet:KX,packedOpSnippet:XX}),JX={kernelName:Mo,backendName:"webgl",kernelFunc:YX},ZX=Oa+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,QX=Je({opSnippet:ZX}),eY={kernelName:Do,backendName:"webgl",kernelFunc:QX},Np=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,c=e.dilationWidth,u=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${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 < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${C} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,T=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${h});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${T}
}
int xC = xCCorner + ${x};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${T}
} else if (${N===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${T}
}
}
setOutput(${v});
}
`}},Ov=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,c=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",v="0.0";if(b||(v="-1.0 / 1e-20"),n){let F=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
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 ${F} 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 * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,$=`
if (${b}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
const float initializationValue = ${v};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${v});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${T}; 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)
);
${$}
}
int xC = xCCorner + ${T};
if (${C===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${$}
} else if (${C===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${$}
} else if (${C===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
);
${$}
}
}
setOutput(${N});
}
}
`}};function tY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;wp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Np(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var nY={kernelName:As,backendName:"webgl",kernelFunc:tY};function aY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=E.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new Ov(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var rY={kernelName:uc,backendName:"webgl",kernelFunc:aY},sY=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,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},iY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${h}, ${m}, ${f});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
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 < ${d};
wC += ${c}) {
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 oY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,p,c,u),h=new iY(d);return n.runWebGLProgram(h,[r],i.dtype)}var lY={kernelName:kd,backendName:"webgl",kernelFunc:oY};function uY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;wp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=E.computePool2DInfo(i.shape,o,l,1,c),p=new sY(u);return n.runWebGLProgram(p,[r],i.dtype)}var cY={kernelName:wd,backendName:"webgl",kernelFunc:uY};function pY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Um({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var dY={kernelName:Fs,backendName:"webgl",kernelFunc:pY},hY=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},mY=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},fY=({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 c=[a,r,s],u=null;i!=null&&(u=i.shape,c.push(i));let p=null;o!=null&&(p=o.shape,c.push(o));let d=te().getBool("WEBGL_PACK_NORMALIZATION")?new mY(a.shape,r.shape,s.shape,u,p,l):new hY(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},gY={kernelName:Vs,backendName:"webgl",kernelFunc:fY},bY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank),n=`uniform int start[${this.rank}];`,a=yY(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Lv[o]} = start[${o}] + coords.${Lv[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)}}},Lv=["x","y","z","w","u","v"];function yY(e){if(e===1)return"sourceLoc";if(e<=6)return Lv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var xY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=gt(this.rank),n=fn("coords",this.rank),a=fn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${a[u]} = ${n[u]} + start[${u}];`).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 vY(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.complexParentRefCount=0,i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=pn.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 Tp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=pn.parseSliceParams(r,s,i);if(pn.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),d=hK(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=pn.isSliceContinous(r.shape,o,l);if(c||!u){let p=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xY(l):new bY(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),vY(r,o,l,n)}var wY={kernelName:yl,backendName:"webgl",kernelFunc:Tp},kY=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,v)=>b*v),l=E.getReshaped(r.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(r.shape,s,o),p=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),h=[],m=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Tp({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},IY={kernelName:cc,backendName:"webgl",kernelFunc:kY};function NY(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),c=rS(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var TY={kernelName:Id,backendName:"webgl",kernelFunc:NY},SY="return float(a != b);",AS=rn({opSnippet:SY,dtype:"bool"}),CY={kernelName:sl,backendName:"webgl",kernelFunc:AS};function Sp(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var _Y={kernelName:Gd,backendName:"webgl",kernelFunc:Sp},EY="return float(int(x));";function AY(e,t){let n=new ds(e.shape,EY),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function zv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Wn({inputs:{x:r},backend:n});let i=bt(r.shape),o=zv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=hs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Sp({inputs:{input:r},backend:n}),o=zv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return AY(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=AS({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var FY={kernelName:$s,backendName:"webgl",kernelFunc:zv},FS="return ceil(x);",$Y=Je({opSnippet:FS,packedOpSnippet:FS,cpuKernelImpl:X8}),DY={kernelName:Ro,backendName:"webgl",kernelFunc:$Y},MY=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)}}},RY=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 PY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;te().getBool("WEBGL_PACK_CLIP")?o=new RY(r.shape):o=new MY(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var OY={kernelName:Vr,backendName:"webgl",kernelFunc:PY},LY=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 $S(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function zY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new LY(a.shape),i=[$S(a,r.complexTensorInfos.real),$S(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var BY={kernelName:pc,backendName:"webgl",kernelFunc:zY},WY=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},VY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=gt(a),s=fn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${Hm(i,l,f)}),
vec2(${Hm(c,l,f)}));
}`}let d=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${d}(${Hm(i,l,h)}),
vec2(${Hm(c,l,h)}));`,this.userCode=`
float getValue(${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 Hm(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function jm(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var UY={kernelName:Od,backendName:"webgl",kernelFunc:jm};function mu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>Sp({inputs:{input:m},backend:n})),u=e.map(m=>jm({inputs:{input:m},backend:n})),p=mu(c,t,n),d=mu(u,t,n),h=hs({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=DS(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=Y8(p,u,a,d),m=E.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>te().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=mu(e.slice(0,c),t,n),p=mu(e.slice(c),t,n),d=mu([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new VY(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=DS(e,t,n),i=new WY(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=ve({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function DS(e,t,n){let a=E.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ve({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function MS(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=E.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return E.assertParamsConsistent(l,s),mu(o,s,n)}var GY={kernelName:Po,backendName:"webgl",kernelFunc:MS},RS=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,c=e.dilationHeight,u=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,v="",x="";n&&(a?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let N=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${b}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${u};
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;
${N}
${x}
setOutput(result);
}
`}},HY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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 < ${u}; 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 < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
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);
}
`}},jY=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:c,dilationHeight:u,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=mn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,v="";for(let x=0;x<=1;x++)for(let N=0;N<=1;N++)v+=`
blockIndex = rc.y + ${N};
pos = rc.x + ${x};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${h};
d0 = offsetY + ${u} * (pos / ${m});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${m}.) / ${r}.));
if(d1 < ${t[b]} && d1 >= 0) {
ch = int(mod(float(pos), ${r}.));
if (${g}) {
innerDims = vec2(d1, ch);
result[${x*2+N}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${x*2+N}] = 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;
${v}
${f.output} = result;
}
`}};function PS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&u>NS,v=l[2]%2!=0&&!!c.isPacked;if(b||!te().getBool("WEBGL_LAZILY_UNPACK")||!te().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!v){let x=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=ve({inputs:{x:e},backend:a,attrs:{shape:[1,x,n.inChannels]}}),T=ve({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=Um({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:C},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(C)}else{let x=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(Lm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let C=ve({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let $=Um({a:N,b:C,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),F=a.texData.get($.dataId);k.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,F.shape=n.outShape,g=Wn({inputs:{x:$},backend:a}),g.shape=n.outShape,y.push($)}for(let x of y)a.disposeIntermediateTensorInfo(x);return g}function OS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*c*u,g=d*p,y=[f,g],b=!0,v=!1,x=[],N=ve({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=ve({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});x.push(N),x.push(T);let C=new jY(y,N.shape,n),$=a.runWebGLProgram(C,[N],"float32"),F=ve({inputs:{x:$},backend:a,attrs:{shape:[1,y[0],y[1]]}});x.push($),x.push(F);let O=r!=null,W=s!=null,V=o==="leakyrelu",H=o?Wm(o,!0):null,K=new bS(F.shape,T.shape,[1,g,n.outChannels],b,v,O,H,W,V),j=[F,T];if(r&&j.push(r),W&&j.push(s),V){let Q=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));j.push(Q),x.push(Q)}let Y=a.runWebGLProgram(K,j,"float32"),J=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],ne=ve({inputs:{x:Y},backend:a,attrs:{shape:J}});x.push(Y);for(let Q of x)a.disposeIntermediateTensorInfo(Q);return ne}function qY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a,p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=PS({x:r,filter:s,convInfo:d,backend:n});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=OS({x:r,filter:s,convInfo:d,backend:n});else{let f=new RS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ve({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var KY={kernelName:Ds,backendName:"webgl",kernelFunc:qY},XY=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);
}
`}},YY=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,c=s?2:3,u=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - 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);
}
`}},JY=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},ZY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=a-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${c});
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 QY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a,p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,s],"float32")}var e7={kernelName:Td,backendName:"webgl",kernelFunc:QY};function t7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a,p=E.convertConv2DDataFormat(c),d=E.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new YY(d);return n.runWebGLProgram(h,[r,s],"float32")}var n7={kernelName:Ms,backendName:"webgl",kernelFunc:t7};function a7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=E.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new HY(c);return n.runWebGLProgram(u,[r,s],"float32")}var r7={kernelName:dc,backendName:"webgl",kernelFunc:a7};function s7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=E.computeConv3DInfo(r.shape,l,i,1,o),u=new JY(c);return n.runWebGLProgram(u,[r,s],"float32")}var i7={kernelName:Sd,backendName:"webgl",kernelFunc:s7};function o7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=E.computeConv3DInfo(l,s.shape,o,1,i),u=new ZY(c);return n.runWebGLProgram(u,[r,s],"float32")}var l7={kernelName:Cd,backendName:"webgl",kernelFunc:o7},u7=yS+`
return cos(x);
`,c7=Je({opSnippet:u7}),p7={kernelName:Rs,backendName:"webgl",kernelFunc:c7},d7=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,h7=Je({opSnippet:d7}),m7={kernelName:Oo,backendName:"webgl",kernelFunc:h7},f7=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,v,x]=p>1?[`${(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(${b});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${g};
float width_scale = ${v};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},g7=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new f7(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},y7={kernelName:Lo,backendName:"webgl",kernelFunc:g7},BS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${LS(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() {
${gt(a)} coords = getOutputCoords();
int end = ${zS(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${zS(a,"coords")} = idx;
val += getX(${LS(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 LS(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 ${e} is not yet supported`)}function zS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function b7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=E.getAxesPermutation([s],l),u=r;c!=null&&(u=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=E.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=r.shape[p],h=Wn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new BS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new BS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=E.getUndoAxesPermutation(c),f=Sn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var x7={kernelName:Ps,backendName:"webgl",kernelFunc:b7};function v7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=rS(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=K8(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var w7={kernelName:_d,backendName:"webgl",kernelFunc:v7},k7=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 I7(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],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new k7(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var N7={kernelName:zo,backendName:"webgl",kernelFunc:I7},WS=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,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?g=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:g=`
