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

var r_=Object.create,dd=Object.defineProperty,s_=Object.getPrototypeOf,i_=Object.prototype.hasOwnProperty,o_=Object.getOwnPropertyNames,l_=Object.getOwnPropertyDescriptor;var Lw=e=>dd(e,"__esModule",{value:!0});var u_=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),hd=(e,t)=>{Lw(e);for(var n in t)dd(e,n,{get:t[n],enumerable:!0})},c_=(e,t,n)=>{if(Lw(e),t&&typeof t=="object"||typeof t=="function")for(let a of o_(t))!i_.call(e,a)&&a!=="default"&&dd(e,a,{get:()=>t[a],enumerable:!(n=l_(t,a))||n.enumerable});return e},p_=e=>e&&e.__esModule?e:c_(dd(e!=null?r_(s_(e)):{},"default",{value:e,enumerable:!0}),e);var tC=u_((Mre,eC)=>{hd(Mre,{isNodejs:()=>Rre});function Rre(){return typeof global=="object"&&!0&&typeof eC!="undefined"&&typeof process!="undefined"&&!!process.version}});var zg={};hd(zg,{Abs:()=>Co,Acos:()=>Eo,Acosh:()=>_o,AdadeltaOptimizer:()=>zh,AdagradOptimizer:()=>Bh,AdamOptimizer:()=>Wh,AdamaxOptimizer:()=>Vh,Add:()=>Ur,AddN:()=>_s,All:()=>vd,Any:()=>wd,ArgMax:()=>Fs,ArgMin:()=>sc,Asin:()=>Fo,Asinh:()=>Ao,Atan:()=>$o,Atan2:()=>Mo,Atanh:()=>Do,AvgPool:()=>As,AvgPool3D:()=>ic,AvgPool3DGrad:()=>Id,AvgPoolGrad:()=>kd,BackendWasm:()=>Y2,BatchMatMul:()=>$s,BatchToSpaceND:()=>oc,Bincount:()=>Nd,BroadcastTo:()=>n0,Callback:()=>JI,CallbackList:()=>X1,Cast:()=>Ds,Ceil:()=>Ro,ClipByValue:()=>Gr,Complex:()=>Td,ComplexAbs:()=>lc,Concat:()=>Po,Conv2D:()=>Ms,Conv2DBackpropFilter:()=>Sd,Conv2DBackpropInput:()=>Rs,Conv3D:()=>uc,Conv3DBackpropFilterV2:()=>Cd,Conv3DBackpropInputV2:()=>Ed,Cos:()=>Ps,Cosh:()=>Oo,CropAndResize:()=>Lo,Cumsum:()=>Os,CustomCallback:()=>J1,DataStorage:()=>gd,DenseBincount:()=>_d,DepthToSpace:()=>zo,DepthwiseConv2dNative:()=>Ls,DepthwiseConv2dNativeBackpropFilter:()=>Fd,DepthwiseConv2dNativeBackpropInput:()=>Ad,Diag:()=>$d,Dilation2D:()=>cc,Dilation2DBackpropFilter:()=>Md,Dilation2DBackpropInput:()=>Dd,ENV:()=>Ug,EarlyStopping:()=>QI,Elu:()=>Bo,EluGrad:()=>Rd,Environment:()=>Qw,Equal:()=>Vo,Erf:()=>Wo,Exp:()=>Bs,ExpandDims:()=>Uo,Expm1:()=>Go,FFT:()=>Pd,Fill:()=>pc,FlipLeftRight:()=>Ho,Floor:()=>Ws,FloorDiv:()=>Vs,FromPixels:()=>Yd,FusedBatchNorm:()=>Us,FusedConv2D:()=>vi,FusedDepthwiseConv2D:()=>wi,GatherNd:()=>qo,GatherV2:()=>jo,GraphModel:()=>EN,Greater:()=>Ko,GreaterEqual:()=>Gs,History:()=>Y1,IFFT:()=>Od,Identity:()=>Xo,Imag:()=>Ld,InputSpec:()=>Zt,IsFinite:()=>Yo,IsInf:()=>Jo,IsNan:()=>Zo,KernelBackend:()=>nc,LRN:()=>mc,LRNGrad:()=>Bd,LayerVariable:()=>G1,LayersModel:()=>Sr,LeakyRelu:()=>Hs,Less:()=>Qo,LessEqual:()=>el,LinSpace:()=>zd,Log:()=>js,Log1p:()=>tl,LogSoftmax:()=>a0,LogicalAnd:()=>nl,LogicalNot:()=>dc,LogicalOr:()=>hc,Max:()=>qs,MaxPool:()=>Xs,MaxPool3D:()=>fc,MaxPool3DGrad:()=>Vd,MaxPoolGrad:()=>Wd,MaxPoolWithArgmax:()=>Ud,Maximum:()=>Ks,Mean:()=>Ys,Min:()=>Js,Minimum:()=>Zs,MirrorPad:()=>gc,Mod:()=>al,MomentumOptimizer:()=>Uh,Multinomial:()=>Gd,Multiply:()=>Qs,Neg:()=>rl,NonMaxSuppressionV3:()=>il,NonMaxSuppressionV4:()=>ol,NonMaxSuppressionV5:()=>ll,NotEqual:()=>sl,OP_SCOPE_SUFFIX:()=>m0,OneHot:()=>ei,OnesLike:()=>ul,Optimizer:()=>Ir,Pack:()=>cl,PadV2:()=>ti,Pool:()=>Z_,Pow:()=>ni,Prelu:()=>ai,Prod:()=>pl,RMSPropOptimizer:()=>Gh,RNN:()=>tr,Range:()=>yc,Rank:()=>Jg,Real:()=>Hd,RealDiv:()=>zs,Reciprocal:()=>dl,Reduction:()=>mn,Relu:()=>ri,Relu6:()=>ii,Reshape:()=>hl,ResizeBilinear:()=>si,ResizeBilinearGrad:()=>qd,ResizeNearestNeighbor:()=>bc,ResizeNearestNeighborGrad:()=>jd,Reverse:()=>oi,RotateWithOffset:()=>El,Round:()=>li,Rsqrt:()=>ui,SGDOptimizer:()=>qc,ScatterNd:()=>ml,Select:()=>fl,Selu:()=>gl,Sequential:()=>Zl,Sigmoid:()=>pi,Sign:()=>xl,Sin:()=>ci,Sinh:()=>bl,Slice:()=>yl,Softmax:()=>mi,Softplus:()=>vl,SpaceToBatchND:()=>xc,SparseToDense:()=>Kd,SplitV:()=>wl,Sqrt:()=>di,Square:()=>vc,SquaredDifference:()=>fi,Step:()=>Cl,StridedSlice:()=>kl,Sub:()=>gi,Sum:()=>hi,SymbolicTensor:()=>$a,Tan:()=>Il,Tanh:()=>yi,Tensor:()=>z,TensorBuffer:()=>Bt,Tile:()=>Hr,TopK:()=>Nl,Transpose:()=>bi,Unique:()=>Xd,Unpack:()=>Tl,UnsortedSegmentSum:()=>wc,Variable:()=>jr,ZerosLike:()=>Sl,_FusedMatMul:()=>xi,abs:()=>Wt,acos:()=>vy,acosh:()=>wy,add:()=>Z,addN:()=>Y0,addStrict:()=>Mk,all:()=>ch,any:()=>Ac,argMax:()=>$c,argMin:()=>ky,asin:()=>Iy,asinh:()=>Ny,atan:()=>Ty,atan2:()=>Sy,atanh:()=>Cy,avgPool:()=>Jn,avgPool3d:()=>Fy,backend:()=>X0,backend_util:()=>_,basicLSTMCell:()=>C$,batchNorm:()=>br,batchNorm2d:()=>ek,batchNorm3d:()=>tk,batchNorm4d:()=>nk,batchToSpaceND:()=>Mc,bincount:()=>ak,booleanMaskAsync:()=>AR,broadcastTo:()=>Rc,browser:()=>Ti,buffer:()=>Le,callbacks:()=>P4,cast:()=>pe,ceil:()=>Ay,clipByValue:()=>Yt,clone:()=>Yr,complex:()=>qr,concat:()=>Ze,concat1d:()=>rk,concat2d:()=>sk,concat3d:()=>ik,concat4d:()=>ok,constraints:()=>y1,conv1d:()=>dh,conv2d:()=>$t,conv2dTranspose:()=>hh,conv3d:()=>Dy,conv3dTranspose:()=>X$,copyRegisteredKernels:()=>tF,cos:()=>Pc,cosh:()=>mh,cosineWindow:()=>rb,cumsum:()=>fh,customGrad:()=>qa,data:()=>FN,denseBincount:()=>uk,deprecationWarn:()=>Pt,depthToSpace:()=>My,depthwiseConv2d:()=>xr,deregisterOp:()=>L4,device_util:()=>nh,diag:()=>aD,dilation2d:()=>Ry,disableDeprecationWarnings:()=>zA,dispose:()=>Ae,disposeVariables:()=>BA,div:()=>ve,divNoNan:()=>Py,divStrict:()=>Rk,dot:()=>ck,dropout:()=>jk,elu:()=>Ll,enableDebugMode:()=>LA,enableProdMode:()=>OA,enclosingPowerOfTwo:()=>qk,engine:()=>Zr,env:()=>ee,equal:()=>vr,equalStrict:()=>Ek,erf:()=>Oy,exp:()=>hn,expandDims:()=>Zn,expm1:()=>Ly,eye:()=>zy,fft:()=>Gc,fill:()=>In,findBackend:()=>qA,findBackendFactory:()=>KA,floor:()=>zl,floorDiv:()=>uh,fused:()=>as,gather:()=>Ei,gatherND:()=>Hk,gather_util:()=>hy,getBackend:()=>HA,getGradient:()=>qg,getKernel:()=>jg,getKernelsForBackend:()=>Jd,grad:()=>$D,grads:()=>DD,greater:()=>Qn,greaterEqual:()=>wr,greaterEqualStrict:()=>_k,greaterStrict:()=>Fk,ifft:()=>Gl,imag:()=>gh,image:()=>Ya,inTopKAsync:()=>aP,initializers:()=>N1,input:()=>O1,io:()=>Kt,irfft:()=>Ah,isFinite:()=>pk,isInf:()=>dk,isNaN:()=>hk,keep:()=>Xt,kernel_impls:()=>Ja,layers:()=>P1,leakyRelu:()=>Oc,less:()=>Lc,lessEqual:()=>es,lessEqualStrict:()=>Ak,lessStrict:()=>$k,linalg:()=>s1,linspace:()=>mk,loadGraphModel:()=>LV,loadLayersModel:()=>n4,localResponseNormalization:()=>By,log:()=>Dn,log1p:()=>yh,logSigmoid:()=>gk,logSoftmax:()=>xh,logSumExp:()=>Uy,logicalAnd:()=>pa,logicalNot:()=>zc,logicalOr:()=>vh,logicalXor:()=>vk,losses:()=>bO,matMul:()=>ze,math:()=>F0,max:()=>ea,maxPool:()=>Dt,maxPool3d:()=>Gy,maxPoolWithArgmax:()=>wk,maximum:()=>Ta,maximumStrict:()=>Pk,mean:()=>Ct,memory:()=>oh,metrics:()=>KI,min:()=>Wl,minimum:()=>Fi,minimumStrict:()=>Ok,mirrorPad:()=>Hy,mod:()=>wh,modStrict:()=>Lk,model:()=>e4,models:()=>XI,moments:()=>kh,movingAverage:()=>XR,mul:()=>L,mulStrict:()=>zk,multiRNNCell:()=>lM,multinomial:()=>kk,neg:()=>St,nextFrame:()=>Hh,norm:()=>Mh,notEqual:()=>ts,notEqualStrict:()=>Dk,oneHot:()=>Ml,ones:()=>Ka,onesLike:()=>Mn,op:()=>R,outerProduct:()=>hM,pad:()=>ta,pad1d:()=>gM,pad2d:()=>bM,pad3d:()=>vM,pad4d:()=>kM,pool:()=>Ik,pow:()=>Xa,powStrict:()=>Bk,prelu:()=>Wc,print:()=>N0,prod:()=>Ih,profile:()=>WA,rand:()=>AM,randomGamma:()=>RM,randomNormal:()=>Nk,randomUniform:()=>Vl,range:()=>Nh,ready:()=>GA,real:()=>Vc,reciprocal:()=>Ky,registerBackend:()=>lh,registerCallbackConstructor:()=>a4,registerGradient:()=>r0,registerKernel:()=>Ic,registerOp:()=>O4,regularizers:()=>YI,relu:()=>Xe,relu6:()=>Th,removeBackend:()=>jA,reshape:()=>j,reverse:()=>Rn,reverse1d:()=>GM,reverse2d:()=>jM,reverse3d:()=>KM,reverse4d:()=>YM,rfft:()=>Hc,round:()=>Xy,rsqrt:()=>Sh,scalar:()=>de,scatterND:()=>Gk,scatter_util:()=>my,selu:()=>Ch,separableConv2d:()=>Ai,sequential:()=>t4,serialization:()=>se,setBackend:()=>UA,setPlatform:()=>XA,setWasmPath:()=>Nre,setWasmPaths:()=>Tre,setdiff1dAsync:()=>Tk,sigmoid:()=>ca,sign:()=>Yy,signal:()=>yO,sin:()=>Eh,sinh:()=>_h,slice:()=>We,slice1d:()=>Fh,slice2d:()=>Jy,slice3d:()=>Ul,slice4d:()=>Uc,slice_util:()=>dn,softmax:()=>Sa,softplus:()=>Bl,spaceToBatchND:()=>Bc,sparseToDense:()=>ab,spectral:()=>gO,split:()=>Pn,sqrt:()=>an,square:()=>ot,squaredDifference:()=>jc,squaredDifferenceStrict:()=>Wk,squeeze:()=>ns,stack:()=>Mt,step:()=>Hl,stridedSlice:()=>Zy,sub:()=>fe,subStrict:()=>Vk,sum:()=>Ce,sumOutType:()=>th,tan:()=>Qy,tanh:()=>Ol,tensor:()=>Xn,tensor1d:()=>et,tensor2d:()=>Ca,tensor3d:()=>sh,tensor4d:()=>Ea,tensor5d:()=>kR,tensor6d:()=>IR,tensor_util:()=>Na,test_util:()=>H0,tidy:()=>D,tile:()=>ja,time:()=>VA,topk:()=>eb,train:()=>Di,transpose:()=>Ue,truncatedNormal:()=>$h,unique:()=>Dh,unregisterGradient:()=>eF,unregisterKernel:()=>Q_,unsortedSegmentSum:()=>tb,unstack:()=>pt,upcastType:()=>ua,util:()=>k,valueAndGrad:()=>MD,valueAndGrads:()=>RD,variable:()=>Sk,variableGrads:()=>fk,version:()=>Pee,version_converter:()=>_N,version_core:()=>K0,version_layers:()=>fm,version_wasm:()=>Sre,where:()=>kn,whereAsync:()=>nb,zeros:()=>yt,zerosLike:()=>He});var d_=Object.create,md=Object.defineProperty,h_=Object.getPrototypeOf,m_=Object.prototype.hasOwnProperty,f_=Object.getOwnPropertyNames,g_=Object.getOwnPropertyDescriptor,zw=e=>md(e,"__esModule",{value:!0}),pn=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),Oe=(e,t)=>{zw(e);for(var n in t)md(e,n,{get:t[n],enumerable:!0})},y_=(e,t,n)=>{if(zw(e),t&&typeof t=="object"||typeof t=="function")for(let a of f_(t))!m_.call(e,a)&&a!=="default"&&md(e,a,{get:()=>t[a],enumerable:!(n=g_(t,a))||n.enumerable});return e},No=e=>e&&e.__esModule?e:y_(md(e!=null?d_(h_(e)):{},"default",{value:e,enumerable:!0}),e),b_=pn(()=>{}),x_=pn((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)}),v_=pn((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)}),w_=pn((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)}),k_=pn((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)}),I_=pn((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=[],x=128;for(d===(d|0)?(m=d,d=null):(d=d+"\0",m=0,x=Math.max(x,d.length)),f=0,g=-32;g<x;++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)}),N_=pn((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)}),T_=pn(()=>{}),S_=pn((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,E){var A=[];T=T==!0?{entropy:!0}:T||{};var $=b(y(T.entropy?[N,v(n)]:N==null?x():N,3),A),O=new f(A),V=function(){for(var W=O.g(i),H=c,X=0;W<u;)W=(W+X)*s,H*=s,X=O.g(1);for(;W>=p;)W/=2,H/=2,X>>>=1;return(W+X)/H};return V.int32=function(){return O.g(4)|0},V.quick=function(){return O.g(4)/4294967296},V.double=V,b(v(O.S),n),(T.pass||E||function(W,H,X,q){return q&&(q.S&&g(q,O),W.state=function(){return g(O,{})}),X?(a[l]=W,H):W})(V,$,"global"in T?T.global:this==a,T.state)}a["seed"+l]=m;function f(N){var T,E=N.length,A=this,$=0,O=A.i=A.j=0,V=A.S=[];for(E||(N=[E++]);$<s;)V[$]=$++;for($=0;$<s;$++)V[$]=V[O=d&O+N[$%E]+(T=V[$])],V[O]=T;(A.g=function(W){for(var H,X=0,q=A.i,K=A.j,J=A.S;W--;)H=J[q=d&q+1],X=X*s+J[d&(J[q]=J[K=d&K+H])+(J[K]=H)];return A.i=q,A.j=K,X})(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 E=[],A=typeof N,$;if(T&&A=="object")for($ in N)try{E.push(y(N[$],T-1))}catch(O){}return E.length?E:A=="string"?N:N+"\0"}function b(N,T){for(var E=N+"",A,$=0;$<E.length;)T[d&$]=d&(A^=T[d&$]*19)+E.charCodeAt($++);return v(T)}function x(){try{var N;return h&&(N=h.randomBytes)?N=N(s):(N=new Uint8Array(s),(r.crypto||r.msCrypto).getRandomValues(N)),v(N)}catch(A){var T=r.navigator,E=T&&T.plugins;return[+new Date,r,E,r.screen,v(n)]}}function v(N){return String.fromCharCode.apply(0,N)}if(b(a.random(),n),typeof t=="object"&&t.exports){t.exports=m;try{h=T_()}catch(N){}}else typeof define=="function"&&define.amd&&define(function(){return m})})([],Math)}),fd=pn((e,t)=>{var n=x_(),a=v_(),r=w_(),s=k_(),i=I_(),o=N_(),l=S_();l.alea=n,l.xor128=a,l.xorwow=r,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),C_=pn(()=>{}),tc=pn(()=>{}),E_=pn(()=>{}),__=pn(()=>{}),F_=pn((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 ae.buffer!=Nt&&wa(ae.buffer),nn}function i(){return ae.buffer!=Nt&&wa(ae.buffer),wn}function o(){return ae.buffer!=Nt&&wa(ae.buffer),cn}function l(){return ae.buffer!=Nt&&wa(ae.buffer),Hn}function c(){return ae.buffer!=Nt&&wa(ae.buffer),cr}var u=typeof r!="undefined"?r:{},p=void 0,d={},h;for(h in u)u.hasOwnProperty(h)&&(d[h]=u[h]);var m=[],f="./this.program",g=function(w,S){throw S},y=!1,b=!1,x=!1,v=!1;y=typeof window=="object",b=typeof importScripts=="function",x=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",v=!y&&!x&&!b;var N=u.ENVIRONMENT_IS_PTHREAD||!1;N&&(Nt=u.buffer,Ia=u.DYNAMIC_BASE,vs=u.DYNAMICTOP_PTR);var T="";function E(w){return u.locateFile?u.locateFile(w,T):T+w}var A,$,O,V,W,H;if(x){b?T=tc().dirname(T)+"/":T=__dirname+"/",A=function(w,S){return W||(W=require("fs")),H||(H=tc()),w=H.normalize(w),W.readFileSync(w,S?null:"utf8")},O=function(w){var S=A(w,!0);return S.buffer||(S=new Uint8Array(S)),Te(S.buffer),S},process.argv.length>1&&(f=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(w){if(!(w instanceof Pw))throw w}),process.on("unhandledRejection",hr),g=function(w){process.exit(w)},u.inspect=function(){return"[Emscripten Module object]"};var X;try{X=E_()}catch(w){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),w}Worker=X.Worker}else v?(typeof read!="undefined"&&(A=function(w){return read(w)}),O=function(w){var S;return typeof readbuffer=="function"?new Uint8Array(readbuffer(w)):(S=read(w,"binary"),Te(typeof S=="object"),S)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(g=function(w){quit(w)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(y||b)&&(b?T=self.location.href:document.currentScript&&(T=document.currentScript.src),p&&(T=p),T.indexOf("blob:")!==0?T=T.substr(0,T.lastIndexOf("/")+1):T="",x?(A=function(w,S){return W||(W=require("fs")),H||(H=tc()),w=H.normalize(w),W.readFileSync(w,S?null:"utf8")},O=function(w){var S=A(w,!0);return S.buffer||(S=new Uint8Array(S)),Te(S.buffer),S}):(A=function(w){var S=new XMLHttpRequest;return S.open("GET",w,!1),S.send(null),S.responseText},b&&(O=function(w){var S=new XMLHttpRequest;return S.open("GET",w,!1),S.responseType="arraybuffer",S.send(null),new Uint8Array(S.response)}),$=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)}),V=function(w){document.title=w});x&&typeof performance=="undefined"&&(performance=__().performance);var q=u.print||console.log.bind(console),K=u.printErr||console.warn.bind(console);for(h in d)d.hasOwnProperty(h)&&(u[h]=d[h]);d=null,u.arguments&&(m=u.arguments),u.thisProgram&&(f=u.thisProgram),u.quit&&(g=u.quit);var J=Atomics.load,te=Atomics.store,Q=Atomics.compareExchange,ie;u.wasmBinary&&(ie=u.wasmBinary);var re;u.noExitRuntime&&(re=u.noExitRuntime),typeof WebAssembly!="object"&&K("no native wasm support detected");var ae,oe=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),he,ce=0,ge=0,be=!1,Ie=0;function Te(w,S){w||hr("Assertion failed: "+S)}function Ee(w){var S=u["_"+w];return Te(S,"Cannot call unknown function "+w+", make sure it is exported"),S}function Me(w,S,M,G,me){var ue={string:function(Kn){var Br=0;if(Kn!=null&&Kn!==0){var ec=(Kn.length<<2)+1;Br=vo(ec),Ge(Kn,Br,ec)}return Br},array:function(Kn){var Br=vo(Kn.length);return ft(Kn,Br),Br}};function le(Kn){return S==="string"?Ve(Kn):S==="boolean"?Boolean(Kn):Kn}var we=Ee(w),Qe=[],Lt=0;if(G)for(var ln=0;ln<G.length;ln++){var ko=ue[M[ln]];ko?(Lt===0&&(Lt=Ju()),Qe[ln]=ko(G[ln])):Qe[ln]=G[ln]}var Qu=we.apply(null,Qe);return Qu=le(Qu),Lt!==0&&wo(Lt),Qu}function Je(w,S,M,G){M=M||[];var me=M.every(function(le){return le==="number"}),ue=S!=="string";return ue&&me&&!G?Ee(w):function(){return Me(w,S,M,arguments,G)}}function nt(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 le=w[S++]&63;if((ue&224)==192){me+=String.fromCharCode((ue&31)<<6|le);continue}var we=w[S++]&63;if((ue&240)==224?ue=(ue&15)<<12|le<<6|we:ue=(ue&7)<<18|le<<12|we<<6|w[S++]&63,ue<65536)me+=String.fromCharCode(ue);else{var Qe=ue-65536;me+=String.fromCharCode(55296|Qe>>10,56320|Qe&1023)}}return me}function Ve(w,S){return w?nt(i(),w,S):""}function ut(w,S,M,G){if(!(G>0))return 0;for(var me=M,ue=M+G-1,le=0;le<w.length;++le){var we=w.charCodeAt(le);if(we>=55296&&we<=57343){var Qe=w.charCodeAt(++le);we=65536+((we&1023)<<10)|Qe&1023}if(we<=127){if(M>=ue)break;S[M++]=we}else if(we<=2047){if(M+1>=ue)break;S[M++]=192|we>>6,S[M++]=128|we&63}else if(we<=65535){if(M+2>=ue)break;S[M++]=224|we>>12,S[M++]=128|we>>6&63,S[M++]=128|we&63}else{if(M+3>=ue)break;S[M++]=240|we>>18,S[M++]=128|we>>12&63,S[M++]=128|we>>6&63,S[M++]=128|we&63}}return S[M]=0,M-me}function Ge(w,S,M){return ut(w,i(),S,M)}function ct(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 ft(w,S){s().set(w,S)}var Fn=65536;function _t(w,S){return w%S>0&&(w+=S-w%S),w}var Nt,nn,wn,ia,An,cn,Hn,ur,cr;function wa(w){Nt=w,u.HEAP8=nn=new Int8Array(w),u.HEAP16=ia=new Int16Array(w),u.HEAP32=cn=new Int32Array(w),u.HEAPU8=wn=new Uint8Array(w),u.HEAPU16=An=new Uint16Array(w),u.HEAPU32=Hn=new Uint32Array(w),u.HEAPF32=ur=new Float32Array(w),u.HEAPF64=cr=new Float64Array(w)}var po=5256464,ka=po,Wa=13584,Ia=5256464,vs=12656,Wp=u.INITIAL_MEMORY||16777216;if(N)ae=u.wasmMemory,Nt=u.buffer;else if(u.wasmMemory)ae=u.wasmMemory;else if(ae=new WebAssembly.Memory({initial:Wp/Fn,maximum:2147483648/Fn,shared:!0}),!(ae.buffer instanceof SharedArrayBuffer))throw K("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"),x&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");ae&&(Nt=ae.buffer),Wp=Nt.byteLength,wa(Nt),N||(o()[vs>>2]=Ia);function pr(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 Bu=[],Vp=[],Up=[],Gp=[],Wu=[],jn=!1;N&&(jn=!0);function Hp(){if(!N){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)ws(u.preRun.shift());pr(Bu)}}function $f(){jn=!0,pr(Vp)}function Df(){N||pr(Up)}function Mf(){if(!N){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)ho(u.postRun.shift());pr(Wu)}}function ws(w){Bu.unshift(w)}function ho(w){Wu.unshift(w)}var Rf=Math.ceil,Pf=Math.floor,dr=0,Vu=null,ks=null;function Of(w){Te(!N,"addRunDependency cannot be used in a pthread worker"),dr++,u.monitorRunDependencies&&u.monitorRunDependencies(dr)}function jp(w){if(dr--,u.monitorRunDependencies&&u.monitorRunDependencies(dr),dr==0&&(Vu!==null&&(clearInterval(Vu),Vu=null),ks)){var S=ks;ks=null,S()}}u.preloadedImages={},u.preloadedAudios={};function hr(w){throw u.onAbort&&u.onAbort(w),N&&console.error("Pthread aborting at "+new Error().stack),w+="",q(w),K(w),be=!0,Ie=1,w="abort("+w+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(w)}function qp(w,S){return String.prototype.startsWith?w.startsWith(S):w.indexOf(S)===0}var Lf="data:application/octet-stream;base64,";function Kp(w){return qp(w,Lf)}var zf="file://";function Xp(w){return qp(w,zf)}var oa="tfjs-backend-wasm-threaded-simd.wasm";Kp(oa)||(oa=E(oa));function Yp(){try{if(ie)return new Uint8Array(ie);if(O)return O(oa);throw"both async and sync fetching of the wasm failed"}catch(w){hr(w)}}function Bf(){return!ie&&(y||b)&&typeof fetch=="function"&&!Xp(oa)?fetch(oa,{credentials:"same-origin"}).then(function(w){if(!w.ok)throw"failed to load wasm binary file at '"+oa+"'";return w.arrayBuffer()}).catch(function(){return Yp()}):new Promise(function(w,S){w(Yp())})}function Wf(){var w={a:od};function S(le,we){var Qe=le.exports;if(u.asm=Qe,he=we,!N){var Lt=ye.unusedWorkers.length;ye.unusedWorkers.forEach(function(ln){ye.loadWasmModuleToWorker(ln,function(){--Lt||jp("wasm-instantiate")})})}}N||Of("wasm-instantiate");function M(le){S(le.instance,le.module)}function G(le){return Bf().then(function(we){return WebAssembly.instantiate(we,w)}).then(le,function(we){K("failed to asynchronously prepare wasm: "+we),hr(we)})}function me(){if(!ie&&typeof WebAssembly.instantiateStreaming=="function"&&!Kp(oa)&&!Xp(oa)&&typeof fetch=="function")fetch(oa,{credentials:"same-origin"}).then(function(le){var we=WebAssembly.instantiateStreaming(le,w);return we.then(M,function(Qe){K("wasm streaming compile failed: "+Qe),K("falling back to ArrayBuffer instantiation"),G(M)})});else return G(M)}if(u.instantiateWasm)try{var ue=u.instantiateWasm(w,S);return ue}catch(le){return K("Module.instantiateWasm callback failed with error: "+le),!1}return me(),{}}var Vf={};function Uf(){ye.initRuntime()}N||Vp.push({func:function(){Hu()}});var Jp=0,Zp=0,Qp=0;function mo(w,S,M){w=w|0,S=S|0,M=M|0,Jp=w,Qp=S,Zp=M}u.__register_pthread_ptr=mo;var Uu={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},fo=13568;function go(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(),fo>>2),G=0;if(M==w){var me=Atomics.compareExchange(o(),fo>>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=go;function Gf(w){if(N)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(N)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(N)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(){mo(ye.mainThreadBlock,!b,1),$w(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}N&&ce&&Aw()},threadExit:function(w){var S=Va();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(),go(S+0,2147483647),mo(0,0,0),ce=0,N&&postMessage({cmd:"exit"}))},threadCancel:function(){ye.runExitHandlers(),Atomics.store(l(),ce+4>>2,-1),Atomics.store(l(),ce+0>>2,1),go(ce+0,2147483647),ce=ge=0,mo(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,Yu(S),Yu(w.threadInfoStruct)}w.threadInfoStruct=0,w.allocatedOwnStack&&w.stackBase&&Yu(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!=Va()){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")Mg();else if(me==="spawnThread")sd(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")q("Thread "+G.threadId+": "+G.text);else if(me==="printErr")K("Thread "+G.threadId+": "+G.text);else if(me==="alert")alert("Thread "+G.threadId+": "+G.text);else if(me==="exit"){var le=w.pthread&&Atomics.load(l(),w.pthread.thread+68>>2);le&&ye.returnWorkerToPool(w)}else me==="cancelDone"?ye.returnWorkerToPool(w):me==="objectTransfer"?ye.receiveObjectTransfer(M.data):M.data.target==="setimmediate"?w.postMessage(M.data):K("worker sent an unknown command "+me);ye.currentProxiedOperationCallerThread=void 0},w.onerror=function(M){K("pthread sent an error! "+M.filename+":"+M.lineno+": "+M.message)},x&&(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||p,wasmMemory:ae,wasmModule:he,DYNAMIC_BASE:Ia,DYNAMICTOP_PTR:vs})},allocateUnusedWorker:function(){var w=E("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){po=ka=w,Wa=S,wo(w)}u.establishStackSpace=qf;function Kf(){return re}u.getNoExitRuntime=Kf;function Xf(w,S,M,G){hr("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 Is;x?Is=function(){var w=process.hrtime();return w[0]*1e3+w[1]/1e6}:N?Is=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?Is=dateNow:Is=function(){return performance.now()};function Jf(w){return o()[_w()>>2]=w,w}function Zf(w,S){if(N)return Or(1,1,w,S);Gp.unshift({func:w,arg:S})}function Qf(w,S){if(w==S)postMessage({cmd:"processQueuedMainThreadWork"});else if(N)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(){hr()}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(b){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(),le=ue+M;Atomics.store(o(),fo>>2,w);for(var we=w;w==we;){if(ue=performance.now(),ue>le)return-73;Mg(),w=Atomics.load(o(),fo>>2)}return 0}}function ag(){return Qp|0}function rg(){return Zp|0}function sg(w,S,M){i().copyWithin(w,S,S+M)}function ig(){return navigator.hardwareConcurrency}function Or(w,S){for(var M=arguments.length-2,G=Ju(),me=vo(M*8),ue=me>>3,le=0;le<M;le++)c()[ue+le]=arguments[2+le];var we=Mw(w,M,me,S);return wo(G),we}var Ns=[];function yo(w,S){yo.array||(yo.array=[]);var M=yo.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){Ns.length=S;for(var G=M>>3,me=0;me<S;me++)Ns[me]=c()[G+me];var ue=w<0,le=ue?Vf[-w-1]:Ag[w];if(ue){var we=Ns[1],Qe=Ns[2],Lt=yo(we,Qe);return le.apply(null,Lt)}return le.apply(null,Ns)}function lg(){return i().length}function ug(w){try{return ae.grow(w-Nt.byteLength+65535>>>16),wa(ae.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 le=S*(1+.2/ue);le=Math.min(le,w+100663296);var we=Math.min(G,_t(Math.max(me,w,le),M)),Qe=ug(we);if(Qe)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||(Gp.push(Pe.removeAllEventListeners),Pe.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(w,S,M){function G(le,we){if(le.length!=we.length)return!1;for(var Qe in le)if(le[Qe]!=we[Qe])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(le,we){return le.precedence<we.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=Ju(),le=vo(12);o()[le>>2]=M,o()[le+4>>2]=G,o()[le+8>>2]=me,Rg(w,637534208,S,G,le),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=ct(w)+1,M=Xu(S);return Ge(w,M,S),M}function dg(w,S,M,G){var me=Ju(),ue=vo(12),le=0;S&&(le=pg(S)),o()[ue>>2]=le,o()[ue+4>>2]=M,o()[ue+8>>2]=G,Rg(w,657457152,0,le,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 Gu(w){return gg(w)}function ed(w,S,M){var G=Gu(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 le=o()[G.canvasSharedPtr+8>>2];return hg(le,w,S,M),1}else return-4;return 0}function td(w,S,M){return N?Or(2,1,w,S,M):ed(w,S,M)}function yg(w,S,M){var G=Gu(w);return G?ed(w,S,M):td(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,le){S.drawElementsInstancedANGLE(M,G,me,ue,le)},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 le=G?o()[G+ue*4>>2]:-1;me+=Ve(o()[M+ue*4>>2],le<0?void 0:le)}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=Xu(8);o()[M+4>>2]=Va();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=Lr=Be.currentContext&&Be.currentContext.GLctx,!(w&&!Lr)},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),Yu(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=Lr.getProgramParameter(S,35718),ue=0;ue<me;++ue){var le=Lr.getActiveUniform(S,ue),we=le.name;M.maxUniformLength=Math.max(M.maxUniformLength,we.length+1),we.slice(-1)=="]"&&(we=we.slice(0,we.lastIndexOf("[")));var Qe=Lr.getUniformLocation(S,we);if(Qe){var Lt=Be.getNewId(Be.uniforms);G[we]=[le.size,Lt],Be.uniforms[Lt]=Qe;for(var ln=1;ln<le.size;++ln){var ko=we+"["+ln+"]";Qe=Lr.getUniformLocation(S,ko),Lt=Be.getNewId(Be.uniforms),Be.uniforms[Lt]=Qe}}}}},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=Gu(w);if(!ue)return-4;if(M.explicitSwapControl)return-1;var le=Be.createContext(ue,M);return le}function Tg(w,S){return Ng(w,S)}var Ts={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=Ts.normalizeArray(w.split("/").filter(function(G){return!!G}),!S).join("/"),!w&&!S&&(w="."),w&&M&&(w+="/"),(S?"/":"")+w},dirname:function(w){var S=Ts.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 Ts.splitPath(w)[3]},join:function(){var w=Array.prototype.slice.call(arguments,0);return Ts.normalize(w.join("/"))},join2:function(w,S){return Ts.normalize(w+"/"+S)}},bo={mappings:{},buffers:[null,[],[]],printChar:function(w,S){var M=bo.buffers[w];S===0||S===10?((w===1?q:K)(nt(M,0)),M.length=0):M.push(S)},varargs:void 0,get:function(){bo.varargs+=4;var w=o()[bo.varargs-4>>2];return w},getStr:function(w){var S=Ve(w);return S},get64:function(w,S){return w}};function nd(w){return N?Or(3,1,w):0}function ad(w,S,M,G,me){if(N)return Or(4,1,w,S,M,G,me)}function rd(w,S,M,G){if(N)return Or(5,1,w,S,M,G);for(var me=0,ue=0;ue<M;ue++){for(var le=o()[S+ue*8>>2],we=o()[S+(ue*8+4)>>2],Qe=0;Qe<we;Qe++)bo.printChar(w,i()[le+Qe]);me+=we}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(){Rw(w,S)})}function sd(w){if(N)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=Xu(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},le=ue.threadInfoStruct>>2;Atomics.store(l(),le+(0>>2),0),Atomics.store(l(),le+(4>>2),0),Atomics.store(l(),le+(8>>2),0),Atomics.store(l(),le+(68>>2),w.detached),Atomics.store(l(),le+(104>>2),M),Atomics.store(l(),le+(48>>2),0),Atomics.store(l(),le+(40>>2),ue.threadInfoStruct),Atomics.store(l(),le+(44>>2),42),Atomics.store(l(),le+(108>>2),w.stackSize),Atomics.store(l(),le+(84>>2),w.stackSize),Atomics.store(l(),le+(80>>2),me),Atomics.store(l(),le+(108+8>>2),me),Atomics.store(l(),le+(108+12>>2),w.detached),Atomics.store(l(),le+(108+20>>2),w.schedPolicy),Atomics.store(l(),le+(108+24>>2),w.schedPrio);var we=Ew(),Qe=we+40;Atomics.store(l(),le+(176>>2),Qe),S.pthread=ue;var Lt={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(){Lt.time=performance.now(),S.postMessage(Lt,w.transferList)},S.loaded&&(S.runPthread(),delete S.runPthread)}function Eg(w,S,M){if(!S&&!M)return Uu.EINVAL;if(!w)return K("pthread_getschedparam called with a null thread pointer!"),Uu.ESRCH;var G=o()[w+12>>2];if(G!==w)return K("pthread_getschedparam attempted on thread "+w+", which does not point to a valid thread, or does not exist anymore!"),Uu.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 Va(){return Jp|0}u._pthread_self=Va;function _g(w,S,M,G){if(typeof SharedArrayBuffer=="undefined")return K("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!w)return K("pthread_create called with a null thread pointer!"),28;var me=[],ue=0;if(N&&(me.length===0||ue))return Dw(687865856,w,S,M,G);if(ue)return ue;var le=0,we=0,Qe=0,Lt=0,ln=0;if(S){le=o()[S>>2],le+=81920,we=o()[S+8>>2],Qe=o()[S+12>>2]!==0;var ko=o()[S+16>>2]===0;if(ko){var Qu=o()[S+20>>2],Kn=o()[S+24>>2],Br=ye.currentProxiedOperationCallerThread?ye.currentProxiedOperationCallerThread:Va();Eg(Br,S+20,S+24),Lt=o()[S+20>>2],ln=o()[S+24>>2],o()[S+20>>2]=Qu,o()[S+24>>2]=Kn}else Lt=o()[S+20>>2],ln=o()[S+24>>2]}else le=2097152;var ec=we==0;ec?we=Fw(16,le):(we-=le,Te(we>0));for(var Io=Xu(232),Og=0;Og<232>>2;++Og)l()[(Io>>2)+Og]=0;o()[w>>2]=Io,o()[Io+12>>2]=Io;var Ow=Io+156;o()[Ow>>2]=Ow;var Lg={stackBase:we,stackSize:le,allocatedOwnStack:ec,schedPolicy:Lt,schedPrio:ln,detached:Qe,startRoutine:M,pthread_ptr:Io,parent_pthread_ptr:Va(),arg:G,transferList:me};return N?(Lg.cmd="spawnThread",postMessage(Lg,me)):sd(Lg),0}function Fg(w){return w=+w,w>=0?+Pf(w+.5):+Rf(w-.5)}function id(w){if(N)return Or(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}N?ye.initWorker():ye.initMainThreadBlock();var Lr;Be.init();var Ag=[null,Zf,td,nd,ad,rd,id],od={e:Xf,r:Yf,w:Qf,a:eg,l:tg,d:ng,c:go,h:Is,g:ag,x:rg,q:sg,B:ig,t:og,A:cg,u:yg,k:bg,s:xg,v:Tg,m:nd,o:ad,i:rd,p:Uf,memory:ae||u.wasmMemory,y:Sg,z:Cg,j:_g,b:Va,f:Fg,n:id,table:oe},xo=Wf();u.asm=xo;var Hu=u.___wasm_call_ctors=function(){return(Hu=u.___wasm_call_ctors=u.asm.C).apply(null,arguments)},Ss=u._init=function(){return(Ss=u._init=u.asm.D).apply(null,arguments)},ju=u._register_tensor=function(){return(ju=u._register_tensor=u.asm.E).apply(null,arguments)},$g=u._dispose_data=function(){return($g=u._dispose_data=u.asm.F).apply(null,arguments)},Dg=u._dispose=function(){return(Dg=u._dispose=u.asm.G).apply(null,arguments)},qu=u._Abs=function(){return(qu=u._Abs=u.asm.H).apply(null,arguments)},ld=u._Add=function(){return(ld=u._Add=u.asm.I).apply(null,arguments)},ud=u._AddN=function(){return(ud=u._AddN=u.asm.J).apply(null,arguments)},U=u._ArgMax=function(){return(U=u._ArgMax=u.asm.K).apply(null,arguments)},ne=u._AvgPool=function(){return(ne=u._AvgPool=u.asm.L).apply(null,arguments)},Ne=u._BatchMatMul=function(){return(Ne=u._BatchMatMul=u.asm.M).apply(null,arguments)},_e=u._ClipByValue=function(){return(_e=u._ClipByValue=u.asm.N).apply(null,arguments)},at=u._Conv2D=function(){return(at=u._Conv2D=u.asm.O).apply(null,arguments)},Ft=u._Conv2DBackpropInput=function(){return(Ft=u._Conv2DBackpropInput=u.asm.P).apply(null,arguments)},Ke=u._Cos=function(){return(Ke=u._Cos=u.asm.Q).apply(null,arguments)},je=u._CropAndResize=function(){return(je=u._CropAndResize=u.asm.R).apply(null,arguments)},jt=u._Cumsum=function(){return(jt=u._Cumsum=u.asm.S).apply(null,arguments)},mr=u._DepthToSpace=function(){return(mr=u._DepthToSpace=u.asm.T).apply(null,arguments)},fr=u._DepthwiseConv2dNative=function(){return(fr=u._DepthwiseConv2dNative=u.asm.U).apply(null,arguments)},cd=u._Equal=function(){return(cd=u._Equal=u.asm.V).apply(null,arguments)},Ku=u._Exp=function(){return(Ku=u._Exp=u.asm.W).apply(null,arguments)},qn=u._FlipLeftRight=function(){return(qn=u._FlipLeftRight=u.asm.X).apply(null,arguments)},zr=u._Floor=function(){return(zr=u._Floor=u.asm.Y).apply(null,arguments)},pd=u._FloorDiv=function(){return(pd=u._FloorDiv=u.asm.Z).apply(null,arguments)},UC=u._FusedBatchNorm=function(){return(UC=u._FusedBatchNorm=u.asm._).apply(null,arguments)},GC=u._FusedConv2D=function(){return(GC=u._FusedConv2D=u.asm.$).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)},eE=u._LogicalAnd=function(){return(eE=u._LogicalAnd=u.asm.ja).apply(null,arguments)},tE=u._Max=function(){return(tE=u._Max=u.asm.ka).apply(null,arguments)},nE=u._MaxPool=function(){return(nE=u._MaxPool=u.asm.la).apply(null,arguments)},aE=u._Maximum=function(){return(aE=u._Maximum=u.asm.ma).apply(null,arguments)},rE=u._Mean=function(){return(rE=u._Mean=u.asm.na).apply(null,arguments)},sE=u._Min=function(){return(sE=u._Min=u.asm.oa).apply(null,arguments)},iE=u._Minimum=function(){return(iE=u._Minimum=u.asm.pa).apply(null,arguments)},oE=u._Multiply=function(){return(oE=u._Multiply=u.asm.qa).apply(null,arguments)},lE=u._Neg=function(){return(lE=u._Neg=u.asm.ra).apply(null,arguments)},uE=u._NonMaxSuppressionV3=function(){return(uE=u._NonMaxSuppressionV3=u.asm.sa).apply(null,arguments)},cE=u._NonMaxSuppressionV4=function(){return(cE=u._NonMaxSuppressionV4=u.asm.ta).apply(null,arguments)},pE=u._NonMaxSuppressionV5=function(){return(pE=u._NonMaxSuppressionV5=u.asm.ua).apply(null,arguments)},dE=u._NotEqual=function(){return(dE=u._NotEqual=u.asm.va).apply(null,arguments)},hE=u._OneHot=function(){return(hE=u._OneHot=u.asm.wa).apply(null,arguments)},mE=u._PadV2=function(){return(mE=u._PadV2=u.asm.xa).apply(null,arguments)},fE=u._Pow=function(){return(fE=u._Pow=u.asm.ya).apply(null,arguments)},gE=u._Prelu=function(){return(gE=u._Prelu=u.asm.za).apply(null,arguments)},yE=u._Prod=function(){return(yE=u._Prod=u.asm.Aa).apply(null,arguments)},bE=u._RealDiv=function(){return(bE=u._RealDiv=u.asm.Ba).apply(null,arguments)},xE=u._Relu=function(){return(xE=u._Relu=u.asm.Ca).apply(null,arguments)},vE=u._Relu6=function(){return(vE=u._Relu6=u.asm.Da).apply(null,arguments)},wE=u._ResizeBilinear=function(){return(wE=u._ResizeBilinear=u.asm.Ea).apply(null,arguments)},kE=u._Reverse=function(){return(kE=u._Reverse=u.asm.Fa).apply(null,arguments)},IE=u._RotateWithOffset=function(){return(IE=u._RotateWithOffset=u.asm.Ga).apply(null,arguments)},NE=u._Round=function(){return(NE=u._Round=u.asm.Ha).apply(null,arguments)},TE=u._Rsqrt=function(){return(TE=u._Rsqrt=u.asm.Ia).apply(null,arguments)},SE=u._ScatterNd=function(){return(SE=u._ScatterNd=u.asm.Ja).apply(null,arguments)},CE=u._SelectV2=function(){return(CE=u._SelectV2=u.asm.Ka).apply(null,arguments)},EE=u._Sigmoid=function(){return(EE=u._Sigmoid=u.asm.La).apply(null,arguments)},_E=u._Sin=function(){return(_E=u._Sin=u.asm.Ma).apply(null,arguments)},FE=u._Softmax=function(){return(FE=u._Softmax=u.asm.Na).apply(null,arguments)},AE=u._Sqrt=function(){return(AE=u._Sqrt=u.asm.Oa).apply(null,arguments)},$E=u._Square=function(){return($E=u._Square=u.asm.Pa).apply(null,arguments)},DE=u._SquaredDifference=function(){return(DE=u._SquaredDifference=u.asm.Qa).apply(null,arguments)},ME=u._StridedSlice=function(){return(ME=u._StridedSlice=u.asm.Ra).apply(null,arguments)},RE=u._Sub=function(){return(RE=u._Sub=u.asm.Sa).apply(null,arguments)},PE=u._Sum=function(){return(PE=u._Sum=u.asm.Ta).apply(null,arguments)},OE=u._Tanh=function(){return(OE=u._Tanh=u.asm.Ua).apply(null,arguments)},LE=u._Tile=function(){return(LE=u._Tile=u.asm.Va).apply(null,arguments)},zE=u._TopK=function(){return(zE=u._TopK=u.asm.Wa).apply(null,arguments)},BE=u._Transpose=function(){return(BE=u._Transpose=u.asm.Xa).apply(null,arguments)},WE=u.__FusedMatMul=function(){return(WE=u.__FusedMatMul=u.asm.Ya).apply(null,arguments)},Xu=u._malloc=function(){return(Xu=u._malloc=u.asm.Za).apply(null,arguments)},Yu=u._free=function(){return(Yu=u._free=u.asm._a).apply(null,arguments)},Ew=u._emscripten_get_global_libc=function(){return(Ew=u._emscripten_get_global_libc=u.asm.$a).apply(null,arguments)},_w=u.___errno_location=function(){return(_w=u.___errno_location=u.asm.ab).apply(null,arguments)},VE=u.___em_js__initPthreadsJS=function(){return(VE=u.___em_js__initPthreadsJS=u.asm.bb).apply(null,arguments)},Fw=u._memalign=function(){return(Fw=u._memalign=u.asm.cb).apply(null,arguments)},Aw=u.___pthread_tsd_run_dtors=function(){return(Aw=u.___pthread_tsd_run_dtors=u.asm.db).apply(null,arguments)},Mg=u._emscripten_main_thread_process_queued_calls=function(){return(Mg=u._emscripten_main_thread_process_queued_calls=u.asm.eb).apply(null,arguments)},UE=u._emscripten_current_thread_process_queued_calls=function(){return(UE=u._emscripten_current_thread_process_queued_calls=u.asm.fb).apply(null,arguments)},$w=u._emscripten_register_main_browser_thread_id=function(){return($w=u._emscripten_register_main_browser_thread_id=u.asm.gb).apply(null,arguments)},GE=u._emscripten_main_browser_thread_id=function(){return(GE=u._emscripten_main_browser_thread_id=u.asm.hb).apply(null,arguments)},HE=u._emscripten_async_run_in_main_thread=function(){return(HE=u._emscripten_async_run_in_main_thread=u.asm.ib).apply(null,arguments)},jE=u._emscripten_sync_run_in_main_thread=function(){return(jE=u._emscripten_sync_run_in_main_thread=u.asm.jb).apply(null,arguments)},qE=u._emscripten_sync_run_in_main_thread_0=function(){return(qE=u._emscripten_sync_run_in_main_thread_0=u.asm.kb).apply(null,arguments)},KE=u._emscripten_sync_run_in_main_thread_1=function(){return(KE=u._emscripten_sync_run_in_main_thread_1=u.asm.lb).apply(null,arguments)},XE=u._emscripten_sync_run_in_main_thread_2=function(){return(XE=u._emscripten_sync_run_in_main_thread_2=u.asm.mb).apply(null,arguments)},YE=u._emscripten_sync_run_in_main_thread_xprintf_varargs=function(){return(YE=u._emscripten_sync_run_in_main_thread_xprintf_varargs=u.asm.nb).apply(null,arguments)},JE=u._emscripten_sync_run_in_main_thread_3=function(){return(JE=u._emscripten_sync_run_in_main_thread_3=u.asm.ob).apply(null,arguments)},Dw=u._emscripten_sync_run_in_main_thread_4=function(){return(Dw=u._emscripten_sync_run_in_main_thread_4=u.asm.pb).apply(null,arguments)},ZE=u._emscripten_sync_run_in_main_thread_5=function(){return(ZE=u._emscripten_sync_run_in_main_thread_5=u.asm.qb).apply(null,arguments)},QE=u._emscripten_sync_run_in_main_thread_6=function(){return(QE=u._emscripten_sync_run_in_main_thread_6=u.asm.rb).apply(null,arguments)},e_=u._emscripten_sync_run_in_main_thread_7=function(){return(e_=u._emscripten_sync_run_in_main_thread_7=u.asm.sb).apply(null,arguments)},Mw=u._emscripten_run_in_main_runtime_thread_js=function(){return(Mw=u._emscripten_run_in_main_runtime_thread_js=u.asm.tb).apply(null,arguments)},Rg=u._emscripten_async_queue_on_thread_=function(){return(Rg=u._emscripten_async_queue_on_thread_=u.asm.ub).apply(null,arguments)},t_=u._emscripten_tls_init=function(){return(t_=u._emscripten_tls_init=u.asm.vb).apply(null,arguments)},Ju=u.stackSave=function(){return(Ju=u.stackSave=u.asm.wb).apply(null,arguments)},vo=u.stackAlloc=function(){return(vo=u.stackAlloc=u.asm.xb).apply(null,arguments)},wo=u.stackRestore=function(){return(wo=u.stackRestore=u.asm.yb).apply(null,arguments)},Rw=u.dynCall_vi=function(){return(Rw=u.dynCall_vi=u.asm.zb).apply(null,arguments)},n_=u.dynCall_v=function(){return(n_=u.dynCall_v=u.asm.Ab).apply(null,arguments)},a_=u.dynCall_ii=function(){return(a_=u.dynCall_ii=u.asm.Bb).apply(null,arguments)};u.asm=xo,u.cwrap=Je,u.PThread=ye,u.PThread=ye,u._pthread_self=Va,u.wasmMemory=ae,u.ExitStatus=Pw;var Zu;u.then=function(w){if(Zu)w(u);else{var S=u.onRuntimeInitialized;u.onRuntimeInitialized=function(){S&&S(),w(u)}}return u};function Pw(w){this.name="ExitStatus",this.message="Program terminated with exit("+w+")",this.status=w}ks=function w(){Zu||Pg(),Zu||(ks=w)};function Pg(w){if(w=w||m,dr>0||(Hp(),dr>0))return;function S(){Zu||(Zu=!0,u.calledRun=!0,!be&&($f(),Df(),u.onRuntimeInitialized&&u.onRuntimeInitialized(),Mf()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),S()},1)):S()}if(u.run=Pg,u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return N||(re=!0),N||Pg(),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)}),A_=pn((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,ne){throw ne},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,x,v,N,T;h?(d?f=tc().dirname(f)+"/":f=__dirname+"/",y=function(U,ne){return N||(N=require("fs")),T||(T=tc()),U=T.normalize(U),N.readFileSync(U,ne?null:"utf8")},x=function(U){var ne=y(U,!0);return ne.buffer||(ne=new Uint8Array(ne)),q(ne.buffer),ne},process.argv.length>1&&(c=process.argv[1].replace(/\\/g,"/")),l=process.argv.slice(2),process.on("uncaughtException",function(U){if(!(U instanceof ju))throw U}),process.on("unhandledRejection",pr),u=function(U){process.exit(U)},s.inspect=function(){return"[Emscripten Module object]"}):m?(typeof read!="undefined"&&(y=function(U){return read(U)}),x=function(U){var ne;return typeof readbuffer=="function"?new Uint8Array(readbuffer(U)):(ne=read(U,"binary"),q(typeof ne=="object"),ne)},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 ne=new XMLHttpRequest;return ne.open("GET",U,!1),ne.send(null),ne.responseText},d&&(x=function(U){var ne=new XMLHttpRequest;return ne.open("GET",U,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),b=function(U,ne,Ne){var _e=new XMLHttpRequest;_e.open("GET",U,!0),_e.responseType="arraybuffer",_e.onload=function(){if(_e.status==200||_e.status==0&&_e.response){ne(_e.response);return}Ne()},_e.onerror=Ne,_e.send(null)},v=function(U){document.title=U});var E=s.print||console.log.bind(console),A=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 $;s.wasmBinary&&($=s.wasmBinary);var O;s.noExitRuntime&&(O=s.noExitRuntime),typeof WebAssembly!="object"&&A("no native wasm support detected");var V,W=new WebAssembly.Table({initial:151,maximum:151+0,element:"anyfunc"}),H=!1,X=0;function q(U,ne){U||pr("Assertion failed: "+ne)}function K(U){var ne=s["_"+U];return q(ne,"Cannot call unknown function "+U+", make sure it is exported"),ne}function J(U,ne,Ne,_e,at){var Ft={string:function(qn){var zr=0;if(qn!=null&&qn!==0){var pd=(qn.length<<2)+1;zr=xo(pd),oe(qn,zr,pd)}return zr},array:function(qn){var zr=xo(qn.length);return he(qn,zr),zr}};function Ke(qn){return ne==="string"?re(qn):ne==="boolean"?Boolean(qn):qn}var je=K(U),jt=[],mr=0;if(_e)for(var fr=0;fr<_e.length;fr++){var cd=Ft[Ne[fr]];cd?(mr===0&&(mr=od()),jt[fr]=cd(_e[fr])):jt[fr]=_e[fr]}var Ku=je.apply(null,jt);return Ku=Ke(Ku),mr!==0&&Hu(mr),Ku}function te(U,ne,Ne,_e){Ne=Ne||[];var at=Ne.every(function(Ke){return Ke==="number"}),Ft=ne!=="string";return Ft&&at&&!_e?K(U):function(){return J(U,ne,Ne,arguments,_e)}}var Q=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function ie(U,ne,Ne){for(var _e=ne+Ne,at=ne;U[at]&&!(at>=_e);)++at;if(at-ne>16&&U.subarray&&Q)return Q.decode(U.subarray(ne,at));for(var Ft="";ne<at;){var Ke=U[ne++];if(!(Ke&128)){Ft+=String.fromCharCode(Ke);continue}var je=U[ne++]&63;if((Ke&224)==192){Ft+=String.fromCharCode((Ke&31)<<6|je);continue}var jt=U[ne++]&63;if((Ke&240)==224?Ke=(Ke&15)<<12|je<<6|jt:Ke=(Ke&7)<<18|je<<12|jt<<6|U[ne++]&63,Ke<65536)Ft+=String.fromCharCode(Ke);else{var mr=Ke-65536;Ft+=String.fromCharCode(55296|mr>>10,56320|mr&1023)}}return Ft}function re(U,ne){return U?ie(be,U,ne):""}function ae(U,ne,Ne,_e){if(!(_e>0))return 0;for(var at=Ne,Ft=Ne+_e-1,Ke=0;Ke<U.length;++Ke){var je=U.charCodeAt(Ke);if(je>=55296&&je<=57343){var jt=U.charCodeAt(++Ke);je=65536+((je&1023)<<10)|jt&1023}if(je<=127){if(Ne>=Ft)break;ne[Ne++]=je}else if(je<=2047){if(Ne+1>=Ft)break;ne[Ne++]=192|je>>6,ne[Ne++]=128|je&63}else if(je<=65535){if(Ne+2>=Ft)break;ne[Ne++]=224|je>>12,ne[Ne++]=128|je>>6&63,ne[Ne++]=128|je&63}else{if(Ne+3>=Ft)break;ne[Ne++]=240|je>>18,ne[Ne++]=128|je>>12&63,ne[Ne++]=128|je>>6&63,ne[Ne++]=128|je&63}}return ne[Ne]=0,Ne-at}function oe(U,ne,Ne){return ae(U,be,ne,Ne)}function he(U,ne){ge.set(U,ne)}var ce,ge,be,Ie,Te,Ee,Me,Je,nt;function Ve(U){ce=U,s.HEAP8=ge=new Int8Array(U),s.HEAP16=Ie=new Int16Array(U),s.HEAP32=Ee=new Int32Array(U),s.HEAPU8=be=new Uint8Array(U),s.HEAPU16=Te=new Uint16Array(U),s.HEAPU32=Me=new Uint32Array(U),s.HEAPF32=Je=new Float32Array(U),s.HEAPF64=nt=new Float64Array(U)}var ut=s.INITIAL_MEMORY||16777216;function Ge(U){for(;U.length>0;){var ne=U.shift();if(typeof ne=="function"){ne(s);continue}var Ne=ne.func;typeof Ne=="number"?ne.arg===void 0?s.dynCall_v(Ne):s.dynCall_vi(Ne,ne.arg):Ne(ne.arg===void 0?null:ne.arg)}}var ct=[],ft=[],Fn=[],_t=[],Nt=!1,nn=!1;function wn(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)ur(s.preRun.shift());Ge(ct)}function ia(){Nt=!0,Ge(ft)}function An(){Ge(Fn)}function cn(){nn=!0}function Hn(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)cr(s.postRun.shift());Ge(_t)}function ur(U){ct.unshift(U)}function cr(U){_t.unshift(U)}var wa=Math.ceil,po=Math.floor,ka=0,Wa=null,Ia=null;function vs(U){ka++,s.monitorRunDependencies&&s.monitorRunDependencies(ka)}function Wp(U){if(ka--,s.monitorRunDependencies&&s.monitorRunDependencies(ka),ka==0&&(Wa!==null&&(clearInterval(Wa),Wa=null),Ia)){var ne=Ia;Ia=null,ne()}}s.preloadedImages={},s.preloadedAudios={};function pr(U){throw s.onAbort&&s.onAbort(U),U+="",E(U),A(U),H=!0,X=1,U="abort("+U+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(U)}function Bu(U,ne){return String.prototype.startsWith?U.startsWith(ne):U.indexOf(ne)===0}var Vp="data:application/octet-stream;base64,";function Up(U){return Bu(U,Vp)}var Gp="file://";function Wu(U){return Bu(U,Gp)}var jn="tfjs-backend-wasm.wasm";Up(jn)||(jn=g(jn));function Hp(){try{if($)return new Uint8Array($);if(x)return x(jn);throw"both async and sync fetching of the wasm failed"}catch(U){pr(U)}}function $f(){return!$&&(p||d)&&typeof fetch=="function"&&!Wu(jn)?fetch(jn,{credentials:"same-origin"}).then(function(U){if(!U.ok)throw"failed to load wasm binary file at '"+jn+"'";return U.arrayBuffer()}).catch(function(){return Hp()}):new Promise(function(U,ne){U(Hp())})}function Df(){var U={env:jp,wasi_snapshot_preview1:jp};function ne(Ke,je){var jt=Ke.exports;s.asm=jt,V=jt.memory,Ve(V.buffer),Wp("wasm-instantiate")}vs("wasm-instantiate");function Ne(Ke){ne(Ke.instance)}function _e(Ke){return $f().then(function(je){return WebAssembly.instantiate(je,U)}).then(Ke,function(je){A("failed to asynchronously prepare wasm: "+je),pr(je)})}function at(){if(!$&&typeof WebAssembly.instantiateStreaming=="function"&&!Up(jn)&&!Wu(jn)&&typeof fetch=="function")fetch(jn,{credentials:"same-origin"}).then(function(Ke){var je=WebAssembly.instantiateStreaming(Ke,U);return je.then(Ne,function(jt){A("wasm streaming compile failed: "+jt),A("falling back to ArrayBuffer instantiation"),_e(Ne)})});else return _e(Ne)}if(s.instantiateWasm)try{var Ft=s.instantiateWasm(U,ne);return Ft}catch(Ke){return A("Module.instantiateWasm callback failed with error: "+Ke),!1}return at(),{}}ft.push();function Mf(U){Ve(V.buffer)}var ws={splitPath:function(U){var ne=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return ne.exec(U).slice(1)},normalizeArray:function(U,ne){for(var Ne=0,_e=U.length-1;_e>=0;_e--){var at=U[_e];at==="."?U.splice(_e,1):at===".."?(U.splice(_e,1),Ne++):Ne&&(U.splice(_e,1),Ne--)}if(ne)for(;Ne;Ne--)U.unshift("..");return U},normalize:function(U){var ne=U.charAt(0)==="/",Ne=U.substr(-1)==="/";return U=ws.normalizeArray(U.split("/").filter(function(_e){return!!_e}),!ne).join("/"),!U&&!ne&&(U="."),U&&Ne&&(U+="/"),(ne?"/":"")+U},dirname:function(U){var ne=ws.splitPath(U),Ne=ne[0],_e=ne[1];return!Ne&&!_e?".":(_e&&(_e=_e.substr(0,_e.length-1)),Ne+_e)},basename:function(U){if(U==="/")return"/";var ne=U.lastIndexOf("/");return ne===-1?U:U.substr(ne+1)},extname:function(U){return ws.splitPath(U)[3]},join:function(){var U=Array.prototype.slice.call(arguments,0);return ws.normalize(U.join("/"))},join2:function(U,ne){return ws.normalize(U+"/"+ne)}},ho={mappings:{},buffers:[null,[],[]],printChar:function(U,ne){var Ne=ho.buffers[U];ne===0||ne===10?((U===1?E:A)(ie(Ne,0)),Ne.length=0):Ne.push(ne)},varargs:void 0,get:function(){ho.varargs+=4;var U=Ee[ho.varargs-4>>2];return U},getStr:function(U){var ne=re(U);return ne},get64:function(U,ne){return U}};function Rf(U){return 0}function Pf(U,ne,Ne,_e,at){}function dr(U,ne,Ne,_e){for(var at=0,Ft=0;Ft<Ne;Ft++){for(var Ke=Ee[ne+Ft*8>>2],je=Ee[ne+(Ft*8+4)>>2],jt=0;jt<je;jt++)ho.printChar(U,be[Ke+jt]);at+=je}return Ee[_e>>2]=at,0}function Vu(U){ld(U)}function ks(U){Vu(U)}function Of(U){return U=+U,U>=0?+po(U+.5):+wa(U-.5)}var jp={emscripten_notify_memory_growth:Mf,fd_close:Rf,fd_seek:Pf,fd_write:dr,proc_exit:ks,roundf:Of},hr=Df();s.asm=hr;var qp=s._init=function(){return(qp=s._init=s.asm.init).apply(null,arguments)},Lf=s._register_tensor=function(){return(Lf=s._register_tensor=s.asm.register_tensor).apply(null,arguments)},Kp=s._dispose_data=function(){return(Kp=s._dispose_data=s.asm.dispose_data).apply(null,arguments)},zf=s._dispose=function(){return(zf=s._dispose=s.asm.dispose).apply(null,arguments)},Xp=s._Abs=function(){return(Xp=s._Abs=s.asm.Abs).apply(null,arguments)},oa=s._Add=function(){return(oa=s._Add=s.asm.Add).apply(null,arguments)},Yp=s._AddN=function(){return(Yp=s._AddN=s.asm.AddN).apply(null,arguments)},Bf=s._ArgMax=function(){return(Bf=s._ArgMax=s.asm.ArgMax).apply(null,arguments)},Wf=s._AvgPool=function(){return(Wf=s._AvgPool=s.asm.AvgPool).apply(null,arguments)},Vf=s._BatchMatMul=function(){return(Vf=s._BatchMatMul=s.asm.BatchMatMul).apply(null,arguments)},Uf=s._ClipByValue=function(){return(Uf=s._ClipByValue=s.asm.ClipByValue).apply(null,arguments)},Jp=s._Conv2D=function(){return(Jp=s._Conv2D=s.asm.Conv2D).apply(null,arguments)},Zp=s._Conv2DBackpropInput=function(){return(Zp=s._Conv2DBackpropInput=s.asm.Conv2DBackpropInput).apply(null,arguments)},Qp=s._Cos=function(){return(Qp=s._Cos=s.asm.Cos).apply(null,arguments)},mo=s._CropAndResize=function(){return(mo=s._CropAndResize=s.asm.CropAndResize).apply(null,arguments)},Uu=s._Cumsum=function(){return(Uu=s._Cumsum=s.asm.Cumsum).apply(null,arguments)},fo=s._DepthToSpace=function(){return(fo=s._DepthToSpace=s.asm.DepthToSpace).apply(null,arguments)},go=s._DepthwiseConv2dNative=function(){return(go=s._DepthwiseConv2dNative=s.asm.DepthwiseConv2dNative).apply(null,arguments)},Gf=s._Equal=function(){return(Gf=s._Equal=s.asm.Equal).apply(null,arguments)},Hf=s._Exp=function(){return(Hf=s._Exp=s.asm.Exp).apply(null,arguments)},jf=s._FlipLeftRight=function(){return(jf=s._FlipLeftRight=s.asm.FlipLeftRight).apply(null,arguments)},ye=s._Floor=function(){return(ye=s._Floor=s.asm.Floor).apply(null,arguments)},qf=s._FloorDiv=function(){return(qf=s._FloorDiv=s.asm.FloorDiv).apply(null,arguments)},Kf=s._FusedBatchNorm=function(){return(Kf=s._FusedBatchNorm=s.asm.FusedBatchNorm).apply(null,arguments)},Xf=s._FusedConv2D=function(){return(Xf=s._FusedConv2D=s.asm.FusedConv2D).apply(null,arguments)},Yf=s._FusedDepthwiseConv2D=function(){return(Yf=s._FusedDepthwiseConv2D=s.asm.FusedDepthwiseConv2D).apply(null,arguments)},Is=s._Gather=function(){return(Is=s._Gather=s.asm.Gather).apply(null,arguments)},Jf=s._GatherNd=function(){return(Jf=s._GatherNd=s.asm.GatherNd).apply(null,arguments)},Zf=s._Greater=function(){return(Zf=s._Greater=s.asm.Greater).apply(null,arguments)},Qf=s._GreaterEqual=function(){return(Qf=s._GreaterEqual=s.asm.GreaterEqual).apply(null,arguments)},eg=s._LeakyRelu=function(){return(eg=s._LeakyRelu=s.asm.LeakyRelu).apply(null,arguments)},tg=s._Less=function(){return(tg=s._Less=s.asm.Less).apply(null,arguments)},ng=s._LessEqual=function(){return(ng=s._LessEqual=s.asm.LessEqual).apply(null,arguments)},ag=s._Log=function(){return(ag=s._Log=s.asm.Log).apply(null,arguments)},rg=s._LogicalAnd=function(){return(rg=s._LogicalAnd=s.asm.LogicalAnd).apply(null,arguments)},sg=s._Max=function(){return(sg=s._Max=s.asm.Max).apply(null,arguments)},ig=s._MaxPool=function(){return(ig=s._MaxPool=s.asm.MaxPool).apply(null,arguments)},Or=s._Maximum=function(){return(Or=s._Maximum=s.asm.Maximum).apply(null,arguments)},Ns=s._Mean=function(){return(Ns=s._Mean=s.asm.Mean).apply(null,arguments)},yo=s._Min=function(){return(yo=s._Min=s.asm.Min).apply(null,arguments)},og=s._Minimum=function(){return(og=s._Minimum=s.asm.Minimum).apply(null,arguments)},lg=s._Multiply=function(){return(lg=s._Multiply=s.asm.Multiply).apply(null,arguments)},ug=s._Neg=function(){return(ug=s._Neg=s.asm.Neg).apply(null,arguments)},cg=s._NonMaxSuppressionV3=function(){return(cg=s._NonMaxSuppressionV3=s.asm.NonMaxSuppressionV3).apply(null,arguments)},Pe=s._NonMaxSuppressionV4=function(){return(Pe=s._NonMaxSuppressionV4=s.asm.NonMaxSuppressionV4).apply(null,arguments)},pg=s._NonMaxSuppressionV5=function(){return(pg=s._NonMaxSuppressionV5=s.asm.NonMaxSuppressionV5).apply(null,arguments)},dg=s._NotEqual=function(){return(dg=s._NotEqual=s.asm.NotEqual).apply(null,arguments)},hg=s._OneHot=function(){return(hg=s._OneHot=s.asm.OneHot).apply(null,arguments)},mg=s._PadV2=function(){return(mg=s._PadV2=s.asm.PadV2).apply(null,arguments)},fg=s._Pow=function(){return(fg=s._Pow=s.asm.Pow).apply(null,arguments)},gg=s._Prelu=function(){return(gg=s._Prelu=s.asm.Prelu).apply(null,arguments)},Gu=s._Prod=function(){return(Gu=s._Prod=s.asm.Prod).apply(null,arguments)},ed=s._RealDiv=function(){return(ed=s._RealDiv=s.asm.RealDiv).apply(null,arguments)},td=s._Relu=function(){return(td=s._Relu=s.asm.Relu).apply(null,arguments)},yg=s._Relu6=function(){return(yg=s._Relu6=s.asm.Relu6).apply(null,arguments)},bg=s._ResizeBilinear=function(){return(bg=s._ResizeBilinear=s.asm.ResizeBilinear).apply(null,arguments)},xg=s._Reverse=function(){return(xg=s._Reverse=s.asm.Reverse).apply(null,arguments)},vg=s._RotateWithOffset=function(){return(vg=s._RotateWithOffset=s.asm.RotateWithOffset).apply(null,arguments)},wg=s._Round=function(){return(wg=s._Round=s.asm.Round).apply(null,arguments)},kg=s._Rsqrt=function(){return(kg=s._Rsqrt=s.asm.Rsqrt).apply(null,arguments)},Be=s._ScatterNd=function(){return(Be=s._ScatterNd=s.asm.ScatterNd).apply(null,arguments)},Ig=s._SelectV2=function(){return(Ig=s._SelectV2=s.asm.SelectV2).apply(null,arguments)},Ng=s._Sigmoid=function(){return(Ng=s._Sigmoid=s.asm.Sigmoid).apply(null,arguments)},Tg=s._Sin=function(){return(Tg=s._Sin=s.asm.Sin).apply(null,arguments)},Ts=s._Softmax=function(){return(Ts=s._Softmax=s.asm.Softmax).apply(null,arguments)},bo=s._Sqrt=function(){return(bo=s._Sqrt=s.asm.Sqrt).apply(null,arguments)},nd=s._Square=function(){return(nd=s._Square=s.asm.Square).apply(null,arguments)},ad=s._SquaredDifference=function(){return(ad=s._SquaredDifference=s.asm.SquaredDifference).apply(null,arguments)},rd=s._StridedSlice=function(){return(rd=s._StridedSlice=s.asm.StridedSlice).apply(null,arguments)},Sg=s._Sub=function(){return(Sg=s._Sub=s.asm.Sub).apply(null,arguments)},Cg=s._Sum=function(){return(Cg=s._Sum=s.asm.Sum).apply(null,arguments)},sd=s._Tanh=function(){return(sd=s._Tanh=s.asm.Tanh).apply(null,arguments)},Eg=s._Tile=function(){return(Eg=s._Tile=s.asm.Tile).apply(null,arguments)},Va=s._TopK=function(){return(Va=s._TopK=s.asm.TopK).apply(null,arguments)},_g=s._Transpose=function(){return(_g=s._Transpose=s.asm.Transpose).apply(null,arguments)},Fg=s.__FusedMatMul=function(){return(Fg=s.__FusedMatMul=s.asm._FusedMatMul).apply(null,arguments)},id=s._malloc=function(){return(id=s._malloc=s.asm.malloc).apply(null,arguments)},Lr=s._free=function(){return(Lr=s._free=s.asm.free).apply(null,arguments)},Ag=s.__start=function(){return(Ag=s.__start=s.asm._start).apply(null,arguments)},od=s.stackSave=function(){return(od=s.stackSave=s.asm.stackSave).apply(null,arguments)},xo=s.stackAlloc=function(){return(xo=s.stackAlloc=s.asm.stackAlloc).apply(null,arguments)},Hu=s.stackRestore=function(){return(Hu=s.stackRestore=s.asm.stackRestore).apply(null,arguments)};s.asm=hr,s.cwrap=te;var Ss;s.then=function(U){if(Ss)U(s);else{var ne=s.onRuntimeInitialized;s.onRuntimeInitialized=function(){ne&&ne(),U(s)}}return s};function ju(U){this.name="ExitStatus",this.message="Program terminated with exit("+U+")",this.status=U}var $g=!1;Ia=function U(){Ss||qu(),Ss||(Ia=U)};function Dg(U){var ne=s.__start;try{ne();var Ne=0;ld(Ne,!0)}catch(at){if(at instanceof ju)return;if(at=="unwind"){O=!0;return}else{var _e=at;at&&typeof at=="object"&&at.stack&&(_e=[at,at.stack]),A("exception thrown: "+_e),u(1,at)}}finally{$g=!0}}function qu(U){if(U=U||l,ka>0||(wn(),ka>0))return;function ne(){Ss||(Ss=!0,s.calledRun=!0,!H&&(ia(),An(),s.onRuntimeInitialized&&s.onRuntimeInitialized(),ud&&Dg(U),Hn()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ne()},1)):ne()}s.run=qu;function ld(U,ne){ne&&O&&U===0||(O||(H=!0,X=U,cn(),s.onExit&&s.onExit(U)),u(U,new ju(U)))}if(s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();var ud=!0;return s.noInitialRun&&(ud=!1),O=!0,qu(),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)}),$_=1e-7,D_=1e-4,gd=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},nc=class{time(e){return Y("time")}read(e){return Y("read")}readSync(e){return Y("readSync")}numDataIds(){return Y("numDataIds")}disposeData(e){return Y("disposeData")}write(e,t,n){return Y("write")}move(e,t,n,a){return Y("move")}memory(){return Y("memory")}floatPrecision(){return Y("floatPrecision")}epsilon(){return this.floatPrecision()===32?$_:D_}batchMatMul(e,t,n,a){return Y("batchMatMul")}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:a,bias:r,activation:s,preluActivationWeights:i}){return Y("fusedBatchMatMul")}slice(e,t,n){return Y("slice")}stridedSlice(e,t,n,a){return Y("stridedSlice")}unstack(e,t){return Y("unstack")}reverse(e,t){return Y("reverse")}concat(e,t){return Y("concat")}neg(e){return Y("neg")}add(e,t){return Y("add")}addN(e){return Y("addN")}subtract(e,t){return Y("subtract")}multiply(e,t){return Y("multiply")}realDivide(e,t){return Y("realDivide")}floorDiv(e,t){return Y("floorDiv")}sum(e,t){return Y("sum")}prod(e,t){return Y("prod")}unsortedSegmentSum(e,t,n){return Y("unsortedSegmentSum")}argMin(e,t){return Y("argMin")}argMax(e,t){return Y("argMax")}equal(e,t){return Y("equal")}notEqual(e,t){return Y("notEqual")}less(e,t){return Y("less")}lessEqual(e,t){return Y("lessEqual")}greater(e,t){return Y("greater")}greaterEqual(e,t){return Y("greaterEqual")}logicalNot(e){return Y("logicalNot")}logicalAnd(e,t){return Y("logicalAnd")}logicalOr(e,t){return Y("logicalOr")}where(e){return Y("where")}select(e,t,n){return Y("select")}topk(e,t,n){return Y("topk")}min(e,t){return Y("min")}minimum(e,t){return Y("minimum")}mod(e,t){return Y("mod")}max(e,t){return Y("max")}maximum(e,t){return Y("maximum")}all(e,t){return Y("all")}any(e,t){return Y("any")}squaredDifference(e,t){return Y("squaredDifference")}ceil(e){return Y("ceil")}floor(e){return Y("floor")}round(e){return Y("round")}sign(e){return Y("sign")}isNaN(e){return Y("isNaN")}isInf(e){return Y("isInf")}isFinite(e){return Y("isFinite")}pow(e,t){return Y("pow")}exp(e){return Y("exp")}expm1(e){return Y("expm1")}softmax(e,t){return Y("softmax")}log(e){return Y("log")}log1p(e){return Y("log1p")}sqrt(e){return Y("sqrt")}rsqrt(e){return Y("rsqrt")}square(e){return Y("square")}reciprocal(e){return Y("reciprocal")}relu(e){return Y("relu")}relu6(e){return Y("relu6")}prelu(e,t){return Y("prelu")}elu(e){return Y("elu")}eluDer(e,t){return Y("eluDer")}selu(e){return Y("selu")}int(e){return Y("int")}clip(e,t,n){return Y("clip")}abs(e){return Y("abs")}complexAbs(e){return Y("complexAbs")}sigmoid(e){return Y("sigmoid")}softplus(e){return Y("softplus")}sin(e){return Y("sin")}cos(e){return Y("cos")}tan(e){return Y("tan")}asin(e){return Y("asin")}acos(e){return Y("acos")}atan(e){return Y("atan")}atan2(e,t){return Y("atan2")}sinh(e){return Y("sinh")}cosh(e){return Y("cosh")}tanh(e){return Y("tanh")}asinh(e){return Y("asinh")}acosh(e){return Y("acosh")}atanh(e){return Y("atanh")}erf(e){return Y("erf")}step(e,t){return Y("step")}fusedConv2d({input:e,filter:t,convInfo:n,bias:a,activation:r,preluActivationWeights:s}){return Y("fusedConv2d")}conv2d(e,t,n){return Y("conv2d")}conv2dDerInput(e,t,n){return Y("conv2dDerInput")}conv2dDerFilter(e,t,n){return Y("conv2dDerFilter")}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:a,activation:r,preluActivationWeights:s}){return Y("fusedDepthwiseConv2D")}depthwiseConv2D(e,t,n){return Y("depthwiseConv2D")}depthwiseConv2DDerInput(e,t,n){return Y("depthwiseConv2DDerInput")}depthwiseConv2DDerFilter(e,t,n){return Y("depthwiseConv2DDerFilter")}conv3d(e,t,n){return Y("conv3d")}conv3dDerInput(e,t,n){return Y("conv3dDerInput")}conv3dDerFilter(e,t,n){return Y("conv3dDerFilter")}maxPool(e,t){return Y("maxPool")}maxPoolBackprop(e,t,n,a){return Y("maxPoolBackprop")}avgPool(e,t){return Y("avgPool")}avgPoolBackprop(e,t,n){return Y("avgPoolBackprop")}avgPool3d(e,t){return Y("avgPool3d")}avgPool3dBackprop(e,t,n){return Y("avgPool3dBackprop")}maxPool3d(e,t){return Y("maxPool3d")}maxPool3dBackprop(e,t,n,a){return Y("maxPool3dBackprop")}reshape(e,t){return Y("reshape")}cast(e,t){return Y("cast")}tile(e,t){return Y("tile")}pad(e,t,n){return Y("pad")}transpose(e,t){return Y("transpose")}gather(e,t,n,a=0){return Y("gather")}gatherND(e,t){return Y("gatherND")}scatterND(e,t,n){return Y("scatterND")}batchToSpaceND(e,t,n){return Y("batchToSpaceND")}spaceToBatchND(e,t,n){return Y("spaceToBatchND")}resizeBilinear(e,t,n,a,r){return Y("resizeBilinear")}resizeBilinearBackprop(e,t,n){return Y("resizeBilinearBackprop")}resizeNearestNeighbor(e,t,n,a,r){return Y("resizeNearestNeighbor")}resizeNearestNeighborBackprop(e,t,n){return Y("resizeNearestNeighborBackprop")}batchNorm(e,t,n,a,r,s){return Y("batchNorm")}localResponseNormalization4D(e,t,n,a,r){return Y("localResponseNormalization4D")}LRNGrad(e,t,n,a,r,s,i){return Y("LRNGrad")}multinomial(e,t,n,a){return Y("multinomial")}oneHot(e,t,n,a){return Y("oneHot")}cumsum(e,t,n,a){return Y("cumsum")}nonMaxSuppression(e,t,n,a,r){return Y("nonMaxSuppression")}fft(e){return Y("fft")}ifft(e){return Y("ifft")}complex(e,t){return Y("complex")}real(e){return Y("real")}imag(e){return Y("imag")}cropAndResize(e,t,n,a,r,s){return Y("cropAndResize")}depthToSpace(e,t,n){return Y("depthToSpace")}split(e,t,n){return Y("split")}sparseToDense(e,t,n,a){return Y("sparseToDense")}diag(e){return Y("diag")}fill(e,t,n){return Y("fill")}onesLike(e){return Y("onesLike")}zerosLike(e){return Y("zerosLike")}linspace(e,t,n){return Y("linspace")}dispose(){return Y("dispose")}};function Y(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 Bw(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 ac(e,t,n){return Math.max(e,Math.min(t,n))}function M_(e){return e%2==0?e:e+1}function R_(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function P_(e,t){let n=Math.random();return t*n+(1-n)*e}function O_(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 F(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function rt(e,t,n=""){F(gr(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function Cs(e){F(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function Es(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||un(e)&&!n)for(let a=0;a<e.length;++a)Es(e[a],t,n);else t.push(e);return t}function zt(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 L_(e){return e.length===0}function gr(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 qt(e){return e%1==0}function z_(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 B_(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function W_(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return Bw(t),t}function rc(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function V_(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 U_(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} / ${n}`);let r=e.slice();return r[a]=t/n,r}function la(e,t){let n=t.length;return e=e==null?t.map((a,r)=>r):[].concat(e),F(e.every(a=>a>=-n&&a<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),F(e.every(a=>qt(a)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(a=>a<0?n+a:a)}function Ww(e,t){let n=[],a=[],r=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||r?null:la(t,e).sort(),i=0;for(let o=0;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 Vw(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 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 if(e==="string")n=new Array(t);else throw new Error(`Unknown data type ${e}`);return n}function Gw(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 Hw(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function jw(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function un(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function qw(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function Kw(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Wr(e){return typeof e=="string"||e instanceof String}function Xw(e){return typeof e=="boolean"}function Yw(e){return typeof e=="number"}function yd(e){return Array.isArray(e)?yd(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Yw(e)?"float32":Wr(e)?"string":Xw(e)?"bool":"float32"}function Vr(e){return!!(e&&e.constructor&&e.call&&e.apply)}function bd(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function To(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let a=t-3;a>=0;--a)n[a]=n[a+1]*e[a+1];return n}function Jw(e,t,n){let a=new Array;if(t.length===1){let r=t[0];for(let s=0;s<r;s++)a[s]=n[e+s]}else{let r=t[0],s=t.slice(1),i=s.reduce((o,l)=>o*l);for(let o=0;o<r;o++)a[o]=Jw(e+o*i,s,n)}return a}function So(e,t){if(e.length===0)return t[0];let n=e.reduce((a,r)=>a*r);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}.