/*
  Human library
  homepage: <https://github.com/vladmandic/human>
  author: <https://github.com/vladmandic>'
  */

var Human=(()=>{var Fk=Object.defineProperty;var Ar=(e,t)=>{for(var n in t)Fk(e,n,{get:t[n],enumerable:!0})};var Og=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var rr=(e,t,n)=>(Og(e,t,"read from private field"),n?n.call(e):t.get(e)),ss=(e,t,n,r)=>(Og(e,t,"write to private field"),r?r.call(e,n):t.set(e,n),n);var Rie={};Ar(Rie,{Human:()=>U8,default:()=>U8});function Yt(e,t){let n=e.endsWith("/")?"":"/",a=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!a.toLocaleLowerCase().includes(".json"))throw new Error(`Human: ModelPath Error: ${a} Expecting JSON file`);return a}function pe(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}var it=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Gn(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Gn(s,i):n[a]=i}),n),{})}function zg(){let e,t;if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);if(n&&n[0]){let r=n[0].match(/\(([^()]+)\)/g);e=r?r[0].replace(/\(|\)/g,""):"",t=navigator.userAgent.replace(n[0],""),e[1]&&(t=t.replace(n[1],"")),t=t.replace(/  /g," ")}}else typeof process!="undefined"&&(e=`${process.platform} ${process.arch}`,t=`NodeJS ${process.version}`);return{platform:e,agent:t}}var cu={};Ar(cu,{Abs:()=>no,Acos:()=>ro,Acosh:()=>ao,AdadeltaOptimizer:()=>Kd,AdagradOptimizer:()=>Zd,AdamOptimizer:()=>Yd,AdamaxOptimizer:()=>Jd,Add:()=>Sa,AddN:()=>ls,All:()=>so,Any:()=>io,ArgMax:()=>us,ArgMin:()=>mu,Asin:()=>oo,Asinh:()=>lo,Atan:()=>uo,Atan2:()=>ho,Atanh:()=>co,AvgPool:()=>cs,AvgPool3D:()=>Au,AvgPool3DGrad:()=>Eh,AvgPoolGrad:()=>Th,BackendWasm:()=>lv,BatchMatMul:()=>hs,BatchToSpaceND:()=>yu,Bincount:()=>Ch,BroadcastTo:()=>Ex,Callback:()=>e4,CallbackList:()=>Yv,Cast:()=>ds,Ceil:()=>ps,ClipByValue:()=>Na,Complex:()=>Rh,ComplexAbs:()=>gu,Concat:()=>po,Conv2D:()=>fs,Conv2DBackpropFilter:()=>Mh,Conv2DBackpropInput:()=>ms,Conv3D:()=>xu,Conv3DBackpropFilterV2:()=>Fh,Conv3DBackpropInputV2:()=>$h,Cos:()=>As,Cosh:()=>fo,CropAndResize:()=>mo,Cumsum:()=>ys,CustomCallback:()=>Qv,DataStorage:()=>kh,DenseBincount:()=>Dh,DepthToSpace:()=>Ao,DepthwiseConv2dNative:()=>gs,DepthwiseConv2dNativeBackpropFilter:()=>Oh,DepthwiseConv2dNativeBackpropInput:()=>zh,Diag:()=>Ph,Dilation2D:()=>wu,Dilation2DBackpropFilter:()=>Wh,Dilation2DBackpropInput:()=>Lh,ENV:()=>yr,EarlyStopping:()=>n4,Einsum:()=>Bh,Elu:()=>yo,EluGrad:()=>Vh,Environment:()=>Nx,Equal:()=>xo,Erf:()=>go,Exp:()=>ws,ExpandDims:()=>wo,Expm1:()=>bo,FFT:()=>jh,Fill:()=>bu,FlipLeftRight:()=>_o,Floor:()=>bs,FloorDiv:()=>_s,FromPixels:()=>sd,FusedBatchNorm:()=>vs,FusedConv2D:()=>ri,FusedDepthwiseConv2D:()=>ai,GPGPUContext:()=>mp,GatherNd:()=>ko,GatherV2:()=>vo,GraphModel:()=>M4,Greater:()=>Io,GreaterEqual:()=>ks,History:()=>Jv,IFFT:()=>Uh,Identity:()=>Is,Imag:()=>Hh,InputSpec:()=>Rt,IsFinite:()=>So,IsInf:()=>No,IsNan:()=>To,KernelBackend:()=>du,LRN:()=>ku,LRNGrad:()=>qh,LayerVariable:()=>Gv,LayersModel:()=>fa,LeakyRelu:()=>Ss,Less:()=>Eo,LessEqual:()=>Co,LinSpace:()=>Gh,Log:()=>Ns,Log1p:()=>Ro,LogSoftmax:()=>Cx,LogicalAnd:()=>Mo,LogicalNot:()=>_u,LogicalOr:()=>vu,MathBackendCPU:()=>np,MathBackendWebGL:()=>Dl,Max:()=>Ts,MaxPool:()=>Cs,MaxPool3D:()=>Iu,MaxPool3DGrad:()=>Kh,MaxPoolGrad:()=>Xh,MaxPoolWithArgmax:()=>Zh,Maximum:()=>Es,Mean:()=>Rs,Min:()=>Ms,Minimum:()=>Fs,MirrorPad:()=>$s,Mod:()=>Fo,MomentumOptimizer:()=>Qd,Multinomial:()=>Yh,Multiply:()=>Ds,Neg:()=>$o,NonMaxSuppressionV3:()=>Oo,NonMaxSuppressionV4:()=>zo,NonMaxSuppressionV5:()=>Po,NotEqual:()=>Do,OP_SCOPE_SUFFIX:()=>Bx,OneHot:()=>Os,OnesLike:()=>Lo,Optimizer:()=>ca,Pack:()=>Wo,PadV2:()=>zs,Pool:()=>$9,Pow:()=>Ps,Prelu:()=>Ls,Prod:()=>Bo,RMSPropOptimizer:()=>ep,RNN:()=>Hr,Range:()=>Su,Rank:()=>If,Real:()=>Jh,RealDiv:()=>xs,Reciprocal:()=>Vo,Reduction:()=>un,Relu:()=>Ws,Relu6:()=>Vs,Reshape:()=>jo,ResizeBilinear:()=>Bs,ResizeBilinearGrad:()=>ed,ResizeNearestNeighbor:()=>Nu,ResizeNearestNeighborGrad:()=>Qh,Reverse:()=>js,RotateWithOffset:()=>nl,Round:()=>Us,Rsqrt:()=>Hs,SGDOptimizer:()=>sc,ScatterNd:()=>Uo,Select:()=>Ho,Selu:()=>Go,Sequential:()=>Ul,Sigmoid:()=>qs,Sign:()=>Ko,Sin:()=>Gs,Sinh:()=>Xo,Slice:()=>qo,Softmax:()=>Zs,Softplus:()=>Zo,SpaceToBatchND:()=>Tu,SparseReshape:()=>td,SparseToDense:()=>nd,SplitV:()=>Yo,Sqrt:()=>Xs,Square:()=>Eu,SquaredDifference:()=>Ys,Step:()=>Ea,StridedSlice:()=>Jo,Sub:()=>Js,Sum:()=>Ks,SymbolicTensor:()=>Nr,Tan:()=>Qs,Tanh:()=>ei,Tensor:()=>Le,TensorBuffer:()=>$t,Tile:()=>Ta,TopK:()=>Qo,Transform:()=>rd,Transpose:()=>ti,Unique:()=>ad,Unpack:()=>el,UnsortedSegmentSum:()=>Cu,Variable:()=>zu,ZerosLike:()=>tl,_FusedMatMul:()=>ni,abs:()=>Dt,acos:()=>Yf,acosh:()=>Jf,add:()=>se,addN:()=>yd,all:()=>gd,any:()=>Vu,argMax:()=>ju,argMin:()=>Qf,asin:()=>em,asinh:()=>tm,atan:()=>nm,atan2:()=>rm,atanh:()=>am,avgPool:()=>Hu,avgPool3d:()=>om,backend:()=>_w,backend_util:()=>E,basicLSTMCell:()=>dN,batchNorm:()=>pi,batchNorm2d:()=>Sw,batchNorm3d:()=>Nw,batchNorm4d:()=>Tw,batchToSpaceND:()=>Gu,bincount:()=>Ew,booleanMaskAsync:()=>gC,broadcastTo:()=>pl,browser:()=>ui,buffer:()=>We,callbacks:()=>mae,cast:()=>Ae,ceil:()=>lm,clipByValue:()=>In,clone:()=>Dr,complex:()=>Ca,concat:()=>ot,concat1d:()=>Cw,concat2d:()=>fl,concat3d:()=>Rw,concat4d:()=>Mw,constraints:()=>xv,conv1d:()=>wd,conv2d:()=>ia,conv2dTranspose:()=>bd,conv3d:()=>cm,conv3dTranspose:()=>$w,copyRegisteredKernels:()=>z9,cos:()=>qu,cosh:()=>_d,cosineWindow:()=>Pm,cumsum:()=>vd,customGrad:()=>zr,data:()=>F4,denseBincount:()=>Dw,deprecationWarn:()=>Kf,depthToSpace:()=>hm,depthwiseConv2d:()=>ml,deregisterOp:()=>yae,device_util:()=>Lu,diag:()=>BN,dilation2d:()=>dm,disableDeprecationWarnings:()=>SS,dispose:()=>Te,disposeVariables:()=>NS,div:()=>ge,divNoNan:()=>pm,dot:()=>Ow,dropout:()=>rb,einsum:()=>zw,elu:()=>Al,enableDebugMode:()=>IS,enableProdMode:()=>kS,enclosingPowerOfTwo:()=>ab,engine:()=>aa,env:()=>J,equal:()=>Da,erf:()=>fm,exp:()=>Xn,expandDims:()=>on,expm1:()=>mm,eye:()=>Am,fft:()=>rc,fill:()=>Xu,findBackend:()=>Zf,findBackendFactory:()=>FS,floor:()=>yl,floorDiv:()=>Ad,forceHalfFloat:()=>w3,fused:()=>La,gather:()=>fi,gatherND:()=>nb,gather_util:()=>Vf,getBackend:()=>RS,getGradient:()=>_f,getKernel:()=>id,getKernelsForBackend:()=>al,gpgpu_util:()=>U_,grad:()=>yT,grads:()=>gT,greater:()=>or,greaterEqual:()=>za,ifft:()=>bl,imag:()=>kd,image:()=>Ye,inTopKAsync:()=>EC,initializers:()=>Sv,input:()=>Lv,io:()=>vn,irfft:()=>Wd,isFinite:()=>Pw,isInf:()=>Lw,isNaN:()=>ym,keep:()=>Vt,kernel_impls:()=>Wr,layers:()=>Pv,leakyRelu:()=>Ku,less:()=>Id,lessEqual:()=>mi,linalg:()=>Ab,linspace:()=>Ww,loadGraphModel:()=>Ht,loadLayersModel:()=>Dre,localResponseNormalization:()=>gm,log:()=>Mn,log1p:()=>Sd,logSigmoid:()=>Vw,logSoftmax:()=>Td,logSumExp:()=>bm,logicalAnd:()=>lr,logicalNot:()=>Zu,logicalOr:()=>Ed,logicalXor:()=>Gw,losses:()=>XR,matMul:()=>Be,math:()=>rw,max:()=>Kn,maxPool:()=>Yu,maxPool3d:()=>_m,maxPoolWithArgmax:()=>qw,maximum:()=>Pr,mean:()=>vt,memory:()=>md,meshgrid:()=>WT,metrics:()=>Y6,min:()=>gl,minimum:()=>xl,mirrorPad:()=>vm,mod:()=>km,model:()=>Fre,models:()=>J6,moments:()=>Cd,movingAverage:()=>bC,mul:()=>B,multiRNNCell:()=>XT,multinomial:()=>Xw,neg:()=>_t,nextFrame:()=>tp,norm:()=>Ud,notEqual:()=>gi,oneHot:()=>ul,ones:()=>Fn,onesLike:()=>$n,op:()=>D,outerProduct:()=>QT,pad:()=>oa,pad1d:()=>nE,pad2d:()=>aE,pad3d:()=>iE,pad4d:()=>lE,pool:()=>Kw,pow:()=>la,prelu:()=>Qu,print:()=>Yx,prod:()=>Rd,profile:()=>sa,rand:()=>yE,randomGamma:()=>bE,randomNormal:()=>Zw,randomUniform:()=>wl,range:()=>Md,ready:()=>CS,real:()=>ec,reciprocal:()=>Nm,registerBackend:()=>hl,registerCallbackConstructor:()=>Ore,registerGradient:()=>Rx,registerKernel:()=>si,registerOp:()=>Aae,regularizers:()=>Q6,relu:()=>Lr,relu6:()=>Fd,removeBackend:()=>MS,reshape:()=>H,reverse:()=>Dn,reverse1d:()=>CE,reverse2d:()=>ME,reverse3d:()=>$E,reverse4d:()=>OE,rfft:()=>ac,round:()=>Tm,rsqrt:()=>$d,scalar:()=>ke,scatterND:()=>tb,scatter_util:()=>jf,selu:()=>Dd,separableConv2d:()=>Em,sequential:()=>$re,serialization:()=>re,setBackend:()=>ES,setPlatform:()=>$S,setWasmPath:()=>EJ,setWasmPaths:()=>CJ,setWebGLContext:()=>hp,setdiff1dAsync:()=>Yw,shared:()=>jm,sigmoid:()=>kn,sign:()=>Cm,signal:()=>qR,sin:()=>Od,sinh:()=>zd,slice:()=>Re,slice1d:()=>Pd,slice2d:()=>Rm,slice3d:()=>Ld,slice4d:()=>tc,slice_util:()=>sn,softmax:()=>nc,softplus:()=>Ai,spaceToBatchND:()=>Ju,sparse:()=>yb,sparseToDense:()=>zm,spectral:()=>GR,split:()=>ln,sqrt:()=>Jt,square:()=>st,squaredDifference:()=>Bd,squeeze:()=>Pa,stack:()=>On,step:()=>_l,stridedSlice:()=>Mm,sub:()=>ye,sum:()=>Ne,sumOutType:()=>cd,tan:()=>Fm,tanh:()=>di,tensor:()=>wr,tensor1d:()=>nn,tensor2d:()=>Zn,tensor3d:()=>pd,tensor4d:()=>lC,tensor5d:()=>uC,tensor6d:()=>cC,tensor_util:()=>gr,test_util:()=>xw,tidy:()=>L,tile:()=>Oa,time:()=>TS,topk:()=>$m,train:()=>wi,transpose:()=>Ze,truncatedNormal:()=>Vd,unique:()=>jd,unregisterGradient:()=>O9,unregisterKernel:()=>D9,unsortedSegmentSum:()=>Dm,unstack:()=>ur,upcastType:()=>ir,util:()=>_,valueAndGrad:()=>xT,valueAndGrads:()=>wT,variable:()=>Jw,variableGrads:()=>Bw,version:()=>rie,version_converter:()=>yse,version_core:()=>vS,version_cpu:()=>Zb,version_layers:()=>ly,version_wasm:()=>cv,version_webgl:()=>x3,webgl:()=>MW,webgl_util:()=>g_,where:()=>Sn,whereAsync:()=>Om,zeros:()=>Et,zerosLike:()=>Ue});var $k=Object.create,vh=Object.defineProperty,Dk=Object.getPrototypeOf,Ok=Object.prototype.hasOwnProperty,zk=Object.getOwnPropertyNames,Pk=Object.getOwnPropertyDescriptor,Lk=e=>vh(e,"__esModule",{value:!0}),wt=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Me=(e,t)=>{for(var n in t)vh(e,n,{get:t[n],enumerable:!0})},Wk=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of zk(t))!Ok.call(e,r)&&r!=="default"&&vh(e,r,{get:()=>t[r],enumerable:!(n=Pk(t,r))||n.enumerable});return e},Qi=e=>Wk(Lk(vh(e!=null?$k(Dk(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),Bk=wt(()=>{}),Vk=wt((e,t)=>{(function(n,r,a){function s(c){var u=this,h=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=h(" "),u.s1=h(" "),u.s2=h(" "),u.s0-=h(c),u.s0<0&&(u.s0+=1),u.s1-=h(c),u.s1<0&&(u.s1+=1),u.s2-=h(c),u.s2<0&&(u.s2+=1),h=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 h=new s(c),d=u&&u.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var c=4022871197,u=function(h){h=h.toString();for(var d=0;d<h.length;d++){c+=h.charCodeAt(d);var p=.02519603282416938*c;c=p>>>0,p-=c,p*=c,c=p>>>0,p-=c,c+=p*4294967296}return(c>>>0)*23283064365386963e-26};return u}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),jk=wt((e,t)=>{(function(n,r,a){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 h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Uk=wt((e,t)=>{(function(n,r,a){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 h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,h==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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Hk=wt((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,d=c.i,p,m,f;return p=h[d],p^=p>>>7,m=p^p<<24,p=h[d+1&7],m^=p^p>>>10,p=h[d+3&7],m^=p^p>>>3,p=h[d+4&7],m^=p^p<<7,p=h[d+7&7],p=p^p<<13,m^=p^p<<9,h[d]=m,c.i=d+1&7,m};function u(h,d){var p,m,f=[];if(d===(d|0))m=f[0]=d;else for(d=""+d,p=0;p<d.length;++p)f[p&7]=f[p&7]<<15^d.charCodeAt(p)+f[p+1&7]<<13;for(;f.length<8;)f.push(0);for(p=0;p<8&&f[p]===0;++p);for(p==8?m=f[7]=-1:m=f[p],h.x=f,h.i=0,p=256;p>0;--p)h.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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(h.x&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Gk=wt((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.w,d=c.X,p=c.i,m,f;return c.w=h=h+1640531527|0,f=d[p+34&127],m=d[p=p+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=d[p]=f^m,c.i=p,f+(h^h>>>16)|0};function u(h,d){var p,m,f,A,y,g=[],x=128;for(d===(d|0)?(m=d,d=null):(d=d+"\0",m=0,x=Math.max(x,d.length)),f=0,A=-32;A<x;++A)d&&(m^=d.charCodeAt((A+32)%d.length)),A===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=m+y,f=p==0?f+1:0);for(f>=128&&(g[(d&&d.length||0)&127]=-1),f=127,A=4*128;A>0;--A)m=g[f+34&127],p=g[f=f+1&127],m^=m<<13,p^=p<<17,m^=m>>>15,p^=p>>>12,g[f]=m^p;h.w=y,h.X=g,h.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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(h.X&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),qk=wt((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var d=c.b,p=c.c,m=c.d,f=c.a;return d=d<<25^d>>>7^p,p=p-m|0,m=m<<24^m>>>8^f,f=f-d|0,c.b=d=d<<20^d>>>12^p,c.c=p=p-m|0,c.d=m<<16^p>>>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 h=0;h<u.length+20;h++)c.b^=u.charCodeAt(h)|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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Pg=wt(()=>{}),Xk=wt((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",c=r.pow(s,i),u=r.pow(2,o),h=u*2,d=s-1,p;function m(w,b,k){var N=[];b=b==!0?{entropy:!0}:b||{};var C=g(y(b.entropy?[w,v(n)]:w==null?x():w,3),N),F=new f(N),O=function(){for(var z=F.g(i),V=c,j=0;z<u;)z=(z+j)*s,V*=s,j=F.g(1);for(;z>=h;)z/=2,V/=2,j>>>=1;return(z+j)/V};return O.int32=function(){return F.g(4)|0},O.quick=function(){return F.g(4)/4294967296},O.double=O,g(v(F.S),n),(b.pass||k||function(z,V,j,U){return U&&(U.S&&A(U,F),z.state=function(){return A(F,{})}),j?(r[l]=z,V):z})(O,C,"global"in b?b.global:this==r,b.state)}r["seed"+l]=m;function f(w){var b,k=w.length,N=this,C=0,F=N.i=N.j=0,O=N.S=[];for(k||(w=[k++]);C<s;)O[C]=C++;for(C=0;C<s;C++)O[C]=O[F=d&F+w[C%k]+(b=O[C])],O[F]=b;(N.g=function(z){for(var V,j=0,U=N.i,X=N.j,G=N.S;z--;)V=G[U=d&U+1],j=j*s+G[d&(G[U]=G[X=d&X+V])+(G[X]=V)];return N.i=U,N.j=X,j})(s)}function A(w,b){return b.i=w.i,b.j=w.j,b.S=w.S.slice(),b}function y(w,b){var k=[],N=typeof w,C;if(b&&N=="object")for(C in w)try{k.push(y(w[C],b-1))}catch(F){}return k.length?k:N=="string"?w:w+"\0"}function g(w,b){for(var k=w+"",N,C=0;C<k.length;)b[d&C]=d&(N^=b[d&C]*19)+k.charCodeAt(C++);return v(b)}function x(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),v(w)}catch(N){var b=a.navigator,k=b&&b.plugins;return[+new Date,a,k,a.screen,v(n)]}}function v(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=m;try{p=Pg()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return m})})([],Math)}),Lg=wt((e,t)=>{var n=Vk(),r=jk(),a=Uk(),s=Hk(),i=Gk(),o=qk(),l=Xk();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),hu=wt(()=>{}),Kk=wt(()=>{}),Zk=wt(()=>{}),Yk=wt((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};function s(){return Q.buffer!=Ve&&Kt(Q.buffer),gn}function i(){return Q.buffer!=Ve&&Kt(Q.buffer),xt}function o(){return Q.buffer!=Ve&&Kt(Q.buffer),xn}function l(){return Q.buffer!=Ve&&Kt(Q.buffer),Un}function c(){return Q.buffer!=Ve&&Kt(Q.buffer),rn}var u=typeof a!="undefined"?a:{},h,d;u.ready=new Promise(function(S,T){h=S,d=T});var p={},m;for(m in u)u.hasOwnProperty(m)&&(p[m]=u[m]);var f=[],A="./this.program",y=function(S,T){throw T},g=!1,x=!1,v=!1,w=!1;g=typeof window=="object",x=typeof importScripts=="function",v=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=!g&&!v&&!x;var b=u.ENVIRONMENT_IS_PTHREAD||!1;b&&(Ve=u.buffer);var k="";function N(S){return u.locateFile?u.locateFile(S,k):k+S}var C,F,O,z,V,j;if(v){x?k=hu().dirname(k)+"/":k=__dirname+"/",C=function(S,T){return V||(V=require("fs")),j||(j=hu()),S=j.normalize(S),V.readFileSync(S,T?null:"utf8")},O=function(S){var T=C(S,!0);return T.buffer||(T=new Uint8Array(T)),de(T.buffer),T},process.argv.length>1&&(A=process.argv[1].replace(/\\/g,"/")),f=process.argv.slice(2),process.on("uncaughtException",function(S){if(!(S instanceof uu))throw S}),process.on("unhandledRejection",Qr),y=function(S){process.exit(S)},u.inspect=function(){return"[Emscripten Module object]"};var U;try{U=Kk()}catch(S){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),S}global.Worker=U.Worker}else w?(typeof read!="undefined"&&(C=function(S){return read(S)}),O=function(S){var T;return typeof readbuffer=="function"?new Uint8Array(readbuffer(S)):(T=read(S,"binary"),de(typeof T=="object"),T)},typeof scriptArgs!="undefined"?f=scriptArgs:typeof arguments!="undefined"&&(f=arguments),typeof quit=="function"&&(y=function(S){quit(S)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(g||x)&&(x?k=self.location.href:typeof document!="undefined"&&document.currentScript&&(k=document.currentScript.src),typeof r!="undefined"&&r&&(k=r),k.indexOf("blob:")!==0?k=k.substr(0,k.lastIndexOf("/")+1):k="",v?(C=function(S,T){return V||(V=require("fs")),j||(j=hu()),S=j.normalize(S),V.readFileSync(S,T?null:"utf8")},O=function(S){var T=C(S,!0);return T.buffer||(T=new Uint8Array(T)),de(T.buffer),T}):(C=function(S){var T=new XMLHttpRequest;return T.open("GET",S,!1),T.send(null),T.responseText},x&&(O=function(S){var T=new XMLHttpRequest;return T.open("GET",S,!1),T.responseType="arraybuffer",T.send(null),new Uint8Array(T.response)}),F=function(S,T,P){var q=new XMLHttpRequest;q.open("GET",S,!0),q.responseType="arraybuffer",q.onload=function(){if(q.status==200||q.status==0&&q.response){T(q.response);return}P()},q.onerror=P,q.send(null)}),z=function(S){document.title=S});v&&typeof performance=="undefined"&&(global.performance=Zk().performance);var X=u.print||console.log.bind(console),G=u.printErr||console.warn.bind(console);for(m in p)p.hasOwnProperty(m)&&(u[m]=p[m]);p=null,u.arguments&&(f=u.arguments),u.thisProgram&&(A=u.thisProgram),u.quit&&(y=u.quit);var ee=Atomics.load,Y=Atomics.store,ae=Atomics.compareExchange,te;u.wasmBinary&&(te=u.wasmBinary);var ie=u.noExitRuntime||!0;typeof WebAssembly!="object"&&Qr("no native wasm support detected");var Q,ce,oe=!1,me;function de(S,T){S||Qr("Assertion failed: "+T)}function ve(S){var T=u["_"+S];return de(T,"Cannot call unknown function "+S+", make sure it is exported"),T}function Ie(S,T,P,q,he){var le={string:function(_n){var Ji=0;if(_n!=null&&_n!==0){var Dg=(_n.length<<2)+1;Ji=Ki(Dg),et(_n,Ji,Dg)}return Ji},array:function(_n){var Ji=Ki(_n.length);return Xe(_n,Ji),Ji}};function ue(_n){return T==="string"?$e(_n):T==="boolean"?Boolean(_n):_n}var we=ve(S),tt=[],Wt=0;if(q)for(var Ft=0;Ft<q.length;Ft++){var va=le[P[Ft]];va?(Wt===0&&(Wt=lu()),tt[Ft]=va(q[Ft])):tt[Ft]=q[Ft]}var Yi=we.apply(null,tt);return Yi=ue(Yi),Wt!==0&&Xi(Wt),Yi}function Fe(S,T,P,q){P=P||[];var he=P.every(function(ue){return ue==="number"}),le=T!=="string";return le&&he&&!q?ve(S):function(){return Ie(S,T,P,arguments,q)}}function Oe(S,T,P){for(var q=T+P,he="";!(T>=q);){var le=S[T++];if(!le)return he;if(!(le&128)){he+=String.fromCharCode(le);continue}var ue=S[T++]&63;if((le&224)==192){he+=String.fromCharCode((le&31)<<6|ue);continue}var we=S[T++]&63;if((le&240)==224?le=(le&15)<<12|ue<<6|we:le=(le&7)<<18|ue<<12|we<<6|S[T++]&63,le<65536)he+=String.fromCharCode(le);else{var tt=le-65536;he+=String.fromCharCode(55296|tt>>10,56320|tt&1023)}}return he}function $e(S,T){return S?Oe(i(),S,T):""}function Qe(S,T,P,q){if(!(q>0))return 0;for(var he=P,le=P+q-1,ue=0;ue<S.length;++ue){var we=S.charCodeAt(ue);if(we>=55296&&we<=57343){var tt=S.charCodeAt(++ue);we=65536+((we&1023)<<10)|tt&1023}if(we<=127){if(P>=le)break;T[P++]=we}else if(we<=2047){if(P+1>=le)break;T[P++]=192|we>>6,T[P++]=128|we&63}else if(we<=65535){if(P+2>=le)break;T[P++]=224|we>>12,T[P++]=128|we>>6&63,T[P++]=128|we&63}else{if(P+3>=le)break;T[P++]=240|we>>18,T[P++]=128|we>>12&63,T[P++]=128|we>>6&63,T[P++]=128|we&63}}return T[P]=0,P-he}function et(S,T,P){return Qe(S,i(),T,P)}function at(S){for(var T=0,P=0;P<S.length;++P){var q=S.charCodeAt(P);q>=55296&&q<=57343&&(q=65536+((q&1023)<<10)|S.charCodeAt(++P)&1023),q<=127?++T:q<=2047?T+=2:q<=65535?T+=3:T+=4}return T}function Xe(S,T){s().set(S,T)}function ht(S,T){return S%T>0&&(S+=T-S%T),S}var Ve,gn,xt,jn,Xt,xn,Un,Rn,rn;function Kt(S){Ve=S,u.HEAP8=gn=new Int8Array(S),u.HEAP16=jn=new Int16Array(S),u.HEAP32=xn=new Int32Array(S),u.HEAPU8=xt=new Uint8Array(S),u.HEAPU16=Xt=new Uint16Array(S),u.HEAPU32=Un=new Uint32Array(S),u.HEAPF32=Rn=new Float32Array(S),u.HEAPF64=rn=new Float64Array(S)}var Rr=u.INITIAL_MEMORY||16777216;if(b)Q=u.wasmMemory,Ve=u.buffer;else if(u.wasmMemory)Q=u.wasmMemory;else if(Q=new WebAssembly.Memory({initial:Rr/65536,maximum:2147483648/65536,shared:!0}),!(Q.buffer instanceof SharedArrayBuffer))throw G("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),v&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Q&&(Ve=Q.buffer),Rr=Ve.byteLength,Kt(Ve);var tr,nr=[],ya=[],Yr=[],ga=[],Vi=[],Mr=!1,th=!1;b||ya.push({func:function(){Ah()}});function W0(){if(!b){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)rh(u.preRun.shift());Ui(nr)}}function Ql(){Mr=!0,!b&&Ui(ya)}function B0(){b||Ui(Yr)}function nh(){b||(th=!0)}function wn(){if(!b){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)V0(u.postRun.shift());Ui(Vi)}}function rh(S){nr.unshift(S)}function V0(S){Vi.unshift(S)}var Jr=0,xa=null,ns=null;function j0(S){de(!b,"addRunDependency cannot be used in a pthread worker"),Jr++,u.monitorRunDependencies&&u.monitorRunDependencies(Jr)}function U0(S){if(Jr--,u.monitorRunDependencies&&u.monitorRunDependencies(Jr),Jr==0&&(xa!==null&&(clearInterval(xa),xa=null),ns)){var T=ns;ns=null,T()}}u.preloadedImages={},u.preloadedAudios={};function Qr(S){u.onAbort&&u.onAbort(S),b&&console.error("Pthread aborting at "+new Error().stack),S+="",G(S),oe=!0,me=1,S="abort("+S+"). Build with -s ASSERTIONS=1 for more info.";var T=new WebAssembly.RuntimeError(S);throw d(T),T}function ah(S,T){return String.prototype.startsWith?S.startsWith(T):S.indexOf(T)===0}var ji="data:application/octet-stream;base64,";function sh(S){return ah(S,ji)}var H0="file://";function ih(S){return ah(S,H0)}var bn="tfjs-backend-wasm-threaded-simd.wasm";sh(bn)||(bn=N(bn));function oh(S){try{if(S==bn&&te)return new Uint8Array(te);if(O)return O(S);throw"both async and sync fetching of the wasm failed"}catch(T){Qr(T)}}function G0(){if(!te&&(g||x)){if(typeof fetch=="function"&&!ih(bn))return fetch(bn,{credentials:"same-origin"}).then(function(S){if(!S.ok)throw"failed to load wasm binary file at '"+bn+"'";return S.arrayBuffer()}).catch(function(){return oh(bn)});if(F)return new Promise(function(S,T){F(bn,function(P){S(new Uint8Array(P))},T)})}return Promise.resolve().then(function(){return oh(bn)})}function q0(){var S={a:z1};function T(ue,we){var tt=ue.exports;if(u.asm=tt,tr=u.asm.F,ce=we,!b){var Wt=_e.unusedWorkers.length;_e.unusedWorkers.forEach(function(Ft){_e.loadWasmModuleToWorker(Ft,function(){--Wt||U0("wasm-instantiate")})})}}b||j0("wasm-instantiate");function P(ue){T(ue.instance,ue.module)}function q(ue){return G0().then(function(we){return WebAssembly.instantiate(we,S)}).then(ue,function(we){G("failed to asynchronously prepare wasm: "+we),Qr(we)})}function he(){return!te&&typeof WebAssembly.instantiateStreaming=="function"&&!sh(bn)&&!ih(bn)&&typeof fetch=="function"?fetch(bn,{credentials:"same-origin"}).then(function(ue){var we=WebAssembly.instantiateStreaming(ue,S);return we.then(P,function(tt){return G("wasm streaming compile failed: "+tt),G("falling back to ArrayBuffer instantiation"),q(P)})}):q(P)}if(u.instantiateWasm)try{var le=u.instantiateWasm(S,T);return le}catch(ue){return G("Module.instantiateWasm callback failed with error: "+ue),!1}return he().catch(d),{}}var X0={9816:function(){throw"Canceled!"},9834:function(S,T){setTimeout(function(){Eg(S,T)},0)}};function lh(){_e.initRuntime()}function Ui(S){for(;S.length>0;){var T=S.shift();if(typeof T=="function"){T(u);continue}var P=T.func;typeof P=="number"?T.arg===void 0?tr.get(P)():tr.get(P)(T.arg):P(T.arg===void 0?null:T.arg)}}function eu(S,T){if(S<=0||S>s().length||S&!0||T<0)return-28;if(T==0)return 0;T>=2147483647&&(T=Infinity);var P=Atomics.load(o(),Zi>>2),q=0;if(P==S){var he=Atomics.compareExchange(o(),Zi>>2,P,0);if(he==P&&(--T,q=1,T<=0))return 1}var le=Atomics.notify(o(),S>>2,T);if(le>=0)return le+q;throw"Atomics.notify returned an unexpected value "+le}u._emscripten_futex_wake=eu;function K0(S){if(b)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!S)throw"Internal Error! Null pthread_ptr in killThread!";o()[S+12>>2]=0;var T=_e.pthreads[S];T.worker.terminate(),_e.freeThreadData(T),_e.runningWorkers.splice(_e.runningWorkers.indexOf(T.worker),1),T.worker.pthread=void 0}function Z0(S){if(b)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!S)throw"Internal Error! Null pthread_ptr in cancelThread!";var T=_e.pthreads[S];T.worker.postMessage({cmd:"cancel"})}function Y0(S){if(b)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!S)throw"Internal Error! Null pthread_ptr in cleanupThread!";var T=_e.pthreads[S];if(T){o()[S+12>>2]=0;var P=T.worker;_e.returnWorkerToPool(P)}}var _e={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var S=Math.min(4,Math.max(1,(navigator.hardwareConcurrency||1)/2)),T=0;T<S;++T)_e.allocateUnusedWorker()},initRuntime:function(){for(var S=as(228),T=0;T<228/4;++T)l()[S/4+T]=0;o()[S+12>>2]=S;var P=S+152;o()[P>>2]=P;for(var q=as(512),T=0;T<128;++T)l()[q/4+T]=0;Atomics.store(l(),S+100>>2,q),Atomics.store(l(),S+40>>2,S),lf(S,!x,1),Tg(S)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;_e.threadExitHandlers.length>0;)_e.threadExitHandlers.pop()();b&&qi()&&Ng()},runExitHandlersAndDeinitThread:function(S,T){Atomics.store(l(),S+56>>2,1),Atomics.store(l(),S+60>>2,0),_e.runExitHandlers(),Atomics.store(l(),S+4>>2,T),Atomics.store(l(),S+0>>2,1),eu(S+0,2147483647),lf(0,0,0)},threadExit:function(S){var T=qi();T&&(_e.runExitHandlersAndDeinitThread(T,S),b&&postMessage({cmd:"exit"}))},threadCancel:function(){_e.runExitHandlersAndDeinitThread(qi(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var S in _e.pthreads){var T=_e.pthreads[S];T&&T.worker&&_e.returnWorkerToPool(T.worker)}_e.pthreads={};for(var P=0;P<_e.unusedWorkers.length;++P){var q=_e.unusedWorkers[P];q.terminate()}_e.unusedWorkers=[];for(var P=0;P<_e.runningWorkers.length;++P){var q=_e.runningWorkers[P],T=q.pthread;_e.freeThreadData(T),q.terminate()}_e.runningWorkers=[]},freeThreadData:function(S){if(S){if(S.threadInfoStruct){var T=o()[S.threadInfoStruct+100>>2];o()[S.threadInfoStruct+100>>2]=0,ou(T),ou(S.threadInfoStruct)}S.threadInfoStruct=0,S.allocatedOwnStack&&S.stackBase&&ou(S.stackBase),S.stackBase=0,S.worker&&(S.worker.pthread=null)}},returnWorkerToPool:function(S){_e.runWithoutMainThreadQueuedCalls(function(){delete _e.pthreads[S.pthread.threadInfoStruct],_e.unusedWorkers.push(S),_e.runningWorkers.splice(_e.runningWorkers.indexOf(S),1),_e.freeThreadData(S.pthread),S.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(S){o()[$g>>2]=0;try{S()}finally{o()[$g>>2]=1}},receiveObjectTransfer:function(S){},loadWasmModuleToWorker:function(S,T){S.onmessage=function(P){var q=P.data,he=q.cmd;if(S.pthread&&(_e.currentProxiedOperationCallerThread=S.pthread.threadInfoStruct),q.targetThread&&q.targetThread!=qi()){var le=_e.pthreads[q.targetThread];le?le.worker.postMessage(P.data,q.transferList):console.error('Internal error! Worker sent a message "'+he+'" to target pthread '+q.targetThread+", but that thread no longer exists!"),_e.currentProxiedOperationCallerThread=void 0;return}if(he==="processQueuedMainThreadWork")sf();else if(he==="spawnThread")fh(P.data);else if(he==="cleanupThread")Y0(q.thread);else if(he==="killThread")K0(q.thread);else if(he==="cancelThread")Z0(q.thread);else if(he==="loaded")S.loaded=!0,T&&T(S),S.runPthread&&(S.runPthread(),delete S.runPthread);else if(he==="print")X("Thread "+q.threadId+": "+q.text);else if(he==="printErr")G("Thread "+q.threadId+": "+q.text);else if(he==="alert")alert("Thread "+q.threadId+": "+q.text);else if(he==="exit"){var ue=S.pthread&&Atomics.load(l(),S.pthread.threadInfoStruct+64>>2);ue&&_e.returnWorkerToPool(S)}else if(he==="exitProcess")try{Mk(q.returnCode)}catch(we){if(we instanceof uu)return;throw we}else he==="cancelDone"?_e.returnWorkerToPool(S):he==="objectTransfer"?_e.receiveObjectTransfer(P.data):P.data.target==="setimmediate"?S.postMessage(P.data):G("worker sent an unknown command "+he);_e.currentProxiedOperationCallerThread=void 0},S.onerror=function(P){G("pthread sent an error! "+P.filename+":"+P.lineno+": "+P.message)},v&&(S.on("message",function(P){S.onmessage({data:P})}),S.on("error",function(P){S.onerror(P)}),S.on("exit",function(P){})),S.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||r,wasmMemory:Q,wasmModule:ce})},allocateUnusedWorker:function(){var S=N("tfjs-backend-wasm-threaded-simd.worker.js");_e.unusedWorkers.push(new Worker(S))},getNewWorker:function(){return _e.unusedWorkers.length==0&&(_e.allocateUnusedWorker(),_e.loadWasmModuleToWorker(_e.unusedWorkers[0])),_e.unusedWorkers.length>0?_e.unusedWorkers.pop():null},busySpinWait:function(S){for(var T=performance.now()+S;performance.now()<T;);}};function J0(S,T){Mg(S,T),Xi(S)}u.establishStackSpace=J0;function Q0(){return ie}u.getNoExitRuntime=Q0;function e1(S,T){return tr.get(S)(T)}u.invokeEntryPoint=e1;function t1(S,T,P,q){Qr("Assertion failed: "+$e(S)+", at: "+[T?$e(T):"unknown filename",P,q?$e(q):"unknown function"])}function n1(S,T){var P=_main(S,T)}var rs;v?rs=function(){var S=process.hrtime();return S[0]*1e3+S[1]/1e6}:b?rs=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?rs=dateNow:rs=function(){return performance.now()};function r1(S){return o()[Ig()>>2]=S,S}function a1(S,T){if(b)return wa(1,1,S,T)}function s1(S,T){if(S==T)postMessage({cmd:"processQueuedMainThreadWork"});else if(b)postMessage({targetThread:S,cmd:"processThreadQueue"});else{var P=_e.pthreads[S],q=P&&P.worker;if(!q)return;q.postMessage({cmd:"processThreadQueue"})}return 1}function i1(){Qr()}function o1(S,T,P){var q=d1(T,P);return X0[S].apply(null,q)}function l1(S,T){}function u1(S,T,P){if(S<=0||S>s().length||S&!0)return-28;if(g){if(Atomics.load(o(),S>>2)!=T)return-6;for(var q=performance.now(),he=q+P,le=Atomics.exchange(o(),Zi>>2,S);;){if(q=performance.now(),q>he)return le=Atomics.exchange(o(),Zi>>2,0),-73;if(le=Atomics.exchange(o(),Zi>>2,0),le==0)break;if(sf(),Atomics.load(o(),S>>2)!=T)return-6;le=Atomics.exchange(o(),Zi>>2,S)}return 0}else{var ue=Atomics.wait(o(),S>>2,T,P);if(ue==="timed-out")return-73;if(ue==="not-equal")return-6;if(ue==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ue}}function c1(S,T,P){i().copyWithin(S,T,T+P)}function h1(){return v?require("os").cpus().length:navigator.hardwareConcurrency}function wa(S,T){for(var P=arguments.length-2,q=lu(),he=P,le=Ki(he*8),ue=le>>3,we=0;we<P;we++){var tt=arguments[2+we];c()[ue+we]=tt}var Wt=Rg(S,he,le,T);return Xi(q),Wt}var tu=[],nu=[];function d1(S,T){nu.length=0;var P;for(T>>=2;P=i()[S++];){var q=P<105;q&&T&1&&T++,nu.push(q?c()[T++>>1]:o()[T]),++T}return nu}function p1(S,T,P){tu.length=T;for(var q=P>>3,he=0;he<T;he++)tu[he]=c()[q+he];var le=S<0,ue=le?X0[-S-1]:O1[S];return ue.apply(null,tu)}function f1(){return i().length}function m1(S){try{return Q.grow(S-Ve.byteLength+65535>>>16),Kt(Q.buffer),1}catch(T){}}function A1(S){var T=f1();if(S<=T)return!1;var P=2147483648;if(S>P)return!1;for(var q=1;q<=4;q*=2){var he=T*(1+.2/q);he=Math.min(he,S+100663296);var le=Math.min(P,ht(Math.max(S,he),65536)),ue=m1(le);if(ue)return!0}return!1}var Pe={inEventHandler:0,removeAllEventListeners:function(){for(var S=Pe.eventHandlers.length-1;S>=0;--S)Pe._removeHandler(S);Pe.eventHandlers=[],Pe.deferredCalls=[]},registerRemoveEventListeners:function(){Pe.removeEventListenersRegistered||(ga.push(Pe.removeAllEventListeners),Pe.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(S,T,P){function q(ue,we){if(ue.length!=we.length)return!1;for(var tt in ue)if(ue[tt]!=we[tt])return!1;return!0}for(var he in Pe.deferredCalls){var le=Pe.deferredCalls[he];if(le.targetFunction==S&&q(le.argsList,P))return}Pe.deferredCalls.push({targetFunction:S,precedence:T,argsList:P}),Pe.deferredCalls.sort(function(ue,we){return ue.precedence<we.precedence})},removeDeferredCalls:function(S){for(var T=0;T<Pe.deferredCalls.length;++T)Pe.deferredCalls[T].targetFunction==S&&(Pe.deferredCalls.splice(T,1),--T)},canPerformEventHandlerRequests:function(){return Pe.inEventHandler&&Pe.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Pe.canPerformEventHandlerRequests())for(var S=0;S<Pe.deferredCalls.length;++S){var T=Pe.deferredCalls[S];Pe.deferredCalls.splice(S,1),--S,T.targetFunction.apply(null,T.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(S,T){for(var P=0;P<Pe.eventHandlers.length;++P)Pe.eventHandlers[P].target==S&&(!T||T==Pe.eventHandlers[P].eventTypeString)&&Pe._removeHandler(P--)},_removeHandler:function(S){var T=Pe.eventHandlers[S];T.target.removeEventListener(T.eventTypeString,T.eventListenerFunc,T.useCapture),Pe.eventHandlers.splice(S,1)},registerOrRemoveHandler:function(S){var T=function(q){++Pe.inEventHandler,Pe.currentEventHandler=S,Pe.runDeferredCalls(),S.handlerFunc(q),Pe.runDeferredCalls(),--Pe.inEventHandler};if(S.callbackfunc)S.eventListenerFunc=T,S.target.addEventListener(S.eventTypeString,T,S.useCapture),Pe.eventHandlers.push(S),Pe.registerRemoveEventListeners();else for(var P=0;P<Pe.eventHandlers.length;++P)Pe.eventHandlers[P].target==S.target&&Pe.eventHandlers[P].eventTypeString==S.eventTypeString&&Pe._removeHandler(P--)},queueEventHandlerOnThread_iiii:function(S,T,P,q,he){var le=lu(),ue=Ki(12);o()[ue>>2]=P,o()[ue+4>>2]=q,o()[ue+8>>2]=he,of(0,S,637534208,T,q,ue),Xi(le)},getTargetThreadForEventCallback:function(S){switch(S){case 1:return 0;case 2:return _e.currentProxiedOperationCallerThread;default:return S}},getNodeNameForTarget:function(S){return S?S==window?"#window":S==screen?"#screen":S&&S.nodeName?S.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function y1(S){var T=at(S)+1,P=as(T);return et(S,P,T),P}function g1(S,T,P,q){var he=lu(),le=Ki(12),ue=0;T&&(ue=y1(T)),o()[le>>2]=ue,o()[le+4>>2]=P,o()[le+8>>2]=q,of(0,S,657457152,0,ue,le),Xi(he)}function x1(S,T,P,q){T=T?$e(T):"",g1(S,T,P,q)}function w1(S){return S>2?$e(S):S}var b1=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function _1(S){S=w1(S);var T=b1[S]||(typeof document!="undefined"?document.querySelector(S):void 0);return T}function ru(S){return _1(S)}function uh(S,T,P){var q=ru(S);if(!q)return-4;if(q.canvasSharedPtr&&(o()[q.canvasSharedPtr>>2]=T,o()[q.canvasSharedPtr+4>>2]=P),q.offscreenCanvas||!q.controlTransferredOffscreen){q.offscreenCanvas&&(q=q.offscreenCanvas);var he=!1;if(q.GLctxObject&&q.GLctxObject.GLctx){var le=q.GLctxObject.GLctx.getParameter(2978);he=le[0]===0&&le[1]===0&&le[2]===q.width&&le[3]===q.height}q.width=T,q.height=P,he&&q.GLctxObject.GLctx.viewport(0,0,T,P)}else if(q.canvasSharedPtr){var ue=o()[q.canvasSharedPtr+8>>2];return x1(ue,S,T,P),1}else return-4;return 0}function ch(S,T,P){return b?wa(2,1,S,T,P):uh(S,T,P)}function v1(S,T,P){var q=ru(S);return q?uh(S,T,P):ch(S,T,P)}function k1(S){}function I1(S,T){}function S1(S){var T=S.getExtension("ANGLE_instanced_arrays");if(T)return S.vertexAttribDivisor=function(P,q){T.vertexAttribDivisorANGLE(P,q)},S.drawArraysInstanced=function(P,q,he,le){T.drawArraysInstancedANGLE(P,q,he,le)},S.drawElementsInstanced=function(P,q,he,le,ue){T.drawElementsInstancedANGLE(P,q,he,le,ue)},1}function N1(S){var T=S.getExtension("OES_vertex_array_object");if(T)return S.createVertexArray=function(){return T.createVertexArrayOES()},S.deleteVertexArray=function(P){T.deleteVertexArrayOES(P)},S.bindVertexArray=function(P){T.bindVertexArrayOES(P)},S.isVertexArray=function(P){return T.isVertexArrayOES(P)},1}function T1(S){var T=S.getExtension("WEBGL_draw_buffers");if(T)return S.drawBuffers=function(P,q){T.drawBuffersWEBGL(P,q)},1}function E1(S){return!!(S.multiDrawWebgl=S.getExtension("WEBGL_multi_draw"))}var Je={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(S){Je.lastError||(Je.lastError=S)},getNewId:function(S){for(var T=Je.counter++,P=S.length;P<T;P++)S[P]=null;return T},getSource:function(S,T,P,q){for(var he="",le=0;le<T;++le){var ue=q?o()[q+le*4>>2]:-1;he+=$e(o()[P+le*4>>2],ue<0?void 0:ue)}return he},createContext:function(S,T){var P=S.getContext("webgl",T);if(!P)return 0;var q=Je.registerContext(P,T);return q},registerContext:function(S,T){var P=as(8);o()[P+4>>2]=qi();var q={handle:P,attributes:T,version:T.majorVersion,GLctx:S};return S.canvas&&(S.canvas.GLctxObject=q),Je.contexts[P]=q,(typeof T.enableExtensionsByDefault=="undefined"||T.enableExtensionsByDefault)&&Je.initExtensions(q),P},makeContextCurrent:function(S){return Je.currentContext=Je.contexts[S],u.ctx=ba=Je.currentContext&&Je.currentContext.GLctx,!(S&&!ba)},getContext:function(S){return Je.contexts[S]},deleteContext:function(S){Je.currentContext===Je.contexts[S]&&(Je.currentContext=null),typeof Pe=="object"&&Pe.removeAllHandlersOnTarget(Je.contexts[S].GLctx.canvas),Je.contexts[S]&&Je.contexts[S].GLctx.canvas&&(Je.contexts[S].GLctx.canvas.GLctxObject=void 0),ou(Je.contexts[S].handle),Je.contexts[S]=null},initExtensions:function(S){if(S||(S=Je.currentContext),!S.initExtensionsDone){S.initExtensionsDone=!0;var T=S.GLctx;S1(T),N1(T),T1(T),T.disjointTimerQueryExt=T.getExtension("EXT_disjoint_timer_query"),E1(T);var P=T.getSupportedExtensions()||[];P.forEach(function(q){q.indexOf("lose_context")<0&&q.indexOf("debug")<0&&T.getExtension(q)})}},populateUniformTable:function(S){for(var T=Je.programs[S],P=Je.programInfos[S]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},q=P.uniforms,he=ba.getProgramParameter(T,35718),le=0;le<he;++le){var ue=ba.getActiveUniform(T,le),we=ue.name;P.maxUniformLength=Math.max(P.maxUniformLength,we.length+1),we.slice(-1)=="]"&&(we=we.slice(0,we.lastIndexOf("[")));var tt=ba.getUniformLocation(T,we);if(tt){var Wt=Je.getNewId(Je.uniforms);q[we]=[ue.size,Wt],Je.uniforms[Wt]=tt;for(var Ft=1;Ft<ue.size;++Ft){var va=we+"["+Ft+"]";tt=ba.getUniformLocation(T,va),Wt=Je.getNewId(Je.uniforms),Je.uniforms[Wt]=tt}}}}},C1=["default","low-power","high-performance"];function R1(S,T){var P=T>>2,q=o()[P+(24>>2)],he={alpha:!!o()[P+(0>>2)],depth:!!o()[P+(4>>2)],stencil:!!o()[P+(8>>2)],antialias:!!o()[P+(12>>2)],premultipliedAlpha:!!o()[P+(16>>2)],preserveDrawingBuffer:!!o()[P+(20>>2)],powerPreference:C1[q],failIfMajorPerformanceCaveat:!!o()[P+(28>>2)],majorVersion:o()[P+(32>>2)],minorVersion:o()[P+(36>>2)],enableExtensionsByDefault:o()[P+(40>>2)],explicitSwapControl:o()[P+(44>>2)],proxyContextToMainThread:o()[P+(48>>2)],renderViaOffscreenBackBuffer:o()[P+(52>>2)]},le=ru(S);if(!le||he.explicitSwapControl)return 0;var ue=Je.createContext(le,he);return ue}function M1(S,T){return R1(S,T)}var Hi={mappings:{},buffers:[null,[],[]],printChar:function(S,T){var P=Hi.buffers[S];T===0||T===10?((S===1?X:G)(Oe(P,0)),P.length=0):P.push(T)},varargs:void 0,get:function(){Hi.varargs+=4;var S=o()[Hi.varargs-4>>2];return S},getStr:function(S){var T=$e(S);return T},get64:function(S,T){return S}};function hh(S){return b?wa(3,1,S):0}function dh(S,T,P,q,he){if(b)return wa(4,1,S,T,P,q,he)}function ph(S,T,P,q){if(b)return wa(5,1,S,T,P,q);for(var he=0,le=0;le<P;le++){for(var ue=o()[T+le*8>>2],we=o()[T+(le*8+4)>>2],tt=0;tt<we;tt++)Hi.printChar(S,i()[ue+tt]);he+=we}return o()[q>>2]=he,0}function F1(S){var T=_e.threadExitHandlers.pop();S&&T()}function $1(S,T){_e.threadExitHandlers.push(function(){tr.get(S)(T)})}function fh(S){if(b)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var T=_e.getNewWorker();if(T.pthread!==void 0)throw"Internal error!";if(!S.pthread_ptr)throw"Internal error, no pthread ptr!";_e.runningWorkers.push(T);for(var P=as(128*4),q=0;q<128;++q)o()[P+q*4>>2]=0;var he=S.stackBase+S.stackSize,le=_e.pthreads[S.pthread_ptr]={worker:T,stackBase:S.stackBase,stackSize:S.stackSize,allocatedOwnStack:S.allocatedOwnStack,threadInfoStruct:S.pthread_ptr},ue=le.threadInfoStruct>>2;Atomics.store(l(),ue+(64>>2),S.detached),Atomics.store(l(),ue+(100>>2),P),Atomics.store(l(),ue+(40>>2),le.threadInfoStruct),Atomics.store(l(),ue+(80>>2),S.stackSize),Atomics.store(l(),ue+(76>>2),he),Atomics.store(l(),ue+(104>>2),S.stackSize),Atomics.store(l(),ue+(104+8>>2),he),Atomics.store(l(),ue+(104+12>>2),S.detached);var we=Sg(),tt=we+40;Atomics.store(l(),ue+(172>>2),tt),T.pthread=le;var Wt={cmd:"run",start_routine:S.startRoutine,arg:S.arg,threadInfoStruct:S.pthread_ptr,stackBase:S.stackBase,stackSize:S.stackSize};T.runPthread=function(){Wt.time=performance.now(),T.postMessage(Wt,S.transferList)},T.loaded&&(T.runPthread(),delete T.runPthread)}function D1(S,T,P,q){if(typeof SharedArrayBuffer=="undefined")return G("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!S)return G("pthread_create called with a null thread pointer!"),28;var he=[],le=0;if(b&&(he.length===0||le))return Cg(687865856,S,T,P,q);if(le)return le;var ue=0,we=0,tt=0;T&&T!=-1?(ue=o()[T>>2],ue+=81920,we=o()[T+8>>2],tt=o()[T+12>>2]!==0):ue=2097152;var Wt=we==0;Wt?we=Fg(16,ue):(we-=ue,de(we>0));for(var Ft=as(228),va=0;va<228>>2;++va)l()[(Ft>>2)+va]=0;o()[S>>2]=Ft,o()[Ft+12>>2]=Ft;var Yi=Ft+152;o()[Yi>>2]=Yi;var _n={stackBase:we,stackSize:ue,allocatedOwnStack:Wt,detached:tt,startRoutine:P,pthread_ptr:Ft,arg:q,transferList:he};return b?(_n.cmd="spawnThread",postMessage(_n,he)):fh(_n),0}function mh(S){if(b)return wa(6,1,S);switch(S){case 30:return 16384;case 85:var T=2147483648;return T/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return r1(28),-1}b||_e.initMainThreadBlock();var ba,O1=[null,a1,ch,hh,dh,ph,mh],z1={e:t1,r:n1,x:s1,b:i1,y:o1,j:l1,c:u1,d:eu,f:rs,p:c1,z:h1,u:p1,q:A1,v:v1,i:k1,t:I1,w:M1,m:hh,n:dh,g:ph,o:lh,a:Q||u.wasmMemory,k:F1,l:$1,h:D1,s:mh},kg=q0(),Ah=u.___wasm_call_ctors=function(){return(Ah=u.___wasm_call_ctors=u.asm.A).apply(null,arguments)},P1=u._init=function(){return(P1=u._init=u.asm.B).apply(null,arguments)},L1=u._register_tensor=function(){return(L1=u._register_tensor=u.asm.C).apply(null,arguments)},W1=u._dispose_data=function(){return(W1=u._dispose_data=u.asm.D).apply(null,arguments)},B1=u._dispose=function(){return(B1=u._dispose=u.asm.E).apply(null,arguments)},V1=u._Abs=function(){return(V1=u._Abs=u.asm.G).apply(null,arguments)},j1=u._Add=function(){return(j1=u._Add=u.asm.H).apply(null,arguments)},U1=u._AddN=function(){return(U1=u._AddN=u.asm.I).apply(null,arguments)},H1=u._All=function(){return(H1=u._All=u.asm.J).apply(null,arguments)},G1=u._Any=function(){return(G1=u._Any=u.asm.K).apply(null,arguments)},q1=u._ArgMax=function(){return(q1=u._ArgMax=u.asm.L).apply(null,arguments)},X1=u._AvgPool=function(){return(X1=u._AvgPool=u.asm.M).apply(null,arguments)},K1=u._BatchMatMul=function(){return(K1=u._BatchMatMul=u.asm.N).apply(null,arguments)},Z1=u._Ceil=function(){return(Z1=u._Ceil=u.asm.O).apply(null,arguments)},Y1=u._ClipByValue=function(){return(Y1=u._ClipByValue=u.asm.P).apply(null,arguments)},J1=u._Conv2D=function(){return(J1=u._Conv2D=u.asm.Q).apply(null,arguments)},Q1=u._Conv2DBackpropInput=function(){return(Q1=u._Conv2DBackpropInput=u.asm.R).apply(null,arguments)},ef=u._Cos=function(){return(ef=u._Cos=u.asm.S).apply(null,arguments)},tf=u._CropAndResize=function(){return(tf=u._CropAndResize=u.asm.T).apply(null,arguments)},nf=u._Cumsum=function(){return(nf=u._Cumsum=u.asm.U).apply(null,arguments)},rf=u._DepthToSpace=function(){return(rf=u._DepthToSpace=u.asm.V).apply(null,arguments)},yh=u._DepthwiseConv2dNative=function(){return(yh=u._DepthwiseConv2dNative=u.asm.W).apply(null,arguments)},gh=u._Equal=function(){return(gh=u._Equal=u.asm.X).apply(null,arguments)},xh=u._Exp=function(){return(xh=u._Exp=u.asm.Y).apply(null,arguments)},au=u._FlipLeftRight=function(){return(au=u._FlipLeftRight=u.asm.Z).apply(null,arguments)},Gi=u._Floor=function(){return(Gi=u._Floor=u.asm._).apply(null,arguments)},af=u._FloorDiv=function(){return(af=u._FloorDiv=u.asm.$).apply(null,arguments)},su=u._FusedBatchNorm=function(){return(su=u._FusedBatchNorm=u.asm.aa).apply(null,arguments)},K=u._FusedConv2D=function(){return(K=u._FusedConv2D=u.asm.ba).apply(null,arguments)},ne=u._FusedDepthwiseConv2D=function(){return(ne=u._FusedDepthwiseConv2D=u.asm.ca).apply(null,arguments)},Se=u._Gather=function(){return(Se=u._Gather=u.asm.da).apply(null,arguments)},Ke=u._GatherNd=function(){return(Ke=u._GatherNd=u.asm.ea).apply(null,arguments)},It=u._Greater=function(){return(It=u._Greater=u.asm.fa).apply(null,arguments)},mt=u._GreaterEqual=function(){return(mt=u._GreaterEqual=u.asm.ga).apply(null,arguments)},je=u._LeakyRelu=function(){return(je=u._LeakyRelu=u.asm.ha).apply(null,arguments)},He=u._Less=function(){return(He=u._Less=u.asm.ia).apply(null,arguments)},Zt=u._LessEqual=function(){return(Zt=u._LessEqual=u.asm.ja).apply(null,arguments)},ea=u._Log=function(){return(ea=u._Log=u.asm.ka).apply(null,arguments)},ta=u._LogicalAnd=function(){return(ta=u._LogicalAnd=u.asm.la).apply(null,arguments)},wh=u._Max=function(){return(wh=u._Max=u.asm.ma).apply(null,arguments)},iu=u._MaxPool=function(){return(iu=u._MaxPool=u.asm.na).apply(null,arguments)},Hn=u._Maximum=function(){return(Hn=u._Maximum=u.asm.oa).apply(null,arguments)},_a=u._Mean=function(){return(_a=u._Mean=u.asm.pa).apply(null,arguments)},bh=u._Min=function(){return(bh=u._Min=u.asm.qa).apply(null,arguments)},H8=u._Minimum=function(){return(H8=u._Minimum=u.asm.ra).apply(null,arguments)},G8=u._MirrorPad=function(){return(G8=u._MirrorPad=u.asm.sa).apply(null,arguments)},q8=u._Multiply=function(){return(q8=u._Multiply=u.asm.ta).apply(null,arguments)},X8=u._Neg=function(){return(X8=u._Neg=u.asm.ua).apply(null,arguments)},K8=u._NonMaxSuppressionV3=function(){return(K8=u._NonMaxSuppressionV3=u.asm.va).apply(null,arguments)},Z8=u._NonMaxSuppressionV4=function(){return(Z8=u._NonMaxSuppressionV4=u.asm.wa).apply(null,arguments)},Y8=u._NonMaxSuppressionV5=function(){return(Y8=u._NonMaxSuppressionV5=u.asm.xa).apply(null,arguments)},J8=u._NotEqual=function(){return(J8=u._NotEqual=u.asm.ya).apply(null,arguments)},Q8=u._OneHot=function(){return(Q8=u._OneHot=u.asm.za).apply(null,arguments)},ek=u._PadV2=function(){return(ek=u._PadV2=u.asm.Aa).apply(null,arguments)},tk=u._Pow=function(){return(tk=u._Pow=u.asm.Ba).apply(null,arguments)},nk=u._Prelu=function(){return(nk=u._Prelu=u.asm.Ca).apply(null,arguments)},rk=u._Prod=function(){return(rk=u._Prod=u.asm.Da).apply(null,arguments)},ak=u._RealDiv=function(){return(ak=u._RealDiv=u.asm.Ea).apply(null,arguments)},sk=u._Relu=function(){return(sk=u._Relu=u.asm.Fa).apply(null,arguments)},ik=u._Relu6=function(){return(ik=u._Relu6=u.asm.Ga).apply(null,arguments)},ok=u._ResizeBilinear=function(){return(ok=u._ResizeBilinear=u.asm.Ha).apply(null,arguments)},lk=u._Reverse=function(){return(lk=u._Reverse=u.asm.Ia).apply(null,arguments)},uk=u._RotateWithOffset=function(){return(uk=u._RotateWithOffset=u.asm.Ja).apply(null,arguments)},ck=u._Round=function(){return(ck=u._Round=u.asm.Ka).apply(null,arguments)},hk=u._Rsqrt=function(){return(hk=u._Rsqrt=u.asm.La).apply(null,arguments)},dk=u._ScatterNd=function(){return(dk=u._ScatterNd=u.asm.Ma).apply(null,arguments)},pk=u._SelectV2=function(){return(pk=u._SelectV2=u.asm.Na).apply(null,arguments)},fk=u._Sigmoid=function(){return(fk=u._Sigmoid=u.asm.Oa).apply(null,arguments)},mk=u._Sin=function(){return(mk=u._Sin=u.asm.Pa).apply(null,arguments)},Ak=u._Softmax=function(){return(Ak=u._Softmax=u.asm.Qa).apply(null,arguments)},yk=u._Sqrt=function(){return(yk=u._Sqrt=u.asm.Ra).apply(null,arguments)},gk=u._Square=function(){return(gk=u._Square=u.asm.Sa).apply(null,arguments)},xk=u._SquaredDifference=function(){return(xk=u._SquaredDifference=u.asm.Ta).apply(null,arguments)},wk=u._Step=function(){return(wk=u._Step=u.asm.Ua).apply(null,arguments)},bk=u._StridedSlice=function(){return(bk=u._StridedSlice=u.asm.Va).apply(null,arguments)},_k=u._Sub=function(){return(_k=u._Sub=u.asm.Wa).apply(null,arguments)},vk=u._Sum=function(){return(vk=u._Sum=u.asm.Xa).apply(null,arguments)},kk=u._Tan=function(){return(kk=u._Tan=u.asm.Ya).apply(null,arguments)},Ik=u._Tanh=function(){return(Ik=u._Tanh=u.asm.Za).apply(null,arguments)},Sk=u._Tile=function(){return(Sk=u._Tile=u.asm._a).apply(null,arguments)},Nk=u._TopK=function(){return(Nk=u._TopK=u.asm.$a).apply(null,arguments)},Tk=u._Transpose=function(){return(Tk=u._Transpose=u.asm.ab).apply(null,arguments)},Ek=u.__FusedMatMul=function(){return(Ek=u.__FusedMatMul=u.asm.bb).apply(null,arguments)},as=u._malloc=function(){return(as=u._malloc=u.asm.cb).apply(null,arguments)},ou=u._free=function(){return(ou=u._free=u.asm.db).apply(null,arguments)},Ig=u.___errno_location=function(){return(Ig=u.___errno_location=u.asm.eb).apply(null,arguments)},Sg=u._emscripten_get_global_libc=function(){return(Sg=u._emscripten_get_global_libc=u.asm.fb).apply(null,arguments)},qi=u._pthread_self=function(){return(qi=u._pthread_self=u.asm.gb).apply(null,arguments)},Ng=u.___pthread_tsd_run_dtors=function(){return(Ng=u.___pthread_tsd_run_dtors=u.asm.hb).apply(null,arguments)},sf=u._emscripten_main_thread_process_queued_calls=function(){return(sf=u._emscripten_main_thread_process_queued_calls=u.asm.ib).apply(null,arguments)},Ck=u._emscripten_current_thread_process_queued_calls=function(){return(Ck=u._emscripten_current_thread_process_queued_calls=u.asm.jb).apply(null,arguments)},Tg=u._emscripten_register_main_browser_thread_id=function(){return(Tg=u._emscripten_register_main_browser_thread_id=u.asm.kb).apply(null,arguments)},Eg=u.__emscripten_do_dispatch_to_thread=function(){return(Eg=u.__emscripten_do_dispatch_to_thread=u.asm.lb).apply(null,arguments)},Cg=u._emscripten_sync_run_in_main_thread_4=function(){return(Cg=u._emscripten_sync_run_in_main_thread_4=u.asm.mb).apply(null,arguments)},Rg=u._emscripten_run_in_main_runtime_thread_js=function(){return(Rg=u._emscripten_run_in_main_runtime_thread_js=u.asm.nb).apply(null,arguments)},of=u.__emscripten_call_on_thread=function(){return(of=u.__emscripten_call_on_thread=u.asm.ob).apply(null,arguments)},Rk=u._emscripten_tls_init=function(){return(Rk=u._emscripten_tls_init=u.asm.pb).apply(null,arguments)},lf=u.__emscripten_thread_init=function(){return(lf=u.__emscripten_thread_init=u.asm.qb).apply(null,arguments)},lu=u.stackSave=function(){return(lu=u.stackSave=u.asm.rb).apply(null,arguments)},Xi=u.stackRestore=function(){return(Xi=u.stackRestore=u.asm.sb).apply(null,arguments)},Ki=u.stackAlloc=function(){return(Ki=u.stackAlloc=u.asm.tb).apply(null,arguments)},Mg=u._emscripten_stack_set_limits=function(){return(Mg=u._emscripten_stack_set_limits=u.asm.ub).apply(null,arguments)},Fg=u._memalign=function(){return(Fg=u._memalign=u.asm.vb).apply(null,arguments)},$g=u.__emscripten_allow_main_runtime_queued_calls=9808,Zi=u.__emscripten_main_thread_futex=11432;u.cwrap=Fe,u.PThread=_e,u.PThread=_e,u.wasmMemory=Q,u.ExitStatus=uu;var _h;function uu(S){this.name="ExitStatus",this.message="Program terminated with exit("+S+")",this.status=S}ns=function S(){_h||uf(),_h||(ns=S)};function uf(S){if(S=S||f,Jr>0)return;if(b){h(u),Ql(),postMessage({cmd:"loaded"});return}if(W0(),Jr>0)return;function T(){_h||(_h=!0,u.calledRun=!0,!oe&&(Ql(),B0(),h(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),wn()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),T()},1)):T()}u.run=uf;function Mk(S,T){if(!(T&&ie&&S===0)){if(!T&&b)throw postMessage({cmd:"exitProcess",returnCode:S}),new uu(S);ie||(_e.terminateAllThreads(),me=S,nh(),u.onExit&&u.onExit(S),oe=!0),y(S,new uu(S))}}if(u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return b&&(ie=!1,_e.initWorker()),uf(),a.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}),Jk=wt((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};var s=typeof a!="undefined"?a:{},i,o;s.ready=new Promise(function(K,ne){i=K,o=ne});var l={},c;for(c in s)s.hasOwnProperty(c)&&(l[c]=s[c]);var u=[],h="./this.program",d=function(K,ne){throw ne},p=!1,m=!1,f=!1,A=!1;p=typeof window=="object",m=typeof importScripts=="function",f=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",A=!p&&!f&&!m;var y="";function g(K){return s.locateFile?s.locateFile(K,y):y+K}var x,v,w,b,k,N;f?(m?y=hu().dirname(y)+"/":y=__dirname+"/",x=function(K,ne){return k||(k=require("fs")),N||(N=hu()),K=N.normalize(K),k.readFileSync(K,ne?null:"utf8")},w=function(K){var ne=x(K,!0);return ne.buffer||(ne=new Uint8Array(ne)),X(ne.buffer),ne},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),u=process.argv.slice(2),process.on("uncaughtException",function(K){if(!(K instanceof af))throw K}),process.on("unhandledRejection",Mr),d=function(K){process.exit(K)},s.inspect=function(){return"[Emscripten Module object]"}):A?(typeof read!="undefined"&&(x=function(K){return read(K)}),w=function(K){var ne;return typeof readbuffer=="function"?new Uint8Array(readbuffer(K)):(ne=read(K,"binary"),X(typeof ne=="object"),ne)},typeof scriptArgs!="undefined"?u=scriptArgs:typeof arguments!="undefined"&&(u=arguments),typeof quit=="function"&&(d=function(K){quit(K)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(p||m)&&(m?y=self.location.href:typeof document!="undefined"&&document.currentScript&&(y=document.currentScript.src),r&&(y=r),y.indexOf("blob:")!==0?y=y.substr(0,y.lastIndexOf("/")+1):y="",x=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.send(null),ne.responseText},m&&(w=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),v=function(K,ne,Se){var Ke=new XMLHttpRequest;Ke.open("GET",K,!0),Ke.responseType="arraybuffer",Ke.onload=function(){if(Ke.status==200||Ke.status==0&&Ke.response){ne(Ke.response);return}Se()},Ke.onerror=Se,Ke.send(null)},b=function(K){document.title=K});var C=s.print||console.log.bind(console),F=s.printErr||console.warn.bind(console);for(c in l)l.hasOwnProperty(c)&&(s[c]=l[c]);l=null,s.arguments&&(u=s.arguments),s.thisProgram&&(h=s.thisProgram),s.quit&&(d=s.quit);var O;s.wasmBinary&&(O=s.wasmBinary);var z=s.noExitRuntime||!0;typeof WebAssembly!="object"&&Mr("no native wasm support detected");var V,j=!1,U;function X(K,ne){K||Mr("Assertion failed: "+ne)}function G(K){var ne=s["_"+K];return X(ne,"Cannot call unknown function "+K+", make sure it is exported"),ne}function ee(K,ne,Se,Ke,It){var mt={string:function(Hn){var _a=0;if(Hn!=null&&Hn!==0){var bh=(Hn.length<<2)+1;_a=au(bh),ce(Hn,_a,bh)}return _a},array:function(Hn){var _a=au(Hn.length);return oe(Hn,_a),_a}};function je(Hn){return ne==="string"?ie(Hn):ne==="boolean"?Boolean(Hn):Hn}var He=G(K),Zt=[],ea=0;if(Ke)for(var ta=0;ta<Ke.length;ta++){var wh=mt[Se[ta]];wh?(ea===0&&(ea=gh()),Zt[ta]=wh(Ke[ta])):Zt[ta]=Ke[ta]}var iu=He.apply(null,Zt);return iu=je(iu),ea!==0&&xh(ea),iu}function Y(K,ne,Se,Ke){Se=Se||[];var It=Se.every(function(je){return je==="number"}),mt=ne!=="string";return mt&&It&&!Ke?G(K):function(){return ee(K,ne,Se,arguments,Ke)}}var ae=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function te(K,ne,Se){for(var Ke=ne+Se,It=ne;K[It]&&!(It>=Ke);)++It;if(It-ne>16&&K.subarray&&ae)return ae.decode(K.subarray(ne,It));for(var mt="";ne<It;){var je=K[ne++];if(!(je&128)){mt+=String.fromCharCode(je);continue}var He=K[ne++]&63;if((je&224)==192){mt+=String.fromCharCode((je&31)<<6|He);continue}var Zt=K[ne++]&63;if((je&240)==224?je=(je&15)<<12|He<<6|Zt:je=(je&7)<<18|He<<12|Zt<<6|K[ne++]&63,je<65536)mt+=String.fromCharCode(je);else{var ea=je-65536;mt+=String.fromCharCode(55296|ea>>10,56320|ea&1023)}}return mt}function ie(K,ne){return K?te(Ie,K,ne):""}function Q(K,ne,Se,Ke){if(!(Ke>0))return 0;for(var It=Se,mt=Se+Ke-1,je=0;je<K.length;++je){var He=K.charCodeAt(je);if(He>=55296&&He<=57343){var Zt=K.charCodeAt(++je);He=65536+((He&1023)<<10)|Zt&1023}if(He<=127){if(Se>=mt)break;ne[Se++]=He}else if(He<=2047){if(Se+1>=mt)break;ne[Se++]=192|He>>6,ne[Se++]=128|He&63}else if(He<=65535){if(Se+2>=mt)break;ne[Se++]=224|He>>12,ne[Se++]=128|He>>6&63,ne[Se++]=128|He&63}else{if(Se+3>=mt)break;ne[Se++]=240|He>>18,ne[Se++]=128|He>>12&63,ne[Se++]=128|He>>6&63,ne[Se++]=128|He&63}}return ne[Se]=0,Se-It}function ce(K,ne,Se){return Q(K,Ie,ne,Se)}function oe(K,ne){ve.set(K,ne)}function me(K,ne){return K%ne>0&&(K+=ne-K%ne),K}var de,ve,Ie,Fe,Oe,$e,Qe,et,at;function Xe(K){de=K,s.HEAP8=ve=new Int8Array(K),s.HEAP16=Fe=new Int16Array(K),s.HEAP32=$e=new Int32Array(K),s.HEAPU8=Ie=new Uint8Array(K),s.HEAPU16=Oe=new Uint16Array(K),s.HEAPU32=Qe=new Uint32Array(K),s.HEAPF32=et=new Float32Array(K),s.HEAPF64=at=new Float64Array(K)}var ht=s.INITIAL_MEMORY||16777216,Ve,gn=[],xt=[],jn=[],Xt=[],xn=!1;xt.push({func:function(){lh()}});function Un(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Rr(s.preRun.shift());xa(gn)}function Rn(){xn=!0,xa(xt)}function rn(){xa(jn)}function Kt(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)tr(s.postRun.shift());xa(Xt)}function Rr(K){gn.unshift(K)}function tr(K){Xt.unshift(K)}var nr=0,ya=null,Yr=null;function ga(K){nr++,s.monitorRunDependencies&&s.monitorRunDependencies(nr)}function Vi(K){if(nr--,s.monitorRunDependencies&&s.monitorRunDependencies(nr),nr==0&&(ya!==null&&(clearInterval(ya),ya=null),Yr)){var ne=Yr;Yr=null,ne()}}s.preloadedImages={},s.preloadedAudios={};function Mr(K){s.onAbort&&s.onAbort(K),K+="",F(K),j=!0,U=1,K="abort("+K+"). Build with -s ASSERTIONS=1 for more info.";var ne=new WebAssembly.RuntimeError(K);throw o(ne),ne}function th(K,ne){return String.prototype.startsWith?K.startsWith(ne):K.indexOf(ne)===0}var W0="data:application/octet-stream;base64,";function Ql(K){return th(K,W0)}var B0="file://";function nh(K){return th(K,B0)}var wn="tfjs-backend-wasm.wasm";Ql(wn)||(wn=g(wn));function rh(K){try{if(K==wn&&O)return new Uint8Array(O);if(w)return w(K);throw"both async and sync fetching of the wasm failed"}catch(ne){Mr(ne)}}function V0(){if(!O&&(p||m)){if(typeof fetch=="function"&&!nh(wn))return fetch(wn,{credentials:"same-origin"}).then(function(K){if(!K.ok)throw"failed to load wasm binary file at '"+wn+"'";return K.arrayBuffer()}).catch(function(){return rh(wn)});if(v)return new Promise(function(K,ne){v(wn,function(Se){K(new Uint8Array(Se))},ne)})}return Promise.resolve().then(function(){return rh(wn)})}function Jr(){var K={a:q0};function ne(je,He){var Zt=je.exports;s.asm=Zt,V=s.asm.i,Xe(V.buffer),Ve=s.asm.o,Vi("wasm-instantiate")}ga("wasm-instantiate");function Se(je){ne(je.instance)}function Ke(je){return V0().then(function(He){return WebAssembly.instantiate(He,K)}).then(je,function(He){F("failed to asynchronously prepare wasm: "+He),Mr(He)})}function It(){return!O&&typeof WebAssembly.instantiateStreaming=="function"&&!Ql(wn)&&!nh(wn)&&typeof fetch=="function"?fetch(wn,{credentials:"same-origin"}).then(function(je){var He=WebAssembly.instantiateStreaming(je,K);return He.then(Se,function(Zt){return F("wasm streaming compile failed: "+Zt),F("falling back to ArrayBuffer instantiation"),Ke(Se)})}):Ke(Se)}if(s.instantiateWasm)try{var mt=s.instantiateWasm(K,ne);return mt}catch(je){return F("Module.instantiateWasm callback failed with error: "+je),!1}return It().catch(o),{}}function xa(K){for(;K.length>0;){var ne=K.shift();if(typeof ne=="function"){ne(s);continue}var Se=ne.func;typeof Se=="number"?ne.arg===void 0?Ve.get(Se)():Ve.get(Se)(ne.arg):Se(ne.arg===void 0?null:ne.arg)}}function ns(){Mr()}function j0(K,ne,Se){Ie.copyWithin(K,ne,ne+Se)}function U0(){return Ie.length}function Qr(K){try{return V.grow(K-de.byteLength+65535>>>16),Xe(V.buffer),1}catch(ne){}}function ah(K){var ne=U0(),Se=2147483648;if(K>Se)return!1;for(var Ke=1;Ke<=4;Ke*=2){var It=ne*(1+.2/Ke);It=Math.min(It,K+100663296);var mt=Math.min(Se,me(Math.max(K,It),65536)),je=Qr(mt);if(je)return!0}return!1}var ji={mappings:{},buffers:[null,[],[]],printChar:function(K,ne){var Se=ji.buffers[K];ne===0||ne===10?((K===1?C:F)(te(Se,0)),Se.length=0):Se.push(ne)},varargs:void 0,get:function(){ji.varargs+=4;var K=$e[ji.varargs-4>>2];return K},getStr:function(K){var ne=ie(K);return ne},get64:function(K,ne){return K}};function sh(K){return 0}function H0(K,ne,Se,Ke,It){}function ih(K,ne,Se,Ke){for(var It=0,mt=0;mt<Se;mt++){for(var je=$e[ne+mt*8>>2],He=$e[ne+(mt*8+4)>>2],Zt=0;Zt<He;Zt++)ji.printChar(K,Ie[je+Zt]);It+=He}return $e[Ke>>2]=It,0}function bn(){return 6}function oh(K){return $e[yh()>>2]=K,K}function G0(K){switch(K){case 30:return 16384;case 85:var ne=2147483648;return ne/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return oh(28),-1}var q0={a:ns,d:j0,e:ah,f:sh,c:H0,b:ih,g:bn,h:G0},X0=Jr(),lh=s.___wasm_call_ctors=function(){return(lh=s.___wasm_call_ctors=s.asm.j).apply(null,arguments)},Ui=s._init=function(){return(Ui=s._init=s.asm.k).apply(null,arguments)},eu=s._register_tensor=function(){return(eu=s._register_tensor=s.asm.l).apply(null,arguments)},K0=s._dispose_data=function(){return(K0=s._dispose_data=s.asm.m).apply(null,arguments)},Z0=s._dispose=function(){return(Z0=s._dispose=s.asm.n).apply(null,arguments)},Y0=s._Abs=function(){return(Y0=s._Abs=s.asm.p).apply(null,arguments)},_e=s._Add=function(){return(_e=s._Add=s.asm.q).apply(null,arguments)},J0=s._AddN=function(){return(J0=s._AddN=s.asm.r).apply(null,arguments)},Q0=s._All=function(){return(Q0=s._All=s.asm.s).apply(null,arguments)},e1=s._Any=function(){return(e1=s._Any=s.asm.t).apply(null,arguments)},t1=s._ArgMax=function(){return(t1=s._ArgMax=s.asm.u).apply(null,arguments)},n1=s._AvgPool=function(){return(n1=s._AvgPool=s.asm.v).apply(null,arguments)},rs=s._BatchMatMul=function(){return(rs=s._BatchMatMul=s.asm.w).apply(null,arguments)},r1=s._Ceil=function(){return(r1=s._Ceil=s.asm.x).apply(null,arguments)},a1=s._ClipByValue=function(){return(a1=s._ClipByValue=s.asm.y).apply(null,arguments)},s1=s._Conv2D=function(){return(s1=s._Conv2D=s.asm.z).apply(null,arguments)},i1=s._Conv2DBackpropInput=function(){return(i1=s._Conv2DBackpropInput=s.asm.A).apply(null,arguments)},o1=s._Cos=function(){return(o1=s._Cos=s.asm.B).apply(null,arguments)},l1=s._CropAndResize=function(){return(l1=s._CropAndResize=s.asm.C).apply(null,arguments)},u1=s._Cumsum=function(){return(u1=s._Cumsum=s.asm.D).apply(null,arguments)},c1=s._DepthToSpace=function(){return(c1=s._DepthToSpace=s.asm.E).apply(null,arguments)},h1=s._DepthwiseConv2dNative=function(){return(h1=s._DepthwiseConv2dNative=s.asm.F).apply(null,arguments)},wa=s._Equal=function(){return(wa=s._Equal=s.asm.G).apply(null,arguments)},tu=s._Exp=function(){return(tu=s._Exp=s.asm.H).apply(null,arguments)},nu=s._FlipLeftRight=function(){return(nu=s._FlipLeftRight=s.asm.I).apply(null,arguments)},d1=s._Floor=function(){return(d1=s._Floor=s.asm.J).apply(null,arguments)},p1=s._FloorDiv=function(){return(p1=s._FloorDiv=s.asm.K).apply(null,arguments)},f1=s._FusedBatchNorm=function(){return(f1=s._FusedBatchNorm=s.asm.L).apply(null,arguments)},m1=s._FusedConv2D=function(){return(m1=s._FusedConv2D=s.asm.M).apply(null,arguments)},A1=s._FusedDepthwiseConv2D=function(){return(A1=s._FusedDepthwiseConv2D=s.asm.N).apply(null,arguments)},Pe=s._Gather=function(){return(Pe=s._Gather=s.asm.O).apply(null,arguments)},y1=s._GatherNd=function(){return(y1=s._GatherNd=s.asm.P).apply(null,arguments)},g1=s._Greater=function(){return(g1=s._Greater=s.asm.Q).apply(null,arguments)},x1=s._GreaterEqual=function(){return(x1=s._GreaterEqual=s.asm.R).apply(null,arguments)},w1=s._LeakyRelu=function(){return(w1=s._LeakyRelu=s.asm.S).apply(null,arguments)},b1=s._Less=function(){return(b1=s._Less=s.asm.T).apply(null,arguments)},_1=s._LessEqual=function(){return(_1=s._LessEqual=s.asm.U).apply(null,arguments)},ru=s._Log=function(){return(ru=s._Log=s.asm.V).apply(null,arguments)},uh=s._LogicalAnd=function(){return(uh=s._LogicalAnd=s.asm.W).apply(null,arguments)},ch=s._Max=function(){return(ch=s._Max=s.asm.X).apply(null,arguments)},v1=s._MaxPool=function(){return(v1=s._MaxPool=s.asm.Y).apply(null,arguments)},k1=s._Maximum=function(){return(k1=s._Maximum=s.asm.Z).apply(null,arguments)},I1=s._Mean=function(){return(I1=s._Mean=s.asm._).apply(null,arguments)},S1=s._Min=function(){return(S1=s._Min=s.asm.$).apply(null,arguments)},N1=s._Minimum=function(){return(N1=s._Minimum=s.asm.aa).apply(null,arguments)},T1=s._MirrorPad=function(){return(T1=s._MirrorPad=s.asm.ba).apply(null,arguments)},E1=s._Multiply=function(){return(E1=s._Multiply=s.asm.ca).apply(null,arguments)},Je=s._Neg=function(){return(Je=s._Neg=s.asm.da).apply(null,arguments)},C1=s._NonMaxSuppressionV3=function(){return(C1=s._NonMaxSuppressionV3=s.asm.ea).apply(null,arguments)},R1=s._NonMaxSuppressionV4=function(){return(R1=s._NonMaxSuppressionV4=s.asm.fa).apply(null,arguments)},M1=s._NonMaxSuppressionV5=function(){return(M1=s._NonMaxSuppressionV5=s.asm.ga).apply(null,arguments)},Hi=s._NotEqual=function(){return(Hi=s._NotEqual=s.asm.ha).apply(null,arguments)},hh=s._OneHot=function(){return(hh=s._OneHot=s.asm.ia).apply(null,arguments)},dh=s._PadV2=function(){return(dh=s._PadV2=s.asm.ja).apply(null,arguments)},ph=s._Pow=function(){return(ph=s._Pow=s.asm.ka).apply(null,arguments)},F1=s._Prelu=function(){return(F1=s._Prelu=s.asm.la).apply(null,arguments)},$1=s._Prod=function(){return($1=s._Prod=s.asm.ma).apply(null,arguments)},fh=s._RealDiv=function(){return(fh=s._RealDiv=s.asm.na).apply(null,arguments)},D1=s._Relu=function(){return(D1=s._Relu=s.asm.oa).apply(null,arguments)},mh=s._Relu6=function(){return(mh=s._Relu6=s.asm.pa).apply(null,arguments)},ba=s._ResizeBilinear=function(){return(ba=s._ResizeBilinear=s.asm.qa).apply(null,arguments)},O1=s._Reverse=function(){return(O1=s._Reverse=s.asm.ra).apply(null,arguments)},z1=s._RotateWithOffset=function(){return(z1=s._RotateWithOffset=s.asm.sa).apply(null,arguments)},kg=s._Round=function(){return(kg=s._Round=s.asm.ta).apply(null,arguments)},Ah=s._Rsqrt=function(){return(Ah=s._Rsqrt=s.asm.ua).apply(null,arguments)},P1=s._ScatterNd=function(){return(P1=s._ScatterNd=s.asm.va).apply(null,arguments)},L1=s._SelectV2=function(){return(L1=s._SelectV2=s.asm.wa).apply(null,arguments)},W1=s._Sigmoid=function(){return(W1=s._Sigmoid=s.asm.xa).apply(null,arguments)},B1=s._Sin=function(){return(B1=s._Sin=s.asm.ya).apply(null,arguments)},V1=s._Softmax=function(){return(V1=s._Softmax=s.asm.za).apply(null,arguments)},j1=s._Sqrt=function(){return(j1=s._Sqrt=s.asm.Aa).apply(null,arguments)},U1=s._Square=function(){return(U1=s._Square=s.asm.Ba).apply(null,arguments)},H1=s._SquaredDifference=function(){return(H1=s._SquaredDifference=s.asm.Ca).apply(null,arguments)},G1=s._Step=function(){return(G1=s._Step=s.asm.Da).apply(null,arguments)},q1=s._StridedSlice=function(){return(q1=s._StridedSlice=s.asm.Ea).apply(null,arguments)},X1=s._Sub=function(){return(X1=s._Sub=s.asm.Fa).apply(null,arguments)},K1=s._Sum=function(){return(K1=s._Sum=s.asm.Ga).apply(null,arguments)},Z1=s._Tan=function(){return(Z1=s._Tan=s.asm.Ha).apply(null,arguments)},Y1=s._Tanh=function(){return(Y1=s._Tanh=s.asm.Ia).apply(null,arguments)},J1=s._Tile=function(){return(J1=s._Tile=s.asm.Ja).apply(null,arguments)},Q1=s._TopK=function(){return(Q1=s._TopK=s.asm.Ka).apply(null,arguments)},ef=s._Transpose=function(){return(ef=s._Transpose=s.asm.La).apply(null,arguments)},tf=s.__FusedMatMul=function(){return(tf=s.__FusedMatMul=s.asm.Ma).apply(null,arguments)},nf=s._malloc=function(){return(nf=s._malloc=s.asm.Na).apply(null,arguments)},rf=s._free=function(){return(rf=s._free=s.asm.Oa).apply(null,arguments)},yh=s.___errno_location=function(){return(yh=s.___errno_location=s.asm.Pa).apply(null,arguments)},gh=s.stackSave=function(){return(gh=s.stackSave=s.asm.Qa).apply(null,arguments)},xh=s.stackRestore=function(){return(xh=s.stackRestore=s.asm.Ra).apply(null,arguments)},au=s.stackAlloc=function(){return(au=s.stackAlloc=s.asm.Sa).apply(null,arguments)};s.cwrap=Y;var Gi;function af(K){this.name="ExitStatus",this.message="Program terminated with exit("+K+")",this.status=K}Yr=function K(){Gi||su(),Gi||(Yr=K)};function su(K){if(K=K||u,nr>0||(Un(),nr>0))return;function ne(){Gi||(Gi=!0,s.calledRun=!0,!j&&(Rn(),rn(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Kt()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ne()},1)):ne()}if(s.run=su,s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();return su(),a.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}),Qk=wt((e,t)=>{(function(n,r,a){function s(c){var u=this,h=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=h(" "),u.s1=h(" "),u.s2=h(" "),u.s0-=h(c),u.s0<0&&(u.s0+=1),u.s1-=h(c),u.s1<0&&(u.s1+=1),u.s2-=h(c),u.s2<0&&(u.s2+=1),h=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 h=new s(c),d=u&&u.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var c=4022871197,u=function(h){h=String(h);for(var d=0;d<h.length;d++){c+=h.charCodeAt(d);var p=.02519603282416938*c;c=p>>>0,p-=c,p*=c,c=p>>>0,p-=c,c+=p*4294967296}return(c>>>0)*23283064365386963e-26};return u}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),e9=wt((e,t)=>{(function(n,r,a){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 h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),t9=wt((e,t)=>{(function(n,r,a){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 h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,h==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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),n9=wt((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,d=c.i,p,m,f;return p=h[d],p^=p>>>7,m=p^p<<24,p=h[d+1&7],m^=p^p>>>10,p=h[d+3&7],m^=p^p>>>3,p=h[d+4&7],m^=p^p<<7,p=h[d+7&7],p=p^p<<13,m^=p^p<<9,h[d]=m,c.i=d+1&7,m};function u(h,d){var p,m,f=[];if(d===(d|0))m=f[0]=d;else for(d=""+d,p=0;p<d.length;++p)f[p&7]=f[p&7]<<15^d.charCodeAt(p)+f[p+1&7]<<13;for(;f.length<8;)f.push(0);for(p=0;p<8&&f[p]===0;++p);for(p==8?m=f[7]=-1:m=f[p],h.x=f,h.i=0,p=256;p>0;--p)h.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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(h.x&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),r9=wt((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.w,d=c.X,p=c.i,m,f;return c.w=h=h+1640531527|0,f=d[p+34&127],m=d[p=p+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=d[p]=f^m,c.i=p,f+(h^h>>>16)|0};function u(h,d){var p,m,f,A,y,g=[],x=128;for(d===(d|0)?(m=d,d=null):(d=d+"\0",m=0,x=Math.max(x,d.length)),f=0,A=-32;A<x;++A)d&&(m^=d.charCodeAt((A+32)%d.length)),A===0&&(y=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=m+y,f=p==0?f+1:0);for(f>=128&&(g[(d&&d.length||0)&127]=-1),f=127,A=4*128;A>0;--A)m=g[f+34&127],p=g[f=f+1&127],m^=m<<13,p^=p<<17,m^=m>>>15,p^=p>>>12,g[f]=m^p;h.w=y,h.X=g,h.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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(h.X&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),a9=wt((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var d=c.b,p=c.c,m=c.d,f=c.a;return d=d<<25^d>>>7^p,p=p-m|0,m=m<<24^m>>>8^f,f=f-d|0,c.b=d=d<<20^d>>>12^p,c.c=p=p-m|0,c.d=m<<16^p>>>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 h=0;h<u.length+20;h++)c.b^=u.charCodeAt(h)|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),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),s9=wt((e,t)=>{(function(n,r,a){var s=256,i=6,o=52,l="random",c=a.pow(s,i),u=a.pow(2,o),h=u*2,d=s-1,p;function m(w,b,k){var N=[];b=b==!0?{entropy:!0}:b||{};var C=g(y(b.entropy?[w,v(r)]:w==null?x():w,3),N),F=new f(N),O=function(){for(var z=F.g(i),V=c,j=0;z<u;)z=(z+j)*s,V*=s,j=F.g(1);for(;z>=h;)z/=2,V/=2,j>>>=1;return(z+j)/V};return O.int32=function(){return F.g(4)|0},O.quick=function(){return F.g(4)/4294967296},O.double=O,g(v(F.S),r),(b.pass||k||function(z,V,j,U){return U&&(U.S&&A(U,F),z.state=function(){return A(F,{})}),j?(a[l]=z,V):z})(O,C,"global"in b?b.global:this==a,b.state)}function f(w){var b,k=w.length,N=this,C=0,F=N.i=N.j=0,O=N.S=[];for(k||(w=[k++]);C<s;)O[C]=C++;for(C=0;C<s;C++)O[C]=O[F=d&F+w[C%k]+(b=O[C])],O[F]=b;(N.g=function(z){for(var V,j=0,U=N.i,X=N.j,G=N.S;z--;)V=G[U=d&U+1],j=j*s+G[d&(G[U]=G[X=d&X+V])+(G[X]=V)];return N.i=U,N.j=X,j})(s)}function A(w,b){return b.i=w.i,b.j=w.j,b.S=w.S.slice(),b}function y(w,b){var k=[],N=typeof w,C;if(b&&N=="object")for(C in w)try{k.push(y(w[C],b-1))}catch(F){}return k.length?k:N=="string"?w:w+"\0"}function g(w,b){for(var k=w+"",N,C=0;C<k.length;)b[d&C]=d&(N^=b[d&C]*19)+k.charCodeAt(C++);return v(b)}function x(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(n.crypto||n.msCrypto).getRandomValues(w)),v(w)}catch(N){var b=n.navigator,k=b&&b.plugins;return[+new Date,n,k,n.screen,v(r)]}}function v(w){return String.fromCharCode.apply(0,w)}if(g(a.random(),r),typeof t=="object"&&t.exports){t.exports=m;try{p=Pg()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return m}):a["seed"+l]=m})(typeof self!="undefined"?self:e,[],Math)}),Wg=wt((e,t)=>{var n=Qk(),r=e9(),a=t9(),s=n9(),i=r9(),o=a9(),l=s9();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),i9=wt(()=>{}),cf={};Me(cf,{bin:()=>Yg,browser:()=>r5,default:()=>o9,dependencies:()=>n5,description:()=>jg,devDependencies:()=>e5,jsdelivr:()=>qg,license:()=>Qg,main:()=>Hg,miniprogram:()=>Zg,module:()=>Gg,name:()=>Bg,private:()=>Ug,repository:()=>Jg,scripts:()=>t5,types:()=>Kg,unpkg:()=>Xg,version:()=>Vg});var Bg="@tensorflow/tfjs",Vg="3.5.0",jg="An open-source machine learning framework.",Ug=!1,Hg="dist/tf.node.js",Gg="dist/index.js",qg="dist/tf.min.js",Xg="dist/tf.min.js",Kg="dist/index.d.ts",Zg="dist/miniprogram",Yg={"tfjs-custom-module":"dist/tools/custom_module/cli.js"},Jg={type:"git",url:"https://github.com/tensorflow/tfjs.git"},Qg="Apache-2.0",e5={"@babel/core":"^7.9.0","@babel/polyfill":"^7.10.4","@babel/preset-env":"^7.9.5","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@types/argparse":"^1.0.38","@types/jasmine":"2.8.7","@types/node":"~10.17.50","@types/shelljs":"^0.8.4","@types/yargs":"^15.0.7","clang-format":"~1.2.2",commander:"~2.14.1",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.2","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.5.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-babel":"^4.4.0","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~4.2.2",shelljs:"~0.8.1","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"1.0.0-pre.50"},t5={build:"tsc && yarn build-cli && yarn bundle","build-ci":"tsc && yarn build-cli && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-converter":"cd ../tfjs-converter && yarn && yarn build","build-converter-ci":"cd ../tfjs-converter && yarn && yarn build-ci","build-data":"cd ../tfjs-data && yarn && yarn build","build-data-ci":"cd ../tfjs-data && yarn && yarn build-ci","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-converter && yarn build-data && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-converter-ci && yarn build-data-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-cli":"tsc --project ./tools/custom_module/tsconfig.json && chmod +x ./dist/tools/custom_module/cli.js","run-custom-build":"ts-node -s ./tools/custom_module/cli.ts","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",test:"yarn && yarn build-deps && yarn build && karma start","test-dev":"karma start","test-tools":"ts-node --project ./tools/custom_module/tsconfig.json run_tools_tests.ts","test-ci":"./scripts/test-ci.sh"},n5={"@tensorflow/tfjs-backend-cpu":"3.5.0","@tensorflow/tfjs-backend-webgl":"3.5.0","@tensorflow/tfjs-converter":"3.5.0","@tensorflow/tfjs-core":"3.5.0","@tensorflow/tfjs-data":"3.5.0","@tensorflow/tfjs-layers":"3.5.0",argparse:"^1.0.10",chalk:"^4.1.0","core-js":"3","regenerator-runtime":"^0.13.5",yargs:"^16.0.3"},r5={"node-fetch":!1,util:!1,crypto:!1},o9={name:Bg,version:Vg,description:jg,private:Ug,main:Hg,module:Gg,jsdelivr:qg,unpkg:Xg,types:Kg,miniprogram:Zg,bin:Yg,repository:Jg,license:Qg,devDependencies:e5,scripts:t5,dependencies:n5,browser:r5},hf={};Me(hf,{browser:()=>b5,default:()=>l9,dependencies:()=>w5,description:()=>i5,devDependencies:()=>g5,engines:()=>m5,jsdelivr:()=>u5,"jsnext:main":()=>d5,license:()=>y5,main:()=>l5,miniprogram:()=>f5,module:()=>p5,name:()=>a5,private:()=>o5,repository:()=>A5,scripts:()=>x5,sideEffects:()=>_5,types:()=>h5,unpkg:()=>c5,version:()=>s5});var a5="@tensorflow/tfjs-core",s5="3.5.0",i5="Hardware-accelerated JavaScript library for machine intelligence",o5=!1,l5="dist/tf-core.node.js",u5="dist/tf-core.min.js",c5="dist/tf-core.min.js",h5="dist/index.d.ts",d5="dist/index.js",p5="dist/index.js",f5="dist/miniprogram",m5={yarn:">= 1.3.2"},A5={type:"git",url:"https://github.com/tensorflow/tfjs-core.git"},y5="Apache-2.0",g5={"@bazel/bazelisk":"^1.3.0","@bazel/typescript":"^0.27.8","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"link:../tfjs-backend-cpu","@types/jasmine":"~3.0.0","@types/node":"~9.6.0","@types/node-fetch":"~2.1.2","clang-format":"~1.2.4",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"6.3.1","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-jasmine":"~1.1.0","karma-typescript":"~4.1.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~5.3.0","rollup-plugin-visualizer":"~3.3.2",shelljs:"~0.8.3","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"~1.0.0-pre.21",yargs:"~13.2.2"},x5={"build-ci":"./scripts/enumerate-tests.js --ci && tsc && yarn bundle-ci && yarn build-test-snippets",build:"node ./scripts/enumerate-tests.js && tsc && yarn bundle",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-npm":"./scripts/build-npm.sh","build-deps":"yarn build && yarn build-cpu-backend","build-cpu-backend":"cd ../tfjs-backend-cpu && yarn && yarn build","build-cpu-backend-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build:bazel":"bazelisk build //...","build-test-snippets":"yarn tsc --project ./scripts/test_snippets/tsconfig.json","format-all":"clang-format -i -style=Google --glob=src/**/*.ts","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build && rollup -c && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-webworker":"karma start --worker","run-browserstack":"karma start --browserstack","test-bundle-size":"./scripts/test-bundle-size.js","test-node":"rimraf dist/ && yarn build-deps && yarn build && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-dev":"tsc && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-ci":"ts-node --transpile-only -P tsconfig.test.json  dist/test_node.js","test-async-backends":"rimraf dist/ && yarn build && ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-async-backends-ci":"ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-snippets":"yarn build && yarn build-cpu-backend && ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts","test-snippets-ci":"ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts"},w5={"@types/offscreencanvas":"~2019.3.0","@types/seedrandom":"2.4.27","@types/webgl-ext":"0.0.30","node-fetch":"~2.6.1",seedrandom:"2.4.3"},b5={"node-fetch":!1,util:!1,crypto:!1,worker_threads:!1},_5=["./dist/index.js","./dist/engine.js","./dist/tensor.js","./dist/base_side_effects.js","./dist/flags.js","./dist/platforms/*.js","./dist/register_all_gradients.js","./dist/public/chained_ops/*.js","./dist/io/*.js"],l9={name:a5,version:s5,description:i5,private:o5,main:l5,jsdelivr:u5,unpkg:c5,types:h5,"jsnext:main":d5,module:p5,miniprogram:f5,engines:m5,repository:A5,license:y5,devDependencies:g5,scripts:x5,dependencies:w5,browser:b5,sideEffects:_5},df={};Me(df,{browser:()=>L5,default:()=>u9,dependencies:()=>P5,description:()=>I5,devDependencies:()=>D5,jsdelivr:()=>T5,"jsnext:main":()=>R5,license:()=>$5,main:()=>N5,miniprogram:()=>F5,module:()=>M5,name:()=>v5,peerDependencies:()=>z5,private:()=>S5,scripts:()=>O5,types:()=>C5,unpkg:()=>E5,version:()=>k5});var v5="@tensorflow/tfjs-data",k5="3.5.0",I5="TensorFlow Data API in JavaScript",S5=!1,N5="dist/tf-data.node.js",T5="dist/tf-data.min.js",E5="dist/tf-data.min.js",C5="dist/index.d.ts",R5="dist/index.js",M5="dist/index.js",F5="dist/miniprogram",$5="Apache-2.0",D5={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.5.0","@tensorflow/tfjs-core":"3.5.0","@tensorflow/tfjs-layers":"3.5.0","@types/jasmine":"~2.5.53","@types/seedrandom":"^2.4.27","@types/utf8":"~2.1.6","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~7.0.0",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"^1.0.0-pre.50"},O5={build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-backend-cpu-ci","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-dev":"tsc && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-browsers":"karma start --browsers='Chrome,Firefox'","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/test_node.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts",lint:"tslint -p . -t verbose"},z5={"@tensorflow/tfjs-core":"3.5.0",seedrandom:"~2.4.3"},P5={"@types/node-fetch":"^2.1.2","node-fetch":"~2.6.1"},L5={fs:!1,"node-fetch":!1,string_decoder:!1,crypto:!1},u9={name:v5,version:k5,description:I5,private:S5,main:N5,jsdelivr:T5,unpkg:E5,types:C5,"jsnext:main":R5,module:M5,miniprogram:F5,license:$5,devDependencies:D5,scripts:O5,peerDependencies:z5,dependencies:P5,browser:L5},pf={};Me(pf,{default:()=>c9,description:()=>V5,devDependencies:()=>J5,jsdelivr:()=>K5,"jsnext:main":()=>q5,license:()=>j5,main:()=>H5,miniprogram:()=>Y5,module:()=>X5,name:()=>W5,peerDependencies:()=>ex,private:()=>U5,scripts:()=>Q5,types:()=>G5,unpkg:()=>Z5,version:()=>B5});var W5="@tensorflow/tfjs-layers",B5="3.5.0",V5="TensorFlow layers API in JavaScript",j5="Apache-2.0 AND MIT",U5=!1,H5="dist/tf-layers.node.js",G5="dist/index.d.ts",q5="dist/index.js",X5="dist/index.js",K5="dist/tf-layers.min.js",Z5="dist/tf-layers.min.js",Y5="dist/miniprogram",J5={"@babel/polyfill":"^7.8.7","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.5.0","@tensorflow/tfjs-backend-webgl":"3.5.0","@tensorflow/tfjs-core":"3.5.0","@types/jasmine":"~2.5.53","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},Q5={prep:"yarn install && yarn build-ci",build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-npm":"./scripts/build-npm.sh",format:"./tools/clang_format_ts.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts","run-browserstack":"karma start --browsers='bs_chrome_mac' --singleRun --reporters='dots,karma-typescript'",lint:"tslint -p . -t verbose"},ex={"@tensorflow/tfjs-core":"3.5.0"},c9={name:W5,version:B5,description:V5,license:j5,private:U5,main:H5,types:G5,"jsnext:main":q5,module:X5,jsdelivr:K5,unpkg:Z5,miniprogram:Y5,devDependencies:J5,scripts:Q5,peerDependencies:ex},ff={};Me(ff,{default:()=>h9,description:()=>rx,devDependencies:()=>fx,jsdelivr:()=>ux,"jsnext:main":()=>sx,license:()=>dx,main:()=>ax,miniprogram:()=>cx,module:()=>ix,name:()=>tx,peerDependencies:()=>px,repository:()=>hx,scripts:()=>mx,types:()=>ox,unpkg:()=>lx,version:()=>nx});var tx="@tensorflow/tfjs-converter",nx="3.5.0",rx="Tensorflow model converter for javascript",ax="dist/tf-converter.node.js",sx="dist/index.js",ix="dist/index.js",ox="dist/index.d.ts",lx="dist/tf-converter.min.js",ux="dist/tf-converter.min.js",cx="dist/miniprogram",hx={type:"git",url:"https://github.com/tensorflow/tfjs-converter.git"},dx="Apache-2.0",px={"@tensorflow/tfjs-core":"3.5.0"},fx={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-replace":"^2.3.3","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.5.0","@tensorflow/tfjs-core":"3.5.0","@types/argparse":"^1.0.38","@types/deep-equal":"^1.0.1","@types/jasmine":"~2.8.6","@types/long":"~3.0.32","@types/node-fetch":"1.6.9",ajv:"~6.3.0",argparse:"^1.0.10","babel-core":"~6.26.3","babel-plugin-external-helpers":"~6.22.0","babel-preset-env":"~1.7.0","clang-format":"~1.2.2",copyfiles:"~1.2.0","deep-equal":"^1.0.1","jasmine-core":"~3.5.0","node-fetch":"~2.6.1",opn:"~5.1.0",protobufjs:"~6.8.6",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-morph":"^7.1.3","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"~0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},mx={build:"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle","build-ci":"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && yarn gen-json --test && yarn gen-kernel2ops && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts","test-dev":"tsc && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts",lint:"tslint -p . -t verbose","make-version":"sh -c ./scripts/make-version","gen-doc":"ts-node -s ./scripts/gen_doc.ts","gen-json":"ts-node -s ./scripts/gen_json.ts","model-summary":"ts-node -s ./tools/model_summary.ts",pb2json:"ts-node -s ./tools/pb2json_converter.ts","build-pip-package":"yarn gen-json --test && cd python && ./build-pip-package.sh --test /tmp/tfjs-pips","run-python-tests":"yarn gen-json --test && cd python && ./run-python-tests.sh","gen-kernel2ops":"ts-node -s scripts/kernels_to_ops.ts --out metadata/kernel2op.json"},h9={name:tx,version:nx,description:rx,main:ax,"jsnext:main":sx,module:ix,types:ox,unpkg:lx,jsdelivr:ux,miniprogram:cx,repository:hx,license:dx,peerDependencies:px,devDependencies:fx,scripts:mx},d9=1e-7,p9=1e-4,kh=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}},du=class{refCount(e){return ar("refCount")}incRef(e){return ar("incRef")}timerAvailable(){return!0}time(e){return ar("time")}read(e){return ar("read")}readSync(e){return ar("readSync")}numDataIds(){return ar("numDataIds")}disposeData(e,t){return ar("disposeData")}write(e,t,n){return ar("write")}move(e,t,n,r,a){return ar("move")}memory(){return ar("memory")}floatPrecision(){return ar("floatPrecision")}epsilon(){return this.floatPrecision()===32?d9:p9}dispose(){return ar("dispose")}};function ar(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 Ax(e){let t=e.length,n=0,r=0;for(;t>0;)r=Math.random()*t|0,t--,n=e[t],e[t]=e[r],e[r]=n}function f9(e,t){if(e.length!==t.length)throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);let n=e.length,r,a,s=0;for(;n>0;)s=Math.random()*n|0,n--,r=e[n],a=t[n],e[n]=e[s],t[n]=t[s],e[s]=r,t[s]=a}function pu(e,t,n){return Math.max(e,Math.min(t,n))}function m9(e){return e%2==0?e:e+1}function A9(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function y9(e,t){let n=Math.random();return t*n+(1-n)*e}function g9(e,t){let n=0;for(let r=0;r<e.length;r++){let a=Number(e[r])-Number(t[r]);n+=a*a}return n}function M(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function an(e,t,n=""){M(na(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function is(e){M(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function os(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||tn(e)&&!n)for(let r=0;r<e.length;++r)os(e[r],t,n);else t.push(e);return t}function Nt(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 x9(e){return e.length===0}function na(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 Bt(e){return e%1==0}function w9(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 b9(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function _9(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return Ax(t),t}function fu(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function v9(e,t=r=>0,n){return new Promise((r,a)=>{let s=0,i=()=>{if(e()){r();return}s++;let o=t(s);if(n!=null&&s>=n){a();return}setTimeout(i,o)};i()})}function k9(e,t){let n=1,r=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)n*=e[s];else if(e[s]===-1){if(r!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${r} and dim ${s}`);r=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(r===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let a=e.slice();return a[r]=t/n,a}function sr(e,t){let n=t.length;return e=e==null?t.map((r,a)=>a):[].concat(e),M(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),M(e.every(r=>Bt(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function yx(e,t){let n=[],r=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:sr(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]),r.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),r.push(o))}return{newShape:n,keptDims:r}}function gx(e,t){let n=null;if(e==null||e==="float32")n=new Float32Array(t);else if(e==="int32")n=new Int32Array(t);else if(e==="bool")n=new Uint8Array(t);else throw new Error(`Unknown data type ${e}`);return n}function xx(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 wx(e,t){for(let n=0;n<e.length;n++){let r=e[n];if(isNaN(r)||!isFinite(r))throw Error(`A tensor of type ${t} being uploaded contains ${r}.`)}}function bx(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function I9(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function tn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function mf(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function _x(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function ka(e){return typeof e=="string"||e instanceof String}function vx(e){return typeof e=="boolean"}function kx(e){return typeof e=="number"}function Ih(e){return Array.isArray(e)?Ih(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":kx(e)?"float32":ka(e)?"string":vx(e)?"bool":"float32"}function Ia(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Sh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function eo(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function Ix(e,t,n,r=!1){let a=new Array;if(t.length===1){let s=t[0]*(r?2:1);for(let i=0;i<s;i++)a[i]=n[e+i]}else{let s=t[0],i=t.slice(1),o=i.reduce((l,c)=>l*c)*(r?2:1);for(let l=0;l<s;l++)a[l]=Ix(e+l*o,i,n,r)}return a}function to(e,t,n=!1){if(e.length===0)return t[0];let r=e.reduce((a,s)=>a*s)*(n?2:1);if(r===0)return[];if(r!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return Ix(0,e,t,n)}function Af(e,t){let n=Nh(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function Nh(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 S9(e,t){let n=e.reduce((r,a)=>r*a,1);if(t==null||t==="float32")return to(e,new Float32Array(n));if(t==="int32")return to(e,new Int32Array(n));if(t==="bool")return to(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function yf(e){e.forEach(t=>{M(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function N9(e,t,n){if(t===0)return 0;if(t===1)return e[0];let r=e[e.length-1];for(let a=0;a<e.length-1;++a)r+=n[a]*e[a];return r}function T9(e,t,n){if(t===0)return[];if(t===1)return[e];let r=new Array(t);for(let a=0;a<r.length-1;++a)r[a]=Math.floor(e/n[a]),e-=r[a]*n[a];return r[r.length-1]=e,r}function gf(e){return e&&e.then&&typeof e.then=="function"}var Sx="tfjsflags",Nx=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=E9,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let r=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${r}.`),this.set(e,r)}}async getAsync(e){return e in this.flags?this.flags[e]:(this.flags[e]=await this.evaluateFlag(e),this.flags[e])}get(e){if(e in this.flags)return this.flags[e];let t=this.evaluateFlag(e);if(gf(t))throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[e]=t,this.flags[e]}getNumber(e){return this.get(e)}getBool(e){return this.get(e)}getFlags(){return this.flags}get features(){return this.flags}set(e,t){if(this.flagRegistry[e]==null)throw new Error(`Cannot set flag ${e} as it has not been registered.`);this.flags[e]=t,this.flagRegistry[e].setHook!=null&&this.flagRegistry[e].setHook(t)}evaluateFlag(e){if(this.flagRegistry[e]==null)throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);return this.flagRegistry[e].evaluationFn()}setFlags(e){this.flags=Object.assign({},e)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;let e=this.getQueryParams(this.global.location.search);Sx in e&&e[Sx].split(",").forEach(t=>{let[n,r]=t.split(":");this.urlFlags[n]=C9(n,r)})}};function E9(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(R9(t,r[0],r[1]),r.join("="))),t}function R9(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function C9(e,t){if(t=t.toLowerCase(),t==="true"||t==="false")return t==="true";if(`${+t}`===t)return+t;throw new Error(`Could not parse value flag value ${t} for flag ${e}.`)}function J(){return yr}var yr=null;function M9(e){yr=e}var xf;function Tx(){if(xf==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");xf=e}return xf}function F9(){let e=Tx();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function wf(e,t){let n=F9();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var no="Abs",ro="Acos",ao="Acosh",Sa="Add",ls="AddN",so="All",io="Any",us="ArgMax",mu="ArgMin",oo="Asin",lo="Asinh",uo="Atan",co="Atanh",ho="Atan2",cs="AvgPool",Th="AvgPoolGrad",Au="AvgPool3D",Eh="AvgPool3DGrad",hs="BatchMatMul",yu="BatchToSpaceND",Ch="Bincount",Ex="BroadcastTo",ds="Cast",ps="Ceil",Na="ClipByValue",Rh="Complex",gu="ComplexAbs",po="Concat",fs="Conv2D",Mh="Conv2DBackpropFilter",ms="Conv2DBackpropInput",xu="Conv3D",Fh="Conv3DBackpropFilterV2",$h="Conv3DBackpropInputV2",As="Cos",fo="Cosh",ys="Cumsum",mo="CropAndResize",Dh="DenseBincount",Ao="DepthToSpace",gs="DepthwiseConv2dNative",Oh="DepthwiseConv2dNativeBackpropFilter",zh="DepthwiseConv2dNativeBackpropInput",Ph="Diag",wu="Dilation2D",Lh="Dilation2DBackpropInput",Wh="Dilation2DBackpropFilter",xs="RealDiv",Bh="Einsum",yo="Elu",Vh="EluGrad",go="Erf",xo="Equal",ws="Exp",wo="ExpandDims",bo="Expm1",jh="FFT",bu="Fill",_o="FlipLeftRight",bs="Floor",_s="FloorDiv",vs="FusedBatchNorm",vo="GatherV2",ko="GatherNd",Io="Greater",ks="GreaterEqual",Is="Identity",Uh="IFFT",Hh="Imag",So="IsFinite",No="IsInf",To="IsNan",Ss="LeakyRelu",Eo="Less",Co="LessEqual",Gh="LinSpace",Ns="Log",Ro="Log1p",Mo="LogicalAnd",_u="LogicalNot",vu="LogicalOr",Cx="LogSoftmax",ku="LRN",qh="LRNGrad",Ts="Max",Es="Maximum",Cs="MaxPool",Xh="MaxPoolGrad",Iu="MaxPool3D",Kh="MaxPool3DGrad",Zh="MaxPoolWithArgmax",Rs="Mean",Ms="Min",Fs="Minimum",$s="MirrorPad",Fo="Mod",Yh="Multinomial",Ds="Multiply",$o="Neg",Do="NotEqual",Oo="NonMaxSuppressionV3",zo="NonMaxSuppressionV4",Po="NonMaxSuppressionV5",Lo="OnesLike",Os="OneHot",Wo="Pack",zs="PadV2",$9="Pool",Ps="Pow",Ls="Prelu",Bo="Prod",Su="Range",Jh="Real",Vo="Reciprocal",Ws="Relu",jo="Reshape",Nu="ResizeNearestNeighbor",Qh="ResizeNearestNeighborGrad",Bs="ResizeBilinear",ed="ResizeBilinearGrad",Vs="Relu6",js="Reverse",Us="Round",Hs="Rsqrt",Uo="ScatterNd",Ho="Select",Go="Selu",qo="Slice",Gs="Sin",Xo="Sinh",Ko="Sign",qs="Sigmoid",Zo="Softplus",Xs="Sqrt",Ks="Sum",Tu="SpaceToBatchND",Yo="SplitV",Zs="Softmax",td="SparseReshape",nd="SparseToDense",Ys="SquaredDifference",Eu="Square",Jo="StridedSlice",Js="Sub",Qs="Tan",ei="Tanh",Ta="Tile",Qo="TopK",rd="Transform",ti="Transpose",ad="Unique",el="Unpack",Cu="UnsortedSegmentSum",tl="ZerosLike",Ea="Step",sd="FromPixels",nl="RotateWithOffset",ni="_FusedMatMul",ri="FusedConv2D",ai="FusedDepthwiseConv2D",rl=wf("kernelRegistry",()=>new Map),Ru=wf("gradRegistry",()=>new Map);function id(e,t){let n=bf(e,t);return rl.get(n)}function _f(e){return Ru.get(e)}function al(e){let t=rl.entries(),n=[];for(;;){let{done:r,value:a}=t.next();if(r)break;let[s,i]=a,[o]=s.split("_");o===e&&n.push(i)}return n}function si(e){let{kernelName:t,backendName:n}=e,r=bf(t,n);rl.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),rl.set(r,e)}function Rx(e){let{kernelName:t}=e;Ru.has(t)&&J().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Ru.set(t,e)}function D9(e,t){let n=bf(e,t);if(!rl.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);rl.delete(n)}function O9(e){if(!Ru.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Ru.delete(e)}function z9(e,t){al(e).forEach(n=>{let r=Object.assign({},n,{backendName:t});si(r)})}function bf(e,t){return`${t}_${e}`}var _={};Me(_,{arraysEqual:()=>na,assert:()=>M,assertNonNegativeIntegerDimensions:()=>yf,assertNonNull:()=>is,assertShapesMatch:()=>an,bytesFromStringArray:()=>_x,bytesPerElement:()=>mf,checkConversionForErrors:()=>wx,clamp:()=>pu,computeStrides:()=>eo,createScalarValue:()=>P9,createShuffledIndices:()=>_9,decodeString:()=>ld,distSquared:()=>g9,encodeString:()=>Fu,fetch:()=>L9,flatten:()=>os,getArrayFromDType:()=>xx,getTypedArrayFromDType:()=>gx,hasEncodingLoss:()=>I9,indexToLoc:()=>T9,inferDtype:()=>Ih,inferFromImplicitShape:()=>k9,isBoolean:()=>vx,isFunction:()=>Ia,isInt:()=>Bt,isNumber:()=>kx,isPromise:()=>gf,isScalarShape:()=>x9,isString:()=>ka,isTypedArray:()=>tn,isValidDtype:()=>bx,locToIndex:()=>N9,makeOnesTypedArray:()=>Af,makeZerosNestedTypedArray:()=>S9,makeZerosTypedArray:()=>Nh,nearestDivisor:()=>Sh,nearestLargerEven:()=>m9,now:()=>Mu,parseAxisParam:()=>sr,randUniform:()=>y9,repeatedTry:()=>v9,rightPad:()=>fu,shuffle:()=>Ax,shuffleCombo:()=>f9,sizeFromShape:()=>Nt,sizeToSquarishShape:()=>b9,squeezeShape:()=>yx,sum:()=>A9,tanh:()=>w9,toNestedArray:()=>to,toTypedArray:()=>od});function P9(e,t){return t==="string"?Fu(e):od([e],t)}function W9(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function od(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=os(e)),J().getBool("DEBUG")&&wx(e,t),W9(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){let n=new Uint8Array(e.length);for(let r=0;r<n.length;++r)Math.round(e[r])!==0&&(n[r]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Mu(){return J().platform.now()}function L9(e,t){return J().platform.fetch(e,t)}function Fu(e,t="utf-8"){return t=t||"utf-8",J().platform.encode(e,t)}function ld(e,t="utf-8"){return t=t||"utf-8",J().platform.decode(e,t)}var j9=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new V9)}profileKernel(e,t,n){let r,a=()=>{r=n()},s,i=Mu();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(a);else{a();for(let o of r)o.dataSync();s=Promise.resolve({kernelMs:Mu()-i})}if(J().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<r.length;o++){let l=r[o];l.data().then(c=>{B9(c,l.dtype,e)})}return{kernelName:e,outputs:r,inputs:t,timeMs:s.then(o=>o.kernelMs),extraInfo:s.then(o=>o.getExtraProfileInfo!=null?o.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:r,inputs:a,extraInfo:s}=e;n.forEach(i=>{Promise.all([i.data(),r,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],a,o[2])})})}};function B9(e,t,n){if(t!=="float32")return!1;for(let r=0;r<e.length;r++){let a=e[r];if(isNaN(a)||!isFinite(a))return console.warn(`Found ${a} in the result of '${n}'`),!0}return!1}var V9=class{logKernelProfile(e,t,n,r,a,s){let i=typeof r=="number"?fu(`${r}ms`,9):r.error,o=fu(e,25),l=t.rank,c=t.size,u=fu(t.shape.toString(),14),h="";for(let d in a){let p=a[d];if(p!=null){let m=p.shape||t.shape,f=m.length;h+=`${d}: ${f}D ${f>0?m:""} `}}console.log(`%c${o}	%c${i}	%c${l}D ${u}	%c${c}	%c${h}	%c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function U9(e,t,n){let r={},a={};for(let l=0;l<t.length;l++)r[t[l].id]=!0;for(let l=0;l<e.length;l++){let c=e[l],u=c.inputs;for(let h in u){let d=u[h],p=!1;for(let m=0;m<t.length;m++)if(r[d.id]){c.outputs.forEach(f=>r[f.id]=!0),p=!0,a[c.id]=!0;break}if(p)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 h=0;h<c.outputs.length;h++)if(s[c.outputs[h].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(a[c.id]&&i[c.id]){let u={};for(let d in c.inputs){let p=c.inputs[d];r[p.id]&&(u[d]=p)}let h=Object.assign({},c);h.inputs=u,h.outputs=c.outputs,o.push(h)}}return o}function H9(e,t,n,r){for(let a=t.length-1;a>=0;a--){let s=t[a],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(!na(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 h=e[u.id];e[u.id]=r(h,c),h.dispose()}}}}var Mx=20,$u=3,vf=7;function q9(e,t,n,r){let a=eo(t),s=G9(e,t,n,a),i=t.length,o=ud(e,t,n,a,s),l=["Tensor"];return r&&(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 G9(e,t,n,r){let a=Nt(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Ou(e):e;if(o>1)for(let c=0;c<a/s;c++){let u=c*s;for(let h=0;h<s;h++)i[h]=Math.max(i[h],Du(l[u+h],0,n).length)}return i}function Du(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(vf))} + ${parseFloat(e[1].toFixed(vf))}j`:ka(e)?r=`'${e}'`:n==="bool"?r=Fx(e):r=parseFloat(e.toFixed(vf)).toString(),fu(r,t)}function Fx(e){return e===0?"false":"true"}function ud(e,t,n,r,a,s=!0){let i=n==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(n==="complex64"){let f=Ou(e);return[Du(f[0],0,n)]}return n==="bool"?[Fx(e[0])]:[e[0].toString()]}if(l===1){if(o>Mx){let A=$u*i,y=Array.from(e.slice(0,A)),g=Array.from(e.slice((o-$u)*i,o*i));return n==="complex64"&&(y=Ou(y),g=Ou(g)),["["+y.map((x,v)=>Du(x,a[v],n)).join(", ")+", ..., "+g.map((x,v)=>Du(x,a[o-$u+v],n)).join(", ")+"]"]}let f=n==="complex64"?Ou(e):Array.from(e);return["["+f.map((A,y)=>Du(A,a[y],n)).join(", ")+"]"]}let c=t.slice(1),u=r.slice(1),h=r[0]*i,d=[];if(o>Mx){for(let f=0;f<$u;f++){let A=f*h,y=A+h;d.push(...ud(e.slice(A,y),c,n,u,a,!1))}d.push("...");for(let f=o-$u;f<o;f++){let A=f*h,y=A+h;d.push(...ud(e.slice(A,y),c,n,u,a,f===o-1))}}else for(let f=0;f<o;f++){let A=f*h,y=A+h;d.push(...ud(e.slice(A,y),c,n,u,a,f===o-1))}let p=l===2?",":"";d[0]="["+d[0]+p;for(let f=1;f<d.length-1;f++)d[f]=" "+d[f]+p;let m=`,
`;for(let f=2;f<l;f++)m+=`