float activation(float x) {
${n}
}
`,y="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=`
${g}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${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 * ${d};
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;
${b}
${y}
setOutput(result);
}
`}},VS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`
vec4 xTexelR${x}C${N*2} = vec4(0.);
vec4 wR${x}C${N} = vec4(0.);
vec4 xR${x}C${N} = vec4(0.);`;for(let x=0;x<h;x++)for(let N=0;N<f;N++){let T=N*2;if(g+=`
xR = xRCorner + ${x*p};
xC = xCCorner + ${T*d};
`,u===1){if(T<m&&(l%2==1?g+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${x}C${T}.zw = vec2(0.);
}
} else {
xTexelR${x}C${T} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${x}C${T} = vec4(previous.zw, xTexelR${x}C${T}.xy);
} else {
xR${x}C${T} = vec4(0, 0, xTexelR${x}C${T}.xy);
}
`:g+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
xR${x}C${T} = xTexelR${x}C${T};
`,T+1<m)){let C=l%2==0?k.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(g+=`
xCOffset = xC + ${l%2} + ${C};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xCOffset, d1);
}
`,d>1&&(g+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
`),g+=`
xR${x}C${T+1} = vec4(
xTexelR${x}C${T}.zw, xTexelR${x}C${T+2}.xy);
`):g+=`
xCOffset = xC + ${C};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xCOffset, d1);
}
xR${x}C${T+1} = xTexelR${x}C${T+2};
`}}else T<m&&(g+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(g+=`
xCOffset = xC + 1 - ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${x}C${T+2} = vec4(0.);
}
xR${x}C${T} = vec4(
xTexelR${x}C${T}.zw, xTexelR${x}C${T+2}.zw);
`,T+1<m&&(g+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${x}C${T+1} = vec4(xTexelR${x}C${T+2}.xy, final.xy);
`)):(g+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
xCOffset = xC + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${T+2} = vec4(0.);
}
xR${x}C${T} = vec4(
xTexelR${x}C${T}.xy, xTexelR${x}C${T+2}.xy);
`,T+1<m&&(g+=`
xR${x}C${T+1} = vec4(
xTexelR${x}C${T}.zw, xTexelR${x}C${T+2}.zw);
`)),g+="}");T<m&&(g+=`
vec4 wTexelR${x}C${T} = getW(${x}, ${T}, d1, q);
wR${x}C${T} = vec4(wTexelR${x}C${T}.xz, wTexelR${x}C${T}.xz);
`,T+1<m&&(g+=`
vec4 wTexelR${x}C${T+1} = getW(${x}, ${T+1}, d1, q);
wR${x}C${T+1} =
vec4(wTexelR${x}C${T+1}.xz, wTexelR${x}C${T+1}.xz);`))}for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`dotProd += xR${x}C${N} * wR${x}C${N};`;let y="",b="";n&&(a?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`vec4 activation(vec4 x) {
${n}
}`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${g}
vec4 result = dotProd;
${v}
${b}
setOutput(result);
}
`}};function T7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a,u=l;u==null&&(u=[1,1]),k.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=E.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return te().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new VS(p):d=new WS(p),n.runWebGLProgram(d,[r,s],"float32")}var S7={kernelName:Os,backendName:"webgl",kernelFunc:T7},C7=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},_7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 E7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=E.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new C7(p);return n.runWebGLProgram(d,[r,s],"float32")}var A7={kernelName:Ed,backendName:"webgl",kernelFunc:E7};function F7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new _7(p);return n.runWebGLProgram(d,[r,s],"float32")}var $7={kernelName:Ad,backendName:"webgl",kernelFunc:F7},D7=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function M7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=ve({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new D7(s),l=n.runWebGLProgram(o,[i],i.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var R7={kernelName:Fd,backendName:"webgl",kernelFunc:M7},P7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
const ivec2 strides = ivec2(${r}, ${s});
const ivec2 pads = ivec2(${u}, ${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 * ${c};
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 O7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=E.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new P7(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var L7={kernelName:hc,backendName:"webgl",kernelFunc:O7},z7="return (x >= 0.0) ? x : (exp(x) - 1.0);",B7=`
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;
`,W7=Je({opSnippet:z7,packedOpSnippet:B7}),V7={kernelName:Bo,backendName:"webgl",kernelFunc:W7},U7="return (b >= 1.0) ? a : a * (b + 1.0);",G7=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,H7=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ip(G7,a.shape,r.shape):new hu(U7,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},j7={kernelName:Md,backendName:"webgl",kernelFunc:H7},q7=`
return vec4(equal(a, b));
`,K7="return float(a == b);",X7=rn({opSnippet:K7,packedOpSnippet:q7,dtype:"bool"}),Y7={kernelName:Vo,backendName:"webgl",kernelFunc:X7},J7=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${E.ERF_P};
float a1 = ${E.ERF_A1};
float a2 = ${E.ERF_A2};
float a3 = ${E.ERF_A3};
float a4 = ${E.ERF_A4};
float a5 = ${E.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Z7=Je({opSnippet:J7}),Q7={kernelName:Wo,backendName:"webgl",kernelFunc:Z7},US="return exp(x);",GS=Je({opSnippet:US,packedOpSnippet:US,cpuKernelImpl:J8}),eJ={kernelName:zs,backendName:"webgl",kernelFunc:GS};function Bv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ve({inputs:{x:s},backend:a,attrs:{shape:o}})}var tJ={kernelName:Uo,backendName:"webgl",kernelFunc:Bv},HS="return exp(x) - 1.0;",nJ=Je({opSnippet:HS,packedOpSnippet:HS,cpuKernelImpl:Z8}),aJ={kernelName:Go,backendName:"webgl",kernelFunc:nJ},jS=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 qS(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=ve({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new jS("real",l,t),u=new jS("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}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=hs({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=ve({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function rJ(e){let{inputs:t,backend:n}=e,{input:a}=t;return qS(a,!1,n)}var sJ={kernelName:Rd,backendName:"webgl",kernelFunc:rJ},iJ=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 Wv(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 iJ(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var oJ={kernelName:mc,backendName:"webgl",kernelFunc:Wv},lJ=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},uJ={kernelName:Ho,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new lJ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},KS="return floor(x);",cJ=Je({opSnippet:KS,packedOpSnippet:KS,cpuKernelImpl:Q8}),pJ={kernelName:Bs,backendName:"webgl",kernelFunc:cJ},dJ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,hJ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,mJ=rn({opSnippet:dJ,packedOpSnippet:hJ,dtype:"int32"}),fJ={kernelName:Ws,backendName:"webgl",kernelFunc:mJ},gJ=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},yJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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);
}
}
${t.output} = result;
}
`}},xJ={kernelName:Xd,backendName:"webgl",kernelFunc:bJ},fu;function bJ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(fu==null&&(fu=document.createElement("canvas").getContext("2d")),fu.canvas.width=c,fu.canvas.height=u,fu.drawImage(r,0,0,c,u),r=fu.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=na.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=te().getBool("WEBGL_PACK")?new yJ(d):new gJ(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function vJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=PS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=OS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let x=i!=null,N=o!=null,T=h==="leakyrelu",C=h?Wm(h,!1):null,$=new RS(g,x,C,N,T),F=[r,s];if(i&&F.push(i),o&&F.push(o),T){let O=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));F.push(O),b.push(O)}y=n.runWebGLProgram($,F,"float32")}let v=ve({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var wJ={kernelName:xi,backendName:"webgl",kernelFunc:vJ};function kJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),k.assert(E.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=E.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=te().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?Wm(d,y):null,v=[r,s],x=i!=null,N=o!=null,T=d==="leakyrelu";if(x&&v.push(i),N&&v.push(o),T){let F=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));v.push(F),m.push(F)}let C;y?C=new VS(g,x,b,N,T):C=new WS(g,x,b,N,T);let $=n.runWebGLProgram(C,v,"float32");return m.forEach(F=>n.disposeIntermediateTensorInfo(F)),$}var IJ={kernelName:vi,backendName:"webgl",kernelFunc:kJ},NJ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=gt(t.length),r=gt(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 TJ(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=E.prepareAndValidate(a,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=ve({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new NJ(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=ve({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var SJ={kernelName:qo,backendName:"webgl",kernelFunc:TJ},_J=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=gt(this.rank),a=CJ(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function CJ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function EJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=ve({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),v=n.bufferSync(d),x=eK(v,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let f=new _J(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=ve({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var AJ={kernelName:jo,backendName:"webgl",kernelFunc:EJ},FJ="return float(a > b);",$J=`
return vec4(greaterThan(a, b));
`,DJ=rn({opSnippet:FJ,packedOpSnippet:$J,cpuKernelImpl:tK,dtype:"bool"}),MJ={kernelName:Ko,backendName:"webgl",kernelFunc:DJ},RJ="return float(a >= b);",PJ=`
return vec4(greaterThanEqual(a, b));
`,OJ=rn({opSnippet:RJ,packedOpSnippet:PJ,dtype:"bool"}),LJ={kernelName:Us,backendName:"webgl",kernelFunc:OJ};function zJ(e){let{inputs:t,backend:n}=e,{input:a}=t;return qS(a,!0,n)}var BJ={kernelName:Pd,backendName:"webgl",kernelFunc:zJ},WJ="return float(!isnan(x) && !isinf(x));",VJ=Je({opSnippet:WJ,dtype:"bool"}),UJ={kernelName:Yo,backendName:"webgl",kernelFunc:VJ},GJ="return float(isinf(x));",HJ=Je({opSnippet:GJ,dtype:"bool"}),jJ={kernelName:Jo,backendName:"webgl",kernelFunc:HJ},qJ="return float(isnan(x));",KJ=Je({opSnippet:qJ,dtype:"bool"}),XJ={kernelName:Zo,backendName:"webgl",kernelFunc:KJ},YJ="return float(a < b);",JJ=`
return vec4(lessThan(a, b));
`,ZJ=rn({opSnippet:YJ,packedOpSnippet:JJ,cpuKernelImpl:nK,dtype:"bool"}),QJ={kernelName:Qo,backendName:"webgl",kernelFunc:ZJ},e9="return float(a <= b);",t9=`
return vec4(lessThanEqual(a, b));
`,n9=rn({opSnippet:e9,packedOpSnippet:t9,dtype:"bool"}),a9={kernelName:el,backendName:"webgl",kernelFunc:n9};function r9(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=aK(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var s9={kernelName:Ld,backendName:"webgl",kernelFunc:r9},i9=`if (x < 0.0) return NAN;
return log(x);`,o9=`
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;
`,l9=Je({opSnippet:i9,packedOpSnippet:o9,cpuKernelImpl:rK}),u9={kernelName:Hs,backendName:"webgl",kernelFunc:l9},c9="return log(1.0 + x);",p9=Je({opSnippet:c9}),d9={kernelName:tl,backendName:"webgl",kernelFunc:p9},h9="return float(a >= 1.0 && b >= 1.0);",m9=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,f9=rn({opSnippet:h9,packedOpSnippet:m9,dtype:"bool"}),g9={kernelName:nl,backendName:"webgl",kernelFunc:f9},y9="return float(!(x >= 1.0));",b9=Je({opSnippet:y9}),x9={kernelName:fc,backendName:"webgl",kernelFunc:b9},v9="return float(a >= 1.0 || b >= 1.0);",w9=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,k9=rn({opSnippet:v9,packedOpSnippet:w9,dtype:"bool"}),I9={kernelName:gc,backendName:"webgl",kernelFunc:k9},N9=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 * ${o};
setOutput(val);
}
`}},T9=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
}
`}},S9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=te().getBool("WEBGL_PACK_NORMALIZATION")?new T9(r.shape,s,i,o,l):new N9(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},C9={kernelName:yc,backendName:"webgl",kernelFunc:S9},_9=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);
}
`}},E9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new _9(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},A9={kernelName:zd,backendName:"webgl",kernelFunc:E9};function F9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=qi(i,e.dtype,"max",a),l=ve({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function XS(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),c=l,u=E.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,v=new Array(o);for(let T=0;T<v.length;T++)v[T]=r.shape[u[T]];let x=Mv(b,r.shape,r.dtype,u,v);h=n.makeTensorInfo(v,r.dtype);let N=n.texData.get(h.dataId);N.values=x}else h=Vm(r,u,n);c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("max",c,o);let[m,f]=E.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=E.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,v=sK(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let x=n.texData.get(y.dataId);x.values=v}else y=F9(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var $9={kernelName:js,backendName:"webgl",kernelFunc:XS},D9=dS+`
return max(a, b);
`,M9=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Bm+`
return result;
`,R9=rn({opSnippet:D9,packedOpSnippet:M9,cpuKernelImpl:iK}),P9={kernelName:qs,backendName:"webgl",kernelFunc:R9};function O9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;wp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Np(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var L9={kernelName:Ks,backendName:"webgl",kernelFunc:O9};function z9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=a,u=[1,1,1],p=E.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new Ov(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var B9={kernelName:bc,backendName:"webgl",kernelFunc:z9},W9=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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},V9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${p}, ${d});
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 < ${c};
