ambiser
/
human
mirror of https://github.com/vladmandic/human
9
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
human/dist/demo-browser-index.js

5156 lines
1.3 MiB
Raw Blame History

/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var S8=Object.defineProperty;var gr=(e,t)=>{for(var n in t)S8(e,n,{get:t[n],enumerable:!0})};var K2=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)},ar=(e,t,n)=>(K2(e,t,"read from private field"),n?n.call(e):t.get(e)),cs=(e,t,n,r)=>(K2(e,t,"write to private field"),r?r.call(e,n):t.set(e,n),n);function Ee(...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 et=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function eo(...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]=eo(s,i):n[a]=i}),n),{})}function Z2(){let e,t;if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);n&&n[0]&&(e=n[0].match(/\(([^()]+)\)/g)[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 ${process.version}`);return{platform:e,agent:t}}var Th={};gr(Th,{Abs:()=>ao,Acos:()=>so,Acosh:()=>io,AdadeltaOptimizer:()=>ep,AdagradOptimizer:()=>tp,AdamOptimizer:()=>np,AdamaxOptimizer:()=>rp,Add:()=>Ea,AddN:()=>ps,All:()=>$h,Any:()=>Dh,ArgMax:()=>fs,ArgMin:()=>xu,Asin:()=>oo,Asinh:()=>lo,Atan:()=>uo,Atan2:()=>ho,Atanh:()=>co,AvgPool:()=>ms,AvgPool3D:()=>wu,AvgPool3DGrad:()=>zh,AvgPoolGrad:()=>Oh,BackendWasm:()=>L3,BatchMatMul:()=>As,BatchToSpaceND:()=>bu,Bincount:()=>Ph,BroadcastTo:()=>p5,Callback:()=>Cv,CallbackList:()=>T7,Cast:()=>ys,Ceil:()=>gs,ClipByValue:()=>Ca,Complex:()=>Lh,ComplexAbs:()=>_u,Concat:()=>po,Conv2D:()=>xs,Conv2DBackpropFilter:()=>Wh,Conv2DBackpropInput:()=>ws,Conv3D:()=>vu,Conv3DBackpropFilterV2:()=>Bh,Conv3DBackpropInputV2:()=>Vh,Cos:()=>bs,Cosh:()=>fo,CropAndResize:()=>mo,Cumsum:()=>_s,CustomCallback:()=>C7,DataStorage:()=>Ch,DenseBincount:()=>Uh,DepthToSpace:()=>Ao,DepthwiseConv2dNative:()=>vs,DepthwiseConv2dNativeBackpropFilter:()=>jh,DepthwiseConv2dNativeBackpropInput:()=>Hh,Diag:()=>Gh,Dilation2D:()=>ku,Dilation2DBackpropFilter:()=>Xh,Dilation2DBackpropInput:()=>qh,ENV:()=>xr,EarlyStopping:()=>Fv,Elu:()=>yo,EluGrad:()=>Kh,Environment:()=>h5,Equal:()=>xo,Erf:()=>go,Exp:()=>Is,ExpandDims:()=>wo,Expm1:()=>bo,FFT:()=>Zh,Fill:()=>Iu,FlipLeftRight:()=>_o,Floor:()=>Ns,FloorDiv:()=>Ss,FromPixels:()=>hd,FusedBatchNorm:()=>Ts,FusedConv2D:()=>ii,FusedDepthwiseConv2D:()=>oi,GPGPUContext:()=>wp,GatherNd:()=>ko,GatherV2:()=>vo,GraphModel:()=>l6,Greater:()=>Io,GreaterEqual:()=>Es,History:()=>E7,IFFT:()=>Yh,Identity:()=>Cs,Imag:()=>Jh,InputSpec:()=>Zt,IsFinite:()=>No,IsInf:()=>So,IsNan:()=>To,KernelBackend:()=>Au,LRN:()=>Tu,LRNGrad:()=>ed,LayerVariable:()=>v7,LayersModel:()=>ma,LeakyRelu:()=>Rs,Less:()=>Eo,LessEqual:()=>Co,LinSpace:()=>Qh,Log:()=>Fs,Log1p:()=>Ro,LogSoftmax:()=>f5,LogicalAnd:()=>Fo,LogicalNot:()=>Nu,LogicalOr:()=>Su,MathBackendCPU:()=>op,MathBackendWebGL:()=>Pl,Max:()=>Ms,MaxPool:()=>Ds,MaxPool3D:()=>Eu,MaxPool3DGrad:()=>nd,MaxPoolGrad:()=>td,MaxPoolWithArgmax:()=>rd,Maximum:()=>$s,Mean:()=>Os,Min:()=>zs,Minimum:()=>Ps,MirrorPad:()=>Cu,Mod:()=>Mo,MomentumOptimizer:()=>ap,Multinomial:()=>ad,Multiply:()=>Ls,Neg:()=>$o,NonMaxSuppressionV3:()=>Oo,NonMaxSuppressionV4:()=>zo,NonMaxSuppressionV5:()=>Po,NotEqual:()=>Do,OP_SCOPE_SUFFIX:()=>I5,OneHot:()=>Ws,OnesLike:()=>Lo,Optimizer:()=>ha,Pack:()=>Wo,PadV2:()=>Bs,Pool:()=>Tk,Pow:()=>Vs,Prelu:()=>Us,Prod:()=>Bo,RMSPropOptimizer:()=>sp,RNN:()=>Kr,Range:()=>Ru,Rank:()=>Nf,Real:()=>sd,RealDiv:()=>ks,Reciprocal:()=>Vo,Reduction:()=>pn,Relu:()=>js,Relu6:()=>Gs,Reshape:()=>Uo,ResizeBilinear:()=>Hs,ResizeBilinearGrad:()=>od,ResizeNearestNeighbor:()=>Fu,ResizeNearestNeighborGrad:()=>id,Reverse:()=>qs,RotateWithOffset:()=>rl,Round:()=>Xs,Rsqrt:()=>Ks,SGDOptimizer:()=>hc,ScatterNd:()=>jo,Select:()=>Ho,Selu:()=>Go,Sequential:()=>Xl,Sigmoid:()=>Ys,Sign:()=>Ko,Sin:()=>Zs,Sinh:()=>Xo,Slice:()=>qo,Softmax:()=>ei,Softplus:()=>Zo,SpaceToBatchND:()=>Mu,SparseToDense:()=>ld,SplitV:()=>Yo,Sqrt:()=>Js,Square:()=>$u,SquaredDifference:()=>ti,Step:()=>Fa,StridedSlice:()=>Jo,Sub:()=>ni,Sum:()=>Qs,SymbolicTensor:()=>Tr,Tan:()=>Qo,Tanh:()=>ri,Tensor:()=>He,TensorBuffer:()=>Pt,Tile:()=>Ra,TopK:()=>el,Transform:()=>ud,Transpose:()=>ai,Unique:()=>cd,Unpack:()=>tl,UnsortedSegmentSum:()=>Du,Variable:()=>Vu,ZerosLike:()=>nl,_FusedMatMul:()=>si,abs:()=>Lt,acos:()=>Jf,acosh:()=>Qf,add:()=>ie,addN:()=>za,all:()=>vd,any:()=>qu,argMax:()=>Xu,argMin:()=>em,asin:()=>tm,asinh:()=>nm,atan:()=>rm,atan2:()=>am,atanh:()=>sm,avgPool:()=>Zu,avgPool3d:()=>lm,backend:()=>ix,backend_util:()=>R,basicLSTMCell:()=>iN,batchNorm:()=>fi,batchNorm2d:()=>cx,batchNorm3d:()=>hx,batchNorm4d:()=>dx,batchToSpaceND:()=>Yu,bincount:()=>px,booleanMaskAsync:()=>hE,broadcastTo:()=>Ju,browser:()=>hl,buffer:()=>Ve,callbacks:()=>Rne,cast:()=>ge,ceil:()=>um,clipByValue:()=>kn,clone:()=>Or,complex:()=>Ma,concat:()=>it,concat1d:()=>fx,concat2d:()=>Al,concat3d:()=>mx,concat4d:()=>Ax,constraints:()=>Z3,conv1d:()=>Id,conv2d:()=>oa,conv2dTranspose:()=>Nd,conv3d:()=>hm,conv3dTranspose:()=>TN,copyRegisteredKernels:()=>Rk,cos:()=>Qu,cosh:()=>Sd,cosineWindow:()=>Pm,cumsum:()=>Td,customGrad:()=>Lr,data:()=>u6,denseBincount:()=>gx,deprecationWarn:()=>Zf,depthToSpace:()=>dm,depthwiseConv2d:()=>yl,deregisterOp:()=>Mne,device_util:()=>ju,diag:()=>ON,dilation2d:()=>pm,disableDeprecationWarnings:()=>wI,dispose:()=>Te,disposeVariables:()=>bI,div:()=>be,divNoNan:()=>fm,dot:()=>xx,dropout:()=>Wx,elu:()=>gl,enableDebugMode:()=>xI,enableProdMode:()=>gI,enclosingPowerOfTwo:()=>Bx,engine:()=>zr,env:()=>J,equal:()=>La,erf:()=>mm,exp:()=>Zn,expandDims:()=>tn,expm1:()=>Am,eye:()=>ym,fft:()=>uc,fill:()=>ec,findBackend:()=>Yf,findBackendFactory:()=>SI,floor:()=>xl,floorDiv:()=>_d,forceHalfFloat:()=>Jb,fused:()=>Ua,gather:()=>mi,gatherND:()=>Lx,gather_util:()=>Uf,getBackend:()=>II,getGradient:()=>vf,getKernel:()=>dd,getKernelsForBackend:()=>sl,gpgpu_util:()=>_b,grad:()=>hS,grads:()=>dS,greater:()=>lr,greaterEqual:()=>Ba,ifft:()=>kl,imag:()=>Ed,image:()=>Ue,inTopKAsync:()=>_E,initializers:()=>r7,input:()=>A7,io:()=>_n,irfft:()=>Hd,isFinite:()=>wx,isInf:()=>bx,isNaN:()=>_x,keep:()=>qt,kernel_impls:()=>Ur,layers:()=>m7,leakyRelu:()=>tc,less:()=>Cd,lessEqual:()=>Ai,linalg:()=>Qx,linspace:()=>vx,loadGraphModel:()=>bt,loadLayersModel:()=>Yte,localResponseNormalization:()=>gm,log:()=>Dn,log1p:()=>Rd,logSigmoid:()=>Ix,logSoftmax:()=>Md,logSumExp:()=>bm,logicalAnd:()=>ur,logicalNot:()=>nc,logicalOr:()=>$d,logicalXor:()=>Ex,losses:()=>LC,matMul:()=>Ke,math:()=>B5,max:()=>Yn,maxPool:()=>rc,maxPool3d:()=>_m,maxPoolWithArgmax:()=>Cx,maximum:()=>Wr,mean:()=>St,memory:()=>bd,metrics:()=>Sv,min:()=>bl,minimum:()=>_l,mirrorPad:()=>vm,mod:()=>km,model:()=>Kte,models:()=>Tv,moments:()=>Dd,movingAverage:()=>fE,mul:()=>P,multiRNNCell:()=>BS,multinomial:()=>Rx,neg:()=>Nt,nextFrame:()=>ip,norm:()=>Kd,notEqual:()=>gi,oneHot:()=>cl,ones:()=>Br,onesLike:()=>On,op:()=>D,outerProduct:()=>GS,pad:()=>la,pad1d:()=>KS,pad2d:()=>YS,pad3d:()=>QS,pad4d:()=>tT,pool:()=>Fx,pow:()=>ua,prelu:()=>sc,print:()=>D5,prod:()=>Od,profile:()=>vn,rand:()=>cT,randomGamma:()=>fT,randomNormal:()=>Mx,randomUniform:()=>vl,range:()=>zd,ready:()=>kI,real:()=>ic,reciprocal:()=>Sm,registerBackend:()=>pl,registerCallbackConstructor:()=>Jte,registerGradient:()=>m5,registerKernel:()=>li,registerOp:()=>Fne,regularizers:()=>Ev,relu:()=>Vr,relu6:()=>Pd,removeBackend:()=>NI,reshape:()=>H,reverse:()=>zn,reverse1d:()=>vT,reverse2d:()=>IT,reverse3d:()=>ST,reverse4d:()=>ET,rfft:()=>cc,round:()=>Tm,rsqrt:()=>Ld,scalar:()=>Ie,scatterND:()=>Px,scatter_util:()=>jf,selu:()=>Wd,separableConv2d:()=>Em,sequential:()=>Zte,serialization:()=>ae,setBackend:()=>vI,setPlatform:()=>TI,setWasmPath:()=>GZ,setWasmPaths:()=>qZ,setWebGLContext:()=>Ap,setdiff1dAsync:()=>$x,shared:()=>Vm,sigmoid:()=>$n,sign:()=>Cm,signal:()=>PC,sin:()=>Bd,sinh:()=>Vd,slice:()=>Me,slice1d:()=>Ud,slice2d:()=>Rm,slice3d:()=>jd,slice4d:()=>oc,slice_util:()=>hn,softmax:()=>lc,softplus:()=>wl,spaceToBatchND:()=>ac,sparseToDense:()=>zm,spectral:()=>zC,split:()=>Bt,sqrt:()=>nn,square:()=>ut,squaredDifference:()=>Gd,squeeze:()=>Va,stack:()=>dn,step:()=>Il,stridedSlice:()=>Fm,sub:()=>we,sum:()=>Fe,sumOutType:()=>Ad,tan:()=>Mm,tanh:()=>ml,tensor:()=>_r,tensor1d:()=>un,tensor2d:()=>Nn,tensor3d:()=>xd,tensor4d:()=>tE,tensor5d:()=>nE,tensor6d:()=>rE,tensor_util:()=>wr,test_util:()=>rx,tidy:()=>z,tile:()=>Wa,time:()=>_I,topk:()=>$m,train:()=>wi,transpose:()=>st,truncatedNormal:()=>qd,unique:()=>Xd,unregisterGradient:()=>Ck,unregisterKernel:()=>Ek,unsortedSegmentSum:()=>Dm,unstack:()=>cr,upcastType:()=>or,util:()=>v,valueAndGrad:()=>pS,valueAndGrads:()=>fS,variable:()=>Dx,variableGrads:()=>kx,version:()=>gae,version_converter:()=>Fre,version_core:()=>yI,version_cpu:()=>Ew,version_layers:()=>oy,version_wasm:()=>B3,version_webgl:()=>Yb,webgl:()=>cL,webgl_util:()=>Zw,where:()=>In,whereAsync:()=>Om,zeros:()=>Mt,zerosLike:()=>qe});var T8=Object.create,Eh=Object.defineProperty,E8=Object.getPrototypeOf,C8=Object.prototype.hasOwnProperty,R8=Object.getOwnPropertyNames,F8=Object.getOwnPropertyDescriptor,M8=e=>Eh(e,"__esModule",{value:!0}),kt=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Le=(e,t)=>{for(var n in t)Eh(e,n,{get:t[n],enumerable:!0})},$8=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of R8(t))!C8.call(e,r)&&r!=="default"&&Eh(e,r,{get:()=>t[r],enumerable:!(n=F8(t,r))||n.enumerable});return e},to=e=>$8(M8(Eh(e!=null?T8(E8(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),D8=kt(()=>{}),O8=kt((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)}),z8=kt((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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),P8=kt((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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),L8=kt((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,d=c.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,c.i=d+1&7,f};function u(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),W8=kt((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,f,m;return c.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,c.i=p,m+(h^h>>>16)|0};function u(h,d){var p,f,m,A,y,g=[],w=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,w=Math.max(w,d.length)),m=0,A=-32;A<w;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),B8=kt((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var d=c.b,p=c.c,f=c.d,m=c.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,c.b=d=d<<20^d>>>12^p,c.c=p=p-f|0,c.d=f<<16^p>>>16^m,c.a=m-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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),Y2=kt(()=>{}),V8=kt((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 f(_,x,N){var T=[];x=x==!0?{entropy:!0}:x||{};var E=g(y(x.entropy?[_,b(n)]:_==null?w():_,3),T),M=new m(T),O=function(){for(var W=M.g(i),V=c,U=0;W<u;)W=(W+U)*s,V*=s,U=M.g(1);for(;W>=h;)W/=2,V/=2,U>>>=1;return(W+U)/V};return O.int32=function(){return M.g(4)|0},O.quick=function(){return M.g(4)/4294967296},O.double=O,g(b(M.S),n),(x.pass||N||function(W,V,U,j){return j&&(j.S&&A(j,M),W.state=function(){return A(M,{})}),U?(r[l]=W,V):W})(O,E,"global"in x?x.global:this==r,x.state)}r["seed"+l]=f;function m(_){var x,N=_.length,T=this,E=0,M=T.i=T.j=0,O=T.S=[];for(N||(_=[N++]);E<s;)O[E]=E++;for(E=0;E<s;E++)O[E]=O[M=d&M+_[E%N]+(x=O[E])],O[M]=x;(T.g=function(W){for(var V,U=0,j=T.i,X=T.j,G=T.S;W--;)V=G[j=d&j+1],U=U*s+G[d&(G[j]=G[X=d&X+V])+(G[X]=V)];return T.i=j,T.j=X,U})(s)}function A(_,x){return x.i=_.i,x.j=_.j,x.S=_.S.slice(),x}function y(_,x){var N=[],T=typeof _,E;if(x&&T=="object")for(E in _)try{N.push(y(_[E],x-1))}catch(M){}return N.length?N:T=="string"?_:_+"\0"}function g(_,x){for(var N=_+"",T,E=0;E<N.length;)x[d&E]=d&(T^=x[d&E]*19)+N.charCodeAt(E++);return b(x)}function w(){try{var _;return p&&(_=p.randomBytes)?_=_(s):(_=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(_)),b(_)}catch(T){var x=a.navigator,N=x&&x.plugins;return[+new Date,a,N,a.screen,b(n)]}}function b(_){return String.fromCharCode.apply(0,_)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=Y2()}catch(_){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),J2=kt((e,t)=>{var n=O8(),r=z8(),a=P8(),s=L8(),i=W8(),o=B8(),l=V8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),mu=kt(()=>{}),U8=kt(()=>{}),j8=kt(()=>{}),H8=kt((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!=je&&Qt(Q.buffer),yn}function i(){return Q.buffer!=je&&Qt(Q.buffer),vt}function o(){return Q.buffer!=je&&Qt(Q.buffer),gn}function l(){return Q.buffer!=je&&Qt(Q.buffer),qn}function c(){return Q.buffer!=je&&Qt(Q.buffer),cn}var u=typeof a!="undefined"?a:{},h,d;u.ready=new Promise(function(I,S){h=I,d=S});var p={},f;for(f in u)u.hasOwnProperty(f)&&(p[f]=u[f]);var m=[],A="./this.program",y=function(I,S){throw S},g=!1,w=!1,b=!1,_=!1;g=typeof window=="object",w=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",_=!g&&!b&&!w;var x=u.ENVIRONMENT_IS_PTHREAD||!1;x&&(je=u.buffer);var N="";function T(I){return u.locateFile?u.locateFile(I,N):N+I}var E,M,O,W,V,U;if(b){w?N=mu().dirname(N)+"/":N=__dirname+"/",E=function(I,S){return V||(V=require("fs")),U||(U=mu()),I=U.normalize(I),V.readFileSync(I,S?null:"utf8")},O=function(I){var S=E(I,!0);return S.buffer||(S=new Uint8Array(S)),me(S.buffer),S},process.argv.length>1&&(A=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(I){if(!(I instanceof fu))throw I}),process.on("unhandledRejection",na),y=function(I){process.exit(I)},u.inspect=function(){return"[Emscripten Module object]"};var j;try{j=U8()}catch(I){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),I}global.Worker=j.Worker}else _?(typeof read!="undefined"&&(E=function(I){return read(I)}),O=function(I){var S;return typeof readbuffer=="function"?new Uint8Array(readbuffer(I)):(S=read(I,"binary"),me(typeof S=="object"),S)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(y=function(I){quit(I)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(g||w)&&(w?N=self.location.href:typeof document!="undefined"&&document.currentScript&&(N=document.currentScript.src),typeof r!="undefined"&&r&&(N=r),N.indexOf("blob:")!==0?N=N.substr(0,N.lastIndexOf("/")+1):N="",b?(E=function(I,S){return V||(V=require("fs")),U||(U=mu()),I=U.normalize(I),V.readFileSync(I,S?null:"utf8")},O=function(I){var S=E(I,!0);return S.buffer||(S=new Uint8Array(S)),me(S.buffer),S}):(E=function(I){var S=new XMLHttpRequest;return S.open("GET",I,!1),S.send(null),S.responseText},w&&(O=function(I){var S=new XMLHttpRequest;return S.open("GET",I,!1),S.responseType="arraybuffer",S.send(null),new Uint8Array(S.response)}),M=function(I,S,L){var q=new XMLHttpRequest;q.open("GET",I,!0),q.responseType="arraybuffer",q.onload=function(){if(q.status==200||q.status==0&&q.response){S(q.response);return}L()},q.onerror=L,q.send(null)}),W=function(I){document.title=I});b&&typeof performance=="undefined"&&(global.performance=j8().performance);var X=u.print||console.log.bind(console),G=u.printErr||console.warn.bind(console);for(f in p)p.hasOwnProperty(f)&&(u[f]=p[f]);p=null,u.arguments&&(m=u.arguments),u.thisProgram&&(A=u.thisProgram),u.quit&&(y=u.quit);var ee=Atomics.load,Y=Atomics.store,se=Atomics.compareExchange,te;u.wasmBinary&&(te=u.wasmBinary);var le=u.noExitRuntime||!0;typeof WebAssembly!="object"&&na("no native wasm support detected");var Q,pe,ue=!1,ye;function me(I,S){I||na("Assertion failed: "+S)}function Ne(I){var S=u["_"+I];return me(S,"Cannot call unknown function "+I+", make sure it is exported"),S}function Ce(I,S,L,q,fe){var ce={string:function(bn){var Qi=0;if(bn!=null&&bn!==0){var X2=(bn.length<<2)+1;Qi=Zi(X2),rt(bn,Qi,X2)}return Qi},array:function(bn){var Qi=Zi(bn.length);return Je(bn,Qi),Qi}};function de(bn){return S==="string"?Oe(bn):S==="boolean"?Boolean(bn):bn}var ve=Ne(I),at=[],Ht=0;if(q)for(var Ot=0;Ot<q.length;Ot++){var Na=ce[L[Ot]];Na?(Ht===0&&(Ht=pu()),at[Ot]=Na(q[Ot])):at[Ot]=q[Ot]}var Ji=ve.apply(null,at);return Ji=de(Ji),Ht!==0&&Ki(Ht),Ji}function De(I,S,L,q){L=L||[];var fe=L.every(function(de){return de==="number"}),ce=S!=="string";return ce&&fe&&!q?Ne(I):function(){return Ce(I,S,L,arguments,q)}}function Pe(I,S,L){for(var q=S+L,fe="";!(S>=q);){var ce=I[S++];if(!ce)return fe;if(!(ce&128)){fe+=String.fromCharCode(ce);continue}var de=I[S++]&63;if((ce&224)==192){fe+=String.fromCharCode((ce&31)<<6|de);continue}var ve=I[S++]&63;if((ce&240)==224?ce=(ce&15)<<12|de<<6|ve:ce=(ce&7)<<18|de<<12|ve<<6|I[S++]&63,ce<65536)fe+=String.fromCharCode(ce);else{var at=ce-65536;fe+=String.fromCharCode(55296|at>>10,56320|at&1023)}}return fe}function Oe(I,S){return I?Pe(i(),I,S):""}function nt(I,S,L,q){if(!(q>0))return 0;for(var fe=L,ce=L+q-1,de=0;de<I.length;++de){var ve=I.charCodeAt(de);if(ve>=55296&&ve<=57343){var at=I.charCodeAt(++de);ve=65536+((ve&1023)<<10)|at&1023}if(ve<=127){if(L>=ce)break;S[L++]=ve}else if(ve<=2047){if(L+1>=ce)break;S[L++]=192|ve>>6,S[L++]=128|ve&63}else if(ve<=65535){if(L+2>=ce)break;S[L++]=224|ve>>12,S[L++]=128|ve>>6&63,S[L++]=128|ve&63}else{if(L+3>=ce)break;S[L++]=240|ve>>18,S[L++]=128|ve>>12&63,S[L++]=128|ve>>6&63,S[L++]=128|ve&63}}return S[L]=0,L-fe}function rt(I,S,L){return nt(I,i(),S,L)}function lt(I){for(var S=0,L=0;L<I.length;++L){var q=I.charCodeAt(L);q>=55296&&q<=57343&&(q=65536+((q&1023)<<10)|I.charCodeAt(++L)&1023),q<=127?++S:q<=2047?S+=2:q<=65535?S+=3:S+=4}return S}function Je(I,S){s().set(I,S)}function pt(I,S){return I%S>0&&(I+=S-I%S),I}var je,yn,vt,Gn,Jt,gn,qn,Mn,cn;function Qt(I){je=I,u.HEAP8=yn=new Int8Array(I),u.HEAP16=Gn=new Int16Array(I),u.HEAP32=gn=new Int32Array(I),u.HEAPU8=vt=new Uint8Array(I),u.HEAPU16=Jt=new Uint16Array(I),u.HEAPU32=qn=new Uint32Array(I),u.HEAPF32=Mn=new Float32Array(I),u.HEAPF64=cn=new Float64Array(I)}var Mr=u.INITIAL_MEMORY||16777216;if(x)Q=u.wasmMemory,je=u.buffer;else if(u.wasmMemory)Q=u.wasmMemory;else if(Q=new WebAssembly.Memory({initial:Mr/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"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Q&&(je=Q.buffer),Mr=je.byteLength,Qt(je);var nr,rr=[],wa=[],ea=[],ba=[],Vi=[],yr=!1,sh=!1;x||wa.push({func:function(){bh()}}),x&&(yr=!0);function e1(){if(!x){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)lh(u.preRun.shift());ji(rr)}}function ih(){yr=!0,ji(wa)}function t1(){x||ji(ea)}function oh(){x||(sh=!0)}function xn(){if(!x){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)n1(u.postRun.shift());ji(Vi)}}function lh(I){rr.unshift(I)}function n1(I){Vi.unshift(I)}var ta=0,_a=null,os=null;function r1(I){me(!x,"addRunDependency cannot be used in a pthread worker"),ta++,u.monitorRunDependencies&&u.monitorRunDependencies(ta)}function a1(I){if(ta--,u.monitorRunDependencies&&u.monitorRunDependencies(ta),ta==0&&(_a!==null&&(clearInterval(_a),_a=null),os)){var S=os;os=null,S()}}u.preloadedImages={},u.preloadedAudios={};function na(I){u.onAbort&&u.onAbort(I),x&&console.error("Pthread aborting at "+new Error().stack),I+="",G(I),ue=!0,ye=1,I="abort("+I+"). Build with -s ASSERTIONS=1 for more info.";var S=new WebAssembly.RuntimeError(I);throw d(S),S}function uh(I,S){return String.prototype.startsWith?I.startsWith(S):I.indexOf(S)===0}var Ui="data:application/octet-stream;base64,";function ch(I){return uh(I,Ui)}var s1="file://";function hh(I){return uh(I,s1)}var wn="tfjs-backend-wasm-threaded-simd.wasm";ch(wn)||(wn=T(wn));function i1(I){try{if(I==wn&&te)return new Uint8Array(te);if(O)return O(I);throw"both async and sync fetching of the wasm failed"}catch(S){na(S)}}function dh(){if(!te&&(g||w)){if(typeof fetch=="function"&&!hh(wn))return fetch(wn,{credentials:"same-origin"}).then(function(I){if(!I.ok)throw"failed to load wasm binary file at '"+wn+"'";return I.arrayBuffer()}).catch(function(){return i1(wn)});if(M)return new Promise(function(I,S){M(wn,function(L){I(new Uint8Array(L))},S)})}return Promise.resolve().then(function(){return i1(wn)})}function o1(){var I={a:Y1};function S(de,ve){var at=de.exports;if(u.asm=at,nr=u.asm.F,pe=ve,!x){var Ht=Se.unusedWorkers.length;Se.unusedWorkers.forEach(function(Ot){Se.loadWasmModuleToWorker(Ot,function(){--Ht||a1("wasm-instantiate")})})}}x||r1("wasm-instantiate");function L(de){S(de.instance,de.module)}function q(de){return dh().then(function(ve){return WebAssembly.instantiate(ve,I)}).then(de,function(ve){G("failed to asynchronously prepare wasm: "+ve),na(ve)})}function fe(){return!te&&typeof WebAssembly.instantiateStreaming=="function"&&!ch(wn)&&!hh(wn)&&typeof fetch=="function"?fetch(wn,{credentials:"same-origin"}).then(function(de){var ve=WebAssembly.instantiateStreaming(de,I);return ve.then(L,function(at){return G("wasm streaming compile failed: "+at),G("falling back to ArrayBuffer instantiation"),q(L)})}):q(L)}if(u.instantiateWasm)try{var ce=u.instantiateWasm(I,S);return ce}catch(de){return G("Module.instantiateWasm callback failed with error: "+de),!1}return fe().catch(d),{}}var ph={8991:function(I,S){setTimeout(function(){V2(I,S)},0)}};function l1(){Se.initRuntime()}function ji(I){for(;I.length>0;){var S=I.shift();if(typeof S=="function"){S(u);continue}var L=S.func;typeof L=="number"?S.arg===void 0?nr.get(L)():nr.get(L)(S.arg):L(S.arg===void 0?null:S.arg)}}function Hi(I,S){if(I<=0||I>s().length||I&!0||S<0)return-28;if(S==0)return 0;S>=2147483647&&(S=Infinity);var L=Atomics.load(o(),Yi>>2),q=0;if(L==I){var fe=Atomics.compareExchange(o(),Yi>>2,L,0);if(fe==L&&(--S,q=1,S<=0))return 1}var ce=Atomics.notify(o(),I>>2,S);if(ce>=0)return ce+q;throw"Atomics.notify returned an unexpected value "+ce}u._emscripten_futex_wake=Hi;function u1(I){if(x)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in killThread!";o()[I+12>>2]=0;var S=Se.pthreads[I];S.worker.terminate(),Se.freeThreadData(S),Se.runningWorkers.splice(Se.runningWorkers.indexOf(S.worker),1),S.worker.pthread=void 0}function c1(I){if(x)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in cancelThread!";var S=Se.pthreads[I];S.worker.postMessage({cmd:"cancel"})}function h1(I){if(x)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in cleanupThread!";o()[I+12>>2]=0;var S=Se.pthreads[I];if(S){var L=S.worker;Se.returnWorkerToPool(L)}}var Se={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var I=8,S=0;S<I;++S)Se.allocateUnusedWorker()},initRuntime:function(){for(var I=us(228),S=0;S<228/4;++S)l()[I/4+S]=0;o()[I+12>>2]=I;var L=I+152;o()[L>>2]=L;for(var q=us(512),S=0;S<128;++S)l()[q/4+S]=0;Atomics.store(l(),I+100>>2,q),Atomics.store(l(),I+40>>2,I),Nh(I,!w,1),B2(I)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Se.threadExitHandlers.length>0;)Se.threadExitHandlers.pop()();x&&Xi()&&W2()},threadExit:function(I){var S=Xi();S&&(Atomics.store(l(),S+4>>2,I),Atomics.store(l(),S+0>>2,1),Atomics.store(l(),S+56>>2,1),Atomics.store(l(),S+60>>2,0),Se.runExitHandlers(),Hi(S+0,2147483647),Nh(0,0,0),x&&postMessage({cmd:"exit"}))},threadCancel:function(){Se.runExitHandlers();var I=Xi();Atomics.store(l(),I+4>>2,-1),Atomics.store(l(),I+0>>2,1),Hi(I+0,2147483647),Nh(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var I in Se.pthreads){var S=Se.pthreads[I];S&&S.worker&&Se.returnWorkerToPool(S.worker)}Se.pthreads={};for(var L=0;L<Se.unusedWorkers.length;++L){var q=Se.unusedWorkers[L];q.terminate()}Se.unusedWorkers=[];for(var L=0;L<Se.runningWorkers.length;++L){var q=Se.runningWorkers[L],S=q.pthread;Se.freeThreadData(S),q.terminate()}Se.runningWorkers=[]},freeThreadData:function(I){if(I){if(I.threadInfoStruct){var S=o()[I.threadInfoStruct+100>>2];o()[I.threadInfoStruct+100>>2]=0,du(S),du(I.threadInfoStruct)}I.threadInfoStruct=0,I.allocatedOwnStack&&I.stackBase&&du(I.stackBase),I.stackBase=0,I.worker&&(I.worker.pthread=null)}},returnWorkerToPool:function(I){Se.runWithoutMainThreadQueuedCalls(function(){delete Se.pthreads[I.pthread.threadInfoStruct],Se.unusedWorkers.push(I),Se.runningWorkers.splice(Se.runningWorkers.indexOf(I),1),Se.freeThreadData(I.pthread),I.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(I){o()[q2>>2]=0;try{I()}finally{o()[q2>>2]=1}},receiveObjectTransfer:function(I){},loadWasmModuleToWorker:function(I,S){I.onmessage=function(L){var q=L.data,fe=q.cmd;if(I.pthread&&(Se.currentProxiedOperationCallerThread=I.pthread.threadInfoStruct),q.targetThread&&q.targetThread!