`);return Jw(0,e,t)}function Bg(e,t){let n=xd(e,t);for(let a=0;a<n.length;a++)n[a]=1;return n}function xd(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 G_(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 ${t}`)}function Wg(e){e.forEach(t=>{F(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function H_(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 j_(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 Vg(e){return e&&e.then&&typeof e.then=="function"}var Zw="tfjsflags",Qw=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(Vg(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=q_(this.global.location.search);Zw in e&&e[Zw].split(",").forEach(t=>{let[n,a]=t.split(":");this.urlFlags[n]=K_(n,a)})}};function q_(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...a)=>(X_(t,a[0],a[1]),a.join("="))),t}function X_(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function K_(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 ${e}.`)}function ee(){return Ug}var Ug=null;function Y_(e){Ug=e}var Gg;function e0(){if(Gg==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");Gg=e}return Gg}function J_(){let e=e0();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function t0(e,t){let n=J_();if(n.has(e))return n.get(e);{let a=t();return n.set(e,a),n.get(e)}}var Co="Abs",Eo="Acos",_o="Acosh",Ur="Add",_s="AddN",vd="All",wd="Any",Fs="ArgMax",sc="ArgMin",Fo="Asin",Ao="Asinh",$o="Atan",Do="Atanh",Mo="Atan2",As="AvgPool",kd="AvgPoolGrad",ic="AvgPool3D",Id="AvgPool3DGrad",$s="BatchMatMul",oc="BatchToSpaceND",Nd="Bincount",n0="BroadcastTo",Ds="Cast",Ro="Ceil",Gr="ClipByValue",Td="Complex",lc="ComplexAbs",Po="Concat",Ms="Conv2D",Sd="Conv2DBackpropFilter",Rs="Conv2DBackpropInput",uc="Conv3D",Cd="Conv3DBackpropFilterV2",Ed="Conv3DBackpropInputV2",Ps="Cos",Oo="Cosh",Os="Cumsum",Lo="CropAndResize",_d="DenseBincount",zo="DepthToSpace",Ls="DepthwiseConv2dNative",Fd="DepthwiseConv2dNativeBackpropFilter",Ad="DepthwiseConv2dNativeBackpropInput",$d="Diag",cc="Dilation2D",Dd="Dilation2DBackpropInput",Md="Dilation2DBackpropFilter",zs="RealDiv",Bo="Elu",Rd="EluGrad",Wo="Erf",Vo="Equal",Bs="Exp",Uo="ExpandDims",Go="Expm1",Pd="FFT",pc="Fill",Ho="FlipLeftRight",Ws="Floor",Vs="FloorDiv",Us="FusedBatchNorm",jo="GatherV2",qo="GatherNd",Ko="Greater",Gs="GreaterEqual",Xo="Identity",Od="IFFT",Ld="Imag",Yo="IsFinite",Jo="IsInf",Zo="IsNan",Hs="LeakyRelu",Qo="Less",el="LessEqual",zd="LinSpace",js="Log",tl="Log1p",nl="LogicalAnd",dc="LogicalNot",hc="LogicalOr",a0="LogSoftmax",mc="LRN",Bd="LRNGrad",qs="Max",Ks="Maximum",Xs="MaxPool",Wd="MaxPoolGrad",fc="MaxPool3D",Vd="MaxPool3DGrad",Ud="MaxPoolWithArgmax",Ys="Mean",Js="Min",Zs="Minimum",gc="MirrorPad",al="Mod",Gd="Multinomial",Qs="Multiply",rl="Neg",sl="NotEqual",il="NonMaxSuppressionV3",ol="NonMaxSuppressionV4",ll="NonMaxSuppressionV5",ul="OnesLike",ei="OneHot",cl="Pack",ti="PadV2",Z_="Pool",ni="Pow",ai="Prelu",pl="Prod",yc="Range",Hd="Real",dl="Reciprocal",ri="Relu",hl="Reshape",bc="ResizeNearestNeighbor",jd="ResizeNearestNeighborGrad",si="ResizeBilinear",qd="ResizeBilinearGrad",ii="Relu6",oi="Reverse",li="Round",ui="Rsqrt",ml="ScatterNd",fl="Select",gl="Selu",yl="Slice",ci="Sin",bl="Sinh",xl="Sign",pi="Sigmoid",vl="Softplus",di="Sqrt",hi="Sum",xc="SpaceToBatchND",wl="SplitV",mi="Softmax",fi="SquaredDifference",vc="Square",gi="Sub",Kd="SparseToDense",kl="StridedSlice",Il="Tan",yi="Tanh",Hr="Tile",Nl="TopK",bi="Transpose",Xd="Unique",Tl="Unpack",wc="UnsortedSegmentSum",Sl="ZerosLike",Cl="Step",Yd="FromPixels",El="RotateWithOffset",xi="_FusedMatMul",vi="FusedConv2D",wi="FusedDepthwiseConv2D",_l=t0("kernelRegistry",()=>new Map),kc=t0("gradRegistry",()=>new Map);function jg(e,t){let n=Hg(e,t);return _l.get(n)}function qg(e){return kc.get(e)}function Jd(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 Ic(e){let{kernelName:t,backendName:n}=e,a=Hg(t,n);_l.has(a)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),_l.set(a,e)}function r0(e){let{kernelName:t}=e;kc.has(t)&&ee().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),kc.set(t,e)}function Q_(e,t){let n=Hg(e,t);if(!_l.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);_l.delete(n)}function eF(e){if(!kc.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);kc.delete(e)}function tF(e,t){Jd(e).forEach(n=>{let a=Object.assign({},n,{backendName:t});Ic(a)})}function Hg(e,t){return`${t}_${e}`}var k={};Oe(k,{arraysEqual:()=>gr,assert:()=>F,assertNonNegativeIntegerDimensions:()=>Wg,assertNonNull:()=>Cs,assertShapesMatch:()=>rt,bytesFromStringArray:()=>Kw,bytesPerElement:()=>qw,checkConversionForErrors:()=>Gw,clamp:()=>ac,computeStrides:()=>To,createScalarValue:()=>nF,createShuffledIndices:()=>W_,decodeString:()=>Qd,distSquared:()=>O_,encodeString:()=>Nc,fetch:()=>aF,flatten:()=>Es,getArrayFromDType:()=>Uw,getTypedArrayFromDType:()=>Vw,hasEncodingLoss:()=>jw,indexToLoc:()=>j_,inferDtype:()=>yd,inferFromImplicitShape:()=>U_,isBoolean:()=>Xw,isFunction:()=>Vr,isInt:()=>qt,isNumber:()=>Yw,isPromise:()=>Vg,isScalarShape:()=>L_,isString:()=>Wr,isTypedArray:()=>un,isValidDtype:()=>Hw,locToIndex:()=>H_,makeOnesTypedArray:()=>Bg,makeZerosNestedTypedArray:()=>G_,makeZerosTypedArray:()=>xd,nearestDivisor:()=>bd,nearestLargerEven:()=>M_,now:()=>Kg,parseAxisParam:()=>la,randUniform:()=>P_,repeatedTry:()=>V_,rightPad:()=>rc,shuffle:()=>Bw,sizeFromShape:()=>zt,sizeToSquarishShape:()=>B_,squeezeShape:()=>Ww,sum:()=>R_,tanh:()=>z_,toNestedArray:()=>So,toTypedArray:()=>Zd});function nF(e,t){return t==="string"?Nc(e):Zd([e],t)}function rF(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function Zd(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=Es(e)),ee().getBool("DEBUG")&&Gw(e,t),rF(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 Kg(){return ee().platform.now()}function aF(e,t){return ee().platform.fetch(e,t)}function Nc(e,t="utf-8"){return t=t||"utf-8",ee().platform.encode(e,t)}function Qd(e,t="utf-8"){return t=t||"utf-8",ee().platform.decode(e,t)}var oF=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new iF)}profileKernel(e,t,n){let a,r=()=>{a=n()},s=this.backendTimer.time(r);if(ee().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<a.length;i++){let o=a[i];o.data().then(l=>{sF(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 sF(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 iF=class{logKernelProfile(e,t,n,a,r,s){let i=typeof a=="number"?rc(`${a}ms`,9):a.error,o=rc(e,25),l=t.rank,c=t.size,u=rc(t.shape.toString(),14),p="";for(let d in r){let h=r[d];if(h!=null){let m=h.shape||t.shape,f=m.length;p+=`${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 lF(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 uF(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(!gr(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 s0=20,Tc=3,Xg=7;function pF(e,t,n,a){let r=To(t),s=cF(e,t,n,r),i=t.length,o=eh(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 cF(e,t,n,a){let r=zt(t),s=a[a.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Cc(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],Sc(l[u+p],0,n).length)}return i}function Sc(e,t,n){let a;return Array.isArray(e)?a=`${parseFloat(e[0].toFixed(Xg))} + ${parseFloat(e[1].toFixed(Xg))}j`:Wr(e)?a=`'${e}'`:n==="bool"?a=i0(e):a=parseFloat(e.toFixed(Xg)).toString(),rc(a,t)}function i0(e){return e===0?"false":"true"}function eh(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=Cc(e);return[Sc(f[0],0,n)]}return n==="bool"?[i0(e[0])]:[e[0].toString()]}if(l===1){if(o>s0){let g=Tc*i,y=Array.from(e.slice(0,g)),b=Array.from(e.slice((o-Tc)*i,o*i));return n==="complex64"&&(y=Cc(y),b=Cc(b)),["["+y.map((x,v)=>Sc(x,r[v],n)).join(", ")+", ..., "+b.map((x,v)=>Sc(x,r[o-Tc+v],n)).join(", ")+"]"]}let f=n==="complex64"?Cc(e):Array.from(e);return["["+f.map((g,y)=>Sc(g,r[y],n)).join(", ")+"]"]}let c=t.slice(1),u=a.slice(1),p=a[0]*i,d=[];if(o>s0){for(let f=0;f<Tc;f++){let g=f*p,y=g+p;d.push(...eh(e.slice(g,y),c,n,u,r,!1))}d.push("...");for(let f=o-Tc;f<o;f++){let g=f*p,y=g+p;d.push(...eh(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(...eh(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 Cc(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Bt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=zt(e),n!=null){let a=n.length;F(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||Uw(t,this.size),this.strides=To(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let a of e){if(a<0||a>=this.shape[t]){let r=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let a=0;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 Ua().makeTensor(this.values,this.shape,this.dtype)}},Ua=null,Fl=null,dF=null;function hF(e){Ua=e}function mF(e){Fl=e}function fF(e){dF=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=zt(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 Fl.buffer(this.shape,this.dtype,e)}bufferSync(){return Fl.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=Ua().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Qd(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=Ua().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Qd(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 Ua().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Ua().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Fl.print(this,e)}clone(){return this.throwIfDisposed(),Fl.clone(this)}toString(e=!1){let t=this.dataSync();return pF(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Fl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ua().makeVariable(this,e,t,n)}};Object.defineProperty(z,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});var jr=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(!gr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ua().disposeTensor(this),this.dataId=e.dataId,Ua().incRef(this,null)}dispose(){Ua().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(jr,Symbol.hasInstance,{value:e=>e instanceof z&&e.assign!=null&&e.assign instanceof Function});var Na={};Oe(Na,{assertTypesMatch:()=>o0,getTensorsInContainer:()=>Yg,isTensorInList:()=>gF,makeTypesMatch:()=>Tt});var Jg;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Jg||(Jg={}));var Zg;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Zg||(Zg={}));var Qg;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Qg||(Qg={}));var ey;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(ey||(ey={}));var ty;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(ty||(ty={}));var yF={float32:ey,int32:Zg,bool:Qg,complex64:ty};function ua(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return yF[e][t]}function th(e){return ua(e,"int32")}function Tt(e,t){if(e.dtype===t.dtype)return[e,t];let n=ua(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function o0(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function gF(e,t){return t.some(n=>n.id===e.id)}function Yg(e){let t=[],n=new Set;return l0(e,t,n),t}function l0(e,t,n){if(e==null)return;if(e instanceof z){t.push(e);return}if(!bF(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),l0(s,t,n))}}function bF(e){return Array.isArray(e)||typeof e=="object"}var u0=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Ec=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new u0}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 oF(this.backendInstance),!0}setupRegisteredKernels(){Jd(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jd(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 nc)&&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 Ec.nextTensorId++}nextVariableId(){return Ec.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,Ds,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=jg(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(x=>{if(x.rank!=null)return x;let{dataId:v,shape:N,dtype:T}=x;return this.makeTensorFromDataId(v,N,T)});if(c){let x=this.getTensorsForGradient(a,t,b);if(x==null){i==null&&(i=[]);let v=b.filter((N,T)=>i[T]);x=(s||[]).slice().concat(v)}l=this.saveTensorsForBackwardMode(x)}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=qg(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,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"&&Wr(e[0])&&(r=e.map(o=>Nc(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=Kw(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 jr(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*qw(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 jr||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)):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=qg(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let p=n[u],d=xd(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=Yg(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(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof z,()=>"The result y returned by f() must be a tensor.");let s=lF(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?xF(r.shape):n,uF(i,s,l=>this.tidy(l),vF);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 F(Vr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(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),F(n.value instanceof z,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(Vr(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];F(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(...)."),F(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=Kg(),n=await this.backend.time(e);return n.wallMs=Kg()-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 u0;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}};Ec.nextTensorId=0;Ec.nextVariableId=0;function xF(e){let t=Bg(zt(e),"float32");return P.makeTensor(t,e,"float32")}function c0(){let e=e0();if(e._tfengine==null){let t=new Qw(e);e._tfengine=new Ec(t)}return Y_(e._tfengine.ENV),hF(()=>e._tfengine),e._tfengine}var P=c0();function vF(e,t){let n={a:e,b:t};return P.runKernel(Ur,n)}var nh={};Oe(nh,{isBrowser:()=>p0,isMobile:()=>wF});function kF(){return typeof navigator!="undefined"&&navigator!=null}function wF(){if(kF()){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 p0(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Ga=ee();Ga.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.")});Ga.registerFlag("IS_BROWSER",()=>p0());Ga.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Ga.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Ga.registerFlag("PROD",()=>!1);Ga.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Ga.getBool("DEBUG"));Ga.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Ga.registerFlag("IS_TEST",()=>!1);Ga.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function Ha(e,t){let n=e;if(un(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let a=[];for(;Array.isArray(n)||un(n)&&t!=="string";)a.push(n.length),n=n[0];return Array.isArray(e)&&ee().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&d0(e,a,[]),a}function d0(e,t,n){if(n=n||[],!Array.isArray(e)&&!un(e)){F(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}F(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),F(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let a=t.slice(1);for(let r=0;r<e.length;++r)d0(e[r],a,n.concat(r))}function h0(e,t,n,a){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${a}' must be ${e} tensor, but got ${t} tensor`)}}function C(e,t,n,a="numeric"){if(e instanceof z)return h0(a,e.dtype,t,n),e;let r=yd(e);if(r!=="string"&&["bool","int32","float32"].indexOf(a)>=0&&(r=a),h0(a,r,t,n),e==null||!un(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Ha(e,r);!un(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?Zd(e,r):Es(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 ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((r,s)=>C(r,`${t}[${s}]`,n,a))}var m0="__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+m0;let r=(...s)=>{P.startScope(n);try{let i=a(...s);return Vg(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 IF(e,t){let n=C(e,"real","complex"),a=C(t,"imag","complex");rt(n.shape,a.shape,`real and imag shapes, ${n.shape} and ${a.shape}, must match in call to tf.complex().`);let r={real:n,imag:a};return P.runKernel(Td,r)}var qr=R({complex_:IF});function Kr(e,t,n,a){if(a==null&&(a=yd(e)),a==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!un(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Wg(t);let r=zt(t),s=zt(n);F(r===s,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==zt(t.slice(i)):!0;F(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!un(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=a!=="string"?Zd(e,a):Es(e,[],!0),P.makeTensor(e,t,a)}function Xn(e,t,n){let a=Ha(e,n);return Kr(e,t,a,n)}var ny={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},ah=4;async function TF(e,t){let n=[],a=[],r=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<r.length;++i){let o=r[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${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)+ah*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+=ah,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:NF(s),specs:n}}function f0(e,t){let n={},a,r=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,c=zt(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=ny[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=SF()),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}': ${o}`);r+=c*d}else if(o==="string"){let p=zt(s.shape);u=[];for(let d=0;d<p;d++){let h=new Uint32Array(e.slice(r,r+ah))[0];r+=ah;let m=new Uint8Array(e.slice(r,r+h));u.push(m),r+=h}}else{let p=ny[o],d=e.slice(r,r+c*p);if(o==="float32")u=new Float32Array(d);else if(o==="int32")u=new Int32Array(d);else if(o==="bool")u=new Uint8Array(d);else if(o==="complex64"){u=new Float32Array(d);let h=new Float32Array(u.length/2),m=new Float32Array(u.length/2);for(let y=0;y<h.length;y++)h[y]=u[y*2],m[y]=u[y*2+1];let f=Xn(h,l,"float32"),g=Xn(m,l,"float32");n[i]=qr(f,g),f.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);r+=c*p}o!=="complex64"&&(n[i]=Xn(u,l,o))}return n}function NF(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 ay=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function g0(e){return ay?Buffer.byteLength(e):new Blob([e]).size}function CF(e){if(ay)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 EF(e){if(ay){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 ry(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 y0(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 Fc(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:g0(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:g0(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function _F(){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 FF(){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 AF(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function SF(){let e=_F(),t=FF(),n=AF();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}},$F=e=>At.registerSaveRouter(e),DF=e=>At.registerLoadRouter(e),MF=e=>At.getSaveHandlers(e),RF=(e,t)=>At.getLoadHandlers(e,t),sy="tensorflowjs",iy=1,ki="models_store",Xr="model_info_store";function b0(){if(!ee().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 oy(e){let t=e.result;t.createObjectStore(ki,{keyPath:"modelPath"}),t.createObjectStore(Xr,{keyPath:"modelPath"})}var Ii=class{constructor(e){if(this.indexedDB=b0(),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(sy,iy);r.onupgradeneeded=()=>oy(r),r.onsuccess=()=>{let s=r.result;if(t==null){let i=s.transaction(ki,"readonly"),o=i.objectStore(ki).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),a(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),a(o.error)),i.oncomplete=()=>s.close()}else{let i=Fc(t),o=s.transaction(Xr,"readwrite"),l=o.objectStore(Xr),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),u;c.onsuccess=()=>{u=s.transaction(ki,"readwrite");let p=u.objectStore(ki).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});p.onsuccess=()=>n({modelArtifactsInfo:i}),p.onerror=d=>{l=o.objectStore(Xr);let h=l.delete(this.modelPath);h.onsuccess=()=>(s.close(),a(p.error)),h.onerror=m=>(s.close(),a(p.error))}},c.onerror=p=>(s.close(),a(c.error)),o.oncomplete=()=>{u==null?s.close():u.oncomplete=()=>s.close()}}},r.onerror=s=>a(r.error)})}};Ii.URL_SCHEME="indexeddb://";var x0=e=>ee().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Ii.URL_SCHEME)?PF(e.slice(Ii.URL_SCHEME.length)):null;At.registerSaveRouter(x0);At.registerLoadRouter(x0);function PF(e){return new Ii(e)}function OF(e){return e.startsWith(Ii.URL_SCHEME)?e.slice(Ii.URL_SCHEME.length):e}var LF=class{constructor(){this.indexedDB=b0()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(sy,iy);n.onupgradeneeded=()=>oy(n),n.onsuccess=()=>{let a=n.result,r=a.transaction(Xr,"readonly"),s=r.objectStore(Xr).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(a.close(),t(s.error)),r.oncomplete=()=>a.close()},n.onerror=a=>t(n.error)})}async removeModel(e){return e=OF(e),new Promise((t,n)=>{let a=this.indexedDB.open(sy,iy);a.onupgradeneeded=()=>oy(a),a.onsuccess=()=>{let r=a.result,s=r.transaction(Xr,"readwrite"),i=s.objectStore(Xr),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=i.delete(e),u=()=>{l=r.transaction(ki,"readwrite");let p=l.objectStore(ki).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)})}},yr="/",Al="tensorflowjs_models",v0="info",zF="model_topology",BF="weight_specs",WF="weight_data",VF="model_metadata";function w0(e){return{info:[Al,e,v0].join(yr),topology:[Al,e,zF].join(yr),weightSpecs:[Al,e,BF].join(yr),weightData:[Al,e,WF].join(yr),modelMetadata:[Al,e,VF].join(yr)}}function UF(e){let t=e.split(yr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(yr)}function GF(e){return e.startsWith(Ni.URL_SCHEME)?e.slice(Ni.URL_SCHEME.length):e}var Ni=class{constructor(e){if(!ee().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=w0(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),a=Fc(e);try{this.LS.setItem(this.keys.info,JSON.stringify(a)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,CF(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:a}}catch(r){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=EF(s),t}};Ni.URL_SCHEME="localstorage://";var k0=e=>ee().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Ni.URL_SCHEME)?HF(e.slice(Ni.URL_SCHEME.length)):null;At.registerSaveRouter(k0);At.registerLoadRouter(k0);function HF(e){return new Ni(e)}var jF=class{constructor(){F(ee().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),F(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Al+yr,n=yr+v0;for(let a=0;a<this.LS.length;++a){let r=this.LS.key(a);if(r.startsWith(t)&&r.endsWith(n)){let s=UF(r);e[s]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=GF(e);let t=w0(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${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}},$l="://",Yn=class{constructor(){this.managers={}}static getInstance(){return Yn.instance==null&&(Yn.instance=new Yn),Yn.instance}static registerManager(e,t){F(e!=null,()=>"scheme must not be undefined or null."),e.endsWith($l)&&(e=e.slice(0,e.indexOf($l))),F(e.length>0,()=>"scheme must not be an empty string.");let n=Yn.getInstance();F(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function rh(e){if(e.indexOf($l)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Yn.getSchemes().join(",")}`);return{scheme:e.split($l)[0],path:e.split($l)[1]}}async function I0(e,t,n=!1){F(e!==t,()=>`Old path and new path are the same: '${e}'`);let a=At.getLoadHandlers(e);F(a.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),F(a.length<2,()=>`Copying failed because more than one (${a.length}) load handlers for source URL ${e}.`);let r=a[0],s=At.getSaveHandlers(t);F(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),F(s.length<2,()=>`Copying failed because more than one (${a.length}) save handlers for destination URL ${t}.`);let i=s[0],o=rh(e).scheme,l=rh(e).path,c=o===rh(e).scheme,u=await r.load();n&&c&&await Yn.getManager(o).removeModel(l);let p=await i.save(u);return n&&!c&&await Yn.getManager(o).removeModel(l),p.modelArtifactsInfo}async function qF(){let e=Yn.getSchemes(),t={};for(let n of e){let a=await Yn.getManager(n).listModels();for(let r in a){let s=n+$l+r;t[s]=a[r]}}return t}async function KF(e){let t=rh(e);return Yn.getManager(t.scheme).removeModel(t.path)}async function XF(e,t){return I0(e,t,!1)}async function YF(e,t){return I0(e,t,!0)}var JF=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(ee().get("IS_BROWSER")){ee().setPlatform("browser",new JF);try{Yn.registerManager(Ni.URL_SCHEME,new jF)}catch(e){}try{Yn.registerManager(Ii.URL_SCHEME,new LF)}catch(e){}}var ZF={importFetch:()=>b_()},ly,QF=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return ee().global.fetch!=null?ee().global.fetch(e,t):(ly==null&&(ly=ZF.importFetch()),ly(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)}};ee().get("IS_NODE")&&ee().setPlatform("node",new QF);function Le(e,t="float32",n){return t=t||"float32",Wg(e),new Bt(e,t,n)}function eA(e,t){let n=C(e,"x","cast");if(!Hw(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let a={x:n},r={dtype:t};return P.runKernel(Ds,a,r)}var pe=R({cast_:eA});function tA(e){let t={x:C(e,"x","clone","string_or_numeric")};return P.runKernel(Xo,t)}var Yr=R({clone_:tA});function N0(e,t=!1){console.log(e.toString(t))}c0();var nA={buffer:Le,cast:pe,clone:Yr,print:N0};mF(nA);var Kt={};Oe(Kt,{browserFiles:()=>aA,browserHTTPRequest:()=>sA,concatenateArrayBuffers:()=>ry,copyModel:()=>XF,decodeWeights:()=>f0,encodeWeights:()=>TF,fromMemory:()=>iA,getLoadHandlers:()=>RF,getModelArtifactsInfoForJSON:()=>Fc,getSaveHandlers:()=>MF,http:()=>cy,isHTTPScheme:()=>uy,listModels:()=>qF,loadWeights:()=>rA,moveModel:()=>YF,registerLoadRouter:()=>DF,registerSaveRouter:()=>$F,removeModel:()=>KF,weightsLoaderFactory:()=>T0,withSaveHandler:()=>oA});var lA="model",uA=".json",cA=".weights.bin";function S0(e){return new Promise(t=>setTimeout(t)).then(e)}var Dl=class{constructor(e){if(!ee().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Dl.URL_SCHEME)&&(e=e.slice(Dl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=lA),this.modelTopologyFileName=e+uA,this.weightDataFileName=e+cA}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 S0(()=>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 S0(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Fc(e)}}}};Dl.URL_SCHEME="downloads://";var pA=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 ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(l,t)}catch(h){a(h);return}let u=[],p=[],d=[];l.forEach(h=>{h.paths.forEach(m=>{p.push(m),d.push(null)}),u.push(...h.weights)}),l.forEach(h=>{h.paths.forEach(m=>{let f=new FileReader;f.onload=g=>{let y=g.target.result,b=p.indexOf(m);if(d[b]=y,d.indexOf(null)===-1){let x={modelTopology:o,weightSpecs:u,weightData:ry(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(x.signature=i.signature),i.userDefinedMetadata!=null&&(x.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(x.modelInitializer=i.modelInitializer),n(x)}},f.onerror=g=>a(`Failed to weights data from file of path '${m}'.`),f.readAsArrayBuffer(c[m])})})},r.onerror=s=>a(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],a=t.map(s=>y0(s.name)),r={};for(let s of e)s.paths.forEach(i=>{let o=y0(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${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}},hA=e=>ee().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Dl.URL_SCHEME)?dA(e.slice(Dl.URL_SCHEME.length)):null;At.registerSaveRouter(hA);function dA(e="model"){return new Dl(e)}function aA(e){return new pA(e)}function C0(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){F(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,c){F(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),F(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),F(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?ee().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 C0(a,t.onProgress,r,s)).map(c=>c.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await C0(i,t.onProgress,o,l)}async function rA(e,t="",n,a){return T0(r=>E0(r,{requestInit:a}))(e,t,n)}function T0(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=ny[y]*zt(g.shape),x=()=>{r[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:g,groupOffset:f,sizeBytes:b})};a!=null?a.forEach((v,N)=>{v===g.name&&(x(),i[N]=!0)}):x(),o.push(g.name),f+=b})}),!i.every(h=>h)){let h=a.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}. 
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=r.reduce((h,m,f)=>(m&&h.push(f),h),[]),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 x=0;x<m;x++)f+=u[d+x].byteLength;let g=new ArrayBuffer(f),y=new Uint8Array(g),b=0;for(let x=0;x<m;x++){let v=new Uint8Array(u[d+x]);y.set(v,b),b+=v.byteLength}s[h].forEach(x=>{let v=g.slice(x.groupOffset,x.groupOffset+x.sizeBytes),N=f0(v,[x.manifestEntry]);for(let T in N)p[T]=N[T]}),d+=m}),p}}var mA="application/octet-stream",fA="application/json",py=class{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(F(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=ee().platform.fetch,F(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&F(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],a={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(a)],{type:fA}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:mA}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Fc(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]=gA(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,ry(l)]}};py.URL_SCHEME_REGEX=/^https?:\/\//;function gA(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),a=e.substring(0,t),r=n>t?e.substring(n):"";return[a+"/",r]}function uy(e){return e.match(py.URL_SCHEME_REGEX)!=null}var _0=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(a=>uy(a)):n=uy(e),n)return cy(e,t)}return null};At.registerSaveRouter(_0);At.registerLoadRouter(_0);function cy(e,t){return new py(e,t)}function sA(e,t){return cy(e,t)}var dy=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},yA=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function iA(e,t,n,a){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new dy(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 dy({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 dy({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:a}))}function oA(e){return new yA(e)}var F0={};Oe(F0,{confusionMatrix:()=>bA});function xA(e,t,n=!1,a=!1){let r=C(e,"a","matMul"),s=C(t,"b","matMul");[r,s]=Tt(r,s);let i={a:r,b:s},o={transposeA:n,transposeB:a};return P.runKernel($s,i,o)}var ze=R({matMul_:xA});function vA(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:C(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:a};return P.runKernel(ei,r,s)}var Ml=R({oneHot_:vA});function wA(e,t){let n=C(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{F(s>=0&&s<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(bi,a,r)}var Ue=R({transpose_:wA});function kA(e,t,n){let a=C(e,"labels","confusionMatrix"),r=C(t,"predictions","confusionMatrix");F(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),F(a.rank===1,()=>`Expected the rank of labels to be 1, but got ${a.rank}`),F(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),F(a.shape[0]===r.shape[0],()=>`Mismatch in the number of examples: ${a.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`),F(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=Ml(pe(a,"int32"),n),i=Ml(pe(r,"int32"),n),o=Ue(s),l=ze(o,i);return pe(l,"int32")}var bA=R({confusionMatrix_:kA}),Ti={};Oe(Ti,{fromPixels:()=>NA,toPixels:()=>IA});function sh(e,t,n){if(Cs(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let a=Ha(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 Kr(e,t,a,n)}var Rl;function TA(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(jg(Yd,P.backendName)!=null){let d={pixels:e},h={numChannels:t};return P.runKernel(Yd,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)&&(Rl==null&&(Rl=document.createElement("canvas").getContext("2d")),Rl.canvas.width=l,Rl.canvas.height=c,Rl.drawImage(e,0,0,l,c),u=Rl.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 sh(p,[c,l,t],"int32")}async function IA(e,t){let n=C(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 NA=R({fromPixels_:TA}),hy={};Oe(hy,{prepareAndValidate:()=>A0});function A0(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(zt(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 my={};Oe(my,{calculateShapes:()=>$0,validateInput:()=>gy,validateUpdateShape:()=>fy});function fy(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 gy(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}`)}fy(n,t,e)}function $0(e,t,n){let a=t.shape.length,r=a>1?t.shape[a-1]:1,s=n.length,i=1;for(let p=r;p<s;++p)i*=n[p];let o=r<1?1:r,l=zt(t.shape)/o,c=[...To(n.slice(0,r)),1],u=zt(n);return{sliceRank:r,numUpdates:l,sliceSize:i,strides:c,outputSize:u}}var dn={};Oe(dn,{assertParamsValid:()=>SA,computeFlatOffset:()=>EA,computeOutShape:()=>D0,getNormalizedAxes:()=>R0,isSliceContinous:()=>CA,maskToAxes:()=>ih,parseSliceParams:()=>W0,sliceInfo:()=>_A,startForAxis:()=>z0,startIndicesWithElidedDims:()=>P0,stopForAxis:()=>B0,stopIndicesWithElidedDims:()=>O0,stridesForAxis:()=>L0,stridesWithElidedDims:()=>M0});function SA(e,t,n){let a=e.shape.length;F(a===t.length,()=>`Error in slice${a}D: Length of begin ${t} must match the rank of the array (${a}).`),F(a===n.length,()=>`Error in slice${a}D: Length of size ${n} must match the rank of the array (${a}).`);for(let r=0;r<a;++r)F(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 ih(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function D0(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 M0(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 V0(e,t,n){return n<=e?n:n-(t-1)}function U0(e,t){let n=[];for(let a=0;a<e;a++)n.push(t+a);return n}function R0(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=P0(i,h,m,a,e),p=O0(o,h,m,r,e),d=M0(s,h,m,e)}else for(let h=0;h<c;h++)u[h]=z0(i,a,s,e,h,l),p[h]=B0(o,r,s,e,h,l),d[h]=L0(s,h,l);return{begin:u,end:p,strides:d}}function P0(e,t,n,a,r){let s=[...r],i=U0(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=V0(t,n,o),c=a[l];e&1<<l&&(c=0),s[o]=c}return s}function O0(e,t,n,a,r){let s=[...r],i=U0(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=V0(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]=ac(0,s[o],r[o])}return s}function L0(e,t,n){let a=e[t];return(n&1<<t||a==null)&&(a=1),a}function z0(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=ac(0,i,l-1),i}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.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=a[r];return i<0&&(i+=l),o>0?i=ac(0,i,l):i=ac(-1,i,l-1),i}function CA(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 EA(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 W0(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=>{F(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(r).fill(-1):typeof n=="number"?s=[n,...new Array(r-1).fill(-1)]:n.length<r?s=n.concat(new Array(r-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(F(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-a[o])),[a,s]}function _A(e,t,n,a,r,s,i,o,l){let c=t.slice(),u=n.slice(),p=a;a==null&&(p=new Array(c.length));let d=ih(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let h=e.length-c.length,m=ih(o),f=e.slice();m.forEach(T=>{c[T]=0,u[T]=1,f.splice(T,0,1)});let{begin:g,end:y,strides:b}=R0(f,d,h,c,u,p,r,s,i);c=g,u=y,p=b;let x=ih(l);x.forEach(T=>{u[T]=c[T]+1,p[T]=1});let v=D0(c,u,p),N=v.filter((T,E)=>x.indexOf(E)===-1);return{nonStrided:p.every(T=>T===1),$begin:c,$end:u,$strides:p,size:v,newShape:f,outShape:N}}var se={};Oe(se,{Serializable:()=>G0,SerializationMap:()=>Si,registerClass:()=>Jr});var G0=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Si=class{constructor(){this.classNameMap={}}static getMap(){return Si.instance==null&&(Si.instance=new Si),Si.instance}static register(e){Si.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Jr(e){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Si.register(e)}var H0={};Oe(H0,{TEST_EPSILON_FLOAT16:()=>j0,encodeStrings:()=>q0,expectArrayBuffersEqual:()=>RA,expectArraysClose:()=>FA,expectArraysEqual:()=>$A,expectNumbersClose:()=>DA,expectPromiseToFail:()=>AA,expectValuesInRange:()=>MA,testEpsilon:()=>yy});var PA=.001,j0=.1;function FA(e,t,n){return n==null&&(n=yy()),by(e,t,(a,r)=>xy(a,r,n))}function yy(){return P.backend.floatPrecision()===32?PA:j0}function by(e,t,n){let a=!0;if((un(e)||un(t))&&(a=!1),un(e)&&un(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: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Ha(e),o=Ha(t);if(!gr(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let r=un(e)?e:Es(e),s=un(t)?t:Es(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 AA(e,t){e().then(()=>t.fail(),()=>t())}function $A(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Wr(e)||Wr(e[0])||Wr(t)||Wr(t[0])?by(e,n,(a,r)=>a==r):by(e,t,(a,r)=>xy(a,r,0))}function DA(e,t,n){if(n==null&&(n=yy()),!xy(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function xy(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function MA(e,t,n){for(let a=0;a<e.length;a++)if(e[a]<t||e[a]>n)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function RA(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function q0(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?q0(n):e[t]=Nc(n)}return e}var K0="2.8.3";function OA(){ee().set("PROD",!0)}function LA(){ee().set("DEBUG",!0)}function zA(){ee().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Pt(e){ee().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}fF(Pt);function BA(){P.disposeVariables()}function Zr(){return P}function oh(){return P.memory()}function WA(e){return P.profile(e)}function D(e,t){return P.tidy(e,t)}function Ae(e){Yg(e).forEach(t=>t.dispose())}function Xt(e){return P.keep(e)}function VA(e){return P.time(e)}function UA(e){return P.setBackend(e)}function GA(){return P.ready()}function HA(){return P.backendName}function jA(e){P.removeBackend(e)}function qA(e){return P.findBackend(e)}function KA(e){return P.findBackendFactory(e)}function lh(e,t,n=1){return P.registerBackend(e,t,n)}function X0(){return P.backend}function XA(e,t){ee().setPlatform(e,t)}function YA(e,t){let n=C(e,"a","add"),a=C(t,"b","add");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Ur,r)}var Z=R({add_:YA});function JA(e,t){let n=C(e,"a","floorDiv"),a=C(t,"b","floorDiv");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Vs,r)}var uh=R({floorDiv_:JA});function ZA(e,t){let n=C(e,"a","div"),a=C(t,"b","div");if([n,a]=Tt(n,a),n.dtype==="int32"&&a.dtype==="int32")return uh(n,a);let r={a:n,b:a},s={};return P.runKernel(zs,r,s)}var ve=R({div_:ZA});function QA(e,t){let n=C(e,"a","mul"),a=C(t,"b","mul");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Qs,r)}var L=R({mul_:QA});function e$(e){let t=C(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return P.runKernel(lc,n)}else{let n={x:t};return P.runKernel(Co,n)}}var Wt=R({abs_:e$});function t$(e){let t={x:C(e,"x","acos")};return P.runKernel(Eo,t)}var vy=R({acos_:t$});function n$(e){let t={x:C(e,"x","acosh")};return P.runKernel(_o,t)}var wy=R({acosh_:n$});function a$(e){F(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),F(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((r,s)=>C(r,`tensors${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(!gr(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 Y0=R({addN_:a$});function r$(e,t=null,n=!1){let a={x:C(e,"x","all","bool")},r={axis:t,keepDims:n};return P.runKernel(vd,a,r)}var ch=R({all_:r$});function s$(e,t=null,n=!1){let a={x:C(e,"x","any","bool")},r={axis:t,keepDims:n};return P.runKernel(wd,a,r)}var Ac=R({any_:s$});function i$(e,t=0){let n={x:C(e,"x","argMax")},a={axis:t};return P.runKernel(Fs,n,a)}var $c=R({argMax_:i$});function o$(e,t=0){let n={x:C(e,"x","argMin")},a={axis:t};return P.runKernel(sc,n,a)}var ky=R({argMin_:o$});function l$(e){let t={x:C(e,"x","asin")};return P.runKernel(Fo,t)}var Iy=R({asin_:l$});function u$(e){let t={x:C(e,"x","asinh")};return P.runKernel(Ao,t)}var Ny=R({asinh_:u$});function c$(e){let t={x:C(e,"x","atan")};return P.runKernel($o,t)}var Ty=R({atan_:c$});function p$(e,t){let n=C(e,"a","atan2"),a=C(t,"b","atan2");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(Mo,r)}var Sy=R({atan2_:p$});function d$(e){let t={x:C(e,"x","atanh")};return P.runKernel(Do,t)}var Cy=R({atanh_:d$});function h$(e,t,n,a,r="NHWC",s){let i=e[3],o=[...t,i],l=J0(r);return Dc(e,o,n,s,a,null,null,l)}function Z0(e,t,n,a,r,s,i="channelsLast"){let[o,l]=ph(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 ${i}`);return Dc(e,c,n,a,r,s,!1,i)}function m$(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 Q0(e,u,n,a,r,!1,p,s)}function Dc(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 ${o}`);let[d,h,,m]=t,[f,g]=ph(n),[y,b]=ph(a),x=Pl(d,y),v=Pl(h,b),{padInfo:N,outHeight:T,outWidth:E}=f$(r,c,u,f,g,x,v,s,o),A=i?m*p:m,$;return o==="channelsFirst"?$=[l,A,T,E]:o==="channelsLast"&&($=[l,T,E,A]),{batchSize:l,dataFormat:o,inHeight:c,inWidth:u,inChannels:p,outHeight:T,outWidth:E,outChannels:A,padInfo:N,strideHeight:f,strideWidth:g,filterHeight:d,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:y,dilationWidth:b,inShape:e,outShape:$,filterShape:t}}function Q0(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 ${i}`);let[h,m,f,,g]=t,[y,b,x]=Ey(n),[v,N,T]=Ey(a),E=Pl(h,v),A=Pl(m,N),$=Pl(f,T),{padInfo:O,outDepth:V,outHeight:W,outWidth:H}=g$(r,c,u,p,y,b,x,E,A,$,o),X=s?g*d:g,q;return i==="channelsFirst"?q=[l,X,V,W,H]:i==="channelsLast"&&(q=[l,V,W,H,X]),{batchSize:l,dataFormat:i,inDepth:c,inHeight:u,inWidth:p,inChannels:d,outDepth:V,outHeight:W,outWidth:H,outChannels:X,padInfo:O,strideDepth:y,strideHeight:b,strideWidth:x,filterDepth:h,filterHeight:m,filterWidth:f,effectiveFilterDepth:E,effectiveFilterHeight:A,effectiveFilterWidth:$,dilationDepth:v,dilationHeight:N,dilationWidth:T,inShape:e,outShape:q,filterShape:t}}function y$(e,t,n,a,r){a==null&&(a=_y(e,t,n));let s=e[0],i=e[1],o=Ci((s-t+2*a)/n+1,r),l=Ci((i-t+2*a)/n+1,r);return[o,l]}function b$(e,t,n,a,r,s){r==null&&(r=_y(e,t,a));let i=e[0],o=e[1],l=e[2],c=Ci((i-t+2*r)/a+1,s),u=Ci((o-t+2*r)/a+1,s),p=Ci((l-t+2*r)/a+1,s);return[c,u,p,n]}function _y(e,t,n,a=1){let r=Pl(t,a);return Math.floor((e[0]*(n-1)-n+r)/2)}function ph(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Ey(e){return typeof e=="number"?[e,e,e]:e}function Pl(e,t){return t<=1?e:e+(e-1)*(t-1)}function f$(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=y$([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=Ci((t-s+d+h)/a+1,o),p=Ci((n-i+m+f)/r+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:p}}function g$(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=b$([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),x=f-b,v=Math.floor(g/2),N=g-v,T=Math.floor(y/2),E=y-T;p={top:v,bottom:N,left:T,right:E,front:b,back:x,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 Ci(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 Qr(e){let[t,n,a]=ph(e);return t===1&&n===1&&a===1}function $n(e,t){return Qr(e)||Qr(t)}function J0(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function x$(e,t){let n={x:C(e,"x","reshape","string_or_numeric")},a={shape:t};return P.runKernel(hl,n,a)}var j=R({reshape_:x$});function v$(e,t,n,a,r){let s=C(e,"x","avgPool","float32"),i=1;F($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=j(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),r!=null&&F(qt(a),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let c={x:o},u={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=P.runKernel(As,c,u);return p=pe(p,s.dtype),l?j(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Jn=R({avgPool_:v$});function w$(e,t,n,a,r,s="NDHWC",i){i==null?i=[1,1,1]:Pt("dilations is deprecated, this field will be gone in v3.0.0.");let o=C(e,"x","avgPool3d","float32"),l=o,c=!1;o.rank===4&&(c=!0,l=j(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),F(l.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${l.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),F($n(n,i),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&F(qt(a),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let u={x:l},p={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s,dilations:i},d=P.runKernel(ic,u,p);return d=pe(d,l.dtype),c?j(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Fy=R({avgPool3d_:w$});function k$(e,t=0){F(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 ${s.dtype}. `)}),n.length===1)return Yr(n[0]);let a=n,r={axis:t};return P.runKernel(Po,a,r)}var Ze=R({concat_:k$});function I$(e){let t={x:C(e,"x","sigmoid")};return P.runKernel(pi,t)}var ca=R({sigmoid_:I$});function N$(e,t,n){let a=C(e,"x","slice","string_or_numeric");if(a.rank===0)throw new Error("Slicing scalar is not possible");let r={x:a},s={begin:t,size:n};return P.runKernel(yl,r,s)}var We=R({slice_:N$});function T$(e){let t={x:C(e,"x","tanh")};return P.runKernel(yi,t)}var Ol=R({tanh_:T$});function S$(e,t,n,a,r,s){let i=C(e,"forgetBias","basicLSTMCell"),o=C(t,"lstmKernel","basicLSTMCell"),l=C(n,"lstmBias","basicLSTMCell"),c=C(a,"data","basicLSTMCell"),u=C(r,"c","basicLSTMCell"),p=C(s,"h","basicLSTMCell"),d=Ze([c,p],1),h=ze(d,o),m=Z(h,l),f=m.shape[0],g=m.shape[1]/4,y=[f,g],b=We(m,[0,0],y),x=We(m,[0,g],y),v=We(m,[0,g*2],y),N=We(m,[0,g*3],y),T=Z(L(ca(b),Ol(x)),L(u,ca(Z(i,v)))),E=L(Ol(T),ca(N));return[T,E]}var C$=R({basicLSTMCell_:S$});function E$(e,t,n){let a=C(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);F(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),F(a.shape[0]%r==0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return P.runKernel(oc,s,i)}var Mc=R({batchToSpaceND_:E$});function _$(e){let t;return e.rank===0||e.rank===1?t=j(e,[1,1,1,e.size]):e.rank===2?t=j(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=j(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function F$(e,t,n,a,r,s){s==null&&(s=.001);let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;r!=null&&(c=C(r,"scale","batchNorm"));let u;a!=null&&(u=C(a,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:_$(i),scale:c,offset:u,mean:o,variance:l},d={varianceEpsilon:s},h=P.runKernel(Us,p,d);return j(h,i.shape)}var br=R({batchNorm_:F$});function A$(e,t,n,a,r,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;r!=null&&(c=C(r,"scale","batchNorm"));let u;return a!=null&&(u=C(a,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),br(i,o,l,u,c,s)}var ek=R({batchNorm2d_:A$});function $$(e,t,n,a,r,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;r!=null&&(c=C(r,"scale","batchNorm"));let u;return a!=null&&(u=C(a,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),br(i,o,l,u,c,s)}var tk=R({batchNorm3d_:$$});function D$(e,t,n,a,r,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;r!=null&&(c=C(r,"scale","batchNorm"));let u;return a!=null&&(u=C(a,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),br(i,o,l,u,c,s)}var nk=R({batchNorm4d_:D$});function M$(e,t,n){let a=C(e,"x","bincount"),r=C(t,"weights","bincount");F(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return P.runKernel(Nd,s,i)}var ak=R({bincount_:M$});function R$(e,t){let n=C(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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=j(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 Yr(n);let i={x:n},o={reps:s};return P.runKernel(Hr,i,o)}var Rc=R({broadcastTo_:R$});function P$(e){let t={x:C(e,"x","ceil")};return P.runKernel(Ro,t)}var Ay=R({ceil_:P$});function O$(e,t,n){let a=C(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return P.runKernel(Gr,r,s)}var Yt=R({clipByValue_:O$});function L$(e){return Ze(e,0)}var rk=R({concat1d_:L$});function z$(e,t){return Ze(e,t)}var sk=R({concat2d_:z$});function B$(e,t){return Ze(e,t)}var ik=R({concat3d_:B$});function W$(e,t){return Ze(e,t)}var ok=R({concat4d_:W$});function V$(e,t,n,a,r="NHWC",s=[1,1],i){let o=C(e,"x","conv2d"),l=C(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=j(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(qt(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p=r==="NHWC"?c.shape[3]:c.shape[1];F(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),F($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(Ms,d,h);return u?j(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var $t=R({conv2d_:V$});function U$(e,t,n,a,r="NWC",s=1,i){let o=C(e,"x","conv1d"),l=C(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=j(o,[1,o.shape[0],o.shape[1]])),F(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(qt(a),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),F(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F($n(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=j(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=j(c,[c.shape[0],1,c.shape[1],c.shape[2]]),h=$t(d,p,[1,n],a,"NHWC",[1,s],i);return u?j(h,[h.shape[2],h.shape[3]]):j(h,[h.shape[0],h.shape[2],h.shape[3]])}var dh=R({conv1d_:U$});function G$(e,t,n,a,r,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=j(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],p=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),F(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(qt(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(Rs,d,h);return c?j(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var $y=R({conv2DBackpropInput_:G$});function H$(e,t,n,a,r,s){let i=C(e,"x","conv2dTranspose"),o=C(t,"filter","conv2dTranspose");return $y(n,i,o,a,r,"NHWC",s)}var hh=R({conv2dTranspose_:H$});function j$(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=C(e,"x","conv3d"),o=C(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=j(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F($n(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:o},p={strides:n,pad:a,dataFormat:r,dilations:s},d=P.runKernel(uc,u,p);return c?j(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Dy=R({conv3d_:j$});function q$(e,t,n,a,r){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=j(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];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},p={pad:r,strides:a,inputShape:s},d=P.runKernel(Ed,u,p);return o?j(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var lk=R({conv3DBackpropInput_:q$});function K$(e,t,n,a,r){let s=C(e,"x","conv3dTranspose"),i=C(t,"filter","conv3dTranspose");return lk(n,s,i,a,r)}var X$=R({conv3dTranspose_:K$});function Y$(e){let t={x:C(e,"x","cos")};return P.runKernel(Ps,t)}var Pc=R({cos_:Y$});function J$(e){let t={x:C(e,"x","cosh")};return P.runKernel(Oo,t)}var mh=R({cosh_:J$});function Z$(e,t=0,n=!1,a=!1){let r={x:C(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return P.runKernel(Os,r,s)}var fh=R({cumsum_:Z$});function Q$(e,t,n,a=!1){let r=C(e,"x","denseBincount"),s=C(t,"weights","denseBincount");F(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),F(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:n,binaryOutput:a};return P.runKernel(_d,i,o)}var uk=R({denseBincount_:Q$});function eD(e,t,n="NHWC"){let a=C(e,"x","depthToSpace"),r=n==="NHWC"?a.shape[1]:a.shape[2],s=n==="NHWC"?a.shape[2]:a.shape[3],i=n==="NHWC"?a.shape[3]:a.shape[1];F(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${r} and ${t}  for depthToSpace with input shape
    ${a.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${s} and ${t} for depthToSpace with input shape
        ${a.shape}`),F(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${a.shape}`);let o={x:a},l={blockSize:t,dataFormat:n};return P.runKernel(zo,o,l)}var My=R({depthToSpace_:eD});function tD(e,t,n,a,r="NHWC",s=[1,1],i){let o=C(e,"x","depthwiseConv2d"),l=C(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=j(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(qt(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:c,filter:l},d={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=P.runKernel(Ls,p,d);return u?j(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var xr=R({depthwiseConv2d_:tD});function nD(e){let t={x:C(e,"x","diag")};return P.runKernel($d,t)}var aD=R({diag_:nD});function rD(e,t,n,a,r=[1,1],s="NHWC"){let i=C(e,"x","dilation2d"),o=C(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=j(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(cc,u,p);return c?j(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Ry=R({dilation2d_:rD});function sD(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 Vt(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 vt(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 iD(e,t){let n=C(e,"a","equal"),a=C(t,"b","equal");[n,a]=Tt(n,a),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Vo,r)}var vr=R({equal_:iD});function oD(e,t,n){let a=C(t,"a","where"),r=C(n,"b","where"),s=C(e,"condition","where","bool"),i=vt(a.shape,r.shape),o=Rc(a,i),l=Rc(r,i);s.rank===1&&F(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 kn=R({where_:oD});function lD(e){let t={x:C(e,"x","zerosLike")};return P.runKernel(Sl,t)}var He=R({zerosLike_:lD});function uD(e,t){let n=C(e,"a","div"),a=C(t,"b","div");[n,a]=Tt(n,a);let r=ve(n,a),s=He(r),i=vr(a,s);return kn(i,s,r)}var Py=R({divNoNan_:uD});function cD(e,t){let n=C(e,"t1","dot"),a=C(t,"t2","dot");F((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(F(r===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${s}.`),n.rank===1&&a.rank===1){let i=j(n,[1,-1]),o=j(a,[-1,1]),l=ze(i,o);return j(l,[])}else if(n.rank===1&&a.rank===2){let i=j(n,[1,-1]),o=j(a,[a.shape[0],a.shape[1]]),l=ze(i,o);return j(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=j(a,[-1,1]),o=ze(n,i);return j(o,[o.size])}else{let i=j(a,[a.shape[0],a.shape[1]]);return ze(n,i)}}var ck=R({dot_:cD});function pD(e){let t={x:C(e,"x","elu")};return P.runKernel(Bo,t)}var Ll=R({elu_:pD});function dD(e){let t=C(e,"x","erf");F(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 Oy=R({erf_:dD});function hD(e){let t={x:C(e,"x","exp")};return P.runKernel(Bs,t)}var hn=R({exp_:hD});function mD(e,t=0){let n=C(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let a={input:n},r={dim:t};return P.runKernel(Uo,a,r)}var Zn=R({expandDims_:mD});function fD(e){let t={x:C(e,"x","expm1")};return P.runKernel(Go,t)}var Ly=R({expm1_:fD});function gD(e,t){let n=C(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let a={x:n},r={reps:t};return P.runKernel(Hr,a,r)}var ja=R({tile_:gD});function yD(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=j(r.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return ja(Zn(i,0),[n[0],1,1]);if(n.length===2)return ja(Zn(Zn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return ja(Zn(Zn(Zn(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 zy=R({eye_:yD});function In(e,t,n){let a={shape:e,value:t,dtype:n};return P.runKernel(pc,{},a)}function bD(e){let t={x:C(e,"x","floor")};return P.runKernel(Ws,t)}var zl=R({floor_:bD});function xD(e,t,n=0,a=0){let r=C(e,"x","gather"),s=C(t,"indices","gather","int32"),i={x:r,indices:s},o={axis:n,batchDims:a};return P.runKernel(jo,i,o)}var Ei=R({gather_:xD});function vD(e,t){let n=C(e,"a","greater"),a=C(t,"b","greater");[n,a]=Tt(n,a),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Ko,r)}var Qn=R({greater_:vD});function wD(e,t){let n=C(e,"a","greaterEqual"),a=C(t,"b","greaterEqual");[n,a]=Tt(n,a),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Gs,r)}var wr=R({greaterEqual_:wD});function kD(e){let t={input:C(e,"input","imag")};return P.runKernel(Ld,t)}var gh=R({imag_:kD});function ID(e){let t={x:C(e,"x","isFinite")};return P.runKernel(Yo,t)}var pk=R({isFinite_:ID});function ND(e){let t={x:C(e,"x","isInf")};return P.runKernel(Jo,t)}var dk=R({isInf_:ND});function TD(e){let t={x:C(e,"x","isNaN")};return P.runKernel(Zo,t)}var hk=R({isNaN_:TD});function SD(e,t=.2){let n={x:C(e,"x","leakyRelu")},a={alpha:t};return P.runKernel(Hs,n,a)}var Oc=R({leakyRelu_:SD});function CD(e,t){let n=C(e,"a","less"),a=C(t,"b","less");[n,a]=Tt(n,a),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Qo,r)}var Lc=R({less_:CD});function ED(e,t){let n=C(e,"a","lessEqual"),a=C(t,"b","lessEqual");[n,a]=Tt(n,a),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(el,r)}var es=R({lessEqual_:ED});function mk(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(zd,{},a)}function _D(e,t=5,n=1,a=1,r=.5){let s=C(e,"x","localResponseNormalization");F(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${s.rank}.`),F(qt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=j(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(mc,l,c);return o?j(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var By=R({localResponseNormalization_:_D});function FD(e){let t={x:C(e,"x","log")};return P.runKernel(js,t)}var Dn=R({log_:FD});function AD(e){let t={x:C(e,"x","log1p")};return P.runKernel(tl,t)}var yh=R({log1p_:AD});function $D(e){return F(Vr(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=C(t,"x","tf.grad","string_or_numeric"),r=n!=null?C(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)"),bh(i),i[0]})}}function DD(e){return F(Vr(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=_c(t,"args","tf.grads","string_or_numeric"),r=n!=null?C(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,...])"),bh(i),i})}}function MD(e){return F(Vr(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof z,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(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 bh(a),{grad:a[0],value:r}}}function RD(e){return F(Vr(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(r=>r instanceof z),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(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,...])"),bh(a.grads),a}}function fk(e,t){F(Vr(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(c=>c instanceof jr),()=>"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),F(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=P.gradients(e,t,null,s);F(o.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),F(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((c,u)=>{o[u]!=null&&(l[c.name]=o[u])}),a!=null&&a.forEach(c=>l[c.name]=null),{value:i,grads:l}}function qa(e){return P.customGrad(e)}function bh(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 PD(e){let t={x:C(e,"x","neg")};return P.runKernel(rl,t)}var St=R({neg_:PD});function OD(e){let t={x:C(e,"x","softplus")};return P.runKernel(vl,t)}var Bl=R({softplus_:OD});function LD(e){let t=C(e,"x","logSigmoid");return qa(n=>({value:St(Bl(St(n))),gradFunc:a=>L(a,ca(St(n)))}))(t)}var gk=R({logSigmoid_:LD});function zD(e,t=null,n=!1){let a={x:C(e,"x","max")},r={reductionIndices:t,keepDims:n};return P.runKernel(qs,a,r)}var ea=R({max_:zD});function BD(e,t){let n=C(e,"a","sub"),a=C(t,"b","sub");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(gi,r)}var fe=R({sub_:BD});function WD(e,t=null,n=!1){let a=C(e,"x","sum");a.dtype==="bool"&&(a=pe(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return P.runKernel(hi,r,s)}var Ce=R({sum_:WD});function VD(e,t=-1){let n=C(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 ${t}`);return qa((a,r)=>{let s=!0,i=ea(a,t,!0),o=fe(a,i),l=fe(pe(o,"float32"),Dn(Ce(hn(o),t,s)));return r([l]),{value:l,gradFunc:(c,u)=>{let[p]=u,d=!0,h=hn(p);return fe(c,L(Ce(c,t,d),h))}}})(n)}var xh=R({logSoftmax_:VD});function Wy(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function yk(e,t,n){let a=e.length+t.length,r=[],s=0,i=0;for(let o=0;o<a;o++)n.indexOf(o)===-1?r.push(e[s++]):r.push(t[i++]);return r}function bk(e,t){let n=[],a=e.length;for(let s=0;s<a;s++)t.indexOf(s)===-1&&n.push(e[s]);let r=t.map(s=>e[s]);return[n,r]}function _i(e,t){let n=t.map(a=>1);return yk(e,n,t)}function UD(e,t,n){F(Wy(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function xk(e,t){if(Wy(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 Vy(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function GD(e,t){let n=[];for(let a=t-e;a<t;++a)n.push(a);return n}function HD(e,t=null,n=!1){let a=C(e,"x","logSumExp"),r=la(t,a.shape),s=ea(a,r,!0),i=fe(a,s),o=hn(i),l=Ce(o,r),c=Dn(l),u=Z(j(s,c.shape),c);if(n){let p=_i(u.shape,r);return j(u,p)}return u}var Uy=R({logSumExp_:HD});function jD(e,t){let n=C(e,"a","logicalAnd","bool"),a=C(t,"b","logicalAnd","bool");vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(nl,r)}var pa=R({logicalAnd_:jD});function qD(e){let t={x:C(e,"x","logicalNot","bool")};return P.runKernel(dc,t)}var zc=R({logicalNot_:qD});function KD(e,t){let n=C(e,"a","logicalOr","bool"),a=C(t,"b","logicalOr","bool");vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(hc,r)}var vh=R({logicalOr_:KD});function XD(e,t){let n=C(e,"a","logicalXor","bool"),a=C(t,"b","logicalXor","bool");return vt(n.shape,a.shape),pa(vh(e,t),zc(pa(e,t)))}var vk=R({logicalXor_:XD});function YD(e,t,n,a,r){let s=C(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=j(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),F($n(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&F(qt(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(Xs,c,u);return l?j(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Dt=R({maxPool_:YD});function JD(e,t=[1,1,1],n,a,r,s="NDHWC",i){i==null?i=[1,1,1]:Pt("dilations is deprecated, this field will be gone in v3.0.0.");let o=C(e,"x","maxPool3d"),l=o,c=!1;o.rank===4&&(c=!0,l=j(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),F(l.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${l.rank}.`),F(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),F($n(n,i),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),r!=null&&F(qt(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(fc,u,p);return c?j(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Gy=R({maxPool3d_:JD});function ZD(e,t,n,a,r=!1){let s={x:C(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:a,includeBatchInIndex:r},o=P.runKernel(Ud,s,i);return{result:o[0],indexes:o[1]}}var wk=R({maxPoolWithArgmax_:ZD});function QD(e,t){let n=C(e,"a","maximum"),a=C(t,"b","maximum");[n,a]=Tt(n,a),n.dtype==="bool"&&(n=pe(n,"int32"),a=pe(a,"int32")),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Ks,r)}var Ta=R({maximum_:QD});function eM(e,t=null,n=!1){let a={x:C(e,"x","mean")},r={axis:t,keepDims:n};return P.runKernel(Ys,a,r)}var Ct=R({mean_:eM});function tM(e,t=null,n=!1){let a={x:C(e,"x","min")},r={axis:t,keepDims:n};return P.runKernel(Js,a,r)}var Wl=R({min_:tM});function nM(e,t){let n=C(e,"a","minimum"),a=C(t,"b","minimum");[n,a]=Tt(n,a),n.dtype==="bool"&&(n=pe(n,"int32"),a=pe(a,"int32")),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Zs,r)}var Fi=R({minimum_:nM});function aM(e,t,n){F(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let a=C(e,"x","mirrorPad");if(a.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");F(t.length===a.rank,()=>`Padding doesn't match input. Must be ${a.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let o=0;o<a.rank;o++)F(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),F(t[o][0]>=0&&t[o][0]<=a.shape[o]-r&&t[o][1]>=0&&t[o][1]<=a.shape[o]-r,()=>`Padding in dimension ${o} cannot be greater than or equal to ${a.shape[o]-r} or less than 0 for input of shape ${a.shape}`);let s={paddings:t,mode:n},i={x:a};return P.runKernel(gc,i,s)}var Hy=R({mirrorPad_:aM});function rM(e,t){let n=C(e,"a","mod"),a=C(t,"b","mod");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(al,r)}var wh=R({mod_:rM});function sM(e){let t=C(e,"x","square"),n={};return P.runKernel("Square",{x:t},n)}var ot=R({square_:sM});function iM(e,t=null,n=!1){e=C(e,"x","moments");let a=la(t,e.shape),r=Ct(e,a,n),s=r.shape;n||(s=_i(r.shape,a));let i=ot(fe(pe(e,"float32"),j(r,s))),o=Ct(i,a,n);return{mean:r,variance:o}}var kh=R({moments_:iM});function oM(e,t,n,a){let r=C(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 lM=R({multiRNNCell_:oM});function uM(e,t,n,a=!1){let r=C(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?j(r,[1,-1]):r},l={numSamples:t,seed:n,normalized:a},c=P.runKernel(Gd,o,l);return i===1?j(c,[c.size]):c}var kk=R({multinomial_:uM});function cM(e,t){let n=C(e,"a","notEqual"),a=C(t,"b","notEqual");[n,a]=Tt(n,a),vt(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(sl,r)}var ts=R({notEqual_:cM});function yt(e,t="float32"){if(t==="complex64"){let a=yt(e,"float32"),r=yt(e,"float32");return qr(a,r)}let n=xd(zt(e),t);return P.makeTensor(n,e,t)}function Ka(e,t="float32"){if(t==="complex64"){let a=Ka(e,"float32"),r=yt(e,"float32");return qr(a,r)}let n=Bg(zt(e),t);return P.makeTensor(n,e,t)}function pM(e){let t={x:C(e,"x","onesLike")};return P.runKernel(ul,t)}var Mn=R({onesLike_:pM});function dM(e,t){let n=C(e,"v1","outerProduct"),a=C(t,"v2","outerProduct");F(n.rank===1&&a.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${a.rank}.