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} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function U9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,p,c,u),h=new Ov(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new V9(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var G9={kernelName:Wd,backendName:"webgl",kernelFunc:U9};function H9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;wp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=E.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new Np(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new W9(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var j9={kernelName:Bd,backendName:"webgl",kernelFunc:H9};function q9(e,t,n,a){let r=new Np(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Np(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var K9={kernelName:Vd,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 c=[1,1];k.assert(E.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,r,s,c,i),[p,d]=q9(a,o,u,l);return[p,d]}};function X9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=qi(i,"float32","mean",a),l=ve({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Y9={kernelName:Xs,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),c=l,u=E.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let v=i.texData.get(m.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[u[C]];let N=Mv(v,a.shape,a.dtype,u,x);m=i.makeTensorInfo(x,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=Vm(a,u,i);h.push(m),c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=E.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=E.expandShapeToKeepDim(f,l));let b=X9(m,g,y,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return b}};function J9(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),c=l,u=E.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",c,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=qi(f,f.dtype,"min",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var Z9={kernelName:Ys,backendName:"webgl",kernelFunc:J9},Q9=dS+`
return min(a, b);
`,eZ=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Bm+`
return result;
`,tZ=rn({opSnippet:Q9,packedOpSnippet:eZ,cpuKernelImpl:oK}),nZ={kernelName:Js,backendName:"webgl",kernelFunc:tZ},aZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let a=e.length,r=gt(a),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).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}));
}
`}},rZ=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=gt(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),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";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;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}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;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},sZ=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rZ(a.shape,r,s):new aZ(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},iZ={kernelName:xc,backendName:"webgl",kernelFunc:sZ},oZ=`if (b == 0.0) return NAN;
return mod(a, b);`,lZ=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Bm+`
return result;
`,uZ=rn({opSnippet:oZ,packedOpSnippet:lZ}),cZ={kernelName:al,backendName:"webgl",kernelFunc:uZ},pZ=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)}}},dZ=`
if (a == b) {
return 1.0;
};
return a / b;`,hZ=`
// 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;
`,YS=rn({opSnippet:dZ,packedOpSnippet:hZ,checkOutOfBounds:!0}),mZ={kernelName:Ls,backendName:"webgl",kernelFunc:YS},JS="return a - b;",ZS=rn({opSnippet:JS,packedOpSnippet:JS,supportsComplex:!0,cpuKernelImpl:fK}),fZ={kernelName:fi,backendName:"webgl",kernelFunc:ZS};function QS(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=XS({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=E.expandShapeToKeepDim(o.shape,i),c=ve({inputs:{x:o},backend:n,attrs:{shape:l}}),u=ZS({inputs:{a:r,b:c},backend:n}),p=GS({inputs:{x:u},backend:n}),d=Pv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=ve({inputs:{x:d},backend:n,attrs:{shape:l}}),m=YS({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var gZ={kernelName:hi,backendName:"webgl",kernelFunc:QS};function yZ(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:QS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new pZ(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var bZ={kernelName:Ud,backendName:"webgl",kernelFunc:yZ},e2="return -x;";function xZ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=uK(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new du(a.shape,e2):r=new ds(a.shape,e2),n.runWebGLProgram(r,[a],a.dtype)}var vZ={kernelName:rl,backendName:"webgl",kernelFunc:xZ},wZ=Za.nonMaxSuppressionV3Impl;function kZ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,c=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=wZ(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var IZ={kernelName:il,backendName:"webgl",kernelFunc:kZ},NZ=Za.nonMaxSuppressionV4Impl;function TZ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=NZ(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var SZ={kernelName:ol,backendName:"webgl",kernelFunc:TZ},CZ=Za.nonMaxSuppressionV5Impl;function _Z(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=CZ(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var EZ={kernelName:ll,backendName:"webgl",kernelFunc:_Z},AZ=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)));
}
`}},FZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),c=new AZ(l,s,i,o),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=ve({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},$Z={kernelName:Qs,backendName:"webgl",kernelFunc:FZ};function qm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Sp({inputs:{input:a},backend:n}),s=qm({inputs:{x:r},backend:n}),i=jm({inputs:{input:a},backend:n}),o=qm({inputs:{x:i},backend:n}),l=hs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Wv({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var DZ={kernelName:Sl,backendName:"webgl",kernelFunc:qm};function t2(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=Sp({inputs:{input:a},backend:n}),s=t2({inputs:{x:r},backend:n}),i=jm({inputs:{input:a},backend:n}),o=qm({inputs:{x:i},backend:n}),l=hs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Wv({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var MZ={kernelName:ul,backendName:"webgl",kernelFunc:t2};function RZ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Bv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=Bv({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=MS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var PZ={kernelName:cl,backendName:"webgl",kernelFunc:RZ},OZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let a=e.length,r=gt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${n}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${n}));
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},LZ=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=gt(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),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
if(${c}) {
`,a===1?"":`}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
if(${c}) {`],d=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
${p[m]}
if (${d}) {
result[${m}] = float(${n});
} else {
${r} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${u});
}
`;h+=a===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},n2=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new LZ(r.shape,s,i):new OZ(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},zZ={kernelName:ei,backendName:"webgl",kernelFunc:n2},BZ=`
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);
`,WZ=`
// 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));
`+Bm+`
return result;
`,VZ=rn({opSnippet:BZ,packedOpSnippet:WZ}),UZ={kernelName:ti,backendName:"webgl",kernelFunc:VZ};function GZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],c=k.parseAxisParam(s,r.shape),u=c,p=E.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=E.getInnerMostAxes(u.length,o),l.push(d)),E.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=cK(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=E.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=eh(r.dtype),v=qi(y,b,"prod",n);h=ve({inputs:{x:v},backend:n,attrs:{shape:m}}),l.push(y),l.push(v)}if(i){l.push(h);let m=E.expandShapeToKeepDim(h.shape,c);h=ve({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var HZ={kernelName:pl,backendName:"webgl",kernelFunc:GZ},a2=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=pK(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},jZ={kernelName:vc,backendName:"webgl",kernelFunc:a2},qZ="return 1.0 / x;",KZ=Je({opSnippet:qZ}),XZ={kernelName:dl,backendName:"webgl",kernelFunc:KZ},YZ=Oa+`
return (x < 0.0) ? 0.0 : x;
`,JZ=`
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;
`,ZZ=Je({opSnippet:YZ,packedOpSnippet:JZ}),QZ={kernelName:ai,backendName:"webgl",kernelFunc:ZZ},eQ=Oa+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,tQ=`
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;
`,nQ=Je({opSnippet:eQ,packedOpSnippet:tQ}),aQ={kernelName:si,backendName:"webgl",kernelFunc:nQ},rQ=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 c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[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(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${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);
}
`}},sQ=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 c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[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(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${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 iQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new sQ(r.shape,l,c,s,i):new rQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var oQ={kernelName:ri,backendName:"webgl",kernelFunc:iQ},lQ=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],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*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(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${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 uQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new lQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var cQ={kernelName:jd,backendName:"webgl",kernelFunc:uQ},pQ=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 c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function dQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new pQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var hQ={kernelName:wc,backendName:"webgl",kernelFunc:dQ},mQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${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),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function fQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new mQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var gQ={kernelName:Hd,backendName:"webgl",kernelFunc:fQ},yQ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=gt(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},bQ=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 < ${this.outputShape[n-2]}`,i=gt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${c(a.slice())};
if(${r}) {
result.a = ${u(a.slice())};
}
}
setOutput(result);
}
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function xQ(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 Wn({inputs:{x:r},backend:n});let l=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bQ(r.shape,o):new yQ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var vQ={kernelName:ii,backendName:"webgl",kernelFunc:xQ},wQ=class{constructor(e,t,n,a){this.variableNames=["Image"],this.outputShape=[];let r=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=E.getImageCenter(a,r,s),u=l.toFixed(3),p=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
vec3 fill = vec3(${n.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - ${u}) * ${o} - (float(y) - ${p}) * ${i};
float coordYFloat = (float(x) - ${u}) * ${i} + (float(y) - ${p}) * ${o};
int coordX = int(round(coordXFloat + ${u}));
int coordY = int(round(coordYFloat + ${p}));
${d}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${r}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},kQ={kernelName:Cl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new wQ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},IQ=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,NQ=Je({opSnippet:IQ}),TQ={kernelName:oi,backendName:"webgl",kernelFunc:NQ},SQ="return inversesqrt(x);",CQ=Je({opSnippet:SQ,cpuKernelImpl:dK}),_Q={kernelName:li,backendName:"webgl",kernelFunc:CQ},r2=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`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 < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function EQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=E.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ve({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new r2(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=ve({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var AQ={kernelName:ml,backendName:"webgl",kernelFunc:EQ},FQ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);a=o.join(),r=l.join()}let s=gt(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function $Q(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new FQ(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],pa(r.dtype,s.dtype))}var DQ={kernelName:fl,backendName:"webgl",kernelFunc:$Q},MQ=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${E.SELU_SCALEALPHA};
float scale = ${E.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,RQ=Je({opSnippet:MQ}),PQ={kernelName:gl,backendName:"webgl",kernelFunc:RQ},OQ="return 1.0 / (1.0 + exp(-1.0 * x));",LQ=Je({opSnippet:OQ}),zQ={kernelName:ci,backendName:"webgl",kernelFunc:LQ},BQ=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,WQ=Je({opSnippet:BQ}),VQ={kernelName:xl,backendName:"webgl",kernelFunc:WQ},UQ=yS+`
return sin(x);
`,GQ=Je({opSnippet:UQ}),HQ={kernelName:ui,backendName:"webgl",kernelFunc:GQ},jQ=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,qQ=Je({opSnippet:jQ}),KQ={kernelName:bl,backendName:"webgl",kernelFunc:qQ},XQ=`
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;