=Xi()){var ce=Se.pthreads[q.targetThread];ce?ce.worker.postMessage(L.data,q.transferList):console.error('Internal error! Worker sent a message "'+fe+'" to target pthread '+q.targetThread+", but that thread no longer exists!"),Se.currentProxiedOperationCallerThread=void 0;return}if(fe==="processQueuedMainThreadWork")pf();else if(fe==="spawnThread")xh(L.data);else if(fe==="cleanupThread")h1(q.thread);else if(fe==="killThread")u1(q.thread);else if(fe==="cancelThread")c1(q.thread);else if(fe==="loaded")I.loaded=!0,S&&S(I),I.runPthread&&(I.runPthread(),delete I.runPthread);else if(fe==="print")X("Thread "+q.threadId+": "+q.text);else if(fe==="printErr")G("Thread "+q.threadId+": "+q.text);else if(fe==="alert")alert("Thread "+q.threadId+": "+q.text);else if(fe==="exit"){var de=I.pthread&&Atomics.load(l(),I.pthread.threadInfoStruct+64>>2);de&&Se.returnWorkerToPool(I)}else if(fe==="exitProcess")try{N8(q.returnCode)}catch(ve){if(ve instanceof fu)return;throw ve}else fe==="cancelDone"?Se.returnWorkerToPool(I):fe==="objectTransfer"?Se.receiveObjectTransfer(L.data):L.data.target==="setimmediate"?I.postMessage(L.data):G("worker sent an unknown command "+fe);Se.currentProxiedOperationCallerThread=void 0},I.onerror=function(L){G("pthread sent an error! "+L.filename+":"+L.lineno+": "+L.message)},b&&(I.on("message",function(L){I.onmessage({data:L})}),I.on("error",function(L){I.onerror(L)}),I.on("exit",function(L){})),I.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||r,wasmMemory:Q,wasmModule:pe})},allocateUnusedWorker:function(){var I=T("tfjs-backend-wasm-threaded-simd.worker.js");Se.unusedWorkers.push(new Worker(I))},getNewWorker:function(){return Se.unusedWorkers.length==0&&(Se.allocateUnusedWorker(),Se.loadWasmModuleToWorker(Se.unusedWorkers[0])),Se.unusedWorkers.length>0?Se.unusedWorkers.pop():null},busySpinWait:function(I){for(var S=performance.now()+I;performance.now()<S;);}};function d1(I,S){H2(I,S),Ki(I)}u.establishStackSpace=d1;function p1(){return le}u.getNoExitRuntime=p1;function f1(I,S){return nr.get(I)(S)}u.invokeEntryPoint=f1;function m1(I,S,L,q){na("Assertion failed: "+Oe(I)+", at: "+[S?Oe(S):"unknown filename",L,q?Oe(q):"unknown function"])}function A1(I,S){var L=_main(I,S)}var ls;b?ls=function(){var I=process.hrtime();return I[0]*1e3+I[1]/1e6}:x?ls=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?ls=dateNow:ls=function(){return performance.now()};function y1(I){return o()[P2()>>2]=I,I}function g1(I,S){if(x)return va(1,1,I,S)}function x1(I,S){if(I==S)postMessage({cmd:"processQueuedMainThreadWork"});else if(x)postMessage({targetThread:I,cmd:"processThreadQueue"});else{var L=Se.pthreads[I],q=L&&L.worker;if(!q)return;q.postMessage({cmd:"processThreadQueue"})}return 1}function w1(){na()}function b1(I,S,L){var q=N1(S,L);return ph[I].apply(null,q)}function _1(I,S){}function v1(I,S,L){if(I<=0||I>s().length||I&!0)return-28;if(g){if(Atomics.load(o(),I>>2)!=S)return-6;for(var q=performance.now(),fe=q+L,ce=Atomics.exchange(o(),Yi>>2,I);;){if(q=performance.now(),q>fe)return ce=Atomics.exchange(o(),Yi>>2,0),-73;if(ce=Atomics.exchange(o(),Yi>>2,0),ce==0)break;if(pf(),Atomics.load(o(),I>>2)!=S)return-6;ce=Atomics.exchange(o(),Yi>>2,I)}return 0}else{var de=Atomics.wait(o(),I>>2,S,L);if(de==="timed-out")return-73;if(de==="not-equal")return-6;if(de==="ok")return 0;throw"Atomics.wait returned an unexpected value "+de}}function k1(I,S,L){i().copyWithin(I,S,S+L)}function I1(){return b?require("os").cpus().length:navigator.hardwareConcurrency}function va(I,S){for(var L=arguments.length-2,q=pu(),fe=L,ce=Zi(fe*8),de=ce>>3,ve=0;ve<L;ve++){var at=arguments[2+ve];c()[de+ve]=at}var Ht=j2(I,fe,ce,S);return Ki(q),Ht}var iu=[],ou=[];function N1(I,S){ou.length=0;var L;for(S>>=2;L=i()[I++];){var q=L<105;q&&S&1&&S++,ou.push(q?c()[S++>>1]:o()[S]),++S}return ou}function S1(I,S,L){iu.length=S;for(var q=L>>3,fe=0;fe<S;fe++)iu[fe]=c()[q+fe];var ce=I<0,de=ce?ph[-I-1]:Z1[I];return de.apply(null,iu)}function T1(){return i().length}function E1(I){try{return Q.grow(I-je.byteLength+65535>>>16),Qt(Q.buffer),1}catch(S){}}function C1(I){var S=T1();if(I<=S)return!1;var L=2147483648;if(I>L)return!1;for(var q=1;q<=4;q*=2){var fe=S*(1+.2/q);fe=Math.min(fe,I+100663296);var ce=Math.min(L,pt(Math.max(I,fe),65536)),de=E1(ce);if(de)return!0}return!1}var Be={inEventHandler:0,removeAllEventListeners:function(){for(var I=Be.eventHandlers.length-1;I>=0;--I)Be._removeHandler(I);Be.eventHandlers=[],Be.deferredCalls=[]},registerRemoveEventListeners:function(){Be.removeEventListenersRegistered||(ba.push(Be.removeAllEventListeners),Be.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(I,S,L){function q(de,ve){if(de.length!=ve.length)return!1;for(var at in de)if(de[at]!=ve[at])return!1;return!0}for(var fe in Be.deferredCalls){var ce=Be.deferredCalls[fe];if(ce.targetFunction==I&&q(ce.argsList,L))return}Be.deferredCalls.push({targetFunction:I,precedence:S,argsList:L}),Be.deferredCalls.sort(function(de,ve){return de.precedence<ve.precedence})},removeDeferredCalls:function(I){for(var S=0;S<Be.deferredCalls.length;++S)Be.deferredCalls[S].targetFunction==I&&(Be.deferredCalls.splice(S,1),--S)},canPerformEventHandlerRequests:function(){return Be.inEventHandler&&Be.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Be.canPerformEventHandlerRequests())for(var I=0;I<Be.deferredCalls.length;++I){var S=Be.deferredCalls[I];Be.deferredCalls.splice(I,1),--I,S.targetFunction.apply(null,S.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(I,S){for(var L=0;L<Be.eventHandlers.length;++L)Be.eventHandlers[L].target==I&&(!S||S==Be.eventHandlers[L].eventTypeString)&&Be._removeHandler(L--)},_removeHandler:function(I){var S=Be.eventHandlers[I];S.target.removeEventListener(S.eventTypeString,S.eventListenerFunc,S.useCapture),Be.eventHandlers.splice(I,1)},registerOrRemoveHandler:function(I){var S=function(q){++Be.inEventHandler,Be.currentEventHandler=I,Be.runDeferredCalls(),I.handlerFunc(q),Be.runDeferredCalls(),--Be.inEventHandler};if(I.callbackfunc)I.eventListenerFunc=S,I.target.addEventListener(I.eventTypeString,S,I.useCapture),Be.eventHandlers.push(I),Be.registerRemoveEventListeners();else for(var L=0;L<Be.eventHandlers.length;++L)Be.eventHandlers[L].target==I.target&&Be.eventHandlers[L].eventTypeString==I.eventTypeString&&Be._removeHandler(L--)},queueEventHandlerOnThread_iiii:function(I,S,L,q,fe){var ce=pu(),de=Zi(12);o()[de>>2]=L,o()[de+4>>2]=q,o()[de+8>>2]=fe,ff(0,I,637534208,S,q,de),Ki(ce)},getTargetThreadForEventCallback:function(I){switch(I){case 1:return 0;case 2:return Se.currentProxiedOperationCallerThread;default:return I}},getNodeNameForTarget:function(I){return I?I==window?"#window":I==screen?"#screen":I&&I.nodeName?I.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function R1(I){var S=lt(I)+1,L=us(S);return rt(I,L,S),L}function F1(I,S,L,q){var fe=pu(),ce=Zi(12),de=0;S&&(de=R1(S)),o()[ce>>2]=de,o()[ce+4>>2]=L,o()[ce+8>>2]=q,ff(0,I,657457152,0,de,ce),Ki(fe)}function M1(I,S,L,q){S=S?Oe(S):"",F1(I,S,L,q)}function $1(I){return I>2?Oe(I):I}var D1=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function O1(I){I=$1(I);var S=D1[I]||(typeof document!="undefined"?document.querySelector(I):void 0);return S}function lu(I){return O1(I)}function fh(I,S,L){var q=lu(I);if(!q)return-4;if(q.canvasSharedPtr&&(o()[q.canvasSharedPtr>>2]=S,o()[q.canvasSharedPtr+4>>2]=L),q.offscreenCanvas||!q.controlTransferredOffscreen){q.offscreenCanvas&&(q=q.offscreenCanvas);var fe=!1;if(q.GLctxObject&&q.GLctxObject.GLctx){var ce=q.GLctxObject.GLctx.getParameter(2978);fe=ce[0]===0&&ce[1]===0&&ce[2]===q.width&&ce[3]===q.height}q.width=S,q.height=L,fe&&q.GLctxObject.GLctx.viewport(0,0,S,L)}else if(q.canvasSharedPtr){var de=o()[q.canvasSharedPtr+8>>2];return M1(de,I,S,L),1}else return-4;return 0}function mh(I,S,L){return x?va(2,1,I,S,L):fh(I,S,L)}function z1(I,S,L){var q=lu(I);return q?fh(I,S,L):mh(I,S,L)}function P1(I){}function L1(I,S){}function W1(I){var S=I.getExtension("ANGLE_instanced_arrays");if(S)return I.vertexAttribDivisor=function(L,q){S.vertexAttribDivisorANGLE(L,q)},I.drawArraysInstanced=function(L,q,fe,ce){S.drawArraysInstancedANGLE(L,q,fe,ce)},I.drawElementsInstanced=function(L,q,fe,ce,de){S.drawElementsInstancedANGLE(L,q,fe,ce,de)},1}function B1(I){var S=I.getExtension("OES_vertex_array_object");if(S)return I.createVertexArray=function(){return S.createVertexArrayOES()},I.deleteVertexArray=function(L){S.deleteVertexArrayOES(L)},I.bindVertexArray=function(L){S.bindVertexArrayOES(L)},I.isVertexArray=function(L){return S.isVertexArrayOES(L)},1}function V1(I){var S=I.getExtension("WEBGL_draw_buffers");if(S)return I.drawBuffers=function(L,q){S.drawBuffersWEBGL(L,q)},1}function U1(I){return!!(I.multiDrawWebgl=I.getExtension("WEBGL_multi_draw"))}var tt={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(I){tt.lastError||(tt.lastError=I)},getNewId:function(I){for(var S=tt.counter++,L=I.length;L<S;L++)I[L]=null;return S},getSource:function(I,S,L,q){for(var fe="",ce=0;ce<S;++ce){var de=q?o()[q+ce*4>>2]:-1;fe+=Oe(o()[L+ce*4>>2],de<0?void 0:de)}return fe},createContext:function(I,S){var L=I.getContext("webgl",S);if(!L)return 0;var q=tt.registerContext(L,S);return q},registerContext:function(I,S){var L=us(8);o()[L+4>>2]=Xi();var q={handle:L,attributes:S,version:S.majorVersion,GLctx:I};return I.canvas&&(I.canvas.GLctxObject=q),tt.contexts[L]=q,(typeof S.enableExtensionsByDefault=="undefined"||S.enableExtensionsByDefault)&&tt.initExtensions(q),L},makeContextCurrent:function(I){return tt.currentContext=tt.contexts[I],u.ctx=ka=tt.currentContext&&tt.currentContext.GLctx,!(I&&!ka)},getContext:function(I){return tt.contexts[I]},deleteContext:function(I){tt.currentContext===tt.contexts[I]&&(tt.currentContext=null),typeof Be=="object"&&Be.removeAllHandlersOnTarget(tt.contexts[I].GLctx.canvas),tt.contexts[I]&&tt.contexts[I].GLctx.canvas&&(tt.contexts[I].GLctx.canvas.GLctxObject=void 0),du(tt.contexts[I].handle),tt.contexts[I]=null},initExtensions:function(I){if(I||(I=tt.currentContext),!I.initExtensionsDone){I.initExtensionsDone=!0;var S=I.GLctx;W1(S),B1(S),V1(S),S.disjointTimerQueryExt=S.getExtension("EXT_disjoint_timer_query"),U1(S);var L=S.getSupportedExtensions()||[];L.forEach(function(q){q.indexOf("lose_context")<0&&q.indexOf("debug")<0&&S.getExtension(q)})}},populateUniformTable:function(I){for(var S=tt.programs[I],L=tt.programInfos[I]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},q=L.uniforms,fe=ka.getProgramParameter(S,35718),ce=0;ce<fe;++ce){var de=ka.getActiveUniform(S,ce),ve=de.name;L.maxUniformLength=Math.max(L.maxUniformLength,ve.length+1),ve.slice(-1)=="]"&&(ve=ve.slice(0,ve.lastIndexOf("[")));var at=ka.getUniformLocation(S,ve);if(at){var Ht=tt.getNewId(tt.uniforms);q[ve]=[de.size,Ht],tt.uniforms[Ht]=at;for(var Ot=1;Ot<de.size;++Ot){var Na=ve+"["+Ot+"]";at=ka.getUniformLocation(S,Na),Ht=tt.getNewId(tt.uniforms),tt.uniforms[Ht]=at}}}}},j1=["default","low-power","high-performance"];function H1(I,S){var L=S>>2,q=o()[L+(24>>2)],fe={alpha:!!o()[L+(0>>2)],depth:!!o()[L+(4>>2)],stencil:!!o()[L+(8>>2)],antialias:!!o()[L+(12>>2)],premultipliedAlpha:!!o()[L+(16>>2)],preserveDrawingBuffer:!!o()[L+(20>>2)],powerPreference:j1[q],failIfMajorPerformanceCaveat:!!o()[L+(28>>2)],majorVersion:o()[L+(32>>2)],minorVersion:o()[L+(36>>2)],enableExtensionsByDefault:o()[L+(40>>2)],explicitSwapControl:o()[L+(44>>2)],proxyContextToMainThread:o()[L+(48>>2)],renderViaOffscreenBackBuffer:o()[L+(52>>2)]},ce=lu(I);if(!ce||fe.explicitSwapControl)return 0;var de=tt.createContext(ce,fe);return de}function G1(I,S){return H1(I,S)}var Gi={mappings:{},buffers:[null,[],[]],printChar:function(I,S){var L=Gi.buffers[I];S===0||S===10?((I===1?X:G)(Pe(L,0)),L.length=0):L.push(S)},varargs:void 0,get:function(){Gi.varargs+=4;var I=o()[Gi.varargs-4>>2];return I},getStr:function(I){var S=Oe(I);return S},get64:function(I,S){return I}};function Ah(I){return x?va(3,1,I):0}function yh(I,S,L,q,fe){if(x)return va(4,1,I,S,L,q,fe)}function gh(I,S,L,q){if(x)return va(5,1,I,S,L,q);for(var fe=0,ce=0;ce<L;ce++){for(var de=o()[S+ce*8>>2],ve=o()[S+(ce*8+4)>>2],at=0;at<ve;at++)Gi.printChar(I,i()[de+at]);fe+=ve}return o()[q>>2]=fe,0}function q1(I){var S=Se.threadExitHandlers.pop();I&&S()}function X1(I,S){Se.threadExitHandlers.push(function(){nr.get(I)(S)})}function xh(I){if(x)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var S=Se.getNewWorker();if(S.pthread!==void 0)throw"Internal error!";if(!I.pthread_ptr)throw"Internal error, no pthread ptr!";Se.runningWorkers.push(S);for(var L=us(128*4),q=0;q<128;++q)o()[L+q*4>>2]=0;var fe=I.stackBase+I.stackSize,ce=Se.pthreads[I.pthread_ptr]={worker:S,stackBase:I.stackBase,stackSize:I.stackSize,allocatedOwnStack:I.allocatedOwnStack,threadInfoStruct:I.pthread_ptr},de=ce.threadInfoStruct>>2;Atomics.store(l(),de+(64>>2),I.detached),Atomics.store(l(),de+(100>>2),L),Atomics.store(l(),de+(40>>2),ce.threadInfoStruct),Atomics.store(l(),de+(80>>2),I.stackSize),Atomics.store(l(),de+(76>>2),fe),Atomics.store(l(),de+(104>>2),I.stackSize),Atomics.store(l(),de+(104+8>>2),fe),Atomics.store(l(),de+(104+12>>2),I.detached);var ve=L2(),at=ve+40;Atomics.store(l(),de+(172>>2),at),S.pthread=ce;var Ht={cmd:"run",start_routine:I.startRoutine,arg:I.arg,threadInfoStruct:I.pthread_ptr,stackBase:I.stackBase,stackSize:I.stackSize};S.runPthread=function(){Ht.time=performance.now(),S.postMessage(Ht,I.transferList)},S.loaded&&(S.runPthread(),delete S.runPthread)}function K1(I,S,L,q){if(typeof SharedArrayBuffer=="undefined")return G("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!I)return G("pthread_create called with a null thread pointer!"),28;var fe=[],ce=0;if(x&&(fe.length===0||ce))return U2(687865856,I,S,L,q);if(ce)return ce;var de=0,ve=0,at=0;S&&S!=-1?(de=o()[S>>2],de+=81920,ve=o()[S+8>>2],at=o()[S+12>>2]!==0):de=2097152;var Ht=ve==0;Ht?ve=G2(16,de):(ve-=de,me(ve>0));for(var Ot=us(228),Na=0;Na<228>>2;++Na)l()[(Ot>>2)+Na]=0;o()[I>>2]=Ot,o()[Ot+12>>2]=Ot;var Ji=Ot+152;o()[Ji>>2]=Ji;var bn={stackBase:ve,stackSize:de,allocatedOwnStack:Ht,detached:at,startRoutine:L,pthread_ptr:Ot,arg:q,transferList:fe};return x?(bn.cmd="spawnThread",postMessage(bn,fe)):xh(bn),0}function wh(I){if(x)return va(6,1,I);switch(I){case 30:return 16384;case 85:var S=2147483648;return S/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 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 y1(28),-1}x||Se.initMainThreadBlock();var ka,Z1=[null,g1,mh,Ah,yh,gh,wh],Y1={e:m1,r:A1,x:x1,b:w1,y:b1,j:_1,c:v1,d:Hi,f:ls,p:k1,z:I1,u:S1,q:C1,v:z1,i:P1,t:L1,w:G1,m:Ah,n:yh,g:gh,o:l1,a:Q||u.wasmMemory,k:q1,l:X1,h:K1,s:wh},z2=o1(),bh=u.___wasm_call_ctors=function(){return(bh=u.___wasm_call_ctors=u.asm.A).apply(null,arguments)},J1=u._init=function(){return(J1=u._init=u.asm.B).apply(null,arguments)},Q1=u._register_tensor=function(){return(Q1=u._register_tensor=u.asm.C).apply(null,arguments)},ef=u._dispose_data=function(){return(ef=u._dispose_data=u.asm.D).apply(null,arguments)},tf=u._dispose=function(){return(tf=u._dispose=u.asm.E).apply(null,arguments)},nf=u._Abs=function(){return(nf=u._Abs=u.asm.G).apply(null,arguments)},rf=u._Add=function(){return(rf=u._Add=u.asm.H).apply(null,arguments)},af=u._AddN=function(){return(af=u._AddN=u.asm.I).apply(null,arguments)},sf=u._ArgMax=function(){return(sf=u._ArgMax=u.asm.J).apply(null,arguments)},of=u._AvgPool=function(){return(of=u._AvgPool=u.asm.K).apply(null,arguments)},lf=u._BatchMatMul=function(){return(lf=u._BatchMatMul=u.asm.L).apply(null,arguments)},uf=u._Ceil=function(){return(uf=u._Ceil=u.asm.M).apply(null,arguments)},cf=u._ClipByValue=function(){return(cf=u._ClipByValue=u.asm.N).apply(null,arguments)},hf=u._Conv2D=function(){return(hf=u._Conv2D=u.asm.O).apply(null,arguments)},_h=u._Conv2DBackpropInput=function(){return(_h=u._Conv2DBackpropInput=u.asm.P).apply(null,arguments)},vh=u._Cos=function(){return(vh=u._Cos=u.asm.Q).apply(null,arguments)},uu=u._CropAndResize=function(){return(uu=u._CropAndResize=u.asm.R).apply(null,arguments)},qi=u._Cumsum=function(){return(qi=u._Cumsum=u.asm.S).apply(null,arguments)},df=u._DepthToSpace=function(){return(df=u._DepthToSpace=u.asm.T).apply(null,arguments)},cu=u._DepthwiseConv2dNative=function(){return(cu=u._DepthwiseConv2dNative=u.asm.U).apply(null,arguments)},K=u._Equal=function(){return(K=u._Equal=u.asm.V).apply(null,arguments)},ne=u._Exp=function(){return(ne=u._Exp=u.asm.W).apply(null,arguments)},Re=u._FlipLeftRight=function(){return(Re=u._FlipLeftRight=u.asm.X).apply(null,arguments)},Qe=u._Floor=function(){return(Qe=u._Floor=u.asm.Y).apply(null,arguments)},Et=u._FloorDiv=function(){return(Et=u._FloorDiv=u.asm.Z).apply(null,arguments)},At=u._FusedBatchNorm=function(){return(At=u._FusedBatchNorm=u.asm._).apply(null,arguments)},Ge=u._FusedConv2D=function(){return(Ge=u._FusedConv2D=u.asm.$).apply(null,arguments)},Xe=u._FusedDepthwiseConv2D=function(){return(Xe=u._FusedDepthwiseConv2D=u.asm.aa).apply(null,arguments)},en=u._Gather=function(){return(en=u._Gather=u.asm.ba).apply(null,arguments)},ra=u._GatherNd=function(){return(ra=u._GatherNd=u.asm.ca).apply(null,arguments)},aa=u._Greater=function(){return(aa=u._Greater=u.asm.da).apply(null,arguments)},kh=u._GreaterEqual=function(){return(kh=u._GreaterEqual=u.asm.ea).apply(null,arguments)},hu=u._LeakyRelu=function(){return(hu=u._LeakyRelu=u.asm.fa).apply(null,arguments)},Xn=u._Less=function(){return(Xn=u._Less=u.asm.ga).apply(null,arguments)},Ia=u._LessEqual=function(){return(Ia=u._LessEqual=u.asm.ha).apply(null,arguments)},Ih=u._Log=function(){return(Ih=u._Log=u.asm.ia).apply(null,arguments)},D4=u._LogicalAnd=function(){return(D4=u._LogicalAnd=u.asm.ja).apply(null,arguments)},O4=u._Max=function(){return(O4=u._Max=u.asm.ka).apply(null,arguments)},z4=u._MaxPool=function(){return(z4=u._MaxPool=u.asm.la).apply(null,arguments)},P4=u._Maximum=function(){return(P4=u._Maximum=u.asm.ma).apply(null,arguments)},L4=u._Mean=function(){return(L4=u._Mean=u.asm.na).apply(null,arguments)},W4=u._Min=function(){return(W4=u._Min=u.asm.oa).apply(null,arguments)},B4=u._Minimum=function(){return(B4=u._Minimum=u.asm.pa).apply(null,arguments)},V4=u._Multiply=function(){return(V4=u._Multiply=u.asm.qa).apply(null,arguments)},U4=u._Neg=function(){return(U4=u._Neg=u.asm.ra).apply(null,arguments)},j4=u._NonMaxSuppressionV3=function(){return(j4=u._NonMaxSuppressionV3=u.asm.sa).apply(null,arguments)},H4=u._NonMaxSuppressionV4=function(){return(H4=u._NonMaxSuppressionV4=u.asm.ta).apply(null,arguments)},G4=u._NonMaxSuppressionV5=function(){return(G4=u._NonMaxSuppressionV5=u.asm.ua).apply(null,arguments)},q4=u._NotEqual=function(){return(q4=u._NotEqual=u.asm.va).apply(null,arguments)},X4=u._OneHot=function(){return(X4=u._OneHot=u.asm.wa).apply(null,arguments)},K4=u._PadV2=function(){return(K4=u._PadV2=u.asm.xa).apply(null,arguments)},Z4=u._Pow=function(){return(Z4=u._Pow=u.asm.ya).apply(null,arguments)},Y4=u._Prelu=function(){return(Y4=u._Prelu=u.asm.za).apply(null,arguments)},J4=u._Prod=function(){return(J4=u._Prod=u.asm.Aa).apply(null,arguments)},Q4=u._RealDiv=function(){return(Q4=u._RealDiv=u.asm.Ba).apply(null,arguments)},e8=u._Relu=function(){return(e8=u._Relu=u.asm.Ca).apply(null,arguments)},t8=u._Relu6=function(){return(t8=u._Relu6=u.asm.Da).apply(null,arguments)},n8=u._ResizeBilinear=function(){return(n8=u._ResizeBilinear=u.asm.Ea).apply(null,arguments)},r8=u._Reverse=function(){return(r8=u._Reverse=u.asm.Fa).apply(null,arguments)},a8=u._RotateWithOffset=function(){return(a8=u._RotateWithOffset=u.asm.Ga).apply(null,arguments)},s8=u._Round=function(){return(s8=u._Round=u.asm.Ha).apply(null,arguments)},i8=u._Rsqrt=function(){return(i8=u._Rsqrt=u.asm.Ia).apply(null,arguments)},o8=u._ScatterNd=function(){return(o8=u._ScatterNd=u.asm.Ja).apply(null,arguments)},l8=u._SelectV2=function(){return(l8=u._SelectV2=u.asm.Ka).apply(null,arguments)},u8=u._Sigmoid=function(){return(u8=u._Sigmoid=u.asm.La).apply(null,arguments)},c8=u._Sin=function(){return(c8=u._Sin=u.asm.Ma).apply(null,arguments)},h8=u._Softmax=function(){return(h8=u._Softmax=u.asm.Na).apply(null,arguments)},d8=u._Sqrt=function(){return(d8=u._Sqrt=u.asm.Oa).apply(null,arguments)},p8=u._Square=function(){return(p8=u._Square=u.asm.Pa).apply(null,arguments)},f8=u._SquaredDifference=function(){return(f8=u._SquaredDifference=u.asm.Qa).apply(null,arguments)},m8=u._Step=function(){return(m8=u._Step=u.asm.Ra).apply(null,arguments)},A8=u._StridedSlice=function(){return(A8=u._StridedSlice=u.asm.Sa).apply(null,arguments)},y8=u._Sub=function(){return(y8=u._Sub=u.asm.Ta).apply(null,arguments)},g8=u._Sum=function(){return(g8=u._Sum=u.asm.Ua).apply(null,arguments)},x8=u._Tanh=function(){return(x8=u._Tanh=u.asm.Va).apply(null,arguments)},w8=u._Tile=function(){return(w8=u._Tile=u.asm.Wa).apply(null,arguments)},b8=u._TopK=function(){return(b8=u._TopK=u.asm.Xa).apply(null,arguments)},_8=u._Transpose=function(){return(_8=u._Transpose=u.asm.Ya).apply(null,arguments)},v8=u.__FusedMatMul=function(){return(v8=u.__FusedMatMul=u.asm.Za).apply(null,arguments)},us=u._malloc=function(){return(us=u._malloc=u.asm._a).apply(null,arguments)},du=u._free=function(){return(du=u._free=u.asm.$a).apply(null,arguments)},P2=u.___errno_location=function(){return(P2=u.___errno_location=u.asm.ab).apply(null,arguments)},L2=u._emscripten_get_global_libc=function(){return(L2=u._emscripten_get_global_libc=u.asm.bb).apply(null,arguments)},Xi=u._pthread_self=function(){return(Xi=u._pthread_self=u.asm.cb).apply(null,arguments)},W2=u.___pthread_tsd_run_dtors=function(){return(W2=u.___pthread_tsd_run_dtors=u.asm.db).apply(null,arguments)},pf=u._emscripten_main_thread_process_queued_calls=function(){return(pf=u._emscripten_main_thread_process_queued_calls=u.asm.eb).apply(null,arguments)},k8=u._emscripten_current_thread_process_queued_calls=function(){return(k8=u._emscripten_current_thread_process_queued_calls=u.asm.fb).apply(null,arguments)},B2=u._emscripten_register_main_browser_thread_id=function(){return(B2=u._emscripten_register_main_browser_thread_id=u.asm.gb).apply(null,arguments)},V2=u.__emscripten_do_dispatch_to_thread=function(){return(V2=u.__emscripten_do_dispatch_to_thread=u.asm.hb).apply(null,arguments)},U2=u._emscripten_sync_run_in_main_thread_4=function(){return(U2=u._emscripten_sync_run_in_main_thread_4=u.asm.ib).apply(null,arguments)},j2=u._emscripten_run_in_main_runtime_thread_js=function(){return(j2=u._emscripten_run_in_main_runtime_thread_js=u.asm.jb).apply(null,arguments)},ff=u.__emscripten_call_on_thread=function(){return(ff=u.__emscripten_call_on_thread=u.asm.kb).apply(null,arguments)},I8=u._emscripten_tls_init=function(){return(I8=u._emscripten_tls_init=u.asm.lb).apply(null,arguments)},Nh=u.__emscripten_thread_init=function(){return(Nh=u.__emscripten_thread_init=u.asm.mb).apply(null,arguments)},pu=u.stackSave=function(){return(pu=u.stackSave=u.asm.nb).apply(null,arguments)},Ki=u.stackRestore=function(){return(Ki=u.stackRestore=u.asm.ob).apply(null,arguments)},Zi=u.stackAlloc=function(){return(Zi=u.stackAlloc=u.asm.pb).apply(null,arguments)},H2=u._emscripten_stack_set_limits=function(){return(H2=u._emscripten_stack_set_limits=u.asm.qb).apply(null,arguments)},G2=u._memalign=function(){return(G2=u._memalign=u.asm.rb).apply(null,arguments)},q2=u.__emscripten_allow_main_runtime_queued_calls=9880,Yi=u.__emscripten_main_thread_futex=11368;u.cwrap=De,u.PThread=Se,u.PThread=Se,u.wasmMemory=Q,u.ExitStatus=fu;var Sh;function fu(I){this.name="ExitStatus",this.message="Program terminated with exit("+I+")",this.status=I}os=function I(){Sh||mf(),Sh||(os=I)};function mf(I){if(I=I||m,ta>0)return;if(x){h(u),postMessage({cmd:"loaded"});return}if(e1(),ta>0)return;function S(){Sh||(Sh=!0,u.calledRun=!0,!ue&&(ih(),t1(),h(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),xn()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),S()},1)):S()}u.run=mf;function N8(I,S){if(!(S&&le&&I===0)){if(!S&&x)throw postMessage({cmd:"exitProcess",returnCode:I}),new fu(I);le||(Se.terminateAllThreads(),ye=I,oh(),u.onExit&&u.onExit(I),ue=!0),y(I,new fu(I))}}if(u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return x&&(le=!1,Se.initWorker()),mf(),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)}),G8=kt((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,f=!1,m=!1,A=!1;p=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",A=!p&&!m&&!f;var y="";function g(K){return s.locateFile?s.locateFile(K,y):y+K}var w,b,_,x,N,T;m?