`);let r=j(n,[-1,1]),s=j(a,[1,-1]);return ze(r,s)}var hM=R({outerProduct_:dM});function mM(e,t,n=0){let a=C(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(ti,s,r)}var ta=R({pad_:mM});function fM(e,t,n=0){return F(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),ta(e,[t],n)}var gM=R({pad1d_:fM});function yM(e,t,n=0){return F(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ta(e,t,n)}var bM=R({pad2d_:yM});function xM(e,t,n=0){return F(t.length===3&&t[0].length===2&&t[1].length===2&&t[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ta(e,t,n)}var vM=R({pad3d_:xM});function wM(e,t,n=0){return F(t.length===4&&t[0].length===2&&t[1].length===2&&t[2].length===2&&t[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ta(e,t,n)}var kM=R({pad4d_:wM});function IM(e,t,n){let a=C(e,"x","spaceToBatchND");F(a.rank>=1+t.length,()=>`input rank ${a.rank} should be > than [blockShape] ${t.length}`),F(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),F(a.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]==0:i,!0),()=>`input spatial dimensions ${a.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let r={x:a},s={blockShape:t,paddings:n};return P.runKernel(xc,r,s)}var Bc=R({spaceToBatchND_:IM});function SM(e,t,n,a,r,s){r==null&&(r=[1,1]),s==null&&(s=1),a===0&&(a="valid");let i=C(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=j(i,[1,i.shape[0],i.shape[1],i.shape[2]])),F($n(s,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${r}'`);let c=Z0(o.shape,t,s,r,a),u=[c.dilationHeight,c.dilationWidth],p;a==="same"?p=TM([c.filterHeight,c.filterWidth],u):p=[[0,0],[0,0]];let d=u[0]===1&&u[1]===1,[h,m]=NM([c.inHeight,c.inWidth],u,p),f=d?a:"valid",g=d?o:Bc(o,u,h),y=(n==="avg"?()=>Jn(g,t,s,f):()=>Dt(g,t,s,f))(),b=d?y:Mc(y,u,m);return l?j(b,[b.shape[1],b.shape[2],b.shape[3]]):b}function NM(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 TM(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 Ik=R({pool_:SM});function CM(e,t){let n=C(e,"base","pow"),a=C(t,"exp","pow");[n,a]=Tt(n,a);let r={a:n,b:a};return P.runKernel(ni,r)}var Xa=R({pow_:CM});function EM(e,t){let n=C(e,"x","prelu"),a=C(t,"alpha","prelu"),r={x:n,alpha:a};return P.runKernel(ai,r)}var Wc=R({prelu_:EM});function _M(e,t=null,n=!1){let a=C(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 Ih=R({prod_:_M});function FM(e,t,n){let a=zt(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 ${n}`);for(let s=0;s<a;s++)r[s]=t();return P.makeTensor(r,e,n)}var AM=R({rand_:FM}),jy=No(fd()),qy=class{constructor(e,t,n,a,r){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=a,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=r||Math.random();this.random=jy.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let a=this.nextVal;return this.nextVal=NaN,a}let e,t,n=!1;for(;!n;){let a,r,s;do a=2*this.random()-1,r=2*this.random()-1,s=a*a+r*r;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*a*i,t=this.mean+this.stdDev*r*i,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},$M=class{constructor(e,t,n,a){this.alpha=e,this.beta=1/t,this.dtype=n;let r=a||Math.random();this.randu=jy.alea(r.toString()),this.randn=new qy(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)}},DM=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=jy.alea(a)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function MM(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 $M(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 RM=R({randomGamma_:MM});function PM(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error(`Unsupported data type ${a}`);let s=new qy(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 Nk=R({randomNormal_:PM});function OM(e,t=0,n=1,a="float32",r){let s=Le(e,a),i=new DM(t,n,null,r);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var Vl=R({randomUniform_:OM});function Nh(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(yc,{},r)}function LM(e){let t={input:C(e,"input","real")};return P.runKernel(Hd,t)}var Vc=R({real_:LM});function zM(e){let t={x:C(e,"x","reciprocal")};return P.runKernel(dl,t)}var Ky=R({reciprocal_:zM});function BM(e){let t={x:C(e,"x","relu")};return P.runKernel(ri,t)}var Xe=R({relu_:BM});function WM(e){let t={x:C(e,"x","relu6")};return P.runKernel(ii,t)}var Th=R({relu6_:WM});function VM(e,t){let n={x:C(e,"x","reverse")},a={dims:t};return P.runKernel(oi,n,a)}var Rn=R({reverse_:VM});function UM(e){let t=C(e,"x","reverse");return F(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Rn(t,0)}var GM=R({reverse1d_:UM});function HM(e,t){let n=C(e,"x","reverse");return F(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Rn(n,t)}var jM=R({reverse2d_:HM});function qM(e,t){let n=C(e,"x","reverse");return F(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Rn(n,t)}var KM=R({reverse3d_:qM});function XM(e,t){let n=C(e,"x","reverse");return F(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Rn(n,t)}var YM=R({reverse4d_:XM});function JM(e){let t={x:C(e,"x","round")};return P.runKernel(li,t)}var Xy=R({round_:JM});function ZM(e){let t={x:C(e,"x","rsqrt")};return P.runKernel(ui,t)}var Sh=R({rsqrt_:ZM});function de(e,t){if((un(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"&&un(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Kr(e,[],[],t)}function QM(e){let t={x:C(e,"x","selu")};return P.runKernel(gl,t)}var Ch=R({selu_:QM});function eR(e,t,n,a,r,s=[1,1],i="NHWC"){let o=C(e,"x","separableConv2d"),l=C(t,"depthwiseFilter","separableConv2d"),c=C(n,"pointwiseFilter","separableConv2d"),u=o,p=!1;if(o.rank===3&&(p=!0,u=j(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");F(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),F(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),F(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${c.shape[0]}.`),F(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];F(c.shape[2]===d*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*h}, but got ${c.shape[2]}.`);let m=xr(u,l,a,r,i,s),f=$t(m,c,1,"valid",i);return p?j(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Ai=R({separableConv2d_:eR});async function tR(e,t){let n=C(e,"x","setdiff1d"),a=C(t,"y","setdiff1d");F(n.dtype===a.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${a.dtype}).`),F(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),F(a.rank===1,()=>`y should be 1D tensor, but got y (${a.shape}).`);let r=await n.data(),s=await a.data(),i=new Set(s),o=0;for(let u=0;u<r.length;u++)i.has(r[u])||o++;let l=new Bt([o],n.dtype),c=new Bt([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 Tk=tR;function nR(e){let t={x:C(e,"x","sign")};return P.runKernel(xl,t)}var Yy=R({sign_:nR});function aR(e){let t={x:C(e,"x","sin")};return P.runKernel(ci,t)}var Eh=R({sin_:aR});function rR(e){let t={x:C(e,"x","sinh")};return P.runKernel(bl,t)}var _h=R({sinh_:rR});function sR(e,t,n){let a=C(e,"x","slice1d");return F(a.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${a.rank} tensor`),We(a,[t],[n])}var Fh=R({slice1d_:sR});function iR(e,t,n){let a=C(e,"x","slice2d");return F(a.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${a.rank} tensor`),We(a,t,n)}var Jy=R({slice2d_:iR});function oR(e,t,n){let a=C(e,"x","slice3d");return F(a.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${a.rank} tensor`),We(a,t,n)}var Ul=R({slice3d_:oR});function lR(e,t,n){let a=C(e,"x","slice4d");return F(a.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${a.rank} tensor`),We(a,t,n)}var Uc=R({slice4d_:lR});function uR(e,t=-1){let n=C(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(mi,a,r)}var Sa=R({softmax_:uR});function cR(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return P.runKernel(Pd,t)}var Gc=R({fft_:cR});function pR(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return P.runKernel(Od,t)}var Gl=R({ifft_:pR});function dR(e){let t=e.shape[e.shape.length-1],n=e.size/t,a;if(t<=2){let r=j(e,[n,t]);a=Gl(r)}else{let r=[n,2*(t-1)],s=j(Vc(e),[n,t]),i=j(gh(e),[n,t]),o=Rn(We(s,[0,1],[n,t-2]),1),l=L(Rn(We(i,[0,1],[n,t-2]),1),de(-1)),c=Ze([s,o],1),u=Ze([i,l],1),p=j(qr(c,u),[r[0],r[1]]);a=Gl(p)}if(a=Vc(a),e.rank===3&&e.shape[0]!==0){let r=a,s=e.shape[0];a=j(a,[s,a.shape[0]/s,a.shape[1]]),r.dispose()}return a}var Ah=R({irfft_:dR});function hR(e,t,n=0){let a={x:C(e,"x","split")},r={numOrSizeSplits:t,axis:n};return P.runKernel(wl,a,r)}var Pn=R({split_:hR});function mR(e,t){F(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],a=e.size/n,r;if(t!=null&&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=Ze([e,yt(m)],e.shape.length-1),n=t}else r=e;let s=He(r),i=j(qr(r,s),[a,n]),o=Gc(i),l=Math.floor(n/2)+1,c=Vc(o),u=gh(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,j(qr(p[0],d[0]),h)}var Hc=R({rfft_:mR});function fR(e){let t={x:C(e,"x","sqrt")};return P.runKernel(di,t)}var an=R({sqrt_:fR});function gR(e,t){let n=C(e,"a","squaredDifference"),a=C(t,"b","squaredDifference");[n,a]=Tt(n,a),vt(n.shape,a.shape);let r={a:n,b:a},s={};return P.runKernel(fi,r,s)}var jc=R({squaredDifference_:gR});function yR(e,t){let n=C(e,"x","squeeze");return j(n,Ww(n.shape,t).newShape)}var ns=R({squeeze_:yR});function bR(e,t=0){let n=_c(e,"tensors","stack","string_or_numeric");F(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&F(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let a=n,r={axis:t};return P.runKernel(cl,a,r)}var Mt=R({stack_:bR});function xR(e,t=0){let n={x:C(e,"x","step")},a={alpha:t};return P.runKernel(Cl,n,a)}var Hl=R({step_:xR});function vR(e,t,n,a,r=0,s=0,i=0,o=0,l=0){let c={x:C(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 Zy=R({stridedSlice_:vR});function wR(e){let t={x:C(e,"x","tan")};return P.runKernel(Il,t)}var Qy=R({tan_:wR});function et(e,t){Cs(e);let n=Ha(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Kr(e,null,n,t)}function Ca(e,t,n){if(Cs(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let a=Ha(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 Kr(e,t,a,n)}function Ea(e,t,n){if(Cs(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let a=Ha(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 Kr(e,t,a,n)}function kR(e,t,n){if(Cs(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let a=Ha(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 Kr(e,t,a,n)}function IR(e,t,n){if(Cs(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let a=Ha(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,Kr(e,t,a,n)}function NR(e,t=1,n=!0){let a=C(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 eb=R({topk_:NR});function TR(e,t=0,n=1,a,r){if(a!=null&&a==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new qy(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 $h=R({truncatedNormal_:TR});function SR(e,t=0){let n=C(e,"x","unique","string_or_numeric");F(n.rank>0,()=>"The input tensor must be at least 1D");let a={x:n},r={axis:t},[s,i]=P.runKernel(Xd,a,r);return{values:s,indices:i}}var Dh=R({unique_:SR});function CR(e,t,n){let a=C(e,"x","unsortedSegmentSum"),r=C(t,"segmentIds","unsortedSegmentSum","int32");F(qt(n),()=>"numSegments must be of dtype int");let s={x:a,segmentIds:r},i={numSegments:n};return P.runKernel(wc,s,i)}var tb=R({unsortedSegmentSum_:CR});function ER(e,t=0){let n=C(e,"x","unstack","string_or_numeric");F(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 pt=R({unstack_:ER});function Sk(e,t=!0,n,a){return P.makeVariable(e,t,n,a)}function Ck(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 _R(e){let t=C(e,"condition","whereAsync","bool"),n=await t.data(),a=Ck(t.shape,n);return e!==t&&t.dispose(),a}var nb=_R;async function FR(e,t,n){let a=C(e,"tensor","boolMask"),r=C(t,"mask","boolMask","bool"),s=n==null?0:n,i=r.rank,o=a.shape;F(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=j(a,c),p=j(r,[-1]),d=await nb(p),h=ns(d,[1]),m=Ei(u,h,s);return e!==a&&a.dispose(),t!==r&&r.dispose(),h.dispose(),u.dispose(),p.dispose(),d.dispose(),m}var AR=FR;function $R(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","notEqualStrict"),a=C(t,"b","notEqualStrict");return rt(n.shape,a.shape,"Error in notEqualStrict: "),ts(n,a)}function DR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","lessStrict"),a=C(t,"b","lessStrict");return rt(n.shape,a.shape,"Error in lessStrict: "),Lc(n,a)}function MR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","equalStrict"),a=C(t,"b","equalStrict");return rt(n.shape,a.shape,"Error in equalStrict: "),vr(n,a)}function RR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","lessEqualStrict"),a=C(t,"b","lessEqualStrict");return rt(n.shape,a.shape,"Error in lessEqualStrict: "),es(n,a)}function PR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","greaterStrict"),a=C(t,"b","greaterStrict");return rt(n.shape,a.shape,"Error in greaterStrict: "),Qn(n,a)}function OR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","greaterEqualStrict"),a=C(t,"b","greaterEqualStrict");return rt(n.shape,a.shape,"Error in greaterEqualStrict: "),wr(n,a)}var Ek=R({equalStrict_:MR}),_k=R({greaterEqualStrict_:OR}),Fk=R({greaterStrict_:PR}),Ak=R({lessEqualStrict_:RR}),$k=R({lessStrict_:DR}),Dk=R({notEqualStrict_:$R});function LR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","addStrict"),a=C(t,"b","addStrict");return rt(n.shape,a.shape,"Error in addStrict: "),Z(n,a)}function zR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","subStrict"),a=C(t,"b","subStrict");return rt(n.shape,a.shape,"Error in subStrict: "),fe(n,a)}function BR(e,t){return Pt("strict variants of ops have been deprecated and will be removed in future"),rt(e.shape,t.shape,"Error in powStrict: "),Xa(e,t)}function WR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","mul"),a=C(t,"b","mul");return rt(n.shape,a.shape,"Error in multiplyStrict: "),L(n,a)}function VR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","div"),a=C(t,"b","div");return rt(n.shape,a.shape,"Error in divideStrict: "),ve(n,a)}function UR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","modStrict"),a=C(t,"b","modStrict");return rt(n.shape,a.shape,"Error in modStrict: "),wh(n,a)}function GR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","minimumStrict"),a=C(t,"b","minimumStrict");return rt(n.shape,a.shape,"Error in minimumStrict: "),Fi(n,a)}function HR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","maximumStrict"),a=C(t,"b","maximumStrict");return rt(n.shape,a.shape,"Error in maximumStrict: "),Ta(n,a)}function jR(e,t){Pt("strict variants of ops have been deprecated and will be removed in future");let n=C(e,"a","squaredDifferenceStrict"),a=C(t,"b","squaredDifferenceStrict");return rt(n.shape,a.shape,"Error in squaredDifferenceStrict: "),jc(n,a)}var Mk=R({addStrict_:LR}),Rk=R({divStrict_:VR}),Pk=R({maximumStrict_:HR}),Ok=R({minimumStrict_:GR}),Lk=R({modStrict_:UR}),zk=R({mulStrict_:WR}),Bk=R({powStrict_:BR}),Wk=R({squaredDifferenceStrict_:jR}),Vk=R({subStrict_:zR});function qR(e,t="euclidean",n=null,a=!1){e=C(e,"x","norm");let r=Uk(e,t,n),s=r.shape;if(a){let i=la(n,e.shape);s=_i(r.shape,i)}return j(r,s)}function Uk(e,t,n=null){if(e.rank===0)return Wt(e);if(e.rank!==1&&n===null)return Uk(j(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Ce(Wt(e),n);if(t===Infinity)return ea(Wt(e),n);if(t===-Infinity)return Wl(Wt(e),n);if(t==="euclidean"||t===2)return an(Ce(Xa(Wt(e),de(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return ea(Ce(Wt(e),n[0]),n[1]-1);if(t===Infinity)return ea(Ce(Wt(e),n[1]),n[0]);if(t===-Infinity)return Wl(Ce(Wt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return an(Ce(ot(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Mh=R({norm_:qR});function KR(e,t,n,a,r=!0){let s=C(e,"v","movingAverage"),i=C(t,"x","movingAverage"),o=C(n,"decay","movingAverage");o0(s,i),F(gr(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=de(1),c=fe(l,o),u=L(fe(i,s),c);if(r){F(a!=null,()=>"When using zeroDebias: true, step is required.");let p=C(a,"step","movingAverage");u=ve(u,fe(l,Xa(o,p)))}return Z(s,u)}var XR=R({movingAverage_:KR});function YR(e,t,n){let a=C(e,"indices","scatterND","int32"),r=C(t,"updates","scatterND");gy(r,a,n);let s={indices:a,updates:r},i={shape:n};return P.runKernel(ml,s,i)}var Gk=R({scatterND_:YR});function JR(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 ZR(e,t,n,a=0){let r=C(e,"sparseIndices","sparseToDense","int32"),s=C(t,"sparseValues","sparseToDense"),i=C(a,"defaultValue","sparseToDense",s.dtype);JR(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:n};return P.runKernel(Kd,o,l)}var ab=R({sparseToDense_:ZR});function QR(e,t){let n=C(t,"indices","gatherND","int32"),a={params:C(e,"x","gatherND"),indices:n};return P.runKernel(qo,a)}var Hk=R({gatherND_:QR});function eP(e,t){if(t==null)return e.shape.slice();if(gr(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 tP(e,t,n,a){let r=C(e,"x","dropout");if(F(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.`),F(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=eP(r,n),i=1-t,o=ve(zl(Z(Vl(s,0,1,"float32",a),i)),i);return L(r,o)}var jk=R({dropout_:tP});function qk(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function rb(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 et(r,"float32")}async function nP(e,t,n=1){let a=C(e,"predictions","inTopK"),r=C(t,"targets","inTopK");F(a.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${a.rank}`),F(a.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${a.rank} and targets rank ${r.rank}`),rt(a.shape.slice(0,a.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=a.shape[a.shape.length-1];F(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await a.data(),o=await r.data(),[l,c]=[i.length/s,s],u=Vw("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(),Xn(u,r.shape,"bool")}var aP=nP,as={};Oe(as,{conv2d:()=>rP,depthwiseConv2d:()=>sP,matMul:()=>iP});function oP(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=j(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=j(t,[1,t.shape[0],t.shape[1],t.shape[2]])),F(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),F(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),F(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let c=s==="NHWC"?o.shape[3]:o.shape[1],u=s==="NHWC"?l.shape[3]:l.shape[1];F(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),F(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),i!=null&&F(qt(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(Sd,p,d)}var sb=R({conv2DBackpropFilter_:oP});function Rh(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return L(e,Hl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Ph(e,t){let n=t,a=Vt(e.shape,t.shape);return a.length>0&&(n=Ce(n,a)),j(n,e.shape)}function Oh(e,t,n,a){if(t==="linear")return e;if(t==="relu")return Xe(e);if(t==="elu")return Ll(e);if(t==="relu6")return Th(e);if(t==="prelu")return Wc(e,n);if(t==="leakyrelu")return Oc(e,a);throw new Error(`Unknown fused activation ${t}.`)}var Lh=(e,t)=>!(e>0)||t==="linear";function lP({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Lh(P.state.gradientDepth,l)===!1){let N=$t(e,t,n,a,r,s,i);return o!=null&&(N=Z(N,o)),Oh(N,l,c,u)}let p=C(e,"x","conv2d"),d=C(t,"filter","conv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=j(p,[1,p.shape[0],p.shape[1],p.shape[2]])),F(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),F(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&F(qt(a),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),F(h.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`),F($n(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let f=Dc(h.shape,d.shape,n,s,a,i),g;o!=null&&(g=C(o,"bias","fused conv2d"),[g]=Tt(g,p),vt(f.outShape,g.shape));let y;c!=null&&(y=C(c,"prelu weights","fused conv2d"));let b=(N,T)=>{let[E,A,$,O]=T,V=Rh(N,$,l);F(Qr(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let W=$y(A.shape,V,E,n,a),H=sb(A,V,E.shape,n,a),X=[W,H];if(O!=null){let q=Ph(O,V);X.push(q)}return X},x={x:h,filter:d,bias:g,preluActivationWeights:y},v={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?qa((N,T,E)=>{let A=P.runKernel(vi,x,v);return E([T,N,A]),m&&(A=j(A,[A.shape[1],A.shape[2],A.shape[3]])),{value:A,gradFunc:b}})(h,d):qa((N,T,E,A)=>{let $=P.runKernel(vi,x,v);return A([T,N,$,E]),m&&($=j($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,d,g)}var rP=R({fusedConv2d_:lP});function uP(e,t,n,a,r,s=[1,1],i){let o=e;e.rank===3&&(o=j(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=j(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={x:o,dy:l},u={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return P.runKernel(Fd,c,u)}var Kk=R({depthwiseConv2dNativeBackpropFilter_:uP});function cP(e,t,n,a,r,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=j(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?j(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Xk=R({depthwiseConv2dNativeBackpropInput_:cP});function pP({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(Lh(P.state.gradientDepth,l)===!1){let N=xr(e,t,n,a,r,s,i);return o!=null&&(N=Z(N,o)),Oh(N,l,c,u)}let p=C(e,"x","depthwiseConv2d"),d=C(t,"filter","depthwiseConv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=j(p,[1,p.shape[0],p.shape[1],p.shape[2]])),F(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),F(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),F(h.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),F($n(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&F(qt(a),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${a}.`);let f=Dc(h.shape,d.shape,n,s,a,i,!0),g;o!=null&&(g=C(o,"bias","fused conv2d"),[g]=Tt(g,p),vt(f.outShape,g.shape));let y;c!=null&&(y=C(c,"prelu weights","fused depthwiseConv2d"));let b=(N,T)=>{F(Qr(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[E,A,$,O]=T,V=Rh(N,$,l),W=Xk(A.shape,V,E,n,a,s,i),H=Kk(A,V,E.shape,n,a,s,i);if(O!=null){let X=Ph(g,V);return[W,H,X]}return[W,H]},x={x:h,filter:d,bias:g,preluActivationWeights:y},v={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?qa((N,T,E)=>{let A=P.runKernel(wi,x,v);return E([T,N,A]),m&&(A=j(A,[A.shape[1],A.shape[2],A.shape[3]])),{value:A,gradFunc:b}})(h,d):qa((N,T,E,A)=>{let $=P.runKernel(wi,x,v);return A([T,N,$,E]),m&&($=j($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,d,g)}var sP=R({fusedDepthwiseConv2d_:pP});function dP({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Lh(P.state.gradientDepth,s)===!1){let O=ze(e,t,n,a);return r!=null&&(O=Z(O,r)),Oh(O,s,i,o)}let l=C(e,"a","fused matMul"),c=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=zt(m),y=zt(f);F(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 ${c.rank}.`),F(gr(m,f),()=>`Error in fused matMul: outer dimensions (${m}) and (${f}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),F(u===p,()=>`Error in fused matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=l.shape.slice(0,-2).concat([d,h]),x=n?j(l,[g,u,d]):j(l,[g,d,u]),v=a?j(c,[y,h,p]):j(c,[y,p,h]),N;r!=null&&(N=C(r,"bias","fused matMul"),[N]=Tt(N,l),vt(b,N.shape));let T;i!=null&&(T=C(i,"prelu weights","fused matMul"));let E=(O,V)=>{let[W,H,X,q]=V,K=Rh(j(O,X.shape),X,s),J,te;if(!n&&!a?(J=ze(K,H,!1,!0),te=ze(W,K,!0,!1)):!n&&a?(J=ze(K,H,!1,!1),te=ze(K,W,!0,!1)):n&&!a?(J=ze(H,K,!1,!0),te=ze(W,K,!1,!1)):(J=ze(H,K,!0,!0),te=ze(K,W,!0,!0)),r!=null){let Q=Ph(q,K);return[J,te,Q]}else return[J,te]},A={a:x,b:v,bias:N,preluActivationWeights:T},$={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?qa((O,V,W)=>{let H=P.runKernel(xi,A,$);return W([O,V,H]),{value:j(H,b),gradFunc:E}})(x,v):qa((O,V,W,H)=>{let X=P.runKernel(xi,A,$);return H([O,V,X,W]),{value:j(X,b),gradFunc:E}})(x,v,N)}var iP=R({fusedMatMul_:dP});function hP(e){return rb(e,.54,.46)}var mP=R({hammingWindow_:hP});function fP(e){return rb(e,.5,.5)}var Yk=R({hannWindow_:fP});function gP(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=Ze([We(e,s,t-o),In([o],r)]);i.push(l),s+=n}return i.length===0?Ca([],[0,t]):j(Ze(i),[i.length,t])}var Jk=R({frame_:gP});function yP(e,t,n,a,r=Yk){a==null&&(a=qk(t));let s=Jk(e,t,n),i=L(s,r(t)),o=[];for(let l=0;l<s.shape[0];l++)o.push(Hc(We(i,[l,0],[1,t]),a));return Ze(o)}var bP=R({stft_:yP});function xP(e,t,n,a,r="bilinear",s=0){let i=C(e,"image","cropAndResize"),o=C(t,"boxes","cropAndResize","float32"),l=C(n,"boxInd","cropAndResize","int32"),c=o.shape[0];F(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${o.shape}.`),F(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${o.shape}.`),F(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${a.length}.`),F(a[0]>=1&&a[1]>=1,()=>`cropSize must be atleast [1,1], but was ${a}`),F(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let u={image:i,boxes:o,boxInd:l},p={method:r,extrapolationValue:s,cropSize:a};return P.runKernel(Lo,u,p)}var vP=R({cropAndResize_:xP});function wP(e){let t=C(e,"image","flipLeftRight","float32");F(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return P.runKernel(Ho,n,{})}var kP=R({flipLeftRight_:wP});function IP(e,t,n=0,a=.5){let r=C(e,"image","rotateWithOffset","float32");F(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return P.runKernel(El,s,i)}var NP=R({rotateWithOffset_:IP});function jl(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),F(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),F(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),F(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),F(t.rank===1,()=>"scores must be a 1D tensor"),F(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),F(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s}}function TP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=C(e,"boxes","nonMaxSuppression"),i=C(t,"scores","nonMaxSuppression"),o=jl(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 SP=R({nonMaxSuppression_:TP});function EP(e,t,n){let a=CP(e,t,n),r=a<0?-(a+1):a;e.splice(r,0,t)}function CP(e,t,n){return FP(e,t,n||_P)}function _P(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 Zk(e,t,n,a,r){return ib(e,t,n,a,r,0)}function Qk(e,t,n,a,r,s){return ib(e,t,n,a,r,0,!1,s,!0)}function e1(e,t,n,a,r,s){return ib(e,t,n,a,r,s,!0)}function ib(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(t1);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:x}=g;if(y<r)break;let v=!1;for(let N=p.length-1;N>=x;--N){let T=AP(e,b,p[N]);if(T>=a){v=!0;break}if(g.score=g.score*$P(a,u,T),g.score<=r)break}g.suppressBeginIndex=p.length,v||(g.score===y?(p.push(b),d.push(g.score)):g.score>r&&EP(c,g,t1))}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 AP(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),x=Math.max(y-f,0)*Math.max(b-g,0);return x/(h+m-x)}function $P(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function t1(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function DP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=C(e,"boxes","nonMaxSuppressionAsync"),i=C(t,"scores","nonMaxSuppressionAsync"),o=jl(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}=Zk(c,u,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),et(p,"int32")}var MP=DP;function RP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=C(e,"boxes","nonMaxSuppression"),o=C(t,"scores","nonMaxSuppression"),l=jl(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 PP=R({nonMaxSuppressionWithScore_:RP});async function OP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=C(e,"boxes","nonMaxSuppressionAsync"),o=C(t,"scores","nonMaxSuppressionAsync"),l=jl(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}=e1(u,p,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:et(d,"int32"),selectedScores:et(h)}}var LP=OP;function zP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=C(e,"boxes","nonMaxSuppression"),o=C(t,"scores","nonMaxSuppression"),l=jl(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 BP=R({nonMaxSuppressionPadded_:zP});async function WP(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=C(e,"boxes","nonMaxSuppressionAsync"),o=C(t,"scores","nonMaxSuppressionAsync"),l=jl(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}=Qk(d,h,c,u,p,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:et(m,"int32"),validOutputs:de(f,"int32")}}var VP=WP;function UP(e,t,n=!1,a=!1){let r=C(e,"images","resizeBilinear");F(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),F(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),F(a===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=j(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(si,o,l);return i?j(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var n1=R({resizeBilinear_:UP});function GP(e,t,n=!1,a=!1){let r=C(e,"images","resizeNearestNeighbor");F(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),F(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),F(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),F(a===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=j(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(bc,o,l);return i?j(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var a1=R({resizeNearestNeighbor_:GP});function HP(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=C(e,"a","bandPart");F(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=j(Nh(0,s,1,"int32"),[-1,1]),l=Nh(0,i,1,"int32"),c=fe(o,l),u=pa(es(c,de(+t,"int32")),wr(c,de(-n,"int32"))),p=yt([s,i],a.dtype);return j(Mt(pt(j(a,[-1,s,i])).map(d=>kn(u,d,p))),r)}var jP=R({bandPart_:HP});function qP(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s<e.length;++s)F(e[s].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${r})`)}else t=!0,e=Pn(e,e.shape[0],0).map(r=>ns(r,[0]));F(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],a=e;for(let r=0;r<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=fe(s,o)}return ve(s,Mh(s,"euclidean"))}));return t?Mt(n,0):n}var KP=R({gramSchmidt_:qP});function XP(e,t=!1){if(F(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return r1(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),a=pt(j(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(l=>{let[c,u]=r1(l,t);r.push(c),s.push(u)});let i=j(Mt(r,0),e.shape),o=j(Mt(s,0),e.shape);return[i,o]}}function r1(e,t=!1){return P.tidy(()=>{F(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],a=e.shape[1],r=zy(n),s=Yr(e),i=Ca([[1]],[1,1]),o=Yr(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=Mh(h),f=We(s,[c,c],[1,1]),g=kn(Qn(f,0),Ca([[-1]]),Ca([[1]])),y=fe(f,L(g,m)),b=ve(h,y);b.shape[0]===1?o=Yr(i):o=Ze([i,We(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let x=St(ve(ze(g,y),m)),v=We(s,[c,0],[n-c,a]),N=L(x,o),T=Ue(o);if(c===0)s=fe(v,ze(N,ze(T,v)));else{let $=fe(v,ze(N,ze(T,v)));s=Ze([We(s,[0,0],[c,a]),$],0)}let E=Ue(N),A=We(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=fe(A,ze(ze(A,o),E));else{let $=fe(A,ze(ze(A,o),E));r=Ze([We(r,[0,0],[n,c]),$],1)}return[o,s,r]}),Ae([u,p,d])}return!t&&n>a&&(r=We(r,[0,0],[n,a]),s=We(s,[0,0],[a,a])),[r,s]})}var YP=R({qr_:XP}),mn;(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"})(mn||(mn={}));function JP(e,t,n=mn.SUM_BY_NONZERO_WEIGHTS){let a=C(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=C(t,"weights","computeWeightedLoss"));let s=r==null?a:L(a,r);if(n===mn.NONE)return s;if(n===mn.SUM)return Ce(s);if(n===mn.MEAN){if(r==null)return Ct(s);{let i=a.size/r.size,o=ve(Ce(s),Ce(r));return i>1?ve(o,de(i)):o}}if(n===mn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return ve(Ce(s),de(a.size));{let i=L(r,Ka(a.shape)),o=pe(Ce(ts(i,de(0))),"float32");return ve(Ce(s),o)}}throw Error(`Unknown reduction: ${n}`)}var kr=R({computeWeightedLoss_:JP});function ZP(e,t,n,a=mn.SUM_BY_NONZERO_WEIGHTS){let r=C(e,"labels","absoluteDifference"),s=C(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=C(n,"weights","absoluteDifference")),rt(r.shape,s.shape,"Error in absoluteDifference: ");let o=Wt(fe(r,s));return kr(o,i,a)}var QP=R({absoluteDifference_:ZP});function eO(e,t,n,a,r=mn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","cosineDistance"),i=C(t,"predictions","cosineDistance"),o=null;a!=null&&(o=C(a,"weights","cosineDistance")),rt(s.shape,i.shape,"Error in cosineDistance: ");let l=de(1),c=fe(l,Ce(L(s,i),n,!0));return kr(c,o,r)}var tO=R({cosineDistance_:eO});function nO(e,t,n,a=mn.SUM_BY_NONZERO_WEIGHTS){let r=C(e,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;n!=null&&(i=C(n,"weights","hingeLoss")),rt(r.shape,s.shape,"Error in hingeLoss: ");let o=de(1);r=fe(L(de(2),r),o);let l=Xe(fe(o,L(r,s)));return kr(l,i,a)}var aO=R({hingeLoss_:nO});function rO(e,t,n,a=1,r=mn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),o=null;n!=null&&(o=C(n,"weights","huberLoss")),rt(s.shape,i.shape,"Error in huberLoss: ");let l=de(a),c=Wt(fe(i,s)),u=Fi(c,l),p=fe(c,u),d=Z(L(de(.5),ot(u)),L(l,p));return kr(d,o,r)}var sO=R({huberLoss_:rO});function iO(e,t,n,a=1e-7,r=mn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","logLoss"),i=C(t,"predictions","logLoss"),o=null;n!=null&&(o=C(n,"weights","logLoss")),rt(s.shape,i.shape,"Error in logLoss: ");let l=de(1),c=de(a),u=St(L(s,Dn(Z(i,c)))),p=L(fe(l,s),Dn(Z(fe(l,i),c))),d=fe(u,p);return kr(d,o,r)}var oO=R({logLoss_:iO});function lO(e,t,n,a=mn.SUM_BY_NONZERO_WEIGHTS){let r=C(e,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=C(n,"weights","meanSquaredError")),rt(r.shape,s.shape,"Error in meanSquaredError: ");let o=jc(r,s);return kr(o,i,a)}var uO=R({meanSquaredError_:lO});function cO(e,t){let n=C(e,"labels","sigmoidCrossEntropyWithLogits"),a=C(t,"logits","sigmoidCrossEntropyWithLogits");rt(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Xe(a),s=L(a,n),i=yh(hn(St(Wt(a))));return Z(fe(r,s),i)}function pO(e,t,n,a=0,r=mn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=C(n,"weights","sigmoidCrossEntropy")),rt(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let c=de(a),u=de(1),p=de(.5);s=Z(L(s,fe(u,c)),L(p,c))}let l=cO(s,i);return kr(l,o,r)}var dO=R({sigmoidCrossEntropy_:pO});function hO(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 qa((a,r,s)=>{let i=Uy(r,[n],!0),o=fe(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(j(c,h),fe(pe(p,"float32"),hn(d))),L(j(c,h),fe(hn(d),pe(p,"float32")))]}}})(e,t)}function mO(e,t,n,a=0,r=mn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"onehotLabels","softmaxCrossEntropy"),i=C(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=C(n,"weights","softmaxCrossEntropy")),rt(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let c=de(a),u=de(1),p=de(s.shape[1]);s=Z(L(s,fe(u,c)),ve(c,p))}let l=hO(s,i);return kr(l,o,r)}var fO=R({softmaxCrossEntropy_:mO}),gO={fft:Gc,ifft:Gl,rfft:Hc,irfft:Ah},yO={hammingWindow:mP,hannWindow:Yk,frame:Jk,stft:bP},Ya={flipLeftRight:kP,resizeNearestNeighbor:a1,resizeBilinear:n1,rotateWithOffset:NP,cropAndResize:vP,nonMaxSuppression:SP,nonMaxSuppressionAsync:MP,nonMaxSuppressionWithScore:PP,nonMaxSuppressionWithScoreAsync:LP,nonMaxSuppressionPadded:BP,nonMaxSuppressionPaddedAsync:VP},s1={bandPart:jP,gramSchmidt:KP,qr:YP},bO={absoluteDifference:QP,computeWeightedLoss:kr,cosineDistance:tO,hingeLoss:aO,huberLoss:sO,logLoss:oO,meanSquaredError:uO,sigmoidCrossEntropy:dO,softmaxCrossEntropy:fO},Ir=class extends G0{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 Ae(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 fk(e,t)}dispose(){this.iterations_!=null&&Ae(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:de(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ir,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var zh=class extends Ir{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:D(()=>He(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:D(()=>He(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;D(()=>{let l=Z(L(i,this.rho),L(ot(s),1-this.rho)),c=L(ve(an(Z(o,this.epsilon)),an(Z(i,this.epsilon))),s),u=Z(L(o,this.rho),L(ot(c),1-this.rho));i.assign(l),o.assign(u);let p=Z(L(c,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ae(this.accumulatedGrads.map(e=>e.variable)),Ae(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};zh.className="Adadelta";Jr(zh);var Bh=class extends Ir{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:D(()=>In(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,ot(r));s.assign(i);let o=Z(L(ve(r,an(Z(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ae(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)}};Bh.className="Adagrad";Jr(Bh);var Wh=class extends Ir{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=de(t).variable(),this.accBeta2=de(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=fe(1,this.accBeta1),a=fe(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:D(()=>He(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:D(()=>He(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(ot(l),1-this.beta2)),h=ve(p,n),m=ve(d,a);c.assign(p),u.assign(d);let f=Z(L(ve(h,Z(an(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&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ae(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(Xa(this.beta1,this.iterations_+1)),this.accBeta2.assign(Xa(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)}};Wh.className="Adam";Jr(Wh);var Vh=class extends Ir{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=de(0).variable(),this.accBeta1=de(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=fe(1,this.accBeta1),a=ve(-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:`${r}/m`,variable:He(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:He(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let 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=Wt(l),m=Ta(d,h);c.assign(p),u.assign(m);let f=Z(L(ve(a,n),ve(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&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ae(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)}};Vh.className="Adamax";Jr(Vh);var qc=class extends Ir{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=Xt(de(-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)}};qc.className="SGD";Jr(qc);var Uh=class extends qc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=de(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:`${t}/momentum`,variable:D(()=>He(a).variable(i))}}let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&D(()=>{let i,o=Z(L(this.m,r),s);this.useNesterov?i=Z(L(this.c,Z(s,L(o,this.m))),a):i=Z(L(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ae(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Uh.className="Momentum";Jr(Uh);var Gh=class extends Ir{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=P.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:D(()=>He(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:D(()=>He(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:D(()=>He(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;D(()=>{let l=Z(L(i,this.decay),L(ot(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=ve(L(s,this.learningRate),an(fe(l,Z(ot(u),this.epsilon)))),d=Z(L(o,this.momentum),p);i.assign(l),c.assign(u),o.assign(d);let h=fe(a,d);a.assign(h)}else{let c=Z(L(i,this.decay),L(ot(s),1-this.decay)),u=Z(L(o,this.momentum),ve(L(s,this.learningRate),an(Z(c,this.epsilon))));i.assign(c),o.assign(u);let p=fe(a,u);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ae(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ae(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ae(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)}};Gh.className="RMSProp";Jr(Gh);var $i=class{static sgd(e){return new qc(e)}static momentum(e,t,n=!1){return new Uh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new Gh(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new Wh(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new zh(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new Vh(e,t,n,a,r)}static adagrad(e,t=.1){return new Bh(e,t)}},Di={sgd:$i.sgd,momentum:$i.momentum,adadelta:$i.adadelta,adagrad:$i.adagrad,rmsprop:$i.rmsprop,adamax:$i.adamax,adam:$i.adam},xO=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Hh(){return new Promise(e=>xO(()=>e()))}var _={};Oe(_,{ERF_A1:()=>FO,ERF_A2:()=>AO,ERF_A3:()=>$O,ERF_A4:()=>DO,ERF_A5:()=>MO,ERF_P:()=>_O,PARALLELIZE_THRESHOLD:()=>ob,SELU_SCALE:()=>o1,SELU_SCALEALPHA:()=>i1,applyActivation:()=>Oh,assertAndGetBroadcastShape:()=>vt,assertAxesAreInnerMostDims:()=>UD,assertParamsConsistent:()=>vO,assignToTypedArray:()=>VO,axesAreInnerMostDims:()=>Wy,calculateShapes:()=>$0,castTensor:()=>jO,combineLocations:()=>yk,complexWithEvenIndex:()=>zO,complexWithOddIndex:()=>BO,computeConv2DInfo:()=>Dc,computeConv3DInfo:()=>Q0,computeDefaultPad:()=>_y,computeDilation2DInfo:()=>h$,computeOptimalWindowSize:()=>kO,computeOutAndReduceShapes:()=>bk,computeOutShape:()=>wO,computePool2DInfo:()=>Z0,computePool3DInfo:()=>m$,convertConv2DDataFormat:()=>J0,eitherStridesOrDilationsAreOne:()=>$n,expandShapeToKeepDim:()=>_i,exponent:()=>GO,exponents:()=>UO,fromStringArrayToUint8:()=>XO,fromUint8ToStringArray:()=>KO,getAxesPermutation:()=>xk,getBroadcastDims:()=>sD,getComplexWithIndex:()=>WO,getFusedBiasGradient:()=>Ph,getFusedDyActivation:()=>Rh,getImageCenter:()=>IO,getInnerMostAxes:()=>GD,getPermuted:()=>TO,getReductionAxes:()=>Vt,getReshaped:()=>NO,getReshapedPermuted:()=>SO,getSliceBeginCoords:()=>CO,getSliceSize:()=>EO,getUndoAxesPermutation:()=>Vy,log:()=>PO,mergeRealAndImagArrays:()=>OO,prepareAndValidate:()=>A0,prepareSplitSize:()=>HO,reshapeTensor:()=>qO,segment_util:()=>l1,shouldFuse:()=>Lh,slice_util:()=>dn,splitRealAndImagArrays:()=>LO,tupleValuesAreOne:()=>Qr,upcastType:()=>ua,validateInput:()=>gy,validateUpdateShape:()=>fy,warn:()=>RO});function vO(e,t){let n=e[0].length;e.forEach((r,s)=>{F(r.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let a=e[0];e.forEach((r,s)=>{for(let i=0;i<n;i++)F(i===t||r[i]===a[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${a}) along the non-concatenated axis ${s}.`)})}function wO(e,t){let n=e[0].slice();for(let a=1;a<e.length;a++)n[t]+=e[a][t];return n}var ob=30;function kO(e){return e<=ob?e:bd(e,Math.floor(Math.sqrt(e)))}function IO(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function NO(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(s+1))}return r}function TO(e,t,n=!0){let a=[];if(n){a.push(t);for(let r=t+1;r<e;++r)r<=2*t?(a.push(r),a.push(r-(t+1))):a.push(r)}else{let r=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function SO(e,t,n,a=!0){let r=[];a?r.push(e[0]/n):r.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?a?r.push(t[s-1]*e[s]):r.push(e[s]/t[s-1]):r.push(e[s]);return r}function CO(e,t){let n=[0];for(let a=0;a<t;++a)n.push(e[a][0]);return n}function EO(e,t,n){let a=e.slice(0,1);for(let r=0;r<n;++r)a.push(e[r+1]-t[r][0]-t[r][1]);return a}var i1=1.7580993408473768,o1=1.0507009873554805,_O=.3275911,FO=.254829592,AO=-.284496736,$O=1.421413741,DO=-1.453152027,MO=1.061405429;function RO(...e){ee().getBool("IS_TEST")||console.warn(...e)}function PO(...e){ee().getBool("IS_TEST")||console.log(...e)}function OO(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let a=0;a<n.length;a+=2)n[a]=e[a/2],n[a+1]=t[a/2];return n}function LO(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let a=0;a<e.length;a+=2)t[a/2]=e[a],n[a/2]=e[a+1];return{real:t,imag:n}}function zO(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function BO(e){let t=Math.floor(e.length/4),n=new Float32Array(t),a=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],a[Math.floor(r/4)]=e[r+1];return{real:n,imag:a}}function WO(e,t){let n=e[t*2],a=e[t*2+1];return{real:n,imag:a}}function VO(e,t,n,a){e[a*2]=t,e[a*2+1]=n}function UO(e,t){let n=new Float32Array(e/2),a=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let s=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(s),a[r]=Math.sin(s)}return{real:n,imag:a}}function GO(e,t,n){let a=(n?2:-2)*Math.PI*(e/t),r=Math.cos(a),s=Math.sin(a);return{real:r,imag:s}}function HO(e,t,n=0){let a=[];if(typeof t=="number")F(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),a=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);F(r<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}F(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),a=t}return a}var l1={};Oe(l1,{collectGatherOpShapeInfo:()=>ZO,computeOutShape:()=>JO,segOpComputeOptimalWindowSize:()=>YO});function YO(e,t){let n=!1,a;for(e<=ob?(a=e,n=!0):a=bd(e,Math.floor(Math.sqrt(e)));!n;)a>t||a===e?n=!0:a=bd(e,a+1);return a}function JO(e,t,n){let a=[],r=e.length;for(let s=0;s<r;s++)s!==t?a.push(e[s]):a.push(n);return a}function ZO(e,t,n,a){let r=t.shape.length,s=e.shape.length;if(a!==0&&(a<-r||a>r))throw new Error(`Expect batchDims in the range of [-${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 jO(e,t,n){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();let a=yt(e.shape),r=pe(e,"float32"),s=n.complex(r,a);return a.dispose(),r.dispose(),s}if(!jw(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=de(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 qO(e,t){return P.makeTensorFromDataId(e.dataId,t,e.dtype)}function KO(e){try{return e.map(t=>Qd(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function XO(e){return e.map(t=>Nc(t))}var Ja={};Oe(Ja,{nonMaxSuppressionV3Impl:()=>Zk,nonMaxSuppressionV4Impl:()=>Qk,nonMaxSuppressionV5Impl:()=>e1,whereImpl:()=>Ck});var u1={kernelName:Co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Hl(pe(n,"float32"),-1))}}},QO={kernelName:Eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=ot(pe(n,"float32")),r=an(fe(de(1),a));return St(ve(e,r))}}}},eL={kernelName:_o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=an(fe(ot(pe(n,"float32")),1));return ve(e,a)}}}},tL={kernelName:Ur,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=vt(n.shape,a.shape);return{a:()=>{let s=e,i=Vt(n.shape,r);return i.length>0&&(s=Ce(s,i)),j(s,n.shape)},b:()=>{let s=e,i=Vt(a.shape,r);return i.length>0&&(s=Ce(s,i)),j(s,a.shape)}}}},nL={kernelName:_s,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((a,r)=>{n[r]=()=>e.clone()}),n}},aL={kernelName:Fs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},rL={kernelName:sc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},sL={kernelName:Fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,an(fe(de(1),ot(pe(n,"float32")))))}}},iL={kernelName:Ao,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=an(Z(de(1),ot(pe(n,"float32"))));return ve(e,a)}}}},oL={kernelName:Mo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=vt(n.shape,a.shape);return{a:()=>{let s=Z(ot(n),ot(a)),i=L(e,ve(a,s)),o=Vt(n.shape,r);return o.length>0&&(i=Ce(i,o)),j(i,n.shape)},b:()=>{let s=Z(ot(n),ot(a)),i=St(L(e,ve(n,s))),o=Vt(a.shape,r);return o.length>0&&(i=Ce(i,o)),j(i,a.shape)}}}},lL={kernelName:$o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,Z(ot(pe(n,"float32")),1))}}},uL={kernelName:Do,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,fe(de(1),ot(pe(n,"float32"))))}}};function cL(e,t,n,a,r=[1,1,1],s,i){let o=C(e,"dy","avgPool3dGrad"),l=C(t,"input","avgPool3dGrad"),c=o,u=l,p=!1;l.rank===4&&(p=!0,c=j(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),u=j(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]])),F(c.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),F(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),F($n(a,r),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`),i!=null&&F(qt(s),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let d={dy:c,input:u},h={filterSize:n,strides:a,dilations:r,pad:s,dimRoundingMode:i},m=P.runKernel(Id,d,h);return p?j(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var pL=R({avgPool3dGrad_:cL}),dL={kernelName:ic,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,dilations:i,pad:o,dimRoundingMode:l}=n,c=i==null?[1,1,1]:i;return{x:()=>pL(e,a,r,s,c,o,l)}}};function hL(e,t,n,a,r){let s=C(e,"dy","avgPoolGrad"),i=C(t,"input","avgPoolGrad");F(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,c=!1;i.rank===3&&(c=!0,o=j(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=j(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),F(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let u={dy:l,input:o},p={filterSize:n,strides:a,pad:r},d=P.runKernel(kd,u,p);return c?j(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var mL=R({avgPoolGrad_:hL}),fL={kernelName:As,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{filterSize:r,strides:s,pad:i}=n;return{x:()=>mL(e,a,r,s,i)}}},gL={kernelName:$s,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)}}},yL={kernelName:oc,gradFunc:(e,t,n)=>{let{blockShape:a,crops:r}=n;return{x:()=>Bc(e,a,r)}}},bL={kernelName:n0,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)}}},xL={kernelName:Ds,gradFunc:e=>({x:()=>e.clone()})},vL={kernelName:Ro,gradFunc:e=>({x:()=>He(e)})},wL={kernelName:Gr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{clipValueMin:r,clipValueMax:s}=n;return{x:()=>kn(pa(wr(a,r),es(a,s)),e,He(e))}}},kL={kernelName:lc,inputsToSave:["x"],gradFunc:u1.gradFunc},IL={kernelName:Po,saveAllInputs:!0,gradFunc:(e,t,n)=>{let a=t.map(o=>o.shape),{axis:r}=n,s=la(r,t[0].shape)[0],i=a.map(o=>o[s]);return Pn(e,i,s).map(o=>()=>o)}},NL={kernelName:Ms,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return F(Qr(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>$y(a.shape,e,r,i,o,l),filter:()=>sb(a,e,r.shape,i,o,l)}}},TL={kernelName:Rs,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[a,r]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>$t(e,r,s,i,o,1,l),filter:()=>sb(e,a,r.shape,s,i,o,l)}}};function SL(e,t,n,a,r){let s=e;e.rank===4&&(s=j(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=j(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),F(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),F(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),F(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),F(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),F(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:a,pad:r,filterShape:n};return P.runKernel(Cd,o,l)}var CL=R({conv3DBackpropFilter_:SL}),EL={kernelName:uc,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s}=n;F(Qr(a),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let[i,o]=t;return{x:()=>lk(i.shape,e,o,r,s),filter:()=>CL(i,e,o.shape,r,s)}}},_L={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(St(Eh(pe(n,"float32"))),e)}}},FL={kernelName:Oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(_h(pe(n,"float32")),e)}}},AL={kernelName:Os,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r,exclusive:s,reverse:i}=n;return{x:()=>{let o=xk([r],a.rank),l=fh(e,r,s,!i);return o!=null&&(l=Ue(l,o)),l}}}},$L={kernelName:Ls,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:a,strides:r,pad:s,dimRoundingMode:i}=n,o=a==null?[1,1]:a;F(Qr(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,c]=t;return F(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),F(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),F(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),F($n(r,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${o}'.`),i!=null&&F(qt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>Xk(l.shape,e,c,r,s,a,i),filter:()=>Kk(l,e,c.shape,r,s,a,i)}}},DL={kernelName:cc,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(Dd,s,n),filter:()=>P.runKernel(Md,i,n)}}},ML={kernelName:Bo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,a={dy:e,y:n};return{x:()=>P.runKernel(Rd,a)}}},RL={kernelName:Wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=L(hn(St(ot(n))),2/Math.sqrt(Math.PI));return{x:()=>L(e,a)}}},PL={kernelName:Bs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,n)}}},OL={kernelName:Uo,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>j(e,n.shape)}}},LL={kernelName:Go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,hn(n))}}},zL={kernelName:Ws,gradFunc:e=>({x:()=>He(e)})},BL={kernelName:Vs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=vt(n.shape,a.shape);return{a:()=>{let s=ve(e,pe(a,"float32")),i=Vt(n.shape,r);return i.length>0?j(Ce(s,i),n.shape):s},b:()=>{let s=L(e,pe(n,"float32")),i=Vt(a.shape,r);i.length>0&&(s=j(Ce(s,i),a.shape));let o=ot(a);return St(ve(s,pe(o,"float32")))}}}},WL={kernelName:Us,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:a}=n,[r,s,i,o]=t,l=o==null?de(1):o,c=Vt(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=fe(r,s),d=L(e,l),h=Sh(Z(i,de(a))),m=L(L(L(h,h),h),de(-.5));return{x:()=>s.rank===1?j(L(L(e,ja(j(h,[1,1,1,s.shape[0]]),u)),l),r.shape):j(L(L(e,h),l),r.shape),mean:()=>{let f=L(L(h,de(-1)),d);return s.rank===1&&(f=Ce(f,c)),j(f,s.shape)},variance:()=>{let f=L(L(m,p),d);return s.rank===1&&(f=Ce(f,c)),j(f,s.shape)},scale:()=>{let f=L(p,h),g=L(e,f);return s.rank===1&&(g=Ce(g,c)),j(g,s.shape)},offset:()=>{let f=e;return s.rank===1&&(f=Ce(f,c)),j(f,s.shape)}}}},VL={kernelName:jo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[a,r]=t,{axis:s}=n,i=la(s,a.shape)[0];return{x:()=>{let o=a.shape,l=r.size,c=o.slice(0,i),u=c.length,p=o.slice(s,o.length).slice(1),d=p.length,h=c1(0,u),m=c1(u+1,u+1+d),f=p1([c,[l],p]),g=j(e,f),y=j(r,[l]),b=p1([[u],h,m]),x=Ue(g,b),v=tb(x,y,a.shape[i]),N=Vy(b);return v=Ue(v,N),v},indices:()=>r}}};function c1(e,t){let n=[];for(let a=e;a<t;++a)n.push(a);return n}function p1(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 UL={kernelName:Gs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>He(n),b:()=>He(a)}}},GL={kernelName:Xo,gradFunc:e=>({x:()=>pe(e,"float32")})},HL={kernelName:Yo,gradFunc:e=>({x:()=>He(e)})},jL={kernelName:Jo,gradFunc:e=>({x:()=>He(e)})},qL={kernelName:Zo,gradFunc:e=>({x:()=>He(e)})},KL={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{alpha:r}=n,s=Qn(a,0);return{x:()=>kn(s,e,L(e,r))}}},XL={kernelName:tl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,Z(n,1))}}},YL={kernelName:js,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,pe(n,"float32"))}}},JL={kernelName:a0,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n;return{logits:()=>{let s=!0,i=hn(a);return fe(e,L(Ce(e,r,s),i))}}}};function ZL(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(Bd,o,l)}var QL=R({localResponseNormalizationBackprop_:ZL}),e3={kernelName:mc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>QL(a,r,e,s,i,o,l)}}};function d1(e,t,n,a){return t.rank<n.rank&&(t=j(t,_i(t.shape,a))),e.rank<n.rank&&(e=j(e,_i(e.shape,a))),{x:()=>L(e,pe(vr(n,t),e.dtype))}}var h1={kernelName:qs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{reductionIndices:r}=a,s=t[0],i=t[1],o=la(r,s.shape),l=d1(e,i,s,o);return{x:()=>l.x()}}},t3={kernelName:Ks,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>L(e,pe(wr(n,a),"float32")),b:()=>L(e,pe(Lc(n,a),"float32"))}}};function n3(e,t,n,a,r,s=[1,1,1],i,o){let l=C(e,"dy","maxPool3dGrad"),c=C(t,"input","maxPool3dGrad"),u=C(n,"output","maxPool3dGrad"),p=l,d=c,h=u,m=!1;c.rank===4&&(m=!0,p=j(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),d=j(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]]),h=j(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),F(p.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${p.rank}.`),F(d.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${d.rank}.`),F(h.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${h.rank}.`),F($n(r,s),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${r} and dilations '${s}'`),o!=null&&F(qt(i),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${i}.`);let f={dy:p,input:d,output:h},g={filterSize:a,strides:r,dilations:s,pad:i,dimRoundingMode:o},y=P.runKernel(Vd,f,g);return m?j(y,[y.shape[1],y.shape[2],y.shape[3],y.shape[4]]):y}var a3=R({maxPool3dGrad_:n3}),r3={kernelName:fc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,dilations:o,pad:l,dimRoundingMode:c}=n,u=o==null?[1,1,1]:o;return{x:()=>a3(e,a,r,s,i,u,l,c)}}};function s3(e,t,n,a,r,s,i){let o=C(e,"dy","maxPoolGrad"),l=C(t,"input","maxPoolGrad"),c=C(n,"output","maxPoolGrad");F(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),F(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),F(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&F(qt(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(Wd,u,p)}var i3=R({maxPoolGrad_:s3}),o3={kernelName:Xs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a,r]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>i3(e,a,r,s,i,o)}}},l3={kernelName:Ys,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{axis:r}=n,s=la(r,a.shape),i=bk(a.shape,s)[1],o=zt(i);return{x:()=>{let l=a.shape.slice();s.forEach(u=>{l[u]=1});let c=j(e,l);return ve(L(c,Ka(a.shape,"float32")),o)}}}},u3={kernelName:Js,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let a=n,{axis:r}=a,[s,i]=t,o=la(r,s.shape),l=d1(e,i,s,o);return{x:()=>l.x()}}},c3={kernelName:Zs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t;return{a:()=>L(e,pe(es(n,a),"float32")),b:()=>L(e,pe(Qn(n,a),"float32"))}}},p3={kernelName:gc,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)}}},d3={kernelName:al,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=vt(n.shape,a.shape);return{a:()=>{let s=Vt(n.shape,r);return s.length>0?j(Ce(e,s),n.shape):e},b:()=>{let s=L(e,St(zl(ve(n,a)))),i=Vt(a.shape,r);return i.length>0?j(Ce(s,i),a.shape):s}}}},h3={kernelName:Qs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=vt(n.shape,a.shape);return{a:()=>{let s=L(e,pe(a,"float32")),i=Vt(n.shape,r);return i.length>0?j(Ce(s,i),n.shape):s},b:()=>{let s=L(e,pe(n,"float32")),i=Vt(a.shape,r);return i.length>0?j(Ce(s,i),a.shape):s}}}},m3={kernelName:rl,gradFunc:e=>({x:()=>St(e)})},f3={kernelName:ei,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>yt(n.shape,"float32")}}},g3={kernelName:ul,gradFunc:e=>({x:()=>He(e)})},y3={kernelName:cl,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:a}=n;return pt(e,a).map(r=>()=>r)}},m1={kernelName:ti,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)}}},b3={kernelName:ni,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,a,r]=t,s=n,i=a,o=vt(s.shape,i.shape);return{a:()=>{let l=pe(i,"float32"),c=L(e,L(l,Xa(s,fe(l,de(1))))),u=Vt(s.shape,o);return u.length>0&&(c=Ce(c,u)),j(c,s.shape)},b:()=>{let l=Qn(s,0),c=kn(l,Dn(s),He(s)),u=L(e,L(r,c)),p=Vt(i.shape,o);return p.length>0&&(u=Ce(u,p)),j(u,i.shape)}}}},x3={kernelName:ai,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,a]=t,r=Qn(n,0);return{x:()=>kn(r,e,L(e,a)),alpha:()=>{let s=kn(r,He(e),L(e,n)),i=Vt(a.shape,e.shape);return i.length>0&&(s=Ce(s,i)),j(s,a.shape)}}}},v3={kernelName:zs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=vt(n.shape,a.shape);return{a:()=>{let s=ve(e,pe(a,"float32")),i=Vt(n.shape,r);return i.length>0?j(Ce(s,i),n.shape):s},b:()=>{let s=L(e,pe(n,"float32")),i=Vt(a.shape,r);i.length>0&&(s=j(Ce(s,i),a.shape));let o=ot(a);return St(ve(s,pe(o,"float32")))}}}},w3={kernelName:dl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,St(ot(n)))}}},k3={kernelName:ii,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,a=L(es(n,6),Hl(n));return{x:()=>L(e,pe(a,"float32"))}}},I3={kernelName:ri,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,pe(Hl(n),"float32"))}}},N3={kernelName:hl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>j(e,n.shape)}}},T3={kernelName:si,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(qd,r,n)}}},S3={kernelName:bc,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[a]=t,r={dy:e,images:a};return{images:()=>P.runKernel(jd,r,n)}}},C3={kernelName:oi,gradFunc:(e,t,n)=>{let{dims:a}=n,r=la(a,e.shape);return{x:()=>Rn(e,r)}}},E3={kernelName:li,gradFunc:e=>({x:()=>He(e)})},_3={kernelName:ui,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>St(ve(e,L(Xa(n,1.5),2)))}}},F3={kernelName:fl,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>pe(He(n),"float32"),t:()=>L(e,pe(n,e.dtype)),e:()=>L(e,pe(zc(n),e.dtype))}}},A3={kernelName:gl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let a=Qn(n,de(0)),r=de(i1),s=de(o1),i=L(e,s),o=L(L(e,r),hn(pe(n,"float32")));return kn(a,i,o)}}}},$3={kernelName:pi,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(n,fe(de(1),n)))}}},D3={kernelName:xl,gradFunc:e=>({x:()=>He(e)})},M3={kernelName:ci,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(Pc(pe(n,"float32")),e)}}},R3={kernelName:bl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(mh(pe(n,"float32")),e)}}},P3={kernelName:yl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{begin:r,size:s}=n,i=a.shape,[o,l]=W0(a,r,s),c=[];for(let u=0;u<e.rank;u++)c.push([o[u],i[u]-o[u]-l[u]]);return{x:()=>ta(e,c)}}},O3={kernelName:mi,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[a]=t,{dim:r}=n,s=!0,i=L(e,a);return{logits:()=>fe(i,L(Ce(i,[r],s),a))}}},L3={kernelName:vl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,ca(n))}}},f1={kernelName:xc,gradFunc:(e,t,n)=>{let{blockShape:a,paddings:r}=n;return{x:()=>Mc(e,a,r)}}},g1={kernelName:wl,gradFunc:(e,t,n)=>{let{axis:a}=n;return{x:()=>Ze(e,a)}}},z3={kernelName:di,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,L(an(pe(n,"float32")),2))}}},B3={kernelName:vc,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(pe(n,"float32"),2))}}},W3={kernelName:fi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=de(2);return{a:()=>L(e,L(r,fe(n,a))),b:()=>L(e,L(r,fe(a,n)))}}},V3={kernelName:Cl,gradFunc:e=>({x:()=>He(e)})},U3={kernelName:gi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,a]=t,r=vt(n.shape,a.shape);return{a:()=>{let s=e,i=Vt(n.shape,r);return i.length>0&&(s=Ce(s,i)),j(s,n.shape)},b:()=>{let s=e,i=Vt(a.shape,r);return i.length>0&&(s=Ce(s,i)),j(St(s),a.shape)}}}},G3={kernelName:hi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,r=a.shape.slice(),{axis:s}=n;la(s,a.shape).forEach(l=>{r[l]=1});let i=j(e,r),o=L(i,Ka(a.shape,"float32"));return{x:()=>o}}},H3={kernelName:Il,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ve(e,ot(Pc(n)))}}},j3={kernelName:yi,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(fe(de(1),ot(n)),e)}}},q3={kernelName:Hr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[a]=t,{reps:r}=n;return{x:()=>{let s=He(a);if(a.rank===1)for(let i=0;i<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}}}},K3={kernelName:bi,gradFunc:(e,t,n)=>{let a=n,{perm:r}=a,s=Vy(r);return{x:()=>Ue(e,s)}}},X3={kernelName:Tl,gradFunc:(e,t,n)=>{let a=n,{axis:r}=a;return{value:()=>Mt(e,r)}}},J3={kernelName:wc,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Y3(e,n)}}};function Y3(e,t){let n=Ta(t,He(t)),a=Ei(e,n),r=wr(t,de(0,"int32")),s=a.rank-r.rank;for(let o=0;o<s;++o)r=Zn(r,o+1);r=pa(r,Ka(a.shape,"bool"));let i=He(a);return kn(r,a,i)}var Z3={kernelName:Sl,gradFunc:e=>({x:()=>He(e)})},Q3=[u1,QO,eL,tL,nL,aL,rL,sL,iL,oL,lL,uL,dL,fL,gL,yL,bL,xL,vL,wL,kL,IL,TL,NL,EL,_L,FL,AL,$L,DL,v3,ML,RL,PL,OL,LL,BL,zL,WL,VL,UL,GL,HL,jL,qL,KL,XL,YL,JL,e3,h1,h1,t3,r3,o3,l3,u3,c3,p3,d3,h3,m3,f3,g3,y3,m1,m1,b3,x3,w3,k3,I3,N3,T3,S3,C3,E3,_3,F3,A3,$3,D3,M3,R3,P3,O3,L3,f1,f1,g1,g1,z3,W3,B3,V3,U3,G3,H3,j3,q3,K3,X3,J3,Z3];for(let e of Q3)r0(e);z.prototype.abs=function(){return this.throwIfDisposed(),Wt(this)};z.prototype.acos=function(){return this.throwIfDisposed(),vy(this)};z.prototype.acosh=function(){return this.throwIfDisposed(),wy(this)};z.prototype.addStrict=function(e){return this.throwIfDisposed(),Mk(this,e)};z.prototype.add=function(e){return this.throwIfDisposed(),Z(this,e)};z.prototype.all=function(e,t){return this.throwIfDisposed(),ch(this,e,t)};z.prototype.any=function(e,t){return this.throwIfDisposed(),Ac(this,e,t)};z.prototype.argMax=function(e){return this.throwIfDisposed(),$c(this,e)};z.prototype.argMin=function(e){return this.throwIfDisposed(),ky(this,e)};z.prototype.asScalar=function(){return this.throwIfDisposed(),F(this.size===1,()=>"The array must have only 1 element."),j(this,[])};z.prototype.asType=function(e){return this.throwIfDisposed(),pe(this,e)};z.prototype.as1D=function(){return this.throwIfDisposed(),j(this,[this.size])};z.prototype.as2D=function(e,t){return this.throwIfDisposed(),j(this,[e,t])};z.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),j(this,[e,t,n])};z.prototype.as4D=function(e,t,n,a){return this.throwIfDisposed(),j(this,[e,t,n,a])};z.prototype.as5D=function(e,t,n,a,r){return this.throwIfDisposed(),j(this,[e,t,n,a,r])};z.prototype.asin=function(){return this.throwIfDisposed(),Iy(this)};z.prototype.asinh=function(){return this.throwIfDisposed(),Ny(this)};z.prototype.atan=function(){return this.throwIfDisposed(),Ty(this)};z.prototype.atan2=function(e){return this.throwIfDisposed(),Sy(this,e)};z.prototype.atanh=function(){return this.throwIfDisposed(),Cy(this)};z.prototype.avgPool=function(e,t,n,a){return this.throwIfDisposed(),Jn(this,e,t,n,a)};z.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Mc(this,e,t)};z.prototype.batchNorm=function(e,t,n,a,r){return this.throwIfDisposed(),br(this,e,t,n,a,r)};z.prototype.broadcastTo=function(e){return this.throwIfDisposed(),Rc(this,e)};z.prototype.cast=function(e){return this.throwIfDisposed(),pe(this,e)};z.prototype.ceil=function(){return this.throwIfDisposed(),Ay(this)};z.