`,YQ=Je({opSnippet:XQ}),JQ={kernelName:vl,backendName:"webgl",kernelFunc:YQ},ZQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=n2({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=E.getReshaped(u.shape,s,o,!1),d=E.getPermuted(p.length,s.length,!1),h=E.getReshapedPermuted(u.shape,s,o,!1),m=ve({inputs:{x:u},backend:n,attrs:{shape:p}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:d}}),g=ve({inputs:{x:f},backend:n,attrs:{shape:h}});return c.push(u),c.push(m),c.push(f),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},QQ={kernelName:kc,backendName:"webgl",kernelFunc:ZQ};function eee(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:c,strides:u,outputSize:p}=E.calculateShapes(s,r,o),d=!1,h=new r2(c,l,r.shape.length,s.shape.length,u,[p,1],d),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=ve({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var tee={kernelName:qd,backendName:"webgl",kernelFunc:eee};function nee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=E.prepareSplitSize(r,s,o),c=r.shape.length,u=new Array(c).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[o]=d;let m=Tp({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[o]+=d,m})}var aee={kernelName:wl,backendName:"webgl",kernelFunc:nee},ree="return sqrt(x);",see=Je({opSnippet:ree}),iee={kernelName:pi,backendName:"webgl",kernelFunc:see},oee="return x * x;",lee=Je({opSnippet:oee}),uee={kernelName:Ic,backendName:"webgl",kernelFunc:lee},s2="return (a - b) * (a - b);",cee=rn({opSnippet:s2,packedOpSnippet:s2}),pee={kernelName:mi,backendName:"webgl",kernelFunc:cee};function dee({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Oa+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new ds(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var hee={kernelName:Gr,backendName:"webgl",kernelFunc:dee},mee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=gt(n.length),s=gt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function fee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:b}=pn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),v=ve({inputs:{x:r},backend:n,attrs:{shape:y}}),x;if(h){let T=Tp({inputs:{x:v},backend:n,attrs:{begin:m,size:g}});x=ve({inputs:{x:T},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(T)}else if(b.some(T=>T===0))x=n.makeTensorInfo(b,r.dtype,[]);else if(n.shouldExecuteOnCPU([v])){let T=n.texData.get(v.dataId).values,C=Le(v.shape,v.dtype,T),$=mK(b,C,f,m);x=n.makeTensorInfo(b,v.dtype,$.values)}else{let T=new mee(m,f,b);x=n.runWebGLProgram(T,[v],v.dtype)}let N=ve({inputs:{x},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(v),n.disposeIntermediateTensorInfo(x),N}var gee={kernelName:kl,backendName:"webgl",kernelFunc:fee},yee="return tan(x);",bee=Je({opSnippet:yee}),xee={kernelName:Il,backendName:"webgl",kernelFunc:bee},vee=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,wee=Je({opSnippet:vee}),kee={kernelName:gi,backendName:"webgl",kernelFunc:wee},Nee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=gt(this.rank),r=Iee(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function Iee(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function i2(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"){let o=n.readSync(r.dataId).map(u=>k.decodeString(u)),l=Le(r.shape,r.dtype,o),c=gK(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new Nee(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var Tee={kernelName:Ur,backendName:"webgl",kernelFunc:i2};function See(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,c]=yK(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var Cee={kernelName:Nl,backendName:"webgl",kernelFunc:See};function _ee(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;wp(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=bK(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([c.length],"int32",c)]}var Eee={kernelName:Kd,backendName:"webgl",kernelFunc:_ee};function Aee(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],c=new Array(o-1),u=0;for(let f=0;f<o;f++)f!==s&&(c[u++]=i.shape[f]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){d[s]=f;let g=Tp({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),y=ve({inputs:{x:g},backend:n,attrs:{shape:c}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var Fee={kernelName:Tl,backendName:"webgl",kernelFunc:Aee},$ee=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",c=Math.floor(n/4)*4,u=n%4,p=`
sumValue += dot(values, segFilter);
`,d="";r%n>0&&(d=`
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) {
${d}
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 < ${c}; 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 + ${c};
if (${u===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
);
${p}
} else if (${u===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
);
${p}
} else if (${u===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 Dee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=eh(r.dtype),g=(x,N,T,C,$)=>{let F=x.shape[0],O=x.shape[1],W=E.segment_util.segOpComputeOptimalWindowSize(O,$),V={windowSize:W,inSize:O,batchSize:F,numSegments:$},H=new $ee(V,N),K=n.compileAndRun(H,[x,T],C);if(l.push(K),K.shape[1]===$)return K;let j=a2({backend:n,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),Y=i2({inputs:{x:j},backend:n,attrs:{reps:[O/W]}});return l.push(j),l.push(Y),g(K,N,Y,C,$)},y=g(m,"unsortedSegmentSum",s,f,i),b=ve({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=E.getUndoAxesPermutation(u);v=Sn({inputs:{x:v},backend:n,attrs:{perm:x}})}return l.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Mee={kernelName:Nc,backendName:"webgl",kernelFunc:Dee},Ree=[C9,A9,fX,yX,vX,IX,TX,_X,AX,$X,PX,LX,WX,GX,JX,qX,eY,rY,nY,lY,cY,dY,gY,IY,TY,FY,DY,OY,BY,YK,GY,e7,n7,KY,i7,l7,r7,p7,m7,y7,x7,w7,N7,A7,$7,S7,R7,L7,V7,j7,Y7,Q7,eJ,tJ,aJ,sJ,oJ,uJ,pJ,fJ,xJ,wJ,IJ,SJ,AJ,MJ,LJ,XK,BJ,UY,UJ,jJ,XJ,ZK,QJ,a9,s9,d9,u9,g9,x9,I9,$9,B9,L9,G9,j9,K9,P9,Y9,Z9,nZ,iZ,cZ,bZ,aX,vZ,IZ,SZ,EZ,CY,$Z,MZ,PZ,zZ,UZ,eX,HZ,jZ,_Y,mZ,XZ,aQ,QZ,sX,oQ,cQ,hQ,gQ,vQ,kQ,TQ,_Q,AQ,DQ,PQ,zQ,VQ,HQ,KQ,wY,gZ,JQ,QQ,tee,aee,iee,uee,pee,hee,gee,fZ,dX,xee,kee,Tee,Cee,hX,Eee,Fee,Mee,DZ];for(let e of Ree)Sc(e);var Pee="2.8.5",Oee={"tfjs-core":X0,"tfjs-backend-cpu":hG,"tfjs-backend-webgl":KK,"tfjs-data":PN,"tfjs-layers":mm,"tfjs-converter":AN,tfjs:Pee},Vn;(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"})(Vn||(Vn={}));var Cp;(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"})(Cp||(Cp={}));var o2;function Lee(e){o2=e.wasm.cwrap(bi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function zee(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:c,activation:u,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let $=n.dataIdMap.get(i.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);m=$.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Cp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=c?s.shape[1]:s.shape[2],v=r.shape[0],x=n.makeOutput([v,y,b],r.dtype),N=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(s.shape).buffer);return o2(d,T,r.shape.length,h,C,s.shape.length,l,c,g,m,f,p||0,N),x}var Bee={kernelName:bi,backendName:"wasm",setupFunc:Lee,kernelFunc:zee};function Un(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),c=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Wee=Un(Co);function gn(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:c,b:u}=l,p=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,m=E.assertAndGetBroadcastShape(c.shape,u.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),b=o.dataIdMap.get(f.dataId).id,v=()=>a(p,g,c.shape.length,d,y,u.shape.length,Vn[c.dtype],b);if(t&&c.dtype==="float32")return v(),f;let x=E.getBroadcastDims(c.shape,m),N=E.getBroadcastDims(u.shape,m),T=x.every(($,F)=>$===F),C=N.every(($,F)=>$===F);if(T&&C)return v(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Vee=!0,Uee=gn(Wr,Vee),l2;function Gee(e){l2=e.wasm.cwrap(_s,null,["array","number","number","number"])}function Hee(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 l2(s,r.length,Vn[a.dtype],i),a}var jee={kernelName:_s,backendName:"wasm",setupFunc:Gee,kernelFunc:Hee};function Km(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 qee={kernelName:Xo,backendName:"wasm",kernelFunc:Km},u2;function Kee(e){u2=e.wasm.cwrap(yi,null,["number","array","number","number","number","array","number"])}function Xm(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Yee(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Xee(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Km({inputs:t,backend:n});return m.shape=o,m}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return u2(u,h,l.shape.length,Vn[l.dtype],p,d,s.length),c}function Xee(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Yee(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Jee={kernelName:yi,backendName:"wasm",kernelFunc:Xm,setupFunc:Kee};function gu(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=E.getAxesPermutation(i,r),l=null,c=!1;if(o!=null){let u=new Array(r);for(let d=0;d<u.length;d++)u[d]=a[o[d]];i=E.getInnerMostAxes(i.length,r),l=Xm({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var c2;function Zee(e){c2=e.wasm.cwrap(Es,null,["number","number","number","number","number"])}function Qee(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:p}=gu(s,r,t);if(p){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),g=l.shape[u[0]];return c2(o,Vn[l.dtype],f,g,m),p&&t.disposeData(c.dataId),h}var ete={kernelName:Es,backendName:"wasm",kernelFunc:Qee,setupFunc:Zee},p2;function tte(e){p2=e.wasm.cwrap(As,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nte(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:c}=n,u=E.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,b=u.strideWidth,v=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let x=a.makeOutput(u.outShape,"float32"),N=a.dataIdMap.get(x.dataId).id;return p2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,N),x}var ate={kernelName:As,backendName:"wasm",setupFunc:tte,kernelFunc:nte};function La(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=k.sizeFromShape(a.shape),i=k.inferFromImplicitShape(r,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),{dataId:a.dataId,shape:i,dtype:a.dtype}}var rte={kernelName:hl,backendName:"wasm",kernelFunc:La},d2;function ste(e){d2=e.wasm.cwrap(Fs,null,["number","array","number","number","array","number","number","number","number"])}function ite(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let v=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);k.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,u,d]:[g,d,u],N=o?[y,h,p]:[y,p,h],T=La({inputs:{x:r},backend:n,attrs:{shape:x}}),C=La({inputs:{x:s},backend:n,attrs:{shape:N}}),$=n.dataIdMap.get(T.dataId).id,F=n.dataIdMap.get(C.dataId).id,O=i?T.shape[2]:T.shape[1],W=o?C.shape[1]:C.shape[2],V=Math.max(g,y),H=n.makeOutput([V,O,W],T.dtype),K=n.dataIdMap.get(H.dataId).id,j=new Uint8Array(new Int32Array(T.shape).buffer),Y=new Uint8Array(new Int32Array(C.shape).buffer);return d2($,j,T.shape.length,F,Y,C.shape.length,i,o,K),H.shape=v,H}var ote={kernelName:Fs,backendName:"wasm",setupFunc:ste,kernelFunc:ite};function Ym(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var lte={kernelName:$s,backendName:"wasm",kernelFunc:Ym},h2;function ute(e){h2=e.wasm.cwrap(Vr,null,["number","number","number","number"])}function cte(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),c=n.dataIdMap.get(l.dataId).id;return h2(o,s,i,c),l}var pte={kernelName:Vr,backendName:"wasm",setupFunc:ute,kernelFunc:cte};function m2(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return Km({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(v=>{let x=k.sizeFromShape(v.shape.slice(a));return La({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=E.computeOutShape(h.map(v=>v.shape),1);let f=h[0].shape[0]===1,g=dv(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(v=>v.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.fromStringArrayToUint8(g),i}let l=k.sizeFromShape(s[0].shape.slice(0,a)),c=0,u=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return c+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let m=h*c;for(let f=0;f<p.length;f++){let g=u[f],y=h*g,b=p[f].subarray(y,y+g);d.set(b,m),m+=g}}return i}var dte={kernelName:Po,backendName:"wasm",kernelFunc:m2},f2;function hte(e){f2=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mte(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:c,pad:u,dimRoundingMode:p,dataFormat:d}=n,h=E.convertConv2DDataFormat(d),m=E.computeConv2DInfo(r.shape,s.shape,l,c,u,p,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,b=m.padInfo.right,v=m.padInfo.bottom,x=m.padInfo.left,N=m.dilationHeight,T=m.dilationWidth,C=m.strideHeight,$=m.strideWidth,F=m.inChannels,O=m.outChannels,W=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let V=a.makeOutput(m.outShape,"float32"),H=a.dataIdMap.get(V.dataId).id;return f2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,v,x,W,N,T,C,$,F,O,H),V}var fte={kernelName:Ds,backendName:"wasm",setupFunc:hte,kernelFunc:mte},g2;function gte(e){g2=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yte(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=E.convertConv2DDataFormat(l),h=E.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:v,outChannels:x,outHeight:N,outWidth:T,strideHeight:C,strideWidth:$}=h,F=f-1-h.padInfo.top,O=g-1-h.padInfo.left,W=h.dataFormat==="channelsLast",V=k.computeStrides(h.inShape),H=k.computeStrides(r.shape),[K,j,Y]=k.computeStrides(s.shape),J=V[0],ne=W?V[1]:V[2],Q=W?V[2]:1,ie=W?1:V[1],ee=H[0],le=W?H[1]:H[2],se=W?H[2]:1,ce=W?1:H[1],de=t.makeOutput(h.inShape,"float32"),fe=t.dataIdMap.get(de.dataId).id,xe=t.dataIdMap.get(r.dataId).id,be=t.dataIdMap.get(s.dataId).id;return g2(xe,be,m,f,g,b,v,y,N,T,x,C,$,F,O,K,j,Y,J,ne,Q,ie,ee,le,se,ce,fe),de}var bte={kernelName:Ms,backendName:"wasm",setupFunc:gte,kernelFunc:yte},xte=Un(Rs),Vv;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Vv||(Vv={}));var y2;function vte(e){y2=e.wasm.cwrap(Lo,null,["number","number","number","number","array","number","number","number","number","number"])}function wte(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Ym({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return y2(g,y,b,u,N,p,d,Vv[r],s,x),f!=null&&t.disposeData(f.dataId),v}var kte={kernelName:Lo,backendName:"wasm",setupFunc:vte,kernelFunc:wte},b2;function Ite(e){b2=e.wasm.cwrap(Ps,null,["number","number","number","number","number","number"])}function Nte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=E.getAxesPermutation([s],l),u=r;c!==null&&(u=Xm({inputs:{x:r},attrs:{perm:c},backend:n}));let p=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;b2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=E.getUndoAxesPermutation(c);g=Xm({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Tte={kernelName:Ps,backendName:"wasm",setupFunc:Ite,kernelFunc:Nte},x2;function Ste(e){x2=e.wasm.cwrap(zo,null,["number","number","number","array","number","array","array","number","number"])}function Cte(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: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return x2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var _te={kernelName:zo,backendName:"wasm",setupFunc:Ste,kernelFunc:Cte},v2;function Ete(e){v2=e.wasm.cwrap(Os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ate(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=E.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,$=h.inChannels,F=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let W=a.makeOutput(h.outShape,"float32"),V=a.dataIdMap.get(W.dataId).id;return v2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,O,x,N,T,C,$,F,V),W}var Fte={kernelName:Os,backendName:"wasm",setupFunc:Ete,kernelFunc:Ate},$te=!1,Dte=gn(Vo,$te,"bool"),Mte=Un(zs);function Uv(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)}, ${i}]`),l=i+s+1),o.splice(l,0,1),La({inputs:{x:r},backend:a,attrs:{shape:o}})}var Rte={kernelName:Uo,backendName:"wasm",kernelFunc:Uv};function Pte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Ote={kernelName:mc,backendName:"wasm",kernelFunc:Pte},w2;function Lte(e){w2=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number"])}function zte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return w2(s,o,l,c,u,i),r}var Bte={kernelName:Ho,backendName:"wasm",kernelFunc:zte,setupFunc:Lte},Wte=Un(Bs),Vte=!1,Ute=gn(Ws,Vte),k2;function Gte(e){k2=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number"])}function Hte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return k2(u,p,d,h,m,r,g),f}var jte={kernelName:Vs,backendName:"wasm",setupFunc:Gte,kernelFunc:Hte},I2;function qte(e){I2=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Kte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Cp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return I2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Xte={kernelName:xi,backendName:"wasm",setupFunc:qte,kernelFunc:Kte},N2;function Yte(e){N2=e.wasm.cwrap(vi,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 Jte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Cp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Zte={kernelName:vi,backendName:"wasm",setupFunc:Yte,kernelFunc:Jte},T2;function Qte(e){T2=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number"])}function ene(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=my.