(f?y=mu().dirname(y)+"/":y=__dirname+"/",w=function(K,ne){return N||(N=require("fs")),T||(T=mu()),K=T.normalize(K),N.readFileSync(K,ne?null:"utf8")},_=function(K){var ne=w(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 df))throw K}),process.on("unhandledRejection",yr),d=function(K){process.exit(K)},s.inspect=function(){return"[Emscripten Module object]"}):A?(typeof read!="undefined"&&(w=function(K){return read(K)}),_=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||f)&&(f?y=self.location.href:typeof document!="undefined"&&document.currentScript&&(y=document.currentScript.src),r&&(y=r),y.indexOf("blob:")!==0?y=y.substr(0,y.lastIndexOf("/")+1):y="",w=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.send(null),ne.responseText},f&&(_=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),b=function(K,ne,Re){var Qe=new XMLHttpRequest;Qe.open("GET",K,!0),Qe.responseType="arraybuffer",Qe.onload=function(){if(Qe.status==200||Qe.status==0&&Qe.response){ne(Qe.response);return}Re()},Qe.onerror=Re,Qe.send(null)},x=function(K){document.title=K});var E=s.print||console.log.bind(console),M=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 W=s.noExitRuntime||!0;typeof WebAssembly!="object"&&yr("no native wasm support detected");var V,U=!1,j;function X(K,ne){K||yr("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,Re,Qe,Et){var At={string:function(Xn){var Ia=0;if(Xn!=null&&Xn!==0){var Ih=(Xn.length<<2)+1;Ia=uu(Ih),pe(Xn,Ia,Ih)}return Ia},array:function(Xn){var Ia=uu(Xn.length);return ue(Xn,Ia),Ia}};function Ge(Xn){return ne==="string"?le(Xn):ne==="boolean"?Boolean(Xn):Xn}var Xe=G(K),en=[],ra=0;if(Qe)for(var aa=0;aa<Qe.length;aa++){var kh=At[Re[aa]];kh?(ra===0&&(ra=_h()),en[aa]=kh(Qe[aa])):en[aa]=Qe[aa]}var hu=Xe.apply(null,en);return hu=Ge(hu),ra!==0&&vh(ra),hu}function Y(K,ne,Re,Qe){Re=Re||[];var Et=Re.every(function(Ge){return Ge==="number"}),At=ne!=="string";return At&&Et&&!Qe?G(K):function(){return ee(K,ne,Re,arguments,Qe)}}var se=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function te(K,ne,Re){for(var Qe=ne+Re,Et=ne;K[Et]&&!(Et>=Qe);)++Et;if(Et-ne>16&&K.subarray&&se)return se.decode(K.subarray(ne,Et));for(var At="";ne<Et;){var Ge=K[ne++];if(!(Ge&128)){At+=String.fromCharCode(Ge);continue}var Xe=K[ne++]&63;if((Ge&224)==192){At+=String.fromCharCode((Ge&31)<<6|Xe);continue}var en=K[ne++]&63;if((Ge&240)==224?Ge=(Ge&15)<<12|Xe<<6|en:Ge=(Ge&7)<<18|Xe<<12|en<<6|K[ne++]&63,Ge<65536)At+=String.fromCharCode(Ge);else{var ra=Ge-65536;At+=String.fromCharCode(55296|ra>>10,56320|ra&1023)}}return At}function le(K,ne){return K?te(Ce,K,ne):""}function Q(K,ne,Re,Qe){if(!(Qe>0))return 0;for(var Et=Re,At=Re+Qe-1,Ge=0;Ge<K.length;++Ge){var Xe=K.charCodeAt(Ge);if(Xe>=55296&&Xe<=57343){var en=K.charCodeAt(++Ge);Xe=65536+((Xe&1023)<<10)|en&1023}if(Xe<=127){if(Re>=At)break;ne[Re++]=Xe}else if(Xe<=2047){if(Re+1>=At)break;ne[Re++]=192|Xe>>6,ne[Re++]=128|Xe&63}else if(Xe<=65535){if(Re+2>=At)break;ne[Re++]=224|Xe>>12,ne[Re++]=128|Xe>>6&63,ne[Re++]=128|Xe&63}else{if(Re+3>=At)break;ne[Re++]=240|Xe>>18,ne[Re++]=128|Xe>>12&63,ne[Re++]=128|Xe>>6&63,ne[Re++]=128|Xe&63}}return ne[Re]=0,Re-Et}function pe(K,ne,Re){return Q(K,Ce,ne,Re)}function ue(K,ne){Ne.set(K,ne)}function ye(K,ne){return K%ne>0&&(K+=ne-K%ne),K}var me,Ne,Ce,De,Pe,Oe,nt,rt,lt;function Je(K){me=K,s.HEAP8=Ne=new Int8Array(K),s.HEAP16=De=new Int16Array(K),s.HEAP32=Oe=new Int32Array(K),s.HEAPU8=Ce=new Uint8Array(K),s.HEAPU16=Pe=new Uint16Array(K),s.HEAPU32=nt=new Uint32Array(K),s.HEAPF32=rt=new Float32Array(K),s.HEAPF64=lt=new Float64Array(K)}var pt=s.INITIAL_MEMORY||16777216,je,yn=[],vt=[],Gn=[],Jt=[],gn=!1;vt.push({func:function(){dh()}});function qn(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Mr(s.preRun.shift());_a(yn)}function Mn(){gn=!0,_a(vt)}function cn(){_a(Gn)}function Qt(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)nr(s.postRun.shift());_a(Jt)}function Mr(K){yn.unshift(K)}function nr(K){Jt.unshift(K)}var rr=0,wa=null,ea=null;function ba(K){rr++,s.monitorRunDependencies&&s.monitorRunDependencies(rr)}function Vi(K){if(rr--,s.monitorRunDependencies&&s.monitorRunDependencies(rr),rr==0&&(wa!==null&&(clearInterval(wa),wa=null),ea)){var ne=ea;ea=null,ne()}}s.preloadedImages={},s.preloadedAudios={};function yr(K){s.onAbort&&s.onAbort(K),K+="",M(K),U=!0,j=1,K="abort("+K+"). Build with -s ASSERTIONS=1 for more info.";var ne=new WebAssembly.RuntimeError(K);throw o(ne),ne}function sh(K,ne){return String.prototype.startsWith?K.startsWith(ne):K.indexOf(ne)===0}var e1="data:application/octet-stream;base64,";function ih(K){return sh(K,e1)}var t1="file://";function oh(K){return sh(K,t1)}var xn="tfjs-backend-wasm.wasm";ih(xn)||(xn=g(xn));function lh(K){try{if(K==xn&&O)return new Uint8Array(O);if(_)return _(K);throw"both async and sync fetching of the wasm failed"}catch(ne){yr(ne)}}function n1(){if(!O&&(p||f)){if(typeof fetch=="function"&&!oh(xn))return fetch(xn,{credentials:"same-origin"}).then(function(K){if(!K.ok)throw"failed to load wasm binary file at '"+xn+"'";return K.arrayBuffer()}).catch(function(){return lh(xn)});if(b)return new Promise(function(K,ne){b(xn,function(Re){K(new Uint8Array(Re))},ne)})}return Promise.resolve().then(function(){return lh(xn)})}function ta(){var K={a:wn};function ne(Ge,Xe){var en=Ge.exports;s.asm=en,V=s.asm.g,Je(V.buffer),je=s.asm.m,Vi("wasm-instantiate")}ba("wasm-instantiate");function Re(Ge){ne(Ge.instance)}function Qe(Ge){return n1().then(function(Xe){return WebAssembly.instantiate(Xe,K)}).then(Ge,function(Xe){M("failed to asynchronously prepare wasm: "+Xe),yr(Xe)})}function Et(){return!O&&typeof WebAssembly.instantiateStreaming=="function"&&!ih(xn)&&!oh(xn)&&typeof fetch=="function"?fetch(xn,{credentials:"same-origin"}).then(function(Ge){var Xe=WebAssembly.instantiateStreaming(Ge,K);return Xe.then(Re,function(en){return M("wasm streaming compile failed: "+en),M("falling back to ArrayBuffer instantiation"),Qe(Re)})}):Qe(Re)}if(s.instantiateWasm)try{var At=s.instantiateWasm(K,ne);return At}catch(Ge){return M("Module.instantiateWasm callback failed with error: "+Ge),!1}return Et().catch(o),{}}function _a(K){for(;K.length>0;){var ne=K.shift();if(typeof ne=="function"){ne(s);continue}var Re=ne.func;typeof Re=="number"?ne.arg===void 0?je.get(Re)():je.get(Re)(ne.arg):Re(ne.arg===void 0?null:ne.arg)}}function os(){yr()}function r1(K,ne,Re){Ce.copyWithin(K,ne,ne+Re)}function a1(){return Ce.length}function na(K){try{return V.grow(K-me.byteLength+65535>>>16),Je(V.buffer),1}catch(ne){}}function uh(K){var ne=a1(),Re=2147483648;if(K>Re)return!1;for(var Qe=1;Qe<=4;Qe*=2){var Et=ne*(1+.2/Qe);Et=Math.min(Et,K+100663296);var At=Math.min(Re,ye(Math.max(K,Et),65536)),Ge=na(At);if(Ge)return!0}return!1}var Ui={mappings:{},buffers:[null,[],[]],printChar:function(K,ne){var Re=Ui.buffers[K];ne===0||ne===10?((K===1?E:M)(te(Re,0)),Re.length=0):Re.push(ne)},varargs:void 0,get:function(){Ui.varargs+=4;var K=Oe[Ui.varargs-4>>2];return K},getStr:function(K){var ne=le(K);return ne},get64:function(K,ne){return K}};function ch(K){return 0}function s1(K,ne,Re,Qe,Et){}function hh(K,ne,Re,Qe){for(var Et=0,At=0;At<Re;At++){for(var Ge=Oe[ne+At*8>>2],Xe=Oe[ne+(At*8+4)>>2],en=0;en<Xe;en++)Ui.printChar(K,Ce[Ge+en]);Et+=Xe}return Oe[Qe>>2]=Et,0}var wn={a:os,d:r1,e:uh,f:ch,c:s1,b:hh},i1=ta(),dh=s.___wasm_call_ctors=function(){return(dh=s.___wasm_call_ctors=s.asm.h).apply(null,arguments)},o1=s._init=function(){return(o1=s._init=s.asm.i).apply(null,arguments)},ph=s._register_tensor=function(){return(ph=s._register_tensor=s.asm.j).apply(null,arguments)},l1=s._dispose_data=function(){return(l1=s._dispose_data=s.asm.k).apply(null,arguments)},ji=s._dispose=function(){return(ji=s._dispose=s.asm.l).apply(null,arguments)},Hi=s._Abs=function(){return(Hi=s._Abs=s.asm.n).apply(null,arguments)},u1=s._Add=function(){return(u1=s._Add=s.asm.o).apply(null,arguments)},c1=s._AddN=function(){return(c1=s._AddN=s.asm.p).apply(null,arguments)},h1=s._ArgMax=function(){return(h1=s._ArgMax=s.asm.q).apply(null,arguments)},Se=s._AvgPool=function(){return(Se=s._AvgPool=s.asm.r).apply(null,arguments)},d1=s._BatchMatMul=function(){return(d1=s._BatchMatMul=s.asm.s).apply(null,arguments)},p1=s._Ceil=function(){return(p1=s._Ceil=s.asm.t).apply(null,arguments)},f1=s._ClipByValue=function(){return(f1=s._ClipByValue=s.asm.u).apply(null,arguments)},m1=s._Conv2D=function(){return(m1=s._Conv2D=s.asm.v).apply(null,arguments)},A1=s._Conv2DBackpropInput=function(){return(A1=s._Conv2DBackpropInput=s.asm.w).apply(null,arguments)},ls=s._Cos=function(){return(ls=s._Cos=s.asm.x).apply(null,arguments)},y1=s._CropAndResize=function(){return(y1=s._CropAndResize=s.asm.y).apply(null,arguments)},g1=s._Cumsum=function(){return(g1=s._Cumsum=s.asm.z).apply(null,arguments)},x1=s._DepthToSpace=function(){return(x1=s._DepthToSpace=s.asm.A).apply(null,arguments)},w1=s._DepthwiseConv2dNative=function(){return(w1=s._DepthwiseConv2dNative=s.asm.B).apply(null,arguments)},b1=s._Equal=function(){return(b1=s._Equal=s.asm.C).apply(null,arguments)},_1=s._Exp=function(){return(_1=s._Exp=s.asm.D).apply(null,arguments)},v1=s._FlipLeftRight=function(){return(v1=s._FlipLeftRight=s.asm.E).apply(null,arguments)},k1=s._Floor=function(){return(k1=s._Floor=s.asm.F).apply(null,arguments)},I1=s._FloorDiv=function(){return(I1=s._FloorDiv=s.asm.G).apply(null,arguments)},va=s._FusedBatchNorm=function(){return(va=s._FusedBatchNorm=s.asm.H).apply(null,arguments)},iu=s._FusedConv2D=function(){return(iu=s._FusedConv2D=s.asm.I).apply(null,arguments)},ou=s._FusedDepthwiseConv2D=function(){return(ou=s._FusedDepthwiseConv2D=s.asm.J).apply(null,arguments)},N1=s._Gather=function(){return(N1=s._Gather=s.asm.K).apply(null,arguments)},S1=s._GatherNd=function(){return(S1=s._GatherNd=s.asm.L).apply(null,arguments)},T1=s._Greater=function(){return(T1=s._Greater=s.asm.M).apply(null,arguments)},E1=s._GreaterEqual=function(){return(E1=s._GreaterEqual=s.asm.N).apply(null,arguments)},C1=s._LeakyRelu=function(){return(C1=s._LeakyRelu=s.asm.O).apply(null,arguments)},Be=s._Less=function(){return(Be=s._Less=s.asm.P).apply(null,arguments)},R1=s._LessEqual=function(){return(R1=s._LessEqual=s.asm.Q).apply(null,arguments)},F1=s._Log=function(){return(F1=s._Log=s.asm.R).apply(null,arguments)},M1=s._LogicalAnd=function(){return(M1=s._LogicalAnd=s.asm.S).apply(null,arguments)},$1=s._Max=function(){return($1=s._Max=s.asm.T).apply(null,arguments)},D1=s._MaxPool=function(){return(D1=s._MaxPool=s.asm.U).apply(null,arguments)},O1=s._Maximum=function(){return(O1=s._Maximum=s.asm.V).apply(null,arguments)},lu=s._Mean=function(){return(lu=s._Mean=s.asm.W).apply(null,arguments)},fh=s._Min=function(){return(fh=s._Min=s.asm.X).apply(null,arguments)},mh=s._Minimum=function(){return(mh=s._Minimum=s.asm.Y).apply(null,arguments)},z1=s._Multiply=function(){return(z1=s._Multiply=s.asm.Z).apply(null,arguments)},P1=s._Neg=function(){return(P1=s._Neg=s.asm._).apply(null,arguments)},L1=s._NonMaxSuppressionV3=function(){return(L1=s._NonMaxSuppressionV3=s.asm.$).apply(null,arguments)},W1=s._NonMaxSuppressionV4=function(){return(W1=s._NonMaxSuppressionV4=s.asm.aa).apply(null,arguments)},B1=s._NonMaxSuppressionV5=function(){return(B1=s._NonMaxSuppressionV5=s.asm.ba).apply(null,arguments)},V1=s._NotEqual=function(){return(V1=s._NotEqual=s.asm.ca).apply(null,arguments)},U1=s._OneHot=function(){return(U1=s._OneHot=s.asm.da).apply(null,arguments)},tt=s._PadV2=function(){return(tt=s._PadV2=s.asm.ea).apply(null,arguments)},j1=s._Pow=function(){return(j1=s._Pow=s.asm.fa).apply(null,arguments)},H1=s._Prelu=function(){return(H1=s._Prelu=s.asm.ga).apply(null,arguments)},G1=s._Prod=function(){return(G1=s._Prod=s.asm.ha).apply(null,arguments)},Gi=s._RealDiv=function(){return(Gi=s._RealDiv=s.asm.ia).apply(null,arguments)},Ah=s._Relu=function(){return(Ah=s._Relu=s.asm.ja).apply(null,arguments)},yh=s._Relu6=function(){return(yh=s._Relu6=s.asm.ka).apply(null,arguments)},gh=s._ResizeBilinear=function(){return(gh=s._ResizeBilinear=s.asm.la).apply(null,arguments)},q1=s._Reverse=function(){return(q1=s._Reverse=s.asm.ma).apply(null,arguments)},X1=s._RotateWithOffset=function(){return(X1=s._RotateWithOffset=s.asm.na).apply(null,arguments)},xh=s._Round=function(){return(xh=s._Round=s.asm.oa).apply(null,arguments)},K1=s._Rsqrt=function(){return(K1=s._Rsqrt=s.asm.pa).apply(null,arguments)},wh=s._ScatterNd=function(){return(wh=s._ScatterNd=s.asm.qa).apply(null,arguments)},ka=s._SelectV2=function(){return(ka=s._SelectV2=s.asm.ra).apply(null,arguments)},Z1=s._Sigmoid=function(){return(Z1=s._Sigmoid=s.asm.sa).apply(null,arguments)},Y1=s._Sin=function(){return(Y1=s._Sin=s.asm.ta).apply(null,arguments)},z2=s._Softmax=function(){return(z2=s._Softmax=s.asm.ua).apply(null,arguments)},bh=s._Sqrt=function(){return(bh=s._Sqrt=s.asm.va).apply(null,arguments)},J1=s._Square=function(){return(J1=s._Square=s.asm.wa).apply(null,arguments)},Q1=s._SquaredDifference=function(){return(Q1=s._SquaredDifference=s.asm.xa).apply(null,arguments)},ef=s._Step=function(){return(ef=s._Step=s.asm.ya).apply(null,arguments)},tf=s._StridedSlice=function(){return(tf=s._StridedSlice=s.asm.za).apply(null,arguments)},nf=s._Sub=function(){return(nf=s._Sub=s.asm.Aa).apply(null,arguments)},rf=s._Sum=function(){return(rf=s._Sum=s.asm.Ba).apply(null,arguments)},af=s._Tanh=function(){return(af=s._Tanh=s.asm.Ca).apply(null,arguments)},sf=s._Tile=function(){return(sf=s._Tile=s.asm.Da).apply(null,arguments)},of=s._TopK=function(){return(of=s._TopK=s.asm.Ea).apply(null,arguments)},lf=s._Transpose=function(){return(lf=s._Transpose=s.asm.Fa).apply(null,arguments)},uf=s.__FusedMatMul=function(){return(uf=s.__FusedMatMul=s.asm.Ga).apply(null,arguments)},cf=s._malloc=function(){return(cf=s._malloc=s.asm.Ha).apply(null,arguments)},hf=s._free=function(){return(hf=s._free=s.asm.Ia).apply(null,arguments)},_h=s.stackSave=function(){return(_h=s.stackSave=s.asm.Ja).apply(null,arguments)},vh=s.stackRestore=function(){return(vh=s.stackRestore=s.asm.Ka).apply(null,arguments)},uu=s.stackAlloc=function(){return(uu=s.stackAlloc=s.asm.La).apply(null,arguments)};s.cwrap=Y;var qi;function df(K){this.name="ExitStatus",this.message="Program terminated with exit("+K+")",this.status=K}ea=function K(){qi||cu(),qi||(ea=K)};function cu(K){if(K=K||u,rr>0||(qn(),rr>0))return;function ne(){qi||(qi=!0,s.calledRun=!0,!U&&(Mn(),cn(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Qt()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ne()},1)):ne()}if(s.run=cu,s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();return cu(),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)}),q8=kt((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)}),X8=kt((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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),K8=kt((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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),Z8=kt((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,d=c.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,c.i=d+1&7,f};function u(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),Y8=kt((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,f,m;return c.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,c.i=p,m+(h^h>>>16)|0};function u(h,d){var p,f,m,A,y,g=[],w=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,w=Math.max(w,d.length)),m=0,A=-32;A<w;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),J8=kt((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var d=c.b,p=c.c,f=c.d,m=c.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,c.b=d=d<<20^d>>>12^p,c.c=p=p-f|0,c.d=f<<16^p>>>16^m,c.a=m-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,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},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)}),Q8=kt((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 f(_,x,N){var T=[];x=x==!0?{entropy:!0}:x||{};var E=g(y(x.entropy?[_,b(r)]:_==null?w():_,3),T),M=new m(T),O=function(){for(var W=M.g(i),V=c,U=0;W<u;)W=(W+U)*s,V*=s,U=M.g(1);for(;W>=h;)W/=2,V/=2,U>>>=1;return(W+U)/V};return O.int32=function(){return M.g(4)|0},O.quick=function(){return M.g(4)/4294967296},O.double=O,g(b(M.S),r),(x.pass||N||function(W,V,U,j){return j&&(j.S&&A(j,M),W.state=function(){return A(M,{})}),U?(a[l]=W,V):W})(O,E,"global"in x?x.global:this==a,x.state)}function m(_){var x,N=_.length,T=this,E=0,M=T.i=T.j=0,O=T.S=[];for(N||(_=[N++]);E<s;)O[E]=E++;for(E=0;E<s;E++)O[E]=O[M=d&M+_[E%N]+(x=O[E])],O[M]=x;(T.g=function(W){for(var V,U=0,j=T.i,X=T.j,G=T.S;W--;)V=G[j=d&j+1],U=U*s+G[d&(G[j]=G[X=d&X+V])+(G[X]=V)];return T.i=j,T.j=X,U})(s)}function A(_,x){return x.i=_.i,x.j=_.j,x.S=_.S.slice(),x}function y(_,x){var N=[],T=typeof _,E;if(x&&T=="object")for(E in _)try{N.push(y(_[E],x-1))}catch(M){}return N.length?N:T=="string"?_:_+"\0"}function g(_,x){for(var N=_+"",T,E=0;E<N.length;)x[d&E]=d&(T^=x[d&E]*19)+N.charCodeAt(E++);return b(x)}function w(){try{var _;return p&&(_=p.randomBytes)?_=_(s):(_=new Uint8Array(s),(n.crypto||n.msCrypto).getRandomValues(_)),b(_)}catch(T){var x=n.navigator,N=x&&x.plugins;return[+new Date,n,N,n.screen,b(r)]}}function b(_){return String.fromCharCode.apply(0,_)}if(g(a.random(),r),typeof t=="object"&&t.exports){t.exports=f;try{p=Y2()}catch(_){}}else typeof define=="function"&&define.amd?define(function(){return f}):a["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}),Q2=kt((e,t)=>{var n=q8(),r=X8(),a=K8(),s=Z8(),i=Y8(),o=J8(),l=Q8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),ek=kt(()=>{}),tk="3.3.0",nk="3.3.0",rk="3.3.0",ak="3.3.0",sk="3.3.0",ik=1e-7,ok=1e-4,Ch=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}},Au=class{refCount(e){return sr("refCount")}incRef(e){return sr("incRef")}timerAvailable(){return!0}time(e){return sr("time")}read(e){return sr("read")}readSync(e){return sr("readSync")}numDataIds(){return sr("numDataIds")}disposeData(e,t){return sr("disposeData")}write(e,t,n){return sr("write")}move(e,t,n,r,a){return sr("move")}memory(){return sr("memory")}floatPrecision(){return sr("floatPrecision")}epsilon(){return this.floatPrecision()===32?ik:ok}dispose(){return sr("dispose")}};function sr(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 e5(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 lk(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 yu(e,t,n){return Math.max(e,Math.min(t,n))}function uk(e){return e%2==0?e:e+1}function ck(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function hk(e,t){let n=Math.random();return t*n+(1-n)*e}function dk(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 F(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function on(e,t,n=""){F(sa(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function hs(e){F(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function ds(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||ln(e)&&!n)for(let r=0;r<e.length;++r)ds(e[r],t,n);else t.push(e);return t}function zt(e){if(e.length===0)return 1;let t=e[0];for(let n=1;n<e.length;n++)t*=e[n];return t}function pk(e){return e.length===0}function sa(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 Gt(e){return e%1==0}function fk(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 mk(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function Ak(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return e5(t),t}function gu(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function yk(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 gk(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} / ${n}`);let a=e.slice();return a[r]=t/n,a}function ir(e,t){let n=t.length;return e=e==null?t.map((r,a)=>a):[].concat(e),F(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),F(e.every(r=>Gt(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function t5(e,t){let n=[],r=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:ir(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 n5(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 r5(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 a5(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 s5(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function xk(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function ln(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function Af(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function i5(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Sa(e){return typeof e=="string"||e instanceof String}function o5(e){return typeof e=="boolean"}function l5(e){return typeof e=="number"}function Rh(e){return Array.isArray(e)?Rh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":l5(e)?"float32":Sa(e)?"string":o5(e)?"bool":"float32"}function Ta(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Fh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function no(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function u5(e,t,n){let r=new Array;if(t.length===1){let a=t[0];for(let s=0;s<a;s++)r[s]=n[e+s]}else{let a=t[0],s=t.slice(1),i=s.reduce((o,l)=>o*l);for(let o=0;o<a;o++)r[o]=u5(e+o*i,s,n)}return r}function ro(e,t){if(e.length===0)return t[0];let n=e.reduce((r,a)=>r*a);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}.`);return u5(0,e,t)}function yf(e,t){let n=Mh(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function Mh(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 wk(e,t){let n=e.reduce((r,a)=>r*a,1);if(t==null||t==="float32")return ro(e,new Float32Array(n));if(t==="int32")return ro(e,new Int32Array(n));if(t==="bool")return ro(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function gf(e){e.forEach(t=>{F(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function bk(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 _k(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 xf(e){return e&&e.then&&typeof e.then=="function"}var c5="tfjsflags",h5=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let 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(xf(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=vk(this.global.location.search);c5 in e&&e[c5].split(",").forEach(t=>{let[n,r]=t.split(":");this.urlFlags[n]=kk(n,r)})}};function vk(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(Ik(t,r[0],r[1]),r.join("="))),t}function Ik(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function kk(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 ${e}.`)}function J(){return xr}var xr=null;function Nk(e){xr=e}var wf;function d5(){if(wf==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");wf=e}return wf}function Sk(){let e=d5();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function bf(e,t){let n=Sk();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var ao="Abs",so="Acos",io="Acosh",Ea="Add",ps="AddN",$h="All",Dh="Any",fs="ArgMax",xu="ArgMin",oo="Asin",lo="Asinh",uo="Atan",co="Atanh",ho="Atan2",ms="AvgPool",Oh="AvgPoolGrad",wu="AvgPool3D",zh="AvgPool3DGrad",As="BatchMatMul",bu="BatchToSpaceND",Ph="Bincount",p5="BroadcastTo",ys="Cast",gs="Ceil",Ca="ClipByValue",Lh="Complex",_u="ComplexAbs",po="Concat",xs="Conv2D",Wh="Conv2DBackpropFilter",ws="Conv2DBackpropInput",vu="Conv3D",Bh="Conv3DBackpropFilterV2",Vh="Conv3DBackpropInputV2",bs="Cos",fo="Cosh",_s="Cumsum",mo="CropAndResize",Uh="DenseBincount",Ao="DepthToSpace",vs="DepthwiseConv2dNative",jh="DepthwiseConv2dNativeBackpropFilter",Hh="DepthwiseConv2dNativeBackpropInput",Gh="Diag",ku="Dilation2D",qh="Dilation2DBackpropInput",Xh="Dilation2DBackpropFilter",ks="RealDiv",yo="Elu",Kh="EluGrad",go="Erf",xo="Equal",Is="Exp",wo="ExpandDims",bo="Expm1",Zh="FFT",Iu="Fill",_o="FlipLeftRight",Ns="Floor",Ss="FloorDiv",Ts="FusedBatchNorm",vo="GatherV2",ko="GatherNd",Io="Greater",Es="GreaterEqual",Cs="Identity",Yh="IFFT",Jh="Imag",No="IsFinite",So="IsInf",To="IsNan",Rs="LeakyRelu",Eo="Less",Co="LessEqual",Qh="LinSpace",Fs="Log",Ro="Log1p",Fo="LogicalAnd",Nu="LogicalNot",Su="LogicalOr",f5="LogSoftmax",Tu="LRN",ed="LRNGrad",Ms="Max",$s="Maximum",Ds="MaxPool",td="MaxPoolGrad",Eu="MaxPool3D",nd="MaxPool3DGrad",rd="MaxPoolWithArgmax",Os="Mean",zs="Min",Ps="Minimum",Cu="MirrorPad",Mo="Mod",ad="Multinomial",Ls="Multiply",$o="Neg",Do="NotEqual",Oo="NonMaxSuppressionV3",zo="NonMaxSuppressionV4",Po="NonMaxSuppressionV5",Lo="OnesLike",Ws="OneHot",Wo="Pack",Bs="PadV2",Tk="Pool",Vs="Pow",Us="Prelu",Bo="Prod",Ru="Range",sd="Real",Vo="Reciprocal",js="Relu",Uo="Reshape",Fu="ResizeNearestNeighbor",id="ResizeNearestNeighborGrad",Hs="ResizeBilinear",od="ResizeBilinearGrad",Gs="Relu6",qs="Reverse",Xs="Round",Ks="Rsqrt",jo="ScatterNd",Ho="Select",Go="Selu",qo="Slice",Zs="Sin",Xo="Sinh",Ko="Sign",Ys="Sigmoid",Zo="Softplus",Js="Sqrt",Qs="Sum",Mu="SpaceToBatchND",Yo="SplitV",ei="Softmax",ti="SquaredDifference",$u="Square",ni="Sub",ld="SparseToDense",Jo="StridedSlice",Qo="Tan",ri="Tanh",Ra="Tile",el="TopK",ud="Transform",ai="Transpose",cd="Unique",tl="Unpack",Du="UnsortedSegmentSum",nl="ZerosLike",Fa="Step",hd="FromPixels",rl="RotateWithOffset",si="_FusedMatMul",ii="FusedConv2D",oi="FusedDepthwiseConv2D",al=bf("kernelRegistry",()=>new Map),Ou=bf("gradRegistry",()=>new Map);function dd(e,t){let n=_f(e,t);return al.get(n)}function vf(e){return Ou.get(e)}function sl(e){let t=al.entries(),n=[];for(;;){let{done:r,value:a}=t.next();if(r)break;let[s,i]=a,[o]=s.split("_");o===e&&n.push(i)}return n}function li(e){let{kernelName:t,backendName:n}=e,r=_f(t,n);al.