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),Yt(this,e,t)};z.prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof z&&(e=[e]),Ze([this,...e],t)};z.prototype.conv1d=function(e,t,n,a,r,s){return this.throwIfDisposed(),dh(this,e,t,n,a,r,s)};z.prototype.conv2dTranspose=function(e,t,n,a,r){return this.throwIfDisposed(),hh(this,e,t,n,a,r)};z.prototype.conv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),$t(this,e,t,n,a,r,s)};z.prototype.cos=function(){return this.throwIfDisposed(),Pc(this)};z.prototype.cosh=function(){return this.throwIfDisposed(),mh(this)};z.prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),fh(this,e,t,n)};z.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),My(this,e,t)};z.prototype.depthwiseConv2D=function(e,t,n,a,r,s){return Pt("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),xr(this,e,t,n,a,r,s)};z.prototype.depthwiseConv2d=function(e,t,n,a,r,s){return this.throwIfDisposed(),xr(this,e,t,n,a,r,s)};z.prototype.dilation2d=function(e,t,n,a,r){return this.throwIfDisposed(),Ry(this,e,t,n,a,r)};z.prototype.divNoNan=function(e){return this.throwIfDisposed(),Py(this,e)};z.prototype.divStrict=function(e){return this.throwIfDisposed(),Rk(this,e)};z.prototype.div=function(e){return this.throwIfDisposed(),ve(this,e)};z.prototype.dot=function(e){return this.throwIfDisposed(),ck(this,e)};z.prototype.elu=function(){return this.throwIfDisposed(),Ll(this)};z.prototype.equalStrict=function(e){return this.throwIfDisposed(),Ek(this,e)};z.prototype.equal=function(e){return this.throwIfDisposed(),vr(this,e)};z.prototype.erf=function(){return this.throwIfDisposed(),Oy(this)};z.prototype.exp=function(){return this.throwIfDisposed(),hn(this)};z.prototype.expandDims=function(e){return this.throwIfDisposed(),Zn(this,e)};z.prototype.expm1=function(){return this.throwIfDisposed(),Ly(this)};z.prototype.fft=function(){return this.throwIfDisposed(),Gc(this)};z.prototype.flatten=function(){return this.throwIfDisposed(),j(this,[this.size])};z.prototype.floor=function(){return this.throwIfDisposed(),zl(this)};z.prototype.floorDiv=function(e){return this.throwIfDisposed(),uh(this,e)};z.prototype.gather=function(e,t){return this.throwIfDisposed(),Ei(this,e,t)};z.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),_k(this,e)};z.prototype.greaterEqual=function(e){return this.throwIfDisposed(),wr(this,e)};z.prototype.greaterStrict=function(e){return this.throwIfDisposed(),Fk(this,e)};z.prototype.greater=function(e){return this.throwIfDisposed(),Qn(this,e)};z.prototype.ifft=function(){return this.throwIfDisposed(),Gl(this)};z.prototype.irfft=function(){return this.throwIfDisposed(),Ah(this)};z.prototype.isFinite=function(){return this.throwIfDisposed(),pk(this)};z.prototype.isInf=function(){return this.throwIfDisposed(),dk(this)};z.prototype.isNaN=function(){return this.throwIfDisposed(),hk(this)};z.prototype.leakyRelu=function(e){return this.throwIfDisposed(),Oc(this,e)};z.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),Ak(this,e)};z.prototype.lessEqual=function(e){return this.throwIfDisposed(),es(this,e)};z.prototype.lessStrict=function(e){return this.throwIfDisposed(),$k(this,e)};z.prototype.less=function(e){return this.throwIfDisposed(),Lc(this,e)};z.prototype.localResponseNormalization=function(e,t,n,a){return this.throwIfDisposed(),By(this,e,t,n,a)};z.prototype.logSigmoid=function(){return this.throwIfDisposed(),gk(this)};z.prototype.logSoftmax=function(e){return this.throwIfDisposed(),xh(this,e)};z.prototype.logSumExp=function(e,t){return this.throwIfDisposed(),Uy(this,e,t)};z.prototype.log=function(){return this.throwIfDisposed(),Dn(this)};z.prototype.log1p=function(){return this.throwIfDisposed(),yh(this)};z.prototype.logicalAnd=function(e){return this.throwIfDisposed(),pa(this,e)};z.prototype.logicalNot=function(){return this.throwIfDisposed(),zc(this)};z.prototype.logicalOr=function(e){return this.throwIfDisposed(),vh(this,e)};z.prototype.logicalXor=function(e){return this.throwIfDisposed(),vk(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(),Dt(this,e,t,n,a)};z.prototype.max=function(e,t){return this.throwIfDisposed(),ea(this,e,t)};z.prototype.maximumStrict=function(e){return this.throwIfDisposed(),Pk(this,e)};z.prototype.maximum=function(e){return this.throwIfDisposed(),Ta(this,e)};z.prototype.mean=function(e,t){return this.throwIfDisposed(),Ct(this,e,t)};z.prototype.min=function(e,t){return this.throwIfDisposed(),Wl(this,e,t)};z.prototype.minimumStrict=function(e){return this.throwIfDisposed(),Ok(this,e)};z.prototype.minimum=function(e){return this.throwIfDisposed(),Fi(this,e)};z.prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),Hy(this,e,t)};z.prototype.modStrict=function(e){return this.throwIfDisposed(),Lk(this,e)};z.prototype.mod=function(e){return this.throwIfDisposed(),wh(this,e)};z.prototype.mulStrict=function(e){return this.throwIfDisposed(),zk(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(),Mh(this,e,t,n)};z.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),Dk(this,e)};z.prototype.notEqual=function(e){return this.throwIfDisposed(),ts(this,e)};z.prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),Ml(this,e,t,n)};z.prototype.onesLike=function(){return this.throwIfDisposed(),Mn(this)};z.prototype.pad=function(e,t){return this.throwIfDisposed(),ta(this,e,t)};z.prototype.pool=function(e,t,n,a,r){return this.throwIfDisposed(),Ik(this,e,t,n,a,r)};z.prototype.powStrict=function(e){return this.throwIfDisposed(),Bk(this,e)};z.prototype.pow=function(e){return this.throwIfDisposed(),Xa(this,e)};z.prototype.prelu=function(e){return this.throwIfDisposed(),Wc(this,e)};z.prototype.prod=function(e,t){return this.throwIfDisposed(),Ih(this,e,t)};z.prototype.reciprocal=function(){return this.throwIfDisposed(),Ky(this)};z.prototype.relu=function(){return this.throwIfDisposed(),Xe(this)};z.prototype.relu6=function(){return this.throwIfDisposed(),Th(this)};z.prototype.reshapeAs=function(e){return this.throwIfDisposed(),j(this,e.shape)};z.prototype.reshape=function(e){return this.throwIfDisposed(),j(this,e)};z.prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),n1(this,e,t,n)};z.prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),a1(this,e,t,n)};z.prototype.reverse=function(e){return this.throwIfDisposed(),Rn(this,e)};z.prototype.rfft=function(){return this.throwIfDisposed(),Hc(this)};z.prototype.round=function(){return this.throwIfDisposed(),Xy(this)};z.prototype.rsqrt=function(){return this.throwIfDisposed(),Sh(this)};z.prototype.selu=function(){return this.throwIfDisposed(),Ch(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(),ca(this)};z.prototype.sign=function(){return this.throwIfDisposed(),Yy(this)};z.prototype.sin=function(){return this.throwIfDisposed(),Eh(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(),Sa(this,e)};z.prototype.softplus=function(){return this.throwIfDisposed(),Bl(this)};z.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Bc(this,e,t)};z.prototype.split=function(e,t){return this.throwIfDisposed(),Pn(this,e,t)};z.prototype.sqrt=function(){return this.throwIfDisposed(),an(this)};z.prototype.square=function(){return this.throwIfDisposed(),ot(this)};z.prototype.squaredDifference=function(e){return this.throwIfDisposed(),jc(this,e)};z.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),Wk(this,e)};z.prototype.squeeze=function(e){return this.throwIfDisposed(),ns(this,e)};z.prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof z?[this,e]:[this,...e];return Mt(n,t)};z.prototype.step=function(e){return this.throwIfDisposed(),Hl(this,e)};z.prototype.stridedSlice=function(e,t,n,a,r,s,i,o){return this.throwIfDisposed(),Zy(this,e,t,n,a,r,s,i,o)};z.prototype.subStrict=function(e){return this.throwIfDisposed(),Vk(this,e)};z.prototype.sub=function(e){return this.throwIfDisposed(),fe(this,e)};z.prototype.sum=function(e,t){return this.throwIfDisposed(),Ce(this,e,t)};z.prototype.tan=function(){return this.throwIfDisposed(),Qy(this)};z.prototype.tanh=function(){return this.throwIfDisposed(),Ol(this)};z.prototype.tile=function(e){return this.throwIfDisposed(),ja(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(),eb(this,e,t)};z.prototype.transpose=function(e){return this.throwIfDisposed(),Ue(this,e)};z.prototype.unique=function(e){return this.throwIfDisposed(),Dh(this,e)};z.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),tb(this,e,t)};z.prototype.unstack=function(e){return this.throwIfDisposed(),pt(this,e)};z.prototype.where=function(e,t){return this.throwIfDisposed(),kn(e,this,t)};z.prototype.zerosLike=function(){return this.throwIfDisposed(),He(this)};var y1={};Oe(y1,{maxNorm:()=>ez,minMaxNorm:()=>az,nonNeg:()=>nz,unitNorm:()=>tz});var lb;function Ut(){return lb==null&&(lb=X0().epsilon()),lb}function _a(){return"channelsLast"}var Nr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Nr.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)}},b1=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,b1.prototype)}},rz=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,rz.prototype)}};function Mi(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 Za(e,t){if(!e)throw new b1(t)}function x1(e,t){let n=0;for(let a of e)a===t&&n++;return n}function Nn(e){return e.length===1?e[0]:e}function bt(e){return Array.isArray(e)?e:[e]}function Tr(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function Ri(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var da={};function ub(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function cb(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>cb(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:cb(a))}}}function Kc(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 da)i=da[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 da?[o,l]=da.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(da))c[h]=da[h];for(let h of Object.keys(n))c[h]=n[h];let u=s.config;u.customObjects=c;let p=Object.assign({},da);for(let h of Object.keys(n))da[h]=n[h];cb(s.config);let d=l(o,s.config,n,r);return da=Object.assign({},p),d}else{let c=Object.assign({},da);for(let p of Object.keys(n))da[p]=n[p];let u=new o(s.config);return da=Object.assign({},c),u}}}function sz(e,t){return e<t?-1:e>t?1:0}function jh(e,t){return-1*sz(e,t)}function rs(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function iz(e){if(e==null)throw new B(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Pi(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new B(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`)}function pb(e,t,n=0,a=Infinity){return Za(n>=0),Za(a>=n),Array.isArray(e)&&e.length>=n&&e.length<=a&&e.every(r=>typeof r===t)}function Jt(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,a)=>Jt(n,`element ${a+1} of ${t}`))):k.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${v1(e)}.`)}function v1(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>v1(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function oz(e,t){let n=k.now(),a;return(...r)=>{let s=k.now();return s-n<t||(n=s,a=e(...r)),a}}function w1(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function db(e,t){return D(()=>an(Ce(L(e,e),t,!0)))}var Xc=class extends se.Serializable{getConfig(){return{}}},hb=class extends Xc{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=db(e,this.axis),n=Yt(t,0,this.maxValue);return L(e,ve(n,Z(Ut(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};hb.className="MaxNorm";se.registerClass(hb);var mb=class extends Xc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return D(()=>ve(e,Z(Ut(),db(e,this.axis))))}getConfig(){return{axis:this.axis}}};mb.className="UnitNorm";se.registerClass(mb);var fb=class extends Xc{apply(e){return Xe(e)}};fb.className="NonNeg";se.registerClass(fb);var gb=class extends Xc{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=db(e,this.axis),n=Z(L(this.rate,Yt(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,ve(n,Z(Ut(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};gb.className="MinMaxNorm";se.registerClass(gb);var k1={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Gt(e){return ub(e)}function I1(e,t={}){return Kc(e,se.SerializationMap.getMap().classNameMap,t,"constraint")}function Ht(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in k1?k1[e]:e,config:{}};return I1(t)}else return e instanceof Xc?e:I1(e)}function ez(e){return new hb(e)}function tz(e){return new mb(e)}function nz(){return new fb}function az(e){return new gb(e)}var N1={};Oe(N1,{constant:()=>cz,glorotNormal:()=>yz,glorotUniform:()=>gz,heNormal:()=>bz,heUniform:()=>xz,identity:()=>mz,leCunNormal:()=>vz,leCunUniform:()=>wz,ones:()=>uz,orthogonal:()=>kz,randomNormal:()=>dz,randomUniform:()=>pz,truncatedNormal:()=>hz,varianceScaling:()=>fz,zeros:()=>lz});var Iz=["channelsFirst","channelsLast"],Nz=["nearest","bilinear"],Tz=["valid","same","causal"],Sz=["max","avg"],Cz=["sum","mul","concat","ave"],ql=new Map;function Rt(e){Pi(Iz,"DataFormat",e)}function Ez(e){Pi(Nz,"InterpolationFormat",e)}function na(e){Pi(Tz,"PaddingMode",e)}function T1(e){Pi(Sz,"PoolMode",e)}var Yc=[],S1="/";function Oi(e,t){Yc.push(e);try{let n=t();return Yc.pop(),n}catch(n){throw Yc.pop(),n}}function _z(){return Yc.length===0?"":Yc.join(S1)+S1}function E1(e){if(!C1(e))throw new Error("Not a valid tensor name: '"+e+"'");return _z()+e}function _1(e){if(!C1(e))throw new Error("Not a valid tensor name: '"+e+"'");ql.has(e)||ql.set(e,0);let t=ql.get(e);if(ql.set(e,ql.get(e)+1),t>0){let n=`${e}_${t}`;return ql.set(n,1),n}else return e}var Fz=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function C1(e){return!!e.match(Fz)}function Az(e){return e===parseInt(e.toString(),10)}function ss(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 F1(e){return e=Array.isArray(e)?new Float32Array(e):e,et(e)}function Kl(e){return Wl(F1(e)).dataSync()[0]}function is(e){return ea(F1(e)).dataSync()[0]}function Aa(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 Jc(e,t){return e.asType(t)}function Zc(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 $z(e,t){return D(()=>{if(e.shape.length!==2)throw new B(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Zc(e,1);return yb(n,[1,t,1])})}function Dz(e){let t=[ss(e.shape)];return e.reshape(t)}function Mz(e){if(e.rank<=1)throw new B(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],ss(e.shape,1)];return e.reshape(t)}function Li(e,t,n){return D(()=>{switch(e.rank){case 1:return Fh(e,t,n);case 2:return Jy(e,[t,0],[n,e.shape[1]]);case 3:return Ul(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Uc(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 bb(e,t,n){return D(()=>{switch(e.rank){case 1:return Fh(e,t,n);case 2:return Jy(e,[0,t],[e.shape[0],n]);case 3:return Ul(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Uc(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 qh(e,t,n,a){return D(()=>{switch(e.rank){case 1:return Fh(e,t,n);case 2:switch(a){case 1:return Li(e,t,n);case 2:return bb(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 Li(e,t,n);case 2:return Ul(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return bb(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 Li(e,t,n);case 2:return Uc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Uc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return bb(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 xb(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),Ze(e,t)}function A1(e,t){switch(e.rank){case 1:return rk([e,t]);case 2:return sk([e,t],0);case 3:return ik([e,t],0);case 4:return ok([e,t],0);default:throw new B(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function yb(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new B(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return ja(e,t)}function Kh(e,t=0,n=1,a,r){return Nk(e,t,n,a,r)}function Qa(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 as.matMul({a:e,b:t,transposeA:r,transposeB:s,bias:a?vb(e.rank,a,_a()):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 as.matMul({a:e,b:t,transposeA:d,transposeB:h,bias:a?vb(e.rank,a,_a()):null,activation:n}).reshape(p)}}function $1(e,t,n){return D(()=>(Array.isArray(t)?t=et(t,"int32"):t=t.toInt(),Ei(e,t,n)))}function Qc(e){return L(e,e)}function vb(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 er(e,t,n){return D(()=>(n==null&&(n=_a()),Rt(n),e.add(vb(e.rank,t,n))))}function Rz(e,t=1){if(t!==1)throw new $e(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Ll(e)}function Pz(e){return D(()=>ve(e,Wt(e).add(1)))}function D1(e,t,n,a){return D(()=>jk(e,t,n,a))}function Oz(e){return D(()=>{let t=Z(.5,L(.2,e));return Yt(t,0,1)})}function ep(e,t,n=!1){return n?e():t()}var Lz=["fanIn","fanOut","fanAvg"],zz=["normal","uniform","truncatedNormal"];function Bz(e){Pi(Lz,"FanMode",e)}function Wz(e){Pi(zz,"Distribution",e)}var ha=class extends se.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},wb=class extends ha{apply(e,t){return yt(e,t)}};wb.className="Zeros";se.registerClass(wb);var Xh=class extends ha{apply(e,t){return Ka(e,t)}};Xh.className="Ones";se.registerClass(Xh);var kb=class extends ha{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(de(this.value),Ka(e,t)))}getConfig(){return{value:this.value}}};kb.className="Constant";se.registerClass(kb);var Ib=class extends ha{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 Vl(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};Ib.className="RandomUniform";se.registerClass(Ib);var Nb=class extends ha{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 Kh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Nb.className="RandomNormal";se.registerClass(Nb);var Tb=class extends ha{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 $h(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Tb.className="TruncatedNormal";se.registerClass(Tb);var Sb=class extends ha{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,zy(e[0]))})}getConfig(){return{gain:this.gain}}};Sb.className="Identity";se.registerClass(Sb);function Vz(e,t="channelsLast"){let n,a;if(Rt(t),e.length===2)n=e[0],a=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=ss(e,2);n=e[1]*r,a=e[0]*r}else if(t==="channelsLast"){let r=ss(e,0,e.length-2);n=e[e.length-2]*r,a=e[e.length-1]*r}}else{let r=ss(e);n=Math.sqrt(r),a=Math.sqrt(r)}return[n,a]}var Tn=class extends ha{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,Bz(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,Wz(this.distribution),this.seed=e.seed}apply(e,t){let n=Vz(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 $h(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return Vl(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Tn.className="VarianceScaling";se.registerClass(Tn);var Yh=class extends Tn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Yh.className="GlorotUniform";se.registerClass(Yh);var Jh=class extends Tn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Jh.className="GlorotNormal";se.registerClass(Jh);var Zh=class extends Tn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Zh.className="HeNormal";se.registerClass(Zh);var Qh=class extends Tn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Qh.className="HeUniform";se.registerClass(Qh);var em=class extends Tn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};em.className="LeCunNormal";se.registerClass(em);var tm=class extends Tn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};tm.className="LeCunNormal";se.registerClass(tm);var Cb=class extends ha{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=Kh(n,0,1,"float32"),r=s1.gramSchmidt(a);return e[0]>e[1]&&(r=r.transpose()),L(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Cb.className="Orthogonal";se.registerClass(Cb);var M1={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 R1(e,t={}){return Kc(e,se.SerializationMap.getMap().classNameMap,t,"initializer")}function Et(e){return ub(e)}function wt(e){if(typeof e=="string"){let t=e in M1?M1[e]:e;if(t==="GlorotNormal")return new Jh;if(t==="GlorotUniform")return new Yh;if(t==="HeNormal")return new Zh;if(t==="HeUniform")return new Qh;if(t==="LeCunNormal")return new em;if(t==="LeCunUniform")return new tm;{let n={};return n.className=t,n.config={},R1(n)}}else return e instanceof ha?e:R1(e)}function lz(){return new wb}function uz(){return new Xh}function cz(e){return new kb(e)}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 Tn(e)}function gz(e){return new Yh(e)}function yz(e){return new Jh(e)}function bz(e){return new Zh(e)}function xz(e){return new Qh(e)}function vz(e){return new em(e)}function wz(e){return new tm(e)}function kz(e){return new Cb(e)}var P1={};Oe(P1,{Layer:()=>qe,RNN:()=>tr,RNNCell:()=>tp,activation:()=>rB,add:()=>hB,alphaDropout:()=>YB,average:()=>mB,averagePooling1d:()=>Eb,averagePooling2d:()=>_b,averagePooling3d:()=>Fb,avgPool1d:()=>IB,avgPool2d:()=>TB,avgPool3d:()=>CB,avgPooling1d:()=>NB,avgPooling2d:()=>SB,avgPooling3d:()=>EB,batchNormalization:()=>vB,bidirectional:()=>VB,concatenate:()=>fB,conv1d:()=>Yz,conv2d:()=>Jz,conv2dTranspose:()=>Zz,conv3d:()=>Qz,convLstm2d:()=>LB,convLstm2dCell:()=>zB,cropping2D:()=>tB,dense:()=>sB,depthwiseConv2d:()=>aB,dot:()=>xB,dropout:()=>iB,elu:()=>Gz,embedding:()=>dB,flatten:()=>lB,gaussianDropout:()=>XB,gaussianNoise:()=>KB,globalAveragePooling1d:()=>_B,globalAveragePooling2d:()=>FB,globalMaxPool1d:()=>GB,globalMaxPool2d:()=>HB,globalMaxPooling1d:()=>L1,globalMaxPooling2d:()=>z1,gru:()=>$B,gruCell:()=>DB,input:()=>O1,inputLayer:()=>Uz,layerNormalization:()=>wB,leakyReLU:()=>jz,lstm:()=>MB,lstmCell:()=>RB,masking:()=>JB,maxPool1d:()=>jB,maxPool2d:()=>qB,maxPooling1d:()=>B1,maxPooling2d:()=>W1,maxPooling3d:()=>AB,maximum:()=>gB,minimum:()=>yB,multiply:()=>bB,permute:()=>pB,prelu:()=>qz,reLU:()=>Hz,repeatVector:()=>uB,reshape:()=>cB,rnn:()=>BB,separableConv2d:()=>eB,simpleRNN:()=>PB,simpleRNNCell:()=>OB,softmax:()=>Kz,spatialDropout1d:()=>oB,stackedRNNCells:()=>WB,thresholdedReLU:()=>Xz,timeDistributed:()=>UB,upSampling2d:()=>nB,zeroPadding2d:()=>kB});var ZB=0;function V1(){return ZB++}var nm={};function am(e=""){return e in nm||(nm[e]=0),nm[e]+=1,e+nm[e].toString()}function Ab(e){return Array.isArray(e)&&Array.isArray(e[0])}function rm(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 dt(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 sm(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 U1="Variable",G1=class{constructor(e,t="float32",n=U1,a=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=V1(),n=n==null?U1:n,this.originalName=E1(n),this.name=_1(this.originalName),this.trainable_=a,this.constraint=r,this.val=Sk(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),QB(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 QB(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function $b(e){return e.map(t=>t.read())}function Db(e){e.forEach(t=>{t[0].write(t[1])})}var Zt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},$a=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=V1(),s!=null&&(this.originalName=E1(s),this.name=_1(this.originalName)),this.rank=t.length}},eW=0,im=class{constructor(e,t){this.callArgs=t,this.id=eW++,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}}},tW=0,qe=class extends se.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=tW++,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=Tr(n)+"_"+am(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 Nn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Nn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Nr(`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 Nr(`Layer ${this.name} is not connected, no input to return.`);return Nn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Nr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Nr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Nn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=bt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=bt(this.inputSpec);if(e.length!==t.length)throw new 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=bt(e),a=!0;for(let s of n)if(!(s instanceof $a)){a=!1;break}let r=!0;for(let s of n)if(s instanceof $a){r=!1;break}if(a===r)throw new B("Arguments to apply() must be all SymbolicTensors or all Tensors");return Oi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of bt(e))s.push(i.shape);this.build(Nn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=bt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Nn(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=nW(e),i=this.computeOutputShape(s),o,l=aW(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 $a(l,c,this,bt(e),t,this.name,u)):o=new $a(l,i,this,bt(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 Nr(`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 Nr(`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 sm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return $b(e?this.trainableWeights:this.weights)}setWeights(e){D(()=>{let t=this.weights;if(t.length!==e.length)throw new B(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],a=$b(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])}Db(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=wt("zeros"));let o=a.apply(t,n),l=new G1(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=bt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=bt(e);t=bt(t),n=bt(n),a=bt(a),r=rm(r),s=rm(s);let l=[],c=[],u=[];for(let p of o)l.push(p.sourceLayer),c.push(p.nodeIndex),u.push(p.tensorIndex);new im({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 nW(e){e=bt(e);let t=[];for(let n of e)t.push(n.shape);return Nn(t)}function aW(e){return"float32"}function H1(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=H1(i,o,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Xl=class extends qe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:am("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 $a(this.dtype,this.batchInputShape,this,[],{},this.name);a.nodeIndex=0,a.tensorIndex=0,new im({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}}};Xl.className="InputLayer";se.registerClass(Xl);function j1(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 Xl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function os(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];Ae(a)}}function q1(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var K1;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(K1||(K1={}));var rW=125,Yl=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){}},X1=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)}},sW=class extends Yl{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(ve(1,this.seen),this.totals[n]);t[n]=a,this.totals[n].dispose(),Xt(t[n])}))}},Y1=class extends Yl{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]}},J1=class extends Yl{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=rW),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=oz(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 os(n),a.push(this.yield(e,t,n))),a.push(Hh()),await Promise.all(a)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await os(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await os(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(Hh()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await os(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await os(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(Hh()):k.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await os(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await os(e),await this.trainEnd(e))}};function Z1(e,t){return e==null&&(e={}),e instanceof Yl?[e]:Array.isArray(e)&&e[0]instanceof Yl?e:bt(e).map(n=>new J1(n,t))}var ma=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}`),ma.checkForDuplicate(t),ma.constructors[e]==null&&(ma.constructors[e]=[]),ma.constructors[e].push(t)}static checkForDuplicate(e){for(let t in ma.constructors)ma.constructors[+t].forEach(n=>{if(n===e)throw new B("Duplicate callback constructor.")})}static clear(){ma.constructors={}}static createCallbacks(e){let t=[];for(let n in ma.constructors){let a=+n;e>=a&&t.push(...ma.constructors[a])}return t.map(n=>new n)}};ma.constructors={};function Q1(e,t,n,a,r,s,i,o,l){let c=new Y1,u=[new sW,...ma.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let p=new X1(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 Da(e,t={},n=!1){return Kc(e,se.SerializationMap.getMap().classNameMap,t,"layer",n)}function om(e,t){return D(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Ce(Qc(e),t,!0),a=In(n.shape,Ut()),r=an(Ta(n,a));return ve(e,r)})}function zi(e,t){return D(()=>Ct(Qc(fe(t,e)),-1))}function lm(e,t){return D(()=>Ct(Wt(fe(t,e)),-1))}function Jl(e,t){return D(()=>{let n=fe(e,t),a=Yt(Wt(e),Ut(),Number.MAX_VALUE),r=Wt(ve(n,a));return L(100,Ct(r,-1))})}function iW(e,t){return D(()=>{let n=Yt(t,Ut(),Number.MAX_VALUE),a=Dn(Z(1,n)),r=Yt(e,Ut(),Number.MAX_VALUE),s=Dn(Z(1,r));return Ct(Qc(fe(a,s)),-1)})}function oW(e,t){return D(()=>{let n=Ta(0,fe(1,L(e,t)));return Ct(Qc(n),-1)})}function lW(e,t){return D(()=>{let n=Ta(0,fe(1,L(e,t)));return Ct(n,-1)})}function uW(e,t){return D(()=>{let n=Ce(L(e,t),-1),a=ea(L(fe(1,e),t),-1);return Ta(0,Z(1,fe(a,n)))})}function cW(e,t){return D(()=>{let n=Math.log(2),a=fe(t,e),r=fe(Z(a,Bl(L(-2,a))),n);return Ct(r,-1)})}function np(e,t,n=!1){return D(()=>{if(n)t=Sa(t);else{let a=Ce(t,t.shape.length-1,!0);t=ve(t,a)}return t=Yt(t,Ut(),1-Ut()),St(Ce(L(e.toFloat(),Dn(t)),t.shape.length-1))})}function um(e,t,n=!1){return D(()=>{let a=zl(Dz(e)).toInt();t=Yt(t,Ut(),1-Ut());let r=t.shape,s=Ml(a,r[r.length-1]).reshape(r);return np(s,t,n)})}function pW(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 cm(e,t){return D(()=>{let n;return n=Yt(t,Ut(),1-Ut()),n=Dn(ve(n,fe(1,n))),Ct(pW(e,n),-1)})}function dW(e,t){return D(()=>{let n=Yt(e,Ut(),1),a=Yt(t,Ut(),1);return Ce(L(e,Dn(ve(n,a))),-1)})}function hW(e,t){return D(()=>{let n=Dn(Z(Ut(),t));return Ct(fe(t,L(e,n)),-1)})}function Mb(e,t){return D(()=>{let n=om(e,-1),a=om(t,-1),r=L(n,a);return St(Ce(r,-1))})}var pm={meanSquaredError:zi,meanAbsoluteError:lm,meanAbsolutePercentageError:Jl,meanSquaredLogarithmicError:iW,squaredHinge:oW,hinge:lW,categoricalHinge:uW,logcosh:cW,categoricalCrossentropy:np,sparseCategoricalCrossentropy:um,binaryCrossentropy:cm,kullbackLeiblerDivergence:dW,poisson:hW,cosineProximity:Mb};function Rb(e){if(typeof e=="string"){if(e in pm)return pm[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new B(t)}else return e}function Pb(e,t){return D(()=>{let n=L(.5,Mn(t)),a=Jc(Qn(t,n),e.dtype);return Ct(vr(e,a),-1)})}function Ob(e,t){return D(()=>Jc(vr($c(e,-1),$c(t,-1)),"float32"))}function eI(e,t){return D(()=>pa(e.equal(1),t.equal(1)).sum().cast("float32"))}function mW(e,t){return D(()=>pa(e.equal(1),t.equal(0)).sum().cast("float32"))}function fW(e,t){return D(()=>pa(e.equal(0),t.equal(1)).sum().cast("float32"))}function tI(e,t){return D(()=>{let n=eI(e,t),a=fW(e,t),r=n.add(a);return kn(Qn(r,0),n.div(r),0).cast("float32")})}function gW(e,t){return D(()=>{let n=eI(e,t),a=mW(e,t),r=n.add(a);return kn(Qn(r,0),n.div(r),0).cast("float32")})}function nI(e,t){return cm(e,t)}function aI(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)),vr(e,t).asType("float32")}var yW=zi,bW=zi,xW=lm,vW=lm,wW=Jl,kW=Jl,Lb=np,IW=Mb,rI=um,dm={binaryAccuracy:Pb,categoricalAccuracy:Ob,precision:tI,categoricalCrossentropy:Lb,sparseCategoricalCrossentropy:rI,mse:yW,MSE:bW,mae:xW,MAE:vW,mape:wW,MAPE:kW,cosine:IW};function NW(e){if(typeof e=="string"&&e in dm)return dm[e];if(typeof e!="string"&&e!=null)return e;throw new B(`Unknown metric ${e}`)}function hm(e){if(Za(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(pm))if(pm[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(dm))if(dm[n]===e){t=n;break}return t!==void 0?t:e.name}}function TW(e){let t={Adagrad:()=>Di.adagrad(.01),Adadelta:()=>Di.adadelta(1,.95,Ut()),Adam:()=>Di.adam(.001,.9,.999,Ut()),Adamax:()=>Di.adamax(.002,.9,.999,Ut(),0),RMSProp:()=>Di.rmsprop(.001,.9,0,Ut()),SGD:()=>Di.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 ${e}`)}var sI=1*1024*1024;function iI(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!zb(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let a=JSON.stringify(e);a.length>sI&&console.warn(`User-defined metadata of model "${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 <= ${sI}.`)}}function zb(e){if(e===null)return!0;if(typeof e=="object")if(Object.getPrototypeOf(e)===Object.prototype){let t=Object.keys(e);for(let n of t)if(typeof n!="string"||!zb(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!zb(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function FW(e,t,n,a=console.log){let r=CW(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)),mm(s,n,a),a("=".repeat(t));let o=e.layers;for(let u=0;u<o.length;++u)r?EW(o[u],n,a):_W(o[u],n,i,a),a((u===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=SW(e),c=sm(e.nonTrainableWeights);a(`Total params: ${l+c}`),a(`Trainable params: ${l}`),a(`Non-trainable params: ${c}`),a("_".repeat(t))}function SW(e){let t;return e.collectedTrainableWeights!=null?t=sm(e.collectedTrainableWeights):t=sm(e.trainableWeights),t}function CW(e){let t=!0,n=[],a=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}a.push(...r)}if(t)for(let r of e.layers){let s=!1;for(let i of r.inboundNodes)if(a.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function mm(e,t,n=console.log){let a="";for(let r=0;r<e.length;++r)r>0&&(a=a.slice(0,a.length-1)+" "),a+=e[r],a=a.slice(0,t[r]),a+=" ".repeat(t[r]-a.length);n(a)}function EW(e,t,n){let a;try{a=JSON.stringify(e.outputShape)}catch(o){a="multiple"}let r=e.name,s=e.getClassName(),i=[`${r} (${s})`,a,e.countParams().toString()];mm(i,t,n)}function _W(e,t,n,a){let r;try{r=JSON.stringify(e.outputShape)}catch(u){r="multiple"}let s=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let p=0;p<u.inboundLayers.length;++p){let d=u.inboundLayers[p].name,h=u.nodeIndices[p],m=u.tensorIndices[p];s.push(`${d}[${h}][${m}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],c=[`${i} (${o})`,r,e.countParams().toString(),l];mm(c,t,a);for(let u=1;u<s.length;++u)mm(["","","",s[u]],t,a)}function oI(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function ap(e,t){if(e===null)return null;if(typeof e=="string")return Ri(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];oI(t,r,s)?n.push(s):n.push(ap(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=Ri(a);n[s]=ap(r,s)}}return n}}function Bb(e,t){if(e==null)return null;if(typeof e=="string")return Tr(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];oI(t,r,s)?n.push(s):n.push(Bb(s,t))}return n}else{let n={};for(let a of Object.keys(e)){let r=e[a],s=Tr(a);(a==="name"||a==="className")&&typeof r=="string"?n[s]=r:n[s]=Bb(r,a)}return n}}var fm="2.8.3";function AW(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}' (${e.dtype}).`)}}var Bi=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Bi)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=AW(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new B(`Duplicate key: name=${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 $a){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 $a){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: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Ae(this.id2Mask)}},Wb={},lI={};function rp(e,t,n,a){let r=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(m=>m.name),l=[],c=t.names();for(let m of o)c.indexOf(m)!==-1?l.push(t.getValue(m)):l.push(null);a!=null&&(a.maxNumTensors=-Infinity,a.minNumTensors=Infinity);let u=o.join(",")+"|"+t.names().join(","),p,d;if(Wb[u]==null){let m=$W(i,t);p=m.sorted,d=m.recipientCounts,Wb[u]=p,lI[u]=d}p=Wb[u],d={},r||Object.assign(d,lI[u]);let h=new Bi(t);for(let m=0;m<p.length;++m){if(a!=null){let $=oh().numTensors;$>a.maxNumTensors&&(a.maxNumTensors=$),$<a.minNumTensors&&(a.minNumTensors=$)}let f=p[m],g=f.sourceLayer;if(g instanceof Xl)continue;let y=[],b=[],x=[],v=!1;for(let $ of f.inputs){let O=h.getValue($),V=h.getMask($);y.push(O),b.push(V),V!=null&&(v=!0),r||(d[$.name]--,d[$.name]===0&&!t.hasKey($)&&o.indexOf($.name)===-1&&!O.isDisposed&&$.sourceLayer.stateful!==!0&&x.push(O))}v&&(n=n||{},n.mask=b[0]);let N=bt(g.apply(y,n)),T=null;g.supportsMasking&&(T=g.computeMask(y,b));let E=DW(f),A=Array.isArray(E)?E:[E];for(let $=0;$<A.length;++$){h.hasKey(A[$])||h.add(A[$],N[$],Array.isArray(T)?T[0]:T);let O=o.indexOf(A[$].name);O!==-1&&(l[O]=N[$])}r||Ae(x)}return h.disposeMasks(),s?l:l[0]}function $W(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=uI(e[0],t);n=r.sorted,a=r.recipientMap}else{let r=new Set;for(let s of e){let{sorted:i,recipientMap:o}=uI(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:MW(a)}}function MW(e){let t={};for(let n in e)t[n]=e[n].size;return t}function uI(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 DW(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 nr=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=am(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],rs(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)}`);rs(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let b=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let b=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;Za(x===0,"input layer has >1 nodes"),Za(v===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof Xl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,b,x,v,N,T)=>{(v==null||N==null||T==null)&&(v=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let E=v.inboundNodes[N];if(x.indexOf(E)!==-1)throw new Fa(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(b.indexOf(E)!==-1)return;this.containerNodes.add(nr.nodeKey(v,N)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(E)===-1&&x.push(E);let A=E.inboundLayers.length;for(let $=0;$<A;$++){let O=E.inputTensors[$],V=E.inboundLayers[$],W=E.nodeIndices[$],H=E.tensorIndices[$];o(O,b,x,V,W,H)}for(b.push(E);x.indexOf(E)>=0;)x.splice(x.indexOf(E),1);i.push(E)},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],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,x),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let v=0;v<y.inboundLayers.length;v++){let N=y.inboundLayers[v],T=y.nodeIndices[v],E=N.inboundNodes[T],A=t[E.id]==null?0:t[E.id];t[E.id]=Math.max(b+1,A),n[E.id]=E}}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(jh);this.layers=[];for(let y of h){let b=d[y];b.sort((x,v)=>{let N=s[x.id],T=s[v.id];return N<T?-1:N>T?1:0});for(let x of b)x instanceof nr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(jh);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let x=b.outboundLayer;if(x!=null){for(let v of b.inputTensors)if(m.indexOf(v)===-1)throw new Fa(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of b.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(x=>x===y).length;if(b!==1)throw new Fa(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new im({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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}`)}Db(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${fm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Bb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=bt(e);let n=new Bi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return rp(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=bt(e);let n;return t==null?n=Mi(null,e.length):n=bt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=rm(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(jh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,x=n[b];u.push(x)}let p=c.computeOutputShape(Nn(u)),d=rm(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];Za(o in n),r.push(n[o])}return Nn(r)}runInternalGraph(e,t){t==null&&(t=Mi(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(jh);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[x,v]=h[0];m.mask==null&&(m.mask=v),y=bt(u.call(x,m)),b=bt(u.computeMask(x,v)),f=[x],g=[v]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),y=bt(u.call(f,m)),b=bt(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 x=0;x<d.length;++x){let v=d[x],N=y[x],T=b[x];n[v.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Za(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 nr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=nr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new 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=nr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],d=nr.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],x=nr.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,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=nr.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=nr.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 x of g){let v=x[0],N=x[1],T=x[2];if(b=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let E=r[v];if(E.inboundNodes.length<=N){i(f,g);return}let A=E.inboundNodes[N];y.push(A.outputTensors[T])}y.length>0&&f.apply(Nn(y),b)}function l(f){let g=f.name,y=Da(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(;!iz(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];Za(g in r);let x=r[g].inboundNodes[y].outputTensors;p.push(x[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];Za(g in r);let x=r[g].inboundNodes[y].outputTensors;d.push(x[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 RW(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: ${JSON.stringify(e)}`)}function cI(e,t){return RW(e,t,"classWeight")}async function pI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());Ae(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])}),et(i,"float32")}else return null}function PW(e,t){return L(e,t)}var OW=32;function hI(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=dI("input",e.inputNames,n),i=dI("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 dI(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 LW(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 BW(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(mI(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=LW(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=Z1(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=Q1(u,p,n.epochs,null,null,zW(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 x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:v,ys:N}=hI(e,x.value),T={};T.batch=b,T.size=v[0].shape[0],await d.onBatchBegin(b,T);let E=[];if(n.classWeight!=null){let O=cI(n.classWeight,e.outputNames);for(let V=0;V<O.length;++V)E.push(await pI(N[V],null,O[V]))}let A=v.concat(N).concat(E),$=o(A);Ae(A);for(let O=0;O<l.length;++O){let V=l[O],W=$[O];T[V]=W,Xt(W)}await d.onBatchEnd(b,T),q1(T),b++,y++}if(a?y>=n.batchesPerEpoch:x.done){if(r){let v;mI(n.validationData)?v=bt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):v=bt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?OW:n.validationBatchSize,verbose:0}));for(let N=0;N<e.metricsNames.length;++N)g[`val_${e.metricsNames[N]}`]=v[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 zW(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function mI(e){return typeof e.iterator=="function"}function WW(e){return typeof e.next=="function"}async function VW(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=WW(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}=hI(e,c.value),d=u.concat(p),h=D(()=>r(d));if(Ae(d),l===0)for(let f=0;f<h.length;++f)s.push(de(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&&Ae(y)}Ae(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]=ve(s[c],o),Ae(u)}return Nn(s)}function Vb(e){k.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function sp(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(a=>Li(a,t,n-t)):Li(e,t,n-t)}function Ub(e,t){return D(()=>e==null?null:Array.isArray(e)?e.map(n=>Ub(n,t)):$1(e,t.dtype==="int32"?t:t.toInt()))}function Gb(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 UW(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=Aa(0,g)),i==null&&(i=1);let{callbackList:b,history:x}=Q1(o,i,s,d,g,h,r,f,p);b.setModel(e),e.history=x,await b.onTrainBegin(),e.stopTraining_=!1;for(let v=d;v<s;++v){await b.onEpochBegin(v);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=et(y),E=Gb(g,r);for(let A=0;A<E.length;++A){let $={};if(await b.onBatchBegin(A,$),D(()=>{let O=E[A][0],V=E[A][1],W=Li(T,O,V-O);$.batch=A,$.size=V-O;let H=Ub(n,W),X=t(H);for(let q=0;q<a.length;++q){let K=a[q],J=X[q];$[K]=J,Xt(J)}if(A===E.length-1&&f){let q=e.testLoop(l,c,r);for(let K=0;K<a.length;++K){let J=a[K],te=q[K];Xt(te),N["val_"+J]=te}}}),await b.onBatchEnd(A,$),q1($),e.stopTraining_)break}T.dispose()}if(await b.onEpochEnd(v,N),e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}async function GW(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;Vb(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,E=await e.standardizeUserData(i,o,null,null,T,p);l=E[0],c=E[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)),E=r[0].shape[0];l=sp(r,T,E),r=sp(r,0,T),c=sp(s,T,E),s=sp(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(),x,v;m?(e.makeTestFunction(),x=e.testFunction,v=b.slice().concat(b.map(T=>"val_"+T))):(x=null,f=[],v=b.slice());let N=Z1(a.callbacks,a.yieldEvery);return await UW(e,y,g,b,p,a.epochs,a.verbose,N,x,f,a.shuffle,v,a.initialEpoch,null,null)}finally{e.isTraining=!1,Wi(r,t),Wi(s,n),Wi(l,i),Wi(c,o),u!=null&&Ae(u)}}function fI(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(Zc(a,1));else{if(a.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(a)}}return t}function Wi(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 HW(e){return e instanceof z}function Hb(e){return Array.isArray(e)}function gI(e){return!HW(e)&&!Hb(e)}function yI(e,t,n,a=!0,r=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(Hb(e)&&e.length>0)i=!0;else if(gI(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 ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(gI(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new B(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(Hb(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=fI(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 jW(e,t,n){let a=rs(e.map(s=>s.shape[0]));a.sort();let r=rs(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 qW(e,t,n){let a=[zi,cm,np];for(let r=0;r<e.length;++r){let s=e[r],i=t[r],o=n[r];if(i!=null){if(i===np&&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 bI(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 KW(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: ${e}`);if(Array.isArray(n))return t.map(a=>n);{let a=[];for(let r of t){let s=n.hasOwnProperty(r)?n[r]:[];Array.isArray(s)||(s=[s]),a.push(s)}return a}}var XW="layers-model",Sr=class extends nr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new B("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");FW(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=TW(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof Ir))throw new B("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new B(`Unknown entry in loss dictionary: "${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(Rb(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=>Rb(s))}else{let s=Rb(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=[],Oi("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=KW(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])};Oi("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]===cm?["accuracy","acc"].indexOf(d)!==-1?u=Pb:["crossentropy","ce"].indexOf(d)!==-1&&(u=nI):this.lossFunctions[s]===um?["accuracy","acc"].indexOf(d)!==-1?u=aI:["crossentropy","ce"].indexOf(d)!==-1&&(u=rI):["accuracy","acc"].indexOf(d)!==-1?u=Ob:["crossentropy","ce"].indexOf(d)!==-1&&(u=Lb);let f;["accuracy","acc"].indexOf(d)!==-1?f="acc":["crossentropy","ce"].indexOf(d)!==-1&&(f="ce"),p=u,c=l+f}else p=NW(d),c=l+hm(d);let h;Oi(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;Vb(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 Nn(l)}finally{Wi(s[0],e),Wi(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),VW(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 ${a} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new B("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),a=n?t:[t],r=this.retrieveSymbolicTensors(a),s=new Bi;if(e instanceof z&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new B(`The number of inputs provided (${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=rp(r,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=Mi(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=Gb(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=sp(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 Bi(u);return rp(this.outputs,p)}).forEach((o,l)=>s[l].push(o));return Nn(s.map(i=>Ze(i,0)))})}predict(e,t={}){let n=fI(e);bI(n,this.inputNames,this.feedInputShapes,!1);try{let a=t.batchSize==null?32:t.batchSize;return Vb(a),this.predictLoop(n,a)}finally{Wi(n,e)}}predictOnBatch(e){bI(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]===um?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=yI(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=yI(t,this.feedOutputNames,r,!1,"target"),jW(e,t,null),qW(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=cI(a,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await pI(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 $e("steps mode in testLoop() is not implemented yet");{let o=Gb(s,n),l=et(Aa(0,s));for(let c=0;c<o.length;++c){let u=o[c][0],p=o[c][1],d=Li(l,u,p-u),h=Ub(t,d),m=e(h);if(c===0)for(let f=0;f<m.length;++f)i.push(de(0));for(let f=0;f<m.length;++f){let g=m[f];i[f]=Z(i[f],L(p-u,g))}}for(let c=0;c<i.length;++c)i[c]=ve(i[c],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let a=e[n],r=a;x1(e,a)>1&&(r+=`_${x1(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 Bi(c),p=rp(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=PW(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]))}Xt(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 Bi(s),o=rp(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 GW(this,e,t,n)}async fitDataset(e,t){return BW(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 Ae(s),Nn(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=oh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-oh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Tr(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=>Tr(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]=Tr(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[Tr(hm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Tr(hm(e)));{let e={};for(let t in this.metrics)e[t]=Tr(hm(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=ap(e.optimizer_config),n=Da(t),a;if(typeof e.loss=="string")a=Ri(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Ri(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Ri(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Ri(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Ri(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Kt.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 '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Kt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:XW,generatedBy:`TensorFlow.js tfjs-layers v${fm}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Kt.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Kt.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;iI(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){iI(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Sr.className="Model";se.registerClass(Sr);var xI=class extends Sr{};xI.className="Functional";se.registerClass(xI);async function YW(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=ap(n),r=Da(a,t);if(e.weightsManifest!=null){let s=await Kt.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),Ae(s)}return r}async function ZW(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Kt.getLoadHandlers(e,t);if(n.length===0)n.push(Kt.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 JW(e,void 0,t)}async function JW(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=Da(ap(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}=QW(a.weightData,a.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Ae(c),Ae(u.map(p=>p.tensor))}return o}function QW(e,t){let n=Kt.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 Zl=class extends Sr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:am("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 Zl||e instanceof Sr,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=j1({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=H1(this.outputs[0])}this.inboundNodes=[],new im({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Mi(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(dt(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 Sr({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 Zl))throw new $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Da(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}}};Zl.className="Sequential";se.registerClass(Zl);function e4(e){return new Sr(e)}function t4(e){return new Zl(e)}function n4(e,t){return t==null&&(t={}),ZW(e,t)}function O1(e){return j1(e)}function a4(e,t){ma.registerCallbackConstructor(e,t)}var On=class extends se.Serializable{getConfig(){return{}}},vI=class extends On{apply(e,t=1){return Rz(e,t)}};vI.className="elu";se.registerClass(vI);var wI=class extends On{apply(e){return Ch(e)}};wI.className="selu";se.registerClass(wI);var kI=class extends On{apply(e){return Xe(e)}};kI.className="relu";se.registerClass(kI);var II=class extends On{apply(e){return D(()=>Fi(6,Xe(e)))}};II.className="relu6";se.registerClass(II);var NI=class extends On{apply(e){return e}};NI.className="linear";se.registerClass(NI);var TI=class extends On{apply(e){return ca(e)}};TI.className="sigmoid";se.registerClass(TI);var SI=class extends On{apply(e){return Oz(e)}};SI.className="hardSigmoid";se.registerClass(SI);var CI=class extends On{apply(e){return Bl(e)}};CI.className="softplus";se.registerClass(CI);var EI=class extends On{apply(e){return Pz(e)}};EI.className="softsign";se.registerClass(EI);var _I=class extends On{apply(e){return Ol(e)}};_I.className="tanh";se.registerClass(_I);var jb=class extends On{apply(e,t=-1){return Sa(e,t)}};jb.className="softmax";se.registerClass(jb);var FI=class extends On{apply(e,t=-1){return xh(e,t)}};FI.className="logSoftmax";se.registerClass(FI);var AI=class extends On{apply(e,t=1){return D(()=>ca(e.mul(t)).mul(e))}};AI.className="swish";se.registerClass(AI);function ls(e){return e.getClassName()}function qb(e,t={}){return Kc(e,se.SerializationMap.getMap().classNameMap,t,"activation")}function us(e){if(e==null){let t={};return t.className="linear",t.config={},qb(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},qb(t)}else return e instanceof On?e:qb(e)}function Kb(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 $I=class extends se.Serializable{},ip=class extends $I{constructor(e){super();Kb(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=yt([1]);return this.hasL1&&(t=Z(t,Ce(L(this.l1,Wt(e))))),this.hasL2&&(t=Z(t,Ce(L(this.l2,Qc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};ip.className="L1L2";se.registerClass(ip);function r4(e){return Kb(e),new ip({l1:e!=null?e.l1:null,l2:0})}function s4(e){return Kb(e),new ip({l2:e!=null?e.l2:null,l1:0})}var DI={l1l2:"L1L2"};function ht(e){return ub(e)}function MI(e,t={}){return Kc(e,se.SerializationMap.getMap().classNameMap,t,"regularizer")}function kt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in DI?DI[e]:e,config:{}};return MI(t)}else return e instanceof $I?e:MI(e)}var Xb=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=Xe(e);return this.maxValue!=null&&(n=Yt(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="ReLU";se.registerClass(Xb);var Yb=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 Oc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Yb.className="LeakyReLU";se.registerClass(Yb);var Jb=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=wt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=kt(e.alphaRegularizer),this.alphaConstraint=Ht(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=dt(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 Zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Re(e),Wc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Et(this.alphaInitializer),alphaRegularizer:ht(this.alphaRegularizer),alphaConstraint:Gt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Jb.className="PReLU";se.registerClass(Jb);var Zb=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 Ll(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Zb.className="ELU";se.registerClass(Zb);var Qb=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(Jc(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="ThresholdedReLU";se.registerClass(Qb);var ex=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new jb().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}};ex.className="Softmax";se.registerClass(ex);function Ql(e,t,n){if(typeof e=="number")return Mi(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(!Az(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 Ma(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 gm(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+is([n-t,0]);else if(a==="same")e=e*t;else throw new B(`Unsupport padding mode: ${a}.`);return e}function tx(e,t){return D(()=>(Rt(t),t==="channelsFirst"?Ue(e,[0,2,3,1]):e))}function RI(e,t){return D(()=>(Rt(t),t==="channelsFirst"?Ue(e,[0,2,3,4,1]):e))}function i4(e,t,n,a=1,r="valid",s,i=1){return D(()=>{if(s==null&&(s=_a()),Rt(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=dh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=er(o,n)),o})}function PI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=_a()),Rt(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=tx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=as.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 o4(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=_a()),Rt(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=RI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Dy(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=er(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var nx=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",nx.verifyArgs(t),this.rank=e,Jt(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=Ql(t.kernelSize,e,"kernelSize"),this.strides=Ql(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,na(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=us(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ht(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=Ql(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new 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(Za("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!pb(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:ls(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),biasConstraint:Gt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},op=class extends nx{constructor(e,t){super(e,t);this.kernel=null,op.verifyArgs(t),this.filters=t.filters,Jt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ht(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=dt(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=w1(this.activation.getClassName());if(r!=null&&this.rank===2)n=PI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=i4(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=PI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=o4(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=dt(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=Ma(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:Et(this.kernelInitializer),kernelRegularizer:ht(this.kernelRegularizer),kernelConstraint:Gt(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)}`)}},lp=class extends op{constructor(e){super(2,e);lp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!pb(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)}.`)}};lp.className="Conv2D";se.registerClass(lp);var ym=class extends op{constructor(e){super(3,e);ym.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)}.`)}};ym.className="Conv3D";se.registerClass(ym);var ax=class extends lp{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Re(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=gm(o,p,c,this.padding),m=gm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let g=hh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ue(g,[0,3,1,2])),this.bias!=null&&(g=er(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=dt(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]=gm(t[a],o,s,this.padding),t[r]=gm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ax.className="Conv2DTranspose";se.registerClass(ax);var OI=class extends op{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Ht(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Ht(t.pointwiseConstraint)}build(e){if(e=dt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${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 Zt({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=er(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=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.pointwiseRegularizer=ht(this.pointwiseRegularizer),e.depthwiseConstraint=Gt(this.depthwiseConstraint),e.pointwiseConstraint=Gt(this.pointwiseConstraint),e}};OI.className="SeparableConv";var rx=class extends OI{constructor(e){super(2,e)}};rx.className="SeparableConv2D";se.registerClass(rx);var bm=class extends op{constructor(e){super(1,e);bm.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"&&!pb(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)}.`)}};bm.className="Conv1D";se.registerClass(bm);var sx=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=qh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return qh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=qh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return qh(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}};sx.className="Cropping2D";se.registerClass(sx);var ix=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,Rt(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}};ix.className="UpSampling2D";se.registerClass(ix);function l4(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=_a()),Rt(r);let i=tx(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=xr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var ox=class extends nx{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ht(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=dt(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=l4(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=er(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ma(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ma(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.depthwiseConstraint=Gt(this.depthwiseRegularizer),e}};ox.className="DepthwiseConv2D";se.registerClass(ox);function LI(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 zI(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(Aa(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Zn(r,-1)),r=Ue(r,c)),a&&(t=Rn(t,0),r!=null&&(r=Rn(r,0)));let u=[],p,d=n,h=t.shape[0],m=pt(t),f;r!=null&&(f=pt(r));for(let y=0;y<h;++y){let b=m[y],x=D(()=>e(b,d));if(r==null)p=x[0],d=x[1];else{let v=D(()=>{let N=f[y],T=Mn(N).sub(N),E=x[0].mul(N).add(d[0].mul(T)),A=d.map(($,O)=>x[1][O].mul(N).add($.mul(T)));return{output:E,newStates:A}});p=v.output,d=v.newStates}o&&u.push(p)}let g;return o&&(g=Mt(u,1)),[p,g,d]})}var tr=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 xm({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 Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Aa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ab(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.");Ab(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Zt({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 Zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Nr("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=>yt([n,a])):this.states_=[yt([n,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>yt([n,a])):this.states_[0]=yt([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()):Ae(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=>Xt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=LI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof $a){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=zI((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=yt(e.shape);return t=Ce(t,[1,2]),t=Zc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?yb(t,[1,n]):t):this.cell.stateSize>1?[yb(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()===tr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Da(a,n);return new e(Object.assign(t,{cell:r}))}};tr.className="RNN";se.registerClass(tr);var tp=class extends qe{},vm=class extends tp{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,Jt(this.units,"units"),this.activation=us(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Kl([1,is([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Kl([1,is([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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=cs({ones:()=>Mn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=cs({ones:()=>Mn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Qa(L(e,s),this.kernel.read()):r=Qa(e,this.kernel.read()),this.bias!