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return T2(d,Vn[a.dtype],h,i,p,o,m,f),c}var tne={kernelName:qo,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},S2;function nne(e){S2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function ane(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],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=La({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=La({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return S2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var rne={kernelName:jo,backendName:"wasm",setupFunc:nne,kernelFunc:ane},sne=!1,ine=gn(Ko,sne,"bool"),one=!1,lne=gn(Us,one,"bool"),C2;function une(e){C2=e.wasm.cwrap(Gs,null,["number","number","number"])}function cne(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;C2(r,n,i)}return s}var pne={kernelName:Gs,backendName:"wasm",setupFunc:une,kernelFunc:cne},dne=!1,hne=gn(Qo,dne,"bool"),mne=!1,fne=gn(el,mne,"bool"),gne=Un(Hs),yne=!1,bne=gn(nl,yne,"bool"),_2;function xne(e){_2=e.wasm.cwrap(js,null,["number, number, number"])}function vne(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:c,axes:u,originalAxes:p,inputWasTransposed:d}=gu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",u,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;_2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var wne={kernelName:js,backendName:"wasm",setupFunc:xne,kernelFunc:vne},kne=!1,Ine=gn(qs,kne),E2;function Nne(e){E2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tne(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:c}=n,u=E.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),$=a.dataIdMap.get(C.dataId).id;return E2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,$),C}var Sne={kernelName:Ks,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne},A2;function Cne(e){A2=e.wasm.cwrap(Xs,null,["number, number, number"])}function _ne(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,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=E.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Ym({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;A2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=E.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Ene={kernelName:Xs,backendName:"wasm",setupFunc:Cne,kernelFunc:_ne},F2;function Ane(e){F2=e.wasm.cwrap(Ys,null,["number, number, number"])}function Fne(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,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;F2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var $ne={kernelName:Ys,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},Dne=!1,Mne=gn(Js,Dne),Rne=!0,Pne=gn(Zs,Rne),One=Un(rl);function Gv(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 $2;function Lne(e){$2=e.wasm.cwrap(il,"number",["number","number","number","number","number"])}function zne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=$2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Gv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Bne={kernelName:il,backendName:"wasm",setupFunc:Lne,kernelFunc:zne},D2;function Wne(e){D2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","bool"])}function Vne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=D2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Gv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Une={kernelName:ol,backendName:"wasm",setupFunc:Wne,kernelFunc:Vne},M2;function Gne(e){M2=e.wasm.cwrap(ll,"number",["number","number","number","number","number","number"])}function Hne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=M2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Gv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var jne={kernelName:ll,backendName:"wasm",setupFunc:Gne,kernelFunc:Hne},qne=!1,Kne=gn(sl,qne,"bool"),R2;function Xne(e){R2=e.wasm.cwrap(Qs,null,["number","number","number","number","number"])}function Yne(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"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return R2(u,s,i,o,c),l}var Jne={kernelName:Qs,backendName:"wasm",setupFunc:Xne,kernelFunc:Yne};function Zne(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Qne={kernelName:ul,backendName:"wasm",kernelFunc:Zne};function eae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Uv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>Uv({inputs:{input:l},backend:n,attrs:{dim:r}}));return m2({inputs:o,backend:n,attrs:{axis:r}})}var tae={kernelName:cl,backendName:"wasm",kernelFunc:eae},P2;function nae(e){P2=e.wasm.cwrap(ei,null,["number","array","number","number","array","array","number","number"])}function aae(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,c=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(p).buffer);return P2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var rae={kernelName:ei,backendName:"wasm",kernelFunc:aae,setupFunc:nae},sae=!1,iae=gn(ti,sae),O2;function oae(e){O2=e.wasm.cwrap(ni,null,["number","number","number"])}function lae(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 O2(s,i,l),o}var uae={kernelName:ni,backendName:"wasm",setupFunc:oae,kernelFunc:lae},L2;function cae(e){L2=e.wasm.cwrap(pl,null,["number","number","number","number"])}function pae(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,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=E.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;L2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var dae={kernelName:pl,backendName:"wasm",setupFunc:cae,kernelFunc:pae},hae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=fv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},mae={kernelName:vc,backendName:"wasm",kernelFunc:hae},fae=!0,gae=gn(Ls,fae),yae=Un(ai),bae=Un(si),z2;function xae(e){z2=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number"])}function vae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Ym({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return z2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var wae={kernelName:ri,backendName:"wasm",setupFunc:xae,kernelFunc:vae},B2;function kae(e){B2=e.wasm.cwrap(ii,null,["number","array","number","array","number","number"])}function Iae(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 Km({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);return B2(l,u,i.length,p,r.shape.length,c),La({inputs:{x:o},attrs:{shape:r.shape},backend:n})}var Nae={kernelName:ii,backendName:"wasm",kernelFunc:Iae,setupFunc:kae},W2;function Tae(e){W2=e.wasm.cwrap(Cl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Sae(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),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=E.getImageCenter(o,d,h),y=i===0,b=255,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return W2(c,p,d,h,m,s,f,g,x,v.length,u),l}var Cae={kernelName:Cl,backendName:"wasm",kernelFunc:Sae,setupFunc:Tae},_ae=Un(oi),Eae=Un(li),V2;function Aae(e){V2=e.wasm.cwrap(ml,null,["number","number","number","number","number","number","array","number","number"])}function Fae(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:c,sliceSize:u,strides:p,outputSize:d}=fy.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),g=t.dataIdMap.get(o.dataId).id;return V2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var $ae={kernelName:ml,backendName:"wasm",setupFunc:Aae,kernelFunc:Fae},U2;function Dae(e){U2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Mae(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,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:k.sizeFromShape(r.shape.slice(1));return U2(i,o,l,h,u),c}var Rae={kernelName:fl,backendName:"wasm",kernelFunc:Mae,setupFunc:Dae},G2;function Pae(e){G2=e.wasm.cwrap(ci,null,["number","number"])}function Oae(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||G2(a,s),r}var Lae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Pae,kernelFunc:Oae},zae=Un(ui);function Jm(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=pn.parseSliceParams(t,n,a),o=pn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),c=r.makeOutput(i,t.dtype),u=k.computeStrides(t.shape),p=r.dataIdMap.get(c.dataId);if(o){let m=pn.computeFlatOffset(s,u);return t.dtype==="string"?p.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(c).set(l.subarray(m,m+k.sizeFromShape(i))),c}if(t.dtype==="string"){let m=Am(l,s,i,t.shape,t.dtype);return p.stringBytes=m,c}let d=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)Bae(l,u[0],d,s,i);else if(h===3)Wae(l,u[0],u[1],d,s,i);else if(h===4)Vae(l,u[0],u[1],u[2],d,s,i);else{let m=Am(l,s,i,t.shape,t.dtype);d.set(m)}return c}function Bae(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+r[1]),s),s+=r[1]}}function Wae(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],c=r[2],u=o+s[0],p=l+s[1];for(let d=o;d<u;d++)for(let h=l;h<p;h++){let m=d*t+h*n+c;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function Vae(e,t,n,a,r,s,i){let o=0,l=s[0],c=s[1],u=s[2],p=l+i[0],d=c+i[1],h=u+i[2],m=s[3];for(let f=l;f<p;f++)for(let g=c;g<d;g++)for(let y=u;y<h;y++){let b=f*t+g*n+y*a+m;r.set(e.subarray(b,b+i[3]),o),o+=i[3]}}var Uae={kernelName:yl,backendName:"wasm",kernelFunc:Jm},H2;function Gae(e){H2=e.wasm.cwrap(hi,null,["number","number","number","number"])}function Hae(e){let{backend:t,inputs:{logits:n},attrs:{dim:a}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[a],l=k.sizeFromShape(n.shape)/o;return k.sizeFromShape(s.shape)===0||H2(r,i,o,l),s}var jae={kernelName:hi,backendName:"wasm",setupFunc:Gae,kernelFunc:Hae};function qae(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=k.parseAxisParam(i,r.shape)[0],l=E.prepareSplitSize(r,s,o),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(p=>{let d=[...u];d[o]=p;let h=Jm({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return c[o]+=p,h})}var Kae={kernelName:wl,backendName:"wasm",kernelFunc:qae},Xae=Un(pi),Yae=Un(Ic),Jae=!0,Zae=gn(mi,Jae),j2;function Qae(e){j2=e.wasm.cwrap(Gr,null,["number","number","number"])}function ere(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 j2(i,r,l),o}var tre={kernelName:Gr,backendName:"wasm",setupFunc:Qae,kernelFunc:ere},q2;function nre(e){q2=e.wasm.cwrap(kl,null,["number","array","number","array","array","array","array","array","number","number"])}function are(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:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,h=E.slice_util.maskToAxes(u);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let m=r.shape.length-s.length,f=E.slice_util.maskToAxes(p),g=r.shape.slice();f.forEach(F=>{s[F]=0,i[F]=1,g.splice(F,0,1)});let y=La({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:b,end:v,strides:x}=E.slice_util.getNormalizedAxes(y.shape,h,m,s,i,o,l,c,u);s=b,i=v,o=x;let N=E.slice_util.maskToAxes(d);N.forEach(F=>{i[F]=s[F]+1,o[F]=1});let T=E.slice_util.computeOutShape(s,i,o),C=T.filter((F,O)=>N.indexOf(O)===-1);if(o.every(F=>F===1)){let F=Jm({inputs:{x:r},attrs:{begin:s,size:T},backend:t});return La({inputs:{x:F},attrs:{shape:C},backend:t})}let $=t.makeOutput(C,"float32");if(!C.some(F=>F===0)){let F=t.dataIdMap.get(y.dataId).id,O=new Uint8Array(new Int32Array(k.computeStrides(y.shape)).buffer),W=new Uint8Array(new Int32Array(s).buffer),V=new Uint8Array(new Int32Array(i).buffer),H=new Uint8Array(new Int32Array(o).buffer),K=new Uint8Array(new Int32Array(C).buffer),j=new Uint8Array(new Int32Array(k.computeStrides(C)).buffer),Y=t.dataIdMap.get($.dataId).id;q2(F,O,y.shape.length,W,V,H,K,j,C.length,Y)}return La({inputs:{x:$},attrs:{shape:C},backend:t})}var rre={kernelName:kl,backendName:"wasm",setupFunc:nre,kernelFunc:are},sre=!0,ire=gn(fi,sre),K2;function ore(e){K2=e.wasm.cwrap(di,null,["number, number, number"])}function lre(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,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("sum",m,c.shape.length);let[f,g]=E.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;K2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var ure={kernelName:di,backendName:"wasm",setupFunc:ore,kernelFunc:lre},cre=Un(gi),X2;function pre(e){X2=e.wasm.cwrap(Ur,null,["number","array","number","array","number","number"])}function dre(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 d=0;d<o.length;d++)o[d]=r.shape[d]*i[d];let l=new Uint8Array(new Int32Array(r.shape).buffer),c=new Uint8Array(new Int32Array(o).buffer),u=n.makeOutput(o,r.dtype),p=n.dataIdMap.get(u.dataId).id;return X2(s,l,r.shape.length,c,o.length,Vn[u.dtype],p),u}var hre={kernelName:Ur,backendName:"wasm",setupFunc:pre,kernelFunc:dre},Y2;function mre(e){Y2=e.wasm.cwrap(Nl,null,["number","array","number","number","number","bool","number","number"])}var fre=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let c=t.makeOutput(l,a.dtype),u=t.dataIdMap.get(c.dataId).id,p=t.makeOutput(l,"int32"),d=t.dataIdMap.get(p.dataId).id;return Y2(i,o,a.shape.length,Vn[a.dtype],r,s,u,d),[c,p]},gre={kernelName:Nl,backendName:"wasm",setupFunc:mre,kernelFunc:fre};function yre(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),c=0;for(let h=0;h<o;h++)h!==s&&(l[c++]=r.shape[h]);let u=new Array(i),p=new Array(o).fill(0),d=r.shape.slice();d[s]=1;for(let h=0;h<u.length;h++)p[s]=h,u[h]=Jm({inputs:{x:r},attrs:{begin:p,size:d},backend:n});return u.map(({dataId:h,dtype:m})=>({dataId:h,dtype:m,shape:l}))}var bre={kernelName:Tl,backendName:"wasm",kernelFunc:yre};function xre(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var vre={kernelName:Sl,backendName:"wasm",kernelFunc:xre},wre=[Wee,Uee,jee,ete,ate,ote,lte,pte,dte,fte,bte,xte,kte,Tte,_te,Fte,Dte,Mte,Rte,Ote,Bte,Wte,Ute,Bee,jte,Xte,Zte,tne,rne,ine,lne,qee,pne,hne,fne,gne,bne,wne,Ine,Sne,Ene,$ne,Mne,Pne,One,Bne,Une,jne,Kne,Jne,Qne,tae,rae,iae,uae,dae,mae,gae,yae,bae,rte,wae,Nae,Cae,Eae,_ae,$ae,Rae,Lae,zae,Uae,jae,Kae,Xae,Yae,Zae,tre,rre,ire,ure,cre,hre,gre,Jee,bre,vre];for(let e of wre)Sc(e);var Hv=te();Hv.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])));Hv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Hv.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 J2=No(FE()),kre='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;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:selfThreadId})}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};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;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)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}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;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;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["dynCall_ii"](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"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){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.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");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()}}}}',Ire=No($E()),Z2=class extends sc{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new fd(this,Jr())}write(e,t,n){let a={};return this.move(a,e,t,n),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}move(e,t,n,a){let r=this.dataIdNextNumber++;if(a==="string"){let l=t;this.dataIdMap.set(e,{id:r,stringBytes:l,shape:n,dtype:a,memoryOffset:null});return}let s=k.sizeFromShape(n),i=s*k.bytesPerElement(a),o=this.wasm._malloc(i);this.dataIdMap.set(e,{id:r,memoryOffset:o,shape:n,dtype:a}),this.wasm.tfjs.registerTensor(r,s,o),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),o)}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 Nre(s.buffer,n)}disposeData(e){let t=this.dataIdMap.get(e);this.wasm._free(t.memoryOffset),this.wasm.tfjs.disposeData(t.id),this.dataIdMap.delete(e)}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let a;if(n==null)a=this.write(null,e,t);else{a={};let r=this.dataIdNextNumber++;this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t});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 ${t}`)}}};function Tre(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance)})})}),{})}function Q2(e,t,n){if(Zm!=null)return Zm;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),_p!=null&&_p[a]!=null?_p[a]:n+a}async function Sre(){let[e,t]=await Promise.all([te().getAsync("WASM_HAS_SIMD_SUPPORT"),te().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(l,c)=>{if(l.endsWith(".worker.js")){let u=kre,p=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(p)}return l.endsWith(".wasm")?Q2(e,t,Ep!