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),al.set(r,e)}function m5(e){let{kernelName:t}=e;Ou.has(t)&&J().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Ou.set(t,e)}function Ek(e,t){let n=_f(e,t);if(!al.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);al.delete(n)}function Ck(e){if(!Ou.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Ou.delete(e)}function Rk(e,t){sl(e).forEach(n=>{let r=Object.assign({},n,{backendName:t});li(r)})}function _f(e,t){return`${t}_${e}`}var v={};Le(v,{arraysEqual:()=>sa,assert:()=>F,assertNonNegativeIntegerDimensions:()=>gf,assertNonNull:()=>hs,assertShapesMatch:()=>on,bytesFromStringArray:()=>i5,bytesPerElement:()=>Af,checkConversionForErrors:()=>a5,clamp:()=>yu,computeStrides:()=>no,createScalarValue:()=>Fk,createShuffledIndices:()=>Ak,decodeString:()=>fd,distSquared:()=>dk,encodeString:()=>Pu,fetch:()=>Mk,flatten:()=>ds,getArrayFromDType:()=>r5,getTypedArrayFromDType:()=>n5,hasEncodingLoss:()=>xk,indexToLoc:()=>_k,inferDtype:()=>Rh,inferFromImplicitShape:()=>gk,isBoolean:()=>o5,isFunction:()=>Ta,isInt:()=>Gt,isNumber:()=>l5,isPromise:()=>xf,isScalarShape:()=>pk,isString:()=>Sa,isTypedArray:()=>ln,isValidDtype:()=>s5,locToIndex:()=>bk,makeOnesTypedArray:()=>yf,makeZerosNestedTypedArray:()=>wk,makeZerosTypedArray:()=>Mh,nearestDivisor:()=>Fh,nearestLargerEven:()=>uk,now:()=>zu,parseAxisParam:()=>ir,randUniform:()=>hk,repeatedTry:()=>yk,rightPad:()=>gu,shuffle:()=>e5,shuffleCombo:()=>lk,sizeFromShape:()=>zt,sizeToSquarishShape:()=>mk,squeezeShape:()=>t5,sum:()=>ck,tanh:()=>fk,toNestedArray:()=>ro,toTypedArray:()=>pd});function Fk(e,t){return t==="string"?Pu(e):pd([e],t)}function $k(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function pd(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=ds(e)),J().getBool("DEBUG")&&a5(e,t),$k(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 zu(){return J().platform.now()}function Mk(e,t){return J().platform.fetch(e,t)}function Pu(e,t="utf-8"){return t=t||"utf-8",J().platform.encode(e,t)}function fd(e,t="utf-8"){return t=t||"utf-8",J().platform.decode(e,t)}var zk=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new Ok)}profileKernel(e,t,n){let r,a=()=>{r=n()},s,i=zu();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(a);else{a();for(let o of r)o.dataSync();s=Promise.resolve({kernelMs:zu()-i})}if(J().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<r.length;o++){let l=r[o];l.data().then(c=>{Dk(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 Dk(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 Ok=class{logKernelProfile(e,t,n,r,a,s){let i=typeof r=="number"?gu(`${r}ms`,9):r.error,o=gu(e,25),l=t.rank,c=t.size,u=gu(t.shape.toString(),14),h="";for(let d in a){let p=a[d];if(p!=null){let f=p.shape||t.shape,m=f.length;h+=`${d}: ${m}D ${m>0?f:""} `}}console.log(`%c${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 Pk(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 f=0;f<t.length;f++)if(r[d.id]){c.outputs.forEach(m=>r[m.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 Lk(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(!sa(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 A5=20,Lu=3,kf=7;function Bk(e,t,n,r){let a=no(t),s=Wk(e,t,n,a),i=t.length,o=md(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 Wk(e,t,n,r){let a=zt(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Bu(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],Wu(l[u+h],0,n).length)}return i}function Wu(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(kf))} + ${parseFloat(e[1].toFixed(kf))}j`:Sa(e)?r=`'${e}'`:n==="bool"?r=y5(e):r=parseFloat(e.toFixed(kf)).toString(),gu(r,t)}function y5(e){return e===0?"false":"true"}function md(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 m=Bu(e);return[Wu(m[0],0,n)]}return n==="bool"?[y5(e[0])]:[e[0].toString()]}if(l===1){if(o>A5){let A=Lu*i,y=Array.from(e.slice(0,A)),g=Array.from(e.slice((o-Lu)*i,o*i));return n==="complex64"&&(y=Bu(y),g=Bu(g)),["["+y.map((w,b)=>Wu(w,a[b],n)).join(", ")+", ..., "+g.map((w,b)=>Wu(w,a[o-Lu+b],n)).join(", ")+"]"]}let m=n==="complex64"?Bu(e):Array.from(e);return["["+m.map((A,y)=>Wu(A,a[y],n)).join(", ")+"]"]}let c=t.slice(1),u=r.slice(1),h=r[0]*i,d=[];if(o>A5){for(let m=0;m<Lu;m++){let A=m*h,y=A+h;d.push(...md(e.slice(A,y),c,n,u,a,!1))}d.push("...");for(let m=o-Lu;m<o;m++){let A=m*h,y=A+h;d.push(...md(e.slice(A,y),c,n,u,a,m===o-1))}}else for(let m=0;m<o;m++){let A=m*h,y=A+h;d.push(...md(e.slice(A,y),c,n,u,a,m===o-1))}let p=l===2?",":"";d[0]="["+d[0]+p;for(let m=1;m<d.length-1;m++)d[m]=" "+d[m]+p;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":f),d}function Bu(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Pt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=zt(e),n!=null){let r=n.length;F(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||r5(t,this.size),this.strides=no(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let 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 $r().makeTensor(this.values,this.shape,this.dtype)}},$r=null,il=null,Vk=null;function Uk(e){$r=e}function jk(e){il=e}function Hk(e){Vk=e}var He=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=zt(e),this.strides=no(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return il.buffer(this.shape,this.dtype,e)}bufferSync(){return il.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ro(this.shape,e)}arraySync(){return ro(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=$r().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>fd(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=$r().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>fd(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 $r().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||($r().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return il.print(this,e)}clone(){return this.throwIfDisposed(),il.clone(this)}toString(e=!1){let t=this.dataSync();return Bk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),il.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),$r().makeVariable(this,e,t,n)}};Object.defineProperty(He,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return bf("Tensor",()=>He)}Z();var Vu=class extends He{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!sa(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);$r().disposeTensor(this),this.dataId=e.dataId,$r().incRef(this,null)}dispose(){$r().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Vu,Symbol.hasInstance,{value:e=>e instanceof He&&e.assign!=null&&e.assign instanceof Function});var wr={};Le(wr,{assertTypesMatch:()=>g5,getTensorsInContainer:()=>If,isTensorInList:()=>Gk,makeTypesMatch:()=>It});var Nf;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Nf||(Nf={}));var Sf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Sf||(Sf={}));var Tf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Tf||(Tf={}));var Ef;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Ef||(Ef={}));var Cf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Cf||(Cf={}));var qk={float32:Ef,int32:Sf,bool:Tf,complex64:Cf};function or(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return qk[e][t]}function Ad(e){return or(e,"int32")}function It(e,t){if(e.dtype===t.dtype)return[e,t];let n=or(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function g5(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Gk(e,t){return t.some(n=>n.id===e.id)}function If(e){let t=[],n=new Set;return x5(e,t,n),t}function x5(e,t,n){if(e==null)return;if(e instanceof He){t.push(e);return}if(!Xk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),x5(s,t,n))}}function Xk(e){return Array.isArray(e)||typeof e=="object"}function Rf(e){return e.kernelName!=null}var w5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Uu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new w5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 zk(this.backendInstance),!0}setupRegisteredKernels(){sl(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){sl(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 Au)&&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 Uu.nextTensorId++}nextVariableId(){return Uu.nextVariableId++}clone(e){let t=$.runKernel(Cs,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(ys,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(dd(e,this.backendName)==null)throw new Error(`Kernel '${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=Rf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Rf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=dd(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:N}=b;return this.makeTensorFromDataId(_,x,N)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=Rf(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=vf(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(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"&&Sa(e[0])&&(a=e.map(o=>Pu(o)));let s=r.write(a,t,n),i=new He(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=i5(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new He(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 Vu(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*Af(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 Vu||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*Af(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=vf(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let h=n[u],d=Mh(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=If(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(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof He,()=>"The result y returned by f() must be a tensor.");let s=Pk(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?Kk(a.shape):n,Lk(i,s,l=>this.tidy(l),Zk);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 F(Ta(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof He),()=>"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),F(n.value instanceof He,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(Ta(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];F(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(...)."),F(c.every(h=>h instanceof He),()=>"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=zu(),n=await this.backend.time(e);return n.wallMs=zu()-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 w5;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}};Uu.nextTensorId=0;Uu.nextVariableId=0;function Kk(e){let t=yf(zt(e),"float32");return $.makeTensor(t,e,"float32")}function b5(){let e=d5();if(e._tfengine==null){let t=new h5(e);e._tfengine=new Uu(t)}return Nk(e._tfengine.ENV),Uk(()=>e._tfengine),e._tfengine}var $=b5();function Zk(e,t){let n={a:e,b:t};return $.runKernel(Ea,n)}var ju={};Le(ju,{isBrowser:()=>_5,isMobile:()=>Yk});function Jk(){return typeof navigator!="undefined"&&navigator!=null}function Yk(){if(Jk()){let e=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function _5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var br=J();br.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.")});br.registerFlag("IS_BROWSER",()=>_5());br.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");br.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));br.registerFlag("PROD",()=>!1);br.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>br.getBool("DEBUG"));br.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);br.registerFlag("IS_TEST",()=>!1);br.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);br.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Dr(e,t){let n=e;if(ln(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||ln(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&v5(e,r,[]),r}function v5(e,t,n){if(n=n||[],!Array.isArray(e)&&!ln(e)){F(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}F(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),F(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let a=0;a<e.length;++a)v5(e[a],r,n.concat(a))}function k5(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function C(e,t,n,r="numeric"){if(e instanceof He)return k5(r,e.dtype,t,n),e;let a=Rh(e);if(a!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(a=r),k5(r,a,t,n),e==null||!ln(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Dr(e,a);!ln(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?pd(e,a):ds(e,[],!0);return $.makeTensor(i,s,a)}function Hu(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>C(a,`${t}[${s}]`,n,r))}var I5="__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 ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+I5;let a=(...s)=>{$.startScope(n);try{let i=r(...s);return xf(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 Qk(e,t){let n=C(e,"real","complex"),r=C(t,"imag","complex");on(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(Lh,a)}var Ma=D({complex_:Qk});function $a(e,t,n,r){if(r==null&&(r=Rh(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!ln(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){gf(t);let a=zt(t),s=zt(n);F(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==zt(t.slice(i)):!0;F(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!ln(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?pd(e,r):ds(e,[],!0),$.makeTensor(e,t,r)}function _r(e,t,n){let r=Dr(e,n);return $a(e,t,r,n)}var Ff={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},yd=4;async function t9(e,t){let n=[],r=[],a=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<a.length;++i){let o=a[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${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)+yd*d.length,f=new Uint8Array(p),m=0;for(let A=0;A<d.length;A++){let y=d[A],g=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(g,m),m+=yd,f.set(y,m),m+=y.length}h(f)});r.push(u)}else r.push(l.data());t!=null&&(c.group=t),n.push(c)}let s=await Promise.all(r);return{data:e9(s),specs:n}}function N5(e,t){let n={},r,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,c=zt(l),u;if("quantization"in s){let 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=Ff[h.dtype],p=e.slice(a,a+c*d),f=h.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(o==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){u=new Float32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];u[m]=A*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=n9()),u=r(f);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 ${h.dtype} for weight type int32.`);u=new Int32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];u[m]=Math.round(A*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*d}else if(o==="string"){let h=zt(s.shape);u=[];for(let d=0;d<h;d++){let p=new Uint32Array(e.slice(a,a+yd))[0];a+=yd;let f=new Uint8Array(e.slice(a,a+p));u.push(f),a+=p}}else{let h=Ff[o],d=e.slice(a,a+c*h);if(o==="float32")u=new Float32Array(d);else if(o==="int32")u=new Int32Array(d);else if(o==="bool")u=new Uint8Array(d);else if(o==="complex64"){u=new Float32Array(d);let p=new Float32Array(u.length/2),f=new Float32Array(u.length/2);for(let y=0;y<p.length;y++)p[y]=u[y*2],f[y]=u[y*2+1];let m=_r(p,l,"float32"),A=_r(f,l,"float32");n[i]=Ma(m,A),m.dispose(),A.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*h}o!=="complex64"&&(n[i]=_r(u,l,o))}return n}function e9(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 S5(e){return Mf?Buffer.byteLength(e):new Blob([e]).size}function r9(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 a9(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 $f(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 T5(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 Gu(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:S5(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:S5(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function s9(){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 i9(){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 o9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function n9(){let e=s9(),t=i9(),n=o9();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 Ct=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Ct.instance==null&&(Ct.instance=new Ct),Ct.instance}static registerSaveRouter(e){Ct.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Ct.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Ct.getHandlers(e,"save")}static getLoadHandlers(e,t){return Ct.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?Ct.getInstance().loadRouters:Ct.getInstance().saveRouters).forEach(a=>{let s=a(e,n);s!==null&&r.push(s)}),r}},l9=e=>Ct.registerSaveRouter(e),u9=e=>Ct.registerLoadRouter(e),c9=e=>Ct.getSaveHandlers(e),h9=(e,t)=>Ct.getLoadHandlers(e,t),Df="tensorflowjs",Of=1,ui="models_store",Da="model_info_store";function E5(){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 zf(e){let t=e.result;t.createObjectStore(ui,{keyPath:"modelPath"}),t.createObjectStore(Da,{keyPath:"modelPath"})}var ci=class{constructor(e){if(this.indexedDB=E5(),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(Df,Of);a.onupgradeneeded=()=>zf(a),a.onsuccess=()=>{let s=a.result;if(t==null){let i=s.transaction(ui,"readonly"),o=i.objectStore(ui).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=Gu(t),o=s.transaction(Da,"readwrite"),l=o.objectStore(Da),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),u;c.onsuccess=()=>{u=s.transaction(ui,"readwrite");let h=u.objectStore(ui).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});h.onsuccess=()=>n({modelArtifactsInfo:i}),h.onerror=d=>{l=o.objectStore(Da);let p=l.delete(this.modelPath);p.onsuccess=()=>(s.close(),r(h.error)),p.onerror=f=>(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)})}};ci.URL_SCHEME="indexeddb://";var C5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ci.URL_SCHEME)?d9(e.slice(ci.URL_SCHEME.length)):null;Ct.registerSaveRouter(C5);Ct.registerLoadRouter(C5);function d9(e){return new ci(e)}function p9(e){return e.startsWith(ci.URL_SCHEME)?e.slice(ci.URL_SCHEME.length):e}var f9=class{constructor(){this.indexedDB=E5()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(Df,Of);n.onupgradeneeded=()=>zf(n),n.onsuccess=()=>{let r=n.result,a=r.transaction(Da,"readonly"),s=a.objectStore(Da).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=p9(e),new Promise((t,n)=>{let r=this.indexedDB.open(Df,Of);r.onupgradeneeded=()=>zf(r),r.onsuccess=()=>{let a=r.result,s=a.transaction(Da,"readwrite"),i=s.objectStore(Da),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(ui,"readwrite");let h=l.objectStore(ui).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)})}},ia="/",ol="tensorflowjs_models",R5="info",m9="model_topology",A9="weight_specs",y9="weight_data",g9="model_metadata";function F5(e){return{info:[ol,e,R5].join(ia),topology:[ol,e,m9].join(ia),weightSpecs:[ol,e,A9].join(ia),weightData:[ol,e,y9].join(ia),modelMetadata:[ol,e,g9].join(ia)}}function x9(e){let t=e.split(ia);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(ia)}function w9(e){return e.startsWith(hi.URL_SCHEME)?e.slice(hi.URL_SCHEME.length):e}var hi=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=F5(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=Gu(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,r9(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:r}}catch(a){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=a9(s),t}};hi.URL_SCHEME="localstorage://";var M5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(hi.URL_SCHEME)?b9(e.slice(hi.URL_SCHEME.length)):null;Ct.registerSaveRouter(M5);Ct.registerLoadRouter(M5);function b9(e){return new hi(e)}var _9=class{constructor(){F(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),F(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=ol+ia,n=ia+R5;for(let r=0;r<this.LS.length;++r){let a=this.LS.key(r);if(a.startsWith(t)&&a.endsWith(n)){let s=x9(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=w9(e);let t=F5(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${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}},ll="://",Kn=class{constructor(){this.managers={}}static getInstance(){return Kn.instance==null&&(Kn.instance=new Kn),Kn.instance}static registerManager(e,t){F(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(ll)&&(e=e.slice(0,e.indexOf(ll))),F(e.length>0,()=>"scheme must not be an empty string.");let n=Kn.getInstance();F(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function gd(e){if(e.indexOf(ll)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Kn.getSchemes().join(",")}`);return{scheme:e.split(ll)[0],path:e.split(ll)[1]}}async function $5(e,t,n=!1){F(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=Ct.getLoadHandlers(e);F(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),F(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let a=r[0],s=Ct.getSaveHandlers(t);F(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),F(s.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let i=s[0],o=gd(e).scheme,l=gd(e).path,c=o===gd(e).scheme,u=await a.load();n&&c&&await Kn.getManager(o).removeModel(l);let h=await i.save(u);return n&&!c&&await Kn.getManager(o).removeModel(l),h.modelArtifactsInfo}async function v9(){let e=Kn.getSchemes(),t={};for(let n of e){let r=await Kn.getManager(n).listModels();for(let a in r){let s=n+ll+a;t[s]=r[a]}}return t}async function k9(e){let t=gd(e);return Kn.getManager(t.scheme).removeModel(t.path)}async function I9(e,t){return $5(e,t,!1)}async function N9(e,t){return $5(e,t,!0)}var S9=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 S9);try{Kn.registerManager(hi.URL_SCHEME,new _9)}catch(e){}try{Kn.registerManager(ci.URL_SCHEME,new f9)}catch(e){}}var T9={importFetch:()=>D8()},Pf,E9=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):(Pf==null&&(Pf=T9.importFetch()),Pf(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 E9);function Ve(e,t="float32",n){return t=t||"float32",gf(e),new Pt(e,t,n)}function C9(e,t){let n=C(e,"x","cast");if(!s5(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(ys,r,a)}var ge=D({cast_:C9});function R9(e){let t={x:C(e,"x","clone","string_or_numeric")};return $.runKernel(Cs,t)}var Or=D({clone_:R9});function D5(e,t=!1){console.log(e.toString(t))}b5();var F9={buffer:Ve,cast:ge,clone:Or,print:D5};jk(F9);var _n={};Le(_n,{browserFiles:()=>M9,browserHTTPRequest:()=>D9,concatenateArrayBuffers:()=>$f,copyModel:()=>I9,decodeWeights:()=>N5,encodeWeights:()=>t9,fromMemory:()=>O9,getLoadHandlers:()=>h9,getModelArtifactsInfoForJSON:()=>Gu,getSaveHandlers:()=>c9,http:()=>Wf,isHTTPScheme:()=>Lf,listModels:()=>v9,loadWeights:()=>$9,moveModel:()=>N9,registerLoadRouter:()=>u9,registerSaveRouter:()=>l9,removeModel:()=>k9,weightsLoaderFactory:()=>O5,withSaveHandler:()=>z9});var P9="model",L9=".json",W9=".weights.bin";function z5(e){return new Promise(t=>setTimeout(t)).then(e)}var ul=class{constructor(e){if(!J().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(ul.URL_SCHEME)&&(e=e.slice(ul.URL_SCHEME.length)),(e==null||e.length===0)&&(e=P9),this.modelTopologyFileName=e+L9,this.weightDataFileName=e+W9}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 z5(()=>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 z5(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Gu(e)}}}};ul.URL_SCHEME="downloads://";var B9=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 ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let u=[],h=[],d=[];l.forEach(p=>{p.paths.forEach(f=>{h.push(f),d.push(null)}),u.