`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":m),d}function Ou(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var $t=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Nt(e),n!=null){let r=n.length;M(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||xx(t,this.size),this.strides=eo(e)}set(e,...t){t.length===0&&(t=[0]),M(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Fr().makeTensor(this.values,this.shape,this.dtype)}},Fr=null,sl=null,X9=null;function K9(e){Fr=e}function Z9(e){sl=e}function Y9(e){X9=e}var Le=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Nt(e),this.strides=eo(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return sl.buffer(this.shape,this.dtype,e)}bufferSync(){return sl.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return to(this.shape,e,this.dtype==="complex64")}arraySync(){return to(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Fr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>ld(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=Fr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>ld(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 Fr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Fr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return sl.print(this,e)}clone(){return this.throwIfDisposed(),sl.clone(this)}toString(e=!1){let t=this.dataSync();return q9(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),sl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Fr().makeVariable(this,e,t,n)}};Object.defineProperty(Le,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return wf("Tensor",()=>Le)}Z();var zu=class extends Le{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!na(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Fr().disposeTensor(this),this.dataId=e.dataId,Fr().incRef(this,null)}dispose(){Fr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(zu,Symbol.hasInstance,{value:e=>e instanceof Le&&e.assign!=null&&e.assign instanceof Function});var gr={};Me(gr,{assertTypesMatch:()=>$x,getTensorsInContainer:()=>kf,isTensorInList:()=>J9,makeTypesMatch:()=>bt});var If;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(If||(If={}));var Sf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Sf||(Sf={}));var Nf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Nf||(Nf={}));var Tf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Tf||(Tf={}));var Ef;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Ef||(Ef={}));var Q9={float32:Tf,int32:Sf,bool:Nf,complex64:Ef};function ir(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Q9[e][t]}function cd(e){return ir(e,"int32")}function bt(e,t){if(e.dtype===t.dtype)return[e,t];let n=ir(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function $x(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function J9(e,t){return t.some(n=>n.id===e.id)}function kf(e){let t=[],n=new Set;return Dx(e,t,n),t}function Dx(e,t,n){if(e==null)return;if(e instanceof Le){t.push(e);return}if(!eI(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),Dx(s,t,n))}}function eI(e){return Array.isArray(e)||typeof e=="object"}function Cf(e){return e.kernelName!=null}var Ox=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Pu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Ox}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 j9(this.backendInstance),!0}setupRegisteredKernels(){al(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){al(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 du)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Pu.nextTensorId++}nextVariableId(){return Pu.nextVariableId++}clone(e){let t=$.runKernel(Is,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(ds,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(id(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Cf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Cf(e)){let{kernelName:p,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=id(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let x=g.map(v=>{if(v.rank!=null)return v;let{dataId:w,shape:b,dtype:k}=v;return this.makeTensorFromDataId(w,b,k)});if(r){let v=this.getTensorsForGradient(p,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:p}=e,m=f=>{!r||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:c,attrs:u}=e,h=Cf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=_f(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&ka(e[0])&&(a=e.map(o=>Fu(o)));let s=r.write(a,t,n),i=new Le(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=_x(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Le(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new zu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*mf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof zu||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*mf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=_f(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let h=n[u],d=Nh(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return c}),r(l.length>1?l:l[0],a,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=kf(e),n=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!n.has(s.id)&&s.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(M(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 a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));M(a instanceof Le,()=>"The result y returned by f() must be a tensor.");let s=U9(this.state.activeTape,t,a);if(!r&&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[a.id]=n==null?tI(a.shape):n,H9(i,s,l=>this.tidy(l),nI);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:a,grads:o}})}customGrad(e){return M(Ia(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{M(t.every(i=>i instanceof Le),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((i,o)=>{r[o]=i});let a=(i,o)=>(n=e(...t,o),M(n.value instanceof Le,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),M(Ia(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),c=Array.isArray(l)?l:[l];M(c.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(...)."),M(c.every(h=>h instanceof Le),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let u={};return c.forEach((h,d)=>{u[d]=()=>h}),u};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=Mu(),n=await this.backend.time(e);return n.wallMs=Mu()-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 Ox;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}};Pu.nextTensorId=0;Pu.nextVariableId=0;function tI(e){let t=Af(Nt(e),"float32");return $.makeTensor(t,e,"float32")}function zx(){let e=Tx();if(e._tfengine==null){let t=new Nx(e);e._tfengine=new Pu(t)}return M9(e._tfengine.ENV),K9(()=>e._tfengine),e._tfengine}var $=zx();function nI(e,t){let n={a:e,b:t};return $.runKernel(Sa,n)}var Lu={};Me(Lu,{isBrowser:()=>Px,isMobile:()=>rI});function aI(){return typeof navigator!="undefined"&&navigator!=null}function rI(e){if(e||aI()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function Px(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var xr=J();xr.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.")});xr.registerFlag("IS_BROWSER",()=>Px());xr.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");xr.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));xr.registerFlag("PROD",()=>!1);xr.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>xr.getBool("DEBUG"));xr.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);xr.registerFlag("IS_TEST",()=>!1);xr.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);xr.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function $r(e,t){let n=e;if(tn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||tn(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Lx(e,r,[]),r}function Lx(e,t,n){if(n=n||[],!Array.isArray(e)&&!tn(e)){M(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}M(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),M(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let a=0;a<e.length;++a)Lx(e[a],r,n.concat(a))}function Wx(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function R(e,t,n,r="numeric"){if(e instanceof Le)return Wx(r,e.dtype,t,n),e;let a=Ih(e);if(a!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(a=r),Wx(r,a,t,n),e==null||!tn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=$r(e,a);!tn(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?od(e,a):os(e,[],!0);return $.makeTensor(i,s,a)}function Wu(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>R(a,`${t}[${s}]`,n,r))}var Bx="__op";function D(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+Bx;let a=(...s)=>{$.startScope(n);try{let i=r(...s);return gf(i)&&console.error("Cannot return a Promise inside of tidy."),$.endScope(i),i}catch(i){throw $.endScope(null),i}};return Object.defineProperty(a,"name",{value:n,configurable:!0}),a}function sI(e,t){let n=R(e,"real","complex"),r=R(t,"imag","complex");an(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);let a={real:n,imag:r};return $.runKernel(Rh,a)}var Ca=D({complex_:sI});function Ra(e,t,n,r){if(r==null&&(r=Ih(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!tn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){yf(t);let a=Nt(t),s=Nt(n);M(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==Nt(t.slice(i)):!0;M(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!tn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?od(e,r):os(e,[],!0),$.makeTensor(e,t,r)}function wr(e,t,n){let r=$r(e,n);return Ra(e,t,r,n)}var Rf={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},hd=4;async function oI(e,t){let n=[],r=[],a=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<a.length;++i){let o=a[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let c={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let u=new Promise(async h=>{let d=await l.bytes(),p=d.reduce((A,y)=>A+y.length,0)+hd*d.length,m=new Uint8Array(p),f=0;for(let A=0;A<d.length;A++){let y=d[A],g=new Uint8Array(new Uint32Array([y.length]).buffer);m.set(g,f),f+=hd,m.set(y,f),f+=y.length}h(m)});r.push(u)}else r.push(l.data());t!=null&&(c.group=t),n.push(c)}let s=await Promise.all(r);return{data:iI(s),specs:n}}function Vx(e,t){let n={},r,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,c=Nt(l),u;if("quantization"in s){let h=s.quantization;if(h.dtype==="uint8"||h.dtype==="uint16"){if(!("min"in h&&"scale"in h))throw new Error(`Weight ${s.name} with quantization ${h.dtype} doesn't have corresponding metadata min and scale.`)}else if(h.dtype==="float16"){if(o!=="float32")throw new Error(`Weight ${s.name} is quantized with ${h.dtype} which only supports weights of type float32 not ${o}.`)}else throw new Error(`Weight ${s.name} has unknown quantization dtype ${h.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let d=Rf[h.dtype],p=e.slice(a,a+c*d),m=h.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(o==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){u=new Float32Array(m.length);for(let f=0;f<m.length;f++){let A=m[f];u[f]=A*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=lI()),u=r(m);else throw new Error(`Unsupported quantization type ${h.dtype} for weight type float32.`);else if(o==="int32"){if(h.dtype!=="uint8"&&h.dtype!=="uint16")throw new Error(`Unsupported quantization type ${h.dtype} for weight type int32.`);u=new Int32Array(m.length);for(let f=0;f<m.length;f++){let A=m[f];u[f]=Math.round(A*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*d}else if(o==="string"){let h=Nt(s.shape);u=[];for(let d=0;d<h;d++){let p=new Uint32Array(e.slice(a,a+hd))[0];a+=hd;let m=new Uint8Array(e.slice(a,a+p));u.push(m),a+=p}}else{let h=Rf[o],d=e.slice(a,a+c*h);if(o==="float32")u=new Float32Array(d);else if(o==="int32")u=new Int32Array(d);else if(o==="bool")u=new Uint8Array(d);else if(o==="complex64"){u=new Float32Array(d);let p=new Float32Array(u.length/2),m=new Float32Array(u.length/2);for(let y=0;y<p.length;y++)p[y]=u[y*2],m[y]=u[y*2+1];let f=wr(p,l,"float32"),A=wr(m,l,"float32");n[i]=Ca(f,A),f.dispose(),A.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*h}o!=="complex64"&&(n[i]=wr(u,l,o))}return n}function iI(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 r=new Uint8Array(t),a=0;return n.forEach(s=>{r.set(new Uint8Array(s.buffer),a),a+=s.byteLength}),r.buffer}var Mf=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function jx(e){return Mf?Buffer.byteLength(e):new Blob([e]).size}function uI(e){if(Mf)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let r=0,a=t.length;r<a;r++)n+=String.fromCharCode(t[r]);return btoa(n)}function cI(e){if(Mf){let r=Buffer.from(e,"base64");return r.buffer.slice(r.byteOffset,r.byteOffset+r.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let r=0;r<t.length;++r)n.set([t.charCodeAt(r)],r);return n.buffer}function Ff(e){if(e.length===1)return e[0];let t=0;e.forEach(a=>{t+=a.byteLength});let n=new Uint8Array(t),r=0;return e.forEach(a=>{n.set(new Uint8Array(a),r),r+=a.byteLength}),n.buffer}function Ux(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 Bu(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:jx(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:jx(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function hI(){let e=n=>{let r=n<<13,a=0;for(;(r&8388608)==0;)a-=8388608,r<<=1;return r&=~8388608,a+=947912704,r|a},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 dI(){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 pI(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function lI(){let e=hI(),t=dI(),n=pI();return r=>{let a=new ArrayBuffer(4*r.length),s=new Uint32Array(a);for(let i=0;i<r.length;i++){let o=r[i],l=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(a)}}var St=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return St.instance==null&&(St.instance=new St),St.instance}static registerSaveRouter(e){St.getInstance().saveRouters.push(e)}static registerLoadRouter(e){St.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return St.getHandlers(e,"save")}static getLoadHandlers(e,t){return St.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?St.getInstance().loadRouters:St.getInstance().saveRouters).forEach(a=>{let s=a(e,n);s!==null&&r.push(s)}),r}},fI=e=>St.registerSaveRouter(e),mI=e=>St.registerLoadRouter(e),AI=e=>St.getSaveHandlers(e),yI=(e,t)=>St.getLoadHandlers(e,t),$f="tensorflowjs",Df=1,ii="models_store",Ma="model_info_store";function Hx(){if(!J().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Of(e){let t=e.result;t.createObjectStore(ii,{keyPath:"modelPath"}),t.createObjectStore(Ma,{keyPath:"modelPath"})}var oi=class{constructor(e){if(this.indexedDB=Hx(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,r)=>{let a=this.indexedDB.open($f,Df);a.onupgradeneeded=()=>Of(a),a.onsuccess=()=>{let s=a.result;if(t==null){let i=s.transaction(ii,"readonly"),o=i.objectStore(ii).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),r(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),r(o.error)),i.oncomplete=()=>s.close()}else{let i=Bu(t),o=s.transaction(Ma,"readwrite"),l=o.objectStore(Ma),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),u;c.onsuccess=()=>{u=s.transaction(ii,"readwrite");let h=u.objectStore(ii).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});h.onsuccess=()=>n({modelArtifactsInfo:i}),h.onerror=d=>{l=o.objectStore(Ma);let p=l.delete(this.modelPath);p.onsuccess=()=>(s.close(),r(h.error)),p.onerror=m=>(s.close(),r(h.error))}},c.onerror=h=>(s.close(),r(c.error)),o.oncomplete=()=>{u==null?s.close():u.oncomplete=()=>s.close()}}},a.onerror=s=>r(a.error)})}};oi.URL_SCHEME="indexeddb://";var Gx=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(oi.URL_SCHEME)?gI(e.slice(oi.URL_SCHEME.length)):null;St.registerSaveRouter(Gx);St.registerLoadRouter(Gx);function gI(e){return new oi(e)}function xI(e){return e.startsWith(oi.URL_SCHEME)?e.slice(oi.URL_SCHEME.length):e}var wI=class{constructor(){this.indexedDB=Hx()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open($f,Df);n.onupgradeneeded=()=>Of(n),n.onsuccess=()=>{let r=n.result,a=r.transaction(Ma,"readonly"),s=a.objectStore(Ma).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(r.close(),t(s.error)),a.oncomplete=()=>r.close()},n.onerror=r=>t(n.error)})}async removeModel(e){return e=xI(e),new Promise((t,n)=>{let r=this.indexedDB.open($f,Df);r.onupgradeneeded=()=>Of(r),r.onsuccess=()=>{let a=r.result,s=a.transaction(Ma,"readwrite"),i=s.objectStore(Ma),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return a.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=i.delete(e),u=()=>{l=a.transaction(ii,"readwrite");let h=l.objectStore(ii).delete(e);h.onsuccess=()=>t(o.result.modelArtifactsInfo),h.onerror=d=>n(o.error)};c.onsuccess=u,c.onerror=h=>(u(),a.close(),n(o.error))}},o.onerror=c=>(a.close(),n(o.error)),s.oncomplete=()=>{l==null?a.close():l.oncomplete=()=>a.close()}},r.onerror=a=>n(r.error)})}},ra="/",il="tensorflowjs_models",qx="info",bI="model_topology",_I="weight_specs",vI="weight_data",kI="model_metadata";function Xx(e){return{info:[il,e,qx].join(ra),topology:[il,e,bI].join(ra),weightSpecs:[il,e,_I].join(ra),weightData:[il,e,vI].join(ra),modelMetadata:[il,e,kI].join(ra)}}function II(e){let t=e.split(ra);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(ra)}function SI(e){return e.startsWith(li.URL_SCHEME)?e.slice(li.URL_SCHEME.length):e}var li=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Xx(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=Bu(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,uI(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:r}}catch(a){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${r.modelTopologyBytes}, weightSpecsBytes=${r.weightSpecsBytes}, weightDataBytes=${r.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;let r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(r==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;let a=this.LS.getItem(this.keys.modelMetadata);if(a!=null){let i=JSON.parse(a);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 '${this.modelPath}' are missing.`);return t.weightData=cI(s),t}};li.URL_SCHEME="localstorage://";var Kx=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(li.URL_SCHEME)?NI(e.slice(li.URL_SCHEME.length)):null;St.registerSaveRouter(Kx);St.registerLoadRouter(Kx);function NI(e){return new li(e)}var TI=class{constructor(){M(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),M(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=il+ra,n=ra+qx;for(let r=0;r<this.LS.length;++r){let a=this.LS.key(r);if(a.startsWith(t)&&a.endsWith(n)){let s=II(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=SI(e);let t=Xx(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${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}},ol="://",qn=class{constructor(){this.managers={}}static getInstance(){return qn.instance==null&&(qn.instance=new qn),qn.instance}static registerManager(e,t){M(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(ol)&&(e=e.slice(0,e.indexOf(ol))),M(e.length>0,()=>"scheme must not be an empty string.");let n=qn.getInstance();M(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 dd(e){if(e.indexOf(ol)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${qn.getSchemes().join(",")}`);return{scheme:e.split(ol)[0],path:e.split(ol)[1]}}async function Zx(e,t,n=!1){M(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=St.getLoadHandlers(e);M(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),M(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let a=r[0],s=St.getSaveHandlers(t);M(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),M(s.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let i=s[0],o=dd(e).scheme,l=dd(e).path,c=o===dd(e).scheme,u=await a.load();n&&c&&await qn.getManager(o).removeModel(l);let h=await i.save(u);return n&&!c&&await qn.getManager(o).removeModel(l),h.modelArtifactsInfo}async function EI(){let e=qn.getSchemes(),t={};for(let n of e){let r=await qn.getManager(n).listModels();for(let a in r){let s=n+ol+a;t[s]=r[a]}}return t}async function CI(e){let t=dd(e);return qn.getManager(t.scheme).removeModel(t.path)}async function RI(e,t){return Zx(e,t,!1)}async function MI(e,t){return Zx(e,t,!0)}var FI=class{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}};if(J().get("IS_BROWSER")){J().setPlatform("browser",new FI);try{qn.registerManager(li.URL_SCHEME,new TI)}catch(e){}try{qn.registerManager(oi.URL_SCHEME,new wI)}catch(e){}}var $I={importFetch:()=>Bk()},zf,DI=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return J().global.fetch!=null?J().global.fetch(e,t):(zf==null&&(zf=$I.importFetch()),zf(e,t))}now(){let e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}};J().get("IS_NODE")&&J().setPlatform("node",new DI);function We(e,t="float32",n){return t=t||"float32",yf(e),new $t(e,t,n)}function OI(e,t){let n=R(e,"x","cast");if(!bx(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let r={x:n},a={dtype:t};return $.runKernel(ds,r,a)}var Ae=D({cast_:OI});function zI(e){let t={x:R(e,"x","clone","string_or_numeric")};return $.runKernel(Is,t)}var Dr=D({clone_:zI});function Yx(e,t=!1){console.log(e.toString(t))}zx();var PI={buffer:We,cast:Ae,clone:Dr,print:Yx};Z9(PI);var vn={};Me(vn,{browserFiles:()=>LI,browserHTTPRequest:()=>BI,concatenateArrayBuffers:()=>Ff,copyModel:()=>RI,decodeWeights:()=>Vx,encodeWeights:()=>oI,fromMemory:()=>VI,getLoadHandlers:()=>yI,getModelArtifactsInfoForJSON:()=>Bu,getSaveHandlers:()=>AI,http:()=>Lf,isHTTPScheme:()=>Pf,listModels:()=>EI,loadWeights:()=>WI,moveModel:()=>MI,registerLoadRouter:()=>mI,registerSaveRouter:()=>fI,removeModel:()=>CI,weightsLoaderFactory:()=>Jx,withSaveHandler:()=>jI});var UI="model",HI=".json",GI=".weights.bin";function Qx(e){return new Promise(t=>setTimeout(t)).then(e)}var ll=class{constructor(e){if(!J().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(ll.URL_SCHEME)&&(e=e.slice(ll.URL_SCHEME.length)),(e==null||e.length===0)&&(e=UI),this.modelTopologyFileName=e+HI,this.weightDataFileName=e+GI}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer);let a=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),s=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(s.download=this.modelTopologyFileName,s.href=a,await Qx(()=>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 Qx(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Bu(e)}}}};ll.URL_SCHEME="downloads://";var qI=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,r)=>{let a=new FileReader;a.onload=s=>{let i=JSON.parse(s.target.result),o=i.modelTopology;if(o==null){r(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:o});let l=i.weightsManifest;if(l==null){r(new Error(`weightManifest field is missing from file ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let u=[],h=[],d=[];l.forEach(p=>{p.paths.forEach(m=>{h.push(m),d.push(null)}),u.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(m=>{let f=new FileReader;f.onload=A=>{let y=A.target.result,g=h.indexOf(m);if(d[g]=y,d.indexOf(null)===-1){let x={modelTopology:o,weightSpecs:u,weightData:Ff(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(x.signature=i.signature),i.userDefinedMetadata!=null&&(x.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(x.modelInitializer=i.modelInitializer),n(x)}},f.onerror=A=>r(`Failed to weights data from file of path '${m}'.`),f.readAsArrayBuffer(c[m])})})},a.onerror=s=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),a.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(s=>Ux(s.name)),a={};for(let s of e)s.paths.forEach(i=>{let o=Ux(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),r.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);a[i]=t[r.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return a}},KI=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ll.URL_SCHEME)?XI(e.slice(ll.URL_SCHEME.length)):null;St.registerSaveRouter(KI);function XI(e="model"){return new ll(e)}function LI(e){return new qI(e)}function ew(e,t,n,r){i(e),n=n==null?0:n,r=r==null?1:r,o(n,r);let a=0,s=l=>(l.then(c=>{let u=n+ ++a/e.length*(r-n);return t(u),c}),l);function i(l){M(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,c){M(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),M(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),M(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(s))}async function tw(e,t){t==null&&(t={});let n=t.fetchFunc==null?J().platform.fetch:t.fetchFunc,r=e.map(c=>n(c,t.requestInit,{isBinary:!0})),a=0,s=.5,i=(t.onProgress==null?await Promise.all(r):await ew(r,t.onProgress,a,s)).map(c=>c.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await ew(i,t.onProgress,o,l)}async function WI(e,t="",n,r){return Jx(a=>tw(a,{requestInit:r}))(e,t,n)}function Jx(e){return async(t,n="",r)=>{let a=t.map(()=>!1),s={},i=r!=null?r.map(()=>!1):[],o=[];if(t.forEach((p,m)=>{let f=0;p.weights.forEach(A=>{let y="quantization"in A?A.quantization.dtype:A.dtype,g=Rf[y]*Nt(A.shape),x=()=>{a[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:A,groupOffset:f,sizeBytes:g})};r!=null?r.forEach((v,w)=>{v===A.name&&(x(),i[w]=!0)}):x(),o.push(A.name),f+=g})}),!i.every(p=>p)){let p=r.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${p.join(", ")}. 
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=a.reduce((p,m,f)=>(m&&p.push(f),p),[]),c=[];l.forEach(p=>{t[p].paths.forEach(m=>{let f=n+(n.endsWith("/")?"":"/")+m;c.push(f)})});let u=await e(c),h={},d=0;return l.forEach(p=>{let m=t[p].paths.length,f=0;for(let x=0;x<m;x++)f+=u[d+x].byteLength;let A=new ArrayBuffer(f),y=new Uint8Array(A),g=0;for(let x=0;x<m;x++){let v=new Uint8Array(u[d+x]);y.set(v,g),g+=v.byteLength}s[p].forEach(x=>{let v=A.slice(x.groupOffset,x.groupOffset+x.sizeBytes),w=Vx(v,[x.manifestEntry]);for(let b in w)h[b]=w[b]}),d+=m}),h}}var ZI="application/octet-stream",YI="application/json",Wf=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?(M(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=J().platform.fetch,M(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&M(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:YI}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:ZI}),"model.weights.bin");let a=await this.fetch(this.path,t);if(a.ok)return{modelArtifactsInfo:Bu(e),responses:[a]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${a.status}.`)}async load(){let e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(p){let 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,r=t.weightsManifest,a=t.generatedBy,s=t.convertedBy,i=t.format,o=t.signature,l=t.userDefinedMetadata;if(n==null&&r==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let c,u;r!=null&&([c,u]=await this.loadWeights(r));let h={modelTopology:n,weightSpecs:c,weightData:u,generatedBy:a,convertedBy:s,format:i};o!=null&&(h.signature=o),l!=null&&(h.userDefinedMetadata=l);let d=t.modelInitializer;return d&&(h.modelInitializer=d),h}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=JI(t),a=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(a+u+r);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await tw(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,Ff(l)]}};Wf.URL_SCHEME_REGEX=/^https?:\/\//;function JI(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),a=n>t?e.substring(n):"";return[r+"/",a]}function Pf(e){return e.match(Wf.URL_SCHEME_REGEX)!=null}var nw=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>Pf(r)):n=Pf(e),n)return Lf(e,t)}return null};St.registerSaveRouter(nw);St.registerLoadRouter(nw);function Lf(e,t){return new Wf(e,t)}function BI(e,t){return Lf(e,t)}var Bf=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},QI=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function VI(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new Bf(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 Bf({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 Bf({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function jI(e){return new QI(e)}var rw={};Me(rw,{confusionMatrix:()=>eS});function tS(e,t,n=!1,r=!1){let a=R(e,"a","matMul"),s=R(t,"b","matMul");[a,s]=bt(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return $.runKernel(hs,i,o)}var Be=D({matMul_:tS});function nS(e,t,n=1,r=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:R(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:r};return $.runKernel(Os,a,s)}var ul=D({oneHot_:nS});function rS(e,t){let n=R(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{M(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 r={x:n},a={perm:t};return $.runKernel(ti,r,a)}var Ze=D({transpose_:rS});function aS(e,t,n){let r=R(e,"labels","confusionMatrix"),a=R(t,"predictions","confusionMatrix");M(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),M(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),M(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),M(r.shape[0]===a.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${a.shape[0]}. Labels and predictions should have the same number of elements.`),M(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=ul(Ae(r,"int32"),n),i=ul(Ae(a,"int32"),n),o=Ze(s),l=Be(o,i);return Ae(l,"int32")}var eS=D({confusionMatrix_:aS}),ui={};Me(ui,{fromPixels:()=>oS,fromPixelsAsync:()=>sS,toPixels:()=>iS});function pd(e,t,n){if(is(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=$r(e,n);if(r.length!==3&&r.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Ra(e,t,r,n)}var cl;function aw(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,a=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)a=!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 ${e.constructor.name}`);if(a){let d=2;if(a&&e.readyState<d)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(id(sd,$.backendName)!=null){let d={pixels:e},p={numChannels:t};return $.runKernel(sd,d,p)}let[l,c]=a?[e.videoWidth,e.videoHeight]:[e.width,e.height],u;i?u=e.getContext("2d").getImageData(0,0,l,c).data:r||n?u=e.data:(s||a||o)&&(cl==null&&(cl=document.createElement("canvas").getContext("2d")),cl.canvas.width=l,cl.canvas.height=c,cl.drawImage(e,0,0,l,c),u=cl.getImageData(0,0,l,c).data);let h;if(t===4)h=new Int32Array(u);else{let d=l*c;h=new Int32Array(d*t);for(let p=0;p<d;p++)for(let m=0;m<t;++m)h[p*t+m]=u[p*4+m]}return pd(h,[c,l,t],"int32")}function lS(e){return e!=null&&e.data instanceof Uint8Array}function uS(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function cS(e){return e!=null&&e.width!==0&&e.height!==0}function hS(e){return uS()&&!(e instanceof ImageBitmap)&&cS(e)&&!lS(e)}async function sS(e,t=3){let n=null;if(J().getBool("WRAP_TO_IMAGEBITMAP")&&hS(e)){let r;try{r=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(a){r=null}r!=null&&r.width===e.width&&r.height===e.height?n=r:n=e}else n=e;return aw(n,t)}async function iS(e,t){let n=R(e,"img","toPixels");if(!(e instanceof Le)){let c=n;n=Ae(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[r,a]=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(a*r*4);for(let c=0;c<r*a;++c){let u=[0,0,0,255];for(let d=0;d<s;d++){let p=i[c*s+d];if(n.dtype==="float32"){if(p<0||p>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${p}.`)}else if(n.dtype==="int32"&&(p<0||p>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${p}.`);s===1?(u[0]=p*o,u[1]=p*o,u[2]=p*o):u[d]=p*o}let h=c*4;l[h+0]=Math.round(u[0]),l[h+1]=Math.round(u[1]),l[h+2]=Math.round(u[2]),l[h+3]=Math.round(u[3])}if(t!=null){t.width=a,t.height=r;let c=t.getContext("2d"),u=new ImageData(l,a,r);c.putImageData(u,0,0)}return n!==e&&n.dispose(),l}var oS=D({fromPixels_:aw}),Vf={};Me(Vf,{prepareAndValidate:()=>sw});function sw(e,t){let n=e.shape.length,r=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(Nt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let a=t.shape,s=a[a.length-1],i=1;for(let h=0;h<a.length-1;++h)i*=a[h];let o=e.shape,l=a.slice();l.pop();let c=1;for(let h=s;h<n;++h)c*=o[h],l.push(o[h]);let u=[...eo(e.shape).map(h=>h/c),1].slice(0,s);return[l,i,c,u]}var jf={};Me(jf,{calculateShapes:()=>iw,validateInput:()=>Hf,validateUpdateShape:()=>Uf});function Uf(e,t,n){let r=t.rank>1?t.shape[t.rank-1]:1,a=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: ${r}, and batchDim: ${a}.`;if(n.rank<a)throw new Error(s+` update.rank < ${a}. `);if(e.length<r+(n.rank-a))throw new Error(s+` Output shape length < ${r+(n.rank-a)}`);if(n.rank!==a+e.length-r)throw new Error(s+` update.rank != ${a+e.length-r}`);for(let i=0;i<a;++i)if(n.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<n.rank-a;++i)if(n.shape[i+a]!==e[i+r])throw new Error(s+` updates.shape[${i+a}] (${n.shape[i+a]}) != shape[${i+a}] (${e[i+a]})`)}function Hf(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: ${e.shape}`)}Uf(n,t,e)}function iw(e,t,n){let r=t.shape.length,a=r>1?t.shape[r-1]:1,s=n.length,i=1;for(let h=a;h<s;++h)i*=n[h];let o=a<1?1:a,l=Nt(t.shape)/o,c=[...eo(n.slice(0,a)),1],u=Nt(n);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:c,outputSize:u}}var sn={};Me(sn,{assertParamsValid:()=>dS,computeFlatOffset:()=>fS,computeOutShape:()=>ow,getNormalizedAxes:()=>uw,isSliceContinous:()=>pS,maskToAxes:()=>fd,parseSliceParams:()=>mw,sliceInfo:()=>mS,startForAxis:()=>pw,startIndicesWithElidedDims:()=>cw,stopForAxis:()=>fw,stopIndicesWithElidedDims:()=>hw,stridesForAxis:()=>dw,stridesWithElidedDims:()=>lw});function dS(e,t,n){let r=e.shape.length;M(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),M(r===n.length,()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`);for(let a=0;a<r;++a)M(t[a]+n[a]<=e.shape[a],()=>`Error in slice${r}D: begin[${a}] + size[${a}] (${t[a]+n[a]}) would overflow input.shape[${a}] (${e.shape[a]})`)}function fd(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function ow(e,t,n){let r=[];for(let a=0;a<e.length;a++)r[a]=Math.ceil((t[a]-e[a])/n[a]);return r}function lw(e,t,n,r){let a=[...e];for(let s=a.length;s<r.length;s++)a.push(1);for(let s=0;s<n;s++)s===0?a[t]=1:(a.splice(t,0,1),a.pop());return a}function Aw(e,t,n){return n<=e?n:n-(t-1)}function yw(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function uw(e,t,n,r,a,s,i,o,l){let c=e.length,u=new Array(c),h=new Array(c),d=new Array(c);if(t.length&&n>0){let p=t[0],m=n+1;u=cw(i,p,m,r,e),h=hw(o,p,m,a,e),d=lw(s,p,m,e)}else for(let p=0;p<c;p++)u[p]=pw(i,r,s,e,p,l),h[p]=fw(o,a,s,e,p,l),d[p]=dw(s,p,l);return{begin:u,end:h,strides:d}}function cw(e,t,n,r,a){let s=[...a],i=yw(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=Aw(t,n,o),c=r[l];e&1<<l&&(c=0),s[o]=c}return s}function hw(e,t,n,r,a){let s=[...a],i=yw(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=Aw(t,n,o),c=r[l];e&1<<l&&(c=Number.MAX_SAFE_INTEGER),s[o]=c}for(let o=0;o<s.length;o++){let l=a[o];s[o]<0&&(s[o]+=l),s[o]=pu(0,s[o],a[o])}return s}function dw(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function pw(e,t,n,r,a,s){let i=t[a],o=n[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=r[a];return i<0&&(i+=l),i=pu(0,i,l-1),i}function fw(e,t,n,r,a,s){let i=t[a],o=n[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=r[a];return i<0&&(i+=l),o>0?i=pu(0,i,l):i=pu(-1,i,l-1),i}function pS(e,t,n){let r=n.length;for(let a=0;a<n.length;a++)if(n[a]>1){r=a;break}for(let a=r+1;a<n.length;a++)if(t[a]>0||n[a]!==e[a])return!1;return!0}function fS(e,t){let n=e.length>0?e[e.length-1]:1;for(let r=0;r<e.length-1;r++)n+=e[r]*t[r];return n}function mw(e,t,n){let r,a=e.shape.length;typeof t=="number"?r=[t,...new Array(a-1).fill(0)]:t.length<a?r=t.concat(new Array(a-t.length).fill(0)):r=t.slice(),r.forEach(i=>{M(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(a).fill(-1):typeof n=="number"?s=[n,...new Array(a-1).fill(-1)]:n.length<a?s=n.concat(new Array(a-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(M(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-r[o])),[r,s]}function mS(e,t,n,r,a,s,i,o,l){let c=t.slice(),u=n.slice(),h=r;r==null&&(h=new Array(c.length));let d=fd(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-c.length,m=fd(o),f=e.slice();m.forEach(b=>{c[b]=0,u[b]=1,f.splice(b,0,1)});let{begin:A,end:y,strides:g}=uw(f,d,p,c,u,h,a,s,i);c=A,u=y,h=g;let x=fd(l);x.forEach(b=>{u[b]=c[b]+1,h[b]=1});let v=ow(c,u,h),w=v.filter((b,k)=>x.indexOf(k)===-1);return{nonStrided:h.every(b=>b===1),$begin:c,$end:u,$strides:h,size:v,newShape:f,outShape:w}}var re={};Me(re,{Serializable:()=>gw,SerializationMap:()=>ci,registerClass:()=>Fa});var gw=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},ci=class{constructor(){this.classNameMap={}}static getMap(){return ci.instance==null&&(ci.instance=new ci),ci.instance}static register(e){ci.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Fa(e){M(e.className!=null,()=>"Class being registered does not have the static className property defined."),M(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),M(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ci.register(e)}var xw={};Me(xw,{TEST_EPSILON_FLOAT16:()=>ww,encodeStrings:()=>bw,expectArrayBuffersEqual:()=>bS,expectArraysClose:()=>AS,expectArraysEqual:()=>gS,expectNumbersClose:()=>xS,expectPromiseToFail:()=>yS,expectValuesInRange:()=>wS,testEpsilon:()=>Gf});var _S=.001,ww=.1;function AS(e,t,n){return n==null&&(n=Gf()),qf(e,t,(r,a)=>Xf(r,a,n))}function Gf(){return $.backend.floatPrecision()===32?_S:ww}function qf(e,t,n){let r=!0;if((tn(e)||tn(t))&&(r=!1),tn(e)&&tn(t)&&(r=!0),r){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=$r(e),o=$r(t);if(!na(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let a=tn(e)?e:os(e),s=tn(t)?t:os(t);if(a.length!==s.length)throw new Error(`Arrays have different lengths actual: ${a.length} vs expected: ${s.length}.
Actual:   ${a}.
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=a[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
Actual:   ${a}.
Expected: ${s}.`)}}function yS(e,t){e().then(()=>t.fail(),()=>t())}function gS(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return ka(e)||ka(e[0])||ka(t)||ka(t[0])?qf(e,n,(r,a)=>r==a):qf(e,t,(r,a)=>Xf(r,a,0))}function xS(e,t,n){if(n==null&&(n=Gf()),!Xf(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Xf(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function wS(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function bS(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function bw(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?bw(n):e[t]=Fu(n)}return e}var vS="3.5.0";function kS(){J().set("PROD",!0)}function IS(){J().set("DEBUG",!0)}function SS(){J().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Kf(e){J().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}Y9(Kf);function NS(){$.disposeVariables()}function aa(){return $}function md(){return $.memory()}function sa(e){return $.profile(e)}function L(e,t){return $.tidy(e,t)}function Te(e){kf(e).forEach(t=>t.dispose())}function Vt(e){return $.keep(e)}function TS(e){return $.time(e)}function ES(e){return $.setBackend(e)}function CS(){return $.ready()}function RS(){return $.backendName}function MS(e){$.removeBackend(e)}function Zf(e){return $.findBackend(e)}function FS(e){return $.findBackendFactory(e)}function hl(e,t,n=1){return $.registerBackend(e,t,n)}function _w(){return $.backend}function $S(e,t){J().setPlatform(e,t)}function DS(e,t){let n=R(e,"a","add"),r=R(t,"b","add");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Sa,a)}var se=D({add_:DS});function OS(e,t){let n=R(e,"a","floorDiv"),r=R(t,"b","floorDiv");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(_s,a)}var Ad=D({floorDiv_:OS});function zS(e,t){let n=R(e,"a","div"),r=R(t,"b","div");if([n,r]=bt(n,r),n.dtype==="int32"&&r.dtype==="int32")return Ad(n,r);let a={a:n,b:r},s={};return $.runKernel(xs,a,s)}var ge=D({div_:zS});function PS(e,t){let n=R(e,"a","mul"),r=R(t,"b","mul");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Ds,a)}var B=D({mul_:PS});function LS(e){let t=R(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return $.runKernel(gu,n)}else{let n={x:t};return $.runKernel(no,n)}}var Dt=D({abs_:LS});function WS(e){let t={x:R(e,"x","acos")};return $.runKernel(ro,t)}var Yf=D({acos_:WS});function BS(e){let t={x:R(e,"x","acosh")};return $.runKernel(ao,t)}var Jf=D({acosh_:BS});function VS(e){M(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),M(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>R(a,`tensors${s}`,"addN")),n=t[0];t.forEach(a=>{if(a.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(a=>{if(!na(a.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return $.runKernel(ls,r)}var yd=D({addN_:VS});function jS(e,t=null,n=!1){let r={x:R(e,"x","all","bool")},a={axis:t,keepDims:n};return $.runKernel(so,r,a)}var gd=D({all_:jS});function US(e,t=null,n=!1){let r={x:R(e,"x","any","bool")},a={axis:t,keepDims:n};return $.runKernel(io,r,a)}var Vu=D({any_:US});function HS(e,t=0){let n={x:R(e,"x","argMax")},r={axis:t};return $.runKernel(us,n,r)}var ju=D({argMax_:HS});function GS(e,t=0){let n={x:R(e,"x","argMin")},r={axis:t};return $.runKernel(mu,n,r)}var Qf=D({argMin_:GS});function qS(e){let t={x:R(e,"x","asin")};return $.runKernel(oo,t)}var em=D({asin_:qS});function XS(e){let t={x:R(e,"x","asinh")};return $.runKernel(lo,t)}var tm=D({asinh_:XS});function KS(e){let t={x:R(e,"x","atan")};return $.runKernel(uo,t)}var nm=D({atan_:KS});function ZS(e,t){let n=R(e,"a","atan2"),r=R(t,"b","atan2");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(ho,a)}var rm=D({atan2_:ZS});function YS(e){let t={x:R(e,"x","atanh")};return $.runKernel(co,t)}var am=D({atanh_:YS});function JS(e,t,n,r,a="NHWC",s){let i=e[3],o=[...t,i],l=vw(a);return Uu(e,o,n,s,r,null,null,l)}function kw(e,t,n,r,a,s,i="channelsLast"){let[o,l]=xd(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 Uu(e,c,n,r,a,s,!1,i)}function QS(e,t,n,r,a,s,i="NDHWC"){let[o,l,c]=sm(t),u,h;if(i==="NDHWC")h="channelsLast",u=[o,l,c,e[4],e[4]];else if(i==="NCDHW")h="channelsFirst",u=[o,l,c,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return Iw(e,u,n,r,a,!1,h,s)}function Uu(e,t,n,r,a,s,i=!1,o="channelsLast"){let[l,c,u,h]=[-1,-1,-1,-1];if(o==="channelsLast")[l,c,u,h]=e;else if(o==="channelsFirst")[l,h,c,u]=e;else throw new Error(`Unknown dataFormat ${o}`);let[d,p,,m]=t,[f,A]=xd(n),[y,g]=xd(r),x=dl(d,y),v=dl(p,g),{padInfo:w,outHeight:b,outWidth:k}=eN(a,c,u,f,A,x,v,s,o),N=i?m*h:m,C;return o==="channelsFirst"?C=[l,N,b,k]:o==="channelsLast"&&(C=[l,b,k,N]),{batchSize:l,dataFormat:o,inHeight:c,inWidth:u,inChannels:h,outHeight:b,outWidth:k,outChannels:N,padInfo:w,strideHeight:f,strideWidth:A,filterHeight:d,filterWidth:p,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:y,dilationWidth:g,inShape:e,outShape:C,filterShape:t}}function Iw(e,t,n,r,a,s=!1,i="channelsLast",o){let[l,c,u,h,d]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,c,u,h,d]=e;else if(i==="channelsFirst")[l,d,c,u,h]=e;else throw new Error(`Unknown dataFormat ${i}`);let[p,m,f,,A]=t,[y,g,x]=sm(n),[v,w,b]=sm(r),k=dl(p,v),N=dl(m,w),C=dl(f,b),{padInfo:F,outDepth:O,outHeight:z,outWidth:V}=tN(a,c,u,h,y,g,x,k,N,C,o),j=s?A*d:A,U;return i==="channelsFirst"?U=[l,j,O,z,V]:i==="channelsLast"&&(U=[l,O,z,V,j]),{batchSize:l,dataFormat:i,inDepth:c,inHeight:u,inWidth:h,inChannels:d,outDepth:O,outHeight:z,outWidth:V,outChannels:j,padInfo:F,strideDepth:y,strideHeight:g,strideWidth:x,filterDepth:p,filterHeight:m,filterWidth:f,effectiveFilterDepth:k,effectiveFilterHeight:N,effectiveFilterWidth:C,dilationDepth:v,dilationHeight:w,dilationWidth:b,inShape:e,outShape:U,filterShape:t}}function nN(e,t,n,r,a){r==null&&(r=im(e,t,n));let s=e[0],i=e[1],o=hi((s-t+2*r)/n+1,a),l=hi((i-t+2*r)/n+1,a);return[o,l]}function rN(e,t,n,r,a,s){a==null&&(a=im(e,t,r));let i=e[0],o=e[1],l=e[2],c=hi((i-t+2*a)/r+1,s),u=hi((o-t+2*a)/r+1,s),h=hi((l-t+2*a)/r+1,s);return[c,u,h,n]}function im(e,t,n,r=1){let a=dl(t,r);return Math.floor((e[0]*(n-1)-n+a)/2)}function xd(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function sm(e){return typeof e=="number"?[e,e,e]:e}function dl(e,t){return t<=1?e:e+(e-1)*(t-1)}function eN(e,t,n,r,a,s,i,o,l){let c,u,h;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let d=nN([t,n],s,r,e,o);u=d[0],h=d[1]}else if(e==="same"){u=Math.ceil(t/r),h=Math.ceil(n/a);let d=Math.max(0,(u-1)*r+s-t),p=Math.max(0,(h-1)*a+i-n),m=Math.floor(d/2),f=d-m,A=Math.floor(p/2),y=p-A;c={top:m,bottom:f,left:A,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)/r),h=Math.ceil((n-i+1)/a);else if(typeof e=="object"){let d=l==="channelsLast"?e[1][0]:e[2][0],p=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:p,left:m,right:f,type:d===0&&p===0&&m===0&&f===0?"VALID":"EXPLICIT"},u=hi((t-s+d+p)/r+1,o),h=hi((n-i+m+f)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:h}}function tN(e,t,n,r,a,s,i,o,l,c,u){let h,d,p,m;if(typeof e=="number"){h={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let f=rN([t,n,r,1],o,1,a,e,u);d=f[0],p=f[1],m=f[2]}else if(e==="same"){d=Math.ceil(t/a),p=Math.ceil(n/s),m=Math.ceil(r/i);let f=(d-1)*a+o-t,A=(p-1)*s+l-n,y=(m-1)*i+c-r,g=Math.floor(f/2),x=f-g,v=Math.floor(A/2),w=A-v,b=Math.floor(y/2),k=y-b;h={top:v,bottom:w,left:b,right:k,front:g,back:x,type:"SAME"}}else if(e==="valid")h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/a),p=Math.ceil((n-l+1)/s),m=Math.ceil((r-c+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:d,outHeight:p,outWidth:m}}function hi(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 $a(e){let[t,n,r]=xd(e);return t===1&&n===1&&r===1}function Or(e,t){return $a(e)||$a(t)}function vw(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function aN(e,t){let n={x:R(e,"x","reshape","string_or_numeric")},r={shape:t};return $.runKernel(jo,n,r)}var H=D({reshape_:aN});function sN(e,t,n,r,a){let s=R(e,"x","avgPool","float32"),i=1;M(Or(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),M(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&M(Bt(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(cs,c,u);return h=Ae(h,s.dtype),l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Hu=D({avgPool_:sN});function iN(e,t,n,r,a,s="NDHWC"){let i=R(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),M(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&M(Bt(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(Au,c,u);return h=Ae(h,o.dtype),l?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var om=D({avgPool3d_:iN});function oN(e,t=0){M(e.length>=1,()=>"Pass at least one tensor to concat");let n=Wu(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 Dr(n[0]);let r=n,a={axis:t};return $.runKernel(po,r,a)}var ot=D({concat_:oN});function lN(e){let t={x:R(e,"x","sigmoid")};return $.runKernel(qs,t)}var kn=D({sigmoid_:lN});function uN(e,t,n){let r=R(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return $.runKernel(qo,a,s)}var Re=D({slice_:uN});function cN(e){let t={x:R(e,"x","tanh")};return $.runKernel(ei,t)}var di=D({tanh_:cN});function hN(e,t,n,r,a,s){let i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(n,"lstmBias","basicLSTMCell"),c=R(r,"data","basicLSTMCell"),u=R(a,"c","basicLSTMCell"),h=R(s,"h","basicLSTMCell"),d=ot([c,h],1),p=Be(d,o),m=se(p,l),f=m.shape[0],A=m.shape[1]/4,y=[f,A],g=Re(m,[0,0],y),x=Re(m,[0,A],y),v=Re(m,[0,A*2],y),w=Re(m,[0,A*3],y),b=se(B(kn(g),di(x)),B(u,kn(se(i,v)))),k=B(di(b),kn(w));return[b,k]}var dN=D({basicLSTMCell_:hN});function pN(e,t,n){let r=R(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);M(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),M(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),M(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return $.runKernel(yu,s,i)}var Gu=D({batchToSpaceND_:pN});function fN(e){let t;return e.rank===0||e.rank===1?t=H(e,[1,1,1,e.size]):e.rank===2?t=H(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function mN(e,t,n,r,a,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;r!=null&&(u=R(r,"offset","batchNorm")),M(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),M(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),M(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:fN(i),scale:c,offset:u,mean:o,variance:l},d={varianceEpsilon:s},p=$.runKernel(vs,h,d);return H(p,i.shape)}var pi=D({batchNorm_:mN});function AN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),M(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),M(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),pi(i,o,l,u,c,s)}var Sw=D({batchNorm2d_:AN});function yN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),M(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),pi(i,o,l,u,c,s)}var Nw=D({batchNorm3d_:yN});function gN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),M(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),pi(i,o,l,u,c,s)}var Tw=D({batchNorm4d_:gN});function xN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");M(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return $.runKernel(Ch,s,i)}var Ew=D({bincount_:xN});function wN(e,t){let n=R(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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=H(n,l)}let a=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return Dr(n);let i={x:n},o={reps:s};return $.runKernel(Ta,i,o)}var pl=D({broadcastTo_:wN});function bN(e){let t={x:R(e,"x","ceil")};return $.runKernel(ps,t)}var lm=D({ceil_:bN});function _N(e,t,n){let r=R(e,"x","clipByValue");M(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Na,a,s)}var In=D({clipByValue_:_N});function vN(e){return ot(e,0)}var Cw=D({concat1d_:vN});function kN(e,t){return ot(e,t)}var fl=D({concat2d_:kN});function IN(e,t){return ot(e,t)}var Rw=D({concat3d_:IN});function SN(e,t){return ot(e,t)}var Mw=D({concat4d_:SN});function NN(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","conv2d"),l=R(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&M(Bt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];M(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),M(Or(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:c,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},m=$.runKernel(fs,d,p);return u?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var ia=D({conv2d_:NN});function TN(e,t,n,r,a="NWC",s=1,i){let o=R(e,"x","conv1d"),l=R(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1]])),M(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&M(Bt(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),M(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(Or(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),M(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=ia(d,h,[1,n],r,"NHWC",[1,s],i);return u?H(p,[p.shape[2],p.shape[3]]):H(p,[p.shape[0],p.shape[2],p.shape[3]])}var wd=D({conv1d_:TN});function EN(e,t,n,r,a,s="NHWC",i){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),M(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&M(Bt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},m=$.runKernel(ms,d,p);return c?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var um=D({conv2DBackpropInput_:EN});function CN(e,t,n,r,a,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return um(n,i,o,r,a,"NHWC",s)}var bd=D({conv2dTranspose_:CN});function RN(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),M(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),M(Or(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),M(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let u={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=$.runKernel(xu,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var cm=D({conv3d_:RN});function MN(e,t,n,r,a){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],c=i.shape[4];M(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),M(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=$.runKernel($h,u,h);return o?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Fw=D({conv3DBackpropInput_:MN});function FN(e,t,n,r,a){let s=R(e,"x","conv3dTranspose"),i=R(t,"filter","conv3dTranspose");return Fw(n,s,i,r,a)}var $w=D({conv3dTranspose_:FN});function $N(e){let t={x:R(e,"x","cos")};return $.runKernel(As,t)}var qu=D({cos_:$N});function DN(e){let t={x:R(e,"x","cosh")};return $.runKernel(fo,t)}var _d=D({cosh_:DN});function ON(e,t=0,n=!1,r=!1){let a={x:R(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return $.runKernel(ys,a,s)}var vd=D({cumsum_:ON});function zN(e,t,n,r=!1){let a=R(e,"x","denseBincount"),s=R(t,"weights","denseBincount");M(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),M(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:n,binaryOutput:r};return $.runKernel(Dh,i,o)}var Dw=D({denseBincount_:zN});function PN(e,t,n="NHWC"){let r=R(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${a} and ${t}  for depthToSpace with input shape
    ${r.shape}`),M(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${s} and ${t} for depthToSpace with input shape
        ${r.shape}`),M(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${r.shape}`);let o={x:r},l={blockSize:t,dataFormat:n};return $.runKernel(Ao,o,l)}var hm=D({depthToSpace_:PN});function LN(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","depthwiseConv2d"),l=R(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),M(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&M(Bt(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h={x:c,filter:l},d={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},p=$.runKernel(gs,h,d);return u?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ml=D({depthwiseConv2d_:LN});function WN(e){let t={x:R(e,"x","diag")};return $.runKernel(Ph,t)}var BN=D({diag_:WN});function VN(e,t,n,r,a=[1,1],s="NHWC"){let i=R(e,"x","dilation2d"),o=R(t,"filter","dilation2d");M(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),M(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),M(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},h={strides:n,pad:r,dilations:a},d=$.runKernel(wu,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var dm=D({dilation2d_:VN});function jN(e,t){let n=e.length,r=[];for(let a=0;a<n;a++){let s=n-1-a,i=e[s]||1;(t[t.length-1-a]||1)>1&&i===1&&r.unshift(s)}return r}function Ot(e,t){let n=[];for(let r=0;r<t.length;r++){let a=e[e.length-r-1],s=t.length-r-1,i=t[s];(a==null||a===1&&i>1)&&n.unshift(s)}return n}function dt(e,t){let n=[],r=Math.max(e.length,t.length);for(let a=0;a<r;a++){let s=e[e.length-a-1];s==null&&(s=1);let i=t[t.length-a-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 UN(e,t){let n=R(e,"a","equal"),r=R(t,"b","equal");[n,r]=bt(n,r),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(xo,a)}var Da=D({equal_:UN});function HN(e,t,n){let r=R(t,"a","where"),a=R(n,"b","where"),s=R(e,"condition","where","bool"),i=dt(dt(s.shape,r.shape),a.shape),o=pl(s,i),l=pl(r,i),c=pl(a,i),u={condition:o,t:l,e:c};return $.runKernel(Ho,u)}var Sn=D({where_:HN});function GN(e){let t={x:R(e,"x","zerosLike")};return $.runKernel(tl,t)}var Ue=D({zerosLike_:GN});function qN(e,t){let n=R(e,"a","div"),r=R(t,"b","div");[n,r]=bt(n,r);let a=ge(n,r),s=Ue(a),i=Da(r,s);return Sn(i,s,a)}var pm=D({divNoNan_:qN});function XN(e,t){let n=R(e,"t1","dot"),r=R(t,"t2","dot");M((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let a=n.rank===1?n.size:n.shape[1],s=r.rank===1?r.size:r.shape[0];if(M(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),n.rank===1&&r.rank===1){let i=H(n,[1,-1]),o=H(r,[-1,1]),l=Be(i,o);return H(l,[])}else if(n.rank===1&&r.rank===2){let i=H(n,[1,-1]),o=H(r,[r.shape[0],r.shape[1]]),l=Be(i,o);return H(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=H(r,[-1,1]),o=Be(n,i);return H(o,[o.size])}else{let i=H(r,[r.shape[0],r.shape[1]]);return Be(n,i)}}var Ow=D({dot_:XN});function KN(e,...t){let n=t.map((a,s)=>R(a,`tensors${s}`,"einsum")),r={equation:e};return $.runKernel(Bh,n,r)}var zw=D({einsum_:KN});function ZN(e){let t={x:R(e,"x","elu")};return $.runKernel(yo,t)}var Al=D({elu_:ZN});function YN(e){let t=R(e,"x","erf");M(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=Ae(t,"float32"));let n={x:t};return $.runKernel(go,n)}var fm=D({erf_:YN});function JN(e){let t={x:R(e,"x","exp")};return $.runKernel(ws,t)}var Xn=D({exp_:JN});function QN(e,t=0){let n=R(e,"x","expandDims","string_or_numeric");M(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return $.runKernel(wo,r,a)}var on=D({expandDims_:QN});function eT(e){let t={x:R(e,"x","expm1")};return $.runKernel(bo,t)}var mm=D({expm1_:eT});function tT(e,t){let n=R(e,"x","tile","string_or_numeric");M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return $.runKernel(Ta,r,a)}var Oa=D({tile_:tT});function nT(e,t,n,r="float32"){t==null&&(t=e);let a=We([e,t],r),s=e<=t?e:t;for(let o=0;o<s;++o)a.set(1,o,o);let i=H(a.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return Oa(on(i,0),[n[0],1,1]);if(n.length===2)return Oa(on(on(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return Oa(on(on(on(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 Am=D({eye_:nT});function Xu(e,t,n){let r={shape:e,value:t,dtype:n};return $.runKernel(bu,{},r)}function rT(e){let t={x:R(e,"x","floor")};return $.runKernel(bs,t)}var yl=D({floor_:rT});function aT(e,t,n=0,r=0){let a=R(e,"x","gather"),s=R(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return $.runKernel(vo,i,o)}var fi=D({gather_:aT});function sT(e,t){let n=R(e,"a","greater"),r=R(t,"b","greater");[n,r]=bt(n,r),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Io,a)}var or=D({greater_:sT});function iT(e,t){let n=R(e,"a","greaterEqual"),r=R(t,"b","greaterEqual");[n,r]=bt(n,r),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(ks,a)}var za=D({greaterEqual_:iT});function oT(e){let t={input:R(e,"input","imag")};return $.runKernel(Hh,t)}var kd=D({imag_:oT});function lT(e){let t={x:R(e,"x","isFinite")};return $.runKernel(So,t)}var Pw=D({isFinite_:lT});function uT(e){let t={x:R(e,"x","isInf")};return $.runKernel(No,t)}var Lw=D({isInf_:uT});function cT(e){let t={x:R(e,"x","isNaN")};return $.runKernel(To,t)}var ym=D({isNaN_:cT});function hT(e,t=.2){let n={x:R(e,"x","leakyRelu")},r={alpha:t};return $.runKernel(Ss,n,r)}var Ku=D({leakyRelu_:hT});function dT(e,t){let n=R(e,"a","less"),r=R(t,"b","less");[n,r]=bt(n,r),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Eo,a)}var Id=D({less_:dT});function pT(e,t){let n=R(e,"a","lessEqual"),r=R(t,"b","lessEqual");[n,r]=bt(n,r),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Co,a)}var mi=D({lessEqual_:pT});function Ww(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let r={start:e,stop:t,num:n};return $.runKernel(Gh,{},r)}function fT(e,t=5,n=1,r=1,a=.5){let s=R(e,"x","localResponseNormalization");M(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${s.rank}.`),M(Bt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=H(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},c={depthRadius:t,bias:n,alpha:r,beta:a},u=$.runKernel(ku,l,c);return o?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var gm=D({localResponseNormalization_:fT});function mT(e){let t={x:R(e,"x","log")};return $.runKernel(Ns,t)}var Mn=D({log_:mT});function AT(e){let t={x:R(e,"x","log1p")};return $.runKernel(Ro,t)}var Sd=D({log1p_:AT});function yT(e){return M(Ia(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=R(t,"x","tf.grad","string_or_numeric"),a=n!=null?R(n,"dy","tf.grad"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(r),[r],a);return a!=null&&an(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Nd(i),i[0]})}}function gT(e){return M(Ia(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{M(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let r=Wu(t,"args","tf.grads","string_or_numeric"),a=n!=null?R(n,"dy","tf.grads"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(...r),r,a);return a!=null&&an(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Nd(i),i})}}function xT(e){return M(Ia(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof Le,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof Le,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=$.gradients(()=>e(t),[t],n);return Nd(r),{grad:r[0],value:a}}}function wT(e){return M(Ia(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(a=>a instanceof Le),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof Le,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=$.gradients(()=>e(...t),t,n);return n!=null&&an(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Nd(r.grads),r}}function Bw(e,t){M(Ia(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(c=>c instanceof zu),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in $.registeredVariables)t.push($.registeredVariables[c])}let r=n?t.filter(c=>!c.trainable):null,a=t.length;t=t.filter(c=>c.trainable),M(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);let s=!0,{value:i,grads:o}=$.gradients(e,t,null,s);M(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()."),M(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])}),r!=null&&r.forEach(c=>l[c.name]=null),{value:i,grads:l}}function zr(e){return $.customGrad(e)}function Nd(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 bT(e){let t={x:R(e,"x","neg")};return $.runKernel($o,t)}var _t=D({neg_:bT});function _T(e){let t={x:R(e,"x","softplus")};return $.runKernel(Zo,t)}var Ai=D({softplus_:_T});function vT(e){let t=R(e,"x","logSigmoid");return zr(n=>({value:_t(Ai(_t(n))),gradFunc:r=>B(r,kn(_t(n)))}))(t)}var Vw=D({logSigmoid_:vT});function kT(e,t=null,n=!1){let r={x:R(e,"x","max")},a={reductionIndices:t,keepDims:n};return $.runKernel(Ts,r,a)}var Kn=D({max_:kT});function IT(e,t){let n=R(e,"a","sub"),r=R(t,"b","sub");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Js,a)}var ye=D({sub_:IT});function ST(e,t=null,n=!1){let r=R(e,"x","sum");r.dtype==="bool"&&(r=Ae(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Ks,a,s)}var Ne=D({sum_:ST});function NT(e,t=-1){let n=R(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 ${t}`);return zr((r,a)=>{let s=!0,i=Kn(r,t,!0),o=ye(r,i),l=ye(Ae(o,"float32"),Mn(Ne(Xn(o),t,s)));return a([l]),{value:l,gradFunc:(c,u)=>{let[h]=u,d=!0,p=Xn(h);return ye(c,B(Ne(c,t,d),p))}}})(n)}var Td=D({logSoftmax_:NT});function xm(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function jw(e,t,n){let r=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<r;o++)n.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function Uw(e,t){let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let a=t.map(s=>e[s]);return[n,a]}function yi(e,t){let n=t.map(r=>1);return jw(e,n,t)}function TT(e,t,n){M(xm(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Hw(e,t){if(xm(e,t))return null;let n=[];for(let r=0;r<t;++r)e.indexOf(r)===-1&&n.push(r);return e.forEach(r=>n.push(r)),n}function wm(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function ET(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function CT(e,t=null,n=!1){let r=R(e,"x","logSumExp"),a=sr(t,r.shape),s=Kn(r,a,!0),i=ye(r,s),o=Xn(i),l=Ne(o,a),c=Mn(l),u=se(H(s,c.shape),c);if(n){let h=yi(u.shape,a);return H(u,h)}return u}var bm=D({logSumExp_:CT});function RT(e,t){let n=R(e,"a","logicalAnd","bool"),r=R(t,"b","logicalAnd","bool");dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Mo,a)}var lr=D({logicalAnd_:RT});function MT(e){let t={x:R(e,"x","logicalNot","bool")};return $.runKernel(_u,t)}var Zu=D({logicalNot_:MT});function FT(e,t){let n=R(e,"a","logicalOr","bool"),r=R(t,"b","logicalOr","bool");dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(vu,a)}var Ed=D({logicalOr_:FT});function $T(e,t){let n=R(e,"a","logicalXor","bool"),r=R(t,"b","logicalXor","bool");return dt(n.shape,r.shape),lr(Ed(e,t),Zu(lr(e,t)))}var Gw=D({logicalXor_:$T});function DT(e,t,n,r,a){let s=R(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),M(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),M(Or(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),a!=null&&M(Bt(r),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(Cs,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Yu=D({maxPool_:DT});function OT(e,t=[1,1,1],n,r,a,s="NDHWC"){let i=R(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),M(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&M(Bt(r),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(Iu,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var _m=D({maxPool3d_:OT});function zT(e,t,n,r,a=!1){let s={x:R(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:a},o=$.runKernel(Zh,s,i);return{result:o[0],indexes:o[1]}}var qw=D({maxPoolWithArgmax_:zT});function PT(e,t){let n=R(e,"a","maximum"),r=R(t,"b","maximum");[n,r]=bt(n,r),n.dtype==="bool"&&(n=Ae(n,"int32"),r=Ae(r,"int32")),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Es,a)}var Pr=D({maximum_:PT});function LT(e,t=null,n=!1){let r={x:R(e,"x","mean")},a={axis:t,keepDims:n};return $.runKernel(Rs,r,a)}var vt=D({mean_:LT});function Et(e,t="float32"){if(t==="complex64"){let r=Et(e,"float32"),a=Et(e,"float32");return Ca(r,a)}let n=Nh(Nt(e),t);return $.makeTensor(n,e,t)}function Fn(e,t="float32"){if(t==="complex64"){let r=Fn(e,"float32"),a=Et(e,"float32");return Ca(r,a)}let n=Af(Nt(e),t);return $.makeTensor(n,e,t)}function WT(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let r=R(e,"x","meshgrid",e instanceof Le?e.dtype:"float32");if(t===void 0)return[r];let a=R(t,"y","meshgrid",t instanceof Le?t.dtype:"float32"),s=Nt(r.shape),i=Nt(a.shape);return n==="xy"?(r=H(r,[1,-1]),a=H(a,[-1,1]),[Be(Fn([i,1],r.dtype),r),Be(a,Fn([1,s],a.dtype))]):(r=H(r,[-1,1]),a=H(a,[1,-1]),[Be(r,Fn([1,i],r.dtype)),Be(Fn([s,1],a.dtype),a)])}function BT(e,t=null,n=!1){let r={x:R(e,"x","min")},a={axis:t,keepDims:n};return $.runKernel(Ms,r,a)}var gl=D({min_:BT});function VT(e,t){let n=R(e,"a","minimum"),r=R(t,"b","minimum");[n,r]=bt(n,r),n.dtype==="bool"&&(n=Ae(n,"int32"),r=Ae(r,"int32")),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Fs,a)}var xl=D({minimum_:VT});function jT(e,t,n){M(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=R(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");M(t.length===r.rank,()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`);let a=n==="reflect"?1:0;for(let o=0;o<r.rank;o++)M(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),M(t[o][0]>=0&&t[o][0]<=r.shape[o]-a&&t[o][1]>=0&&t[o][1]<=r.shape[o]-a,()=>`Padding in dimension ${o} cannot be greater than or equal to ${r.shape[o]-a} or less than 0 for input of shape ${r.shape}`);let s={paddings:t,mode:n},i={x:r};return $.runKernel($s,i,s)}var vm=D({mirrorPad_:jT});function UT(e,t){let n=R(e,"a","mod"),r=R(t,"b","mod");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Fo,a)}var km=D({mod_:UT});function HT(e){let t=R(e,"x","square"),n={};return $.runKernel("Square",{x:t},n)}var st=D({square_:HT});function GT(e,t=null,n=!1){e=R(e,"x","moments");let r=sr(t,e.shape),a=vt(e,r,n),s=a.shape;n||(s=yi(a.shape,r));let i=st(ye(Ae(e,"float32"),H(a,s))),o=vt(i,r,n);return{mean:a,variance:o}}var Cd=D({moments_:GT});function qT(e,t,n,r){let a=R(t,"data","multiRNNCell"),s=Wu(n,"c","multiRNNCell"),i=Wu(r,"h","multiRNNCell"),o=a,l=[];for(let h=0;h<e.length;h++){let d=e[h](o,s[h],i[h]);l.push(d[0]),l.push(d[1]),o=d[1]}let c=[],u=[];for(let h=0;h<l.length;h+=2)c.push(l[h]),u.push(l[h+1]);return[c,u]}var XT=D({multiRNNCell_:qT});function KT(e,t,n,r=!1){let a=R(e,"logits","multinomial"),s=a.size,i=a.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 ${i}`);n=n||Math.random();let o={logits:i===1?H(a,[1,-1]):a},l={numSamples:t,seed:n,normalized:r},c=$.runKernel(Yh,o,l);return i===1?H(c,[c.size]):c}var Xw=D({multinomial_:KT});function ZT(e,t){let n=R(e,"a","notEqual"),r=R(t,"b","notEqual");[n,r]=bt(n,r),dt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Do,a)}var gi=D({notEqual_:ZT});function YT(e){let t={x:R(e,"x","onesLike")};return $.runKernel(Lo,t)}var $n=D({onesLike_:YT});function JT(e,t){let n=R(e,"v1","outerProduct"),r=R(t,"v2","outerProduct");M(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let a=H(n,[-1,1]),s=H(r,[1,-1]);return Be(a,s)}var QT=D({outerProduct_:JT});function eE(e,t,n=0){let r=R(e,"x","pad");if(r.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let a={paddings:t,constantValue:n},s={x:r};return $.runKernel(zs,s,a)}var oa=D({pad_:eE});function tE(e,t,n=0){return M(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),oa(e,[t],n)}var nE=D({pad1d_:tE});function rE(e,t,n=0){return M(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),oa(e,t,n)}var aE=D({pad2d_:rE});function sE(e,t,n=0){return M(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."),oa(e,t,n)}var iE=D({pad3d_:sE});function oE(e,t,n=0){return M(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."),oa(e,t,n)}var lE=D({pad4d_:oE});function uE(e,t,n){let r=R(e,"x","spaceToBatchND");M(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),M(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),M(r.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 ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let a={x:r},s={blockShape:t,paddings:n};return $.runKernel(Tu,a,s)}var Ju=D({spaceToBatchND_:uE});function dE(e,t,n,r,a,s){a==null&&(a=[1,1]),s==null&&(s=1),r===0&&(r="valid");let i=R(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(Or(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let c=kw(o.shape,t,s,a,r),u=[c.dilationHeight,c.dilationWidth],h;r==="same"?h=hE([c.filterHeight,c.filterWidth],u):h=[[0,0],[0,0]];let d=u[0]===1&&u[1]===1,[p,m]=cE([c.inHeight,c.inWidth],u,h),f=d?r:"valid",A=d?o:Ju(o,u,p),y=(n==="avg"?()=>Hu(A,t,s,f):()=>Yu(A,t,s,f))(),g=d?y:Gu(y,u,m);return l?H(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function cE(e,t,n){let r=n.map(u=>u[0]),a=n.map(u=>u[1]),s=e.concat(r,a),i=t.map((u,h)=>(u-s[h]%u)%u),o=a.map((u,h)=>u+i[h]),l=t.map((u,h)=>[r[h],o[h]]),c=t.map((u,h)=>[0,i[h]]);return[l,c]}function hE(e,t){let n=e.map((s,i)=>s+(s-1)*(t[i]-1)).map(s=>s-1),r=n.map(s=>Math.floor(s/2)),a=n.map((s,i)=>s-r[i]);return n.map((s,i)=>[r[i],a[i]])}var Kw=D({pool_:dE});function pE(e,t){let n=R(e,"base","pow"),r=R(t,"exp","pow");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Ps,a)}var la=D({pow_:pE});function fE(e,t){let n=R(e,"x","prelu"),r=R(t,"alpha","prelu"),a={x:n,alpha:r};return $.runKernel(Ls,a)}var Qu=D({prelu_:fE});function mE(e,t=null,n=!1){let r=R(e,"x","prod");r.dtype==="bool"&&(r=Ae(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Bo,a,s)}var Rd=D({prod_:mE});function AE(e,t,n){let r=Nt(e),a=null;if(n==null||n==="float32")a=new Float32Array(r);else if(n==="int32")a=new Int32Array(r);else if(n==="bool")a=new Uint8Array(r);else throw new Error(`Unknown data type ${n}`);for(let s=0;s<r;s++)a[s]=t();return $.makeTensor(a,e,n)}var yE=D({rand_:AE}),Im=Qi(Lg()),Sm=class{constructor(e,t,n,r,a){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=r,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=a||Math.random();this.random=Im.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let r=this.nextVal;return this.nextVal=NaN,r}let e,t,n=!1;for(;!n;){let r,a,s;do r=2*this.random()-1,a=2*this.random()-1,s=r*r+a*a;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*r*i,t=this.mean+this.stdDev*a*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}},gE=class{constructor(e,t,n,r){this.alpha=e,this.beta=1/t,this.dtype=n;let a=r||Math.random();this.randu=Im.alea(a.toString()),this.randn=new Sm(0,1,n,!1,this.randu()),e<1?this.d=e+2/3:this.d=e-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let e,t,n,r,a,s;for(;;){do r=this.randn.nextValue(),s=1+this.c*r;while(s<=0);if(s*=s*s,e=r*r,t=1-.331*e*e,n=.5*e+this.d*(1-s+Math.log(s)),a=this.randu(),a<t||Math.log(a)<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)}},xE=class{constructor(e=0,t=1,n,r){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,r==null&&(r=Math.random()),typeof r=="number"&&(r=r.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=Im.alea(r)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function wE(e,t,n=1,r="float32",a){if(n==null&&(n=1),r==null&&(r="float32"),r!=="float32"&&r!=="int32")throw new Error(`Unsupported data type ${r}`);let s=new gE(t,n,r,a),i=We(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var bE=D({randomGamma_:wE});function _E(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let s=new Sm(t,n,r,!1,a),i=We(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Zw=D({randomNormal_:_E});function vE(e,t=0,n=1,r="float32",a){let s=We(e,r),i=new xE(t,n,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var wl=D({randomUniform_:vE});function Md(e,t,n=1,r="float32"){if(n===0)throw new Error("Cannot have a step of zero");let a={start:e,stop:t,step:n,dtype:r};return $.runKernel(Su,{},a)}function kE(e){let t={input:R(e,"input","real")};return $.runKernel(Jh,t)}var ec=D({real_:kE});function IE(e){let t={x:R(e,"x","reciprocal")};return $.runKernel(Vo,t)}var Nm=D({reciprocal_:IE});function SE(e){let t={x:R(e,"x","relu")};return $.runKernel(Ws,t)}var Lr=D({relu_:SE});function NE(e){let t={x:R(e,"x","relu6")};return $.runKernel(Vs,t)}var Fd=D({relu6_:NE});function TE(e,t){let n={x:R(e,"x","reverse")},r={dims:t};return $.runKernel(js,n,r)}var Dn=D({reverse_:TE});function EE(e){let t=R(e,"x","reverse");return M(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Dn(t,0)}var CE=D({reverse1d_:EE});function RE(e,t){let n=R(e,"x","reverse");return M(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Dn(n,t)}var ME=D({reverse2d_:RE});function FE(e,t){let n=R(e,"x","reverse");return M(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Dn(n,t)}var $E=D({reverse3d_:FE});function DE(e,t){let n=R(e,"x","reverse");return M(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Dn(n,t)}var OE=D({reverse4d_:DE});function zE(e){let t={x:R(e,"x","round")};return $.runKernel(Us,t)}var Tm=D({round_:zE});function PE(e){let t={x:R(e,"x","rsqrt")};return $.runKernel(Hs,t)}var $d=D({rsqrt_:PE});function ke(e,t){if((tn(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"&&tn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Ra(e,[],[],t)}function LE(e){let t={x:R(e,"x","selu")};return $.runKernel(Go,t)}var Dd=D({selu_:LE});function WE(e,t,n,r,a,s=[1,1],i="NHWC"){let o=R(e,"x","separableConv2d"),l=R(t,"depthwiseFilter","separableConv2d"),c=R(n,"pointwiseFilter","separableConv2d"),u=o,h=!1;if(o.rank===3&&(h=!0,u=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");M(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),M(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),M(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),M(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${c.shape[0]}.`),M(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],p=l.shape[3];M(c.shape[2]===d*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*p}, but got ${c.shape[2]}.`);let m=ml(u,l,r,a,i,s),f=ia(m,c,1,"valid",i);return h?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Em=D({separableConv2d_:WE});async function BE(e,t){let n=R(e,"x","setdiff1d"),r=R(t,"y","setdiff1d");M(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),M(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),M(r.rank===1,()=>`y should be 1D tensor, but got y (${r.shape}).`);let a=await n.data(),s=await r.data(),i=new Set(s),o=0;for(let u=0;u<a.length;u++)i.has(a[u])||o++;let l=new $t([o],n.dtype),c=new $t([o],"int32");for(let u=0,h=0;u<a.length;u++)i.has(a[u])||(l.values[h]=a[u],c.values[h]=u,h++);return[l.toTensor(),c.toTensor()]}var Yw=BE;function VE(e){let t={x:R(e,"x","sign")};return $.runKernel(Ko,t)}var Cm=D({sign_:VE});function jE(e){let t={x:R(e,"x","sin")};return $.runKernel(Gs,t)}var Od=D({sin_:jE});function UE(e){let t={x:R(e,"x","sinh")};return $.runKernel(Xo,t)}var zd=D({sinh_:UE});function HE(e,t,n){let r=R(e,"x","slice1d");return M(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Re(r,[t],[n])}var Pd=D({slice1d_:HE});function GE(e,t,n){let r=R(e,"x","slice2d");return M(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var Rm=D({slice2d_:GE});function qE(e,t,n){let r=R(e,"x","slice3d");return M(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var Ld=D({slice3d_:qE});function XE(e,t,n){let r=R(e,"x","slice4d");return M(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var tc=D({slice4d_:XE});function KE(e,t=-1){let n=R(e,"logits","softmax","float32");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);let r={logits:n},a={dim:t};return $.runKernel(Zs,r,a)}var nc=D({softmax_:KE});function ZE(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(jh,t)}var rc=D({fft_:ZE});function YE(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Uh,t)}var bl=D({ifft_:YE});function JE(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let a=H(e,[n,t]);r=bl(a)}else{let a=[n,2*(t-1)],s=H(ec(e),[n,t]),i=H(kd(e),[n,t]),o=Dn(Re(s,[0,1],[n,t-2]),1),l=B(Dn(Re(i,[0,1],[n,t-2]),1),ke(-1)),c=ot([s,o],1),u=ot([i,l],1),h=H(Ca(c,u),[a[0],a[1]]);r=bl(h)}if(r=ec(r),e.rank===3&&e.shape[0]!==0){let a=r,s=e.shape[0];r=H(r,[s,r.shape[0]/s,r.shape[1]]),a.dispose()}return r}var Wd=D({irfft_:JE});function QE(e,t,n=0){let r={x:R(e,"x","split")},a={numOrSizeSplits:t,axis:n};return $.runKernel(Yo,r,a)}var ln=D({split_:QE});function eC(e,t){M(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,a;if(t!=null&&t<n){let m=e.shape.map(A=>0),f=e.shape.map(A=>A);f[e.shape.length-1]=t,a=Re(e,m,f),n=t}else if(t!=null&&t>n){let m=e.shape.map(f=>f);m[e.shape.length-1]=t-n,a=ot([e,Et(m)],e.shape.length-1),n=t}else a=e;let s=Ue(a),i=H(Ca(a,s),[r,n]),o=rc(i),l=Math.floor(n/2)+1,c=ec(o),u=kd(o),h=ln(c,[l,n-l],c.shape.length-1),d=ln(u,[l,n-l],u.shape.length-1),p=a.shape.slice();return p[a.shape.length-1]=l,H(Ca(h[0],d[0]),p)}var ac=D({rfft_:eC});function tC(e){let t={x:R(e,"x","sqrt")};return $.runKernel(Xs,t)}var Jt=D({sqrt_:tC});function nC(e,t){let n=R(e,"a","squaredDifference"),r=R(t,"b","squaredDifference");[n,r]=bt(n,r),dt(n.shape,r.shape);let a={a:n,b:r},s={};return $.runKernel(Ys,a,s)}var Bd=D({squaredDifference_:nC});function rC(e,t){let n=R(e,"x","squeeze");return H(n,yx(n.shape,t).newShape)}var Pa=D({squeeze_:rC});function aC(e,t=0){let n=Wu(e,"tensors","stack","string_or_numeric");M(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&M(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,a={axis:t};return $.runKernel(Wo,r,a)}var On=D({stack_:aC});function sC(e,t=0){let n={x:R(e,"x","step")},r={alpha:t};return $.runKernel(Ea,n,r)}var _l=D({step_:sC});function iC(e,t,n,r,a=0,s=0,i=0,o=0,l=0){let c={x:R(e,"x","stridedSlice")},u={begin:t,end:n,strides:r,beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return $.runKernel(Jo,c,u)}var Mm=D({stridedSlice_:iC});function oC(e){let t={x:R(e,"x","tan")};return $.runKernel(Qs,t)}var Fm=D({tan_:oC});function nn(e,t){is(e);let n=$r(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Ra(e,null,n,t)}function Zn(e,t,n){if(is(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=$r(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Ra(e,t,r,n)}function lC(e,t,n){if(is(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=$r(e,n);if(r.length!==4&&r.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Ra(e,t,r,n)}function uC(e,t,n){if(is(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=$r(e,n);if(r.length!==5&&r.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Ra(e,t,r,n)}function cC(e,t,n){if(is(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=$r(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,Ra(e,t,r,n)}function hC(e,t=1,n=!0){let r=R(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let a=r.shape[r.shape.length-1];if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${a}) but got ${t}`);let s={x:r},i={k:t,sorted:n},[o,l]=$.runKernel(Qo,s,i);return{values:o,indices:l}}var $m=D({topk_:hC});function dC(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new Sm(t,n,r,!0,a),i=We(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Vd=D({truncatedNormal_:dC});function pC(e,t=0){let n=R(e,"x","unique","string_or_numeric");M(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},a={axis:t},[s,i]=$.runKernel(ad,r,a);return{values:s,indices:i}}var jd=D({unique_:pC});function fC(e,t,n){let r=R(e,"x","unsortedSegmentSum"),a=R(t,"segmentIds","unsortedSegmentSum","int32");M(Bt(n),()=>"numSegments must be of dtype int");let s={x:r,segmentIds:a},i={numSegments:n};return $.runKernel(Cu,s,i)}var Dm=D({unsortedSegmentSum_:fC});function mC(e,t=0){let n=R(e,"x","unstack","string_or_numeric");M(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},a={axis:t};return $.runKernel(el,r,a)}var ur=D({unstack_:mC});function Jw(e,t=!0,n,r){return $.makeVariable(e,t,n,r)}function Qw(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let r=We(e,"int32"),a=We([n.length,e.length],"int32");for(let s=0;s<n.length;s++){let i=r.indexToLoc(n[s]),o=s*e.length;a.values.set(i,o)}return a.toTensor()}async function AC(e){let t=R(e,"condition","whereAsync","bool"),n=await t.data(),r=Qw(t.shape,n);return e!==t&&t.dispose(),r}var Om=AC;async function yC(e,t,n){let r=R(e,"tensor","boolMask"),a=R(t,"mask","boolMask","bool"),s=n==null?0:n,i=a.rank,o=r.shape;M(i>0,()=>"mask cannot be scalar"),an(o.slice(s,s+i),a.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=H(r,c),h=H(a,[-1]),d=await Om(h),p=Pa(d,[1]),m=fi(u,p,s);return e!==r&&r.dispose(),t!==a&&a.dispose(),p.dispose(),u.dispose(),h.dispose(),d.dispose(),m}var gC=yC;function xC(e,t="euclidean",n=null,r=!1){e=R(e,"x","norm");let a=eb(e,t,n),s=a.shape;if(r){let i=sr(n,e.shape);s=yi(a.shape,i)}return H(a,s)}function eb(e,t,n=null){if(e.rank===0)return Dt(e);if(e.rank!==1&&n===null)return eb(H(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Ne(Dt(e),n);if(t===Infinity)return Kn(Dt(e),n);if(t===-Infinity)return gl(Dt(e),n);if(t==="euclidean"||t===2)return Jt(Ne(la(Dt(e),ke(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Kn(Ne(Dt(e),n[0]),n[1]-1);if(t===Infinity)return Kn(Ne(Dt(e),n[1]),n[0]);if(t===-Infinity)return gl(Ne(Dt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return Jt(Ne(st(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Ud=D({norm_:xC});function wC(e,t,n,r,a=!0){let s=R(e,"v","movingAverage"),i=R(t,"x","movingAverage"),o=R(n,"decay","movingAverage");$x(s,i),M(na(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=ke(1),c=ye(l,o),u=B(ye(i,s),c);if(a){M(r!=null,()=>"When using zeroDebias: true, step is required.");let h=R(r,"step","movingAverage");u=ge(u,ye(l,la(o,h)))}return se(s,u)}var bC=D({movingAverage_:wC});function _C(e,t,n){let r=R(e,"indices","scatterND","int32"),a=R(t,"updates","scatterND");Hf(a,r,n);let s={indices:r,updates:a},i={shape:n};return $.runKernel(Uo,s,i)}var tb=D({scatterND_:_C});function vC(e,t,n,r){if(e.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);let a=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===a))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${a}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function kC(e,t,n,r=0){let a=R(e,"sparseIndices","sparseToDense","int32"),s=R(t,"sparseValues","sparseToDense"),i=R(r,"defaultValue","sparseToDense",s.dtype);vC(a,s,n,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:n};return $.runKernel(nd,o,l)}var zm=D({sparseToDense_:kC});function IC(e,t){let n=R(t,"indices","gatherND","int32"),r={params:R(e,"x","gatherND"),indices:n};return $.runKernel(ko,r)}var nb=D({gatherND_:IC});function SC(e,t){if(t==null)return e.shape.slice();if(na(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let r=0;r<e.shape.length;r++)t[r]==null&&e.shape[r]!=null?n.push(e.shape[r]):n.push(t[r]);return n}return t}function NC(e,t,n,r){let a=R(e,"x","dropout");if(M(a.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${a.dtype} tensor instead.`),M(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Le?a.clone():a;let s=SC(a,n),i=1-t,o=ge(yl(se(wl(s,0,1,"float32",r),i)),i);return B(a,o)}var rb=D({dropout_:NC});function ab(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function Pm(e,t,n){let r=1-e%2,a=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+r-1);a[s]=t-n*Math.cos(i)}return nn(a,"float32")}async function TC(e,t,n=1){let r=R(e,"predictions","inTopK"),a=R(t,"targets","inTopK");M(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),M(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),an(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=r.shape[r.shape.length-1];M(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await r.data(),o=await a.data(),[l,c]=[i.length/s,s],u=gx("bool",l);for(let h=0;h<l;h++){let d=h*c,p=i.subarray(d,d+c),m=[];for(let f=0;f<p.length;f++)m.push({value:p[f],index:f});m.sort((f,A)=>A.value-f.value),u[h]=0;for(let f=0;f<n;f++)if(m[f].index===o[h]){u[h]=1;break}}return e!==r&&r.dispose(),t!==a&&a.dispose(),wr(u,a.shape,"bool")}var EC=TC,La={};Me(La,{conv2d:()=>CC,depthwiseConv2d:()=>RC,matMul:()=>MC});function FC(e,t,n,r,a,s="NHWC",i){let o=e;e.rank===3&&(o=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]])),M(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),M(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),M(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];M(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),M(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),i!=null&&M(Bt(a),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let h={x:o,dy:l},d={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,filterShape:n};return $.runKernel(Mh,h,d)}var Lm=D({conv2DBackpropFilter_:FC});function Hd(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return B(e,_l(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Gd(e,t){let n=t,r=Ot(e.shape,t.shape);return r.length>0&&(n=Ne(n,r)),H(n,e.shape)}function qd(e,t,n,r){if(t==="linear")return e;if(t==="relu")return Lr(e);if(t==="elu")return Al(e);if(t==="relu6")return Fd(e);if(t==="prelu")return Qu(e,n);if(t==="leakyrelu")return Ku(e,r);if(t==="sigmoid")return kn(e);throw new Error(`Unknown fused activation ${t}.`)}var Xd=(e,t)=>!(e>0)||t==="linear";function $C({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Xd($.state.gradientDepth,l)===!1){let w=ia(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),qd(w,l,c,u)}let h=R(e,"x","conv2d"),d=R(t,"filter","conv2d"),p=h,m=!1;h.rank===3&&(m=!0,p=H(h,[1,h.shape[0],h.shape[1],h.shape[2]])),M(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),M(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&M(Bt(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),M(p.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${d.shape[2]}.`),M(Or(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),M(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let f=Uu(p.shape,d.shape,n,s,r,i),A;o!=null&&(A=R(o,"bias","fused conv2d"),[A]=bt(A,h),dt(f.outShape,A.shape));let y;c!=null&&(y=R(c,"prelu weights","fused conv2d"));let g=(w,b)=>{let[k,N,C,F]=b,O=Hd(w,C,l);M($a(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let z=um(N.shape,O,k,n,r),V=Lm(N,O,k.shape,n,r),j=[z,V];if(F!=null){let U=Gd(F,O);j.push(U)}return j},x={x:p,filter:d,bias:A,preluActivationWeights:y},v={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?zr((w,b,k)=>{let N=$.runKernel(ri,x,v);return k([b,w,N]),m&&(N=H(N,[N.shape[1],N.shape[2],N.shape[3]])),{value:N,gradFunc:g}})(p,d):zr((w,b,k,N)=>{let C=$.runKernel(ri,x,v);return N([b,w,C,k]),m&&(C=H(C,[C.shape[1],C.shape[2],C.shape[3]])),{value:C,gradFunc:g}})(p,d,A)}var CC=D({fusedConv2d_:$C});function DC(e,t,n,r,a,s=[1,1],i){let o=e;e.rank===3&&(o=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={x:o,dy:l},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,filterShape:n};return $.runKernel(Oh,c,u)}var sb=D({depthwiseConv2dNativeBackpropFilter_:DC});function OC(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:o,filter:n},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=$.runKernel(zh,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var ib=D({depthwiseConv2dNativeBackpropInput_:OC});function zC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(Xd($.state.gradientDepth,l)===!1){let w=ml(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),qd(w,l,c,u)}let h=R(e,"x","depthwiseConv2d"),d=R(t,"filter","depthwiseConv2d"),p=h,m=!1;h.rank===3&&(m=!0,p=H(h,[1,h.shape[0],h.shape[1],h.shape[2]])),M(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),M(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),M(p.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),M(Or(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&M(Bt(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${r}.`);let f=Uu(p.shape,d.shape,n,s,r,i,!0),A;o!=null&&(A=R(o,"bias","fused conv2d"),[A]=bt(A,h),dt(f.outShape,A.shape));let y;c!=null&&(y=R(c,"prelu weights","fused depthwiseConv2d"));let g=(w,b)=>{M($a(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[k,N,C,F]=b,O=Hd(w,C,l),z=ib(N.shape,O,k,n,r,s,i),V=sb(N,O,k.shape,n,r,s,i);if(F!=null){let j=Gd(A,O);return[z,V,j]}return[z,V]},x={x:p,filter:d,bias:A,preluActivationWeights:y},v={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?zr((w,b,k)=>{let N=$.runKernel(ai,x,v);return k([b,w,N]),m&&(N=H(N,[N.shape[1],N.shape[2],N.shape[3]])),{value:N,gradFunc:g}})(p,d):zr((w,b,k,N)=>{let C=$.runKernel(ai,x,v);return N([b,w,C,k]),m&&(C=H(C,[C.shape[1],C.shape[2],C.shape[3]])),{value:C,gradFunc:g}})(p,d,A)}var RC=D({fusedDepthwiseConv2d_:zC});function PC({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Xd($.state.gradientDepth,s)===!1){let F=Be(e,t,n,r);return a!=null&&(F=se(F,a)),qd(F,s,i,o)}let l=R(e,"a","fused matMul"),c=R(t,"b","fused matMul");[l,c]=bt(l,c);let u=n?l.shape[l.rank-2]:l.shape[l.rank-1],h=r?c.shape[c.rank-1]:c.shape[c.rank-2],d=n?l.shape[l.rank-1]:l.shape[l.rank-2],p=r?c.shape[c.rank-2]:c.shape[c.rank-1],m=l.shape.slice(0,-2),f=c.shape.slice(0,-2),A=Nt(m),y=Nt(f);M(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 ${c.rank}.`),M(na(m,f),()=>`Error in fused matMul: outer dimensions (${m}) and (${f}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),M(u===h,()=>`Error in fused matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${r} must match.`);let g=l.shape.slice(0,-2).concat([d,p]),x=n?H(l,[A,u,d]):H(l,[A,d,u]),v=r?H(c,[y,p,h]):H(c,[y,h,p]),w;a!=null&&(w=R(a,"bias","fused matMul"),[w]=bt(w,l),dt(g,w.shape));let b;i!=null&&(b=R(i,"prelu weights","fused matMul"));let k=(F,O)=>{let[z,V,j,U]=O,X=Hd(H(F,j.shape),j,s),G,ee;if(!n&&!r?(G=Be(X,V,!1,!0),ee=Be(z,X,!0,!1)):!n&&r?(G=Be(X,V,!1,!1),ee=Be(X,z,!0,!1)):n&&!r?(G=Be(V,X,!1,!0),ee=Be(z,X,!1,!1)):(G=Be(V,X,!0,!0),ee=Be(X,z,!0,!0)),a!=null){let Y=Gd(U,X);return[G,ee,Y]}else return[G,ee]},N={a:x,b:v,bias:w,preluActivationWeights:b},C={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return a==null?zr((F,O,z)=>{let V=$.runKernel(ni,N,C);return z([F,O,V]),{value:H(V,g),gradFunc:k}})(x,v):zr((F,O,z,V)=>{let j=$.runKernel(ni,N,C);return V([F,O,j,z]),{value:H(j,g),gradFunc:k}})(x,v,w)}var MC=D({fusedMatMul_:PC});function LC(e){return Pm(e,.54,.46)}var WC=D({hammingWindow_:LC});function BC(e){return Pm(e,.5,.5)}var ob=D({hannWindow_:BC});function VC(e,t,n,r=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Re(e,s,t)),s+=n;if(r)for(;s<e.size;){let o=s+t-e.size,l=ot([Re(e,s,t-o),Xu([o],a)]);i.push(l),s+=n}return i.length===0?Zn([],[0,t]):H(ot(i),[i.length,t])}var lb=D({frame_:VC});function jC(e,t,n,r,a=ob){r==null&&(r=ab(t));let s=lb(e,t,n),i=B(s,a(t));return ac(i,r)}var UC=D({stft_:jC});function HC(e,t,n,r,a="bilinear",s=0){let i=R(e,"image","cropAndResize"),o=R(t,"boxes","cropAndResize","float32"),l=R(n,"boxInd","cropAndResize","int32"),c=o.shape[0];M(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),M(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${o.shape}.`),M(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${o.shape}.`),M(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),M(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),M(a==="bilinear"||a==="nearest",()=>`method must be bilinear or nearest, but was ${a}`);let u={image:i,boxes:o,boxInd:l},h={method:a,extrapolationValue:s,cropSize:r};return $.runKernel(mo,u,h)}var GC=D({cropAndResize_:HC});function qC(e){let t=R(e,"image","flipLeftRight","float32");M(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return $.runKernel(_o,n,{})}var XC=D({flipLeftRight_:qC});function KC(e,t,n=0,r=.5){let a=R(e,"image","rotateWithOffset","float32");M(a.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`);let s={image:a},i={radians:t,fillValue:n,center:r};return $.runKernel(nl,s,i)}var ZC=D({rotateWithOffset_:KC});function vl(e,t,n,r,a,s){r==null&&(r=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),M(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),M(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),M(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),M(t.rank===1,()=>"scores must be a 1D tensor"),M(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),M(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function YC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppression"),i=R(t,"scores","nonMaxSuppression"),o=vl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:a};return $.runKernel(Oo,{boxes:s,scores:i},l)}var JC=D({nonMaxSuppression_:YC});function eR(e,t,n){let r=QC(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function QC(e,t,n){return nR(e,t,n||tR)}function tR(e,t){return e>t?1:e<t?-1:0}function nR(e,t,n){let r=0,a=e.length,s=0,i=!1;for(;r<a;){s=r+(a-r>>>1);let o=n(t,e[s]);o>0?r=s+1:(a=s,i=!o)}return i?r:-r-1}function ub(e,t,n,r,a){return Wm(e,t,n,r,a,0)}function cb(e,t,n,r,a,s){return Wm(e,t,n,r,a,0,!1,s,!0)}function hb(e,t,n,r,a,s){return Wm(e,t,n,r,a,s,!0)}function Wm(e,t,n,r,a,s,i=!1,o=!1,l=!1){let c=[];for(let A=0;A<t.length;A++)t[A]>a&&c.push({score:t[A],boxIndex:A,suppressBeginIndex:0});c.sort(db);let u=s>0?-.5/s:0,h=[],d=[];for(;h.length<n&&c.length>0;){let A=c.pop(),{score:y,boxIndex:g,suppressBeginIndex:x}=A;if(y<a)break;let v=!1;for(let w=h.length-1;w>=x;--w){let b=rR(e,g,h[w]);if(b>=r){v=!0;break}if(A.score=A.score*aR(r,u,b),A.score<=a)break}A.suppressBeginIndex=h.length,v||(A.score===y?(h.push(g),d.push(A.score)):A.score>a&&eR(c,A,db))}let p=h.length,m=n-p;o&&m>0&&(h.push(...new Array(m).fill(0)),d.push(...new Array(m).fill(0)));let f={selectedIndices:h};return i&&(f.selectedScores=d),l&&(f.validOutputs=p),f}function rR(e,t,n){let r=e.subarray(t*4,t*4+4),a=e.subarray(n*4,n*4+4),s=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),o=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),c=Math.min(a[0],a[2]),u=Math.min(a[1],a[3]),h=Math.max(a[0],a[2]),d=Math.max(a[1],a[3]),p=(o-s)*(l-i),m=(h-c)*(d-u);if(p<=0||m<=0)return 0;let f=Math.max(s,c),A=Math.max(i,u),y=Math.min(o,h),g=Math.min(l,d),x=Math.max(y-f,0)*Math.max(g-A,0);return x/(p+m-x)}function aR(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function db(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function sR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppressionAsync"),i=R(t,"scores","nonMaxSuppressionAsync"),o=vl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),c=l[0],u=l[1],{selectedIndices:h}=ub(c,u,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),nn(h,"int32")}var iR=sR;function oR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=R(e,"boxes","nonMaxSuppression"),o=R(t,"scores","nonMaxSuppression"),l=vl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let c={boxes:i,scores:o},u={maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s},h=$.runKernel(Po,c,u);return{selectedIndices:h[0],selectedScores:h[1]}}var lR=D({nonMaxSuppressionWithScore_:oR});async function uR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=R(e,"boxes","nonMaxSuppressionAsync"),o=R(t,"scores","nonMaxSuppressionAsync"),l=vl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let c=await Promise.all([i.data(),o.data()]),u=c[0],h=c[1],{selectedIndices:d,selectedScores:p}=hb(u,h,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:nn(d,"int32"),selectedScores:nn(p)}}var cR=uR;function hR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=R(e,"boxes","nonMaxSuppression"),o=R(t,"scores","nonMaxSuppression"),l=vl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,d={boxes:i,scores:o},p={maxOutputSize:c,iouThreshold:u,scoreThreshold:h,padToMaxOutputSize:s},m=$.runKernel(zo,d,p);return{selectedIndices:m[0],validOutputs:m[1]}}var dR=D({nonMaxSuppressionPadded_:hR});async function pR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=R(e,"boxes","nonMaxSuppressionAsync"),o=R(t,"scores","nonMaxSuppressionAsync"),l=vl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,[d,p]=await Promise.all([i.data(),o.data()]),{selectedIndices:m,validOutputs:f}=cb(d,p,c,u,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:nn(m,"int32"),validOutputs:ke(f,"int32")}}var fR=pR;function mR(e,t,n=!1,r=!1){let a=R(e,"images","resizeBilinear");M(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),M(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),M(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(Bs,o,l);return i?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var pb=D({resizeBilinear_:mR});function AR(e,t,n=!1,r=!1){let a=R(e,"images","resizeNearestNeighbor");M(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),M(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),M(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),M(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(Nu,o,l);return i?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var fb=D({resizeNearestNeighbor_:AR});function yR(e,t,n="nearest",r="constant",a=0,s){let i=R(e,"image","transform","float32"),o=R(t,"transforms","transform","float32");M(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),M(o.rank===2&&(o.shape[0]===i.shape[0]||o.shape[0]===1)&&o.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),M(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},c={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return $.runKernel(rd,l,c)}var gR=D({transform_:yR});function xR(e,t,n){M(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),M(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=R(e,"a","bandPart");M(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let a=r.shape,[s,i]=r.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=H(Md(0,s,1,"int32"),[-1,1]),l=Md(0,i,1,"int32"),c=ye(o,l),u=lr(mi(c,ke(+t,"int32")),za(c,ke(-n,"int32"))),h=Et([s,i],r.dtype);return H(On(ur(H(r,[-1,s,i])).map(d=>Sn(u,d,h))),a)}var wR=D({bandPart_:xR});function bR(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)M(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=ln(e,e.shape[0],0).map(a=>Pa(a,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let a=0;a<e.length;++a)n.push($.tidy(()=>{let s=r[a];if(a>0)for(let i=0;i<a;++i){let o=B(Ne(B(n[i],s)),n[i]);s=ye(s,o)}return ge(s,Ud(s,"euclidean"))}));return t?On(n,0):n}var _R=D({gramSchmidt_:bR});function vR(e,t=!1){if(M(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return mb(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),r=ur(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(l=>{let[c,u]=mb(l,t);a.push(c),s.push(u)});let i=H(On(a,0),e.shape),o=H(On(s,0),e.shape);return[i,o]}}function mb(e,t=!1){return $.tidy(()=>{M(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],a=Am(n),s=Dr(e),i=Zn([[1]],[1,1]),o=Dr(i),l=n>=r?r:n;for(let c=0;c<l;++c){let u=s,h=o,d=a;[o,s,a]=$.tidy(()=>{let p=Re(s,[c,c],[n-c,1]),m=Ud(p),f=Re(s,[c,c],[1,1]),A=Sn(or(f,0),Zn([[-1]]),Zn([[1]])),y=ye(f,B(A,m)),g=ge(p,y);g.shape[0]===1?o=Dr(i):o=ot([i,Re(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let x=_t(ge(Be(A,y),m)),v=Re(s,[c,0],[n-c,r]),w=B(x,o),b=Ze(o);if(c===0)s=ye(v,Be(w,Be(b,v)));else{let C=ye(v,Be(w,Be(b,v)));s=ot([Re(s,[0,0],[c,r]),C],0)}let k=Ze(w),N=Re(a,[0,c],[n,a.shape[1]-c]);if(c===0)a=ye(N,Be(Be(N,o),k));else{let C=ye(N,Be(Be(N,o),k));a=ot([Re(a,[0,0],[n,c]),C],1)}return[o,s,a]}),Te([u,h,d])}return!t&&n>r&&(a=Re(a,[0,0],[n,r]),s=Re(s,[0,0],[r,r])),[a,s]})}var kR=D({qr_:vR}),un;(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"})(un||(un={}));function IR(e,t,n=un.SUM_BY_NONZERO_WEIGHTS){let r=R(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=R(t,"weights","computeWeightedLoss"));let s=a==null?r:B(r,a);if(n===un.NONE)return s;if(n===un.SUM)return Ne(s);if(n===un.MEAN){if(a==null)return vt(s);{let i=r.size/a.size,o=ge(Ne(s),Ne(a));return i>1?ge(o,ke(i)):o}}if(n===un.SUM_BY_NONZERO_WEIGHTS){if(a==null)return ge(Ne(s),ke(r.size));{let i=B(a,Fn(r.shape)),o=Ae(Ne(gi(i,ke(0))),"float32");return ge(Ne(s),o)}}throw Error(`Unknown reduction: ${n}`)}var ua=D({computeWeightedLoss_:IR});function SR(e,t,n,r=un.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","absoluteDifference"),s=R(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=R(n,"weights","absoluteDifference")),an(a.shape,s.shape,"Error in absoluteDifference: ");let o=Dt(ye(a,s));return ua(o,i,r)}var NR=D({absoluteDifference_:SR});function TR(e,t,n,r,a=un.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","cosineDistance"),i=R(t,"predictions","cosineDistance"),o=null;r!=null&&(o=R(r,"weights","cosineDistance")),an(s.shape,i.shape,"Error in cosineDistance: ");let l=ke(1),c=ye(l,Ne(B(s,i),n,!0));return ua(c,o,a)}var ER=D({cosineDistance_:TR});function CR(e,t,n,r=un.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","hingeLoss"),s=R(t,"predictions","hingeLoss"),i=null;n!=null&&(i=R(n,"weights","hingeLoss")),an(a.shape,s.shape,"Error in hingeLoss: ");let o=ke(1);a=ye(B(ke(2),a),o);let l=Lr(ye(o,B(a,s)));return ua(l,i,r)}var RR=D({hingeLoss_:CR});function MR(e,t,n,r=1,a=un.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","huberLoss"),i=R(t,"predictions","huberLoss"),o=null;n!=null&&(o=R(n,"weights","huberLoss")),an(s.shape,i.shape,"Error in huberLoss: ");let l=ke(r),c=Dt(ye(i,s)),u=xl(c,l),h=ye(c,u),d=se(B(ke(.5),st(u)),B(l,h));return ua(d,o,a)}var FR=D({huberLoss_:MR});function $R(e,t,n,r=1e-7,a=un.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","logLoss"),i=R(t,"predictions","logLoss"),o=null;n!=null&&(o=R(n,"weights","logLoss")),an(s.shape,i.shape,"Error in logLoss: ");let l=ke(1),c=ke(r),u=_t(B(s,Mn(se(i,c)))),h=B(ye(l,s),Mn(se(ye(l,i),c))),d=ye(u,h);return ua(d,o,a)}var DR=D({logLoss_:$R});function OR(e,t,n,r=un.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","meanSquaredError"),s=R(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=R(n,"weights","meanSquaredError")),an(a.shape,s.shape,"Error in meanSquaredError: ");let o=Bd(a,s);return ua(o,i,r)}var zR=D({meanSquaredError_:OR});function PR(e,t){let n=R(e,"labels","sigmoidCrossEntropyWithLogits"),r=R(t,"logits","sigmoidCrossEntropyWithLogits");an(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=Lr(r),s=B(r,n),i=Sd(Xn(_t(Dt(r))));return se(ye(a,s),i)}function LR(e,t,n,r=0,a=un.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"multiClassLabels","sigmoidCrossEntropy"),i=R(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","sigmoidCrossEntropy")),an(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let c=ke(r),u=ke(1),h=ke(.5);s=se(B(s,ye(u,c)),B(h,c))}let l=PR(s,i);return ua(l,o,a)}var WR=D({sigmoidCrossEntropy_:LR});function BR(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 zr((r,a,s)=>{let i=bm(a,[n],!0),o=ye(Ae(a,"float32"),i);s([r,o]);let l=_t(B(o,r));return{value:Ne(l,[n]),gradFunc:(c,u)=>{let[h,d]=u,p=yi(c.shape,[n]);return[B(H(c,p),ye(Ae(h,"float32"),Xn(d))),B(H(c,p),ye(Xn(d),Ae(h,"float32")))]}}})(e,t)}function VR(e,t,n,r=0,a=un.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"onehotLabels","softmaxCrossEntropy"),i=R(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","softmaxCrossEntropy")),an(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let c=ke(r),u=ke(1),h=ke(s.shape[1]);s=se(B(s,ye(u,c)),ge(c,h))}let l=BR(s,i);return ua(l,o,a)}var jR=D({softmaxCrossEntropy_:VR});function UR(e,t,n){let r=R(e,"inputIndices","sparseReshape"),a=R(t,"inputShape","sparseReshape"),s=R(n,"newShape","sparseReshape");if(r.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