=null?Ep:c):c+l},jv&&(r.instantiateWasm=Tre(Q2(e,t,Ep!=null?Ep:"")));let s;t&&e&&Zm==null?(s=J2.default(r),s.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+J2.default.toString()],{type:"text/javascript"})):s=Ire.default(r);let i=null;s.tfjs={init:s.cwrap("init",null,[]),registerTensor:s.cwrap("register_tensor",null,["number","number","number"]),disposeData:s.cwrap("dispose_data",i,["number"]),dispose:s.cwrap("dispose",i,[])};let o=!1;s.onRuntimeInitialized=()=>{o=!0,Ap=!1,n({wasm:s})},s.onAbort=()=>{o||Ap||(Ap=!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"}))}})}function Nre(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 Cre=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Zm=null,Ep=null,_p={},Ap=!1,jv=!1;function _re(e,t=!1){if(Rt("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Ap)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Zm=e,jv=t}function Ere(e,t=!1){if(Ap)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")Ep=e;else{_p=e;let n=Cre.filter(a=>_p[a]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}jv=t}var Are="2.8.5",Fre=2;oh("wasm",async()=>{let{wasm:e}=await Sre();return new Z2(e)},Fre);var bw={};dd(bw,{AnchorPosition:()=>lr,DrawBox:()=>nf,DrawBoxOptions:()=>ow,DrawFaceLandmarks:()=>yw,DrawFaceLandmarksOptions:()=>gw,DrawTextField:()=>fs,DrawTextFieldOptions:()=>Mp,drawContour:()=>_r,drawDetections:()=>Vre,drawFaceExpressions:()=>jre,drawFaceLandmarks:()=>qre});function _r(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:a,y:r},s)=>{let i=t[s];e.moveTo(i.x,i.y),e.lineTo(a,r)}),n){let a=t[t.length-1],r=t[0];if(!a||!r)return;e.moveTo(a.x,a.y),e.lineTo(r.x,r.y)}e.stroke()}var qv={};dd(qv,{computeReshapedDimensions:()=>Yv,getCenterPoint:()=>Yi,isDimensions:()=>ef,isEven:()=>Qm,isFloat:()=>Xv,isTensor:()=>Ki,isTensor1D:()=>$re,isTensor2D:()=>Kv,isTensor3D:()=>Er,isTensor4D:()=>aa,isValidNumber:()=>za,isValidProbablitiy:()=>yu,range:()=>ir,round:()=>Xi});var yn=class{constructor(t,n){if(!za(t)||!za(n))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new yn(1/this.width,1/this.height)}};function Ki(e,t){return e instanceof z&&e.shape.length===t}function $re(e){return Ki(e,1)}function Kv(e){return Ki(e,2)}function Er(e){return Ki(e,3)}function aa(e){return Ki(e,4)}function Xv(e){return e%1!=0}function Qm(e){return e%2==0}function Xi(e,t=2){let n=10**t;return Math.floor(e*n)/n}function ef(e){return e&&e.width&&e.height}function Yv({width:e,height:t},n){let a=n/Math.max(t,e);return new yn(Math.round(e*a),Math.round(t*a))}function Yi(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function ir(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function za(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function yu(e){return za(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var ct=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(za)}static assertIsValidBox(t,n,a=!1){if(!ct.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!a&&(t.width<0||t.height<0))throw new Error(`${n} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,n=!0){let a=t||{},r=[a.left,a.top,a.right,a.bottom].every(za),s=[a.x,a.y,a.width,a.height].every(za);if(!s&&!r)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(a)}`);let[i,o,l,c]=s?[a.x,a.y,a.width,a.height]:[a.left,a.top,a.right-a.left,a.bottom-a.top];ct.assertIsValidBox({x:i,y:o,width:l,height:c},"Box.constructor",n),this._x=i,this._y=o,this._width=l,this._height=c}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new De(this.left,this.top)}get topRight(){return new De(this.right,this.top)}get bottomLeft(){return new De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ct({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ct({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return a<r&&(t-=s/2,a+=s),r<a&&(n-=s/2,r+=s),new ct({x:t,y:n,width:a,height:r})}rescale(t){let n=ef(t)?t.width:t,a=ef(t)?t.height:t;return new ct({x:this.x*n,y:this.y*a,width:this.width*n,height:this.height*a})}pad(t,n){let[a,r,s,i]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new ct({x:a,y:r,width:s,height:i})}clipAtImageBorders(t,n){let{x:a,y:r,right:s,bottom:i}=this,o=Math.max(a,0),l=Math.max(r,0),c=s-o,u=i-l,p=Math.min(c,t-o),d=Math.min(u,n-l);return new ct({x:o,y:l,width:p,height:d}).floor()}shift(t,n){let{width:a,height:r}=this,s=this.x+t,i=this.y+n;return new ct({x:s,y:i,width:a,height:r})}padAtBorders(t,n){let a=this.width+1,r=this.height+1,s=1,i=1,o=a,l=r,c=this.left,u=this.top,p=this.right,d=this.bottom;return p>n&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),c<1&&(l=2-c,c=1),u<1&&(l=2-u,u=1),{dy:i,edy:l,dx:s,edx:o,y:u,ey:d,x:c,ex:p,w:a,h:r}}calibrate(t){return new ct({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var bu=class extends ct{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var ms=class{constructor(t,n,a,r,s){this._imageDims=new yn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ct(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ct(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new ms(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var vt=class extends ms{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new vt(a,r,s)}};function Jv(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function Zv(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>l<o?l:o,Infinity),r=n.reduce((o,l)=>l<o?l:o,Infinity),s=t.reduce((o,l)=>o<l?l:o,0),i=n.reduce((o,l)=>o<l?l:o,0);return new bu(a,r,s,i)}function Qv(e,t,n,a=!0){let r=t.map((i,o)=>({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let c=0;c<o.length;c++){let u=o[c],p=e[i],d=e[u];l.push(Jv(p,d,a))}r=r.filter((c,u)=>l[u]<=n)}return s}function Ba(e,t){return D(()=>{let[n,a,r]=t,s=kn([...e.shape.slice(0,3),1],n,"float32"),i=kn([...e.shape.slice(0,3),1],a,"float32"),o=kn([...e.shape.slice(0,3),1],r,"float32"),l=Qe([s,i,o],3);return ge(e,l)})}function ew(e,t=!1){return D(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,kn(h,0,"float32")},l=o(s),c=r-l.shape[i],p=[t&&c?o(c):null,e,l].filter(d=>!!d).map(d=>pe(d,"float32"));return Qe(p,i)})}function Dre(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Fp(e){return 1/(1+Math.exp(-e))}function Mre(e){return Math.log(e/(1-e))}var xu=class extends ct{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Rre=.5,Pre=.43,Ore=.45,ra=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new yn(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new De(r,s)).add(a))}get shift(){return new De(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new De(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new De(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof vt?t.box.floor():new ct(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/Ore),l=Yi(t),c=Math.floor(Math.max(0,l.x-Rre*o)),u=Math.floor(Math.max(0,l.y-Pre*o));return new xu(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=Zv(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var eC=class extends ra{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Yi([t[3],t[4]])]}};var vu=class extends ra{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Yi)}};var $p=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Xi(this.distance)})`:""}`}};var Dp=class extends ct{static assertIsValidLabeledBox(t,n){if(ct.assertIsValidBox(t,n),!za(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var Ar=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new Ar(t.label,n)}};var tC=class extends Dp{static assertIsValidPredictedBox(t,n){if(Dp.assertIsValidLabeledBox(t,n),!yu(t.score)||!yu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function or(e){return e.detection instanceof vt}function Ji(e,t){return{...e,...{detection:t}}}function tw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");let t=()=>{throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:t}}function tf(e){let t="";if(!e)try{e=require("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function nw(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},r=global.fetch,s=tf();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:a,fetch:r,...s}}function aw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var rw=dE(aC()),en;function Bre(){if(!en)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return en}function sw(e){en=e}function iw(){return aw()?sw(tw()):rw.isNodejs()?sw(nw()):null}function Wre(e){if(en||iw(),!en)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=en.Canvas,Image:n=en.Image}=e;en.Canvas=t,en.Image=n,en.createCanvasElement=e.createCanvasElement||(()=>new t),en.createImageElement=e.createImageElement||(()=>new n),en.ImageData=e.ImageData||en.ImageData,en.Video=e.Video||en.Video,en.fetch=e.fetch||en.fetch,en.readFile=e.readFile||en.readFile}var it={getEnv:Bre,setEnv:sw,initialize:iw,createBrowserEnv:tw,createFileSystem:tf,createNodejsEnv:nw,monkeyPatch:Wre,isBrowser:aw,isNodejs:rw.isNodejs};iw();function Zi(e){return!it.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Cn(e){let{Canvas:t,CanvasRenderingContext2D:n}=it.getEnv();if(e instanceof n)return e;let a=Zi(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var lr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lr||(lr={}));var Mp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||lr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},fs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof fs?t.text:t,this.anchor=n,this.options=new Mp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a===lr.BOTTOM_RIGHT||a===lr.TOP_RIGHT,s=a===lr.BOTTOM_LEFT||a===lr.BOTTOM_RIGHT,i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,c=s?this.anchor.y-o:this.anchor.y;if(n){let{width:u,height:p}=n,d=Math.max(Math.min(l,u-i),0),h=Math.max(Math.min(c,p-o),0);return{x:d,y:h}}return{x:l,y:c}}draw(t){let n=Zi(t),a=Cn(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let c=this.measureWidth(a),u=this.measureHeight();a.fillStyle=r;let p=this.getUpperLeft(a,n);a.fillRect(p.x,p.y,c,u),a.fillStyle=s,this.text.forEach((d,h)=>{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var ow=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:lr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Mp({...i,...s})}},nf=class{constructor(t,n={}){this.box=new ct(t),this.options=new ow(n)}draw(t){let n=Cn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new fs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Vre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof vt?a.score:or(a)?a.detection.score:void 0,s=a instanceof vt?a.box:or(a)?a.detection.box:new ct(a),i=r?`${Xi(r)}`:void 0;new nf(s,{label:i}).draw(e)})}function Rp(e){let{Image:t,Video:n}=it.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function lw(e){return new Promise((t,n)=>{if(e instanceof it.getEnv().Canvas||Rp(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function uw(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=it.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function Qi(e){let{Image:t,Video:n}=it.getEnv();return e instanceof t?new yn(e.naturalWidth,e.naturalHeight):e instanceof n?new yn(e.videoWidth,e.videoHeight):new yn(e.width,e.height)}function wu({width:e,height:t}){let{createCanvasElement:n}=it.getEnv(),a=n();return a.width=e,a.height=t,a}function Pp(e,t){let{ImageData:n}=it.getEnv();if(!(e instanceof n)&&!Rp(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||Qi(e),s=wu({width:a,height:r});return e instanceof n?Cn(s).putImageData(e,0,0):Cn(s).drawImage(e,0,0,a,r),s}async function cw(e,t){let n=t||it.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(aa(e)?1:0),i=D(()=>e.as3D(a,r,s).toInt());return await Ni.toPixels(i,n),i.dispose(),n}function af(e){let{Image:t,Canvas:n,Video:a}=it.getEnv();return e instanceof t||e instanceof n||e instanceof a}function pw(e,t,n=!1){let{Image:a,Canvas:r}=it.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let s=Qi(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,c=wu({width:t,height:t}),u=e instanceof r?e:Pp(e),p=Math.abs(o-l)/2,d=n&&o<l?p:0,h=n&&l<o?p:0;return Cn(c).drawImage(u,d,h,o,l),c}var Fr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Er(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(aa(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof it.getEnv().Canvas?a:Pp(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ir(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Yv({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,D(()=>{let a=ir(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof z){let o=aa(i)?i:i.expandDims();return o=ew(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ja.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof it.getEnv().Canvas)return Ni.fromPixels(pw(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>pe(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function yt(e){if(e instanceof Fr)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(Zi);return a.forEach((r,s)=>{if(!af(r)&&!Er(r)&&!aa(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(aa(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>af(r)&&lw(r))),new Fr(a,Array.isArray(e))}async function ku(e,t){let{Canvas:n}=it.getEnv(),a=e;if(!(e instanceof n)){let i=await yt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await cw(o)}let r=Cn(a);return t.map(i=>i instanceof vt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:c})=>{let u=wu({width:l,height:c});return Cn(u).putImageData(r.getImageData(i,o,l,c),0,0),u})}async function Iu(e,t){if(!Er(e)&&!aa(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(aa(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return D(()=>{let[n,a,r]=e.shape.slice(aa(e)?1:0);return t.map(o=>o instanceof vt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:c,height:u})=>Vl(e.as3D(n,a,r),[l,o,0],[u,c,r]))})}async function eo(e,t){let{fetch:n}=it.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function Ure(e){let t=await eo(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return uw(n)}async function dw(e){return(await eo(e)).json()}async function Gre(e){return new Float32Array(await(await eo(e)).arrayBuffer())}function rf(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function hw(e,t){let{manifestUri:n,modelBaseUri:a}=rf(e,t),r=await dw(n);return qt.loadWeights(r,a)}function Hre(e,t,n=!1){let{width:a,height:r}=n?Qi(t):t;return e.width=a,e.height=r,{width:a,height:r}}var sn=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof Hr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof Hr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Kn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await hw(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=it.getEnv(),{manifestUri:a,modelBaseUri:r}=rf(t,this.getDefaultModelName()),s=c=>Promise.all(c.map(u=>n(u).then(p=>p.buffer))),i=qt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof z))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function _n(e,t,n){return D(()=>{let a=Ai(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=Z(a,t.bias),a})}function sf(e,t,n=!1){return D(()=>{let a=Ye(n?Z(Ft(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):_n(e,t.conv0,[2,2])),r=_n(a,t.conv1,[1,1]),s=Ye(Z(a,r)),i=_n(s,t.conv2,[1,1]);return Ye(Z(a,Z(r,i)))})}function Op(e,t,n=!1,a=!0){return D(()=>{let r=Ye(n?Z(Ft(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):_n(e,t.conv0,a?[2,2]:[1,1])),s=_n(r,t.conv1,[1,1]),i=Ye(Z(r,s)),o=_n(i,t.conv2,[1,1]),l=Ye(Z(r,Z(s,o))),c=_n(l,t.conv3,[1,1]);return Ye(Z(r,Z(s,Z(o,c))))})}function to(e,t,n="same",a=!1){return D(()=>{let r=Z(Ft(e,t.filters,[1,1],n),t.bias);return a?Ye(r):r})}function bn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Nu(e,t){return(n,a,r,s)=>{let i=Ea(e(n*a*r*r),[r,r,n,a]),o=tt(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function of(e,t){return(n,a,r)=>{let s=_a(e(n*a),[n,a]),i=tt(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var lf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function Tu(e,t){return(n,a,r)=>{let s=Ea(e(3*3*n),[3,3,n,1]),i=Ea(e(n*a),[1,1,n,a]),o=tt(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new lf(s,i,o)}}function Su(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new lf(n,a,r)}}function Gn(e,t){return(n,a,r)=>{let s=e[n];if(!Ki(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function xn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function uf(e,t){let n=Nu(e,t),a=Tu(e,t);function r(i,o,l,c=!1){let u=c?