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(f=>{let m=new FileReader;m.onload=A=>{let y=A.target.result,g=h.indexOf(f);if(d[g]=y,d.indexOf(null)===-1){let w={modelTopology:o,weightSpecs:u,weightData:$f(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(w.signature=i.signature),i.userDefinedMetadata!=null&&(w.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(w.modelInitializer=i.modelInitializer),n(w)}},m.onerror=A=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(c[f])})})},a.onerror=s=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),a.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(s=>T5(s.name)),a={};for(let s of e)s.paths.forEach(i=>{let o=T5(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}},U9=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ul.URL_SCHEME)?V9(e.slice(ul.URL_SCHEME.length)):null;Ct.registerSaveRouter(U9);function V9(e="model"){return new ul(e)}function M9(e){return new B9(e)}function P5(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){F(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,c){F(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),F(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),F(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(s))}async function L5(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 P5(r,t.onProgress,a,s)).map(c=>c.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await P5(i,t.onProgress,o,l)}async function $9(e,t="",n,r){return O5(a=>L5(a,{requestInit:r}))(e,t,n)}function O5(e){return async(t,n="",r)=>{let a=t.map(()=>!1),s={},i=r!=null?r.map(()=>!1):[],o=[];if(t.forEach((p,f)=>{let m=0;p.weights.forEach(A=>{let y="quantization"in A?A.quantization.dtype:A.dtype,g=Ff[y]*zt(A.shape),w=()=>{a[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:A,groupOffset:m,sizeBytes:g})};r!=null?r.forEach((b,_)=>{b===A.name&&(w(),i[_]=!0)}):w(),o.push(A.name),m+=g})}),!i.every(p=>p)){let p=r.filter((f,m)=>!i[m]);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,f,m)=>(f&&p.push(m),p),[]),c=[];l.forEach(p=>{t[p].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;c.push(m)})});let u=await e(c),h={},d=0;return l.forEach(p=>{let f=t[p].paths.length,m=0;for(let w=0;w<f;w++)m+=u[d+w].byteLength;let A=new ArrayBuffer(m),y=new Uint8Array(A),g=0;for(let w=0;w<f;w++){let b=new Uint8Array(u[d+w]);y.set(b,g),g+=b.byteLength}s[p].forEach(w=>{let b=A.slice(w.groupOffset,w.groupOffset+w.sizeBytes),_=N5(b,[w.manifestEntry]);for(let x in _)h[x]=_[x]}),d+=f}),h}}var j9="application/octet-stream",H9="application/json",Bf=class{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(F(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=J().platform.fetch,F(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&F(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],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:H9}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:j9}),"model.weights.bin");let a=await this.fetch(this.path,t);if(a.ok)return{modelArtifactsInfo:Gu(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 f=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?f+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":f+=" Please make sure the server is serving valid JSON for this request.",new Error(f)}let n=t.modelTopology,r=t.weightsManifest,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 ${this.path} contains neither model topology or manifest for weights.`);let c,u;r!=null&&([c,u]=await this.loadWeights(r));let h={modelTopology:n,weightSpecs:c,weightData:u,generatedBy:a,convertedBy:s,format:i};o!=null&&(h.signature=o),l!=null&&(h.userDefinedMetadata=l);let d=t.modelInitializer;return d&&(h.modelInitializer=d),h}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=G9(t),a=this.weightPathPrefix||n,s=[];for(let c of e)s.push(...c.weights);let i=[],o=[];for(let c of e)for(let u of c.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(u)):i.push(a+u+r);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await L5(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,$f(l)]}};Bf.URL_SCHEME_REGEX=/^https?:\/\//;function G9(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),a=n>t?e.substring(n):"";return[r+"/",a]}function Lf(e){return e.match(Bf.URL_SCHEME_REGEX)!=null}var W5=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>Lf(r)):n=Lf(e),n)return Wf(e,t)}return null};Ct.registerSaveRouter(W5);Ct.registerLoadRouter(W5);function Wf(e,t){return new Bf(e,t)}function D9(e,t){return Wf(e,t)}var Vf=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},q9=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function O9(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new Vf(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 Vf({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 Vf({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function z9(e){return new q9(e)}var B5={};Le(B5,{confusionMatrix:()=>X9});function K9(e,t,n=!1,r=!1){let a=C(e,"a","matMul"),s=C(t,"b","matMul");[a,s]=It(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return $.runKernel(As,i,o)}var Ke=D({matMul_:K9});function Z9(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:C(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:r};return $.runKernel(Ws,a,s)}var cl=D({oneHot_:Z9});function Y9(e,t){let n=C(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{F(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let r={x:n},a={perm:t};return $.runKernel(ai,r,a)}var st=D({transpose_:Y9});function J9(e,t,n){let r=C(e,"labels","confusionMatrix"),a=C(t,"predictions","confusionMatrix");F(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),F(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),F(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),F(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.`),F(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=cl(ge(r,"int32"),n),i=cl(ge(a,"int32"),n),o=st(s),l=Ke(o,i);return ge(l,"int32")}var X9=D({confusionMatrix_:J9}),hl={};Le(hl,{fromPixels:()=>tI,fromPixelsAsync:()=>Q9,toPixels:()=>eI});function xd(e,t,n){if(hs(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=Dr(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 $a(e,t,r,n)}var dl;function V5(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 ${e.constructor.name}`);if(a){let d=2;if(a&&e.readyState<d)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(dd(hd,$.backendName)!=null){let d={pixels:e},p={numChannels:t};return $.runKernel(hd,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)&&(dl==null&&(dl=document.createElement("canvas").getContext("2d")),dl.canvas.width=l,dl.canvas.height=c,dl.drawImage(e,0,0,l,c),u=dl.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 f=0;f<t;++f)h[p*t+f]=u[p*4+f]}return xd(h,[c,l,t],"int32")}function nI(e){return e!=null&&e.data instanceof Uint8Array}function rI(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function aI(e){return e!=null&&e.width!==0&&e.height!==0}function sI(e){return rI()&&!(e instanceof ImageBitmap)&&aI(e)&&!nI(e)}async function Q9(e,t=3){let n=null;if(J().getBool("WRAP_TO_IMAGEBITMAP")&&sI(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 V5(n,t)}async function eI(e,t){let n=C(e,"img","toPixels");if(!(e instanceof He)){let c=n;n=ge(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 tI=D({fromPixels_:V5}),Uf={};Le(Uf,{prepareAndValidate:()=>U5});function U5(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(zt(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=[...no(e.shape).map(h=>h/c),1].slice(0,s);return[l,i,c,u]}var jf={};Le(jf,{calculateShapes:()=>j5,validateInput:()=>Gf,validateUpdateShape:()=>Hf});function Hf(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 Gf(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: ${e.shape}`)}Hf(n,t,e)}function j5(e,t,n){let r=t.shape.length,a=r>1?t.shape[r-1]:1,s=n.length,i=1;for(let h=a;h<s;++h)i*=n[h];let o=a<1?1:a,l=zt(t.shape)/o,c=[...no(n.slice(0,a)),1],u=zt(n);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:c,outputSize:u}}var hn={};Le(hn,{assertParamsValid:()=>iI,computeFlatOffset:()=>lI,computeOutShape:()=>H5,getNormalizedAxes:()=>q5,isSliceContinous:()=>oI,maskToAxes:()=>wd,parseSliceParams:()=>Q5,sliceInfo:()=>uI,startForAxis:()=>Y5,startIndicesWithElidedDims:()=>X5,stopForAxis:()=>J5,stopIndicesWithElidedDims:()=>K5,stridesForAxis:()=>Z5,stridesWithElidedDims:()=>G5});function iI(e,t,n){let r=e.shape.length;F(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),F(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)F(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 wd(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function H5(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 G5(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 ex(e,t,n){return n<=e?n:n-(t-1)}function tx(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function q5(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],f=n+1;u=X5(i,p,f,r,e),h=K5(o,p,f,a,e),d=G5(s,p,f,e)}else for(let p=0;p<c;p++)u[p]=Y5(i,r,s,e,p,l),h[p]=J5(o,a,s,e,p,l),d[p]=Z5(s,p,l);return{begin:u,end:h,strides:d}}function X5(e,t,n,r,a){let s=[...a],i=tx(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=ex(t,n,o),c=r[l];e&1<<l&&(c=0),s[o]=c}return s}function K5(e,t,n,r,a){let s=[...a],i=tx(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=ex(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]=yu(0,s[o],a[o])}return s}function Z5(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function Y5(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=yu(0,i,l-1),i}function J5(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=yu(0,i,l):i=yu(-1,i,l-1),i}function oI(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 lI(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 Q5(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=>{F(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:(F(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-r[o])),[r,s]}function uI(e,t,n,r,a,s,i,o,l){let c=t.slice(),u=n.slice(),h=r;r==null&&(h=new Array(c.length));let d=wd(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-c.length,f=wd(o),m=e.slice();f.forEach(x=>{c[x]=0,u[x]=1,m.splice(x,0,1)});let{begin:A,end:y,strides:g}=q5(m,d,p,c,u,h,a,s,i);c=A,u=y,h=g;let w=wd(l);w.forEach(x=>{u[x]=c[x]+1,h[x]=1});let b=H5(c,u,h),_=b.filter((x,N)=>w.indexOf(N)===-1);return{nonStrided:h.every(x=>x===1),$begin:c,$end:u,$strides:h,size:b,newShape:m,outShape:_}}var ae={};Le(ae,{Serializable:()=>nx,SerializationMap:()=>di,registerClass:()=>Oa});var nx=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},di=class{constructor(){this.classNameMap={}}static getMap(){return di.instance==null&&(di.instance=new di),di.instance}static register(e){di.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Oa(e){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),di.register(e)}var rx={};Le(rx,{TEST_EPSILON_FLOAT16:()=>ax,encodeStrings:()=>sx,expectArrayBuffersEqual:()=>mI,expectArraysClose:()=>cI,expectArraysEqual:()=>dI,expectNumbersClose:()=>pI,expectPromiseToFail:()=>hI,expectValuesInRange:()=>fI,testEpsilon:()=>qf});var AI=.001,ax=.1;function cI(e,t,n){return n==null&&(n=qf()),Xf(e,t,(r,a)=>Kf(r,a,n))}function qf(){return $.backend.floatPrecision()===32?AI:ax}function Xf(e,t,n){let r=!0;if((ln(e)||ln(t))&&(r=!1),ln(e)&&ln(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: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Dr(e),o=Dr(t);if(!sa(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let a=ln(e)?e:ds(e),s=ln(t)?t:ds(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 hI(e,t){e().then(()=>t.fail(),()=>t())}function dI(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Sa(e)||Sa(e[0])||Sa(t)||Sa(t[0])?Xf(e,n,(r,a)=>r==a):Xf(e,t,(r,a)=>Kf(r,a,0))}function pI(e,t,n){if(n==null&&(n=qf()),!Kf(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Kf(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function fI(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function mI(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function sx(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?sx(n):e[t]=Pu(n)}return e}var yI="3.3.0";function gI(){J().set("PROD",!0)}function xI(){J().set("DEBUG",!0)}function wI(){J().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Zf(e){J().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}Hk(Zf);function bI(){$.disposeVariables()}function zr(){return $}function bd(){return $.memory()}function vn(e){return $.profile(e)}function z(e,t){return $.tidy(e,t)}function Te(e){If(e).forEach(t=>t.dispose())}function qt(e){return $.keep(e)}function _I(e){return $.time(e)}function vI(e){return $.setBackend(e)}function kI(){return $.ready()}function II(){return $.backendName}function NI(e){$.removeBackend(e)}function Yf(e){return $.findBackend(e)}function SI(e){return $.findBackendFactory(e)}function pl(e,t,n=1){return $.registerBackend(e,t,n)}function ix(){return $.backend}function TI(e,t){J().setPlatform(e,t)}function EI(e,t){let n=C(e,"a","add"),r=C(t,"b","add");[n,r]=It(n,r);let a={a:n,b:r};return $.runKernel(Ea,a)}var ie=D({add_:EI});function CI(e,t){let n=C(e,"a","floorDiv"),r=C(t,"b","floorDiv");[n,r]=It(n,r);let a={a:n,b:r};return $.runKernel(Ss,a)}var _d=D({floorDiv_:CI});function RI(e,t){let n=C(e,"a","div"),r=C(t,"b","div");if([n,r]=It(n,r),n.dtype==="int32"&&r.dtype==="int32")return _d(n,r);let a={a:n,b:r},s={};return $.runKernel(ks,a,s)}var be=D({div_:RI});function FI(e,t){let n=C(e,"a","mul"),r=C(t,"b","mul");[n,r]=It(n,r);let a={a:n,b:r};return $.runKernel(Ls,a)}var P=D({mul_:FI});function MI(e){let t=C(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return $.runKernel(_u,n)}else{let n={x:t};return $.runKernel(ao,n)}}var Lt=D({abs_:MI});function $I(e){let t={x:C(e,"x","acos")};return $.runKernel(so,t)}var Jf=D({acos_:$I});function DI(e){let t={x:C(e,"x","acosh")};return $.runKernel(io,t)}var Qf=D({acosh_:DI});function OI(e){F(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),F(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>C(a,`tensors${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(!sa(a.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return $.runKernel(ps,r)}var za=D({addN_:OI});function zI(e,t=null,n=!1){let r={x:C(e,"x","all","bool")},a={axis:t,keepDims:n};return $.runKernel($h,r,a)}var vd=D({all_:zI});function PI(e,t=null,n=!1){let r={x:C(e,"x","any","bool")},a={axis:t,keepDims:n};return $.runKernel(Dh,r,a)}var qu=D({any_:PI});function LI(e,t=0){let n={x:C(e,"x","argMax")},r={axis:t};return $.runKernel(fs,n,r)}var Xu=D({argMax_:LI});function WI(e,t=0){let n={x:C(e,"x","argMin")},r={axis:t};return $.runKernel(xu,n,r)}var em=D({argMin_:WI});function BI(e){let t={x:C(e,"x","asin")};return $.runKernel(oo,t)}var tm=D({asin_:BI});function VI(e){let t={x:C(e,"x","asinh")};return $.runKernel(lo,t)}var nm=D({asinh_:VI});function UI(e){let t={x:C(e,"x","atan")};return $.runKernel(uo,t)}var rm=D({atan_:UI});function jI(e,t){let n=C(e,"a","atan2"),r=C(t,"b","atan2");[n,r]=It(n,r);let a={a:n,b:r};return $.runKernel(ho,a)}var am=D({atan2_:jI});function HI(e){let t={x:C(e,"x","atanh")};return $.runKernel(co,t)}var sm=D({atanh_:HI});function GI(e,t,n,r,a="NHWC",s){let i=e[3],o=[...t,i],l=ox(a);return Ku(e,o,n,s,r,null,null,l)}function lx(e,t,n,r,a,s,i="channelsLast"){let[o,l]=kd(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 Ku(e,c,n,r,a,s,!1,i)}function qI(e,t,n,r,a,s,i="NDHWC"){let[o,l,c]=im(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 ux(e,u,n,r,a,!1,h,s)}function Ku(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,,f]=t,[m,A]=kd(n),[y,g]=kd(r),w=fl(d,y),b=fl(p,g),{padInfo:_,outHeight:x,outWidth:N}=XI(a,c,u,m,A,w,b,s,o),T=i?f*h:f,E;return o==="channelsFirst"?E=[l,T,x,N]:o==="channelsLast"&&(E=[l,x,N,T]),{batchSize:l,dataFormat:o,inHeight:c,inWidth:u,inChannels:h,outHeight:x,outWidth:N,outChannels:T,padInfo:_,strideHeight:m,strideWidth:A,filterHeight:d,filterWidth:p,effectiveFilterHeight:w,effectiveFilterWidth:b,dilationHeight:y,dilationWidth:g,inShape:e,outShape:E,filterShape:t}}function ux(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,f,m,,A]=t,[y,g,w]=im(n),[b,_,x]=im(r),N=fl(p,b),T=fl(f,_),E=fl(m,x),{padInfo:M,outDepth:O,outHeight:W,outWidth:V}=KI(a,c,u,h,y,g,w,N,T,E,o),U=s?A*d:A,j;return i==="channelsFirst"?j=[l,U,O,W,V]:i==="channelsLast"&&(j=[l,O,W,V,U]),{batchSize:l,dataFormat:i,inDepth:c,inHeight:u,inWidth:h,inChannels:d,outDepth:O,outHeight:W,outWidth:V,outChannels:U,padInfo:M,strideDepth:y,strideHeight:g,strideWidth:w,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:N,effectiveFilterHeight:T,effectiveFilterWidth:E,dilationDepth:b,dilationHeight:_,dilationWidth:x,inShape:e,outShape:j,filterShape:t}}function ZI(e,t,n,r,a){r==null&&(r=om(e,t,n));let s=e[0],i=e[1],o=pi((s-t+2*r)/n+1,a),l=pi((i-t+2*r)/n+1,a);return[o,l]}function YI(e,t,n,r,a,s){a==null&&(a=om(e,t,r));let i=e[0],o=e[1],l=e[2],c=pi((i-t+2*a)/r+1,s),u=pi((o-t+2*a)/r+1,s),h=pi((l-t+2*a)/r+1,s);return[c,u,h,n]}function om(e,t,n,r=1){let a=fl(t,r);return Math.floor((e[0]*(n-1)-n+a)/2)}function kd(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function im(e){return typeof e=="number"?[e,e,e]:e}function fl(e,t){return t<=1?e:e+(e-1)*(t-1)}function XI(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=ZI([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),f=Math.floor(d/2),m=d-f,A=Math.floor(p/2),y=p-A;c={top:f,bottom:m,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],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];c={top:d,bottom:p,left:f,right:m,type:d===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},u=pi((t-s+d+p)/r+1,o),h=pi((n-i+f+m)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:h}}function KI(e,t,n,r,a,s,i,o,l,c,u){let h,d,p,f;if(typeof e=="number"){h={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let m=YI([t,n,r,1],o,1,a,e,u);d=m[0],p=m[1],f=m[2]}else if(e==="same"){d=Math.ceil(t/a),p=Math.ceil(n/s),f=Math.ceil(r/i);let m=(d-1)*a+o-t,A=(p-1)*s+l-n,y=(f-1)*i+c-r,g=Math.floor(m/2),w=m-g,b=Math.floor(A/2),_=A-b,x=Math.floor(y/2),N=y-x;h={top:b,bottom:_,left:x,right:N,front:g,back:w,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),f=Math.ceil((r-c+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:d,outHeight:p,outWidth:f}}function pi(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 Pa(e){let[t,n,r]=kd(e);return t===1&&n===1&&r===1}function Pr(e,t){return Pa(e)||Pa(t)}function ox(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function JI(e,t){let n={x:C(e,"x","reshape","string_or_numeric")},r={shape:t};return $.runKernel(Uo,n,r)}var H=D({reshape_:JI});function QI(e,t,n,r,a){let s=C(e,"x","avgPool","float32"),i=1;F(Pr(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&F(Gt(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(ms,c,u);return h=ge(h,s.dtype),l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Zu=D({avgPool_:QI});function eN(e,t,n,r,a,s="NDHWC"){let i=C(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(Gt(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(wu,c,u);return h=ge(h,o.dtype),l?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var lm=D({avgPool3d_:eN});function tN(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=Hu(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 Or(n[0]);let r=n,a={axis:t};return $.runKernel(po,r,a)}var it=D({concat_:tN});function nN(e){let t={x:C(e,"x","sigmoid")};return $.runKernel(Ys,t)}var $n=D({sigmoid_:nN});function rN(e,t,n){let r=C(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 Me=D({slice_:rN});function aN(e){let t={x:C(e,"x","tanh")};return $.runKernel(ri,t)}var ml=D({tanh_:aN});function sN(e,t,n,r,a,s){let i=C(e,"forgetBias","basicLSTMCell"),o=C(t,"lstmKernel","basicLSTMCell"),l=C(n,"lstmBias","basicLSTMCell"),c=C(r,"data","basicLSTMCell"),u=C(a,"c","basicLSTMCell"),h=C(s,"h","basicLSTMCell"),d=it([c,h],1),p=Ke(d,o),f=ie(p,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Me(f,[0,0],y),w=Me(f,[0,A],y),b=Me(f,[0,A*2],y),_=Me(f,[0,A*3],y),x=ie(P($n(g),ml(w)),P(u,$n(ie(i,b)))),N=P(ml(x),$n(_));return[x,N]}var iN=D({basicLSTMCell_:sN});function oN(e,t,n){let r=C(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);F(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(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 $.runKernel(bu,s,i)}var Yu=D({batchToSpaceND_:oN});function lN(e){let t;return e.rank===0||e.rank===1?t=H(e,[1,1,1,e.size]):e.rank===2?t=H(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function uN(e,t,n,r,a,s){s==null&&(s=.001);let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;a!=null&&(c=C(a,"scale","batchNorm"));let u;r!=null&&(u=C(r,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:lN(i),scale:c,offset:u,mean:o,variance:l},d={varianceEpsilon:s},p=$.runKernel(Ts,h,d);return H(p,i.shape)}var fi=D({batchNorm_:uN});function cN(e,t,n,r,a,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;a!=null&&(c=C(a,"scale","batchNorm"));let u;return r!=null&&(u=C(r,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),fi(i,o,l,u,c,s)}var cx=D({batchNorm2d_:cN});function hN(e,t,n,r,a,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;a!=null&&(c=C(a,"scale","batchNorm"));let u;return r!=null&&(u=C(r,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),fi(i,o,l,u,c,s)}var hx=D({batchNorm3d_:hN});function dN(e,t,n,r,a,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;a!=null&&(c=C(a,"scale","batchNorm"));let u;return r!=null&&(u=C(r,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),fi(i,o,l,u,c,s)}var dx=D({batchNorm4d_:dN});function pN(e,t,n){let r=C(e,"x","bincount"),a=C(t,"weights","bincount");F(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(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 $.runKernel(Ph,s,i)}var px=D({bincount_:pN});function fN(e,t){let n=C(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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 Or(n);let i={x:n},o={reps:s};return $.runKernel(Ra,i,o)}var Ju=D({broadcastTo_:fN});function mN(e){let t={x:C(e,"x","ceil")};return $.runKernel(gs,t)}var um=D({ceil_:mN});function AN(e,t,n){let r=C(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Ca,a,s)}var kn=D({clipByValue_:AN});function yN(e){return it(e,0)}var fx=D({concat1d_:yN});function gN(e,t){return it(e,t)}var Al=D({concat2d_:gN});function xN(e,t){return it(e,t)}var mx=D({concat3d_:xN});function wN(e,t){return it(e,t)}var Ax=D({concat4d_:wN});function bN(e,t,n,r,a="NHWC",s=[1,1],i){let o=C(e,"x","conv2d"),l=C(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(Gt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];F(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),F(Pr(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},f=$.runKernel(xs,d,p);return u?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var oa=D({conv2d_:bN});function _N(e,t,n,r,a="NWC",s=1,i){let o=C(e,"x","conv1d"),l=C(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1]])),F(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(Gt(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(Pr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=oa(d,h,[1,n],r,"NHWC",[1,s],i);return u?H(p,[p.shape[2],p.shape[3]]):H(p,[p.shape[0],p.shape[2],p.shape[3]])}var Id=D({conv1d_:_N});function vN(e,t,n,r,a,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),F(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(Gt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=$.runKernel(ws,d,p);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var cm=D({conv2DBackpropInput_:vN});function kN(e,t,n,r,a,s){let i=C(e,"x","conv2dTranspose"),o=C(t,"filter","conv2dTranspose");return cm(n,i,o,r,a,"NHWC",s)}var Nd=D({conv2dTranspose_:kN});function IN(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=C(e,"x","conv3d"),o=C(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(Pr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(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(vu,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var hm=D({conv3d_:IN});function NN(e,t,n,r,a){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],c=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=$.runKernel(Vh,u,h);return o?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var yx=D({conv3DBackpropInput_:NN});function SN(e,t,n,r,a){let s=C(e,"x","conv3dTranspose"),i=C(t,"filter","conv3dTranspose");return yx(n,s,i,r,a)}var TN=D({conv3dTranspose_:SN});function EN(e){let t={x:C(e,"x","cos")};return $.runKernel(bs,t)}var Qu=D({cos_:EN});function CN(e){let t={x:C(e,"x","cosh")};return $.runKernel(fo,t)}var Sd=D({cosh_:CN});function RN(e,t=0,n=!1,r=!1){let a={x:C(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return $.runKernel(_s,a,s)}var Td=D({cumsum_:RN});function FN(e,t,n,r=!1){let a=C(e,"x","denseBincount"),s=C(t,"weights","denseBincount");F(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),F(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(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(Uh,i,o)}var gx=D({denseBincount_:FN});function MN(e,t,n="NHWC"){let r=C(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];F(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${a} and ${t} for depthToSpace with input shape