        ${r.shape}`);if(a.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${a.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:r,inputShape:a,newShape:s},o=$.runKernel(td,i);return{outputIndices:o[0],outputShape:o[1]}}var HR=D({sparseReshape_:UR}),GR={fft:rc,ifft:bl,rfft:ac,irfft:Wd},qR={hammingWindow:WC,hannWindow:ob,frame:lb,stft:UC},Ye={flipLeftRight:XC,resizeNearestNeighbor:fb,resizeBilinear:pb,rotateWithOffset:ZC,cropAndResize:GC,nonMaxSuppression:JC,nonMaxSuppressionAsync:iR,nonMaxSuppressionWithScore:lR,nonMaxSuppressionWithScoreAsync:cR,nonMaxSuppressionPadded:dR,nonMaxSuppressionPaddedAsync:fR,transform:gR},Ab={bandPart:wR,gramSchmidt:_R,qr:kR},XR={absoluteDifference:NR,computeWeightedLoss:ua,cosineDistance:ER,hingeLoss:RR,huberLoss:FR,logLoss:DR,meanSquaredError:zR,sigmoidCrossEntropy:WR,softmaxCrossEntropy:jR},yb={sparseReshape:HR},ca=class extends gw{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Te(a),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Bw(e,t)}dispose(){this.iterations_!=null&&Te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(ca,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Kd=class extends ca{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:L(()=>Ue(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:L(()=>Ue(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;L(()=>{let l=se(B(i,this.rho),B(st(s),1-this.rho)),c=B(ge(Jt(se(o,this.epsilon)),Jt(se(i,this.epsilon))),s),u=se(B(o,this.rho),B(st(c),1-this.rho));i.assign(l),o.assign(u);let h=se(B(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Te(this.accumulatedGrads.map(e=>e.variable)),Te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Kd.className="Adadelta";Fa(Kd);var Zd=class extends ca{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:L(()=>Xu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;L(()=>{let i=se(s,st(a));s.assign(i);let o=se(B(ge(a,Jt(se(i,$.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Te(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)}};Zd.className="Adagrad";Fa(Zd);var Yd=class extends ca{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],L(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);L(()=>{let n=ye(1,this.accBeta1),r=ye(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:L(()=>Ue(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:L(()=>Ue(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,h=se(B(c,this.beta1),B(l,1-this.beta1)),d=se(B(u,this.beta2),B(st(l),1-this.beta2)),p=ge(h,n),m=ge(d,r);c.assign(h),u.assign(d);let f=se(B(ge(p,se(Jt(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(B(this.accBeta1,this.beta1)),this.accBeta2.assign(B(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Te(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),L(()=>{this.accBeta1.assign(la(this.beta1,this.iterations_+1)),this.accBeta2.assign(la(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Yd.className="Adam";Fa(Yd);var Jd=class extends ca{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],L(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);L(()=>{let n=ye(1,this.accBeta1),r=ge(-this.learningRate,se(B(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:Ue(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:Ue(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,h=se(B(c,this.beta1),B(l,1-this.beta1)),d=B(u,this.beta2),p=Dt(l),m=Pr(d,p);c.assign(h),u.assign(m);let f=se(B(ge(r,n),ge(h,se(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(B(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Te(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)}};Jd.className="Adamax";Fa(Jd);var sc=class extends ca{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 r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=$.registeredVariables[t];L(()=>{let s=se(B(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Vt(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};sc.className="SGD";Fa(sc);var Qd=class extends sc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:L(()=>Ue(r).variable(i))}}let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&L(()=>{let i,o=se(B(this.m,a),s);this.useNesterov?i=se(B(this.c,se(s,B(o,this.m))),r):i=se(B(this.c,o),r),a.assign(o),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Te(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Qd.className="Momentum";Fa(Qd);var ep=class extends ca{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.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 r=$.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:L(()=>Ue(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:L(()=>Ue(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:L(()=>Ue(r).variable(a))});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;L(()=>{let l=se(B(i,this.decay),B(st(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=se(B(c,this.decay),B(s,1-this.decay)),h=ge(B(s,this.learningRate),Jt(ye(l,se(st(u),this.epsilon)))),d=se(B(o,this.momentum),h);i.assign(l),c.assign(u),o.assign(d);let p=ye(r,d);r.assign(p)}else{let c=se(B(i,this.decay),B(st(s),1-this.decay)),u=se(B(o,this.momentum),ge(B(s,this.learningRate),Jt(se(c,this.epsilon))));i.assign(c),o.assign(u);let h=ye(r,u);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Te(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Te(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Te(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};ep.className="RMSProp";Fa(ep);var xi=class{static sgd(e){return new sc(e)}static momentum(e,t,n=!1){return new Qd(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new ep(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new Yd(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Kd(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Jd(e,t,n,r,a)}static adagrad(e,t=.1){return new Zd(e,t)}},wi={sgd:xi.sgd,momentum:xi.momentum,adadelta:xi.adadelta,adagrad:xi.adagrad,rmsprop:xi.rmsprop,adamax:xi.adamax,adam:xi.adam},KR=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function tp(){return new Promise(e=>KR(()=>e()))}var E={};Me(E,{ERF_A1:()=>iM,ERF_A2:()=>oM,ERF_A3:()=>lM,ERF_A4:()=>uM,ERF_A5:()=>cM,ERF_P:()=>sM,PARALLELIZE_THRESHOLD:()=>Bm,SELU_SCALE:()=>xb,SELU_SCALEALPHA:()=>gb,applyActivation:()=>qd,assertAndGetBroadcastShape:()=>dt,assertAxesAreInnerMostDims:()=>TT,assertParamsConsistent:()=>ZR,assignToTypedArray:()=>gM,axesAreInnerMostDims:()=>xm,calculateShapes:()=>iw,checkEinsumDimSizes:()=>vM,combineLocations:()=>jw,complexWithEvenIndex:()=>mM,complexWithOddIndex:()=>AM,computeConv2DInfo:()=>Uu,computeConv3DInfo:()=>Iw,computeDefaultPad:()=>im,computeDilation2DInfo:()=>JS,computeOptimalWindowSize:()=>JR,computeOutAndReduceShapes:()=>Uw,computeOutShape:()=>YR,computePool2DInfo:()=>kw,computePool3DInfo:()=>QS,convertConv2DDataFormat:()=>vw,decodeEinsumEquation:()=>bM,eitherStridesOrDilationsAreOne:()=>Or,expandShapeToKeepDim:()=>yi,exponent:()=>wM,exponents:()=>xM,fromStringArrayToUint8:()=>TM,fromUint8ToStringArray:()=>NM,getAxesPermutation:()=>Hw,getBroadcastDims:()=>jN,getComplexWithIndex:()=>yM,getEinsumComputePath:()=>kM,getEinsumPermutation:()=>_M,getFusedBiasGradient:()=>Gd,getFusedDyActivation:()=>Hd,getImageCenter:()=>QR,getInnerMostAxes:()=>ET,getPermuted:()=>tM,getReductionAxes:()=>Ot,getReshaped:()=>eM,getReshapedPermuted:()=>nM,getSliceBeginCoords:()=>rM,getSliceSize:()=>aM,getUndoAxesPermutation:()=>wm,isIdentityPermutation:()=>IM,log:()=>dM,mergeRealAndImagArrays:()=>pM,prepareAndValidate:()=>sw,prepareSplitSize:()=>SM,segment_util:()=>wb,shouldFuse:()=>Xd,slice_util:()=>sn,splitRealAndImagArrays:()=>fM,tupleValuesAreOne:()=>$a,upcastType:()=>ir,validateInput:()=>Hf,validateUpdateShape:()=>Uf,warn:()=>hM});function ZR(e,t){let n=e[0].length;e.forEach((a,s)=>{M(a.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),M(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((a,s)=>{for(let i=0;i<n;i++)M(i===t||a[i]===r[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`)})}function YR(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var Bm=30;function JR(e){return e<=Bm?e:Sh(e,Math.floor(Math.sqrt(e)))}function QR(e,t,n){let r=n*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[r,a]}function eM(e,t,n,r=!0){let a=[];if(r)a=a.concat(t.slice(0)),a.push(e[0]/n),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function tM(e,t,n=!0){let r=[];if(n){r.push(t);for(let a=t+1;a<e;++a)a<=2*t?(r.push(a),r.push(a-(t+1))):r.push(a)}else{let a=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):a.push(i);r.push(...a),r.push(0),r.push(...s)}return r}function nM(e,t,n,r=!0){let a=[];r?a.push(e[0]/n):a.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?r?a.push(t[s-1]*e[s]):a.push(e[s]/t[s-1]):a.push(e[s]);return a}function rM(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function aM(e,t,n){let r=e.slice(0,1);for(let a=0;a<n;++a)r.push(e[a+1]-t[a][0]-t[a][1]);return r}var gb=1.7580993408473768,xb=1.0507009873554805,sM=.3275911,iM=.254829592,oM=-.284496736,lM=1.421413741,uM=-1.453152027,cM=1.061405429;function hM(...e){J().getBool("IS_TEST")||console.warn(...e)}function dM(...e){J().getBool("IS_TEST")||console.log(...e)}function pM(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function fM(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function mM(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=0;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function AM(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=2;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function yM(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function gM(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function xM(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);n[a]=Math.cos(s),r[a]=Math.sin(s)}return{real:n,imag:r}}function wM(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),a=Math.cos(r),s=Math.sin(r);return{real:a,imag:s}}var Vm="->",EM=/->/g,bb=",",_b="...";function bM(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(EM,"").length)/Vm.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${Vm}").`);let[r,a]=e.split(Vm);M(r.indexOf(_b)===-1,()=>`The ellipsis notation ("${_b}") is not supported yet.`);let s=r.split(bb),i=s.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let o=[];for(let d=0;d<a.length;++d){let p=a[d];if(!s.some(m=>m.indexOf(p)!==-1))throw new Error(`Output subscripts contain the label ${p} not present in the input subscripts.`);o.indexOf(p)===-1&&o.push(p)}for(let d=0;d<r.length;++d){let p=r[d];o.indexOf(p)===-1&&p!==bb&&o.push(p)}let l=new Array(s.length);for(let d=0;d<i;++d){if(new Set(s[d].split("")).size!==s[d].length)throw new Error(`Found duplicate axes in input component ${s[d]}. Support for duplicate axes in input is not implemented yet.`);l[d]=[];for(let p=0;p<s[d].length;++p)l[d].push(o.indexOf(s[d][p]))}let c=o.length,u=a.length,h=[];for(let d=u;d<c;++d)h.push(d);return{allDims:o,summedDims:h,idDims:l}}function _M(e,t){let n=new Array(e);n.fill(-1);for(let a=0;a<t.length;++a)n[t[a]]=a;let r=[];for(let a=0;a<e;++a)n[a]===-1&&r.push(a);return n=n.filter(a=>a!==-1),{permutationIndices:n,expandDims:r}}function vM(e,t,n){let r=new Array(e);for(let a=0;a<n.length;++a){let s=n[a].shape;for(let i=0;i<t[a].length;++i)r[t[a][i]]===void 0?r[t[a][i]]=s[i]:M(r[t[a][i]]===s[i],()=>`Expected dimension ${r[t[a][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function kM(e,t){let n=e,r=[],a=0;e.length===0&&n.push(-1),a=e.length+1;for(let i=0;i<a;++i)r.push([]);let s=[];for(let i=0;i<n.length;++i){let o=n[i],l=CM(t,o);for(let c of l)s.indexOf(c)===-1&&(r[i].push(c),s.push(c))}return{path:n,steps:r}}function IM(e){return e.every((t,n)=>t===n)}function CM(e,t){let n=[];for(let r=0;r<e.length;++r)(e[r].length===0||e[r].indexOf(t)!==-1||t===-1)&&n.push(r);return n}function SM(e,t,n=0){let r=[];if(typeof t=="number")M(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);M(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}M(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var wb={};Me(wb,{collectGatherOpShapeInfo:()=>FM,computeOutShape:()=>MM,segOpComputeOptimalWindowSize:()=>RM});function RM(e,t){let n=!1,r;for(e<=Bm?(r=e,n=!0):r=Sh(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=Sh(e,r+1);return r}function MM(e,t,n){let r=[],a=e.length;for(let s=0;s<a;s++)s!==t?r.push(e[s]):r.push(n);return r}function FM(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${a}, ${a}], but got ${r}`);if(r<0&&(r+=a),r>s)throw new Error(`batchDims (${r}) must be less than rank(x) (
    ${s}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let h=0;h<r;++h)if(e.shape[h]!==t.shape[h])throw new Error(`x.shape[${h}]: ${e.shape[h]} should be equal to indices.shape[${h}]: ${t.shape[h]}.`);let i=e.shape[n],o=[],l=1,c=1,u=1;for(let h=0;h<r;++h)o.push(e.shape[h]),l*=e.shape[h];for(let h=r;h<n;h++)o.push(e.shape[h]),c*=e.shape[h];for(let h=r;h<a;h++)o.push(t.shape[h]);for(let h=n+1;h<s;h++)o.push(e.shape[h]),u*=e.shape[h];return{batchSize:l,sliceSize:u,outerSize:c,dimSize:i,outputShape:o}}function NM(e){try{return e.map(t=>ld(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function TM(e){return e.map(t=>Fu(t))}var Wr={};Me(Wr,{nonMaxSuppressionV3Impl:()=>ub,nonMaxSuppressionV4Impl:()=>cb,nonMaxSuppressionV5Impl:()=>hb,whereImpl:()=>Qw});function be(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&_.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var $M=Wr.whereImpl,np=class extends du{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new kh(this,aa())}nextDataId(){return np.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&E.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&_.isString(n[0])){let a=n.map(s=>_.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return E.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>_.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return aa().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=_.now();return e(),{kernelMs:_.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){be([e],"where");let t=this.readSync(e.dataId);return $M(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};np.nextDataId=0;var jm={};Me(jm,{addImpl:()=>kb,bincountImpl:()=>Um,bincountReduceImpl:()=>Ib,ceilImpl:()=>Sb,concatImpl:()=>Hm,expImpl:()=>Nb,expm1Impl:()=>Tb,floorImpl:()=>Eb,gatherV2Impl:()=>Cb,greaterImpl:()=>Rb,lessImpl:()=>Mb,linSpaceImpl:()=>Fb,logImpl:()=>$b,maxImpl:()=>Db,maximumImpl:()=>Ob,minimumImpl:()=>zb,multiplyImpl:()=>Gm,negImpl:()=>Pb,notEqualImpl:()=>Lb,prodImpl:()=>Wb,rangeImpl:()=>Xm,rsqrtImpl:()=>Bb,simpleAbsImpl:()=>vb,sliceImpl:()=>rp,sparseReshapeImpl:()=>Vb,squaredDifferenceImpl:()=>jb,stridedSliceImpl:()=>Ub,subImpl:()=>Hb,tileImpl:()=>Gb,topKImpl:()=>qb,transposeImpl:()=>qm,uniqueImpl:()=>Xb});function vb(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var DM=e=>{let{x:t}=e.inputs,n=e.backend;be(t,"abs");let r=new Float32Array(_.sizeFromShape(t.shape)),a=n.data.get(t.dataId).values;return r=vb(a),n.makeOutput(r,t.shape,"float32")},OM={kernelName:no,backendName:"cpu",kernelFunc:DM};function Ct(e){return(t,n,r,a,s)=>{let i=E.assertAndGetBroadcastShape(t,n),o=i.length,l=_.computeStrides(i),c=_.sizeFromShape(i),u=_.getTypedArrayFromDType(s,c),h=t.length,d=n.length,p=_.computeStrides(t),m=_.computeStrides(n),f=E.getBroadcastDims(t,i),A=E.getBroadcastDims(n,i);if(f.length+A.length===0)for(let y=0;y<u.length;++y)u[y]=e(r[y%r.length],a[y%a.length]);else for(let y=0;y<u.length;++y){let g=_.indexToLoc(y,o,l),x=g.slice(-h);f.forEach(k=>x[k]=0);let v=_.locToIndex(x,h,p),w=g.slice(-d);A.forEach(k=>w[k]=0);let b=_.locToIndex(w,d,m);u[y]=e(r[v],a[b])}return[u,i]}}function zn(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,o=n.makeTensorInfo(r.shape,"complex64"),l=n.data.get(o.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",s),imag:n.makeTensorInfo(a.shape,"float32",i)},o}var zM={kernelName:Rh,backendName:"cpu",kernelFunc:zn};function ap(e,t,n="float32"){if(n==="complex64"){let a=ap(e,t,"float32"),s=ap(e,t,"float32");return zn({inputs:{real:a,imag:s},backend:e})}let r=_.makeZerosTypedArray(_.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function Br(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var PM={kernelName:Is,backendName:"cpu",kernelFunc:Br};function bi(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.real,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var LM={kernelName:Jh,backendName:"cpu",kernelFunc:bi};function Wa(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Br({inputs:{x:a},backend:n});let i=ap(n,a.shape,a.dtype),o=Wa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=zn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=bi({inputs:{input:a},backend:n}),o=Wa({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=Br({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(a.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(a.shape,"int32",o)}if(s==="bool"){let i=n.data.get(a.dataId).values,o=_.toTypedArray([0],a.dtype),[l,c]=Ct((u,h)=>u!==h?1:0)(a.shape,[],i,o,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var WM={kernelName:ds,backendName:"cpu",kernelFunc:Wa};function jt(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;be([i,o],e);let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,c,u,h);return l.makeTensorInfo(p,h,d)}:({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let c=Wa({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),u=l.data.get(c.dataId),h=u.complexTensorInfos.real,d=u.complexTensorInfos.imag,p=l.data.get(h.dataId).values,m=l.data.get(d.dataId).values,f=Wa({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),A=l.data.get(f.dataId),y=A.complexTensorInfos.real,g=A.complexTensorInfos.imag,x=l.data.get(y.dataId).values,v=l.data.get(g.dataId).values,[w,b,k]=n(i.shape,o.shape,p,m,x,v),N=l.makeTensorInfo(k,"float32",w),C=l.makeTensorInfo(k,"float32",b),F=zn({inputs:{real:N,imag:C},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(f),l.disposeIntermediateTensorInfo(N),l.disposeIntermediateTensorInfo(C),F}else{let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,c,u,h);return l.makeTensorInfo(p,h,d)}}}function Km(e){return(t,n,r,a,s,i)=>{let o=E.assertAndGetBroadcastShape(t,n),l=_.sizeFromShape(o),c=o.length,u=_.computeStrides(o),h=_.getTypedArrayFromDType("float32",l),d=_.getTypedArrayFromDType("float32",l),p=E.getBroadcastDims(t,o),m=E.getBroadcastDims(n,o),f=E.mergeRealAndImagArrays(r,a),A=E.mergeRealAndImagArrays(s,i),y=t.length,g=_.computeStrides(t),x=n.length,v=_.computeStrides(n);if(p.length+m.length===0)for(let w=0;w<h.length;w++){let b=w%f.length,k=w%A.length,N=e(f[b*2],f[b*2+1],A[k*2],A[k*2+1]);h[w]=N.real,d[w]=N.imag}else for(let w=0;w<h.length;w++){let b=_.indexToLoc(w,c,u),k=b.slice(-y);p.forEach(z=>k[z]=0);let N=_.locToIndex(k,y,g),C=b.slice(-x);m.forEach(z=>C[z]=0);let F=_.locToIndex(C,x,v),O=e(f[N*2],f[N*2+1],A[F*2],A[F*2+1]);h[w]=O.real,d[w]=O.imag}return[h,d,o]}}var kb=Ct((e,t)=>e+t),BM=Km((e,t,n,r)=>({real:e+n,imag:t+r})),ic=jt(Sa,kb,BM),VM={kernelName:Sa,backendName:"cpu",kernelFunc:ic};function Um(e,t,n,r,a){let s=_.sizeFromShape(r),i=_.makeZerosTypedArray(a,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>=a||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function Ib(e,t,n,r=!1){let a=e.shape[0],s=e.shape[1],i=We([a,n],t.dtype);for(let o=0;o<a;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||(r?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 kl(e){return(t,n,r)=>{let a=_.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function nt(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(be(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=_.sizeFromShape(i.shape),u=n||i.dtype,h=_.getArrayFromDType(u,c);for(let d=0;d<c;++d)h[d]=t(l[d],a);return o.makeTensorInfo(i.shape,u,h)}}function Il(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(be(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,a);return o.makeTensorInfo(i.shape,c,u)}}var Sb=kl(e=>Math.ceil(e)),jM=Il(ps,Sb),UM={kernelName:ps,backendName:"cpu",kernelFunc:jM};function Hm(e,t,n,r){let a=_.getArrayFromDType(n,_.sizeFromShape(t));if(r&&n!=="string"){let s=0;e.forEach(i=>{let o=_.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?E.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let c=0;c<i.shape[0];++c){let u=c*t[1]+s;for(let h=0;h<i.shape[1];++h)a[u+h]=o[l++]}s+=i.shape[1]})}return a}var Nb=kl(e=>Math.exp(e)),Kb=Il(ws,Nb),HM={kernelName:ws,backendName:"cpu",kernelFunc:Kb},Tb=kl(e=>Math.expm1(e)),GM=Il(bo,Tb),qM={kernelName:bo,backendName:"cpu",kernelFunc:GM},Eb=kl(e=>Math.floor(e)),XM=Il(bs,Eb),KM={kernelName:bs,backendName:"cpu",kernelFunc:XM};function Cb(e,t,n){let r=We(n,e.dtype);for(let a=0;a<r.size;++a){let s=r.indexToLoc(a).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let c=e.locToIndex(s);r.values[a]=e.values[c]}return r}var Rb=Ct((e,t)=>e>t?1:0),ZM=jt(Io,Rb,null,"bool"),YM={kernelName:Io,backendName:"cpu",kernelFunc:ZM},Mb=Ct((e,t)=>e<t?1:0),JM=jt(Eo,Mb,null,"bool"),QM={kernelName:Eo,backendName:"cpu",kernelFunc:JM};function Fb(e,t,n){let r=(t-e)/(n-1),a=_.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}var $b=kl(e=>Math.log(e)),eF=Il(Ns,$b),tF={kernelName:Ns,backendName:"cpu",kernelFunc:eF};function Db(e,t,n,r){let a=_.getTypedArrayFromDType(r,_.sizeFromShape(n));for(let s=0;s<a.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)}a[s]=o}return a}var Ob=Ct((e,t)=>Math.max(e,t)),nF=jt(Es,Ob),rF={kernelName:Es,backendName:"cpu",kernelFunc:nF},zb=Ct((e,t)=>Math.min(e,t)),aF=jt(Fs,zb),sF={kernelName:Fs,backendName:"cpu",kernelFunc:aF},Gm=Ct((e,t)=>e*t),iF=Km((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),sp=jt(Ds,Gm,iF),oF={kernelName:Ds,backendName:"cpu",kernelFunc:sp};function Pb(e,t,n){let r=_.createScalarValue(-1,n);return Gm([],t,r,e,n)}function lF(e){let{inputs:t,backend:n}=e,{x:r}=t;be(r,"neg");let a=n.data.get(r.dataId).values,[s,i]=Pb(a,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,s)}var uF={kernelName:$o,backendName:"cpu",kernelFunc:lF},Lb=Ct((e,t)=>e!==t?1:0),cF=jt(Do,Lb,null,"bool"),hF={kernelName:Do,backendName:"cpu",kernelFunc:cF};function qm(e,t,n,r,a){let s=t.length,i=_.sizeFromShape(t),o=_.computeStrides(t),l=_.computeStrides(a),c=_.getTypedArrayFromDType(n,_.sizeFromShape(a));for(let u=0;u<i;++u){let h=_.indexToLoc(u,s,o),d=new Array(h.length);for(let m=0;m<d.length;m++)d[m]=h[r[m]];let p=_.locToIndex(d,s,l);c[p]=e[u]}return c}function Yn(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{perm:s}=n;be(a,"transpose");let i=a.shape.length,o=new Array(i);for(let u=0;u<o.length;u++)o[u]=a.shape[s[u]];let l=r.data.get(a.dataId).values,c=qm(l,a.shape,a.dtype,s,o);return{dataId:r.write(c,o,a.dtype),shape:o,dtype:a.dtype}}var dF={kernelName:ti,backendName:"cpu",kernelFunc:Yn};function Wb(e,t,n,r){let[a,s]=E.computeOutAndReduceShapes(e,r),i=ir(t,"int32"),o=_.makeZerosTypedArray(_.sizeFromShape(a),i),l=_.sizeFromShape(s);for(let c=0;c<o.length;++c){let u=c*l,h=1;for(let d=0;d<l;++d)h*=n[u+d];o[c]=h}return{outVals:o,outShape:a,outDtype:i}}function pF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;be(a,"prod");let o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=E.getAxesPermutation(l,o),u=l,h=a,d=[];c!=null&&(h=Yn({inputs:{x:a},backend:n,attrs:{perm:c}}),d.push(h),u=E.getInnerMostAxes(u.length,o));let p=n.data.get(h.dataId).values,{outVals:m,outShape:f,outDtype:A}=Wb(h.shape,h.dtype,p,u),y=f;return i&&(y=E.expandShapeToKeepDim(f,l)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(y,A,m)}var fF={kernelName:Bo,backendName:"cpu",kernelFunc:pF};function Xm(e,t,n,r){let a=e===t,s=e<t&&n<0,i=t<e&&n>1;if(a||s||i)return _.makeZerosTypedArray(0,r);let o=Math.abs(Math.ceil((t-e)/n)),l=_.makeZerosTypedArray(o,r);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 Bb=kl(e=>1/Math.sqrt(e)),mF=Il(Hs,Bb),AF={kernelName:Hs,backendName:"cpu",kernelFunc:mF};function rp(e,t,n,r,a){let s=sn.isSliceContinous(r,t,n),i=_.sizeFromShape(n),o=_.computeStrides(r);if(s){let h=sn.computeFlatOffset(t,o);return a==="string"?e.slice(h,h+i):e.subarray(h,h+i)}let l=a==="string"?E.fromUint8ToStringArray(e):e,c=We(r,a,l),u=We(n,a);for(let h=0;h<u.size;++h){let d=u.indexToLoc(h),p=d.map((m,f)=>m+t[f]);u.set(c.get(...p),...d)}return a==="string"?E.fromStringArrayToUint8(u.values):u.values}function _i(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r;be(a,"slice");let[o,l]=sn.parseSliceParams(a,s,i);sn.assertParamsValid(a,o,l);let c=n.data.get(a.dataId).values,u=rp(c,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,u)}var yF={kernelName:qo,backendName:"cpu",kernelFunc:_i};function Vb(e,t,n,r,a){let s=_.sizeFromShape(r),i=t[0],o=a.length,l=[],c=1,u=-1;for(let A=0;A<o;++A){let y=a[A];if(y===-1){if(u!==-1)throw new Error(`only one output dimension may be -1, not both ${u} and ${A}`);u=A,l.push(1)}else{if(y<0)throw new Error(`size ${A} must be non-negative, not ${y}`);c*=y,l.push(y)}}if(u!==-1){if(c<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let A=Math.trunc(s/c);if(c*A!==s)throw new Error(`Input to reshape is a SparseTensor with ${s}
          dense values, but the requested shape requires a multiple of ${c}. inputShape=${r} outputShape= ${l}`);l[u]=A}let h=_.sizeFromShape(l);if(h!==s)throw new Error(`Input to reshape is a tensor with ${s} dense values, but the requested shape has ${h}. inputShape=${r} outputShape=${l}`);let d=r.length,p=[];if(d>0){p[d-1]=1;for(let A=d-2;A>=0;--A)p[A]=p[A+1]*r[A+1]}let m=[];if(o>0){m[o-1]=1;for(let A=o-2;A>=0;--A)m[A]=m[A+1]*l[A+1]}let f=_.getArrayFromDType(n,i*o);for(let A=0;A<i;++A){let y=0;for(let g=0;g<d;++g)y+=e[A*d+g]*p[g];for(let g=0;g<o;++g)f[A*o+g]=Math.trunc(y/m[g]),y%=m[g]}return[f,[i,o],l]}var jb=Ct((e,t)=>{let n=e-t;return n*n}),gF=jt(Ys,jb),xF={kernelName:Ys,backendName:"cpu",kernelFunc:gF};function Ub(e,t,n,r){let a=We(e,t.dtype);for(let s=0;s<a.size;s++){let i=a.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*n[l]+r[l];a.set(t.get(...o),...i)}return a}var Hb=Ct((e,t)=>e-t),wF=Km((e,t,n,r)=>({real:e-n,imag:t-r})),Zm=jt(Js,Hb,wF),bF={kernelName:Js,backendName:"cpu",kernelFunc:Zm};function Gb(e,t){let n=new Array(e.rank);for(let a=0;a<n.length;a++)n[a]=e.shape[a]*t[a];let r=We(n,e.dtype);for(let a=0;a<r.values.length;++a){let s=r.indexToLoc(a),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);r.values[a]=e.values[o]}return r}function qb(e,t,n,r,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=_.getTypedArrayFromDType(n,i*r),c=_.getTypedArrayFromDType("int32",i*r);for(let h=0;h<i;h++){let d=h*o,p=e.subarray(d,d+o),m=[];for(let g=0;g<p.length;g++)m.push({value:p[g],index:g});m.sort((g,x)=>x.value-g.value);let f=h*r,A=l.subarray(f,f+r),y=c.subarray(f,f+r);for(let g=0;g<r;g++)A[g]=m[g].value,y[g]=m[g].index}let u=t.slice();return u[u.length-1]=r,[We(u,n,l),We(u,"int32",c)]}function Xb(e,t,n,r){let a=_.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let m=0;m<a;m++)s[0]*=n[m];s[1]=n[a];for(let m=a+1;m<n.length;m++)s[2]*=n[m];let i={},o=new Int32Array(n[a]),l=new $t(s,r,e),c=[],u=s[0]===1&&s[2]===1;for(let m=0;m<n[a];m++){let f;if(u)f=e[m].toString();else{let A=[];for(let y=0;y<s[0];y++)for(let g=0;g<s[2];g++)A.push(l.get(y,m,g));f=A.join(",")}if(i[f]!==void 0)o[m]=i[f];else{let A=Object.keys(i).length;i[f]=A,o[m]=A,c.push(m)}}let h=s.slice();h[1]=Object.keys(i).length;let d=new $t(h,r);c.forEach((m,f)=>{for(let A=0;A<s[0];A++)for(let y=0;y<s[2];y++)d.set(l.get(A,m,y),A,f,y)});let p=n.slice();return p[a]=h[1],{outputValues:d.values,outputShape:p,indices:o}}var Zb="3.5.0";hl("cpu",()=>new np,1);var Yb=nt(yo,e=>e>=0?e:Math.exp(e)-1),_F={kernelName:yo,backendName:"cpu",kernelFunc:Yb};function Jb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;be([a],"leakyRelu");let i=_.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=_.getTypedArrayFromDType("float32",i);for(let c=0;c<o.length;c++)l[c]=o[c]<0?s*o[c]:o[c];return n.makeTensorInfo(a.shape,"float32",l)}var vF={kernelName:Ss,backendName:"cpu",kernelFunc:Jb},kF=Ct((e,t)=>e<0?t*e:e);function Qb(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t;be([r,a],"prelu");let s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[o,l]=kF(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var IF={kernelName:Ls,backendName:"cpu",kernelFunc:Qb},e_=nt(Ws,e=>Math.max(0,e)),SF={kernelName:Ws,backendName:"cpu",kernelFunc:e_},t_=nt(Vs,e=>Math.min(Math.max(0,e),6)),NF={kernelName:Vs,backendName:"cpu",kernelFunc:t_},n_=nt(qs,e=>1/(1+Math.exp(-e))),TF={kernelName:qs,backendName:"cpu",kernelFunc:n_};function Ym(e,t,n,r,a){if(n==="linear")return Br({inputs:{x:t},backend:e});if(n==="relu")return e_({inputs:{x:t},backend:e});if(n==="elu")return Yb({inputs:{x:t},backend:e});if(n==="relu6")return t_({inputs:{x:t},backend:e});if(n==="prelu")return Qb({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return Jb({inputs:{x:t},backend:e,attrs:{alpha:a}});if(n==="sigmoid")return n_({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function pt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=_.sizeFromShape(a.shape),o=_.inferFromImplicitShape(s,i),l=_.sizeFromShape(o);_.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(a.dataId);let c=n.data.get(a.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,h=c.complexTensorInfos.imag;u.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var EF={kernelName:jo,backendName:"cpu",kernelFunc:pt};function r_(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;be([a,s],"matMul");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[c-2]:s.shape[c-1],m=a.shape.slice(0,-2),f=s.shape.slice(0,-2),A=_.sizeFromShape(m),y=_.sizeFromShape(f),g=A===y||A===1||y===1;_.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);_.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[A,u,d]:[A,d,u],w=o?[y,p,h]:[y,h,p],b=pt({inputs:{x:a},backend:n,attrs:{shape:v}}),k=pt({inputs:{x:s},backend:n,attrs:{shape:w}}),N=i?b.shape[1]:b.shape[2],C=i?b.shape[2]:b.shape[1],F=o?k.shape[1]:k.shape[2],O=Math.max(A,y),z=n.data.get(b.dataId).values,V=n.data.get(k.dataId).values,j=_.computeStrides(b.shape),U=_.computeStrides(k.shape),[X,G,ee]=i?[j[0],1,j[1]]:[j[0],j[1],1],[Y,ae,te]=o?[1,U[1],U[0]]:[U[1],1,U[0]],ie=C*F,Q=We([O,C,F],b.dtype),ce=Q.values,oe=n.blockSize;for(let me=0;me<O;me++)for(let de=0;de<C;de+=oe)for(let ve=0;ve<F;ve+=oe)for(let Ie=0;Ie<N;Ie+=oe){let Fe=Math.min(de+oe,C),Oe=Math.min(ve+oe,F),$e=Math.min(Ie+oe,N);for(let Qe=de;Qe<Fe;Qe++)for(let et=ve;et<Oe;et++){let at=0;for(let Xe=Ie;Xe<$e;Xe++){let ht=Math.min(me,A-1)*X,Ve=Math.min(me,y-1)*te,gn=z[ht+Qe*G+Xe*ee],xt=V[Xe*Y+et*ae+Ve];at+=gn*xt}ce[me*ie+(Qe*F+et)]+=at}}return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(k),n.makeTensorInfo(x,Q.dtype,Q.values)}var CF={kernelName:hs,backendName:"cpu",kernelFunc:r_};function RF(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,d,p,m,f=[];d=r_({inputs:{a,b:s},attrs:{transposeA:l,transposeB:c},backend:n}),i&&(p=ic({inputs:{a:d,b:i},backend:n}),f.push(d),d=p),u&&(m=Ym(n,d,u,o,h),f.push(d),d=m);for(let A of f)n.disposeIntermediateTensorInfo(A);return d}var MF={kernelName:ni,backendName:"cpu",kernelFunc:RF},FF=nt(ro,e=>Math.acos(e)),$F={kernelName:ro,backendName:"cpu",kernelFunc:FF},DF=nt(ao,e=>Math.acosh(e)),OF={kernelName:ao,backendName:"cpu",kernelFunc:DF};function zF(e){let{inputs:t,backend:n}=e,r=t;be(t,"addN");let a=r.map(o=>n.data.get(o.dataId).values),s=We(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var PF={kernelName:ls,backendName:"cpu",kernelFunc:zF};function LF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;be(a,"all");let o=_.parseAxisParam(s,a.shape),l=o,c=E.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=Yn({inputs:{x:a},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,a.shape.length)),E.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=E.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),m=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let g=y*p,x=f[g];for(let v=0;v<p;++v){let w=f[g+v];x=x&&w}m[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,m);if(i){let y=E.expandShapeToKeepDim(h,o),g=pt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var WF={kernelName:so,backendName:"cpu",kernelFunc:LF};function BF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;be(a,"any");let o=_.parseAxisParam(s,a.shape),l=o,c=E.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=Yn({inputs:{x:a},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,a.shape.length)),E.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=E.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),m=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let g=y*p,x=f[g];for(let v=0;v<p;++v){let w=f[g+v];x=x||w}m[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,m);if(i){let y=E.expandShapeToKeepDim(h,o),g=pt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var VF={kernelName:io,backendName:"cpu",kernelFunc:BF};function jF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;be(a,"argMax");let i=_.parseAxisParam(s,a.shape),o=E.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=Yn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],E.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=E.computeOutAndReduceShapes(l.shape,i),d=_.sizeFromShape(u),p=_.makeZerosTypedArray(d,"int32"),m=_.sizeFromShape(h),f=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*m,g=f[y],x=0;for(let v=0;v<m;++v){let w=f[y+v];w>g&&(g=w,x=v)}p[A]=x}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var UF={kernelName:us,backendName:"cpu",kernelFunc:jF};function HF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;be(a,"argMin");let i=_.parseAxisParam(s,a.shape),o=E.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=Yn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],E.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=E.computeOutAndReduceShapes(l.shape,i),d=_.sizeFromShape(u),p=_.makeZerosTypedArray(d,"int32"),m=_.sizeFromShape(h),f=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*m,g=f[y],x=0;for(let v=0;v<m;++v){let w=f[y+v];w<g&&(g=w,x=v)}p[A]=x}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var GF={kernelName:mu,backendName:"cpu",kernelFunc:HF},qF=nt(oo,e=>Math.asin(e)),XF={kernelName:oo,backendName:"cpu",kernelFunc:qF},KF=nt(lo,e=>Math.asinh(e)),ZF={kernelName:lo,backendName:"cpu",kernelFunc:KF},YF=nt(uo,e=>Math.atan(e)),JF={kernelName:uo,backendName:"cpu",kernelFunc:YF},QF=Ct((e,t)=>Math.atan2(e,t)),e$=jt(ho,QF),t$={kernelName:ho,backendName:"cpu",kernelFunc:e$},n$=nt(co,e=>Math.atanh(e)),r$={kernelName:co,backendName:"cpu",kernelFunc:n$};function Jm(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,c=a.dilationWidth,u=a.effectiveFilterHeight,h=a.effectiveFilterWidth,d=a.padInfo.top,p=a.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=We(a.outShape,n),A=f.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],x=a.outShape[3];for(let v=0;v<a.batchSize;++v){let w=v*y,b=v*r[0];for(let k=0;k<a.inChannels;++k)for(let N=0;N<a.outHeight;++N){let C=N*i-d,F=Math.max(0,C),O=Math.min(a.inHeight,u+C),z=w+N*g;for(let V=0;V<a.outWidth;++V){let j=V*o-p,U=Math.max(0,j),X=Math.min(a.inWidth,h+j),G=m,ee=0,Y=0;for(let te=F;te<O;te+=l){let ie=b+te*r[1];for(let Q=U;Q<X;Q+=c){let ce=ie+Q*r[2],oe=e[ce+k];s==="max"&&oe>G?G=oe:s==="avg"&&(ee+=oe,Y++)}if(isNaN(G))break}let ae=z+V*x+k;A[ae]=s==="avg"?ee/Y:G}}}return f}function a_(e,t,n,r,a=!1,s=!1){let i=We(r.outShape,"int32"),o=r.strideHeight,l=r.strideWidth,c=r.dilationHeight,u=r.dilationWidth,h=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,m=r.padInfo.left,f=We(t,n,e);for(let A=0;A<r.batchSize;++A)for(let y=0;y<r.inChannels;++y)for(let g=0;g<r.outHeight;++g){let x=g*o-p,v=x;for(;v<0;)v+=c;let w=Math.min(r.inHeight,h+x);for(let b=0;b<r.outWidth;++b){let k=b*l-m,N=k;for(;N<0;)N+=u;let C=Math.min(r.inWidth,d+k),F=Number.NEGATIVE_INFINITY,O=-1;for(let z=v;z<w;z+=c){let V=z-x;for(let j=N;j<C;j+=u){let U=j-k,X=f.get(A,z,j,y);X>F&&(F=X,a?O=s?((A*r.inHeight+z)*r.inWidth+j)*r.inChannels+y:(z*r.inWidth+j)*r.inChannels+y:O=V*d+U)}}i.set(O,A,g,b,y)}}return i}function s_(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,m=a.effectiveFilterWidth,f=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(a.outShape,n),v=x.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],b=a.outShape[2]*a.outShape[3]*a.outShape[4],k=a.outShape[3]*a.outShape[4],N=a.outShape[4];for(let C=0;C<a.batchSize;++C){let F=C*w,O=C*r[0];for(let z=0;z<a.inChannels;++z)for(let V=0;V<a.outDepth;++V){let j=V*i-f,U=j;for(;U<0;)U+=c;let X=Math.min(a.inDepth,d+j),G=F+V*b;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-A,ae=Y;for(;ae<0;)ae+=u;let te=Math.min(a.inHeight,p+Y),ie=G+ee*k;for(let Q=0;Q<a.outWidth;++Q){let ce=Q*l-y,oe=ce;for(;oe<0;)oe+=h;let me=Math.min(a.inWidth,m+ce),de=ie+Q*N,ve=g,Ie=0,Fe=0;for(let $e=U;$e<X;$e+=c){let Qe=O+$e*r[1];for(let et=ae;et<te;et+=u){let at=Qe+et*r[2];for(let Xe=oe;Xe<me;Xe+=h){let ht=at+Xe*r[3],Ve=e[ht+z];if(s==="max"&&Ve>ve?ve=Ve:s==="avg"&&(Ie+=Ve,Fe++),isNaN(ve))break}if(isNaN(ve))break}if(isNaN(ve))break}let Oe=de+z;v[Oe]=s==="avg"?Ie/Fe:ve}}}}return x}function a$(e,t){let n=We(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-d,x=g;for(;x<0;)x+=i;let v=Math.min(t.inDepth,c+g);for(let w=0;w<t.outHeight;++w){let b=w*a-p,k=b;for(;k<0;)k+=o;let N=Math.min(t.inHeight,u+b);for(let C=0;C<t.outWidth;++C){let F=C*s-m,O=F;for(;O<0;)O+=l;let z=Math.min(t.inWidth,h+F),V=Number.NEGATIVE_INFINITY,j=-1;for(let U=x;U<v;U+=i){let X=U-g;for(let G=k;G<N;G+=o){let ee=G-b;for(let Y=O;Y<z;Y+=l){let ae=Y-F,te=e.get(f,U,G,Y,A);te>=V&&(V=te,j=X*u*h+ee*u+ae)}}}n.set(j,f,y,w,C,A)}}}return n}function s$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;be(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))h=Br({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=_.computeStrides(a.shape),m=Jm(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,m.values)}return h}var i$={kernelName:cs,backendName:"cpu",kernelFunc:s$};function o$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;be(a,"avgPool3d");let u=E.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=s_(h,a.shape,a.dtype,_.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var l$={kernelName:Au,backendName:"cpu",kernelFunc:o$};function u$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;be([a,s],"avgPool3DGrad");let u=E.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,m=u.filterDepth,f=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,x=u.dilationWidth,v=u.effectiveFilterDepth,w=u.effectiveFilterHeight,b=u.effectiveFilterWidth,k=v-1-u.padInfo.front,N=b-1-u.padInfo.left,C=w-1-u.padInfo.top,F=We(s.shape,"float32"),O=1/(m*f*A),z=n.bufferSync(a);for(let V=0;V<u.batchSize;++V)for(let j=0;j<u.inChannels;++j)for(let U=0;U<u.inDepth;++U)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=U-k,Y=X-C,ae=G-N,te=0;for(let ie=0;ie<v;ie+=y){let Q=(ee+ie)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let ce=0;ce<w;ce+=g){let oe=(Y+ce)/d;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let me=0;me<b;me+=x){let de=(ae+me)/p;de<0||de>=u.outWidth||Math.floor(de)!==de||(te+=z.get(V,Q,oe,de,j))}}}F.set(te*O,V,U,X,G,j)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var c$={kernelName:Eh,backendName:"cpu",kernelFunc:u$};function h$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;be([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=E.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,m=u.filterWidth,f=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,x=g-1-u.padInfo.left,v=y-1-u.padInfo.top,w=We(i.shape,"float32"),b=1/(p*m),k=n.data.get(a.dataId).values,N=We(a.shape,"float32",k);for(let C=0;C<u.batchSize;++C)for(let F=0;F<u.inChannels;++F)for(let O=0;O<u.inHeight;++O)for(let z=0;z<u.inWidth;++z){let V=O-v,j=z-x,U=0;for(let X=0;X<y;X+=f){let G=(V+X)/h;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=A){let Y=(j+ee)/d;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(U+=N.get(C,G,Y,F))}}w.set(U*b,C,O,z,F)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var d$={kernelName:Th,backendName:"cpu",kernelFunc:h$};function p$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;_.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),be([a,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=r;c==null&&(c=.001);let u=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=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),A=m.length,y=p.length,g=d.length,x=h.length,v=0,w=0,b=0,k=0;for(let N=0;N<u.length;++N)f[N]=m[v++]+(u[N]-h[w++])*p[b++]/Math.sqrt(d[k++]+c),v>=A&&(v=0),w>=x&&(w=0),b>=y&&(b=0),k>=g&&(k=0);return n.makeTensorInfo(a.shape,a.dtype,f)}var f$={kernelName:vs,backendName:"cpu",kernelFunc:p$};function m$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;be([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=E.getReshaped(a.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(a.shape,s,o),h=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),p=pt({inputs:{x:a},backend:n,attrs:{shape:l}}),m=Yn({inputs:{x:p},backend:n,attrs:{perm:c}}),f=pt({inputs:{x:m},backend:n,attrs:{shape:u}}),A=_i({inputs:{x:f},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var A$={kernelName:yu,backendName:"cpu",kernelFunc:m$};function y$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=Um(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var g$={kernelName:Ch,backendName:"cpu",kernelFunc:y$},x$=nt(Na,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),w$={kernelName:Na,backendName:"cpu",kernelFunc:x$},b$=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(_.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.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],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},_$={kernelName:gu,backendName:"cpu",kernelFunc:b$};function Sl(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var v$={kernelName:Hh,backendName:"cpu",kernelFunc:Sl};function Nl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=E.computeOutShape(t.map(f=>f.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>_.sizeFromShape(f.shape)>0);if(o.length===1)return Br({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(E.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>bi({inputs:{input:v},backend:n})),A=o.map(v=>Sl({inputs:{input:v},backend:n})),y=Nl({inputs:f,backend:n,attrs:{axis:s}}),g=Nl({inputs:A,backend:n,attrs:{axis:s}}),x=zn({inputs:{real:y,imag:g},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),A.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),x}let c=o.map(f=>{let A=_.sizeFromShape(f.shape.slice(s));return pt({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),u=c.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=E.computeOutShape(c.map(f=>f.shape),1);let h=c[0].shape[0]===1,d=Hm(u,i,t[0].dtype,h),p=E.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(p,t[0].dtype,d);return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var k$={kernelName:po,backendName:"cpu",kernelFunc:Nl};function i_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r;be([a,s],"conv2d");let h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,x=d.dataFormat==="channelsLast",v=new $t(d.outShape,a.dtype),w=_.computeStrides(a.shape),b=_.computeStrides(s.shape),k=w[0],N=x?w[1]:w[2],C=x?w[2]:1,F=x?1:w[1],O=v.strides[0],z=x?v.strides[1]:v.strides[2],V=x?v.strides[2]:1,j=x?1:v.strides[1],U=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=v.values;for(let ee=0;ee<d.batchSize;++ee){let Y=ee*k,ae=ee*O;for(let te=0;te<d.outHeight;++te){let ie=ae+te*z,Q=te*d.strideHeight-g;for(let ce=0;ce<p;++ce){let oe=Q+ce*f;if(oe<0||oe>=d.inHeight)continue;let me=ce*b[0],de=Y+oe*N;for(let ve=0;ve<d.outWidth;++ve){let Ie=ie+ve*V,Fe=ve*d.strideWidth-y;for(let Oe=0;Oe<m;++Oe){let $e=Fe+Oe*A;if($e<0||$e>=d.inWidth)continue;let Qe=me+Oe*b[1],et=de+$e*C,at=Qe;for(let Xe=0;Xe<d.inChannels;++Xe){let ht=U[et+Xe*F];for(let Ve=0;Ve<d.outChannels;++Ve)G[Ie+Ve*j]+=ht*X[at+Ve];at+=d.outChannels}}}}}}return n.makeTensorInfo(v.shape,v.dtype,G)}var I$={kernelName:fs,backendName:"cpu",kernelFunc:i_};function S$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r;be([a,s],"conv2dBackpropFilter");let h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),{strideHeight:p,strideWidth:m,filterHeight:f,filterWidth:A}=d,y=d.dataFormat==="channelsLast",g=new $t(d.filterShape,"float32"),x=d.padInfo.left,v=d.padInfo.top,w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,k=new $t(a.shape,a.dtype,w),N=new $t(s.shape,s.dtype,b);for(let C=0;C<f;++C){let F=Math.max(0,Math.ceil((v-C)/p)),O=Math.min(d.outHeight,(d.inHeight+v-C)/p);for(let z=0;z<A;++z){let V=Math.max(0,Math.ceil((x-z)/m)),j=Math.min(d.outWidth,(d.inWidth+x-z)/m);for(let U=0;U<d.inChannels;++U)for(let X=0;X<d.outChannels;++X){let G=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=F;Y<O;++Y){let ae=C+Y*p-v;for(let te=V;te<j;++te){let ie=z+te*m-x;y?G+=k.get(ee,ae,ie,U)*N.get(ee,Y,te,X):G+=k.get(ee,U,ae,ie)*N.get(ee,X,Y,te)}}g.set(G,C,z,U,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var N$={kernelName:Mh,backendName:"cpu",kernelFunc:S$};function T$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;be([a,s],"conv2dBackpropInput");let h=_.computeStrides(s.shape),d=_.computeStrides(a.shape),p=E.convertConv2DDataFormat(c),m=E.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),f=new $t(m.inShape,"float32"),A=f.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[x,v,w]=h,{batchSize:b,filterHeight:k,filterWidth:N,inChannels:C,inHeight:F,inWidth:O,outChannels:z,outHeight:V,outWidth:j,strideHeight:U,strideWidth:X}=m;p=m.dataFormat;let G=k-1-m.padInfo.top,ee=N-1-m.padInfo.left,Y=p==="channelsLast",ae=f.strides[0],te=Y?f.strides[1]:f.strides[2],ie=Y?f.strides[2]:1,Q=Y?1:f.strides[1],ce=d[0],oe=Y?d[1]:d[2],me=Y?d[2]:1,de=Y?1:d[1];for(let ve=0;ve<b;++ve)for(let Ie=0;Ie<C;++Ie)for(let Fe=0;Fe<F;++Fe){let Oe=Fe-G,$e=Math.max(0,Math.ceil(Oe/U)),Qe=Math.min(V,(k+Oe)/U);for(let et=0;et<O;++et){let at=et-ee,Xe=Math.max(0,Math.ceil(at/X)),ht=Math.min(j,(N+at)/X),Ve=0;for(let xt=$e;xt<Qe;++xt){let jn=xt*U-Oe;for(let Xt=Xe;Xt<ht;++Xt){let xn=Xt*X-at,Un=ce*ve+oe*xt+me*Xt,Rn=x*(k-1-jn)+v*(N-1-xn)+w*Ie;for(let rn=0;rn<z;++rn){let Kt=y[Un+de*rn],Rr=g[Rn+rn];Ve+=Kt*Rr}}}let gn=ae*ve+te*Fe+ie*et+Q*Ie;A[gn]=Ve}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var E$={kernelName:ms,backendName:"cpu",kernelFunc:T$};function C$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;be([a,s],"conv3d");let c=E.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:m,dilationWidth:f,padInfo:A}=c,y=A.front,g=A.left,x=A.top,v=new $t(c.outShape,a.dtype),w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,k=v.values,N=_.computeStrides(a.shape),C=_.computeStrides(s.shape);for(let F=0;F<c.batchSize;++F){let O=F*N[0],z=F*v.strides[0];for(let V=0;V<c.outDepth;++V){let j=z+V*v.strides[1],U=V*c.strideDepth-y;for(let X=0;X<u;++X){let G=U+X*p;if(G<0||G>=c.inDepth)continue;let ee=X*C[0],Y=O+G*N[1];for(let ae=0;ae<c.outHeight;++ae){let te=j+ae*v.strides[2],ie=ae*c.strideHeight-x;for(let Q=0;Q<h;++Q){let ce=ie+Q*m;if(ce<0||ce>=c.inHeight)continue;let oe=ee+Q*C[1],me=Y+ce*N[2];for(let de=0;de<c.outWidth;++de){let ve=te+de*c.outChannels,Ie=de*c.strideWidth-g;for(let Fe=0;Fe<d;++Fe){let Oe=Ie+Fe*f;if(Oe<0||Oe>=c.inWidth)continue;let $e=oe+Fe*C[2],Qe=me+Oe*c.inChannels,et=$e;for(let at=0;at<c.inChannels;++at){let Xe=w[Qe+at];for(let ht=0;ht<c.outChannels;++ht)k[ve+ht]+=Xe*b[et+ht];et+=c.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var R$={kernelName:xu,backendName:"cpu",kernelFunc:C$};function M$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;be([a,s],"conv3dBackpropFilterV2");let c=_.computeStrides(a.shape),u=_.computeStrides(s.shape),h=E.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,m=h.strideWidth,f=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=new $t(h.filterShape,"float32"),x=g.values,[v,w,b,k]=g.strides,N=n.data.get(s.dataId).values,[C,F,O,z]=u,V=n.data.get(a.dataId).values,[j,U,X,G]=c,ee=h.padInfo.front,Y=h.padInfo.left,ae=h.padInfo.top;for(let te=0;te<f;++te){let ie=Math.max(0,Math.ceil((ee-te)/d)),Q=Math.min(h.outDepth,(h.inDepth+ee-te)/d),ce=te*v;for(let oe=0;oe<A;++oe){let me=Math.max(0,Math.ceil((ae-oe)/p)),de=Math.min(h.outHeight,(h.inHeight+ae-oe)/p),ve=oe*w+ce;for(let Ie=0;Ie<y;++Ie){let Fe=Math.max(0,Math.ceil((Y-Ie)/m)),Oe=Math.min(h.outWidth,(h.inWidth+Y-Ie)/m),$e=Ie*b+ve;for(let Qe=0;Qe<h.inChannels;++Qe){let et=Qe*k+$e;for(let at=0;at<h.outChannels;++at){let Xe=0;for(let ht=0;ht<h.batchSize;++ht){let Ve=ht*j,gn=ht*C;for(let xt=ie;xt<Q;++xt){let jn=(te+xt*d-ee)*U+Ve,Xt=xt*F+gn;for(let xn=me;xn<de;++xn){let Un=(oe+xn*p-ae)*X+jn,Rn=xn*O+Xt;for(let rn=Fe;rn<Oe;++rn){let Kt=(Ie+rn*m-Y)*G+Un,Rr=rn*z+Rn;Xe+=V[Kt+Qe]*N[Rr+at]}}}}x[et+at]=Xe}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var F$={kernelName:Fh,backendName:"cpu",kernelFunc:M$};function $$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;be([a],"conv3dBackpropInputV2");let c=_.computeStrides(a.shape),u=_.computeStrides(s.shape),h=E.computeConv3DInfo(l,s.shape,o,1,i),d=new $t(h.inShape,"float32"),p=d.values,[m,f,A,y]=d.strides,g=n.data.get(a.dataId).values,[x,v,w,b]=c,k=n.data.get(s.dataId).values,[N,C,F,O]=u,{batchSize:z,filterDepth:V,filterHeight:j,filterWidth:U,inChannels:X,inDepth:G,inHeight:ee,inWidth:Y,outChannels:ae,outDepth:te,outHeight:ie,outWidth:Q,strideDepth:ce,strideHeight:oe,strideWidth:me}=h,de=V-1-h.padInfo.front,ve=j-1-h.padInfo.top,Ie=U-1-h.padInfo.left;for(let Fe=0;Fe<z;++Fe)for(let Oe=0;Oe<X;++Oe)for(let $e=0;$e<G;++$e){let Qe=$e-de,et=Math.max(0,Math.ceil(Qe/ce)),at=Math.min(te,(V+Qe)/ce);for(let Xe=0;Xe<ee;++Xe){let ht=Xe-ve,Ve=Math.max(0,Math.ceil(ht/oe)),gn=Math.min(ie,(j+ht)/oe);for(let xt=0;xt<Y;++xt){let jn=xt-Ie,Xt=Math.max(0,Math.ceil(jn/me)),xn=Math.min(Q,(U+jn)/me),Un=0;for(let Rn=et;Rn<at;++Rn){let rn=Rn*ce-Qe;for(let Kt=Ve;Kt<gn;++Kt){let Rr=Kt*oe-ht;for(let tr=Xt;tr<xn;++tr){let nr=tr*me-jn,ya=x*Fe+v*Rn+w*Kt+b*tr,Yr=N*(V-1-rn)+C*(j-1-Rr)+F*(U-1-nr)+O*Oe;for(let ga=0;ga<ae;++ga){let Vi=g[ya+ga],Mr=k[Yr+ga];Un+=Vi*Mr}}}}p[m*Fe+f*$e+A*Xe+y*xt+Oe]=Un}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var D$={kernelName:$h,backendName:"cpu",kernelFunc:$$},O$=nt(As,e=>Math.cos(e)),z$={kernelName:As,backendName:"cpu",kernelFunc:O$},P$=nt(fo,e=>Math.cosh(e)),L$={kernelName:fo,backendName:"cpu",kernelFunc:P$};function W$(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,[u,h,d,p]=a.shape,m=s.shape[0],[f,A]=o,y=We([m,f,A,p],"float32"),g=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(a.dataId).values,w=_.computeStrides(a.shape),b=_.computeStrides(y.shape);for(let k=0;k<m;k++){let N=k*4,C=g[N],F=g[N+1],O=g[N+2],z=g[N+3],V=x[k];if(V>=u)continue;let j=f>1?(O-C)*(h-1)/(f-1):0,U=A>1?(z-F)*(d-1)/(A-1):0;for(let X=0;X<f;X++){let G=f>1?C*(h-1)+X*j:.5*(C+O)*(h-1);if(G<0||G>h-1){for(let ee=0;ee<A;ee++)for(let Y=0;Y<p;Y++){let ae=Y+ee*b[2]+X*b[1]+k*b[0];y.values[ae]=c}continue}if(l==="bilinear"){let ee=Math.floor(G),Y=Math.ceil(G),ae=G-ee;for(let te=0;te<A;te++){let ie=A>1?F*(d-1)+te*U:.5*(F+z)*(d-1);if(ie<0||ie>d-1){for(let me=0;me<p;me++){let de=me+te*b[2]+X*b[1]+k*b[0];y.values[de]=c}continue}let Q=Math.floor(ie),ce=Math.ceil(ie),oe=ie-Q;for(let me=0;me<p;me++){let de=me+Q*w[2]+ee*w[1]+V*w[0],ve=v[de];de=me+ce*w[2]+ee*w[1]+V*w[0];let Ie=v[de];de=me+Q*w[2]+Y*w[1]+V*w[0];let Fe=v[de];de=me+ce*w[2]+Y*w[1]+V*w[0];let Oe=v[de],$e=ve+(Ie-ve)*oe,Qe=Fe+(Oe-Fe)*oe;de=me+te*b[2]+X*b[1]+k*b[0],y.values[de]=$e+(Qe-$e)*ae}}}else for(let ee=0;ee<A;++ee){let Y=A>1?F*(d-1)+ee*U:.5*(F+z)*(d-1);if(Y<0||Y>d-1){for(let ie=0;ie<p;ie++){let Q=ie+ee*b[2]+X*b[1]+k*b[0];y.values[Q]=c}continue}let ae=Math.round(Y),te=Math.round(G);for(let ie=0;ie<p;ie++){let Q=ie+ae*w[2]+te*w[1]+V*w[0],ce=ie+ee*b[2]+X*b[1]+k*b[0];y.values[ce]=v[Q]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var B$={kernelName:mo,backendName:"cpu",kernelFunc:W$};function V$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;be(a,"cumsum");let l=E.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=Yn({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=E.getInnerMostAxes(1,a.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let h=ir(c.dtype,"int32"),d=_.makeZerosTypedArray(_.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,g)=>y+m-g-1:(y,g)=>y+g;for(let y=0;y<p.length;y+=m)for(let g=0;g<m;g++){let x=f(y,g);if(g===0)d[x]=i?0:p[x];else{let v=f(y,g-1);d[x]=i?p[v]+d[v]:p[x]+d[v]}}let A=n.makeTensorInfo(c.shape,h,d);if(l!=null){let y=E.getUndoAxesPermutation(l),g=Yn({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(c),g}return A}var j$={kernelName:ys,backendName:"cpu",kernelFunc:V$};function U$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=Um(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=Ib(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${a.shape.length}.`)}var H$={kernelName:Dh,backendName:"cpu",kernelFunc:U$};function G$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],c=a.shape[2],u=a.shape[3],h=l*s,d=c*s,p=u/(s*s),m=n.data.get(a.dataId).values,f=new Float32Array(o*h*d*p),A=0;for(let y=0;y<o;++y)for(let g=0;g<h;++g){let x=Math.floor(g/s),v=g%s;for(let w=0;w<d;++w){let b=Math.floor(w/s),k=w%s,N=(v*s+k)*p;for(let C=0;C<p;++C){let F=C+N+u*(b+c*(x+l*y));f[A++]=m[F]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,f)}var q$={kernelName:Ao,backendName:"cpu",kernelFunc:G$};function o_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r;be([a,s],"depthwiseConv2DNative");let u=_.computeStrides(a.shape),h=_.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),_.assert(E.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=E.computeConv2DInfo(a.shape,s.shape,i,d,o,c,!0),{filterHeight:m,filterWidth:f,dilationHeight:A,dilationWidth:y,padInfo:g}=p,x=g.left,v=g.top,w=p.outChannels/p.inChannels,b=new $t(p.outShape,a.dtype),k=n.data.get(a.dataId).values,N=n.data.get(s.dataId).values,C=b.values;for(let F=0;F<p.batchSize;++F){let O=F*u[0],z=F*b.strides[0];for(let V=0;V<p.outHeight;++V){let j=z+V*b.strides[1],U=V*p.strideHeight-v;for(let X=0;X<m;++X){let G=U+X*A;if(G<0||G>=p.inHeight)continue;let ee=X*h[0],Y=O+G*u[1];for(let ae=0;ae<p.outWidth;++ae){let te=j+ae*b.strides[2],ie=ae*p.strideWidth-x;for(let Q=0;Q<f;++Q){let ce=ie+Q*y;if(ce<0||ce>=p.inWidth)continue;let oe=ee+Q*h[1],me=Y+ce*p.inChannels,de=te,ve=oe;for(let Ie=0;Ie<p.inChannels;++Ie){let Fe=k[me+Ie];for(let Oe=0;Oe<w;++Oe)C[de+Oe]+=Fe*N[ve+Oe];de+=w,ve+=w}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var X$={kernelName:gs,backendName:"cpu",kernelFunc:o_};function K$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r;be([a,s],"depthwiseConv2dNativeBackpropFilter");let h=E.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:p,filterHeight:m,filterWidth:f}=h,A=new $t(h.filterShape,"float32"),y=h.padInfo.left,g=h.padInfo.top,x=h.outChannels/h.inChannels,v=n.data.get(a.dataId).values,w=new $t(a.shape,a.dtype,v),b=n.data.get(s.dataId).values,k=new $t(s.shape,s.dtype,b);for(let N=0;N<m;++N){let C=Math.max(0,Math.ceil((g-N)/d)),F=Math.min(h.outHeight,(h.inHeight+g-N)/d);for(let O=0;O<f;++O){let z=Math.max(0,Math.ceil((y-O)/p)),V=Math.min(h.outWidth,(h.inWidth+y-O)/p);for(let j=0;j<h.outChannels;++j){let U=Math.trunc(j/x),X=j%x,G=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=C;Y<F;++Y){let ae=N+Y*d-g;for(let te=z;te<V;++te){let ie=O+te*p-y;G+=w.get(ee,ae,ie,U)*k.get(ee,Y,te,j)}}A.set(G,N,O,U,X)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var Z$={kernelName:Oh,backendName:"cpu",kernelFunc:K$};function Y$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;be([a,s],"depthwiseConv2DNativeBackpropInput");let h=_.computeStrides(a.shape),d=_.computeStrides(s.shape),p=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new $t(p.inShape,"float32"),f=m.values,[A,y,g]=m.strides,x=n.data.get(a.dataId).values,[v,w,b]=h,k=n.data.get(s.dataId).values,[N,C,F]=d,{batchSize:O,filterHeight:z,filterWidth:V,inChannels:j,inHeight:U,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,ie=z-1-p.padInfo.top,Q=V-1-p.padInfo.left,ce=G/j;for(let oe=0;oe<O;++oe)for(let me=0;me<j;++me)for(let de=0;de<U;++de){let ve=de-ie,Ie=Math.max(0,Math.ceil(ve/ae)),Fe=Math.min(ee,(z+ve)/ae);for(let Oe=0;Oe<X;++Oe){let $e=Oe-Q,Qe=Math.max(0,Math.ceil($e/te)),et=Math.min(Y,(V+$e)/te),at=0;for(let Xe=Ie;Xe<Fe;++Xe){let ht=Xe*ae-ve;for(let Ve=Qe;Ve<et;++Ve){let gn=Ve*te-$e,xt=v*oe+w*Xe+b*Ve,jn=N*(z-1-ht)+C*(V-1-gn)+F*me;for(let Xt=0;Xt<ce;++Xt){let xn=me*ce+Xt,Un=x[xt+xn],Rn=k[jn+Xt];at+=Un*Rn}}}f[A*oe+y*de+g*Oe+me]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var J$={kernelName:zh,backendName:"cpu",kernelFunc:Y$};function Q$(e){let{inputs:t,backend:n}=e,{x:r}=t,a=_.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=We([a,a],r.dtype),o=i.values;for(let c=0;c<s.length;c++)o[c*a+c]=s[c];let l=[...r.shape,...r.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var eD={kernelName:Ph,backendName:"cpu",kernelFunc:Q$},tD={kernelName:wu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(r.dataId).values,u=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:m,inWidth:f,inChannels:A,outHeight:y,outWidth:g,padInfo:x,strideHeight:v,strideWidth:w,filterHeight:b,filterWidth:k,dilationHeight:N,dilationWidth:C,outShape:F}=E.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),O=_.sizeFromShape(F),z=F.length,V=_.getArrayFromDType(r.dtype,O);for(let j=0;j<p;++j)for(let U=0;U<y;++U){let X=U*v-x.top;for(let G=0;G<g;++G){let ee=G*w-x.left;for(let Y=0;Y<A;++Y){let ae=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<b;++ie){let Q=X+ie*N;if(Q>=0&&Q<m)for(let ce=0;ce<k;++ce){let oe=ee+ce*C;if(oe>=0&&oe<f){let me=_.locToIndex([j,Q,oe,Y],u,_.computeStrides(r.shape)),de=_.locToIndex([ie,ce,Y],d,_.computeStrides(a.shape)),ve=c[me]+h[de];ve>ae&&(ae=ve)}}}let te=_.locToIndex([j,U,G,Y],z,_.computeStrides(F));V[te]=ae}}}return{dataId:l.write(_.toTypedArray(V,r.dtype),F,r.dtype),shape:F,dtype:r.dtype}}},nD={kernelName:Wh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=_.toNestedArray(r.shape,c.data.get(r.dataId).values),h=_.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:b,dilationHeight:k,dilationWidth:N,outShape:C}=E.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);_.assert(s.rank===C.length,()=>`Error in ${Wh}, dy must have the same rank as output ${C.length}, but got ${s.rank}`);let F=_.toNestedArray(C,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(a.shape,a.dtype);for(let z=0;z<d;++z)for(let V=0;V<A;++V){let j=V*x-g.top;for(let U=0;U<y;++U){let X=U*v-g.left;for(let G=0;G<f;++G){let ee=Number.MIN_SAFE_INTEGER,Y=0,ae=0;for(let te=0;te<w;++te){let ie=j+te*k;if(ie>=0&&ie<p)for(let Q=0;Q<b;++Q){let ce=X+Q*N;if(ce>=0&&ce<m){let oe=u[z][ie][ce][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=te,ae=Q)}}}O[Y][ae][G]+=F[z][V][U][G]}}}return{dataId:c.write(_.toTypedArray(O,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},rD={kernelName:Lh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=_.toNestedArray(r.shape,c.data.get(r.dataId).values),h=_.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:b,dilationHeight:k,dilationWidth:N,outShape:C}=E.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);_.assert(s.rank===C.length,()=>`Error in ${Lh}, dy must have the same rank as output ${C.length}, but got ${s.rank}`);let F=_.toNestedArray(C,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(r.shape,r.dtype);for(let z=0;z<d;++z)for(let V=0;V<A;++V){let j=V*x-g.top;for(let U=0;U<y;++U){let X=U*v-g.left;for(let G=0;G<f;++G){let ee=Number.MIN_SAFE_INTEGER,Y=j<0?0:j,ae=X<0?0:X;for(let te=0;te<w;++te){let ie=j+te*k;if(ie>=0&&ie<p)for(let Q=0;Q<b;++Q){let ce=X+Q*N;if(ce>=0&&ce<m){let oe=u[z][ie][ce][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=ie,ae=ce)}}}O[z][Y][ae][G]+=F[z][V][U][G]}}}return{dataId:c.write(_.toTypedArray(O,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function oc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;be(a,"sum");let o;a.dtype==="bool"?o=Wa({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Br({inputs:{x:a},backend:n});let l=o.shape.length,c=_.parseAxisParam(s,o.shape),u=E.getAxesPermutation(c,l),h=c,d=o;u!=null&&(d=Yn({inputs:{x:o},backend:n,attrs:{perm:u}}),h=E.getInnerMostAxes(h.length,l)),E.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,m]=E.computeOutAndReduceShapes(d.shape,h),f=E.upcastType(d.dtype,"int32"),A=ap(n,p,f),y=_.sizeFromShape(m),g=n.data.get(A.dataId).values,x=n.data.get(d.dataId).values;for(let v=0;v<g.length;++v){let w=v*y,b=0;for(let k=0;k<y;++k)b+=x[w+k];g[v]=b}if(i){let v=E.expandShapeToKeepDim(A.shape,c),w=A;A=pt({inputs:{x:A},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),A}var aD={kernelName:Ks,backendName:"cpu",kernelFunc:oc};function sD(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=E.decodeEinsumEquation(a,s.length);E.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=E.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,m=[];for(let f=0;f<h;++f){for(let A of u[f]){let{permutationIndices:y,expandDims:g}=E.getEinsumPermutation(p,l[A]),x;E.isIdentityPermutation(y)?x=s[A]:(x=Yn({inputs:{x:s[A]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=pt({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),d===null?d=x:(d=sp({inputs:{a:x,b:d},backend:n}),m.push(d))}f<h-1&&(c[f]>=0&&(d=oc({inputs:{x:d},backend:n,attrs:{axis:c[f]-(i.length-p),keepDims:!1}}),m.push(d)),p--)}for(let f of m)f!==d&&n.disposeIntermediateTensorInfo(f);return d}var iD={kernelName:Bh,backendName:"cpu",kernelFunc:sD};function oD(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;be([r,a],"eluGrad");let s=new Float32Array(_.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.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(a.shape,"float32",s)}var lD={kernelName:Vh,backendName:"cpu",kernelFunc:oD},uD=Ct((e,t)=>e===t?1:0),l_=jt(xo,uD,null,"bool"),cD={kernelName:xo,backendName:"cpu",kernelFunc:l_},hD=E.ERF_P,dD=E.ERF_A1,pD=E.ERF_A2,fD=E.ERF_A3,mD=E.ERF_A4,AD=E.ERF_A5,yD=nt(go,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+hD*n);return t*(1-((((AD*r+mD)*r+fD)*r+pD)*r+dD)*r*Math.exp(-n*n))}),gD={kernelName:go,backendName:"cpu",kernelFunc:yD};function ip(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),pt({inputs:{x:a},backend:n,attrs:{shape:o}})}var xD={kernelName:wo,backendName:"cpu",kernelFunc:ip},wD=Ct((e,t)=>e/t),Qm=jt(xs,wD),eA={kernelName:xs,backendName:"cpu",kernelFunc:Qm};function u_(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,c=[a,s],u=_.sizeFromShape(c),h=_.getTypedArrayFromDType("float32",u),d=_.getTypedArrayFromDType("float32",u);for(let A=0;A<a;A++){let y=_i({inputs:{x:o},backend:n,attrs:{begin:[A,0],size:[1,s]}}),g=_i({inputs:{x:l},backend:n,attrs:{begin:[A,0],size:[1,s]}}),x=zn({inputs:{real:y,imag:g},backend:n}),{real:v,imag:w}=bD(x,t,n),b=E.mergeRealAndImagArrays(v,w);for(let k=0;k<s;k++){let N=E.getComplexWithIndex(b,k);h[A*s+k]=N.real,d[A*s+k]=N.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x)}let p=n.makeTensorInfo(c,"float32",h),m=n.makeTensorInfo(c,"float32",d),f=zn({inputs:{real:p,imag:m},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),f}function bD(e,t,n){let r=_.sizeFromShape(e.shape),a=n.data.get(e.dataId),s=n.data.get(a.complexTensorInfos.real.dataId).values,i=n.data.get(a.complexTensorInfos.imag.dataId).values;if(_D(r)){let o=tA(s,i,r,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),h=n.makeTensorInfo([],"float32",_.createScalarValue(r,"float32")),d=Br({inputs:{x:h},backend:n}),p=eA.kernelFunc({inputs:{a:c,b:h},backend:n}),m=eA.kernelFunc({inputs:{a:u,b:d},backend:n}),f=n.data.get(p.dataId).values,A=n.data.get(m.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),{real:f,imag:A}}return o}else{let o=E.mergeRealAndImagArrays(s,i),l=vD(o,r,t);return E.splitRealAndImagArrays(l)}}function _D(e){return(e&e-1)==0}function tA(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=E.mergeRealAndImagArrays(e,t),i=n/2,o=E.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],h=a.makeTensorInfo(u,"float32",l),d=a.makeTensorInfo(u,"float32",c),p=zn({inputs:{real:h,imag:d},backend:a}),m=E.complexWithOddIndex(s),f=m.real,A=m.imag,y=[f.length],g=a.makeTensorInfo(y,"float32",f),x=a.makeTensorInfo(y,"float32",A),v=zn({inputs:{real:g,imag:x},backend:a}),w=tA(l,c,i,r,a),b=w.real,k=w.imag,N=[b.length],C=a.makeTensorInfo(N,"float32",b),F=a.makeTensorInfo(N,"float32",k),O=zn({inputs:{real:C,imag:F},backend:a}),z=tA(f,A,i,r,a),V=z.real,j=z.imag,U=[V.length],X=a.makeTensorInfo(U,"float32",V),G=a.makeTensorInfo(U,"float32",j),ee=zn({inputs:{real:X,imag:G},backend:a}),Y=E.exponents(n,r),ae=[Y.real.length],te=a.makeTensorInfo(ae,"float32",Y.real),ie=a.makeTensorInfo(ae,"float32",Y.imag),Q=zn({inputs:{real:te,imag:ie},backend:a}),ce=sp({inputs:{a:Q,b:ee},backend:a}),oe=ic({inputs:{a:O,b:ce},backend:a}),me=Zm({inputs:{a:O,b:ce},backend:a}),de=bi({inputs:{input:oe},backend:a}),ve=bi({inputs:{input:me},backend:a}),Ie=Sl({inputs:{input:oe},backend:a}),Fe=Sl({inputs:{input:me},backend:a}),Oe=Nl({inputs:[de,ve],backend:a,attrs:{axis:0}}),$e=Nl({inputs:[Ie,Fe],backend:a,attrs:{axis:0}}),Qe=a.data.get(Oe.dataId).values,et=a.data.get($e.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(v),a.disposeIntermediateTensorInfo(C),a.disposeIntermediateTensorInfo(F),a.disposeIntermediateTensorInfo(O),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(G),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(ie),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(ce),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(me),a.disposeIntermediateTensorInfo(de),a.disposeIntermediateTensorInfo(Ie),a.disposeIntermediateTensorInfo(ve),a.disposeIntermediateTensorInfo(Fe),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo($e),{real:Qe,imag:et}}function vD(e,t,n){let r=new Float32Array(t*2);for(let a=0;a<t;a++){let s=0,i=0;for(let o=0;o<t;o++){let l=E.exponent(a*o,t,n),c=E.getComplexWithIndex(e,o);s+=c.real*l.real-c.imag*l.imag,i+=c.real*l.imag+c.imag*l.real}n&&(s/=t,i/=t),E.assignToTypedArray(r,s,i,a)}return r}function kD(e){let{inputs:t,backend:n}=e,{input:r}=t,a=_.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=pt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=u_(o,!1,n),c=pt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var ID={kernelName:jh,backendName:"cpu",kernelFunc:kD};function nA(e){let{backend:t,attrs:n}=e,{shape:r,value:a,dtype:s}=n,i=s||_.inferDtype(a),o=_.getArrayFromDType(i,_.sizeFromShape(r));return SD(o,a,i),t.makeTensorInfo(r,i,o)}var ND={kernelName:bu,backendName:"cpu",kernelFunc:nA};function SD(e,t,n){e.fill(t)}var TD={kernelName:_o,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=_.getTypedArrayFromDType(r.dtype,_.sizeFromShape(r.shape)),[i,o,l,c]=r.shape,u=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let d=h*l*o*c;for(let p=0;p<o;p++){let m=p*(l*c);for(let f=0;f<l;f++){let A=f*c;for(let y=0;y<c;y++){let g=[i,p,f,y][2],x=Math.round(l-g),v=d+m+A+y,w=u[v];if(x>=0&&x<l){let b=x*c,k=d+m+b+y;w=u[k]}s[v]=w}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},ED=Ct((e,t)=>Math.floor(e/t)),CD=jt(_s,ED,null,"int32"),RD={kernelName:_s,backendName:"cpu",kernelFunc:CD};function MD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=i_({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=ic({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=Ym(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var FD={kernelName:ri,backendName:"cpu",kernelFunc:MD};function $D(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=o_({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=ic({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=Ym(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var DD={kernelName:ai,backendName:"cpu",kernelFunc:$D};function OD(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=_.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,c,u,h]=E.prepareAndValidate(r,a);if(c===0)return n.makeTensorInfo(l,r.dtype,[]);let d=We([c,u],r.dtype),p=n.data.get(a.dataId).values,m=n.data.get(r.dataId).values;for(let f=0;f<c;f++){let A=[],y=0;for(let g=0;g<o;g++){let x=p[f*o+g];y+=x*h[g],A.push(x)}if(y<0||y>=s/u)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;g<u;g++)d.values[f*u+g]=m[y*u+g]}return n.makeTensorInfo(l,d.dtype,d.values)}var zD={kernelName:ko,backendName:"cpu",kernelFunc:OD};function PD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r;be([a,s],"gatherV2");let l=o;o==null&&(l=0);let c=_.sizeFromShape(s.shape),u=_.parseAxisParam(i,a.shape)[0],h=E.segment_util.collectGatherOpShapeInfo(a,s,u,l),d=pt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),p=pt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),m=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],f=n.bufferSync(p),A=n.bufferSync(d),y=Cb(A,f,m);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var LD={kernelName:vo,backendName:"cpu",kernelFunc:PD},WD=Ct((e,t)=>e>=t?1:0),BD=jt(ks,WD,null,"bool"),VD={kernelName:ks,backendName:"cpu",kernelFunc:BD};function jD(e){let{inputs:t,backend:n}=e,{input:r}=t,a=_.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=pt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=u_(o,!0,n),c=pt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var UD={kernelName:Uh,backendName:"cpu",kernelFunc:jD},HD=nt(So,e=>Number.isFinite(e)?1:0,"bool"),GD={kernelName:So,backendName:"cpu",kernelFunc:HD},qD=nt(No,e=>Math.abs(e)===Infinity?1:0,"bool"),XD={kernelName:No,backendName:"cpu",kernelFunc:qD},KD=nt(To,e=>Number.isNaN(e)?1:0,"bool"),ZD={kernelName:To,backendName:"cpu",kernelFunc:KD},YD=Ct((e,t)=>e<=t?1:0),JD=jt(Co,YD,null,"bool"),QD={kernelName:Co,backendName:"cpu",kernelFunc:JD};function eO(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Fb(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var tO={kernelName:Gh,backendName:"cpu",kernelFunc:eO},nO=nt(Ro,e=>Math.log1p(e)),rO={kernelName:Ro,backendName:"cpu",kernelFunc:nO},aO=Ct((e,t)=>e&&t),sO=jt(Mo,aO,null,"bool"),iO={kernelName:Mo,backendName:"cpu",kernelFunc:sO},oO=nt(_u,e=>e?0:1,"bool"),lO={kernelName:_u,backendName:"cpu",kernelFunc:oO},uO=Ct((e,t)=>e||t),cO=jt(vu,uO,null,"bool"),hO={kernelName:vu,backendName:"cpu",kernelFunc:cO};function dO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;be(a,"LRN");let c=a.shape[3],u=c-1,h=n.data.get(a.dataId).values,d=_.sizeFromShape(a.shape),p=new Float32Array(d);function m(f){let A=f%c,y=f-A+Math.max(0,A-s),g=f-A+Math.min(A+s,u),x=0;for(;y<=g;y++){let v=h[y];x+=v*v}return x}for(let f=0;f<d;f++){let A=m(f),y=h[f]*Math.pow(i+o*A,-l);p[f]=y}return n.makeTensorInfo(a.shape,a.dtype,p)}var pO={kernelName:ku,backendName:"cpu",kernelFunc:dO};function fO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r;be(i,"LRNGrad");let h=_.sizeFromShape(i.shape),d=i.shape[3],p=n.data.get(i.dataId).values,m=n.data.get(a.dataId).values,f=n.data.get(s.dataId).values,A=new Float32Array(h),y=h;for(let g=0;g<y;g++){let x=g%d,v=g-x+Math.max(0,x-o),w=g-x+Math.min(d,x+o+1),b=0;for(let k=v;k<w;k++)b+=Math.pow(m[k],2);b=c*b+l;for(let k=v;k<w;k++){let N=-2*c*u*m[k]*f[g]/b;g===k&&(N+=Math.pow(b,-u)),N*=p[g],A[k]+=N}}return n.makeTensorInfo(i.shape,a.dtype,A)}var mO={kernelName:qh,backendName:"cpu",kernelFunc:fO};function c_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=n,l=a.shape,c=l.length,u=_.parseAxisParam(s,l),h=u,d=E.getAxesPermutation(h,c),p=o.data.get(a.dataId).values;if(d!=null){let v=new Array(c);for(let w=0;w<v.length;w++)v[w]=l[d[w]];p=qm(p,l,a.dtype,d,v),h=E.getInnerMostAxes(h.length,c),l=v}be(a,"max"),E.assertAxesAreInnerMostDims("max",h,c);let[m,f]=E.computeOutAndReduceShapes(l,h),A=_.sizeFromShape(f),y=Db(p,A,m,a.dtype),g=o.write(y,m,a.dtype),x=m;return i&&(x=E.expandShapeToKeepDim(m,u)),{dataId:g,shape:x,dtype:a.dtype}}var AO={kernelName:Ts,backendName:"cpu",kernelFunc:c_};function yO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;be(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))h=Br({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=_.computeStrides(a.shape),m=Jm(d,a.shape,a.dtype,p,u,"max");h=n.makeTensorInfo(u.outShape,a.dtype,m.values)}return h}var gO={kernelName:Cs,backendName:"cpu",kernelFunc:yO};function xO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;be(a,"maxPool3d");let u=E.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=s_(h,a.shape,a.dtype,_.computeStrides(a.shape),u,"max");return n.makeTensorInfo(d.shape,"float32",d.values)}var wO={kernelName:Iu,backendName:"cpu",kernelFunc:xO};function bO(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;be([a,s],"maxPool3DGrad");let u=E.computePool3DInfo(s.shape,i,o,1,l,c),h=n.bufferSync(s),d=a$(h,u),p=u.strideDepth,m=u.strideHeight,f=u.strideWidth,A=u.dilationDepth,y=u.dilationHeight,g=u.dilationWidth,x=u.effectiveFilterDepth,v=u.effectiveFilterHeight,w=u.effectiveFilterWidth,b=x-1-u.padInfo.front,k=w-1-u.padInfo.left,N=v-1-u.padInfo.top,C=We(s.shape,"float32"),F=n.bufferSync(a);for(let O=0;O<u.batchSize;++O)for(let z=0;z<u.inChannels;++z)for(let V=0;V<u.inDepth;++V)for(let j=0;j<u.inHeight;++j)for(let U=0;U<u.inWidth;++U){let X=V-b,G=j-N,ee=U-k,Y=0;for(let ae=0;ae<x;ae+=A){let te=(X+ae)/p;if(!(te<0||te>=u.outDepth||Math.floor(te)!==te))for(let ie=0;ie<v;ie+=y){let Q=(G+ie)/m;if(!(Q<0||Q>=u.outHeight||Math.floor(Q)!==Q))for(let ce=0;ce<w;ce+=g){let oe=(ee+ce)/f;if(oe<0||oe>=u.outWidth||Math.floor(oe)!==oe)continue;let me=x*v*w-1-d.get(O,te,Q,oe,z),de=ae*v*w+ie*w+ce,ve=me===de?1:0;ve!==0&&(Y+=F.get(O,te,Q,oe,z)*ve)}}}C.set(Y,O,V,j,U,z)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var _O={kernelName:Kh,backendName:"cpu",kernelFunc:bO};function vO(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;be([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=E.computePool2DInfo(o.shape,l,c,1,u,h),p=n.data.get(o.dataId).values,m=We(d.outShape,o.dtype,a_(p,o.shape,o.dtype,d).values),f=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,x=d.effectiveFilterHeight,v=d.effectiveFilterWidth,w=v-1-d.padInfo.left,b=x-1-d.padInfo.top,k=We(o.shape,"float32"),N=n.data.get(a.dataId).values,C=We(a.shape,"float32",N);for(let F=0;F<d.batchSize;++F)for(let O=0;O<d.inChannels;++O)for(let z=0;z<d.inHeight;++z)for(let V=0;V<d.inWidth;++V){let j=z-b,U=V-w,X=0;for(let G=0;G<x;G+=y){let ee=(j+G)/f;if(!(ee<0||ee>=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y<v;Y+=g){let ae=(U+Y)/A;if(ae<0||ae>=d.outWidth||Math.floor(ae)!==ae)continue;let te=x*v-1-m.get(F,ee,ae,O),ie=G*v+Y,Q=te===ie?1:0;Q!==0&&(X+=C.get(F,ee,ae,O)*Q)}}k.set(X,F,z,V,O)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var kO={kernelName:Xh,backendName:"cpu",kernelFunc:vO};function IO(e,t,n,r,a){let s=_.computeStrides(t),i=Jm(e,t,n,s,a,"max"),o=a_(e,t,n,a,!0,r);return[i.values,o.values]}var SO={kernelName:Zh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;be(r,"MaxPoolWithArgmax");let c=l.data.get(r.dataId).values,u=E.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=IO(c,r.shape,r.dtype,o,u),p=l.write(h,u.outShape,r.dtype),m=l.write(d,u.outShape,r.dtype);return[{dataId:p,shape:u.outShape,dtype:r.dtype},{dataId:m,shape:u.outShape,dtype:"int32"}]}};function NO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=_.parseAxisParam(s,a.shape),l=E.computeOutAndReduceShapes(a.shape,o)[1],c=_.sizeFromShape(l),u=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(h);let d=Wa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});u.push(d);let p=Qm({inputs:{a:d,b:h},backend:n});u.push(p);let m=oc({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var TO={kernelName:Rs,backendName:"cpu",kernelFunc:NO};function EO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;be(a,"min");let o=_.parseAxisParam(s,a.shape),l=o,c=E.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=Yn({inputs:{x:a},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,a.shape.length)),E.assertAxesAreInnerMostDims("min",l,u.shape.length);let[h,d]=E.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),m=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let g=y*p,x=f[g];for(let v=0;v<p;++v){let w=f[g+v];w<x&&(x=w)}m[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,m);if(i){let y=E.expandShapeToKeepDim(h,o),g=pt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var CO={kernelName:Ms,backendName:"cpu",kernelFunc:EO};function RO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,mode:i}=r;be(a,"mirrorPad");let o=s.map((g,x)=>g[0]+a.shape[x]+g[1]),l=s.map(g=>g[0]),c=s.map((g,x)=>g[0]+a.shape[x]),u=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=_.computeStrides(a.shape),m=_.sizeFromShape(o),f=o.length,A=_.computeStrides(o),y=_.getTypedArrayFromDType(a.dtype,m);for(let g=0;g<m;g++){let x=_.indexToLoc(g,f,A);for(let w=0;w<f;w++)x[w]<l[w]?x[w]=l[w]*2-x[w]-u:x[w]>=c[w]&&(x[w]=(c[w]-1)*2-x[w]+u);x=x.map((w,b)=>w-l[b]);let v=_.locToIndex(x,d,p);y[g]=h[v]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var MO={kernelName:$s,backendName:"cpu",kernelFunc:RO},FO=Ct((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),$O=jt(Fo,FO),DO={kernelName:Fo,backendName:"cpu",kernelFunc:$O},OO=Qi(Lg());function h_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=a.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=_.parseAxisParam([o],a.shape),c=c_({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=E.expandShapeToKeepDim(c.shape,l),h=pt({inputs:{x:c},backend:n,attrs:{shape:u}}),d=Zm({inputs:{a,b:h},backend:n}),p=Kb({inputs:{x:d},backend:n}),m=oc({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),f=pt({inputs:{x:m},backend:n,attrs:{shape:u}}),A=Qm({inputs:{a:p,b:f},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var zO={kernelName:Zs,backendName:"cpu",kernelFunc:h_};function PO(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r;be(a,"multinomial");let l=o?a:h_({inputs:{logits:a},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],h=n.data.get(l.dataId).values,d=[c,s],p=_.makeZerosTypedArray(_.sizeFromShape(d),"int32");for(let m=0;m<c;++m){let f=m*u,A=new Float32Array(u-1);A[0]=h[f];for(let x=1;x<A.length;++x)A[x]=A[x-1]+h[f+x];let y=OO.alea(i.toString()),g=m*s;for(let x=0;x<s;++x){let v=y();p[g+x]=A.length;for(let w=0;w<A.length;w++)if(v<A[w]){p[g+x]=w;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",p)}var LO={kernelName:Yh,backendName:"cpu",kernelFunc:PO},WO=Wr.nonMaxSuppressionV3Impl;function BO(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r;be(a,"NonMaxSuppression");let c=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:h}=WO(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var VO={kernelName:Oo,backendName:"cpu",kernelFunc:BO},jO=Wr.nonMaxSuppressionV4Impl;function UO(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r;be(a,"NonMaxSuppressionPadded");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:p}=jO(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var HO={kernelName:zo,backendName:"cpu",kernelFunc:UO},GO=Wr.nonMaxSuppressionV5Impl;function qO(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r;be(a,"NonMaxSuppressionWithScore");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,d=i,p=o,m=l,f=c,{selectedIndices:A,selectedScores:y}=GO(u,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var XO={kernelName:Po,backendName:"cpu",kernelFunc:qO};function KO(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;be(a,"oneHot");let l=_.sizeFromShape(a.shape),c=new Float32Array(l*s);c.fill(o);let u=n.data.get(a.dataId).values;for(let h=0;h<l;++h)u[h]>=0&&u[h]<s&&(c[h*s+u[h]]=i);return n.makeTensorInfo([...a.shape,s],"int32",c)}var ZO={kernelName:Os,backendName:"cpu",kernelFunc:KO};function op(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(r.dtype==="complex64"){let a=bi({inputs:{input:r},backend:n}),s=op({inputs:{x:a},backend:n}),i=Sl({inputs:{input:r},backend:n}),o=op({inputs:{x:i},backend:n}),l=zn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return nA({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var YO={kernelName:tl,backendName:"cpu",kernelFunc:op};function d_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(r.dtype==="complex64"){let a=bi({inputs:{input:r},backend:n}),s=d_({inputs:{x:a},backend:n}),i=Sl({inputs:{input:r},backend:n}),o=op({inputs:{x:i},backend:n}),l=zn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return nA({backend:n,attrs:{shape:r.shape,value:1,dtype:r.dtype}})}var JO={kernelName:Lo,backendName:"cpu",kernelFunc:d_};function p_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return ip({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=ip({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=Nl({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var QO={kernelName:Wo,backendName:"cpu",kernelFunc:p_};function ez(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r;be(a,"pad");let o=s.map((y,g)=>y[0]+a.shape[g]+y[1]),l=s.map(y=>y[0]),c=n.data.get(a.dataId).values,u=_.sizeFromShape(a.shape),h=a.shape.length,d=_.computeStrides(a.shape),p=_.sizeFromShape(o),m=o.length,f=_.computeStrides(o),A=_.getTypedArrayFromDType(a.dtype,p);i!==0&&A.fill(i);for(let y=0;y<u;y++){let g=_.indexToLoc(y,h,d).map((v,w)=>v+l[w]),x=_.locToIndex(g,m,f);A[x]=c[y]}return{dataId:n.write(A,o,a.dtype),shape:o,dtype:a.dtype}}var f_={kernelName:zs,backendName:"cpu",kernelFunc:ez},tz=Ct((e,t)=>Math.pow(e,t)),nz=jt(Ps,tz),rz={kernelName:Ps,backendName:"cpu",kernelFunc:nz};function az(e){let{backend:t,attrs:n}=e,{start:r,stop:a,dtype:s,step:i}=n,o=Xm(r,a,i,s);return t.makeTensorInfo([o.length],s,o)}var sz={kernelName:Su,backendName:"cpu",kernelFunc:az},iz=nt(Vo,e=>1/e),oz={kernelName:Vo,backendName:"cpu",kernelFunc:iz};function lz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;be(a,"resizeBilinear");let l=_.computeStrides(a.shape),[c,u]=o,[h,d,p,m]=a.shape,f=n.data.get(a.dataId).values,A=new Float32Array(_.sizeFromShape([h,c,u,m])),y=[s&&c>1?d-1:d,s&&u>1?p-1:p],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],x=0,v=y[0]/g[0],w=y[1]/g[1];for(let b=0;b<h;b++)for(let k=0;k<c;k++){let N;i?N=v*(k+.5)-.5:N=v*k;let C=Math.max(0,Math.floor(N)),F=N-C,O=Math.min(d-1,Math.ceil(N)),z=b*l[0]+C*l[1],V=b*l[0]+O*l[1];for(let j=0;j<u;j++){let U;i?U=w*(j+.5)-.5:U=w*j;let X=Math.max(0,Math.floor(U)),G=U-X,ee=Math.min(p-1,Math.ceil(U)),Y=z+X*l[2],ae=V+X*l[2],te=z+ee*l[2],ie=V+ee*l[2];for(let Q=0;Q<m;Q++){let ce=f[Y+Q],oe=f[ae+Q],me=f[te+Q],de=f[ie+Q],ve=ce+(me-ce)*G,Ie=oe+(de-oe)*G,Fe=ve+(Ie-ve)*F;A[x++]=Fe}}}return n.makeTensorInfo([h,c,u,m],"float32",A)}var uz={kernelName:Bs,backendName:"cpu",kernelFunc:lz};function cz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;be([s,a],"resizeBilinearGrad");let o=_.computeStrides(a.shape),[l,c,u,h]=a.shape,[,d,p]=s.shape,m=new Float32Array(l*c*u*h),f=[i&&d>1?c-1:c,i&&p>1?u-1:u],A=[i&&d>1?d-1:d,i&&p>1?p-1:p],y=f[0]/A[0],g=f[1]/A[1],x=n.data.get(s.dataId).values,v=0;for(let w=0;w<l;w++){let b=w*o[0];for(let k=0;k<d;k++){let N=k*y,C=Math.floor(N),F=Math.min(Math.ceil(N),c-1),O=b+C*o[1],z=b+F*o[1],V=N-C,j=1-V;for(let U=0;U<p;U++){let X=U*g,G=Math.floor(X),ee=Math.min(Math.ceil(X),u-1),Y=X-G,ae=1-Y,te=O+G*o[2],ie=O+ee*o[2],Q=z+G*o[2],ce=z+ee*o[2],oe=j*ae,me=j*Y,de=V*ae,ve=V*Y;for(let Ie=0;Ie<h;Ie++){let Fe=x[v++];m[te+Ie]+=Fe*oe,m[ie+Ie]+=Fe*me,m[Q+Ie]+=Fe*de,m[ce+Ie]+=Fe*ve}}}}return n.makeTensorInfo([l,u,c,h],"float32",m)}var hz={kernelName:ed,backendName:"cpu",kernelFunc:cz};function dz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;be(a,"resizeNearestNeighbor");let l=_.computeStrides(a.shape),[c,u]=o,[h,d,p,m]=a.shape,f=n.data.get(a.dataId).values,A=new Float32Array(h*c*u*m),y=[s&&c>1?d-1:d,s&&u>1?p-1:p],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],x=y[0]/g[0],v=y[1]/g[1],w=0;for(let b=0;b<h;b++){let k=b*l[0];for(let N=0;N<c;N++){let C=i?x*(N+.5):x*N,F=Math.min(d-1,s?Math.round(C):Math.floor(C));i&&(F=Math.max(0,F));let O=k+F*l[1];for(let z=0;z<u;z++){let V=i?v*(z+.5):v*z,j=Math.min(p-1,s?Math.round(V):Math.floor(V));i&&(j=Math.max(0,j));let U=O+j*l[2];for(let X=0;X<m;X++){let G=f[U+X];A[w++]=G}}}}return n.makeTensorInfo([h,c,u,m],a.dtype,A)}var pz={kernelName:Nu,backendName:"cpu",kernelFunc:dz};function fz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;be([s,a],"resizeNearestNeighborGrad");let o=_.computeStrides(a.shape),l=_.computeStrides(s.shape),[c,u,h,d]=a.shape,[,p,m]=s.shape,f=new Float32Array(c*u*h*d),A=n.data.get(s.dataId).values,y=[i&&p>1?u-1:u,i&&m>1?h-1:h],g=[i&&p>1?p-1:p,i&&m>1?m-1:m],x=y[0]/g[0],v=y[1]/g[1],w=1/x,b=1/v,k=Math.ceil(w)*2+2,N=Math.ceil(b)*2+2;for(let C=0;C<c;C++){let F=C*o[0];for(let O=0;O<u;O++){let z=F+O*o[1],V=Math.floor(O*w),j=Math.floor(V-k/2);for(let U=0;U<h;U++){let X=z+U*o[2],G=Math.floor(U*b),ee=Math.floor(G-N/2);for(let Y=0;Y<d;Y++){let ae=0;for(let te=0;te<k;te++){let ie=te+j;if(ie<0||ie>=p)continue;let Q=F+ie*l[1],ce=ie*x,oe=Math.min(u-1,i?Math.round(ce):Math.floor(ce));if(O===oe)for(let me=0;me<N;me++){let de=me+ee;if(de<0||de>=m)continue;let ve=Q+de*l[2],Ie=de*v,Fe=Math.min(h-1,i?Math.round(Ie):Math.floor(Ie));U===Fe&&(ae+=A[ve+Y])}}f[X+Y]=ae}}}}return n.makeTensorInfo(a.shape,a.dtype,f)}var mz={kernelName:Qh,backendName:"cpu",kernelFunc:fz};function Az(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r;be(a,"reverse");let i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return Br({inputs:{x:a},backend:n});let l=new $t(a.shape,a.dtype),c=n.bufferSync(a);for(let u=0;u<l.size;u++){let h=l.indexToLoc(u),d=h.slice();o.forEach(p=>d[p]=a.shape[p]-1-d[p]),l.set(c.get(...d),...h)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var yz={kernelName:js,backendName:"cpu",kernelFunc:Az},gz={kernelName:nl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=_.getTypedArrayFromDType(r.dtype,_.sizeFromShape(r.shape)),[c,u,h,d]=r.shape,[p,m]=E.getImageCenter(i,u,h),f=255,A=Math.sin(a),y=Math.cos(a),g=o.data.get(r.dataId).values;for(let x=0;x<c;x++){let v=x*h*u*d;for(let w=0;w<u;w++){let b=w*(h*d);for(let k=0;k<h;k++){let N=k*d;for(let C=0;C<d;C++){let F=[c,w,k,C],O=F[2],z=F[1],V=(O-p)*y-(z-m)*A,j=(O-p)*A+(z-m)*y;V=Math.round(V+p),j=Math.round(j+m);let U=s;if(typeof s!="number"&&(C===3?U=f:U=s[C]),V>=0&&V<h&&j>=0&&j<u){let G=j*(h*d),ee=V*d,Y=v+G+ee+C;U=g[Y]}let X=v+b+N+C;l[X]=U}}}}return{dataId:o.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},xz=nt(Us,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}),wz={kernelName:Us,backendName:"cpu",kernelFunc:xz};function m_(e,t,n,r,a,s,i,o,l,c){let u=[r/a,a],h=e.values,d=t.values;if(r===0)return We(n,t.dtype);let p=We(u,t.dtype);p.values.fill(l);for(let m=0;m<s;m++){let f=[],A=0;for(let y=0;y<i;y++){let g=h[m*i+y];f.push(g),A+=g*o[y]}if(A<0||A>=r/a)throw new Error(`Invalid indices: ${f} does not index into ${n}`);for(let y=0;y<a;y++)c?p.values[A*a+y]+=d[m*a+y]:p.values[A*a+y]=t.rank===0?d[0]:d[m*a+y]}return p}function bz(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=E.calculateShapes(s,a,i),d=!0,p=n.bufferSync(a),m=n.bufferSync(s),f=m_(p,m,i,h,c,l,o,u,0,d);return n.makeTensorInfo(i,f.dtype,f.values)}var _z={kernelName:Uo,backendName:"cpu",kernelFunc:bz};function vz(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t;be([r,a,s],"select");let i=r.shape.length,o=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=ir(a.dtype,s.dtype),h=_.makeZerosTypedArray(_.sizeFromShape(a.shape),u),d=0,p=i===0||i>1||a.shape.length===1?1:_.sizeFromShape(a.shape.slice(1));for(let m=0;m<o.length;m++)for(let f=0;f<p;f++)o[m]===1?h[d++]=l[m]:h[d++]=c[m];return n.makeTensorInfo(a.shape,u,h)}var kz={kernelName:Ho,backendName:"cpu",kernelFunc:vz},Iz=E.SELU_SCALEALPHA,Sz=E.SELU_SCALE,Nz=nt(Go,e=>e>=0?Sz*e:Iz*(Math.exp(e)-1)),Tz={kernelName:Go,backendName:"cpu",kernelFunc:Nz},Ez=nt(Ko,e=>e<0?-1:e>0?1:0),Cz={kernelName:Ko,backendName:"cpu",kernelFunc:Ez},Rz=nt(Gs,e=>Math.sin(e)),Mz={kernelName:Gs,backendName:"cpu",kernelFunc:Rz},Fz=nt(Xo,e=>Math.sinh(e)),$z={kernelName:Xo,backendName:"cpu",kernelFunc:Fz},Dz=11920928955078125e-23,A_=Math.log(Dz)+2,Oz=nt(Zo,e=>{let t=e>-A_,n=e<A_,r=Math.exp(e),a;return n?a=r:t?a=e:a=Math.log(1+r),a}),zz={kernelName:Zo,backendName:"cpu",kernelFunc:Oz};function Pz(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;be([a],"spaceToBatchND");let o=_.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let c=f_.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),u=E.getReshaped(c.shape,s,o,!1),h=E.getPermuted(u.length,s.length,!1),d=E.getReshapedPermuted(c.shape,s,o,!1),p=pt({inputs:{x:c},backend:n,attrs:{shape:u}}),m=Yn({inputs:{x:p},backend:n,attrs:{perm:h}}),f=pt({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),f}var Lz={kernelName:Tu,backendName:"cpu",kernelFunc:Pz};function Wz(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:s}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${r.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.data.get(a.dataId).values),o=n.data.get(r.dataId).values,l=Array.from(n.data.get(s.dataId).values),[c,u,h]=Vb(o,r.shape,r.dtype,i,l);return[n.makeTensorInfo(u,r.dtype,c),n.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Bz={kernelName:td,backendName:"cpu",kernelFunc:Wz};function Vz(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,sliceSize:u,strides:h,outputSize:d}=E.calculateShapes(s,a,o),p=!1,m=n.bufferSync(a),f=n.bufferSync(s),A=n.data.get(i.dataId).values[0],y=m_(m,f,o,d,u,c,l,h,A,p);return n.makeTensorInfo(o,y.dtype,y.values)}var jz={kernelName:nd,backendName:"cpu",kernelFunc:Vz};function Uz(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=E.prepareSplitSize(a,s,o),c=new Array(a.shape.length).fill(0),u=a.shape.slice();return l.map(h=>{let d=[...u];d[o]=h;let p=_i({inputs:{x:a},backend:n,attrs:{begin:c,size:d}});return c[o]+=h,p})}var Hz={kernelName:Yo,backendName:"cpu",kernelFunc:Uz},Gz=nt(Xs,e=>Math.sqrt(e)),qz={kernelName:Xs,backendName:"cpu",kernelFunc:Gz},Xz={kernelName:Eu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;be(n,"square");let a=r.data.get(n.dataId).values,s=new Float32Array(a.length);for(let i=0;i<a.length;++i){let o=a[i];s[i]=o*o}return{dataId:r.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},Kz=nt(Ea,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),Zz={kernelName:Ea,backendName:"cpu",kernelFunc:Kz};function Yz(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r;be(a,"stridedSlice");let{nonStrided:p,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=sn.sliceInfo(a.shape,s,i,o,l,c,u,h,d),x=pt({inputs:{x:a},backend:n,attrs:{shape:y}}),v;if(p){let b=_i({inputs:{x},backend:n,attrs:{begin:m,size:A}});v=pt({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))v=n.makeTensorInfo(g,a.dtype,[]);else{let b=n.bufferSync(x),k=Ub(g,b,f,m);v=n.makeTensorInfo(k.shape,k.dtype,k.values)}let w=pt({inputs:{x:v},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),w}var Jz={kernelName:Jo,backendName:"cpu",kernelFunc:Yz},Qz=nt(Qs,e=>Math.tan(e)),eP={kernelName:Qs,backendName:"cpu",kernelFunc:Qz},tP=nt(ei,e=>Math.tanh(e)),nP={kernelName:ei,backendName:"cpu",kernelFunc:tP};function rP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;be(a,"tile");let i=Gb(n.bufferSync(a),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var aP={kernelName:Ta,backendName:"cpu",kernelFunc:rP};function sP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r;be(a,"topk");let o=n.data.get(a.dataId).values,[l,c]=qb(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var iP={kernelName:Qo,backendName:"cpu",kernelFunc:sP};function uP(e){let{inputs:t,attrs:n,backend:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=n,[u,h,d,p]=a.shape,[m,f]=c!=null?c:[h,d],A=[u,m,f,p],y=_.computeStrides(a.shape),g=y[0],x=y[1],v=y[2],w=_.getTypedArrayFromDType(a.dtype,_.sizeFromShape(A));w.fill(l);let b=r.data.get(a.dataId).values,k=r.data.get(s.dataId).values;for(let N=0;N<u;++N){let C=s.shape[0]===1?k:k.subarray(N*8,N*8+8);for(let F=0;F<m;++F)for(let O=0;O<f;++O)for(let z=0;z<p;++z){let V,j=C[6]*O+C[7]*F+1;if(j===0)continue;let U=(C[0]*O+C[1]*F+C[2])/j,X=(C[3]*O+C[4]*F+C[5])/j,G=y_(U,d,o),ee=y_(X,h,o);switch(i){case"nearest":V=oP(b,h,d,g,x,v,N,ee,G,z,l);break;case"bilinear":V=lP(b,h,d,g,x,v,N,ee,G,z,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let Y=N*g+F*x+O*v+z;w[Y]=V}return r.makeTensorInfo(A,a.dtype,w)}return{dataId:r.write(w,A,a.dtype),shape:a.shape,dtype:a.dtype}}var cP={kernelName:rd,backendName:"cpu",kernelFunc:uP};function y_(e,t,n){switch(n){case"reflect":return hP(e,t);case"wrap":return dP(e,t);case"nearest":return fP(e,t);case"constant":default:return pP(e,t)}}function hP(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=2*t;n<r&&(n=r*Math.trunc(-n/r)+n),n=n<-t?n+r:-n-1}else if(n>t-1)if(t<=1)n=0;else{let r=2*t;n-=r*Math.trunc(n/r),n>=t&&(n=r-n-1)}return _.clamp(0,n,t-1)}function dP(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=t-1;n+=t*(Math.trunc(-n/r)+1)}else if(n>t-1)if(t<=1)n=0;else{let r=t-1;n-=t*Math.trunc(n/r)}return _.clamp(0,n,t-1)}function pP(e,t){return e}function fP(e,t){return _.clamp(0,e,t-1)}function lc(e,t,n,r,a,s,i,o,l,c,u){let h=i*r+o*a+l*s+c;return 0<=o&&o<t&&0<=l&&l<n?e[h]:u}function oP(e,t,n,r,a,s,i,o,l,c,u){let h=Math.round(o),d=Math.round(l);return lc(e,t,n,r,a,s,i,h,d,c,u)}function lP(e,t,n,r,a,s,i,o,l,c,u){let h=Math.floor(o),d=Math.floor(l),p=h+1,m=d+1,f=(m-l)*lc(e,t,n,r,a,s,i,h,d,c,u)+(l-d)*lc(e,t,n,r,a,s,i,h,m,c,u),A=(m-l)*lc(e,t,n,r,a,s,i,p,d,c,u)+(l-d)*lc(e,t,n,r,a,s,i,p,m,c,u);return(p-o)*f+(o-h)*A}function mP(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;be(s,"unique");let i=r.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:c}=Xb(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var AP={kernelName:ad,backendName:"cpu",kernelFunc:mP};function yP(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape.length,o=a.shape[s],l=new Array(i-1),c=0;for(let p=0;p<i;p++)p!==s&&(l[c++]=a.shape[p]);let u=new Array(i).fill(0),h=a.shape.slice();h[s]=1;let d=new Array(o);for(let p=0;p<d.length;p++){u[s]=p;let m=_i({inputs:{x:a},backend:n,attrs:{begin:u,size:h}});d[p]=pt({inputs:{x:m},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(m)}return d}var gP={kernelName:el,backendName:"cpu",kernelFunc:yP};function xP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r;be(a,"unsortedSegmentSum");let o=a.shape.length,l=s.shape.length,c=[],u=[],h=o-l,d=s;for(let m=0;m<h;++m){let f=ip({inputs:{input:d},backend:n,attrs:{dim:m+1}});d=f,u.push(f)}for(let m=0;m<i;++m){let f=_.createScalarValue(m,"int32"),A=n.makeTensorInfo([],"int32",f),y=l_({inputs:{a:A,b:d},backend:n}),g=Wa({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=sp({inputs:{a:g,b:a},backend:n}),v=oc({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});c.push(v),u.push(A),u.push(y),u.push(g),u.push(x),u.push(v)}let p=p_({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var wP={kernelName:Cu,backendName:"cpu",kernelFunc:xP},bP=[MF,OM,$F,OF,VM,PF,WF,VF,UF,GF,XF,ZF,JF,t$,r$,i$,l$,c$,d$,CF,f$,A$,g$,WM,UM,w$,zM,_$,k$,N$,E$,I$,F$,D$,R$,z$,L$,B$,j$,H$,q$,X$,Z$,J$,eD,tD,rD,nD,eA,iD,_F,lD,cD,gD,HM,xD,qM,ID,ND,TD,KM,RD,FD,DD,zD,LD,YM,VD,PM,UD,v$,GD,XD,ZD,vF,QM,QD,tO,tF,rO,iO,lO,hO,pO,mO,rF,gO,wO,_O,kO,SO,AO,TO,CO,sF,MO,DO,LO,oF,uF,VO,HO,XO,hF,ZO,JO,QO,f_,rz,IF,fF,sz,LM,oz,SF,NF,EF,uz,hz,pz,mz,yz,gz,wz,AF,_z,kz,Tz,TF,Cz,Mz,$z,yF,zO,zz,Lz,Bz,jz,Hz,qz,Xz,xF,Zz,Jz,bF,aD,eP,nP,aP,iP,dF,cP,AP,gP,wP,YO];for(let e of bP)si(e);var g_={};Me(g_,{assertNotComplex:()=>Tl,bindCanvasToFramebuffer:()=>kP,bindColorTextureToFramebuffer:()=>up,bindTextureToProgramUniformSampler:()=>F_,bindTextureUnit:()=>C_,bindVertexBufferToProgramAttribute:()=>rA,callAndCheck:()=>xe,canBeRepresented:()=>x_,createFragmentShader:()=>__,createFramebuffer:()=>E_,createProgram:()=>v_,createStaticIndexBuffer:()=>S_,createStaticVertexBuffer:()=>I_,createTexture:()=>N_,createVertexShader:()=>b_,getBatchDim:()=>vi,getExtensionOrThrow:()=>uc,getFramebufferErrorMessage:()=>$_,getMaxTexturesInShader:()=>z_,getNumChannels:()=>_P,getProgramUniformLocation:()=>M_,getProgramUniformLocationOrThrow:()=>R_,getRowsCols:()=>ki,getShapeAs3D:()=>cp,getTextureShapeFromLogicalShape:()=>D_,getWebGLDisjointQueryTimerVersion:()=>P_,getWebGLErrorMessage:()=>w_,getWebGLMaxTextureSize:()=>O_,hasExtension:()=>Jn,isCapableOfRenderingToFloatTexture:()=>L_,isDownloadFloatTextureEnabled:()=>W_,isReshapeFree:()=>hc,isWebGLFenceEnabled:()=>B_,isWebGLVersionEnabled:()=>sA,linkProgram:()=>k_,resetMaxTextureSize:()=>IP,resetMaxTexturesInShader:()=>SP,unbindColorTextureFromFramebuffer:()=>aA,unbindTextureUnit:()=>vP,validateFramebuffer:()=>cc,validateProgram:()=>lp,validateTextureSize:()=>T_});var Ii={},iA={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function hp(e,t){Ii[e]=t}function Vr(e){if(!(e in Ii)){let n=NP(e);if(n!==null)Ii[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Ii[e];return t.isContextLost()?(delete Ii[e],Vr(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),Ii[e])}function TP(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 NP(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=TP(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Ii[e]},!1),e===1?t.getContext("webgl",iA)||t.getContext("experimental-webgl",iA):t.getContext("webgl2",iA)}var dc;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(dc||(dc={}));var Qn;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(Qn||(Qn={}));var Qt;(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"})(Qt||(Qt={}));function pc(e,t){return[t,e]}function EP(e,t){return e*t}function fc(e){let t=_.sizeFromShape(e),n=Math.ceil(t/4);return _.sizeToSquarishShape(n)}function El(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function CP(e,t){let[n,r]=El(e,t);return n*r*4}function oA(e,t){let n=e,r,a,s,i,o,l,c,u,h,d;return J().getNumber("WEBGL_VERSION")===2?(r=n.R32F,a=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,c=4,u=1,h=n.HALF_FLOAT,d=n.FLOAT):(r=e.RGBA,a=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,c=4,u=4,h=t!=null?t.HALF_FLOAT_OES:null,d=e.FLOAT),l=e.RGBA,{internalFormatFloat:r,internalFormatHalfFloat:a,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:c,defaultNumChannels:u,textureTypeHalfFloat:h,textureTypeFloat:d}}function xe(e,t){let n=t();return J().getBool("DEBUG")&&RP(e),n}function RP(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+w_(e,t))}var MP=596e-10,FP=65504;function x_(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||MP<Math.abs(e)&&Math.abs(e)<FP)}function w_(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 uc(e,t){return ha(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function b_(e,t){let n=ha(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function __(e,t){let n=ha(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw $P(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var DP=/ERROR: [0-9]+:([0-9]+):/g;function $P(e,t){let n=DP.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let r=+n[1],a=e.split(`
`),s=a.length.toString().length+2,i=a.map((h,d)=>_.rightPad((d+1).toString(),s)+h),o=0;for(let h=0;h<i.length;h++)o=Math.max(i[h].length,o);let l=i.slice(0,r-1),c=i.slice(r-1,r),u=i.slice(r);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${_.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
`))}function v_(e){return ha(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function k_(e,t){if(xe(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function lp(e,t){if(xe(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function I_(e,t){let n=ha(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function S_(e,t){let n=ha(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function _P(){return J().getNumber("WEBGL_VERSION")===2?1:4}function N_(e){return ha(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function T_(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,a=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+a+".")}}function E_(e){return ha(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function rA(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),xe(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),xe(e,()=>e.enableVertexAttribArray(o)),!0)}function C_(e,t,n){V_(e,n),xe(e,()=>e.activeTexture(e.TEXTURE0+n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function vP(e,t){V_(e,t),xe(e,()=>e.activeTexture(e.TEXTURE0+t)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function R_(e,t,n){return ha(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function M_(e,t,n){return e.getUniformLocation(t,n)}function F_(e,t,n,r){xe(e,()=>C_(e,t,r)),xe(e,()=>e.uniform1i(n,r))}function kP(e){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),xe(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function up(e,t,n){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function aA(e,t){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function cc(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+$_(e,t))}function $_(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 ${t}`}}function ha(e,t,n){let r=xe(e,()=>t());if(r==null)throw new Error(n);return r}function V_(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function vi(e,t=2){return _.sizeFromShape(e.slice(0,e.length-t))}function ki(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 cp(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[vi(e),...ki(e)]),t}function D_(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?_.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=_.squeezeShape(e).newShape);let r=_.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=vi(e),s=2,i=2;return e.length&&([s,i]=ki(e)),r=a*(s/2)*(i/2),_.sizeToSquarishShape(r).map(o=>o*2)}return _.sizeToSquarishShape(r)}function dp(e){return e%2==0}function hc(e,t){if(e=e.slice(-2),t=t.slice(-2),_.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||dp(n)&&dp(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&dp(e[0])&&dp(t[0])}var pp,fp;function O_(e){if(pp==null){let t=Vr(e);pp=t.getParameter(t.MAX_TEXTURE_SIZE)}return pp}function IP(){pp=null}function SP(){fp=null}function z_(e){if(fp==null){let t=Vr(e);fp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,fp)}function P_(e){if(e===0)return 0;let t,n=Vr(e);return Jn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Jn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Jn(e,t){return e.getExtension(t)!=null}function sA(e){try{if(Vr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function L_(e){if(e===0)return!1;let t=Vr(e);if(e===1){if(!Jn(t,"OES_texture_float"))return!1}else if(!Jn(t,"EXT_color_buffer_float"))return!1;return lA(t)}function W_(e){if(e===0)return!1;let t=Vr(e);if(e===1){if(!Jn(t,"OES_texture_float")||!Jn(t,"WEBGL_color_buffer_float"))return!1}else{if(Jn(t,"EXT_color_buffer_float"))return lA(t);let n="EXT_color_buffer_half_float";if(Jn(t,n)){let r=t.getExtension(n);return OP(t,r)}return!1}return lA(t)}function lA(e){let t=oA(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,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 OP(e,t){let n=oA(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,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,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function B_(e){return e!==2?!1:Vr(e).fenceSync!=null}function Tl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&_.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ce=J();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>sA(2)?2:sA(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>O_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>z_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ce.getNumber("WEBGL_VERSION");return e===0?0:P_(e)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Lu.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>L_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>W_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>B_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.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}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Lu.isMobile()&&Ce.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function cn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="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",r="varying",a="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:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Si(e,t,n="index"){let r=_.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function uA(e){let t=_.computeStrides(e).map(n=>n.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}var j_=`
  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;
  }
`,zP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=dc.DENSE;let t=fc(e),n=cn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${Si(["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;
      }
    `}},PP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=dc.DENSE;let t=fc(e),n=cn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${Si(["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;
      }
    `}},LP=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Qn.DOWNLOAD;let t=cn();this.outputShape=e,this.userCode=`
      ${j_}