=null&&(r=er(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,Qa(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:ls(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),recurrentConstraint:Gt(this.recurrentConstraint),biasConstraint:Gt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};vm.className="SimpleRNNCell";se.registerClass(vm);var lx=class extends tr{constructor(e){e.cell=new vm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(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)}};lx.className="SimpleRNN";se.registerClass(lx);var wm=class extends tp{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,Jt(this.units,"units"),this.activation=us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Kl([1,is([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Kl([1,is([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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=cs({ones:()=>Mn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=cs({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=Qa(e,this.kernel.read());this.useBias&&(c=er(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=Qa(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 x=Qa(L(o,a),d);l=this.activation.apply(Z(g,x));let v=Z(L(i,a),L(Z(1,St(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ls(this.activation),recurrentActivation:ls(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),recurrentConstraint:Gt(this.recurrentConstraint),biasConstraint:Gt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};wm.className="GRUCell";se.registerClass(wm);var ux=class extends tr{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 wm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(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)}};ux.className="GRU";se.registerClass(ux);var up=class extends tp{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,Jt(this.units,"units"),this.activation=us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Kl([1,is([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Kl([1,is([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=dt(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 ha{apply(i,o){let l=r.apply([s]),c=new Xh().apply([s]),u=r.apply([s*2]);return A1(A1(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=cs({ones:()=>Mn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=cs({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=Qa(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,Qa(a,this.recurrentKernel.read())),this.useBias&&(p=er(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:ls(this.activation),recurrentActivation:ls(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),recurrentConstraint:Gt(this.recurrentConstraint),biasConstraint:Gt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};up.className="LSTMCell";se.registerClass(up);var cx=class extends tr{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 up(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(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="LSTM";se.registerClass(cx);var xm=class extends tp{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){Ab(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Oi(`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(Da(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 $b(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]])}Db(t)}};xm.className="StackedRNNCells";se.registerClass(xm);function cs(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>D1(t(),n),i=()=>ep(s,t,a);return!r||r<=1?Xt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Xt(o.clone()))}var u4=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},BI=class extends tr{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 Zt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(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=yt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Nr("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(()=>yt(r)):this.states_=[yt(r)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>yt(r)):this.states_[0]=yt(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()):Ae(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=>Xt(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=Ma(l,a[0],r,s[0],i[0]),p=Ma(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};BI.className="ConvRNN2D";var km=class extends up{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,Jt(this.filters,"filters"),this.kernelSize=Ql(n,2,"kernelSize"),this.kernelSize.forEach(o=>Jt(o,"kernelSize")),this.strides=Ql(a||1,2,"strides"),this.strides.forEach(o=>Jt(o,"strides")),this.padding=r||"valid",na(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Ql(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Jt(o,"dilationRate"))}build(e){var t;e=dt(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 ha{apply(u,p){let d=l.apply([c]),h=Ka([c]),m=l.apply([c*2]);return xb([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 ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=cs({ones:()=>Mn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Q,ie,re)=>!ie||!ie[re]?Q:L(ie[re],Q),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=cs({ones:()=>Mn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,N,T]=Pn(this.kernel.read(),i,b),[E,A,$,O]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,E,this.padding),u=this.inputConv(u,v,A,this.padding),p=this.inputConv(p,N,$,this.padding),d=this.inputConv(d,T,O,this.padding);let[V,W,H,X]=Pn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,V),f=this.recurrentConv(f,W),g=this.recurrentConv(g,H),y=this.recurrentConv(y,X);let q=this.recurrentActivation.apply(Z(c,m)),K=this.recurrentActivation.apply(Z(u,f)),J=Z(L(K,s),L(q,this.activation.apply(Z(p,g)))),te=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(J));return[te,te,J]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=u4(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?er(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};km.className="ConvLSTM2DCell";se.registerClass(km);var px=class extends BI{constructor(e){let t=new km(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};px.className="ConvLSTM2D";se.registerClass(px);var Im=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 ep(()=>D1(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()}};Im.className="Dropout";se.registerClass(Im);var dx=class extends Im{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};dx.className="SpatialDropout1D";se.registerClass(dx);var hx=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,Jt(this.units,"units"),this.activation=us(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ht(e.kernelConstraint),this.biasConstraint=Ht(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=dt(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=dt(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=w1(this.activation.getClassName()),r;return a!=null?r=Qa(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Qa(n,this.kernel.read()),this.bias!=null&&(r=er(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ls(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),biasConstraint:Gt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};hx.className="Dense";se.registerClass(hx);var mx=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=dt(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 ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],ss(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return Mz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};mx.className="Flatten";se.registerClass(mx);var fx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=us(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.activation.apply(n)})}getConfig(){let e={activation:ls(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Activation";se.registerClass(fx);var gx=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return D(()=>(e=Re(e),$z(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};gx.className="RepeatVector";se.registerClass(gx);var yx=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=ss(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}};yx.className="Reshape";se.registerClass(yx);var bx=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=Aa(1,e.dims.length+1);if(!k.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=dt(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}};bx.className="Permute";se.registerClass(bx);var xx=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 Ac(ts(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e),a=-1,r=!0,s=Ac(ts(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};xx.className="Masking";se.registerClass(xx);var vx=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(bt(e.inputLength))}this.inputDim=e.inputDim,Jt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Jt(this.outputDim,"outputDim"),this.embeddingsInitializer=wt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=kt(e.embeddingsRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.embeddingsConstraint=Ht(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),ts(e,He(e))):null)}computeOutputShape(e){if(e=dt(e),this.inputLength==null)return[...e,this.outputDim];let t=bt(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=Jc(n,"int32")),$1(this.embeddings.read(),n.as1D()).reshape(dt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Et(this.embeddingsInitializer),embeddingsRegularizer:ht(this.embeddingsRegularizer),activityRegularizer:ht(this.activityRegularizer),embeddingsConstraint:Gt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vx.className="Embedding";se.registerClass(vx);var Vi=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=[dt(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=rs(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&rs(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=is(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Zc(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(ss(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=Aa(1,l).concat([0]);n.push(Ue(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ue(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Aa(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=rs(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Zn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=pa(n,t[a]);return n})}},wx=class extends Vi{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})}};wx.className="Add";se.registerClass(wx);var kx=class extends Vi{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})}};kx.className="Multiply";se.registerClass(kx);var Ix=class extends Vi{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)})}};Ix.className="Average";se.registerClass(Ix);var Nx=class extends Vi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ta(t,e[n]);return t})}};Nx.className="Maximum";se.registerClass(Nx);var Tx=class extends Vi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Fi(t,e[n]);return t})}};Tx.className="Minimum";se.registerClass(Tx);var Sx=class extends Vi{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(()=>xb(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(Zn(t[s],-1)):a.push(t[s]);let r=Ze(a,this.axis);return ch(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Sx.className="Concatenate";se.registerClass(Sx);function cp(e,t){for(;e<0;)e+=t;return e}function c4(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 Cx=class extends Vi{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)=>cp(r,e[s].shape.length)):a=[cp(this.axes,t.shape.length),cp(this.axes,n.shape.length)],this.normalize&&(t=om(t,a[0]),n=om(n,a[1])),c4(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[cp(this.axes,e.length),cp(this.axes,t.length)],n}computeOutputShape(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new $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}};Cx.className="Dot";se.registerClass(Cx);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 ep(()=>Kh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Ex.className="GaussianNoise";se.registerClass(Ex);var _x=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?ep(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(Kh(n.shape,1,a))},()=>n,t.training||!1):n})}};_x.className="GaussianDropout";se.registerClass(_x);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 ep(()=>{let a=Re(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=wr(Vl(n),this.rate);o=Jc(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";se.registerClass(Fx);function pp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=ek(e,t,n,a,r,s);else if(e.rank===3)i=tk(e,t,n,a,r,s);else if(e.rank===4)i=nk(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function p4(e,t,n,a,r=.001){return D(()=>{let s=kh(e,a),i=s.mean,o=s.variance;return[pp(e,i,o,n,t,r),i,o]})}function d4(e,t,n,a,r=.001){return D(()=>{let s=kh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Aa(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[pp(e,c,u,d,p,r),i,o]})}function h4(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Aa(0,e.rank-1))?p4(e,t,n,a,r):d4(e,t,n,a,r)}var Ax=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ht(e.betaConstraint),this.gammaConstraint=Ht(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=dt(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 Zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Re(e),r=a.shape,s=r.length,i=Aa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Mi(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,Aa(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,x=this.scale?this.gamma.read().reshape(l):null;return pp(a,g,y,b,x,this.epsilon)}else return pp(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]=h4(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let x=1-b,v=g.read(),N=v.sub(y).mul(x);g.write(v.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:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),movingMeanInitializer:Et(this.movingMeanInitializer),movingVarianceInitializer:Et(this.movingVarianceInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer),betaConstraint:Gt(this.betaConstraint),gammaConstraint:Gt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="BatchNormalization";se.registerClass(Ax);var $x=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=dt(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!==rs(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Re(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=kh(n,this.axis,s),l=Mi(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),pp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};$x.className="LayerNormalization";se.registerClass($x);function m4(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=_a()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ta(e,a)})}var Dx=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?_a():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 Zt({ndim:4})]}computeOutputShape(e){e=dt(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(()=>m4(Re(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="ZeroPadding2D";se.registerClass(Dx);function Nm(e,t,n,a,r,s){return D(()=>{Rt(r),T1(s),na(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=_a()),s==null&&(s="max"),e=tx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Dt(e,t,n,o):i=Jn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function WI(e,t,n,a,r,s){return D(()=>{Rt(r),T1(s),na(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=_a()),s==null&&(s="max"),e=RI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Gy(e,t,n,o):i=Fy(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var VI=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(Jt(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)}`);Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,na(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=dt(e);let t=Ma(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=Zc(Re(e),2);let n=this.poolingFunction(Re(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ns(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Mx=class extends VI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),Nm(e,t,n,a,r,"max")}};Mx.className="MaxPooling1D";se.registerClass(Mx);var Rx=class extends VI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),Nm(e,t,n,a,r,"avg")}};Rx.className="AveragePooling1D";se.registerClass(Rx);var UI=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];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),na(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ma(t,this.poolSize[0],this.padding,this.strides[0]),n=Ma(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return 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}},Px=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),Nm(e,t,n,a,r,"max")}};Px.className="MaxPooling2D";se.registerClass(Px);var Ox=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),Nm(e,t,n,a,r,"avg")}};Ox.className="AveragePooling2D";se.registerClass(Ox);var GI=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];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),na(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ma(t,this.poolSize[0],this.padding,this.strides[0]),n=Ma(n,this.poolSize[1],this.padding,this.strides[1]),a=Ma(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return 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}},Lx=class extends GI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),WI(e,t,n,a,r,"max")}};Lx.className="MaxPooling3D";se.registerClass(Lx);var zx=class extends GI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),WI(e,t,n,a,r,"avg")}};zx.className="AveragePooling3D";se.registerClass(zx);var HI=class extends qe{constructor(e){super(e);this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Bx=class extends HI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return Ct(n,1)})}};Bx.className="GlobalAveragePooling1D";se.registerClass(Bx);var Wx=class extends HI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return ea(n,1)})}};Wx.className="GlobalMaxPooling1D";se.registerClass(Wx);var jI=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Vx=class extends jI{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};Vx.className="GlobalAveragePooling2D";se.registerClass(Vx);var Ux=class extends jI{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?ea(n,[1,2]):ea(n,[2,3])})}};Ux.className="GlobalMaxPooling2D";se.registerClass(Ux);var qI=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=Da(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},Gx=class extends qI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=dt(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=dt(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),zI((n,a)=>[Re(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Gx.className="TimeDistributed";se.registerClass(Gx);function f4(e){Pi(Cz,"BidirectionalMergeMode",e)}var g4="concat",Hx=class extends qI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Da(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Da(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?g4:e.mergeMode,f4(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()):Nn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=LI(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 Zt({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 $a;for(let l of s)if(l instanceof $a!==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=xb([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){Oi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Oi(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=Da(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)}};Hx.className="Bidirectional";se.registerClass(Hx);function Uz(e){return new Xl(e)}function Gz(e){return new Zb(e)}function Hz(e){return new Xb(e)}function jz(e){return new Yb(e)}function qz(e){return new Jb(e)}function Kz(e){return new ex(e)}function Xz(e){return new Qb(e)}function Yz(e){return new bm(e)}function Jz(e){return new lp(e)}function Zz(e){return new ax(e)}function Qz(e){return new ym(e)}function eB(e){return new rx(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 fx(e)}function sB(e){return new hx(e)}function iB(e){return new Im(e)}function oB(e){return new dx(e)}function lB(e){return new mx(e)}function uB(e){return new gx(e)}function cB(e){return new yx(e)}function pB(e){return new bx(e)}function dB(e){return new vx(e)}function hB(e){return new wx(e)}function mB(e){return new Ix(e)}function fB(e){return new Sx(e)}function gB(e){return new Nx(e)}function yB(e){return new Tx(e)}function bB(e){return new kx(e)}function xB(e){return new Cx(e)}function vB(e){return new Ax(e)}function wB(e){return new $x(e)}function kB(e){return new Dx(e)}function Eb(e){return new Rx(e)}function IB(e){return Eb(e)}function NB(e){return Eb(e)}function _b(e){return new Ox(e)}function TB(e){return _b(e)}function SB(e){return _b(e)}function Fb(e){return new zx(e)}function CB(e){return Fb(e)}function EB(e){return Fb(e)}function _B(e){return new Bx(e)}function FB(e){return new Vx(e)}function L1(e){return new Wx(e)}function z1(e){return new Ux(e)}function B1(e){return new Mx(e)}function W1(e){return new Px(e)}function AB(e){return new Lx(e)}function $B(e){return new ux(e)}function DB(e){return new wm(e)}function MB(e){return new cx(e)}function RB(e){return new up(e)}function PB(e){return new lx(e)}function OB(e){return new vm(e)}function LB(e){return new px(e)}function zB(e){return new km(e)}function BB(e){return new tr(e)}function WB(e){return new xm(e)}function VB(e){return new Hx(e)}function UB(e){return new Gx(e)}var GB=L1,HB=z1,jB=B1,qB=W1;function KB(e){return new Ex(e)}function XB(e){return new _x(e)}function YB(e){return new Fx(e)}function JB(e){return new xx(e)}var KI={};Oe(KI,{MAPE:()=>C4,MSE:()=>F4,binaryAccuracy:()=>y4,binaryCrossentropy:()=>b4,categoricalAccuracy:()=>v4,categoricalCrossentropy:()=>w4,cosineProximity:()=>N4,mape:()=>E4,meanAbsoluteError:()=>T4,meanAbsolutePercentageError:()=>S4,meanSquaredError:()=>_4,mse:()=>A4,precision:()=>k4,recall:()=>I4,sparseCategoricalAccuracy:()=>x4});function y4(e,t){return Pb(e,t)}function b4(e,t){return nI(e,t)}function x4(e,t){return aI(e,t)}function v4(e,t){return Ob(e,t)}function w4(e,t){return Lb(e,t)}function k4(e,t){return tI(e,t)}function I4(e,t){return gW(e,t)}function N4(e,t){return Mb(e,t)}function T4(e,t){return lm(e,t)}function S4(e,t){return Jl(e,t)}function C4(e,t){return Jl(e,t)}function E4(e,t){return Jl(e,t)}function _4(e,t){return zi(e,t)}function F4(e,t){return zi(e,t)}function A4(e,t){return zi(e,t)}var XI={};Oe(XI,{modelFromJSON:()=>YW});var YI={};Oe(YI,{l1:()=>D4,l1l2:()=>$4,l2:()=>M4});function $4(e){return new ip(e)}function D4(e){return r4(e)}function M4(e){return s4(e)}var JI=class extends Yl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof Sr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function Tm(e,t){return e<t}function ZI(e,t){return e>t}var QI=class extends JI{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=Tm:this.mode==="max"?this.monitorFunc=ZI:this.monitor.indexOf("acc")!==-1?this.monitorFunc=ZI:this.monitorFunc=Tm,this.monitorFunc===Tm&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===Tm?Infinity:-Infinity}async onEpochEnd(e,t){await os(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 R4(e){return new QI(e)}var P4={earlyStopping:R4},Ra;(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"})(Ra||(Ra={}));var eN;(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={}))})(eN||(eN={}));var jx={};function O4(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};jx[e]=n}function tN(e){return jx[e]}function L4(e){delete jx[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 Sn(t.inputNames[s.inputIndexStart],n,a,r);if(s.type==="tensors")return t.inputNames.slice(o,l).map(p=>Sn(p,n,a,r));let c=Sn(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 Sn(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[Sm(r,o)]);return i!==void 0?t[Sm(r,i)][s]:void 0}function z4(e,t,n){return t[Sm(e,n.currentContextId)]}function Cr(e,t){let[n,a]=Ln(e);return[Sm(n,t&&t.currentContextId),a]}function Sm(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 Cm(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 Er(e){return e.kept?e:Yr(e)}var nN={};Oe(nN,{json:()=>B4});var B4=[{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}]}],aN={};Oe(aN,{json:()=>W4});var W4=[{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}]}],rN={};Oe(rN,{json:()=>V4});var V4=[{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"}]}],sN={};Oe(sN,{json:()=>U4});var U4=[{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"}]}],iN={};Oe(iN,{json:()=>G4});var G4=[{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"}]}],oN={};Oe(oN,{json:()=>H4});var H4=[{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}]}],lN={};Oe(lN,{json:()=>j4});var j4=[{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"}]}],uN={};Oe(uN,{json:()=>q4});var q4=[{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"}]}],cN={};Oe(cN,{json:()=>K4});var K4=[{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}]}],pN={};Oe(pN,{json:()=>X4});var X4=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}],dN={};Oe(dN,{json:()=>Y4});var Y4=[{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}]}],hN={};Oe(hN,{json:()=>J4});var J4=[{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}]}],mN={};Oe(mN,{json:()=>Z4});var Z4=[{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}]}],fN={};Oe(fN,{json:()=>Q4});var Q4=[{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"}]}],gN={};Oe(gN,{json:()=>eV});var eV=[{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}]}],yN={};Oe(yN,{json:()=>tV});var tV=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],bN={};Oe(bN,{json:()=>nV});var nV=[{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:[]}],vN=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[nN,aN,rN,sN,iN,oN,lN,dN,pN,uN,hN,mN,fN,gN,yN,bN,cN],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]=Cr(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]=Cr(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]=Cr(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=tN(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=qx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=qx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":i=tv(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=tv(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":i=Xx(e.attr,r.tfName,r.defaultValue||0),i===void 0&&!!r.tfDeprecatedName&&(i=Xx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":i=ev(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=ev(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":i=Kx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Kx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":i=av(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=av(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":i=Qx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Qx(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":i=nv(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=nv(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":i=Jx(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=Jx(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"func":i=xN(e.attr,r.tfName,r.defaultValue),i===void 0&&!!r.tfDeprecatedName&&(i=xN(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]=Cr(c.name),p={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Yx(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]=Cr(p);u.inputs.push(r[d]),r[d].children.push(u)})});let o=e.ret;e.signature.outputArg.forEach(c=>{let[u,p]=Cr(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 aV(e){let t=ee().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 wN(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):aV(e);return t?n:n.toLowerCase()}function qx(e,t,n,a=!1){let r=e[t];return r!=null?wN(r.s,a):n}function Kx(e,t,n){let a=e[t];return a?a.b:n}function Xx(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 Yx(e){switch(typeof e=="string"&&(e=Ra[e]),e){case Ra.DT_FLOAT:return"float32";case Ra.DT_INT32:case Ra.DT_INT64:case Ra.DT_INT8:case Ra.DT_UINT8:return"int32";case Ra.DT_BOOL:return"bool";case Ra.DT_DOUBLE:return"float32";case Ra.DT_STRING:return"string";default:return null}}function xN(e,t,n){let a=e[t];return a&&a.func?a.func.name:n}function Jx(e,t,n){let a=e[t];return a&&a.type?Yx(a.type):n}function Zx(e,t,n){let a=e[t];return a&&a.list&&a.list.type?a.list.type.map(r=>Yx(r)):n}function kN(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function Qx(e,t,n){let a=e[t];return a&&a.shape?kN(a.shape):n}function ev(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 tv(e,t,n,a=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(s=>wN(s,a)):n}function nv(e,t,n){let a=e[t];return a&&a.list&&a.list.shape?a.list.shape.map(r=>kN(r)):n}function av(e,t,n){let a=e[t];return a&&a.list&&a.list.b?a.list.b:n}var rV=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 Sn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Sn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Xx(this.node.rawAttrs,e,t);if(n.s!=null)return qx(this.node.rawAttrs,e,t);if(n.b!=null)return Kx(this.node.rawAttrs,e,t);if(n.shape!=null)return Qx(this.node.rawAttrs,e,t);if(n.type!=null)return Jx(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return ev(this.node.rawAttrs,e,t);if(n.list.s!=null)return tv(this.node.rawAttrs,e,t);if(n.list.shape!=null)return nv(this.node.rawAttrs,e,t);if(n.list.b!=null)return av(this.node.rawAttrs,e,t);if(n.list.type!=null)return Zx(this.node.rawAttrs,e,t)}return t}},sV=(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[Y0(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[wh(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[ve(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[Py(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[uh(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[fe(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[Fi(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[Ta(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[Xa(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[jc(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},iV=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Wt(I("x",e,t,n))];case"Acos":return[vy(I("x",e,t,n))];case"Acosh":return[wy(I("x",e,t,n))];case"Asin":return[Iy(I("x",e,t,n))];case"Asinh":return[Ny(I("x",e,t,n))];case"Atan":return[Ty(I("x",e,t,n))];case"Atan2":return[Sy(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[Cy(I("x",e,t,n))];case"Ceil":return[Ay(I("x",e,t,n))];case"Complex":return[qr(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[Pc(I("x",e,t,n))];case"Cosh":return[mh(I("x",e,t,n))];case"Elu":return[Ll(I("x",e,t,n))];case"Erf":return[Oy(I("x",e,t,n))];case"Exp":return[hn(I("x",e,t,n))];case"Expm1":return[Ly(I("x",e,t,n))];case"Floor":return[zl(I("x",e,t,n))];case"Log":return[Dn(I("x",e,t,n))];case"Log1p":return[yh(I("x",e,t,n))];case"Imag":return[gh(I("x",e,t,n))];case"Neg":return[St(I("x",e,t,n))];case"Reciprocal":return[Ky(I("x",e,t,n))];case"Real":return[Vc(I("x",e,t,n))];case"Relu":return[Xe(I("x",e,t,n))];case"Round":return[Xy(I("x",e,t,n))];case"Selu":return[Ch(I("x",e,t,n))];case"Sigmoid":return[ca(I("x",e,t,n))];case"Sin":return[Eh(I("x",e,t,n))];case"Sign":return[Yy(I("x",e,t,n))];case"Sinh":return[_h(I("x",e,t,n))];case"Softplus":return[Bl(I("x",e,t,n))];case"Sqrt":return[an(I("x",e,t,n))];case"Square":return[ot(I("x",e,t,n))];case"Tanh":return[Ol(I("x",e,t,n))];case"Tan":return[Qy(I("x",e,t,n))];case"ClipByValue":return[Yt(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Th(I("x",e,t,n))];case"Rsqrt":return[Sh(Sn(e.inputNames[0],t,n))];case"Prod":return[Ih(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[Oc(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[Wc(I("x",e,t,n),I("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function fa(e,t,n=""){k.assert(oV(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function oV(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 lV=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=de(0),Xt(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),fa(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,Xt(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 Xn([],[0].concat(this.elementShape));let n=this.readMany(e);return fa(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Mt(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Xn([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return fa(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Ze(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,pt(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=j(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]=j(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)}},dp=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}`);fa(t,r.shape,"TensorList shape mismatch: "),Xt(r)}),this.idTensor=de(0),this.maxNumElements=a,Xt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new dp([...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 fa(e,this.elementShape,"TensorList shape mismatch: "),D(()=>{let a=this.tensors.map(r=>j(r,e));return Mt(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 fa(n.shape,e,"TensorList shape mismatch: "),j(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(fa(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Xt(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 fa(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.`);fa(this.elementShape,t.shape,"TensorList shape mismatch: "),Xt(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 fa(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?Xn([],[0].concat(this.elementShape)):D(()=>{let a=e.map(r=>j(this.tensors[r],n));return Mt(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return fa(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?Xn([],[0].concat(this.elementShape)):D(()=>{let n=this.tensors.map(a=>j(a,t));return Ze(n,0)})}};function uV(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);fa(r,t,"TensorList shape mismatch: ");let s=pt(e);return new dp(s,t,a)}function cV(e,t,n){return new dp([],e,t,n)}function pV(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 dp([],n,e.dtype,a),i=pt(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function dV(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=j(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]=j(We(e,p,d),n)}return e.dispose(),l}),o=new dp([],n,e.dtype,t.length);for(let l=0;l<i.length;l++)o.setItem(l,i[l]);return o}var hV=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[Er(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Er(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>Sn(r,t,n)!==void 0);if(a){let r=Sn(a,t,n);return[Er(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Er(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Er(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Er(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 lV(c,r,a,s,l,i,o);return n.addTensorArray(u),[u.idTensor,de(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[de(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=pV(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=cV(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=uV(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=dV(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function IN(e,t,n){let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=a==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let c=I("strides",e,t,n),u=Cm(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[h,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:p,dilations:d,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var mV=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[dh(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=Cm(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[$t(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}=IN(e,t,n);return[as.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}=IN(e,t,n);return[as.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=Cm(e,t,n);return[hh(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=Cm(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[xr(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[Dy(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[Jn(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[Dt(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}=wk(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[Gy(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[Ry(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},fV=(e,t,n)=>{switch(e.op){case"Fill":{let a=I("shape",e,t,n),r=I("dtype",e,t,n),s=I("value",e,t,n);return[In(a,s,r)]}case"LinSpace":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("num",e,t,n);return[mk(a,r,s)]}case"Multinomial":{let a=I("logits",e,t,n),r=I("numSamples",e,t,n),s=I("seed",e,t,n);return[kk(a,r,s)]}case"OneHot":{let a=I("indices",e,t,n),r=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[Ml(a,r,s,i)]}case"Ones":return[Ka(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Mn(I("x",e,t,n))];case"RandomUniform":return[Vl(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("step",e,t,n);return[Nh(a,r,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let a=I("shape",e,t,n),r=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[$h(a,r,s,I("dtype",e,t,n),i)]}case"Zeros":return[yt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[He(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function rv(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 gV=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=rv(e,t,n),c=await Ya.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}=rv(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Ya.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}=rv(e,t,n);return[await Ya.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=pe(I("condition",e,t,n),"bool"),r=[await nb(a)];return a.dispose(),r}case"ListDiff":return Tk(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},yV=(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=eb(a,r,s);return[i.values,i.indices]}case"Unique":{let a=I("x",e,t,n),r=Dh(a);return[r.values,r.indices]}case"UniqueV2":{let a=I("x",e,t,n),r=I("axis",e,t,n),s=Dh(a,r);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},bV=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let a=I("default",e,t,n);return[Sn(e.name,t,n)||a];case"Placeholder":return[Sn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",e,t,n);return[Er(c)]}case"IdentityN":return I("x",e,t,n).map(c=>Er(c));case"Snapshot":let r=I("x",e,t,n);return[Er(r)];case"Shape":return[et(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(c=>et(c.shape));case"Size":return[de(I("x",e,t,n).size,"int32")];case"Rank":return[de(I("x",e,t,n).rank,"int32")];case"NoOp":return[de(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`)}},xV=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=de(0),this.tensorMap=new Map,Xt(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=pt(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];Xt(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 Mt(a)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},vV=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 xV(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`)}},wV=(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[Ya.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[Ya.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[Ya.cropAndResize(a,r,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},kV=(e,t,n)=>{switch(e.op){case"Equal":return[vr(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[ts(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Qn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[wr(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Lc(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[es(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[pa(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[zc(I("a",e,t,n))];case"LogicalOr":return[vh(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[kn(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`)}},IV=(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[as.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`)}},NV=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[br(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[br(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[By(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[Sa(I("x",e,t,n))];case"LogSoftmax":return[xh(I("x",e,t,n))];case"SparseToDense":return[ab(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 ${e.op} is not implemented`)}},TV=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ea(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ct(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Wl(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ce(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ch(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ac(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[$c(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[ky(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ih(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[fh(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[ak(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[uk(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},SV=(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),[Ze(s,r)]}case"Gather":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[Ei(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[Ei(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[Zy(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=ns(r[0]).shape,o=r.map(l=>{let c=k.arraysEqual(l.shape,s);if(!c&&!k.arraysEqual(ns(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:j(l,s)});return[Mt(o,a)]});case"Unpack":{let a=I("axis",e,t,n),r=I("tensor",e,t,n);return pt(r,a)}case"Tile":{let a=I("reps",e,t,n);return[ja(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[Gk(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[Hk(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[ab(a,s,r,s.dtype===i.dtype?i:pe(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},CV=(e,t,n)=>{switch(e.op){case"FFT":return[Gc(I("x",e,t,n))];case"IFFT":return[Gl(I("x",e,t,n))];case"RFFT":return[Hc(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[Zn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[ns(I("x",e,t,n),a)]}case"Reshape":return[j(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[Hy(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ta(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[Bc(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Mc(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[My(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Rc(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function NN(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return D(()=>sV(s,i,o));case"basic_math":return D(()=>iV(s,i,o));case"control":return hV(s,i,o);case"convolution":return D(()=>mV(s,i,o));case"creation":return D(()=>fV(s,i,o));case"dynamic":return gV(s,i,o);case"evaluation":return D(()=>yV(s,i,o));case"image":return D(()=>wV(s,i,o));case"graph":return D(()=>bV(s,i,o));case"logical":return D(()=>kV(s,i,o));case"matrices":return D(()=>IV(s,i,o));case"normalization":return D(()=>NV(s,i,o));case"reduction":return D(()=>TV(s,i,o));case"slice_join":return D(()=>SV(s,i,o));case"spectral":return D(()=>CV(s,i,o));case"transformation":return D(()=>EV(s,i,o));case"hash_table":return vV(s,i,o,a);case"custom":let l=tN(s.op);if(l&&l.customExecutor)return l.customExecutor(new rV(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 TN=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 CN(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((SN(d)||_V(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 AV(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 $V=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],MV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function SN(e){return $V.indexOf(e.op)>=0}function _V(e){return DV.indexOf(e.op)>=0}function FV(e){return MV.indexOf(e.op)>=0}var sv=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new sv(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=CN(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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: [${a}]`)}return AV(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(u=>this.graph.nodes[Ln(u)[0]]),r=t.map(u=>Ln(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return D(()=>{let u=new TN(this.weightMap,l,c,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Ln(m),y=[];y[g]=e[m],p[f]=y});let d=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=NN(f,p,u,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${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=>Sn(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=z4(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 TN(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>Sn(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}=CN(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[x,v]=Ln(b),N=[];N[v]=e[b],h[x]=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=>!SN(b)&&!Sn(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}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let p="";if(u.node.op==="Enter"&&I("isConstant",u.node,a,n)&&([p]=Cr(u.node.name,n)),a[u.node.name]==null){let d=NN(u.node,a,n,this._resourceManager);p||([p]=Cr(u.node.name,n));let h=n.currentContext;k.isPromise(d)?c.push(d.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l),m))):(a[p]=d,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l))}else this.processChildNodes(u.node,t,n,a,r,l)}return c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Cr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Sn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Sn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Ln(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${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 '${t}' is not found in the graph`)})}},RV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},PV="?tfjs-format=file",OV="model.json",EN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new RV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Kt.browserHTTPRequest(e,this.loadOptions);else{let t=Kt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Kt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Kt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new sv(vN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=vN.Instance.transformGraph(e.modelInitializer);this.initializer=new sv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Kt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function LV(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${OV}${PV}`);let n=new EN(e,t);return await n.load(),n}var _N="2.8.3",FN={};Oe(FN,{CSVDataset:()=>$N,Dataset:()=>eu,FileDataSource:()=>DN,TextLineDataset:()=>AN,URLDataSource:()=>MN,array:()=>zV,csv:()=>WV,func:()=>VV,generator:()=>UV,microphone:()=>HV,version_data:()=>RN,webcam:()=>GV,zip:()=>BV});var jV=No(fd()),qV=No(fd());function KV(e,t){return Em(e,t)}function Em(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(tu(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=Em(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 XV(e,t=ON){return PN(e,t)}function PN(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(tu(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(c=>c[i]),l=PN(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 ON(e){return e===null?null:tu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function LN(e,t){let n=new Map;Em(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 Em(e,t,n)}function tu(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof z))}function JV(e){return e==null||YV(e)||Array.isArray(e)||typeof e=="object"&&e instanceof z||k.isTypedArray(e)}function YV(e){return e===null||typeof e!="object"&&typeof e!="function"}function QV(e){return KV(e,ZV)}function ZV(e){return e instanceof z?{value:e.clone(),recurse:!1}:tu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var zN=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}},iv=class extends zN{constructor(){super(iv.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}};iv.INITIAL_CAPACITY=32;function BN(e){return new eU(e)}function ov(e){return new tU(e)}function nU(e,t){return new WN(e,t)}function rU(e,t=ps.FAIL){return new aU(e,t)}var Qt=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 pU(this,e)}filter(e){return new uU(this,e)}map(e){return new cU(this,e)}mapAsync(e){return new VN(this,e)}serialMapAsync(e){return new VN(this,e).serial()}flatmap(e){return new dU(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 lU(this,e,t)}columnMajorBatch(e,t=!0,n=ON){return this.rowMajorBatch(e,t).map(a=>XV(a,n))}concatenate(e,t){return new WN(BN([this,e]),t)}take(e){return e<0||e==null?this:new oU(this,e)}skip(e){return e<0||e==null?this:new iU(this,e)}prefetch(e){return new UN(this,e)}shuffle(e,t){return new hU(this,e,t)}serial(){return new sU(this)}},eU=class extends Qt{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:QV(e),done:!1}}},tU=class extends Qt{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: ${e.message}`,e}}},sU=class extends Qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},iU=class extends Qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ae(e.value)}return this.upstream.next()}},oU=class extends Qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},lU=class extends Qt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},uU=class extends Qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Ae(e.value)}}},cU=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Na.getTensorsInContainer(e.value),n=this.transform(e.value),a=Na.getTensorsInContainer(n);for(let r of t)Na.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},pU=class extends Qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},VN=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Na.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Na.getTensorsInContainer(n);for(let r of t)Na.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},lv=class extends Qt{constructor(){super();this.outputQueue=new iv,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},dU=class extends lv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Na.getTensorsInContainer(e.value),n=this.transform(e.value),a=Na.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Na.isTensorInList(r,a)||r.dispose();return!0}},WN=class extends Qt{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}},ps;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ps||(ps={}));var aU=class extends Qt{constructor(e,t=ps.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 Qt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await LN(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ps.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ps.SHORTEST:return{value:null,done:!0};case ps.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},UN=class extends Qt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new zN(t)}summary(){return`${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()}},hU=class extends UN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=qV.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}}},eu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),zn(async()=>(await n.iterator()).columnMajorBatch(e,t,mU),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,zn(async()=>(await t.iterator()).filter(a=>D(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return zn(async()=>(await t.iterator()).map(n=>D(()=>e(n))),this.size)}mapAsync(e){let t=this;return zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,zn(async()=>{let a=ov(async()=>({value:await t.iterator(),done:!1}));return nU(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,zn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=jV.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()}};eu.MAX_BUFFER_SIZE=1e4;function zn(e,t=null){return new class extends eu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function zV(e){return zn(async()=>BN(e),e.length)}function BV(e){if(!tu(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 LN(e,a=>{if(a instanceof eu)return{value:a.iterator(),recurse:!1};if(tu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return rU(n,ps.SHORTEST)},t)}function mU(e){if(e===null)return null;let t=e[0];return JV(t)?{value:fU(e),recurse:!1}:{value:null,recurse:!0}}function fU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof z?Mt(e):Xn(e)}var AN=class extends eu{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='"',hp=Symbol("out"),GN=Symbol("field"),Fm=Symbol("quote"),uv=Symbol("quoteafterquote"),HN=Symbol("quoteinquote"),$N=class extends eu{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 AN(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=hp;for(let i=0;i<r;i++)switch(s){case hp:switch(e.charAt(i)){case _m:a=i+1,s=Fm;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=hp;break;default:s=GN,a=i;break}break;case GN:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=hp,a=i+1;break;default:}break;case Fm:switch(e.charAt(i)){case _m:s=uv;break;default:}break;case uv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=hp,a=i+1;break;case _m:s=Fm;break;default:s=HN;break}break;case HN:switch(e.charAt(i)){case _m:s=Fm;break;default:}break;default:}if(s===uv?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}},jN=class extends Qt{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(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new jN(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),Xn(n,t)}},qN=class extends Qt{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=et([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=Ca([s,r,o,i],[1,4])}else this.cropBox=Ca([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().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 qN(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=Ti.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=e.toFloat().expandDims(0),n;n=Ya.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return n.reshape(a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},KN=class{},XN=class extends Qt{split(e){return new gU(this,e)}},gU=class extends XN{constructor(e,t){super();this.upstream=e,this.impl=new yU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yU=class extends lv{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},xU=class extends Qt{decodeUTF8(){return new bU(this)}},bU=class extends XN{constructor(e){super();this.upstream=e,this.impl=new vU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},vU=class extends lv{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=C_();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return ee().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},YN=class extends xU{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(ee().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 kU(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=wU(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new YN(s,t)}else throw new Error(r.statusText)}var wU=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 JN(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var DN=class extends KN{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(JN(this.input)&&ee().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new YN(this.input,this.options)}},MN=class extends KN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return JN(this.url)?new DN(this.url,this.fileOptions).iterator():kU(this.url,this.fileOptions)}};function WV(e,t={}){return new $N(new MN(e),t)}function VV(e){let t=ov(e);return zn(async()=>t)}function UV(e){return zn(async()=>{let t=await e();return ov(()=>t.next())})}async function GV(e,t){return qN.create(e,t)}async function HV(e){return jN.create(e)}var RN="2.8.3";function ke(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var IU=Ja.whereImpl,NU=class extends nc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new gd(this,Zr())}write(e,t,n){this.firstUse&&(this.firstUse=!1,ee().get("IS_NODE")&&_.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let a={};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 _.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 Zr().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){ke([e],"where");let t=this.readSync(e.dataId);return IU(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},ZN={};Oe(ZN,{addImpl:()=>eT,bincountImpl:()=>cv,bincountReduceImpl:()=>tT,ceilImpl:()=>nT,concatImpl:()=>pv,expImpl:()=>aT,expm1Impl:()=>rT,floorImpl:()=>sT,gatherV2Impl:()=>iT,greaterImpl:()=>oT,lessImpl:()=>lT,linSpaceImpl:()=>uT,logImpl:()=>cT,maxImpl:()=>pT,maximumImpl:()=>dT,minimumImpl:()=>hT,multiplyImpl:()=>dv,negImpl:()=>mT,notEqualImpl:()=>fT,prodImpl:()=>gT,rangeImpl:()=>mv,rsqrtImpl:()=>yT,simpleAbsImpl:()=>QN,sliceImpl:()=>Am,squaredDifferenceImpl:()=>bT,stridedSliceImpl:()=>xT,subImpl:()=>vT,tileImpl:()=>wT,topKImpl:()=>kT,transposeImpl:()=>hv,uniqueImpl:()=>IT});function QN(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var TU=e=>{let{x:t}=e.inputs,n=e.backend;ke(t,"abs");let a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=QN(r),n.makeOutput(a,t.shape,"float32")},SU={kernelName:Co,backendName:"cpu",kernelFunc:TU};function Ot(e){return(t,n,a,r,s)=>{let i=_.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=_.getBroadcastDims(t,i),g=_.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),x=b.slice(-p);f.forEach(E=>x[E]=0);let v=k.locToIndex(x,p,h),N=b.slice(-d);g.forEach(E=>N[E]=0);let T=k.locToIndex(N,d,m);u[y]=e(a[v],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 CU={kernelName:Td,backendName:"cpu",kernelFunc:Bn};function $m(e,t,n="float32"){if(n==="complex64"){let r=$m(e,t,"float32"),s=$m(e,t,"float32");return Bn({inputs:{real:r,imag:s},backend:e})}let a=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,a)}function ar(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var EU={kernelName:Xo,backendName:"cpu",kernelFunc:ar};function Ui(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.real,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var _U={kernelName:Hd,backendName:"cpu",kernelFunc:Ui};function ds(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return ar({inputs:{x:r},backend:n});let i=$m(n,r.shape,r.dtype),o=ds({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=Ui({inputs:{input:r},backend:n}),o=ds({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=ar({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]=Ot((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 ${s}`)}var FU={kernelName:Ds,backendName:"cpu",kernelFunc:ds};function en(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;ke([i,o],e);let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,p=a||i.dtype,[d,h]=t(i.shape,o.shape,c,u,p);return l.makeTensorInfo(h,p,d)}:({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let c=ds({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),u=l.data.get(c.dataId),p=u.complexTensorInfos.real,d=u.complexTensorInfos.imag,h=l.data.get(p.dataId).values,m=l.data.get(d.dataId).values,f=ds({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(f.dataId),y=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,v=l.data.get(b.dataId).values,[N,T,E]=n(i.shape,o.shape,h,m,x,v),A=l.makeTensorInfo(E,"float32",N),$=l.makeTensorInfo(E,"float32",T),O=Bn({inputs:{real:A,imag:$},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(f),l.disposeIntermediateTensorInfo(A),l.disposeIntermediateTensorInfo($),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 fv(e){return(t,n,a,r,s,i)=>{let o=_.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=_.getBroadcastDims(t,o),m=_.getBroadcastDims(n,o),f=_.mergeRealAndImagArrays(a,r),g=_.mergeRealAndImagArrays(s,i),y=t.length,b=k.computeStrides(t),x=n.length,v=k.computeStrides(n);if(h.length+m.length===0)for(let N=0;N<p.length;N++){let T=N%f.length,E=N%g.length,A=e(f[T*2],f[T*2+1],g[E*2],g[E*2+1]);p[N]=A.real,d[N]=A.imag}else for(let N=0;N<p.length;N++){let T=k.indexToLoc(N,c,u),E=T.slice(-y);h.forEach(W=>E[W]=0);let A=k.locToIndex(E,y,b),$=T.slice(-x);m.forEach(W=>$[W]=0);let O=k.locToIndex($,x,v),V=e(f[A*2],f[A*2+1],g[O*2],g[O*2+1]);p[N]=V.real,d[N]=V.imag}return[p,d,o]}}var eT=Ot((e,t)=>e+t),AU=fv((e,t,n,a)=>({real:e+n,imag:t+a})),mp=en(Ur,eT,AU),$U={kernelName:Ur,backendName:"cpu",kernelFunc:mp};function cv(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 tT(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 nu(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 it(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(ke(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 au(e,t,n){return({inputs:a,attrs:r,backend:s})=>{let{x:i}=a;if(ke(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 nT=nu(e=>Math.ceil(e)),DU=au(Ro,nT),MU={kernelName:Ro,backendName:"cpu",kernelFunc:DU};function pv(e,t,n,a){let r=k.getArrayFromDType(n,k.sizeFromShape(t));if(a&&n!=="string"){let s=0;e.forEach(i=>{let o=k.sizeFromShape(i.shape);r.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?_.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let 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 aT=nu(e=>Math.exp(e)),NT=au(Bs,aT),RU={kernelName:Bs,backendName:"cpu",kernelFunc:NT},rT=nu(e=>Math.expm1(e)),PU=au(Go,rT),OU={kernelName:Go,backendName:"cpu",kernelFunc:PU},sT=nu(e=>Math.floor(e)),LU=au(Ws,sT),zU={kernelName:Ws,backendName:"cpu",kernelFunc:LU};function iT(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 oT=Ot((e,t)=>e>t?1:0),BU=en(Ko,oT,null,"bool"),WU={kernelName:Ko,backendName:"cpu",kernelFunc:BU},lT=Ot((e,t)=>e<t?1:0),VU=en(Qo,lT,null,"bool"),UU={kernelName:Qo,backendName:"cpu",kernelFunc:VU};function uT(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 cT=nu(e=>Math.log(e)),GU=au(js,cT),HU={kernelName:js,backendName:"cpu",kernelFunc:GU};function pT(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 dT=Ot((e,t)=>Math.max(e,t)),jU=en(Ks,dT),qU={kernelName:Ks,backendName:"cpu",kernelFunc:jU},hT=Ot((e,t)=>Math.min(e,t)),KU=en(Zs,hT),XU={kernelName:Zs,backendName:"cpu",kernelFunc:KU},dv=Ot((e,t)=>e*t),YU=fv((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),gv=en(Qs,dv,YU),JU={kernelName:Qs,backendName:"cpu",kernelFunc:gv};function mT(e,t,n){let a=k.createScalarValue(-1,n);return dv([],t,a,e,n)}function ZU(e){let{inputs:t,backend:n}=e,{x:a}=t;ke(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=mT(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var QU={kernelName:rl,backendName:"cpu",kernelFunc:ZU},fT=Ot((e,t)=>e!==t?1:0),eG=en(sl,fT,null,"bool"),tG={kernelName:sl,backendName:"cpu",kernelFunc:eG};function hv(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 ga(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{perm:s}=n;ke(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=hv(l,r.shape,r.dtype,s,o);return{dataId:a.write(c,o,r.dtype),shape:o,dtype:r.dtype}}var nG={kernelName:bi,backendName:"cpu",kernelFunc:ga};function gT(e,t,n,a){let[r,s]=_.computeOutAndReduceShapes(e,a),i=ua(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 aG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ke(r,"prod");let o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=_.getAxesPermutation(l,o),u=l,p=r,d=[];c!=null&&(p=ga({inputs:{x:r},backend:n,attrs:{perm:c}}),d.push(p),u=_.getInnerMostAxes(u.length,o));let h=n.data.get(p.dataId).values,{outVals:m,outShape:f,outDtype:g}=gT(p.shape,p.dtype,h,u),y=f;return i&&(y=_.expandShapeToKeepDim(f,l)),d.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(y,g,m)}var rG={kernelName:pl,backendName:"cpu",kernelFunc:aG};function mv(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 yT=nu(e=>1/Math.sqrt(e)),sG=au(ui,yT),iG={kernelName:ui,backendName:"cpu",kernelFunc:sG};function Am(e,t,n,a,r){let s=dn.isSliceContinous(a,t,n),i=k.sizeFromShape(n),o=k.computeStrides(a);if(s){let p=dn.computeFlatOffset(t,o);return r==="string"?e.slice(p,p+i):e.subarray(p,p+i)}let l=r==="string"?