${r.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${s} and ${t} for depthToSpace with input shape
${r.shape}`),F(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 dm=D({depthToSpace_:MN});function $N(e,t,n,r,a="NHWC",s=[1,1],i){let o=C(e,"x","depthwiseConv2d"),l=C(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(Gt(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(vs,h,d);return u?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var yl=D({depthwiseConv2d_:$N});function DN(e){let t={x:C(e,"x","diag")};return $.runKernel(Gh,t)}var ON=D({diag_:DN});function zN(e,t,n,r,a=[1,1],s="NHWC"){let i=C(e,"x","dilation2d"),o=C(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},h={strides:n,pad:r,dilations:a},d=$.runKernel(ku,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var pm=D({dilation2d_:zN});function PN(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 Wt(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 yt(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 LN(e,t){let n=C(e,"a","equal"),r=C(t,"b","equal");[n,r]=It(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(xo,a)}var La=D({equal_:LN});function WN(e,t,n){let r=C(t,"a","where"),a=C(n,"b","where"),s=C(e,"condition","where","bool"),i=yt(r.shape,a.shape),o=Ju(r,i),l=Ju(a,i);s.rank===1&&F(s.shape[0]===r.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&on(s.shape,l.shape,"Error in where: ");let c={condition:s,t:o,e:l};return $.runKernel(Ho,c)}var In=D({where_:WN});function BN(e){let t={x:C(e,"x","zerosLike")};return $.runKernel(nl,t)}var qe=D({zerosLike_:BN});function VN(e,t){let n=C(e,"a","div"),r=C(t,"b","div");[n,r]=It(n,r);let a=be(n,r),s=qe(a),i=La(r,s);return In(i,s,a)}var fm=D({divNoNan_:VN});function UN(e,t){let n=C(e,"t1","dot"),r=C(t,"t2","dot");F((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(F(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=Ke(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=Ke(i,o);return H(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=H(r,[-1,1]),o=Ke(n,i);return H(o,[o.size])}else{let i=H(r,[r.shape[0],r.shape[1]]);return Ke(n,i)}}var xx=D({dot_:UN});function jN(e){let t={x:C(e,"x","elu")};return $.runKernel(yo,t)}var gl=D({elu_:jN});function HN(e){let t=C(e,"x","erf");F(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=ge(t,"float32"));let n={x:t};return $.runKernel(go,n)}var mm=D({erf_:HN});function GN(e){let t={x:C(e,"x","exp")};return $.runKernel(Is,t)}var Zn=D({exp_:GN});function qN(e,t=0){let n=C(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return $.runKernel(wo,r,a)}var tn=D({expandDims_:qN});function XN(e){let t={x:C(e,"x","expm1")};return $.runKernel(bo,t)}var Am=D({expm1_:XN});function KN(e,t){let n=C(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return $.runKernel(Ra,r,a)}var Wa=D({tile_:KN});function ZN(e,t,n,r="float32"){t==null&&(t=e);let a=Ve([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 Wa(tn(i,0),[n[0],1,1]);if(n.length===2)return Wa(tn(tn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return Wa(tn(tn(tn(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 ym=D({eye_:ZN});function ec(e,t,n){let r={shape:e,value:t,dtype:n};return $.runKernel(Iu,{},r)}function YN(e){let t={x:C(e,"x","floor")};return $.runKernel(Ns,t)}var xl=D({floor_:YN});function JN(e,t,n=0,r=0){let a=C(e,"x","gather"),s=C(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return $.runKernel(vo,i,o)}var mi=D({gather_:JN});function QN(e,t){let n=C(e,"a","greater"),r=C(t,"b","greater");[n,r]=It(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Io,a)}var lr=D({greater_:QN});function eS(e,t){let n=C(e,"a","greaterEqual"),r=C(t,"b","greaterEqual");[n,r]=It(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Es,a)}var Ba=D({greaterEqual_:eS});function tS(e){let t={input:C(e,"input","imag")};return $.runKernel(Jh,t)}var Ed=D({imag_:tS});function nS(e){let t={x:C(e,"x","isFinite")};return $.runKernel(No,t)}var wx=D({isFinite_:nS});function rS(e){let t={x:C(e,"x","isInf")};return $.runKernel(So,t)}var bx=D({isInf_:rS});function aS(e){let t={x:C(e,"x","isNaN")};return $.runKernel(To,t)}var _x=D({isNaN_:aS});function sS(e,t=.2){let n={x:C(e,"x","leakyRelu")},r={alpha:t};return $.runKernel(Rs,n,r)}var tc=D({leakyRelu_:sS});function iS(e,t){let n=C(e,"a","less"),r=C(t,"b","less");[n,r]=It(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Eo,a)}var Cd=D({less_:iS});function oS(e,t){let n=C(e,"a","lessEqual"),r=C(t,"b","lessEqual");[n,r]=It(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Co,a)}var Ai=D({lessEqual_:oS});function vx(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(Qh,{},r)}function lS(e,t=5,n=1,r=1,a=.5){let s=C(e,"x","localResponseNormalization");F(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${s.rank}.`),F(Gt(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(Tu,l,c);return o?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var gm=D({localResponseNormalization_:lS});function uS(e){let t={x:C(e,"x","log")};return $.runKernel(Fs,t)}var Dn=D({log_:uS});function cS(e){let t={x:C(e,"x","log1p")};return $.runKernel(Ro,t)}var Rd=D({log1p_:cS});function hS(e){return F(Ta(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=C(t,"x","tf.grad","string_or_numeric"),a=n!=null?C(n,"dy","tf.grad"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(r),[r],a);return a!=null&&on(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Fd(i),i[0]})}}function dS(e){return F(Ta(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let r=Hu(t,"args","tf.grads","string_or_numeric"),a=n!=null?C(n,"dy","tf.grads"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(...r),r,a);return a!=null&&on(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Fd(i),i})}}function pS(e){return F(Ta(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof He,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(n==null||n instanceof He,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=$.gradients(()=>e(t),[t],n);return Fd(r),{grad:r[0],value:a}}}function fS(e){return F(Ta(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(a=>a instanceof He),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(n==null||n instanceof He,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=$.gradients(()=>e(...t),t,n);return n!=null&&on(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Fd(r.grads),r}}function kx(e,t){F(Ta(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(c=>c instanceof Vu),()=>"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),F(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);F(o.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),F(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((c,u)=>{o[u]!=null&&(l[c.name]=o[u])}),r!=null&&r.forEach(c=>l[c.name]=null),{value:i,grads:l}}function Lr(e){return $.customGrad(e)}function Fd(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 mS(e){let t={x:C(e,"x","neg")};return $.runKernel($o,t)}var Nt=D({neg_:mS});function AS(e){let t={x:C(e,"x","softplus")};return $.runKernel(Zo,t)}var wl=D({softplus_:AS});function yS(e){let t=C(e,"x","logSigmoid");return Lr(n=>({value:Nt(wl(Nt(n))),gradFunc:r=>P(r,$n(Nt(n)))}))(t)}var Ix=D({logSigmoid_:yS});function gS(e,t=null,n=!1){let r={x:C(e,"x","max")},a={reductionIndices:t,keepDims:n};return $.runKernel(Ms,r,a)}var Yn=D({max_:gS});function xS(e,t){let n=C(e,"a","sub"),r=C(t,"b","sub");[n,r]=It(n,r);let a={a:n,b:r};return $.runKernel(ni,a)}var we=D({sub_:xS});function wS(e,t=null,n=!1){let r=C(e,"x","sum");r.dtype==="bool"&&(r=ge(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Qs,a,s)}var Fe=D({sum_:wS});function bS(e,t=-1){let n=C(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);return Lr((r,a)=>{let s=!0,i=Yn(r,t,!0),o=we(r,i),l=we(ge(o,"float32"),Dn(Fe(Zn(o),t,s)));return a([l]),{value:l,gradFunc:(c,u)=>{let[h]=u,d=!0,p=Zn(h);return we(c,P(Fe(c,t,d),p))}}})(n)}var Md=D({logSoftmax_:bS});function xm(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function Nx(e,t,n){let r=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<r;o++)n.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function Sx(e,t){let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let a=t.map(s=>e[s]);return[n,a]}function yi(e,t){let n=t.map(r=>1);return Nx(e,n,t)}function _S(e,t,n){F(xm(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Tx(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 vS(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function kS(e,t=null,n=!1){let r=C(e,"x","logSumExp"),a=ir(t,r.shape),s=Yn(r,a,!0),i=we(r,s),o=Zn(i),l=Fe(o,a),c=Dn(l),u=ie(H(s,c.shape),c);if(n){let h=yi(u.shape,a);return H(u,h)}return u}var bm=D({logSumExp_:kS});function IS(e,t){let n=C(e,"a","logicalAnd","bool"),r=C(t,"b","logicalAnd","bool");yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Fo,a)}var ur=D({logicalAnd_:IS});function NS(e){let t={x:C(e,"x","logicalNot","bool")};return $.runKernel(Nu,t)}var nc=D({logicalNot_:NS});function SS(e,t){let n=C(e,"a","logicalOr","bool"),r=C(t,"b","logicalOr","bool");yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Su,a)}var $d=D({logicalOr_:SS});function TS(e,t){let n=C(e,"a","logicalXor","bool"),r=C(t,"b","logicalXor","bool");return yt(n.shape,r.shape),ur($d(e,t),nc(ur(e,t)))}var Ex=D({logicalXor_:TS});function ES(e,t,n,r,a){let s=C(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]])),F(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),F(Pr(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),a!=null&&F(Gt(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(Ds,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var rc=D({maxPool_:ES});function CS(e,t=[1,1,1],n,r,a,s="NDHWC"){let i=C(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(Gt(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(Eu,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var _m=D({maxPool3d_:CS});function RS(e,t,n,r,a=!1){let s={x:C(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:a},o=$.runKernel(rd,s,i);return{result:o[0],indexes:o[1]}}var Cx=D({maxPoolWithArgmax_:RS});function FS(e,t){let n=C(e,"a","maximum"),r=C(t,"b","maximum");[n,r]=It(n,r),n.dtype==="bool"&&(n=ge(n,"int32"),r=ge(r,"int32")),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel($s,a)}var Wr=D({maximum_:FS});function MS(e,t=null,n=!1){let r={x:C(e,"x","mean")},a={axis:t,keepDims:n};return $.runKernel(Os,r,a)}var St=D({mean_:MS});function $S(e,t=null,n=!1){let r={x:C(e,"x","min")},a={axis:t,keepDims:n};return $.runKernel(zs,r,a)}var bl=D({min_:$S});function DS(e,t){let n=C(e,"a","minimum"),r=C(t,"b","minimum");[n,r]=It(n,r),n.dtype==="bool"&&(n=ge(n,"int32"),r=ge(r,"int32")),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Ps,a)}var _l=D({minimum_:DS});function OS(e,t,n){F(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=C(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");F(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++)F(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),F(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(Cu,i,s)}var vm=D({mirrorPad_:OS});function zS(e,t){let n=C(e,"a","mod"),r=C(t,"b","mod");[n,r]=It(n,r);let a={a:n,b:r};return $.runKernel(Mo,a)}var km=D({mod_:zS});function PS(e){let t=C(e,"x","square"),n={};return $.runKernel("Square",{x:t},n)}var ut=D({square_:PS});function LS(e,t=null,n=!1){e=C(e,"x","moments");let r=ir(t,e.shape),a=St(e,r,n),s=a.shape;n||(s=yi(a.shape,r));let i=ut(we(ge(e,"float32"),H(a,s))),o=St(i,r,n);return{mean:a,variance:o}}var Dd=D({moments_:LS});function WS(e,t,n,r){let a=C(t,"data","multiRNNCell"),s=Hu(n,"c","multiRNNCell"),i=Hu(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 BS=D({multiRNNCell_:WS});function VS(e,t,n,r=!1){let a=C(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(ad,o,l);return i===1?H(c,[c.size]):c}var Rx=D({multinomial_:VS});function US(e,t){let n=C(e,"a","notEqual"),r=C(t,"b","notEqual");[n,r]=It(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Do,a)}var gi=D({notEqual_:US});function Mt(e,t="float32"){if(t==="complex64"){let r=Mt(e,"float32"),a=Mt(e,"float32");return Ma(r,a)}let n=Mh(zt(e),t);return $.makeTensor(n,e,t)}function Br(e,t="float32"){if(t==="complex64"){let r=Br(e,"float32"),a=Mt(e,"float32");return Ma(r,a)}let n=yf(zt(e),t);return $.makeTensor(n,e,t)}function jS(e){let t={x:C(e,"x","onesLike")};return $.runKernel(Lo,t)}var On=D({onesLike_:jS});function HS(e,t){let n=C(e,"v1","outerProduct"),r=C(t,"v2","outerProduct");F(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 Ke(a,s)}var GS=D({outerProduct_:HS});function qS(e,t,n=0){let r=C(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(Bs,s,a)}var la=D({pad_:qS});function XS(e,t,n=0){return F(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),la(e,[t],n)}var KS=D({pad1d_:XS});function ZS(e,t,n=0){return F(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),la(e,t,n)}var YS=D({pad2d_:ZS});function JS(e,t,n=0){return F(t.length===3&&t[0].length===2&&t[1].length===2&&t[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),la(e,t,n)}var QS=D({pad3d_:JS});function eT(e,t,n=0){return F(t.length===4&&t[0].length===2&&t[1].length===2&&t[2].length===2&&t[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),la(e,t,n)}var tT=D({pad4d_:eT});function nT(e,t,n){let r=C(e,"x","spaceToBatchND");F(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),F(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),F(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(Mu,a,s)}var ac=D({spaceToBatchND_:nT});function sT(e,t,n,r,a,s){a==null&&(a=[1,1]),s==null&&(s=1),r===0&&(r="valid");let i=C(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2]])),F(Pr(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let c=lx(o.shape,t,s,a,r),u=[c.dilationHeight,c.dilationWidth],h;r==="same"?h=aT([c.filterHeight,c.filterWidth],u):h=[[0,0],[0,0]];let d=u[0]===1&&u[1]===1,[p,f]=rT([c.inHeight,c.inWidth],u,h),m=d?r:"valid",A=d?o:ac(o,u,p),y=(n==="avg"?()=>Zu(A,t,s,m):()=>rc(A,t,s,m))(),g=d?y:Yu(y,u,f);return l?H(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function rT(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 aT(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 Fx=D({pool_:sT});function iT(e,t){let n=C(e,"base","pow"),r=C(t,"exp","pow");[n,r]=It(n,r);let a={a:n,b:r};return $.runKernel(Vs,a)}var ua=D({pow_:iT});function oT(e,t){let n=C(e,"x","prelu"),r=C(t,"alpha","prelu"),a={x:n,alpha:r};return $.runKernel(Us,a)}var sc=D({prelu_:oT});function lT(e,t=null,n=!1){let r=C(e,"x","prod");r.dtype==="bool"&&(r=ge(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Bo,a,s)}var Od=D({prod_:lT});function uT(e,t,n){let r=zt(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 cT=D({rand_:uT}),Im=to(J2()),Nm=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}},hT=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 Nm(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)}},dT=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 pT(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 hT(t,n,r,a),i=Ve(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var fT=D({randomGamma_:pT});function mT(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let s=new Nm(t,n,r,!1,a),i=Ve(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Mx=D({randomNormal_:mT});function AT(e,t=0,n=1,r="float32",a){let s=Ve(e,r),i=new dT(t,n,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var vl=D({randomUniform_:AT});function zd(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(Ru,{},a)}function yT(e){let t={input:C(e,"input","real")};return $.runKernel(sd,t)}var ic=D({real_:yT});function gT(e){let t={x:C(e,"x","reciprocal")};return $.runKernel(Vo,t)}var Sm=D({reciprocal_:gT});function xT(e){let t={x:C(e,"x","relu")};return $.runKernel(js,t)}var Vr=D({relu_:xT});function wT(e){let t={x:C(e,"x","relu6")};return $.runKernel(Gs,t)}var Pd=D({relu6_:wT});function bT(e,t){let n={x:C(e,"x","reverse")},r={dims:t};return $.runKernel(qs,n,r)}var zn=D({reverse_:bT});function _T(e){let t=C(e,"x","reverse");return F(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),zn(t,0)}var vT=D({reverse1d_:_T});function kT(e,t){let n=C(e,"x","reverse");return F(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),zn(n,t)}var IT=D({reverse2d_:kT});function NT(e,t){let n=C(e,"x","reverse");return F(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),zn(n,t)}var ST=D({reverse3d_:NT});function TT(e,t){let n=C(e,"x","reverse");return F(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),zn(n,t)}var ET=D({reverse4d_:TT});function CT(e){let t={x:C(e,"x","round")};return $.runKernel(Xs,t)}var Tm=D({round_:CT});function RT(e){let t={x:C(e,"x","rsqrt")};return $.runKernel(Ks,t)}var Ld=D({rsqrt_:RT});function Ie(e,t){if((ln(e)&&t!=="string"||Array.isArray(e))&&t!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(t==="string"&&ln(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return $a(e,[],[],t)}function FT(e){let t={x:C(e,"x","selu")};return $.runKernel(Go,t)}var Wd=D({selu_:FT});function MT(e,t,n,r,a,s=[1,1],i="NHWC"){let o=C(e,"x","separableConv2d"),l=C(t,"depthwiseFilter","separableConv2d"),c=C(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");F(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),F(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),F(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${c.shape[0]}.`),F(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${c.shape[1]}.`);let d=l.shape[2],p=l.shape[3];F(c.shape[2]===d*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*p}, but got ${c.shape[2]}.`);let f=yl(u,l,r,a,i,s),m=oa(f,c,1,"valid",i);return h?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Em=D({separableConv2d_:MT});async function $T(e,t){let n=C(e,"x","setdiff1d"),r=C(t,"y","setdiff1d");F(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),F(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),F(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 Pt([o],n.dtype),c=new Pt([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 $x=$T;function DT(e){let t={x:C(e,"x","sign")};return $.runKernel(Ko,t)}var Cm=D({sign_:DT});function OT(e){let t={x:C(e,"x","sin")};return $.runKernel(Zs,t)}var Bd=D({sin_:OT});function zT(e){let t={x:C(e,"x","sinh")};return $.runKernel(Xo,t)}var Vd=D({sinh_:zT});function PT(e,t,n){let r=C(e,"x","slice1d");return F(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Me(r,[t],[n])}var Ud=D({slice1d_:PT});function LT(e,t,n){let r=C(e,"x","slice2d");return F(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Me(r,t,n)}var Rm=D({slice2d_:LT});function WT(e,t,n){let r=C(e,"x","slice3d");return F(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Me(r,t,n)}var jd=D({slice3d_:WT});function BT(e,t,n){let r=C(e,"x","slice4d");return F(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Me(r,t,n)}var oc=D({slice4d_:BT});function VT(e,t=-1){let n=C(e,"logits","softmax","float32");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);let r={logits:n},a={dim:t};return $.runKernel(ei,r,a)}var lc=D({softmax_:VT});function UT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Zh,t)}var uc=D({fft_:UT});function jT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Yh,t)}var kl=D({ifft_:jT});function HT(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let a=H(e,[n,t]);r=kl(a)}else{let a=[n,2*(t-1)],s=H(ic(e),[n,t]),i=H(Ed(e),[n,t]),o=zn(Me(s,[0,1],[n,t-2]),1),l=P(zn(Me(i,[0,1],[n,t-2]),1),Ie(-1)),c=it([s,o],1),u=it([i,l],1),h=H(Ma(c,u),[a[0],a[1]]);r=kl(h)}if(r=ic(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 Hd=D({irfft_:HT});function GT(e,t,n=0){let r={x:C(e,"x","split")},a={numOrSizeSplits:t,axis:n};return $.runKernel(Yo,r,a)}var Bt=D({split_:GT});function qT(e,t){F(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,a;if(t!=null&&t<n){let f=e.shape.map(A=>0),m=e.shape.map(A=>A);m[e.shape.length-1]=t,a=Me(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,a=it([e,Mt(f)],e.shape.length-1),n=t}else a=e;let s=qe(a),i=H(Ma(a,s),[r,n]),o=uc(i),l=Math.floor(n/2)+1,c=ic(o),u=Ed(o),h=Bt(c,[l,n-l],c.shape.length-1),d=Bt(u,[l,n-l],u.shape.length-1),p=a.shape.slice();return p[a.shape.length-1]=l,H(Ma(h[0],d[0]),p)}var cc=D({rfft_:qT});function XT(e){let t={x:C(e,"x","sqrt")};return $.runKernel(Js,t)}var nn=D({sqrt_:XT});function KT(e,t){let n=C(e,"a","squaredDifference"),r=C(t,"b","squaredDifference");[n,r]=It(n,r),yt(n.shape,r.shape);let a={a:n,b:r},s={};return $.runKernel(ti,a,s)}var Gd=D({squaredDifference_:KT});function ZT(e,t){let n=C(e,"x","squeeze");return H(n,t5(n.shape,t).newShape)}var Va=D({squeeze_:ZT});function YT(e,t=0){let n=Hu(e,"tensors","stack","string_or_numeric");F(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&F(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,a={axis:t};return $.runKernel(Wo,r,a)}var dn=D({stack_:YT});function JT(e,t=0){let n={x:C(e,"x","step")},r={alpha:t};return $.runKernel(Fa,n,r)}var Il=D({step_:JT});function QT(e,t,n,r,a=0,s=0,i=0,o=0,l=0){let c={x:C(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 Fm=D({stridedSlice_:QT});function eE(e){let t={x:C(e,"x","tan")};return $.runKernel(Qo,t)}var Mm=D({tan_:eE});function un(e,t){hs(e);let n=Dr(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return $a(e,null,n,t)}function Nn(e,t,n){if(hs(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=Dr(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 $a(e,t,r,n)}function tE(e,t,n){if(hs(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=Dr(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 $a(e,t,r,n)}function nE(e,t,n){if(hs(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=Dr(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 $a(e,t,r,n)}function rE(e,t,n){if(hs(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=Dr(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,$a(e,t,r,n)}function aE(e,t=1,n=!0){let r=C(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(el,s,i);return{values:o,indices:l}}var $m=D({topk_:aE});function sE(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new Nm(t,n,r,!0,a),i=Ve(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var qd=D({truncatedNormal_:sE});function iE(e,t=0){let n=C(e,"x","unique","string_or_numeric");F(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},a={axis:t},[s,i]=$.runKernel(cd,r,a);return{values:s,indices:i}}var Xd=D({unique_:iE});function oE(e,t,n){let r=C(e,"x","unsortedSegmentSum"),a=C(t,"segmentIds","unsortedSegmentSum","int32");F(Gt(n),()=>"numSegments must be of dtype int");let s={x:r,segmentIds:a},i={numSegments:n};return $.runKernel(Du,s,i)}var Dm=D({unsortedSegmentSum_:oE});function lE(e,t=0){let n=C(e,"x","unstack","string_or_numeric");F(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},a={axis:t};return $.runKernel(tl,r,a)}var cr=D({unstack_:lE});function Dx(e,t=!0,n,r){return $.makeVariable(e,t,n,r)}function Ox(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let r=Ve(e,"int32"),a=Ve([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 uE(e){let t=C(e,"condition","whereAsync","bool"),n=await t.data(),r=Ox(t.shape,n);return e!