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

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},BP=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=cn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
      ${uA(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, ${a}.0);
        vec4 values = ${r.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${r.output} = vec4(${i}, 0., 0., 0.);
      }
    `}},VP=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=cn(),[a,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, ${a}.0);
              values = ${r.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=`
      ${uA(e)}

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

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

        ${i}

        ${r.output} = ${o};
      }
    `}},U_={};Me(U_,{bindVertexProgramAttributeStreams:()=>Q_,createBufferFromOutputTexture:()=>n3,createFloat16MatrixTexture:()=>K_,createFloat16PackedMatrixTexture:()=>J_,createFloat32MatrixTexture:()=>X_,createIndexBuffer:()=>q_,createPackedMatrixTexture:()=>Y_,createUnsignedBytesMatrixTexture:()=>Z_,createVertexBuffer:()=>G_,createVertexShader:()=>H_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>a3,downloadFloat32MatrixFromBuffer:()=>r3,downloadMatrixFromPackedOutputTexture:()=>i3,downloadPackedMatrixFromBuffer:()=>s3,getInternalFormatForFloat16MatrixTexture:()=>hA,getInternalFormatForFloat16PackedMatrixTexture:()=>fA,getInternalFormatForFloat32MatrixTexture:()=>cA,getInternalFormatForPackedMatrixTexture:()=>pA,getInternalFormatForUnsignedBytesMatrixTexture:()=>dA,uploadDenseMatrixToTexture:()=>e3,uploadPixelDataToTexture:()=>t3});function H_(e){let t=cn(),n=`${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 b_(e,n)}function G_(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 I_(e,t)}function q_(e){let t=new Uint16Array([0,1,2,2,1,3]);return S_(e,t)}function mc(e,t,n,r,a,s){T_(t,n);let i=N_(e),o=e.TEXTURE_2D;return xe(e,()=>e.bindTexture(o,i)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),xe(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function cA(e){return e.internalFormatFloat}function X_(e,t,n,r){let[a,s]=pc(t,n);return mc(e,a,s,cA(r),r.textureFormatFloat,e.FLOAT)}function hA(e){return e.internalFormatHalfFloat}function K_(e,t,n,r){let[a,s]=pc(t,n);return mc(e,a,s,hA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function dA(e){return e.downloadTextureFormat}function Z_(e,t,n,r){let[a,s]=pc(t,n);return mc(e,a,s,dA(r),e.RGBA,e.UNSIGNED_BYTE)}function pA(e){return e.internalFormatPackedFloat}function Y_(e,t,n,r){let[a,s]=El(t,n);return mc(e,a,s,pA(r),e.RGBA,e.FLOAT)}function fA(e){return e.internalFormatPackedHalfFloat}function J_(e,t,n,r){let[a,s]=El(t,n);return mc(e,a,s,fA(r),e.RGBA,r.textureTypeHalfFloat)}function Q_(e,t,n){let r=0,a=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),rA(e,t,"clipSpacePos",n,3,s,r)&&rA(e,t,"uv",n,2,s,a)}function e3(e,t,n,r,a,s){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function t3(e,t,n){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function n3(e,t,n,r){let a=e.createBuffer();xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return xe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function r3(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function a3(e,t,n,r){let[a,s]=pc(t,n),i=4,o=new Uint8Array(EP(t*n,i));return xe(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function s3(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(CP(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 i3(e,t,n){let r=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var mp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,hp(t,e)):this.gl=Vr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=uc(this.gl,a),Jn(this.gl,s))this.textureHalfFloatExtension=uc(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Jn(this.gl,r))this.colorBufferHalfFloatExtension=uc(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Jn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Jn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=G_(this.gl),this.indexBuffer=q_(this.gl),this.framebuffer=E_(this.gl),this.textureConfig=oA(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;xe(e,()=>e.finish()),xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.deleteFramebuffer(this.framebuffer)),xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),xe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),X_(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),K_(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Z_(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),t3(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),e3(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),J_(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Y_(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(aA(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>a3(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return s3(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return r3(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=n3(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>i3(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=__(t,e),r=H_(t),a=v_(t);return xe(t,()=>t.attachShader(a,r)),xe(t,()=>t.attachShader(a,n)),k_(t,a),this.debug&&lp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Q_(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&lp(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?R_(this.gl,e,t):M_(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),xe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),F_(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=El(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&lp(this.gl,this.program),cc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),xe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),xe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=uc(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await _.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=jP(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)&&_.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),up(this.gl,e,this.framebuffer),this.debug&&cc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(up(this.gl,this.outputTexture,this.framebuffer),this.debug&&cc(this.gl)):aA(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;up(r,e,this.framebuffer),this.debug&&cc(r),this.outputTexture=e,xe(r,()=>r.viewport(0,0,t,n)),xe(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function jP(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:o3}=E;function JP(e,t,n,r){let a=[];e.forEach(p=>{let m=_.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${m>1?`[${m}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
`),i=e.map(p=>UP(p,t,r)).join(`
`),o=t.texShape,l=cn(),c=qP(l),u,h,d=ZP(l);return t.isPacked?(u=HP(t.logicalShape,o),h=KP(l)):(u=GP(t.logicalShape,o),h=XP(l)),r&&(d+=YP),[d,c,h,s,u,i,n].join(`
`)}function Cl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return QP(e);case 1:return eL(e);case 2:return tL(e);case 3:return nL(e);case 4:return rL(e);case 5:return aL(e);case 6:return sL(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function l3(e){switch(e.shapeInfo.logicalShape.length){case 0:return iL(e);case 1:return oL(e);case 2:return lL(e);case 3:return uL(e);default:return cL(e)}}function UP(e,t,n=!1){let r="";n?r+=l3(e):r+=Cl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=hL(e,t):r+=dL(e,t)),r}function HP(e,t){switch(e.length){case 0:return u3();case 1:return pL(e,t);case 2:return AL(e,t);case 3:return fL(e,t);default:return mL(e,t)}}function GP(e,t){switch(e.length){case 0:return u3();case 1:return yL(e,t);case 2:return _L(e,t);case 3:return gL(e,t);case 4:return xL(e,t);case 5:return wL(e,t);case 6:return bL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function qP(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function XP(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function KP(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function ZP(e){return`${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFs} vec2 resultUV;
    ${e.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${e.defineSpecialNaN}
    ${e.defineSpecialInf}
    ${e.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${vL}
    ${kL}
    ${IL}
  `}var vL=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,kL=`
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);
}
`,IL=`
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);
}
`,YP=`
  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 u3(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function pL(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 yL(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 * ${t[1]} + resTexRC.y;
    }
  `}function fL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

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

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

      return ivec3(b, r, c);
    }
  `}function gL(e,t){let n=Si(["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;
      ${n}
      return ivec3(r, c, d);
    }
  `}function mL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
      int b${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 / ${a};
      index -= b * ${a};

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

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

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

      ${n}

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

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

      return ivec2(r, c);
    }
  `}function _L(e,t){return _.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 * ${e[1]};
      return ivec2(r, c);
    }
  `}function Ni(e){return`offset${e}`}function iL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=cn();return`
    vec4 ${n}() {
      return ${r.texture2D}(${t}, halfCR);
    }
  `}function QP(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
      float ${n}() {
        return sampleTexture(${t}, halfCR);
      }
    `;let[s,i]=e.shapeInfo.texShape,o=Ni(t);return`
    float ${n}() {
      vec2 uv = uvFromFlat(${s}, ${i}, ${o});
      return sampleTexture(${t}, uv);
    }
  `}function oL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=cn();return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${a[0]}, ${a[1]}, index);
      return ${s.texture2D}(${t}, uv);
    }
  `}function eL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${Rl(e)}
      }
    `;let r=e.shapeInfo.texShape,a=r[0],s=r[1];if(s===1&&a===1)return`
      float ${n}(int index) {
        return sampleTexture(${t}, halfCR);
      }
    `;let i=Ni(t);return s===1?`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
        return sampleTexture(${t}, uv);
      }
    `:a===1?`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
        return sampleTexture(${t}, uv);
      }
    `:`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
      return sampleTexture(${t}, uv);
    }
  `}function lL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=cn();if(a!=null&&_.arraysEqual(t,a))return`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);

        return ${o.texture2D}(${n}, uv);
      }
    `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
    vec4 ${r}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
      return ${o.texture2D}(${n}, uv);
    }
  `}function tL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&_.arraysEqual(t,a)){let h=a[0],d=a[1];return`
    float ${r}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
      return sampleTexture(${n}, uv);
    }
  `}let{newShape:s,keptDims:i}=_.squeezeShape(t),o=s;if(o.length<t.length){let h=Ml(e,o),d=["row","col"];return`
      ${Cl(h)}
      float ${r}(int row, int col) {
        return ${r}(${Fl(d,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${Rl(e)}
      }
    `;let l=a[0],c=a[1],u=Ni(n);return c===1?`
    float ${r}(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 ${r}(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 ${r}(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 uL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Ml(e,h),m=["b","row","col"];return`
        ${l3(p)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${Fl(m,d)});
        }
      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=cn();return`
    vec4 ${r}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${i}, ${o}, ${c}, ${l}, b, row, col);
      return ${u.texture2D}(${n}, uv);
    }
  `}function nL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=_.squeezeShape(t),l=i;if(l.length<t.length){let m=Ml(e,l),f=["row","col","depth"];return`
        ${Cl(m)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${Fl(f,o)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${a}, ${s}, 1)));
        ${Rl(e)}
      }
    `;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
        float ${r}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${s}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${h}.0, ${u}.0);
          return sampleTexture(${n}, uv);
        }
      `;if(h===s&&d==null)return`
    float ${r}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
      return sampleTexture(${n}, uv);
    }
  `;let p=Ni(n);return`
      float ${r}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a} + col * ${s} + depth + ${p};
        vec2 uv = uvFromFlat(${u}, ${h}, index);
        return sampleTexture(${n}, uv);
      }
  `}function cL(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.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),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)h=`int b${m}, `+h,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let p=cn();return`
    vec4 ${a}(${h}) {
      int index = ${d};
      int texR = index / ${l};
      int texC = index - texR * ${l};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
      return ${p.texture2D}(${r}, uv);
    }
  `}function rL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=_.squeezeShape(t);if(o.length<t.length){let m=Ml(e,o),f=["row","col","depth","depth2"];return`
      ${Cl(m)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${Fl(f,l)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${i}, ${s}, ${a}, 1)));
        ${Rl(e)}
      }
    `;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${s}, ${a}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(d===a&&c==null)return`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;let p=Ni(n);return`
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${s} +
          depth * ${a} + depth2;
      vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
      return sampleTexture(${n}, uv);
    }
  `}function aL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=_.squeezeShape(t);if(l.length<t.length){let f=Ml(e,l),A=["row","col","depth","depth2","depth3"];return`
      ${Cl(f)}
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        return ${r}(${Fl(A,c)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${o}, ${i}, ${s}, ${a})) +
          depth3;
        ${Rl(e)}
      }
    `;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${i}, ${s}, ${a}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${p}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(p===a&&u==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${p}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;let m=Ni(n);return`
    float ${r}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} + depth * ${s} +
          depth2 * ${a} + depth3 + ${m};
      vec2 uv = uvFromFlat(${d}, ${p}, index);
      return sampleTexture(${n}, uv);
    }
  `}function sL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=_.squeezeShape(t);if(a.length<t.length){let A=Ml(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${Cl(A)}
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${r}(${Fl(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 ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${u}, ${c}, ${l}, ${o})) +
          dot(
            vec2(depth3, depth4),
            vec2(${i}, 1)));
        ${Rl(e)}
      }
    `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],m=d[1];if(m===u&&h==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${c}, ${l}, ${o}, ${i})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${m}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(m===i&&h==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${m}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;let f=Ni(n);return`
    float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${u} + col * ${c} + depth * ${l} +
          depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
      vec2 uv = uvFromFlat(${p}, ${m}, index);
      return sampleTexture(${n}, uv);
    }
  `}function Rl(e){let t=e.name,n=_.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function hL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=o3(e.shapeInfo.logicalShape,t.logicalShape),l=lt(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",m=_.sizeFromShape(e.shapeInfo.logicalShape)===1,f=_.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)p=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!f)i===1?p=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:p=`
        return vec4(outputValue.x);
      `;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${a}() {
      ${l} coords = getOutputCoords();
      ${u}
      vec4 outputValue = get${r}(${d});
      ${p}
    }
  `}function dL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"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&&_.arraysEqual(i,s))return`
      float ${a}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let c=lt(l),u=o3(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${p[f+h]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,A)=>`coords.${p[A+h]}`).join(", "),`
    float ${a}() {
      ${c} coords = getOutputCoords();
      ${d}
      return get${r}(${m});
    }
  `}function lt(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 ${e} is not yet supported`)}function Ml(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Fl(e,t){return t.map(n=>e[n]).join(", ")}function SL(e,t,n,r){let a=t.userCode,s=n.map((p,m)=>{let f={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(f.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=JP(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let m=t.variableNames[p],f=!1;d[m]=e.getUniformLocation(c,m,f),d[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function c3(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!_.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!_.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function NL(e,t,n,r,a){c3(t.inShapeInfos,n),c3([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(_.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&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function TL(e,t,n){let r="";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;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:EL,bincountImpl:h3,bincountReduceImpl:CL,ceilImpl:RL,concatImpl:ML,expImpl:FL,expm1Impl:$L,floorImpl:DL,gatherV2Impl:OL,greaterImpl:zL,lessImpl:PL,linSpaceImpl:LL,logImpl:WL,maxImpl:BL,maximumImpl:VL,minimumImpl:jL,multiplyImpl:UL,negImpl:HL,prodImpl:GL,rangeImpl:qL,rsqrtImpl:XL,simpleAbsImpl:d3,sliceImpl:KL,sparseReshapeImpl:ZL,stridedSliceImpl:YL,subImpl:JL,tileImpl:QL,topKImpl:eW,transposeImpl:mA,uniqueImpl:tW}=jm;function p3(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function hn(e,t){return t===1?[e]:p3(e,t)}function nW(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var iW=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=hn("rc",t),r=lt(t),a=rW(t,e,n),s=aW(t,e[e.length-1],e[e.length-2],n),i=sW(e,n);this.userCode=`
        void main() {
          ${r} rc = getOutputCoords();

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

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

    bool cEdge = cp1 >= ${t};
    bool rEdge = rp1 >= ${n};
  `}function sW(e,t){let n=e.length,r=oW(n,t);return n===1?`getA(rc),
            rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
            0, 0`:`getA(${r[0]}),
          cEdge ? 0. : getA(${r[1]}),
          rEdge ? 0. : getA(${r[2]}),
          rEdge || cEdge ? 0. : getA(${r[3]})`}var f3=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
        ${a}
        ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${r}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${r>0?"}":""}
      `}this.userCode=`
      ${lW(t)}
      ${uA(e)}

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

        vec4 result = vec4(0.);

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

        ${n}

        setOutput(result);
      }
    `}};function lW(e){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${Si(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var uW=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=A3(t,n),a=y3(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=m3(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Qt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Qt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Qt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=A3(n,r),s=y3(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=m3(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 cW(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 m3(e,t,n,r,a){let s=hW(t,r),i;if(a){let[l,c]=El(e[0],e[1]);i=l*c}else{let[l,c]=pc(e[0],e[1]);i=l*c}let o=cW(n,s);return i*o}function hW(e,t){switch(e){case Qt.PACKED_2X2_FLOAT32:return pA(t);case Qt.PACKED_2X2_FLOAT16:return fA(t);case Qt.UNPACKED_FLOAT32:return cA(t);case Qt.UNPACKED_FLOAT16:return hA(t);case Qt.PACKED_4X1_UNSIGNED_BYTE:return dA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function dW(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Qt.PACKED_2X2_FLOAT32:Qt.UNPACKED_FLOAT32:e?Qt.PACKED_2X2_FLOAT16:Qt.UNPACKED_FLOAT16}function A3(e,t){if(e===Qn.UPLOAD)return Qt.PACKED_2X2_FLOAT32;if(e===Qn.RENDER||e==null)return dW(t);if(e===Qn.DOWNLOAD||e===Qn.PIXELS)return Qt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function y3(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ba=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);
      }
    `}},br="if (isnan(x)) return x;",pW="return x;",g3="return abs(x);",fW="return (x >= 0.0) ? x : (exp(x) - 1.0);",mW=br+`
  return (x < 0.0) ? 0.0 : x;
`,AW=br+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Ap="return x;",yW="return 1.0 / (1.0 + exp(-1.0 * x));",gW="return x;",xW=`
  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;
`,wW=`
  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;
`,bW=`
  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;
`,_W="return 1.0 / (1.0 + exp(-1.0 * x));",$l=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);
      }
    `}},vW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=hn("rc",t),r=lt(t),a=nW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
      void main() {
        ${r} rc = getOutputCoords();
        vec4 packedInput = getA(${a});

        setOutput(getChannel(packedInput, ${i}));
      }
    `}},kW=Wr.whereImpl,IW=1e-7,SW=1e-4,AA={};function NW(e){return e in AA||(AA[e]={}),AA[e]}var TW=128,EW=600;function CW(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*EW/1024/1024}var Dl=class extends du{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Vr(J().getNumber("WEBGL_VERSION"));this.binaryCache=NW(J().getNumber("WEBGL_VERSION")),this.gpgpu=new mp(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 uW(this.gpgpu),this.numMBBeforeWarning=CW(),this.texData=new kh(this,aa())}nextDataId(){return Dl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Qn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Qn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new $l(i,Ap):h=new Ba(i,Ap);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=_.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=E.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=_.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(m=>p.push(m))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new $l(r,Ap):p=new Ba(r,Ap);let m=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...fc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=p[0],f=p[1];u=E.mergeRealAndImagArrays(m,f)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=_.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&aa().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>_.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!x_(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=_.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...fc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?cp(t):t,o=s?new WP(i):new LP(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=_.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=_.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=_.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:_.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=_.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,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(r,n),this.textureManager.releaseTexture(t,r,a,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)}shouldExecuteOnCPU(e,t=TW){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&_.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return kW(e.shape,t)}packedUnaryOp(e,t,n){let r=new $l(e.shape,t),a=this.compileAndRun(r,[e],n);return aa().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=d3(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,g3,e.dtype);let t=new Ba(e.shape,g3),n=this.compileAndRun(t,[e]);return aa().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&_.isString(n[0])){let a=n.map(s=>_.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return aa().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new vW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new iW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[vi(e.shape),...ki(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[vi(t),...ki(t)],s=new f3(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=cp(r),i;n?i=new PP(s):i=new zP(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===dc.DENSE){let f=fc(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),_.sizeFromShape(s.shape)===0)return i.values=_.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&_.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!hc(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=TL(e,l,c),h=this.getAndSaveBinary(u,()=>SL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),NL(this.gpgpu,h,l,c,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=_.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=L(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?IW:SW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=_.now());let u=t.texShape;if(u==null&&(u=D_(n,o),t.texShape=u),a!=null){let h=cp(n),d,p=u[1],m=u[0],f=a instanceof Uint8Array;o?([p,m]=El(u[0],u[1]),d=new VP(h,[m,p],f)):d=new BP(h,[m,p],f);let A=this.makeTensorInfo([m,p],r);f?this.texData.get(A.dataId).usage=Qn.PIXELS:this.texData.get(A.dataId).usage=Qn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,m,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=_.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=RW(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*_.bytesPerElement(t)}};Dl.nextDataId=0;function RW(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var x3="3.5.0";function w3(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Lu.isBrowser()&&hl("webgl",()=>new Dl,2);var MW={forceHalfFloat:w3},b3=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,Ol=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},yp=`
  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;
`,Ac=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||_.sizeFromShape(this.outputShape)===1)s=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(s=`
          ${lt(a)} coords = getOutputCoords();
        `,a===1)s+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=hn("coords",a);s+=`
            bool nextRowOutOfBounds =
              (${i[a-2]} + 1) >= ${this.outputShape[a-2]};
            bool nextColOutOfBounds =
              (${i[a-1]} + 1) >= ${this.outputShape[a-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 Pn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var FW={kernelName:Is,backendName:"webgl",kernelFunc:Pn};function Va(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Pn({inputs:{x:r},backend:n}),l=Pn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var $W={kernelName:Rh,backendName:"webgl",kernelFunc:Va},_3="return (a < 0.) ? b * a : a;",v3=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function DW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",_.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ac(v3,a.shape,i.shape):new Ol(_3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var OW={kernelName:Ss,backendName:"webgl",kernelFunc:DW},k3="return (a < 0.) ? b * a : a;",I3=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function zW(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ac(I3,r.shape,a.shape):new Ol(k3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var PW={kernelName:Ls,backendName:"webgl",kernelFunc:zW},S3="if (isnan(x)) return x;",LW=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,WW=`
  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 qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new $l(i.shape,t):u=new Ba(i.shape,e),o.runWebGLProgram(u,[i],l)}}function en({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,w]=x,b={dataId:v.dataId,dtype:v.dtype,shape:l.shape},k={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new Ol(e,l.shape,c.shape);return u.runWebGLProgram(N,[b,k],ir(v.dtype,w.dtype))}),g=Va({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||ir(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,m.values,f.values,h),g=u.makeTensorInfo(y,h),x=u.texData.get(g.dataId);return x.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new Ac(t,l.shape,c.shape,n):p=new Ol(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function gp(e,t=!1){if(e==="linear")return t?gW:pW;if(e==="relu")return t?wW:mW;if(e==="elu")return t?xW:fW;if(e==="relu6")return t?bW:AW;if(e==="prelu")return t?I3:k3;if(e==="leakyrelu")return t?v3:_3;if(e==="sigmoid")return t?_W:yW;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var N3=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";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}
        }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`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 = ${g};
          int batchB = ${x};
          vec4 a = getMatrixA(batchA, ${h});
          vec4 b = getMatrixB(batchB, ${d});

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

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

        ${y}

        ${A}

        setOutput(result);
      }
    `}},T3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},E3=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},C3="return a * b;";function yA(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=E.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new E3(T3.REAL,r.shape,a.shape),u=new E3(T3.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),m=Va({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=UL(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ac(C3,r.shape,a.shape):i=new Ol(C3,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var BW={kernelName:Ds,backendName:"webgl",kernelFunc:yA};function VW(e,t,n){let r=[vi(e.shape),...ki(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[vi(t),...ki(t)],i=new f3(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function fe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=_.sizeFromShape(a.shape),l=_.inferFromImplicitShape(s,o),c=_.sizeFromShape(l);_.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!hc(a.shape,l)&&!(u.texture!==null&&hc(u.shape,l))?VW(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var jW={kernelName:jo,backendName:"webgl",kernelFunc:fe},R3=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,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 * ${_.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
        if (inIdx < 0 || inIdx >= ${a}) {
          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);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},UW=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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,h=`
      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",h=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,d="bvec4"):t==="any"&&(i="0.0",h=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,d="bvec4");let p="";a%n>0&&(p=`
        if (inIdx < 0 || inIdx >= ${a}) {
          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) {
        ${p}
        return getX(batch, inIdx);
      }

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

        vec4 minMaxValue = vec4(${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)
          );

          ${h}
        }

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

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

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

          ${h}
        }
        setOutput(${l});
      }
    `}};function HW(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ti(e,t,n,r){let a=HW(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new R3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new R3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new UW({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var qW=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 r=lt(this.rank),a=GW(t);this.userCode=`
    void main() {
      ${r} resRC = getOutputCoords();
      setOutput(getA(${a}));
    }
    `}};function GW(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var XW=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 ${this.rank} is not yet supported.`);let r=lt(this.rank),a=p3("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
    void main() {
      ${r} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${o}) {
        result[1] = ${l};
      }
      --${a[this.rank-1]};
      if(++${a[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${o}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function xp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XW(e.shape,t):new qW(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function KW(e,t,n,r){let a=t,s=e.shape.length,i=_.parseAxisParam(a,e.shape),o=i,l=E.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=xp(e,l,r),o=E.getInnerMostAxes(o.length,s)),E.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=E.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=E.expandShapeToKeepDim(h,i));let m=_.sizeFromShape(d),f=_.sizeFromShape(e.shape)/m,A=fe({inputs:{x:u},attrs:{shape:[f,m]},backend:r}),y=cd(e.dtype),g=Ti(A,y,"sum",r),x=fe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),x}function wp(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return KW(a,s,i,n)}var ZW={kernelName:Ks,backendName:"webgl",kernelFunc:wp};function dn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=mA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=xp(a,s,i);return c}var YW={kernelName:ti,backendName:"webgl",kernelFunc:dn},M3=1e3;function bp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],m=r?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=_.sizeFromShape(f),g=_.sizeFromShape(A),x=y===g||y===1||g===1;_.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${A}).`);let v=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,m]);_.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,p]:[y,p,h],b=r?[g,m,d]:[g,d,m],k=fe({inputs:{x:e},backend:a,attrs:{shape:w}}),N=fe({inputs:{x:t},backend:a,attrs:{shape:b}}),C=[k,N],F=Math.max(y,g),O=n?k.shape[1]:k.shape[2],z=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?gp(l,!0):null,X=z||V||j||U!=null,G;if((p===1||m===1)&&O>M3&&X===!1){let Y=k,ae=N;n&&(Y=dn({inputs:{x:k},backend:a,attrs:{perm:[0,2,1]}}),C.push(Y)),r&&(ae=dn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),C.push(ae));let te=m!==1,ie=m===1,Q=Y;te&&(Q=fe({inputs:{x:Y},backend:a,attrs:{shape:[F,O,1]}}),C.push(Q));let ce=m===1?2:1,oe=ae;ie&&(oe=fe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,O]}}),C.push(oe));let me=yA({inputs:{a:Q,b:oe},backend:a});G=wp({inputs:{x:me},backend:a,attrs:{axis:ce,keepDims:!0}}),C.push(me)}else{let Y=ir(e.dtype,t.dtype),ae=new N3(w,b,[F,p,m],n,r,z,U,V,j),te=[k,N];if(s!=null&&te.push(s),V&&te.push(i),j){let ie=a.makeTensorInfo([],"float32",_.createScalarValue(o,"float32"));te.push(ie),C.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=fe({inputs:{x:G},backend:a,attrs:{shape:v}});C.push(G);for(let Y of C)a.disposeIntermediateTensorInfo(Y);return ee}function JW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return bp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var QW={kernelName:ni,backendName:"webgl",kernelFunc:JW},F3="return abs(x);";function eB(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=d3(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new $l(r.shape,F3):a=new Ba(r.shape,F3),n.runWebGLProgram(a,[r],r.dtype)}var tB={kernelName:no,backendName:"webgl",kernelFunc:eB},nB=br+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,rB=qe({opSnippet:nB}),aB={kernelName:ro,backendName:"webgl",kernelFunc:rB},sB=br+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,iB=qe({opSnippet:sB}),oB={kernelName:ao,backendName:"webgl",kernelFunc:iB},$3="return a + b;",lB=en({opSnippet:$3,packedOpSnippet:$3,supportsComplex:!0,cpuKernelImpl:EL}),uB={kernelName:Sa,backendName:"webgl",kernelFunc:lB},cB=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

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

        vec4 result = ${r};
        setOutput(result);
      }
    `}};function _p(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Pn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=_p({inputs:r.slice(0,o),backend:n}),c=_p({inputs:r.slice(o),backend:n});return _p({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>ir(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new hB(r[0].shape,s):new cB(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var dB={kernelName:ls,backendName:"webgl",kernelFunc:_p};function pB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=a;u!=null&&(h=dn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("all",c,o);let[d,p]=E.computeOutAndReduceShapes(h.shape,c),m=_.sizeFromShape(p),f=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=Ti(f,f.dtype,"all",n),y;if(i){let g=E.expandShapeToKeepDim(d,l);y=fe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=fe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var fB={kernelName:so,backendName:"webgl",kernelFunc:pB};function mB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=a;u!=null&&(h=dn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("any",c,o);let[d,p]=E.computeOutAndReduceShapes(h.shape,c),m=_.sizeFromShape(p),f=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=Ti(f,f.dtype,"any",n),y;if(i){let g=E.expandShapeToKeepDim(d,l);y=fe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=fe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var AB={kernelName:io,backendName:"webgl",kernelFunc:mB},yB=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,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 * ${r};

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

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

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

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function D3(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=E.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new yB(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=D3(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function O3(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=E.computeOptimalWindowSize(s),o=new gB(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=O3(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function z3(e,t,n,r){let a=[n];if(E.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=E.computeOutAndReduceShapes(t.shape,a),l=_.sizeFromShape(o),c=fe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=D3(e,c,r);s.push(u);let h=fe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return O3(e,t,r)}function xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=E.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=dn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=z3(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var wB={kernelName:us,backendName:"webgl",kernelFunc:xB};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=E.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=dn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=z3(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var _B={kernelName:mu,backendName:"webgl",kernelFunc:bB},vB=br+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,kB=qe({opSnippet:vB}),IB={kernelName:oo,backendName:"webgl",kernelFunc:kB},SB=br+"return log(x + sqrt(x * x + 1.0));",NB=qe({opSnippet:SB}),TB={kernelName:lo,backendName:"webgl",kernelFunc:NB},EB=br+`
  return atan(x);
`,CB=qe({opSnippet:EB}),RB={kernelName:uo,backendName:"webgl",kernelFunc:CB},MB=LW+`
  return atan(a, b);
`,FB=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+WW+`
  return result;
`,$B=en({opSnippet:MB,packedOpSnippet:FB}),DB={kernelName:ho,backendName:"webgl",kernelFunc:$B},OB=br+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=qe({opSnippet:OB}),PB={kernelName:co,backendName:"webgl",kernelFunc:zB},yc=class{constructor(e,t,n,r=!1,a=!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,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let k=">=";this.userCode=`
        const ivec2 strides = ivec2(${i}, ${o});
        const ivec2 pads = ivec2(${d}, ${p});

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

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

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

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

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

            for (int wC = 0; wC < ${h};
                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 ${k} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${r?a?f:A:`wR * ${h} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,w=s%4,b=`
      if (${m}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${g}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${d}, ${p});
      const float initializationValue = ${y};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

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

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

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

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

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

            ${b}
          }

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

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

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

            ${b}
          }
        }
        setOutput(${x});
      }
    `}},gA=class{constructor(e,t,n,r=!1,a=!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,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${o}, ${l});
        const ivec3 pads = ivec3(${f}, ${A}, ${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 < ${p};
                wR += ${u}) {
              int xR = xRCorner + wR;

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

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

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

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

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

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

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

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${h}, ch),
                getValue(batch, xD, xR, xC + 2 * ${h}, ch),
                getValue(batch, xD, xR, xC + 3 * ${h}, ch)
              );

              ${N}
            }

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

              ${N}
            } else if (${k===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${h}, ch),
                initializationValue,
                initializationValue
              );

              ${N}
            } else if (${k===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${h}, ch),
                getValue(batch, xD, xR, xC + 2 * ${h}, ch),
                initializationValue
              );

              ${N}
            }
          }
          setOutput(${w});
        }
      }
    `}};function LB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Tl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Pn({inputs:{x:a},backend:n});let h=new yc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var WB={kernelName:cs,backendName:"webgl",kernelFunc:LB};function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=E.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new gA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var VB={kernelName:Au,backendName:"webgl",kernelFunc:BB},jB=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=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,h=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${c}, ${u});
      const float avgMultiplier = float(${h});

      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) / ${r}.0;

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

          for (int wC = 0; wC < ${l};
            wC+= ${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(b, idyR, idyC, d);

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

      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) / ${a}.0;

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

          for (int wR = 0; wR < ${h};
              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 HB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,h,c,u),p=new UB(d);return n.runWebGLProgram(p,[a],i.dtype)}var GB={kernelName:Eh,backendName:"webgl",kernelFunc:HB};function qB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Tl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=E.computePool2DInfo(i.shape,o,l,1,c),h=new jB(u);return n.runWebGLProgram(h,[a],i.dtype)}var XB={kernelName:Th,backendName:"webgl",kernelFunc:qB};function KB(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return bp({a,b:s,transposeA:i,transposeB:o,backend:n})}var ZB={kernelName:hs,backendName:"webgl",kernelFunc:KB},YB=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(E.assertAndGetBroadcastShape(e,a),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)));
      }
    `}},JB=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(E.assertAndGetBroadcastShape(e,a),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);
      }
    `}},QB=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;_.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(o==null||a.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=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new JB(r.shape,a.shape,s.shape,u,h,l):new YB(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},eV={kernelName:vs,backendName:"webgl",kernelFunc:QB},nV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=tV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${xA[o]} = start[${o}] + coords.${xA[o]};`);a=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      ${n}
      void main() {
        ${a}
        setOutput(getSource(${r}));
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},xA=["x","y","z","w","u","v"];function tV(e){if(e===1)return"sourceLoc";if(e<=6)return xA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var rV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=hn("coords",this.rank),r=hn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
      result.x = ${s};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${r[this.rank-1]};
        result.y = ${s};
        --${r[this.rank-1]};
      }
    `,o=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${r[this.rank-2]};
        result.z = ${s};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${r[this.rank-1]};
          result.w = ${s};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[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 aV(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=sn.computeFlatOffset(t,_.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function gc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=sn.parseSliceParams(a,s,i);if(sn.assertParamsValid(a,o,l),_.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=KL(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=sn.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rV(l):new nV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),aV(a,o,l,n)}var sV={kernelName:qo,backendName:"webgl",kernelFunc:gc},iV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=E.getReshaped(a.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(a.shape,s,o),h=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),p=[],m=fe({inputs:{x:a},backend:n,attrs:{shape:l}}),f=dn({inputs:{x:m},backend:n,attrs:{perm:c}}),A=fe({inputs:{x:f},backend:n,attrs:{shape:u}}),y=gc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(m),p.push(f),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},oV={kernelName:yu,backendName:"webgl",kernelFunc:iV};function lV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=h3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var uV={kernelName:Ch,backendName:"webgl",kernelFunc:lV},cV="return float(a != b);",P3=en({opSnippet:cV,dtype:"bool"}),hV={kernelName:Do,backendName:"webgl",kernelFunc:P3};function xc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Pn({inputs:{x:a.complexTensorInfos.real},backend:n})}var dV={kernelName:Jh,backendName:"webgl",kernelFunc:xc},pV="return float(int(x));";function fV(e,t){let n=new Ba(e.shape,pV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function wA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Pn({inputs:{x:a},backend:n});let i=Et(a.shape),o=wA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Va({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=xc({inputs:{input:a},backend:n}),o=wA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=Pn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return fV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",_.getTypedArrayFromDType("bool",1)),o=P3({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var mV={kernelName:ds,backendName:"webgl",kernelFunc:wA},L3="return ceil(x);",AV=qe({opSnippet:L3,packedOpSnippet:L3,cpuKernelImpl:RL}),yV={kernelName:ps,backendName:"webgl",kernelFunc:AV},gV=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

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

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

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

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

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function wV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new xV(a.shape):o=new gV(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var bV={kernelName:Na,backendName:"webgl",kernelFunc:wV},_V=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 W3(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function vV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new _V(r.shape),i=[W3(r,a.complexTensorInfos.real),W3(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var kV={kernelName:gu,backendName:"webgl",kernelFunc:vV},IV=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

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

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

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

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

        ${s[r-1]} = ${s[r-1]} - 1;
        if (${s[r-2]} < ${n[r-2]} &&
            ${s[r-1]} < ${n[r-1]}) {
          result.b = getValue(${s});
        }
        setOutput(result);
      }
    `}};function vp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function kp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Pn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var NV={kernelName:Hh,backendName:"webgl",kernelFunc:kp};function zl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>xc({inputs:{input:f},backend:n})),h=e.map(f=>kp({inputs:{input:f},backend:n})),d=zl(u,t,n),p=zl(h,t,n),m=Va({inputs:{real:d,imag:p},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}let a=n.shouldExecuteOnCPU(e);if(r==="string"&&(a=!0),a){let u=e.map(y=>{let g=_.sizeFromShape(y.shape.slice(t));return fe({inputs:{x:y},backend:n,attrs:{shape:[-1,g]}})}),h=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),d=E.computeOutShape(u.map(y=>y.shape),1),p=u[0].shape[0]===1,m=ML(h,d,r,p),f=E.computeOutShape(e.map(y=>y.shape),t),A=n.makeTensorInfo(f,r,m);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),h=zl(e.slice(0,u),t,n),d=zl(e.slice(u),t,n),p=zl([h,d],t,n);return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new SV(e.map(h=>h.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:s,outShape:i}=TV(e,t,n),o=new IV(s.map(u=>u.shape)),l=n.runWebGLProgram(o,s,r);s.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=fe({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),c}function TV(e,t,n){let r=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>fe({inputs:{x:a},attrs:{shape:[-1,_.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function B3(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=E.computeOutShape(t.map(c=>c.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>_.sizeFromShape(c.shape)>0);if(o.length===1)return Pn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return E.assertParamsConsistent(l,s),zl(o,s,n)}var EV={kernelName:po,backendName:"webgl",kernelFunc:B3},V3=class{constructor(e,t=!1,n=null,r=!1,a=!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,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,x="",v="";n&&(r?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:a?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:x=`
          float activation(float x) {
            ${n}
          }
        `,v="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

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

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

        ivec2 xRCCorner =
            ivec2(coords[${A}], coords[${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 < ${h}; 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 < ${p}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

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

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

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

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

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

            }
          }
        }

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

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

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

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

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

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

                dotProd += dot(xValues, wValues);
              }

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

          if(blockIndex < ${e[1]} && pos < ${e[0]}) {
            offsetY = int(blockIndex / (${l})) * ${i} - ${p};
            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}.) / ${a}.));

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

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

                if (${A}) {
                  innerDims = vec2(d1, ch);
                  result[${v*2+w}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${v*2+w}] = 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 j3({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(h===1||d===1)&&u>M3,x=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=fe({inputs:{x:e},backend:r,attrs:{shape:[1,v,n.inChannels]}}),b=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),k=bp({a:w,b,transposeA:m,transposeB:f,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=fe({inputs:{x:k},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(b),y.push(k)}else{let v=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,_.assert(hc(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let k=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(k);let N=bp({a:w,b:k,backend:r,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=r.texData.get(N.dataId);_.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,C.shape=n.outShape,A=Pn({inputs:{x:N},backend:r}),A.shape=n.outShape,y.push(N)}for(let v of y)r.disposeIntermediateTensorInfo(v);return A}function U3({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,m=p==="channelsLast",f=l*c*u,A=d*h,y=[f,A],g=!0,x=!1,v=[],w=fe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=fe({inputs:{x:t},backend:r,attrs:{shape:[1,f,_.sizeFromShape(t.shape)/f]}});v.push(w),v.push(b);let k=new RV(y,w.shape,n),N=r.runWebGLProgram(k,[w],"float32"),C=fe({inputs:{x:N},backend:r,attrs:{shape:[1,y[0],y[1]]}});v.push(N),v.push(C);let F=a!=null,O=s!=null,z=o==="leakyrelu",V=o?gp(o,!0):null,j=new N3(C.shape,b.shape,[1,A,n.outChannels],g,x,F,V,O,z),U=[C,b];if(a&&U.push(a),O&&U.push(s),z){let Y=r.makeTensorInfo([],"float32",_.createScalarValue(i,"float32"));U.push(Y),v.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=m?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=fe({inputs:{x:X},backend:r,attrs:{shape:G}});v.push(X);for(let Y of v)r.disposeIntermediateTensorInfo(Y);return ee}function MV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;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"))p=j3({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=U3({x:a,filter:s,convInfo:d,backend:n});else{let f=new V3(d);p=n.runWebGLProgram(f,[a,s],"float32")}let m=fe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),m}var FV={kernelName:fs,backendName:"webgl",kernelFunc:MV},$V=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=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} - ${r};

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

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

              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);
      }
    `}},DV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=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) / ${r}.0;

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

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

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${a}.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);
      }
    `}},OV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=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} - ${a};

            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 * ${r} - ${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);
      }
    `}},zV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-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) / ${a}.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 < ${r}; 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 = ${r} - 1 - wC;

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function PV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new $V(d);return n.runWebGLProgram(p,[a,s],"float32")}var LV={kernelName:Mh,backendName:"webgl",kernelFunc:PV};function WV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=E.convertConv2DDataFormat(c),d=E.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new DV(d);return n.runWebGLProgram(p,[a,s],"float32")}var BV={kernelName:ms,backendName:"webgl",kernelFunc:WV};function VV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=E.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new CV(c);return n.runWebGLProgram(u,[a,s],"float32")}var jV={kernelName:xu,backendName:"webgl",kernelFunc:VV};function UV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=E.computeConv3DInfo(a.shape,l,i,1,o),u=new OV(c);return n.runWebGLProgram(u,[a,s],"float32")}var HV={kernelName:Fh,backendName:"webgl",kernelFunc:UV};function GV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=E.computeConv3DInfo(l,s.shape,o,1,i),u=new zV(c);return n.runWebGLProgram(u,[a,s],"float32")}var qV={kernelName:$h,backendName:"webgl",kernelFunc:GV},XV=S3+`
  return cos(x);
`,KV=qe({opSnippet:XV}),ZV={kernelName:As,backendName:"webgl",kernelFunc:KV},YV=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,JV=qe({opSnippet:YV}),QV={kernelName:fo,backendName:"webgl",kernelFunc:JV},ej=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,x,v]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
      const float height_ratio = float(${f});
      const float width_ratio = float(${g});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

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

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

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

        float in_y = ${y};
        if( in_y < 0.0 || in_y > ${p} ) {
          setOutput(float(${a}));
          return;
        }
        float in_x = ${v};
        if( in_x < 0.0 || in_x > ${m} ) {
          setOutput(float(${a}));
          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);
        }
      }
    `}},tj=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new ej(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},nj={kernelName:mo,backendName:"webgl",kernelFunc:tj},q3=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${H3(r,"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() {
        ${lt(r)} coords = getOutputCoords();
        int end = ${G3(r,"coords")};
        float val = ${a};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${G3(r,"coords")} = idx;
          val += getX(${H3(r,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function H3(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 ${e} is not yet supported`)}function G3(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function rj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=E.getAxesPermutation([s],l),u=a;c!=null&&(u=dn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=E.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Pn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new q3(u.shape,!1,o),A=f.getCustomSetupFunc(m),y=p;p=n.runWebGLProgram(f,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new q3(u.shape,i,o),f=p;p=n.runWebGLProgram(m,[p],p.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=E.getUndoAxesPermutation(c),f=dn({inputs:{x:p},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),f}return p}var aj={kernelName:ys,backendName:"webgl",kernelFunc:rj};function sj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=h3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=CL(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var ij={kernelName:Dh,backendName:"webgl",kernelFunc:sj},oj=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 lj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=new oj(m,s,i);return n.runWebGLProgram(f,[a],a.dtype)}var uj={kernelName:Ao,backendName:"webgl",kernelFunc:lj},X3=class{constructor(e,t=!1,n=null,r=!1,a=!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,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:a?A=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:A=`
          float activation(float x) {
            ${n}
          }
        `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${A}

      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 < ${p}; wR++) {
          int xR = xRCorner + wR * ${h};

          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;
        ${g}
        ${y}
        setOutput(result);
      }
    `}},K3=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,h=e.strideWidth,d=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,A=f,y=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let w=0;w<f;w++)y+=`
          vec4 xTexelC${w*2};
          vec4 xC${w};`;for(let w=0;w<m;w++){for(let b=0;b<f;b++)y+=`
          xTexelC${b*2} = vec4(0.0);
          xC${b} = vec4(0.0);`;y+=`
        xR = xRCorner + ${w*d};
        if (xR >=0 && xR < ${i}) {
      `;for(let b=0;b<A/2+1;b++){let k=b*2;if(y+=`
          xC = xCCorner + ${k*p};
          `,h===1){if(k<f&&(c%2==1?(y+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < ${o}) {
                  xTexelC${k} = getX(batch, xR, xCOffset, d1);

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

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

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

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

                xC${k} = xTexelC${k};
                `,k+1<f)){let N=c%2==0?_.nearestLargerEven(p):p;p%2==0&&c%2==1||p%2!=0&&c%2!=1?(y+=`
                  xCOffset = xC + ${c%2} + ${N};

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

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

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

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

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

                xC${k} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
              `,k+1<f&&(y+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + ${h};
                  if(xCOffset >= 0 && xCOffset < ${o}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${k+1} = vec4(xTexelC${k+2}.xy, final.xy);
                `)):(y+=`
                if(xC >= 0 && xC < ${o}) {
                  xTexelC${k} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= ${o}) {
                    xTexelC${k}.zw = vec2(0.0);
                  }
                }

                xCOffset = xC + ${h};
                if(xCOffset >= 0 && xCOffset < ${o}) {
                  xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= ${o}) {
                    xTexelC${k+2}.zw = vec2(0.);
                  }
                }

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

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

      void main() {

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

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

        ${y}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${v}
        ${x}
        setOutput(result);
      }
    `}};function cj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),_.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=E.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new K3(h):d=new X3(h),n.runWebGLProgram(d,[a,s],"float32")}var hj={kernelName:gs,backendName:"webgl",kernelFunc:cj},dj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=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} - ${r};

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

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

              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);
      }
    `}},pj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=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) / ${r}.0;

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

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

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${a}.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 * ${o} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function fj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=E.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new dj(h);return n.runWebGLProgram(d,[a,s],"float32")}var mj={kernelName:Oh,backendName:"webgl",kernelFunc:fj};function Aj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new pj(h);return n.runWebGLProgram(d,[a,s],"float32")}var yj={kernelName:zh,backendName:"webgl",kernelFunc:Aj},gj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function xj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=_.sizeFromShape(r.shape),i=fe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new gj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=fe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var wj={kernelName:Ph,backendName:"webgl",kernelFunc:xj},bj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
      const ivec2 strides = ivec2(${a}, ${s});
      const ivec2 pads = ivec2(${u}, ${h});
      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 _j(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=E.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new bj(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=fe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var vj={kernelName:wu,backendName:"webgl",kernelFunc:_j};function kj(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=E.decodeEinsumEquation(a,s.length);E.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=E.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,m=[];for(let f=0;f<h;++f){for(let A of u[f]){let{permutationIndices:y,expandDims:g}=E.getEinsumPermutation(p,l[A]),x;E.isIdentityPermutation(y)?x=s[A]:(x=dn({inputs:{x:s[A]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=fe({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),d===null?d=x:(d=yA({inputs:{a:x,b:d},backend:n}),m.push(d))}f<h-1&&(c[f]>=0&&(d=wp({inputs:{x:d},backend:n,attrs:{axis:c[f]-(i.length-p),keepDims:!1}}),m.push(d)),p--)}for(let f of m)f!==d&&n.disposeIntermediateTensorInfo(f);return d}var Ij={kernelName:Bh,backendName:"webgl",kernelFunc:kj},Sj="return (x >= 0.0) ? x : (exp(x) - 1.0);",Nj=`
  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;
`,Tj=qe({opSnippet:Sj,packedOpSnippet:Nj}),Ej={kernelName:yo,backendName:"webgl",kernelFunc:Tj},Cj="return (b >= 1.0) ? a : a * (b + 1.0);",Rj=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Mj=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ac(Rj,r.shape,a.shape):new Ol(Cj,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},Fj={kernelName:Vh,backendName:"webgl",kernelFunc:Mj},$j=`
  return vec4(equal(a, b));
`,Dj="return float(a == b);",Oj=en({opSnippet:Dj,packedOpSnippet:$j,dtype:"bool"}),zj={kernelName:xo,backendName:"webgl",kernelFunc:Oj},Pj=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${E.ERF_P};
  float a1 = ${E.ERF_A1};
  float a2 = ${E.ERF_A2};
  float a3 = ${E.ERF_A3};
  float a4 = ${E.ERF_A4};
  float a5 = ${E.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Lj=qe({opSnippet:Pj}),Wj={kernelName:go,backendName:"webgl",kernelFunc:Lj},Z3="return exp(x);",Y3=qe({opSnippet:Z3,packedOpSnippet:Z3,cpuKernelImpl:FL}),Bj={kernelName:ws,backendName:"webgl",kernelFunc:Y3};function bA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(_.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),fe({inputs:{x:s},backend:r,attrs:{shape:o}})}var Vj={kernelName:wo,backendName:"webgl",kernelFunc:bA},J3="return exp(x) - 1.0;",jj=qe({opSnippet:J3,packedOpSnippet:J3,cpuKernelImpl:$L}),Uj={kernelName:bo,backendName:"webgl",kernelFunc:jj},Q3=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.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 = ${a};

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

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

        float result = 0.0;

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

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

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function e7(e,t,n){let r=n.texData.get(e.dataId),a=_.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=fe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new Q3("real",l,t),u=new Q3("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),m=Va({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let f=fe({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Hj(e){let{inputs:t,backend:n}=e,{input:r}=t;return e7(r,!1,n)}var Gj={kernelName:jh,backendName:"webgl",kernelFunc:Hj},qj=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 _A(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||_.inferDtype(a),s==="string"){let i=_.getArrayFromDType(s,_.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new qj(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var Xj={kernelName:bu,backendName:"webgl",kernelFunc:_A},Kj=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);
        }
    `}},Zj={kernelName:_o,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new Kj(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},t7="return floor(x);",Yj=qe({opSnippet:t7,packedOpSnippet:t7,cpuKernelImpl:DL}),Jj={kernelName:bs,backendName:"webgl",kernelFunc:Yj},Qj=`
  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;
  }
`,eU=`
  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);
`,tU=en({opSnippet:Qj,packedOpSnippet:eU,dtype:"int32"}),nU={kernelName:_s,backendName:"webgl",kernelFunc:tU},rU=class{constructor(e){this.variableNames=["A"];let t=cn(),[n,r]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);

        vec4 values = ${t.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}},aU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=cn(),[n,r]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${r}.0, ${n}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${t.output} = result;
      }
    `}},iU={kernelName:sd,backendName:"webgl",kernelFunc:sU},Pl;function sU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(Pl==null&&(Pl=document.createElement("canvas").getContext("2d")),Pl.canvas.width=l,Pl.canvas.height=c,Pl.drawImage(a,0,0,l,c),a=Pl.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=Qn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new aU(h):new rU(h),m=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),m}function oU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=E.convertConv2DDataFormat(u),A=E.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=j3({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=U3({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,w=o!=null,b=p==="leakyrelu",k=p?gp(p,!1):null,N=new V3(A,v,k,w,b),C=[a,s];if(i&&C.push(i),o&&C.push(o),b){let F=n.makeTensorInfo([],"float32",_.createScalarValue(m,"float32"));C.push(F),g.push(F)}y=n.runWebGLProgram(N,C,"float32")}let x=fe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var lU={kernelName:ri,backendName:"webgl",kernelFunc:oU};function uU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,m=[],f=u;f==null&&(f=[1,1]),_.assert(E.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let A=E.computeConv2DInfo(a.shape,s.shape,l,f,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?gp(d,y):null,x=[a,s],v=i!=null,w=o!=null,b=d==="leakyrelu";if(v&&x.push(i),w&&x.push(o),b){let C=n.makeTensorInfo([],"float32",_.createScalarValue(p,"float32"));x.push(C),m.push(C)}let k;y?k=new K3(A,v,g,w,b):k=new X3(A,v,g,w,b);let N=n.runWebGLProgram(k,x,"float32");return m.forEach(C=>n.disposeIntermediateTensorInfo(C)),N}var cU={kernelName:ai,backendName:"webgl",kernelFunc:uU},hU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${r} strides = ${r}(${this.strides});
         void main() {
          ${a} 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 dU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=E.prepareAndValidate(r,a),h=fe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=fe({inputs:{x:r},backend:n,attrs:{shape:[_.sizeFromShape(r.shape)/c,c]}}),p=new hU(i,u,[l,c]),m=n.runWebGLProgram(p,[d,h],d.dtype),f=fe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var pU={kernelName:ko,backendName:"webgl",kernelFunc:dU},mU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=fU(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function fU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function AU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=_.parseAxisParam(i,a.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=_.sizeFromShape(s.shape),h=[],d=fe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=fe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),x=n.bufferSync(d),v=OL(x,g,m);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let f=new mU(d.shape,m),A=n.runWebGLProgram(f,[d,p],d.dtype);h.push(A);let y=fe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var yU={kernelName:vo,backendName:"webgl",kernelFunc:AU},gU="return float(a > b);",xU=`
  return vec4(greaterThan(a, b));
`,wU=en({opSnippet:gU,packedOpSnippet:xU,cpuKernelImpl:zL,dtype:"bool"}),bU={kernelName:Io,backendName:"webgl",kernelFunc:wU},_U="return float(a >= b);",vU=`
  return vec4(greaterThanEqual(a, b));
`,kU=en({opSnippet:_U,packedOpSnippet:vU,dtype:"bool"}),IU={kernelName:ks,backendName:"webgl",kernelFunc:kU};function SU(e){let{inputs:t,backend:n}=e,{input:r}=t;return e7(r,!0,n)}var NU={kernelName:Uh,backendName:"webgl",kernelFunc:SU},TU="return float(!isnan(x) && !isinf(x));",EU=qe({opSnippet:TU,dtype:"bool"}),CU={kernelName:So,backendName:"webgl",kernelFunc:EU},RU="return float(isinf(x));",MU=qe({opSnippet:RU,dtype:"bool"}),FU={kernelName:No,backendName:"webgl",kernelFunc:MU},$U="return float(isnan(x));",DU=qe({opSnippet:$U,dtype:"bool"}),OU={kernelName:To,backendName:"webgl",kernelFunc:DU},zU="return float(a < b);",PU=`
  return vec4(lessThan(a, b));
`,LU=en({opSnippet:zU,packedOpSnippet:PU,cpuKernelImpl:PL,dtype:"bool"}),WU={kernelName:Eo,backendName:"webgl",kernelFunc:LU},BU="return float(a <= b);",VU=`
  return vec4(lessThanEqual(a, b));
`,jU=en({opSnippet:BU,packedOpSnippet:VU,dtype:"bool"}),UU={kernelName:Co,backendName:"webgl",kernelFunc:jU};function HU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=LL(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var GU={kernelName:Gh,backendName:"webgl",kernelFunc:HU},qU=`if (x < 0.0) return NAN;
  return log(x);`,XU=`
  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;
`,KU=qe({opSnippet:qU,packedOpSnippet:XU,cpuKernelImpl:WL}),ZU={kernelName:Ns,backendName:"webgl",kernelFunc:KU},YU="return log(1.0 + x);",JU=qe({opSnippet:YU}),QU={kernelName:Ro,backendName:"webgl",kernelFunc:JU},eH="return float(a >= 1.0 && b >= 1.0);",tH=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,nH=en({opSnippet:eH,packedOpSnippet:tH,dtype:"bool"}),rH={kernelName:Mo,backendName:"webgl",kernelFunc:nH},aH="return float(!(x >= 1.0));",sH=qe({opSnippet:aH}),iH={kernelName:_u,backendName:"webgl",kernelFunc:sH},oH="return float(a >= 1.0 || b >= 1.0);",lH=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,uH=en({opSnippet:oH,packedOpSnippet:lH,dtype:"bool"}),cH={kernelName:vu,backendName:"webgl",kernelFunc:uH},hH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,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 * ${o};
        setOutput(val);
      }
    `}},dH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,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);
      }
    `}},pH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new dH(a.shape,s,i,o,l):new hH(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},fH={kernelName:ku,backendName:"webgl",kernelFunc:pH},mH=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${t})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${t} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${r}) * norm + float(${n});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${r})
                * float(${a})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${a});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},AH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new mH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},yH={kernelName:qh,backendName:"webgl",kernelFunc:AH};function gH(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=fe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ti(i,e.dtype,"max",r),l=fe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function n7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,x=new Array(o);for(let b=0;b<x.length;b++)x[b]=a.shape[u[b]];let v=mA(g,a.shape,a.dtype,u,x);p=n.makeTensorInfo(x,a.dtype);let w=n.texData.get(p.dataId);w.values=v}else p=xp(a,u,n);c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("max",c,o);let[m,f]=E.computeOutAndReduceShapes(p.shape,c),A=m;i&&(A=E.expandShapeToKeepDim(m,l));let y;if(d){let g=n.texData.get(p.dataId).values,x=BL(g,_.sizeFromShape(f),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=gH(p,f,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var xH={kernelName:Ts,backendName:"webgl",kernelFunc:n7},wH=b3+`
  return max(a, b);
`,bH=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+yp+`
  return result;
`,_H=en({opSnippet:wH,packedOpSnippet:bH,cpuKernelImpl:VL}),vH={kernelName:Es,backendName:"webgl",kernelFunc:_H};function kH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Tl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Pn({inputs:{x:a},backend:n});let h=new yc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var IH={kernelName:Cs,backendName:"webgl",kernelFunc:kH};function SH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=E.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new gA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var NH={kernelName:Iu,backendName:"webgl",kernelFunc:SH},TH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*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 < ${a};
          wR += ${r}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

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

          for (int wC = 0; wC < ${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);
      }
    `}},EH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
      const ivec3 pads = ivec3(${u}, ${h}, ${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 += ${a}) {
          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) / ${r}.0;

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

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

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function CH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,h,c,u),p=new gA(d,"max",!0),m=n.runWebGLProgram(p,[i],i.dtype),f=new EH(d),A=n.runWebGLProgram(f,[a,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var RH={kernelName:Kh,backendName:"webgl",kernelFunc:CH};function MH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Tl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=E.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,m=new yc(d,"max",p),f=n.runWebGLProgram(m,[o],o.dtype),A=new TH(d),y=n.runWebGLProgram(A,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var FH={kernelName:Xh,backendName:"webgl",kernelFunc:MH};function $H(e,t,n,r){let a=new yc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new yc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var DH={kernelName:Zh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];_.assert(E.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,s,c,i),[h,d]=$H(r,o,u,l);return[h,d]}};function OH(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=fe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ti(i,"float32","mean",r),l=fe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var zH={kernelName:Rs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=_.parseAxisParam(s,r.shape),c=l,u=E.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],m=r;if(h){if(d){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let k=0;k<v.length;k++)v[k]=r.shape[u[k]];let w=mA(x,r.shape,r.dtype,u,v);m=i.makeTensorInfo(v,r.dtype);let b=i.texData.get(m.dataId);b.values=w}else m=xp(r,u,i);p.push(m),c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("sum",c,o);let[f,A]=E.computeOutAndReduceShapes(m.shape,c),y=f;a&&(y=E.expandShapeToKeepDim(f,l));let g=OH(m,A,y,i);for(let x of p)i.disposeIntermediateTensorInfo(x);return g}};function PH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=a;u!=null&&(h=dn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,a.shape.length)),E.assertAxesAreInnerMostDims("min",c,o);let[d,p]=E.computeOutAndReduceShapes(h.shape,c),m=_.sizeFromShape(p),f=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=Ti(f,f.dtype,"min",n),y;if(i){let g=E.expandShapeToKeepDim(d,l);y=fe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=fe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var LH={kernelName:Ms,backendName:"webgl",kernelFunc:PH},WH=b3+`
  return min(a, b);
`,BH=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+yp+`
  return result;
`,VH=en({opSnippet:WH,packedOpSnippet:BH,cpuKernelImpl:jL}),jH={kernelName:Fs,backendName:"webgl",kernelFunc:VH},UH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=lt(r),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,r),l=n==="reflect"?0:1;if(r===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=`
      ${a} start = ${a}(${s});
      ${a} end = ${a}(${i});

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

      void main() {
        ${a} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}},GH=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HH(r.shape,a,s):new UH(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},qH={kernelName:$s,backendName:"webgl",kernelFunc:GH},XH=`if (b == 0.0) return NAN;
  return mod(a, b);`,KH=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+yp+`
  return result;
`,ZH=en({opSnippet:XH,packedOpSnippet:KH}),YH={kernelName:Fo,backendName:"webgl",kernelFunc:ZH},JH=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)}}},QH=`
if (a == b) {
  return 1.0;
};
return a / b;`,eG=`
  // 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;
`,r7=en({opSnippet:QH,packedOpSnippet:eG,checkOutOfBounds:!0}),tG={kernelName:xs,backendName:"webgl",kernelFunc:r7},a7="return a - b;",s7=en({opSnippet:a7,packedOpSnippet:a7,supportsComplex:!0,cpuKernelImpl:JL}),nG={kernelName:Js,backendName:"webgl",kernelFunc:s7};function i7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=_.parseAxisParam([s],a.shape),o=n7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=E.expandShapeToKeepDim(o.shape,i),c=fe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=s7({inputs:{a,b:c},backend:n}),h=Y3({inputs:{x:u},backend:n}),d=wp({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=fe({inputs:{x:d},backend:n,attrs:{shape:l}}),m=r7({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}var rG={kernelName:Zs,backendName:"webgl",kernelFunc:i7};function aG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:i7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new JH(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var sG={kernelName:Yh,backendName:"webgl",kernelFunc:aG},o7="return -x;";function iG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=HL(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new $l(r.shape,o7):a=new Ba(r.shape,o7),n.runWebGLProgram(a,[r],r.dtype)}var oG={kernelName:$o,backendName:"webgl",kernelFunc:iG},lG=Wr.nonMaxSuppressionV3Impl;function uG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=lG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var cG={kernelName:Oo,backendName:"webgl",kernelFunc:uG},hG=Wr.nonMaxSuppressionV4Impl;function dG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=hG(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var pG={kernelName:zo,backendName:"webgl",kernelFunc:dG},fG=Wr.nonMaxSuppressionV5Impl;function mG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,m=l,f=c,{selectedIndices:A,selectedScores:y}=fG(u,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var AG={kernelName:Po,backendName:"webgl",kernelFunc:mG},yG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${r}), float(${n}),
                      float(index == coords.y)));
      }
    `}},gG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=_.sizeFromShape(a.shape),c=new yG(l,s,i,o),u=fe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=fe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},xG={kernelName:Os,backendName:"webgl",kernelFunc:gG};function Ip(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=xc({inputs:{input:r},backend:n}),s=Ip({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Va({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return _A({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var wG={kernelName:tl,backendName:"webgl",kernelFunc:Ip};function l7(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=xc({inputs:{input:r},backend:n}),s=l7({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Va({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return _A({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var bG={kernelName:Lo,backendName:"webgl",kernelFunc:l7};function _G(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return bA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=bA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=B3({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var vG={kernelName:Wo,backendName:"webgl",kernelFunc:_G},kG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=lt(r),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,r);if(r===1){this.userCode=`
        int start = ${s};
        int end = ${i};
        uniform float value;

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

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

      void main() {
        ${a} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${p}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},u7=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IG(a.shape,s,i):new kG(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},SG={kernelName:zs,backendName:"webgl",kernelFunc:u7},NG=`
  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);
`,TG=`
  // 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));
  `+yp+`
  return result;
`,EG=en({opSnippet:NG,packedOpSnippet:TG}),CG={kernelName:Ps,backendName:"webgl",kernelFunc:EG};function RG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=_.parseAxisParam(s,a.shape),u=c,h=E.getAxesPermutation(u,o),d=a;h!=null&&(d=dn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=E.getInnerMostAxes(u.length,o),l.push(d)),E.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:A,outDtype:y}=GL(d.shape,d.dtype,m,u);p=n.makeTensorInfo(A,y,f)}else{let[m,f]=E.computeOutAndReduceShapes(d.shape,u),A=_.sizeFromShape(f),y=fe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=cd(a.dtype),x=Ti(y,g,"prod",n);p=fe({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(p);let m=E.expandShapeToKeepDim(p.shape,c);p=fe({inputs:{x:p},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var MG={kernelName:Bo,backendName:"webgl",kernelFunc:RG},c7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=qL(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},FG={kernelName:Su,backendName:"webgl",kernelFunc:c7},$G="return 1.0 / x;",DG=qe({opSnippet:$G}),OG={kernelName:Vo,backendName:"webgl",kernelFunc:DG},zG=br+`
  return (x < 0.0) ? 0.0 : x;
`,PG=`
  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;
`,LG=qe({opSnippet:zG,packedOpSnippet:PG}),WG={kernelName:Ws,backendName:"webgl",kernelFunc:LG},BG=br+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,VG=`
  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;
`,jG=qe({opSnippet:BG,packedOpSnippet:VG}),UG={kernelName:Vs,backendName:"webgl",kernelFunc:jG},HG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="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 = ${h};

        // 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);
      }
    `}},GG=class{constructor(e,t,n,r,a){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=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="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 = ${h};

        // 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 qG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new GG(a.shape,l,c,s,i):new HG(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var XG={kernelName:Bs,backendName:"webgl",kernelFunc:qG},KG=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*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(${h});
        const float invWidthScale = float(${d});

        const int winHeight = int(${p});
        const int winWidth = int(${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), ${r-1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${a-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 ZG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new KG(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var YG={kernelName:ed,backendName:"webgl",kernelFunc:ZG},JG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?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 + ${h})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}};function QG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new JG(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var eq={kernelName:Nu,backendName:"webgl",kernelFunc:QG},tq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*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(${h});
        const float invWidthScale = float(${d});

        const int winHeight = int(${p});
        const int winWidth = int(${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(${r}) - 1),
                ${n} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${a}) - 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 nq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new tq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var rq={kernelName:Qh,backendName:"webgl",kernelFunc:nq},aq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=lt(n);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${a}));
      }
    `}},sq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=hn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${a}){
              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 = ${o(r.slice())};
          if(${a}){
            result.g = ${l(r.slice())};
          }
          if(${s}) {
            result.b = ${c(r.slice())};
            if(${a}) {
              result.a = ${u(r.slice())};
            }
          }
          setOutput(result);
        }
    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let m=e.map((y,g)=>d(g,p)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function d(p,m){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${m[p]} - 1`:`${m[p]}`}}};function iq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return Pn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sq(a.shape,o):new aq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var oq={kernelName:js,backendName:"webgl",kernelFunc:iq},lq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        uniform vec4 params;
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${a}
          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},uq={kernelName:nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new lq(r.shape,s),[c,u]=E.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},cq=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,hq=qe({opSnippet:cq}),dq={kernelName:Us,backendName:"webgl",kernelFunc:hq},pq="return inversesqrt(x);",fq=qe({opSnippet:pq,cpuKernelImpl:XL}),mq={kernelName:Hs,backendName:"webgl",kernelFunc:fq},h7=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
        ${o} strides = ${o}(${a});

        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 * ${p};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${d};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function Aq(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=E.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=fe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),m=fe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new h7(l,o,p.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(A,[m,p,f],m.dtype),g=fe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),g}var yq={kernelName:Uo,backendName:"webgl",kernelFunc:Aq},gq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=lt(n);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${r});
        if (cVal >= 1.0) {
          setOutput(getA(${a}));
        } else {
          setOutput(getB(${a}));
        }
      }
    `}};function xq(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new gq(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],ir(a.dtype,s.dtype))}var wq={kernelName:Ho,backendName:"webgl",kernelFunc:xq},bq=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${E.SELU_SCALEALPHA};
  float scale = ${E.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,_q=qe({opSnippet:bq}),vq={kernelName:Go,backendName:"webgl",kernelFunc:_q},kq="return 1.0 / (1.0 + exp(-1.0 * x));",Iq=qe({opSnippet:kq}),Sq={kernelName:qs,backendName:"webgl",kernelFunc:Iq},Nq=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,Tq=qe({opSnippet:Nq}),Eq={kernelName:Ko,backendName:"webgl",kernelFunc:Tq},Cq=S3+`
  return sin(x);
`,Rq=qe({opSnippet:Cq}),Mq={kernelName:Gs,backendName:"webgl",kernelFunc:Rq},Fq=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,$q=qe({opSnippet:Fq}),Dq={kernelName:Xo,backendName:"webgl",kernelFunc:$q},Oq=`
  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;
`,zq=qe({opSnippet:Oq}),Pq={kernelName:Zo,backendName:"webgl",kernelFunc:zq},Lq=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=u7({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=E.getReshaped(u.shape,s,o,!1),d=E.getPermuted(h.length,s.length,!1),p=E.getReshapedPermuted(u.shape,s,o,!1),m=fe({inputs:{x:u},backend:n,attrs:{shape:h}}),f=dn({inputs:{x:m},backend:n,attrs:{perm:d}}),A=fe({inputs:{x:f},backend:n,attrs:{shape:p}});return c.push(u),c.push(m),c.push(f),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},Wq={kernelName:Tu,backendName:"webgl",kernelFunc:Lq};function Bq(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:s}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(a.dataId)),o=n.readSync(r.dataId),l=Array.from(n.readSync(s.dataId)),[c,u,h]=ZL(o,r.shape,r.dtype,i,l);return[n.makeTensorInfo(u,r.dtype,c),n.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Vq={kernelName:td,backendName:"webgl",kernelFunc:Bq};function jq(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=E.calculateShapes(s,a,o),d=!1,p=new h7(c,l,a.shape.length,s.shape.length,u,[h,1],d),m=n.runWebGLProgram(p,[s,a,i],s.dtype),f=fe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var Uq={kernelName:nd,backendName:"webgl",kernelFunc:jq};function Hq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=E.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let m=gc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,m})}var Gq={kernelName:Yo,backendName:"webgl",kernelFunc:Hq},qq="return sqrt(x);",Xq=qe({opSnippet:qq}),Kq={kernelName:Xs,backendName:"webgl",kernelFunc:Xq},Zq="return x * x;",Yq=qe({opSnippet:Zq}),Jq={kernelName:Eu,backendName:"webgl",kernelFunc:Yq},d7="return (a - b) * (a - b);",Qq=en({opSnippet:d7,packedOpSnippet:d7}),eX={kernelName:Ys,backendName:"webgl",kernelFunc:Qq};function tX({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=br+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Ba(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var nX={kernelName:Ea,backendName:"webgl",kernelFunc:tX},rX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=lt(n.length),s=lt(n.length),i="";if(r===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=`
      ${a} begin = ${a}(${e});
      ${a} strides = ${a}(${t});

      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function aX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=sn.sliceInfo(a.shape,s,i,o,l,c,u,h,d),x=fe({inputs:{x:a},backend:n,attrs:{shape:y}}),v;if(p){let b=gc({inputs:{x},backend:n,attrs:{begin:m,size:A}});v=fe({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))v=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let b=n.texData.get(x.dataId).values,k=We(x.shape,x.dtype,b),N=YL(g,k,f,m);v=n.makeTensorInfo(g,x.dtype,N.values)}else{let b=new rX(m,f,g);v=n.runWebGLProgram(b,[x],x.dtype)}let w=fe({inputs:{x:v},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),w}var sX={kernelName:Jo,backendName:"webgl",kernelFunc:aX},iX="return tan(x);",oX=qe({opSnippet:iX}),lX={kernelName:Qs,backendName:"webgl",kernelFunc:oX},uX=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,cX=qe({opSnippet:uX}),hX={kernelName:ei,backendName:"webgl",kernelFunc:cX},pX=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 r=lt(this.rank),a=dX(e);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        setOutput(getA(${a}));
      }
    `}};function dX(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function p7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"||a.shape.length>5){let o=n.readSync(a.dataId).map(u=>_.decodeString(u)),l=We(a.shape,a.dtype,o),c=QL(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new pX(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var fX={kernelName:Ta,backendName:"webgl",kernelFunc:p7};function mX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=eW(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var AX={kernelName:Qo,backendName:"webgl",kernelFunc:mX},yX=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${o} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${a});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${a});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

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

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

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

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

          ${h}
        }

        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
          );