_.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"?_.fromStringArrayToUint8(u.values):u.values}function Gi(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;ke(r,"slice");let[o,l]=dn.parseSliceParams(r,s,i);dn.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 oG={kernelName:yl,backendName:"cpu",kernelFunc:Gi},bT=Ot((e,t)=>{let n=e-t;return n*n}),lG=en(fi,bT),uG={kernelName:fi,backendName:"cpu",kernelFunc:lG};function xT(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 vT=Ot((e,t)=>e-t),cG=fv((e,t,n,a)=>({real:e-n,imag:t-a})),yv=en(gi,vT,cG),pG={kernelName:gi,backendName:"cpu",kernelFunc:yv};function wT(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 kT(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,x)=>x.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 IT(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 Bt(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 Bt(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 dG="2.8.3";lh("cpu",()=>new NU,1);var TT=it(Bo,e=>e>=0?e:Math.exp(e)-1),hG={kernelName:Bo,backendName:"cpu",kernelFunc:TT};function ST(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;ke([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 mG={kernelName:Hs,backendName:"cpu",kernelFunc:ST},fG=Ot((e,t)=>e<0?t*e:e);function CT(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;ke([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=fG(a.shape,r.shape,s,i,a.dtype);return n.makeTensorInfo(l,a.dtype,o)}var gG={kernelName:ai,backendName:"cpu",kernelFunc:CT},ET=it(ri,e=>Math.max(0,e)),yG={kernelName:ri,backendName:"cpu",kernelFunc:ET},_T=it(ii,e=>Math.min(Math.max(0,e),6)),bG={kernelName:ii,backendName:"cpu",kernelFunc:_T};function bv(e,t,n,a,r){if(n==="linear")return ar({inputs:{x:t},backend:e});if(n==="relu")return ET({inputs:{x:t},backend:e});if(n==="elu")return TT({inputs:{x:t},backend:e});if(n==="relu6")return _T({inputs:{x:t},backend:e});if(n==="prelu")return CT({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return ST({inputs:{x:t},backend:e,attrs:{alpha:r}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function It(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let c=n.data.get(r.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,p=c.complexTensorInfos.imag;u.shape=o,p.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var xG={kernelName:hl,backendName:"cpu",kernelFunc:It};function FT(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;ke([r,s],"matMul");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);k.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,u,d]:[g,d,u],N=o?[y,h,p]:[y,p,h],T=It({inputs:{x:r},backend:n,attrs:{shape:v}}),E=It({inputs:{x:s},backend:n,attrs:{shape:N}}),A=i?T.shape[1]:T.shape[2],$=i?T.shape[2]:T.shape[1],O=o?E.shape[1]:E.shape[2],V=Math.max(g,y),W=n.data.get(T.dataId).values,H=n.data.get(E.dataId).values,X=k.computeStrides(T.shape),q=k.computeStrides(E.shape),[K,J,te]=i?[X[0],1,X[1]]:[X[0],X[1],1],[Q,ie,re]=o?[1,q[1],q[0]]:[q[1],1,q[0]],ae=$*O,oe=Le([V,$,O],T.dtype),he=oe.values,ce=n.blockSize;for(let ge=0;ge<V;ge++)for(let be=0;be<$;be+=ce)for(let Ie=0;Ie<O;Ie+=ce)for(let Te=0;Te<A;Te+=ce){let Ee=Math.min(be+ce,$),Me=Math.min(Ie+ce,O),Je=Math.min(Te+ce,A);for(let nt=be;nt<Ee;nt++)for(let Ve=Ie;Ve<Me;Ve++){let ut=0;for(let Ge=Te;Ge<Je;Ge++){let ct=Math.min(ge,g-1)*K,ft=Math.min(ge,y-1)*re,Fn=W[ct+nt*J+Ge*te],_t=H[Ge*Q+Ve*ie+ft];ut+=Fn*_t}he[ge*ae+(nt*O+Ve)]+=ut}}return n.disposeIntermediateTensorInfo(T),n.disposeIntermediateTensorInfo(E),n.makeTensorInfo(x,oe.dtype,oe.values)}var vG={kernelName:$s,backendName:"cpu",kernelFunc:FT};function wG(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=mp({inputs:{a:d,b:i},backend:n}),f.push(d),d=h),u&&(m=bv(n,d,u,o,p),f.push(d),d=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return d}var kG={kernelName:xi,backendName:"cpu",kernelFunc:wG},IG=it(Eo,e=>Math.acos(e)),NG={kernelName:Eo,backendName:"cpu",kernelFunc:IG},TG=it(_o,e=>Math.acosh(e)),SG={kernelName:_o,backendName:"cpu",kernelFunc:TG};function CG(e){let{inputs:t,backend:n}=e,a=t;ke(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:CG};function _G(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ke(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,c=_.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ga({inputs:{x:r},backend:n,attrs:{perm:c}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("all",l,u.shape.length);let[p,d]=_.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,x=f[b];for(let v=0;v<h;++v){let N=f[b+v];x=x&&N}m[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let y=_.expandShapeToKeepDim(p,o),b=It({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var FG={kernelName:vd,backendName:"cpu",kernelFunc:_G};function AG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ke(r,"any");let o=k.parseAxisParam(s,r.shape),l=o,c=_.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ga({inputs:{x:r},backend:n,attrs:{perm:c}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("any",l,u.shape.length);let[p,d]=_.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,x=f[b];for(let v=0;v<h;++v){let N=f[b+v];x=x||N}m[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let y=_.expandShapeToKeepDim(p,o),b=It({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var $G={kernelName:wd,backendName:"cpu",kernelFunc:AG};function DG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ke(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=ga({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],_.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,p]=_.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),h=k.makeZerosTypedArray(d,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let v=0;v<m;++v){let N=f[y+v];N>b&&(b=N,x=v)}h[g]=x}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var MG={kernelName:Fs,backendName:"cpu",kernelFunc:DG};function RG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ke(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=ga({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],_.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,p]=_.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),h=k.makeZerosTypedArray(d,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let v=0;v<m;++v){let N=f[y+v];N<b&&(b=N,x=v)}h[g]=x}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var PG={kernelName:sc,backendName:"cpu",kernelFunc:RG},OG=it(Fo,e=>Math.asin(e)),LG={kernelName:Fo,backendName:"cpu",kernelFunc:OG},zG=it(Ao,e=>Math.asinh(e)),BG={kernelName:Ao,backendName:"cpu",kernelFunc:zG},WG=it($o,e=>Math.atan(e)),VG={kernelName:$o,backendName:"cpu",kernelFunc:WG},UG=Ot((e,t)=>Math.atan2(e,t)),GG=en(Mo,UG),HG={kernelName:Mo,backendName:"cpu",kernelFunc:GG},jG=it(Do,e=>Math.atanh(e)),qG={kernelName:Do,backendName:"cpu",kernelFunc:jG};function xv(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],x=r.outShape[3];for(let v=0;v<r.batchSize;++v){let N=v*y,T=v*a[0];for(let E=0;E<r.inChannels;++E)for(let A=0;A<r.outHeight;++A){let $=A*i-d,O=Math.max(0,$),V=Math.min(r.inHeight,u+$),W=N+A*b;for(let H=0;H<r.outWidth;++H){let X=H*o-h,q=Math.max(0,X),K=Math.min(r.inWidth,p+X),J=m,te=0,Q=0;for(let re=O;re<V;re+=l){let ae=T+re*a[1];for(let oe=q;oe<K;oe+=c){let he=ae+oe*a[2],ce=e[he+E];s==="max"&&ce>J?J=ce:s==="avg"&&(te+=ce,Q++)}if(isNaN(J))break}let ie=W+H*x+E;g[ie]=s==="avg"?te/Q:J}}}return f}function AT(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 x=b*o-h,v=x;for(;v<0;)v+=c;let N=Math.min(a.inHeight,p+x);for(let T=0;T<a.outWidth;++T){let E=T*l-m,A=E;for(;A<0;)A+=u;let $=Math.min(a.inWidth,d+E),O=Number.NEGATIVE_INFINITY,V=-1;for(let W=v;W<N;W+=c){let H=W-x;for(let X=A;X<$;X+=u){let q=X-E,K=f.get(g,W,X,y);K>O&&(O=K,r?V=s?((g*a.inHeight+W)*a.inWidth+X)*a.inChannels+y:(W*a.inWidth+X)*a.inChannels+y:V=H*d+q)}}i.set(V,g,b,T,y)}}return i}function $T(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,x=Le(r.outShape,n),v=x.values,N=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],E=r.outShape[3]*r.outShape[4],A=r.outShape[4];for(let $=0;$<r.batchSize;++$){let O=$*N,V=$*a[0];for(let W=0;W<r.inChannels;++W)for(let H=0;H<r.outDepth;++H){let X=H*i-f,q=X;for(;q<0;)q+=c;let K=Math.min(r.inDepth,d+X),J=O+H*T;for(let te=0;te<r.outHeight;++te){let Q=te*o-g,ie=Q;for(;ie<0;)ie+=u;let re=Math.min(r.inHeight,h+Q),ae=J+te*E;for(let oe=0;oe<r.outWidth;++oe){let he=oe*l-y,ce=he;for(;ce<0;)ce+=p;let ge=Math.min(r.inWidth,m+he),be=ae+oe*A,Ie=b,Te=0,Ee=0;for(let Je=q;Je<K;Je+=c){let nt=V+Je*a[1];for(let Ve=ie;Ve<re;Ve+=u){let ut=nt+Ve*a[2];for(let Ge=ce;Ge<ge;Ge+=p){let ct=ut+Ge*a[3],ft=e[ct+W];if(s==="max"&&ft>Ie?Ie=ft:s==="avg"&&(Te+=ft,Ee++),isNaN(Ie))break}if(isNaN(Ie))break}if(isNaN(Ie))break}let Me=be+W;v[Me]=s==="avg"?Te/Ee:Ie}}}}return x}function KG(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,x=b;for(;x<0;)x+=i;let v=Math.min(t.inDepth,c+b);for(let N=0;N<t.outHeight;++N){let T=N*r-h,E=T;for(;E<0;)E+=o;let A=Math.min(t.inHeight,u+T);for(let $=0;$<t.outWidth;++$){let O=$*s-m,V=O;for(;V<0;)V+=l;let W=Math.min(t.inWidth,p+O),H=Number.NEGATIVE_INFINITY,X=-1;for(let q=x;q<v;q+=i){let K=q-b;for(let J=E;J<A;J+=o){let te=J-T;for(let Q=V;Q<W;Q+=l){let ie=Q-O,re=e.get(f,q,J,Q,g);re>=H&&(H=re,X=K*u*p+te*u+ie)}}}n.set(X,f,y,N,$,g)}}}return n}function XG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ke(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l),p;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))p=ar({inputs:{x:r},backend:n});else{let d=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=xv(d,r.shape,r.dtype,h,u,"avg");p=n.makeTensorInfo(u.outShape,r.dtype,m.values)}return p}var YG={kernelName:As,backendName:"cpu",kernelFunc:XG};function JG(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;ke(r,"avgPool3d");let p=u;p==null&&(p=[1,1,1]);let d=_.computePool3DInfo(r.shape,s,i,p,o,l,c),h=n.data.get(r.dataId).values,m=$T(h,r.shape,r.dtype,k.computeStrides(r.shape),d,"avg");return n.makeTensorInfo(m.shape,"float32",m.values)}var ZG={kernelName:ic,backendName:"cpu",kernelFunc:JG};function QG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dilations:c,dimRoundingMode:u}=a;ke([r,s],"avgPool3DGrad");let p=_.computePool3DInfo(s.shape,i,o,c,l,u),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=p.dilationDepth,x=p.dilationHeight,v=p.dilationWidth,N=p.effectiveFilterDepth,T=p.effectiveFilterHeight,E=p.effectiveFilterWidth,A=N-1-p.padInfo.front,$=E-1-p.padInfo.left,O=T-1-p.padInfo.top,V=Le(s.shape,"float32"),W=1/(f*g*y),H=n.bufferSync(r);for(let X=0;X<p.batchSize;++X)for(let q=0;q<p.inChannels;++q)for(let K=0;K<p.inDepth;++K)for(let J=0;J<p.inHeight;++J)for(let te=0;te<p.inWidth;++te){let Q=K-A,ie=J-O,re=te-$,ae=0;for(let oe=0;oe<N;oe+=b){let he=(Q+oe)/d;if(!(he<0||he>=p.outDepth||Math.floor(he)!==he))for(let ce=0;ce<T;ce+=x){let ge=(ie+ce)/h;if(!(ge<0||ge>=p.outHeight||Math.floor(ge)!==ge))for(let be=0;be<E;be+=v){let Ie=(re+be)/m;Ie<0||Ie>=p.outWidth||Math.floor(Ie)!==Ie||(ae+=H.get(X,he,ge,Ie,q))}}}V.set(ae*W,X,K,J,te,q)}return n.makeTensorInfo(V.shape,V.dtype,V.values)}var eH={kernelName:Id,backendName:"cpu",kernelFunc:QG};function tH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ke([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.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,x=b-1-u.padInfo.left,v=y-1-u.padInfo.top,N=Le(i.shape,"float32"),T=1/(h*m),E=n.data.get(r.dataId).values,A=Le(r.shape,"float32",E);for(let $=0;$<u.batchSize;++$)for(let O=0;O<u.inChannels;++O)for(let V=0;V<u.inHeight;++V)for(let W=0;W<u.inWidth;++W){let H=V-v,X=W-x,q=0;for(let K=0;K<y;K+=f){let J=(H+K)/p;if(!(J<0||J>=u.outHeight||Math.floor(J)!==J))for(let te=0;te<b;te+=g){let Q=(X+te)/d;Q<0||Q>=u.outWidth||Math.floor(Q)!==Q||(q+=A.get($,J,Q,O))}}N.set(q*T,$,V,W,O)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var nH={kernelName:kd,backendName:"cpu",kernelFunc:tH};function aH(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."),ke([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,x=p.length,v=0,N=0,T=0,E=0;for(let A=0;A<u.length;++A)f[A]=m[v++]+(u[A]-p[N++])*h[T++]/Math.sqrt(d[E++]+c),v>=g&&(v=0),N>=x&&(N=0),T>=y&&(T=0),E>=b&&(E=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var rH={kernelName:Us,backendName:"cpu",kernelFunc:aH};function sH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ke([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=It({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ga({inputs:{x:h},backend:n,attrs:{perm:c}}),f=It({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Gi({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var iH={kernelName:oc,backendName:"cpu",kernelFunc:sH};function oH(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=cv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var lH={kernelName:Nd,backendName:"cpu",kernelFunc:oH},uH=it(Gr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),cH={kernelName:Gr,backendName:"cpu",kernelFunc:uH},pH=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")},dH={kernelName:lc,backendName:"cpu",kernelFunc:pH};function ru(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 hH={kernelName:Ld,backendName:"cpu",kernelFunc:ru};function su(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return ar({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(_.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>Ui({inputs:{input:v},backend:n})),g=o.map(v=>ru({inputs:{input:v},backend:n})),y=su({inputs:f,backend:n,attrs:{axis:s}}),b=su({inputs:g,backend:n,attrs:{axis:s}}),x=Bn({inputs:{real:y,imag:b},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),x}let c=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return It({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=_.computeOutShape(c.map(f=>f.shape),1);let p=c[0].shape[0]===1,d=pv(u,i,t[0].dtype,p),h=_.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,d);return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var mH={kernelName:Po,backendName:"cpu",kernelFunc:su};function DT(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;ke([r,s],"conv2d");let p=_.convertConv2DDataFormat(l),d=_.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,x=d.dataFormat==="channelsLast",v=new Bt(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),E=N[0],A=x?N[1]:N[2],$=x?N[2]:1,O=x?1:N[1],V=v.strides[0],W=x?v.strides[1]:v.strides[2],H=x?v.strides[2]:1,X=x?1:v.strides[1],q=n.data.get(r.dataId).values,K=n.data.get(s.dataId).values,J=v.values;for(let te=0;te<d.batchSize;++te){let Q=te*E,ie=te*V;for(let re=0;re<d.outHeight;++re){let ae=ie+re*W,oe=re*d.strideHeight-b;for(let he=0;he<h;++he){let ce=oe+he*f;if(ce<0||ce>=d.inHeight)continue;let ge=he*T[0],be=Q+ce*A;for(let Ie=0;Ie<d.outWidth;++Ie){let Te=ae+Ie*H,Ee=Ie*d.strideWidth-y;for(let Me=0;Me<m;++Me){let Je=Ee+Me*g;if(Je<0||Je>=d.inWidth)continue;let nt=ge+Me*T[1],Ve=be+Je*$,ut=nt;for(let Ge=0;Ge<d.inChannels;++Ge){let ct=q[Ve+Ge*O];for(let ft=0;ft<d.outChannels;++ft)J[Te+ft*X]+=ct*K[ut+ft];ut+=d.outChannels}}}}}}return n.makeTensorInfo(v.shape,v.dtype,J)}var fH={kernelName:Ms,backendName:"cpu",kernelFunc:DT};function gH(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;ke([r,s],"conv2dBackpropFilter");let p=_.convertConv2DDataFormat(l),d=_.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 Bt(d.filterShape,"float32"),x=d.padInfo.left,v=d.padInfo.top,N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,E=new Bt(r.shape,r.dtype,N),A=new Bt(s.shape,s.dtype,T);for(let $=0;$<f;++$){let O=Math.max(0,Math.ceil((v-$)/h)),V=Math.min(d.outHeight,(d.inHeight+v-$)/h);for(let W=0;W<g;++W){let H=Math.max(0,Math.ceil((x-W)/m)),X=Math.min(d.outWidth,(d.inWidth+x-W)/m);for(let q=0;q<d.inChannels;++q)for(let K=0;K<d.outChannels;++K){let J=0;for(let te=0;te<d.batchSize;++te)for(let Q=O;Q<V;++Q){let ie=$+Q*h-v;for(let re=H;re<X;++re){let ae=W+re*m-x;y?J+=E.get(te,ie,ae,q)*A.get(te,Q,re,K):J+=E.get(te,q,ie,ae)*A.get(te,K,Q,re)}}b.set(J,$,W,q,K)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var yH={kernelName:Sd,backendName:"cpu",kernelFunc:gH};function bH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a;ke([r,s],"conv2dBackpropInput");let p=k.computeStrides(s.shape),d=k.computeStrides(r.shape),h=_.convertConv2DDataFormat(c),m=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),f=new Bt(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,b=n.data.get(s.dataId).values,[x,v,N]=p,{batchSize:T,filterHeight:E,filterWidth:A,inChannels:$,inHeight:O,inWidth:V,outChannels:W,outHeight:H,outWidth:X,strideHeight:q,strideWidth:K}=m;h=m.dataFormat;let J=E-1-m.padInfo.top,te=A-1-m.padInfo.left,Q=h==="channelsLast",ie=f.strides[0],re=Q?f.strides[1]:f.strides[2],ae=Q?f.strides[2]:1,oe=Q?1:f.strides[1],he=d[0],ce=Q?d[1]:d[2],ge=Q?d[2]:1,be=Q?1:d[1];for(let Ie=0;Ie<T;++Ie)for(let Te=0;Te<$;++Te)for(let Ee=0;Ee<O;++Ee){let Me=Ee-J,Je=Math.max(0,Math.ceil(Me/q)),nt=Math.min(H,(E+Me)/q);for(let Ve=0;Ve<V;++Ve){let ut=Ve-te,Ge=Math.max(0,Math.ceil(ut/K)),ct=Math.min(X,(A+ut)/K),ft=0;for(let _t=Je;_t<nt;++_t){let Nt=_t*q-Me;for(let nn=Ge;nn<ct;++nn){let wn=nn*K-ut,ia=he*Ie+ce*_t+ge*nn,An=x*(E-1-Nt)+v*(A-1-wn)+N*Te;for(let cn=0;cn<W;++cn){let Hn=y[ia+be*cn],ur=b[An+cn];ft+=Hn*ur}}}let Fn=ie*Ie+re*Ee+ae*Ve+oe*Te;g[Fn]=ft}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var xH={kernelName:Rs,backendName:"cpu",kernelFunc:bH};function vH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;ke([r,s],"conv3d");let c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,x=g.top,v=new Bt(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,E=v.values,A=k.computeStrides(r.shape),$=k.computeStrides(s.shape);for(let O=0;O<c.batchSize;++O){let V=O*A[0],W=O*v.strides[0];for(let H=0;H<c.outDepth;++H){let X=W+H*v.strides[1],q=H*c.strideDepth-y;for(let K=0;K<u;++K){let J=q+K*h;if(J<0||J>=c.inDepth)continue;let te=K*$[0],Q=V+J*A[1];for(let ie=0;ie<c.outHeight;++ie){let re=X+ie*v.strides[2],ae=ie*c.strideHeight-x;for(let oe=0;oe<p;++oe){let he=ae+oe*m;if(he<0||he>=c.inHeight)continue;let ce=te+oe*$[1],ge=Q+he*A[2];for(let be=0;be<c.outWidth;++be){let Ie=re+be*c.outChannels,Te=be*c.strideWidth-b;for(let Ee=0;Ee<d;++Ee){let Me=Te+Ee*f;if(Me<0||Me>=c.inWidth)continue;let Je=ce+Ee*$[2],nt=ge+Me*c.inChannels,Ve=Je;for(let ut=0;ut<c.inChannels;++ut){let Ge=N[nt+ut];for(let ct=0;ct<c.outChannels;++ct)E[Ie+ct]+=Ge*T[Ve+ct];Ve+=c.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var wH={kernelName:uc,backendName:"cpu",kernelFunc:vH};function kH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;ke([r,s],"conv3dBackpropFilterV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(r.shape,l,i,1,o),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=new Bt(p.filterShape,"float32"),x=b.values,[v,N,T,E]=b.strides,A=n.data.get(s.dataId).values,[$,O,V,W]=u,H=n.data.get(r.dataId).values,[X,q,K,J]=c,te=p.padInfo.front,Q=p.padInfo.left,ie=p.padInfo.top;for(let re=0;re<f;++re){let ae=Math.max(0,Math.ceil((te-re)/d)),oe=Math.min(p.outDepth,(p.inDepth+te-re)/d),he=re*v;for(let ce=0;ce<g;++ce){let ge=Math.max(0,Math.ceil((ie-ce)/h)),be=Math.min(p.outHeight,(p.inHeight+ie-ce)/h),Ie=ce*N+he;for(let Te=0;Te<y;++Te){let Ee=Math.max(0,Math.ceil((Q-Te)/m)),Me=Math.min(p.outWidth,(p.inWidth+Q-Te)/m),Je=Te*T+Ie;for(let nt=0;nt<p.inChannels;++nt){let Ve=nt*E+Je;for(let ut=0;ut<p.outChannels;++ut){let Ge=0;for(let ct=0;ct<p.batchSize;++ct){let ft=ct*X,Fn=ct*$;for(let _t=ae;_t<oe;++_t){let Nt=(re+_t*d-te)*q+ft,nn=_t*O+Fn;for(let wn=ge;wn<be;++wn){let ia=(ce+wn*h-ie)*K+Nt,An=wn*V+nn;for(let cn=Ee;cn<Me;++cn){let Hn=(Te+cn*m-Q)*J+ia,ur=cn*W+An;Ge+=H[Hn+nt]*A[ur+ut]}}}}x[Ve+ut]=Ge}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var IH={kernelName:Cd,backendName:"cpu",kernelFunc:kH};function NH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;ke([r],"conv3dBackpropInputV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(l,s.shape,o,1,i),d=new Bt(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[x,v,N,T]=c,E=n.data.get(s.dataId).values,[A,$,O,V]=u,{batchSize:W,filterDepth:H,filterHeight:X,filterWidth:q,inChannels:K,inDepth:J,inHeight:te,inWidth:Q,outChannels:ie,outDepth:re,outHeight:ae,outWidth:oe,strideDepth:he,strideHeight:ce,strideWidth:ge}=p,be=H-1-p.padInfo.front,Ie=X-1-p.padInfo.top,Te=q-1-p.padInfo.left;for(let Ee=0;Ee<W;++Ee)for(let Me=0;Me<K;++Me)for(let Je=0;Je<J;++Je){let nt=Je-be,Ve=Math.max(0,Math.ceil(nt/he)),ut=Math.min(re,(H+nt)/he);for(let Ge=0;Ge<te;++Ge){let ct=Ge-Ie,ft=Math.max(0,Math.ceil(ct/ce)),Fn=Math.min(ae,(X+ct)/ce);for(let _t=0;_t<Q;++_t){let Nt=_t-Te,nn=Math.max(0,Math.ceil(Nt/ge)),wn=Math.min(oe,(q+Nt)/ge),ia=0;for(let An=Ve;An<ut;++An){let cn=An*he-nt;for(let Hn=ft;Hn<Fn;++Hn){let ur=Hn*ce-ct;for(let cr=nn;cr<wn;++cr){let wa=cr*ge-Nt,po=x*Ee+v*An+N*Hn+T*cr,ka=A*(H-1-cn)+$*(X-1-ur)+O*(q-1-wa)+V*Me;for(let Wa=0;Wa<ie;++Wa){let Ia=b[po+Wa],vs=E[ka+Wa];ia+=Ia*vs}}}}h[m*Ee+f*Je+g*Ge+y*_t+Me]=ia}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var TH={kernelName:Ed,backendName:"cpu",kernelFunc:NH},SH=it(Ps,e=>Math.cos(e)),CH={kernelName:Ps,backendName:"cpu",kernelFunc:SH},EH=it(Oo,e=>Math.cosh(e)),_H={kernelName:Oo,backendName:"cpu",kernelFunc:EH};function FH(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,[u,p,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,N=k.computeStrides(r.shape),T=k.computeStrides(y.shape);for(let E=0;E<m;E++){let A=E*4,$=b[A],O=b[A+1],V=b[A+2],W=b[A+3],H=x[E];if(H>=u)continue;let X=f>1?(V-$)*(p-1)/(f-1):0,q=g>1?(W-O)*(d-1)/(g-1):0;for(let K=0;K<f;K++){let J=f>1?$*(p-1)+K*X:.5*($+V)*(p-1);if(J<0||J>p-1){for(let te=0;te<g;te++)for(let Q=0;Q<h;Q++){let ie=Q+te*T[2]+K*T[1]+E*T[0];y.values[ie]=c}continue}if(l==="bilinear"){let te=Math.floor(J),Q=Math.ceil(J),ie=J-te;for(let re=0;re<g;re++){let ae=g>1?O*(d-1)+re*q:.5*(O+W)*(d-1);if(ae<0||ae>d-1){for(let ge=0;ge<h;ge++){let be=ge+re*T[2]+K*T[1]+E*T[0];y.values[be]=c}continue}let oe=Math.floor(ae),he=Math.ceil(ae),ce=ae-oe;for(let ge=0;ge<h;ge++){let be=ge+oe*N[2]+te*N[1]+H*N[0],Ie=v[be];be=ge+he*N[2]+te*N[1]+H*N[0];let Te=v[be];be=ge+oe*N[2]+Q*N[1]+H*N[0];let Ee=v[be];be=ge+he*N[2]+Q*N[1]+H*N[0];let Me=v[be],Je=Ie+(Te-Ie)*ce,nt=Ee+(Me-Ee)*ce;be=ge+re*T[2]+K*T[1]+E*T[0],y.values[be]=Je+(nt-Je)*ie}}}else for(let te=0;te<g;++te){let Q=g>1?O*(d-1)+te*q:.5*(O+W)*(d-1);if(Q<0||Q>d-1){for(let ae=0;ae<h;ae++){let oe=ae+te*T[2]+K*T[1]+E*T[0];y.values[oe]=c}continue}let ie=Math.round(Q),re=Math.round(J);for(let ae=0;ae<h;ae++){let oe=ae+ie*N[2]+re*N[1]+H*N[0],he=ae+te*T[2]+K*T[1]+E*T[0];y.values[he]=v[oe]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var AH={kernelName:Lo,backendName:"cpu",kernelFunc:FH};function $H(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;ke(r,"cumsum");let l=_.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=ga({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=_.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=ua(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)d[x]=i?0:h[x];else{let v=f(y,b-1);d[x]=i?h[v]+d[v]:h[x]+d[v]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=_.getUndoAxesPermutation(l),b=ga({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var DH={kernelName:Os,backendName:"cpu",kernelFunc:$H};function MH(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=cv(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=tT(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 RH={kernelName:_d,backendName:"cpu",kernelFunc:MH};function PH(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 x=Math.floor(b/s),v=b%s;for(let N=0;N<d;++N){let T=Math.floor(N/s),E=N%s,A=(v*s+E)*h;for(let $=0;$<h;++$){let O=$+A+u*(T+c*(x+l*y));f[g++]=m[O]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,f)}var OH={kernelName:zo,backendName:"cpu",kernelFunc:PH};function MT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a;ke([r,s],"depthwiseConv2DNative");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=_.computeConv2DInfo(r.shape,s.shape,i,d,o,c,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,v=b.top,N=h.outChannels/h.inChannels,T=new Bt(h.outShape,r.dtype),E=n.data.get(r.dataId).values,A=n.data.get(s.dataId).values,$=T.values;for(let O=0;O<h.batchSize;++O){let V=O*u[0],W=O*T.strides[0];for(let H=0;H<h.outHeight;++H){let X=W+H*T.strides[1],q=H*h.strideHeight-x;for(let K=0;K<m;++K){let J=q+K*g;if(J<0||J>=h.inHeight)continue;let te=K*p[0],Q=V+J*u[1];for(let ie=0;ie<h.outWidth;++ie){let re=X+ie*T.strides[2],ae=ie*h.strideWidth-v;for(let oe=0;oe<f;++oe){let he=ae+oe*y;if(he<0||he>=h.inWidth)continue;let ce=te+oe*p[1],ge=Q+he*h.inChannels,be=re,Ie=ce;for(let Te=0;Te<h.inChannels;++Te){let Ee=E[ge+Te];for(let Me=0;Me<N;++Me)$[be+Me]+=Ee*A[Ie+Me];be+=N,Ie+=N}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var LH={kernelName:Ls,backendName:"cpu",kernelFunc:MT};function zH(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;ke([r,s],"depthwiseConv2dNativeBackpropFilter");let p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Bt(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,x=p.outChannels/p.inChannels,v=n.data.get(r.dataId).values,N=new Bt(r.shape,r.dtype,v),T=n.data.get(s.dataId).values,E=new Bt(s.shape,s.dtype,T);for(let A=0;A<m;++A){let $=Math.max(0,Math.ceil((b-A)/d)),O=Math.min(p.outHeight,(p.inHeight+b-A)/d);for(let V=0;V<f;++V){let W=Math.max(0,Math.ceil((y-V)/h)),H=Math.min(p.outWidth,(p.inWidth+y-V)/h);for(let X=0;X<p.outChannels;++X){let q=Math.trunc(X/x),K=X%x,J=0;for(let te=0;te<p.batchSize;++te)for(let Q=$;Q<O;++Q){let ie=A+Q*d-b;for(let re=W;re<H;++re){let ae=V+re*h-y;J+=N.get(te,ie,ae,q)*E.get(te,Q,re,X)}}g.set(J,A,V,q,K)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var BH={kernelName:Fd,backendName:"cpu",kernelFunc:zH};function WH(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;ke([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new Bt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,x=n.data.get(r.dataId).values,[v,N,T]=p,E=n.data.get(s.dataId).values,[A,$,O]=d,{batchSize:V,filterHeight:W,filterWidth:H,inChannels:X,inHeight:q,inWidth:K,outChannels:J,outHeight:te,outWidth:Q,strideHeight:ie,strideWidth:re}=h,ae=W-1-h.padInfo.top,oe=H-1-h.padInfo.left,he=J/X;for(let ce=0;ce<V;++ce)for(let ge=0;ge<X;++ge)for(let be=0;be<q;++be){let Ie=be-ae,Te=Math.max(0,Math.ceil(Ie/ie)),Ee=Math.min(te,(W+Ie)/ie);for(let Me=0;Me<K;++Me){let Je=Me-oe,nt=Math.max(0,Math.ceil(Je/re)),Ve=Math.min(Q,(H+Je)/re),ut=0;for(let Ge=Te;Ge<Ee;++Ge){let ct=Ge*ie-Ie;for(let ft=nt;ft<Ve;++ft){let Fn=ft*re-Je,_t=v*ce+N*Ge+T*ft,Nt=A*(W-1-ct)+$*(H-1-Fn)+O*ge;for(let nn=0;nn<he;++nn){let wn=ge*he+nn,ia=x[_t+wn],An=E[Nt+nn];ut+=ia*An}}}f[g*ce+y*be+b*Me+ge]=ut}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var VH={kernelName:Ad,backendName:"cpu",kernelFunc:WH};function UH(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 GH={kernelName:$d,backendName:"cpu",kernelFunc:UH},HH={kernelName:cc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(a.dataId).values,u=a.shape.length,p=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:v,strideWidth:N,filterHeight:T,filterWidth:E,dilationHeight:A,dilationWidth:$,outShape:O}=_.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),V=k.sizeFromShape(O),W=O.length,H=k.getArrayFromDType(a.dtype,V);for(let X=0;X<h;++X)for(let q=0;q<y;++q){let K=q*v-x.top;for(let J=0;J<b;++J){let te=J*N-x.left;for(let Q=0;Q<g;++Q){let ie=Number.MIN_SAFE_INTEGER;for(let ae=0;ae<T;++ae){let oe=K+ae*A;if(oe>=0&&oe<m)for(let he=0;he<E;++he){let ce=te+he*$;if(ce>=0&&ce<f){let ge=k.locToIndex([X,oe,ce,Q],u,k.computeStrides(a.shape)),be=k.locToIndex([ae,he,Q],d,k.computeStrides(r.shape)),Ie=c[ge]+p[be];Ie>ie&&(ie=Ie)}}}let re=k.locToIndex([X,q,J,Q],W,k.computeStrides(O));H[re]=ie}}}return{dataId:l.write(k.toTypedArray(H,a.dtype),O,a.dtype),shape:O,dtype:a.dtype}}},jH={kernelName:Md,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(a.shape,c.data.get(a.dataId).values),p=k.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:v,filterHeight:N,filterWidth:T,dilationHeight:E,dilationWidth:A,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Md}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let O=k.toNestedArray($,c.data.get(s.dataId).values),V=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let W=0;W<d;++W)for(let H=0;H<g;++H){let X=H*x-b.top;for(let q=0;q<y;++q){let K=q*v-b.left;for(let J=0;J<f;++J){let te=Number.MIN_SAFE_INTEGER,Q=0,ie=0;for(let re=0;re<N;++re){let ae=X+re*E;if(ae>=0&&ae<h)for(let oe=0;oe<T;++oe){let he=K+oe*A;if(he>=0&&he<m){let ce=u[W][ae][he][J]+p[re][oe][J];ce>te&&(te=ce,Q=re,ie=oe)}}}V[Q][ie][J]+=O[W][H][q][J]}}}return{dataId:c.write(k.toTypedArray(V,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.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:x,strideWidth:v,filterHeight:N,filterWidth:T,dilationHeight:E,dilationWidth:A,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Dd}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let O=k.toNestedArray($,c.data.get(s.dataId).values),V=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let W=0;W<d;++W)for(let H=0;H<g;++H){let X=H*x-b.top;for(let q=0;q<y;++q){let K=q*v-b.left;for(let J=0;J<f;++J){let te=Number.MIN_SAFE_INTEGER,Q=X<0?0:X,ie=K<0?0:K;for(let re=0;re<N;++re){let ae=X+re*E;if(ae>=0&&ae<h)for(let oe=0;oe<T;++oe){let he=K+oe*A;if(he>=0&&he<m){let ce=u[W][ae][he][J]+p[re][oe][J];ce>te&&(te=ce,Q=ae,ie=he)}}}V[W][Q][ie][J]+=O[W][H][q][J]}}}return{dataId:c.write(k.toTypedArray(V,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function KH(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ke([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 XH={kernelName:Rd,backendName:"cpu",kernelFunc:KH},YH=Ot((e,t)=>e===t?1:0),RT=en(Vo,YH,null,"bool"),JH={kernelName:Vo,backendName:"cpu",kernelFunc:RT},ZH=_.ERF_P,QH=_.ERF_A1,e6=_.ERF_A2,t6=_.ERF_A3,n6=_.ERF_A4,a6=_.ERF_A5,r6=it(Wo,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+ZH*n);return t*(1-((((a6*a+n6)*a+t6)*a+e6)*a+QH)*a*Math.exp(-n*n))}),s6={kernelName:Wo,backendName:"cpu",kernelFunc:r6};function Dm(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),It({inputs:{x:r},backend:n,attrs:{shape:o}})}var i6={kernelName:Uo,backendName:"cpu",kernelFunc:Dm},o6=Ot((e,t)=>e/t),vv=en(zs,o6),wv={kernelName:zs,backendName:"cpu",kernelFunc:vv};function PT(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=Gi({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,s]}}),b=Gi({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,s]}}),x=Bn({inputs:{real:y,imag:b},backend:n}),{real:v,imag:N}=l6(x,t,n),T=_.mergeRealAndImagArrays(v,N);for(let E=0;E<s;E++){let A=_.getComplexWithIndex(T,E);p[g*s+E]=A.real,d[g*s+E]=A.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(x)}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 l6(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(u6(a)){let o=kv(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=ar({inputs:{x:p},backend:n}),h=wv.kernelFunc({inputs:{a:c,b:p},backend:n}),m=wv.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=_.mergeRealAndImagArrays(s,i),l=c6(o,a,t);return _.splitRealAndImagArrays(l)}}function u6(e){return(e&e-1)==0}function kv(e,t,n,a,r){if(n===1)return{real:e,imag:t};let s=_.mergeRealAndImagArrays(e,t),i=n/2,o=_.complexWithEvenIndex(s),l=o.real,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=_.complexWithOddIndex(s),f=m.real,g=m.imag,y=[f.length],b=r.makeTensorInfo(y,"float32",f),x=r.makeTensorInfo(y,"float32",g),v=Bn({inputs:{real:b,imag:x},backend:r}),N=kv(l,c,i,a,r),T=N.real,E=N.imag,A=[T.length],$=r.makeTensorInfo(A,"float32",T),O=r.makeTensorInfo(A,"float32",E),V=Bn({inputs:{real:$,imag:O},backend:r}),W=kv(f,g,i,a,r),H=W.real,X=W.imag,q=[H.length],K=r.makeTensorInfo(q,"float32",H),J=r.makeTensorInfo(q,"float32",X),te=Bn({inputs:{real:K,imag:J},backend:r}),Q=_.exponents(n,a),ie=[Q.real.length],re=r.makeTensorInfo(ie,"float32",Q.real),ae=r.makeTensorInfo(ie,"float32",Q.imag),oe=Bn({inputs:{real:re,imag:ae},backend:r}),he=gv({inputs:{a:oe,b:te},backend:r}),ce=mp({inputs:{a:V,b:he},backend:r}),ge=yv({inputs:{a:V,b:he},backend:r}),be=Ui({inputs:{input:ce},backend:r}),Ie=Ui({inputs:{input:ge},backend:r}),Te=ru({inputs:{input:ce},backend:r}),Ee=ru({inputs:{input:ge},backend:r}),Me=su({inputs:[be,Ie],backend:r,attrs:{axis:0}}),Je=su({inputs:[Te,Ee],backend:r,attrs:{axis:0}}),nt=r.data.get(Me.dataId).values,Ve=r.data.get(Je.dataId).values;return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo($),r.disposeIntermediateTensorInfo(O),r.disposeIntermediateTensorInfo(V),r.disposeIntermediateTensorInfo(K),r.disposeIntermediateTensorInfo(J),r.disposeIntermediateTensorInfo(te),r.disposeIntermediateTensorInfo(re),r.disposeIntermediateTensorInfo(ae),r.disposeIntermediateTensorInfo(oe),r.disposeIntermediateTensorInfo(he),r.disposeIntermediateTensorInfo(ce),r.disposeIntermediateTensorInfo(ge),r.disposeIntermediateTensorInfo(be),r.disposeIntermediateTensorInfo(Te),r.disposeIntermediateTensorInfo(Ie),r.disposeIntermediateTensorInfo(Ee),r.disposeIntermediateTensorInfo(Me),r.disposeIntermediateTensorInfo(Je),{real:nt,imag:Ve}}function c6(e,t,n){let a=new Float32Array(t*2);for(let r=0;r<t;r++){let s=0,i=0;for(let o=0;o<t;o++){let l=_.exponent(r*o,t,n),c=_.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),_.assignToTypedArray(a,s,i,r)}return a}function p6(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=It({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=PT(o,!1,n),c=It({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var d6={kernelName:Pd,backendName:"cpu",kernelFunc:p6};function Iv(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 h6(o,r,i),t.makeTensorInfo(a,i,o)}var m6={kernelName:pc,backendName:"cpu",kernelFunc:Iv};function h6(e,t,n){e.fill(t)}var f6={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],x=Math.round(l-b),v=d+m+g+y,N=u[v];if(x>=0&&x<l){let T=x*c,E=d+m+T+y;N=u[E]}s[v]=N}}}}return{dataId:r.write(s,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},g6=Ot((e,t)=>Math.floor(e/t)),y6=en(Vs,g6,null,"int32"),b6={kernelName:Vs,backendName:"cpu",kernelFunc:y6};function x6(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=DT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d}});if(i){let g=f;f=mp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=bv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var v6={kernelName:vi,backendName:"cpu",kernelFunc:x6};function w6(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=mp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=bv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var k6={kernelName:wi,backendName:"cpu",kernelFunc:w6};function I6(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]=_.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 x=h[f*o+b];y+=x*p[b],g.push(x)}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 N6={kernelName:qo,backendName:"cpu",kernelFunc:I6};function T6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;ke([r,s],"gatherV2");let l=o;o==null&&(l=0);let c=k.sizeFromShape(s.shape),u=k.parseAxisParam(i,r.shape)[0],p=_.segment_util.collectGatherOpShapeInfo(r,s,u,l),d=It({inputs:{x:r},backend:n,attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]}}),h=It({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=iT(g,f,m);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(p.outputShape,y.dtype,y.values)}var S6={kernelName:jo,backendName:"cpu",kernelFunc:T6},C6=Ot((e,t)=>e>=t?1:0),E6=en(Gs,C6,null,"bool"),_6={kernelName:Gs,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=It({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=PT(o,!0,n),c=It({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var A6={kernelName:Od,backendName:"cpu",kernelFunc:F6},$6=it(Yo,e=>Number.isFinite(e)?1:0,"bool"),D6={kernelName:Yo,backendName:"cpu",kernelFunc:$6},M6=it(Jo,e=>Math.abs(e)===Infinity?1:0,"bool"),R6={kernelName:Jo,backendName:"cpu",kernelFunc:M6},P6=it(Zo,e=>Number.isNaN(e)?1:0,"bool"),O6={kernelName:Zo,backendName:"cpu",kernelFunc:P6},L6=Ot((e,t)=>e<=t?1:0),z6=en(el,L6,null,"bool"),B6={kernelName:el,backendName:"cpu",kernelFunc:z6};function W6(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=uT(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var V6={kernelName:zd,backendName:"cpu",kernelFunc:W6},U6=it(tl,e=>Math.log1p(e)),G6={kernelName:tl,backendName:"cpu",kernelFunc:U6},H6=Ot((e,t)=>e&&t),j6=en(nl,H6,null,"bool"),q6={kernelName:nl,backendName:"cpu",kernelFunc:j6},K6=it(dc,e=>e?0:1,"bool"),X6={kernelName:dc,backendName:"cpu",kernelFunc:K6},Y6=Ot((e,t)=>e||t),J6=en(hc,Y6,null,"bool"),Z6={kernelName:hc,backendName:"cpu",kernelFunc:J6};function Q6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ke(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),x=0;for(;y<=b;y++){let v=p[y];x+=v*v}return x}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 ej={kernelName:mc,backendName:"cpu",kernelFunc:Q6};function tj(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;ke(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 x=b%d,v=b-x+Math.max(0,x-o),N=b-x+Math.min(d,x+o+1),T=0;for(let E=v;E<N;E++)T+=Math.pow(m[E],2);T=c*T+l;for(let E=v;E<N;E++){let A=-2*c*u*m[E]*f[b]/T;b===E&&(A+=Math.pow(T,-u)),A*=h[b],g[E]+=A}}return n.makeTensorInfo(i.shape,r.dtype,g)}var nj={kernelName:Bd,backendName:"cpu",kernelFunc:tj};function OT(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=_.getAxesPermutation(p,c),h=o.data.get(r.dataId).values;if(d!=null){let v=new Array(c);for(let N=0;N<v.length;N++)v[N]=l[d[N]];h=hv(h,l,r.dtype,d,v),p=_.getInnerMostAxes(p.length,c),l=v}ke(r,"max"),_.assertAxesAreInnerMostDims("max",p,c);let[m,f]=_.computeOutAndReduceShapes(l,p),g=k.sizeFromShape(f),y=pT(h,g,m,r.dtype),b=o.write(y,m,r.dtype),x=m;return i&&(x=_.expandShapeToKeepDim(m,u)),{dataId:b,shape:x,dtype:r.dtype}}var aj={kernelName:qs,backendName:"cpu",kernelFunc:OT};function rj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ke(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l),p;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))p=ar({inputs:{x:r},backend:n});else{let d=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=xv(d,r.shape,r.dtype,h,u,"max");p=n.makeTensorInfo(u.outShape,r.dtype,m.values)}return p}var sj={kernelName:Xs,backendName:"cpu",kernelFunc:rj};function ij(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;ke(r,"maxPool3d");let p=u;p==null&&(p=[1,1,1]);let d=_.computePool3DInfo(r.shape,s,i,p,o,l,c),h=n.data.get(r.dataId).values,m=$T(h,r.shape,r.dtype,k.computeStrides(r.shape),d,"max");return n.makeTensorInfo(m.shape,"float32",m.values)}var oj={kernelName:fc,backendName:"cpu",kernelFunc:ij};function lj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dilations:c,dimRoundingMode:u}=a;ke([r,s],"maxPool3DGrad");let p=_.computePool3DInfo(s.shape,i,o,c,l,u),d=n.bufferSync(s),h=KG(d,p),m=p.strideDepth,f=p.strideHeight,g=p.strideWidth,y=p.dilationDepth,b=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,N=p.effectiveFilterHeight,T=p.effectiveFilterWidth,E=v-1-p.padInfo.front,A=T-1-p.padInfo.left,$=N-1-p.padInfo.top,O=Le(s.shape,"float32"),V=n.bufferSync(r);for(let W=0;W<p.batchSize;++W)for(let H=0;H<p.inChannels;++H)for(let X=0;X<p.inDepth;++X)for(let q=0;q<p.inHeight;++q)for(let K=0;K<p.inWidth;++K){let J=X-E,te=q-$,Q=K-A,ie=0;for(let re=0;re<v;re+=y){let ae=(J+re)/m;if(!(ae<0||ae>=p.outDepth||Math.floor(ae)!==ae))for(let oe=0;oe<N;oe+=b){let he=(te+oe)/f;if(!(he<0||he>=p.outHeight||Math.floor(he)!==he))for(let ce=0;ce<T;ce+=x){let ge=(Q+ce)/g;if(ge<0||ge>=p.outWidth||Math.floor(ge)!==ge)continue;let be=v*N*T-1-h.get(W,ae,he,ge,H),Ie=re*N*T+oe*T+ce,Te=be===Ie?1:0;Te!==0&&(ie+=V.get(W,ae,he,ge,H)*Te)}}}O.set(ie,W,X,q,K,H)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var uj={kernelName:Vd,backendName:"cpu",kernelFunc:lj};function cj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ke([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=n.data.get(o.dataId).values,m=Le(d.outShape,o.dtype,AT(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,b=d.dilationWidth,x=d.effectiveFilterHeight,v=d.effectiveFilterWidth,N=v-1-d.padInfo.left,T=x-1-d.padInfo.top,E=Le(o.shape,"float32"),A=n.data.get(r.dataId).values,$=Le(r.shape,"float32",A);for(let O=0;O<d.batchSize;++O)for(let V=0;V<d.inChannels;++V)for(let W=0;W<d.inHeight;++W)for(let H=0;H<d.inWidth;++H){let X=W-T,q=H-N,K=0;for(let J=0;J<x;J+=y){let te=(X+J)/f;if(!(te<0||te>=d.outHeight||Math.floor(te)!==te))for(let Q=0;Q<v;Q+=b){let ie=(q+Q)/g;if(ie<0||ie>=d.outWidth||Math.floor(ie)!==ie)continue;let re=x*v-1-m.get(O,te,ie,V),ae=J*v+Q,oe=re===ae?1:0;oe!==0&&(K+=$.get(O,te,ie,V)*oe)}}E.set(K,O,W,H,V)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var pj={kernelName:Wd,backendName:"cpu",kernelFunc:cj};function dj(e,t,n,a,r){let s=k.computeStrides(t),i=xv(e,t,n,s,r,"max"),o=AT(e,t,n,r,!0,a);return[i.values,o.values]}var hj={kernelName:Ud,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ke(a,"MaxPoolWithArgmax");let c=l.data.get(a.dataId).values,u=_.computePool2DInfo(a.shape,r,s,[1,1],i),[p,d]=dj(c,a.shape,a.dtype,o,u),h=l.write(p,u.outShape,a.dtype),m=l.write(d,u.outShape,a.dtype);return[{dataId:h,shape:u.outShape,dtype:a.dtype},{dataId:m,shape:u.outShape,dtype:"int32"}]}};function Mm(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ke(r,"sum");let o;r.dtype==="bool"?o=ds({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=ar({inputs:{x:r},backend:n});let l=o.shape.length,c=k.parseAxisParam(s,o.shape),u=_.getAxesPermutation(c,l),p=c,d=o;u!=null&&(d=ga({inputs:{x:o},backend:n,attrs:{perm:u}}),p=_.getInnerMostAxes(p.length,l)),_.assertAxesAreInnerMostDims("sum",p,d.shape.length);let[h,m]=_.computeOutAndReduceShapes(d.shape,p),f=_.upcastType(d.dtype,"int32"),g=$m(n,h,f),y=k.sizeFromShape(m),b=n.data.get(g.dataId).values,x=n.data.get(d.dataId).values;for(let v=0;v<b.length;++v){let N=v*y,T=0;for(let E=0;E<y;++E)T+=x[N+E];b[v]=T}if(i){let v=_.expandShapeToKeepDim(g.shape,c),N=g;g=It({inputs:{x:g},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(N)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),g}var mj={kernelName:hi,backendName:"cpu",kernelFunc:Mm};function fj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),l=_.computeOutAndReduceShapes(r.shape,o)[1],c=k.sizeFromShape(l),u=[],p=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(p);let d=ds({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});u.push(d);let h=vv({inputs:{a:d,b:p},backend:n});u.push(h);let m=Mm({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var gj={kernelName:Ys,backendName:"cpu",kernelFunc:fj};function yj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ke(r,"min");let o=k.parseAxisParam(s,r.shape),l=o,c=_.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ga({inputs:{x:r},backend:n,attrs:{perm:c}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",l,u.shape.length);let[p,d]=_.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,x=f[b];for(let v=0;v<h;++v){let N=f[b+v];N<x&&(x=N)}m[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let y=_.expandShapeToKeepDim(p,o),b=It({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var bj={kernelName:Js,backendName:"cpu",kernelFunc:yj};function xj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,mode:i}=a;ke(r,"mirrorPad");let o=s.map((b,x)=>b[0]+r.shape[x]+b[1]),l=s.map(b=>b[0]),c=s.map((b,x)=>b[0]+r.shape[x]),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 x=k.indexToLoc(b,f,g);for(let N=0;N<f;N++)x[N]<l[N]?x[N]=l[N]*2-x[N]-u:x[N]>=c[N]&&(x[N]=(c[N]-1)*2-x[N]+u);x=x.map((N,T)=>N-l[T]);let v=k.locToIndex(x,d,h);y[b]=p[v]}return{dataId:n.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var vj={kernelName:gc,backendName:"cpu",kernelFunc:xj},wj=Ot((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),kj=en(al,wj),Ij={kernelName:al,backendName:"cpu",kernelFunc:kj},Nj=No(fd());function LT(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=OT({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=_.expandShapeToKeepDim(c.shape,l),p=It({inputs:{x:c},backend:n,attrs:{shape:u}}),d=yv({inputs:{a:r,b:p},backend:n}),h=NT({inputs:{x:d},backend:n}),m=Mm({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=It({inputs:{x:m},backend:n,attrs:{shape:u}}),g=vv({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 Tj={kernelName:mi,backendName:"cpu",kernelFunc:LT};function Sj(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;ke(r,"multinomial");let l=o?r:LT({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 x=1;x<g.length;++x)g[x]=g[x-1]+p[f+x];let y=Nj.alea(i.toString()),b=m*s;for(let x=0;x<s;++x){let v=y();h[b+x]=g.length;for(let N=0;N<g.length;N++)if(v<g[N]){h[b+x]=N;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",h)}var Cj={kernelName:Gd,backendName:"cpu",kernelFunc:Sj},Ej=Ja.nonMaxSuppressionV3Impl;function _j(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a;ke(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:_j},Aj=Ja.nonMaxSuppressionV4Impl;function $j(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a;ke(r,"NonMaxSuppressionPadded");let u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:h}=Aj(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Dj={kernelName:ol,backendName:"cpu",kernelFunc:$j},Mj=Ja.nonMaxSuppressionV5Impl;function Rj(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a;ke(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}=Mj(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Pj={kernelName:ll,backendName:"cpu",kernelFunc:Rj};function Oj(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;ke(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 Lj={kernelName:ei,backendName:"cpu",kernelFunc:Oj};function Rm(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=Ui({inputs:{input:a},backend:n}),s=Rm({inputs:{x:r},backend:n}),i=ru({inputs:{input:a},backend:n}),o=Rm({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 Iv({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var zj={kernelName:Sl,backendName:"cpu",kernelFunc:Rm};function zT(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=Ui({inputs:{input:a},backend:n}),s=zT({inputs:{x:r},backend:n}),i=ru({inputs:{input:a},backend:n}),o=Rm({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 Iv({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var Bj={kernelName:ul,backendName:"cpu",kernelFunc:zT};function BT(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Dm({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=Dm({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=su({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Wj={kernelName:cl,backendName:"cpu",kernelFunc:BT};function Vj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ke(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((v,N)=>v+l[N]),x=k.locToIndex(b,m,f);g[x]=c[y]}return{dataId:n.write(g,o,r.dtype),shape:o,dtype:r.dtype}}var WT={kernelName:ti,backendName:"cpu",kernelFunc:Vj},Uj=Ot((e,t)=>Math.pow(e,t)),Gj=en(ni,Uj),Hj={kernelName:ni,backendName:"cpu",kernelFunc:Gj};function jj(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=mv(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var qj={kernelName:yc,backendName:"cpu",kernelFunc:jj},Kj=it(dl,e=>1/e),Xj={kernelName:dl,backendName:"cpu",kernelFunc:Kj};function Yj(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ke(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],x=0,v=y[0]/b[0],N=y[1]/b[1];for(let T=0;T<p;T++)for(let E=0;E<c;E++){let A;i?A=v*(E+.5)-.5:A=v*E;let $=Math.max(0,Math.floor(A)),O=A-$,V=Math.min(d-1,Math.ceil(A)),W=T*l[0]+$*l[1],H=T*l[0]+V*l[1];for(let X=0;X<u;X++){let q;i?q=N*(X+.5)-.5:q=N*X;let K=Math.max(0,Math.floor(q)),J=q-K,te=Math.min(h-1,Math.ceil(q)),Q=W+K*l[2],ie=H+K*l[2],re=W+te*l[2],ae=H+te*l[2];for(let oe=0;oe<m;oe++){let he=f[Q+oe],ce=f[ie+oe],ge=f[re+oe],be=f[ae+oe],Ie=he+(ge-he)*J,Te=ce+(be-ce)*J,Ee=Ie+(Te-Ie)*O;g[x++]=Ee}}}return n.makeTensorInfo([p,c,u,m],"float32",g)}var Jj={kernelName:si,backendName:"cpu",kernelFunc:Yj};function Zj(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ke([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,c,u,p]=r.shape,[,d,h]=s.shape,m=new Float32Array(l*c*u*p),f=[i&&d>1?c-1:c,i&&h>1?u-1:u],g=[i&&d>1?d-1:d,i&&h>1?h-1:h],y=f[0]/g[0],b=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let N=0;N<l;N++){let T=N*o[0];for(let E=0;E<d;E++){let A=E*y,$=Math.floor(A),O=Math.min(Math.ceil(A),c-1),V=T+$*o[1],W=T+O*o[1],H=A-$,X=1-H;for(let q=0;q<h;q++){let K=q*b,J=Math.floor(K),te=Math.min(Math.ceil(K),u-1),Q=K-J,ie=1-Q,re=V+J*o[2],ae=V+te*o[2],oe=W+J*o[2],he=W+te*o[2],ce=X*ie,ge=X*Q,be=H*ie,Ie=H*Q;for(let Te=0;Te<p;Te++){let Ee=x[v++];m[re+Te]+=Ee*ce,m[ae+Te]+=Ee*ge,m[oe+Te]+=Ee*be,m[he+Te]+=Ee*Ie}}}}return n.makeTensorInfo([l,u,c,p],"float32",m)}var Qj={kernelName:qd,backendName:"cpu",kernelFunc:Zj};function e5(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ke(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],x=y[0]/b[0],v=y[1]/b[1],N=0;for(let T=0;T<p;T++){let E=T*l[0];for(let A=0;A<c;A++){let $=i?x*(A+.5):x*A,O=Math.min(d-1,s?Math.round($):Math.floor($));i&&(O=Math.max(0,O));let V=E+O*l[1];for(let W=0;W<u;W++){let H=i?v*(W+.5):v*W,X=Math.min(h-1,s?Math.round(H):Math.floor(H));i&&(X=Math.max(0,X));let q=V+X*l[2];for(let K=0;K<m;K++){let J=f[q+K];g[N++]=J}}}}return n.makeTensorInfo([p,c,u,m],r.dtype,g)}var t5={kernelName:bc,backendName:"cpu",kernelFunc:e5};function n5(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ke([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],x=y[0]/b[0],v=y[1]/b[1],N=1/x,T=1/v,E=Math.ceil(N)*2+2,A=Math.ceil(T)*2+2;for(let $=0;$<c;$++){let O=$*o[0];for(let V=0;V<u;V++){let W=O+V*o[1],H=Math.floor(V*N),X=Math.floor(H-E/2);for(let q=0;q<p;q++){let K=W+q*o[2],J=Math.floor(q*T),te=Math.floor(J-A/2);for(let Q=0;Q<d;Q++){let ie=0;for(let re=0;re<E;re++){let ae=re+X;if(ae<0||ae>=h)continue;let oe=O+ae*l[1],he=ae*x,ce=Math.min(u-1,i?Math.round(he):Math.floor(he));if(V===ce)for(let ge=0;ge<A;ge++){let be=ge+te;if(be<0||be>=m)continue;let Ie=oe+be*l[2],Te=be*v,Ee=Math.min(p-1,i?Math.round(Te):Math.floor(Te));q===Ee&&(ie+=g[Ie+Q])}}f[K+Q]=ie}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var a5={kernelName:jd,backendName:"cpu",kernelFunc:n5};function r5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;ke(r,"reverse");let i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return ar({inputs:{x:r},backend:n});let l=new Bt(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 s5={kernelName:oi,backendName:"cpu",kernelFunc:r5},i5={kernelName:El,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]=_.getImageCenter(i,u,p),f=255,g=Math.sin(r),y=Math.cos(r),b=o.data.get(a.dataId).values;for(let x=0;x<c;x++){let v=x*p*u*d;for(let N=0;N<u;N++){let T=N*(p*d);for(let E=0;E<p;E++){let A=E*d;for(let $=0;$<d;$++){let O=[c,N,E,$],V=O[2],W=O[1],H=(V-h)*y-(W-m)*g,X=(V-h)*g+(W-m)*y;H=Math.round(H+h),X=Math.round(X+m);let q=s;if(typeof s!="number"&&($===3?q=f:q=s[$]),H>=0&&H<p&&X>=0&&X<u){let J=X*(p*d),te=H*d,Q=v+J+te+$;q=b[Q]}let K=v+T+A+$;l[K]=q}}}}return{dataId:o.write(l,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},o5=it(li,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}),l5={kernelName:li,backendName:"cpu",kernelFunc:o5};function VT(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 u5(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}=_.calculateShapes(s,r,i),d=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=VT(h,m,i,p,c,l,o,u,0,d);return n.makeTensorInfo(i,f.dtype,f.values)}var c5={kernelName:ml,backendName:"cpu",kernelFunc:u5};function p5(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t;ke([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=ua(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 d5={kernelName:fl,backendName:"cpu",kernelFunc:p5},h5=_.SELU_SCALEALPHA,m5=_.SELU_SCALE,f5=it(gl,e=>e>=0?m5*e:h5*(Math.exp(e)-1)),g5={kernelName:gl,backendName:"cpu",kernelFunc:f5},y5=it(pi,e=>1/(1+Math.exp(-e))),b5={kernelName:pi,backendName:"cpu",kernelFunc:y5},x5=it(xl,e=>e<0?-1:e>0?1:0),v5={kernelName:xl,backendName:"cpu",kernelFunc:x5},w5=it(ci,e=>Math.sin(e)),k5={kernelName:ci,backendName:"cpu",kernelFunc:w5},I5=it(bl,e=>Math.sinh(e)),N5={kernelName:bl,backendName:"cpu",kernelFunc:I5},T5=11920928955078125e-23,UT=Math.log(T5)+2,S5=it(vl,e=>{let t=e>-UT,n=e<UT,a=Math.exp(e),r;return n?r=a:t?r=e:r=Math.log(1+a),r}),C5={kernelName:vl,backendName:"cpu",kernelFunc:S5};function E5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;ke([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=WT.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=_.getReshaped(c.shape,s,o,!1),p=_.getPermuted(u.length,s.length,!1),d=_.getReshapedPermuted(c.shape,s,o,!1),h=It({inputs:{x:c},backend:n,attrs:{shape:u}}),m=ga({inputs:{x:h},backend:n,attrs:{perm:p}}),f=It({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}var _5={kernelName:xc,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}=_.calculateShapes(s,r,o),h=!1,m=n.bufferSync(r),f=n.bufferSync(s),g=n.data.get(i.dataId).values[0],y=VT(m,f,o,d,u,c,l,p,g,h);return n.makeTensorInfo(o,y.dtype,y.values)}var A5={kernelName:Kd,backendName:"cpu",kernelFunc:F5};function $5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(p=>{let d=[...u];d[o]=p;let h=Gi({inputs:{x:r},backend:n,attrs:{begin:c,size:d}});return c[o]+=p,h})}var D5={kernelName:wl,backendName:"cpu",kernelFunc:$5},M5=it(di,e=>Math.sqrt(e)),R5={kernelName:di,backendName:"cpu",kernelFunc:M5},P5={kernelName:vc,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;ke(n,"square");let r=a.data.get(n.dataId).values,s=new Float32Array(r.length);for(let i=0;i<r.length;++i){let o=r[i];s[i]=o*o}return{dataId:a.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},O5=it(Cl,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),L5={kernelName:Cl,backendName:"cpu",kernelFunc:O5};function z5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a;ke(r,"stridedSlice");let{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:b}=dn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),x=It({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let T=Gi({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=It({inputs:{x:T},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(T)}else if(b.some(T=>T===0))v=n.makeTensorInfo(b,r.dtype,[]);else{let T=n.bufferSync(x),E=xT(b,T,f,m);v=n.makeTensorInfo(E.shape,E.dtype,E.values)}let N=It({inputs:{x:v},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),N}var B5={kernelName:kl,backendName:"cpu",kernelFunc:z5},W5=it(Il,e=>Math.tan(e)),V5={kernelName:Il,backendName:"cpu",kernelFunc:W5},U5=it(yi,e=>Math.tanh(e)),G5={kernelName:yi,backendName:"cpu",kernelFunc:U5};function H5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;ke(r,"tile");let i=wT(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var j5={kernelName:Hr,backendName:"cpu",kernelFunc:H5};function q5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;ke(r,"topk");let o=n.data.get(r.dataId).values,[l,c]=kT(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 K5={kernelName:Nl,backendName:"cpu",kernelFunc:q5};function X5(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;ke(s,"unique");let i=a.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:c}=IT(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([c.length],"int32",c)]}var Y5={kernelName:Xd,backendName:"cpu",kernelFunc:X5};function J5(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=Gi({inputs:{x:r},backend:n,attrs:{begin:u,size:p}});d[h]=It({inputs:{x:m},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(m)}return d}var Z5={kernelName:Tl,backendName:"cpu",kernelFunc:J5};function Q5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a;ke(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=Dm({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=RT({inputs:{a:g,b:d},backend:n}),b=ds({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=gv({inputs:{a:b,b:r},backend:n}),v=Mm({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});c.push(v),u.push(g),u.push(y),u.push(b),u.push(x),u.push(v)}let h=BT({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var eq={kernelName:wc,backendName:"cpu",kernelFunc:Q5},tq=[kG,SU,NG,SG,$U,EG,FG,$G,MG,PG,LG,BG,VG,HG,qG,YG,ZG,eH,nH,vG,rH,iH,lH,FU,MU,cH,CU,dH,mH,yH,xH,fH,IH,TH,wH,CH,_H,AH,DH,RH,OH,LH,BH,VH,GH,HH,qH,jH,wv,hG,XH,JH,s6,RU,i6,OU,d6,m6,f6,zU,b6,v6,k6,N6,S6,WU,_6,EU,A6,hH,D6,R6,O6,mG,UU,B6,V6,HU,G6,q6,X6,Z6,ej,nj,qU,sj,oj,uj,pj,hj,aj,gj,bj,XU,vj,Ij,Cj,JU,QU,Fj,Dj,Pj,tG,Lj,Bj,Wj,WT,Hj,gG,rG,qj,_U,Xj,yG,bG,xG,Jj,Qj,t5,a5,s5,i5,l5,iG,c5,d5,g5,b5,v5,k5,N5,oG,Tj,C5,_5,A5,D5,R5,P5,uG,L5,B5,pG,mj,V5,G5,j5,K5,nG,Y5,Z5,eq,zj];for(let e of tq)Ic(e);var Hi={},Nv={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function nq(e,t){Hi[e]=t}function rr(e){if(!(e in Hi)){let n=aq(e);if(n!==null)Hi[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Hi[e];return t.isContextLost()?(delete Hi[e],rr(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),Hi[e])}function rq(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 aq(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=rq(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Hi[e]},!1),e===1?t.getContext("webgl",Nv)||t.getContext("experimental-webgl",Nv):t.getContext("webgl2",Nv)}var fp;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(fp||(fp={}));var aa;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(aa||(aa={}));var rn;(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"})(rn||(rn={}));function gp(e,t){return[t,e]}function sq(e,t){return e*t}function yp(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function iu(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function iq(e,t){let[n,a]=iu(e,t);return n*a*4}function Tv(e,t){let n=e,a,r,s,i,o,l,c,u,p,d;return ee().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 Se(e,t){let n=t();return ee().getBool("DEBUG")&&oq(e),n}function oq(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+lq(e,t))}var uq=596e-10,cq=65504;function pq(e){return!!(ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||uq<Math.abs(e)&&Math.abs(e)<cq)}function lq(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 Pm(e,t){return _r(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function dq(e,t){let n=_r(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(Se(e,()=>e.shaderSource(n,t)),Se(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 mq(e,t){let n=_r(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(Se(e,()=>e.shaderSource(n,t)),Se(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw hq(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var fq=/ERROR: [0-9]+:([0-9]+):/g;function hq(e,t){let n=fq.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 ${k.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
`))}function gq(e){return _r(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function yq(e,t){if(Se(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function Sv(e,t){if(Se(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function bq(e,t){let n=_r(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Se(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Se(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function xq(e,t){let n=_r(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return Se(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),Se(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function vq(e){return _r(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function wq(e,t){let n=ee().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${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 kq(e){return _r(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function GT(e,t,n,a,r,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(Se(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),Se(e,()=>e.vertexAttribPointer(o,r,e.FLOAT,!1,s,i)),Se(e,()=>e.enableVertexAttribArray(o)),!0)}function Nq(e,t,n){Iq(e,n),Se(e,()=>e.activeTexture(e.TEXTURE0+n)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function Tq(e,t,n){return _r(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Sq(e,t,n){return e.getUniformLocation(t,n)}function Cq(e,t,n,a){Se(e,()=>Nq(e,t,a)),Se(e,()=>e.uniform1i(n,a))}function Cv(e,t,n){Se(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),Se(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function HT(e,t){Se(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),Se(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Om(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 ${t}`}}function _r(e,t,n){let a=Se(e,()=>t());if(a==null)throw new Error(n);return a}function Iq(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function ou(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function lu(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=[ou(e),...lu(e)]),t}function _q(e,t=!1){let n=ee().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=ou(e),s=2,i=2;return e.length&&([s,i]=lu(e)),a=r*(s/2)*(i/2),k.sizeToSquarishShape(a).map(o=>o*2)}return k.sizeToSquarishShape(a)}function Lm(e){return e%2==0}function zm(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||Lm(n)&&Lm(a)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Lm(e[0])&&Lm(t[0])}var _v,Fv;function Fq(e){if(_v==null){let t=rr(e);_v=t.getParameter(t.MAX_TEXTURE_SIZE)}return _v}function Aq(e){if(Fv==null){let t=rr(e);Fv=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Fv)}function $q(e){if(e===0)return 0;let t,n=rr(e);return ya(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:ya(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function ya(e,t){return e.getExtension(t)!=null}function jT(e){try{if(rr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function Dq(e){if(e===0)return!1;let t=rr(e);if(e===1){if(!ya(t,"OES_texture_float"))return!1}else if(!ya(t,"EXT_color_buffer_float"))return!1;return Av(t)}function Rq(e){if(e===0)return!1;let t=rr(e);if(e===1){if(!ya(t,"OES_texture_float")||!ya(t,"WEBGL_color_buffer_float"))return!1}else{if(ya(t,"EXT_color_buffer_float"))return Av(t);let n="EXT_color_buffer_half_float";if(ya(t,n)){let a=t.getExtension(n);return Mq(t,a)}return!1}return Av(t)}function Av(e){let t=Tv(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function Mq(e,t){let n=Tv(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function Pq(e){return e!==2?!1:rr(e).fenceSync!=null}function bp(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Fe=ee();Fe.registerFlag("HAS_WEBGL",()=>Fe.getNumber("WEBGL_VERSION")>0);Fe.registerFlag("WEBGL_VERSION",()=>jT(2)?2:jT(1)?1:0);Fe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Fe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Fe.get("WEBGL_VERSION")===2);Fe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Fe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Fe.registerFlag("WEBGL_PACK",()=>Fe.getBool("HAS_WEBGL"));Fe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_CLIP",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Fe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_REDUCE",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_CONV_IM2COL",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>Fq(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Aq(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Fe.getNumber("WEBGL_VERSION");return e===0?0:$q(e)});Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Fe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!nh.isMobile());Fe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Dq(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Fe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Fe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Fe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Rq(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Pq(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Fe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Fe.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 fn(){let e,t,n,a,r,s,i,o,l,c;return ee().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 ji(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 $v(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 qT=`
  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;
  }
`,Oq=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=fp.DENSE;let t=yp(e),n=fn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${ji(["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;
      }
    `}},Lq=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=fp.DENSE;let t=yp(e),n=fn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${ji(["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;
      }
    `}},zq=class{constructor(e){this.variableNames=["A"],this.outTexUsage=aa.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
      ${qT}