==t&&t.dispose(),r}var Om=uE;async function cE(e,t,n){let r=C(e,"tensor","boolMask"),a=C(t,"mask","boolMask","bool"),s=n==null?0:n,i=a.rank,o=r.shape;F(i>0,()=>"mask cannot be scalar"),on(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 m=s;m<s+i;m++)l*=o[m];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=Va(d,[1]),f=mi(u,p,s);return e!==r&&r.dispose(),t!==a&&a.dispose(),p.dispose(),u.dispose(),h.dispose(),d.dispose(),f}var hE=cE;function dE(e,t="euclidean",n=null,r=!1){e=C(e,"x","norm");let a=zx(e,t,n),s=a.shape;if(r){let i=ir(n,e.shape);s=yi(a.shape,i)}return H(a,s)}function zx(e,t,n=null){if(e.rank===0)return Lt(e);if(e.rank!==1&&n===null)return zx(H(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Fe(Lt(e),n);if(t===Infinity)return Yn(Lt(e),n);if(t===-Infinity)return bl(Lt(e),n);if(t==="euclidean"||t===2)return nn(Fe(ua(Lt(e),Ie(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Yn(Fe(Lt(e),n[0]),n[1]-1);if(t===Infinity)return Yn(Fe(Lt(e),n[1]),n[0]);if(t===-Infinity)return bl(Fe(Lt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return nn(Fe(ut(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Kd=D({norm_:dE});function pE(e,t,n,r,a=!0){let s=C(e,"v","movingAverage"),i=C(t,"x","movingAverage"),o=C(n,"decay","movingAverage");g5(s,i),F(sa(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=Ie(1),c=we(l,o),u=P(we(i,s),c);if(a){F(r!=null,()=>"When using zeroDebias: true, step is required.");let h=C(r,"step","movingAverage");u=be(u,we(l,ua(o,h)))}return ie(s,u)}var fE=D({movingAverage_:pE});function mE(e,t,n){let r=C(e,"indices","scatterND","int32"),a=C(t,"updates","scatterND");Gf(a,r,n);let s={indices:r,updates:a},i={shape:n};return $.runKernel(jo,s,i)}var Px=D({scatterND_:mE});function AE(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 yE(e,t,n,r=0){let a=C(e,"sparseIndices","sparseToDense","int32"),s=C(t,"sparseValues","sparseToDense"),i=C(r,"defaultValue","sparseToDense",s.dtype);AE(a,s,n,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:n};return $.runKernel(ld,o,l)}var zm=D({sparseToDense_:yE});function gE(e,t){let n=C(t,"indices","gatherND","int32"),r={params:C(e,"x","gatherND"),indices:n};return $.runKernel(ko,r)}var Lx=D({gatherND_:gE});function xE(e,t){if(t==null)return e.shape.slice();if(sa(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 wE(e,t,n,r){let a=C(e,"x","dropout");if(F(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.`),F(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof He?a.clone():a;let s=xE(a,n),i=1-t,o=be(xl(ie(vl(s,0,1,"float32",r),i)),i);return P(a,o)}var Wx=D({dropout_:wE});function Bx(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 un(a,"float32")}async function bE(e,t,n=1){let r=C(e,"predictions","inTopK"),a=C(t,"targets","inTopK");F(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),F(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),on(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=r.shape[r.shape.length-1];F(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await r.data(),o=await a.data(),[l,c]=[i.length/s,s],u=n5("bool",l);for(let h=0;h<l;h++){let d=h*c,p=i.subarray(d,d+c),f=[];for(let m=0;m<p.length;m++)f.push({value:p[m],index:m});f.sort((m,A)=>A.value-m.value),u[h]=0;for(let m=0;m<n;m++)if(f[m].index===o[h]){u[h]=1;break}}return e!==r&&r.dispose(),t!==a&&a.dispose(),_r(u,a.shape,"bool")}var _E=bE,Ua={};Le(Ua,{conv2d:()=>vE,depthwiseConv2d:()=>kE,matMul:()=>IE});function NE(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]])),F(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),F(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),F(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let c=s==="NHWC"?o.shape[3]:o.shape[1],u=s==="NHWC"?l.shape[3]:l.shape[1];F(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),F(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),i!=null&&F(Gt(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(Wh,h,d)}var Lm=D({conv2DBackpropFilter_:NE});function Zd(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return P(e,Il(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Yd(e,t){let n=t,r=Wt(e.shape,t.shape);return r.length>0&&(n=Fe(n,r)),H(n,e.shape)}function Jd(e,t,n,r){if(t==="linear")return e;if(t==="relu")return Vr(e);if(t==="elu")return gl(e);if(t==="relu6")return Pd(e);if(t==="prelu")return sc(e,n);if(t==="leakyrelu")return tc(e,r);throw new Error(`Unknown fused activation ${t}.`)}var Qd=(e,t)=>!(e>0)||t==="linear";function SE({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Qd($.state.gradientDepth,l)===!1){let _=oa(e,t,n,r,a,s,i);return o!=null&&(_=ie(_,o)),Jd(_,l,c,u)}let h=C(e,"x","conv2d"),d=C(t,"filter","conv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=H(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),F(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&F(Gt(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(p.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${d.shape[2]}.`),F(Pr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let m=Ku(p.shape,d.shape,n,s,r,i),A;o!=null&&(A=C(o,"bias","fused conv2d"),[A]=It(A,h),yt(m.outShape,A.shape));let y;c!=null&&(y=C(c,"prelu weights","fused conv2d"));let g=(_,x)=>{let[N,T,E,M]=x,O=Zd(_,E,l);F(Pa(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let W=cm(T.shape,O,N,n,r),V=Lm(T,O,N.shape,n,r),U=[W,V];if(M!=null){let j=Yd(M,O);U.push(j)}return U},w={x:p,filter:d,bias:A,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Lr((_,x,N)=>{let T=$.runKernel(ii,w,b);return N([x,_,T]),f&&(T=H(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(p,d):Lr((_,x,N,T)=>{let E=$.runKernel(ii,w,b);return T([x,_,E,N]),f&&(E=H(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(p,d,A)}var vE=D({fusedConv2d_:SE});function TE(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 $.runKernel(jh,c,u)}var Vx=D({depthwiseConv2dNativeBackpropFilter_:TE});function EE(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:o,filter:n},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=$.runKernel(Hh,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Ux=D({depthwiseConv2dNativeBackpropInput_:EE});function CE({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(Qd($.state.gradientDepth,l)===!1){let _=yl(e,t,n,r,a,s,i);return o!=null&&(_=ie(_,o)),Jd(_,l,c,u)}let h=C(e,"x","depthwiseConv2d"),d=C(t,"filter","depthwiseConv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=H(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),F(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),F(p.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),F(Pr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&F(Gt(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${r}.`);let m=Ku(p.shape,d.shape,n,s,r,i,!0),A;o!=null&&(A=C(o,"bias","fused conv2d"),[A]=It(A,h),yt(m.outShape,A.shape));let y;c!=null&&(y=C(c,"prelu weights","fused depthwiseConv2d"));let g=(_,x)=>{F(Pa(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[N,T,E,M]=x,O=Zd(_,E,l),W=Ux(T.shape,O,N,n,r,s,i),V=Vx(T,O,N.shape,n,r,s,i);if(M!=null){let U=Yd(A,O);return[W,V,U]}return[W,V]},w={x:p,filter:d,bias:A,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Lr((_,x,N)=>{let T=$.runKernel(oi,w,b);return N([x,_,T]),f&&(T=H(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(p,d):Lr((_,x,N,T)=>{let E=$.runKernel(oi,w,b);return T([x,_,E,N]),f&&(E=H(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(p,d,A)}var kE=D({fusedDepthwiseConv2d_:CE});function RE({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Qd($.state.gradientDepth,s)===!1){let M=Ke(e,t,n,r);return a!=null&&(M=ie(M,a)),Jd(M,s,i,o)}let l=C(e,"a","fused matMul"),c=C(t,"b","fused matMul");[l,c]=It(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],f=l.shape.slice(0,-2),m=c.shape.slice(0,-2),A=zt(f),y=zt(m);F(l.rank>=2&&c.rank>=2&&l.rank===c.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${c.rank}.`),F(sa(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),F(u===h,()=>`Error in fused matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${r} must match.`);let g=l.shape.slice(0,-2).concat([d,p]),w=n?H(l,[A,u,d]):H(l,[A,d,u]),b=r?H(c,[y,p,h]):H(c,[y,h,p]),_;a!=null&&(_=C(a,"bias","fused matMul"),[_]=It(_,l),yt(g,_.shape));let x;i!=null&&(x=C(i,"prelu weights","fused matMul"));let N=(M,O)=>{let[W,V,U,j]=O,X=Zd(H(M,U.shape),U,s),G,ee;if(!n&&!r?(G=Ke(X,V,!1,!0),ee=Ke(W,X,!0,!1)):!n&&r?(G=Ke(X,V,!1,!1),ee=Ke(X,W,!0,!1)):n&&!r?(G=Ke(V,X,!1,!0),ee=Ke(W,X,!1,!1)):(G=Ke(V,X,!0,!0),ee=Ke(X,W,!0,!0)),a!=null){let Y=Yd(j,X);return[G,ee,Y]}else return[G,ee]},T={a:w,b,bias:_,preluActivationWeights:x},E={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return a==null?Lr((M,O,W)=>{let V=$.runKernel(si,T,E);return W([M,O,V]),{value:H(V,g),gradFunc:N}})(w,b):Lr((M,O,W,V)=>{let U=$.runKernel(si,T,E);return V([M,O,U,W]),{value:H(U,g),gradFunc:N}})(w,b,_)}var IE=D({fusedMatMul_:RE});function FE(e){return Pm(e,.54,.46)}var ME=D({hammingWindow_:FE});function $E(e){return Pm(e,.5,.5)}var jx=D({hannWindow_:$E});function DE(e,t,n,r=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Me(e,s,t)),s+=n;if(r)for(;s<e.size;){let o=s+t-e.size,l=it([Me(e,s,t-o),ec([o],a)]);i.push(l),s+=n}return i.length===0?Nn([],[0,t]):H(it(i),[i.length,t])}var Hx=D({frame_:DE});function OE(e,t,n,r,a=jx){r==null&&(r=Bx(t));let s=Hx(e,t,n),i=P(s,a(t)),o=[];for(let l=0;l<s.shape[0];l++)o.push(cc(Me(i,[l,0],[1,t]),r));return it(o)}var zE=D({stft_:OE});function PE(e,t,n,r,a="bilinear",s=0){let i=C(e,"image","cropAndResize"),o=C(t,"boxes","cropAndResize","float32"),l=C(n,"boxInd","cropAndResize","int32"),c=o.shape[0];F(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${o.shape}.`),F(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${o.shape}.`),F(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),F(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),F(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 LE=D({cropAndResize_:PE});function WE(e){let t=C(e,"image","flipLeftRight","float32");F(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return $.runKernel(_o,n,{})}var BE=D({flipLeftRight_:WE});function VE(e,t,n=0,r=.5){let a=C(e,"image","rotateWithOffset","float32");F(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 $.runKernel(rl,s,i)}var UE=D({rotateWithOffset_:VE});function Nl(e,t,n,r,a,s){r==null&&(r=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),F(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),F(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),F(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),F(t.rank===1,()=>"scores must be a 1D tensor"),F(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),F(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function jE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=C(e,"boxes","nonMaxSuppression"),i=C(t,"scores","nonMaxSuppression"),o=Nl(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 HE=D({nonMaxSuppression_:jE});function qE(e,t,n){let r=GE(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function GE(e,t,n){return KE(e,t,n||XE)}function XE(e,t){return e>t?1:e<t?-1:0}function KE(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 Gx(e,t,n,r,a){return Wm(e,t,n,r,a,0)}function qx(e,t,n,r,a,s){return Wm(e,t,n,r,a,0,!1,s,!0)}function Xx(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(Kx);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:w}=A;if(y<a)break;let b=!1;for(let _=h.length-1;_>=w;--_){let x=ZE(e,g,h[_]);if(x>=r){b=!0;break}if(A.score=A.score*YE(r,u,x),A.score<=a)break}A.suppressBeginIndex=h.length,b||(A.score===y?(h.push(g),d.push(A.score)):A.score>a&&qE(c,A,Kx))}let p=h.length,f=n-p;o&&f>0&&(h.push(...new Array(f).fill(0)),d.push(...new Array(f).fill(0)));let m={selectedIndices:h};return i&&(m.selectedScores=d),l&&(m.validOutputs=p),m}function ZE(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),f=(h-c)*(d-u);if(p<=0||f<=0)return 0;let m=Math.max(s,c),A=Math.max(i,u),y=Math.min(o,h),g=Math.min(l,d),w=Math.max(y-m,0)*Math.max(g-A,0);return w/(p+f-w)}function YE(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function Kx(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function JE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=C(e,"boxes","nonMaxSuppressionAsync"),i=C(t,"scores","nonMaxSuppressionAsync"),o=Nl(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}=Gx(c,u,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),un(h,"int32")}var QE=JE;function eC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=C(e,"boxes","nonMaxSuppression"),o=C(t,"scores","nonMaxSuppression"),l=Nl(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 tC=D({nonMaxSuppressionWithScore_:eC});async function nC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=C(e,"boxes","nonMaxSuppressionAsync"),o=C(t,"scores","nonMaxSuppressionAsync"),l=Nl(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}=Xx(u,h,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:un(d,"int32"),selectedScores:un(p)}}var rC=nC;function aC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=C(e,"boxes","nonMaxSuppression"),o=C(t,"scores","nonMaxSuppression"),l=Nl(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},f=$.runKernel(zo,d,p);return{selectedIndices:f[0],validOutputs:f[1]}}var sC=D({nonMaxSuppressionPadded_:aC});async function iC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=C(e,"boxes","nonMaxSuppressionAsync"),o=C(t,"scores","nonMaxSuppressionAsync"),l=Nl(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:f,validOutputs:m}=qx(d,p,c,u,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:un(f,"int32"),validOutputs:Ie(m,"int32")}}var oC=iC;function lC(e,t,n=!1,r=!1){let a=C(e,"images","resizeBilinear");F(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),F(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(Hs,o,l);return i?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Zx=D({resizeBilinear_:lC});function uC(e,t,n=!1,r=!1){let a=C(e,"images","resizeNearestNeighbor");F(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),F(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),F(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(Fu,o,l);return i?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Yx=D({resizeNearestNeighbor_:uC});function cC(e,t,n="nearest",r="constant",a=0,s){let i=C(e,"image","transform","float32"),o=C(t,"transforms","transform","float32");F(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&(o.shape[0]===i.shape[0]||o.shape[0]===1)&&o.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),F(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},c={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return $.runKernel(ud,l,c)}var hC=D({transform_:cC});function dC(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=C(e,"a","bandPart");F(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 (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=H(zd(0,s,1,"int32"),[-1,1]),l=zd(0,i,1,"int32"),c=we(o,l),u=ur(Ai(c,Ie(+t,"int32")),Ba(c,Ie(-n,"int32"))),h=Mt([s,i],r.dtype);return H(dn(cr(H(r,[-1,s,i])).map(d=>In(u,d,h))),a)}var pC=D({bandPart_:dC});function fC(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)F(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=Bt(e,e.shape[0],0).map(a=>Va(a,[0]));F(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let a=0;a<e.length;++a)n.push($.tidy(()=>{let s=r[a];if(a>0)for(let i=0;i<a;++i){let o=P(Fe(P(n[i],s)),n[i]);s=we(s,o)}return be(s,Kd(s,"euclidean"))}));return t?dn(n,0):n}var mC=D({gramSchmidt_:fC});function AC(e,t=!1){if(F(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return Jx(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),r=cr(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(l=>{let[c,u]=Jx(l,t);a.push(c),s.push(u)});let i=H(dn(a,0),e.shape),o=H(dn(s,0),e.shape);return[i,o]}}function Jx(e,t=!1){return $.tidy(()=>{F(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],a=ym(n),s=Or(e),i=Nn([[1]],[1,1]),o=Or(i),l=n>=r?r:n;for(let c=0;c<l;++c){let u=s,h=o,d=a;[o,s,a]=$.tidy(()=>{let p=Me(s,[c,c],[n-c,1]),f=Kd(p),m=Me(s,[c,c],[1,1]),A=In(lr(m,0),Nn([[-1]]),Nn([[1]])),y=we(m,P(A,f)),g=be(p,y);g.shape[0]===1?o=Or(i):o=it([i,Me(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let w=Nt(be(Ke(A,y),f)),b=Me(s,[c,0],[n-c,r]),_=P(w,o),x=st(o);if(c===0)s=we(b,Ke(_,Ke(x,b)));else{let E=we(b,Ke(_,Ke(x,b)));s=it([Me(s,[0,0],[c,r]),E],0)}let N=st(_),T=Me(a,[0,c],[n,a.shape[1]-c]);if(c===0)a=we(T,Ke(Ke(T,o),N));else{let E=we(T,Ke(Ke(T,o),N));a=it([Me(a,[0,0],[n,c]),E],1)}return[o,s,a]}),Te([u,h,d])}return!t&&n>r&&(a=Me(a,[0,0],[n,r]),s=Me(s,[0,0],[r,r])),[a,s]})}var yC=D({qr_:AC}),pn;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(pn||(pn={}));function gC(e,t,n=pn.SUM_BY_NONZERO_WEIGHTS){let r=C(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=C(t,"weights","computeWeightedLoss"));let s=a==null?r:P(r,a);if(n===pn.NONE)return s;if(n===pn.SUM)return Fe(s);if(n===pn.MEAN){if(a==null)return St(s);{let i=r.size/a.size,o=be(Fe(s),Fe(a));return i>1?be(o,Ie(i)):o}}if(n===pn.SUM_BY_NONZERO_WEIGHTS){if(a==null)return be(Fe(s),Ie(r.size));{let i=P(a,Br(r.shape)),o=ge(Fe(gi(i,Ie(0))),"float32");return be(Fe(s),o)}}throw Error(`Unknown reduction: ${n}`)}var ca=D({computeWeightedLoss_:gC});function xC(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=C(e,"labels","absoluteDifference"),s=C(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=C(n,"weights","absoluteDifference")),on(a.shape,s.shape,"Error in absoluteDifference: ");let o=Lt(we(a,s));return ca(o,i,r)}var wC=D({absoluteDifference_:xC});function bC(e,t,n,r,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","cosineDistance"),i=C(t,"predictions","cosineDistance"),o=null;r!=null&&(o=C(r,"weights","cosineDistance")),on(s.shape,i.shape,"Error in cosineDistance: ");let l=Ie(1),c=we(l,Fe(P(s,i),n,!0));return ca(c,o,a)}var _C=D({cosineDistance_:bC});function vC(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=C(e,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;n!=null&&(i=C(n,"weights","hingeLoss")),on(a.shape,s.shape,"Error in hingeLoss: ");let o=Ie(1);a=we(P(Ie(2),a),o);let l=Vr(we(o,P(a,s)));return ca(l,i,r)}var kC=D({hingeLoss_:vC});function IC(e,t,n,r=1,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),o=null;n!=null&&(o=C(n,"weights","huberLoss")),on(s.shape,i.shape,"Error in huberLoss: ");let l=Ie(r),c=Lt(we(i,s)),u=_l(c,l),h=we(c,u),d=ie(P(Ie(.5),ut(u)),P(l,h));return ca(d,o,a)}var NC=D({huberLoss_:IC});function SC(e,t,n,r=1e-7,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","logLoss"),i=C(t,"predictions","logLoss"),o=null;n!=null&&(o=C(n,"weights","logLoss")),on(s.shape,i.shape,"Error in logLoss: ");let l=Ie(1),c=Ie(r),u=Nt(P(s,Dn(ie(i,c)))),h=P(we(l,s),Dn(ie(we(l,i),c))),d=we(u,h);return ca(d,o,a)}var TC=D({logLoss_:SC});function EC(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=C(e,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=C(n,"weights","meanSquaredError")),on(a.shape,s.shape,"Error in meanSquaredError: ");let o=Gd(a,s);return ca(o,i,r)}var CC=D({meanSquaredError_:EC});function RC(e,t){let n=C(e,"labels","sigmoidCrossEntropyWithLogits"),r=C(t,"logits","sigmoidCrossEntropyWithLogits");on(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=Vr(r),s=P(r,n),i=Rd(Zn(Nt(Lt(r))));return ie(we(a,s),i)}function FC(e,t,n,r=0,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=C(n,"weights","sigmoidCrossEntropy")),on(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let c=Ie(r),u=Ie(1),h=Ie(.5);s=ie(P(s,we(u,c)),P(h,c))}let l=RC(s,i);return ca(l,o,a)}var MC=D({sigmoidCrossEntropy_:FC});function $C(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 ${n}`);return Lr((r,a,s)=>{let i=bm(a,[n],!0),o=we(ge(a,"float32"),i);s([r,o]);let l=Nt(P(o,r));return{value:Fe(l,[n]),gradFunc:(c,u)=>{let[h,d]=u,p=yi(c.shape,[n]);return[P(H(c,p),we(ge(h,"float32"),Zn(d))),P(H(c,p),we(Zn(d),ge(h,"float32")))]}}})(e,t)}function DC(e,t,n,r=0,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"onehotLabels","softmaxCrossEntropy"),i=C(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=C(n,"weights","softmaxCrossEntropy")),on(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let c=Ie(r),u=Ie(1),h=Ie(s.shape[1]);s=ie(P(s,we(u,c)),be(c,h))}let l=$C(s,i);return ca(l,o,a)}var OC=D({softmaxCrossEntropy_:DC}),zC={fft:uc,ifft:kl,rfft:cc,irfft:Hd},PC={hammingWindow:ME,hannWindow:jx,frame:Hx,stft:zE},Ue={flipLeftRight:BE,resizeNearestNeighbor:Yx,resizeBilinear:Zx,rotateWithOffset:UE,cropAndResize:LE,nonMaxSuppression:HE,nonMaxSuppressionAsync:QE,nonMaxSuppressionWithScore:tC,nonMaxSuppressionWithScoreAsync:rC,nonMaxSuppressionPadded:sC,nonMaxSuppressionPaddedAsync:oC,transform:hC},Qx={bandPart:pC,gramSchmidt:mC,qr:yC},LC={absoluteDifference:wC,computeWeightedLoss:ca,cosineDistance:_C,hingeLoss:kC,huberLoss:NC,logLoss:TC,meanSquaredError:CC,sigmoidCrossEntropy:MC,softmaxCrossEntropy:OC},ha=class extends nx{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 kx(e,t)}dispose(){this.iterations_!=null&&Te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ie(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(ha,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var ep=class extends ha{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.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:z(()=>qe(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:z(()=>qe(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;z(()=>{let l=ie(P(i,this.rho),P(ut(s),1-this.rho)),c=P(be(nn(ie(o,this.epsilon)),nn(ie(i,this.epsilon))),s),u=ie(P(o,this.rho),P(ut(c),1-this.rho));i.assign(l),o.assign(u);let h=ie(P(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Te(this.accumulatedGrads.map(e=>e.variable)),Te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ep.className="Adadelta";Oa(ep);var tp=class extends ha{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:z(()=>ec(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;z(()=>{let i=ie(s,ut(a));s.assign(i);let o=ie(P(be(a,nn(ie(i,$.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)}};tp.className="Adagrad";Oa(tp);var np=class extends ha{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],z(()=>{this.accBeta1=Ie(t).variable(),this.accBeta2=Ie(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);z(()=>{let n=we(1,this.accBeta1),r=we(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:z(()=>qe(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:z(()=>qe(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=ie(P(c,this.beta1),P(l,1-this.beta1)),d=ie(P(u,this.beta2),P(ut(l),1-this.beta2)),p=be(h,n),f=be(d,r);c.assign(h),u.assign(d);let m=ie(P(be(p,ie(nn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(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),z(()=>{this.accBeta1.assign(ua(this.beta1,this.iterations_+1)),this.accBeta2.assign(ua(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)}};np.className="Adam";Oa(np);var rp=class extends ha{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=[],z(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);z(()=>{let n=we(1,this.accBeta1),r=be(-this.learningRate,ie(P(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:qe(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:qe(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=ie(P(c,this.beta1),P(l,1-this.beta1)),d=P(u,this.beta2),p=Lt(l),f=Wr(d,p);c.assign(h),u.assign(f);let m=ie(P(be(r,n),be(h,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(P(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)}};rp.className="Adamax";Oa(rp);var hc=class extends ha{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=$.registeredVariables[t];z(()=>{let s=ie(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=qt(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};hc.className="SGD";Oa(hc);var ap=class extends hc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ie(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:z(()=>qe(r).variable(i))}}let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&z(()=>{let i,o=ie(P(this.m,a),s);this.useNesterov?i=ie(P(this.c,ie(s,P(o,this.m))),r):i=ie(P(this.c,o),r),a.assign(o),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Te(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ap.className="Momentum";Oa(ap);var sp=class extends ha{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.