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:u,conv1:p,conv2:d}}function s(i,o,l,c=!1){let{conv0:u,conv1:p,conv2:d}=r(i,o,l,c),h=a(o,o,`${l}/conv3`);return{conv0:u,conv1:p,conv2:d,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function rC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock4Params:r}=uf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function cf(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function pf(e,t){let n=Gn(e,t),a=cf(n),r=Su(n);function s(o,l=!1){let c=l?a(`${o}/conv0`):r(`${o}/conv0`),u=r(`${o}/conv1`),p=r(`${o}/conv2`);return{conv0:c,conv1:u,conv2:p}}function i(o,l=!1){let c=l?a(`${o}/conv0`):r(`${o}/conv0`),u=r(`${o}/conv1`),p=r(`${o}/conv2`),d=r(`${o}/conv3`);return{conv0:c,conv1:u,conv2:p,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function sC(e){let t=[],{extractDenseBlock4Params:n}=pf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return bn(e,t),{params:a,paramMappings:t}}var Lp=class extends sn{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=Ba(a,[122.782,117.001,104.298]).div(he(255)),i=Op(s,n.dense0,!0);return i=Op(i,n.dense1),i=Op(i,n.dense2),i=Op(i,n.dense3),i=Yn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await yt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return sC(t)}extractParams(t){return rC(t)}};function zp(e,t){return D(()=>Z(ze(e,t.weights),t.bias))}function iC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=xn(e),o=of(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function oC(e){let t=[],n=Gn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return bn(e,t),{params:r,paramMappings:t}}function df(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Bp=class extends sn{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof Fr?this.faceFeatureExtractor.forwardInput(t):t;return zp(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return iC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=df(t);return this.faceFeatureExtractor.loadFromWeightMap(n),oC(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var mw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],gs=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);mw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return mw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var hf=class extends Bp{constructor(t=new Lp){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Ca(this.runNet(t)))}async forward(t){return this.forwardInput(await yt(t))}async predictExpressions(t){let n=await yt(t),a=await this.forwardInput(n),r=await Promise.all(ht(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new gs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function fw(e){return e.expressions instanceof gs}function mf(e,t){return{...e,...{expressions:t}}}function jre(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof gs?s:fw(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=or(s)?s.detection.box.bottomLeft:a||new De(0,0);new fs(l.map(p=>`${p.expression} (${Xi(p.probability)})`),c).draw(e)})}function no(e){return or(e)&&e.landmarks instanceof ra&&e.unshiftedLandmarks instanceof ra&&e.alignedRect instanceof vt}function Cu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new vt(e.detection.score,r.rescale(s.reverse()),s);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i}}}var gw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},yw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new gw(n)}draw(t){let n=Cn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof vu&&(n.strokeStyle=i,n.lineWidth=s,_r(n,this.faceLandmarks.getJawOutline()),_r(n,this.faceLandmarks.getLeftEyeBrow()),_r(n,this.faceLandmarks.getRightEyeBrow()),_r(n,this.faceLandmarks.getNose()),_r(n,this.faceLandmarks.getLeftEye(),!0),_r(n,this.faceLandmarks.getRightEye(),!0),_r(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let c=u=>{n.beginPath(),n.arc(u.x,u.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(c)}}};function qre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ra?a:no(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new yw(r).draw(e)})}var lC="0.11.5";function Kre(e,t){let n=Nu(e,t),a=Tu(e,t);function r(i,o,l){let c=a(i,o,`${l}/separable_conv0`),u=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:u,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),c=a(i,i,`${o}/separable_conv1`),u=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function uC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=xn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Kre(a,n),c=s(3,32,3,"entry_flow/conv_in"),u=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:c,reduction_block_0:u,reduction_block_1:p},h={};ir(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Xre(e,t){let n=Gn(e,t),a=cf(n),r=Su(n);function s(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:c,expansion_conv:u}}function i(o){let l=r(`${o}/separable_conv0`),c=r(`${o}/separable_conv1`),u=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function cC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Xre(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),c=s("entry_flow/reduction_block_1"),u={conv_in:o,reduction_block_0:l,reduction_block_1:c},p={};ir(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return bn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function pC(e,t,n){return Z(Ft(e,t.filters,n,"same"),t.bias)}function xw(e,t,n=!0){let a=n?Ye(e):e;return a=_n(a,t.separable_conv0,[1,1]),a=_n(Ye(a),t.separable_conv1,[1,1]),a=$t(a,[3,3],[2,2],"same"),a=Z(a,pC(e,t.expansion_conv,[2,2])),a}function Yre(e,t){let n=_n(Ye(e),t.separable_conv0,[1,1]);return n=_n(Ye(n),t.separable_conv1,[1,1]),n=_n(Ye(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var vw=class extends sn{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=Ba(a,[122.782,117.001,104.298]).div(he(256)),i=Ye(pC(s,n.entry_flow.conv_in,[2,2]));return i=xw(i,n.entry_flow.reduction_block_0,!1),i=xw(i,n.entry_flow.reduction_block_1),ir(this._numMainBlocks,0,1).forEach(o=>{i=Yre(i,n.middle_flow[`main_block_${o}`])}),i=xw(i,n.exit_flow.reduction_block),i=Ye(_n(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await yt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return cC(t,this._numMainBlocks)}extractParams(t){return uC(t,this._numMainBlocks)}};function dC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),r=of(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function hC(e){let t=[],n=Gn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return bn(e,t),{params:r,paramMappings:t}}var $r;(function(e){e.FEMALE="female",e.MALE="male"})($r||($r={}));var ff=class extends sn{constructor(t=new vw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof Fr?this.faceFeatureExtractor.forwardInput(t):t,r=Yn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=zp(r,n.fc.age).as1D(),i=zp(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return D(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Ca(a)}})}async forward(t){return this.forwardInput(await yt(t))}async predictAgeAndGender(t){let n=await yt(t),a=await this.forwardInput(n),r=ht(a.age),s=ht(a.gender),i=r.map((l,c)=>({ageTensor:l,genderTensor:s[c]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:c})=>{let u=(await l.data())[0],p=(await c.data())[0],d=p>.5,h=d?$r.MALE:$r.FEMALE,m=d?p:1-p;return l.dispose(),c.dispose(),{age:u,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return dC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=df(t);return this.faceFeatureExtractor.loadFromWeightMap(n),hC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var Wp=class extends Bp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return D(()=>{let i=(p,d)=>Dt([kn([68],p,"float32"),kn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return d(h,m)?Math.abs(h-m)/2:0},l=p=>o(p,(d,h)=>d<h),c=p=>o(p,(d,h)=>h<d);return t.mul(kn([s,136],n,"float32")).sub(Dt(Array.from(Array(s),(p,d)=>i(l(d),c(d))))).div(Dt(Array.from(Array(s),(p,d)=>i(r[d].width,r[d].height))))})}forwardInput(t){return D(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await yt(t))}async detectLandmarks(t){let n=await yt(t),a=D(()=>ht(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(await s.data()),l=o.filter((u,p)=>Qm(p)),c=o.filter((u,p)=>!Qm(p));return new vu(Array(68).fill(0).map((u,p)=>new De(l[p],c[p])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var _u=class extends Wp{constructor(t=new Lp){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function mC(e){let t=[],{extractDenseBlock3Params:n}=pf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return bn(e,t),{params:a,paramMappings:t}}function fC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock3Params:r}=uf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var ww=class extends sn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=Ba(a,[122.782,117.001,104.298]).div(he(255)),i=sf(s,n.dense0,!0);return i=sf(i,n.dense1),i=sf(i,n.dense2),i=Yn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await yt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return mC(t)}extractParams(t){return fC(t)}};var gf=class extends Wp{constructor(t=new ww){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var gC=class extends _u{};function yC(e,t){return Z(L(e,t.weights),t.biases)}function kw(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Ft(e,s,n,r);return o=Z(o,i),o=yC(o,t.scale),a?Ye(o):o}function bC(e,t){return kw(e,t,[1,1],!0)}function Iw(e,t){return kw(e,t,[1,1],!1)}function yf(e,t){return kw(e,t,[2,2],!0,"valid")}function Jre(e,t){function n(o,l,c){let u=e(o),p=u.length/(l*c*c);if(Xv(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${u.length}, numFilters: ${l}, filterSize: ${c}`);return D(()=>Ue(Ea(u,[l,p,c,c]),[2,3,1,0]))}function a(o,l,c,u){let p=n(o,l,c),d=tt(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/bias`}),{filters:p,bias:d}}function r(o,l){let c=tt(e(o)),u=tt(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:c,biases:u}}function s(o,l,c,u){let p=a(o,l,c,`${u}/conv`),d=r(l,`${u}/scale`);return{conv:p,scale:d}}function i(o,l,c,u,p=!1){let d=s((p?.5:1)*o,l,c,`${u}/conv1`),h=s(o,l,c,`${u}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function xC(e){let{extractWeights:t,getRemainingWeights:n}=xn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=Jre(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),c=s(9216,32,3,"conv32_3"),u=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),v=s(589824,256,3,"conv256_2"),x=s(589824,256,3,"conv256_down_out"),N=D(()=>Ue(_a(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:c,conv64_down:u,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:v,conv256_down_out:x,fc:N},paramMappings:a}}function Zre(e,t){let n=Gn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),c=a(i);return{conv:{filters:o,bias:l},scale:c}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function vC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Zre(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),c=a("conv64_1"),u=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:v}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!Kv(v))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${v}`);let x={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:c,conv64_2:u,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:v};return bn(e,t),{params:x,paramMappings:t}}function Wa(e,t){let n=bC(e,t.conv1);return n=Iw(n,t.conv2),n=Z(n,e),n=Ye(n),n}function Vp(e,t){let n=yf(e,t.conv1);n=Iw(n,t.conv2);let a=Yn(e,2,2,"valid"),r=bt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=bt(o);n=Qe([n,l],1);let c=[...n.shape];c[2]=1;let u=bt(c);n=Qe([n,u],2)}return a=s?Qe([a,r],3):a,n=Z(a,n),n=Ye(n),n}var Eu=class extends sn{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(150,!0),"float32"),s=Ba(a,[122.782,117.001,104.298]).div(he(256)),i=yf(s,n.conv32_down);i=$t(i,3,2,"valid"),i=Wa(i,n.conv32_1),i=Wa(i,n.conv32_2),i=Wa(i,n.conv32_3),i=Vp(i,n.conv64_down),i=Wa(i,n.conv64_1),i=Wa(i,n.conv64_2),i=Wa(i,n.conv64_3),i=Vp(i,n.conv128_down),i=Wa(i,n.conv128_1),i=Wa(i,n.conv128_2),i=Vp(i,n.conv256_down),i=Wa(i,n.conv256_1),i=Wa(i,n.conv256_2),i=Vp(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await yt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)==null?void 0:s.some(i=>i<=0))return new Float32Array(128);let n=await yt(t),a=D(()=>ht(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return vC(t)}extractParams(t){return xC(t)}};function Qre(e){let t=new Eu;return t.extractWeights(e),t}function bf(e,t){return{...e,...{descriptor:t}}}function ese(e){return typeof e.age=="number"}function xf(e,t){return{...e,...{age:t}}}function tse(e){return(e.gender===$r.MALE||e.gender===$r.FEMALE)&&yu(e.genderProbability)}function vf(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function nse(e,t){function n(l,c){let u=Ea(e(3*3*l),[3,3,l,1]),p=tt(e(l)),d=tt(e(l)),h=tt(e(l)),m=tt(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/batch_norm_scale`},{paramPath:`${c}/batch_norm_offset`},{paramPath:`${c}/batch_norm_mean`},{paramPath:`${c}/batch_norm_variance`}),{filters:u,batch_norm_scale:p,batch_norm_offset:d,batch_norm_mean:h,batch_norm_variance:m}}function a(l,c,u,p,d){let h=Ea(e(l*c*u*u),[u,u,l,c]),m=tt(e(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/${d?