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

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},Wq=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=fn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
      ${$v(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.);
      }
    `}},Vq=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=fn(),[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=`
      ${$v(e)}

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

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

        ${i}

        ${a.output} = ${o};
      }
    `}};function Uq(e){let t=fn(),n=`${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 dq(e,n)}function Gq(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 bq(e,t)}function Hq(e){let t=new Uint16Array([0,1,2,2,1,3]);return xq(e,t)}function xp(e,t,n,a,r,s){wq(t,n);let i=vq(e),o=e.TEXTURE_2D;return Se(e,()=>e.bindTexture(o,i)),Se(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Se(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Se(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Se(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Se(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function KT(e){return e.internalFormatFloat}function jq(e,t,n,a){let[r,s]=gp(t,n);return xp(e,r,s,KT(a),a.textureFormatFloat,e.FLOAT)}function XT(e){return e.internalFormatHalfFloat}function qq(e,t,n,a){let[r,s]=gp(t,n);return xp(e,r,s,XT(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function YT(e){return e.downloadTextureFormat}function Kq(e,t,n,a){let[r,s]=gp(t,n);return xp(e,r,s,YT(a),e.RGBA,e.UNSIGNED_BYTE)}function JT(e){return e.internalFormatPackedFloat}function Xq(e,t,n,a){let[r,s]=iu(t,n);return xp(e,r,s,JT(a),e.RGBA,e.FLOAT)}function ZT(e){return e.internalFormatPackedHalfFloat}function Yq(e,t,n,a){let[r,s]=iu(t,n);return xp(e,r,s,ZT(a),e.RGBA,a.textureTypeHalfFloat)}function Jq(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Se(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),GT(e,t,"clipSpacePos",n,3,s,a)&&GT(e,t,"uv",n,2,s,r)}function Zq(e,t,n,a,r,s){Se(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),Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Qq(e,t,n){Se(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function e8(e,t,n,a){let r=e.createBuffer();Se(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Se(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Se(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Se(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function t8(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 n8(e,t,n,a){let[r,s]=gp(t,n),i=4,o=new Uint8Array(sq(t*n,i));return Se(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function a8(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(iq(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 r8(e,t,n){let a=new Float32Array(t*n*4);return Se(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var i8=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,nq(t,e)):this.gl=rr(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Pm(this.gl,r),ya(this.gl,s))this.textureHalfFloatExtension=Pm(this.gl,s);else if(ee().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),ya(this.gl,a))this.colorBufferHalfFloatExtension=Pm(this.gl,a);else if(ee().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",ya(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ya(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Gq(this.gl),this.indexBuffer=Hq(this.gl),this.framebuffer=kq(this.gl),this.textureConfig=Tv(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().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;Se(e,()=>e.finish()),Se(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Se(e,()=>e.deleteFramebuffer(this.framebuffer)),Se(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Se(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Se(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),jq(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),qq(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Kq(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Qq(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),Zq(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Yq(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Xq(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(HT(this.gl,this.framebuffer),this.outputTexture=null),Se(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>n8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return a8(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return t8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=e8(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(ee().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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>r8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=mq(t,e),a=Uq(t),r=gq(t);return Se(t,()=>t.attachShader(r,a)),Se(t,()=>t.attachShader(r,n)),yq(t,r),this.debug&&Sv(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=Jq(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Se(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Sv(this.gl,this.program),Se(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Tq(this.gl,e,t):Sq(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Se(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(),Cq(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=iu(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&&Sv(this.gl,this.program),Om(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Se(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Se(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Pm(this.gl,ee().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(ee().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(ee().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,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ee().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=s8(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(),Cv(this.gl,e,this.framebuffer),this.debug&&Om(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Cv(this.gl,this.outputTexture,this.framebuffer),this.debug&&Om(this.gl)):HT(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;Cv(a,e,this.framebuffer),this.debug&&Om(a),this.outputTexture=e,Se(a,()=>a.viewport(0,0,t,n)),Se(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Se(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 s8(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:QT}=_;function f8(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=>o8(h,t,a)).join(`
`),o=t.texShape,l=fn(),c=c8(l),u,p,d=h8(l);return t.isPacked?(u=l8(t.logicalShape,o),p=d8(l)):(u=u8(t.logicalShape,o),p=p8(l)),a&&(d+=m8),[d,c,p,s,u,i,n].join(`
`)}function uu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return g8(e);case 1:return y8(e);case 2:return b8(e);case 3:return x8(e);case 4:return v8(e);case 5:return w8(e);case 6:return k8(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function eS(e){switch(e.shapeInfo.logicalShape.length){case 0:return I8(e);case 1:return N8(e);case 2:return T8(e);case 3:return S8(e);default:return C8(e)}}function o8(e,t,n=!1){let a="";n?a+=eS(e):a+=uu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=E8(e,t):a+=_8(e,t)),a}function l8(e,t){switch(e.length){case 0:return tS();case 1:return F8(e,t);case 2:return D8(e,t);case 3:return A8(e,t);default:return $8(e,t)}}function u8(e,t){switch(e.length){case 0:return tS();case 1:return M8(e,t);case 2:return z8(e,t);case 3:return R8(e,t);case 4:return P8(e,t);case 5:return O8(e,t);case 6:return L8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function c8(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function p8(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function d8(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function h8(e){return`${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);
    }

    ${B8}
    ${W8}
    ${V8}
  `}var B8=`
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);
}
`,W8=`
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);
}
`,V8=`
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);
}
`,m8=`
  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 tS(){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 M8(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 * ${t[1]} + resTexRC.y;
    }
  `}function A8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${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 R8(e,t){let n=ji(["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 $8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
      int b${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 P8(e,t){let n=ji(["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;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function O8(e,t){let n=ji(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function L8(e,t){let n=ji(["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 D8(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 z8(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 * ${e[1]};
      return ivec2(r, c);
    }
  `}function qi(e){return`offset${e}`}function I8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=fn();return`
    vec4 ${n}() {
      return ${a.texture2D}(${t}, halfCR);
    }
  `}function g8(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=qi(t);return`
    float ${n}() {
      vec2 uv = uvFromFlat(${s}, ${i}, ${o});
      return sampleTexture(${t}, uv);
    }
  `}function N8(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=fn();return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${r[0]}, ${r[1]}, index);
      return ${s.texture2D}(${t}, uv);
    }
  `}function y8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${cu(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=qi(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 T8(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=fn();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 b8(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=pu(e,o),d=["row","col"];return`
      ${uu(p)}
      float ${a}(int row, int col) {
        return ${a}(${du(d,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${cu(e)}
      }
    `;let l=r[0],c=r[1],u=qi(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 S8(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=pu(e,p),m=["b","row","col"];return`
        ${eS(h)}
        vec4 ${a}(int b, int row, int col) {
          return ${a}(${du(m,d)});
        }
      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=fn();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 x8(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=pu(e,l),f=["row","col","depth"];return`
        ${uu(m)}
        float ${a}(int row, int col, int depth) {
          return ${a}(${du(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)));
        ${cu(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=qi(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 C8(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=fn();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 v8(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=pu(e,o),f=["row","col","depth","depth2"];return`
      ${uu(m)}
      float ${a}(int row, int col, int depth, int depth2) {
        return ${a}(${du(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)));
        ${cu(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=qi(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 w8(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=pu(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${uu(f)}
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        return ${a}(${du(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;
        ${cu(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=qi(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 k8(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=pu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${uu(g)}
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${a}(${du(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)));
        ${cu(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=qi(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 cu(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 ${t}[i];
      }
    }
  `}function E8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=QT(e.shapeInfo.logicalShape,t.logicalShape),l=mt(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${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 _8(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=mt(l),u=QT(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 mt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function pu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function du(e,t){return t.map(n=>e[n]).join(", ")}function U8(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=f8(s,o,r,t.packedInputs),c=e.createProgram(l),u=null,p=e.getUniformLocation(c,"NAN",!1);ee().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;d[m]=e.getUniformLocation(c,m,f),d[`offset${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 nS(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 ${l} must match`)})}function G8(e,t,n,a,r){nS(t.inShapeInfos,n),nS([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),ee().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],p=t.uniformLocations[`offset${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 H8(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}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:j8,bincountImpl:aS,bincountReduceImpl:q8,ceilImpl:K8,concatImpl:X8,expImpl:Y8,expm1Impl:J8,floorImpl:Z8,gatherV2Impl:Q8,greaterImpl:eK,lessImpl:tK,linSpaceImpl:nK,logImpl:aK,maxImpl:rK,maximumImpl:sK,minimumImpl:iK,multiplyImpl:oK,negImpl:lK,prodImpl:uK,rangeImpl:cK,rsqrtImpl:pK,simpleAbsImpl:rS,sliceImpl:dK,stridedSliceImpl:hK,subImpl:mK,tileImpl:fK,topKImpl:gK,transposeImpl:Dv,uniqueImpl:yK}=ZN;function sS(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function gn(e,t){return t===1?[e]:sS(e,t)}function bK(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 kK=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=gn("rc",t),a=mt(t),r=xK(t,e,n),s=vK(t,e[e.length-1],e[e.length-2],n),i=wK(e,n);this.userCode=`
        void main() {
          ${a} rc = getOutputCoords();

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

            setOutput(vec4(${i}));
          }
        }
      `}}};function IK(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${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 xK(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 vK(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 wK(e,t){let n=e.length,a=IK(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 iS=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=`
      ${NK(t)}
      ${$v(e)}

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

        vec4 result = vec4(0.);

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

        ${n}

        setOutput(result);
      }
    `}};function NK(e){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${ji(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var TK=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=lS(t,n),r=uS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=oS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===rn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===rn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===rn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===rn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===rn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=lS(n,a),s=uS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=oS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=ee().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 SK(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 oS(e,t,n,a,r){let s=CK(t,a),i;if(r){let[l,c]=iu(e[0],e[1]);i=l*c}else{let[l,c]=gp(e[0],e[1]);i=l*c}let o=SK(n,s);return i*o}function CK(e,t){switch(e){case rn.PACKED_2X2_FLOAT32:return JT(t);case rn.PACKED_2X2_FLOAT16:return ZT(t);case rn.UNPACKED_FLOAT32:return KT(t);case rn.UNPACKED_FLOAT16:return XT(t);case rn.PACKED_4X1_UNSIGNED_BYTE:return YT(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function EK(e){return ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?rn.PACKED_2X2_FLOAT32:rn.UNPACKED_FLOAT32:e?rn.PACKED_2X2_FLOAT16:rn.UNPACKED_FLOAT16}function lS(e,t){if(e===aa.UPLOAD)return rn.PACKED_2X2_FLOAT32;if(e===aa.RENDER||e==null)return EK(t);if(e===aa.DOWNLOAD||e===aa.PIXELS)return rn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function uS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var hs=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);
      }
    `}},Pa="if (isnan(x)) return x;",_K="return x;",cS="return abs(x);",FK="return (x >= 0.0) ? x : (exp(x) - 1.0);",AK=Pa+`
  return (x < 0.0) ? 0.0 : x;
`,$K=Pa+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Bm="return x;",DK="return x;",MK=`
  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;
`,RK=`
  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;
`,PK=`
  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;
`,hu=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);
      }
    `}},OK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=gn("rc",t),a=mt(t),r=bK(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}));
      }
    `}},LK=Ja.whereImpl,zK=1e-7,BK=1e-4,Mv={};function WK(e){return e in Mv||(Mv[e]={}),Mv[e]}var VK=128,UK=600;function GK(){return ee().global.screen==null?1024:ee().global.screen.height*ee().global.screen.width*window.devicePixelRatio*UK/1024/1024}var jK=class extends nc{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,!ee().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=rr(ee().getNumber("WEBGL_VERSION"));this.binaryCache=WK(ee().getNumber("WEBGL_VERSION")),this.gpgpu=new i8(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 TK(this.gpgpu),this.numMBBeforeWarning=GK(),this.texData=new gd(this,Zr())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((ee().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ee().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:aa.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--}}move(e,t,n,a){if(ee().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:aa.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 hu(i,Bm):p=new hs(i,Bm);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=_.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 hu(a,Bm):h=new hs(a,Bm);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(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().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"&&ee().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...yp(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=_.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(!pq(n))throw ee().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...yp(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=ee().getBool("WEBGL_PACK")&&a===!0,i=s?Ev(t):t,o=s?new Bq(i):new zq(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ee().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 ee().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Zr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=VK){let n=this.getCPUBackend();return!ee().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){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return LK(e.shape,t)}packedUnaryOp(e,t,n){let a=new hu(e.shape,t);return this.compileAndRun(a,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=rS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(ee().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,cS,e.dtype);let t=new hs(e.shape,cS);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 Zr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new OK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new kK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ou(e.shape),...lu(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ou(t),...lu(t)],s=new iS(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 Lq(s):i=new Oq(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===fp.DENSE){let m=yp(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)<=ee().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&&!zm(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=H8(e,l,c),p=this.getAndSaveBinary(u,()=>U8(this.gpgpu,e,l,c)),d=this.activeTimers!=null,h;if(d&&(h=this.startTimer()),G8(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)})),!ee().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 Zr().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||(ee().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(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(de(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zK:BK}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=_q(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]=iu(u[0],u[1]),d=new Vq(p,[m,h],f)):d=new Wq(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=aa.PIXELS:this.texData.get(g.dataId).usage=aa.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,b=this.runWebGLProgram(d,[g],a,null,y),x=this.texData.get(b.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.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=HK(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 HK(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 qK="2.8.3";nh.isBrowser()&&lh("webgl",()=>new jK,2);var pS=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,mu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},Wm=`
  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;
`,vp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(s=`
          ${mt(r)} coords = getOutputCoords();
        `,r===1)s+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=gn("coords",r);s+=`
            bool nextRowOutOfBounds =
              (${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 KK={kernelName:Xo,backendName:"webgl",kernelFunc:Wn};function ms(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 XK={kernelName:Td,backendName:"webgl",kernelFunc:ms},dS="return (a < 0.) ? b * a : a;",hS=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function YK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vp(hS,r.shape,i.shape):new mu(dS,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var JK={kernelName:Hs,backendName:"webgl",kernelFunc:YK},mS="return (a < 0.) ? b * a : a;",fS=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function ZK(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vp(fS,a.shape,r.shape):new mu(mS,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var QK={kernelName:ai,backendName:"webgl",kernelFunc:ZK},gS="if (isnan(x)) return x;",eX=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,tX=`
  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 Ye({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=ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new hu(i.shape,t):u=new hs(i.shape,e),o.runWebGLProgram(u,[i],l)}}function sn({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(x=>{let[v,N]=x,T={dataId:v.dataId,dtype:v.dtype,shape:l.shape},E={dataId:N.dataId,dtype:N.dtype,shape:c.shape},A=new mu(e,l.shape,c.shape);return u.runWebGLProgram(A,[T,E],ua(v.dtype,N.dtype))}),b=ms({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),b}let p=s||ua(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),x=u.texData.get(b.dataId);return x.values=g,b}let d=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new vp(t,l.shape,c.shape,n):h=new mu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],p)}}function Vm(e,t=!1){if(e==="linear")return t?DK:_K;if(e==="relu")return t?RK:AK;if(e==="elu")return t?MK:FK;if(e==="relu6")return t?PK:$K;if(e==="prelu")return t?fS:mS;if(e==="leakyrelu")return t?hS:dS;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var yS=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",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${f}

      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 = ${x};
          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);
      }
    `}},bS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},xS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},vS="return a * b;";function wS(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new xS(bS.REAL,a.shape,r.shape),u=new xS(bS.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=ms({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]=oK(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 ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new vp(vS,a.shape,r.shape):i=new mu(vS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var nX={kernelName:Qs,backendName:"webgl",kernelFunc:wS};function aX(e,t,n){let a=[ou(e.shape),...lu(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[ou(t),...lu(t)],i=new iS(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(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&&!zm(r.shape,l)&&!(u.texture!==null&&zm(u.shape,l))?aX(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var rX={kernelName:hl,backendName:"webgl",kernelFunc:xe},kS=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);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},sX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 iX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Ki(e,t,n,a){let r=iX(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 kS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new kS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new sX({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 lX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=mt(this.rank),r=oX(t);this.userCode=`
    void main() {
      ${a} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `}};function oX(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 uX=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 ${this.rank} is not yet supported.`);let a=mt(this.rank),r=sS("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${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] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function Um(e,t,n){let a=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uX(e.shape,t):new lX(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function cX(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=Um(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=_.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=_.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=xe({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=th(e.dtype),b=Ki(g,y,"sum",a),x=xe({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),x}function Rv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return cX(r,s,i,n)}var pX={kernelName:hi,backendName:"webgl",kernelFunc:Rv};function Cn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Dv(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=Um(r,s,i);return c}var dX={kernelName:bi,backendName:"webgl",kernelFunc:Cn},IS=1e3;function Gm({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),x=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],E=xe({inputs:{x:e},backend:r,attrs:{shape:N}}),A=xe({inputs:{x:t},backend:r,attrs:{shape:T}}),$=[E,A],O=Math.max(y,b),V=n?E.shape[1]:E.shape[2],W=s!=null,H=i!=null,X=l==="leakyrelu",q=l!=null?Vm(l,!0):null,K=W||H||X||q!=null,J;if((h===1||m===1)&&V>IS&&K===!1){let Q=E,ie=A;n&&(Q=Cn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),$.push(Q)),a&&(ie=Cn({inputs:{x:A},backend:r,attrs:{perm:[0,2,1]}}),$.push(ie));let re=m!==1,ae=m===1,oe=Q;re&&(oe=xe({inputs:{x:Q},backend:r,attrs:{shape:[O,V,1]}}),$.push(oe));let he=m===1?2:1,ce=ie;ae&&(ce=xe({inputs:{x:ie},backend:r,attrs:{shape:[O,1,V]}}),$.push(ce));let ge=wS({inputs:{a:oe,b:ce},backend:r});J=Rv({inputs:{x:ge},backend:r,attrs:{axis:he,keepDims:!0}}),$.push(ge)}else{let Q=ua(e.dtype,t.dtype),ie=new yS(N,T,[O,h,m],n,a,W,q,H,X),re=[E,A];if(s!=null&&re.push(s),H&&re.push(i),X){let ae=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));re.push(ae),$.push(ae)}J=r.runWebGLProgram(ie,re,Q)}let te=xe({inputs:{x:J},backend:r,attrs:{shape:v}});$.push(J);for(let Q of $)r.disposeIntermediateTensorInfo(Q);return te}function hX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return Gm({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var mX={kernelName:xi,backendName:"webgl",kernelFunc:hX},NS="return abs(x);";function fX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=rS(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hu(a.shape,NS):r=new hs(a.shape,NS),n.runWebGLProgram(r,[a],a.dtype)}var gX={kernelName:Co,backendName:"webgl",kernelFunc:fX},yX=Pa+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,bX=Ye({opSnippet:yX}),xX={kernelName:Eo,backendName:"webgl",kernelFunc:bX},vX=Pa+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,wX=Ye({opSnippet:vX}),kX={kernelName:_o,backendName:"webgl",kernelFunc:wX},TS="return a + b;",IX=sn({opSnippet:TS,packedOpSnippet:TS,supportsComplex:!0,cpuKernelImpl:j8}),NX={kernelName:Ur,backendName:"webgl",kernelFunc:IX},TX=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 = ${a};
        setOutput(result);
      }
    `}},SX=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 Hm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Wn({inputs:{x:a[0]},backend:n});if(a.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Hm({inputs:a.slice(0,o),backend:n}),c=Hm({inputs:a.slice(o),backend:n});return Hm({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ua(o,l)),s=a.map(o=>o.shape),i=ee().getBool("WEBGL_PACK")?new SX(a[0].shape,s):new TX(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var CX={kernelName:_s,backendName:"webgl",kernelFunc:Hm};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=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("all",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ki(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=xe({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=xe({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: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=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("any",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ki(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=xe({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=xe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var AX={kernelName:wd,backendName:"webgl",kernelFunc:FX},$X=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 ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},DX=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=mt(o),c=gn("coords",o),u,p;if(s===1){p=o+1;let E=mt(p);u=`
        ${E} sourceLocR = ${E}(${c.join()}, 0);
        ++${c[o-1]};
        ${E} sourceLocG = ${E}(${c.join()}, 0);
        ++${c[o-2]};
        ${E} sourceLocA = ${E}(${c.join()}, 0);
        --${c[o-1]};
        ${E} sourceLocB = ${E}(${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(E=>"int "+E),f=gn("sourceLocR",p-1).concat("inIdx.r"),g=gn("sourceLocG",p-1).concat("inIdx.g"),y=gn("sourceLocB",p-1).concat("inIdx.b"),b=gn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${b.join()})));`,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;
          ${v}
          vec4 candidate = ${N};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function SS(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new $X(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=SS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function CS(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new DX(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=CS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function ES(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=_.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=SS(e,c,a);s.push(u);let p=xe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return CS(e,t,a)}function MX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Cn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=ES(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var RX={kernelName:Fs,backendName:"webgl",kernelFunc:MX};function PX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Cn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=ES(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var OX={kernelName:sc,backendName:"webgl",kernelFunc:PX},LX=Pa+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,zX=Ye({opSnippet:LX}),BX={kernelName:Fo,backendName:"webgl",kernelFunc:zX},WX=Pa+"return log(x + sqrt(x * x + 1.0));",VX=Ye({opSnippet:WX}),UX={kernelName:Ao,backendName:"webgl",kernelFunc:VX},GX=Pa+`
  return atan(x);
`,HX=Ye({opSnippet:GX}),jX={kernelName:$o,backendName:"webgl",kernelFunc:HX},qX=eX+`
  return atan(a, b);
`,KX=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+tX+`
  return result;
`,XX=sn({opSnippet:qX,packedOpSnippet:KX}),YX={kernelName:Mo,backendName:"webgl",kernelFunc:XX},JX=Pa+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZX=Ye({opSnippet:JX}),QX={kernelName:Do,backendName:"webgl",kernelFunc:ZX},wp=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) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
        const ivec2 strides = ivec2(${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 ${E} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,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 < ${v}; 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 + ${v};
          if (${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
            );

            ${T}
          }
        }
        setOutput(${x});
      }
    `}},Pv=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",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${o}, ${l});
        const ivec3 pads = ivec3(${f}, ${g}, ${y});

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

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

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

          for (int wD = 0; wD < ${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 ${$} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
                      wR * ${m} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let v="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,E=s%4,A=`
      if (${b}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${v}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${i}, ${o}, ${l});
      const ivec3 pads = ivec3(${f}, ${g}, ${y});
      const float initializationValue = ${x};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

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

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

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${x});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${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)
              );

              ${A}
            }

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

              ${A}
            } else if (${E===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                initializationValue,
                initializationValue
              );

              ${A}
            } else if (${E===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
              );

              ${A}
            }
          }
          setOutput(${N});
        }
      }
    `}};function eY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;bp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.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 wp(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var tY={kernelName:As,backendName:"webgl",kernelFunc:eY};function nY(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=_.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new Pv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var aY={kernelName:ic,backendName:"webgl",kernelFunc:nY},rY=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);
      }
    `}},sY=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 iY(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=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new sY(d);return n.runWebGLProgram(h,[r],i.dtype)}var oY={kernelName:Id,backendName:"webgl",kernelFunc:iY};function lY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;bp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=new rY(u);return n.runWebGLProgram(p,[r],i.dtype)}var uY={kernelName:kd,backendName:"webgl",kernelFunc:lY};function cY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Gm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var pY={kernelName:$s,backendName:"webgl",kernelFunc:cY},dY=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${i};
        float scale = ${o};
        float inv = scale * inversesqrt(variance + float(${s}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},hY=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${i};
        vec4 scale = ${o};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `}},mY=({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=ee().getBool("WEBGL_PACK_NORMALIZATION")?new hY(a.shape,r.shape,s.shape,u,p,l):new dY(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},fY={kernelName:Us,backendName:"webgl",kernelFunc:mY},yY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=mt(this.rank),n=`uniform int start[${this.rank}];`,a=gY(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Ov[o]} = start[${o}] + coords.${Ov[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)}}},Ov=["x","y","z","w","u","v"];function gY(e){if(e===1)return"sourceLoc";if(e<=6)return Ov.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var bY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=mt(this.rank),n=gn("coords",this.rank),a=gn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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 xY(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.shape=n,i.dtype=e.dtype;let o=dn.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 kp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=dn.parseSliceParams(r,s,i);if(dn.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=dK(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=dn.isSliceContinous(r.shape,o,l);if(c||!u){let p=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bY(l):new yY(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),xY(r,o,l,n)}var vY={kernelName:yl,backendName:"webgl",kernelFunc:kp},wY=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=[],m=xe({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Cn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=xe({inputs:{x:f},backend:n,attrs:{shape:u}}),y=kp({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},kY={kernelName:oc,backendName:"webgl",kernelFunc:wY};function IY(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=aS(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var NY={kernelName:Nd,backendName:"webgl",kernelFunc:IY},TY="return float(a != b);",_S=sn({opSnippet:TY,dtype:"bool"}),SY={kernelName:sl,backendName:"webgl",kernelFunc:_S};function Ip(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 CY={kernelName:Hd,backendName:"webgl",kernelFunc:Ip},EY="return float(int(x));";function _Y(e,t){let n=new hs(e.shape,EY),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Lv(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=yt(r.shape),o=Lv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ms({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Ip({inputs:{input:r},backend:n}),o=Lv({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 _Y(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=_S({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var FY={kernelName:Ds,backendName:"webgl",kernelFunc:Lv},FS="return ceil(x);",AY=Ye({opSnippet:FS,packedOpSnippet:FS,cpuKernelImpl:K8}),$Y={kernelName:Ro,backendName:"webgl",kernelFunc:AY},DY=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)}}},MY=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 RY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;ee().getBool("WEBGL_PACK_CLIP")?o=new MY(r.shape):o=new DY(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var PY={kernelName:Gr,backendName:"webgl",kernelFunc:RY},OY=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 AS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function LY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new OY(a.shape),i=[AS(a,r.complexTensorInfos.real),AS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var zY={kernelName:lc,backendName:"webgl",kernelFunc:LY},BY=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}},WY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=mt(a),s=gn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${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}(${jm(i,l,f)}),
            vec2(${jm(c,l,f)}));
        }`}let d=o.length,h=o[o.length-1];p+=`
        return getChannel(
          getT${d}(${jm(i,l,h)}),
          vec2(${jm(c,l,h)}));`,this.userCode=`
      float getValue(${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 jm(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function qm(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var VY={kernelName:Ld,backendName:"webgl",kernelFunc:qm};function fu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>Ip({inputs:{input:m},backend:n})),u=e.map(m=>qm({inputs:{input:m},backend:n})),p=fu(c,t,n),d=fu(u,t,n),h=ms({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=$S(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=X8(p,u,a,d),m=_.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=fu(e.slice(0,c),t,n),p=fu(e.slice(c),t,n),d=fu([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new WY(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=$S(e,t,n),i=new BY(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function $S(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>xe({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function DS(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.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 _.assertParamsConsistent(l,s),fu(o,s,n)}var UY={kernelName:Po,backendName:"webgl",kernelFunc:DS},MS=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,x="",v="";n&&(a?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:x=`
          float activation(float x) {
            ${n}
          }
        `,v="result = activation(result);");let N=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

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

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

        ivec2 xRCCorner =
            ivec2(coords[${g}], coords[${y}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${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}
        ${v}
        setOutput(result);
      }
    `}},GY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${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);
      }
    `}},HY=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=fn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,x="";for(let v=0;v<=1;v++)for(let N=0;N<=1;N++)x+=`
          blockIndex = rc.y + ${N};
          pos = rc.x + ${v};

          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 (${g}) {
                  innerDims = vec2(d1, ch);
                  result[${v*2+N}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${v*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;

        ${x}

        ${f.output} = result;
      }
    `}};function RS({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>IS,x=l[2]%2!=0&&!!c.isPacked;if(b||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=xe({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),E=Gm({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=xe({inputs:{x:E},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(E)}else{let v=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(zm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let E=xe({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(E);let A=Gm({a:N,b:E,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),$=a.texData.get(A.dataId);k.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,$.shape=n.outShape,g=Wn({inputs:{x:A},backend:a}),g.shape=n.outShape,y.push(A)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function PS({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,x=!1,v=[],N=xe({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(N),v.push(T);let E=new HY(y,N.shape,n),A=a.runWebGLProgram(E,[N],"float32"),$=xe({inputs:{x:A},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(A),v.push($);let O=r!=null,V=s!=null,W=o==="leakyrelu",H=o?Vm(o,!0):null,X=new yS($.shape,T.shape,[1,g,n.outChannels],b,x,O,H,V,W),q=[$,T];if(r&&q.push(r),V&&q.push(s),W){let Q=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));q.push(Q),v.push(Q)}let K=a.runWebGLProgram(X,q,"float32"),J=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],te=xe({inputs:{x:K},backend:a,attrs:{shape:J}});v.push(K);for(let Q of v)a.disposeIntermediateTensorInfo(Q);return te}function jY(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=_.convertConv2DDataFormat(l),d=_.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=RS({x:r,filter:s,convInfo:d,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=PS({x:r,filter:s,convInfo:d,backend:n});else{let f=new MS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=xe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var qY={kernelName:Ms,backendName:"webgl",kernelFunc:jY},KY=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);
      }
    `}},XY=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);
      }
    `}},YY=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);
      }
    `}},JY=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 ZY(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=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new KY(d);return n.runWebGLProgram(h,[r,s],"float32")}var QY={kernelName:Sd,backendName:"webgl",kernelFunc:ZY};function e7(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=_.convertConv2DDataFormat(c),d=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,s],"float32")}var t7={kernelName:Rs,backendName:"webgl",kernelFunc:e7};function n7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new GY(c);return n.runWebGLProgram(u,[r,s],"float32")}var a7={kernelName:uc,backendName:"webgl",kernelFunc:n7};function r7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=_.computeConv3DInfo(r.shape,l,i,1,o),u=new YY(c);return n.runWebGLProgram(u,[r,s],"float32")}var s7={kernelName:Cd,backendName:"webgl",kernelFunc:r7};function i7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=_.computeConv3DInfo(l,s.shape,o,1,i),u=new JY(c);return n.runWebGLProgram(u,[r,s],"float32")}var o7={kernelName:Ed,backendName:"webgl",kernelFunc:i7},l7=gS+`
  return cos(x);
`,u7=Ye({opSnippet:l7}),c7={kernelName:Ps,backendName:"webgl",kernelFunc:u7},p7=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,d7=Ye({opSnippet:p7}),h7={kernelName:Oo,backendName:"webgl",kernelFunc:d7},m7=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) * ${h}`],[b,x,v]=p>1?[`${(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 = ${x};

        float in_y = ${y};
        if( in_y < 0.0 || in_y > ${h} ) {
          setOutput(float(${r}));
          return;
        }
        float in_x = ${v};
        if( in_x < 0.0 || in_x > ${m} ) {
          setOutput(float(${r}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${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);
        }
      }
    `}},f7=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 m7(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},g7={kernelName:Lo,backendName:"webgl",kernelFunc:f7},zS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${OS(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() {
        ${mt(a)} coords = getOutputCoords();
        int end = ${LS(a,"coords")};
        float val = ${r};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${LS(a,"coords")} = idx;
          val += getX(${OS(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 OS(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 ${e} is not yet supported`)}function LS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function y7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=_.getAxesPermutation([s],l),u=r;c!=null&&(u=Cn({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=_.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=r.shape[p],h=Wn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new zS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new zS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=_.getUndoAxesPermutation(c),f=Cn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var b7={kernelName:Os,backendName:"webgl",kernelFunc:y7};function x7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=aS(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=q8(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var v7={kernelName:_d,backendName:"webgl",kernelFunc:x7},w7=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 k7(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 w7(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var I7={kernelName:zo,backendName:"webgl",kernelFunc:k7},BS=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}
        ${y}
        setOutput(result);
      }
    `}},WS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let v=0;v<h;v++)for(let N=0;N<m;N++)g+=`
          vec4 xTexelR${v}C${N*2} = vec4(0.);
          vec4 wR${v}C${N} = vec4(0.);
          vec4 xR${v}C${N} = vec4(0.);`;for(let v=0;v<h;v++)for(let N=0;N<f;N++){let T=N*2;if(g+=`
          xR = xRCorner + ${v*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 < ${i}) {
                  xTexelR${v}C${T} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${i}) {
                    xTexelR${v}C${T}.zw = vec2(0.);
                  }
                } else {
                  xTexelR${v}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${v}C${T} = vec4(previous.zw, xTexelR${v}C${T}.xy);
                } else {
                  xR${v}C${T} = vec4(0, 0, xTexelR${v}C${T}.xy);
                }
              `:g+=`
                if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
                  xTexelR${v}C${T} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${v}C${T} = vec4(0.);
                }

                xR${v}C${T} = xTexelR${v}C${T};
              `,T+1<m)){let E=l%2==0?k.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(g+=`
                  xCOffset = xC + ${l%2} + ${E};

                  if(xR >= 0 && xR < ${s} &&
                    xCOffset >= 0 && xCOffset < ${i}) {
                    xTexelR${v}C${T+2} = getX(batch, xR, xCOffset, d1);
                  }
                `,d>1&&(g+=`
                    xCOffset -= 2;
                    if(xR >= 0 && xR < ${s} &&
                      xCOffset >= 0 && xCOffset < ${i}) {
                      xTexelR${v}C${T} = getX(batch, xR, xCOffset, d1);
                    } else {
                      xTexelR${v}C${T} = vec4(0.);
                    }
                  `),g+=`
                  xR${v}C${T+1} = vec4(
                    xTexelR${v}C${T}.zw, xTexelR${v}C${T+2}.xy);
                `):g+=`
                  xCOffset = xC + ${E};

                  if(xR >= 0 && xR < ${s} &&
                    xCOffset >= 0 && xCOffset < ${i}) {
                    xTexelR${v}C${T+2} = getX(batch, xR, xCOffset, d1);
                  }

                  xR${v}C${T+1} = xTexelR${v}C${T+2};
                `}}else T<m&&(g+=`
              if(xR >= 0 && xR < ${s}) {
            `,l%2==1?(g+=`
                xCOffset = xC + 1 - ${u};
                if(xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${v}C${T} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${v}C${T} = vec4(0.);
                }

                if(xC + 1 >= 0 && xC + 1 < ${i}) {
                  xTexelR${v}C${T+2} = getX(batch, xR, xC + 1, d1);
                } else {
                  xTexelR${v}C${T+2} = vec4(0.);
                }

                xR${v}C${T} = vec4(
                  xTexelR${v}C${T}.zw, xTexelR${v}C${T+2}.zw);
              `,T+1<m&&(g+=`
                  vec4 final = vec4(0.);
                  xCOffset = xC + 1 + ${u};
                  if(xCOffset >= 0 && xCOffset < ${i}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xR${v}C${T+1} = vec4(xTexelR${v}C${T+2}.xy, final.xy);
                `)):(g+=`
                if(xC >= 0 && xC < ${i}) {
                  xTexelR${v}C${T} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${v}C${T} = vec4(0.);
                }

                xCOffset = xC + ${u};
                if(xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${v}C${T+2} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${v}C${T+2} = vec4(0.);
                }

                xR${v}C${T} = vec4(
                  xTexelR${v}C${T}.xy, xTexelR${v}C${T+2}.xy);
              `,T+1<m&&(g+=`
                  xR${v}C${T+1} = vec4(
                    xTexelR${v}C${T}.zw, xTexelR${v}C${T+2}.zw);
                `)),g+="}");T<m&&(g+=`
            vec4 wTexelR${v}C${T} = getW(${v}, ${T}, d1, q);
            wR${v}C${T} = vec4(wTexelR${v}C${T}.xz, wTexelR${v}C${T}.xz);
          `,T+1<m&&(g+=`
              vec4 wTexelR${v}C${T+1} = getW(${v}, ${T+1}, d1, q);
              wR${v}C${T+1} =
                vec4(wTexelR${v}C${T+1}.xz, wTexelR${v}C${T+1}.xz);`))}for(let v=0;v<h;v++)for(let N=0;N<m;N++)g+=`dotProd += xR${v}C${N} * wR${v}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 x=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;
        ${x}
        ${b}
        setOutput(result);
      }
    `}};function N7(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(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new WS(p):d=new BS(p),n.runWebGLProgram(d,[r,s],"float32")}var T7={kernelName:Ls,backendName:"webgl",kernelFunc:N7},S7=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);
      }
    `}},C7=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=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new S7(p);return n.runWebGLProgram(d,[r,s],"float32")}var _7={kernelName:Fd,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=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new C7(p);return n.runWebGLProgram(d,[r,s],"float32")}var A7={kernelName:Ad,backendName:"webgl",kernelFunc:F7},$7=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 D7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=xe({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new $7(s),l=n.runWebGLProgram(o,[i],i.dtype),c=xe({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var M7={kernelName:$d,backendName:"webgl",kernelFunc:D7},R7=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 P7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new R7(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=xe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var O7={kernelName:cc,backendName:"webgl",kernelFunc:P7},L7="return (x >= 0.0) ? x : (exp(x) - 1.0);",z7=`
  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;
`,B7=Ye({opSnippet:L7,packedOpSnippet:z7}),W7={kernelName:Bo,backendName:"webgl",kernelFunc:B7},V7="return (b >= 1.0) ? a : a * (b + 1.0);",U7=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,G7=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vp(U7,a.shape,r.shape):new mu(V7,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},H7={kernelName:Rd,backendName:"webgl",kernelFunc:G7},j7=`
  return vec4(equal(a, b));
`,q7="return float(a == b);",K7=sn({opSnippet:q7,packedOpSnippet:j7,dtype:"bool"}),X7={kernelName:Vo,backendName:"webgl",kernelFunc:K7},Y7=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${_.ERF_P};
  float a1 = ${_.ERF_A1};
  float a2 = ${_.ERF_A2};
  float a3 = ${_.ERF_A3};
  float a4 = ${_.ERF_A4};
  float a5 = ${_.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,J7=Ye({opSnippet:Y7}),Z7={kernelName:Wo,backendName:"webgl",kernelFunc:J7},VS="return exp(x);",US=Ye({opSnippet:VS,packedOpSnippet:VS,cpuKernelImpl:Y8}),Q7={kernelName:Bs,backendName:"webgl",kernelFunc:US};function zv(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),xe({inputs:{x:s},backend:a,attrs:{shape:o}})}var eJ={kernelName:Uo,backendName:"webgl",kernelFunc:zv},GS="return exp(x) - 1.0;",tJ=Ye({opSnippet:GS,packedOpSnippet:GS,cpuKernelImpl:J8}),nJ={kernelName:Go,backendName:"webgl",kernelFunc:tJ},HS=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 jS(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=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}).shape,l=new HS("real",o,t),c=new HS("imag",o,t),u=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:o},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:o}],p=n.runWebGLProgram(l,u,"float32"),d=n.runWebGLProgram(c,u,"float32"),h=ms({inputs:{real:p,imag:d},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d);let m=xe({inputs:{x:h},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(m),m}function aJ(e){let{inputs:t,backend:n}=e,{input:a}=t;return jS(a,!1,n)}var rJ={kernelName:Pd,backendName:"webgl",kernelFunc:aJ},sJ=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 Bv(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 sJ(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var iJ={kernelName:pc,backendName:"webgl",kernelFunc:Bv},oJ=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);
        }
    `}},lJ={kernelName:Ho,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new oJ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},qS="return floor(x);",uJ=Ye({opSnippet:qS,packedOpSnippet:qS,cpuKernelImpl:Z8}),cJ={kernelName:Ws,backendName:"webgl",kernelFunc:uJ},pJ=`
  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;
  }
`,dJ=`
  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);
`,hJ=sn({opSnippet:pJ,packedOpSnippet:dJ,dtype:"int32"}),mJ={kernelName:Vs,backendName:"webgl",kernelFunc:hJ},fJ=class{constructor(e){this.variableNames=["A"];let t=fn(),[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));
      }
    `}},gJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[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);
          }
        }