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

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

          ${h}
        }
        setOutput(${l});
      }
    `}};function IX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=E.getAxesPermutation([c],o),h=a;u!=null&&(h=dn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(h.shape,c,i),p=_.sizeFromShape([h.shape[c]]),m=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(m);let f=cd(a.dtype),A=(v,w,b,k,N)=>{let C=v.shape[0],F=v.shape[1],O=E.segment_util.segOpComputeOptimalWindowSize(F,N),z={windowSize:O,inSize:F,batchSize:C,numSegments:N},V=new kX(z,w),j=n.compileAndRun(V,[v,b],k);if(l.push(j),j.shape[1]===N)return j;let U=c7({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),X=p7({inputs:{x:U},backend:n,attrs:{reps:[F/O]}});return l.push(U),l.push(X),A(j,w,X,k,N)},y=A(m,"unsortedSegmentSum",s,f,i),g=fe({inputs:{x:y},backend:n,attrs:{shape:d}}),x=g;if(u!=null){l.push(g);let v=E.getUndoAxesPermutation(u);x=dn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var SX={kernelName:Cu,backendName:"webgl",kernelFunc:IX},NX=[fH,yH,QW,tB,aB,oB,uB,dB,fB,AB,wB,_B,IB,TB,DB,RB,PB,VB,WB,GB,XB,ZB,eV,oV,uV,mV,yV,bV,kV,$W,EV,LV,BV,FV,HV,qV,jV,ZV,QV,nj,aj,ij,uj,mj,yj,hj,wj,vj,Ij,Ej,Fj,zj,Wj,Bj,Vj,Uj,Gj,Xj,Zj,Jj,nU,iU,lU,cU,pU,yU,bU,IU,FW,NU,NV,CU,FU,OU,OW,WU,UU,GU,QU,ZU,rH,iH,cH,xH,NH,IH,RH,FH,DH,vH,zH,LH,jH,qH,YH,sG,BW,oG,cG,pG,AG,hV,xG,bG,vG,SG,CG,PW,MG,FG,dV,tG,OG,UG,WG,jW,XG,YG,eq,rq,oq,uq,dq,mq,yq,wq,vq,Sq,Eq,Mq,Dq,sV,rG,Pq,Wq,Vq,Uq,Gq,Kq,Jq,eX,nX,sX,nG,ZW,lX,hX,fX,AX,xX,YW,bX,vX,SX,wG];for(let e of NX)si(e);var Nn;(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"})(Nn||(Nn={}));var wc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid"})(wc||(wc={}));var f7;function TX(e){f7=e.wasm.cwrap(ni,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function EX(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.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:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let N=n.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);m=N.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,A=wc[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],x=a.shape[0],v=n.makeOutput([x,y,g],a.dtype),w=n.dataIdMap.get(v.dataId).id,b=new Uint8Array(new Int32Array(a.shape).buffer),k=new Uint8Array(new Int32Array(s.shape).buffer);return f7(d,b,a.shape.length,p,k,s.shape.length,l,c,A,m,f,h||0,w),v}var CX={kernelName:ni,backendName:"wasm",setupFunc:TX,kernelFunc:EX};function pn(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),c=s.dataIdMap.get(l.dataId).id;return _.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var RX=pn(no);function fn(e,t,n){let r;function a(i){r=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,h=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,p=n!=null?n:c.dtype,m=E.assertAndGetBroadcastShape(c.shape,u.shape),f=o.makeOutput(m,p);if(_.sizeFromShape(m)===0)return f;let A=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(f.dataId).id,x=()=>r(h,A,c.shape.length,d,y,u.shape.length,Nn[c.dtype],g);if(t&&c.dtype==="float32")return x(),f;let v=E.getBroadcastDims(c.shape,m),w=E.getBroadcastDims(u.shape,m),b=v.every((N,C)=>N===C),k=w.every((N,C)=>N===C);if(b&&k)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var MX=!0,FX=fn(Sa,MX),m7;function $X(e){m7=e.wasm.cwrap(ls,null,["array","number","number","number"])}function DX(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(_.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return m7(s,a.length,Nn[r.dtype],i),r}var OX={kernelName:ls,backendName:"wasm",setupFunc:$X,kernelFunc:DX};function Sp(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var zX={kernelName:Is,backendName:"wasm",kernelFunc:Sp},A7;function PX(e){A7=e.wasm.cwrap(ti,null,["number","array","number","number","number","array","number"])}function Np(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=WX(t.x.shape,r.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=LX(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let m=Sp({inputs:t,backend:n});return m.shape=o,m}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return A7(u,p,l.shape.length,Nn[l.dtype],h,d,s.length),c}function LX(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function WX(e,t){let n=[],r=[];for(let a=0;a<e.length;++a)e[a]!==1&&n.push(e[a]),e[t[a]]!==1&&r.push(t[a]);for(let a=0;a<r.length;++a){let s=-1;for(let i=0;i<r.length;++i)r[i]>=a&&(s===-1||r[s]>r[i])&&(s=i);r[s]=a}return[n,r]}var BX={kernelName:ti,backendName:"wasm",kernelFunc:Np,setupFunc:PX};function ja(e,t,n){let r=e.shape,a=e.shape.length,s=_.parseAxisParam(t,r),i=s,o=E.getAxesPermutation(i,a),l=null,c=!1;if(o!=null){let u=new Array(a);for(let d=0;d<u.length;d++)u[d]=r[o[d]];i=E.getInnerMostAxes(i.length,a),l=Np({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var y7;function VX(e){y7=e.wasm.cwrap(so,null,["number, number, number"])}function jX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=ja(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;E.assertAxesAreInnerMostDims("all",u,p);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),A=_.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;y7(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=E.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var UX={kernelName:so,backendName:"wasm",setupFunc:VX,kernelFunc:jX},g7;function HX(e){g7=e.wasm.cwrap(io,null,["number, number, number"])}function GX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=ja(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;E.assertAxesAreInnerMostDims("any",u,p);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),A=_.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;g7(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=E.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var qX={kernelName:io,backendName:"wasm",setupFunc:HX,kernelFunc:GX},x7;function XX(e){x7=e.wasm.cwrap(us,null,["number","number","number","number","number"])}function KX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:h}=ja(s,a,t);if(h){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),p=t.makeOutput(d,"int32"),m=t.dataIdMap.get(p.dataId).id,f=_.sizeFromShape(p.shape),A=l.shape[u[0]];return x7(o,Nn[l.dtype],f,A,m),h&&t.disposeData(c.dataId),p}var ZX={kernelName:us,backendName:"wasm",kernelFunc:KX,setupFunc:XX},w7;function YX(e){w7=e.wasm.cwrap(cs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function JX(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,A=u.padInfo.left,y=u.strideHeight,g=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=r.makeOutput(u.outShape,"float32"),w=r.dataIdMap.get(v.dataId).id;return w7(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,m,f,A,y,g,x,w),v}var QX={kernelName:cs,backendName:"wasm",setupFunc:YX,kernelFunc:JX};function _r(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=_.sizeFromShape(r.shape),i=_.inferFromImplicitShape(a,s);return _.assert(s===_.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:i,dtype:r.dtype}}var eK={kernelName:jo,backendName:"wasm",kernelFunc:_r},b7;function tK(e){b7=e.wasm.cwrap(hs,null,["number","array","number","number","array","number","number","number","number"])}function nK(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[c-2]:s.shape[c-1],m=a.shape.slice(0,-2),f=s.shape.slice(0,-2),A=_.sizeFromShape(m),y=_.sizeFromShape(f),g=A===y||A===1||y===1;_.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);_.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[A,u,d]:[A,d,u],w=o?[y,p,h]:[y,h,p],b=_r({inputs:{x:a},backend:n,attrs:{shape:v}}),k=_r({inputs:{x:s},backend:n,attrs:{shape:w}}),N=n.dataIdMap.get(b.dataId).id,C=n.dataIdMap.get(k.dataId).id,F=i?b.shape[2]:b.shape[1],O=o?k.shape[1]:k.shape[2],z=Math.max(A,y),V=n.makeOutput([z,F,O],b.dtype),j=n.dataIdMap.get(V.dataId).id,U=new Uint8Array(new Int32Array(b.shape).buffer),X=new Uint8Array(new Int32Array(k.shape).buffer);return b7(N,U,b.shape.length,C,X,k.shape.length,i,o,j),n.disposeData(b.dataId),n.disposeData(k.dataId),V.shape=x,V}var rK={kernelName:hs,backendName:"wasm",setupFunc:tK,kernelFunc:nK};function Tp(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,a=r.makeOutput(t.shape,n),s=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(a).set(s),a}var aK={kernelName:ds,backendName:"wasm",kernelFunc:Tp},sK=pn(ps),_7;function iK(e){_7=e.wasm.cwrap(Na,null,["number","number","number","number"])}function oK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(l.dataId).id;return _7(o,s,i,c),l}var lK={kernelName:Na,backendName:"wasm",setupFunc:iK,kernelFunc:oK};function v7(e){let{inputs:t,backend:n}=e,r=_.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=E.computeOutShape(t.map(p=>p.shape),r),s=t.filter(p=>_.sizeFromShape(p.shape)>0);if(s.length===1)return Sp({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(_.sizeFromShape(a)===0)return i;let o=s.map(p=>p.shape);if(E.assertParamsConsistent(o,r),s[0].dtype==="string"){let p=s.map(x=>{let v=_.sizeFromShape(x.shape.slice(r));return _r({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));a=E.computeOutShape(p.map(x=>x.shape),1);let f=p[0].shape[0]===1,A=Hm(m,a,t[0].dtype,f),y=E.computeOutShape(s.map(x=>x.shape),r);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=E.fromStringArrayToUint8(A),p.forEach(x=>n.disposeData(x.dataId)),i}let l=_.sizeFromShape(s[0].shape.slice(0,r)),c=0,u=s.map(p=>{let m=_.sizeFromShape(p.shape.slice(r));return c+=m,m}),h=s.map(p=>n.typedArrayFromHeap(p)),d=n.typedArrayFromHeap(i);for(let p=0;p<l;p++){let m=p*c;for(let f=0;f<h.length;f++){let A=u[f],y=p*A,g=h[f].subarray(y,y+A);d.set(g,m),m+=A}}return i}var uK={kernelName:po,backendName:"wasm",kernelFunc:v7},k7;function cK(e){k7=e.wasm.cwrap(fs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hK(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h,dataFormat:d}=n,p=E.convertConv2DDataFormat(d),m=E.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!1,p),f=m.filterHeight,A=m.filterWidth,y=m.padInfo.top,g=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,w=m.dilationHeight,b=m.dilationWidth,k=m.strideHeight,N=m.strideWidth,C=m.inChannels,F=m.outChannels,O=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 z=r.makeOutput(m.outShape,"float32"),V=r.dataIdMap.get(z.dataId).id;return k7(i,a.shape[0],a.shape[1],a.shape[2],o,f,A,y,g,x,v,O,w,b,k,N,C,F,V),z}var dK={kernelName:fs,backendName:"wasm",setupFunc:cK,kernelFunc:hK},I7;function pK(e){I7=e.wasm.cwrap(ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fK(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=r,h=1,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(u,s.shape,i,h,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:A,inChannels:y,inHeight:g,inWidth:x,outChannels:v,outHeight:w,outWidth:b,strideHeight:k,strideWidth:N}=p,C=f-1-p.padInfo.top,F=A-1-p.padInfo.left,O=p.dataFormat==="channelsLast",z=_.computeStrides(p.inShape),V=_.computeStrides(a.shape),[j,U,X]=_.computeStrides(s.shape),G=z[0],ee=O?z[1]:z[2],Y=O?z[2]:1,ae=O?1:z[1],te=V[0],ie=O?V[1]:V[2],Q=O?V[2]:1,ce=O?1:V[1],oe=t.makeOutput(p.inShape,"float32"),me=t.dataIdMap.get(oe.dataId).id,de=t.dataIdMap.get(a.dataId).id,ve=t.dataIdMap.get(s.dataId).id;return I7(de,ve,m,f,A,g,x,y,w,b,v,k,N,C,F,j,U,X,G,ee,Y,ae,te,ie,Q,ce,me),oe}var mK={kernelName:ms,backendName:"wasm",setupFunc:pK,kernelFunc:fK},AK=pn(As),vA;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(vA||(vA={}));var S7;function yK(e){S7=e.wasm.cwrap(mo,null,["number","number","number","number","array","number","number","number","number","number"])}function gK(e){let{backend:t,inputs:n,attrs:r}=e,{method:a,extrapolationValue:s,cropSize:i}=r,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[h,d]=i,p=[u,h,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Tp({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let A=m.id,y=t.dataIdMap.get(l.dataId).id,g=t.dataIdMap.get(c.dataId).id,x=t.makeOutput(p,"float32"),v=t.dataIdMap.get(x.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return S7(A,y,g,u,w,h,d,vA[a],s,v),f!=null&&t.disposeData(f.dataId),x}var xK={kernelName:mo,backendName:"wasm",setupFunc:yK,kernelFunc:gK},N7;function wK(e){N7=e.wasm.cwrap(ys,null,["number","number","number","number","number","number"])}function bK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;_.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let c=E.getAxesPermutation([s],l),u=a;c!==null&&(u=Np({inputs:{x:a},attrs:{perm:c},backend:n}));let h=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[h],l);let d=n.makeOutput(u.shape,u.dtype),p=u.shape[h],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;N7(m,i?1:0,o?1:0,p,f,Nn[a.dtype]);let A=d;if(c!==null){let y=E.getUndoAxesPermutation(c);A=Np({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return A}var _K={kernelName:ys,backendName:"wasm",setupFunc:wK,kernelFunc:bK},T7;function vK(e){T7=e.wasm.cwrap(Ao,null,["number","number","number","array","number","array","array","number","number"])}function kK(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r;_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=t.makeOutput(m,"float32"),A=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(_.computeStrides(a.shape)).buffer),g=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(_.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return T7(A,s,i==="NHWC"?1:0,y,a.shape.length-1,g,x,m.length,v),f}var IK={kernelName:Ao,backendName:"wasm",setupFunc:vK,kernelFunc:kK},E7;function SK(e){E7=e.wasm.cwrap(gs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function NK(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h}=n,d=c==null?[1,1]:c,p=E.computeConv2DInfo(a.shape,s.shape,l,d,u,h,!0),m=p.filterHeight,f=p.filterWidth,A=p.padInfo.top,y=p.padInfo.right,g=p.padInfo.bottom,x=p.padInfo.left,v=p.dilationHeight,w=p.dilationWidth,b=p.strideHeight,k=p.strideWidth,N=p.inChannels,C=p.outChannels,F=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let O=r.makeOutput(p.outShape,"float32"),z=r.dataIdMap.get(O.dataId).id;return E7(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,A,y,g,x,F,v,w,b,k,N,C,z),O}var TK={kernelName:gs,backendName:"wasm",setupFunc:SK,kernelFunc:NK},EK=!1,CK=fn(xo,EK,"bool"),RK=pn(ws);function kA(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),_r({inputs:{x:a},backend:r,attrs:{shape:o}})}var MK={kernelName:wo,backendName:"wasm",kernelFunc:kA};function FK(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var $K={kernelName:bu,backendName:"wasm",kernelFunc:FK},C7;function DK(e){C7=e.wasm.cwrap(_o,null,["number","number","number","number","number","number"])}function OK(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return C7(s,o,l,c,u,i),a}var zK={kernelName:_o,backendName:"wasm",kernelFunc:OK,setupFunc:DK},PK=pn(bs),LK=!1,WK=fn(_s,LK),R7;function BK(e){R7=e.wasm.cwrap(vs,null,["number","number","number","number","number","number","number"])}function VK(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(_.sizeFromShape(s.shape)===0)return f;let A=t.dataIdMap.get(f.dataId).id;return R7(u,h,d,p,m,a,A),f}var jK={kernelName:vs,backendName:"wasm",setupFunc:BK,kernelFunc:VK},M7;function UK(e){M7=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function HK(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(a.shape,s.shape,l,u,c,d),A=wc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=f.filterHeight,b=f.filterWidth,k=f.padInfo.top,N=f.padInfo.right,C=f.padInfo.bottom,F=f.padInfo.left,O=f.dilationHeight,z=f.dilationWidth,V=f.strideHeight,j=f.strideWidth,U=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return M7(y,G,ee,Y,g,w,b,v,k,N,C,F,X,O,z,V,j,U,x,A,ie,m||0,te),ae}var GK={kernelName:ri,backendName:"wasm",setupFunc:UK,kernelFunc:HK},F7;function qK(e){F7=e.wasm.cwrap(ai,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 XK(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(a.shape,s.shape,l,u,c,d,!0),A=wc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=f.filterHeight,b=f.filterWidth,k=f.padInfo.top,N=f.padInfo.right,C=f.padInfo.bottom,F=f.padInfo.left,O=f.dilationHeight,z=f.dilationWidth,V=f.strideHeight,j=f.strideWidth,U=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return F7(y,G,ee,Y,g,w,b,v,k,N,C,F,X,O,z,V,j,U,x,A,ie,m||0,te),ae}var KK={kernelName:ai,backendName:"wasm",setupFunc:qK,kernelFunc:XK},$7;function ZK(e){$7=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","array","number"])}function YK(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=Vf.prepareAndValidate(r,a),c=t.makeOutput(s,r.dtype);if(i===0)return c;let u=a.shape,h=u[u.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return $7(d,Nn[r.dtype],p,i,h,o,m,f),c}var JK={kernelName:ko,backendName:"wasm",setupFunc:ZK,kernelFunc:YK},D7;function QK(e){D7=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function eZ(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=_.parseAxisParam(i,a.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=_r({inputs:{x:a},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=_.sizeFromShape(s.shape),d=_r({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),p=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],m=t.makeOutput(p,a.dtype);if(_.sizeFromShape(a.shape)===0)return m;let f=u.shape.length-1,A=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(_.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(_.computeStrides(p)).buffer);return D7(A,Nn[a.dtype],x,f,y,c.batchSize,v,g),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var tZ={kernelName:vo,backendName:"wasm",setupFunc:QK,kernelFunc:eZ},nZ=!1,rZ=fn(Io,nZ,"bool"),aZ=!1,sZ=fn(ks,aZ,"bool"),O7;function iZ(e){O7=e.wasm.cwrap(Ss,null,["number","number","number"])}function oZ(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(_.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;O7(a,n,i)}return s}var lZ={kernelName:Ss,backendName:"wasm",setupFunc:iZ,kernelFunc:oZ},uZ=!1,cZ=fn(Eo,uZ,"bool"),hZ=!1,dZ=fn(Co,hZ,"bool"),pZ=pn(Ns),fZ=!1,mZ=fn(Mo,fZ,"bool"),z7;function AZ(e){z7=e.wasm.cwrap(Ts,null,["number, number, number"])}function yZ(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=ja(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;E.assertAxesAreInnerMostDims("max",u,p);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),A=_.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;z7(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=E.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var gZ={kernelName:Ts,backendName:"wasm",setupFunc:AZ,kernelFunc:yZ},xZ=!1,wZ=fn(Es,xZ),P7;function bZ(e){P7=e.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _Z(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,A=u.padInfo.left,y=u.dilationHeight,g=u.dilationWidth,x=u.strideHeight,v=u.strideWidth,w=u.inChannels,b=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let k=r.makeOutput(u.outShape,"float32"),N=r.dataIdMap.get(k.dataId).id;return P7(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,m,f,A,y,g,x,v,w,b,N),k}var vZ={kernelName:Cs,backendName:"wasm",setupFunc:bZ,kernelFunc:_Z},L7;function kZ(e){L7=e.wasm.cwrap(Rs,null,["number, number, number"])}function IZ(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=ja(i,a,t),m=h;if(p){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,A]=E.computeOutAndReduceShapes(c.shape,m),y=_.sizeFromShape(A),g=c;c.dtype!=="float32"&&(g=Tp({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let x=t.makeOutput(f,"float32");if(_.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;L7(l,y,v)}if(p&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(g.dataId),x}var SZ={kernelName:Rs,backendName:"wasm",setupFunc:kZ,kernelFunc:IZ},W7;function NZ(e){W7=e.wasm.cwrap(Ms,null,["number, number, number"])}function TZ(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=ja(i,a,t);if(p){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let m=c.shape.length;E.assertAxesAreInnerMostDims("min",h,m);let[f,A]=E.computeOutAndReduceShapes(c.shape,h),y=_.sizeFromShape(A),g=t.makeOutput(f,c.dtype);if(_.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;W7(l,y,x)}if(p&&t.disposeData(u.dataId),s){let x=E.expandShapeToKeepDim(g.shape,d);g.shape=x}return g}var EZ={kernelName:Ms,backendName:"wasm",setupFunc:NZ,kernelFunc:TZ},CZ=!1,RZ=fn(Fs,CZ),IA;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(IA||(IA={}));var B7;function MZ(e){B7=e.wasm.cwrap($s,null,["number","array","number","number","array","array","number","number"])}function FZ(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:a}}=e,s=r.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=r.map(m=>m[0]),h=r.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return B7(i,c,t.shape.length,Nn[t.dtype],d,p,IA[a],l),o}var $Z={kernelName:$s,backendName:"wasm",kernelFunc:FZ,setupFunc:MZ},DZ=!0,OZ=fn(Ds,DZ),zZ=pn($o);function SA(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],a=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var V7;function PZ(e){V7=e.wasm.cwrap(Oo,"number",["number","number","number","number","number"])}function LZ(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=r,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,h=V7(c,u,s,a,i),{pSelectedIndices:d,selectedSize:p,pSelectedScores:m,pValidOutputs:f}=SA(t,h);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([p],"int32",d)}var WZ={kernelName:Oo,backendName:"wasm",setupFunc:PZ,kernelFunc:LZ},j7;function BZ(e){j7=e.wasm.cwrap(zo,"number",["number","number","number","number","number","bool"])}function VZ(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=j7(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:m,pSelectedScores:f,pValidOutputs:A}=SA(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var jZ={kernelName:zo,backendName:"wasm",setupFunc:BZ,kernelFunc:VZ},U7;function UZ(e){U7=e.wasm.cwrap(Po,"number",["number","number","number","number","number","number"])}function HZ(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=U7(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:m,pSelectedScores:f,pValidOutputs:A}=SA(t,d);t.wasm._free(A);let y=t.makeOutput([m],"int32",p),g=t.makeOutput([m],"float32",f);return[y,g]}var GZ={kernelName:Po,backendName:"wasm",setupFunc:UZ,kernelFunc:HZ},qZ=!1,XZ=fn(Do,qZ,"bool"),H7;function KZ(e){H7=e.wasm.cwrap(Os,null,["number","number","number","number","number"])}function ZZ(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(a.dataId).id;return H7(u,s,i,o,c),l}var YZ={kernelName:Os,backendName:"wasm",setupFunc:KZ,kernelFunc:ZZ};function JZ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var QZ={kernelName:Lo,backendName:"wasm",kernelFunc:JZ};function eY(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return kA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=kA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=v7({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeData(u.dataId)),c}var tY={kernelName:Wo,backendName:"wasm",kernelFunc:eY},G7;function nY(e){G7=e.wasm.cwrap(zs,null,["number","array","number","number","array","array","number","number"])}function rY(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.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=r.map(m=>m[0]),h=r.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return G7(i,c,t.shape.length,Nn[t.dtype],d,p,a,l),o}var aY={kernelName:zs,backendName:"wasm",kernelFunc:rY,setupFunc:nY},sY=!1,iY=fn(Ps,sY),q7;function oY(e){q7=e.wasm.cwrap(Ls,null,["number","number","number"])}function lY(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return q7(s,i,l),o}var uY={kernelName:Ls,backendName:"wasm",setupFunc:oY,kernelFunc:lY},X7;function cY(e){X7=e.wasm.cwrap(Bo,null,["number","number","number","number"])}function hY(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=ja(i,a,t),m=h;if(p){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,A]=E.computeOutAndReduceShapes(c.shape,m),y=_.sizeFromShape(A),g=t.makeOutput(f,c.dtype);if(_.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;X7(l,y,Nn[g.dtype],x)}if(p&&t.disposeData(u.dataId),s){let x=E.expandShapeToKeepDim(g.shape,d);g.shape=x}return g}var dY={kernelName:Bo,backendName:"wasm",setupFunc:cY,kernelFunc:hY},pY=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Xm(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},fY={kernelName:Su,backendName:"wasm",kernelFunc:pY},mY=!0,AY=fn(xs,mY),yY=pn(Ws),gY=pn(Vs),K7;function xY(e){K7=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number"])}function wY(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,[u,h,d,p]=a.shape,m=[u,l,c,p],f=t.dataIdMap.get(a.dataId),A;f.dtype!=="float32"&&(A=Tp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(A.dataId));let y=f.id,g=t.makeOutput(m,"float32");if(_.sizeFromShape(a.shape)===0)return g;let x=t.dataIdMap.get(g.dataId).id;return K7(y,u,h,d,p,l,c,s?1:0,i?1:0,x),A!=null&&t.disposeData(A.dataId),g}var bY={kernelName:Bs,backendName:"wasm",setupFunc:xY,kernelFunc:wY},Z7;function _Y(e){Z7=e.wasm.cwrap(js,null,["number","array","number","array","number","number"])}function vY(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=_.parseAxisParam(s,a.shape);if(a.shape.length===0)return Sp({inputs:{x:a},backend:n});let o=n.makeOutput(a.shape,a.dtype),l=n.dataIdMap.get(a.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);Z7(l,u,i.length,h,a.shape.length,c);let d=_r({inputs:{x:o},attrs:{shape:a.shape},backend:n});return n.disposeData(o.dataId),d}var kY={kernelName:js,backendName:"wasm",kernelFunc:vY,setupFunc:_Y},Y7;function IY(e){Y7=e.wasm.cwrap(nl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function SY(e){let{inputs:t,backend:n,attrs:r}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=r,l=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(a.dataId).id,u=n.dataIdMap.get(l.dataId).id,[h,d,p,m]=a.shape,[f,A]=E.getImageCenter(o,d,p),y=i===0,g=255,x=typeof i=="number"?[i,i,i,y?0:g]:[...i,g],v=new Uint8Array(new Int32Array(x).buffer);return Y7(c,h,d,p,m,s,f,A,v,x.length,u),l}var NY={kernelName:nl,backendName:"wasm",kernelFunc:SY,setupFunc:IY},TY=pn(Us),EY=pn(Hs),J7;function CY(e){J7=e.wasm.cwrap(Uo,null,["number","number","number","number","number","number","array","number","number"])}function RY(e){let{backend:t,inputs:n,attrs:r}=e,{indices:a,updates:s}=n,{shape:i}=r,o=t.makeOutput(i,s.dtype);if(_.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:h,outputSize:d}=jf.calculateShapes(s,a,i),p=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(h).buffer),A=t.dataIdMap.get(o.dataId).id;return J7(p,m,Nn[s.dtype],l,c,u,f,d,A),o}var MY={kernelName:Uo,backendName:"wasm",setupFunc:CY,kernelFunc:RY},Q7;function FY(e){Q7=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function $Y(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(a.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(c.dataId).id,h=r.shape.length,d=a.shape.length,p=h===0||h>1||d===1?1:_.sizeFromShape(a.shape.slice(1));return Q7(i,o,l,p,u),c}var DY={kernelName:Ho,backendName:"wasm",kernelFunc:$Y,setupFunc:FY},ev;function OY(e){ev=e.wasm.cwrap(qs,null,["number","number"])}function zY(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(a.dataId).id;return _.sizeFromShape(a.shape)===0||ev(r,s),a}var PY={kernelName:"Sigmoid",backendName:"wasm",setupFunc:OY,kernelFunc:zY},LY=pn(Gs);function Ep(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=sn.parseSliceParams(t,n,r),o=sn.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),c=a.makeOutput(i,t.dtype),u=_.computeStrides(t.shape),h=a.dataIdMap.get(c.dataId);if(o){let m=sn.computeFlatOffset(s,u);return t.dtype==="string"?h.stringBytes=l.slice(m,m+_.sizeFromShape(i)):a.typedArrayFromHeap(c).set(l.subarray(m,m+_.sizeFromShape(i))),c}if(t.dtype==="string"){let m=rp(l,s,i,t.shape,t.dtype);return h.stringBytes=m,c}let d=a.typedArrayFromHeap(c),p=t.shape.length;if(p===2)WY(l,u[0],d,s,i);else if(p===3)BY(l,u[0],u[1],d,s,i);else if(p===4)VY(l,u[0],u[1],u[2],d,s,i);else{let m=rp(l,s,i,t.shape,t.dtype);d.set(m)}return c}function WY(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+a[1]),s),s+=a[1]}}function BY(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],c=a[2],u=o+s[0],h=l+s[1];for(let d=o;d<u;d++)for(let p=l;p<h;p++){let m=d*t+p*n+c;r.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function VY(e,t,n,r,a,s,i){let o=0,l=s[0],c=s[1],u=s[2],h=l+i[0],d=c+i[1],p=u+i[2],m=s[3];for(let f=l;f<h;f++)for(let A=c;A<d;A++)for(let y=u;y<p;y++){let g=f*t+A*n+y*r+m;a.set(e.subarray(g,g+i[3]),o),o+=i[3]}}var jY={kernelName:qo,backendName:"wasm",kernelFunc:Ep},tv;function UY(e){tv=e.wasm.cwrap(Zs,null,["number","number","number","number"])}function HY(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,a=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[r],l=_.sizeFromShape(n.shape)/o;return _.sizeFromShape(s.shape)===0||tv(a,i,o,l),s}var GY={kernelName:Zs,backendName:"wasm",setupFunc:UY,kernelFunc:HY};function qY(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=_.parseAxisParam(i,a.shape)[0],l=E.prepareSplitSize(a,s,o),c=new Array(a.shape.length).fill(0),u=a.shape.slice();return l.map(h=>{let d=[...u];d[o]=h;let p=Ep({inputs:{x:a},attrs:{begin:c,size:d},backend:r});return c[o]+=h,p})}var XY={kernelName:Yo,backendName:"wasm",kernelFunc:qY},KY=pn(Xs),ZY=pn(Eu),YY=!0,JY=fn(Ys,YY),nv;function QY(e){nv=e.wasm.cwrap(Ea,null,["number","number","number"])}function eJ(e){let{backend:t,inputs:n,attrs:r}=e,{alpha:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return nv(i,a,l),o}var tJ={kernelName:Ea,backendName:"wasm",setupFunc:QY,kernelFunc:eJ},rv;function nJ(e){rv=e.wasm.cwrap(Jo,null,["number","array","number","array","array","array","array","array","number","number"])}function rJ(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{begin:s,end:i,strides:o}=r;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,p=E.slice_util.maskToAxes(u);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&h!==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=a.shape.length-s.length,f=E.slice_util.maskToAxes(h),A=a.shape.slice();f.forEach(F=>{s[F]=0,i[F]=1,A.splice(F,0,1)});let y=_r({inputs:{x:a},attrs:{shape:A},backend:t}),{begin:g,end:x,strides:v}=E.slice_util.getNormalizedAxes(y.shape,p,m,s,i,o,l,c,u);s=g,i=x,o=v;let w=E.slice_util.maskToAxes(d);w.forEach(F=>{i[F]=s[F]+1,o[F]=1});let b=E.slice_util.computeOutShape(s,i,o),k=b.filter((F,O)=>w.indexOf(O)===-1);if(o.every(F=>F===1)){let F=Ep({inputs:{x:y},attrs:{begin:s,size:b},backend:t});t.disposeData(y.dataId);let O=_r({inputs:{x:F},attrs:{shape:k},backend:t});return t.disposeData(F.dataId),O}let N=t.makeOutput(k,"float32");if(!k.some(F=>F===0)){let F=t.dataIdMap.get(y.dataId).id,O=new Uint8Array(new Int32Array(_.computeStrides(y.shape)).buffer),z=new Uint8Array(new Int32Array(s).buffer),V=new Uint8Array(new Int32Array(i).buffer),j=new Uint8Array(new Int32Array(o).buffer),U=new Uint8Array(new Int32Array(k).buffer),X=new Uint8Array(new Int32Array(_.computeStrides(k)).buffer),G=t.dataIdMap.get(N.dataId).id;rv(F,O,y.shape.length,z,V,j,U,X,k.length,G)}t.disposeData(y.dataId);let C=_r({inputs:{x:N},attrs:{shape:k},backend:t});return t.disposeData(N.dataId),C}var aJ={kernelName:Jo,backendName:"wasm",setupFunc:nJ,kernelFunc:rJ},sJ=!0,iJ=fn(Js,sJ),av;function oJ(e){av=e.wasm.cwrap(Ks,null,["number, number, number"])}function lJ(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=ja(i,a,t),m=h;if(p){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("sum",m,c.shape.length);let[f,A]=E.computeOutAndReduceShapes(c.shape,m),y=_.sizeFromShape(A),g=t.makeOutput(f,c.dtype);if(_.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;av(l,y,x)}if(p&&t.disposeData(u.dataId),s){let x=E.expandShapeToKeepDim(g.shape,d);g.shape=x}return g}var uJ={kernelName:Ks,backendName:"wasm",setupFunc:oJ,kernelFunc:lJ},cJ=pn(Qs),hJ=pn(ei),sv;function dJ(e){sv=e.wasm.cwrap(Ta,null,["number","array","number","array","number","number"])}function pJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,s=n.dataIdMap.get(a.dataId).id,{reps:i}=r,o=new Array(a.shape.length);for(let d=0;d<o.length;d++)o[d]=a.shape[d]*i[d];let l=new Uint8Array(new Int32Array(a.shape).buffer),c=new Uint8Array(new Int32Array(o).buffer),u=n.makeOutput(o,a.dtype),h=n.dataIdMap.get(u.dataId).id;return sv(s,l,a.shape.length,c,o.length,Nn[u.dtype],h),u}var fJ={kernelName:Ta,backendName:"wasm",setupFunc:dJ,kernelFunc:pJ},iv;function mJ(e){iv=e.wasm.cwrap(Qo,null,["number","array","number","number","number","bool","number","number"])}var AJ=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{k:a,sorted:s}=n,i=t.dataIdMap.get(r.dataId).id,o=new Uint8Array(new Int32Array(r.shape).buffer),l=r.shape.slice();l[l.length-1]=a;let c=t.makeOutput(l,r.dtype),u=t.dataIdMap.get(c.dataId).id,h=t.makeOutput(l,"int32"),d=t.dataIdMap.get(h.dataId).id;return iv(i,o,r.shape.length,Nn[r.dtype],a,s,u,d),[c,h]},yJ={kernelName:Qo,backendName:"wasm",setupFunc:mJ,kernelFunc:AJ};function gJ(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape[s],o=a.shape.length,l=new Array(o-1),c=0;for(let p=0;p<o;p++)p!==s&&(l[c++]=a.shape[p]);let u=new Array(i),h=new Array(o).fill(0),d=a.shape.slice();d[s]=1;for(let p=0;p<u.length;p++)h[s]=p,u[p]=Ep({inputs:{x:a},attrs:{begin:h,size:d},backend:n});return u.map(({dataId:p,dtype:m})=>({dataId:p,dtype:m,shape:l}))}var xJ={kernelName:el,backendName:"wasm",kernelFunc:gJ};function wJ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var bJ={kernelName:tl,backendName:"wasm",kernelFunc:wJ},_J=[RX,FX,OX,UX,qX,ZX,QX,rK,aK,sK,lK,uK,dK,mK,AK,xK,_K,IK,TK,CK,RK,MK,$K,zK,PK,WK,CX,jK,GK,KK,JK,tZ,rZ,sZ,zX,lZ,cZ,dZ,pZ,mZ,gZ,wZ,vZ,SZ,EZ,RZ,$Z,OZ,zZ,WZ,jZ,GZ,XZ,YZ,QZ,tY,aY,iY,uY,dY,fY,AY,yY,gY,eK,bY,kY,NY,EY,TY,MY,DY,PY,LY,jY,GY,XY,KY,ZY,JY,tJ,aJ,iJ,uJ,cJ,hJ,fJ,yJ,BX,xJ,bJ];for(let e of _J)si(e);var NA=J();NA.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])));NA.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(NA.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 ov=Qi(Yk()),vJ='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',kJ=Qi(Jk()),lv=class extends du{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new kh(this,aa())}write(e,t,n){let r={id:this.dataIdNextNumber++};return this.move(r,e,t,n,1),r}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=_.now();return e(),{kernelMs:_.now()-t}}move(e,t,n,r,a){let s=this.dataIdNextNumber++;if(r==="string"){let c=t;this.dataIdMap.set(e,{id:s,stringBytes:c,shape:n,dtype:r,memoryOffset:null,refCount:a});return}let i=_.sizeFromShape(n),o=i*_.bytesPerElement(r),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:r,refCount:a}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:r,stringBytes:a}=this.dataIdMap.get(e);if(n==="string")return a;let s=this.wasm.HEAPU8.slice(t,t+_.sizeFromShape(r)*_.bytesPerElement(n));return IJ(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let r;if(n==null)r=this.write(null,e,t);else{let a=this.dataIdNextNumber++;r={id:a},this.dataIdMap.set(r,{id:a,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=_.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,s,n)}return{dataId:r,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let r=this.wasm.HEAPU8.buffer,{memoryOffset:a}=this.dataIdMap.get(n),s=_.sizeFromShape(e);switch(t){case"float32":return new Float32Array(r,a,s);case"int32":return new Int32Array(r,a,s);case"bool":return new Uint8Array(r,a,s);default:throw new Error(`Unknown dtype ${t}`)}}};function SJ(e){return(t,n)=>(_.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{n(s.instance,s.module)})})}),{})}function uv(e,t,n){if(Cp!=null)return Cp;let r="tfjs-backend-wasm.wasm";return e&&t?r="tfjs-backend-wasm-threaded-simd.wasm":e&&(r="tfjs-backend-wasm-simd.wasm"),bc!=null&&bc[r]!=null?bc[r]:n+r}async function NJ(){let[e,t]=await Promise.all([J().getAsync("WASM_HAS_SIMD_SUPPORT"),J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,r)=>{let a={};a.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let c=vJ,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return o.endsWith(".wasm")?uv(e,t,_c!=null?_c:l):l+o},TA&&(a.instantiateWasm=SJ(uv(e,t,_c!=null?_c:"")));let s=!1;a.onAbort=()=>{s||vc||(vc=!0,r({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&Cp==null?(a.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+ov.default.toString()],{type:"text/javascript"}),i=(0,ov.default)(a)):i=(0,kJ.default)(a),i.then(o=>{s=!0,vc=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function IJ(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 TJ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Cp=null,_c=null,bc={},vc=!1,TA=!1;function EJ(e,t=!1){if(Kf("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),vc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Cp=e,TA=t}function CJ(e,t=!1){if(vc)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")_c=e;else{bc=e;let n=TJ.filter(r=>bc[r]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}TA=t}var cv="3.5.0",RJ=2;hl("wasm",async()=>{let{wasm:e}=await NJ();return new lv(e)},RJ);Z().prototype.abs=function(){return this.throwIfDisposed(),Dt(this)};Z().prototype.acos=function(){return this.throwIfDisposed(),Yf(this)};Z().prototype.acosh=function(){return this.throwIfDisposed(),Jf(this)};Z().prototype.add=function(e){return this.throwIfDisposed(),se(this,e)};Z().prototype.all=function(e,t){return this.throwIfDisposed(),gd(this,e,t)};Z().prototype.any=function(e,t){return this.throwIfDisposed(),Vu(this,e,t)};Z().prototype.argMax=function(e){return this.throwIfDisposed(),ju(this,e)};Z().prototype.argMin=function(e){return this.throwIfDisposed(),Qf(this,e)};Z().prototype.asScalar=function(){return this.throwIfDisposed(),M(this.size===1,()=>"The array must have only 1 element."),H(this,[])};Z().prototype.asType=function(e){return this.throwIfDisposed(),Ae(this,e)};Z().prototype.as1D=function(){return this.throwIfDisposed(),H(this,[this.size])};Z().prototype.as2D=function(e,t){return this.throwIfDisposed(),H(this,[e,t])};Z().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),H(this,[e,t,n])};Z().prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),H(this,[e,t,n,r])};Z().prototype.as5D=function(e,t,n,r,a){return this.throwIfDisposed(),H(this,[e,t,n,r,a])};Z().prototype.asin=function(){return this.throwIfDisposed(),em(this)};Z().prototype.asinh=function(){return this.throwIfDisposed(),tm(this)};Z().prototype.atan=function(){return this.throwIfDisposed(),nm(this)};Z().prototype.atan2=function(e){return this.throwIfDisposed(),rm(this,e)};Z().prototype.atanh=function(){return this.throwIfDisposed(),am(this)};Z().prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),Hu(this,e,t,n,r)};Z().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Gu(this,e,t)};Z().prototype.batchNorm=function(e,t,n,r,a){return this.throwIfDisposed(),pi(this,e,t,n,r,a)};Z().prototype.broadcastTo=function(e){return this.throwIfDisposed(),pl(this,e)};Z().prototype.cast=function(e){return this.throwIfDisposed(),Ae(this,e)};Z().prototype.ceil=function(){return this.throwIfDisposed(),lm(this)};Z().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),In(this,e,t)};Z().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Le&&(e=[e]),ot([this,...e],t)};Z().prototype.conv1d=function(e,t,n,r,a,s){return this.throwIfDisposed(),wd(this,e,t,n,r,a,s)};Z().prototype.conv2dTranspose=function(e,t,n,r,a){return this.throwIfDisposed(),bd(this,e,t,n,r,a)};Z().prototype.conv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),ia(this,e,t,n,r,a,s)};Z().prototype.cos=function(){return this.throwIfDisposed(),qu(this)};Z().prototype.cosh=function(){return this.throwIfDisposed(),_d(this)};Z().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),vd(this,e,t,n)};Z().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),hm(this,e,t)};Z().prototype.depthwiseConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),ml(this,e,t,n,r,a,s)};Z().prototype.dilation2d=function(e,t,n,r,a){return this.throwIfDisposed(),dm(this,e,t,n,r,a)};Z().prototype.divNoNan=function(e){return this.throwIfDisposed(),pm(this,e)};Z().prototype.div=function(e){return this.throwIfDisposed(),ge(this,e)};Z().prototype.dot=function(e){return this.throwIfDisposed(),Ow(this,e)};Z().prototype.elu=function(){return this.throwIfDisposed(),Al(this)};Z().prototype.equal=function(e){return this.throwIfDisposed(),Da(this,e)};Z().prototype.erf=function(){return this.throwIfDisposed(),fm(this)};Z().prototype.exp=function(){return this.throwIfDisposed(),Xn(this)};Z().prototype.expandDims=function(e){return this.throwIfDisposed(),on(this,e)};Z().prototype.expm1=function(){return this.throwIfDisposed(),mm(this)};Z().prototype.fft=function(){return this.throwIfDisposed(),rc(this)};Z().prototype.flatten=function(){return this.throwIfDisposed(),H(this,[this.size])};Z().prototype.floor=function(){return this.throwIfDisposed(),yl(this)};Z().prototype.floorDiv=function(e){return this.throwIfDisposed(),Ad(this,e)};Z().prototype.gather=function(e,t){return this.throwIfDisposed(),fi(this,e,t)};Z().prototype.greaterEqual=function(e){return this.throwIfDisposed(),za(this,e)};Z().prototype.greater=function(e){return this.throwIfDisposed(),or(this,e)};Z().prototype.ifft=function(){return this.throwIfDisposed(),bl(this)};Z().prototype.irfft=function(){return this.throwIfDisposed(),Wd(this)};Z().prototype.isFinite=function(){return this.throwIfDisposed(),Pw(this)};Z().prototype.isInf=function(){return this.throwIfDisposed(),Lw(this)};Z().prototype.isNaN=function(){return this.throwIfDisposed(),ym(this)};Z().prototype.leakyRelu=function(e){return this.throwIfDisposed(),Ku(this,e)};Z().prototype.lessEqual=function(e){return this.throwIfDisposed(),mi(this,e)};Z().prototype.less=function(e){return this.throwIfDisposed(),Id(this,e)};Z().prototype.localResponseNormalization=function(e,t,n,r){return this.throwIfDisposed(),gm(this,e,t,n,r)};Z().prototype.logSigmoid=function(){return this.throwIfDisposed(),Vw(this)};Z().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Td(this,e)};Z().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),bm(this,e,t)};Z().prototype.log=function(){return this.throwIfDisposed(),Mn(this)};Z().prototype.log1p=function(){return this.throwIfDisposed(),Sd(this)};Z().prototype.logicalAnd=function(e){return this.throwIfDisposed(),lr(this,e)};Z().prototype.logicalNot=function(){return this.throwIfDisposed(),Zu(this)};Z().prototype.logicalOr=function(e){return this.throwIfDisposed(),Ed(this,e)};Z().prototype.logicalXor=function(e){return this.throwIfDisposed(),Gw(this,e)};Z().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Be(this,e,t,n)};Z().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),Yu(this,e,t,n,r)};Z().prototype.max=function(e,t){return this.throwIfDisposed(),Kn(this,e,t)};Z().prototype.maximum=function(e){return this.throwIfDisposed(),Pr(this,e)};Z().prototype.mean=function(e,t){return this.throwIfDisposed(),vt(this,e,t)};Z().prototype.min=function(e,t){return this.throwIfDisposed(),gl(this,e,t)};Z().prototype.minimum=function(e){return this.throwIfDisposed(),xl(this,e)};Z().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),vm(this,e,t)};Z().prototype.mod=function(e){return this.throwIfDisposed(),km(this,e)};Z().prototype.mul=function(e){return this.throwIfDisposed(),B(this,e)};Z().prototype.neg=function(){return this.throwIfDisposed(),_t(this)};Z().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Ud(this,e,t,n)};Z().prototype.notEqual=function(e){return this.throwIfDisposed(),gi(this,e)};Z().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),ul(this,e,t,n)};Z().prototype.onesLike=function(){return this.throwIfDisposed(),$n(this)};Z().prototype.pad=function(e,t){return this.throwIfDisposed(),oa(this,e,t)};Z().prototype.pool=function(e,t,n,r,a){return this.throwIfDisposed(),Kw(this,e,t,n,r,a)};Z().prototype.pow=function(e){return this.throwIfDisposed(),la(this,e)};Z().prototype.prelu=function(e){return this.throwIfDisposed(),Qu(this,e)};Z().prototype.prod=function(e,t){return this.throwIfDisposed(),Rd(this,e,t)};Z().prototype.reciprocal=function(){return this.throwIfDisposed(),Nm(this)};Z().prototype.relu=function(){return this.throwIfDisposed(),Lr(this)};Z().prototype.relu6=function(){return this.throwIfDisposed(),Fd(this)};Z().prototype.reshapeAs=function(e){return this.throwIfDisposed(),H(this,e.shape)};Z().prototype.reshape=function(e){return this.throwIfDisposed(),H(this,e)};Z().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),pb(this,e,t,n)};Z().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),fb(this,e,t,n)};Z().prototype.reverse=function(e){return this.throwIfDisposed(),Dn(this,e)};Z().prototype.rfft=function(){return this.throwIfDisposed(),ac(this)};Z().prototype.round=function(){return this.throwIfDisposed(),Tm(this)};Z().prototype.rsqrt=function(){return this.throwIfDisposed(),$d(this)};Z().prototype.selu=function(){return this.throwIfDisposed(),Dd(this)};Z().prototype.separableConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),Em(this,e,t,n,r,a,s)};Z().prototype.sigmoid=function(){return this.throwIfDisposed(),kn(this)};Z().prototype.sign=function(){return this.throwIfDisposed(),Cm(this)};Z().prototype.sin=function(){return this.throwIfDisposed(),Od(this)};Z().prototype.sinh=function(){return this.throwIfDisposed(),zd(this)};Z().prototype.slice=function(e,t){return this.throwIfDisposed(),Re(this,e,t)};Z().prototype.softmax=function(e){return this.throwIfDisposed(),nc(this,e)};Z().prototype.softplus=function(){return this.throwIfDisposed(),Ai(this)};Z().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Ju(this,e,t)};Z().prototype.split=function(e,t){return this.throwIfDisposed(),ln(this,e,t)};Z().prototype.sqrt=function(){return this.throwIfDisposed(),Jt(this)};Z().prototype.square=function(){return this.throwIfDisposed(),st(this)};Z().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Bd(this,e)};Z().prototype.squeeze=function(e){return this.throwIfDisposed(),Pa(this,e)};Z().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Le?[this,e]:[this,...e];return On(n,t)};Z().prototype.step=function(e){return this.throwIfDisposed(),_l(this,e)};Z().prototype.stridedSlice=function(e,t,n,r,a,s,i,o){return this.throwIfDisposed(),Mm(this,e,t,n,r,a,s,i,o)};Z().prototype.sub=function(e){return this.throwIfDisposed(),ye(this,e)};Z().prototype.sum=function(e,t){return this.throwIfDisposed(),Ne(this,e,t)};Z().prototype.tan=function(){return this.throwIfDisposed(),Fm(this)};Z().prototype.tanh=function(){return this.throwIfDisposed(),di(this)};Z().prototype.tile=function(e){return this.throwIfDisposed(),Oa(this,e)};Z().prototype.toBool=function(){return this.throwIfDisposed(),Ae(this,"bool")};Z().prototype.toFloat=function(){return this.throwIfDisposed(),Ae(this,"float32")};Z().prototype.toInt=function(){return this.throwIfDisposed(),Ae(this,"int32")};Z().prototype.topk=function(e,t){return this.throwIfDisposed(),$m(this,e,t)};Z().prototype.transpose=function(e){return this.throwIfDisposed(),Ze(this,e)};Z().prototype.unique=function(e){return this.throwIfDisposed(),jd(this,e)};Z().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),Dm(this,e,t)};Z().prototype.unstack=function(e){return this.throwIfDisposed(),ur(this,e)};Z().prototype.where=function(e,t){return this.throwIfDisposed(),Sn(e,this,t)};Z().prototype.zerosLike=function(){return this.throwIfDisposed(),Ue(this)};var hv={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,_l(Ae(n,"float32"),-1))}}},MJ={kernelName:ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=st(Ae(n,"float32")),a=Jt(ye(ke(1),r));return _t(ge(e,a))}}}},FJ={kernelName:ao,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Jt(ye(st(Ae(n,"float32")),1));return ge(e,r)}}}},$J={kernelName:Sa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=dt(n.shape,r.shape);return{a:()=>{let s=e,i=Ot(n.shape,a);return i.length>0&&(s=Ne(s,i)),H(s,n.shape)},b:()=>{let s=e,i=Ot(r.shape,a);return i.length>0&&(s=Ne(s,i)),H(s,r.shape)}}}},DJ={kernelName:ls,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,a)=>{n[a]=()=>e.clone()}),n}},OJ={kernelName:us,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ue(n)}}},zJ={kernelName:mu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ue(n)}}},PJ={kernelName:oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,Jt(ye(ke(1),st(Ae(n,"float32")))))}}},LJ={kernelName:lo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Jt(se(ke(1),st(Ae(n,"float32"))));return ge(e,r)}}}},WJ={kernelName:ho,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=dt(n.shape,r.shape);return{a:()=>{let s=se(st(n),st(r)),i=B(e,ge(r,s)),o=Ot(n.shape,a);return o.length>0&&(i=Ne(i,o)),H(i,n.shape)},b:()=>{let s=se(st(n),st(r)),i=_t(B(e,ge(n,s))),o=Ot(r.shape,a);return o.length>0&&(i=Ne(i,o)),H(i,r.shape)}}}},BJ={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(st(Ae(n,"float32")),1))}}},VJ={kernelName:co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,ye(ke(1),st(Ae(n,"float32"))))}}};function jJ(e,t,n,r,a,s){let i=R(e,"dy","avgPool3dGrad"),o=R(t,"input","avgPool3dGrad"),l=i,c=o,u=!1;o.rank===4&&(u=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),c=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),M(c.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${c.rank}.`),s!=null&&M(Bt(a),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${a}.`);let h={dy:l,input:c},d={filterSize:n,strides:r,pad:a,dimRoundingMode:s},p=$.runKernel(Eh,h,d);return u?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var UJ=D({avgPool3dGrad_:jJ}),HJ={kernelName:Au,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>UJ(e,r,a,s,i,o)}}};function GJ(e,t,n,r,a){let s=R(e,"dy","avgPoolGrad"),i=R(t,"input","avgPoolGrad");M(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=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),M(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),M(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let u={dy:l,input:o},h={filterSize:n,strides:r,pad:a},d=$.runKernel(Th,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var qJ=D({avgPoolGrad_:GJ}),XJ={kernelName:cs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i}=n;return{x:()=>qJ(e,r,a,s,i)}}},KJ={kernelName:hs,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[r,a]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>Be(e,a,!1,!0),b:()=>Be(r,e,!0,!1)}:!s&&i?{a:()=>Be(e,a,!1,!1),b:()=>Be(e,r,!0,!1)}:s&&!i?{a:()=>Be(a,e,!1,!0),b:()=>Be(r,e,!1,!1)}:{a:()=>Be(a,e,!0,!0),b:()=>Be(e,r,!0,!0)}}},ZJ={kernelName:yu,gradFunc:(e,t,n)=>{let{blockShape:r,crops:a}=n;return{x:()=>Ju(e,r,a)}}},YJ={kernelName:Ex,gradFunc:(e,t,n)=>{let r=n,a=r.inputShape,s=r.shape,i=Array.from(s);for(let l=a.length-1;l>=0;l--)if(a[l]===s[l])i[l]=1;else if(a[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l<i.length;l++)i[l]>1&&o.push(l);return{x:()=>Ne(e,o,!0)}}},JJ={kernelName:ds,gradFunc:e=>({x:()=>e.clone()})},QJ={kernelName:ps,gradFunc:e=>({x:()=>Ue(e)})},eQ={kernelName:Na,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:a,clipValueMax:s}=n;return{x:()=>Sn(lr(za(r,a),mi(r,s)),e,Ue(e))}}},tQ={kernelName:gu,inputsToSave:["x"],gradFunc:hv.gradFunc},nQ={kernelName:po,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(o=>o.shape),{axis:a}=n,s=sr(a,t[0].shape)[0],i=r.map(o=>o[s]);return ln(e,i,s).map(o=>()=>o)}},rQ={kernelName:fs,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return M($a(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>um(r.shape,e,a,i,o,l),filter:()=>Lm(r,e,a.shape,i,o,l)}}},aQ={kernelName:ms,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>ia(e,a,s,i,o,1,l),filter:()=>Lm(e,r,a.shape,s,i,o,l)}}};function sQ(e,t,n,r,a){let s=e;e.rank===4&&(s=H(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),M(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),M(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),M(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),M(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),M(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:r,pad:a,filterShape:n};return $.runKernel(Fh,o,l)}var iQ=D({conv3DBackpropFilter_:sQ}),oQ={kernelName:xu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s}=n;M($a(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[i,o]=t;return{x:()=>Fw(i.shape,e,o,a,s),filter:()=>iQ(i,e,o.shape,a,s)}}},lQ={kernelName:As,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(_t(Od(Ae(n,"float32"))),e)}}},uQ={kernelName:fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(zd(Ae(n,"float32")),e)}}},cQ={kernelName:ys,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a,exclusive:s,reverse:i}=n;return{x:()=>{let o=Hw([a],r.rank),l=vd(e,a,s,!i);return o!=null&&(l=Ze(l,o)),l}}}},hQ={kernelName:gs,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s,dimRoundingMode:i}=n,o=r==null?[1,1]:r;M($a(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,c]=t;return M(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),M(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),M(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),M(Or(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${a} and dilations '${o}'.`),i!=null&&M(Bt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>ib(l.shape,e,c,a,s,r,i),filter:()=>sb(l,e,c.shape,a,s,r,i)}}},dQ={kernelName:wu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,s={x:r,filter:a,dy:e},i={x:r,filter:a,dy:e};return{x:()=>$.runKernel(Lh,s,n),filter:()=>$.runKernel(Wh,i,n)}}},pQ={kernelName:yo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>$.runKernel(Vh,r)}}},fQ={kernelName:go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=B(Xn(_t(st(n))),2/Math.sqrt(Math.PI));return{x:()=>B(e,r)}}},mQ={kernelName:ws,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,n)}}},AQ={kernelName:wo,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>H(e,n.shape)}}},yQ={kernelName:bo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,Xn(n))}}},gQ={kernelName:bs,gradFunc:e=>({x:()=>Ue(e)})},xQ={kernelName:_s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=dt(n.shape,r.shape);return{a:()=>{let s=ge(e,Ae(r,"float32")),i=Ot(n.shape,a);return i.length>0?H(Ne(s,i),n.shape):s},b:()=>{let s=B(e,Ae(n,"float32")),i=Ot(r.shape,a);i.length>0&&(s=H(Ne(s,i),r.shape));let o=st(r);return _t(ge(s,Ae(o,"float32")))}}}},wQ={kernelName:vs,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:r}=n,[a,s,i,o]=t,l=o==null?ke(1):o,c=Ot(s.shape,a.shape),u=[];if(s.rank===1){for(let f=0;f<a.shape.length-1;++f)u.push(a.shape[f]);u.push(1)}let h=ye(a,s),d=B(e,l),p=$d(se(i,ke(r))),m=B(B(B(p,p),p),ke(-.5));return{x:()=>s.rank===1?H(B(B(e,Oa(H(p,[1,1,1,s.shape[0]]),u)),l),a.shape):H(B(B(e,p),l),a.shape),mean:()=>{let f=B(B(p,ke(-1)),d);return s.rank===1&&(f=Ne(f,c)),H(f,s.shape)},variance:()=>{let f=B(B(m,h),d);return s.rank===1&&(f=Ne(f,c)),H(f,s.shape)},scale:()=>{let f=B(h,p),A=B(e,f);return s.rank===1&&(A=Ne(A,c)),H(A,s.shape)},offset:()=>{let f=e;return s.rank===1&&(f=Ne(f,c)),H(f,s.shape)}}}},bQ={kernelName:vo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,a]=t,{axis:s}=n,i=sr(s,r.shape)[0];return{x:()=>{let o=r.shape,l=a.size,c=o.slice(0,i),u=c.length,h=o.slice(s,o.length).slice(1),d=h.length,p=dv(0,u),m=dv(u+1,u+1+d),f=pv([c,[l],h]),A=H(e,f),y=H(a,[l]),g=pv([[u],p,m]),x=Ze(A,g),v=Dm(x,y,r.shape[i]),w=wm(g);return v=Ze(v,w),v},indices:()=>a}}};function dv(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function pv(e){let t=[];for(let n=0;n<e.length;++n)for(let r=0;r<e[n].length;++r)t.push(e[n][r]);return t}var _Q={kernelName:ks,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>Ue(n),b:()=>Ue(r)}}},vQ={kernelName:Is,gradFunc:e=>({x:()=>Ae(e,"float32")})},kQ={kernelName:So,gradFunc:e=>({x:()=>Ue(e)})},IQ={kernelName:No,gradFunc:e=>({x:()=>Ue(e)})},SQ={kernelName:To,gradFunc:e=>({x:()=>Ue(e)})},NQ={kernelName:Ss,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:a}=n,s=or(r,0);return{x:()=>Sn(s,e,B(e,a))}}},TQ={kernelName:Ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(n,1))}}},EQ={kernelName:Ns,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,Ae(n,"float32"))}}},CQ={kernelName:Cx,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n;return{logits:()=>{let s=!0,i=Xn(r);return ye(e,B(Ne(e,a,s),i))}}}};function RQ(e,t,n,r=5,a=1,s=1,i=.5){let o={x:e,y:t,dy:n},l={depthRadius:r,bias:a,alpha:s,beta:i};return $.runKernel(qh,o,l)}var MQ=D({localResponseNormalizationBackprop_:RQ}),FQ={kernelName:ku,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>MQ(r,a,e,s,i,o,l)}}};function fv(e,t,n,r){return t.rank<n.rank&&(t=H(t,yi(t.shape,r))),e.rank<n.rank&&(e=H(e,yi(e.shape,r))),{x:()=>B(e,Ae(Da(n,t),e.dtype))}}var mv={kernelName:Ts,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{reductionIndices:a}=r,s=t[0],i=t[1],o=sr(a,s.shape),l=fv(e,i,s,o);return{x:()=>l.x()}}},$Q={kernelName:Es,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>B(e,Ae(za(n,r),"float32")),b:()=>B(e,Ae(Id(n,r),"float32"))}}};function DQ(e,t,n,r,a,s,i){let o=R(e,"dy","maxPool3dGrad"),l=R(t,"input","maxPool3dGrad"),c=R(n,"output","maxPool3dGrad"),u=o,h=l,d=c,p=!1;l.rank===4&&(p=!0,u=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),h=H(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),d=H(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]])),M(u.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),M(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),M(d.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`),i!=null&&M(Bt(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let m={dy:u,input:h,output:d},f={filterSize:r,strides:a,pad:s,dimRoundingMode:i},A=$.runKernel(Kh,m,f);return p?H(A,[A.shape[1],A.shape[2],A.shape[3],A.shape[4]]):A}var OQ=D({maxPool3dGrad_:DQ}),zQ={kernelName:Iu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>OQ(e,r,a,s,i,o,l)}}};function PQ(e,t,n,r,a,s,i){let o=R(e,"dy","maxPoolGrad"),l=R(t,"input","maxPoolGrad"),c=R(n,"output","maxPoolGrad");M(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),M(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),M(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&M(Bt(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},h={filterSize:r,strides:a,pad:s,dimRoundingMode:i};return $.runKernel(Xh,u,h)}var LQ=D({maxPoolGrad_:PQ}),WQ={kernelName:Cs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>LQ(e,r,a,s,i,o)}}},BQ={kernelName:Rs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n,s=sr(a,r.shape),i=Uw(r.shape,s)[1],o=Nt(i);return{x:()=>{let l=r.shape.slice();s.forEach(u=>{l[u]=1});let c=H(e,l);return ge(B(c,Fn(r.shape,"float32")),o)}}}},VQ={kernelName:Ms,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:a}=r,[s,i]=t,o=sr(a,s.shape),l=fv(e,i,s,o);return{x:()=>l.x()}}},jQ={kernelName:Fs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>B(e,Ae(mi(n,r),"float32")),b:()=>B(e,Ae(or(n,r),"float32"))}}},UQ={kernelName:$s,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Re(e,s,r.shape)}}},HQ={kernelName:Fo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=dt(n.shape,r.shape);return{a:()=>{let s=Ot(n.shape,a);return s.length>0?H(Ne(e,s),n.shape):e},b:()=>{let s=B(e,_t(yl(ge(n,r)))),i=Ot(r.shape,a);return i.length>0?H(Ne(s,i),r.shape):s}}}},GQ={kernelName:Ds,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=dt(n.shape,r.shape);return{a:()=>{let s=B(e,Ae(r,"float32")),i=Ot(n.shape,a);return i.length>0?H(Ne(s,i),n.shape):s},b:()=>{let s=B(e,Ae(n,"float32")),i=Ot(r.shape,a);return i.length>0?H(Ne(s,i),r.shape):s}}}},qQ={kernelName:$o,gradFunc:e=>({x:()=>_t(e)})},XQ={kernelName:Os,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Et(n.shape,"float32")}}},KQ={kernelName:Lo,gradFunc:e=>({x:()=>Ue(e)})},ZQ={kernelName:Wo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return ur(e,r).map(a=>()=>a)}},Av={kernelName:zs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Re(e,s,r.shape)}}},YQ={kernelName:Ps,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,a]=t,s=n,i=r,o=dt(s.shape,i.shape);return{a:()=>{let l=Ae(i,"float32"),c=B(e,B(l,la(s,ye(l,ke(1))))),u=Ot(s.shape,o);return u.length>0&&(c=Ne(c,u)),H(c,s.shape)},b:()=>{let l=or(s,0),c=Sn(l,Mn(s),Ue(s)),u=B(e,B(a,c)),h=Ot(i.shape,o);return h.length>0&&(u=Ne(u,h)),H(u,i.shape)}}}},JQ={kernelName:Ls,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,a=or(n,0);return{x:()=>Sn(a,e,B(e,r)),alpha:()=>{let s=Sn(a,Ue(e),B(e,n)),i=Ot(r.shape,e.shape);return i.length>0&&(s=Ne(s,i)),H(s,r.shape)}}}},QQ={kernelName:xs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=dt(n.shape,r.shape);return{a:()=>{let s=ge(e,Ae(r,"float32")),i=Ot(n.shape,a);return i.length>0?H(Ne(s,i),n.shape):s},b:()=>{let s=B(e,Ae(n,"float32")),i=Ot(r.shape,a);i.length>0&&(s=H(Ne(s,i),r.shape));let o=st(r);return _t(ge(s,Ae(o,"float32")))}}}},eee={kernelName:Vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,_t(st(n)))}}},tee={kernelName:Vs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=B(mi(n,6),_l(n));return{x:()=>B(e,Ae(r,"float32"))}}},nee={kernelName:Ws,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,Ae(_l(n),"float32"))}}},ree={kernelName:jo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>H(e,n.shape)}}},aee={kernelName:Bs,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(ed,a,n)}}},see={kernelName:Nu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(Qh,a,n)}}},iee={kernelName:js,gradFunc:(e,t,n)=>{let{dims:r}=n,a=sr(r,e.shape);return{x:()=>Dn(e,a)}}},oee={kernelName:Us,gradFunc:e=>({x:()=>Ue(e)})},lee={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>_t(ge(e,B(la(n,1.5),2)))}}},uee={kernelName:Ho,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>Ae(Ue(n),"float32"),t:()=>B(e,Ae(n,e.dtype)),e:()=>B(e,Ae(Zu(n),e.dtype))}}},cee={kernelName:Go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=or(n,ke(0)),a=ke(gb),s=ke(xb),i=B(e,s),o=B(B(e,a),Xn(Ae(n,"float32")));return Sn(r,i,o)}}}},hee={kernelName:qs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(n,ye(ke(1),n)))}}},dee={kernelName:Ko,gradFunc:e=>({x:()=>Ue(e)})},pee={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(qu(Ae(n,"float32")),e)}}},fee={kernelName:Xo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(_d(Ae(n,"float32")),e)}}},mee={kernelName:qo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:a,size:s}=n,i=r.shape,[o,l]=mw(r,a,s),c=[];for(let u=0;u<e.rank;u++)c.push([o[u],i[u]-o[u]-l[u]]);return{x:()=>oa(e,c)}}},Aee={kernelName:Zs,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:a}=n,s=!0,i=B(e,r);return{logits:()=>ye(i,B(Ne(i,[a],s),r))}}},yee={kernelName:Zo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,kn(n))}}},yv={kernelName:Tu,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:a}=n;return{x:()=>Gu(e,r,a)}}},gv={kernelName:Yo,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>ot(e,r)}}},gee={kernelName:Xs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,B(Jt(Ae(n,"float32")),2))}}},xee={kernelName:Eu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(e,B(Ae(n,"float32"),2))}}},wee={kernelName:Ys,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ke(2);return{a:()=>B(e,B(a,ye(n,r))),b:()=>B(e,B(a,ye(r,n)))}}},bee={kernelName:Ea,gradFunc:e=>({x:()=>Ue(e)})},_ee={kernelName:Js,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=dt(n.shape,r.shape);return{a:()=>{let s=e,i=Ot(n.shape,a);return i.length>0&&(s=Ne(s,i)),H(s,n.shape)},b:()=>{let s=e,i=Ot(r.shape,a);return i.length>0&&(s=Ne(s,i)),H(_t(s),r.shape)}}}},vee={kernelName:Ks,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,a=r.shape.slice(),{axis:s}=n;sr(s,r.shape).forEach(l=>{a[l]=1});let i=H(e,a),o=B(i,Fn(r.shape,"float32"));return{x:()=>o}}},kee={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,st(qu(n)))}}},Iee={kernelName:ei,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>B(ye(ke(1),st(n)),e)}}},See={kernelName:Ta,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{reps:a}=n;return{x:()=>{let s=Ue(r);if(r.rank===1)for(let i=0;i<a[0];++i)s=se(s,Re(e,[i*r.shape[0]],[r.shape[0]]));else if(r.rank===2)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1]],[r.shape[0],r.shape[1]]));else if(r.rank===3)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2]],[r.shape[0],r.shape[1],r.shape[2]]));else if(r.rank===4)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)for(let c=0;c<a[3];++c)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2],c*r.shape[3]],[r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${r.rank} tensors yet.`);return s}}}},Nee={kernelName:ti,gradFunc:(e,t,n)=>{let r=n,{perm:a}=r,s=wm(a);return{x:()=>Ze(e,s)}}},Tee={kernelName:el,gradFunc:(e,t,n)=>{let r=n,{axis:a}=r;return{value:()=>On(e,a)}}},Cee={kernelName:Cu,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Eee(e,n)}}};function Eee(e,t){let n=Pr(t,Ue(t)),r=fi(e,n),a=za(t,ke(0,"int32")),s=r.rank-a.rank;for(let o=0;o<s;++o)a=on(a,o+1);a=lr(a,Fn(r.shape,"bool"));let i=Ue(r);return Sn(a,r,i)}var Ree={kernelName:tl,gradFunc:e=>({x:()=>Ue(e)})},Mee=[hv,MJ,FJ,$J,DJ,OJ,zJ,PJ,LJ,WJ,BJ,VJ,HJ,XJ,KJ,ZJ,YJ,JJ,QJ,eQ,tQ,nQ,aQ,rQ,oQ,lQ,uQ,cQ,hQ,dQ,QQ,pQ,fQ,mQ,AQ,yQ,xQ,gQ,wQ,bQ,_Q,vQ,kQ,IQ,SQ,NQ,TQ,EQ,CQ,FQ,mv,mv,$Q,zQ,WQ,BQ,VQ,jQ,UQ,HQ,GQ,qQ,XQ,KQ,ZQ,Av,Av,YQ,JQ,eee,tee,nee,ree,aee,see,iee,oee,lee,uee,cee,hee,dee,pee,fee,mee,Aee,yee,yv,yv,gv,gv,gee,wee,xee,bee,_ee,vee,kee,Iee,See,Nee,Tee,Cee,Ree];for(let e of Mee)Rx(e);var xv={};Me(xv,{maxNorm:()=>Fee,minMaxNorm:()=>Oee,nonNeg:()=>Dee,unitNorm:()=>$ee});var EA;function zt(){return EA==null&&(EA=_w().epsilon()),EA}function vr(){return"channelsLast"}var da=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,da.prototype)}},kr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,kr.prototype)}},W=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,W.prototype)}},De=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,De.prototype)}},wv=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,wv.prototype)}};function Ei(e,t){if(Array.isArray(e)){let n=[];for(let r=0;r<t;r++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function jr(e,t){if(!e)throw new wv(t)}function bv(e,t){let n=0;for(let r of e)r===t&&n++;return n}function Tn(e){return e.length===1?e[0]:e}function ft(e){return Array.isArray(e)?e:[e]}function pa(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function Ci(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var cr={};function CA(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function RA(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>RA(t));else{let t=Object.keys(e);for(let n of t){let r=e[n];r!=null&&typeof r=="object"&&(!Array.isArray(r)&&r.type==="ndarray"&&typeof r.value=="number"?e[n]=r.value:RA(r))}}}function kc(e,t={},n={},r="object",a=!1){if(typeof e=="string"){let s=e,i;if(s in n)i=n[s];else if(s in cr)i=cr[s];else if(i=t[s],i==null)throw new W(`Unknown ${r}: ${e}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new W(`${r}: 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 cr?[o,l]=cr.className:i in t&&([o,l]=t[i]),o==null)throw new W(`Unknown ${r}: ${i}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let c={};for(let p of Object.keys(cr))c[p]=cr[p];for(let p of Object.keys(n))c[p]=n[p];let u=s.config;u.customObjects=c;let h=Object.assign({},cr);for(let p of Object.keys(n))cr[p]=n[p];RA(s.config);let d=l(o,s.config,n,a);return cr=Object.assign({},h),d}else{let c=Object.assign({},cr);for(let h of Object.keys(n))cr[h]=n[h];let u=new o(s.config);return cr=Object.assign({},c),u}}}function zee(e,t){return e<t?-1:e>t?1:0}function Rp(e,t){return-1*zee(e,t)}function Ua(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function Pee(e){if(e==null)throw new W(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Ri(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new W(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`)}function MA(e,t,n=0,r=Infinity){return jr(n>=0),jr(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(a=>typeof a===t)}function Ut(e,t){Array.isArray(e)?(_.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>Ut(n,`element ${r+1} of ${t}`))):_.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${_v(e)}.`)}function _v(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>_v(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function Lee(e,t){let n=_.now(),r;return(...a)=>{let s=_.now();return s-n<t||(n=s,r=e(...a)),r}}function vv(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function FA(e,t){return L(()=>Jt(Ne(B(e,e),t,!0)))}var Ic=class extends re.Serializable{getConfig(){return{}}},$A=class extends Ic{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 L(()=>{let t=FA(e,this.axis),n=In(t,0,this.maxValue);return B(e,ge(n,se(zt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};$A.className="MaxNorm";re.registerClass($A);var DA=class extends Ic{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return L(()=>ge(e,se(zt(),FA(e,this.axis))))}getConfig(){return{axis:this.axis}}};DA.className="UnitNorm";re.registerClass(DA);var OA=class extends Ic{apply(e){return Lr(e)}};OA.className="NonNeg";re.registerClass(OA);var zA=class extends Ic{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 L(()=>{let t=FA(e,this.axis),n=se(B(this.rate,In(t,this.minValue,this.maxValue)),B(1-this.rate,t));return B(e,ge(n,se(zt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};zA.className="MinMaxNorm";re.registerClass(zA);var kv={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Pt(e){return CA(e)}function Iv(e,t={}){return kc(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function Lt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in kv?kv[e]:e,config:{}};return Iv(t)}else return e instanceof Ic?e:Iv(e)}function Fee(e){return new $A(e)}function $ee(e){return new DA(e)}function Dee(){return new OA}function Oee(e){return new zA(e)}var Sv={};Me(Sv,{constant:()=>Vee,glorotNormal:()=>Kee,glorotUniform:()=>Xee,heNormal:()=>Zee,heUniform:()=>Yee,identity:()=>Gee,leCunNormal:()=>Jee,leCunUniform:()=>Qee,ones:()=>Bee,orthogonal:()=>ete,randomNormal:()=>Uee,randomUniform:()=>jee,truncatedNormal:()=>Hee,varianceScaling:()=>qee,zeros:()=>Wee});var tte=["channelsFirst","channelsLast"],nte=["nearest","bilinear"],rte=["valid","same","causal"],ate=["max","avg"],ste=["sum","mul","concat","ave"],Ll=new Map;function Tt(e){Ri(tte,"DataFormat",e)}function ite(e){Ri(nte,"InterpolationFormat",e)}function er(e){Ri(rte,"PaddingMode",e)}function Nv(e){Ri(ate,"PoolMode",e)}var Sc=[],Tv="/";function Mi(e,t){Sc.push(e);try{let n=t();return Sc.pop(),n}catch(n){throw Sc.pop(),n}}function ote(){return Sc.length===0?"":Sc.join(Tv)+Tv}function Cv(e){if(!Ev(e))throw new Error("Not a valid tensor name: '"+e+"'");return ote()+e}function Rv(e){if(!Ev(e))throw new Error("Not a valid tensor name: '"+e+"'");Ll.has(e)||Ll.set(e,0);let t=Ll.get(e);if(Ll.set(e,Ll.get(e)+1),t>0){let n=`${e}_${t}`;return Ll.set(n,1),n}else return e}var lte=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Ev(e){return!!e.match(lte)}function ute(e){return e===parseInt(e.toString(),10)}function Ha(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let r=1;for(let a=t;a<n;++a)r*=e[a];return r}function Mv(e){return e=Array.isArray(e)?new Float32Array(e):e,nn(e)}function Wl(e){return gl(Mv(e)).dataSync()[0]}function Ga(e){return Kn(Mv(e)).dataSync()[0]}function Ir(e,t){if(t<e)throw new W(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let r=e;r<t;++r)n.push(r);return n}function Nc(e,t){return e.asType(t)}function Tc(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 cte(e,t){return L(()=>{if(e.shape.length!==2)throw new W(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Tc(e,1);return PA(n,[1,t,1])})}function hte(e){let t=[Ha(e.shape)];return e.reshape(t)}function dte(e){if(e.rank<=1)throw new W(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Ha(e.shape,1)];return e.reshape(t)}function Fi(e,t,n){return L(()=>{switch(e.rank){case 1:return Pd(e,t,n);case 2:return Rm(e,[t,0],[n,e.shape[1]]);case 3:return Ld(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return tc(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Re(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Re(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new W(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function LA(e,t,n){return L(()=>{switch(e.rank){case 1:return Pd(e,t,n);case 2:return Rm(e,[0,t],[e.shape[0],n]);case 3:return Ld(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return tc(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new W(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Mp(e,t,n,r){return L(()=>{switch(e.rank){case 1:return Pd(e,t,n);case 2:switch(r){case 1:return Fi(e,t,n);case 2:return LA(e,t,n);default:throw new W(`The axis is not within the rank of the tensor ${r}`)}case 3:switch(r){case 1:return Fi(e,t,n);case 2:return Ld(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return LA(e,t,n);default:throw new W(`The axis is not within the rank of the tensor ${r}`)}case 4:switch(r){case 1:return Fi(e,t,n);case 2:return tc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return tc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return LA(e,t,n);default:throw new W(`The axis is not within the rank of the tensor ${r}`)}default:throw new W(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function WA(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),ot(e,t)}function Fv(e,t){switch(e.rank){case 1:return Cw([e,t]);case 2:return fl([e,t],0);case 3:return Rw([e,t],0);case 4:return Mw([e,t],0);default:throw new W(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function PA(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new W(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Oa(e,t)}function Fp(e,t=0,n=1,r,a){return Zw(e,t,n,r,a)}function Ur(e,t,n,r){if(e.rank<2||t.rank<2)throw new De(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let a=e.shape.slice(-1)[0],s=t.shape.slice(-2)[0];if(a!==s)throw new De(`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 a=!1,s=!1;return La.matMul({a:e,b:t,transposeA:a,transposeB:s,bias:r?BA(e.rank,r,vr()):null,activation:n})}else{let a=e.shape.slice(),s=a.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 h=[...a,...c],d=!1,p=!1;return La.matMul({a:e,b:t,transposeA:d,transposeB:p,bias:r?BA(e.rank,r,vr()):null,activation:n}).reshape(h)}}function $v(e,t,n){return L(()=>(Array.isArray(t)?t=nn(t,"int32"):t=t.toInt(),fi(e,t,n)))}function Ec(e){return B(e,e)}function BA(e,t,n){let r=t.shape;if(t.rank!==1&&t.rank!==e)throw new W(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1,1]):t.reshape([1,r[3],r[0],r[1],r[2]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===4){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1]):t.reshape([1,r[2],r[0],r[1]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===3){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1]):t.reshape([1,r[1],r[0]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,r[0]]):t.reshape([1].concat(r))}else if(e<3)return t;throw new W(`Unsupported input rank by biasAdd: ${t.rank}`)}function Sr(e,t,n){return L(()=>(n==null&&(n=vr()),Tt(n),e.add(BA(e.rank,t,n))))}function pte(e,t=1){if(t!==1)throw new De(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Al(e)}function fte(e){return L(()=>ge(e,Dt(e).add(1)))}function Dv(e,t,n,r){return L(()=>rb(e,t,n,r))}function mte(e){return L(()=>{let t=se(.5,B(.2,e));return In(t,0,1)})}function Cc(e,t,n=!1){return n?e():t()}var Ate=["fanIn","fanOut","fanAvg"],yte=["normal","uniform","truncatedNormal"];function gte(e){Ri(Ate,"FanMode",e)}function xte(e){Ri(yte,"Distribution",e)}var hr=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},VA=class extends hr{apply(e,t){return Et(e,t)}};VA.className="Zeros";re.registerClass(VA);var $p=class extends hr{apply(e,t){return Fn(e,t)}};$p.className="Ones";re.registerClass($p);var jA=class extends hr{constructor(e){super();if(typeof e!="object")throw new W(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new W(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return L(()=>B(ke(this.value),Fn(e,t)))}getConfig(){return{value:this.value}}};jA.className="Constant";re.registerClass(jA);var UA=class extends hr{constructor(e){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=e.minval||this.DEFAULT_MINVAL,this.maxval=e.maxval||this.DEFAULT_MAXVAL,this.seed=e.seed}apply(e,t){return wl(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};UA.className="RandomUniform";re.registerClass(UA);var HA=class extends hr{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 De(`randomNormal does not support dType ${t}.`);return Fp(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};HA.className="RandomNormal";re.registerClass(HA);var GA=class extends hr{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 De(`truncatedNormal does not support dType ${t}.`);return Vd(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};GA.className="TruncatedNormal";re.registerClass(GA);var qA=class extends hr{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return L(()=>{if(e.length!==2||e[0]!==e[1])throw new W("Identity matrix initializer can only be used for 2D square matrices.");return B(this.gain,Am(e[0]))})}getConfig(){return{gain:this.gain}}};qA.className="Identity";re.registerClass(qA);function wte(e,t="channelsLast"){let n,r;if(Tt(t),e.length===2)n=e[0],r=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let a=Ha(e,2);n=e[1]*a,r=e[0]*a}else if(t==="channelsLast"){let a=Ha(e,0,e.length-2);n=e[e.length-2]*a,r=e[e.length-1]*a}}else{let a=Ha(e);n=Math.sqrt(a),r=Math.sqrt(a)}return[n,r]}var En=class extends hr{constructor(e){super();if(e.scale<0)throw new W(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,gte(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,xte(this.distribution),this.seed=e.seed}apply(e,t){let n=wte(e),r=n[0],a=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,r):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new De(`${this.getClassName()} does not support dType ${t}.`);return Vd(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return wl(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};En.className="VarianceScaling";re.registerClass(En);var Dp=class extends En{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return En.className}};Dp.className="GlorotUniform";re.registerClass(Dp);var Op=class extends En{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return En.className}};Op.className="GlorotNormal";re.registerClass(Op);var zp=class extends En{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return En.className}};zp.className="HeNormal";re.registerClass(zp);var Pp=class extends En{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return En.className}};Pp.className="HeUniform";re.registerClass(Pp);var Lp=class extends En{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return En.className}};Lp.className="LeCunNormal";re.registerClass(Lp);var Wp=class extends En{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return En.className}};Wp.className="LeCunNormal";re.registerClass(Wp);var XA=class extends hr{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new De("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return L(()=>{if(e.length<2)throw new De("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=Fp(n,0,1,"float32"),a=Ab.gramSchmidt(r);return e[0]>e[1]&&(a=a.transpose()),B(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};XA.className="Orthogonal";re.registerClass(XA);var Ov={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 zv(e,t={}){return kc(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function kt(e){return CA(e)}function At(e){if(typeof e=="string"){let t=e in Ov?Ov[e]:e;if(t==="GlorotNormal")return new Op;if(t==="GlorotUniform")return new Dp;if(t==="HeNormal")return new zp;if(t==="HeUniform")return new Pp;if(t==="LeCunNormal")return new Lp;if(t==="LeCunUniform")return new Wp;{let n={};return n.className=t,n.config={},zv(n)}}else return e instanceof hr?e:zv(e)}function Wee(){return new VA}function Bee(){return new $p}function Vee(e){return new jA(e)}function jee(e){return new UA(e)}function Uee(e){return new HA(e)}function Hee(e){return new GA(e)}function Gee(e){return new qA(e)}function qee(e){return new En(e)}function Xee(e){return new Dp(e)}function Kee(e){return new Op(e)}function Zee(e){return new zp(e)}function Yee(e){return new Pp(e)}function Jee(e){return new Lp(e)}function Qee(e){return new Wp(e)}function ete(e){return new XA(e)}var Pv={};Me(Pv,{Layer:()=>Ge,RNN:()=>Hr,RNNCell:()=>Rc,activation:()=>zte,add:()=>Gte,alphaDropout:()=>Ene,average:()=>qte,averagePooling1d:()=>KA,averagePooling2d:()=>ZA,averagePooling3d:()=>YA,avgPool1d:()=>nne,avgPool2d:()=>ane,avgPool3d:()=>ine,avgPooling1d:()=>rne,avgPooling2d:()=>sne,avgPooling3d:()=>one,batchNormalization:()=>Qte,bidirectional:()=>bne,concatenate:()=>Xte,conv1d:()=>Tte,conv2d:()=>Ete,conv2dTranspose:()=>Cte,conv3d:()=>Rte,conv3dTranspose:()=>Mte,convLstm2d:()=>yne,convLstm2dCell:()=>gne,cropping2D:()=>$te,dense:()=>Pte,depthwiseConv2d:()=>Ote,dot:()=>Jte,dropout:()=>Lte,elu:()=>_te,embedding:()=>Hte,flatten:()=>Bte,gaussianDropout:()=>Tne,gaussianNoise:()=>Nne,globalAveragePooling1d:()=>lne,globalAveragePooling2d:()=>une,globalMaxPool1d:()=>vne,globalMaxPool2d:()=>kne,globalMaxPooling1d:()=>Wv,globalMaxPooling2d:()=>Bv,gru:()=>hne,gruCell:()=>dne,input:()=>Lv,inputLayer:()=>bte,layerNormalization:()=>ene,leakyReLU:()=>kte,lstm:()=>pne,lstmCell:()=>fne,masking:()=>Cne,maxPool1d:()=>Ine,maxPool2d:()=>Sne,maxPooling1d:()=>Vv,maxPooling2d:()=>jv,maxPooling3d:()=>cne,maximum:()=>Kte,minimum:()=>Zte,multiply:()=>Yte,permute:()=>Ute,prelu:()=>Ite,reLU:()=>vte,repeatVector:()=>Vte,reshape:()=>jte,rnn:()=>xne,separableConv2d:()=>Fte,simpleRNN:()=>mne,simpleRNNCell:()=>Ane,softmax:()=>Ste,spatialDropout1d:()=>Wte,stackedRNNCells:()=>wne,thresholdedReLU:()=>Nte,timeDistributed:()=>_ne,upSampling2d:()=>Dte,zeroPadding2d:()=>tne});var Rne=0;function Uv(){return Rne++}var Bp={};function Vp(e=""){return e in Bp||(Bp[e]=0),Bp[e]+=1,e+Bp[e].toString()}function JA(e){return Array.isArray(e)&&Array.isArray(e[0])}function jp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function ze(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new W(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function rt(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new W(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Up(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((r,a)=>r*a);return t}var Hv="Variable",Gv=class{constructor(e,t="float32",n=Hv,r=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=Uv(),n=n==null?Hv:n,this.originalName=Cv(n),this.name=Rv(this.originalName),this.trainable_=r,this.constraint=a,this.val=Jw(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Mne(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 ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function Mne(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function QA(e){return e.map(t=>t.read())}function ey(e){e.forEach(t=>{t[0].write(t[1])})}var Rt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Nr=class{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=Uv(),s!=null&&(this.originalName=Cv(s),this.name=Rv(this.originalName)),this.rank=t.length}},Fne=0,Hp=class{constructor(e,t){this.callArgs=t,this.id=Fne++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},$ne=0,Ge=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=$ne++,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=pa(n)+"_"+Vp(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 a=null;e.batchSize!=null&&(a=e.batchSize),n=[a].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new kr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new W(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Tn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Tn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new da(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new da(`Layer ${this.name} is not connected, no input to return.`);return Tn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new da(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new da(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Tn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=ft(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=ft(this.inputSpec);if(e.length!==t.length)throw new W(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],a=t[n];if(a==null)continue;let s=r.rank;if(a.ndim!=null&&s!==a.ndim)throw new W(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new W(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new W(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&r.dtype!==a.dtype)throw new W(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${r.dtype}.`);if(a.axes){let i=r.shape;for(let o in a.axes){let l=Number(o),c=a.axes[o],u=l>=0?i[l]:i[i.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new W(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=r.shape[i];if(o!=null&&l!=null&&o!==l)throw new W(`Input ${n} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ft(e),r=!0;for(let s of n)if(!(s instanceof Nr)){r=!1;break}let a=!0;for(let s of n)if(s instanceof Nr){a=!1;break}if(r===a)throw new W("Arguments to apply() must be all SymbolicTensors or all Tensors");return Mi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of ft(e))s.push(i.shape);this.build(Tn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=ft(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Tn(o),this.activityRegularizer!=null)throw new De("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=Dne(e),i=this.computeOutputShape(s),o,l=One(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 Nr(l,c,this,ft(e),t,this.name,u)):o=new Nr(l,i,this,ft(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new De("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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new da(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new da(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new kr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Up(this.weights)}build(e){this.built=!0}getWeights(e=!1){return QA(e?this.trainableWeights:this.weights)}setWeights(e){L(()=>{let t=this.weights;if(t.length!==e.length)throw new W(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],r=QA(t);for(let a=0;a<r.length;++a){let s=r[a],i=t[a],o=e[a];if(!_.arraysEqual(s.shape,o.shape))throw new W(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}ey(n)})}addWeight(e,t,n,r,a,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new W(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(r=At("zeros"));let o=r.apply(t,n),l=new Gv(o,n,e,s,i);return o.dispose(),a!=null&&this.addLoss(()=>a.apply(l.read())),s==null&&(s=!0),s?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=ft(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,a,s,i=null){let o=ft(e);t=ft(t),n=ft(n),r=ft(r),a=jp(a),s=jp(s);let l=[],c=[],u=[];for(let h of o)l.push(h.sourceLayer),c.push(h.nodeIndex),u.push(h.tensorIndex);new Hp({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 Dne(e){e=ft(e);let t=[];for(let n of e)t.push(n.shape);return Tn(t)}function One(e){return"float32"}function qv(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let r=t.inboundNodes[n];if(r.inboundLayers.length===0)return r.inputTensors;{let a=[];for(let s=0;s<r.inboundLayers.length;s++){let i=r.inputTensors[s],o=r.inboundLayers[s],l=r.nodeIndices[s],c=qv(i,o,l);for(let u of c)a.indexOf(u)===-1&&a.push(u)}return a}}}var Bl=class extends Ge{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Vp("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 W("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 W("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new W("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let r=new Nr(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new Hp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new W(`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}}};Bl.className="InputLayer";re.registerClass(Bl);function Xv(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 W("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 Bl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function qa(e){if(e==null)return;let t=[],n=[],r=[];for(let a in e){let s=e[a];if(typeof s!="number"){let i=s;t.push(i.data()),n.push(a),r.push(i)}}if(t.length>0){let a=await Promise.all(t);for(let s=0;s<a.length;++s)e[n[s]]=a[s][0];Te(r)}}function Kv(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var Zv;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(Zv||(Zv={}));var zne=125,Vl=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){}},Yv=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)}},Pne=class extends Vl{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let n=t.size==null?0:t.size;this.seen+=n;for(let r in t){let a=t[r];if(typeof a=="number")this.totals.hasOwnProperty(r)||(this.totals[r]=0),this.totals[r]=this.totals[r]+a*n;else{let s;r in this.totals?s=this.totals[r]:this.totals[r]=0;let i=L(()=>se(this.totals[r],B(a,n)));this.totals[r]=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:L(()=>{let r=B(ge(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),Vt(t[n])}))}},Jv=class extends Vl{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 a in this.history){let s=this.history[a];for(let i=0;i<s.length;++i)if(typeof s[i]!="number"){let o=s[i];e.push(o.data()),t.push(a),n.push(i)}}let r=await Promise.all(e);for(let a=0;a<r.length;++a)this.history[t[a]][n[a]].dispose(),this.history[t[a]][n[a]]=r[a][0]}},Qv=class extends Vl{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=zne),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");_.isNumber(this.yieldEvery)&&(this.maybeWait=Lee(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let r=[];this.yield!=null&&(await qa(n),r.push(this.yield(e,t,n))),r.push(tp()),await Promise.all(r)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await qa(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await qa(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(tp()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await qa(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await qa(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(tp()):_.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await qa(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await qa(e),await this.trainEnd(e))}};function e6(e,t){return e==null&&(e={}),e instanceof Vl?[e]:Array.isArray(e)&&e[0]instanceof Vl?e:ft(e).map(n=>new Qv(n,t))}var dr=class{constructor(){}static registerCallbackConstructor(e,t){_.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),dr.checkForDuplicate(t),dr.constructors[e]==null&&(dr.constructors[e]=[]),dr.constructors[e].push(t)}static checkForDuplicate(e){for(let t in dr.constructors)dr.constructors[+t].forEach(n=>{if(n===e)throw new W("Duplicate callback constructor.")})}static clear(){dr.constructors={}}static createCallbacks(e){let t=[];for(let n in dr.constructors){let r=+n;e>=r&&t.push(...dr.constructors[r])}return t.map(n=>new n)}};dr.constructors={};function t6(e,t,n,r,a,s,i,o,l){let c=new Jv,u=[new Pne,...dr.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let h=new Yv(u);return h.setParams({epochs:n,initialEpoch:r,samples:a,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:c}}function Tr(e,t={},n=!1){return kc(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function Gp(e,t){return L(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Ne(Ec(e),t,!0),r=Xu(n.shape,zt()),a=Jt(Pr(n,r));return ge(e,a)})}function $i(e,t){return L(()=>vt(Ec(ye(t,e)),-1))}function qp(e,t){return L(()=>vt(Dt(ye(t,e)),-1))}function jl(e,t){return L(()=>{let n=ye(e,t),r=In(Dt(e),zt(),Number.MAX_VALUE),a=Dt(ge(n,r));return B(100,vt(a,-1))})}function Lne(e,t){return L(()=>{let n=In(t,zt(),Number.MAX_VALUE),r=Mn(se(1,n)),a=In(e,zt(),Number.MAX_VALUE),s=Mn(se(1,a));return vt(Ec(ye(r,s)),-1)})}function Wne(e,t){return L(()=>{let n=Pr(0,ye(1,B(e,t)));return vt(Ec(n),-1)})}function Bne(e,t){return L(()=>{let n=Pr(0,ye(1,B(e,t)));return vt(n,-1)})}function Vne(e,t){return L(()=>{let n=Ne(B(e,t),-1),r=Kn(B(ye(1,e),t),-1);return Pr(0,se(1,ye(r,n)))})}function jne(e,t){return L(()=>{let n=Math.log(2),r=ye(t,e),a=ye(se(r,Ai(B(-2,r))),n);return vt(a,-1)})}function Mc(e,t,n=!1){return L(()=>{if(n)t=nc(t);else{let r=Ne(t,t.shape.length-1,!0);t=ge(t,r)}return t=In(t,zt(),1-zt()),_t(Ne(B(e.toFloat(),Mn(t)),t.shape.length-1))})}function Xp(e,t,n=!1){return L(()=>{let r=yl(hte(e)).toInt();t=In(t,zt(),1-zt());let a=t.shape,s=ul(r,a[a.length-1]).reshape(a);return Mc(s,t,n)})}function Une(e,t){if(!_.arraysEqual(e.shape,t.shape))throw new W(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return L(()=>{let n=t.relu(),r=t.abs().neg();return n.sub(t.mul(e)).add(r.exp().log1p())})}function Kp(e,t){return L(()=>{let n;return n=In(t,zt(),1-zt()),n=Mn(ge(n,ye(1,n))),vt(Une(e,n),-1)})}function Hne(e,t){return L(()=>{let n=In(e,zt(),1),r=In(t,zt(),1);return Ne(B(e,Mn(ge(n,r))),-1)})}function Gne(e,t){return L(()=>{let n=Mn(se(zt(),t));return vt(ye(t,B(e,n)),-1)})}function ty(e,t){return L(()=>{let n=Gp(e,-1),r=Gp(t,-1),a=B(n,r);return _t(Ne(a,-1))})}var Zp={meanSquaredError:$i,meanAbsoluteError:qp,meanAbsolutePercentageError:jl,meanSquaredLogarithmicError:Lne,squaredHinge:Wne,hinge:Bne,categoricalHinge:Vne,logcosh:jne,categoricalCrossentropy:Mc,sparseCategoricalCrossentropy:Xp,binaryCrossentropy:Kp,kullbackLeiblerDivergence:Hne,poisson:Gne,cosineProximity:ty};function ny(e){if(typeof e=="string"){if(e in Zp)return Zp[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new W(t)}else return e}function ry(e,t){return L(()=>{let n=B(.5,$n(t)),r=Nc(or(t,n),e.dtype);return vt(Da(e,r),-1)})}function ay(e,t){return L(()=>Nc(Da(ju(e,-1),ju(t,-1)),"float32"))}function n6(e,t){return L(()=>lr(e.equal(1),t.equal(1)).sum().cast("float32"))}function qne(e,t){return L(()=>lr(e.equal(1),t.equal(0)).sum().cast("float32"))}function Xne(e,t){return L(()=>lr(e.equal(0),t.equal(1)).sum().cast("float32"))}function r6(e,t){return L(()=>{let n=n6(e,t),r=Xne(e,t),a=n.add(r);return Sn(or(a,0),n.div(a),0).cast("float32")})}function Kne(e,t){return L(()=>{let n=n6(e,t),r=qne(e,t),a=n.add(r);return Sn(or(a,0),n.div(a),0).cast("float32")})}function a6(e,t){return Kp(e,t)}function s6(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)),Da(e,t).asType("float32")}var Zne=$i,Yne=$i,Jne=qp,Qne=qp,ere=jl,tre=jl,sy=Mc,nre=ty,i6=Xp,Yp={binaryAccuracy:ry,categoricalAccuracy:ay,precision:r6,categoricalCrossentropy:sy,sparseCategoricalCrossentropy:i6,mse:Zne,MSE:Yne,mae:Jne,MAE:Qne,mape:ere,MAPE:tre,cosine:nre};function rre(e){if(typeof e=="string"&&e in Yp)return Yp[e];if(typeof e!="string"&&e!=null)return e;throw new W(`Unknown metric ${e}`)}function Jp(e){if(jr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Zp))if(Zp[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(Yp))if(Yp[n]===e){t=n;break}return t!==void 0?t:e.name}}function are(e){let t={Adagrad:()=>wi.adagrad(.01),Adadelta:()=>wi.adadelta(1,.95,zt()),Adam:()=>wi.adam(.001,.9,.999,zt()),Adamax:()=>wi.adamax(.002,.9,.999,zt(),0),RMSProp:()=>wi.rmsprop(.001,.9,0,zt()),SGD:()=>wi.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 W(`Unknown Optimizer ${e}`)}var o6=1*1024*1024;function l6(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!iy(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>o6&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${r.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${o6}.`)}}function iy(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"||!iy(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!iy(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function ure(e,t,n,r=console.log){let a=ire(e),s=["Layer (type)","Output shape","Param #"];a?(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(!a){s.push("Receives inputs"),i=[];for(let u in e.nodesByDepth)i.push(...e.nodesByDepth[u])}r("_".repeat(t)),Qp(s,n,r),r("=".repeat(t));let o=e.layers;for(let u=0;u<o.length;++u)a?ore(o[u],n,r):lre(o[u],n,i,r),r((u===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=sre(e),c=Up(e.nonTrainableWeights);r(`Total params: ${l+c}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${c}`),r("_".repeat(t))}function sre(e){let t;return e.collectedTrainableWeights!=null?t=Up(e.collectedTrainableWeights):t=Up(e.trainableWeights),t}function ire(e){let t=!0,n=[],r=[];for(let a in e.nodesByDepth)n.push(e.nodesByDepth[a]);for(let a of n){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}r.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(r.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function Qp(e,t,n=console.log){let r="";for(let a=0;a<e.length;++a)a>0&&(r=r.slice(0,r.length-1)+" "),r+=e[a],r=r.slice(0,t[a]),r+=" ".repeat(t[a]-r.length);n(r)}function ore(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(o){r="multiple"}let a=e.name,s=e.getClassName(),i=[`${a} (${s})`,r,e.countParams().toString()];Qp(i,t,n)}function lre(e,t,n,r){let a;try{a=JSON.stringify(e.outputShape)}catch(u){a="multiple"}let s=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let h=0;h<u.inboundLayers.length;++h){let d=u.inboundLayers[h].name,p=u.nodeIndices[h],m=u.tensorIndices[h];s.push(`${d}[${p}][${m}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],c=[`${i} (${o})`,a,e.countParams().toString(),l];Qp(c,t,r);for(let u=1;u<s.length;++u)Qp(["","","",s[u]],t,r)}function u6(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Fc(e,t){if(e===null)return null;if(typeof e=="string")return Ci(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let a=0;a<r;++a){let s=e[a];u6(t,a,s)?n.push(s):n.push(Fc(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r];if(r==="name"&&typeof a=="string")n[r]=a;else{let s=Ci(r);n[s]=Fc(a,s)}}return n}}function oy(e,t){if(e==null)return null;if(typeof e=="string")return pa(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let a=0;a<r;++a){let s=e[a];u6(t,a,s)?n.push(s):n.push(oy(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r],s=pa(r);(r==="name"||r==="className")&&typeof a=="string"?n[s]=a:n[s]=oy(a,r)}return n}}var ly="3.5.0";function cre(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return Ae(t,e.dtype)}catch(n){throw new W(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Di=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Di)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=cre(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new W(`Duplicate key: name=${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 Nr){if(this.id2Value[e.id]==null)throw new W(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new W(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Nr){if(this.id2Value[e.id]==null)throw new W(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new W(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Te(this.id2Mask)}},uy={},c6={};function $c(e,t,n,r){let a=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);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let u=o.join(",")+"|"+t.names().join(","),h,d;if(uy[u]==null){let m=hre(i,t);h=m.sorted,d=m.recipientCounts,uy[u]=h,c6[u]=d}h=uy[u],d={},a||Object.assign(d,c6[u]);let p=new Di(t);for(let m=0;m<h.length;++m){if(r!=null){let C=md().numTensors;C>r.maxNumTensors&&(r.maxNumTensors=C),C<r.minNumTensors&&(r.minNumTensors=C)}let f=h[m],A=f.sourceLayer;if(A instanceof Bl)continue;let y=[],g=[],x=[],v=!1;for(let C of f.inputs){let F=p.getValue(C),O=p.getMask(C);y.push(F),g.push(O),O!=null&&(v=!0),a||(d[C.name]--,d[C.name]===0&&!t.hasKey(C)&&o.indexOf(C.name)===-1&&!F.isDisposed&&C.sourceLayer.stateful!==!0&&x.push(F))}v&&(n=n||{},n.mask=g[0]);let w=ft(A.apply(y,n)),b=null;A.supportsMasking&&(b=A.computeMask(y,g));let k=dre(f),N=Array.isArray(k)?k:[k];for(let C=0;C<N.length;++C){p.hasKey(N[C])||p.add(N[C],w[C],Array.isArray(b)?b[0]:b);let F=o.indexOf(N[C].name);F!==-1&&(l[F]=w[C])}a||Te(x)}return p.disposeMasks(),s?l:l[0]}function hre(e,t){_.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let a=h6(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=h6(s,t);for(let l of i)a.has(l.name)||(n.push(l),a.add(l.name));for(let l in o)r[l]==null&&(r[l]=new Set),o[l].forEach(c=>r[l].add(c))}}return{sorted:n,recipientCounts:pre(r)}}function pre(e){let t={};for(let n in e)t[n]=e[n].size;return t}function h6(e,t){let n=new Set,r=[],a={};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(),r.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let c of o.inputs)a[c.name]==null&&(a[c.name]=new Set),a[c.name].add(o.name),!n.has(c.name)&&s.push(c)}}return{sorted:r,recipientMap:a}}function dre(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let r=0;r<e.sourceLayer.inboundNodes.length;++r)for(let a of e.sourceLayer.inboundNodes[r].outputTensors)if(a.id===e.id){n=r;break}t=e.sourceLayer.getOutputAt(n)}return t}var Gr=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Vp(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],Ua(this.inputs).length!==this.inputs.length)throw new W(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Ua(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 g=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let g=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;jr(x===0,"input layer has >1 nodes"),jr(v===0,"input layer has >1 tensors"),this.inputLayers.push(g),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 g=this.inputLayers[y];if(!(g instanceof Bl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},r={},a={},s={},i=[],o=(y,g,x,v,w,b)=>{(v==null||w==null||b==null)&&(v=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex);let k=v.inboundNodes[w];if(x.indexOf(k)!==-1)throw new kr(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(g.indexOf(k)!==-1)return;this.containerNodes.add(Gr.nodeKey(v,w)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(k)===-1&&x.push(k);let N=k.inboundLayers.length;for(let C=0;C<N;C++){let F=k.inputTensors[C],O=k.inboundLayers[C],z=k.nodeIndices[C],V=k.tensorIndices[C];o(F,g,x,O,z,V)}for(g.push(k);x.indexOf(k)>=0;)x.splice(x.indexOf(k),1);i.push(k)},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 g=t[y.id],x=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,x),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let v=0;v<y.inboundLayers.length;v++){let w=y.inboundLayers[v],b=y.nodeIndices[v],k=w.inboundNodes[b],N=t[k.id]==null?0:t[k.id];t[k.id]=Math.max(g+1,N),n[k.id]=k}}let h={};for(let y in t){let g=t[y];g in h||(h[g]=[]),h[g].push(n[y])}let d={};for(let y in r){let g=r[y];g in d||(d[g]=[]),d[g].push(a[y])}let p=Object.keys(d).map(y=>parseInt(y,10)).sort(Rp);this.layers=[];for(let y of p){let g=d[y];g.sort((x,v)=>{let w=s[x.id],b=s[v.id];return w<b?-1:w>b?1:0});for(let x of g)x instanceof Gr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(Rp);let m=this.inputs.slice(),f=[];for(let y of p)for(let g of h[y]){let x=g.outboundLayer;if(x!=null){for(let v of g.inputTensors)if(m.indexOf(v)===-1)throw new kr(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of g.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=h;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(x=>x===y).length;if(g!==1)throw new kr(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Hp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 W("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new W(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,r++}let a=[];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)a.push([n[i],e[s]]);else if(t)throw new W(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new W(`${s.length} of ${r} weights are not set: ${s}`)}ey(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${ly}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=oy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return L(()=>{e=ft(e);let n=new Di;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return $c(this.outputs,n,t)})}computeMask(e,t){return L(()=>{e=ft(e);let n;return t==null?n=Ei(null,e.length):n=ft(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=jp(e);if(t.length!