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,c,u,p){let{filters:d,bias:h}=a(l,c,u,p,!0);return{filters:d,batch_norm_offset:h}}function s(l,c,u){let p=n(l,`${u}/depthwise_conv`),d=r(l,c,1,`${u}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),c=s(32,64,"mobilenetv1/conv_1"),u=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),v=s(512,512,"mobilenetv1/conv_11"),x=s(512,1024,"mobilenetv1/conv_12"),N=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:c,conv_2:u,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:v,conv_12:x,conv_13:N}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),c=r(256,512,3,"prediction_layer/conv_1"),u=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),v=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),x=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),N=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),T=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),C=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),F=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),O=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),W=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:c,conv_2:u,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:v},box_predictor_2:{box_encoding_predictor:x,class_predictor:N},box_predictor_3:{box_encoding_predictor:T,class_predictor:C},box_predictor_4:{box_encoding_predictor:$,class_predictor:F},box_predictor_5:{box_encoding_predictor:O,class_predictor:W}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function wC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=nse(n,t),i=r(),o=s(),c={extra_dim:rh(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:c},paramMappings:t}}function ase(e,t){let n=Gn(e,t);function a(c,u,p){let d=n(`${c}/Conv2d_${u}_pointwise/weights`,4,`${p}/filters`),h=n(`${c}/Conv2d_${u}_pointwise/convolution_bn_offset`,1,`${p}/batch_norm_offset`);return{filters:d,batch_norm_offset:h}}function r(c){let u=`mobilenetv1/conv_${c}`,p=`MobilenetV1/Conv2d_${c}_depthwise`,d=`${u}/depthwise_conv`,h=`${u}/pointwise_conv`,m=n(`${p}/depthwise_weights`,4,`${d}/filters`),f=n(`${p}/BatchNorm/gamma`,1,`${d}/batch_norm_scale`),g=n(`${p}/BatchNorm/beta`,1,`${d}/batch_norm_offset`),y=n(`${p}/BatchNorm/moving_mean`,1,`${d}/batch_norm_mean`),b=n(`${p}/BatchNorm/moving_variance`,1,`${d}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",c,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(c,u){let p=n(`${c}/weights`,4,`${u}/filters`),d=n(`${c}/biases`,1,`${u}/bias`);return{filters:p,bias:d}}function o(c){let u=i(`Prediction/BoxPredictor_${c}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${c}/box_encoding_predictor`),p=i(`Prediction/BoxPredictor_${c}/ClassPredictor`,`prediction_layer/box_predictor_${c}/class_predictor`);return{box_encoding_predictor:u,class_predictor:p}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function kC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=ase(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Er(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return bn(e,t),{params:s,paramMappings:t}}function va(e,t,n){return D(()=>{let a=Ft(e,t.filters,n,"same");return a=Z(a,t.batch_norm_offset),Xt(a,0,6)})}var rse=.0010000000474974513;function sse(e,t,n){return D(()=>{let a=yr(e,t.filters,n,"same");return a=gr(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,rse),Xt(a,0,6)})}function ise(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function IC(e,t){return D(()=>{let n,a=va(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=ise(o);a=sse(a,s.depthwise_conv,l),a=va(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function ose(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),c=Math.min(a[n][1],a[n][3]),u=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(u-l)*(p-c);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,c),g=Math.min(i,u),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function NC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((u,p)=>({score:u,boxIndex:p})).filter(u=>u.score>r).sort((u,p)=>p.score-u.score),l=u=>u<=a?1:0,c=[];return o.forEach(u=>{if(c.length>=i)return;let p=u.score;for(let d=c.length-1;d>=0;--d){let h=ose(e,u.boxIndex,c[d]);if(h!==0&&(u.score*=l(h),u.score<=r))break}p===u.score&&c.push(u.boxIndex)}),c}function lse(e){let t=ht(Ue(e,[1,0])),n=[ge(t[2],t[0]),ge(t[3],t[1])],a=[Z(t[0],we(n[0],he(2))),Z(t[1],we(n[1],he(2)))];return{sizes:n,centers:a}}function use(e,t){let{sizes:n,centers:a}=lse(e),r=ht(Ue(t,[1,0])),s=we(L(dn(we(r[2],he(5))),n[0]),he(2)),i=Z(L(we(r[0],he(10)),n[0]),a[0]),o=we(L(dn(we(r[3],he(5))),n[1]),he(2)),l=Z(L(we(r[1],he(10)),n[1]),a[1]);return Ue(Dt([ge(i,s),ge(l,o),Z(i,s),Z(l,o)]),[1,0])}function TC(e,t,n){return D(()=>{let a=e.shape[0],r=use(q(qa(n.extra_dim,[a,1,1]),[-1,4]),q(e,[-1,4]));r=q(r,[a,r.shape[0]/a,4]);let s=da(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=q(i,[a,i.shape[1]]);let o=ht(r),l=ht(i);return{boxes:o,scores:l}})}function ao(e,t){return D(()=>{let n=e.shape[0],a=q(to(e,t.box_encoding_predictor),[n,-1,1,4]),r=q(to(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function SC(e,t,n){return D(()=>{let a=va(e,n.conv_0,[1,1]),r=va(a,n.conv_1,[2,2]),s=va(r,n.conv_2,[1,1]),i=va(s,n.conv_3,[2,2]),o=va(i,n.conv_4,[1,1]),l=va(o,n.conv_5,[2,2]),c=va(l,n.conv_6,[1,1]),u=va(c,n.conv_7,[2,2]),p=ao(t,n.box_predictor_0),d=ao(e,n.box_predictor_1),h=ao(r,n.box_predictor_2),m=ao(i,n.box_predictor_3),f=ao(l,n.box_predictor_4),g=ao(u,n.box_predictor_5),y=Qe([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Qe([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var wa=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ro=class extends sn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(512,!1),"float32"),r=ge(L(a,he(.007843137718737125)),he(1)),s=IC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=SC(s.out,s.conv11,n.prediction_layer);return TC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await yt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new wa(n),s=await yt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let v=1;v<i.length;v++)i[v].dispose(),o[v].dispose();let u=Array.from(await c.data()),d=NC(l,u,a,.5,r),h=s.getReshapedInputDimensions(0),m=s.inputSize,f=m/h.width,g=m/h.height,y=l.arraySync(),b=d.map(v=>{let[x,N]=[Math.max(0,y[v][0]),Math.min(1,y[v][2])].map($=>$*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map($=>$*f);return new vt(u[v],new xu(T,x,C-T,N-x),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return kC(t)}extractParams(t){return wC(t)}};function CC(e){let t=new ro;return t.extractWeights(e),t}function cse(e){return CC(e)}var _C=class extends ro{};var EC=.4,AC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],FC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],$C=[117.001,114.697,97.404],DC="tiny_yolov2_model",MC="tiny_yolov2_separable_conv_model";var wf=e=>typeof e=="number";function Nw(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!wf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>wf(t.x)&&wf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(wf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Au(e){return D(()=>{let t=L(e,he(.10000000149011612));return Z(Ye(ge(e,t)),t)})}function Dr(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=ge(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Au(n)})}function Mr(e,t){return D(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ai(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Au(n)})}function pse(e,t){let n=Nu(e,t);function a(i,o){let l=tt(e(i)),c=tt(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=Tu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function RC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=pse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,W=v?c(b,v,"conv7"):void 0,V=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),W=l(b,v,"conv7"),V=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function dse(e,t){let n=Gn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function PC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=dse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var ur=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Tw=class extends sn{constructor(t){super("TinyYolov2");Nw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Dr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(to(t,n.conv0,"valid",!1)):Mr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Mr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Mr(a,n.conv6):a,a=n.conv7?Mr(a,n.conv7):a,to(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=pe(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ba(r,this.config.meanRgb):r,r=r.div(he(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await yt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await yt(t),i=await this.forwardInput(s,a),o=D(()=>ht(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return Qv(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return PC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Tw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return RC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ca(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):he(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Fp(f[y][b][v][0]);if(!a||x>a){let N=(b+Fp(g[y][b][v][0]))/c*o,T=(y+Fp(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,W={row:y,col:b,anchor:v},{classScore:V,label:H}=this.withClassScores?await this.extractPredictedClass(h,W):{classScore:1,label:0};m.push({box:new bu(F,O,F+C,O+$),score:x,classScore:x*V,label:H,...W})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Fu=Tw;Fu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $u=class extends Fu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:EC,classes:["face"],...t?{anchors:FC,meanRgb:$C}:{anchors:AC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?MC:DC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function hse(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var kf=class extends ur{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ka=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof z?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof z&&l.dispose()),o}async function Du(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var OC=.4,LC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],zC=[117.001,114.697,97.404];var Mu=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:OC,classes:["face"],anchors:LC,meanRgb:zC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new ro,tinyFaceDetector:new Mu,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new gf,faceRecognitionNet:new Eu,faceExpressionNet:new hf,ageGenderNet:new ff},BC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),mse=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),fse=(e,t)=>nt.tinyYolov2.locateFaces(e,t),WC=e=>nt.faceLandmark68Net.detectLandmarks(e),gse=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),yse=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),bse=e=>nt.faceExpressionNet.predictExpressions(e),xse=e=>nt.ageGenderNet.predictAgeAndGender(e),VC=e=>nt.ssdMobilenetv1.load(e),vse=e=>nt.tinyFaceDetector.load(e),wse=e=>nt.tinyYolov2.load(e),kse=e=>nt.faceLandmark68Net.load(e),Ise=e=>nt.faceLandmark68TinyNet.load(e),Nse=e=>nt.faceRecognitionNet.load(e),Tse=e=>nt.faceExpressionNet.load(e),Sse=e=>nt.ageGenderNet.load(e),Cse=VC,_se=BC,Ese=WC;var Sw=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Sw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>mf(a,n[r]))}withAgeAndGender(){return new Ru(this,this.input)}},Lu=class extends Sw{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return mf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},lo=class extends Ou{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Lu{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Cw=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ru=class extends Cw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return xf(vf(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Cw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Du(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return xf(vf(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},io=class extends Ru{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Pu{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var If=class extends ka{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends If{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>bf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends If{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return bf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var Nf=class extends ka{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},Tf=class extends Nf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof z?await Iu(this.input,n):await ku(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof z&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},Sf=class extends Nf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof z?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof z&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Cf=class extends ka{constructor(t,n=new wa){super();this.input=t;this.options=n}},Up=class extends Cf{async run(){let{input:t,options:n}=this,a=n instanceof kf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof wa?r=>nt.ssdMobilenetv1.locateFaces(r,n):n instanceof ur?r=>nt.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Ji({},a)))})}withFaceLandmarks(t=!1){return new Tf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ru(this.runAndExtendWithFaceDetections(),this.input)}},_f=class extends Cf{async run(){let t=await new Up(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Ji({},n):void 0)})}withFaceLandmarks(t=!1){return new Sf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function Ase(e,t=new wa){return new _f(e,t)}function Ef(e,t=new wa){return new Up(e,t)}async function UC(e,t){return Ef(e,new wa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Fse(e,t={}){return Ef(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var $se=UC;function _w(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Af=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Ar)return i;if(i instanceof Float32Array)return new Ar(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Ar(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>_w(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new $p(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new $p("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Ar.fromJSON(a));return new Af(n,t.distanceThreshold)}};function Dse(e){let t=new Mu;return t.extractWeights(e),t}function GC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>GC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof vt?e.forSize(n,a):e}var Mse=typeof process!="undefined",Rse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Pse={faceapi:lC,node:Mse,browser:Rse};export{ff as AgeGenderNet,bu as BoundingBox,ct as Box,ka as ComposableTask,ys as ComputeAllFaceDescriptorsTask,If as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,Tf as DetectAllFaceLandmarksTask,Up as DetectAllFacesTask,Nf as DetectFaceLandmarksTaskBase,Cf as DetectFacesTaskBase,Sf as DetectSingleFaceLandmarksTask,_f as DetectSingleFaceTask,yn as Dimensions,mw as FACE_EXPRESSION_LABELS,vt as FaceDetection,_C as FaceDetectionNet,hf as FaceExpressionNet,gs as FaceExpressions,_u as FaceLandmark68Net,gf as FaceLandmark68TinyNet,gC as FaceLandmarkNet,ra as FaceLandmarks,eC as FaceLandmarks5,vu as FaceLandmarks68,$p as FaceMatch,Af as FaceMatcher,Eu as FaceRecognitionNet,$r as Gender,Dp as LabeledBox,Ar as LabeledFaceDescriptors,Fr as NetInput,sn as NeuralNetwork,ms as ObjectDetection,De as Point,tC as PredictedBox,xu as Rect,ro as SsdMobilenetv1,wa as SsdMobilenetv1Options,Mu as TinyFaceDetector,kf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,$se as allFaces,UC as allFacesSsdMobilenetv1,Fse as allFacesTinyYolov2,lw as awaitMediaLoaded,uw as bufferToImage,yse as computeFaceDescriptor,wu as createCanvas,Pp as createCanvasFromMedia,cse as createFaceDetectionNet,Qre as createFaceRecognitionNet,CC as createSsdMobilenetv1,Dse as createTinyFaceDetector,hse as createTinyYolov2,Ef as detectAllFaces,WC as detectFaceLandmarks,gse as detectFaceLandmarksTiny,Ese as detectLandmarks,Ase as detectSingleFace,bw as draw,it as env,_w as euclideanDistance,xf as extendWithAge,bf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,mf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,vf as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,Ure as fetchImage,dw as fetchJson,Gre as fetchNetWeights,eo as fetchOrThrow,Cn as getContext2dOrThrow,Qi as getMediaDimensions,cw as imageTensorToCanvas,pw as imageToSquare,Mre as inverseSigmoid,Jv as iou,af as isMediaElement,Rp as isMediaLoaded,ese as isWithAge,or as isWithFaceDetection,fw as isWithFaceExpressions,no as isWithFaceLandmarks,tse as isWithGender,Sse as loadAgeGenderModel,Cse as loadFaceDetectionModel,Tse as loadFaceExpressionModel,kse as loadFaceLandmarkModel,Ise as loadFaceLandmarkTinyModel,Nse as loadFaceRecognitionModel,VC as loadSsdMobilenetv1Model,vse as loadTinyFaceDetectorModel,wse as loadTinyYolov2Model,hw as loadWeightMap,_se as locateFaces,Hre as matchDimensions,Zv as minBbox,nt as nets,Qv as nonMaxSuppression,Ba as normalize,ew as padToSquare,xse as predictAgeAndGender,bse as recognizeFaceExpressions,GC as resizeResults,Zi as resolveInput,Dre as shuffleArray,Fp as sigmoid,BC as ssdMobilenetv1,Bg as tf,mse as tinyFaceDetector,fse as tinyYolov2,yt as toNetInput,qv as utils,Nw as validateConfig,Pse as version};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=face-api.esm.js.map
Reference in New Issue View Git Blame Copy Permalink