        ${t.output} = result;
      }
    `}},bJ={kernelName:Yd,backendName:"webgl",kernelFunc:yJ},gu;function yJ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(gu==null&&(gu=document.createElement("canvas").getContext("2d")),gu.canvas.width=c,gu.canvas.height=u,gu.drawImage(r,0,0,c,u),r=gu.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=aa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=ee().getBool("WEBGL_PACK")?new gJ(d):new fJ(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function xJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=RS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=PS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,N=o!=null,T=h==="leakyrelu",E=h?Vm(h,!1):null,A=new MS(g,v,E,N,T),$=[r,s];if(i&&$.push(i),o&&$.push(o),T){let O=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));$.push(O),b.push(O)}y=n.runWebGLProgram(A,$,"float32")}let x=xe({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var vJ={kernelName:vi,backendName:"webgl",kernelFunc:xJ};function wJ(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(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?Vm(d,y):null,x=[r,s],v=i!=null,N=o!=null,T=d==="leakyrelu";if(v&&x.push(i),N&&x.push(o),T){let $=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push($),m.push($)}let E;y?E=new WS(g,v,b,N,T):E=new BS(g,v,b,N,T);let A=n.runWebGLProgram(E,x,"float32");return m.forEach($=>n.disposeIntermediateTensorInfo($)),A}var kJ={kernelName:wi,backendName:"webgl",kernelFunc:wJ},IJ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=mt(t.length),r=mt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${a} strides = ${a}(${this.strides});
         void main() {
          ${r} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${s};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function NJ(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=_.prepareAndValidate(a,r),p=xe({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new IJ(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=xe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var TJ={kernelName:qo,backendName:"webgl",kernelFunc:NJ},CJ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=mt(this.rank),a=SJ(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${a}));
      }
    `}};function SJ(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(`${n[r]}`);return a.join()}function EJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=xe({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=xe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),x=n.bufferSync(d),v=Q8(x,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let f=new CJ(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=xe({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var _J={kernelName:jo,backendName:"webgl",kernelFunc:EJ},FJ="return float(a > b);",AJ=`
  return vec4(greaterThan(a, b));
`,$J=sn({opSnippet:FJ,packedOpSnippet:AJ,cpuKernelImpl:eK,dtype:"bool"}),DJ={kernelName:Ko,backendName:"webgl",kernelFunc:$J},MJ="return float(a >= b);",RJ=`
  return vec4(greaterThanEqual(a, b));
`,PJ=sn({opSnippet:MJ,packedOpSnippet:RJ,dtype:"bool"}),OJ={kernelName:Gs,backendName:"webgl",kernelFunc:PJ};function LJ(e){let{inputs:t,backend:n}=e,{input:a}=t;return jS(a,!0,n)}var zJ={kernelName:Od,backendName:"webgl",kernelFunc:LJ},BJ="return float(!isnan(x) && !isinf(x));",WJ=Ye({opSnippet:BJ,dtype:"bool"}),VJ={kernelName:Yo,backendName:"webgl",kernelFunc:WJ},UJ="return float(isinf(x));",GJ=Ye({opSnippet:UJ,dtype:"bool"}),HJ={kernelName:Jo,backendName:"webgl",kernelFunc:GJ},jJ="return float(isnan(x));",qJ=Ye({opSnippet:jJ,dtype:"bool"}),KJ={kernelName:Zo,backendName:"webgl",kernelFunc:qJ},XJ="return float(a < b);",YJ=`
  return vec4(lessThan(a, b));
`,JJ=sn({opSnippet:XJ,packedOpSnippet:YJ,cpuKernelImpl:tK,dtype:"bool"}),ZJ={kernelName:Qo,backendName:"webgl",kernelFunc:JJ},QJ="return float(a <= b);",eZ=`
  return vec4(lessThanEqual(a, b));
`,tZ=sn({opSnippet:QJ,packedOpSnippet:eZ,dtype:"bool"}),nZ={kernelName:el,backendName:"webgl",kernelFunc:tZ};function aZ(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=nK(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var rZ={kernelName:zd,backendName:"webgl",kernelFunc:aZ},sZ=`if (x < 0.0) return NAN;
  return log(x);`,iZ=`
  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;
`,oZ=Ye({opSnippet:sZ,packedOpSnippet:iZ,cpuKernelImpl:aK}),lZ={kernelName:js,backendName:"webgl",kernelFunc:oZ},uZ="return log(1.0 + x);",cZ=Ye({opSnippet:uZ}),pZ={kernelName:tl,backendName:"webgl",kernelFunc:cZ},dZ="return float(a >= 1.0 && b >= 1.0);",hZ=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,mZ=sn({opSnippet:dZ,packedOpSnippet:hZ,dtype:"bool"}),fZ={kernelName:nl,backendName:"webgl",kernelFunc:mZ},gZ="return float(!(x >= 1.0));",yZ=Ye({opSnippet:gZ}),bZ={kernelName:dc,backendName:"webgl",kernelFunc:yZ},xZ="return float(a >= 1.0 || b >= 1.0);",vZ=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,wZ=sn({opSnippet:xZ,packedOpSnippet:vZ,dtype:"bool"}),kZ={kernelName:hc,backendName:"webgl",kernelFunc:wZ},IZ=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 * ${o};
        setOutput(val);
      }
    `}},NZ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
      }
    `}},TZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new NZ(r.shape,s,i,o,l):new IZ(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},SZ={kernelName:mc,backendName:"webgl",kernelFunc:TZ},CZ=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 * ${r});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},EZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new CZ(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},_Z={kernelName:Bd,backendName:"webgl",kernelFunc:EZ};function FZ(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=xe({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ki(i,e.dtype,"max",a),l=xe({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function KS(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[u[T]];let v=Dv(b,r.shape,r.dtype,u,x);h=n.makeTensorInfo(x,r.dtype);let N=n.texData.get(h.dataId);N.values=v}else h=Um(r,u,n);c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("max",c,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,x=rK(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=FZ(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var AZ={kernelName:qs,backendName:"webgl",kernelFunc:KS},$Z=pS+`
  return max(a, b);
`,DZ=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Wm+`
  return result;
`,MZ=sn({opSnippet:$Z,packedOpSnippet:DZ,cpuKernelImpl:sK}),RZ={kernelName:Ks,backendName:"webgl",kernelFunc:MZ};function PZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;bp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.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 wp(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var OZ={kernelName:Xs,backendName:"webgl",kernelFunc:PZ};function LZ(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=_.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new Pv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var zZ={kernelName:fc,backendName:"webgl",kernelFunc:LZ},BZ=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);
      }
    `}},WZ=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 VZ(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=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new Pv(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new WZ(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var UZ={kernelName:Vd,backendName:"webgl",kernelFunc:VZ};function GZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;bp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new wp(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new BZ(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var HZ={kernelName:Wd,backendName:"webgl",kernelFunc:GZ};function jZ(e,t,n,a){let r=new wp(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new wp(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var qZ={kernelName:Ud,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(_.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=_.computePool2DInfo(a.shape,r,s,c,i),[p,d]=jZ(a,o,u,l);return[p,d]}};function KZ(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=xe({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ki(i,"float32","mean",a),l=xe({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var XZ={kernelName:Ys,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=_.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let E=0;E<v.length;E++)v[E]=a.shape[u[E]];let N=Dv(x,a.shape,a.dtype,u,v);m=i.makeTensorInfo(v,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=Um(a,u,i);h.push(m),c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=KZ(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function YZ(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=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ki(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=xe({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=xe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var JZ={kernelName:Js,backendName:"webgl",kernelFunc:YZ},ZZ=pS+`
  return min(a, b);
`,QZ=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Wm+`
  return result;
`,e9=sn({opSnippet:ZZ,packedOpSnippet:QZ,cpuKernelImpl:iK}),t9={kernelName:Zs,backendName:"webgl",kernelFunc:e9},n9=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=mt(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}));
      }
    `}},a9=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=mt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=gn("rc",a),l=gn("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);
      }
    `}},r9=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new a9(a.shape,r,s):new n9(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},s9={kernelName:gc,backendName:"webgl",kernelFunc:r9},i9=`if (b == 0.0) return NAN;
  return mod(a, b);`,o9=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Wm+`
  return result;
`,l9=sn({opSnippet:i9,packedOpSnippet:o9}),u9={kernelName:al,backendName:"webgl",kernelFunc:l9},c9=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)}}},p9=`
if (a == b) {
  return 1.0;
};
return a / b;`,d9=`
  // 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;
`,XS=sn({opSnippet:p9,packedOpSnippet:d9,checkOutOfBounds:!0}),h9={kernelName:zs,backendName:"webgl",kernelFunc:XS},YS="return a - b;",JS=sn({opSnippet:YS,packedOpSnippet:YS,supportsComplex:!0,cpuKernelImpl:mK}),m9={kernelName:gi,backendName:"webgl",kernelFunc:JS};function ZS(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=KS({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),c=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=JS({inputs:{a:r,b:c},backend:n}),p=US({inputs:{x:u},backend:n}),d=Rv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),m=XS({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 f9={kernelName:mi,backendName:"webgl",kernelFunc:ZS};function g9(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:ZS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new c9(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var y9={kernelName:Gd,backendName:"webgl",kernelFunc:g9},QS="return -x;";function b9(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=lK(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hu(a.shape,QS):r=new hs(a.shape,QS),n.runWebGLProgram(r,[a],a.dtype)}var x9={kernelName:rl,backendName:"webgl",kernelFunc:b9},v9=Ja.nonMaxSuppressionV3Impl;function w9(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}=v9(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var k9={kernelName:il,backendName:"webgl",kernelFunc:w9},I9=Ja.nonMaxSuppressionV4Impl;function N9(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}=I9(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var T9={kernelName:ol,backendName:"webgl",kernelFunc:N9},S9=Ja.nonMaxSuppressionV5Impl;function C9(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}=S9(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var E9={kernelName:ll,backendName:"webgl",kernelFunc:C9},_9=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)));
      }
    `}},F9=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 _9(l,s,i,o),u=xe({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=xe({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},A9={kernelName:ei,backendName:"webgl",kernelFunc:F9};function Km(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Ip({inputs:{input:a},backend:n}),s=Km({inputs:{x:r},backend:n}),i=qm({inputs:{input:a},backend:n}),o=Km({inputs:{x:i},backend:n}),l=ms({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Bv({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var $9={kernelName:Sl,backendName:"webgl",kernelFunc:Km};function e2(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=Ip({inputs:{input:a},backend:n}),s=e2({inputs:{x:r},backend:n}),i=qm({inputs:{input:a},backend:n}),o=Km({inputs:{x:i},backend:n}),l=ms({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Bv({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var D9={kernelName:ul,backendName:"webgl",kernelFunc:e2};function M9(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return zv({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=zv({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=DS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var R9={kernelName:cl,backendName:"webgl",kernelFunc:M9},P9=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=mt(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}));
        }
      }
    `}},O9=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=mt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=gn("rc",a),l=gn("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);
      }
    `}},t2=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new O9(r.shape,s,i):new P9(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},L9={kernelName:ti,backendName:"webgl",kernelFunc:t2},z9=`
  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);
`,B9=`
  // 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));
  `+Wm+`
  return result;
`,W9=sn({opSnippet:z9,packedOpSnippet:B9}),V9={kernelName:ni,backendName:"webgl",kernelFunc:W9};function U9(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=_.getAxesPermutation(u,o),d=r;p!=null&&(d=Cn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o),l.push(d)),_.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=uK(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=th(r.dtype),x=Ki(y,b,"prod",n);h=xe({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,c);h=xe({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var G9={kernelName:pl,backendName:"webgl",kernelFunc:U9},n2=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=cK(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},H9={kernelName:yc,backendName:"webgl",kernelFunc:n2},j9="return 1.0 / x;",q9=Ye({opSnippet:j9}),K9={kernelName:dl,backendName:"webgl",kernelFunc:q9},X9=Pa+`
  return (x < 0.0) ? 0.0 : x;
`,Y9=`
  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;
`,J9=Ye({opSnippet:X9,packedOpSnippet:Y9}),Z9={kernelName:ri,backendName:"webgl",kernelFunc:J9},Q9=Pa+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,eQ=`
  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;
`,tQ=Ye({opSnippet:Q9,packedOpSnippet:eQ}),nQ={kernelName:ii,backendName:"webgl",kernelFunc:tQ},aQ=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);
      }
    `}},rQ=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 sQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rQ(r.shape,l,c,s,i):new aQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var iQ={kernelName:si,backendName:"webgl",kernelFunc:sQ},oQ=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 lQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new oQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var uQ={kernelName:qd,backendName:"webgl",kernelFunc:lQ},cQ=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 pQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new cQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var dQ={kernelName:bc,backendName:"webgl",kernelFunc:pQ},hQ=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 mQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new hQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var fQ={kernelName:jd,backendName:"webgl",kernelFunc:mQ},gQ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=mt(n);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `}},yQ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=gn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=mt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${r}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${o(a.slice())};
          if(${r}){
            result.g = ${l(a.slice())};
          }
          if(${s}) {
            result.b = ${c(a.slice())};
            if(${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 bQ(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=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yQ(r.shape,o):new gQ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var xQ={kernelName:oi,backendName:"webgl",kernelFunc:bQ},vQ=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]=_.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 < ${r}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},wQ={kernelName:El,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new vQ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},kQ=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,IQ=Ye({opSnippet:kQ}),NQ={kernelName:li,backendName:"webgl",kernelFunc:IQ},TQ="return inversesqrt(x);",SQ=Ye({opSnippet:TQ,cpuKernelImpl:pK}),CQ={kernelName:ui,backendName:"webgl",kernelFunc:SQ},a2=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=mt(r.length),l=mt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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}=_.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=xe({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=xe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new a2(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var _Q={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=mt(n);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${a});
        if (cVal >= 1.0) {
          setOutput(getA(${r}));
        } else {
          setOutput(getB(${r}));
        }
      }
    `}};function AQ(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],ua(r.dtype,s.dtype))}var $Q={kernelName:fl,backendName:"webgl",kernelFunc:AQ},DQ=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${_.SELU_SCALEALPHA};
  float scale = ${_.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,MQ=Ye({opSnippet:DQ}),RQ={kernelName:gl,backendName:"webgl",kernelFunc:MQ},PQ="return 1.0 / (1.0 + exp(-1.0 * x));",OQ=Ye({opSnippet:PQ}),LQ={kernelName:pi,backendName:"webgl",kernelFunc:OQ},zQ=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,BQ=Ye({opSnippet:zQ}),WQ={kernelName:xl,backendName:"webgl",kernelFunc:BQ},VQ=gS+`
  return sin(x);
`,UQ=Ye({opSnippet:VQ}),GQ={kernelName:ci,backendName:"webgl",kernelFunc:UQ},HQ=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,jQ=Ye({opSnippet:HQ}),qQ={kernelName:bl,backendName:"webgl",kernelFunc:jQ},KQ=`
  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;
`,XQ=Ye({opSnippet:KQ}),YQ={kernelName:vl,backendName:"webgl",kernelFunc:XQ},JQ=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=t2({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=_.getReshaped(u.shape,s,o,!1),d=_.getPermuted(p.length,s.length,!1),h=_.getReshapedPermuted(u.shape,s,o,!1),m=xe({inputs:{x:u},backend:n,attrs:{shape:p}}),f=Cn({inputs:{x:m},backend:n,attrs:{perm:d}}),g=xe({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},ZQ={kernelName:xc,backendName:"webgl",kernelFunc:JQ};function QQ(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}=_.calculateShapes(s,r,o),d=!1,h=new a2(c,l,r.shape.length,s.shape.length,u,[p,1],d),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=xe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var eee={kernelName:Kd,backendName:"webgl",kernelFunc:QQ};function tee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),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=kp({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[o]+=d,m})}var nee={kernelName:wl,backendName:"webgl",kernelFunc:tee},aee="return sqrt(x);",ree=Ye({opSnippet:aee}),see={kernelName:di,backendName:"webgl",kernelFunc:ree},iee="return x * x;",oee=Ye({opSnippet:iee}),lee={kernelName:vc,backendName:"webgl",kernelFunc:oee},r2="return (a - b) * (a - b);",uee=sn({opSnippet:r2,packedOpSnippet:r2}),cee={kernelName:fi,backendName:"webgl",kernelFunc:uee};function pee({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Pa+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new hs(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var dee={kernelName:Cl,backendName:"webgl",kernelFunc:pee},hee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=mt(n.length),s=mt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${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 mee(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}=dn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),x=xe({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let T=kp({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=xe({inputs:{x:T},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(T)}else if(b.some(T=>T===0))v=n.makeTensorInfo(b,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let T=n.texData.get(x.dataId).values,E=Le(x.shape,x.dtype,T),A=hK(b,E,f,m);v=n.makeTensorInfo(b,x.dtype,A.values)}else{let T=new hee(m,f,b);v=n.runWebGLProgram(T,[x],x.dtype)}let N=xe({inputs:{x:v},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),N}var fee={kernelName:kl,backendName:"webgl",kernelFunc:mee},gee="return tan(x);",yee=Ye({opSnippet:gee}),bee={kernelName:Il,backendName:"webgl",kernelFunc:yee},xee=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,vee=Ye({opSnippet:xee}),wee={kernelName:yi,backendName:"webgl",kernelFunc:vee},Iee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=mt(this.rank),r=kee(e);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function kee(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 s2(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=fK(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new Iee(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var Nee={kernelName:Hr,backendName:"webgl",kernelFunc:s2};function Tee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,c]=gK(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 See={kernelName:Nl,backendName:"webgl",kernelFunc:Tee};function Cee(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;bp(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}=yK(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([c.length],"int32",c)]}var Eee={kernelName:Xd,backendName:"webgl",kernelFunc:Cee};function _ee(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=kp({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),y=xe({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:_ee},Aee=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 $ee(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=_.getAxesPermutation([c],o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=_.getInnerMostAxes(1,o)[0]);let d=_.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=th(r.dtype),g=(v,N,T,E,A)=>{let $=v.shape[0],O=v.shape[1],V=_.segment_util.segOpComputeOptimalWindowSize(O,A),W={windowSize:V,inSize:O,batchSize:$,numSegments:A},H=new Aee(W,N),X=n.compileAndRun(H,[v,T],E);if(l.push(X),X.shape[1]===A)return X;let q=n2({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),K=s2({inputs:{x:q},backend:n,attrs:{reps:[O/V]}});return l.push(q),l.push(K),g(X,N,K,E,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=xe({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(u!=null){l.push(b);let v=_.getUndoAxesPermutation(u);x=Cn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Dee={kernelName:wc,backendName:"webgl",kernelFunc:$ee},Mee=[SZ,_Z,mX,gX,xX,kX,NX,CX,_X,AX,RX,OX,BX,UX,YX,jX,QX,aY,tY,oY,uY,pY,fY,kY,NY,FY,$Y,PY,zY,XK,UY,QY,t7,qY,s7,o7,a7,c7,h7,g7,b7,v7,I7,_7,A7,T7,M7,O7,W7,H7,X7,Z7,Q7,eJ,nJ,rJ,iJ,lJ,cJ,mJ,bJ,vJ,kJ,TJ,_J,DJ,OJ,KK,zJ,VY,VJ,HJ,KJ,JK,ZJ,nZ,rZ,pZ,lZ,fZ,bZ,kZ,AZ,zZ,OZ,UZ,HZ,qZ,RZ,XZ,JZ,t9,s9,u9,y9,nX,x9,k9,T9,E9,SY,A9,D9,R9,L9,V9,QK,G9,H9,CY,h9,K9,nQ,Z9,rX,iQ,uQ,dQ,fQ,xQ,wQ,NQ,CQ,_Q,$Q,RQ,LQ,WQ,GQ,qQ,vY,f9,YQ,ZQ,eee,nee,see,lee,cee,dee,fee,m9,pX,bee,wee,Nee,See,dX,Eee,Fee,Dee,$9];for(let e of Mee)Ic(e);var Ree="2.8.3",Pee={"tfjs-core":K0,"tfjs-backend-cpu":dG,"tfjs-backend-webgl":qK,"tfjs-data":RN,"tfjs-layers":fm,"tfjs-converter":_N,tfjs:Ree},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 Np;(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"})(Np||(Np={}));var i2;function Oee(e){i2=e.wasm.cwrap(xi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Lee(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 A=n.dataIdMap.get(i.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Np[u];if(g==null)throw new Error(`${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],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),N=n.dataIdMap.get(v.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),E=new Uint8Array(new Int32Array(s.shape).buffer);return i2(d,T,r.shape.length,h,E,s.shape.length,l,c,g,m,f,p||0,N),v}var zee={kernelName:xi,backendName:"wasm",setupFunc:Oee,kernelFunc:Lee};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 Bee=Un(Co);function yn(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=_.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,x=()=>a(p,g,c.shape.length,d,y,u.shape.length,Vn[c.dtype],b);if(t&&c.dtype==="float32")return x(),f;let v=_.getBroadcastDims(c.shape,m),N=_.getBroadcastDims(u.shape,m),T=v.every((A,$)=>A===$),E=N.every((A,$)=>A===$);if(T&&E)return x(),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 Wee=!0,Vee=yn(Ur,Wee),o2;function Uee(e){o2=e.wasm.cwrap(_s,null,["array","number","number","number"])}function Gee(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 o2(s,r.length,Vn[a.dtype],i),a}var Hee={kernelName:_s,backendName:"wasm",setupFunc:Uee,kernelFunc:Gee};function Xm(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 jee={kernelName:Xo,backendName:"wasm",kernelFunc:Xm},l2;function qee(e){l2=e.wasm.cwrap(bi,null,["number","array","number","number","number","array","number"])}function Ym(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Xee(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Kee(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Xm({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 l2(u,h,l.shape.length,Vn[l.dtype],p,d,s.length),c}function Kee(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Xee(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 Yee={kernelName:bi,backendName:"wasm",kernelFunc:Ym,setupFunc:qee};function yu(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=_.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=_.getInnerMostAxes(i.length,r),l=Ym({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 u2;function Jee(e){u2=e.wasm.cwrap(Fs,null,["number","number","number","number","number"])}function Zee(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}=yu(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 u2(o,Vn[l.dtype],f,g,m),p&&t.disposeData(c.dataId),h}var Qee={kernelName:Fs,backendName:"wasm",kernelFunc:Zee,setupFunc:Jee},c2;function ete(e){c2=e.wasm.cwrap(As,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tte(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=_.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,x=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 v=a.makeOutput(u.outShape,"float32"),N=a.dataIdMap.get(v.dataId).id;return c2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,N),v}var nte={kernelName:As,backendName:"wasm",setupFunc:ete,kernelFunc:tte};function Oa(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: ${a.shape}. New shape and old shape must have the same number of elements.`),{dataId:a.dataId,shape:i,dtype:a.dtype}}var ate={kernelName:hl,backendName:"wasm",kernelFunc:Oa},p2;function rte(e){p2=e.wasm.cwrap($s,null,["number","array","number","number","array","number","number","number","number"])}function ste(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 x=(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=${o} must match.`);let v=i?[g,u,d]:[g,d,u],N=o?[y,h,p]:[y,p,h],T=Oa({inputs:{x:r},backend:n,attrs:{shape:v}}),E=Oa({inputs:{x:s},backend:n,attrs:{shape:N}}),A=n.dataIdMap.get(T.dataId).id,$=n.dataIdMap.get(E.dataId).id,O=i?T.shape[2]:T.shape[1],V=o?E.shape[1]:E.shape[2],W=Math.max(g,y),H=n.makeOutput([W,O,V],T.dtype),X=n.dataIdMap.get(H.dataId).id,q=new Uint8Array(new Int32Array(T.shape).buffer),K=new Uint8Array(new Int32Array(E.shape).buffer);return p2(A,q,T.shape.length,$,K,E.shape.length,i,o,X),H.shape=x,H}var ite={kernelName:$s,backendName:"wasm",setupFunc:rte,kernelFunc:ste};function Jm(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 ote={kernelName:Ds,backendName:"wasm",kernelFunc:Jm},d2;function lte(e){d2=e.wasm.cwrap(Gr,null,["number","number","number","number"])}function ute(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 d2(o,s,i,c),l}var cte={kernelName:Gr,backendName:"wasm",setupFunc:lte,kernelFunc:ute};function h2(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=_.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return Xm({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(_.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=k.sizeFromShape(x.shape.slice(a));return Oa({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=_.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=pv(m,r,t[0].dtype,f),y=_.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=_.fromStringArrayToUint8(g),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 pte={kernelName:Po,backendName:"wasm",kernelFunc:h2},m2;function dte(e){m2=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hte(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=_.convertConv2DDataFormat(d),m=_.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,x=m.padInfo.bottom,v=m.padInfo.left,N=m.dilationHeight,T=m.dilationWidth,E=m.strideHeight,A=m.strideWidth,$=m.inChannels,O=m.outChannels,V=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),H=a.dataIdMap.get(W.dataId).id;return m2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,v,V,N,T,E,A,$,O,H),W}var mte={kernelName:Ms,backendName:"wasm",setupFunc:dte,kernelFunc:hte},f2;function fte(e){f2=e.wasm.cwrap(Rs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gte(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:v,outHeight:N,outWidth:T,strideHeight:E,strideWidth:A}=h,$=f-1-h.padInfo.top,O=g-1-h.padInfo.left,V=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),H=k.computeStrides(r.shape),[X,q,K]=k.computeStrides(s.shape),J=W[0],te=V?W[1]:W[2],Q=V?W[2]:1,ie=V?1:W[1],re=H[0],ae=V?H[1]:H[2],oe=V?H[2]:1,he=V?1:H[1],ce=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ce.dataId).id,be=t.dataIdMap.get(r.dataId).id,Ie=t.dataIdMap.get(s.dataId).id;return f2(be,Ie,m,f,g,b,x,y,N,T,v,E,A,$,O,X,q,K,J,te,Q,ie,re,ae,oe,he,ge),ce}var yte={kernelName:Rs,backendName:"wasm",setupFunc:fte,kernelFunc:gte},bte=Un(Ps),Wv;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Wv||(Wv={}));var g2;function xte(e){g2=e.wasm.cwrap(Lo,null,["number","number","number","number","array","number","number","number","number","number"])}function vte(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=Jm({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,x=t.makeOutput(h,"float32"),v=t.dataIdMap.get(x.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return g2(g,y,b,u,N,p,d,Wv[r],s,v),f!=null&&t.disposeData(f.dataId),x}var wte={kernelName:Lo,backendName:"wasm",setupFunc:xte,kernelFunc:vte},y2;function kte(e){y2=e.wasm.cwrap(Os,null,["number","number","number","number","number","number"])}function Ite(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=_.getAxesPermutation([s],l),u=r;c!==null&&(u=Ym({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.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;y2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=Ym({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Nte={kernelName:Os,backendName:"wasm",setupFunc:kte,kernelFunc:Ite},b2;function Tte(e){b2=e.wasm.cwrap(zo,null,["number","number","number","array","number","array","array","number","number"])}function Ste(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: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return b2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var Cte={kernelName:zo,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste},x2;function Ete(e){x2=e.wasm.cwrap(Ls,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _te(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,E=h.strideWidth,A=h.inChannels,$=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let V=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(V.dataId).id;return x2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,O,v,N,T,E,A,$,W),V}var Fte={kernelName:Ls,backendName:"wasm",setupFunc:Ete,kernelFunc:_te},Ate=!1,$te=yn(Vo,Ate,"bool"),Dte=Un(Bs);function Vv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Mte={kernelName:Uo,backendName:"wasm",kernelFunc:Vv};function Rte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Pte={kernelName:pc,backendName:"wasm",kernelFunc:Rte},v2;function Ote(e){v2=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number"])}function Lte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return v2(s,o,l,c,u,i),r}var zte={kernelName:Ho,backendName:"wasm",kernelFunc:Lte,setupFunc:Ote},Bte=Un(Ws),Wte=!1,Vte=yn(Vs,Wte),w2;function Ute(e){w2=e.wasm.cwrap(Us,null,["number","number","number","number","number","number","number"])}function Gte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return w2(u,p,d,h,m,r,g),f}var Hte={kernelName:Us,backendName:"wasm",setupFunc:Ute,kernelFunc:Gte},k2;function jte(e){k2=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Np[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let oe=a.dataIdMap.get(i.dataId);if(oe.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${oe.shape.length}.`);if(oe.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${oe.shape}) does not match the number of output channels (${x})`);v=oe.id}let N=f.filterHeight,T=f.filterWidth,E=f.padInfo.top,A=f.padInfo.right,$=f.padInfo.bottom,O=f.padInfo.left,V=f.dilationHeight,W=f.dilationWidth,H=f.strideHeight,X=f.strideWidth,q=f.inChannels,K=f.padInfo.type==="SAME"?1:0,J=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),re=a.dataIdMap.get(ie.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return k2(y,J,te,Q,b,N,T,v,E,A,$,O,K,V,W,H,X,q,x,g,ae,m||0,re),ie}var Kte={kernelName:vi,backendName:"wasm",setupFunc:jte,kernelFunc:qte},I2;function Xte(e){I2=e.wasm.cwrap(wi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yte(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=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Np[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let oe=a.dataIdMap.get(i.dataId);if(oe.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${oe.shape.length}.`);if(oe.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${oe.shape}) does not match the number of output channels (${x})`);v=oe.id}let N=f.filterHeight,T=f.filterWidth,E=f.padInfo.top,A=f.padInfo.right,$=f.padInfo.bottom,O=f.padInfo.left,V=f.dilationHeight,W=f.dilationWidth,H=f.strideHeight,X=f.strideWidth,q=f.inChannels,K=f.padInfo.type==="SAME"?1:0,J=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),re=a.dataIdMap.get(ie.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return I2(y,J,te,Q,b,N,T,v,E,A,$,O,K,V,W,H,X,q,x,g,ae,m||0,re),ie}var Jte={kernelName:wi,backendName:"wasm",setupFunc:Xte,kernelFunc:Yte},N2;function Zte(e){N2=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number"])}function Qte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=hy.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 N2(d,Vn[a.dtype],h,i,p,o,m,f),c}var ene={kernelName:qo,backendName:"wasm",setupFunc:Zte,kernelFunc:Qte},T2;function tne(e){T2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function nne(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=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Oa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Oa({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,x=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return T2(g,Vn[r.dtype],x,f,y,c.batchSize,v,b),m.shape=c.outputShape,m}var ane={kernelName:jo,backendName:"wasm",setupFunc:tne,kernelFunc:nne},rne=!1,sne=yn(Ko,rne,"bool"),ine=!1,one=yn(Gs,ine,"bool"),S2;function lne(e){S2=e.wasm.cwrap(Hs,null,["number","number","number"])}function une(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;S2(r,n,i)}return s}var cne={kernelName:Hs,backendName:"wasm",setupFunc:lne,kernelFunc:une},pne=!1,dne=yn(Qo,pne,"bool"),hne=!1,mne=yn(el,hne,"bool"),fne=Un(js),gne=!1,yne=yn(nl,gne,"bool"),C2;function bne(e){C2=e.wasm.cwrap(qs,null,["number, number, number"])}function xne(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}=yu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.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;C2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var vne={kernelName:qs,backendName:"wasm",setupFunc:bne,kernelFunc:xne},wne=!1,kne=yn(Ks,wne),E2;function Ine(e){E2=e.wasm.cwrap(Xs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nne(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=_.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,x=u.strideHeight,v=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let E=a.makeOutput(u.outShape,"float32"),A=a.dataIdMap.get(E.dataId).id;return E2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,N,T,A),E}var Tne={kernelName:Xs,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne},_2;function Sne(e){_2=e.wasm.cwrap(Ys,null,["number, number, number"])}function Cne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=yu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Jm({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;_2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(b.dataId),x}var Ene={kernelName:Ys,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},F2;function _ne(e){F2=e.wasm.cwrap(Js,null,["number, number, number"])}function Fne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=yu(i,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;F2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Ane={kernelName:Js,backendName:"wasm",setupFunc:_ne,kernelFunc:Fne},$ne=!1,Dne=yn(Zs,$ne),Mne=!0,Rne=yn(Qs,Mne),Pne=Un(rl);function Uv(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 A2;function One(e){A2=e.wasm.cwrap(il,"number",["number","number","number","number","number"])}function Lne(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=A2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Uv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var zne={kernelName:il,backendName:"wasm",setupFunc:One,kernelFunc:Lne},$2;function Bne(e){$2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","bool"])}function Wne(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=$2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Uv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Vne={kernelName:ol,backendName:"wasm",setupFunc:Bne,kernelFunc:Wne},D2;function Une(e){D2=e.wasm.cwrap(ll,"number",["number","number","number","number","number","number"])}function Gne(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=D2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Uv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Hne={kernelName:ll,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},jne=!1,qne=yn(sl,jne,"bool"),M2;function Kne(e){M2=e.wasm.cwrap(ei,null,["number","number","number","number","number"])}function Xne(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 M2(u,s,i,o,c),l}var Yne={kernelName:ei,backendName:"wasm",setupFunc:Kne,kernelFunc:Xne};function Jne(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Zne={kernelName:ul,backendName:"wasm",kernelFunc:Jne};function Qne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Vv({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=>Vv({inputs:{input:l},backend:n,attrs:{dim:r}}));return h2({inputs:o,backend:n,attrs:{axis:r}})}var eae={kernelName:cl,backendName:"wasm",kernelFunc:Qne},R2;function tae(e){R2=e.wasm.cwrap(ti,null,["number","array","number","number","array","array","number","number"])}function nae(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 R2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var aae={kernelName:ti,backendName:"wasm",kernelFunc:nae,setupFunc:tae},rae=!1,sae=yn(ni,rae),P2;function iae(e){P2=e.wasm.cwrap(ai,null,["number","number","number"])}function oae(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 P2(s,i,l),o}var lae={kernelName:ai,backendName:"wasm",setupFunc:iae,kernelFunc:oae},O2;function uae(e){O2=e.wasm.cwrap(pl,null,["number","number","number","number"])}function cae(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}=yu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;O2(l,y,Vn[b.dtype],x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var pae={kernelName:pl,backendName:"wasm",setupFunc:uae,kernelFunc:cae},dae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=mv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},hae={kernelName:yc,backendName:"wasm",kernelFunc:dae},mae=!0,fae=yn(zs,mae),gae=Un(ri),yae=Un(ii),L2;function bae(e){L2=e.wasm.cwrap(si,null,["number","number","number","number","number","number","number","number","number","number"])}function xae(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=Jm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return L2(y,u,p,d,h,l,c,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var vae={kernelName:si,backendName:"wasm",setupFunc:bae,kernelFunc:xae},z2;function wae(e){z2=e.wasm.cwrap(oi,null,["number","array","number","array","number","number"])}function kae(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 Xm({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 z2(l,u,i.length,p,r.shape.length,c),Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n})}var Iae={kernelName:oi,backendName:"wasm",kernelFunc:kae,setupFunc:wae},B2;function Nae(e){B2=e.wasm.cwrap(El,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Tae(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]=_.getImageCenter(o,d,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return B2(c,p,d,h,m,s,f,g,v,x.length,u),l}var Sae={kernelName:El,backendName:"wasm",kernelFunc:Tae,setupFunc:Nae},Cae=Un(li),Eae=Un(ui),W2;function _ae(e){W2=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}=my.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 W2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var Aae={kernelName:ml,backendName:"wasm",setupFunc:_ae,kernelFunc:Fae},V2;function $ae(e){V2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Dae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:k.sizeFromShape(r.shape.slice(1));return V2(i,o,l,h,u),c}var Mae={kernelName:fl,backendName:"wasm",kernelFunc:Dae,setupFunc:$ae},U2;function Rae(e){U2=e.wasm.cwrap(pi,null,["number","number"])}function Pae(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||U2(a,s),r}var Oae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Rae,kernelFunc:Pae},Lae=Un(ci);function Zm(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=dn.parseSliceParams(t,n,a),o=dn.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=dn.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)zae(l,u[0],d,s,i);else if(h===3)Bae(l,u[0],u[1],d,s,i);else if(h===4)Wae(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 zae(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 Bae(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 Wae(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 Vae={kernelName:yl,backendName:"wasm",kernelFunc:Zm},G2;function Uae(e){G2=e.wasm.cwrap(mi,null,["number","number","number","number"])}function Gae(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||G2(r,i,o,l),s}var Hae={kernelName:mi,backendName:"wasm",setupFunc:Uae,kernelFunc:Gae};function jae(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(p=>{let d=[...u];d[o]=p;let h=Zm({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return c[o]+=p,h})}var qae={kernelName:wl,backendName:"wasm",kernelFunc:jae},Kae=Un(di),Xae=Un(vc),Yae=!0,Jae=yn(fi,Yae),H2;function Zae(e){H2=e.wasm.cwrap(kl,null,["number","array","number","array","array","array","array","array","number","number"])}function Qae(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=_.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=_.slice_util.maskToAxes(p),g=r.shape.slice();f.forEach($=>{s[$]=0,i[$]=1,g.splice($,0,1)});let y=Oa({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:b,end:x,strides:v}=_.slice_util.getNormalizedAxes(y.shape,h,m,s,i,o,l,c,u);s=b,i=x,o=v;let N=_.slice_util.maskToAxes(d);N.forEach($=>{i[$]=s[$]+1,o[$]=1});let T=_.slice_util.computeOutShape(s,i,o),E=T.filter(($,O)=>N.indexOf(O)===-1);if(o.every($=>$===1)){let $=Zm({inputs:{x:r},attrs:{begin:s,size:T},backend:t});return Oa({inputs:{x:$},attrs:{shape:E},backend:t})}let A=t.makeOutput(E,"float32");if(!E.some($=>$===0)){let $=t.dataIdMap.get(y.dataId).id,O=new Uint8Array(new Int32Array(k.computeStrides(y.shape)).buffer),V=new Uint8Array(new Int32Array(s).buffer),W=new Uint8Array(new Int32Array(i).buffer),H=new Uint8Array(new Int32Array(o).buffer),X=new Uint8Array(new Int32Array(E).buffer),q=new Uint8Array(new Int32Array(k.computeStrides(E)).buffer),K=t.dataIdMap.get(A.dataId).id;H2($,O,y.shape.length,V,W,H,X,q,E.length,K)}return Oa({inputs:{x:A},attrs:{shape:E},backend:t})}var ere={kernelName:kl,backendName:"wasm",setupFunc:Zae,kernelFunc:Qae},tre=!0,nre=yn(gi,tre),j2;function are(e){j2=e.wasm.cwrap(hi,null,["number, number, number"])}function rre(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}=yu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("sum",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;j2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var sre={kernelName:hi,backendName:"wasm",setupFunc:are,kernelFunc:rre},ire=Un(yi),q2;function ore(e){q2=e.wasm.cwrap(Hr,null,["number","array","number","array","number","number"])}function lre(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 q2(s,l,r.shape.length,c,o.length,Vn[u.dtype],p),u}var ure={kernelName:Hr,backendName:"wasm",setupFunc:ore,kernelFunc:lre},K2;function cre(e){K2=e.wasm.cwrap(Nl,null,["number","array","number","number","number","bool","number","number"])}var pre=({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 K2(i,o,a.shape.length,Vn[a.dtype],r,s,u,d),[c,p]},dre={kernelName:Nl,backendName:"wasm",setupFunc:cre,kernelFunc:pre};function hre(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]=Zm({inputs:{x:r},attrs:{begin:p,size:d},backend:n});return u.map(({dataId:h,dtype:m})=>({dataId:h,dtype:m,shape:l}))}var mre={kernelName:Tl,backendName:"wasm",kernelFunc:hre};function fre(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var gre={kernelName:Sl,backendName:"wasm",kernelFunc:fre},yre=[Bee,Vee,Hee,Qee,nte,ite,ote,cte,pte,mte,yte,bte,wte,Nte,Cte,Fte,$te,Dte,Mte,Pte,zte,Bte,Vte,zee,Hte,Kte,Jte,ene,ane,sne,one,jee,cne,dne,mne,fne,yne,vne,kne,Tne,Ene,Ane,Dne,Rne,Pne,zne,Vne,Hne,qne,Yne,Zne,eae,aae,sae,lae,pae,hae,fae,gae,yae,ate,vae,Iae,Sae,Eae,Cae,Aae,Mae,Oae,Lae,Vae,Hae,qae,Kae,Xae,Jae,ere,nre,sre,ire,ure,dre,Yee,mre,gre];for(let e of yre)Ic(e);var Gv=ee();Gv.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])));Gv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Gv.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 X2=No(F_()),bre='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()}}}}',xre=No(A_()),Y2=class extends nc{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new gd(this,Zr())}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 vre(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 ${t}`)}}};function wre(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance)})})}),{})}function J2(e,t,n){if(Qm!=null)return Qm;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),Tp!=null&&Tp[a]!=null?Tp[a]:n+a}async function kre(){let[e,t]=await Promise.all([ee().getAsync("WASM_HAS_SIMD_SUPPORT"),ee().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(l,c)=>{if(l.endsWith(".worker.js")){let u=bre,p=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(p)}return l.endsWith(".wasm")?J2(e,t,Sp!=null?Sp:c):c+l},Hv&&(r.instantiateWasm=wre(J2(e,t,Sp!=null?Sp:"")));let s;t&&e&&Qm==null?(s=X2.default(r),s.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+X2.default.toString()],{type:"text/javascript"})):s=xre.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,Cp=!1,n({wasm:s})},s.onAbort=()=>{o||Cp||(Cp=!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 vre(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 Ire=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Qm=null,Sp=null,Tp={},Cp=!1,Hv=!1;function Nre(e,t=!1){if(Pt("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Cp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Qm=e,Hv=t}function Tre(e,t=!1){if(Cp)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")Sp=e;else{Tp=e;let n=Ire.filter(a=>Tp[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.`)}Hv=t}var Sre="2.8.3",Cre=2;lh("wasm",async()=>{let{wasm:e}=await kre();return new Y2(e)},Cre);var yw={};hd(yw,{AnchorPosition:()=>or,DrawBox:()=>af,DrawBoxOptions:()=>iw,DrawFaceLandmarks:()=>gw,DrawFaceLandmarksOptions:()=>fw,DrawTextField:()=>gs,DrawTextFieldOptions:()=>Ap,drawContour:()=>Fr,drawDetections:()=>Lre,drawFaceExpressions:()=>Vre,drawFaceLandmarks:()=>Ure});function Fr(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:a,y:r},s)=>{let i=t[s];e.moveTo(i.x,i.y),e.lineTo(a,r)}),n){let a=t[t.length-1],r=t[0];if(!a||!r)return;e.moveTo(a.x,a.y),e.lineTo(r.x,r.y)}e.stroke()}var jv={};hd(jv,{computeReshapedDimensions:()=>Xv,getCenterPoint:()=>Ji,isDimensions:()=>tf,isEven:()=>ef,isFloat:()=>Kv,isTensor:()=>Xi,isTensor1D:()=>Ere,isTensor2D:()=>qv,isTensor3D:()=>Ar,isTensor4D:()=>ra,isValidNumber:()=>La,isValidProbablitiy:()=>bu,range:()=>sr,round:()=>Yi});var bn=class{constructor(t,n){if(!La(t)||!La(n))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new bn(1/this.width,1/this.height)}};function Xi(e,t){return e instanceof z&&e.shape.length===t}function Ere(e){return Xi(e,1)}function qv(e){return Xi(e,2)}function Ar(e){return Xi(e,3)}function ra(e){return Xi(e,4)}function Kv(e){return e%1!=0}function ef(e){return e%2==0}function Yi(e,t=2){let n=10**t;return Math.floor(e*n)/n}function tf(e){return e&&e.width&&e.height}function Xv({width:e,height:t},n){let a=n/Math.max(t,e);return new bn(Math.round(e*a),Math.round(t*a))}function Ji(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function sr(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function La(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function bu(e){return La(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var lt=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(La)}static assertIsValidBox(t,n,a=!1){if(!lt.isRect(t))throw new Error(`${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(La),s=[a.x,a.y,a.width,a.height].every(La);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];lt.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 lt({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 lt({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 lt({x:t,y:n,width:a,height:r})}rescale(t){let n=tf(t)?t.width:t,a=tf(t)?t.height:t;return new lt({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 lt({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 lt({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 lt({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 lt({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 xu=class extends lt{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var fs=class{constructor(t,n,a,r,s){this._imageDims=new bn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new lt(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 lt(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new fs(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var xt=class extends fs{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 xt(a,r,s)}};function Yv(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 Jv(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 xu(a,r,s,i)}function Zv(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(Yv(p,d,a))}r=r.filter((c,u)=>l[u]<=n)}return s}function za(e,t){return D(()=>{let[n,a,r]=t,s=In([...e.shape.slice(0,3),1],n,"float32"),i=In([...e.shape.slice(0,3),1],a,"float32"),o=In([...e.shape.slice(0,3),1],r,"float32"),l=Ze([s,i,o],3);return fe(e,l)})}function Qv(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,In(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 Ze(p,i)})}function _re(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 Ep(e){return 1/(1+Math.exp(-e))}function Fre(e){return Math.log(e/(1-e))}var vu=class extends lt{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Are=.5,$re=.43,Dre=.45,sa=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new bn(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 xt?t.box.floor():new lt(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/Dre),l=Ji(t),c=Math.floor(Math.max(0,l.x-Are*o)),u=Math.floor(Math.max(0,l.y-$re*o));return new vu(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=Jv(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Z2=class extends sa{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Ji([t[3],t[4]])]}};var wu=class extends sa{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Ji)}};var _p=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Yi(this.distance)})`:""}`}};var Fp=class extends lt{static assertIsValidLabeledBox(t,n){if(lt.assertIsValidBox(t,n),!La(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 $r=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 $r(t.label,n)}};var Q2=class extends Fp{static assertIsValidPredictedBox(t,n){if(Fp.assertIsValidLabeledBox(t,n),!bu(t.score)||!bu(t.classScore))throw new Error(`${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 ir(e){return e.detection instanceof xt}function Zi(e,t){return{...e,...{detection:t}}}function ew(){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 nf(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 tw(){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=nf();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 nw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var aw=p_(tC()),tn;function Pre(){if(!tn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return tn}function rw(e){tn=e}function sw(){return nw()?rw(ew()):aw.isNodejs()?rw(tw()):null}function Ore(e){if(tn||sw(),!tn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=tn.Canvas,Image:n=tn.Image}=e;tn.Canvas=t,tn.Image=n,tn.createCanvasElement=e.createCanvasElement||(()=>new t),tn.createImageElement=e.createImageElement||(()=>new n),tn.ImageData=e.ImageData||tn.ImageData,tn.Video=e.Video||tn.Video,tn.fetch=e.fetch||tn.fetch,tn.readFile=e.readFile||tn.readFile}var st={getEnv:Pre,setEnv:rw,initialize:sw,createBrowserEnv:ew,createFileSystem:nf,createNodejsEnv:tw,monkeyPatch:Ore,isBrowser:nw,isNodejs:aw.isNodejs};sw();function Qi(e){return!st.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function En(e){let{Canvas:t,CanvasRenderingContext2D:n}=st.getEnv();if(e instanceof n)return e;let a=Qi(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 or;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(or||(or={}));var Ap=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||or.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}},gs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof gs?t.text:t,this.anchor=n,this.options=new Ap(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===or.BOTTOM_RIGHT||a===or.TOP_RIGHT,s=a===or.BOTTOM_LEFT||a===or.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=Qi(t),a=En(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let c=this.measureWidth(a),u=this.measureHeight();a.fillStyle=r;let p=this.getUpperLeft(a,n);a.fillRect(p.x,p.y,c,u),a.fillStyle=s,this.text.forEach((d,h)=>{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var iw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:or.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ap({...i,...s})}},af=class{constructor(t,n={}){this.box=new lt(t),this.options=new iw(n)}draw(t){let n=En(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new gs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Lre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof xt?a.score:ir(a)?a.detection.score:void 0,s=a instanceof xt?a.box:ir(a)?a.detection.box:new lt(a),i=r?`${Yi(r)}`:void 0;new af(s,{label:i}).draw(e)})}function $p(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function ow(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||$p(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 lw(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=st.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function eo(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new bn(e.naturalWidth,e.naturalHeight):e instanceof n?new bn(e.videoWidth,e.videoHeight):new bn(e.width,e.height)}function ku({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Dp(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!$p(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||eo(e),s=ku({width:a,height:r});return e instanceof n?En(s).putImageData(e,0,0):En(s).drawImage(e,0,0,a,r),s}async function uw(e,t){let n=t||st.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ra(e)?1:0),i=D(()=>e.as3D(a,r,s).toInt());return await Ti.toPixels(i,n),i.dispose(),n}function rf(e){let{Image:t,Canvas:n,Video:a}=st.getEnv();return e instanceof t||e instanceof n||e instanceof a}function cw(e,t,n=!1){let{Image:a,Canvas:r}=st.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let s=eo(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,c=ku({width:t,height:t}),u=e instanceof r?e:Dp(e),p=Math.abs(o-l)/2,d=n&&o<l?p:0,h=n&&l<o?p:0;return En(c).drawImage(u,d,h,o,l),c}var Dr=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(Ar(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ra(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 st.getEnv().Canvas?a:Dp(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 sr(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 Xv({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,D(()=>{let a=sr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof z){let o=ra(i)?i:i.expandDims();return o=Qv(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ya.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof st.getEnv().Canvas)return Ti.fromPixels(cw(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Mt(a.map(s=>pe(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function gt(e){if(e instanceof Dr)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 ${r}:`:"",a=t.map(Qi);return a.forEach((r,s)=>{if(!rf(r)&&!Ar(r)&&!ra(r))throw typeof t[s]=="string"?new Error(`toNetInput -${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(ra(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>rf(r)&&ow(r))),new Dr(a,Array.isArray(e))}async function Iu(e,t){let{Canvas:n}=st.getEnv(),a=e;if(!(e instanceof n)){let i=await gt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await uw(o)}let r=En(a);return t.map(i=>i instanceof xt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:c})=>{let u=ku({width:l,height:c});return En(u).putImageData(r.getImageData(i,o,l,c),0,0),u})}async function Nu(e,t){if(!Ar(e)&&!ra(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ra(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return D(()=>{let[n,a,r]=e.shape.slice(ra(e)?1:0);return t.map(o=>o instanceof xt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:c,height:u})=>Ul(e.as3D(n,a,r),[l,o,0],[u,c,r]))})}async function to(e,t){let{fetch:n}=st.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function zre(e){let t=await to(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: ${t.url}`);return lw(n)}async function pw(e){return(await to(e)).json()}async function Bre(e){return new Float32Array(await(await to(e)).arrayBuffer())}function sf(e,t){let n=`${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 dw(e,t){let{manifestUri:n,modelBaseUri:a}=sf(e,t),r=await pw(n);return Kt.loadWeights(r,a)}function Wre(e,t,n=!1){let{width:a,height:r}=n?eo(t):t;return e.width=a,e.height=r,{width:a,height:r}}var on=class{constructor(){this._params=void 0;this._paramMappings=[]}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 jr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof jr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Xn(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 dw(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}=st.getEnv(),{manifestUri:a,modelBaseUri:r}=sf(t,this.getDefaultModelName()),s=c=>Promise.all(c.map(u=>n(u).then(p=>p.buffer))),i=Kt.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 ${t}`);return{obj:a,objProp:r}}};function _n(e,t,n){return D(()=>{let a=Ai(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=Z(a,t.bias),a})}function of(e,t,n=!1){return D(()=>{let a=Xe(n?Z($t(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):_n(e,t.conv0,[2,2])),r=_n(a,t.conv1,[1,1]),s=Xe(Z(a,r)),i=_n(s,t.conv2,[1,1]);return Xe(Z(a,Z(r,i)))})}function Mp(e,t,n=!1,a=!0){return D(()=>{let r=Xe(n?Z($t(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):_n(e,t.conv0,a?[2,2]:[1,1])),s=_n(r,t.conv1,[1,1]),i=Xe(Z(r,s)),o=_n(i,t.conv2,[1,1]),l=Xe(Z(r,Z(s,o))),c=_n(l,t.conv3,[1,1]);return Xe(Z(r,Z(s,Z(o,c))))})}function no(e,t,n="same",a=!1){return D(()=>{let r=Z($t(e,t.filters,[1,1],n),t.bias);return a?Xe(r):r})}function xn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Tu(e,t){return(n,a,r,s)=>{let i=Ea(e(n*a*r*r),[r,r,n,a]),o=et(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function lf(e,t){return(n,a,r)=>{let s=Ca(e(n*a),[n,a]),i=et(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var uf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function Su(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=et(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new uf(s,i,o)}}function Cu(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new uf(n,a,r)}}function Gn(e,t){return(n,a,r)=>{let s=e[n];if(!Xi(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function vn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function cf(e,t){let n=Tu(e,t),a=Su(e,t);function r(i,o,l,c=!1){let u=c?n(i,o,3,`${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 nC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock4Params:r}=cf(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: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function pf(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function df(e,t){let n=Gn(e,t),a=pf(n),r=Cu(n);function s(o,l=!1){let c=l?a(`${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 aC(e){let t=[],{extractDenseBlock4Params:n}=df(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return xn(e,t),{params:a,paramMappings:t}}var Rp=class extends on{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=za(a,[122.782,117.001,104.298]).div(de(255)),i=Mp(s,n.dense0,!0);return i=Mp(i,n.dense1),i=Mp(i,n.dense2),i=Mp(i,n.dense3),i=Jn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await gt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return aC(t)}extractParams(t){return nC(t)}};function Pp(e,t){return D(()=>Z(ze(e,t.weights),t.bias))}function rC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=vn(e),o=lf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function sC(e){let t=[],n=Gn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return xn(e,t),{params:r,paramMappings:t}}function hf(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Op=class extends on{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof Dr?this.faceFeatureExtractor.forwardInput(t):t;return Pp(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 rC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=hf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),sC(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 hw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],ys=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);hw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return hw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var mf=class extends Op{constructor(t=new Rp){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Sa(this.runNet(t)))}async forward(t){return this.forwardInput(await gt(t))}async predictExpressions(t){let n=await gt(t),a=await this.forwardInput(n),r=await Promise.all(pt(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new ys(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function mw(e){return e.expressions instanceof ys}function ff(e,t){return{...e,...{expressions:t}}}function Vre(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof ys?s:mw(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=ir(s)?s.detection.box.bottomLeft:a||new De(0,0);new gs(l.map(p=>`${p.expression} (${Yi(p.probability)})`),c).draw(e)})}function ao(e){return ir(e)&&e.landmarks instanceof sa&&e.unshiftedLandmarks instanceof sa&&e.alignedRect instanceof xt}function Eu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new xt(e.detection.score,r.rescale(s.reverse()),s);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i}}}var fw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},gw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new fw(n)}draw(t){let n=En(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof wu&&(n.strokeStyle=i,n.lineWidth=s,Fr(n,this.faceLandmarks.getJawOutline()),Fr(n,this.faceLandmarks.getLeftEyeBrow()),Fr(n,this.faceLandmarks.getRightEyeBrow()),Fr(n,this.faceLandmarks.getNose()),Fr(n,this.faceLandmarks.getLeftEye(),!0),Fr(n,this.faceLandmarks.getRightEye(),!0),Fr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let c=u=>{n.beginPath(),n.arc(u.x,u.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(c)}}};function Ure(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof sa?a:ao(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new gw(r).draw(e)})}var iC="0.11.2";function Gre(e,t){let n=Tu(e,t),a=Su(e,t);function r(i,o,l){let c=a(i,o,`${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,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function oC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=vn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Gre(a,n),c=s(3,32,3,"entry_flow/conv_in"),u=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:c,reduction_block_0:u,reduction_block_1:p},h={};sr(t,0,1).forEach(y=>{h[`main_block_${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: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Hre(e,t){let n=Gn(e,t),a=pf(n),r=Cu(n);function s(o){let l=r(`${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(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function lC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Hre(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),c=s("entry_flow/reduction_block_1"),u={conv_in:o,reduction_block_0:l,reduction_block_1:c},p={};sr(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return xn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function uC(e,t,n){return Z($t(e,t.filters,n,"same"),t.bias)}function bw(e,t,n=!0){let a=n?Xe(e):e;return a=_n(a,t.separable_conv0,[1,1]),a=_n(Xe(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=Z(a,uC(e,t.expansion_conv,[2,2])),a}function jre(e,t){let n=_n(Xe(e),t.separable_conv0,[1,1]);return n=_n(Xe(n),t.separable_conv1,[1,1]),n=_n(Xe(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var xw=class extends on{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=za(a,[122.782,117.001,104.298]).div(de(256)),i=Xe(uC(s,n.entry_flow.conv_in,[2,2]));return i=bw(i,n.entry_flow.reduction_block_0,!1),i=bw(i,n.entry_flow.reduction_block_1),sr(this._numMainBlocks,0,1).forEach(o=>{i=jre(i,n.middle_flow[`main_block_${o}`])}),i=bw(i,n.exit_flow.reduction_block),i=Xe(_n(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await gt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return lC(t,this._numMainBlocks)}extractParams(t){return oC(t,this._numMainBlocks)}};function cC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),r=lf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function pC(e){let t=[],n=Gn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return xn(e,t),{params:r,paramMappings:t}}var Mr;(function(e){e.FEMALE="female",e.MALE="male"})(Mr||(Mr={}));var gf=class extends on{constructor(t=new xw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof Dr?this.faceFeatureExtractor.forwardInput(t):t,r=Jn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Pp(r,n.fc.age).as1D(),i=Pp(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:Sa(a)}})}async forward(t){return this.forwardInput(await gt(t))}async predictAgeAndGender(t){let n=await gt(t),a=await this.forwardInput(n),r=pt(a.age),s=pt(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?Mr.MALE:Mr.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 cC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=hf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),pC(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 Lp=class extends Op{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)=>Mt([In([68],p,"float32"),In([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(In([s,136],n,"float32")).sub(Mt(Array.from(Array(s),(p,d)=>i(l(d),c(d))))).div(Mt(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 gt(t))}async detectLandmarks(t){let n=await gt(t),a=D(()=>pt(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(await s.data()),l=o.filter((u,p)=>ef(p)),c=o.filter((u,p)=>!ef(p));return new wu(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 Lp{constructor(t=new Rp){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function dC(e){let t=[],{extractDenseBlock3Params:n}=df(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return xn(e,t),{params:a,paramMappings:t}}function hC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock3Params:r}=cf(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: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var vw=class extends on{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=za(a,[122.782,117.001,104.298]).div(de(255)),i=of(s,n.dense0,!0);return i=of(i,n.dense1),i=of(i,n.dense2),i=Jn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await gt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return dC(t)}extractParams(t){return hC(t)}};var yf=class extends Lp{constructor(t=new vw){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var mC=class extends _u{};function fC(e,t){return Z(L(e,t.weights),t.biases)}function ww(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=$t(e,s,n,r);return o=Z(o,i),o=fC(o,t.scale),a?Xe(o):o}function gC(e,t){return ww(e,t,[1,1],!0)}function kw(e,t){return ww(e,t,[1,1],!1)}function bf(e,t){return ww(e,t,[2,2],!0,"valid")}function qre(e,t){function n(o,l,c){let u=e(o),p=u.length/(l*c*c);if(Kv(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${u.length}, numFilters: ${l}, filterSize: ${c}`);return D(()=>Ue(Ea(u,[l,p,c,c]),[2,3,1,0]))}function a(o,l,c,u){let p=n(o,l,c),d=et(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/bias`}),{filters:p,bias:d}}function r(o,l){let c=et(e(o)),u=et(e(o));return t.push({paramPath:`${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 yC(e){let{extractWeights:t,getRemainingWeights:n}=vn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=qre(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"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),N=D(()=>Ue(Ca(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:c,conv64_down:u,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:N},paramMappings:a}}function Kre(e,t){let n=Gn(e,t);function a(i){let o=n(`${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 bC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Kre(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:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!qv(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1: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:x};return xn(e,t),{params:v,paramMappings:t}}function Ba(e,t){let n=gC(e,t.conv1);return n=kw(n,t.conv2),n=Z(n,e),n=Xe(n),n}function zp(e,t){let n=bf(e,t.conv1);n=kw(n,t.conv2);let a=Jn(e,2,2,"valid"),r=yt(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=yt(o);n=Ze([n,l],1);let c=[...n.shape];c[2]=1;let u=yt(c);n=Ze([n,u],2)}return a=s?Ze([a,r],3):a,n=Z(a,n),n=Xe(n),n}var Fu=class extends on{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=za(a,[122.782,117.001,104.298]).div(de(256)),i=bf(s,n.conv32_down);i=Dt(i,3,2,"valid"),i=Ba(i,n.conv32_1),i=Ba(i,n.conv32_2),i=Ba(i,n.conv32_3),i=zp(i,n.conv64_down),i=Ba(i,n.conv64_1),i=Ba(i,n.conv64_2),i=Ba(i,n.conv64_3),i=zp(i,n.conv128_down),i=Ba(i,n.conv128_1),i=Ba(i,n.conv128_2),i=zp(i,n.conv256_down),i=Ba(i,n.conv256_1),i=Ba(i,n.conv256_2),i=zp(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await gt(t))}async computeFaceDescriptor(t){let n=await gt(t),a=D(()=>pt(this.forwardInput(n))),r=await Promise.all(a.map(s=>s.data()));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return bC(t)}extractParams(t){return yC(t)}};function Xre(e){let t=new Fu;return t.extractWeights(e),t}function xf(e,t){return{...e,...{descriptor:t}}}function Yre(e){return typeof e.age=="number"}function vf(e,t){return{...e,...{age:t}}}function Jre(e){return(e.gender===Mr.MALE||e.gender===Mr.FEMALE)&&bu(e.genderProbability)}function wf(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function Zre(e,t){function n(l,c){let u=Ea(e(3*3*l),[3,3,l,1]),p=et(e(l)),d=et(e(l)),h=et(e(l)),m=et(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=et(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,`${u}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),c=s(32,64,"mobilenetv1/conv_1"),u=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),N=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:c,conv_2:u,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:N}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),c=r(256,512,3,"prediction_layer/conv_1"),u=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),N=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),T=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),E=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),A=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),$=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),O=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),V=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:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:N},box_predictor_3:{box_encoding_predictor:T,class_predictor:E},box_predictor_4:{box_encoding_predictor:A,class_predictor:$},box_predictor_5:{box_encoding_predictor:O,class_predictor:V}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function xC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=Zre(n,t),i=r(),o=s(),c={extra_dim:sh(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:c},paramMappings:t}}function Qre(e,t){let n=Gn(e,t);function a(c,u,p){let d=n(`${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 vC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Qre(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Ar(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return xn(e,t),{params:s,paramMappings:t}}function ba(e,t,n){return D(()=>{let a=$t(e,t.filters,n,"same");return a=Z(a,t.batch_norm_offset),Yt(a,0,6)})}var ese=.0010000000474974513;function tse(e,t,n){return D(()=>{let a=xr(e,t.filters,n,"same");return a=br(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,ese),Yt(a,0,6)})}function nse(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function wC(e,t){return D(()=>{let n,a=ba(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=nse(o);a=tse(a,s.depthwise_conv,l),a=ba(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 ase(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 kC(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=ase(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 rse(e){let t=pt(Ue(e,[1,0])),n=[fe(t[2],t[0]),fe(t[3],t[1])],a=[Z(t[0],ve(n[0],de(2))),Z(t[1],ve(n[1],de(2)))];return{sizes:n,centers:a}}function sse(e,t){let{sizes:n,centers:a}=rse(e),r=pt(Ue(t,[1,0])),s=ve(L(hn(ve(r[2],de(5))),n[0]),de(2)),i=Z(L(ve(r[0],de(10)),n[0]),a[0]),o=ve(L(hn(ve(r[3],de(5))),n[1]),de(2)),l=Z(L(ve(r[1],de(10)),n[1]),a[1]);return Ue(Mt([fe(i,s),fe(l,o),Z(i,s),Z(l,o)]),[1,0])}function IC(e,t,n){return D(()=>{let a=e.shape[0],r=sse(j(ja(n.extra_dim,[a,1,1]),[-1,4]),j(e,[-1,4]));r=j(r,[a,r.shape[0]/a,4]);let s=ca(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=j(i,[a,i.shape[1]]);let o=pt(r),l=pt(i);return{boxes:o,scores:l}})}function ro(e,t){return D(()=>{let n=e.shape[0],a=j(no(e,t.box_encoding_predictor),[n,-1,1,4]),r=j(no(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function NC(e,t,n){return D(()=>{let a=ba(e,n.conv_0,[1,1]),r=ba(a,n.conv_1,[2,2]),s=ba(r,n.conv_2,[1,1]),i=ba(s,n.conv_3,[2,2]),o=ba(i,n.conv_4,[1,1]),l=ba(o,n.conv_5,[2,2]),c=ba(l,n.conv_6,[1,1]),u=ba(c,n.conv_7,[2,2]),p=ro(t,n.box_predictor_0),d=ro(e,n.box_predictor_1),h=ro(r,n.box_predictor_2),m=ro(i,n.box_predictor_3),f=ro(l,n.box_predictor_4),g=ro(u,n.box_predictor_5),y=Ze([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Ze([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var xa=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 so=class extends on{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=fe(L(a,de(.007843137718737125)),de(1)),s=wC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=NC(s.out,s.conv11,n.prediction_layer);return IC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await gt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new xa(n),s=await gt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let x=1;x<i.length;x++)i[x].dispose(),o[x].dispose();let u=Array.from(await c.data()),d=kC(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(x=>{let[v,N]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(A=>A*g),[T,E]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(A=>A*f);return new xt(u[x],new vu(T,v,E-T,N-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return vC(t)}extractParams(t){return xC(t)}};function TC(e){let t=new so;return t.extractWeights(e),t}function ise(e){return TC(e)}var SC=class extends so{};var CC=.4,EC=[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)],_C=[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)],FC=[117.001,114.697,97.404],AC="tiny_yolov2_model",$C="tiny_yolov2_separable_conv_model";var kf=e=>typeof e=="number";function Iw(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(!kf(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=>kf(t.x)&&kf(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(kf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Au(e){return D(()=>{let t=L(e,de(.10000000149011612));return Z(Xe(fe(e,t)),t)})}function Rr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Au(n)})}function Pr(e,t){return D(()=>{let n=ta(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 ose(e,t){let n=Tu(e,t);function a(i,o){let l=et(e(i)),c=et(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=Su(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function DC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=vn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=ose(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),E=c(m,f,"conv3"),A=c(f,g,"conv4"),$=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,V=x?c(b,x,"conv7"):void 0,W=o(x||b||y,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:E,conv4:A,conv5:$,conv6:O,conv7:V,conv8:W}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),E=l(m,f,"conv3"),A=l(f,g,"conv4"),$=l(g,y,"conv5"),O=l(y,b,"conv6"),V=l(b,x,"conv7"),W=o(x,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:E,conv4:A,conv5:$,conv6:O,conv7:V,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function lse(e,t){let n=Gn(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Cu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function MC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=lse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var lr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 Nw=class extends on{constructor(t){super("TinyYolov2");Iw(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=Rr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Rr(a,n.conv6),a=Rr(a,n.conv7),no(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(no(t,n.conv0,"valid",!1)):Pr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Pr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Pr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Pr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Pr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Pr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Pr(a,n.conv6):a,a=n.conv7?Pr(a,n.conv7):a,no(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?za(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new lr(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>pt(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 Zv(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new fs(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return MC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Nw.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 ${a} filterSizes in config`);return DC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),x=y.slice([0,0,0,4],[c,c,u,1]),v=this.withClassScores?Sa(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let x=0;x<u;x++){let v=Ep(f[y][b][x][0]);if(!a||v>a){let N=(b+Ep(g[y][b][x][0]))/c*o,T=(y+Ep(g[y][b][x][1]))/c*l,E=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/c*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/c*l,$=N-E/2,O=T-A/2,V={row:y,col:b,anchor:x},{classScore:W,label:H}=this.withClassScores?await this.extractPredictedClass(h,V):{classScore:1,label:0};m.push({box:new xu($,O,$+E,O+A),score:v,classScore:v*W,label:H,...V})}}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)}},$u=Nw;$u.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Du=class extends $u{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:CC,classes:["face"],...t?{anchors:_C,meanRgb:FC}:{anchors:EC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$C:AC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function use(e,t=!0){let n=new Du(t);return n.extractWeights(e),n}var If=class extends lr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var va=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function io(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ao(l)?r(l):l.detection),i=a||(t instanceof z?await Nu(t,s):await Iu(t,s)),o=await n(i);return i.forEach(l=>l instanceof z&&l.dispose()),o}async function Mu(e,t,n,a,r){return io([e],t,async s=>n(s[0]),a,r)}var RC=.4,PC=[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)],OC=[117.001,114.697,97.404];var Ru=class extends $u{constructor(){let t={withSeparableConvs:!0,iouThreshold:RC,classes:["face"],anchors:PC,meanRgb:OC,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 xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var tt={ssdMobilenetv1:new so,tinyFaceDetector:new Ru,tinyYolov2:new Du,faceLandmark68Net:new _u,faceLandmark68TinyNet:new yf,faceRecognitionNet:new Fu,faceExpressionNet:new mf,ageGenderNet:new gf},LC=(e,t)=>tt.ssdMobilenetv1.locateFaces(e,t),cse=(e,t)=>tt.tinyFaceDetector.locateFaces(e,t),pse=(e,t)=>tt.tinyYolov2.locateFaces(e,t),zC=e=>tt.faceLandmark68Net.detectLandmarks(e),dse=e=>tt.faceLandmark68TinyNet.detectLandmarks(e),hse=e=>tt.faceRecognitionNet.computeFaceDescriptor(e),mse=e=>tt.faceExpressionNet.predictExpressions(e),fse=e=>tt.ageGenderNet.predictAgeAndGender(e),BC=e=>tt.ssdMobilenetv1.load(e),gse=e=>tt.tinyFaceDetector.load(e),yse=e=>tt.tinyYolov2.load(e),bse=e=>tt.faceLandmark68Net.load(e),xse=e=>tt.faceLandmark68TinyNet.load(e),vse=e=>tt.faceRecognitionNet.load(e),wse=e=>tt.faceExpressionNet.load(e),kse=e=>tt.ageGenderNet.load(e),Ise=BC,Nse=LC,Tse=zC;var Tw=class extends va{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Lu=class extends Tw{async run(){let t=await this.parentTask,n=await io(t,this.input,async a=>Promise.all(a.map(r=>tt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>ff(a,n[r]))}withAgeAndGender(){return new Pu(this,this.input)}},zu=class extends Tw{async run(){let t=await this.parentTask;if(!t)return;let n=await Mu(t,this.input,a=>tt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return ff(t,n)}withAgeAndGender(){return new Ou(this,this.input)}},uo=class extends Lu{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new bs(this,this.input)}},co=class extends zu{withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new xs(this,this.input)}};var Sw=class extends va{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Pu=class extends Sw{async run(){let t=await this.parentTask,n=await io(t,this.input,async a=>Promise.all(a.map(r=>tt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return vf(wf(a,i,o),s)})}withFaceExpressions(){return new Lu(this,this.input)}},Ou=class extends Sw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Mu(t,this.input,s=>tt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return vf(wf(t,a,r),n)}withFaceExpressions(){return new zu(this,this.input)}},oo=class extends Pu{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptors(){return new bs(this,this.input)}},lo=class extends Ou{withFaceExpressions(){return new co(this,this.input)}withFaceDescriptor(){return new xs(this,this.input)}};var Nf=class extends va{constructor(t,n){super();this.parentTask=t;this.input=n}},bs=class extends Nf{async run(){let t=await this.parentTask;return(await io(t,this.input,a=>Promise.all(a.map(r=>tt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>xf(t[r],a))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}},xs=class extends Nf{async run(){let t=await this.parentTask;if(!t)return;let n=await Mu(t,this.input,a=>tt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return xf(t,n)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}};var Tf=class extends va{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?tt.faceLandmark68TinyNet:tt.faceLandmark68Net}},Sf=class extends Tf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof z?await Nu(this.input,n):await Iu(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)=>Eu(s,r[i]))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new bs(this,this.input)}},Cf=class extends Tf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof z?await Nu(this.input,[n]):await Iu(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof z&&s.dispose()),Eu(t,r)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new xs(this,this.input)}};var Ef=class extends va{constructor(t,n=new xa){super();this.input=t;this.options=n}},Bp=class extends Ef{async run(){let{input:t,options:n}=this,a=n instanceof If?r=>tt.tinyFaceDetector.locateFaces(r,n):n instanceof xa?r=>tt.ssdMobilenetv1.locateFaces(r,n):n instanceof lr?r=>tt.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=>Zi({},a)))})}withFaceLandmarks(t=!1){return new Sf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetections(),this.input)}},_f=class extends Ef{async run(){let t=await new Bp(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?Zi({},n):void 0)})}withFaceLandmarks(t=!1){return new Cf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new zu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ou(this.runAndExtendWithFaceDetection(),this.input)}};function Sse(e,t=new xa){return new _f(e,t)}function Ff(e,t=new xa){return new Bp(e,t)}async function WC(e,t){return Ff(e,new xa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Cse(e,t={}){return Ff(e,new lr(t)).withFaceLandmarks().withFaceDescriptors()}var Ese=WC;function Cw(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 $r)return i;if(i instanceof Float32Array)return new $r(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new $r(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Cw(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new _p(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new _p("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>$r.fromJSON(a));return new Af(n,t.distanceThreshold)}};function _se(e){let t=new Ru;return t.extractWeights(e),t}function VC(e,t){let{width:n,height:a}=new bn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>VC(r,{width:n,height:a}));if(ao(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Eu(Zi(e,r),s)}return ir(e)?Zi(e,e.detection.forSize(n,a)):e instanceof sa||e instanceof xt?e.forSize(n,a):e}var Fse=typeof process!="undefined",Ase=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",$se={faceapi:iC,node:Fse,browser:Ase};export{gf as AgeGenderNet,xu as BoundingBox,lt as Box,va as ComposableTask,bs as ComputeAllFaceDescriptorsTask,Nf as ComputeFaceDescriptorsTaskBase,xs as ComputeSingleFaceDescriptorTask,Sf as DetectAllFaceLandmarksTask,Bp as DetectAllFacesTask,Tf as DetectFaceLandmarksTaskBase,Ef as DetectFacesTaskBase,Cf as DetectSingleFaceLandmarksTask,_f as DetectSingleFaceTask,bn as Dimensions,hw as FACE_EXPRESSION_LABELS,xt as FaceDetection,SC as FaceDetectionNet,mf as FaceExpressionNet,ys as FaceExpressions,_u as FaceLandmark68Net,yf as FaceLandmark68TinyNet,mC as FaceLandmarkNet,sa as FaceLandmarks,Z2 as FaceLandmarks5,wu as FaceLandmarks68,_p as FaceMatch,Af as FaceMatcher,Fu as FaceRecognitionNet,Mr as Gender,Fp as LabeledBox,$r as LabeledFaceDescriptors,Dr as NetInput,on as NeuralNetwork,fs as ObjectDetection,De as Point,Q2 as PredictedBox,vu as Rect,so as SsdMobilenetv1,xa as SsdMobilenetv1Options,Ru as TinyFaceDetector,If as TinyFaceDetectorOptions,Du as TinyYolov2,lr as TinyYolov2Options,Ese as allFaces,WC as allFacesSsdMobilenetv1,Cse as allFacesTinyYolov2,ow as awaitMediaLoaded,lw as bufferToImage,hse as computeFaceDescriptor,ku as createCanvas,Dp as createCanvasFromMedia,ise as createFaceDetectionNet,Xre as createFaceRecognitionNet,TC as createSsdMobilenetv1,_se as createTinyFaceDetector,use as createTinyYolov2,Ff as detectAllFaces,zC as detectFaceLandmarks,dse as detectFaceLandmarksTiny,Tse as detectLandmarks,Sse as detectSingleFace,yw as draw,st as env,Cw as euclideanDistance,vf as extendWithAge,xf as extendWithFaceDescriptor,Zi as extendWithFaceDetection,ff as extendWithFaceExpressions,Eu as extendWithFaceLandmarks,wf as extendWithGender,Nu as extractFaceTensors,Iu as extractFaces,zre as fetchImage,pw as fetchJson,Bre as fetchNetWeights,to as fetchOrThrow,En as getContext2dOrThrow,eo as getMediaDimensions,uw as imageTensorToCanvas,cw as imageToSquare,Fre as inverseSigmoid,Yv as iou,rf as isMediaElement,$p as isMediaLoaded,Yre as isWithAge,ir as isWithFaceDetection,mw as isWithFaceExpressions,ao as isWithFaceLandmarks,Jre as isWithGender,kse as loadAgeGenderModel,Ise as loadFaceDetectionModel,wse as loadFaceExpressionModel,bse as loadFaceLandmarkModel,xse as loadFaceLandmarkTinyModel,vse as loadFaceRecognitionModel,BC as loadSsdMobilenetv1Model,gse as loadTinyFaceDetectorModel,yse as loadTinyYolov2Model,dw as loadWeightMap,Nse as locateFaces,Wre as matchDimensions,Jv as minBbox,tt as nets,Zv as nonMaxSuppression,za as normalize,Qv as padToSquare,fse as predictAgeAndGender,mse as recognizeFaceExpressions,VC as resizeResults,Qi as resolveInput,_re as shuffleArray,Ep as sigmoid,LC as ssdMobilenetv1,zg as tf,cse as tinyFaceDetector,pse as tinyYolov2,gt as toNetInput,jv as utils,Iw as validateConfig,$se 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