==this.inputLayers.length)throw new W(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Rp);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],A=l.nodeIndices[m],y=l.tensorIndices[m],g=`${f.name}_${A}_${y}`,x=n[g];u.push(x)}let h=c.computeOutputShape(Tn(u)),d=jp(h),p=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${p}_${m}`;n[f]=d[m]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];jr(o in n),a.push(n[o])}return Tn(a)}runInternalGraph(e,t){t==null&&(t=Ei(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 r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Rp);for(let o of r){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,h=c.inputTensors,d=c.outputTensors,p=new Array;for(let m of h)m.id in n&&p.push(n[m.id]);if(p.length===h.length){let m={},f,A,y,g;if(c.callArgs!=null&&(m=c.callArgs),p.length===1){let[x,v]=p[0];m.mask==null&&(m.mask=v),y=ft(u.call(x,m)),g=ft(u.computeMask(x,v)),f=[x],A=[v]}else f=p.map(x=>x[0]),A=p.map(x=>x[1]),m.mask==null&&(m.mask=A),y=ft(u.call(f,m)),g=ft(u.computeMask(f,A));if(u.activityRegularizer)throw new De("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],w=y[x],b=g[x];n[v.id]=[w,b]}}}}let a=[],s=[],i=[];for(let o of this.outputs){jr(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),a.push(l),s.push(c)}return[a,s,i]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof Gr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=Gr.nodeKey(r,a);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new W(`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 W("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new W(`No such layer: ${e}`)}calculateLosses(){return L(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=Gr.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let h=s.inboundNodes[u],d=Gr.nodeKey(s,u),p={};if(this.containerNodes.has(d)){if(h.callArgs)try{JSON.stringify(h.callArgs),p=h.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),p={}}if(h.inboundLayers.length>0){let m=[];for(let f=0;f<h.inboundLayers.length;f++){let A=h.inboundLayers[f],y=h.nodeIndices[f],g=h.tensorIndices[f],x=Gr.nodeKey(A,y),v=t[x];v==null&&(v=0),m.push([A.name,v,g,p])}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 r=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Gr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];r.push([i.name,c,u])}e.inputLayers=r;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Gr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];a.push([i.name,c,u])}return e.outputLayers=a,e}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(f,A){f.name in s?s[f.name].push(A):s[f.name]=[A]}function o(f,A){let y=[],g;for(let x of A){let v=x[0],w=x[1],b=x[2];if(g=x[3]==null?{}:x[3],!(v in a)){i(f,A);return}let k=a[v];if(k.inboundNodes.length<=w){i(f,A);return}let N=k.inboundNodes[w];y.push(N.outputTensors[b])}y.length>0&&f.apply(Tn(y),g)}function l(f){let A=f.name,y=Tr(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,f.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new W(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!Pee(s);)for(let f of u){let A=a[f.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let h=[],d=[],p=t.inputLayers;for(let f of p){let A=f[0],y=f[1],g=f[2];jr(A in a);let x=a[A].inboundNodes[y].outputTensors;h.push(x[g])}let m=t.outputLayers;for(let f of m){let A=f[0],y=f[1],g=f[2];jr(A in a);let x=a[A].inboundNodes[y].outputTensors;d.push(x[g])}return new e({inputs:h,outputs:d,name:c})}get stateful(){if(this._stateful)throw new W("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(){L(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function fre(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function d6(e,t){return fre(e,t,"classWeight")}async function p6(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=L(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 a.data());Te(a);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])}),nn(i,"float32")}else return null}function mre(e,t){return B(e,t)}var Are=32;function m6(e,t){let n,r,a=t;n=a.xs,r=a.ys,_.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=f6("input",e.inputNames,n),i=f6("output",e.outputNames,r),o=s[0].shape[0];_.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)})`),_.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++)_.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++)_.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 f6(e,t,n){if(n instanceof Le)return[n];if(Array.isArray(n))return _.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new W(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function yre(e){if(e.length===3)throw new De("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function xre(e,t,n){let r=n.batchesPerEpoch!=null;if(_.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),_.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),_.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}`),_.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),_.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 a=n.validationData!=null,s,i;if(a)if(A6(n.validationData))_.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 A=yre(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(A=>"val_"+A)):c=l.slice();let u=e6(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=t6(u,h,n.epochs,null,null,gre(t,n),null,a,c);d.setModel(e),e.history=p,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let A={};await d.onEpochBegin(m);let y=0,g=0;for(r||(f=await t.iterator());r?y<n.batchesPerEpoch:!0;){let x=await f.next();if(r&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:v,ys:w}=m6(e,x.value),b={};b.batch=g,b.size=v[0].shape[0],await d.onBatchBegin(g,b);let k=[];if(n.classWeight!=null){let F=d6(n.classWeight,e.outputNames);for(let O=0;O<F.length;++O)k.push(await p6(w[O],null,F[O]))}let N=v.concat(w).concat(k),C=o(N);Te(N);for(let F=0;F<l.length;++F){let O=l[F],z=C[F];b[O]=z,Vt(z)}await d.onBatchEnd(g,b),Kv(b),g++,y++}if(r?y>=n.batchesPerEpoch:x.done){if(a){let v;A6(n.validationData)?v=ft(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):v=ft(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?Are:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)A[`val_${e.metricsNames[w]}`]=v[w]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(m,A),m++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function gre(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function A6(e){return typeof e.iterator=="function"}function wre(e){return typeof e.next=="function"}async function bre(e,t,n){n=n||{};let r=n.batches!=null,a=e.testFunction,s=[];if(n.verbose>0)throw new De("Verbose mode is not implemented yet.");_.assert(!r||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=wre(t)?t:await t.iterator(),o=0,l=0;for(;r?l<n.batches:!0;){let c=await i.next();if(s=L(()=>{if(c.value){let{xs:u,ys:h}=m6(e,c.value),d=u.concat(h),p=L(()=>a(d));if(Te(d),l===0)for(let f=0;f<p.length;++f)s.push(ke(0));let m=d[0].shape[0];for(let f=0;f<p.length;++f){let A=p[f],y=s[f];s[f]=L(()=>se(s[f],B(m,A))),l>0&&Te(y)}Te(p),o+=m,++l}return s}),c.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let c=0;c<s.length;++c){let u=s[c];s[c]=ge(s[c],o),Te(u)}return Tn(s)}function cy(e){_.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Dc(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(r=>Fi(r,t,n-t)):Fi(e,t,n-t)}function hy(e,t){return L(()=>e==null?null:Array.isArray(e)?e.map(n=>hy(n,t)):$v(e,t.dtype==="int32"?t:t.toInt()))}function dy(e,t){let n=[],r=0,a=null;for(;r<e;)a=r+t,a>=e&&(a=e),n.push([r,a]),r=a;return n}async function _re(e,t,n,r,a,s,i,o,l,c,u,h,d,p,m){a==null&&(a=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,p==null))throw new W("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let A=e.checkNumSamples(n,a,p,"steps_per_epoch"),y;A!=null&&(y=Ir(0,A)),i==null&&(i=1);let{callbackList:g,history:x}=t6(o,i,s,d,A,p,a,f,h);g.setModel(e),e.history=x,await g.onTrainBegin(),e.stopTraining_=!1;for(let v=d;v<s;++v){await g.onEpochBegin(v);let w={};if(p!=null)throw new De("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new De("batch shuffling is not implemneted yet");u&&_.shuffle(y);let b=nn(y),k=dy(A,a);for(let N=0;N<k.length;++N){let C={};if(await g.onBatchBegin(N,C),L(()=>{let F=k[N][0],O=k[N][1],z=Fi(b,F,O-F);C.batch=N,C.size=O-F;let V=hy(n,z),j=t(V);for(let U=0;U<r.length;++U){let X=r[U],G=j[U];C[X]=G,Vt(G)}if(N===k.length-1&&f){let U=e.testLoop(l,c,a);for(let X=0;X<r.length;++X){let G=r[X],ee=U[X];Vt(ee),w["val_"+G]=ee}}}),await g.onBatchEnd(N,C),Kv(C),e.stopTraining_)break}b.dispose()}if(await g.onEpochEnd(v,w),e.stopTraining_)break}return await g.onTrainEnd(),await e.history.syncData(),e.history}async function vre(e,t,n,r={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let a,s,i,o,l,c,u;try{let h=r.batchSize==null?32:r.batchSize;cy(h);let d=!1,p=await e.standardizeUserData(t,n,r.sampleWeight,r.classWeight,d,h);a=p[0],s=p[1],u=p[2];let m=!1,f;if(r.validationData!=null&&r.validationData.length>0){if(m=!0,r.validationData.length===2)i=r.validationData[0],o=r.validationData[1];else throw r.validationData.length===3?new De("validationData including sample weights is not supported yet."):new W(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);let b=!0,k=await e.standardizeUserData(i,o,null,null,b,h);l=k[0],c=k[1],f=l.concat(c)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){m=!0;let b=Math.floor(a[0].shape[0]*(1-r.validationSplit)),k=a[0].shape[0];l=Dc(a,b,k),a=Dc(a,0,b),c=Dc(s,b,k),s=Dc(s,0,b),f=l.concat(c)}else r.validationSteps!=null&&(m=!0);let A=a.concat(s).concat(u);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),x,v;m?(e.makeTestFunction(),x=e.testFunction,v=g.slice().concat(g.map(b=>"val_"+b))):(x=null,f=[],v=g.slice());let w=e6(r.callbacks,r.yieldEvery);return await _re(e,y,A,g,h,r.epochs,r.verbose,w,x,f,r.shuffle,v,r.initialEpoch,null,null)}finally{e.isTraining=!1,Oi(a,t),Oi(s,n),Oi(l,i),Oi(c,o),u!=null&&Te(u)}}function y6(e){let t=[];e instanceof Le&&(e=[e]);for(let n=0;n<e.length;++n){let r=e[n];if(r.rank===1)t.push(Tc(r,1));else{if(r.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(r)}}return t}function Oi(e,t){if(e==null)return;let n=[];if(t instanceof Le)n.push(t.id);else if(Array.isArray(t))t.forEach(a=>n.push(a.id));else if(t!=null)for(let a in t){let s=t[a];n.push(s.id)}let r=[];if(e instanceof Le)n.indexOf(e.id)===-1&&r.push(e);else if(Array.isArray(e))e.forEach(a=>{n.indexOf(a.id)===-1&&r.push(a)});else if(e!=null)for(let a in e){let s=e[a];n.indexOf(s.id)===-1&&r.push(s)}r.forEach(a=>{a.isDisposed||a.dispose()})}function kre(e){return e instanceof Le}function py(e){return Array.isArray(e)}function g6(e){return!kre(e)&&!py(e)}function x6(e,t,n,r=!0,a=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(py(e)&&e.length>0)i=!0;else if(g6(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new W(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(g6(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new W(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(py(e)){if(e=e,e.length!==t.length)throw new W(`Error when checking model ${a}: 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 W(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=y6(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 W(`Error when checking ${a}: 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&&!r)continue;let c=o.shape[l],u=n[i][l];if(u!=null&&u>=0&&c!==u)throw new W(`Error when checking ${a}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function Ire(e,t,n){let r=Ua(e.map(s=>s.shape[0]));r.sort();let a=Ua(t.map(s=>s.shape[0]));if(a.sort(),r.length>1)throw new W(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(a.length>1)throw new W(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(r.length>0&&a.length>0&&!_.arraysEqual(r,a))throw new W(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${a[0]} target sample(s).`)}function Sre(e,t,n){let r=[$i,Kp,Mc];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=n[a];if(i!=null){if(i===Mc&&s.shape[s.shape.length-1]===1)throw new W(`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(r.indexOf(i)!==-1){let l=s.shape.slice(1),c=o.slice(1);for(let u=0;u<l.length;++u){let h=l[u],d=c[u];if(d!=null&&h!==d)throw new W(`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 w6(e,t,n,r=!0,a=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new W(`Error when checking model ${a}: 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 W(`The model expects ${t.length} ${a} 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 W(`Error when checking ${a}: 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&&!r)continue;let c=o.shape[l],u=n[i][l];if(u!=null&&u!==c)throw new W(`Error when checking ${a}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function Nre(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>[]);let n;if(typeof e=="string"||typeof e=="function")n=[e];else if(Array.isArray(e)||typeof e=="object")n=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let a of t){let s=n.hasOwnProperty(a)?n[a]:[];Array.isArray(s)||(s=[s]),r.push(s)}return r}}var Tre="layers-model",fa=class extends Gr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new W("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");ure(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=are(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof ca))throw new W("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new W(`Unknown entry in loss dictionary: "${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(ny(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new W(`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=>ny(s))}else{let s=ny(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=[],Mi("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 r=Nre(e.metrics,this.outputNames),a=(s,i,o)=>{this.outputNames.length>1&&(i=this.outputNames[s]+"_"+i),this.metricsNames.push(i),this.metricsTensors.push([o,s])};Mi("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=r[s];(o=>{let l="",c,u,h;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]===Kp?["accuracy","acc"].indexOf(d)!==-1?u=ry:["crossentropy","ce"].indexOf(d)!==-1&&(u=a6):this.lossFunctions[s]===Xp?["accuracy","acc"].indexOf(d)!==-1?u=s6:["crossentropy","ce"].indexOf(d)!==-1&&(u=i6):["accuracy","acc"].indexOf(d)!==-1?u=ay:["crossentropy","ce"].indexOf(d)!==-1&&(u=sy);let f;["accuracy","acc"].indexOf(d)!==-1?f="acc":["crossentropy","ce"].indexOf(d)!==-1&&(f="ce"),h=u,c=l+f}else h=rre(d),c=l+Jp(d);let p;Mi(c,()=>{p=h}),a(s,c,p)}})(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 r=n.batchSize==null?32:n.batchSize;cy(r);let a=!0,s=this.standardizeUserDataXY(e,t,a,r);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,l=this.testLoop(o,i,r,n.verbose,n.steps);return Tn(l)}finally{Oi(s[0],e),Oi(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),bre(this,e,t)}checkNumSamples(e,t,n,r="steps"){let a;if(n!=null){if(a=null,t!=null)throw new W(`If ${r} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?a=e[0].shape[0]:a=e.shape[0];else throw new W(`Either the input data should have a defined shape, or ${r} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new W("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Di;if(e instanceof Le&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new W(`The number of inputs provided (${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 W(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=$c(a,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=Ei(null,e.length),n=e.length;for(let r of this.layers){let a=Array.isArray(r.output)?r.output:[r.output],s=a.map(i=>i.name);for(let i=0;i<e.length;++i){let o=s.indexOf(e[i]);if(o!==-1&&(t[i]=a[o],n--),n===0)break}if(n===0)break}if(n>0){let r=[];throw t.forEach((a,s)=>{a==null&&r.push(e[s])}),new W(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(r)}`)}return t}predictLoop(e,t=32,n=!1){return L(()=>{let r=this.checkNumSamples(e);if(n)throw new De("Verbose predictLoop() is not implemented yet.");let a=dy(r,t),s=this.outputs.map(i=>[]);for(let i=0;i<a.length;++i)L(()=>{let o=a[i][0],l=a[i][1],c=Dc(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 h=new Di(u);return $c(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return Tn(s.map(i=>ot(i,0)))})}predict(e,t={}){let n=y6(e);w6(n,this.inputNames,this.feedInputShapes,!1);try{let r=t.batchSize==null?32:t.batchSize;return cy(r),this.predictLoop(n,r)}finally{Oi(n,e)}}predictOnBatch(e){w6(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,r){if(this.optimizer_==null)throw new kr("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let a=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===Xp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=x6(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=x6(t,this.feedOutputNames,a,!1,"target"),Ire(e,t,null),Sre(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new W(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let c=d6(r,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await p6(o[u],null,c[u]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return L(()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new De("Verbose mode is not implemented yet.");if(a!=null)throw new De("steps mode in testLoop() is not implemented yet");{let o=dy(s,n),l=nn(Ir(0,s));for(let c=0;c<o.length;++c){let u=o[c][0],h=o[c][1],d=Fi(l,u,h-u),p=hy(t,d),m=e(p);if(c===0)for(let f=0;f<m.length;++f)i.push(ke(0));for(let f=0;f<m.length;++f){let A=m[f];i[f]=se(i[f],B(h-u,A))}}for(let c=0;c<i.length;++c)i[c]=ge(i[c],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;bv(e,r)>1&&(a+=`_${bv(e.slice(0,n),r)}`),t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new Di(c),h=$c(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let m=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(m=mre(m,a[p]));let f=vt(m);t.push(f),p===0?d=m:d=se(d,m)}for(let p=0;p<this.metricsTensors.length;++p){let m;if(this.outputs.length>1&&p<this.outputs.length)m=t[p];else{let f=this.metricsTensors[p][0],A=this.metricsTensors[p][1];m=vt(f(r[A],h[A]))}Vt(m),s.push(m)}return d=vt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>L(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Di(s),o=$c(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=vt(c(a[l],o[l]));l===0?n=u:n=se(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],h=vt(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return vre(this,e,t,n)}async fitDataset(e,t){return xre(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Te(s),Tn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[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=md().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-md().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=pa(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=>pa(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=pa(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[pa(Jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>pa(Jp(e)));{let e={};for(let t in this.metrics)e[t]=pa(Jp(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=Fc(e.optimizer_config),n=Tr(t),r;if(typeof e.loss=="string")r=Ci(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>Ci(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=Ci(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>Ci(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=Ci(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=vn.getSaveHandlers(e);if(i.length===0)throw new W(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new W(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new W("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await vn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Tre,generatedBy:`TensorFlow.js tfjs-layers v${ly}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await vn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=vn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;l6(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){l6(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};fa.className="Model";re.registerClass(fa);var b6=class extends fa{};b6.className="Functional";re.registerClass(b6);async function Ere(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Fc(n),a=Tr(r,t);if(e.weightsManifest!=null){let s=await vn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),Te(s)}return a}async function Rre(e,t){if(t==null&&(t={}),typeof e=="string"){let n=vn.getLoadHandlers(e,t);if(n.length===0)n.push(vn.browserHTTPRequest(e,t));else if(n.length>1)throw new W(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Cre(e,void 0,t)}async function Cre(e,t,n){if(n==null&&(n={}),e.load==null)throw new W("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=Tr(Fc(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new W("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=Mre(r.weightData,r.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Te(c),Te(u.map(h=>h.tensor))}return o}function Mre(e,t){let n=vn.decodeWeights(e,t),r={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:n[s.name]}):r[s.name]=n[s.name]}),{modelWeights:r,optimizerWeights:a}}var Ul=class extends fa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Vp("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 W(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Ul||e instanceof fa,n;if(t){if(n=e,n.outputs.length!==1)throw new W("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 W("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 W("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=Xv({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new W(`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 W("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=qv(this.outputs[0])}this.inboundNodes=[],new Hp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Ei(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(r=>r.shape),outputShapes:this.outputs[0].shape})}else{let r=e.apply(this.outputs[0]);if(Array.isArray(r))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[r],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(rt(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 fa({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 kr("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 kr("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 kr("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 kr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new W("Legacy serialization format not supported yet.");a=t}else _.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."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Ul))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Tr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new W("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 W("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}}};Ul.className="Sequential";re.registerClass(Ul);function Fre(e){return new fa(e)}function $re(e){return new Ul(e)}function Dre(e,t){return t==null&&(t={}),Rre(e,t)}function Lv(e){return Xv(e)}function Ore(e,t){dr.registerCallbackConstructor(e,t)}var Cn=class extends re.Serializable{getConfig(){return{}}},_6=class extends Cn{apply(e,t=1){return pte(e,t)}};_6.className="elu";re.registerClass(_6);var v6=class extends Cn{apply(e){return Dd(e)}};v6.className="selu";re.registerClass(v6);var k6=class extends Cn{apply(e){return Lr(e)}};k6.className="relu";re.registerClass(k6);var I6=class extends Cn{apply(e){return L(()=>xl(6,Lr(e)))}};I6.className="relu6";re.registerClass(I6);var S6=class extends Cn{apply(e){return e}};S6.className="linear";re.registerClass(S6);var N6=class extends Cn{apply(e){return kn(e)}};N6.className="sigmoid";re.registerClass(N6);var T6=class extends Cn{apply(e){return mte(e)}};T6.className="hardSigmoid";re.registerClass(T6);var E6=class extends Cn{apply(e){return Ai(e)}};E6.className="softplus";re.registerClass(E6);var C6=class extends Cn{apply(e){return fte(e)}};C6.className="softsign";re.registerClass(C6);var R6=class extends Cn{apply(e){return di(e)}};R6.className="tanh";re.registerClass(R6);var fy=class extends Cn{apply(e,t=-1){return nc(e,t)}};fy.className="softmax";re.registerClass(fy);var M6=class extends Cn{apply(e,t=-1){return Td(e,t)}};M6.className="logSoftmax";re.registerClass(M6);var F6=class extends Cn{apply(e,t=1){return L(()=>kn(e.mul(t)).mul(e))}};F6.className="swish";re.registerClass(F6);var $6=class extends Cn{apply(e){return L(()=>B(e,di(Ai(e))))}};$6.className="mish";re.registerClass($6);function Xa(e){return e.getClassName()}function my(e,t={}){return kc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Ka(e){if(e==null){let t={};return t.className="linear",t.config={},my(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},my(t)}else return e instanceof Cn?e:my(e)}function Ay(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 D6=class extends re.Serializable{},Oc=class extends D6{constructor(e){super();Ay(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 L(()=>{let t=Et([1]);return this.hasL1&&(t=se(t,Ne(B(this.l1,Dt(e))))),this.hasL2&&(t=se(t,Ne(B(this.l2,Ec(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Oc.className="L1L2";re.registerClass(Oc);function zre(e){return Ay(e),new Oc({l1:e!=null?e.l1:null,l2:0})}function Pre(e){return Ay(e),new Oc({l2:e!=null?e.l2:null,l1:0})}var O6={l1l2:"L1L2"};function ut(e){return CA(e)}function z6(e,t={}){return kc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function yt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in O6?O6[e]:e,config:{}};return z6(t)}else return e instanceof D6?e:z6(e)}var yy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=Lr(e);return this.maxValue!=null&&(n=In(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};yy.className="ReLU";re.registerClass(yy);var gy=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return Ku(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};gy.className="LeakyReLU";re.registerClass(gy);var xy=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=At(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=yt(e.alphaRegularizer),this.alphaConstraint=Lt(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 W(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=rt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Rt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=ze(e),Qu(e,this.alpha.read())}getConfig(){let e={alphaInitializer:kt(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:Pt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};xy.className="PReLU";re.registerClass(xy);var wy=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return Al(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wy.className="ELU";re.registerClass(wy);var by=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=ze(e);return n.mul(Nc(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};by.className="ThresholdedReLU";re.registerClass(by);var _y=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new fy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=ze(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};_y.className="Softmax";re.registerClass(_y);function Hl(e,t,n){if(typeof e=="number")return Ei(e,t);if(e.length!==t)throw new W(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!ute(a))throw new W(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Er(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function qr(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ga([n-t,0]);else if(r==="same")e=e*t;else throw new W(`Unsupport padding mode: ${r}.`);return e}function vy(e,t){return L(()=>(Tt(t),t==="channelsFirst"?Ze(e,[0,2,3,1]):e))}function P6(e,t){return L(()=>(Tt(t),t==="channelsFirst"?Ze(e,[0,2,3,4,1]):e))}function Lre(e,t,n,r=1,a="valid",s,i=1){return L(()=>{if(s==null&&(s=vr()),Tt(s),e.shape.length!==3)throw new W(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new W(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new W(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ze(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=wd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Sr(o,n)),o})}function L6(e,t,n,r=[1,1],a="valid",s,i,o=null){return L(()=>{if(s==null&&(s=vr()),Tt(s),e.rank!==3&&e.rank!==4)throw new W(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new W(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=vy(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=La.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ze(l,[0,3,1,2])),l})}function Wre(e,t,n,r=[1,1,1],a="valid",s,i){return L(()=>{if(s==null&&(s=vr()),Tt(s),e.rank!==4&&e.rank!==5)throw new W(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new W(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=P6(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Sr(o,n)),s==="channelsFirst"&&(o=Ze(o,[0,4,1,2,3])),o})}var ky=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ky.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Hl(t.kernelSize,e,"kernelSize"),this.strides=Hl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,er(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Tt(this.dataFormat),this.activation=Ka(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=At(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Lt(t.biasConstraint),this.biasRegularizer=yt(t.biasRegularizer),this.activityRegularizer=yt(t.activityRegularizer),this.dilationRate=Hl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new W(`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 W(`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 W(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(jr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!MA(e.kernelSize,"number",1,3))throw new W(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Xa(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},zc=class extends ky{constructor(e,t){super(e,t);this.kernel=null,zc.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=At(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Lt(t.kernelConstraint),this.kernelRegularizer=yt(t.kernelRegularizer)}build(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new W(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return L(()=>{e=ze(e);let n,r=this.bias==null?null:this.bias.read(),a=vv(this.activation.getClassName());if(a!=null&&this.rank===2)n=L6(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Lre(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=L6(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Wre(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=rt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Er(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Pt(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 W(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Pc=class extends zc{constructor(e){super(2,e);Pc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!MA(e.kernelSize,"number",1,2))throw new W(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Pc.className="Conv2D";re.registerClass(Pc);var Lc=class extends zc{constructor(e){super(3,e);Lc.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 W(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Lc.className="Conv3D";re.registerClass(Lc);var Iy=class extends Pc{constructor(e){super(e);if(this.inputSpec=[new Rt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new W(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=rt(e),e.length!==4)throw new W("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new W("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Rt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return L(()=>{let n=ze(e);if(n.shape.length!==4)throw new W(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=qr(o,h,c,this.padding),m=qr(l,d,u,this.padding),f=[a,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,1]));let A=bd(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Ze(A,[0,3,1,2])),this.bias!=null&&(A=Sr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=rt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=qr(t[r],o,s,this.padding),t[a]=qr(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Iy.className="Conv2DTranspose";re.registerClass(Iy);var Sy=class extends Lc{constructor(e){super(e);if(this.inputSpec=[new Rt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new W(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=rt(e),e.length!==5)throw new W("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new W("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Rt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return L(()=>{let n=ze(e);if(n.shape.length!==5)throw new W(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=r[o],c=r[s],u=r[i],h=this.kernelSize[0],d=this.kernelSize[1],p=this.kernelSize[2],m=this.strides[0],f=this.strides[1],A=this.strides[2],y=qr(l,m,h,this.padding),g=qr(c,f,d,this.padding),x=qr(u,A,p,this.padding),v=[a,y,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,4,1]));let w=$w(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Ze(w,[0,4,1,2,3])),this.bias!==null&&(w=Sr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=rt(e);let t=e.slice(),n,r,a,s;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3,s=4):(n=4,r=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],h=this.strides[2];return t[n]=this.filters,t[r]=qr(t[r],c,i,this.padding),t[a]=qr(t[a],u,o,this.padding),t[s]=qr(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Sy.className="Conv3DTranspose";re.registerClass(Sy);var W6=class extends zc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new W("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new W("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new W(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=yt(t.depthwiseRegularizer),this.depthwiseConstraint=Lt(t.depthwiseConstraint),this.pointwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=yt(t.pointwiseRegularizer),this.pointwiseConstraint=Lt(t.pointwiseConstraint)}build(e){if(e=rt(e),e.length<this.rank+2)throw new W(`Inputs to SeparableConv${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 W(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"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 Rt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return L(()=>{e=ze(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ze(e,[0,2,3,1])),n=Em(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Sr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ze(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=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseConstraint),e.pointwiseConstraint=Pt(this.pointwiseConstraint),e}};W6.className="SeparableConv";var Ny=class extends W6{constructor(e){super(2,e)}};Ny.className="SeparableConv2D";re.registerClass(Ny);var e0=class extends zc{constructor(e){super(1,e);e0.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"&&!MA(e.kernelSize,"number",1,1))throw new W(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};e0.className="Conv1D";re.registerClass(e0);var Ty=class extends Ge{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 L(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=Mp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Mp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Mp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Mp(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}};Ty.className="Cropping2D";re.registerClass(Ty);var Ey=class extends Ge{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,Tt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,ite(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 L(()=>{let n=ze(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Ze(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return Ze(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="UpSampling2D";re.registerClass(Ey);function Bre(e,t,n=[1,1],r="valid",a,s){return L(()=>{a==null&&(a=vr()),Tt(a);let i=vy(e,a);if(e.rank!==4)throw new W(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new W(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ml(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=Ze(i,[0,3,1,2])),i})}var Cy=class extends ky{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=At(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Lt(e.depthwiseConstraint),this.depthwiseRegularizer=yt(e.depthwiseRegularizer)}build(e){if(e=rt(e),e.length<4)throw new W(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new W(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return L(()=>{e=ze(e);let n=Bre(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Sr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Er(t,this.kernelSize[0],this.padding,this.strides[0]),s=Er(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseRegularizer),e}};Cy.className="DepthwiseConv2D";re.registerClass(Cy);function B6(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new W("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function V6(e,t,n,r=!1,a,s,i=!1,o=!1){return L(()=>{let l=t.shape.length;if(l<3)throw new W(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Ir(2,l));if(t=Ze(t,c),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=on(a,-1)),a=Ze(a,c)),r&&(t=Dn(t,0),a!=null&&(a=Dn(a,0)));let u=[],h,d=n,p=t.shape[0],m=ur(t),f;a!=null&&(f=ur(a));for(let y=0;y<p;++y){let g=m[y],x=L(()=>e(g,d));if(a==null)h=x[0],d=x[1];else{let v=L(()=>{let w=f[y],b=$n(w).sub(w),k=x[0].mul(w).add(d[0].mul(b)),N=d.map((C,F)=>x[1][F].mul(w).add(C.mul(b)));return{output:k,newStates:N}});h=v.output,d=v.newStates}o&&u.push(h)}let A;return o&&(A=On(u,1)),[h,A,d]})}var Hr=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new W("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new t0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new W("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 Rt({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 Ir(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){JA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return L(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");JA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Rt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!_.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new W(`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 Rt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){L(()=>{if(!this.stateful)throw new da("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new W("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Et([n,r])):this.states_=[Et([n,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Et([n,r])):this.states_[0]=Et([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new W(`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()):Te(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!_.arraysEqual(a.shape,i))throw new W(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Vt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=B6(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.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 Rt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Nr){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 L(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=ze(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new W(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=V6((d,p)=>{let m=this.cell.call([d].concat(p),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return L(()=>{let t=Et(e.shape);return t=Ne(t,[1,2]),t=Tc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?PA(t,[1,n]):t):this.cell.stateSize>1?[PA(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()===Hr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Tr(r,n);return new e(Object.assign(t,{cell:a}))}};Hr.className="RNN";re.registerClass(Hr);var Rc=class extends Ge{},n0=class extends Rc{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,Ut(this.units,"units"),this.activation=Ka(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Wl([1,Ga([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Wl([1,Ga([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=rt(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 L(()=>{if(e=e,e.length!==2)throw new W(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Za({ones:()=>$n(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Za({ones:()=>$n(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ur(B(e,s),this.kernel.read()):a=Ur(e,this.kernel.read()),this.bias!=null&&(a=Sr(a,this.bias.read())),i!=null&&(n=B(n,i));let o=se(a,Ur(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:Xa(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};n0.className="SimpleRNNCell";re.registerClass(n0);var Ry=class extends Hr{constructor(e){e.cell=new n0(e),super(e)}call(e,t){return L(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Ry.className="SimpleRNN";re.registerClass(Ry);var r0=class extends Rc{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 W("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=Ka(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ka(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Wl([1,Ga([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Wl([1,Ga([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=rt(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 L(()=>{if(e=e,e.length!==2)throw new W(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Za({ones:()=>$n(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Za({ones:()=>$n(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=B(e,a[0]));let c=Ur(e,this.kernel.read());this.useBias&&(c=Sr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=B(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=ln(u,[2*this.units,this.units],u.rank-1),p=Ur(r,h),[m,f,A]=ln(c,3,c.rank-1),[y,g]=ln(p,2,p.rank-1);i=this.recurrentActivation.apply(se(m,y)),o=this.recurrentActivation.apply(se(f,g));let x=Ur(B(o,r),d);l=this.activation.apply(se(A,x));let v=se(B(i,r),B(se(1,_t(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Xa(this.activation),recurrentActivation:Xa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};r0.className="GRUCell";re.registerClass(r0);var My=class extends Hr{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 r0(e),super(e)}call(e,t){return L(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};My.className="GRU";re.registerClass(My);var Wc=class extends Rc{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,Ut(this.units,"units"),this.activation=Ka(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ka(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Wl([1,Ga([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Wl([1,Ga([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=rt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends hr{apply(i,o){let l=a.apply([s]),c=new $p().apply([s]),u=a.apply([s*2]);return Fv(Fv(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return L(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new W(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Za({ones:()=>$n(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Za({ones:()=>$n(r),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=B(e,s[0]));let h=Ur(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=B(r,i[0])),h=se(h,Ur(r,this.recurrentKernel.read())),this.useBias&&(h=Sr(h,this.bias.read()));let[d,p,m,f]=ln(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(B(l,a),B(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let A=B(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Xa(this.activation),recurrentActivation:Xa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Wc.className="LSTMCell";re.registerClass(Wc);var Fy=class extends Hr{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 Wc(e),super(e)}call(e,t){return L(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fy.className="LSTM";re.registerClass(Fy);var t0=class extends Rc{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 L(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){JA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Mi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Tr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return QA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}ey(t)}};t0.className="StackedRNNCells";re.registerClass(t0);function Za(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>Dv(t(),n),i=()=>Cc(s,t,r);return!a||a<=1?Vt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Vt(o.clone()))}var Vre=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},j6=class extends Hr{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Rt({ndim:5})]}call(e,t){return L(()=>{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new W("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}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 L(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Et(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){L(()=>{if(!this.stateful)throw new da("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new W("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(()=>Et(a)):this.states_=[Et(a)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Et(a)):this.states_[0]=Et(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new W(`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()):Te(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!_.arraysEqual(i.shape,o))throw new W(`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=>Vt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Er(l,r[0],a,s[0],i[0]),h=Er(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};j6.className="ConvRNN2D";var a0=class extends Wc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=Hl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ut(o,"kernelSize")),this.strides=Hl(r||1,2,"strides"),this.strides.forEach(o=>Ut(o,"strides")),this.padding=a||"valid",er(this.padding),this.dataFormat=s||"channelsLast",Tt(this.dataFormat),this.dilationRate=Hl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ut(o,"dilationRate"))}build(e){var t;e=rt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new W(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);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 hr{apply(u,h){let d=l.apply([c]),p=Fn([c]),m=l.apply([c*2]);return WA([d,p,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return L(()=>{if(e.length!==3)throw new W(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Za({ones:()=>$n(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:B(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Za({ones:()=>$n(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,m=l(a,p,0),f=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[x,v,w,b]=ln(this.kernel.read(),i,g),[k,N,C,F]=this.useBias?ln(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,k,this.padding),u=this.inputConv(u,v,N,this.padding),h=this.inputConv(h,w,C,this.padding),d=this.inputConv(d,b,F,this.padding);let[O,z,V,j]=ln(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,O),f=this.recurrentConv(f,z),A=this.recurrentConv(A,V),y=this.recurrentConv(y,j);let U=this.recurrentActivation.apply(se(c,m)),X=this.recurrentActivation.apply(se(u,f)),G=se(B(X,s),B(U,this.activation.apply(se(h,A)))),ee=B(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Vre(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=ia(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Sr(a,n,this.dataFormat):a}recurrentConv(e,t){return ia(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};a0.className="ConvLSTM2DCell";re.registerClass(a0);var $y=class extends j6{constructor(e){let t=new a0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};$y.className="ConvLSTM2D";re.registerClass($y);var s0=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=ze(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Cc(()=>Dv(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};s0.className="Dropout";re.registerClass(s0);var Dy=class extends s0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Dy.className="SpatialDropout1D";re.registerClass(Dy);var Oy=class extends Ge{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,Ut(this.units,"units"),this.activation=Ka(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Lt(e.kernelConstraint),this.biasConstraint=Lt(e.biasConstraint),this.kernelRegularizer=yt(e.kernelRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.activityRegularizer=yt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=rt(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=rt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=ze(e),r=vv(this.activation.getClassName()),a;return r!=null?a=Ur(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Ur(n,this.kernel.read()),this.bias!=null&&(a=Sr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Xa(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="Dense";re.registerClass(Oy);var zy=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=rt(e);for(let t of e.slice(1))if(t==null)throw new W(`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],Ha(e,1)]}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=ze(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return dte(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};zy.className="Flatten";re.registerClass(zy);var Py=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ka(e.activation)}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.activation.apply(n)})}getConfig(){let e={activation:Xa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Py.className="Activation";re.registerClass(Py);var Ly=class extends Ge{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 L(()=>(e=ze(e),cte(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="RepeatVector";re.registerClass(Ly);var Wy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new W("Can only specifiy one unknown dimension.");else a*=l}let i=Ha(e);if(s!==null){if(a===0||i%a!=0)throw new W(n);r[s]=i/a}else if(i!==a)throw new W(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=ze(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="Reshape";re.registerClass(Wy);var By=class extends Ge{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=Ir(1,e.dims.length+1);if(!_.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 Rt({ndim:this.dims.length+1})]}computeOutputShape(e){e=rt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Ze(ze(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};By.className="Permute";re.registerClass(By);var Vy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=ze(e),r=-1;return Vu(gi(n,this.maskValue),r)}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=ze(e),r=-1,a=!0,s=Vu(gi(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Vy.className="Masking";re.registerClass(Vy);var jy=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ft(e.inputLength))}this.inputDim=e.inputDim,Ut(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ut(this.outputDim,"outputDim"),this.embeddingsInitializer=At(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=yt(e.embeddingsRegularizer),this.activityRegularizer=yt(e.activityRegularizer),this.embeddingsConstraint=Lt(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 L(()=>this.maskZero?(e=ze(e),gi(e,Ue(e))):null)}computeOutputShape(e){if(e=rt(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new W(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new W(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=ze(e);return n.dtype!=="int32"&&(n=Nc(n,"int32")),$v(this.embeddings.read(),n.as1D()).reshape(rt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Pt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};jy.className="Embedding";re.registerClass(jy);var zi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new W("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[rt(e)]),e=e,e.length<2)throw new W(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ua(t),t.length>1)throw new W(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ua(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return L(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Ga(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Tc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Ha(c.slice(1))));d=Ze(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=Ir(1,l).concat([0]);n.push(Ze(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ze(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Ir(0,i-1));s=Ze(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ua(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return L(()=>{if(t==null)return null;if(!Array.isArray(t))throw new W("`mask` should be an Array");if(!Array.isArray(e))throw new W("`inputs` should be an Array");if(t.length!==e.length)throw new W(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:on(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=lr(n,t[r]);return n})}},Uy=class extends zi{constructor(e){super(e)}mergeFunction(e){return L(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};Uy.className="Add";re.registerClass(Uy);var Hy=class extends zi{constructor(e){super(e)}mergeFunction(e){return L(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=B(t,e[n]);return t})}};Hy.className="Multiply";re.registerClass(Hy);var Gy=class extends zi{constructor(e){super(e)}mergeFunction(e){return L(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return B(1/e.length,t)})}};Gy.className="Average";re.registerClass(Gy);var qy=class extends zi{constructor(e){super(e)}mergeFunction(e){return L(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Pr(t,e[n]);return t})}};qy.className="Maximum";re.registerClass(qy);var Xy=class extends zi{constructor(e){super(e)}mergeFunction(e){return L(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=xl(t,e[n]);return t})}};Xy.className="Minimum";re.registerClass(Xy);var Ky=class extends zi{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 W("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(_.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new W("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return L(()=>WA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new W("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new W("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new W("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new W(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return L(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push($n(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(on(t[s],-1)):r.push(t[s]);let a=ot(r,this.axis);return gd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ky.className="Concatenate";re.registerClass(Ky);function Bc(e,t){for(;e<0;)e+=t;return e}function jre(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(_.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),_.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 De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return L(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;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;r>a?l=r+a-3:l=r-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 Zy=class extends zi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){_.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 De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new W(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new W(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>Bc(a,e[s].shape.length)):r=[Bc(this.axes,t.shape.length),Bc(this.axes,n.shape.length)],this.normalize&&(t=Gp(t,r[0]),n=Gp(n,r[1])),jre(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Bc(this.axes,e.length),Bc(this.axes,t.length)],n}computeOutputShape(e){_.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 De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Zy.className="Dot";re.registerClass(Zy);var Yy=class extends Ge{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 L(()=>{this.invokeCallHook(e,t);let n=ze(e);return Cc(()=>Fp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Yy.className="GaussianNoise";re.registerClass(Yy);var Jy=class extends Ge{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 L(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?Cc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Fp(n.shape,1,r))},()=>n,t.training||!1):n})}};Jy.className="GaussianDropout";re.registerClass(Jy);var Qy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return L(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Cc(()=>{let r=ze(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=za(wl(n),this.rate);o=Nc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>ze(e),t.training||!1)}return e})}};Qy.className="AlphaDropout";re.registerClass(Qy);function Vc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=Sw(e,t,n,r,a,s);else if(e.rank===3)i=Nw(e,t,n,r,a,s);else if(e.rank===4)i=Tw(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Ure(e,t,n,r,a=.001){return L(()=>{let s=Cd(e,r),i=s.mean,o=s.variance;return[Vc(e,i,o,n,t,a),i,o]})}function Hre(e,t,n,r,a=.001){return L(()=>{let s=Cd(e,r),i=s.mean,o=s.variance,l=[];for(let p of Ir(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Vc(e,c,u,d,h,a),i,o]})}function Gre(e,t,n,r,a=.001){return _.arraysEqual(r.slice().sort(),Ir(0,e.rank-1))?Ure(e,t,n,r,a):Hre(e,t,n,r,a)}var e2=class extends Ge{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=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.movingMeanInitializer=At(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=At(e.movingVarianceInitializer||"ones"),this.betaConstraint=Lt(e.betaConstraint),this.gammaConstraint=Lt(e.gammaConstraint),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer)}build(e){e=rt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new W(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Rt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return L(()=>{let n=t.training==null?!1:t.training,r=ze(e),a=r.shape,s=a.length,i=Ir(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Ei(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!_.arraysEqual(c,Ir(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return Vc(r,A,y,g,x,this.epsilon)}else return Vc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,m]=Gre(r,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(A,y,g)=>{L(()=>{let x=1-g,v=A.read(),w=v.sub(y).mul(x);A.write(v.sub(w))})};return(()=>{f(this.movingMean,p,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:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Pt(this.betaConstraint),gammaConstraint:Pt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};e2.className="BatchNormalization";re.registerClass(e2);var t2=class extends Ge{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=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=rt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ua(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=ze(e),r=n.shape,a=r.length;return L(()=>{let s=!0,{mean:i,variance:o}=Cd(n,this.axis,s),l=Ei(1,a);for(let m of this.axis)l[m]=r[m];let c=m=>m!=null&&m.shape.length!==a&&this.axis!==[a-1]?m.reshape(l):m,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let m=0;m<a;++m)this.axis.indexOf(m)!==-1?(d.push(r[m]),p.push(1)):(d.push(1),p.push(r[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),Vc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};t2.className="LayerNormalization";re.registerClass(t2);function qre(e,t,n){return L(()=>{if(e.rank!==4)throw new W(`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 W("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=vr()),n!=="channelsLast"&&n!=="channelsFirst")throw new W(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],oa(e,r)})}var n2=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?vr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new W(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 W(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new W(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Rt({ndim:4})]}computeOutputShape(e){e=rt(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 L(()=>qre(ze(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};n2.className="ZeroPadding2D";re.registerClass(n2);function i0(e,t,n,r,a,s){return L(()=>{Tt(a),Nv(s),er(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=vr()),s==null&&(s="max"),e=vy(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Yu(e,t,n,o):i=Hu(e,t,n,o),a==="channelsFirst"&&(i=Ze(i,[0,3,1,2])),i})}function U6(e,t,n,r,a,s){return L(()=>{Tt(a),Nv(s),er(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=vr()),s==null&&(s="max"),e=P6(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=_m(e,t,n,o):i=om(e,t,n,o),a==="channelsFirst"&&(i=Ze(i,[0,4,1,2,3])),i})}var H6=class extends Ge{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 W(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(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 W(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,er(this.padding),this.inputSpec=[new Rt({ndim:3})]}computeOutputShape(e){e=rt(e);let t=Er(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return L(()=>{this.invokeCallHook(e,t),e=Tc(ze(e),2);let n=this.poolingFunction(ze(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Pa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},r2=class extends H6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),er(r),i0(e,t,n,r,a,"max")}};r2.className="MaxPooling1D";re.registerClass(r2);var a2=class extends H6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),er(r),i0(e,t,n,r,a,"avg")}};a2.className="AveragePooling1D";re.registerClass(a2);var G6=class extends Ge{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 W(`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];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),er(this.padding),this.inputSpec=[new Rt({ndim:4})]}computeOutputShape(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Er(t,this.poolSize[0],this.padding,this.strides[0]),n=Er(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 L(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},s2=class extends G6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),er(r),i0(e,t,n,r,a,"max")}};s2.className="MaxPooling2D";re.registerClass(s2);var i2=class extends G6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),er(r),i0(e,t,n,r,a,"avg")}};i2.className="AveragePooling2D";re.registerClass(i2);var q6=class extends Ge{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 W(`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];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),er(this.padding),this.inputSpec=[new Rt({ndim:5})]}computeOutputShape(e){e=rt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Er(t,this.poolSize[0],this.padding,this.strides[0]),n=Er(n,this.poolSize[1],this.padding,this.strides[1]),r=Er(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return L(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},o2=class extends q6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),er(r),U6(e,t,n,r,a,"max")}};o2.className="MaxPooling3D";re.registerClass(o2);var l2=class extends q6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),er(r),U6(e,t,n,r,a,"avg")}};l2.className="AveragePooling3D";re.registerClass(l2);var X6=class extends Ge{constructor(e){super(e);this.inputSpec=[new Rt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},u2=class extends X6{constructor(e){super(e||{})}call(e,t){return L(()=>{let n=ze(e);return vt(n,1)})}};u2.className="GlobalAveragePooling1D";re.registerClass(u2);var c2=class extends X6{constructor(e){super(e||{})}call(e,t){return L(()=>{let n=ze(e);return Kn(n,1)})}};c2.className="GlobalMaxPooling1D";re.registerClass(c2);var K6=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.inputSpec=[new Rt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},h2=class extends K6{call(e,t){return L(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?vt(n,[1,2]):vt(n,[2,3])})}};h2.className="GlobalAveragePooling2D";re.registerClass(h2);var d2=class extends K6{call(e,t){return L(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?Kn(n,[1,2]):Kn(n,[2,3])})}};d2.className="GlobalMaxPooling2D";re.registerClass(d2);var Z6=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=Tr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},p2=class extends Z6{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=rt(e),e.length<3)throw new W(`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=rt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return L(()=>(e=ze(e),V6((n,r)=>[ze(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};p2.className="TimeDistributed";re.registerClass(p2);function Xre(e){Ri(ste,"BidirectionalMergeMode",e)}var Kre="concat",f2=class extends Z6{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Tr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Tr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Kre:e.mergeMode,Xre(this.mergeMode),e.weights)throw new De("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Tn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=B6(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new W("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 Rt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(r!=null)throw new De("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Nr;for(let l of s)if(l instanceof Nr!==o)throw new W("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 h=super.apply(l,t);return this.inputSpec=u,h}else return super.apply(e,t)}call(e,t){return L(()=>{let n=t.initialState,r,a;if(n==null)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Dn(a,1));let i;return this.mergeMode==="concat"?i=WA([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=B(.5,se(r,a)):this.mergeMode==="mul"?i=B(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Mi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Mi(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 r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}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=Tr(t.layer);if(delete t.layer,t.numConstants!=null)throw new De("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};f2.className="Bidirectional";re.registerClass(f2);function bte(e){return new Bl(e)}function _te(e){return new wy(e)}function vte(e){return new yy(e)}function kte(e){return new gy(e)}function Ite(e){return new xy(e)}function Ste(e){return new _y(e)}function Nte(e){return new by(e)}function Tte(e){return new e0(e)}function Ete(e){return new Pc(e)}function Cte(e){return new Iy(e)}function Rte(e){return new Lc(e)}function Mte(e){return new Sy(e)}function Fte(e){return new Ny(e)}function $te(e){return new Ty(e)}function Dte(e){return new Ey(e)}function Ote(e){return new Cy(e)}function zte(e){return new Py(e)}function Pte(e){return new Oy(e)}function Lte(e){return new s0(e)}function Wte(e){return new Dy(e)}function Bte(e){return new zy(e)}function Vte(e){return new Ly(e)}function jte(e){return new Wy(e)}function Ute(e){return new By(e)}function Hte(e){return new jy(e)}function Gte(e){return new Uy(e)}function qte(e){return new Gy(e)}function Xte(e){return new Ky(e)}function Kte(e){return new qy(e)}function Zte(e){return new Xy(e)}function Yte(e){return new Hy(e)}function Jte(e){return new Zy(e)}function Qte(e){return new e2(e)}function ene(e){return new t2(e)}function tne(e){return new n2(e)}function KA(e){return new a2(e)}function nne(e){return KA(e)}function rne(e){return KA(e)}function ZA(e){return new i2(e)}function ane(e){return ZA(e)}function sne(e){return ZA(e)}function YA(e){return new l2(e)}function ine(e){return YA(e)}function one(e){return YA(e)}function lne(e){return new u2(e)}function une(e){return new h2(e)}function Wv(e){return new c2(e)}function Bv(e){return new d2(e)}function Vv(e){return new r2(e)}function jv(e){return new s2(e)}function cne(e){return new o2(e)}function hne(e){return new My(e)}function dne(e){return new r0(e)}function pne(e){return new Fy(e)}function fne(e){return new Wc(e)}function mne(e){return new Ry(e)}function Ane(e){return new n0(e)}function yne(e){return new $y(e)}function gne(e){return new a0(e)}function xne(e){return new Hr(e)}function wne(e){return new t0(e)}function bne(e){return new f2(e)}function _ne(e){return new p2(e)}var vne=Wv,kne=Bv,Ine=Vv,Sne=jv;function Nne(e){return new Yy(e)}function Tne(e){return new Jy(e)}function Ene(e){return new Qy(e)}function Cne(e){return new Vy(e)}var Y6={};Me(Y6,{MAPE:()=>iae,MSE:()=>uae,binaryAccuracy:()=>Zre,binaryCrossentropy:()=>Yre,categoricalAccuracy:()=>Qre,categoricalCrossentropy:()=>eae,cosineProximity:()=>rae,mape:()=>oae,meanAbsoluteError:()=>aae,meanAbsolutePercentageError:()=>sae,meanSquaredError:()=>lae,mse:()=>cae,precision:()=>tae,recall:()=>nae,sparseCategoricalAccuracy:()=>Jre});function Zre(e,t){return ry(e,t)}function Yre(e,t){return a6(e,t)}function Jre(e,t){return s6(e,t)}function Qre(e,t){return ay(e,t)}function eae(e,t){return sy(e,t)}function tae(e,t){return r6(e,t)}function nae(e,t){return Kne(e,t)}function rae(e,t){return ty(e,t)}function aae(e,t){return qp(e,t)}function sae(e,t){return jl(e,t)}function iae(e,t){return jl(e,t)}function oae(e,t){return jl(e,t)}function lae(e,t){return $i(e,t)}function uae(e,t){return $i(e,t)}function cae(e,t){return $i(e,t)}var J6={};Me(J6,{modelFromJSON:()=>Ere});var Q6={};Me(Q6,{l1:()=>dae,l1l2:()=>hae,l2:()=>pae});function hae(e){return new Oc(e)}function dae(e){return zre(e)}function pae(e){return Pre(e)}var e4=class extends Vl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof fa))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function o0(e,t){return e<t}function t4(e,t){return e>t}var n4=class extends e4{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new De("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=o0:this.mode==="max"?this.monitorFunc=t4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=t4:this.monitorFunc=o0,this.monitorFunc===o0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===o0?Infinity:-Infinity}async onEpochEnd(e,t){await qa(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 fae(e){return new n4(e)}var mae={earlyStopping:fae},Cr;(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"})(Cr||(Cr={}));var r4;(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={}))})(r4||(r4={}));var m2={};function Aae(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};m2[e]=n}function a4(e){return m2[e]}function yae(e){delete m2[e]}function I(e,t,n,r,a){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 mn(t.inputNames[s.inputIndexStart],n,r,a);if(s.type==="tensors")return t.inputNames.slice(o,l).map(h=>mn(h,n,r,a));let c=mn(t.inputNames.slice(o)[0],n,r,a),u=c.dataSync();return s.type==="number"?u[0]:_.toNestedArray(c.shape,u)}let i=t.attrParams[e];return i&&i.value}function mn(e,t,n,r){let[a,s]=Ln(e);if(r!=null){let o=r.getHashTableHandleByName(a);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[l0(a,o)]);return i!==void 0?t[l0(a,i)][s]:void 0}function gae(e,t,n){return t[l0(e,n.currentContextId)]}function ma(e,t){let[n,r]=Ln(e);return[l0(n,t&&t.currentContextId),r]}function l0(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 u0(e,t,n){let r=I("pad",e,t,n);if(r==="explicit"){r=I("explicitPaddings",e,t,n);let a=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)a[s][0]=r[s*2],a[s][1]=r[s*2+1];return a}return r}function Aa(e){return e.kept?e:Dr(e)}var s4={};Me(s4,{json:()=>xae});var xae=[{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}]}],i4={};Me(i4,{json:()=>wae});var wae=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"IsNan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],o4={};Me(o4,{json:()=>bae});var bae=[{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"}]}],l4={};Me(l4,{json:()=>_ae});var _ae=[{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"}]}],u4={};Me(u4,{json:()=>vae});var vae=[{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"}]}],c4={};Me(c4,{json:()=>kae});var kae=[{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}]}],h4={};Me(h4,{json:()=>Iae});var Iae=[{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"}]}],d4={};Me(d4,{json:()=>Sae});var Sae=[{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"}]}],p4={};Me(p4,{json:()=>Nae});var Nae=[{tfOpName:"HashTable",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"HashTableV2",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"LookupTableImport",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableImportV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFind",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFindV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableSize",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"}]},{tfOpName:"LookupTableSizeV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"}]}],f4={};Me(f4,{json:()=>Tae});var Tae=[{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"}]}],m4={};Me(m4,{json:()=>Eae});var Eae=[{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}]}],A4={};Me(A4,{json:()=>Cae});var Cae=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Einsum",category:"matrices",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"equation",name:"equation",type:"string"},{tfName:"N",name:"n",type:"number",defaultValue:2},{tfName:"T",name:"dtype",type:"dtype"}]}],y4={};Me(y4,{json:()=>Rae});var Rae=[{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}]}],g4={};Me(g4,{json:()=>Mae});var Mae=[{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"}]}],x4={};Me(x4,{json:()=>Fae});var Fae=[{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}]}],w4={};Me(w4,{json:()=>$ae});var $ae=[{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}]}],b4={};Me(b4,{json:()=>Dae});var Dae=[{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:[]}],v4=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[s4,i4,o4,l4,u4,c4,h4,m4,f4,d4,A4,y4,g4,x4,w4,b4,p4],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,r)=>(n[r.tfOpName]=r,n),{})}transformGraph(e,t={}){let n=e.node,r=[],a=[],s=[],i=n.reduce((m,f)=>(m[f.name]=this.mapNode(f),f.op.startsWith("Placeholder")?r.push(m[f.name]):f.op==="Const"?a.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 h=Object.keys(i);h.forEach(m=>{let f=i[m];f.inputNames.forEach(A=>{let[y]=ma(A);f.inputs.push(i[y]),i[y].children.push(f)})}),Object.keys(u).length===0?h.forEach(m=>{let f=i[m];f.children.length===0&&l.push(f)}):Object.keys(u).forEach(m=>{let[f]=ma(m),A=i[f];A!=null&&(A.signatureKey=u[m],l.push(A))}),Object.keys(c).length>0?Object.keys(c).forEach(m=>{let[f]=ma(m),A=i[f];A&&(A.signatureKey=c[m],o.push(A))}):o=r;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 p={nodes:i,inputs:o,outputs:l,weights:a,placeholders:r,signature:t,functions:d};return s.length>0&&(p.initNodes=s),p}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=a4(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(r=>r.startsWith("^")?r.substr(1):r),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((r,a)=>(r[a.name]={type:a.type,inputIndexStart:a.start,inputIndexEnd:a.end},r),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((r,a)=>{let s=a.type,i;switch(a.type){case"string":i=A2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=A2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"string[]":i=k2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=k2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number":i=g2(e.attr,a.tfName,a.defaultValue||0),i===void 0&&!!a.tfDeprecatedName&&(i=g2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number[]":i=v2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=v2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool":i=y2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=y2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool[]":i=S2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=S2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape":i=_2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=_2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape[]":i=I2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=I2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype":i=w2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=w2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype[]":i=b2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=b2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"func":i=_4(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=_4(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${a.type} for op: ${e.op}`)}return r[a.name]={value:i,type:s},r},{})),n}mapFunction(e){let t=e.nodeDef,n=[],r=[],a={};t!=null&&(a=t.reduce((c,u)=>(c[u.name]=this.mapNode(u),u.op==="Const"&&r.push(c[u.name]),c),{}));let s=[],i=[];e.signature.inputArg.forEach(c=>{let[u]=ma(c.name),h={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:x2(c.type),type:"dtype"}},children:[]};h.signatureKey=c.name,s.push(h),a[u]=h}),Object.keys(a).forEach(c=>{let u=a[c];u.inputNames.forEach(h=>{let[d]=ma(h);u.inputs.push(a[d]),a[d].children.push(u)})});let o=e.ret;e.signature.outputArg.forEach(c=>{let[u,h]=ma(o[c.name]),d=a[u];d!=null&&(d.defaultOutput=h,i.push(d))});let l=this.mapArgsToSignature(e);return{nodes:a,inputs:s,outputs:i,weights:r,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 Oae(e){let t=J().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function k4(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Oae(e);return t?n:n.toLowerCase()}function A2(e,t,n,r=!1){let a=e[t];return a!=null?k4(a.s,r):n}function y2(e,t,n){let r=e[t];return r?r.b:n}function g2(e,t,n){let r=e[t]||{},a=r.i!=null?r.i:r.f!=null?r.f:n;return typeof a=="number"?a:parseInt(a,10)}function x2(e){switch(typeof e=="string"&&(e=Cr[e]),e){case Cr.DT_FLOAT:return"float32";case Cr.DT_INT32:case Cr.DT_INT64:case Cr.DT_INT8:case Cr.DT_UINT8:return"int32";case Cr.DT_BOOL:return"bool";case Cr.DT_DOUBLE:return"float32";case Cr.DT_STRING:return"string";default:return null}}function _4(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function w2(e,t,n){let r=e[t];return r&&r.type?x2(r.type):n}function b2(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>x2(a)):n}function I4(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function _2(e,t,n){let r=e[t];return r&&r.shape?I4(r.shape):n}function v2(e,t,n){let r=e[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map(a=>typeof a=="number"?a:parseInt(a,10)):n}function k2(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>k4(s,r)):n}function I2(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>I4(a)):n}function S2(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var zae=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(r=>this.getInput(r)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((r,a)=>(r[a]=this.getAttr(a),r),{}))}getInput(e){return mn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return mn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return g2(this.node.rawAttrs,e,t);if(n.s!=null)return A2(this.node.rawAttrs,e,t);if(n.b!=null)return y2(this.node.rawAttrs,e,t);if(n.shape!=null)return _2(this.node.rawAttrs,e,t);if(n.type!=null)return w2(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return v2(this.node.rawAttrs,e,t);if(n.list.s!=null)return k2(this.node.rawAttrs,e,t);if(n.list.shape!=null)return I2(this.node.rawAttrs,e,t);if(n.list.b!=null)return S2(this.node.rawAttrs,e,t);if(n.list.type!=null)return b2(this.node.rawAttrs,e,t)}return t}},Pae=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[se(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[yd(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[km(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[B(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[ge(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[pm(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[Ad(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[ye(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[xl(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[Pr(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[la(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[Bd(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Lae=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Dt(I("x",e,t,n))];case"Acos":return[Yf(I("x",e,t,n))];case"Acosh":return[Jf(I("x",e,t,n))];case"Asin":return[em(I("x",e,t,n))];case"Asinh":return[tm(I("x",e,t,n))];case"Atan":return[nm(I("x",e,t,n))];case"Atan2":return[rm(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[am(I("x",e,t,n))];case"Ceil":return[lm(I("x",e,t,n))];case"Complex":return[Ca(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[qu(I("x",e,t,n))];case"Cosh":return[_d(I("x",e,t,n))];case"Elu":return[Al(I("x",e,t,n))];case"Erf":return[fm(I("x",e,t,n))];case"Exp":return[Xn(I("x",e,t,n))];case"Expm1":return[mm(I("x",e,t,n))];case"Floor":return[yl(I("x",e,t,n))];case"Log":return[Mn(I("x",e,t,n))];case"Log1p":return[Sd(I("x",e,t,n))];case"Imag":return[kd(I("x",e,t,n))];case"Neg":return[_t(I("x",e,t,n))];case"Reciprocal":return[Nm(I("x",e,t,n))];case"Real":return[ec(I("x",e,t,n))];case"Relu":return[Lr(I("x",e,t,n))];case"Round":return[Tm(I("x",e,t,n))];case"Selu":return[Dd(I("x",e,t,n))];case"Sigmoid":return[kn(I("x",e,t,n))];case"Sin":return[Od(I("x",e,t,n))];case"Sign":return[Cm(I("x",e,t,n))];case"Sinh":return[zd(I("x",e,t,n))];case"Softplus":return[Ai(I("x",e,t,n))];case"Sqrt":return[Jt(I("x",e,t,n))];case"Square":return[st(I("x",e,t,n))];case"Tanh":return[di(I("x",e,t,n))];case"Tan":return[Fm(I("x",e,t,n))];case"ClipByValue":return[In(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Fd(I("x",e,t,n))];case"Rsqrt":return[$d(mn(e.inputNames[0],t,n))];case"Prod":return[Rd(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[Ku(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[Qu(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[ym(mn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function pr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){_.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let r=0;r<e.length;r++){let a=e[r],s=t[r];_.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function S4(e){return!(typeof e=="number"||e.some(t=>t<0))}function jc(e,t,n){let r=N2(e,n),a=!S4(r);if(a&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(a&&t.forEach(s=>{r=N2(s.shape,r)}),!S4(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function N2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let r=0;r<e.length;++r){let a=e[r],s=t[r];if(a>=0&&s>=0&&a!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[r]=a>=0?a:s}return n}var Wae=class{constructor(e,t,n,r,a,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Vt(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),pr(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,Vt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,r)=>this.write(n,t[r]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let r=0;r<this.size();r++)e.push(r)}if(e.length===0)return wr([],[0].concat(this.elementShape));let n=this.readMany(e);return pr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),On(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return wr([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return pr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),ot(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,ur(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,r=e.map(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 a=n===0?0:t.size/n,s=[];L(()=>{t=H(t,[1,n,a]);for(let o=0;o<e.length;++o){let l=o===0?0:r[o-1],c=[0,l,0],u=[1,e[o],a];s[o]=H(Re(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},Uc=class{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(a=>{if(n!==a.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${a.dtype}`);pr(t,a.shape,"TensorList shape mismatch: "),Vt(a)}),this.idTensor=ke(0),this.maxNumElements=r,Vt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Uc([...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.`);pr(e,this.elementShape,"TensorList shape mismatch: ");let r=jc(this.elementShape,this.tensors,e);return L(()=>{let a=this.tensors.map(s=>H(s,r));return On(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=jc(this.elementShape,this.tensors,e),r=this.tensors.pop();return pr(r.shape,e,"TensorList shape mismatch: "),H(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(pr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Vt(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.`);pr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=jc(this.elementShape,this.tensors,t);return H(this.tensors[e],r)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);pr(this.elementShape,t.shape,"TensorList shape mismatch: "),Vt(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}`);pr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=jc(this.elementShape,this.tensors,n);return e.length===0?wr([],[0].concat(r)):L(()=>{let a=e.map(s=>H(this.tensors[s],r));return On(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);pr(this.elementShape,t,"TensorList shape mismatch: ");let n=jc(this.elementShape,this.tensors,t);return this.size()===0?wr([],[0].concat(n)):L(()=>{let r=this.tensors.map(a=>H(a,n));return ot(r,0)})}};function Bae(e,t,n){let r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let a=e.shape.slice(1);pr(a,t,"TensorList shape mismatch: ");let s=ur(e);return new Uc(s,t,r)}function Vae(e,t,n){return new Uc([],e,t,n)}function jae(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(r!=null&&r!==-1&&a>=r)throw new Error(`Max index must be < array size (${a}  vs. ${r})`);let s=new Uc([],n,e.dtype,r),i=ur(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function Uae(e,t,n){let r=0,a=t.map(u=>(r+=u,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${r}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=N2(s,n),o=r===0?0:e.size/r,l=L(()=>{let u=[];e=H(e,[1,r,o]);for(let h=0;h<t.length;++h){let d=h===0?0:a[h-1],p=[0,d,0],m=[1,t[h],o];u[h]=H(Re(e,p,m),i)}return e.dispose(),u}),c=new Uc([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var Hae=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=I("thenBranch",e,t,n),a=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=I("body",e,t,n),a=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[a].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[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let h=c.map(p=>p.id);u.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()});let d=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()})}return c}case"LoopCond":{let r=I("pred",e,t,n);return[Aa(r)]}case"Switch":{let r=I("pred",e,t,n),a=I("data",e,t,n);return a.kept||(a=Aa(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>mn(a,t,n)!==void 0);if(r){let a=mn(r,t,n);return[Aa(a)]}return}case"Enter":{let r=I("frameName",e,t,n),a=I("tensor",e,t,n);return n.enterFrame(r),[Aa(a)]}case"Exit":{let r=I("tensor",e,t,n);return n.exitFrame(),[Aa(r)]}case"NextIteration":{let r=I("tensor",e,t,n);return n.nextIteration(),[Aa(r)]}case"TensorArrayV3":{let r=I("size",e,t,n),a=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 Wae(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,ke(1)]}case"TensorArrayWriteV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=I("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=I("tensorArrayId",e,t,n),a=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[ke(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=I("indices",e,t,n),a=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=jae(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=Vae(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),a=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=Bae(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),a=n.getTensorList(r.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),a=I("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=Uae(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function N4(e,t,n){let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="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=u0(e,t,n),h=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[p,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:h,dilations:d,biasArg:p,preluArg:m,activationFunc:a,leakyreluAlpha:f}}var Gae=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=I("stride",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[wd(I("x",e,t,n),I("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=I("strides",e,t,n),a=u0(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[ia(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=N4(e,t,n);return[La.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=N4(e,t,n);return[La.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=I("outputShape",e,t,n),a=I("strides",e,t,n),s=u0(e,t,n);return[bd(I("x",e,t,n),I("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=I("strides",e,t,n),a=u0(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[ml(I("input",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[cm(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Hu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Yu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=qw(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[om(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[_m(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dilations",e,t,n),i=r[1],o=r[2],l=s[1],c=s[2];return[dm(I("x",e,t,n),I("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},qae=(e,t,n)=>{switch(e.op){case"Fill":{let r=I("shape",e,t,n),a=I("dtype",e,t,n),s=I("value",e,t,n);return[Xu(r,s,a)]}case"LinSpace":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("num",e,t,n);return[Ww(r,a,s)]}case"Multinomial":{let r=I("logits",e,t,n),a=I("numSamples",e,t,n),s=I("seed",e,t,n);return[Xw(r,a,s)]}case"OneHot":{let r=I("indices",e,t,n),a=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[ul(r,a,s,i)]}case"Ones":return[Fn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[$n(I("x",e,t,n))];case"RandomUniform":return[wl(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("step",e,t,n);return[Md(r,a,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let r=I("shape",e,t,n),a=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[Vd(r,a,s,I("dtype",e,t,n),i)]}case"Zeros":return[Et(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[Ue(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function T2(e,t,n){let r=I("boxes",e,t,n),a=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:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var Xae=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=T2(e,t,n),c=await Ye.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=T2(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Ye.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=T2(e,t,n);return[await Ye.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=Ae(I("condition",e,t,n),"bool"),a=[await Om(r)];return r.dispose(),a}case"ListDiff":return Yw(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},Kae=(e,t,n)=>{switch(e.op){case"TopKV2":{let r=I("x",e,t,n),a=I("k",e,t,n),s=I("sorted",e,t,n),i=$m(r,a,s);return[i.values,i.indices]}case"Unique":{let r=I("x",e,t,n),a=jd(r);return[a.values,a.indices]}case"UniqueV2":{let r=I("x",e,t,n),a=I("axis",e,t,n),s=jd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Zae=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=I("default",e,t,n);return[mn(e.name,t,n)||r];case"Placeholder":return[mn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",e,t,n);return[Aa(c)]}case"IdentityN":return I("x",e,t,n).map(c=>Aa(c));case"Snapshot":let a=I("x",e,t,n);return[Aa(a)];case"Shape":return[nn(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(c=>nn(c.shape));case"Size":return[ke(I("x",e,t,n).size,"int32")];case"Rank":return[ke(I("x",e,t,n).rank,"int32")];case"NoOp":return[ke(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let 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`)}},Yae=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=ke(0),this.tensorMap=new Map,Vt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),L(()=>{let r=ur(t),a=n.length,s=r.length;_.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=n[i],l=r[i];Vt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return L(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return On(r)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},Jae=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new Yae(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=I("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Qae=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Ye.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Ye.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=I("image",e,t,n),a=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[Ye.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ese=(e,t,n)=>{switch(e.op){case"Equal":return[Da(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[gi(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[or(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[za(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Id(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[mi(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[lr(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Zu(I("a",e,t,n))];case"LogicalOr":return[Ed(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[Sn(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`)}},tse=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Be(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[zw(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Ze(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="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[La.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:a,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},nse=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[pi(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[pi(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[gm(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[nc(I("x",e,t,n))];case"LogSoftmax":return[Td(I("x",e,t,n))];case"SparseToDense":return[zm(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`)}},rse=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Kn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[vt(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[gl(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ne(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[gd(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Vu(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[ju(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[Qf(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Rd(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[vd(I("x",e,t,n),i,o,l)]}case"Bincount":let r=I("x",e,t,n),a=I("weights",e,t,n),s=I("size",e,t,n);return[Ew(r,a,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[Dw(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ase=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=I("n",e,t,n),a=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,r),[ot(s,a)]}case"Gather":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[fi(r,Ae(a,"int32"),0)]}case"GatherV2":{let r=I("axis",e,t,n),a=I("batchDims",e,t,n),s=I("x",e,t,n),i=I("indices",e,t,n);return[fi(s,Ae(i,"int32"),r,a)]}case"Reverse":{let r=I("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let s=I("x",e,t,n);return[Dn(s,a)]}case"ReverseV2":{let r=I("axis",e,t,n),a=I("x",e,t,n);return[Dn(a,r)]}case"Slice":{let r=I("begin",e,t,n),a=I("size",e,t,n);return[Re(I("x",e,t,n),r,a)]}case"StridedSlice":{let r=I("begin",e,t,n),a=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),h=I("x",e,t,n);return[Mm(h,r,a,s,i,o,l,c,u)]}case"Pack":return L(()=>{let r=I("axis",e,t,n),a=I("tensors",e,t,n),s=a[0].shape,i=Pa(a[0]).shape,o=a.map(l=>{let c=_.arraysEqual(l.shape,s);if(!c&&!_.arraysEqual(Pa(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:H(l,s)});return[On(o,r)]});case"Unpack":{let r=I("axis",e,t,n),a=I("tensor",e,t,n);return ur(a,r)}case"Tile":{let r=I("reps",e,t,n);return[Oa(I("x",e,t,n),r)]}case"Split":case"SplitV":{let r=I("axis",e,t,n),a=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return ln(s,a,r)}case"ScatterNd":{let r=I("indices",e,t,n),a=I("values",e,t,n),s=I("shape",e,t,n);return[tb(r,a,s)]}case"GatherNd":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[nb(r,a)]}case"SparseToDense":{let r=I("sparseIndices",e,t,n),a=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[zm(r,s,a,s.dtype===i.dtype?i:Ae(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},sse=(e,t,n)=>{switch(e.op){case"SparseReshape":{let{outputIndices:r,outputShape:a}=yb.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[r,a]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ise=(e,t,n)=>{switch(e.op){case"FFT":return[rc(I("x",e,t,n))];case"IFFT":return[bl(I("x",e,t,n))];case"RFFT":return[ac(I("x",e,t,n))];case"IRFFT":return[Wd(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ose=(e,t,n)=>{switch(e.op){case"Cast":return[Ae(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[on(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[Pa(I("x",e,t,n),r)]}case"Reshape":return[H(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[vm(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[oa(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let r=I("blockShape",e,t,n),a=I("paddings",e,t,n);return[Ju(I("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=I("blockShape",e,t,n),a=I("crops",e,t,n);return[Gu(I("x",e,t,n),r,a)]}case"DepthToSpace":{let r=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[hm(I("x",e,t,n),r,a)]}case"BroadcastTo":return[pl(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function T4(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return L(()=>Pae(s,i,o));case"basic_math":return L(()=>Lae(s,i,o));case"control":return Hae(s,i,o);case"convolution":return L(()=>Gae(s,i,o));case"creation":return L(()=>qae(s,i,o));case"dynamic":return Xae(s,i,o);case"evaluation":return L(()=>Kae(s,i,o));case"image":return L(()=>Qae(s,i,o));case"graph":return L(()=>Zae(s,i,o));case"logical":return L(()=>ese(s,i,o));case"matrices":return L(()=>tse(s,i,o));case"normalization":return L(()=>nse(s,i,o));case"reduction":return L(()=>rse(s,i,o));case"slice_join":return L(()=>ase(s,i,o));case"sparse":return L(()=>sse(s,i,o));case"spectral":return L(()=>ise(s,i,o));case"transformation":return L(()=>ose(s,i,o));case"hash_table":return Jae(s,i,o,r);case"custom":let l=a4(s.op);if(l&&l.customExecutor)return l.customExecutor(new zae(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 _.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var E4=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function R4(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Ln(d)[0]),u=[];r!=null&&(u=r.map(d=>Ln(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((C4(d)||lse(d)||use(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.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(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function cse(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Ln(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.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(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var hse=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],dse=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],pse=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function C4(e){return hse.indexOf(e.op)>=0}function lse(e){return dse.indexOf(e.op)>=0}function use(e){return pse.indexOf(e.op)>=0}var E2=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 E2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=R4(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return cse(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[Ln(u)[0]]),a=t.map(u=>Ln(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return L(()=>{let u=new E4(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=Ln(m),y=[];y[A]=e[m],h[f]=y});let d=this.getFrozenTensorIds(h),p={};for(let m=0;m<o.length;m++){let f=o[m];if(!h[f.name]){let A=T4(f,h,u,this._resourceManager);if(_.isPromise(A))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);h[f.name]=A,this.checkTensorForDisposal(f.name,f,h,u,d,a,p)}}return this.parent==null&&u.dispose(d),t.map(m=>mn(m,h,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,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=gae(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!a.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,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new E4(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>mn(h,i,s)),l=o.map(h=>h.id),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.kept&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[Ln(g)[0]]),i=n.map(g=>Ln(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:h}=R4(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[x,v]=Ln(g),w=[];w[v]=e[g],p[x]=w});let m={},f=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,f,i,m,l);await Promise.all(g)}u==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!C4(g)&&!mn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw u!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=ma(u.node.name,n)),r[u.node.name]==null){let d=T4(u.node,r,n,this._resourceManager);h||([h]=ma(u.node.name,n));let p=n.currentContext;_.isPromise(d)?c.push(d.then(m=>(r[h]=m,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),m))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ma(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!mn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!mn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Ln(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);_.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&_.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Ln(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Ln(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},fse=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},mse="?tfjs-format=file",Ase="model.json",M4=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new fse}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=vn.browserHTTPRequest(e,this.loadOptions);else{let t=vn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(vn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=vn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new E2(v4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=v4.Instance.transformGraph(e.modelInitializer);this.initializer=new E2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=vn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Le)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Ht(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Ase}${mse}`);let n=new M4(e,t);return await n.load(),n}var yse="3.5.0",F4={};Me(F4,{CSVDataset:()=>D4,Dataset:()=>Gl,FileDataSource:()=>O4,TextLineDataset:()=>$4,URLDataSource:()=>z4,array:()=>gse,csv:()=>wse,func:()=>bse,generator:()=>_se,microphone:()=>kse,version_data:()=>Ise,webcam:()=>vse,zip:()=>xse});var Sse=Qi(Wg()),Nse=Qi(Wg());function Tse(e,t){return c0(e,t)}function c0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(ql(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=c0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function Ese(e,t=L4){return P4(e,t)}function P4(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(ql(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=P4(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function L4(e){return e===null?null:ql(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function W4(e,t){let n=new Map;c0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(_.isPromise(a)){let s=await a;n.set(r,s)}}return c0(e,t,n)}function ql(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Le))}function Rse(e){return e==null||Cse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Le||_.isTypedArray(e)}function Cse(e){return e===null||typeof e!="object"&&typeof e!="function"}function Fse(e){return Tse(e,Mse)}function Mse(e){return e instanceof Le?{value:e.clone(),recurse:!1}:ql(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var B4=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}},C2=class extends B4{constructor(){super(C2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};C2.INITIAL_CAPACITY=32;function V4(e){return new $se(e)}function R2(e){return new Dse(e)}function Ose(e,t){return new j4(e,t)}function Pse(e,t=Ya.FAIL){return new zse(e,t)}var Gt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new Hse(this,e)}filter(e){return new jse(this,e)}map(e){return new Use(this,e)}mapAsync(e){return new U4(this,e)}serialMapAsync(e){return new U4(this,e).serial()}flatmap(e){return new Gse(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new Vse(this,e,t)}columnMajorBatch(e,t=!0,n=L4){return this.rowMajorBatch(e,t).map(r=>Ese(r,n))}concatenate(e,t){return new j4(V4([this,e]),t)}take(e){return e<0||e==null?this:new Bse(this,e)}skip(e){return e<0||e==null?this:new Wse(this,e)}prefetch(e){return new H4(this,e)}shuffle(e,t){return new qse(this,e,t)}serial(){return new Lse(this)}},$se=class extends Gt{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:Fse(e),done:!1}}},Dse=class extends Gt{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: ${e.message}`,e}}},Lse=class extends Gt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Wse=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Te(e.value)}return this.upstream.next()}},Bse=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Vse=class extends Gt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},jse=class extends Gt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Te(e.value)}}},Use=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=gr.getTensorsInContainer(e.value),n=this.transform(e.value),r=gr.getTensorsInContainer(n);for(let a of t)gr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Hse=class extends Gt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},U4=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=gr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=gr.getTensorsInContainer(n);for(let a of t)gr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},M2=class extends Gt{constructor(){super();this.outputQueue=new C2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Gse=class extends M2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=gr.getTensorsInContainer(e.value),n=this.transform(e.value),r=gr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)gr.isTensorInList(a,r)||a.dispose();return!0}},j4=class extends Gt{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}},Ya;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Ya||(Ya={}));var zse=class extends Gt{constructor(e,t=Ya.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 r(s){return s instanceof Gt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await W4(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ya.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ya.SHORTEST:return{value:null,done:!0};case Ya.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},H4=class extends Gt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new B4(t)}summary(){return`${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()}},qse=class extends H4{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Nse.alea(n||_.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}}},Gl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;_.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Wn(async()=>(await n.iterator()).columnMajorBatch(e,t,Xse),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Wn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Wn(async()=>(await t.iterator()).filter(r=>L(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Wn(async()=>(await t.iterator()).map(n=>L(()=>e(n))),this.size)}mapAsync(e){let t=this;return Wn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Wn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Wn(async()=>{let r=R2(async()=>({value:await t.iterator(),done:!1}));return Ose(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Wn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Sse.alea(t||_.now().toString());return Wn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.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,Wn(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()}};Gl.MAX_BUFFER_SIZE=1e4;function Wn(e,t=null){return new class extends Gl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function gse(e){return Wn(async()=>V4(e),e.length)}function xse(e){if(!ql(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 Wn(async()=>{let n=await W4(e,r=>{if(r instanceof Gl)return{value:r.iterator(),recurse:!1};if(ql(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Pse(n,Ya.SHORTEST)},t)}function Xse(e){if(e===null)return null;let t=e[0];return Rse(t)?{value:Kse(e),recurse:!1}:{value:null,recurse:!0}}function Kse(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Le?On(e):wr(e)}var $4=class extends Gl{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))}},h0='"',Hc=Symbol("out"),G4=Symbol("field"),d0=Symbol("quote"),F2=Symbol("quoteafterquote"),q4=Symbol("quoteinquote"),D4=class extends Gl{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 $4(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(_.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&&_.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(_.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let 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={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],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?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Hc;for(let i=0;i<a;i++)switch(s){case Hc:switch(e.charAt(i)){case h0:r=i+1,s=d0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Hc;break;default:s=G4,r=i;break}break;case G4:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Hc,r=i+1;break;default:}break;case d0:switch(e.charAt(i)){case h0:s=F2;break;default:}break;case F2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Hc,r=i+1;break;case h0:s=d0;break;default:s=q4;break}break;case q4:switch(e.charAt(i)){case h0:s=d0;break;default:}break;default:}if(s===F2?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},X4=class extends Gt{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(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new X4(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(_.sizeFromShape(t));return n.set(e,n.length-e.length),wr(n,t)}},K4=class extends Gt{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=nn([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=Zn([s,a,o,i],[1,4])}else this.cropBox=Zn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new K4(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&_.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=ui.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return L(()=>{let t=on(Ae(e,"float32"),0),n;n=Ye.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Z4=class{},Y4=class extends Gt{split(e){return new Zse(this,e)}},Zse=class extends Y4{constructor(e,t){super();this.upstream=e,this.impl=new Yse(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Yse=class extends M2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Qse=class extends Gt{decodeUTF8(){return new Jse(this)}},Jse=class extends Y4{constructor(e){super();this.upstream=e,this.impl=new eie(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},eie=class extends M2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=i9();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},J4=class extends Qse{constructor(e,t={}){super();this.file=e,this.options=t,_.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function nie(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=tie(e));let a=await _.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new J4(s,t)}else throw new Error(a.statusText)}var tie=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 Q4(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var O4=class extends Z4{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Q4(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new J4(this.input,this.options)}},z4=class extends Z4{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Q4(this.url)?new O4(this.url,this.fileOptions).iterator():nie(this.url,this.fileOptions)}};function wse(e,t={}){return new D4(new z4(e),t)}function bse(e){let t=R2(e);return Wn(async()=>t)}function _se(e){return Wn(async()=>{let t=await e();return R2(()=>t.next())})}async function vse(e,t){return K4.create(e,t)}async function kse(e){return X4.create(e)}var Ise="3.5.0",rie={tfjs:(cf==null?void 0:cf.version)||void 0,"tfjs-core":(hf==null?void 0:hf.version)||void 0,"tfjs-data":(df==null?void 0:df.version)||void 0,"tfjs-layers":(pf==null?void 0:pf.version)||void 0,"tfjs-converter":(ff==null?void 0:ff.version)||void 0,"tfjs-backend-cpu":Zb||void 0,"tfjs-backend-webgl":x3||void 0,"tfjs-backend-wasm":cv||void 0};var Bn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function e8(){if(!Zf(Bn.name)){pe("backend registration:",Bn.name);try{Bn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Bn.width,Bn.height):document.createElement("canvas")}catch(e){pe("error: cannot create canvas:",e);return}try{Bn.gl=Bn.canvas.getContext("webgl2",Bn.webGLattr)}catch(e){pe("error: cannot get WebGL2 context:",e);return}try{hp(2,Bn.gl)}catch(e){pe("error: cannot set WebGL2 context:",e);return}try{let e=new mp(Bn.gl);hl(Bn.name,()=>new Dl(e),Bn.priority)}catch(e){pe("error: cannot register WebGL backend:",e);return}try{al("webgl").forEach(t=>{let n={...t,backendName:Bn.name};si(n)})}catch(e){pe("error: cannot update WebGL backend registration:",e);return}try{yr.set("WEBGL_VERSION",2)}catch(e){pe("error: cannot set WebGL backend flags:",e);return}pe("backend registered:",Bn.name)}}var $2={};Ar($2,{load:()=>z2,predict:()=>m0});var p0={};function Ja(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(i=>i.kernelTimeMs>0).reduce((i,o)=>i+=o.kernelTimeMs,0),a=t.kernels.map((i,o)=>(i.id=o,i)).filter(i=>i.kernelTimeMs>0).sort((i,o)=>o.kernelTimeMs-i.kernelTimeMs),s=t.kernels.map((i,o)=>(i.id=o,i)).filter(i=>i.totalBytesSnapshot>0).sort((i,o)=>o.totalBytesSnapshot-i.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n),p0[e]={model:e,newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s},pe("profiler",e,p0[e])}var aie=["angry","disgust","fear","happy","sad","surprise","neutral"],fr,D2=[],f0=Number.MAX_SAFE_INTEGER,O2=[.2989,.587,.114];async function z2(e){return fr?e.debug&&pe("cached model:",fr.modelUrl):(fr=await Ht(Yt(e.modelBasePath,e.face.emotion.modelPath)),!fr||!fr.modelUrl?pe("load model failed:",e.face.emotion.modelPath):e.debug&&pe("load model:",fr.modelUrl)),fr}async function m0(e,t){return fr?f0<t.face.emotion.skipFrames&&t.videoOptimized&&D2.length>0?(f0++,D2):(t.videoOptimized?f0=0:f0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Ye.resizeBilinear(e,[fr.inputs[0].shape[2],fr.inputs[0].shape[1]],!1),[a,s,i]=ln(r,3,3);r.dispose();let o=B(a,O2[0]),l=B(s,O2[1]),c=B(i,O2[2]);a.dispose(),s.dispose(),i.dispose();let u=yd([o,l,c]);o.dispose(),l.dispose(),c.dispose();let h=L(()=>u.sub(.5).mul(2));u.dispose();let d=[];if(t.face.emotion.enabled){let p;if(t.profile){let m=await sa(()=>fr.predict(h));p=m.result.dataSync(),m.result.dispose(),Ja("emotion",m)}else{let m=await fr.predict(h);p=m.dataSync(),Te(m)}for(let m=0;m<p.length;m++)p[m]>t.face.emotion.minConfidence&&d.push({score:Math.min(.99,Math.trunc(100*p[m])/100),emotion:aie[m]});d.sort((m,f)=>f.score-m.score)}h.dispose(),D2=d,n(d)})):null}var P2={};Ar(P2,{enhance:()=>B2,load:()=>L2,match:()=>t8,predict:()=>g0,similarity:()=>W2});var Vn,A0={age:0},y0=Number.MAX_SAFE_INTEGER;async function L2(e){return Vn?e.debug&&pe("cached model:",Vn.modelUrl):(Vn=await Ht(Yt(e.modelBasePath,e.face.description.modelPath)),!Vn||!Vn.modelUrl?pe("load model failed:",e.face.description.modelPath):e.debug&&pe("load model:",Vn.modelUrl)),Vn}function W2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let r=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-r)/100}function t8(e,t,n=0){let r={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return r;for(let a of t)if(a.embedding&&a.name){let s=W2(e,a.embedding);s>n&&s>r.similarity&&(r={...a,similarity:s})}return r}function B2(e){return L(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Le))return null;let r=[[.05,.15,.85,.85]];return(n.shape.length===3?Ye.cropAndResize(on(n,0),r,[0],[Vn.inputs[0].shape[2],Vn.inputs[0].shape[1]]):Ye.cropAndResize(n,r,[0],[Vn.inputs[0].shape[2],Vn.inputs[0].shape[1]])).mul(255)})}async function g0(e,t){return Vn?y0<t.face.description.skipFrames&&t.videoOptimized&&A0.age&&A0.age>0?(y0++,A0):(t.videoOptimized?y0=0:y0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=B2(e),a,s={age:0,gender:"unknown",genderConfidence:0,descriptor:[]};if(!t.profile)t.face.description.enabled&&(a=await Vn.predict(r));else{let i=t.face.description.enabled?await sa(()=>Vn.predict(r)):{};a=i.result,Ja("faceres",i)}Te(r),a&&(L(()=>{let i=a.find(h=>h.shape[1]===1).dataSync(),o=Math.trunc(200*Math.abs(i[0]-.5))/100;o>t.face.description.minConfidence&&(s.gender=i[0]<=.5?"female":"male",s.genderConfidence=Math.min(.99,o));let l=a.find(h=>h.shape[1]===100).argMax(1).dataSync()[0],c=a.find(h=>h.shape[1]===100).dataSync();s.age=Math.round(c[l-1]>c[l+1]?10*l-100*c[l-1]:10*l+100*c[l+1])/10;let u=a.find(h=>h.shape[1]===1024);s.descriptor=[...u.dataSync()]}),a.forEach(i=>Te(i))),A0=s,n(s)})):null}var sie=(e,t)=>{let n=A=>A*180/Math.PI,r=A=>{let y=Math.sqrt(A[0]*A[0]+A[1]*A[1]+A[2]*A[2]);return A[0]/=y,A[1]/=y,A[2]/=y,A},a=(A,y)=>{let g=A[0]-y[0],x=A[1]-y[1],v=A[2]-y[2];return[g,x,v]},s=(A,y)=>{let g=A[1]*y[2]-A[2]*y[1],x=A[2]*y[0]-A[0]*y[2],v=A[0]*y[1]-A[1]*y[0];return[g,x,v]},i=A=>{let[y,g,x,v,w,b,k,N,C]=A,F,O,z;return v<1?v>-1?(z=Math.asin(v),O=Math.atan2(-k,y),F=Math.atan2(-b,w)):(z=-Math.PI/2,O=-Math.atan2(N,C),F=0):(z=Math.PI/2,O=Math.atan2(N,C),F=0),{pitch:2*-F,yaw:2*-O,roll:2*-z}},o=A=>{let y=(x,v,w,b)=>Math.atan2(b-v,w-x);return{pitch:y(A[10][1],A[10][2],A[152][1],A[152][2]),yaw:y(A[33][0],A[33][2],A[263][0],A[263][2]),roll:y(A[33][0],A[33][1],A[263][0],A[263][1])}},l=e.meshRaw;if(!l||l.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1]};let c=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[l[10],l[152],l[234],l[454]].map(A=>[A[0]*t[0]/c,A[1]*t[1]/c,A[2]]),h=r(a(u[1],u[0])),d=r(a(u[3],u[2])),p=r(s(d,h));d=s(h,p);let m=[d[0],d[1],d[2],h[0],h[1],h[2],p[0],p[1],p[2]];return{angle:i(m),matrix:m}},V2=async(e,t)=>{var u,h,d,p,m,f,A;let n,r,a,s,i,o,l=[];e.state="run:face",n=it();let c=await((u=e.models.face)==null?void 0:u.estimateFaces(t,e.config));if(e.perf.face=Math.trunc(it()-n),!c)return[];for(let y of c){if(e.analyze("Get Face"),!y.image||y.image.isDisposedInternal){pe("Face object is disposed:",y.image);continue}let g=sie(y,[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?m0(y.image,e.config):{}:(e.state="run:emotion",n=it(),s=e.config.face.emotion.enabled?await m0(y.image,e.config):{},e.perf.emotion=Math.trunc(it()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?g0(y,e.config):[]:(e.state="run:description",n=it(),o=e.config.face.description.enabled?await g0(y.image,e.config):[],e.perf.embedding=Math.trunc(it()-n)),e.analyze("End Description:"),e.config.async&&([r,a,s,i,o]=await Promise.all([r,a,s,i,o])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((h=y==null?void 0:y.annotations)==null?void 0:h.leftEyeIris)&&((d=y==null?void 0:y.annotations)==null?void 0:d.rightEyeIris)&&(delete y.annotations.leftEyeIris,delete y.annotations.rightEyeIris);let x=((p=y.annotations)==null?void 0:p.leftEyeIris)&&((m=y.annotations)==null?void 0:m.rightEyeIris)?11.7*Math.max(Math.abs(y.annotations.leftEyeIris[3][0]-y.annotations.leftEyeIris[1][0]),Math.abs(y.annotations.rightEyeIris[4][1]-y.annotations.rightEyeIris[2][1])):0;l.push({...y,age:o.age,gender:o.gender,genderConfidence:o.genderConfidence,embedding:o.descriptor,emotion:s,iris:x!==0?Math.trunc(x)/100:0,rotation:g,tensor:e.config.face.detector.return?(f=y.image)==null?void 0:f.squeeze():null}),(A=y.image)==null||A.dispose(),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.perf.face&&delete e.perf.face,e.perf.age&&delete e.perf.age,e.perf.gender&&delete e.perf.gender,e.perf.emotion&&delete e.perf.emotion),l};var X2={};Ar(X2,{MediaPipeFaceMesh:()=>K2,load:()=>Z2,triangulation:()=>h8,uvmap:()=>d8});var n8=6;function iie(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r<t.strides.length;r++){let a=t.strides[r],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[r];for(let l=0;l<s;l++){let c=a*(l+.5);for(let u=0;u<i;u++){let h=a*(u+.5);for(let d=0;d<o;d++)n.push([h,c])}}}return n}var oie=e=>({startEndTensor:e,startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});function lie(e,t,n){let r=Re(e,[0,1],[-1,2]),a=se(r,t),s=Re(e,[0,3],[-1,2]),i=ge(s,n),o=ge(a,n),l=ge(i,2),c=ye(o,l),u=se(o,l),h=B(c,n),d=B(u,n);return fl([h,d],1)}var r8=class{constructor(t,n){this.model=t,this.anchorsData=iie(t.inputs[0].shape[1]),this.anchors=Zn(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=L(()=>{let d=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),p=this.model.predict(d),m;if(Array.isArray(p)){let g=p.sort((b,k)=>b.size-k.size),x=ot([g[0],g[2]],2),v=ot([g[1],g[3]],2);m=ot([v,x],1).squeeze(0)}else m=p.squeeze();let f=lie(m,this.anchors,[this.inputSize,this.inputSize]),A=Re(m,[0,0],[-1,1]),y=kn(A).squeeze();return[m,f,y]}),s=await Ye.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Re(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),c=a.dataSync(),u=[];for(let h=0;h<l.length;h++){let d=i[h],p=c[d];if(p>this.config.face.detector.minConfidence){let m=oie(l[h]),f=this.anchorsData[d],A=L(()=>Re(n,[d,n8-1],[1,-1]).squeeze().reshape([n8,-1]));u.push({box:m,landmarks:A,anchor:f,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),{boxes:u,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function a8(e){let t=await Ht(Yt(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new r8(t,e);return!t||!t.modelUrl?pe("load model failed:",e.face.detector.modelPath):e.debug&&pe("load model:",t.modelUrl),n}function s8(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function Gc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Xl(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Kl(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Ye.cropAndResize(t,s,[0],n)}function x0(e,t=1.5){let n=Xl(e),r=Gc(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function w0(e){let t=Xl(e),n=Gc(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}var b0=[[1,0,0],[0,1,0],[0,0,1]];function uie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function j2(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return uie(n)}function i8(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function Qa(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function cie(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function o8(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(Qa(e[a],cie(t,s)))}return n}function _0(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=i8(t[0],t[1]),i=o8(s,a),o=i8(-t[0],-t[1]);return o8(i,o)}function l8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-Qa(t[0],n),-Qa(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function u8(e,t){return[Qa(e,t[0]),Qa(e,t[1])]}var Xr={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],rightEyeIris:[473,474,475,476,477],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],leftEyeIris:[468,469,470,471,472],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]},U2=[{key:"EyeUpper0",indices:[9,10,11,12,13,14,15]},{key:"EyeUpper1",indices:[25,26,27,28,29,30,31]},{key:"EyeUpper2",indices:[41,42,43,44,45,46,47]},{key:"EyeLower0",indices:[0,1,2,3,4,5,6,7,8]},{key:"EyeLower1",indices:[16,17,18,19,20,21,22,23,24]},{key:"EyeLower2",indices:[32,33,34,35,36,37,38,39,40]},{key:"EyeLower3",indices:[54,55,56,57,58,59,60,61,62]}],qc=[[.499976992607117,.652534008026123],[.500025987625122,.547487020492554],[.499974012374878,.602371990680695],[.482113003730774,.471979022026062],[.500150978565216,.527155995368958],[.499909996986389,.498252987861633],[.499523013830185,.40106201171875],[.289712011814117,.380764007568359],[.499954998493195,.312398016452789],[.499987006187439,.269918978214264],[.500023007392883,.107050001621246],[.500023007392883,.666234016418457],[.5000159740448,.679224014282227],[.500023007392883,.692348003387451],[.499976992607117,.695277988910675],[.499976992607117,.70593398809433],[.499976992607117,.719385027885437],[.499976992607117,.737019002437592],[.499967992305756,.781370997428894],[.499816000461578,.562981009483337],[.473773002624512,.573909997940063],[.104906998574734,.254140973091125],[.365929991006851,.409575998783112],[.338757991790771,.41302502155304],[.311120003461838,.409460008144379],[.274657994508743,.389131009578705],[.393361985683441,.403706014156342],[.345234006643295,.344011008739471],[.370094001293182,.346076011657715],[.319321990013123,.347265005111694],[.297903001308441,.353591024875641],[.24779200553894,.410809993743896],[.396889001131058,.842755019664764],[.280097991228104,.375599980354309],[.106310002505779,.399955987930298],[.2099249958992,.391353011131287],[.355807989835739,.534406006336212],[.471751004457474,.65040397644043],[.474155008792877,.680191993713379],[.439785003662109,.657229006290436],[.414617002010345,.66654098033905],[.450374007225037,.680860996246338],[.428770989179611,.682690978050232],[.374971002340317,.727805018424988],[.486716985702515,.547628998756409],[.485300987958908,.527395009994507],[.257764995098114,.314490020275116],[.401223003864288,.455172002315521],[.429818987846375,.548614978790283],[.421351999044418,.533740997314453],[.276895999908447,.532056987285614],[.483370006084442,.499586999416351],[.33721199631691,.282882988452911],[.296391993761063,.293242990970612],[.169294998049736,.193813979625702],[.447580009698868,.302609980106354],[.392390012741089,.353887975215912],[.354490011930466,.696784019470215],[.067304998636246,.730105042457581],[.442739009857178,.572826027870178],[.457098007202148,.584792017936707],[.381974011659622,.694710969924927],[.392388999462128,.694203019142151],[.277076005935669,.271932005882263],[.422551989555359,.563233017921448],[.385919004678726,.281364023685455],[.383103013038635,.255840003490448],[.331431001424789,.119714021682739],[.229923993349075,.232002973556519],[.364500999450684,.189113974571228],[.229622006416321,.299540996551514],[.173287004232407,.278747975826263],[.472878992557526,.666198015213013],[.446828007698059,.668527007102966],[.422762006521225,.673889994621277],[.445307999849319,.580065965652466],[.388103008270264,.693961024284363],[.403039008378983,.706539988517761],[.403629004955292,.693953037261963],[.460041999816895,.557139039039612],[.431158006191254,.692366003990173],[.452181994915009,.692366003990173],[.475387006998062,.692366003990173],[.465828001499176,.779190003871918],[.472328990697861,.736225962638855],[.473087012767792,.717857003211975],[.473122000694275,.704625964164734],[.473033010959625,.695277988910675],[.427942007780075,.695277988910675],[.426479011774063,.703539967536926],[.423162013292313,.711845993995667],[.4183090031147,.720062971115112],[.390094995498657,.639572978019714],[.013953999616206,.560034036636353],[.499913990497589,.58014702796936],[.413199990987778,.69539999961853],[.409626007080078,.701822996139526],[.468080013990402,.601534962654114],[.422728985548019,.585985004901886],[.463079988956451,.593783974647522],[.37211999297142,.47341400384903],[.334562003612518,.496073007583618],[.411671012639999,.546965003013611],[.242175996303558,.14767599105835],[.290776997804642,.201445996761322],[.327338010072708,.256527006626129],[.399509996175766,.748921036720276],[.441727995872498,.261676013469696],[.429764986038208,.187834024429321],[.412198007106781,.108901023864746],[.288955003023148,.398952007293701],[.218936994671822,.435410976409912],[.41278201341629,.398970007896423],[.257135003805161,.355440020561218],[.427684992551804,.437960982322693],[.448339998722076,.536936044692993],[.178560003638268,.45755398273468],[.247308000922203,.457193970680237],[.286267012357712,.467674970626831],[.332827985286713,.460712015628815],[.368755996227264,.447206974029541],[.398963987827301,.432654976844788],[.476410001516342,.405806005001068],[.189241006970406,.523923993110657],[.228962004184723,.348950982093811],[.490725994110107,.562400996685028],[.404670000076294,.485132992267609],[.019469000399113,.401564002037048],[.426243007183075,.420431017875671],[.396993011236191,.548797011375427],[.266469985246658,.376977026462555],[.439121007919312,.51895797252655],[.032313998788595,.644356966018677],[.419054001569748,.387154996395111],[.462783008813858,.505746960639954],[.238978996872902,.779744982719421],[.198220998048782,.831938028335571],[.107550002634525,.540755033493042],[.183610007166862,.740257024765015],[.134409993886948,.333683013916016],[.385764002799988,.883153975009918],[.490967005491257,.579378008842468],[.382384985685349,.508572995662689],[.174399003386497,.397670984268188],[.318785011768341,.39623498916626],[.343364000320435,.400596976280212],[.396100014448166,.710216999053955],[.187885001301765,.588537991046906],[.430987000465393,.944064974784851],[.318993002176285,.898285031318665],[.266247987747192,.869701027870178],[.500023007392883,.190576016902924],[.499976992607117,.954452991485596],[.366169989109039,.398822009563446],[.393207013607025,.39553701877594],[.410373002290726,.391080021858215],[.194993004202843,.342101991176605],[.388664990663528,.362284004688263],[.365961998701096,.355970978736877],[.343364000320435,.355356991291046],[.318785011768341,.35834002494812],[.301414996385574,.363156020641327],[.058132998645306,.319076001644135],[.301414996385574,.387449026107788],[.499987989664078,.618434011936188],[.415838003158569,.624195992946625],[.445681989192963,.566076993942261],[.465844005346298,.620640993118286],[.49992299079895,.351523995399475],[.288718998432159,.819945991039276],[.335278987884521,.852819979190826],[.440512001514435,.902418971061707],[.128294005990028,.791940987110138],[.408771991729736,.373893976211548],[.455606997013092,.451801002025604],[.499877005815506,.908990025520325],[.375436991453171,.924192011356354],[.11421000212431,.615022003650665],[.448662012815475,.695277988910675],[.4480200111866,.704632043838501],[.447111994028091,.715808033943176],[.444831997156143,.730794012546539],[.430011987686157,.766808986663818],[.406787008047104,.685672998428345],[.400738000869751,.681069016456604],[.392399996519089,.677703022956848],[.367855995893478,.663918972015381],[.247923001646996,.601333022117615],[.452769994735718,.420849978923798],[.43639200925827,.359887003898621],[.416164010763168,.368713974952698],[.413385987281799,.692366003990173],[.228018000721931,.683571994304657],[.468268007040024,.352671027183533],[.411361992359161,.804327011108398],[.499989002943039,.469825029373169],[.479153990745544,.442654013633728],[.499974012374878,.439637005329132],[.432112008333206,.493588984012604],[.499886006116867,.866917014122009],[.49991300702095,.821729004383087],[.456548988819122,.819200992584229],[.344549000263214,.745438992977142],[.37890899181366,.574010014533997],[.374292999505997,.780184984207153],[.319687992334366,.570737957954407],[.357154995203018,.604269981384277],[.295284003019333,.621580958366394],[.447750002145767,.862477004528046],[.410986006259918,.508723020553589],[.31395098567009,.775308012962341],[.354128003120422,.812552988529205],[.324548006057739,.703992962837219],[.189096003770828,.646299958229065],[.279776990413666,.71465802192688],[.1338230073452,.682700991630554],[.336768001317978,.644733011722565],[.429883986711502,.466521978378296],[.455527991056442,.548622965812683],[.437114000320435,.558896005153656],[.467287987470627,.529924988746643],[.414712011814117,.335219979286194],[.37704598903656,.322777986526489],[.344107985496521,.320150971412659],[.312875986099243,.32233202457428],[.283526003360748,.333190023899078],[.241245999932289,.382785975933075],[.102986000478268,.468762993812561],[.267612010240555,.424560010433197],[.297879010438919,.433175981044769],[.333433985710144,.433878004550934],[.366427004337311,.426115989685059],[.396012008190155,.416696012020111],[.420121014118195,.41022801399231],[.007561000064015,.480777025222778],[.432949006557465,.569517970085144],[.458638995885849,.479089021682739],[.473466008901596,.545744001865387],[.476087987422943,.563830018043518],[.468472003936768,.555056989192963],[.433990985155106,.582361996173859],[.483518004417419,.562983989715576],[.482482999563217,.57784903049469],[.42645001411438,.389798998832703],[.438998997211456,.39649498462677],[.450067013502121,.400434017181396],[.289712011814117,.368252992630005],[.276670008897781,.363372981548309],[.517862021923065,.471948027610779],[.710287988185883,.380764007568359],[.526226997375488,.573909997940063],[.895093023777008,.254140973091125],[.634069979190826,.409575998783112],[.661242008209229,.41302502155304],[.688880026340485,.409460008144379],[.725341975688934,.389131009578705],[.606630027294159,.40370500087738],[.654766023159027,.344011008739471],[.629905998706818,.346076011657715],[.680678009986877,.347265005111694],[.702096998691559,.353591024875641],[.75221198797226,.410804986953735],[.602918028831482,.842862963676453],[.719901978969574,.375599980354309],[.893692970275879,.399959981441498],[.790081977844238,.391354024410248],[.643998026847839,.534487962722778],[.528249025344849,.65040397644043],[.525849997997284,.680191040039062],[.560214996337891,.657229006290436],[.585384011268616,.66654098033905],[.549625992774963,.680860996246338],[.57122802734375,.682691991329193],[.624852001667023,.72809898853302],[.513050019741058,.547281980514526],[.51509702205658,.527251958847046],[.742246985435486,.314507007598877],[.598631024360657,.454979002475739],[.570338010787964,.548575043678284],[.578631997108459,.533622980117798],[.723087012767792,.532054007053375],[.516445994377136,.499638974666595],[.662801027297974,.282917976379395],[.70362401008606,.293271005153656],[.830704987049103,.193813979625702],[.552385985851288,.302568018436432],[.607609987258911,.353887975215912],[.645429015159607,.696707010269165],[.932694971561432,.730105042457581],[.557260990142822,.572826027870178],[.542901992797852,.584792017936707],[.6180260181427,.694710969924927],[.607590973377228,.694203019142151],[.722943007946014,.271963000297546],[.577413976192474,.563166975975037],[.614082992076874,.281386971473694],[.616907000541687,.255886018276215],[.668509006500244,.119913995265961],[.770092010498047,.232020974159241],[.635536015033722,.189248979091644],[.77039098739624,.299556016921997],[.826722025871277,.278755009174347],[.527121007442474,.666198015213013],[.553171992301941,.668527007102966],[.577238023281097,.673889994621277],[.554691970348358,.580065965652466],[.611896991729736,.693961024284363],[.59696102142334,.706539988517761],[.596370995044708,.693953037261963],[.539958000183105,.557139039039612],[.568841993808746,.692366003990173],[.547818005084991,.692366003990173],[.52461302280426,.692366003990173],[.534089982509613,.779141008853912],[.527670979499817,.736225962638855],[.526912987232208,.717857003211975],[.526877999305725,.704625964164734],[.526966989040375,.695277988910675],[.572058022022247,.695277988910675],[.573521018028259,.703539967536926],[.57683801651001,.711845993995667],[.581691026687622,.720062971115112],[.609944999217987,.639909982681274],[.986046016216278,.560034036636353],[.5867999792099,.69539999961853],[.590372025966644,.701822996139526],[.531915009021759,.601536989212036],[.577268004417419,.585934996604919],[.536915004253387,.593786001205444],[.627542972564697,.473352015018463],[.665585994720459,.495950996875763],[.588353991508484,.546862006187439],[.757824003696442,.14767599105835],[.709249973297119,.201507985591888],[.672684013843536,.256581008434296],[.600408971309662,.74900496006012],[.55826598405838,.261672019958496],[.570303976535797,.187870979309082],[.588165998458862,.109044015407562],[.711045026779175,.398952007293701],[.781069993972778,.435405015945435],[.587247014045715,.398931980133057],[.742869973182678,.355445981025696],[.572156012058258,.437651991844177],[.55186802148819,.536570012569427],[.821442008018494,.457556009292603],[.752701997756958,.457181990146637],[.71375697851181,.467626988887787],[.66711300611496,.460672974586487],[.631101012229919,.447153985500336],[.6008620262146,.432473003864288],[.523481011390686,.405627012252808],[.810747981071472,.523926019668579],[.771045982837677,.348959028720856],[.509127020835876,.562718033790588],[.595292985439301,.485023975372314],[.980530977249146,.401564002037048],[.573499977588654,.420000016689301],[.602994978427887,.548687994480133],[.733529984951019,.376977026462555],[.560611009597778,.519016981124878],[.967685997486115,.644356966018677],[.580985009670258,.387160003185272],[.537728011608124,.505385041236877],[.760966002941132,.779752969741821],[.801778972148895,.831938028335571],[.892440974712372,.54076099395752],[.816350996494293,.740260004997253],[.865594983100891,.333687007427216],[.614073991775513,.883246004581451],[.508952975273132,.579437971115112],[.617941975593567,.508316040039062],[.825608015060425,.397674977779388],[.681214988231659,.39623498916626],[.656635999679565,.400596976280212],[.603900015354156,.710216999053955],[.81208598613739,.588539004325867],[.56801301240921,.944564998149872],[.681007981300354,.898285031318665],[.733752012252808,.869701027870178],[.633830010890961,.398822009563446],[.606792986392975,.39553701877594],[.589659988880157,.391062021255493],[.805015981197357,.342108011245728],[.611334979534149,.362284004688263],[.634037971496582,.355970978736877],[.656635999679565,.355356991291046],[.681214988231659,.35834002494812],[.698584973812103,.363156020641327],[.941866993904114,.319076001644135],[.698584973812103,.387449026107788],[.584177017211914,.624107003211975],[.554318010807037,.566076993942261],[.534153997898102,.62064003944397],[.711217999458313,.819975018501282],[.664629995822906,.852871000766754],[.559099972248077,.902631998062134],[.871706008911133,.791940987110138],[.591234028339386,.373893976211548],[.544341027736664,.451583981513977],[.624562978744507,.924192011356354],[.88577002286911,.615028977394104],[.551338016986847,.695277988910675],[.551980018615723,.704632043838501],[.552887976169586,.715808033943176],[.555167973041534,.730794012546539],[.569944024085999,.767035007476807],[.593203008174896,.685675978660583],[.599261999130249,.681069016456604],[.607599973678589,.677703022956848],[.631937980651855,.663500010967255],[.752032995223999,.601315021514893],[.547226011753082,.420395016670227],[.563543975353241,.359827995300293],[.583841025829315,.368713974952698],[.586614012718201,.692366003990173],[.771915018558502,.683578014373779],[.531597018241882,.352482974529266],[.588370978832245,.804440975189209],[.52079701423645,.442565023899078],[.567984998226166,.493479013442993],[.543282985687256,.819254994392395],[.655317008495331,.745514988899231],[.621008992195129,.574018001556396],[.625559985637665,.78031200170517],[.680198013782501,.570719003677368],[.64276397228241,.604337990283966],[.704662978649139,.621529996395111],[.552012026309967,.862591981887817],[.589071989059448,.508637011051178],[.685944974422455,.775357007980347],[.645735025405884,.812640011310577],[.675342977046967,.703978002071381],[.810858011245728,.646304965019226],[.72012197971344,.714666962623596],[.866151988506317,.682704985141754],[.663187026977539,.644596993923187],[.570082008838654,.466325998306274],[.544561982154846,.548375964164734],[.562758982181549,.558784961700439],[.531987011432648,.530140042304993],[.585271000862122,.335177004337311],[.622952997684479,.32277899980545],[.655896008014679,.320163011550903],[.687132000923157,.322345972061157],[.716481983661652,.333200991153717],[.758756995201111,.382786989212036],[.897013008594513,.468769013881683],[.732392013072968,.424547016620636],[.70211398601532,.433162987232208],[.66652500629425,.433866024017334],[.633504986763,.426087975502014],[.603875994682312,.416586995124817],[.579657971858978,.409945011138916],[.992439985275269,.480777025222778],[.567192018032074,.569419980049133],[.54136598110199,.478899002075195],[.526564002037048,.546118021011353],[.523913025856018,.563830018043518],[.531529009342194,.555056989192963],[.566035985946655,.582329034805298],[.51631098985672,.563053965568542],[.5174720287323,.577877044677734],[.573594987392426,.389806985855103],[.560697972774506,.395331978797913],[.549755990505219,.399751007556915],[.710287988185883,.368252992630005],[.723330020904541,.363372981548309]],Pi=[127,34,139,11,0,37,232,231,120,72,37,39,128,121,47,232,121,128,104,69,67,175,171,148,157,154,155,118,50,101,73,39,40,9,151,108,48,115,131,194,204,211,74,40,185,80,42,183,40,92,186,230,229,118,202,212,214,83,18,17,76,61,146,160,29,30,56,157,173,106,204,194,135,214,192,203,165,98,21,71,68,51,45,4,144,24,23,77,146,91,205,50,187,201,200,18,91,106,182,90,91,181,85,84,17,206,203,36,148,171,140,92,40,39,193,189,244,159,158,28,247,246,161,236,3,196,54,68,104,193,168,8,117,228,31,189,193,55,98,97,99,126,47,100,166,79,218,155,154,26,209,49,131,135,136,150,47,126,217,223,52,53,45,51,134,211,170,140,67,69,108,43,106,91,230,119,120,226,130,247,63,53,52,238,20,242,46,70,156,78,62,96,46,53,63,143,34,227,173,155,133,123,117,111,44,125,19,236,134,51,216,206,205,154,153,22,39,37,167,200,201,208,36,142,100,57,212,202,20,60,99,28,158,157,35,226,113,160,159,27,204,202,210,113,225,46,43,202,204,62,76,77,137,123,116,41,38,72,203,129,142,64,98,240,49,102,64,41,73,74,212,216,207,42,74,184,169,170,211,170,149,176,105,66,69,122,6,168,123,147,187,96,77,90,65,55,107,89,90,180,101,100,120,63,105,104,93,137,227,15,86,85,129,102,49,14,87,86,55,8,9,100,47,121,145,23,22,88,89,179,6,122,196,88,95,96,138,172,136,215,58,172,115,48,219,42,80,81,195,3,51,43,146,61,171,175,199,81,82,38,53,46,225,144,163,110,246,33,7,52,65,66,229,228,117,34,127,234,107,108,69,109,108,151,48,64,235,62,78,191,129,209,126,111,35,143,163,161,246,117,123,50,222,65,52,19,125,141,221,55,65,3,195,197,25,7,33,220,237,44,70,71,139,122,193,245,247,130,33,71,21,162,153,158,159,170,169,150,188,174,196,216,186,92,144,160,161,2,97,167,141,125,241,164,167,37,72,38,12,145,159,160,38,82,13,63,68,71,226,35,111,158,153,154,101,50,205,206,92,165,209,198,217,165,167,97,220,115,218,133,112,243,239,238,241,214,135,169,190,173,133,171,208,32,125,44,237,86,87,178,85,86,179,84,85,180,83,84,181,201,83,182,137,93,132,76,62,183,61,76,184,57,61,185,212,57,186,214,207,187,34,143,156,79,239,237,123,137,177,44,1,4,201,194,32,64,102,129,213,215,138,59,166,219,242,99,97,2,94,141,75,59,235,24,110,228,25,130,226,23,24,229,22,23,230,26,22,231,112,26,232,189,190,243,221,56,190,28,56,221,27,28,222,29,27,223,30,29,224,247,30,225,238,79,20,166,59,75,60,75,240,147,177,215,20,79,166,187,147,213,112,233,244,233,128,245,128,114,188,114,217,174,131,115,220,217,198,236,198,131,134,177,132,58,143,35,124,110,163,7,228,110,25,356,389,368,11,302,267,452,350,349,302,303,269,357,343,277,452,453,357,333,332,297,175,152,377,384,398,382,347,348,330,303,304,270,9,336,337,278,279,360,418,262,431,304,408,409,310,415,407,270,409,410,450,348,347,422,430,434,313,314,17,306,307,375,387,388,260,286,414,398,335,406,418,364,367,416,423,358,327,251,284,298,281,5,4,373,374,253,307,320,321,425,427,411,421,313,18,321,405,406,320,404,405,315,16,17,426,425,266,377,400,369,322,391,269,417,465,464,386,257,258,466,260,388,456,399,419,284,332,333,417,285,8,346,340,261,413,441,285,327,460,328,355,371,329,392,439,438,382,341,256,429,420,360,364,394,379,277,343,437,443,444,283,275,440,363,431,262,369,297,338,337,273,375,321,450,451,349,446,342,467,293,334,282,458,461,462,276,353,383,308,324,325,276,300,293,372,345,447,382,398,362,352,345,340,274,1,19,456,248,281,436,427,425,381,256,252,269,391,393,200,199,428,266,330,329,287,273,422,250,462,328,258,286,384,265,353,342,387,259,257,424,431,430,342,353,276,273,335,424,292,325,307,366,447,345,271,303,302,423,266,371,294,455,460,279,278,294,271,272,304,432,434,427,272,407,408,394,430,431,395,369,400,334,333,299,351,417,168,352,280,411,325,319,320,295,296,336,319,403,404,330,348,349,293,298,333,323,454,447,15,16,315,358,429,279,14,15,316,285,336,9,329,349,350,374,380,252,318,402,403,6,197,419,318,319,325,367,364,365,435,367,397,344,438,439,272,271,311,195,5,281,273,287,291,396,428,199,311,271,268,283,444,445,373,254,339,263,466,249,282,334,296,449,347,346,264,447,454,336,296,299,338,10,151,278,439,455,292,407,415,358,371,355,340,345,372,390,249,466,346,347,280,442,443,282,19,94,370,441,442,295,248,419,197,263,255,359,440,275,274,300,383,368,351,412,465,263,467,466,301,368,389,380,374,386,395,378,379,412,351,419,436,426,322,373,390,388,2,164,393,370,462,461,164,0,267,302,11,12,374,373,387,268,12,13,293,300,301,446,261,340,385,384,381,330,266,425,426,423,391,429,355,437,391,327,326,440,457,438,341,382,362,459,457,461,434,430,394,414,463,362,396,369,262,354,461,457,316,403,402,315,404,403,314,405,404,313,406,405,421,418,406,366,401,361,306,408,407,291,409,408,287,410,409,432,436,410,434,416,411,264,368,383,309,438,457,352,376,401,274,275,4,421,428,262,294,327,358,433,416,367,289,455,439,462,370,326,2,326,370,305,460,455,254,449,448,255,261,446,253,450,449,252,451,450,256,452,451,341,453,452,413,464,463,441,413,414,258,442,441,257,443,442,259,444,443,260,445,444,467,342,445,459,458,250,289,392,290,290,328,460,376,433,435,250,290,392,411,416,433,341,463,464,453,464,465,357,465,412,343,412,399,360,363,440,437,399,456,420,456,363,401,435,288,372,383,353,339,255,249,448,261,255,133,243,190,133,155,112,33,246,247,33,130,25,398,384,286,362,398,414,362,463,341,263,359,467,263,249,255,466,467,260,75,60,166,238,239,79,162,127,139,72,11,37,121,232,120,73,72,39,114,128,47,233,232,128,103,104,67,152,175,148,173,157,155,119,118,101,74,73,40,107,9,108,49,48,131,32,194,211,184,74,185,191,80,183,185,40,186,119,230,118,210,202,214,84,83,17,77,76,146,161,160,30,190,56,173,182,106,194,138,135,192,129,203,98,54,21,68,5,51,4,145,144,23,90,77,91,207,205,187,83,201,18,181,91,182,180,90,181,16,85,17,205,206,36,176,148,140,165,92,39,245,193,244,27,159,28,30,247,161,174,236,196,103,54,104,55,193,8,111,117,31,221,189,55,240,98,99,142,126,100,219,166,218,112,155,26,198,209,131,169,135,150,114,47,217,224,223,53,220,45,134,32,211,140,109,67,108,146,43,91,231,230,120,113,226,247,105,63,52,241,238,242,124,46,156,95,78,96,70,46,63,116,143,227,116,123,111,1,44,19,3,236,51,207,216,205,26,154,22,165,39,167,199,200,208,101,36,100,43,57,202,242,20,99,56,28,157,124,35,113,29,160,27,211,204,210,124,113,46,106,43,204,96,62,77,227,137,116,73,41,72,36,203,142,235,64,240,48,49,64,42,41,74,214,212,207,183,42,184,210,169,211,140,170,176,104,105,69,193,122,168,50,123,187,89,96,90,66,65,107,179,89,180,119,101,120,68,63,104,234,93,227,16,15,85,209,129,49,15,14,86,107,55,9,120,100,121,153,145,22,178,88,179,197,6,196,89,88,96,135,138,136,138,215,172,218,115,219,41,42,81,5,195,51,57,43,61,208,171,199,41,81,38,224,53,225,24,144,110,105,52,66,118,229,117,227,34,234,66,107,69,10,109,151,219,48,235,183,62,191,142,129,126,116,111,143,7,163,246,118,117,50,223,222,52,94,19,141,222,221,65,196,3,197,45,220,44,156,70,139,188,122,245,139,71,162,145,153,159,149,170,150,122,188,196,206,216,92,163,144,161,164,2,167,242,141,241,0,164,37,11,72,12,144,145,160,12,38,13,70,63,71,31,226,111,157,158,154,36,101,205,203,206,165,126,209,217,98,165,97,237,220,218,237,239,241,210,214,169,140,171,32,241,125,237,179,86,178,180,85,179,181,84,180,182,83,181,194,201,182,177,137,132,184,76,183,185,61,184,186,57,185,216,212,186,192,214,187,139,34,156,218,79,237,147,123,177,45,44,4,208,201,32,98,64,129,192,213,138,235,59,219,141,242,97,97,2,141,240,75,235,229,24,228,31,25,226,230,23,229,231,22,230,232,26,231,233,112,232,244,189,243,189,221,190,222,28,221,223,27,222,224,29,223,225,30,224,113,247,225,99,60,240,213,147,215,60,20,166,192,187,213,243,112,244,244,233,245,245,128,188,188,114,174,134,131,220,174,217,236,236,198,134,215,177,58,156,143,124,25,110,7,31,228,25,264,356,368,0,11,267,451,452,349,267,302,269,350,357,277,350,452,357,299,333,297,396,175,377,381,384,382,280,347,330,269,303,270,151,9,337,344,278,360,424,418,431,270,304,409,272,310,407,322,270,410,449,450,347,432,422,434,18,313,17,291,306,375,259,387,260,424,335,418,434,364,416,391,423,327,301,251,298,275,281,4,254,373,253,375,307,321,280,425,411,200,421,18,335,321,406,321,320,405,314,315,17,423,426,266,396,377,369,270,322,269,413,417,464,385,386,258,248,456,419,298,284,333,168,417,8,448,346,261,417,413,285,326,327,328,277,355,329,309,392,438,381,382,256,279,429,360,365,364,379,355,277,437,282,443,283,281,275,363,395,431,369,299,297,337,335,273,321,348,450,349,359,446,467,283,293,282,250,458,462,300,276,383,292,308,325,283,276,293,264,372,447,346,352,340,354,274,19,363,456,281,426,436,425,380,381,252,267,269,393,421,200,428,371,266,329,432,287,422,290,250,328,385,258,384,446,265,342,386,387,257,422,424,430,445,342,276,422,273,424,306,292,307,352,366,345,268,271,302,358,423,371,327,294,460,331,279,294,303,271,304,436,432,427,304,272,408,395,394,431,378,395,400,296,334,299,6,351,168,376,352,411,307,325,320,285,295,336,320,319,404,329,330,349,334,293,333,366,323,447,316,15,315,331,358,279,317,14,316,8,285,9,277,329,350,253,374,252,319,318,403,351,6,419,324,318,325,397,367,365,288,435,397,278,344,439,310,272,311,248,195,281,375,273,291,175,396,199,312,311,268,276,283,445,390,373,339,295,282,296,448,449,346,356,264,454,337,336,299,337,338,151,294,278,455,308,292,415,429,358,355,265,340,372,388,390,466,352,346,280,295,442,282,354,19,370,285,441,295,195,248,197,457,440,274,301,300,368,417,351,465,251,301,389,385,380,386,394,395,379,399,412,419,410,436,322,387,373,388,326,2,393,354,370,461,393,164,267,268,302,12,386,374,387,312,268,13,298,293,301,265,446,340,380,385,381,280,330,425,322,426,391,420,429,437,393,391,326,344,440,438,458,459,461,364,434,394,428,396,262,274,354,457,317,316,402,316,315,403,315,314,404,314,313,405,313,421,406,323,366,361,292,306,407,306,291,408,291,287,409,287,432,410,427,434,411,372,264,383,459,309,457,366,352,401,1,274,4,418,421,262,331,294,358,435,433,367,392,289,439,328,462,326,94,2,370,289,305,455,339,254,448,359,255,446,254,253,449,253,252,450,252,256,451,256,341,452,414,413,463,286,441,414,286,258,441,258,257,442,257,259,443,259,260,444,260,467,445,309,459,250,305,289,290,305,290,460,401,376,435,309,250,392,376,411,433,453,341,464,357,453,465,343,357,412,437,343,399,344,360,440,420,437,456,360,420,363,361,401,288,265,372,353,390,339,249,339,448,255];var hie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],die=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],pie=[33,133,362,263,1,78,308],roe=hie.map(e=>qc[e]),aoe=die.map(e=>qc[e]),soe=pie.map(e=>qc[e]);var H2=Xr.leftEyeLower0,G2=Xr.rightEyeLower0,Zl={leftBounds:[H2[0],H2[H2.length-1]],rightBounds:[G2[0],G2[G2.length-1]]},v0={count:468,mouth:13,symmetryLine:[13,Xr.midwayBetweenEyes[0]]},c8={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Yl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function k0(e,t,n,r){for(let a=0;a<U2.length;a++){let{key:s,indices:i}=U2[a],o=Xr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var q2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Gc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?_0(r,[0,0]):b0,l=r!==0?i.map(h=>[...u8(h,o),h[2]]):i,c=r!==0?l8(a):b0,u=[...Xl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Qa(u,c[0]),h[1]+Qa(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[Zl.leftBounds[0]][2],r=t[Zl.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=w0(x0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Gc(i),l=Ye.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&yr.flags.IS_BROWSER&&(l=Ye.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<Yl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Yl.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Xr[`${r}EyeUpper0`][Yl.upperCenter]][2],s=t[Xr[`${r}EyeLower0`][Yl.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=s8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=x0(o),c=w0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=L(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&yr.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=v0.count?v0.symmetryLine:c8.symmetryLine;u=j2(i.landmarks[w],i.landmarks[b]);let k=Xl({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],C=Ye.rotateWithOffset(t,u,0,N);h=_0(-u,k),n.face.mesh.enabled?c=Kl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255):c=Kl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.boxSize,this.boxSize]).div(255)}else{h=b0;let w=t.clone();n.face.mesh.enabled?c=Kl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):c=Kl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),m=d.dataSync()[0];if(m<n.face.detector.minConfidence)return null;let A=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:b,crop:k}=this.getEyeBox(A,c,Zl.leftBounds[0],Zl.leftBounds[1],!0),{box:N,boxSize:C,crop:F}=this.getEyeBox(A,c,Zl.rightBounds[0],Zl.rightBounds[1]),z=this.irisModel.predict(ot([k,F])).dataSync(),V=z.slice(0,Yl.numCoordinates*3),{rawCoords:j,iris:U}=this.getEyeCoords(V,w,b,!0),X=z.slice(Yl.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,N,C),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(k0(A,j,"left",null),k0(A,G,"right",null)):Y<1?k0(A,j,"left",["EyeUpper0","EyeLower0"]):k0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,U,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=x0(this.calculateLandmarksBoundingBox(y),1.5);let g=Zn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&yr.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=v0.count?v0.symmetryLine:c8.symmetryLine;u=j2(i.landmarks[w],i.landmarks[b]);let k=Xl({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],C=Ye.rotateWithOffset(t.toFloat(),u,0,N);h=_0(-u,k),c=Kl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255)}let x={coords:g,box:i,faceConfidence:m,boxConfidence:l,image:c,rawCoords:A},v=w0(i);return this.storedBoxes[o]={...v,landmarks:y,confidence:i.confidence,faceConfidence:m},x}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var K2=class{constructor(t,n,r,a){this.facePipeline=new q2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Xr))l[h]=Xr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},qt=[null,null,null];async function Z2(e){return!qt[0]&&e.face.enabled||!qt[1]&&e.face.mesh.enabled||!qt[2]&&e.face.iris.enabled?(qt=await Promise.all([!qt[0]&&e.face.enabled?a8(e):null,!qt[1]&&e.face.mesh.enabled?Ht(Yt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!qt[2]&&e.face.iris.enabled?Ht(Yt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!qt[1]||!qt[1].modelUrl?pe("load model failed:",e.face.mesh.modelPath):e.debug&&pe("load model:",qt[1].modelUrl)),e.face.iris.enabled&&(!qt[2]||!qt[1].modelUrl?pe("load model failed:",e.face.iris.modelPath):e.debug&&pe("load model:",qt[2].modelUrl))):e.debug&&(pe("cached model:",qt[0].model.modelUrl),pe("cached model:",qt[1].modelUrl),pe("cached model:",qt[2].modelUrl)),new K2(qt[0],qt[1],qt[2],e)}var h8=Pi,d8=qc;var ng={};Ar(ng,{load:()=>ag,predict:()=>rg});var Xc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],p8=Xc.length,Kc=Xc.reduce((e,t,n)=>(e[t]=n,e),{}),fie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],mie=fie.map(([e,t])=>[Kc[e],Kc[t]]),f8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function m8(e){let t=e.reduce(({maxX:n,maxY:r,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(n,i),maxY:Math.max(r,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function A8(e,[t,n],[r,a]){let s=(o,l,c)=>({score:o.score,box:[Math.trunc(o.box[0]*c),Math.trunc(o.box[1]*l),Math.trunc(o.box[2]*c),Math.trunc(o.box[3]*l)],keypoints:o.keypoints.map(({score:u,part:h,position:d})=>({score:u,part:h,position:{x:Math.trunc(d.x*c),y:Math.trunc(d.y*l)}}))});return e.map(o=>s(o,t/r,n/a))}var Y2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function J2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+p8)}}function Q2(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=J2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function eg(e,t,n){return e<t?t:e>n?n:e}function y8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function tg(e,t){return{x:e.x+t.x,y:e.y+t.y}}var I0=1,g8=16;function x8(e,t,n,r,a,s,i,o=2){let l=g=>({y:i.get(g.y,g.x,e),x:i.get(g.y,g.x,i.shape[2]/2+e)}),c=(g,x,v)=>({y:eg(Math.round(g.y/s),0,x-1),x:eg(Math.round(g.x/s),0,v-1)}),[u,h]=r.shape,d=c(t.position,u,h),p=l(d),f=tg(t.position,p);for(let g=0;g<o;g++){let x=c(f,u,h),v=J2(x.y,x.x,n,a);f=tg({x:x.x*s,y:x.y*s},{x:v.x,y:v.y})}let A=c(f,u,h),y=r.get(A.y,A.x,n);return{position:f,part:Xc[n],score:y}}function Aie(e,t,n,r,a,s){let i=f8.map(([f,A])=>[Kc[f],Kc[A]]),o=i.map(([,f])=>f),l=i.map(([f])=>f),c=t.shape[2],u=o.length,h=new Array(c),{part:d,score:p}=e,m=Q2(d,r,n);h[d.id]={score:p,part:Xc[d.id],position:m};for(let f=u-1;f>=0;--f){let A=o[f],y=l[f];h[A]&&!h[y]&&(h[y]=x8(f,h[A],y,t,n,r,s))}for(let f=0;f<u;++f){let A=l[f],y=o[f];h[A]&&!h[y]&&(h[y]=x8(f,h[A],y,t,n,r,a))}return h}function yie(e,t,n,r,a){let[s,i]=a.shape,o=!0,l=Math.max(n-I0,0),c=Math.min(n+I0+1,s);for(let u=l;u<c;++u){let h=Math.max(r-I0,0),d=Math.min(r+I0+1,i);for(let p=h;p<d;++p)if(a.get(u,p,e)>t){o=!1;break}if(!o)break}return o}function gie(e,t){let[n,r,a]=t.shape,s=new Y2(n*r*a,({score:i})=>i);for(let i=0;i<n;++i)for(let o=0;o<r;++o)for(let l=0;l<a;++l){let c=t.get(i,o,l);c<e||yie(l,c,i,o,t)&&s.enqueue({score:c,part:{heatmapY:i,heatmapX:o,id:l}})}return s}function w8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return y8(r,n,i.y,i.x)<=t})}function xie(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(w8(e,t,s,o)||(a+=i),a),0)/n.length}function b8(e,t,n,r,a,s,i){let o=[],l=gie(i,t),c=a**2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=Q2(u.part,g8,e);if(w8(o,c,h,u.part.id))continue;let p=Aie(u,t,e,g8,n,r).filter(A=>A.score>i),m=xie(o,c,p),f=m8(p);m>i&&o.push({keypoints:p,box:f,score:Math.round(100*m)/100})}return o}var mr,wie=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"];async function rg(e,t){let n=L(()=>{let o=e.resizeBilinear([mr.inputs[0].shape[2],mr.inputs[0].shape[1]]).toFloat().div(127.5).sub(1),c=mr.execute(o,wie).map(u=>u.squeeze([0]));return c[1]=c[1].sigmoid(),c}),r=await Promise.all(n.map(i=>i.buffer()));for(let i of n)i.dispose();let a=await b8(r[0],r[1],r[2],r[3],t.body.nmsRadius,t.body.maxDetections,t.body.scoreThreshold);return A8(a,[e.shape[1],e.shape[2]],[mr.inputs[0].shape[2],mr.inputs[0].shape[1]])}async function ag(e){return mr?e.debug&&pe("cached model:",mr.modelUrl):(mr=await Ht(Yt(e.modelBasePath,e.body.modelPath)),!mr||!mr.modelUrl?pe("load model failed:",e.body.modelPath):e.debug&&pe("load model:",mr.modelUrl)),mr}var ug={};Ar(ug,{HandPose:()=>hg,load:()=>dg});function S0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Zc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function _8(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Ye.cropAndResize(t,s,[0],n)}function v8(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function N0(e,t=1.5){let n=Zc(e),r=S0(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function T0(e){let t=Zc(e),n=S0(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var sg=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=Zn(this.anchors),this.inputSize=n,this.inputSizeTensor=nn([n,n]),this.doubleInputSizeTensor=nn([n*2,n*2])}normalizeBoxes(t){return L(()=>{let n=Re(t,[0,0],[-1,2]),r=Re(t,[0,2],[-1,2]),a=se(ge(n,this.inputSizeTensor),this.anchorsTensor),s=ge(r,this.doubleInputSizeTensor),i=B(ye(a,s),this.inputSizeTensor),o=B(se(a,s),this.inputSizeTensor);return fl([i,o],1)})}normalizeLandmarks(t,n){return L(()=>{let r=se(ge(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return B(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=L(()=>kn(Re(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Re(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let c=await Ye.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),u=c.arraySync();s.dispose(),c.dispose();let h=[];for(let d of u)if(i[d]>=n.hand.minConfidence){let p=Re(l,[d,0],[1,-1]),m=Re(a,[d,5],[1,14]),f=L(()=>this.normalizeLandmarks(m,d).reshape([-1,2]));m.dispose(),h.push({box:p,palmLandmarks:f,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=L(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let c=l.box.dataSync(),u=c.slice(0,2),h=c.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(v8({startPoint:u,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function bie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function k8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return bie(n)}var I8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function es(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function _ie(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function S8(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(es(e[a],_ie(t,s)))}return n}function ig(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=I8(t[0],t[1]),i=S8(s,a),o=I8(-t[0],-t[1]);return S8(i,o)}function N8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-es(t[0],n),-es(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function og(e,t){return[es(e,t[0]),es(e,t[1])]}var vie=5,T8=1.65,E8=[0,5,9,13,17,1,2],kie=0,Iie=2,lg=class{constructor(t,n,r){this.handDetector=t,this.landmarkDetector=n,this.inputSize=r,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(t,n){let r=t.map(s=>og([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return N0(T0(a),vie)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=N0(T0(n),T8);r.palmLandmarks=[];for(let a=0;a<E8.length;a++)r.palmLandmarks.push(t[E8[a]].slice(0,2));return r}transformRawCoords(t,n,r,a){let s=S0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(p=>[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=ig(r,[0,0]),c=o.map(p=>[...og(p,l),p[2]]),u=N8(a),h=[...Zc(n),1],d=[es(h,u[0]),es(h,u[1])];return c.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let l=n.hand.rotation?k8(o.palmLandmarks[kie],o.palmLandmarks[Iie]):0,c=Zc(o),u=[c[0]/t.shape[2],c[1]/t.shape[1]],h=n.hand.rotation?Ye.rotateWithOffset(t,l,0,u):t.clone(),d=ig(-l,c),p=r?this.getBoxForPalmLandmarks(o.palmLandmarks,d):o,m=_8(p,h,[this.inputSize,this.inputSize]),f=m.div(255);m.dispose(),h.dispose();let[A,y]=await this.landmarkDetector.predict(f);f.dispose();let g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let x=H(y,[-1,3]),v=x.arraySync();y.dispose(),x.dispose();let w=this.transformRawCoords(v,p,l,d),b=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=b;let k={landmarks:w,confidence:g,box:{topLeft:b.startPoint,bottomRight:b.endPoint}};s.push(k)}else this.storedBoxes[i]=null;y.dispose()}else{let l=N0(T0(o),T8),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var C8=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.765625,y_center:.046875},{w:1,h:1,x_center:.765625,y_center:.046875},{w:1,h:1,x_center:.796875,y_center:.046875},{w:1,h:1,x_center:.796875,y_center:.046875},{w:1,h:1,x_center:.828125,y_center:.046875},{w:1,h:1,x_center:.828125,y_center:.046875},{w:1,h:1,x_center:.859375,y_center:.046875},{w:1,h:1,x_center:.859375,y_center:.046875},{w:1,h:1,x_center:.890625,y_center:.046875},{w:1,h:1,x_center:.890625,y_center:.046875},{w:1,h:1,x_center:.921875,y_center:.046875},{w:1,h:1,x_center:.921875,y_center:.046875},{w:1,h:1,x_center:.953125,y_center:.046875},{w:1,h:1,x_center:.953125,y_center:.046875},{w:1,h:1,x_center:.984375,y_center:.046875},{w:1,h:1,x_center:.984375,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.078125},{w:1,h:1,x_center:.015625,y_center:.078125},{w:1,h:1,x_center:.046875,y_center:.078125},{w:1,h:1,x_center:.046875,y_center:.078125},{w:1,h:1,x_center:.078125,y_center:.078125},{w:1,h:1,x_center:.078125,y_center:.078125},{w:1,h:1,x_center:.109375,y_center:.078125},{w:1,h:1,x_center:.109375,y_center:.078125},{w:1,h:1,x_center:.140625,y_center:.078125},{w:1,h:1,x_center:.140625,y_center:.078125},{w:1,h:1,x_center:.171875,y_center:.078125},{w:1,h:1,x_center:.171875,y_center:.078125},{w:1,h:1,x_center:.203125,y_center:.078125},{w:1,h:1,x_center:.203125,y_center:.078125},{w:1,h:1,x_center:.234375,y_center:.078125},{w:1,h:1,x_center:.234375,y_center:.078125},{w:1,h:1,x_center:.265625,y_center:.078125},{w:1,h:1,x_center:.265625,y_center:.078125},{w:1,h:1,x_center:.296875,y_center:.078125},{w:1,h:1,x_center:.296875,y_center:.078125},{w:1,h:1,x_center:.328125,y_center:.078125},{w:1,h:1,x_center:.328125,y_center:.078125},{w:1,h:1,x_center:.359375,y_center:.078125},{w:1,h:1,x_center:.359375,y_center:.078125},{w:1,h:1,x_center:.390625,y_center:.078125},{w:1,h:1,x_center:.390625,y_center:.078125},{w:1,h:1,x_center:.421875,y_center:.078125},{w:1,h:1,x_center:.421875,y_center:.078125},{w:1,h:1,x_center:.453125,y_center:.078125},{w:1,h:1,x_center:.453125,y_center:.078125},{w:1,h:1,x_center:.484375,y_center:.078125},{w:1,h:1,x_center:.484375,y_center:.078125},{w:1,h:1,x_center:.515625,y_center:.078125},{w:1,h:1,x_center:.515625,y_center:.078125},{w:1,h:1,x_center:.546875,y_center:.078125},{w:1,h:1,x_center:.546875,y_center:.078125},{w:1,h:1,x_center:.578125,y_center:.078125},{w:1,h:1,x_center:.578125,y_center:.078125},{w:1,h:1,x_center:.609375,y_center:.078125},{w:1,h:1,x_center:.609375,y_center:.078125},{w:1,h:1,x_center:.640625,y_center:.078125},{w:1,h:1,x_center:.640625,y_center:.078125},{w:1,h:1,x_center:.671875,y_center:.078125},{w:1,h:1,x_center:.671875,y_center:.078125},{w:1,h:1,x_center:.703125,y_center:.078125},{w:1,h:1,x_center:.703125,y_center:.078125},{w:1,h:1,x_center:.734375,y_center:.078125},{w:1,h:1,x_center:.734375,y_center:.078125},{w:1,h:1,x_center:.765625,y_center:.078125},{w:1,h:1,x_center:.765625,y_center:.078125},{w:1,h:1,x_center:.796875,y_center:.078125},{w:1,h:1,x_center:.796875,y_center:.078125},{w:1,h:1,x_center:.828125,y_center:.078125},{w:1,h:1,x_center:.828125,y_center:.078125},{w:1,h:1,x_center:.859375,y_center:.078125},{w:1,h:1,x_center:.859375,y_center:.078125},{w:1,h:1,x_center:.890625,y_center:.078125},{w:1,h:1,x_center:.890625,y_center:.078125},{w:1,h:1,x_center:.921875,y_center:.078125},{w:1,h:1,x_center:.921875,y_center:.078125},{w:1,h:1,x_center:.953125,y_center:.078125},{w:1,h:1,x_center:.953125,y_center:.078125},{w:1,h:1,x_center:.984375,y_center:.078125},{w:1,h:1,x_center:.984375,y_center:.078125},{w:1,h:1,x_center:.015625,y_center:.109375},{w:1,h:1,x_center:.015625,y_center:.109375},{w:1,h:1,x_center:.046875,y_center:.109375},{w:1,h:1,x_center:.046875,y_center:.109375},{w:1,h:1,x_center:.078125,y_center:.109375},{w:1,h:1,x_center:.078125,y_center:.109375},{w:1,h:1,x_center:.109375,y_center:.109375},{w:1,h:1,x_center:.109375,y_center:.109375},{w:1,h:1,x_center:.140625,y_center:.109375},{w:1,h:1,x_center:.140625,y_center:.109375},{w:1,h:1,x_center:.171875,y_center:.109375},{w:1,h:1,x_center:.171875,y_center:.109375},{w:1,h:1,x_center:.203125,y_center:.109375},{w:1,h:1,x_center:.203125,y_center:.109375},{w:1,h:1,x_center:.234375,y_center:.109375},{w:1,h:1,x_center:.234375,y_center:.109375},{w:1,h:1,x_center:.265625,y_center:.109375},{w:1,h:1,x_center:.265625,y_center:.109375},{w:1,h:1,x_center:.296875,y_center:.109375},{w:1,h:1,x_center:.296875,y_center:.109375},{w:1,h:1,x_center:.328125,y_center:.109375},{w:1,h:1,x_center:.328125,y_center:.109375},{w:1,h:1,x_center:.359375,y_center:.109375},{w:1,h:1,x_center:.359375,y_center:.109375},{w:1,h:1,x_center:.390625,y_center:.109375},{w:1,h:1,x_center:.390625,y_center:.109375},{w:1,h:1,x_center:.421875,y_center:.109375},{w:1,h:1,x_center:.421875,y_center:.109375},{w:1,h:1,x_center:.453125,y_center:.109375},{w:1,h:1,x_center:.453125,y_center:.109375},{w:1,h:1,x_center:.484375,y_center:.109375},{w:1,h:1,x_center:.484375,y_center:.109375},{w:1,h:1,x_center:.515625,y_center:.109375},{w:1,h:1,x_center:.515625,y_center:.109375},{w:1,h:1,x_center:.546875,y_center:.109375},{w:1,h:1,x_center:.546875,y_center:.109375},{w:1,h:1,x_center:.578125,y_center:.109375},{w:1,h:1,x_center:.578125,y_center:.109375},{w:1,h:1,x_center:.609375,y_center:.109375},{w:1,h:1,x_center:.609375,y_center:.109375},{w:1,h:1,x_center:.640625,y_center:.109375},{w:1,h:1,x_center:.640625,y_center:.109375},{w:1,h:1,x_center:.671875,y_center:.109375},{w:1,h:1,x_center:.671875,y_center:.109375},{w:1,h:1,x_center:.703125,y_center:.109375},{w:1,h:1,x_center:.703125,y_center:.109375},{w:1,h:1,x_center:.734375,y_center:.109375},{w:1,h:1,x_center:.734375,y_center:.109375},{w:1,h:1,x_center:.765625,y_center:.109375},{w:1,h:1,x_center:.765625,y_center:.109375},{w:1,h:1,x_center:.796875,y_center:.109375},{w:1,h:1,x_center:.796875,y_center:.109375},{w:1,h:1,x_center:.828125,y_center:.109375},{w:1,h:1,x_center:.828125,y_center:.109375},{w:1,h:1,x_center:.859375,y_center:.109375},{w:1,h:1,x_center:.859375,y_center:.109375},{w:1,h:1,x_center:.890625,y_center:.109375},{w:1,h:1,x_center:.890625,y_center:.109375},{w:1,h:1,x_center:.921875,y_center:.109375},{w:1,h:1,x_center:.921875,y_center:.109375},{w:1,h:1,x_center:.953125,y_center:.109375},{w:1,h:1,x_center:.953125,y_center:.109375},{w:1,h:1,x_center:.984375,y_center:.109375},{w:1,h:1,x_center:.984375,y_center:.109375},{w:1,h:1,x_center:.015625,y_center:.140625},{w:1,h:1,x_center:.015625,y_center:.140625},{w:1,h:1,x_center:.046875,y_center:.140625},{w:1,h:1,x_center:.046875,y_center:.140625},{w:1,h:1,x_center:.078125,y_center:.140625},{w:1,h:1,x_center:.078125,y_center:.140625},{w:1,h:1,x_center:.109375,y_center:.140625},{w:1,h:1,x_center:.109375,y_center:.140625},{w:1,h:1,x_center:.140625,y_center:.140625},{w:1,h:1,x_center:.140625,y_center:.140625},{w:1,h:1,x_center:.171875,y_center:.140625},{w:1,h:1,x_center:.171875,y_center:.140625},{w:1,h:1,x_center:.203125,y_center:.140625},{w:1,h:1,x_center:.203125,y_center:.140625},{w:1,h:1,x_center:.234375,y_center:.140625},{w:1,h:1,x_center:.234375,y_center:.140625},{w:1,h:1,x_center:.265625,y_center:.140625},{w:1,h:1,x_center:.265625,y_center:.140625},{w:1,h:1,x_center:.296875,y_center:.140625},{w:1,h:1,x_center:.296875,y_center:.140625},{w:1,h:1,x_center:.328125,y_center:.140625},{w:1,h:1,x_center:.328125,y_center:.140625},{w:1,h:1,x_center:.359375,y_center:.140625},{w:1,h:1,x_center:.359375,y_center:.140625},{w:1,h:1,x_center:.390625,y_center:.140625},{w:1,h:1,x_center:.390625,y_center:.140625},{w:1,h:1,x_center:.421875,y_center:.140625},{w:1,h:1,x_center:.421875,y_center:.140625},{w:1,h:1,x_center:.453125,y_center:.140625},{w:1,h:1,x_center:.453125,y_center:.140625},{w:1,h:1,x_center:.484375,y_center:.140625},{w:1,h:1,x_center:.484375,y_center:.140625},{w:1,h:1,x_center:.515625,y_center:.140625},{w:1,h:1,x_center:.515625,y_center:.140625},{w:1,h:1,x_center:.546875,y_center:.140625},{w:1,h:1,x_center:.546875,y_center:.140625},{w:1,h:1,x_center:.578125,y_center:.140625},{w:1,h:1,x_center:.578125,y_center:.140625},{w:1,h:1,x_center:.609375,y_center:.140625},{w:1,h:1,x_center:.609375,y_center:.140625},{w:1,h:1,x_center:.640625,y_center:.140625},{w:1,h:1,x_center:.640625,y_center:.140625},{w:1,h:1,x_center:.671875,y_center:.140625},{w:1,h:1,x_center:.671875,y_center:.140625},{w:1,h:1,x_center:.703125,y_center:.140625},{w:1,h:1,x_center:.703125,y_center:.140625},{w:1,h:1,x_center:.734375,y_center:.140625},{w:1,h:1,x_center:.734375,y_center:.140625},{w:1,h:1,x_center:.765625,y_center:.140625},{w:1,h:1,x_center:.765625,y_center:.140625},{w:1,h:1,x_center:.796875,y_center:.140625},{w:1,h:1,x_center:.796875,y_center:.140625},{w:1,h:1,x_center:.828125,y_center:.140625},{w:1,h:1,x_center:.828125,y_center:.140625},{w:1,h:1,x_center:.859375,y_center:.140625},{w:1,h:1,x_center:.859375,y_center:.140625},{w:1,h:1,x_center:.890625,y_center:.140625},{w:1,h:1,x_center:.890625,y_center:.140625},{w:1,h:1,x_center:.921875,y_center:.140625},{w:1,h:1,x_center:.921875,y_center:.140625},{w:1,h:1,x_center:.953125,y_center:.140625},{w:1,h:1,x_center:.953125,y_center:.140625},{w:1,h:1,x_center:.984375,y_center:.140625},{w:1,h:1,x_center:.984375,y_center:.140625},{w:1,h:1,x_center:.015625,y_center:.171875},{w:1,h:1,x_center:.015625,y_center:.171875},{w:1,h:1,x_center:.046875,y_center:.171875},{w:1,h:1,x_center:.046875,y_center:.171875},{w:1,h:1,x_center:.078125,y_center:.171875},{w:1,h:1,x_center:.078125,y_center:.171875},{w:1,h:1,x_center:.109375,y_center:.171875},{w:1,h:1,x_center:.109375,y_center:.171875},{w:1,h:1,x_center:.140625,y_center:.171875},{w:1,h:1,x_center:.140625,y_center:.171875},{w:1,h:1,x_center:.171875,y_center:.171875},{w:1,h:1,x_center:.171875,y_center:.171875},{w:1,h:1,x_center:.203125,y_center:.171875},{w:1,h:1,x_center:.203125,y_center:.171875},{w:1,h:1,x_center:.234375,y_center:.171875},{w:1,h:1,x_center:.234375,y_center:.171875},{w:1,h:1,x_center:.265625,y_center:.171875},{w:1,h:1,x_center:.265625,y_center:.171875},{w:1,h:1,x_center:.296875,y_center:.171875},{w:1,h:1,x_center:.296875,y_center:.171875},{w:1,h:1,x_center:.328125,y_center:.171875},{w:1,h:1,x_center:.328125,y_center:.171875},{w:1,h:1,x_center:.359375,y_center:.171875},{w:1,h:1,x_center:.359375,y_center:.171875},{w:1,h:1,x_center:.390625,y_center:.171875},{w:1,h:1,x_center:.390625,y_center:.171875},{w:1,h:1,x_center:.421875,y_center:.171875},{w:1,h:1,x_center:.421875,y_center:.171875},{w:1,h:1,x_center:.453125,y_center:.171875},{w:1,h:1,x_center:.453125,y_center:.171875},{w:1,h:1,x_center:.484375,y_center:.171875},{w:1,h:1,x_center:.484375,y_center:.171875},{w:1,h:1,x_center:.515625,y_center:.171875},{w:1,h:1,x_center:.515625,y_center:.171875},{w:1,h:1,x_center:.546875,y_center:.171875},{w:1,h:1,x_center:.546875,y_center:.171875},{w:1,h:1,x_center:.578125,y_center:.171875},{w:1,h:1,x_center:.578125,y_center:.171875},{w:1,h:1,x_center:.609375,y_center:.171875},{w:1,h:1,x_center:.609375,y_center:.171875},{w:1,h:1,x_center:.640625,y_center:.171875},{w:1,h:1,x_center:.640625,y_center:.171875},{w:1,h:1,x_center:.671875,y_center:.171875},{w:1,h:1,x_center:.671875,y_center:.171875},{w:1,h:1,x_center:.703125,y_center:.171875},{w:1,h:1,x_center:.703125,y_center:.171875},{w:1,h:1,x_center:.734375,y_center:.171875},{w:1,h:1,x_center:.734375,y_center:.171875},{w:1,h:1,x_center:.765625,y_center:.171875},{w:1,h:1,x_center:.765625,y_center:.171875},{w:1,h:1,x_center:.796875,y_center:.171875},{w:1,h:1,x_center:.796875,y_center:.171875},{w:1,h:1,x_center:.828125,y_center:.171875},{w:1,h:1,x_center:.828125,y_center:.171875},{w:1,h:1,x_center:.859375,y_center:.171875},{w:1,h:1,x_center:.859375,y_center:.171875},{w:1,h:1,x_center:.890625,y_center:.171875},{w:1,h:1,x_center:.890625,y_center:.171875},{w:1,h:1,x_center:.921875,y_center:.171875},{w:1,h:1,x_center:.921875,y_center:.171875},{w:1,h:1,x_center:.953125,y_center:.171875},{w:1,h:1,x_center:.953125,y_center:.171875},{w:1,h:1,x_center:.984375,y_center:.171875},{w:1,h:1,x_center:.984375,y_center:.171875},{w:1,h:1,x_center:.015625,y_center:.203125},{w:1,h:1,x_center:.015625,y_center:.203125},{w:1,h:1,x_center:.046875,y_center:.203125},{w:1,h:1,x_center:.046875,y_center:.203125},{w:1,h:1,x_center:.078125,y_center:.203125},{w:1,h:1,x_center:.078125,y_center:.203125},{w:1,h:1,x_center:.109375,y_center:.203125},{w:1,h:1,x_center:.109375,y_center:.203125},{w:1,h:1,x_center:.140625,y_center:.203125},{w:1,h:1,x_center:.140625,y_center:.203125},{w:1,h:1,x_center:.171875,y_center:.203125},{w:1,h:1,x_center:.171875,y_center:.203125},{w:1,h:1,x_center:.203125,y_center:.203125},{w:1,h:1,x_center:.203125,y_center:.203125},{w:1,h:1,x_center:.234375,y_center:.203125},{w:1,h:1,x_center:.234375,y_center:.203125},{w:1,h:1,x_center:.265625,y_center:.203125},{w:1,h:1,x_center:.265625,y_center:.203125},{w:1,h:1,x_center:.296875,y_center:.203125},{w:1,h:1,x_center:.296875,y_center:.203125},{w:1,h:1,x_center:.328125,y_center:.203125},{w:1,h:1,x_center:.328125,y_center:.203125},{w:1,h:1,x_center:.359375,y_center:.203125},{w:1,h:1,x_center:.359375,y_center:.203125},{w:1,h:1,x_center:.390625,y_center:.203125},{w:1,h:1,x_center:.390625,y_center:.203125},{w:1,h:1,x_center:.421875,y_center:.203125},{w:1,h:1,x_center:.421875,y_center:.203125},{w:1,h:1,x_center:.453125,y_center:.203125},{w:1,h:1,x_center:.453125,y_center:.203125},{w:1,h:1,x_center:.484375,y_center:.203125},{w:1,h:1,x_center:.484375,y_center:.203125},{w:1,h:1,x_center:.515625,y_center:.203125},{w:1,h:1,x_center:.515625,y_center:.203125},{w:1,h:1,x_center:.546875,y_center:.203125},{w:1,h:1,x_center:.546875,y_center:.203125},{w:1,h:1,x_center:.578125,y_center:.203125},{w:1,h:1,x_center:.578125,y_center:.203125},{w:1,h:1,x_center:.609375,y_center:.203125},{w:1,h:1,x_center:.609375,y_center:.203125},{w:1,h:1,x_center:.640625,y_center:.203125},{w:1,h:1,x_center:.640625,y_center:.203125},{w:1,h:1,x_center:.671875,y_center:.203125},{w:1,h:1,x_center:.671875,y_center:.203125},{w:1,h:1,x_center:.703125,y_center:.203125},{w:1,h:1,x_center:.703125,y_center:.203125},{w:1,h:1,x_center:.734375,y_center:.203125},{w:1,h:1,x_center:.734375,y_center:.203125},{w:1,h:1,x_center:.765625,y_center:.203125},{w:1,h:1,x_center:.765625,y_center:.203125},{w:1,h:1,x_center:.796875,y_center:.203125},{w:1,h:1,x_center:.796875,y_center:.203125},{w:1,h:1,x_center:.828125,y_center:.203125},{w:1,h:1,x_center:.828125,y_center:.203125},{w:1,h:1,x_center:.859375,y_center:.203125},{w:1,h:1,x_center:.859375,y_center:.203125},{w:1,h:1,x_center:.890625,y_center:.203125},{w:1,h:1,x_center:.890625,y_center:.203125},{w:1,h:1,x_center:.921875,y_center:.203125},{w:1,h:1,x_center:.921875,y_center:.203125},{w:1,h:1,x_center:.953125,y_center:.203125},{w:1,h:1,x_center:.953125,y_center:.203125},{w:1,h:1,x_center:.984375,y_center:.203125},{w:1,h:1,x_center:.984375,y_center:.203125},{w:1,h:1,x_center:.015625,y_center:.234375},{w:1,h:1,x_center:.015625,y_center:.234375},{w:1,h:1,x_center:.046875,y_center:.234375},{w:1,h:1,x_center:.046875,y_center:.234375},{w:1,h:1,x_center:.078125,y_center:.234375},{w:1,h:1,x_center:.078125,y_center:.234375},{w:1,h:1,x_center:.109375,y_center:.234375},{w:1,h:1,x_center:.109375,y_center:.234375},{w:1,h:1,x_center:.140625,y_center:.234375},{w:1,h:1,x_center:.140625,y_center:.234375},{w:1,h:1,x_center:.171875,y_center:.234375},{w:1,h:1,x_center:.171875,y_center:.234375},{w:1,h:1,x_center:.203125,y_center:.234375},{w:1,h:1,x_center:.203125,y_center:.234375},{w:1,h:1,x_center:.234375,y_center:.234375},{w:1,h:1,x_center:.234375,y_center:.234375},{w:1,h:1,x_center:.265625,y_center:.234375},{w:1,h:1,x_center:.265625,y_center:.234375},{w:1,h:1,x_center:.296875,y_center:.234375},{w:1,h:1,x_center:.296875,y_center:.234375},{w:1,h:1,x_center:.328125,y_center:.234375},{w:1,h:1,x_center:.328125,y_center:.234375},{w:1,h:1,x_center:.359375,y_center:.234375},{w:1,h:1,x_center:.359375,y_center:.234375},{w:1,h:1,x_center:.390625,y_center:.234375},{w:1,h:1,x_center:.390625,y_center:.234375},{w:1,h:1,x_center:.421875,y_center:.234375},{w:1,h:1,x_center:.421875,y_center:.234375},{w:1,h:1,x_center:.453125,y_center:.234375},{w:1,h:1,x_center:.453125,y_center:.234375},{w:1,h:1,x_center:.484375,y_center:.234375},{w:1,h:1,x_center:.484375,y_center:.234375},{w:1,h:1,x_center:.515625,y_center:.234375},{w:1,h:1,x_center:.515625,y_center:.234375},{w:1,h:1,x_center:.546875,y_center:.234375},{w:1,h:1,x_center:.546875,y_center:.234375},{w:1,h:1,x_center:.578125,y_center:.234375},{w:1,h:1,x_center:.578125,y_center:.234375},{w:1,h:1,x_center:.609375,y_center:.234375},{w:1,h:1,x_center:.609375,y_center:.234375},{w:1,h:1,x_center:.640625,y_center:.234375},{w:1,h:1,x_center:.640625,y_center:.234375},{w:1,h:1,x_center:.671875,y_center:.234375},{w:1,h:1,x_center:.671875,y_center:.234375},{w:1,h:1,x_center:.703125,y_center:.234375},{w:1,h:1,x_center:.703125,y_center:.234375},{w:1,h:1,x_center:.734375,y_center:.234375},{w:1,h:1,x_center:.734375,y_center:.234375},{w:1,h:1,x_center:.765625,y_center:.234375},{w:1,h:1,x_center:.765625,y_center:.234375},{w:1,h:1,x_center:.796875,y_center:.234375},{w:1,h:1,x_center:.796875,y_center:.234375},{w:1,h:1,x_center:.828125,y_center:.234375},{w:1,h:1,x_center:.828125,y_center:.234375},{w:1,h:1,x_center:.859375,y_center:.234375},{w:1,h:1,x_center:.859375,y_center:.234375},{w:1,h:1,x_center:.890625,y_center:.234375},{w:1,h:1,x_center:.890625,y_center:.234375},{w:1,h:1,x_center:.921875,y_center:.234375},{w:1,h:1,x_center:.921875,y_center:.234375},{w:1,h:1,x_center:.953125,y_center:.234375},{w:1,h:1,x_center:.953125,y_center:.234375},{w:1,h:1,x_center:.984375,y_center:.234375},{w:1,h:1,x_center:.984375,y_center:.234375},{w:1,h:1,x_center:.015625,y_center:.265625},{w:1,h:1,x_center:.015625,y_center:.265625},{w:1,h:1,x_center:.046875,y_center:.265625},{w:1,h:1,x_center:.046875,y_center:.265625},{w:1,h:1,x_center:.078125,y_center:.265625},{w:1,h:1,x_center:.078125,y_center:.265625},{w:1,h:1,x_center:.109375,y_center:.265625},{w:1,h:1,x_center:.109375,y_center:.265625},{w:1,h:1,x_center:.140625,y_center:.265625},{w:1,h:1,x_center:.140625,y_center:.265625},{w:1,h:1,x_center:.171875,y_center:.265625},{w:1,h:1,x_center:.171875,y_center:.265625},{w:1,h:1,x_center:.203125,y_center:.265625},{w:1,h:1,x_center:.203125,y_center:.265625},{w:1,h:1,x_center:.234375,y_center:.265625},{w:1,h:1,x_center:.234375,y_center:.265625},{w:1,h:1,x_center:.265625,y_center:.265625},{w:1,h:1,x_center:.265625,y_center:.265625},{w:1,h:1,x_center:.296875,y_center:.265625},{w:1,h:1,x_center:.296875,y_center:.265625},{w:1,h:1,x_center:.328125,y_center:.265625},{w:1,h:1,x_center:.328125,y_center:.265625},{w:1,h:1,x_center:.359375,y_center:.265625},{w:1,h:1,x_center:.359375,y_center:.265625},{w:1,h:1,x_center:.390625,y_center:.265625},{w:1,h:1,x_center:.390625,y_center:.265625},{w:1,h:1,x_center:.421875,y_center:.265625},{w:1,h:1,x_center:.421875,y_center:.265625},{w:1,h:1,x_center:.453125,y_center:.265625},{w:1,h:1,x_center:.453125,y_center:.265625},{w:1,h:1,x_center:.484375,y_center:.265625},{w:1,h:1,x_center:.484375,y_center:.265625},{w:1,h:1,x_center:.515625,y_center:.265625},{w:1,h:1,x_center:.515625,y_center:.265625},{w:1,h:1,x_center:.546875,y_center:.265625},{w:1,h:1,x_center:.546875,y_center:.265625},{w:1,h:1,x_center:.578125,y_center:.265625},{w:1,h:1,x_center:.578125,y_center:.265625},{w:1,h:1,x_center:.609375,y_center:.265625},{w:1,h:1,x_center:.609375,y_center:.265625},{w:1,h:1,x_center:.640625,y_center:.265625},{w:1,h:1,x_center:.640625,y_center:.265625},{w:1,h:1,x_center:.671875,y_center:.265625},{w:1,h:1,x_center:.671875,y_center:.265625},{w:1,h:1,x_center:.703125,y_center:.265625},{w:1,h:1,x_center:.703125,y_center:.265625},{w:1,h:1,x_center:.734375,y_center:.265625},{w:1,h:1,x_center:.734375,y_center:.265625},{w:1,h:1,x_center:.765625,y_center:.265625},{w:1,h:1,x_center:.765625,y_center:.265625},{w:1,h:1,x_center:.796875,y_center:.265625},{w:1,h:1,x_center:.796875,y_center:.265625},{w:1,h:1,x_center:.828125,y_center:.265625},{w:1,h:1,x_center:.828125,y_center:.265625},{w:1,h:1,x_center:.859375,y_center:.265625},{w:1,h:1,x_center:.859375,y_center:.265625},{w:1,h:1,x_center:.890625,y_center:.265625},{w:1,h:1,x_center:.890625,y_center:.265625},{w:1,h:1,x_center:.921875,y_center:.265625},{w:1,h:1,x_center:.921875,y_center:.265625},{w:1,h:1,x_center:.953125,y_center:.265625},{w:1,h:1,x_center:.953125,y_center:.265625},{w:1,h:1,x_center:.984375,y_center:.265625},{w:1,h:1,x_center:.984375,y_center:.265625},{w:1,h:1,x_center:.015625,y_center:.296875},{w:1,h:1,x_center:.015625,y_center:.296875},{w:1,h:1,x_center:.046875,y_center:.296875},{w:1,h:1,x_center:.046875,y_center:.296875},{w:1,h:1,x_center:.078125,y_center:.296875},{w:1,h:1,x_center:.078125,y_center:.296875},{w:1,h:1,x_center:.109375,y_center:.296875},{w:1,h:1,x_center:.109375,y_center:.296875},{w:1,h:1,x_center:.140625,y_center:.296875},{w:1,h:1,x_center:.140625,y_center:.296875},{w:1,h:1,x_center:.171875,y_center:.296875},{w:1,h:1,x_center:.171875,y_center:.296875},{w:1,h:1,x_center:.203125,y_center:.296875},{w:1,h:1,x_center:.203125,y_center:.296875},{w:1,h:1,x_center:.234375,y_center:.296875},{w:1,h:1,x_center:.234375,y_center:.296875},{w:1,h:1,x_center:.265625,y_center:.296875},{w:1,h:1,x_center:.265625,y_center:.296875},{w:1,h:1,x_center:.296875,y_center:.296875},{w:1,h:1,x_center:.296875,y_center:.296875},{w:1,h:1,x_center:.328125,y_center:.296875},{w:1,h:1,x_center:.328125,y_center:.296875},{w:1,h:1,x_center:.359375,y_center:.296875},{w:1,h:1,x_center:.359375,y_center:.296875},{w:1,h:1,x_center:.390625,y_center:.296875},{w:1,h:1,x_center:.390625,y_center:.296875},{w:1,h:1,x_center:.421875,y_center:.296875},{w:1,h:1,x_center:.421875,y_center:.296875},{w:1,h:1,x_center:.453125,y_center:.296875},{w:1,h:1,x_center:.453125,y_center:.296875},{w:1,h:1,x_center:.484375,y_center:.296875},{w:1,h:1,x_center:.484375,y_center:.296875},{w:1,h:1,x_center:.515625,y_center:.296875},{w:1,h:1,x_center:.515625,y_center:.296875},{w:1,h:1,x_center:.546875,y_center:.296875},{w:1,h:1,x_center:.546875,y_center:.296875},{w:1,h:1,x_center:.578125,y_center:.296875},{w:1,h:1,x_center:.578125,y_center:.296875},{w:1,h:1,x_center:.609375,y_center:.296875},{w:1,h:1,x_center:.609375,y_center:.296875},{w:1,h:1,x_center:.640625,y_center:.296875},{w:1,h:1,x_center:.640625,y_center:.296875},{w:1,h:1,x_center:.671875,y_center:.296875},{w:1,h:1,x_center:.671875,y_center:.296875},{w:1,h:1,x_center:.703125,y_center:.296875},{w:1,h:1,x_center:.703125,y_center:.296875},{w:1,h:1,x_center:.734375,y_center:.296875},{w:1,h:1,x_center:.734375,y_center:.296875},{w:1,h:1,x_center:.765625,y_center:.296875},{w:1,h:1,x_center:.765625,y_center:.296875},{w:1,h:1,x_center:.796875,y_center:.296875},{w:1,h:1,x_center:.796875,y_center:.296875},{w:1,h:1,x_center:.828125,y_center:.296875},{w:1,h:1,x_center:.828125,y_center:.296875},{w:1,h:1,x_center:.859375,y_center:.296875},{w:1,h:1,x_center:.859375,y_center:.296875},{w:1,h:1,x_center:.890625,y_center:.296875},{w:1,h:1,x_center:.890625,y_center:.296875},{w:1,h:1,x_center:.921875,y_center:.296875},{w:1,h:1,x_center:.921875,y_center:.296875},{w:1,h:1,x_center:.953125,y_center:.296875},{w:1,h:1,x_center:.953125,y_center:.296875},{w:1,h:1,x_center:.984375,y_center:.296875},{w:1,h:1,x_center:.984375,y_center:.296875},{w:1,h:1,x_center:.015625,y_center:.328125},{w:1,h:1,x_center:.015625,y_center:.328125},{w:1,h:1,x_center:.046875,y_center:.328125},{w:1,h:1,x_center:.046875,y_center:.328125},{w:1,h:1,x_center:.078125,y_center:.328125},{w:1,h:1,x_center:.078125,y_center:.328125},{w:1,h:1,x_center:.109375,y_center:.328125},{w:1,h:1,x_center:.109375,y_center:.328125},{w:1,h:1,x_center:.140625,y_center:.328125},{w:1,h:1,x_center:.140625,y_center:.328125},{w:1,h:1,x_center:.171875,y_center:.328125},{w:1,h:1,x_center:.171875,y_center:.328125},{w:1,h:1,x_center:.203125,y_center:.328125},{w:1,h:1,x_center:.203125,y_center:.328125},{w:1,h:1,x_center:.234375,y_center:.328125},{w:1,h:1,x_center:.234375,y_center:.328125},{w:1,h:1,x_center:.265625,y_center:.328125},{w:1,h:1,x_center:.265625,y_center:.328125},{w:1,h:1,x_center:.296875,y_center:.328125},{w:1,h:1,x_center:.296875,y_center:.328125},{w:1,h:1,x_center:.328125,y_center:.328125},{w:1,h:1,x_center:.328125,y_center:.328125},{w:1,h:1,x_center:.359375,y_center:.328125},{w:1,h:1,x_center:.359375,y_center:.328125},{w:1,h:1,x_center:.390625,y_center:.328125},{w:1,h:1,x_center:.390625,y_center:.328125},{w:1,h:1,x_center:.421875,y_center:.328125},{w:1,h:1,x_center:.421875,y_center:.328125},{w:1,h:1,x_center:.453125,y_center:.328125},{w:1,h:1,x_center:.453125,y_center:.328125},{w:1,h:1,x_center:.484375,y_center:.328125},{w:1,h:1,x_center:.484375,y_center:.328125},{w:1,h:1,x_center:.515625,y_center:.328125},{w:1,h:1,x_center:.515625,y_center:.328125},{w:1,h:1,x_center:.546875,y_center:.328125},{w:1,h:1,x_center:.546875,y_center:.328125},{w:1,h:1,x_center:.578125,y_center:.328125},{w:1,h:1,x_center:.578125,y_center:.328125},{w:1,h:1,x_center:.609375,y_center:.328125},{w:1,h:1,x_center:.609375,y_center:.328125},{w:1,h:1,x_center:.640625,y_center:.328125},{w:1,h:1,x_center:.640625,y_center:.328125},{w:1,h:1,x_center:.671875,y_center:.328125},{w:1,h:1,x_center:.671875,y_center:.328125},{w:1,h:1,x_center:.703125,y_center:.328125},{w:1,h:1,x_center:.703125,y_center:.328125},{w:1,h:1,x_center:.734375,y_center:.328125},{w:1,h:1,x_center:.734375,y_center:.328125},{w:1,h:1,x_center:.765625,y_center:.328125},{w:1,h:1,x_center:.765625,y_center:.328125},{w:1,h:1,x_center:.796875,y_center:.328125},{w:1,h:1,x_center:.796875,y_center:.328125},{w:1,h:1,x_center:.828125,y_center:.328125},{w:1,h:1,x_center:.828125,y_center:.328125},{w:1,h:1,x_center:.859375,y_center:.328125},{w:1,h:1,x_center:.859375,y_center:.328125},{w:1,h:1,x_center:.890625,y_center:.328125},{w:1,h:1,x_center:.890625,y_center:.328125},{w:1,h:1,x_center:.921875,y_center:.328125},{w:1,h:1,x_center:.921875,y_center:.328125},{w:1,h:1,x_center:.953125,y_center:.328125},{w:1,h:1,x_center:.953125,y_center:.328125},{w:1,h:1,x_center:.984375,y_center:.328125},{w:1,h:1,x_center:.984375,y_center:.328125},{w:1,h:1,x_center:.015625,y_center:.359375},{w:1,h:1,x_center:.015625,y_center:.359375},{w:1,h:1,x_center:.046875,y_center:.359375},{w:1,h:1,x_center:.046875,y_center:.359375},{w:1,h:1,x_center:.078125,y_center:.359375},{w:1,h:1,x_center:.078125,y_center:.359375},{w:1,h:1,x_center:.109375,y_center:.359375},{w:1,h:1,x_center:.109375,y_center:.359375},{w:1,h:1,x_center:.140625,y_center:.359375},{w:1,h:1,x_center:.140625,y_center:.359375},{w:1,h:1,x_center:.171875,y_center:.359375},{w:1,h:1,x_center:.171875,y_center:.359375},{w:1,h:1,x_center:.203125,y_center:.359375},{w:1,h:1,x_center:.203125,y_center:.359375},{w:1,h:1,x_center:.234375,y_center:.359375},{w:1,h:1,x_center:.234375,y_center:.359375},{w:1,h:1,x_center:.265625,y_center:.359375},{w:1,h:1,x_center:.265625,y_center:.359375},{w:1,h:1,x_center:.296875,y_center:.359375},{w:1,h:1,x_center:.296875,y_center:.359375},{w:1,h:1,x_center:.328125,y_center:.359375},{w:1,h:1,x_center:.328125,y_center:.359375},{w:1,h:1,x_center:.359375,y_center:.359375},{w:1,h:1,x_center:.359375,y_center:.359375},{w:1,h:1,x_center:.390625,y_center:.359375},{w:1,h:1,x_center:.390625,y_center:.359375},{w:1,h:1,x_center:.421875,y_center:.359375},{w:1,h:1,x_center:.421875,y_center:.359375},{w:1,h:1,x_center:.453125,y_center:.359375},{w:1,h:1,x_center:.453125,y_center:.359375},{w:1,h:1,x_center:.484375,y_center:.359375},{w:1,h:1,x_center:.484375,y_center:.359375},{w:1,h:1,x_center:.515625,y_center:.359375},{w:1,h:1,x_center:.515625,y_center:.359375},{w:1,h:1,x_center:.546875,y_center:.359375},{w:1,h:1,x_center:.546875,y_center:.359375},{w:1,h:1,x_center:.578125,y_center:.359375},{w:1,h:1,x_center:.578125,y_center:.359375},{w:1,h:1,x_center:.609375,y_center:.359375},{w:1,h:1,x_center:.609375,y_center:.359375},{w:1,h:1,x_center:.640625,y_center:.359375},{w:1,h:1,x_center:.640625,y_center:.359375},{w:1,h:1,x_center:.671875,y_center:.359375},{w:1,h:1,x_center:.671875,y_center:.359375},{w:1,h:1,x_center:.703125,y_center:.359375},{w:1,h:1,x_center:.703125,y_center:.359375},{w:1,h:1,x_center:.734375,y_center:.359375},{w:1,h:1,x_center:.734375,y_center:.359375},{w:1,h:1,x_center:.765625,y_center:.359375},{w:1,h:1,x_center:.765625,y_center:.359375},{w:1,h:1,x_center:.796875,y_center:.359375},{w:1,h:1,x_center:.796875,y_center:.359375},{w:1,h:1,x_center:.828125,y_center:.359375},{w:1,h:1,x_center:.828125,y_center:.359375},{w:1,h:1,x_center:.859375,y_center:.359375},{w:1,h:1,x_center:.859375,y_center:.359375},{w:1,h:1,x_center:.890625,y_center:.359375},{w:1,h:1,x_center:.890625,y_center:.359375},{w:1,h:1,x_center:.921875,y_center:.359375},{w:1,h:1,x_center:.921875,y_center:.359375},{w:1,h:1,x_center:.953125,y_center:.359375},{w:1,h:1,x_center:.953125,y_center:.359375},{w:1,h:1,x_center:.984375,y_center:.359375},{w:1,h:1,x_center:.984375,y_center:.359375},{w:1,h:1,x_center:.015625,y_center:.390625},{w:1,h:1,x_center:.015625,y_center:.390625},{w:1,h:1,x_center:.046875,y_center:.390625},{w:1,h:1,x_center:.046875,y_center:.390625},{w:1,h:1,x_center:.078125,y_center:.390625},{w:1,h:1,x_center:.078125,y_center:.390625},{w:1,h:1,x_center:.109375,y_center:.390625},{w:1,h:1,x_center:.109375,y_center:.390625},{w:1,h:1,x_center:.140625,y_center:.390625},{w:1,h:1,x_center:.140625,y_center:.390625},{w:1,h:1,x_center:.171875,y_center:.390625},{w:1,h:1,x_center:.171875,y_center:.390625},{w:1,h:1,x_center:.203125,y_center:.390625},{w:1,h:1,x_center:.203125,y_center:.390625},{w:1,h:1,x_center:.234375,y_center:.390625},{w:1,h:1,x_center:.234375,y_center:.390625},{w:1,h:1,x_center:.265625,y_center:.390625},{w:1,h:1,x_center:.265625,y_center:.390625},{w:1,h:1,x_center:.296875,y_center:.390625},{w:1,h:1,x_center:.296875,y_center:.390625},{w:1,h:1,x_center:.328125,y_center:.390625},{w:1,h:1,x_center:.328125,y_center:.390625},{w:1,h:1,x_center:.359375,y_center:.390625},{w:1,h:1,x_center:.359375,y_center:.390625},{w:1,h:1,x_center:.390625,y_center:.390625},{w:1,h:1,x_center:.390625,y_center:.390625},{w:1,h:1,x_center:.421875,y_center:.390625},{w:1,h:1,x_center:.421875,y_center:.390625},{w:1,h:1,x_center:.453125,y_center:.390625},{w:1,h:1,x_center:.453125,y_center:.390625},{w:1,h:1,x_center:.484375,y_center:.390625},{w:1,h:1,x_center:.484375,y_center:.390625},{w:1,h:1,x_center:.515625,y_center:.390625},{w:1,h:1,x_center:.515625,y_center:.390625},{w:1,h:1,x_center:.546875,y_center:.390625},{w:1,h:1,x_center:.546875,y_center:.390625},{w:1,h:1,x_center:.578125,y_center:.390625},{w:1,h:1,x_center:.578125,y_center:.390625},{w:1,h:1,x_center:.609375,y_center:.390625},{w:1,h:1,x_center:.609375,y_center:.390625},{w:1,h:1,x_center:.640625,y_center:.390625},{w:1,h:1,x_center:.640625,y_center:.390625},{w:1,h:1,x_center:.671875,y_center:.390625},{w:1,h:1,x_center:.671875,y_center:.390625},{w:1,h:1,x_center:.703125,y_center:.390625},{w:1,h:1,x_center:.703125,y_center:.390625},{w:1,h:1,x_center:.734375,y_center:.390625},{w:1,h:1,x_center:.734375,y_center:.390625},{w:1,h:1,x_center:.765625,y_center:.390625},{w:1,h:1,x_center:.765625,y_center:.390625},{w:1,h:1,x_center:.796875,y_center:.390625},{w:1,h:1,x_center:.796875,y_center:.390625},{w:1,h:1,x_center:.828125,y_center:.390625},{w:1,h:1,x_center:.828125,y_center:.390625},{w:1,h:1,x_center:.859375,y_center:.390625},{w:1,h:1,x_center:.859375,y_center:.390625},{w:1,h:1,x_center:.890625,y_center:.390625},{w:1,h:1,x_center:.890625,y_center:.390625},{w:1,h:1,x_center:.921875,y_center:.390625},{w:1,h:1,x_center:.921875,y_center:.390625},{w:1,h:1,x_center:.953125,y_center:.390625},{w:1,h:1,x_center:.953125,y_center:.390625},{w:1,h:1,x_center:.984375,y_center:.390625},{w:1,h:1,x_center:.984375,y_center:.390625},{w:1,h:1,x_center:.015625,y_center:.421875},{w:1,h:1,x_center:.015625,y_center:.421875},{w:1,h:1,x_center:.046875,y_center:.421875},{w:1,h:1,x_center:.046875,y_center:.421875},{w:1,h:1,x_center:.078125,y_center:.421875},{w:1,h:1,x_center:.078125,y_center:.421875},{w:1,h:1,x_center:.109375,y_center:.421875},{w:1,h:1,x_center:.109375,y_center:.421875},{w:1,h:1,x_center:.140625,y_center:.421875},{w:1,h:1,x_center:.140625,y_center:.421875},{w:1,h:1,x_center:.171875,y_center:.421875},{w:1,h:1,x_center:.171875,y_center:.421875},{w:1,h:1,x_center:.203125,y_center:.421875},{w:1,h:1,x_center:.203125,y_center:.421875},{w:1,h:1,x_center:.234375,y_center:.421875},{w:1,h:1,x_center:.234375,y_center:.421875},{w:1,h:1,x_center:.265625,y_center:.421875},{w:1,h:1,x_center:.265625,y_center:.421875},{w:1,h:1,x_center:.296875,y_center:.421875},{w:1,h:1,x_center:.296875,y_center:.421875},{w:1,h:1,x_center:.328125,y_center:.421875},{w:1,h:1,x_center:.328125,y_center:.421875},{w:1,h:1,x_center:.359375,y_center:.421875},{w:1,h:1,x_center:.359375,y_center:.421875},{w:1,h:1,x_center:.390625,y_center:.421875},{w:1,h:1,x_center:.390625,y_center:.421875},{w:1,h:1,x_center:.421875,y_center:.421875},{w:1,h:1,x_center:.421875,y_center:.421875},{w:1,h:1,x_center:.453125,y_center:.421875},{w:1,h:1,x_center:.453125,y_center:.421875},{w:1,h:1,x_center:.484375,y_center:.421875},{w:1,h:1,x_center:.484375,y_center:.421875},{w:1,h:1,x_center:.515625,y_center:.421875},{w:1,h:1,x_center:.515625,y_center:.421875},{w:1,h:1,x_center:.546875,y_center:.421875},{w:1,h:1,x_center:.546875,y_center:.421875},{w:1,h:1,x_center:.578125,y_center:.421875},{w:1,h:1,x_center:.578125,y_center:.421875},{w:1,h:1,x_center:.609375,y_center:.421875},{w:1,h:1,x_center:.609375,y_center:.421875},{w:1,h:1,x_center:.640625,y_center:.421875},{w:1,h:1,x_center:.640625,y_center:.421875},{w:1,h:1,x_center:.671875,y_center:.421875},{w:1,h:1,x_center:.671875,y_center:.421875},{w:1,h:1,x_center:.703125,y_center:.421875},{w:1,h:1,x_center:.703125,y_center:.421875},{w:1,h:1,x_center:.734375,y_center:.421875},{w:1,h:1,x_center:.734375,y_center:.421875},{w:1,h:1,x_center:.765625,y_center:.421875},{w:1,h:1,x_center:.765625,y_center:.421875},{w:1,h:1,x_center:.796875,y_center:.421875},{w:1,h:1,x_center:.796875,y_center:.421875},{w:1,h:1,x_center:.828125,y_center:.421875},{w:1,h:1,x_center:.828125,y_center:.421875},{w:1,h:1,x_center:.859375,y_center:.421875},{w:1,h:1,x_center:.859375,y_center:.421875},{w:1,h:1,x_center:.890625,y_center:.421875},{w:1,h:1,x_center:.890625,y_center:.421875},{w:1,h:1,x_center:.921875,y_center:.421875},{w:1,h:1,x_center:.921875,y_center:.421875},{w:1,h:1,x_center:.953125,y_center:.421875},{w:1,h:1,x_center:.953125,y_center:.421875},{w:1,h:1,x_center:.984375,y_center:.421875},{w:1,h:1,x_center:.984375,y_center:.421875},{w:1,h:1,x_center:.015625,y_center:.453125},{w:1,h:1,x_center:.015625,y_center:.453125},{w:1,h:1,x_center:.046875,y_center:.453125},{w:1,h:1,x_center:.046875,y_center:.453125},{w:1,h:1,x_center:.078125,y_center:.453125},{w:1,h:1,x_center:.078125,y_center:.453125},{w:1,h:1,x_center:.109375,y_center:.453125},{w:1,h:1,x_center:.109375,y_center:.453125},{w:1,h:1,x_center:.140625,y_center:.453125},{w:1,h:1,x_center:.140625,y_center:.453125},{w:1,h:1,x_center:.171875,y_center:.453125},{w:1,h:1,x_center:.171875,y_center:.453125},{w:1,h:1,x_center:.203125,y_center:.453125},{w:1,h:1,x_center:.203125,y_center:.453125},{w:1,h:1,x_center:.234375,y_center:.453125},{w:1,h:1,x_center:.234375,y_center:.453125},{w:1,h:1,x_center:.265625,y_center:.453125},{w:1,h:1,x_center:.265625,y_center:.453125},{w:1,h:1,x_center:.296875,y_center:.453125},{w:1,h:1,x_center:.296875,y_center:.453125},{w:1,h:1,x_center:.328125,y_center:.453125},{w:1,h:1,x_center:.328125,y_center:.453125},{w:1,h:1,x_center:.359375,y_center:.453125},{w:1,h:1,x_center:.359375,y_center:.453125},{w:1,h:1,x_center:.390625,y_center:.453125},{w:1,h:1,x_center:.390625,y_center:.453125},{w:1,h:1,x_center:.421875,y_center:.453125},{w:1,h:1,x_center:.421875,y_center:.453125},{w:1,h:1,x_center:.453125,y_center:.453125},{w:1,h:1,x_center:.453125,y_center:.453125},{w:1,h:1,x_center:.484375,y_center:.453125},{w:1,h:1,x_center:.484375,y_center:.453125},{w:1,h:1,x_center:.515625,y_center:.453125},{w:1,h:1,x_center:.515625,y_center:.453125},{w:1,h:1,x_center:.546875,y_center:.453125},{w:1,h:1,x_center:.546875,y_center:.453125},{w:1,h:1,x_center:.578125,y_center:.453125},{w:1,h:1,x_center:.578125,y_center:.453125},{w:1,h:1,x_center:.609375,y_center:.453125},{w:1,h:1,x_center:.609375,y_center:.453125},{w:1,h:1,x_center:.640625,y_center:.453125},{w:1,h:1,x_center:.640625,y_center:.453125},{w:1,h:1,x_center:.671875,y_center:.453125},{w:1,h:1,x_center:.671875,y_center:.453125},{w:1,h:1,x_center:.703125,y_center:.453125},{w:1,h:1,x_center:.703125,y_center:.453125},{w:1,h:1,x_center:.734375,y_center:.453125},{w:1,h:1,x_center:.734375,y_center:.453125},{w:1,h:1,x_center:.765625,y_center:.453125},{w:1,h:1,x_center:.765625,y_center:.453125},{w:1,h:1,x_center:.796875,y_center:.453125},{w:1,h:1,x_center:.796875,y_center:.453125},{w:1,h:1,x_center:.828125,y_center:.453125},{w:1,h:1,x_center:.828125,y_center:.453125},{w:1,h:1,x_center:.859375,y_center:.453125},{w:1,h:1,x_center:.859375,y_center:.453125},{w:1,h:1,x_center:.890625,y_center:.453125},{w:1,h:1,x_center:.890625,y_center:.453125},{w:1,h:1,x_center:.921875,y_center:.453125},{w:1,h:1,x_center:.921875,y_center:.453125},{w:1,h:1,x_center:.953125,y_center:.453125},{w:1,h:1,x_center:.953125,y_center:.453125},{w:1,h:1,x_center:.984375,y_center:.453125},{w:1,h:1,x_center:.984375,y_center:.453125},{w:1,h:1,x_center:.015625,y_center:.484375},{w:1,h:1,x_center:.015625,y_center:.484375},{w:1,h:1,x_center:.046875,y_center:.484375},{w:1,h:1,x_center:.046875,y_center:.484375},{w:1,h:1,x_center:.078125,y_center:.484375},{w:1,h:1,x_center:.078125,y_center:.484375},{w:1,h:1,x_center:.109375,y_center:.484375},{w:1,h:1,x_center:.109375,y_center:.484375},{w:1,h:1,x_center:.140625,y_center:.484375},{w:1,h:1,x_center:.140625,y_center:.484375},{w:1,h:1,x_center:.171875,y_center:.484375},{w:1,h:1,x_center:.171875,y_center:.484375},{w:1,h:1,x_center:.203125,y_center:.484375},{w:1,h:1,x_center:.203125,y_center:.484375},{w:1,h:1,x_center:.234375,y_center:.484375},{w:1,h:1,x_center:.234375,y_center:.484375},{w:1,h:1,x_center:.265625,y_center:.484375},{w:1,h:1,x_center:.265625,y_center:.484375},{w:1,h:1,x_center:.296875,y_center:.484375},{w:1,h:1,x_center:.296875,y_center:.484375},{w:1,h:1,x_center:.328125,y_center:.484375},{w:1,h:1,x_center:.328125,y_center:.484375},{w:1,h:1,x_center:.359375,y_center:.484375},{w:1,h:1,x_center:.359375,y_center:.484375},{w:1,h:1,x_center:.390625,y_center:.484375},{w:1,h:1,x_center:.390625,y_center:.484375},{w:1,h:1,x_center:.421875,y_center:.484375},{w:1,h:1,x_center:.421875,y_center:.484375},{w:1,h:1,x_center:.453125,y_center:.484375},{w:1,h:1,x_center:.453125,y_center:.484375},{w:1,h:1,x_center:.484375,y_center:.484375},{w:1,h:1,x_center:.484375,y_center:.484375},{w:1,h:1,x_center:.515625,y_center:.484375},{w:1,h:1,x_center:.515625,y_center:.484375},{w:1,h:1,x_center:.546875,y_center:.484375},{w:1,h:1,x_center:.546875,y_center:.484375},{w:1,h:1,x_center:.578125,y_center:.484375},{w:1,h:1,x_center:.578125,y_center:.484375},{w:1,h:1,x_center:.609375,y_center:.484375},{w:1,h:1,x_center:.609375,y_center:.484375},{w:1,h:1,x_center:.640625,y_center:.484375},{w:1,h:1,x_center:.640625,y_center:.484375},{w:1,h:1,x_center:.671875,y_center:.484375},{w:1,h:1,x_center:.671875,y_center:.484375},{w:1,h:1,x_center:.703125,y_center:.484375},{w:1,h:1,x_center:.703125,y_center:.484375},{w:1,h:1,x_center:.734375,y_center:.484375},{w:1,h:1,x_center:.734375,y_center:.484375},{w:1,h:1,x_center:.765625,y_center:.484375},{w:1,h:1,x_center:.765625,y_center:.484375},{w:1,h:1,x_center:.796875,y_center:.484375},{w:1,h:1,x_center:.796875,y_center:.484375},{w:1,h:1,x_center:.828125,y_center:.484375},{w:1,h:1,x_center:.828125,y_center:.484375},{w:1,h:1,x_center:.859375,y_center:.484375},{w:1,h:1,x_center:.859375,y_center:.484375},{w:1,h:1,x_center:.890625,y_center:.484375},{w:1,h:1,x_center:.890625,y_center:.484375},{w:1,h:1,x_center:.921875,y_center:.484375},{w:1,h:1,x_center:.921875,y_center:.484375},{w:1,h:1,x_center:.953125,y_center:.484375},{w:1,h:1,x_center:.953125,y_center:.484375},{w:1,h:1,x_center:.984375,y_center:.484375},{w:1,h:1,x_center:.984375,y_center:.484375},{w:1,h:1,x_center:.015625,y_center:.515625},{w:1,h:1,x_center:.015625,y_center:.515625},{w:1,h:1,x_center:.046875,y_center:.515625},{w:1,h:1,x_center:.046875,y_center:.515625},{w:1,h:1,x_center:.078125,y_center:.515625},{w:1,h:1,x_center:.078125,y_center:.515625},{w:1,h:1,x_center:.109375,y_center:.515625},{w:1,h:1,x_center:.109375,y_center:.515625},{w:1,h:1,x_center:.140625,y_center:.515625},{w:1,h:1,x_center:.140625,y_center:.515625},{w:1,h:1,x_center:.171875,y_center:.515625},{w:1,h:1,x_center:.171875,y_center:.515625},{w:1,h:1,x_center:.203125,y_center:.515625},{w:1,h:1,x_center:.203125,y_center:.515625},{w:1,h:1,x_center:.234375,y_center:.515625},{w:1,h:1,x_center:.234375,y_center:.515625},{w:1,h:1,x_center:.265625,y_center:.515625},{w:1,h:1,x_center:.265625,y_center:.515625},{w:1,h:1,x_center:.296875,y_center:.515625},{w:1,h:1,x_center:.296875,y_center:.515625},{w:1,h:1,x_center:.328125,y_center:.515625},{w:1,h:1,x_center:.328125,y_center:.515625},{w:1,h:1,x_center:.359375,y_center:.515625},{w:1,h:1,x_center:.359375,y_center:.515625},{w:1,h:1,x_center:.390625,y_center:.515625},{w:1,h:1,x_center:.390625,y_center:.515625},{w:1,h:1,x_center:.421875,y_center:.515625},{w:1,h:1,x_center:.421875,y_center:.515625},{w:1,h:1,x_center:.453125,y_center:.515625},{w:1,h:1,x_center:.453125,y_center:.515625},{w:1,h:1,x_center:.484375,y_center:.515625},{w:1,h:1,x_center:.484375,y_center:.515625},{w:1,h:1,x_center:.515625,y_center:.515625},{w:1,h:1,x_center:.515625,y_center:.515625},{w:1,h:1,x_center:.546875,y_center:.515625},{w:1,h:1,x_center:.546875,y_center:.515625},{w:1,h:1,x_center:.578125,y_center:.515625},{w:1,h:1,x_center:.578125,y_center:.515625},{w:1,h:1,x_center:.609375,y_center:.515625},{w:1,h:1,x_center:.609375,y_center:.515625},{w:1,h:1,x_center:.640625,y_center:.515625},{w:1,h:1,x_center:.640625,y_center:.515625},{w:1,h:1,x_center:.671875,y_center:.515625},{w:1,h:1,x_center:.671875,y_center:.515625},{w:1,h:1,x_center:.703125,y_center:.515625},{w:1,h:1,x_center:.703125,y_center:.515625},{w:1,h:1,x_center:.734375,y_center:.515625},{w:1,h:1,x_center:.734375,y_center:.515625},{w:1,h:1,x_center:.765625,y_center:.515625},{w:1,h:1,x_center:.765625,y_center:.515625},{w:1,h:1,x_center:.796875,y_center:.515625},{w:1,h:1,x_center:.796875,y_center:.515625},{w:1,h:1,x_center:.828125,y_center:.515625},{w:1,h:1,x_center:.828125,y_center:.515625},{w:1,h:1,x_center:.859375,y_center:.515625},{w:1,h:1,x_center:.859375,y_center:.515625},{w:1,h:1,x_center:.890625,y_center:.515625},{w:1,h:1,x_center:.890625,y_center:.515625},{w:1,h:1,x_center:.921875,y_center:.515625},{w:1,h:1,x_center:.921875,y_center:.515625},{w:1,h:1,x_center:.953125,y_center:.515625},{w:1,h:1,x_center:.953125,y_center:.515625},{w:1,h:1,x_center:.984375,y_center:.515625},{w:1,h:1,x_center:.984375,y_center:.515625},{w:1,h:1,x_center:.015625,y_center:.546875},{w:1,h:1,x_center:.015625,y_center:.546875},{w:1,h:1,x_center:.046875,y_center:.546875},{w:1,h:1,x_center:.046875,y_center:.546875},{w:1,h:1,x_center:.078125,y_center:.546875},{w:1,h:1,x_center:.078125,y_center:.546875},{w:1,h:1,x_center:.109375,y_center:.546875},{w:1,h:1,x_center:.109375,y_center:.546875},{w:1,h:1,x_center:.140625,y_center:.546875},{w:1,h:1,x_center:.140625,y_center:.546875},{w:1,h:1,x_center:.171875,y_center:.546875},{w:1,h:1,x_center:.171875,y_center:.546875},{w:1,h:1,x_center:.203125,y_center:.546875},{w:1,h:1,x_center:.203125,y_center:.546875},{w:1,h:1,x_center:.234375,y_center:.546875},{w:1,h:1,x_center:.234375,y_center:.546875},{w:1,h:1,x_center:.265625,y_center:.546875},{w:1,h:1,x_center:.265625,y_center:.546875},{w:1,h:1,x_center:.296875,y_center:.546875},{w:1,h:1,x_center:.296875,y_center:.546875},{w:1,h:1,x_center:.328125,y_center:.546875},{w:1,h:1,x_center:.328125,y_center:.546875},{w:1,h:1,x_center:.359375,y_center:.546875},{w:1,h:1,x_center:.359375,y_center:.546875},{w:1,h:1,x_center:.390625,y_center:.546875},{w:1,h:1,x_center:.390625,y_center:.546875},{w:1,h:1,x_center:.421875,y_center:.546875},{w:1,h:1,x_center:.421875,y_center:.546875},{w:1,h:1,x_center:.453125,y_center:.546875},{w:1,h:1,x_center:.453125,y_center:.546875},{w:1,h:1,x_center:.484375,y_center:.546875},{w:1,h:1,x_center:.484375,y_center:.546875},{w:1,h:1,x_center:.515625,y_center:.546875},{w:1,h:1,x_center:.515625,y_center:.546875},{w:1,h:1,x_center:.546875,y_center:.546875},{w:1,h:1,x_center:.546875,y_center:.546875},{w:1,h:1,x_center:.578125,y_center:.546875},{w:1,h:1,x_center:.578125,y_center:.546875},{w:1,h:1,x_center:.609375,y_center:.546875},{w:1,h:1,x_center:.609375,y_center:.546875},{w:1,h:1,x_center:.640625,y_center:.546875},{w:1,h:1,x_center:.640625,y_center:.546875},{w:1,h:1,x_center:.671875,y_center:.546875},{w:1,h:1,x_center:.671875,y_center:.546875},{w:1,h:1,x_center:.703125,y_center:.546875},{w:1,h:1,x_center:.703125,y_center:.546875},{w:1,h:1,x_center:.734375,y_center:.546875},{w:1,h:1,x_center:.734375,y_center:.546875},{w:1,h:1,x_center:.765625,y_center:.546875},{w:1,h:1,x_center:.765625,y_center:.546875},{w:1,h:1,x_center:.796875,y_center:.546875},{w:1,h:1,x_center:.796875,y_center:.546875},{w:1,h:1,x_center:.828125,y_center:.546875},{w:1,h:1,x_center:.828125,y_center:.546875},{w:1,h:1,x_center:.859375,y_center:.546875},{w:1,h:1,x_center:.859375,y_center:.546875},{w:1,h:1,x_center:.890625,y_center:.546875},{w:1,h:1,x_center:.890625,y_center:.546875},{w:1,h:1,x_center:.921875,y_center:.546875},{w:1,h:1,x_center:.921875,y_center:.546875},{w:1,h:1,x_center:.953125,y_center:.546875},{w:1,h:1,x_center:.953125,y_center:.546875},{w:1,h:1,x_center:.984375,y_center:.546875},{w:1,h:1,x_center:.984375,y_center:.546875},{w:1,h:1,x_center:.015625,y_center:.578125},{w:1,h:1,x_center:.015625,y_center:.578125},{w:1,h:1,x_center:.046875,y_center:.578125},{w:1,h:1,x_center:.046875,y_center:.578125},{w:1,h:1,x_center:.078125,y_center:.578125},{w:1,h:1,x_center:.078125,y_center:.578125},{w:1,h:1,x_center:.109375,y_center:.578125},{w:1,h:1,x_center:.109375,y_center:.578125},{w:1,h:1,x_center:.140625,y_center:.578125},{w:1,h:1,x_center:.140625,y_center:.578125},{w:1,h:1,x_center:.171875,y_center:.578125},{w:1,h:1,x_center:.171875,y_center:.578125},{w:1,h:1,x_center:.203125,y_center:.578125},{w:1,h:1,x_center:.203125,y_center:.578125},{w:1,h:1,x_center:.234375,y_center:.578125},{w:1,h:1,x_center:.234375,y_center:.578125},{w:1,h:1,x_center:.265625,y_center:.578125},{w:1,h:1,x_center:.265625,y_center:.578125},{w:1,h:1,x_center:.296875,y_center:.578125},{w:1,h:1,x_center:.296875,y_center:.578125},{w:1,h:1,x_center:.328125,y_center:.578125},{w:1,h:1,x_center:.328125,y_center:.578125},{w:1,h:1,x_center:.359375,y_center:.578125},{w:1,h:1,x_center:.359375,y_center:.578125},{w:1,h:1,x_center:.390625,y_center:.578125},{w:1,h:1,x_center:.390625,y_center:.578125},{w:1,h:1,x_center:.421875,y_center:.578125},{w:1,h:1,x_center:.421875,y_center:.578125},{w:1,h:1,x_center:.453125,y_center:.578125},{w:1,h:1,x_center:.453125,y_center:.578125},{w:1,h:1,x_center:.484375,y_center:.578125},{w:1,h:1,x_center:.484375,y_center:.578125},{w:1,h:1,x_center:.515625,y_center:.578125},{w:1,h:1,x_center:.515625,y_center:.578125},{w:1,h:1,x_center:.546875,y_center:.578125},{w:1,h:1,x_center:.546875,y_center:.578125},{w:1,h:1,x_center:.578125,y_center:.578125},{w:1,h:1,x_center:.578125,y_center:.578125},{w:1,h:1,x_center:.609375,y_center:.578125},{w:1,h:1,x_center:.609375,y_center:.578125},{w:1,h:1,x_center:.640625,y_center:.578125},{w:1,h:1,x_center:.640625,y_center:.578125},{w:1,h:1,x_center:.671875,y_center:.578125},{w:1,h:1,x_center:.671875,y_center:.578125},{w:1,h:1,x_center:.703125,y_center:.578125},{w:1,h:1,x_center:.703125,y_center:.578125},{w:1,h:1,x_center:.734375,y_center:.578125},{w:1,h:1,x_center:.734375,y_center:.578125},{w:1,h:1,x_center:.765625,y_center:.578125},{w:1,h:1,x_center:.765625,y_center:.578125},{w:1,h:1,x_center:.796875,y_center:.578125},{w:1,h:1,x_center:.796875,y_center:.578125},{w:1,h:1,x_center:.828125,y_center:.578125},{w:1,h:1,x_center:.828125,y_center:.578125},{w:1,h:1,x_center:.859375,y_center:.578125},{w:1,h:1,x_center:.859375,y_center:.578125},{w:1,h:1,x_center:.890625,y_center:.578125},{w:1,h:1,x_center:.890625,y_center:.578125},{w:1,h:1,x_center:.921875,y_center:.578125},{w:1,h:1,x_center:.921875,y_center:.578125},{w:1,h:1,x_center:.953125,y_center:.578125},{w:1,h:1,x_center:.953125,y_center:.578125},{w:1,h:1,x_center:.984375,y_center:.578125},{w:1,h:1,x_center:.984375,y_center:.578125},{w:1,h:1,x_center:.015625,y_center:.609375},{w:1,h:1,x_center:.015625,y_center:.609375},{w:1,h:1,x_center:.046875,y_center:.609375},{w:1,h:1,x_center:.046875,y_center:.609375},{w:1,h:1,x_center:.078125,y_center:.609375},{w:1,h:1,x_center:.078125,y_center:.609375},{w:1,h:1,x_center:.109375,y_center:.609375},{w:1,h:1,x_center:.109375,y_center:.609375},{w:1,h:1,x_center:.140625,y_center:.609375},{w:1,h:1,x_center:.140625,y_center:.609375},{w:1,h:1,x_center:.171875,y_center:.609375},{w:1,h:1,x_center:.171875,y_center:.609375},{w:1,h:1,x_center:.203125,y_center:.609375},{w:1,h:1,x_center:.203125,y_center:.609375},{w:1,h:1,x_center:.234375,y_center:.609375},{w:1,h:1,x_center:.234375,y_center:.609375},{w:1,h:1,x_center:.265625,y_center:.609375},{w:1,h:1,x_center:.265625,y_center:.609375},{w:1,h:1,x_center:.296875,y_center:.609375},{w:1,h:1,x_center:.296875,y_center:.609375},{w:1,h:1,x_center:.328125,y_center:.609375},{w:1,h:1,x_center:.328125,y_center:.609375},{w:1,h:1,x_center:.359375,y_center:.609375},{w:1,h:1,x_center:.359375,y_center:.609375},{w:1,h:1,x_center:.390625,y_center:.609375},{w:1,h:1,x_center:.390625,y_center:.609375},{w:1,h:1,x_center:.421875,y_center:.609375},{w:1,h:1,x_center:.421875,y_center:.609375},{w:1,h:1,x_center:.453125,y_center:.609375},{w:1,h:1,x_center:.453125,y_center:.609375},{w:1,h:1,x_center:.484375,y_center:.609375},{w:1,h:1,x_center:.484375,y_center:.609375},{w:1,h:1,x_center:.515625,y_center:.609375},{w:1,h:1,x_center:.515625,y_center:.609375},{w:1,h:1,x_center:.546875,y_center:.609375},{w:1,h:1,x_center:.546875,y_center:.609375},{w:1,h:1,x_center:.578125,y_center:.609375},{w:1,h:1,x_center:.578125,y_center:.609375},{w:1,h:1,x_center:.609375,y_center:.609375},{w:1,h:1,x_center:.609375,y_center:.609375},{w:1,h:1,x_center:.640625,y_center:.609375},{w:1,h:1,x_center:.640625,y_center:.609375},{w:1,h:1,x_center:.671875,y_center:.609375},{w:1,h:1,x_center:.671875,y_center:.609375},{w:1,h:1,x_center:.703125,y_center:.609375},{w:1,h:1,x_center:.703125,y_center:.609375},{w:1,h:1,x_center:.734375,y_center:.609375},{w:1,h:1,x_center:.734375,y_center:.609375},{w:1,h:1,x_center:.765625,y_center:.609375},{w:1,h:1,x_center:.765625,y_center:.609375},{w:1,h:1,x_center:.796875,y_center:.609375},{w:1,h:1,x_center:.796875,y_center:.609375},{w:1,h:1,x_center:.828125,y_center:.609375},{w:1,h:1,x_center:.828125,y_center:.609375},{w:1,h:1,x_center:.859375,y_center:.609375},{w:1,h:1,x_center:.859375,y_center:.609375},{w:1,h:1,x_center:.890625,y_center:.609375},{w:1,h:1,x_center:.890625,y_center:.609375},{w:1,h:1,x_center:.921875,y_center:.609375},{w:1,h:1,x_center:.921875,y_center:.609375},{w:1,h:1,x_center:.953125,y_center:.609375},{w:1,h:1,x_center:.953125,y_center:.609375},{w:1,h:1,x_center:.984375,y_center:.609375},{w:1,h:1,x_center:.984375,y_center:.609375},{w:1,h:1,x_center:.015625,y_center:.640625},{w:1,h:1,x_center:.015625,y_center:.640625},{w:1,h:1,x_center:.046875,y_center:.640625},{w:1,h:1,x_center:.046875,y_center:.640625},{w:1,h:1,x_center:.078125,y_center:.640625},{w:1,h:1,x_center:.078125,y_center:.640625},{w:1,h:1,x_center:.109375,y_center:.640625},{w:1,h:1,x_center:.109375,y_center:.640625},{w:1,h:1,x_center:.140625,y_center:.640625},{w:1,h:1,x_center:.140625,y_center:.640625},{w:1,h:1,x_center:.171875,y_center:.640625},{w:1,h:1,x_center:.171875,y_center:.640625},{w:1,h:1,x_center:.203125,y_center:.640625},{w:1,h:1,x_center:.203125,y_center:.640625},{w:1,h:1,x_center:.234375,y_center:.640625},{w:1,h:1,x_center:.234375,y_center:.640625},{w:1,h:1,x_center:.265625,y_center:.640625},{w:1,h:1,x_center:.265625,y_center:.640625},{w:1,h:1,x_center:.296875,y_center:.640625},{w:1,h:1,x_center:.296875,y_center:.640625},{w:1,h:1,x_center:.328125,y_center:.640625},{w:1,h:1,x_center:.328125,y_center:.640625},{w:1,h:1,x_center:.359375,y_center:.640625},{w:1,h:1,x_center:.359375,y_center:.640625},{w:1,h:1,x_center:.390625,y_center:.640625},{w:1,h:1,x_center:.390625,y_center:.640625},{w:1,h:1,x_center:.421875,y_center:.640625},{w:1,h:1,x_center:.421875,y_center:.640625},{w:1,h:1,x_center:.453125,y_center:.640625},{w:1,h:1,x_center:.453125,y_center:.640625},{w:1,h:1,x_center:.484375,y_center:.640625},{w:1,h:1,x_center:.484375,y_center:.640625},{w:1,h:1,x_center:.515625,y_center:.640625},{w:1,h:1,x_center:.515625,y_center:.640625},{w:1,h:1,x_center:.546875,y_center:.640625},{w:1,h:1,x_center:.546875,y_center:.640625},{w:1,h:1,x_center:.578125,y_center:.640625},{w:1,h:1,x_center:.578125,y_center:.640625},{w:1,h:1,x_center:.609375,y_center:.640625},{w:1,h:1,x_center:.609375,y_center:.640625},{w:1,h:1,x_center:.640625,y_center:.640625},{w:1,h:1,x_center:.640625,y_center:.640625},{w:1,h:1,x_center:.671875,y_center:.640625},{w:1,h:1,x_center:.671875,y_center:.640625},{w:1,h:1,x_center:.703125,y_center:.640625},{w:1,h:1,x_center:.703125,y_center:.640625},{w:1,h:1,x_center:.734375,y_center:.640625},{w:1,h:1,x_center:.734375,y_center:.640625},{w:1,h:1,x_center:.765625,y_center:.640625},{w:1,h:1,x_center:.765625,y_center:.640625},{w:1,h:1,x_center:.796875,y_center:.640625},{w:1,h:1,x_center:.796875,y_center:.640625},{w:1,h:1,x_center:.828125,y_center:.640625},{w:1,h:1,x_center:.828125,y_center:.640625},{w:1,h:1,x_center:.859375,y_center:.640625},{w:1,h:1,x_center:.859375,y_center:.640625},{w:1,h:1,x_center:.890625,y_center:.640625},{w:1,h:1,x_center:.890625,y_center:.640625},{w:1,h:1,x_center:.921875,y_center:.640625},{w:1,h:1,x_center:.921875,y_center:.640625},{w:1,h:1,x_center:.953125,y_center:.640625},{w:1,h:1,x_center:.953125,y_center:.640625},{w:1,h:1,x_center:.984375,y_center:.640625},{w:1,h:1,x_center:.984375,y_center:.640625},{w:1,h:1,x_center:.015625,y_center:.671875},{w:1,h:1,x_center:.015625,y_center:.671875},{w:1,h:1,x_center:.046875,y_center:.671875},{w:1,h:1,x_center:.046875,y_center:.671875},{w:1,h:1,x_center:.078125,y_center:.671875},{w:1,h:1,x_center:.078125,y_center:.671875},{w:1,h:1,x_center:.109375,y_center:.671875},{w:1,h:1,x_center:.109375,y_center:.671875},{w:1,h:1,x_center:.140625,y_center:.671875},{w:1,h:1,x_center:.140625,y_center:.671875},{w:1,h:1,x_center:.171875,y_center:.671875},{w:1,h:1,x_center:.171875,y_center:.671875},{w:1,h:1,x_center:.203125,y_center:.671875},{w:1,h:1,x_center:.203125,y_center:.671875},{w:1,h:1,x_center:.234375,y_center:.671875},{w:1,h:1,x_center:.234375,y_center:.671875},{w:1,h:1,x_center:.265625,y_center:.671875},{w:1,h:1,x_center:.265625,y_center:.671875},{w:1,h:1,x_center:.296875,y_center:.671875},{w:1,h:1,x_center:.296875,y_center:.671875},{w:1,h:1,x_center:.328125,y_center:.671875},{w:1,h:1,x_center:.328125,y_center:.671875},{w:1,h:1,x_center:.359375,y_center:.671875},{w:1,h:1,x_center:.359375,y_center:.671875},{w:1,h:1,x_center:.390625,y_center:.671875},{w:1,h:1,x_center:.390625,y_center:.671875},{w:1,h:1,x_center:.421875,y_center:.671875},{w:1,h:1,x_center:.421875,y_center:.671875},{w:1,h:1,x_center:.453125,y_center:.671875},{w:1,h:1,x_center:.453125,y_center:.671875},{w:1,h:1,x_center:.484375,y_center:.671875},{w:1,h:1,x_center:.484375,y_center:.671875},{w:1,h:1,x_center:.515625,y_center:.671875},{w:1,h:1,x_center:.515625,y_center:.671875},{w:1,h:1,x_center:.546875,y_center:.671875},{w:1,h:1,x_center:.546875,y_center:.671875},{w:1,h:1,x_center:.578125,y_center:.671875},{w:1,h:1,x_center:.578125,y_center:.671875},{w:1,h:1,x_center:.609375,y_center:.671875},{w:1,h:1,x_center:.609375,y_center:.671875},{w:1,h:1,x_center:.640625,y_center:.671875},{w:1,h:1,x_center:.640625,y_center:.671875},{w:1,h:1,x_center:.671875,y_center:.671875},{w:1,h:1,x_center:.671875,y_center:.671875},{w:1,h:1,x_center:.703125,y_center:.671875},{w:1,h:1,x_center:.703125,y_center:.671875},{w:1,h:1,x_center:.734375,y_center:.671875},{w:1,h:1,x_center:.734375,y_center:.671875},{w:1,h:1,x_center:.765625,y_center:.671875},{w:1,h:1,x_center:.765625,y_center:.671875},{w:1,h:1,x_center:.796875,y_center:.671875},{w:1,h:1,x_center:.796875,y_center:.671875},{w:1,h:1,x_center:.828125,y_center:.671875},{w:1,h:1,x_center:.828125,y_center:.671875},{w:1,h:1,x_center:.859375,y_center:.671875},{w:1,h:1,x_center:.859375,y_center:.671875},{w:1,h:1,x_center:.890625,y_center:.671875},{w:1,h:1,x_center:.890625,y_center:.671875},{w:1,h:1,x_center:.921875,y_center:.671875},{w:1,h:1,x_center:.921875,y_center:.671875},{w:1,h:1,x_center:.953125,y_center:.671875},{w:1,h:1,x_center:.953125,y_center:.671875},{w:1,h:1,x_center:.984375,y_center:.671875},{w:1,h:1,x_center:.984375,y_center:.671875},{w:1,h:1,x_center:.015625,y_center:.703125},{w:1,h:1,x_center:.015625,y_center:.703125},{w:1,h:1,x_center:.046875,y_center:.703125},{w:1,h:1,x_center:.046875,y_center:.703125},{w:1,h:1,x_center:.078125,y_center:.703125},{w:1,h:1,x_center:.078125,y_center:.703125},{w:1,h:1,x_center:.109375,y_center:.703125},{w:1,h:1,x_center:.109375,y_center:.703125},{w:1,h:1,x_center:.140625,y_center:.703125},{w:1,h:1,x_center:.140625,y_center:.703125},{w:1,h:1,x_center:.171875,y_center:.703125},{w:1,h:1,x_center:.171875,y_center:.703125},{w:1,h:1,x_center:.203125,y_center:.703125},{w:1,h:1,x_center:.203125,y_center:.703125},{w:1,h:1,x_center:.234375,y_center:.703125},{w:1,h:1,x_center:.234375,y_center:.703125},{w:1,h:1,x_center:.265625,y_center:.703125},{w:1,h:1,x_center:.265625,y_center:.703125},{w:1,h:1,x_center:.296875,y_center:.703125},{w:1,h:1,x_center:.296875,y_center:.703125},{w:1,h:1,x_center:.328125,y_center:.703125},{w:1,h:1,x_center:.328125,y_center:.703125},{w:1,h:1,x_center:.359375,y_center:.703125},{w:1,h:1,x_center:.359375,y_center:.703125},{w:1,h:1,x_center:.390625,y_center:.703125},{w:1,h:1,x_center:.390625,y_center:.703125},{w:1,h:1,x_center:.421875,y_center:.703125},{w:1,h:1,x_center:.421875,y_center:.703125},{w:1,h:1,x_center:.453125,y_center:.703125},{w:1,h:1,x_center:.453125,y_center:.703125},{w:1,h:1,x_center:.484375,y_center:.703125},{w:1,h:1,x_center:.484375,y_center:.703125},{w:1,h:1,x_center:.515625,y_center:.703125},{w:1,h:1,x_center:.515625,y_center:.703125},{w:1,h:1,x_center:.546875,y_center:.703125},{w:1,h:1,x_center:.546875,y_center:.703125},{w:1,h:1,x_center:.578125,y_center:.703125},{w:1,h:1,x_center:.578125,y_center:.703125},{w:1,h:1,x_center:.609375,y_center:.703125},{w:1,h:1,x_center:.609375,y_center:.703125},{w:1,h:1,x_center:.640625,y_center:.703125},{w:1,h:1,x_center:.640625,y_center:.703125},{w:1,h:1,x_center:.671875,y_center:.703125},{w:1,h:1,x_center:.671875,y_center:.703125},{w:1,h:1,x_center:.703125,y_center:.703125},{w:1,h:1,x_center:.703125,y_center:.703125},{w:1,h:1,x_center:.734375,y_center:.703125},{w:1,h:1,x_center:.734375,y_center:.703125},{w:1,h:1,x_center:.765625,y_center:.703125},{w:1,h:1,x_center:.765625,y_center:.703125},{w:1,h:1,x_center:.796875,y_center:.703125},{w:1,h:1,x_center:.796875,y_center:.703125},{w:1,h:1,x_center:.828125,y_center:.703125},{w:1,h:1,x_center:.828125,y_center:.703125},{w:1,h:1,x_center:.859375,y_center:.703125},{w:1,h:1,x_center:.859375,y_center:.703125},{w:1,h:1,x_center:.890625,y_center:.703125},{w:1,h:1,x_center:.890625,y_center:.703125},{w:1,h:1,x_center:.921875,y_center:.703125},{w:1,h:1,x_center:.921875,y_center:.703125},{w:1,h:1,x_center:.953125,y_center:.703125},{w:1,h:1,x_center:.953125,y_center:.703125},{w:1,h:1,x_center:.984375,y_center:.703125},{w:1,h:1,x_center:.984375,y_center:.703125},{w:1,h:1,x_center:.015625,y_center:.734375},{w:1,h:1,x_center:.015625,y_center:.734375},{w:1,h:1,x_center:.046875,y_center:.734375},{w:1,h:1,x_center:.046875,y_center:.734375},{w:1,h:1,x_center:.078125,y_center:.734375},{w:1,h:1,x_center:.078125,y_center:.734375},{w:1,h:1,x_center:.109375,y_center:.734375},{w:1,h:1,x_center:.109375,y_center:.734375},{w:1,h:1,x_center:.140625,y_center:.734375},{w:1,h:1,x_center:.140625,y_center:.734375},{w:1,h:1,x_center:.171875,y_center:.734375},{w:1,h:1,x_center:.171875,y_center:.734375},{w:1,h:1,x_center:.203125,y_center:.734375},{w:1,h:1,x_center:.203125,y_center:.734375},{w:1,h:1,x_center:.234375,y_center:.734375},{w:1,h:1,x_center:.234375,y_center:.734375},{w:1,h:1,x_center:.265625,y_center:.734375},{w:1,h:1,x_center:.265625,y_center:.734375},{w:1,h:1,x_center:.296875,y_center:.734375},{w:1,h:1,x_center:.296875,y_center:.734375},{w:1,h:1,x_center:.328125,y_center:.734375},{w:1,h:1,x_center:.328125,y_center:.734375},{w:1,h:1,x_center:.359375,y_center:.734375},{w:1,h:1,x_center:.359375,y_center:.734375},{w:1,h:1,x_center:.390625,y_center:.734375},{w:1,h:1,x_center:.390625,y_center:.734375},{w:1,h:1,x_center:.421875,y_center:.734375},{w:1,h:1,x_center:.421875,y_center:.734375},{w:1,h:1,x_center:.453125,y_center:.734375},{w:1,h:1,x_center:.453125,y_center:.734375},{w:1,h:1,x_center:.484375,y_center:.734375},{w:1,h:1,x_center:.484375,y_center:.734375},{w:1,h:1,x_center:.515625,y_center:.734375},{w:1,h:1,x_center:.515625,y_center:.734375},{w:1,h:1,x_center:.546875,y_center:.734375},{w:1,h:1,x_center:.546875,y_center:.734375},{w:1,h:1,x_center:.578125,y_center:.734375},{w:1,h:1,x_center:.578125,y_center:.734375},{w:1,h:1,x_center:.609375,y_center:.734375},{w:1,h:1,x_center:.609375,y_center:.734375},{w:1,h:1,x_center:.640625,y_center:.734375},{w:1,h:1,x_center:.640625,y_center:.734375},{w:1,h:1,x_center:.671875,y_center:.734375},{w:1,h:1,x_center:.671875,y_center:.734375},{w:1,h:1,x_center:.703125,y_center:.734375},{w:1,h:1,x_center:.703125,y_center:.734375},{w:1,h:1,x_center:.734375,y_center:.734375},{w:1,h:1,x_center:.734375,y_center:.734375},{w:1,h:1,x_center:.765625,y_center:.734375},{w:1,h:1,x_center:.765625,y_center:.734375},{w:1,h:1,x_center:.796875,y_center:.734375},{w:1,h:1,x_center:.796875,y_center:.734375},{w:1,h:1,x_center:.828125,y_center:.734375},{w:1,h:1,x_center:.828125,y_center:.734375},{w:1,h:1,x_center:.859375,y_center:.734375},{w:1,h:1,x_center:.859375,y_center:.734375},{w:1,h:1,x_center:.890625,y_center:.734375},{w:1,h:1,x_center:.890625,y_center:.734375},{w:1,h:1,x_center:.921875,y_center:.734375},{w:1,h:1,x_center:.921875,y_center:.734375},{w:1,h:1,x_center:.953125,y_center:.734375},{w:1,h:1,x_center:.953125,y_center:.734375},{w:1,h:1,x_center:.984375,y_center:.734375},{w:1,h:1,x_center:.984375,y_center:.734375},{w:1,h:1,x_center:.015625,y_center:.765625},{w:1,h:1,x_center:.015625,y_center:.765625},{w:1,h:1,x_center:.046875,y_center:.765625},{w:1,h:1,x_center:.046875,y_center:.765625},{w:1,h:1,x_center:.078125,y_center:.765625},{w:1,h:1,x_center:.078125,y_center:.765625},{w:1,h:1,x_center:.109375,y_center:.765625},{w:1,h:1,x_center:.109375,y_center:.765625},{w:1,h:1,x_center:.140625,y_center:.765625},{w:1,h:1,x_center:.140625,y_center:.765625},{w:1,h:1,x_center:.171875,y_center:.765625},{w:1,h:1,x_center:.171875,y_center:.765625},{w:1,h:1,x_center:.203125,y_center:.765625},{w:1,h:1,x_center:.203125,y_center:.765625},{w:1,h:1,x_center:.234375,y_center:.765625},{w:1,h:1,x_center:.234375,y_center:.765625},{w:1,h:1,x_center:.265625,y_center:.765625},{w:1,h:1,x_center:.265625,y_center:.765625},{w:1,h:1,x_center:.296875,y_center:.765625},{w:1,h:1,x_center:.296875,y_center:.765625},{w:1,h:1,x_center:.328125,y_center:.765625},{w:1,h:1,x_center:.328125,y_center:.765625},{w:1,h:1,x_center:.359375,y_center:.765625},{w:1,h:1,x_center:.359375,y_center:.765625},{w:1,h:1,x_center:.390625,y_center:.765625},{w:1,h:1,x_center:.390625,y_center:.765625},{w:1,h:1,x_center:.421875,y_center:.765625},{w:1,h:1,x_center:.421875,y_center:.765625},{w:1,h:1,x_center:.453125,y_center:.765625},{w:1,h:1,x_center:.453125,y_center:.765625},{w:1,h:1,x_center:.484375,y_center:.765625},{w:1,h:1,x_center:.484375,y_center:.765625},{w:1,h:1,x_center:.515625,y_center:.765625},{w:1,h:1,x_center:.515625,y_center:.765625},{w:1,h:1,x_center:.546875,y_center:.765625},{w:1,h:1,x_center:.546875,y_center:.765625},{w:1,h:1,x_center:.578125,y_center:.765625},{w:1,h:1,x_center:.578125,y_center:.765625},{w:1,h:1,x_center:.609375,y_center:.765625},{w:1,h:1,x_center:.609375,y_center:.765625},{w:1,h:1,x_center:.640625,y_center:.765625},{w:1,h:1,x_center:.640625,y_center:.765625},{w:1,h:1,x_center:.671875,y_center:.765625},{w:1,h:1,x_center:.671875,y_center:.765625},{w:1,h:1,x_center:.703125,y_center:.765625},{w:1,h:1,x_center:.703125,y_center:.765625},{w:1,h:1,x_center:.734375,y_center:.765625},{w:1,h:1,x_center:.734375,y_center:.765625},{w:1,h:1,x_center:.765625,y_center:.765625},{w:1,h:1,x_center:.765625,y_center:.765625},{w:1,h:1,x_center:.796875,y_center:.765625},{w:1,h:1,x_center:.796875,y_center:.765625},{w:1,h:1,x_center:.828125,y_center:.765625},{w:1,h:1,x_center:.828125,y_center:.765625},{w:1,h:1,x_center:.859375,y_center:.765625},{w:1,h:1,x_center:.859375,y_center:.765625},{w:1,h:1,x_center:.890625,y_center:.765625},{w:1,h:1,x_center:.890625,y_center:.765625},{w:1,h:1,x_center:.921875,y_center:.765625},{w:1,h:1,x_center:.921875,y_center:.765625},{w:1,h:1,x_center:.953125,y_center:.765625},{w:1,h:1,x_center:.953125,y_center:.765625},{w:1,h:1,x_center:.984375,y_center:.765625},{w:1,h:1,x_center:.984375,y_center:.765625},{w:1,h:1,x_center:.015625,y_center:.796875},{w:1,h:1,x_center:.015625,y_center:.796875},{w:1,h:1,x_center:.046875,y_center:.796875},{w:1,h:1,x_center:.046875,y_center:.796875},{w:1,h:1,x_center:.078125,y_center:.796875},{w:1,h:1,x_center:.078125,y_center:.796875},{w:1,h:1,x_center:.109375,y_center:.796875},{w:1,h:1,x_center:.109375,y_center:.796875},{w:1,h:1,x_center:.140625,y_center:.796875},{w:1,h:1,x_center:.140625,y_center:.796875},{w:1,h:1,x_center:.171875,y_center:.796875},{w:1,h:1,x_center:.171875,y_center:.796875},{w:1,h:1,x_center:.203125,y_center:.796875},{w:1,h:1,x_center:.203125,y_center:.796875},{w:1,h:1,x_center:.234375,y_center:.796875},{w:1,h:1,x_center:.234375,y_center:.796875},{w:1,h:1,x_center:.265625,y_center:.796875},{w:1,h:1,x_center:.265625,y_center:.796875},{w:1,h:1,x_center:.296875,y_center:.796875},{w:1,h:1,x_center:.296875,y_center:.796875},{w:1,h:1,x_center:.328125,y_center:.796875},{w:1,h:1,x_center:.328125,y_center:.796875},{w:1,h:1,x_center:.359375,y_center:.796875},{w:1,h:1,x_center:.359375,y_center:.796875},{w:1,h:1,x_center:.390625,y_center:.796875},{w:1,h:1,x_center:.390625,y_center:.796875},{w:1,h:1,x_center:.421875,y_center:.796875},{w:1,h:1,x_center:.421875,y_center:.796875},{w:1,h:1,x_center:.453125,y_center:.796875},{w:1,h:1,x_center:.453125,y_center:.796875},{w:1,h:1,x_center:.484375,y_center:.796875},{w:1,h:1,x_center:.484375,y_center:.796875},{w:1,h:1,x_center:.515625,y_center:.796875},{w:1,h:1,x_center:.515625,y_center:.796875},{w:1,h:1,x_center:.546875,y_center:.796875},{w:1,h:1,x_center:.546875,y_center:.796875},{w:1,h:1,x_center:.578125,y_center:.796875},{w:1,h:1,x_center:.578125,y_center:.796875},{w:1,h:1,x_center:.609375,y_center:.796875},{w:1,h:1,x_center:.609375,y_center:.796875},{w:1,h:1,x_center:.640625,y_center:.796875},{w:1,h:1,x_center:.640625,y_center:.796875},{w:1,h:1,x_center:.671875,y_center:.796875},{w:1,h:1,x_center:.671875,y_center:.796875},{w:1,h:1,x_center:.703125,y_center:.796875},{w:1,h:1,x_center:.703125,y_center:.796875},{w:1,h:1,x_center:.734375,y_center:.796875},{w:1,h:1,x_center:.734375,y_center:.796875},{w:1,h:1,x_center:.765625,y_center:.796875},{w:1,h:1,x_center:.765625,y_center:.796875},{w:1,h:1,x_center:.796875,y_center:.796875},{w:1,h:1,x_center:.796875,y_center:.796875},{w:1,h:1,x_center:.828125,y_center:.796875},{w:1,h:1,x_center:.828125,y_center:.796875},{w:1,h:1,x_center:.859375,y_center:.796875},{w:1,h:1,x_center:.859375,y_center:.796875},{w:1,h:1,x_center:.890625,y_center:.796875},{w:1,h:1,x_center:.890625,y_center:.796875},{w:1,h:1,x_center:.921875,y_center:.796875},{w:1,h:1,x_center:.921875,y_center:.796875},{w:1,h:1,x_center:.953125,y_center:.796875},{w:1,h:1,x_center:.953125,y_center:.796875},{w:1,h:1,x_center:.984375,y_center:.796875},{w:1,h:1,x_center:.984375,y_center:.796875},{w:1,h:1,x_center:.015625,y_center:.828125},{w:1,h:1,x_center:.015625,y_center:.828125},{w:1,h:1,x_center:.046875,y_center:.828125},{w:1,h:1,x_center:.046875,y_center:.828125},{w:1,h:1,x_center:.078125,y_center:.828125},{w:1,h:1,x_center:.078125,y_center:.828125},{w:1,h:1,x_center:.109375,y_center:.828125},{w:1,h:1,x_center:.109375,y_center:.828125},{w:1,h:1,x_center:.140625,y_center:.828125},{w:1,h:1,x_center:.140625,y_center:.828125},{w:1,h:1,x_center:.171875,y_center:.828125},{w:1,h:1,x_center:.171875,y_center:.828125},{w:1,h:1,x_center:.203125,y_center:.828125},{w:1,h:1,x_center:.203125,y_center:.828125},{w:1,h:1,x_center:.234375,y_center:.828125},{w:1,h:1,x_center:.234375,y_center:.828125},{w:1,h:1,x_center:.265625,y_center:.828125},{w:1,h:1,x_center:.265625,y_center:.828125},{w:1,h:1,x_center:.296875,y_center:.828125},{w:1,h:1,x_center:.296875,y_center:.828125},{w:1,h:1,x_center:.328125,y_center:.828125},{w:1,h:1,x_center:.328125,y_center:.828125},{w:1,h:1,x_center:.359375,y_center:.828125},{w:1,h:1,x_center:.359375,y_center:.828125},{w:1,h:1,x_center:.390625,y_center:.828125},{w:1,h:1,x_center:.390625,y_center:.828125},{w:1,h:1,x_center:.421875,y_center:.828125},{w:1,h:1,x_center:.421875,y_center:.828125},{w:1,h:1,x_center:.453125,y_center:.828125},{w:1,h:1,x_center:.453125,y_center:.828125},{w:1,h:1,x_center:.484375,y_center:.828125},{w:1,h:1,x_center:.484375,y_center:.828125},{w:1,h:1,x_center:.515625,y_center:.828125},{w:1,h:1,x_center:.515625,y_center:.828125},{w:1,h:1,x_center:.546875,y_center:.828125},{w:1,h:1,x_center:.546875,y_center:.828125},{w:1,h:1,x_center:.578125,y_center:.828125},{w:1,h:1,x_center:.578125,y_center:.828125},{w:1,h:1,x_center:.609375,y_center:.828125},{w:1,h:1,x_center:.609375,y_center:.828125},{w:1,h:1,x_center:.640625,y_center:.828125},{w:1,h:1,x_center:.640625,y_center:.828125},{w:1,h:1,x_center:.671875,y_center:.828125},{w:1,h:1,x_center:.671875,y_center:.828125},{w:1,h:1,x_center:.703125,y_center:.828125},{w:1,h:1,x_center:.703125,y_center:.828125},{w:1,h:1,x_center:.734375,y_center:.828125},{w:1,h:1,x_center:.734375,y_center:.828125},{w:1,h:1,x_center:.765625,y_center:.828125},{w:1,h:1,x_center:.765625,y_center:.828125},{w:1,h:1,x_center:.796875,y_center:.828125},{w:1,h:1,x_center:.796875,y_center:.828125},{w:1,h:1,x_center:.828125,y_center:.828125},{w:1,h:1,x_center:.828125,y_center:.828125},{w:1,h:1,x_center:.859375,y_center:.828125},{w:1,h:1,x_center:.859375,y_center:.828125},{w:1,h:1,x_center:.890625,y_center:.828125},{w:1,h:1,x_center:.890625,y_center:.828125},{w:1,h:1,x_center:.921875,y_center:.828125},{w:1,h:1,x_center:.921875,y_center:.828125},{w:1,h:1,x_center:.953125,y_center:.828125},{w:1,h:1,x_center:.953125,y_center:.828125},{w:1,h:1,x_center:.984375,y_center:.828125},{w:1,h:1,x_center:.984375,y_center:.828125},{w:1,h:1,x_center:.015625,y_center:.859375},{w:1,h:1,x_center:.015625,y_center:.859375},{w:1,h:1,x_center:.046875,y_center:.859375},{w:1,h:1,x_center:.046875,y_center:.859375},{w:1,h:1,x_center:.078125,y_center:.859375},{w:1,h:1,x_center:.078125,y_center:.859375},{w:1,h:1,x_center:.109375,y_center:.859375},{w:1,h:1,x_center:.109375,y_center:.859375},{w:1,h:1,x_center:.140625,y_center:.859375},{w:1,h:1,x_center:.140625,y_center:.859375},{w:1,h:1,x_center:.171875,y_center:.859375},{w:1,h:1,x_center:.171875,y_center:.859375},{w:1,h:1,x_center:.203125,y_center:.859375},{w:1,h:1,x_center:.203125,y_center:.859375},{w:1,h:1,x_center:.234375,y_center:.859375},{w:1,h:1,x_center:.234375,y_center:.859375},{w:1,h:1,x_center:.265625,y_center:.859375},{w:1,h:1,x_center:.265625,y_center:.859375},{w:1,h:1,x_center:.296875,y_center:.859375},{w:1,h:1,x_center:.296875,y_center:.859375},{w:1,h:1,x_center:.328125,y_center:.859375},{w:1,h:1,x_center:.328125,y_center:.859375},{w:1,h:1,x_center:.359375,y_center:.859375},{w:1,h:1,x_center:.359375,y_center:.859375},{w:1,h:1,x_center:.390625,y_center:.859375},{w:1,h:1,x_center:.390625,y_center:.859375},{w:1,h:1,x_center:.421875,y_center:.859375},{w:1,h:1,x_center:.421875,y_center:.859375},{w:1,h:1,x_center:.453125,y_center:.859375},{w:1,h:1,x_center:.453125,y_center:.859375},{w:1,h:1,x_center:.484375,y_center:.859375},{w:1,h:1,x_center:.484375,y_center:.859375},{w:1,h:1,x_center:.515625,y_center:.859375},{w:1,h:1,x_center:.515625,y_center:.859375},{w:1,h:1,x_center:.546875,y_center:.859375},{w:1,h:1,x_center:.546875,y_center:.859375},{w:1,h:1,x_center:.578125,y_center:.859375},{w:1,h:1,x_center:.578125,y_center:.859375},{w:1,h:1,x_center:.609375,y_center:.859375},{w:1,h:1,x_center:.609375,y_center:.859375},{w:1,h:1,x_center:.640625,y_center:.859375},{w:1,h:1,x_center:.640625,y_center:.859375},{w:1,h:1,x_center:.671875,y_center:.859375},{w:1,h:1,x_center:.671875,y_center:.859375},{w:1,h:1,x_center:.703125,y_center:.859375},{w:1,h:1,x_center:.703125,y_center:.859375},{w:1,h:1,x_center:.734375,y_center:.859375},{w:1,h:1,x_center:.734375,y_center:.859375},{w:1,h:1,x_center:.765625,y_center:.859375},{w:1,h:1,x_center:.765625,y_center:.859375},{w:1,h:1,x_center:.796875,y_center:.859375},{w:1,h:1,x_center:.796875,y_center:.859375},{w:1,h:1,x_center:.828125,y_center:.859375},{w:1,h:1,x_center:.828125,y_center:.859375},{w:1,h:1,x_center:.859375,y_center:.859375},{w:1,h:1,x_center:.859375,y_center:.859375},{w:1,h:1,x_center:.890625,y_center:.859375},{w:1,h:1,x_center:.890625,y_center:.859375},{w:1,h:1,x_center:.921875,y_center:.859375},{w:1,h:1,x_center:.921875,y_center:.859375},{w:1,h:1,x_center:.953125,y_center:.859375},{w:1,h:1,x_center:.953125,y_center:.859375},{w:1,h:1,x_center:.984375,y_center:.859375},{w:1,h:1,x_center:.984375,y_center:.859375},{w:1,h:1,x_center:.015625,y_center:.890625},{w:1,h:1,x_center:.015625,y_center:.890625},{w:1,h:1,x_center:.046875,y_center:.890625},{w:1,h:1,x_center:.046875,y_center:.890625},{w:1,h:1,x_center:.078125,y_center:.890625},{w:1,h:1,x_center:.078125,y_center:.890625},{w:1,h:1,x_center:.109375,y_center:.890625},{w:1,h:1,x_center:.109375,y_center:.890625},{w:1,h:1,x_center:.140625,y_center:.890625},{w:1,h:1,x_center:.140625,y_center:.890625},{w:1,h:1,x_center:.171875,y_center:.890625},{w:1,h:1,x_center:.171875,y_center:.890625},{w:1,h:1,x_center:.203125,y_center:.890625},{w:1,h:1,x_center:.203125,y_center:.890625},{w:1,h:1,x_center:.234375,y_center:.890625},{w:1,h:1,x_center:.234375,y_center:.890625},{w:1,h:1,x_center:.265625,y_center:.890625},{w:1,h:1,x_center:.265625,y_center:.890625},{w:1,h:1,x_center:.296875,y_center:.890625},{w:1,h:1,x_center:.296875,y_center:.890625},{w:1,h:1,x_center:.328125,y_center:.890625},{w:1,h:1,x_center:.328125,y_center:.890625},{w:1,h:1,x_center:.359375,y_center:.890625},{w:1,h:1,x_center:.359375,y_center:.890625},{w:1,h:1,x_center:.390625,y_center:.890625},{w:1,h:1,x_center:.390625,y_center:.890625},{w:1,h:1,x_center:.421875,y_center:.890625},{w:1,h:1,x_center:.421875,y_center:.890625},{w:1,h:1,x_center:.453125,y_center:.890625},{w:1,h:1,x_center:.453125,y_center:.890625},{w:1,h:1,x_center:.484375,y_center:.890625},{w:1,h:1,x_center:.484375,y_center:.890625},{w:1,h:1,x_center:.515625,y_center:.890625},{w:1,h:1,x_center:.515625,y_center:.890625},{w:1,h:1,x_center:.546875,y_center:.890625},{w:1,h:1,x_center:.546875,y_center:.890625},{w:1,h:1,x_center:.578125,y_center:.890625},{w:1,h:1,x_center:.578125,y_center:.890625},{w:1,h:1,x_center:.609375,y_center:.890625},{w:1,h:1,x_center:.609375,y_center:.890625},{w:1,h:1,x_center:.640625,y_center:.890625},{w:1,h:1,x_center:.640625,y_center:.890625},{w:1,h:1,x_center:.671875,y_center:.890625},{w:1,h:1,x_center:.671875,y_center:.890625},{w:1,h:1,x_center:.703125,y_center:.890625},{w:1,h:1,x_center:.703125,y_center:.890625},{w:1,h:1,x_center:.734375,y_center:.890625},{w:1,h:1,x_center:.734375,y_center:.890625},{w:1,h:1,x_center:.765625,y_center:.890625},{w:1,h:1,x_center:.765625,y_center:.890625},{w:1,h:1,x_center:.796875,y_center:.890625},{w:1,h:1,x_center:.796875,y_center:.890625},{w:1,h:1,x_center:.828125,y_center:.890625},{w:1,h:1,x_center:.828125,y_center:.890625},{w:1,h:1,x_center:.859375,y_center:.890625},{w:1,h:1,x_center:.859375,y_center:.890625},{w:1,h:1,x_center:.890625,y_center:.890625},{w:1,h:1,x_center:.890625,y_center:.890625},{w:1,h:1,x_center:.921875,y_center:.890625},{w:1,h:1,x_center:.921875,y_center:.890625},{w:1,h:1,x_center:.953125,y_center:.890625},{w:1,h:1,x_center:.953125,y_center:.890625},{w:1,h:1,x_center:.984375,y_center:.890625},{w:1,h:1,x_center:.984375,y_center:.890625},{w:1,h:1,x_center:.015625,y_center:.921875},{w:1,h:1,x_center:.015625,y_center:.921875},{w:1,h:1,x_center:.046875,y_center:.921875},{w:1,h:1,x_center:.046875,y_center:.921875},{w:1,h:1,x_center:.078125,y_center:.921875},{w:1,h:1,x_center:.078125,y_center:.921875},{w:1,h:1,x_center:.109375,y_center:.921875},{w:1,h:1,x_center:.109375,y_center:.921875},{w:1,h:1,x_center:.140625,y_center:.921875},{w:1,h:1,x_center:.140625,y_center:.921875},{w:1,h:1,x_center:.171875,y_center:.921875},{w:1,h:1,x_center:.171875,y_center:.921875},{w:1,h:1,x_center:.203125,y_center:.921875},{w:1,h:1,x_center:.203125,y_center:.921875},{w:1,h:1,x_center:.234375,y_center:.921875},{w:1,h:1,x_center:.234375,y_center:.921875},{w:1,h:1,x_center:.265625,y_center:.921875},{w:1,h:1,x_center:.265625,y_center:.921875},{w:1,h:1,x_center:.296875,y_center:.921875},{w:1,h:1,x_center:.296875,y_center:.921875},{w:1,h:1,x_center:.328125,y_center:.921875},{w:1,h:1,x_center:.328125,y_center:.921875},{w:1,h:1,x_center:.359375,y_center:.921875},{w:1,h:1,x_center:.359375,y_center:.921875},{w:1,h:1,x_center:.390625,y_center:.921875},{w:1,h:1,x_center:.390625,y_center:.921875},{w:1,h:1,x_center:.421875,y_center:.921875},{w:1,h:1,x_center:.421875,y_center:.921875},{w:1,h:1,x_center:.453125,y_center:.921875},{w:1,h:1,x_center:.453125,y_center:.921875},{w:1,h:1,x_center:.484375,y_center:.921875},{w:1,h:1,x_center:.484375,y_center:.921875},{w:1,h:1,x_center:.515625,y_center:.921875},{w:1,h:1,x_center:.515625,y_center:.921875},{w:1,h:1,x_center:.546875,y_center:.921875},{w:1,h:1,x_center:.546875,y_center:.921875},{w:1,h:1,x_center:.578125,y_center:.921875},{w:1,h:1,x_center:.578125,y_center:.921875},{w:1,h:1,x_center:.609375,y_center:.921875},{w:1,h:1,x_center:.609375,y_center:.921875},{w:1,h:1,x_center:.640625,y_center:.921875},{w:1,h:1,x_center:.640625,y_center:.921875},{w:1,h:1,x_center:.671875,y_center:.921875},{w:1,h:1,x_center:.671875,y_center:.921875},{w:1,h:1,x_center:.703125,y_center:.921875},{w:1,h:1,x_center:.703125,y_center:.921875},{w:1,h:1,x_center:.734375,y_center:.921875},{w:1,h:1,x_center:.734375,y_center:.921875},{w:1,h:1,x_center:.765625,y_center:.921875},{w:1,h:1,x_center:.765625,y_center:.921875},{w:1,h:1,x_center:.796875,y_center:.921875},{w:1,h:1,x_center:.796875,y_center:.921875},{w:1,h:1,x_center:.828125,y_center:.921875},{w:1,h:1,x_center:.828125,y_center:.921875},{w:1,h:1,x_center:.859375,y_center:.921875},{w:1,h:1,x_center:.859375,y_center:.921875},{w:1,h:1,x_center:.890625,y_center:.921875},{w:1,h:1,x_center:.890625,y_center:.921875},{w:1,h:1,x_center:.921875,y_center:.921875},{w:1,h:1,x_center:.921875,y_center:.921875},{w:1,h:1,x_center:.953125,y_center:.921875},{w:1,h:1,x_center:.953125,y_center:.921875},{w:1,h:1,x_center:.984375,y_center:.921875},{w:1,h:1,x_center:.984375,y_center:.921875},{w:1,h:1,x_center:.015625,y_center:.953125},{w:1,h:1,x_center:.015625,y_center:.953125},{w:1,h:1,x_center:.046875,y_center:.953125},{w:1,h:1,x_center:.046875,y_center:.953125},{w:1,h:1,x_center:.078125,y_center:.953125},{w:1,h:1,x_center:.078125,y_center:.953125},{w:1,h:1,x_center:.109375,y_center:.953125},{w:1,h:1,x_center:.109375,y_center:.953125},{w:1,h:1,x_center:.140625,y_center:.953125},{w:1,h:1,x_center:.140625,y_center:.953125},{w:1,h:1,x_center:.171875,y_center:.953125},{w:1,h:1,x_center:.171875,y_center:.953125},{w:1,h:1,x_center:.203125,y_center:.953125},{w:1,h:1,x_center:.203125,y_center:.953125},{w:1,h:1,x_center:.234375,y_center:.953125},{w:1,h:1,x_center:.234375,y_center:.953125},{w:1,h:1,x_center:.265625,y_center:.953125},{w:1,h:1,x_center:.265625,y_center:.953125},{w:1,h:1,x_center:.296875,y_center:.953125},{w:1,h:1,x_center:.296875,y_center:.953125},{w:1,h:1,x_center:.328125,y_center:.953125},{w:1,h:1,x_center:.328125,y_center:.953125},{w:1,h:1,x_center:.359375,y_center:.953125},{w:1,h:1,x_center:.359375,y_center:.953125},{w:1,h:1,x_center:.390625,y_center:.953125},{w:1,h:1,x_center:.390625,y_center:.953125},{w:1,h:1,x_center:.421875,y_center:.953125},{w:1,h:1,x_center:.421875,y_center:.953125},{w:1,h:1,x_center:.453125,y_center:.953125},{w:1,h:1,x_center:.453125,y_center:.953125},{w:1,h:1,x_center:.484375,y_center:.953125},{w:1,h:1,x_center:.484375,y_center:.953125},{w:1,h:1,x_center:.515625,y_center:.953125},{w:1,h:1,x_center:.515625,y_center:.953125},{w:1,h:1,x_center:.546875,y_center:.953125},{w:1,h:1,x_center:.546875,y_center:.953125},{w:1,h:1,x_center:.578125,y_center:.953125},{w:1,h:1,x_center:.578125,y_center:.953125},{w:1,h:1,x_center:.609375,y_center:.953125},{w:1,h:1,x_center:.609375,y_center:.953125},{w:1,h:1,x_center:.640625,y_center:.953125},{w:1,h:1,x_center:.640625,y_center:.953125},{w:1,h:1,x_center:.671875,y_center:.953125},{w:1,h:1,x_center:.671875,y_center:.953125},{w:1,h:1,x_center:.703125,y_center:.953125},{w:1,h:1,x_center:.703125,y_center:.953125},{w:1,h:1,x_center:.734375,y_center:.953125},{w:1,h:1,x_center:.734375,y_center:.953125},{w:1,h:1,x_center:.765625,y_center:.953125},{w:1,h:1,x_center:.765625,y_center:.953125},{w:1,h:1,x_center:.796875,y_center:.953125},{w:1,h:1,x_center:.796875,y_center:.953125},{w:1,h:1,x_center:.828125,y_center:.953125},{w:1,h:1,x_center:.828125,y_center:.953125},{w:1,h:1,x_center:.859375,y_center:.953125},{w:1,h:1,x_center:.859375,y_center:.953125},{w:1,h:1,x_center:.890625,y_center:.953125},{w:1,h:1,x_center:.890625,y_center:.953125},{w:1,h:1,x_center:.921875,y_center:.953125},{w:1,h:1,x_center:.921875,y_center:.953125},{w:1,h:1,x_center:.953125,y_center:.953125},{w:1,h:1,x_center:.953125,y_center:.953125},{w:1,h:1,x_center:.984375,y_center:.953125},{w:1,h:1,x_center:.984375,y_center:.953125},{w:1,h:1,x_center:.015625,y_center:.984375},{w:1,h:1,x_center:.015625,y_center:.984375},{w:1,h:1,x_center:.046875,y_center:.984375},{w:1,h:1,x_center:.046875,y_center:.984375},{w:1,h:1,x_center:.078125,y_center:.984375},{w:1,h:1,x_center:.078125,y_center:.984375},{w:1,h:1,x_center:.109375,y_center:.984375},{w:1,h:1,x_center:.109375,y_center:.984375},{w:1,h:1,x_center:.140625,y_center:.984375},{w:1,h:1,x_center:.140625,y_center:.984375},{w:1,h:1,x_center:.171875,y_center:.984375},{w:1,h:1,x_center:.171875,y_center:.984375},{w:1,h:1,x_center:.203125,y_center:.984375},{w:1,h:1,x_center:.203125,y_center:.984375},{w:1,h:1,x_center:.234375,y_center:.984375},{w:1,h:1,x_center:.234375,y_center:.984375},{w:1,h:1,x_center:.265625,y_center:.984375},{w:1,h:1,x_center:.265625,y_center:.984375},{w:1,h:1,x_center:.296875,y_center:.984375},{w:1,h:1,x_center:.296875,y_center:.984375},{w:1,h:1,x_center:.328125,y_center:.984375},{w:1,h:1,x_center:.328125,y_center:.984375},{w:1,h:1,x_center:.359375,y_center:.984375},{w:1,h:1,x_center:.359375,y_center:.984375},{w:1,h:1,x_center:.390625,y_center:.984375},{w:1,h:1,x_center:.390625,y_center:.984375},{w:1,h:1,x_center:.421875,y_center:.984375},{w:1,h:1,x_center:.421875,y_center:.984375},{w:1,h:1,x_center:.453125,y_center:.984375},{w:1,h:1,x_center:.453125,y_center:.984375},{w:1,h:1,x_center:.484375,y_center:.984375},{w:1,h:1,x_center:.484375,y_center:.984375},{w:1,h:1,x_center:.515625,y_center:.984375},{w:1,h:1,x_center:.515625,y_center:.984375},{w:1,h:1,x_center:.546875,y_center:.984375},{w:1,h:1,x_center:.546875,y_center:.984375},{w:1,h:1,x_center:.578125,y_center:.984375},{w:1,h:1,x_center:.578125,y_center:.984375},{w:1,h:1,x_center:.609375,y_center:.984375},{w:1,h:1,x_center:.609375,y_center:.984375},{w:1,h:1,x_center:.640625,y_center:.984375},{w:1,h:1,x_center:.640625,y_center:.984375},{w:1,h:1,x_center:.671875,y_center:.984375},{w:1,h:1,x_center:.671875,y_center:.984375},{w:1,h:1,x_center:.703125,y_center:.984375},{w:1,h:1,x_center:.703125,y_center:.984375},{w:1,h:1,x_center:.734375,y_center:.984375},{w:1,h:1,x_center:.734375,y_center:.984375},{w:1,h:1,x_center:.765625,y_center:.984375},{w:1,h:1,x_center:.765625,y_center:.984375},{w:1,h:1,x_center:.796875,y_center:.984375},{w:1,h:1,x_center:.796875,y_center:.984375},{w:1,h:1,x_center:.828125,y_center:.984375},{w:1,h:1,x_center:.828125,y_center:.984375},{w:1,h:1,x_center:.859375,y_center:.984375},{w:1,h:1,x_center:.859375,y_center:.984375},{w:1,h:1,x_center:.890625,y_center:.984375},{w:1,h:1,x_center:.890625,y_center:.984375},{w:1,h:1,x_center:.921875,y_center:.984375},{w:1,h:1,x_center:.921875,y_center:.984375},{w:1,h:1,x_center:.953125,y_center:.984375},{w:1,h:1,x_center:.953125,y_center:.984375},{w:1,h:1,x_center:.984375,y_center:.984375},{w:1,h:1,x_center:.984375,y_center:.984375},{w:1,h:1,x_center:.03125,y_center:.03125},{w:1,h:1,x_center:.03125,y_center:.03125},{w:1,h:1,x_center:.09375,y_center:.03125},{w:1,h:1,x_center:.09375,y_center:.03125},{w:1,h:1,x_center:.15625,y_center:.03125},{w:1,h:1,x_center:.15625,y_center:.03125},{w:1,h:1,x_center:.21875,y_center:.03125},{w:1,h:1,x_center:.21875,y_center:.03125},{w:1,h:1,x_center:.28125,y_center:.03125},{w:1,h:1,x_center:.28125,y_center:.03125},{w:1,h:1,x_center:.34375,y_center:.03125},{w:1,h:1,x_center:.34375,y_center:.03125},{w:1,h:1,x_center:.40625,y_center:.03125},{w:1,h:1,x_center:.40625,y_center:.03125},{w:1,h:1,x_center:.46875,y_center:.03125},{w:1,h:1,x_center:.46875,y_center:.03125},{w:1,h:1,x_center:.53125,y_center:.03125},{w:1,h:1,x_center:.53125,y_center:.03125},{w:1,h:1,x_center:.59375,y_center:.03125},{w:1,h:1,x_center:.59375,y_center:.03125},{w:1,h:1,x_center:.65625,y_center:.03125},{w:1,h:1,x_center:.65625,y_center:.03125},{w:1,h:1,x_center:.71875,y_center:.03125},{w:1,h:1,x_center:.71875,y_center:.03125},{w:1,h:1,x_center:.78125,y_center:.03125},{w:1,h:1,x_center:.78125,y_center:.03125},{w:1,h:1,x_center:.84375,y_center:.03125},{w:1,h:1,x_center:.84375,y_center:.03125},{w:1,h:1,x_center:.90625,y_center:.03125},{w:1,h:1,x_center:.90625,y_center:.03125},{w:1,h:1,x_center:.96875,y_center:.03125},{w:1,h:1,x_center:.96875,y_center:.03125},{w:1,h:1,x_center:.03125,y_center:.09375},{w:1,h:1,x_center:.03125,y_center:.09375},{w:1,h:1,x_center:.09375,y_center:.09375},{w:1,h:1,x_center:.09375,y_center:.09375},{w:1,h:1,x_center:.15625,y_center:.09375},{w:1,h:1,x_center:.15625,y_center:.09375},{w:1,h:1,x_center:.21875,y_center:.09375},{w:1,h:1,x_center:.21875,y_center:.09375},{w:1,h:1,x_center:.28125,y_center:.09375},{w:1,h:1,x_center:.28125,y_center:.09375},{w:1,h:1,x_center:.34375,y_center:.09375},{w:1,h:1,x_center:.34375,y_center:.09375},{w:1,h:1,x_center:.40625,y_center:.09375},{w:1,h:1,x_center:.40625,y_center:.09375},{w:1,h:1,x_center:.46875,y_center:.09375},{w:1,h:1,x_center:.46875,y_center:.09375},{w:1,h:1,x_center:.53125,y_center:.09375},{w:1,h:1,x_center:.53125,y_center:.09375},{w:1,h:1,x_center:.59375,y_center:.09375},{w:1,h:1,x_center:.59375,y_center:.09375},{w:1,h:1,x_center:.65625,y_center:.09375},{w:1,h:1,x_center:.65625,y_center:.09375},{w:1,h:1,x_center:.71875,y_center:.09375},{w:1,h:1,x_center:.71875,y_center:.09375},{w:1,h:1,x_center:.78125,y_center:.09375},{w:1,h:1,x_center:.78125,y_center:.09375},{w:1,h:1,x_center:.84375,y_center:.09375},{w:1,h:1,x_center:.84375,y_center:.09375},{w:1,h:1,x_center:.90625,y_center:.09375},{w:1,h:1,x_center:.90625,y_center:.09375},{w:1,h:1,x_center:.96875,y_center:.09375},{w:1,h:1,x_center:.96875,y_center:.09375},{w:1,h:1,x_center:.03125,y_center:.15625},{w:1,h:1,x_center:.03125,y_center:.15625},{w:1,h:1,x_center:.09375,y_center:.15625},{w:1,h:1,x_center:.09375,y_center:.15625},{w:1,h:1,x_center:.15625,y_center:.15625},{w:1,h:1,x_center:.15625,y_center:.15625},{w:1,h:1,x_center:.21875,y_center:.15625},{w:1,h:1,x_center:.21875,y_center:.15625},{w:1,h:1,x_center:.28125,y_center:.15625},{w:1,h:1,x_center:.28125,y_center:.15625},{w:1,h:1,x_center:.34375,y_center:.15625},{w:1,h:1,x_center:.34375,y_center:.15625},{w:1,h:1,x_center:.40625,y_center:.15625},{w:1,h:1,x_center:.40625,y_center:.15625},{w:1,h:1,x_center:.46875,y_center:.15625},{w:1,h:1,x_center:.46875,y_center:.15625},{w:1,h:1,x_center:.53125,y_center:.15625},{w:1,h:1,x_center:.53125,y_center:.15625},{w:1,h:1,x_center:.59375,y_center:.15625},{w:1,h:1,x_center:.59375,y_center:.15625},{w:1,h:1,x_center:.65625,y_center:.15625},{w:1,h:1,x_center:.65625,y_center:.15625},{w:1,h:1,x_center:.71875,y_center:.15625},{w:1,h:1,x_center:.71875,y_center:.15625},{w:1,h:1,x_center:.78125,y_center:.15625},{w:1,h:1,x_center:.78125,y_center:.15625},{w:1,h:1,x_center:.84375,y_center:.15625},{w:1,h:1,x_center:.84375,y_center:.15625},{w:1,h:1,x_center:.90625,y_center:.15625},{w:1,h:1,x_center:.90625,y_center:.15625},{w:1,h:1,x_center:.96875,y_center:.15625},{w:1,h:1,x_center:.96875,y_center:.15625},{w:1,h:1,x_center:.03125,y_center:.21875},{w:1,h:1,x_center:.03125,y_center:.21875},{w:1,h:1,x_center:.09375,y_center:.21875},{w:1,h:1,x_center:.09375,y_center:.21875},{w:1,h:1,x_center:.15625,y_center:.21875},{w:1,h:1,x_center:.15625,y_center:.21875},{w:1,h:1,x_center:.21875,y_center:.21875},{w:1,h:1,x_center:.21875,y_center:.21875},{w:1,h:1,x_center:.28125,y_center:.21875},{w:1,h:1,x_center:.28125,y_center:.21875},{w:1,h:1,x_center:.34375,y_center:.21875},{w:1,h:1,x_center:.34375,y_center:.21875},{w:1,h:1,x_center:.40625,y_center:.21875},{w:1,h:1,x_center:.40625,y_center:.21875},{w:1,h:1,x_center:.46875,y_center:.21875},{w:1,h:1,x_center:.46875,y_center:.21875},{w:1,h:1,x_center:.53125,y_center:.21875},{w:1,h:1,x_center:.53125,y_center:.21875},{w:1,h:1,x_center:.59375,y_center:.21875},{w:1,h:1,x_center:.59375,y_center:.21875},{w:1,h:1,x_center:.65625,y_center:.21875},{w:1,h:1,x_center:.65625,y_center:.21875},{w:1,h:1,x_center:.71875,y_center:.21875},{w:1,h:1,x_center:.71875,y_center:.21875},{w:1,h:1,x_center:.78125,y_center:.21875},{w:1,h:1,x_center:.78125,y_center:.21875},{w:1,h:1,x_center:.84375,y_center:.21875},{w:1,h:1,x_center:.84375,y_center:.21875},{w:1,h:1,x_center:.90625,y_center:.21875},{w:1,h:1,x_center:.90625,y_center:.21875},{w:1,h:1,x_center:.96875,y_center:.21875},{w:1,h:1,x_center:.96875,y_center:.21875},{w:1,h:1,x_center:.03125,y_center:.28125},{w:1,h:1,x_center:.03125,y_center:.28125},{w:1,h:1,x_center:.09375,y_center:.28125},{w:1,h:1,x_center:.09375,y_center:.28125},{w:1,h:1,x_center:.15625,y_center:.28125},{w:1,h:1,x_center:.15625,y_center:.28125},{w:1,h:1,x_center:.21875,y_center:.28125},{w:1,h:1,x_center:.21875,y_center:.28125},{w:1,h:1,x_center:.28125,y_center:.28125},{w:1,h:1,x_center:.28125,y_center:.28125},{w:1,h:1,x_center:.34375,y_center:.28125},{w:1,h:1,x_center:.34375,y_center:.28125},{w:1,h:1,x_center:.40625,y_center:.28125},{w:1,h:1,x_center:.40625,y_center:.28125},{w:1,h:1,x_center:.46875,y_center:.28125},{w:1,h:1,x_center:.46875,y_center:.28125},{w:1,h:1,x_center:.53125,y_center:.28125},{w:1,h:1,x_center:.53125,y_center:.28125},{w:1,h:1,x_center:.59375,y_center:.28125},{w:1,h:1,x_center:.59375,y_center:.28125},{w:1,h:1,x_center:.65625,y_center:.28125},{w:1,h:1,x_center:.65625,y_center:.28125},{w:1,h:1,x_center:.71875,y_center:.28125},{w:1,h:1,x_center:.71875,y_center:.28125},{w:1,h:1,x_center:.78125,y_center:.28125},{w:1,h:1,x_center:.78125,y_center:.28125},{w:1,h:1,x_center:.84375,y_center:.28125},{w:1,h:1,x_center:.84375,y_center:.28125},{w:1,h:1,x_center:.90625,y_center:.28125},{w:1,h:1,x_center:.90625,y_center:.28125},{w:1,h:1,x_center:.96875,y_center:.28125},{w:1,h:1,x_center:.96875,y_center:.28125},{w:1,h:1,x_center:.03125,y_center:.34375},{w:1,h:1,x_center:.03125,y_center:.34375},{w:1,h:1,x_center:.09375,y_center:.34375},{w:1,h:1,x_center:.09375,y_center:.34375},{w:1,h:1,x_center:.15625,y_center:.34375},{w:1,h:1,x_center:.15625,y_center:.34375},{w:1,h:1,x_center:.21875,y_center:.34375},{w:1,h:1,x_center:.21875,y_center:.34375},{w:1,h:1,x_center:.28125,y_center:.34375},{w:1,h:1,x_center:.28125,y_center:.34375},{w:1,h:1,x_center:.34375,y_center:.34375},{w:1,h:1,x_center:.34375,y_center:.34375},{w:1,h:1,x_center:.40625,y_center:.34375},{w:1,h:1,x_center:.40625,y_center:.34375},{w:1,h:1,x_center:.46875,y_center:.34375},{w:1,h:1,x_center:.46875,y_center:.34375},{w:1,h:1,x_center:.53125,y_center:.34375},{w:1,h:1,x_center:.53125,y_center:.34375},{w:1,h:1,x_center:.59375,y_center:.34375},{w:1,h:1,x_center:.59375,y_center:.34375},{w:1,h:1,x_center:.65625,y_center:.34375},{w:1,h:1,x_center:.65625,y_center:.34375},{w:1,h:1,x_center:.71875,y_center:.34375},{w:1,h:1,x_center:.71875,y_center:.34375},{w:1,h:1,x_center:.78125,y_center:.34375},{w:1,h:1,x_center:.78125,y_center:.34375},{w:1,h:1,x_center:.84375,y_center:.34375},{w:1,h:1,x_center:.84375,y_center:.34375},{w:1,h:1,x_center:.90625,y_center:.34375},{w:1,h:1,x_center:.90625,y_center:.34375},{w:1,h:1,x_center:.96875,y_center:.34375},{w:1,h:1,x_center:.96875,y_center:.34375},{w:1,h:1,x_center:.03125,y_center:.40625},{w:1,h:1,x_center:.03125,y_center:.40625},{w:1,h:1,x_center:.09375,y_center:.40625},{w:1,h:1,x_center:.09375,y_center:.40625},{w:1,h:1,x_center:.15625,y_center:.40625},{w:1,h:1,x_center:.15625,y_center:.40625},{w:1,h:1,x_center:.21875,y_center:.40625},{w:1,h:1,x_center:.21875,y_center:.40625},{w:1,h:1,x_center:.28125,y_center:.40625},{w:1,h:1,x_center:.28125,y_center:.40625},{w:1,h:1,x_center:.34375,y_center:.40625},{w:1,h:1,x_center:.34375,y_center:.40625},{w:1,h:1,x_center:.40625,y_center:.40625},{w:1,h:1,x_center:.40625,y_center:.40625},{w:1,h:1,x_center:.46875,y_center:.40625},{w:1,h:1,x_center:.46875,y_center:.40625},{w:1,h:1,x_center:.53125,y_center:.40625},{w:1,h:1,x_center:.53125,y_center:.40625},{w:1,h:1,x_center:.59375,y_center:.40625},{w:1,h:1,x_center:.59375,y_center:.40625},{w:1,h:1,x_center:.65625,y_center:.40625},{w:1,h:1,x_center:.65625,y_center:.40625},{w:1,h:1,x_center:.71875,y_center:.40625},{w:1,h:1,x_center:.71875,y_center:.40625},{w:1,h:1,x_center:.78125,y_center:.40625},{w:1,h:1,x_center:.78125,y_center:.40625},{w:1,h:1,x_center:.84375,y_center:.40625},{w:1,h:1,x_center:.84375,y_center:.40625},{w:1,h:1,x_center:.90625,y_center:.40625},{w:1,h:1,x_center:.90625,y_center:.40625},{w:1,h:1,x_center:.96875,y_center:.40625},{w:1,h:1,x_center:.96875,y_center:.40625},{w:1,h:1,x_center:.03125,y_center:.46875},{w:1,h:1,x_center:.03125,y_center:.46875},{w:1,h:1,x_center:.09375,y_center:.46875},{w:1,h:1,x_center:.09375,y_center:.46875},{w:1,h:1,x_center:.15625,y_center:.46875},{w:1,h:1,x_center:.15625,y_center:.46875},{w:1,h:1,x_center:.21875,y_center:.46875},{w:1,h:1,x_center:.21875,y_center:.46875},{w:1,h:1,x_center:.28125,y_center:.46875},{w:1,h:1,x_center:.28125,y_center:.46875},{w:1,h:1,x_center:.34375,y_center:.46875},{w:1,h:1,x_center:.34375,y_center:.46875},{w:1,h:1,x_center:.40625,y_center:.46875},{w:1,h:1,x_center:.40625,y_center:.46875},{w:1,h:1,x_center:.46875,y_center:.46875},{w:1,h:1,x_center:.46875,y_center:.46875},{w:1,h:1,x_center:.53125,y_center:.46875},{w:1,h:1,x_center:.53125,y_center:.46875},{w:1,h:1,x_center:.59375,y_center:.46875},{w:1,h:1,x_center:.59375,y_center:.46875},{w:1,h:1,x_center:.65625,y_center:.46875},{w:1,h:1,x_center:.65625,y_center:.46875},{w:1,h:1,x_center:.71875,y_center:.46875},{w:1,h:1,x_center:.71875,y_center:.46875},{w:1,h:1,x_center:.78125,y_center:.46875},{w:1,h:1,x_center:.78125,y_center:.46875},{w:1,h:1,x_center:.84375,y_center:.46875},{w:1,h:1,x_center:.84375,y_center:.46875},{w:1,h:1,x_center:.90625,y_center:.46875},{w:1,h:1,x_center:.90625,y_center:.46875},{w:1,h:1,x_center:.96875,y_center:.46875},{w:1,h:1,x_center:.96875,y_center:.46875},{w:1,h:1,x_center:.03125,y_center:.53125},{w:1,h:1,x_center:.03125,y_center:.53125},{w:1,h:1,x_center:.09375,y_center:.53125},{w:1,h:1,x_center:.09375,y_center:.53125},{w:1,h:1,x_center:.15625,y_center:.53125},{w:1,h:1,x_center:.15625,y_center:.53125},{w:1,h:1,x_center:.21875,y_center:.53125},{w:1,h:1,x_center:.21875,y_center:.53125},{w:1,h:1,x_center:.28125,y_center:.53125},{w:1,h:1,x_center:.28125,y_center:.53125},{w:1,h:1,x_center:.34375,y_center:.53125},{w:1,h:1,x_center:.34375,y_center:.53125},{w:1,h:1,x_center:.40625,y_center:.53125},{w:1,h:1,x_center:.40625,y_center:.53125},{w:1,h:1,x_center:.46875,y_center:.53125},{w:1,h:1,x_center:.46875,y_center:.53125},{w:1,h:1,x_center:.53125,y_center:.53125},{w:1,h:1,x_center:.53125,y_center:.53125},{w:1,h:1,x_center:.59375,y_center:.53125},{w:1,h:1,x_center:.59375,y_center:.53125},{w:1,h:1,x_center:.65625,y_center:.53125},{w:1,h:1,x_center:.65625,y_center:.53125},{w:1,h:1,x_center:.71875,y_center:.53125},{w:1,h:1,x_center:.71875,y_center:.53125},{w:1,h:1,x_center:.78125,y_center:.53125},{w:1,h:1,x_center:.78125,y_center:.53125},{w:1,h:1,x_center:.84375,y_center:.53125},{w:1,h:1,x_center:.84375,y_center:.53125},{w:1,h:1,x_center:.90625,y_center:.53125},{w:1,h:1,x_center:.90625,y_center:.53125},{w:1,h:1,x_center:.96875,y_center:.53125},{w:1,h:1,x_center:.96875,y_center:.53125},{w:1,h:1,x_center:.03125,y_center:.59375},{w:1,h:1,x_center:.03125,y_center:.59375},{w:1,h:1,x_center:.09375,y_center:.59375},{w:1,h:1,x_center:.09375,y_center:.59375},{w:1,h:1,x_center:.15625,y_center:.59375},{w:1,h:1,x_center:.15625,y_center:.59375},{w:1,h:1,x_center:.21875,y_center:.59375},{w:1,h:1,x_center:.21875,y_center:.59375},{w:1,h:1,x_center:.28125,y_center:.59375},{w:1,h:1,x_center:.28125,y_center:.59375},{w:1,h:1,x_center:.34375,y_center:.59375},{w:1,h:1,x_center:.34375,y_center:.59375},{w:1,h:1,x_center:.40625,y_center:.59375},{w:1,h:1,x_center:.40625,y_center:.59375},{w:1,h:1,x_center:.46875,y_center:.59375},{w:1,h:1,x_center:.46875,y_center:.59375},{w:1,h:1,x_center:.53125,y_center:.59375},{w:1,h:1,x_center:.53125,y_center:.59375},{w:1,h:1,x_center:.59375,y_center:.59375},{w:1,h:1,x_center:.59375,y_center:.59375},{w:1,h:1,x_center:.65625,y_center:.59375},{w:1,h:1,x_center:.65625,y_center:.59375},{w:1,h:1,x_center:.71875,y_center:.59375},{w:1,h:1,x_center:.71875,y_center:.59375},{w:1,h:1,x_center:.78125,y_center:.59375},{w:1,h:1,x_center:.78125,y_center:.59375},{w:1,h:1,x_center:.84375,y_center:.59375},{w:1,h:1,x_center:.84375,y_center:.59375},{w:1,h:1,x_center:.90625,y_center:.59375},{w:1,h:1,x_center:.90625,y_center:.59375},{w:1,h:1,x_center:.96875,y_center:.59375},{w:1,h:1,x_center:.96875,y_center:.59375},{w:1,h:1,x_center:.03125,y_center:.65625},{w:1,h:1,x_center:.03125,y_center:.65625},{w:1,h:1,x_center:.09375,y_center:.65625},{w:1,h:1,x_center:.09375,y_center:.65625},{w:1,h:1,x_center:.15625,y_center:.65625},{w:1,h:1,x_center:.15625,y_center:.65625},{w:1,h:1,x_center:.21875,y_center:.65625},{w:1,h:1,x_center:.21875,y_center:.65625},{w:1,h:1,x_center:.28125,y_center:.65625},{w:1,h:1,x_center:.28125,y_center:.65625},{w:1,h:1,x_center:.34375,y_center:.65625},{w:1,h:1,x_center:.34375,y_center:.65625},{w:1,h:1,x_center:.40625,y_center:.65625},{w:1,h:1,x_center:.40625,y_center:.65625},{w:1,h:1,x_center:.46875,y_center:.65625},{w:1,h:1,x_center:.46875,y_center:.65625},{w:1,h:1,x_center:.53125,y_center:.65625},{w:1,h:1,x_center:.53125,y_center:.65625},{w:1,h:1,x_center:.59375,y_center:.65625},{w:1,h:1,x_center:.59375,y_center:.65625},{w:1,h:1,x_center:.65625,y_center:.65625},{w:1,h:1,x_center:.65625,y_center:.65625},{w:1,h:1,x_center:.71875,y_center:.65625},{w:1,h:1,x_center:.71875,y_center:.65625},{w:1,h:1,x_center:.78125,y_center:.65625},{w:1,h:1,x_center:.78125,y_center:.65625},{w:1,h:1,x_center:.84375,y_center:.65625},{w:1,h:1,x_center:.84375,y_center:.65625},{w:1,h:1,x_center:.90625,y_center:.65625},{w:1,h:1,x_center:.90625,y_center:.65625},{w:1,h:1,x_center:.96875,y_center:.65625},{w:1,h:1,x_center:.96875,y_center:.65625},{w:1,h:1,x_center:.03125,y_center:.71875},{w:1,h:1,x_center:.03125,y_center:.71875},{w:1,h:1,x_center:.09375,y_center:.71875},{w:1,h:1,x_center:.09375,y_center:.71875},{w:1,h:1,x_center:.15625,y_center:.71875},{w:1,h:1,x_center:.15625,y_center:.71875},{w:1,h:1,x_center:.21875,y_center:.71875},{w:1,h:1,x_center:.21875,y_center:.71875},{w:1,h:1,x_center:.28125,y_center:.71875},{w:1,h:1,x_center:.28125,y_center:.71875},{w:1,h:1,x_center:.34375,y_center:.71875},{w:1,h:1,x_center:.34375,y_center:.71875},{w:1,h:1,x_center:.40625,y_center:.71875},{w:1,h:1,x_center:.40625,y_center:.71875},{w:1,h:1,x_center:.46875,y_center:.71875},{w:1,h:1,x_center:.46875,y_center:.71875},{w:1,h:1,x_center:.53125,y_center:.71875},{w:1,h:1,x_center:.53125,y_center:.71875},{w:1,h:1,x_center:.59375,y_center:.71875},{w:1,h:1,x_center:.59375,y_center:.71875},{w:1,h:1,x_center:.65625,y_center:.71875},{w:1,h:1,x_center:.65625,y_center:.71875},{w:1,h:1,x_center:.71875,y_center:.71875},{w:1,h:1,x_center:.71875,y_center:.71875},{w:1,h:1,x_center:.78125,y_center:.71875},{w:1,h:1,x_center:.78125,y_center:.71875},{w:1,h:1,x_center:.84375,y_center:.71875},{w:1,h:1,x_center:.84375,y_center:.71875},{w:1,h:1,x_center:.90625,y_center:.71875},{w:1,h:1,x_center:.90625,y_center:.71875},{w:1,h:1,x_center:.96875,y_center:.71875},{w:1,h:1,x_center:.96875,y_center:.71875},{w:1,h:1,x_center:.03125,y_center:.78125},{w:1,h:1,x_center:.03125,y_center:.78125},{w:1,h:1,x_center:.09375,y_center:.78125},{w:1,h:1,x_center:.09375,y_center:.78125},{w:1,h:1,x_center:.15625,y_center:.78125},{w:1,h:1,x_center:.15625,y_center:.78125},{w:1,h:1,x_center:.21875,y_center:.78125},{w:1,h:1,x_center:.21875,y_center:.78125},{w:1,h:1,x_center:.28125,y_center:.78125},{w:1,h:1,x_center:.28125,y_center:.78125},{w:1,h:1,x_center:.34375,y_center:.78125},{w:1,h:1,x_center:.34375,y_center:.78125},{w:1,h:1,x_center:.40625,y_center:.78125},{w:1,h:1,x_center:.40625,y_center:.78125},{w:1,h:1,x_center:.46875,y_center:.78125},{w:1,h:1,x_center:.46875,y_center:.78125},{w:1,h:1,x_center:.53125,y_center:.78125},{w:1,h:1,x_center:.53125,y_center:.78125},{w:1,h:1,x_center:.59375,y_center:.78125},{w:1,h:1,x_center:.59375,y_center:.78125},{w:1,h:1,x_center:.65625,y_center:.78125},{w:1,h:1,x_center:.65625,y_center:.78125},{w:1,h:1,x_center:.71875,y_center:.78125},{w:1,h:1,x_center:.71875,y_center:.78125},{w:1,h:1,x_center:.78125,y_center:.78125},{w:1,h:1,x_center:.78125,y_center:.78125},{w:1,h:1,x_center:.84375,y_center:.78125},{w:1,h:1,x_center:.84375,y_center:.78125},{w:1,h:1,x_center:.90625,y_center:.78125},{w:1,h:1,x_center:.90625,y_center:.78125},{w:1,h:1,x_center:.96875,y_center:.78125},{w:1,h:1,x_center:.96875,y_center:.78125},{w:1,h:1,x_center:.03125,y_center:.84375},{w:1,h:1,x_center:.03125,y_center:.84375},{w:1,h:1,x_center:.09375,y_center:.84375},{w:1,h:1,x_center:.09375,y_center:.84375},{w:1,h:1,x_center:.15625,y_center:.84375},{w:1,h:1,x_center:.15625,y_center:.84375},{w:1,h:1,x_center:.21875,y_center:.84375},{w:1,h:1,x_center:.21875,y_center:.84375},{w:1,h:1,x_center:.28125,y_center:.84375},{w:1,h:1,x_center:.28125,y_center:.84375},{w:1,h:1,x_center:.34375,y_center:.84375},{w:1,h:1,x_center:.34375,y_center:.84375},{w:1,h:1,x_center:.40625,y_center:.84375},{w:1,h:1,x_center:.40625,y_center:.84375},{w:1,h:1,x_center:.46875,y_center:.84375},{w:1,h:1,x_center:.46875,y_center:.84375},{w:1,h:1,x_center:.53125,y_center:.84375},{w:1,h:1,x_center:.53125,y_center:.84375},{w:1,h:1,x_center:.59375,y_center:.84375},{w:1,h:1,x_center:.59375,y_center:.84375},{w:1,h:1,x_center:.65625,y_center:.84375},{w:1,h:1,x_center:.65625,y_center:.84375},{w:1,h:1,x_center:.71875,y_center:.84375},{w:1,h:1,x_center:.71875,y_center:.84375},{w:1,h:1,x_center:.78125,y_center:.84375},{w:1,h:1,x_center:.78125,y_center:.84375},{w:1,h:1,x_center:.84375,y_center:.84375},{w:1,h:1,x_center:.84375,y_center:.84375},{w:1,h:1,x_center:.90625,y_center:.84375},{w:1,h:1,x_center:.90625,y_center:.84375},{w:1,h:1,x_center:.96875,y_center:.84375},{w:1,h:1,x_center:.96875,y_center:.84375},{w:1,h:1,x_center:.03125,y_center:.90625},{w:1,h:1,x_center:.03125,y_center:.90625},{w:1,h:1,x_center:.09375,y_center:.90625},{w:1,h:1,x_center:.09375,y_center:.90625},{w:1,h:1,x_center:.15625,y_center:.90625},{w:1,h:1,x_center:.15625,y_center:.90625},{w:1,h:1,x_center:.21875,y_center:.90625},{w:1,h:1,x_center:.21875,y_center:.90625},{w:1,h:1,x_center:.28125,y_center:.90625},{w:1,h:1,x_center:.28125,y_center:.90625},{w:1,h:1,x_center:.34375,y_center:.90625},{w:1,h:1,x_center:.34375,y_center:.90625},{w:1,h:1,x_center:.40625,y_center:.90625},{w:1,h:1,x_center:.40625,y_center:.90625},{w:1,h:1,x_center:.46875,y_center:.90625},{w:1,h:1,x_center:.46875,y_center:.90625},{w:1,h:1,x_center:.53125,y_center:.90625},{w:1,h:1,x_center:.53125,y_center:.90625},{w:1,h:1,x_center:.59375,y_center:.90625},{w:1,h:1,x_center:.59375,y_center:.90625},{w:1,h:1,x_center:.65625,y_center:.90625},{w:1,h:1,x_center:.65625,y_center:.90625},{w:1,h:1,x_center:.71875,y_center:.90625},{w:1,h:1,x_center:.71875,y_center:.90625},{w:1,h:1,x_center:.78125,y_center:.90625},{w:1,h:1,x_center:.78125,y_center:.90625},{w:1,h:1,x_center:.84375,y_center:.90625},{w:1,h:1,x_center:.84375,y_center:.90625},{w:1,h:1,x_center:.90625,y_center:.90625},{w:1,h:1,x_center:.90625,y_center:.90625},{w:1,h:1,x_center:.96875,y_center:.90625},{w:1,h:1,x_center:.96875,y_center:.90625},{w:1,h:1,x_center:.03125,y_center:.96875},{w:1,h:1,x_center:.03125,y_center:.96875},{w:1,h:1,x_center:.09375,y_center:.96875},{w:1,h:1,x_center:.09375,y_center:.96875},{w:1,h:1,x_center:.15625,y_center:.96875},{w:1,h:1,x_center:.15625,y_center:.96875},{w:1,h:1,x_center:.21875,y_center:.96875},{w:1,h:1,x_center:.21875,y_center:.96875},{w:1,h:1,x_center:.28125,y_center:.96875},{w:1,h:1,x_center:.28125,y_center:.96875},{w:1,h:1,x_center:.34375,y_center:.96875},{w:1,h:1,x_center:.34375,y_center:.96875},{w:1,h:1,x_center:.40625,y_center:.96875},{w:1,h:1,x_center:.40625,y_center:.96875},{w:1,h:1,x_center:.46875,y_center:.96875},{w:1,h:1,x_center:.46875,y_center:.96875},{w:1,h:1,x_center:.53125,y_center:.96875},{w:1,h:1,x_center:.53125,y_center:.96875},{w:1,h:1,x_center:.59375,y_center:.96875},{w:1,h:1,x_center:.59375,y_center:.96875},{w:1,h:1,x_center:.65625,y_center:.96875},{w:1,h:1,x_center:.65625,y_center:.96875},{w:1,h:1,x_center:.71875,y_center:.96875},{w:1,h:1,x_center:.71875,y_center:.96875},{w:1,h:1,x_center:.78125,y_center:.96875},{w:1,h:1,x_center:.78125,y_center:.96875},{w:1,h:1,x_center:.84375,y_center:.96875},{w:1,h:1,x_center:.84375,y_center:.96875},{w:1,h:1,x_center:.90625,y_center:.96875},{w:1,h:1,x_center:.90625,y_center:.96875},{w:1,h:1,x_center:.96875,y_center:.96875},{w:1,h:1,x_center:.96875,y_center:.96875},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375}];var cg={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},hg=class{constructor(t){this.handPipeline=t}static getAnnotations(){return cg}async estimateHands(t,n){let r=await this.handPipeline.estimateHands(t,n);if(!r)return[];let a=[];for(let s of r){let i={};if(s.landmarks)for(let c of Object.keys(cg))i[c]=cg[c].map(u=>s.landmarks[u]);let o=s.box?[Math.max(0,s.box.topLeft[0]),Math.max(0,s.box.topLeft[1]),Math.min(t.shape[2],s.box.bottomRight[0])-Math.max(0,s.box.topLeft[0]),Math.min(t.shape[1],s.box.bottomRight[1])-Math.max(0,s.box.topLeft[1])]:[],l=[s.box.topLeft[0]/t.shape[2],s.box.topLeft[1]/t.shape[1],(s.box.bottomRight[0]-s.box.topLeft[0])/t.shape[2],(s.box.bottomRight[1]-s.box.topLeft[1])/t.shape[1]];a.push({confidence:Math.round(100*s.confidence)/100,box:o,boxRaw:l,landmarks:s.landmarks,annotations:i})}return a}},Kr,Zr;async function dg(e){!Kr||!Zr?([Kr,Zr]=await Promise.all([e.hand.enabled?Ht(Yt(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?Ht(Yt(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!Kr||!Kr.modelUrl?pe("load model failed:",e.hand.detector.modelPath):e.debug&&pe("load model:",Kr.modelUrl),!Zr||!Zr.modelUrl?pe("load model failed:",e.hand.skeleton.modelPath):e.debug&&pe("load model:",Zr.modelUrl))):(e.debug&&pe("cached model:",Kr.modelUrl),e.debug&&pe("cached model:",Zr.modelUrl));let t=new sg(Kr,Kr==null?void 0:Kr.inputs[0].shape[2],C8),n=new lg(t,Zr,Zr==null?void 0:Zr.inputs[0].shape[2]);return new hg(n)}var pg={};Ar(pg,{load:()=>fg,predict:()=>mg});var R8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],M8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var An;async function fg(e){return An?e.debug&&pe("cached model:",An.modelUrl):(An=await Ht(Yt(e.modelBasePath,e.body.modelPath)),An.width=parseInt(An.signature.inputs["input_1:0"].tensorShape.dim[2].size),An.height=parseInt(An.signature.inputs["input_1:0"].tensorShape.dim[1].size),!An||!An.modelUrl?pe("load model failed:",e.body.modelPath):e.debug&&pe("load model:",An.modelUrl)),An}async function mg(e,t){if(!An||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Ye.resizeBilinear(e,[An.width,An.height],!1),a=ge(r,[255]);r.dispose();let s;if(t.profile){let u=await sa(()=>An.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),Ja("blazepose",u)}else{let u=await An.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?R8:M8,l=5;for(let u=0;u<s.length/l;u++)i.push({id:u,part:o[u],position:{x:Math.trunc(n.width*s[l*u+0]/255),y:Math.trunc(n.height*s[l*u+1]/255),z:Math.trunc(s[l*u+2])+0},score:(100-Math.trunc(100/(1+Math.exp(s[l*u+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(s[l*u+4]))))/100});return[{score:i.reduce((u,h)=>h.score>u?h.score:u,0),keypoints:i}]}var Ag={};Ar(Ag,{load:()=>gg,predict:()=>xg});var E0=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var yn,yg=[],C0=Number.MAX_SAFE_INTEGER,R0=2.5;async function gg(e){if(yn)e.debug&&pe("cached model:",yn.modelUrl);else{yn=await Ht(Yt(e.modelBasePath,e.object.modelPath));let t=Object.values(yn.modelSignature.inputs);if(yn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!yn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!yn||!yn.modelUrl?pe("load model failed:",e.object.modelPath):e.debug&&pe("load model:",yn.modelUrl)}return yn}async function Sie(e,t,n,r){let a=0,s=[];for(let c of[1,2,4])L(()=>{var A,y;let u=c*13,h=(A=e.find(g=>g.shape[1]===u**2&&g.shape[2]===E0.length))==null?void 0:A.squeeze(),d=(y=e.find(g=>g.shape[1]===u**2&&g.shape[2]<E0.length))==null?void 0:y.squeeze(),m=d.reshape([-1,4,d.shape[1]/4]).argMax(2).arraySync(),f=h.arraySync();for(let g=0;g<h.shape[0];g++)for(let x=0;x<h.shape[1];x++){let v=f[g][x];if(v>r.object.minConfidence&&x!==61){let w=(.5+Math.trunc(g%u))/u,b=(.5+Math.trunc(g/u))/u,k=m[g].map(U=>U*(u/c/t)),[N,C]=[w-R0/c*k[0],b-R0/c*k[1]],[F,O]=[w+R0/c*k[2]-N,b+R0/c*k[3]-C],z=[N,C,F,O];z=z.map(U=>Math.max(0,Math.min(U,1)));let V=[z[0]*n[0],z[1]*n[1],z[2]*n[0],z[3]*n[1]],j={id:a++,strideSize:c,score:Math.round(100*v)/100,class:x+1,label:E0[x].label,center:[Math.trunc(n[0]*w),Math.trunc(n[1]*b)],centerRaw:[w,b],box:V.map(U=>Math.trunc(U)),boxRaw:z};s.push(j)}}});e.forEach(c=>Te(c));let i=s.map(c=>c.boxRaw),o=s.map(c=>c.score),l=[];if(i&&i.length>0){let c=await Ye.nonMaxSuppressionAsync(i,o,r.object.maxResults,r.object.iouThreshold,r.object.minConfidence);l=c.dataSync(),Te(c)}return s=s.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),s}async function xg(e,t){return yn?C0<t.object.skipFrames&&t.videoOptimized&&yg.length>0?(C0++,yg):(t.videoOptimized?C0=0:C0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=[e.shape[2],e.shape[1]],a=Ye.resizeBilinear(e,[yn.inputSize,yn.inputSize],!1),s=a.div(255),i=s.transpose([0,3,1,2]);s.dispose(),a.dispose();let o;if(!t.profile)t.object.enabled&&(o=await yn.predict(i));else{let c=t.object.enabled?await sa(()=>yn.predict(i)):{};o=c.result,Ja("object",c)}i.dispose();let l=await Sie(o,yn.inputSize,r,t);yg=l,n(l)})):null}var F8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=e[n].keypoints.find(l=>l.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.y<s.position.y&&a.position.y<s.position.y?t.push({body:n,gesture:"i give up"}):s&&r&&r.position.y<s.position.y?t.push({body:n,gesture:"raise left hand"}):s&&a&&a.position.y<s.position.y&&t.push({body:n,gesture:"raise right hand"});let i=e[n].keypoints.find(l=>l.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},$8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let r=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${r<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},D8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],a=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(r*a),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o),c=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let h=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].annotations.rightEyeIris[0][0],d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].annotations.leftEyeIris[0][0];(d>.033||h>.033)&&(c=!1),d>.033&&t.push({iris:n,gesture:"looking right"}),h>.033&&t.push({iris:n,gesture:"looking left"});let p=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].annotations.rightEyeIris[0][1],m=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].annotations.leftEyeIris[0][1];(m<.015||p<.015||m>.03||p>.03)&&(c=!1),(m<.015||p<.015)&&t.push({iris:n,gesture:"looking down"}),(m>.03||p>.03)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},O8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[a,s]of Object.entries(e[n].annotations))a!=="palmBase"&&Array.isArray(s)&&r.push({name:a.toLowerCase(),position:s[0]});if(r&&r.length>0){let a=r.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=r.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${a.name} forward ${s.name} up`})}}return t};var wg={};Ar(wg,{process:()=>bg});function Nie(e,t,n){let r=function(o,l,c){let u=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");o.replace(u,(h,d)=>(c[d]=0,h))},a=function(o,l){let c=e.createShader(l);if(e.shaderSource(c,o),e.compileShader(c),!e.getShaderParameter(c,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(c));return c};this.uniform={},this.attribute={};let s=a(t,e.VERTEX_SHADER),i=a(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),r(t,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=e.getAttribLocation(this.id,o);r(t,"uniform",this.uniform),r(n,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=e.getUniformLocation(this.id,o)}function z8(e){e||(e={});let t=0,n=null,r=!1,a=-1,s=[null,null],i=[],o=-1,l=-1,c=null,u=null,h={},d=e.canvas||document.createElement("canvas"),p={},m={INTERMEDIATE:1},f=d.getContext("webgl");if(!f)throw new Error("Filter: getContext() failed");this.addFilter=function(w){let b=Array.prototype.slice.call(arguments,1),k=h[w];i.push({func:k,args:b})},this.reset=function(){i=[]};let A=function(w,b){if(!(w===o&&b===l)){if(d.width=w,o=w,d.height=b,l=b,!c){let k=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);c=f.createBuffer(),f.bindBuffer(f.ARRAY_BUFFER,c),f.bufferData(f.ARRAY_BUFFER,k,f.STATIC_DRAW),f.pixelStorei(f.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}f.viewport(0,0,o,l),s=[null,null]}},y=function(w,b){let k=f.createFramebuffer();f.bindFramebuffer(f.FRAMEBUFFER,k);let N=f.createRenderbuffer();f.bindRenderbuffer(f.RENDERBUFFER,N);let C=f.createTexture();return f.bindTexture(f.TEXTURE_2D,C),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,w,b,0,f.RGBA,f.UNSIGNED_BYTE,null),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.framebufferTexture2D(f.FRAMEBUFFER,f.COLOR_ATTACHMENT0,f.TEXTURE_2D,C,0),f.bindTexture(f.TEXTURE_2D,null),f.bindFramebuffer(f.FRAMEBUFFER,null),{fbo:k,texture:C}},g=function(w){return s[w]=s[w]||y(o,l),s[w]},x=function(w=null){var C,F;let b=null,k=null,N=!1;t===0?b=n:b=(C=g(a))==null?void 0:C.texture,t++,r&&!(w&m.INTERMEDIATE)?(k=null,N=t%2==0):(a=(a+1)%2,k=(F=g(a))==null?void 0:F.fbo),f.bindTexture(f.TEXTURE_2D,b),f.bindFramebuffer(f.FRAMEBUFFER,k),f.uniform1f(u.uniform.flipY,N?-1:1),f.drawArrays(f.TRIANGLES,0,6)};this.apply=function(w){if(A(w.width,w.height),t=0,n||(n=f.createTexture()),f.bindTexture(f.TEXTURE_2D,n),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.NEAREST),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.NEAREST),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,f.RGBA,f.UNSIGNED_BYTE,w),i.length===0)return x(),d;for(let b=0;b<i.length;b++){r=b===i.length-1;let k=i[b];k.func.apply(this,k.args||[])}return d};let v=function(w){if(p[w])return u=p[w],f.useProgram(u.id),u;let b={};b.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
`),b.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`),u=new Nie(f,b.VERTEX_IDENTITY,w);let k=Float32Array.BYTES_PER_ELEMENT,N=4*k;return f.enableVertexAttribArray(u.attribute.pos),f.vertexAttribPointer(u.attribute.pos,2,f.FLOAT,!1,N,0*k),f.enableVertexAttribArray(u.attribute.uv),f.vertexAttribPointer(u.attribute.uv,2,f.FLOAT,!1,N,2*k),p[w]=u,u};h.colorMatrix=function(w){let b=new Float32Array(w);b[4]/=255,b[9]/=255,b[14]/=255,b[19]/=255;let k=b[18]===1&&b[3]===0&&b[8]===0&&b[13]===0&&b[15]===0&&b[16]===0&&b[17]===0&&b[19]===0?h.colorMatrix.SHADER.WITHOUT_ALPHA:h.colorMatrix.SHADER.WITH_ALPHA,N=v(k);f.uniform1fv(N.uniform.m,b),x()},h.colorMatrix.SHADER={},h.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
`),h.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
`),h.brightness=function(w){let b=(w||0)+1;h.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},h.saturation=function(w){let b=(w||0)*2/3+1,k=(b-1)*-.5;h.colorMatrix([b,k,k,0,0,k,b,k,0,0,k,k,b,0,0,0,0,0,1,0])},h.desaturate=function(){h.saturation(-1)},h.contrast=function(w){let b=(w||0)+1,k=-128*(b-1);h.colorMatrix([b,0,0,0,k,0,b,0,0,k,0,0,b,0,k,0,0,0,1,0])},h.negative=function(){h.contrast(-2)},h.hue=function(w){w=(w||0)/180*Math.PI;let b=Math.cos(w),k=Math.sin(w),N=.213,C=.715,F=.072;h.colorMatrix([N+b*(1-N)+k*-N,C+b*-C+k*-C,F+b*-F+k*(1-F),0,0,N+b*-N+k*.143,C+b*(1-C)+k*.14,F+b*-F+k*-.283,0,0,N+b*-N+k*-(1-N),C+b*-C+k*C,F+b*(1-F)+k*F,0,0,0,0,0,1,0])},h.desaturateLuminance=function(){h.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},h.sepia=function(){h.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},h.brownie=function(){h.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},h.vintagePinhole=function(){h.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},h.kodachrome=function(){h.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},h.technicolor=function(){h.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},h.polaroid=function(){h.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},h.shiftToBGR=function(){h.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},h.convolution=function(w){let b=new Float32Array(w),k=1/o,N=1/l,C=v(h.convolution.SHADER);f.uniform1fv(C.uniform.m,b),f.uniform2f(C.uniform.px,k,N),x()},h.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
`),h.detectEdges=function(){h.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},h.sobelX=function(){h.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},h.sobelY=function(){h.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},h.sharpen=function(w){let b=w||1;h.convolution.call(this,[0,-1*b,0,-1*b,1+4*b,-1*b,0,-1*b,0])},h.emboss=function(w){let b=w||1;h.convolution.call(this,[-2*b,-1*b,0,-1*b,1,1*b,0,1*b,2*b])},h.blur=function(w){let b=w/7/o,k=w/7/l,N=v(h.blur.SHADER);f.uniform2f(N.uniform.px,0,k),x(m.INTERMEDIATE),f.uniform2f(N.uniform.px,b,0),x()},h.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;","}"].join(`
`),h.pixelate=function(w){let b=w/o,k=w/l,N=v(h.pixelate.SHADER);f.uniform2f(N.uniform.size,b,k),x()},h.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
`)}var M0=2048,Ee,gt,Mt;function bg(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof Le)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof Le)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Dr(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>M0&&(i=M0,o=i*s/a),o>M0&&(o=M0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Ee||(Ee==null?void 0:Ee.width)!==i||(Ee==null?void 0:Ee.height)!==o)&&(Ee=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ee==null?void 0:Ee.width)!==i&&(Ee.width=i),(Ee==null?void 0:Ee.height)!==o&&(Ee.height=o));let l=Ee.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(a,0),l.scale(-1,1),l.drawImage(e,0,0,a,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,a,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),t.filter.enabled){if((!Mt||!gt||Ee.width!==gt.width||(Ee==null?void 0:Ee.height)!==(gt==null?void 0:gt.height))&&(gt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height):document.createElement("canvas"),(gt==null?void 0:gt.width)!==(Ee==null?void 0:Ee.width)&&(gt.width=Ee==null?void 0:Ee.width),(gt==null?void 0:gt.height)!==(Ee==null?void 0:Ee.height)&&(gt.height=Ee==null?void 0:Ee.height),Mt=yr.flags.IS_BROWSER?new z8({canvas:gt}):null),!Mt)return{tensor:null,canvas:Ee};Mt.reset(),Mt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Mt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Mt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Mt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Mt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Mt.addFilter("hue",t.filter.hue),t.filter.negative&&Mt.addFilter("negative"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.vintage&&Mt.addFilter("brownie"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.kodachrome&&Mt.addFilter("kodachrome"),t.filter.technicolor&&Mt.addFilter("technicolor"),t.filter.polaroid&&Mt.addFilter("polaroid"),t.filter.pixelate!==0&&Mt.addFilter("pixelate",t.filter.pixelate),Mt.apply(Ee)}else gt=Ee,Mt&&(Mt=null);let c;if(gt.data){let h=[gt.height,gt.width,3];c=pd(gt.data,h,"int32")}else if(gt instanceof ImageData)c=ui.fromPixels(gt);else if(t.backend==="webgl"||t.backend==="humangl"){let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(gt,0,0),c=ui.fromPixels(h)}else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(gt,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=ui.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?gt:null;return{tensor:n,canvas:r}}var _g={};Ar(_g,{all:()=>Eie,body:()=>W8,canvas:()=>Tie,face:()=>L8,gesture:()=>P8,hand:()=>B8,object:()=>V8,options:()=>Li});var ct={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31,minConfidence:.1},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetections:1,scoreThreshold:.2,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var Li={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!1,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1,calculateHandBox:!0};function F0(e,t,n,r=0,a){e.fillStyle=a.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,n,a.pointSize,0,2*Math.PI),e.fill()}function Wi(e,t,n,r,a,s){if(e.beginPath(),s.useCurves){let i=(t+t+r)/2,o=(n+n+a)/2;e.ellipse(i,o,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+r-s.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+s.roundRect),e.lineTo(t+r,n+a-s.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-s.roundRect,n+a),e.lineTo(t+s.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function vg(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let r of t)e.strokeStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.lineTo(r[0],parseInt(r[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Yc(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){vg(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let r=0;r<t.length-2;r++){let a=(t[r][0]+t[r+1][0])/2,s=(t[r][1]+t[r+1][1])/2;e.quadraticCurveTo(t[r][0],t[r][1],a,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function P8(e,t,n){let r=Gn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!a)return;a.font=r.font,a.fillStyle=r.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let c=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${c}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(u,8,2+s*r.lineHeight)),a.fillStyle=r.labelColor,a.fillText(u,6,0+s*r.lineHeight),s+=1}}}async function L8(e,t,n){let r=Gn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a)for(let s of t){a.font=r.font,a.strokeStyle=r.color,a.fillStyle=r.color,r.drawBoxes&&(r.useRawBoxes?Wi(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):Wi(a,s.box[0],s.box[1],s.box[2],s.box[3],r));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}s.rotation&&s.rotation.angle&&s.rotation.angle.roll&&i.push(`roll: ${Math.trunc(100*s.rotation.angle.roll)/100} yaw:${Math.trunc(100*s.rotation.angle.yaw)/100} pitch:${Math.trunc(100*s.rotation.angle.pitch)/100}`),i.length===0&&i.push("face"),a.fillStyle=r.color;for(let o=i.length-1;o>=0;o--){let l=Math.max(s.box[0],0),c=o*r.lineHeight+s.box[1];r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i[o],l+5,c+16)),a.fillStyle=r.labelColor,a.fillText(i[o],l+4,c+15)}if(a.lineWidth=1,s.mesh&&s.mesh.length>0){if(r.drawPoints)for(let o of s.mesh)F0(a,o[0],o[1],o[2],r);if(r.drawPolygons){a.lineWidth=1;for(let o=0;o<Pi.length/3;o++){let l=[Pi[o*3+0],Pi[o*3+1],Pi[o*3+2]].map(c=>s.mesh[c]);vg(a,l,r)}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}}}}}var ts=[];async function W8(e,t,n){let r=Gn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round";for(let s=0;s<t.length;s++){if(!ts[s]&&r.bufferedOutput&&(ts[s]={...t[s]}),a.strokeStyle=r.color,a.fillStyle=r.color,a.lineWidth=r.lineWidth,a.font=r.font,r.drawBoxes&&(Wi(a,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(`body ${100*t[s].score}%`,t[s].box[0]+3,1+t[s].box[1]+r.lineHeight,t[s].box[2])),a.fillStyle=r.labelColor,a.fillText(`body ${100*t[s].score}%`,t[s].box[0]+2,0+t[s].box[1]+r.lineHeight,t[s].box[2]))),r.drawPoints)for(let i=0;i<t[s].keypoints.length;i++)a.fillStyle=r.useDepth&&t[s].keypoints[i].position.z?`rgba(${127.5+2*t[s].keypoints[i].position.z}, ${127.5-2*t[s].keypoints[i].position.z}, 255, 0.5)`:r.color,r.bufferedOutput?(ts[s].keypoints[i][0]=(ts[s].keypoints[i][0]+t[s].keypoints[i].position.x)/2,ts[s].keypoints[i][1]=(ts[s].keypoints[i][1]+t[s].keypoints[i].position.y)/2,F0(a,ts[s].keypoints[i][0],ts[s].keypoints[i][1],0,r)):F0(a,t[s].keypoints[i].position.x,t[s].keypoints[i].position.y,0,r);if(r.drawLabels&&(a.font=r.font,t[s].keypoints))for(let i of t[s].keypoints)a.fillStyle=r.useDepth&&i.position.z?`rgba(${127.5+2*i.position.z}, ${127.5-2*i.position.z}, 255, 0.5)`:r.color,a.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position.x+4,i.position.y+4);if(r.drawPolygons&&t[s].keypoints){let i,o=[];o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Yc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),o.length===4&&vg(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftKnee"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftAnkle"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHeel"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftFoot"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Yc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightKnee"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightAnkle"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHeel"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightFoot"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Yc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftElbow"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftWrist"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftPalm"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Yc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightElbow"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightWrist"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightPalm"),i&&i.score>ct.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Yc(a,o,r)}}}}async function B8(e,t,n){let r=Gn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t){if(r.drawBoxes){a.strokeStyle=r.color,a.fillStyle=r.color;let i;if(!r.calculateHandBox)i=r.useRawBoxes?s.boxRaw:s.box;else if(i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],s.landmarks&&s.landmarks.length>0){for(let o of s.landmarks)o[0]<i[0]&&(i[0]=o[0]),o[1]<i[1]&&(i[1]=o[1]),o[0]>i[2]&&(i[2]=o[0]),o[1]>i[3]&&(i[3]=o[1]);i[2]-=i[0],i[3]-=i[1]}r.useRawBoxes?Wi(a,e.width*i[0],e.height*i[1],e.width*i[2],e.height*i[3],r):Wi(a,i[0],i[1],i[2],i[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText("hand",i[0]+3,1+i[1]+r.lineHeight,i[2])),a.fillStyle=r.labelColor,a.fillText("hand",i[0]+2,0+i[1]+r.lineHeight,i[2])),a.stroke()}if(r.drawPoints&&s.landmarks&&s.landmarks.length>0)for(let i of s.landmarks)a.fillStyle=r.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:r.color,F0(a,i[0],i[1],0,r);if(r.drawPolygons){let i=o=>{if(!!o)for(let l=0;l<o.length;l++)a.lineWidth=r.lineWidth,a.beginPath(),a.strokeStyle=r.useDepth?`rgba(${127.5+2*o[l][2]}, ${127.5-2*o[l][2]}, 255, 0.5)`:r.color,a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()};i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function V8(e,t,n){let r=Gn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t)if(r.drawBoxes){if(a.strokeStyle=r.color,a.fillStyle=r.color,r.useRawBoxes?Wi(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):Wi(a,s.box[0],s.box[1],s.box[2],s.box[3],r),r.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+r.lineHeight,s.box[2])),a.fillStyle=r.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+r.lineHeight,s.box[2])}a.stroke()}}}async function Tie(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Eie(e,t,n){let r=Gn(Li,n);!t||!e||e instanceof HTMLCanvasElement&&(L8(e,t.face,r),W8(e,t.body,r),B8(e,t.hand,r),P8(e,t.gesture,r),V8(e,t.object,r))}var $0=`
/9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA
AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob
IxwWFiAsICMmJykqKRkfLTAtKDAlKCko/9sAQwEHBwcKCAoTCgoTKBoWGigoKCgoKCgoKCgoKCgo
KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgo/8AAEQgBAAEAAwEhAAIRAQMRAf/E
AB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAE
EQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZH
SElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1
tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEB
AQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXET
IjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFla
Y2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG
x8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A+qaKACigApGOKAML
Xp8xlF5A7V4X8RtYs7PzfNImnx8sa8Kp9z3q2tEgp6angWs62ZZ5CTGoJ6DArGNz5p+UrID6EUrF
PUlW1EuN0XNW7PQ2L5j3JnoKXN0KijqNP0eYoqXBdgPuuo+ZPeupisWn2Jd4+0r924XgsQOCff3/
AJ1FzRKxDqGii6m3siiQ8F1XGfXI6YNWLfRbiRQMkcZI9fpTDluT2/h6Qy8gDPbtmtG38JeY480Z
5zSLUTZg8M28YwYxjAArXtdPt402qgHbpSaLWhma3o0Uqk7Nx9DWLaaVblgPs6qRyds2M/gRSQp9
zZOni2iWS2hlQ+kjYz9OMGrdjq89vIPPVhj+8M/lQyDq9P1WOYBlMZz1AOD+VdDaTiReOKulK0jO
tHmi0WDTlr0TyxRVhT8tJjIX+9SUxHXUV553BRQAVBcPhSBTSuxPY86+IGti0s5I7dsORy9fM3i6
8e8mfDO5P90ZrWWiJicNPpZZtxV/xrW0jQt4DOv6Vk2dEEdTY6BHuB25rpbPSo0QARjP0qTRI17W
wA/hFaMWmoQMgflQXYsDS142rU9tpqqenfNA7GgtihxkdKuRW6qMY/GkDZY8sY4Ap4hXbyB+VArk
EtuH4wPyrk/EGkOm+a3jw3suRQLc5i38SX9hJ9nnY+XnBUdPyNdFY6pa3KkkAE9l6f8AfJ/pSJT6
GhDmI+Zb4ZRycdv6ium0nUhKFydrelTsNnS2829RnrVgV6NKXNG55lWPLIM81Op+WrZkRMfmNNzT
A7GivPO4KKAEY4XNYWt3vkwPg4OK0giJdjw/xrqhm87Zs8tc7pX5A+leSajf6aHYJ50kn4AZpTep
rBWRm2Vobm4BXfyehPFdnpmnBFUY5rI2SN63tlToK0YI+KZpFF+3QdavwoKTLtoW0Toaswpk5pCb
LCxipAhoIuP2dKevHXoaYDylRyxhlwRQI4nxVoCXWZI1GfpXGtbSWjYPGP73+NIGupt6TqMsLruZ
ih4xnP5V09mQ+JLd8gn0xSYJnVaVdkook69K34zuUGunDS3Rx4qOzHVIp4rrOMY3NJQI7GivPO8K
KAILt9kZrz3xlebYiu8KCCWb0XvW0NFch6ysfO3jLVjfXLIn+pQkKorl7WxNxIPl71g2dUUdpo+l
pBGvHPet23iC8ihFosrxirkHQUFo0IF4FXI1O726CpKLacCrMJoJLYHAPpTwucHpSRJJ5e4AZI9x
UqpxzVpCuOC8cUpQUMRnXttuB4rjNdsYyeVwfXpmpGmcvcQyafMCFJjPY10eg34BUg4DcZP8jUO4
HaRq3lLNF+IHet7R7jz7c56rwa2wz9+xhiVeFy/T1PFegeaNPWigDsc0ZrzzvDNIaAM7VpNqdegr
xL4l6kywyRhseZ19lrdfAZL4jxYg3Fw20d63tJsdrDI5rm3Z3R0R0Mce1eKnQYAplIkWrMJ45oZS
NO3PHbNXIyfpSGWowSOasxLUiZdjFSqtNEMkUemKlAGKsRJjAppFAiORMjmsTVrNZEO4cfSoZSOD
1eJ7WXBUzQZ+7nkfSo7e2Ei+ZaMzxntjBX2NSU1Y6/wxqojiEFzkA8KTXYaUoWRyv3W5rSjpNHPX
+BmpSg8V6J5gUUAdhRXnneFFAGHrTfu5PpXzj8S70/aZtxzztXFbv4DKHxHI+H4GZiz9zxXXW8G3
GBXMjvLRXAx0oPGPSmMVeOnWrMTYpFI0bcg1fh54xmgovRcD3qxETSIZcRvzp+/BpEkqsBUqsM9K
q4Em4Gkxk0yRGXrVW6i8yFhkg+tJjRxGsWrxllkUMh9eK5uMz6bcebbnfG33kPcVkay2OntPKuo0
nhXI67c8qa7Lw3c+adjcEDGK1paSRhVV4s6A0or0jyRRQ1AHX0V553hRQBz+vNtt5z3xXzX8Qbdm
uic5YnOMdK3l8JnTXvlbwpYl+WySOgrp5YfLOOB9O1c62O7qQkc+9RsKChFPWp4DluOlSykaNruH
ArUgHShFNF2NT1qxGO3NBmyxGcE1N2560CFzjrUysO9JAPDDjFOVuKoQuSRTWouBkazbCa3cd8cV
wF7IISQccHBzUSWpV9C3o1x5b5GAjdQD1rs9DjC3kckbEhqKfxIzn8LOupRXqnkPccBSkUAzraK8
87wooA5rxMSI3HqK8B8bQl9Q8sffY5b/AAraXwkUviNrw9pH2W1ViMMRTdRjw4HpWNtDti9TPc4P
FQs2M5qdyyMHLcfjV63HTAoBGtap0wK0YxigpsuRDtVhVYd6GQydVwwIqdRnqKCR23I5pCMUW6gD
YNKuetAEise9KTxQBWuFyhrznxNZkXjFeN3I+tTIZg2OqmzmxNF0PO3vXp/g2+hukVl4zyPanTXv
JmVR+60dpThXpnlPceopWFAbnV0V553hSGgRynjC5FujOey14Ssp1HxNmTnc+a3kvcIpv37HoEYQ
QmMdVHSsnVbYJF5jVk0dsNzlruVIsl2wKxbjWrVHILjg1CRbZJb+ILHPzyhfStODWLQgFJFYd+el
UJM27HUIXxhga1Y5lLVLKLkMnoauxnPPrSEx7ShF+Y/n2qrc6xBbhizDAqkK1zJuvG9nbg8ZA681
ly/Ei052RO3uKAsZlx8QGd8xxvt9Aa1NH8dK7AXMcip64zigdkdrZX8F7EJLdwwNXMkrz1qRMRly
CK4TxmpidWI49felPYSOMmi80NIoOV6qRzXYeA5SskYPfirpfEjGr8LPWVHyD6U4CvQPL3ZItOYc
UDOoNFeed4Uhpks4H4iE/Z5MeleMeGULeLgjds10S+BGdL+Jc9OSBU2Huc5Nc74yvUtrcDBrJnZF
63PJdXvLy/lKWw46bvQVz82jXhkLO5Y+9ZlsYthcRnbIjY9R3q3awTRkEM3WmJI6C0ea3dGRsr1x
XY6TqW9FLHnjrUs0izpLK5DDjofSta3ckH09KRUkZuuTvFGdvPauE1Y3U6Mqbssf/rUxHPTaJPK2
ZmJPbBqzY6DCZh5xJC9s9aBJHU6dpemJjfEmfetJtI0+VPkUr/unFOxdiextHs33W07YHQHk11mk
Xb3KbZ1xIvcd6LEyWho4Nct41sTPYb16ipexCPPZN+wYGCvH1rrPAEJmvkPoc1VL4kZVvgZ6yFwK
cBXoHkkqinFaVyzo80GuE7WJRQSziPiGdthK5HQV4x4J/wBI8WPIewNdEvgRNL42emO/yj1UHNef
eNpRczbC+I17DvWT2OqJxc0sMK4TCisy41q0hfEkqj8aixdwTXNOlwvmqD9anS9tXH7uVG+hosO4
/wC0oOhrR0+6G4YNIEzsNEuCxAPNdjZruA4xxUmjINSjURksOlcbqFykbnjFA1sYGoassaknCqO5
rl7rxhGm7yBnBxuJq0rkSlYpw+NLlsfd5P8AerVsvHEqSBHwPVgcgVpyMyVXU3rXxcHYETAk+hru
/DWti6ZSTyOKzZqndHaxvvUGq2rQ+dYyqR24qWI8dvbr7LqDxyDAzXpvw6FvIxePGSM06Xxoyr/A
zviKFHNegeX1J41zUhXioGbuaSuM6wpCaBHG/EcA6HN/exxXjXw2jL67cv8A3Qa6H8CFR+NnoWpO
I4XI44rxLxrqjQzSEsQM1gdSPM9U1uR1YbmWIdXHf2rmpIb67YS28UrRlsLI3c/jW0VZGUpO5pW1
jfLNOjahawzwReYI5cjzMkDavHJ5/SrVv9uhtPtVxCPLBwzxnlT9KGghLU3tKvvPjHzbl7EGuisJ
GRxWLOg7nRXJEbDjmvSNK+aFSfSoZr0KutRkphc4NcRrdkVjL9aVio7Hk3iqS8ubhrWzUlsZY9kG
cZNc5D4aee5MclzJIFTzHAO0MfatqSOWu7bFS1srDUZEis0vIZoUxPvfcC+4/dx2xjr712XiTwXb
WmlQ6hol3cRhoFd4rlg3zY5wR0GelavQwjq7GD4etdVvSnk2wAB+9v8A8mvcfA2kXiRo0/UdcDis
ZnTTulqeoWqbUAJqWUb42X1FZlnjfjSwlGrr5S/eNdD4RkvLAAQ4yRyaUZcruVKl7TQ9I0G+mnzH
ckFwM8VuIK7ac3KF2eXiKapz5UWYxipNtMyNejNch0jSar3cjR27uoyQCRVRWom9DxTx54gu5fMi
lbKdMVjfCZPNlv5v9rFbVHpYqjGzbOn8SzFI9o715L4u0r7arYzk+lYdTqSujy7U/C0u4vHk+WwO
xuh9q3J9dgvbdVukMV1EwbDDgn04rZMwlHoZ+orZ6hfQ3RWVnQYCgZAq+8U0ln5NtBsV2yxYcfgK
JtW0CnB31LlroVwJ1nQLGDjeP7w+lb0dsFxjrWB0tHS6NuWPJ6A16ToUm63T3Gallr4S7cxiTjrX
PaxaF7dlVeSMUhxZ5jd+H7qCa4eF3DSE5x3zXN3Wk6jbyeaiFWUY6ZyPStYS5SalPmVipFbX0E4c
W0alvmPHJrag0rVvEE6LdljGpG2NRtQD+tW5XMI0uU9M8NeFo9PiQhecDIIrtrOMIoG3H4VlJm9t
C6CB06VPGM1IHLeItGS6uw+ORT7e3jsbQvj7gzUNam0JaWE+HN7NqOqX80n3FO1RXo8YzXdS+BHk
4z+KyzGPapcU2YIv7qQtiuaxvcaWqG4O6FwfSrS1JbPnrxoxkv7qIfejcitj4V2f2exumI+8+aKn
xHTT+G5d8Txlm4rjLxMsQwzWT3OiK0Mm6sEkVsAcjFc1d+FEmlGwEDPQVopaEuOpr6f4ZWNAu3tW
vHpAj5ZQcUFIWaDjGMVUMQ3cVDBmvbhY7QAV2nh+T/R1yeKhlrY31+b61FcQK6nIoJMi401WblRi
qr6PCw5UYq9y+YgOgWzNkRrx3xWjp+nx2v3FQcelAbmko9anQ4GBUNisPHWr1qMrQhS2K11HvmYV
hamcxSRZ5xRIqluS/DKAQQXZxyXrvo2FdlL4EeZjH+/ZbjNSZpswLNBrE1Gt7VE4ODVIlnh/j61F
j4lmeTGyUbq6LwdEqWbeX0YbhSqfEddP4Bddj4JIrhL5d8h7VjI6oLQqKNzelWre3yc4/ClFjaL6
wqBxxUUxwCKu5BmXRA6c+9ZjP83FSBoQuPs4BrsNBlUW659KmRrDY6G1lyQtW3Hy0lqQ1qVJnAbm
oy3b9KYJCqRj3o4zRctIlhjLHmpSuOBRbQOpLGpPFaES7UqkZzKN1KsEc87/AHUUmvPLTVGv72aQ
k7WJwKmRrQ3ud74Ltilgz4++2a6iNDXdS0gjyMU71my7GpqTbxSbMki3SViajTTHqkSeR/GeyZmg
nQHkEE1S+F+oPPavBL96I4/Cia1udVF+4dVrkW+Fq8+v4tjMDWUkdVJ6WM0cNV+F+MVmjUcZgqnP
1qpNNnkcVRLiZtxIS1UzzIF7mghlxUZpVQdq6nTVdAoAOKzkbQWhvwM6gMM1twOJYx3NOJE11Kt1
H1/pVVlwBkk+9NocXoOQ45FPj+fkUJFF2NSB700v/hTEty5ZpkjvVyUgcCq6GM9zC14/8Se6GcZQ
1574Xs5WkI2HBPHFQ1dm1KSSZ7Rotn9l0+KPHIHNacae1dy0Vjxaj5ptlhVp+2s2CJ9ppCKzuWNx
zSFc1SYrHNeNdIGpaYw25ZeRXmvheyk0jVpEdcLJ0q3ZxNKTa0O3vQHg/DNcHrsJDmsmjspnNzNt
fFIJ24GazOhC+azDmgZIOOKBsp3J2qSaZodubq58yQ4QAnmhGT3NO18pb7BORmu205LfYpyKVkWp
Oxr5gKYWoIZWgfGfloFq1qTPLubnGO1RPtxg4P0oBAkY/hBz6VNDDkZ6AU0W2WSdqkdKr9ZOaGSj
VtcLHmnOcgmmYvcz7mBLy3MbdD1q9ouiRK6bUAVeelOC1InPlidSsWMDFOCEdq3uefykqrinYqGy
rFvApMVka2DAowKAsMkRXQqwyDXn/iWyitNQ3qPl6itIvRoF8RXinW4tQ6HI6GuW8SIVBPalc6qe
5x9x97r3qruwTjrWZ0ksZ9TUmcDNAmZ9/wAoao63rR0+w22MLPtAzt6mghmfofiB76LdJBJBIp5D
d/oa7bSdWLIPnpDi9TM8TeKdas51XTbIyxd3J/pXS+E/EFxqNoFu7do5OmD60maHWrnZyDRkn/69
MlEyOR0xntVoNx+FUgYjPxg4FLCuWDZyKQr2RoRnP0qO+nEFpJITgAUzLqZnhu6+0rknOTXpOmwJ
Fbrt5yMmnHYyr6Oxb2ijaKLnPYMClwKQWK3n0hn+lachHOJ9pNNN0apQFzsY10a4v4hXQh0xpieQ
MA1XLZNjhK80cT8OdV+3Wl3A7ZZJCw+hrR1qLcjZ/CsbnfHRnFXseHJArOYYbrUs1uPhYbuatqFP
ByfSkMq3UIINYkto+87Tx6GkSxfsDbflGD7CtTw/pk4nzITtPIFMFudsukh4Rxz71paTpKwP5jcn
0qTRy0NORMDgVCqewoJTJgAoxjntTiTu7fWmFxAcnn1q3EPl+X8KZMi4gKqB1Peob/Tv7Us5bfeU
yOoq4R5nYxqT5I8xieH9J1DTbvyJELRg8ODwa9Ms5mSFV9BWiptbnNVrKdmif7Q1KLg96XIZc5Is
pNL5pqeUrmMtZs0jzV08phchaY00zH1p2ZNxjS1g+LdJOt6U9ssmxjyGp2urDjLlaZzng/wUPDqz
TSTmWeTrjpVjVk3Rvjr2rnqQ5dDvo1XUd2cTqSNk9OKxXGCeKxZ1DAxHTr2q5C/y8GokUhsz54qu
uCxzSQjQ0+FZblR2ro4bZYiMVQ0dBb7Qi5x0qzuG5QOh71LYErDufpSeWrHnimIXbjkUjLkH1Hem
gGxryc+tXI19KYmWegq9YLiLJ7mtqS945cS7QsWehqxA9dEjz4krPSxyZqbFFhGxUm6smjRM55Lk
HvSvNxXTY57kLT+9MNwKdhXGm5FIbkU7Bca1wMEVhaiuQcVhXWiZ14R6tHGanGBI2OtYkqEHjgVy
s9ErEeo6UBsHipKEZs5qpPdRxcbhx70NCSuybTNWihc5brW9Fq6vjMnFSdEIdDRi8RRKygZbHFbu
m6nb3RA3gMegNJhOm0jbXGOoxTuCc1Rz3FyoGKawz9KaAVcZqeMgCmIkB4FaUTbYwB6V00Fuzixb
0SFMuDU8Mlbs4UPeXHeiOXkUrDuXYnyKk3cVk0ap6HMxxketSMhrcwRC0dMMZFMQ3yzSeVQAeUaz
9Vj8uPd271nVV4m+GdpnHX67pCeKyLtBtNcR6xlk9RVeWTb3qRnO6trgttyIfm71z7ai8j7/AJmN
DNqUVa5Yi1AnjynHuBV+11YJhWWXcP8AZNSzqgmaEerSsf3NtIQP4mGKtRavdRgMIpVI9KjU0a7n
R6T43uYQI7qN2Tpkqciu503VVuQGAYZHQjFVc4alPlZrpKGAznpTwxOc9+lWjIlUACnM4XApiLNk
nmvnsK0NvpXZRVonmYqV52GsmanhXitTmFkSiJTSAvwrxUxXIrJ7miOfjf1pzNWxkRlqYWpgJupu
6gQbuahvIxPA6eo4pNXVioS5WmefakGhndH4INZs5DJXA10PaTurmLO21uKpSZqGMoXGnRzBiyjd
9Kx5rcQS428fSkjanLoaOliHGZFB56VswW+mtPufcBsGOAfmxz+tFkd8HpoaUx09FAtFY8DO71qb
Sms/Nb7RbecG6AEjFLS5c78t+p0djpVs9wsyQiJAdyr1rW+zqjErzSe559Sbk9S3C+MA1bjbgE1S
MSXzMVG0vNUI2tPKrAuCMnrVzNd0PhR49W/O2xrHmp4TxVMzQshpIzzQBehqesnuaI5VGzT2bitz
FEbNTC1ADS1JupgG6l3UAc14s04yR/aYRll+8BXCtLncDXFWjys9TCz5oW7GddH5qqNzWDOgQnC8
VSuo1kHzAGkPYopEY2+RWxV23Vzj5G/Kg3jWaNazhZuqNXS6TaKhB2c0jR1nJWOlhOxRxU4YkCgx
Y0OQatQyDbyaaFYe8uF4NY3iC9ltbVGj43NTIL3h7WzMihjzXVQXYYDdW9Cf2WcOJpfaRZ3g9KsQ
mupnCLIabGeaAL0LcVY3cVmzRHIxtUhetzEjZqjLUAIWpN1ArhupwagAfDKQ3Q1594v0c2bm6tx+
5Y8j+6ayrR5onThp8s7dzkZjuqAAmuBnqC7c0iwgtzSA0rWzjfGRW3ZadDu4AoNYo2rfS4v7orSh
05UA2r0pDbsTm29KRottBNyJ0wpJ9KhD7f6U0ikNWffIFBz60zVUW52ow4UcUN6EPcx44WsbgOmd
ua7TT5Bd24KHnFKnLlZFSN4koluLdueRWvp14swweG9DXoxldHlTjYtzGoo25qzEvwtUxas2jRPQ
5CNqkLVsYoYzUzdQA3dSFqBBmnqaBhuqhriCXTpVIzxUz+Fl03aSPI9QTypW2/dz0qKNw3SvOPZR
Mqin8VLKRcs3O4Cuk0w/MDjt1NBtHY6O2IIHY1pxgFaETIRwMkjtVSUEk4570MlFW5bap6dKzWm8
1tqH8aY+hp2FvGoGayNevVt7/ap4xzUvYjqTLtvLPcvJxSaVcyWsxTnFZlnT2t15xHmCtOBYwQy4
B9q7cPO+jPPxFO2qLEj5HWo42+aus4HpoX4W4FTF+KlotbHII9SFuK0MUNZqiLUDE3UbqBBupwag
Bc1DefPbyD/ZND2KjujyPWlKzuPesRZjHJXms9lMuw3StjnmphKDSLTJ7OfE3JrpbO4GQc9qlnRA
3LO82k5NbFvdADkjBoCSHyXIIIzgVQvdRigT7wzjgUzO1jHknlvG7qnp61etYFQDIpCZoqVijzXn
3iC8EmsOuaCGb/heR/s0ijkVv6fbxy3QMg5xmsnuX0Ldzut3+UYTPWk+2GJSe+M1pFtamcldalmx
1eO4XaThhWnC+TXqR2PHqL3maUJ4qRjxSEjj42qXdxVmaGs1MJoATfSbqBAG5p6mgAzTJTmNvpQU
tzzHXY83D/U1zF5FhjgV5r3Pa6FMsV5HWnLe7RhqBRdmTwagN2d2K2rPU1C5LAnPrUs6Iysbdrq6
f3gK0BrUKj/WClY05iM6xLOcQAj3NT29uznfKSzHuadzNu7NSBFjHNSm5VO9IRnajqoWMhTzXFtA
bvUfMduSeg702Qz0rS7FbTToQFwzjJqaGTFyfK5PQViyzUuFmuIdgGABya5u/vTaN5cnUHFUmLoZ
zyskwlgJweSK6zQdUEwVJeGr0aUrxPLxEfe0OrhPAqVjxWhznGRtUwatDK4jNxURbmkAm6jNABup
6tQAFqhupNtu59qUnZFwV5JHnWsHdIx96w5lz15rzT2uhRmt85xWbcxMnUGmZlB0bdxmrNvFIcfM
350mWjbs7YkDJY/jW5ZWW4jikWkdNp9mqYJFaJdEHHakUULu/VB1rLn1Ld/FgetMGYd/qWSQmSa0
/AemS32pfa7piLeLkg9z6UmQtz0W7uQ2cZx0A9BVzR7cAea6j2rPqX0L99KRat5A6Dk1wOoKZ52a
YfMORTYRLujiGWEq6/NWza2yKQVHNdOHerRy4laJo6TTnbbtb8KuM3Fdh5z3OJjbmpt3FaMxAtUZ
agBN1GaQBzTwaAAms3VbjERUGsa07RsdeFpuUuY4jUjljWTKK4j02RE4IpJYFk6imQkVl0xWarsO
mAEcUi0bNnZBR0rWtoguMCkUi21wI161mXuocEKaYXMS4u+pY/hVCSWSY4HT0pEmlouiSahdpEBl
mOceleiwWcNjClvHgJH97Hc1EmVFFi3Czy7mwIl/WtJbjP7uLgd/apQ2VNVvtsBhiPzdK5S4nAuR
nqOCaTGi9pcytPlU+XpmumtWII44rah8ZjiNIXRuWeNvvViQ/LXpJWPJbu7nCRvVkNxVsxBmqJmo
EPiXca0YLMuOlJsuKuPlsSi5IrNuG8s4HWs5VEkbwoOTKsk+FJY4rC1K53k1xTk5O7PSpwVNWRzt
4cms+WpKICtSLTETQj5q0YeBSGiys23pUguGxQMq3E59ayrm4x3yaAKiRtO2WPHcmhruKFxFajzZ
ScA44qRHoXhuMaLpxaUg6hcDLMf4F9KlhuDeXGASIl+8azZslYma68y48m1+7nFW5rtbRNhb5z1p
iMKbUg0zuW4A4rPgb7VdKXOMmpA7HRbMS7nUYiUda0lkQOBngVrS+JGdbWLRt2bAx5BqeQ/LXpnj
PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l
c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1
8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3
ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY
euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,D0=`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`;var j8="1.7.1";var Jl,Jc,Qc,Bi,O0,eh,z0,P0,L0,U8=class{constructor(t={}){Jl.set(this,void 0);Jc.set(this,void 0);Qc.set(this,void 0);Bi.set(this,void 0);this.analyze=(...t)=>{if(!rr(this,Jc))return;let n=this.tf.engine().state.numTensors,r=rr(this,Jl);ss(this,Jl,n);let a=n-r;a!==0&&pe(...t,a)};O0.set(this,t=>{if(!rr(this,Qc))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Le))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});eh.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=it();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&pe("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&pe("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&pe("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&pe(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!a&&pe("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&e8();try{await this.tf.setBackend(this.config.backend)}catch(a){pe("error: cannot set backend:",this.config.backend,a)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(pe("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let a=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&pe(`gl version:${a.getParameter(a.VERSION)} renderer:${a.getParameter(a.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(it()-r)}});z0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t($0);break;case"full":n=await t(D0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});P0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+$0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+D0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));L0.set(this,async()=>{let t=a=>Buffer.from(a,"base64"),n;if(this.config.warmup==="face"&&(n=t($0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(D0)),!n)return null;let r;if(typeof void 0!="undefined"){let a=(void 0).decodeJpeg(n),s=a.expandDims(0);this.tf.dispose(a),r=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&pe("Warmup tfjs-node not loaded");return r});this.tf=cu,this.draw=_g,this.version=j8,this.config=Gn(ct,t),this.state="idle",ss(this,Jl,0),ss(this,Jc,!1),ss(this,Qc,!1),ss(this,Bi,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>bg(n,this.config),this.classes={facemesh:X2,emotion:$2,faceres:P2,body:this.config.body.modelPath.includes("posenet")?ng:pg,hand:ug,nanodet:Ag},this.faceTriangulation=h8,this.faceUVMap=d8,this.sysinfo=zg()}profileData(){return this.config.profile?p0:{}}similarity(t,n){return W2(t,n)}enhance(t){return B2(t)}match(t,n,r=0){return t8(t,n,r)}async load(t={}){this.state="load";let n=it();t&&(this.config=Gn(this.config,t)),rr(this,Bi)&&(this.config.debug&&pe(`version: ${this.version}`),this.config.debug&&pe(`tfjs version: ${this.tf.version_core}`),this.config.debug&&pe("platform:",this.sysinfo.platform),this.config.debug&&pe("agent:",this.sysinfo.agent),await rr(this,eh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&pe("configuration:",this.config),this.config.debug&&pe("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?Z2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?z2(this.config):null),this.models.handpose||(this.config.hand.enabled?dg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?ag(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?fg(this.config):null),this.models.nanodet||(this.config.object.enabled?gg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?L2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await Z2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await z2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await dg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await ag(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await fg(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await gg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await L2(this.config))),rr(this,Bi)&&(this.config.debug&&pe("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ss(this,Bi,!1));let r=Math.trunc(it()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var A,y;this.state="config";let a;this.config=Gn(this.config,n),this.state="check";let s=rr(this,O0).call(this,t);s&&(pe(s,t),r({error:s}));let i=it();await rr(this,eh).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&typeof window!="undefined"&&typeof WorkerGlobalScope!="undefined"&&(typeof HTMLImageElement!="undefined"&&t instanceof HTMLImageElement||typeof Image!="undefined"&&t instanceof Image||typeof ImageData!="undefined"&&t instanceof ImageData||typeof ImageBitmap!="undefined"&&wg instanceof ImageBitmap)&&(pe("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=it();let l=bg(t,this.config);if(!l||!l.tensor){pe("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(it()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?V2(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=it(),h=this.config.face.enabled?await V2(this,l.tensor):[],p=Math.trunc(it()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?rg(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")&&(c=this.config.body.enabled?mg(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=it(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await rg(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")&&(c=this.config.body.enabled?await mg(l.tensor,this.config):[]),p=Math.trunc(it()-a),p>0&&(this.perf.body=p)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(A=this.models.handpose)==null?void 0:A.estimateHands(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=it(),u=this.config.hand.enabled?await((y=this.models.handpose)==null?void 0:y.estimateHands(l.tensor,this.config)):[],p=Math.trunc(it()-a),p>0&&(this.perf.hand=p)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(d=this.config.object.enabled?xg(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=it(),d=this.config.object.enabled?await xg(l.tensor,this.config):[],p=Math.trunc(it()-a),p>0&&(this.perf.object=p)),this.analyze("End Object:"),this.config.async&&([h,c,u,d]=await Promise.all([h,c,u,d])),Te(l.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let m=[];this.config.gesture.enabled&&(a=it(),m=[...$8(h),...F8(c),...O8(u),...D8(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(it()-a)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(it()-i),this.state="idle";let f={face:h,body:c,hand:u,gesture:m,object:d,performance:this.perf,canvas:l.canvas};r(f)})}async warmup(t={}){let n=it();if(t&&(this.config=Gn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await rr(this,z0).call(this):typeof Image!="undefined"?a=await rr(this,P0).call(this):a=await rr(this,L0).call(this),this.config.videoOptimized=r;let s=it();return this.config.debug&&pe("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};Jl=new WeakMap,Jc=new WeakMap,Qc=new WeakMap,Bi=new WeakMap,O0=new WeakMap,eh=new WeakMap,z0=new WeakMap,P0=new WeakMap,L0=new WeakMap;return Rie;})();
/**
 * @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=human.js.map