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:z(()=>qe(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:z(()=>qe(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:z(()=>qe(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;z(()=>{let l=ie(P(i,this.decay),P(ut(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=ie(P(c,this.decay),P(s,1-this.decay)),h=be(P(s,this.learningRate),nn(we(l,ie(ut(u),this.epsilon)))),d=ie(P(o,this.momentum),h);i.assign(l),c.assign(u),o.assign(d);let p=we(r,d);r.assign(p)}else{let c=ie(P(i,this.decay),P(ut(s),1-this.decay)),u=ie(P(o,this.momentum),be(P(s,this.learningRate),nn(ie(c,this.epsilon))));i.assign(c),o.assign(u);let h=we(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)}};sp.className="RMSProp";Oa(sp);var xi=class{static sgd(e){return new hc(e)}static momentum(e,t,n=!1){return new ap(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new sp(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new np(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new ep(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new rp(e,t,n,r,a)}static adagrad(e,t=.1){return new tp(e,t)}},wi={sgd:xi.sgd,momentum:xi.momentum,adadelta:xi.adadelta,adagrad:xi.adagrad,rmsprop:xi.rmsprop,adamax:xi.adamax,adam:xi.adam},WC=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function ip(){return new Promise(e=>WC(()=>e()))}var R={};Le(R,{ERF_A1:()=>YC,ERF_A2:()=>JC,ERF_A3:()=>QC,ERF_A4:()=>eR,ERF_A5:()=>tR,ERF_P:()=>ZC,PARALLELIZE_THRESHOLD:()=>Bm,SELU_SCALE:()=>tw,SELU_SCALEALPHA:()=>ew,applyActivation:()=>Jd,assertAndGetBroadcastShape:()=>yt,assertAxesAreInnerMostDims:()=>_S,assertParamsConsistent:()=>BC,assignToTypedArray:()=>uR,axesAreInnerMostDims:()=>xm,calculateShapes:()=>j5,combineLocations:()=>Nx,complexWithEvenIndex:()=>iR,complexWithOddIndex:()=>oR,computeConv2DInfo:()=>Ku,computeConv3DInfo:()=>ux,computeDefaultPad:()=>om,computeDilation2DInfo:()=>GI,computeOptimalWindowSize:()=>UC,computeOutAndReduceShapes:()=>Sx,computeOutShape:()=>VC,computePool2DInfo:()=>lx,computePool3DInfo:()=>qI,convertConv2DDataFormat:()=>ox,eitherStridesOrDilationsAreOne:()=>Pr,expandShapeToKeepDim:()=>yi,exponent:()=>hR,exponents:()=>cR,fromStringArrayToUint8:()=>fR,fromUint8ToStringArray:()=>pR,getAxesPermutation:()=>Tx,getBroadcastDims:()=>PN,getComplexWithIndex:()=>lR,getFusedBiasGradient:()=>Yd,getFusedDyActivation:()=>Zd,getImageCenter:()=>jC,getInnerMostAxes:()=>vS,getPermuted:()=>GC,getReductionAxes:()=>Wt,getReshaped:()=>HC,getReshapedPermuted:()=>qC,getSliceBeginCoords:()=>XC,getSliceSize:()=>KC,getUndoAxesPermutation:()=>wm,log:()=>rR,mergeRealAndImagArrays:()=>aR,prepareAndValidate:()=>U5,prepareSplitSize:()=>dR,segment_util:()=>nw,shouldFuse:()=>Qd,slice_util:()=>hn,splitRealAndImagArrays:()=>sR,tupleValuesAreOne:()=>Pa,upcastType:()=>or,validateInput:()=>Gf,validateUpdateShape:()=>Hf,warn:()=>nR});function BC(e,t){let n=e[0].length;e.forEach((a,s)=>{F(a.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((a,s)=>{for(let i=0;i<n;i++)F(i===t||a[i]===r[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`)})}function VC(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var Bm=30;function UC(e){return e<=Bm?e:Fh(e,Math.floor(Math.sqrt(e)))}function jC(e,t,n){let r=n*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[r,a]}function HC(e,t,n,r=!0){let a=[];if(r)a=a.concat(t.slice(0)),a.push(e[0]/n),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function GC(e,t,n=!0){let r=[];if(n){r.push(t);for(let a=t+1;a<e;++a)a<=2*t?(r.push(a),r.push(a-(t+1))):r.push(a)}else{let a=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):a.push(i);r.push(...a),r.push(0),r.push(...s)}return r}function qC(e,t,n,r=!0){let a=[];r?a.push(e[0]/n):a.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?r?a.push(t[s-1]*e[s]):a.push(e[s]/t[s-1]):a.push(e[s]);return a}function XC(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function KC(e,t,n){let r=e.slice(0,1);for(let a=0;a<n;++a)r.push(e[a+1]-t[a][0]-t[a][1]);return r}var ew=1.7580993408473768,tw=1.0507009873554805,ZC=.3275911,YC=.254829592,JC=-.284496736,QC=1.421413741,eR=-1.453152027,tR=1.061405429;function nR(...e){J().getBool("IS_TEST")||console.warn(...e)}function rR(...e){J().getBool("IS_TEST")||console.log(...e)}function aR(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function sR(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function iR(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=0;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function oR(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=2;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function lR(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function uR(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function cR(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);n[a]=Math.cos(s),r[a]=Math.sin(s)}return{real:n,imag:r}}function hR(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),a=Math.cos(r),s=Math.sin(r);return{real:a,imag:s}}function dR(e,t,n=0){let r=[];if(typeof t=="number")F(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);F(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}F(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var nw={};Le(nw,{collectGatherOpShapeInfo:()=>yR,computeOutShape:()=>AR,segOpComputeOptimalWindowSize:()=>mR});function mR(e,t){let n=!1,r;for(e<=Bm?(r=e,n=!0):r=Fh(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=Fh(e,r+1);return r}function AR(e,t,n){let r=[],a=e.length;for(let s=0;s<a;s++)s!==t?r.push(e[s]):r.push(n);return r}function yR(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${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 pR(e){try{return e.map(t=>fd(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function fR(e){return e.map(t=>Pu(t))}var Ur={};Le(Ur,{nonMaxSuppressionV3Impl:()=>Gx,nonMaxSuppressionV4Impl:()=>qx,nonMaxSuppressionV5Impl:()=>Xx,whereImpl:()=>Ox});function ke(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var gR=Ur.whereImpl,op=class extends Au{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ch(this,zr())}nextDataId(){return op.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&R.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let 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&&v.isString(n[0])){let a=n.map(s=>v.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 R.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=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ve(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return zr().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=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){ke([e],"where");let t=this.readSync(e.dataId);return gR(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};op.nextDataId=0;var Vm={};Le(Vm,{addImpl:()=>aw,bincountImpl:()=>Um,bincountReduceImpl:()=>sw,ceilImpl:()=>iw,concatImpl:()=>jm,expImpl:()=>ow,expm1Impl:()=>lw,floorImpl:()=>uw,gatherV2Impl:()=>cw,greaterImpl:()=>hw,lessImpl:()=>dw,linSpaceImpl:()=>pw,logImpl:()=>fw,maxImpl:()=>mw,maximumImpl:()=>Aw,minimumImpl:()=>yw,multiplyImpl:()=>Hm,negImpl:()=>gw,notEqualImpl:()=>xw,prodImpl:()=>ww,rangeImpl:()=>qm,rsqrtImpl:()=>bw,simpleAbsImpl:()=>rw,sliceImpl:()=>lp,squaredDifferenceImpl:()=>_w,stridedSliceImpl:()=>vw,subImpl:()=>kw,tileImpl:()=>Iw,topKImpl:()=>Nw,transposeImpl:()=>Gm,uniqueImpl:()=>Sw});function rw(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var xR=e=>{let{x:t}=e.inputs,n=e.backend;ke(t,"abs");let r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId).values;return r=rw(a),n.makeOutput(r,t.shape,"float32")},wR={kernelName:ao,backendName:"cpu",kernelFunc:xR};function $t(e){return(t,n,r,a,s)=>{let i=R.assertAndGetBroadcastShape(t,n),o=i.length,l=v.computeStrides(i),c=v.sizeFromShape(i),u=v.getTypedArrayFromDType(s,c),h=t.length,d=n.length,p=v.computeStrides(t),f=v.computeStrides(n),m=R.getBroadcastDims(t,i),A=R.getBroadcastDims(n,i);if(m.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=v.indexToLoc(y,o,l),w=g.slice(-h);m.forEach(N=>w[N]=0);let b=v.locToIndex(w,h,p),_=g.slice(-d);A.forEach(N=>_[N]=0);let x=v.locToIndex(_,d,f);u[y]=e(r[b],a[x])}return[u,i]}}function Pn(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 bR={kernelName:Lh,backendName:"cpu",kernelFunc:Pn};function up(e,t,n="float32"){if(n==="complex64"){let a=up(e,t,"float32"),s=up(e,t,"float32");return Pn({inputs:{real:a,imag:s},backend:e})}let r=v.makeZerosTypedArray(v.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function jr(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 _R={kernelName:Cs,backendName:"cpu",kernelFunc:jr};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 vR={kernelName:sd,backendName:"cpu",kernelFunc:bi};function ja(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return jr({inputs:{x:a},backend:n});let i=up(n,a.shape,a.dtype),o=ja({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Pn({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=ja({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=jr({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=v.toTypedArray([0],a.dtype),[l,c]=$t((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 kR={kernelName:ys,backendName:"cpu",kernelFunc:ja};function Xt(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;ke([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=ja({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,f=l.data.get(d.dataId).values,m=ja({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),A=l.data.get(m.dataId),y=A.complexTensorInfos.real,g=A.complexTensorInfos.imag,w=l.data.get(y.dataId).values,b=l.data.get(g.dataId).values,[_,x,N]=n(i.shape,o.shape,p,f,w,b),T=l.makeTensorInfo(N,"float32",_),E=l.makeTensorInfo(N,"float32",x),M=Pn({inputs:{real:T,imag:E},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(T),l.disposeIntermediateTensorInfo(E),M}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 Xm(e){return(t,n,r,a,s,i)=>{let o=R.assertAndGetBroadcastShape(t,n),l=v.sizeFromShape(o),c=o.length,u=v.computeStrides(o),h=v.getTypedArrayFromDType("float32",l),d=v.getTypedArrayFromDType("float32",l),p=R.getBroadcastDims(t,o),f=R.getBroadcastDims(n,o),m=R.mergeRealAndImagArrays(r,a),A=R.mergeRealAndImagArrays(s,i),y=t.length,g=v.computeStrides(t),w=n.length,b=v.computeStrides(n);if(p.length+f.length===0)for(let _=0;_<h.length;_++){let x=_%m.length,N=_%A.length,T=e(m[x*2],m[x*2+1],A[N*2],A[N*2+1]);h[_]=T.real,d[_]=T.imag}else for(let _=0;_<h.length;_++){let x=v.indexToLoc(_,c,u),N=x.slice(-y);p.forEach(W=>N[W]=0);let T=v.locToIndex(N,y,g),E=x.slice(-w);f.forEach(W=>E[W]=0);let M=v.locToIndex(E,w,b),O=e(m[T*2],m[T*2+1],A[M*2],A[M*2+1]);h[_]=O.real,d[_]=O.imag}return[h,d,o]}}var aw=$t((e,t)=>e+t),IR=Xm((e,t,n,r)=>({real:e+n,imag:t+r})),dc=Xt(Ea,aw,IR),NR={kernelName:Ea,backendName:"cpu",kernelFunc:dc};function Um(e,t,n,r,a){let s=v.sizeFromShape(r),i=v.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 sw(e,t,n,r=!1){let a=e.shape[0],s=e.shape[1],i=Ve([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 Sl(e){return(t,n,r)=>{let a=v.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function ot(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(ke(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,c=v.sizeFromShape(i.shape),u=n||i.dtype,h=v.getArrayFromDType(u,c);for(let d=0;d<c;++d)h[d]=t(l[d],a);return o.makeTensorInfo(i.shape,u,h)}}function Tl(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(ke(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,c=n||i.dtype,u=t(l,c,a);return o.makeTensorInfo(i.shape,c,u)}}var iw=Sl(e=>Math.ceil(e)),SR=Tl(gs,iw),TR={kernelName:gs,backendName:"cpu",kernelFunc:SR};function jm(e,t,n,r){let a=v.getArrayFromDType(n,v.sizeFromShape(t));if(r&&n!=="string"){let s=0;e.forEach(i=>{let o=v.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?R.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let 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 ow=Sl(e=>Math.exp(e)),Tw=Tl(Is,ow),ER={kernelName:Is,backendName:"cpu",kernelFunc:Tw},lw=Sl(e=>Math.expm1(e)),CR=Tl(bo,lw),RR={kernelName:bo,backendName:"cpu",kernelFunc:CR},uw=Sl(e=>Math.floor(e)),FR=Tl(Ns,uw),MR={kernelName:Ns,backendName:"cpu",kernelFunc:FR};function cw(e,t,n){let r=Ve(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 hw=$t((e,t)=>e>t?1:0),$R=Xt(Io,hw,null,"bool"),DR={kernelName:Io,backendName:"cpu",kernelFunc:$R},dw=$t((e,t)=>e<t?1:0),OR=Xt(Eo,dw,null,"bool"),zR={kernelName:Eo,backendName:"cpu",kernelFunc:OR};function pw(e,t,n){let r=(t-e)/(n-1),a=v.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}var fw=Sl(e=>Math.log(e)),PR=Tl(Fs,fw),LR={kernelName:Fs,backendName:"cpu",kernelFunc:PR};function mw(e,t,n,r){let a=v.getTypedArrayFromDType(r,v.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 Aw=$t((e,t)=>Math.max(e,t)),WR=Xt($s,Aw),BR={kernelName:$s,backendName:"cpu",kernelFunc:WR},yw=$t((e,t)=>Math.min(e,t)),VR=Xt(Ps,yw),UR={kernelName:Ps,backendName:"cpu",kernelFunc:VR},Hm=$t((e,t)=>e*t),jR=Xm((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),Km=Xt(Ls,Hm,jR),HR={kernelName:Ls,backendName:"cpu",kernelFunc:Km};function gw(e,t,n){let r=v.createScalarValue(-1,n);return Hm([],t,r,e,n)}function GR(e){let{inputs:t,backend:n}=e,{x:r}=t;ke(r,"neg");let a=n.data.get(r.dataId).values,[s,i]=gw(a,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,s)}var qR={kernelName:$o,backendName:"cpu",kernelFunc:GR},xw=$t((e,t)=>e!==t?1:0),XR=Xt(Do,xw,null,"bool"),KR={kernelName:Do,backendName:"cpu",kernelFunc:XR};function Gm(e,t,n,r,a){let s=t.length,i=v.sizeFromShape(t),o=v.computeStrides(t),l=v.computeStrides(a),c=v.getTypedArrayFromDType(n,v.sizeFromShape(a));for(let u=0;u<i;++u){let h=v.indexToLoc(u,s,o),d=new Array(h.length);for(let f=0;f<d.length;f++)d[f]=h[r[f]];let p=v.locToIndex(d,s,l);c[p]=e[u]}return c}function hr(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{perm:s}=n;ke(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=Gm(l,a.shape,a.dtype,s,o);return{dataId:r.write(c,o,a.dtype),shape:o,dtype:a.dtype}}var ZR={kernelName:ai,backendName:"cpu",kernelFunc:hr};function ww(e,t,n,r){let[a,s]=R.computeOutAndReduceShapes(e,r),i=or(t,"int32"),o=v.makeZerosTypedArray(v.sizeFromShape(a),i),l=v.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 YR(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ke(a,"prod");let o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=R.getAxesPermutation(l,o),u=l,h=a,d=[];c!=null&&(h=hr({inputs:{x:a},backend:n,attrs:{perm:c}}),d.push(h),u=R.getInnerMostAxes(u.length,o));let p=n.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:A}=ww(h.shape,h.dtype,p,u),y=m;return i&&(y=R.expandShapeToKeepDim(m,l)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(y,A,f)}var JR={kernelName:Bo,backendName:"cpu",kernelFunc:YR};function qm(e,t,n,r){let a=e===t,s=e<t&&n<0,i=t<e&&n>1;if(a||s||i)return v.makeZerosTypedArray(0,r);let o=Math.abs(Math.ceil((t-e)/n)),l=v.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 bw=Sl(e=>1/Math.sqrt(e)),QR=Tl(Ks,bw),eF={kernelName:Ks,backendName:"cpu",kernelFunc:QR};function lp(e,t,n,r,a){let s=hn.isSliceContinous(r,t,n),i=v.sizeFromShape(n),o=v.computeStrides(r);if(s){let h=hn.computeFlatOffset(t,o);return a==="string"?e.slice(h,h+i):e.subarray(h,h+i)}let l=a==="string"?R.fromUint8ToStringArray(e):e,c=Ve(r,a,l),u=Ve(n,a);for(let h=0;h<u.size;++h){let d=u.indexToLoc(h),p=d.map((f,m)=>f+t[m]);u.set(c.get(...p),...d)}return a==="string"?R.fromStringArrayToUint8(u.values):u.values}function _i(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r;ke(a,"slice");let[o,l]=hn.parseSliceParams(a,s,i);hn.assertParamsValid(a,o,l);let c=n.data.get(a.dataId).values,u=lp(c,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,u)}var tF={kernelName:qo,backendName:"cpu",kernelFunc:_i},_w=$t((e,t)=>{let n=e-t;return n*n}),nF=Xt(ti,_w),rF={kernelName:ti,backendName:"cpu",kernelFunc:nF};function vw(e,t,n,r){let a=Ve(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 kw=$t((e,t)=>e-t),aF=Xm((e,t,n,r)=>({real:e-n,imag:t-r})),Zm=Xt(ni,kw,aF),sF={kernelName:ni,backendName:"cpu",kernelFunc:Zm};function Iw(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=Ve(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 Nw(e,t,n,r,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=v.getTypedArrayFromDType(n,i*r),c=v.getTypedArrayFromDType("int32",i*r);for(let h=0;h<i;h++){let d=h*o,p=e.subarray(d,d+o),f=[];for(let g=0;g<p.length;g++)f.push({value:p[g],index:g});f.sort((g,w)=>w.value-g.value);let m=h*r,A=l.subarray(m,m+r),y=c.subarray(m,m+r);for(let g=0;g<r;g++)A[g]=f[g].value,y[g]=f[g].index}let u=t.slice();return u[u.length-1]=r,[Ve(u,n,l),Ve(u,"int32",c)]}function Sw(e,t,n,r){let a=v.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let f=0;f<a;f++)s[0]*=n[f];s[1]=n[a];for(let f=a+1;f<n.length;f++)s[2]*=n[f];let i={},o=new Int32Array(n[a]),l=new Pt(s,r,e),c=[],u=s[0]===1&&s[2]===1;for(let f=0;f<n[a];f++){let m;if(u)m=e[f].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,f,g));m=A.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let A=Object.keys(i).length;i[m]=A,o[f]=A,c.push(f)}}let h=s.slice();h[1]=Object.keys(i).length;let d=new Pt(h,r);c.forEach((f,m)=>{for(let A=0;A<s[0];A++)for(let y=0;y<s[2];y++)d.set(l.get(A,f,y),A,m,y)});let p=n.slice();return p[a]=h[1],{outputValues:d.values,outputShape:p,indices:o}}var Ew="3.3.0";pl("cpu",()=>new op,1);var Cw=ot(yo,e=>e>=0?e:Math.exp(e)-1),iF={kernelName:yo,backendName:"cpu",kernelFunc:Cw};function Rw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;ke([a],"leakyRelu");let i=v.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=v.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 oF={kernelName:Rs,backendName:"cpu",kernelFunc:Rw},lF=$t((e,t)=>e<0?t*e:e);function Fw(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t;ke([r,a],"prelu");let s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[o,l]=lF(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var uF={kernelName:Us,backendName:"cpu",kernelFunc:Fw},Mw=ot(js,e=>Math.max(0,e)),cF={kernelName:js,backendName:"cpu",kernelFunc:Mw},$w=ot(Gs,e=>Math.min(Math.max(0,e),6)),hF={kernelName:Gs,backendName:"cpu",kernelFunc:$w};function Ym(e,t,n,r,a){if(n==="linear")return jr({inputs:{x:t},backend:e});if(n==="relu")return Mw({inputs:{x:t},backend:e});if(n==="elu")return Cw({inputs:{x:t},backend:e});if(n==="relu6")return $w({inputs:{x:t},backend:e});if(n==="prelu")return Fw({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return Rw({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function gt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=v.sizeFromShape(a.shape),o=v.inferFromImplicitShape(s,i),l=v.sizeFromShape(o);v.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(a.dataId);let c=n.data.get(a.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,h=c.complexTensorInfos.imag;u.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var dF={kernelName:Uo,backendName:"cpu",kernelFunc:gt};function Dw(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;ke([a,s],"matMul");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[c-2]:s.shape[c-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=v.sizeFromShape(f),y=v.sizeFromShape(m),g=A===y||A===1||y===1;v.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let w=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);v.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let b=i?[A,u,d]:[A,d,u],_=o?[y,p,h]:[y,h,p],x=gt({inputs:{x:a},backend:n,attrs:{shape:b}}),N=gt({inputs:{x:s},backend:n,attrs:{shape:_}}),T=i?x.shape[1]:x.shape[2],E=i?x.shape[2]:x.shape[1],M=o?N.shape[1]:N.shape[2],O=Math.max(A,y),W=n.data.get(x.dataId).values,V=n.data.get(N.dataId).values,U=v.computeStrides(x.shape),j=v.computeStrides(N.shape),[X,G,ee]=i?[U[0],1,U[1]]:[U[0],U[1],1],[Y,se,te]=o?[1,j[1],j[0]]:[j[1],1,j[0]],le=E*M,Q=Ve([O,E,M],x.dtype),pe=Q.values,ue=n.blockSize;for(let ye=0;ye<O;ye++)for(let me=0;me<E;me+=ue)for(let Ne=0;Ne<M;Ne+=ue)for(let Ce=0;Ce<T;Ce+=ue){let De=Math.min(me+ue,E),Pe=Math.min(Ne+ue,M),Oe=Math.min(Ce+ue,T);for(let nt=me;nt<De;nt++)for(let rt=Ne;rt<Pe;rt++){let lt=0;for(let Je=Ce;Je<Oe;Je++){let pt=Math.min(ye,A-1)*X,je=Math.min(ye,y-1)*te,yn=W[pt+nt*G+Je*ee],vt=V[Je*Y+rt*se+je];lt+=yn*vt}pe[ye*le+(nt*M+rt)]+=lt}}return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(w,Q.dtype,Q.values)}var pF={kernelName:As,backendName:"cpu",kernelFunc:Dw};function fF(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,f,m=[];d=Dw({inputs:{a,b:s},attrs:{transposeA:l,transposeB:c},backend:n}),i&&(p=dc({inputs:{a:d,b:i},backend:n}),m.push(d),d=p),u&&(f=Ym(n,d,u,o,h),m.push(d),d=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return d}var mF={kernelName:si,backendName:"cpu",kernelFunc:fF},AF=ot(so,e=>Math.acos(e)),yF={kernelName:so,backendName:"cpu",kernelFunc:AF},gF=ot(io,e=>Math.acosh(e)),xF={kernelName:io,backendName:"cpu",kernelFunc:gF};function wF(e){let{inputs:t,backend:n}=e,r=t;ke(t,"addN");let a=r.map(o=>n.data.get(o.dataId).values),s=Ve(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 bF={kernelName:ps,backendName:"cpu",kernelFunc:wF};function _F(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ke(a,"all");let o=v.parseAxisParam(s,a.shape),l=o,c=R.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=hr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=R.computeOutAndReduceShapes(u.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let b=0;b<p;++b){let _=m[g+b];w=w&&_}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var vF={kernelName:$h,backendName:"cpu",kernelFunc:_F};function kF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ke(a,"any");let o=v.parseAxisParam(s,a.shape),l=o,c=R.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=hr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=R.computeOutAndReduceShapes(u.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let b=0;b<p;++b){let _=m[g+b];w=w||_}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var IF={kernelName:Dh,backendName:"cpu",kernelFunc:kF};function NF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ke(a,"argMax");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=hr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(u),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let b=0;b<f;++b){let _=m[y+b];_>g&&(g=_,w=b)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var SF={kernelName:fs,backendName:"cpu",kernelFunc:NF};function TF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ke(a,"argMin");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=hr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(u),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let b=0;b<f;++b){let _=m[y+b];_<g&&(g=_,w=b)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var EF={kernelName:xu,backendName:"cpu",kernelFunc:TF},CF=ot(oo,e=>Math.asin(e)),RF={kernelName:oo,backendName:"cpu",kernelFunc:CF},FF=ot(lo,e=>Math.asinh(e)),MF={kernelName:lo,backendName:"cpu",kernelFunc:FF},$F=ot(uo,e=>Math.atan(e)),DF={kernelName:uo,backendName:"cpu",kernelFunc:$F},OF=$t((e,t)=>Math.atan2(e,t)),zF=Xt(ho,OF),PF={kernelName:ho,backendName:"cpu",kernelFunc:zF},LF=ot(co,e=>Math.atanh(e)),WF={kernelName:co,backendName:"cpu",kernelFunc:LF};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,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ve(a.outShape,n),A=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],w=a.outShape[3];for(let b=0;b<a.batchSize;++b){let _=b*y,x=b*r[0];for(let N=0;N<a.inChannels;++N)for(let T=0;T<a.outHeight;++T){let E=T*i-d,M=Math.max(0,E),O=Math.min(a.inHeight,u+E),W=_+T*g;for(let V=0;V<a.outWidth;++V){let U=V*o-p,j=Math.max(0,U),X=Math.min(a.inWidth,h+U),G=f,ee=0,Y=0;for(let te=M;te<O;te+=l){let le=x+te*r[1];for(let Q=j;Q<X;Q+=c){let pe=le+Q*r[2],ue=e[pe+N];s==="max"&&ue>G?G=ue:s==="avg"&&(ee+=ue,Y++)}if(isNaN(G))break}let se=W+V*w+N;A[se]=s==="avg"?ee/Y:G}}}return m}function Ow(e,t,n,r,a=!1,s=!1){let i=Ve(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,f=r.padInfo.left,m=Ve(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 w=g*o-p,b=w;for(;b<0;)b+=c;let _=Math.min(r.inHeight,h+w);for(let x=0;x<r.outWidth;++x){let N=x*l-f,T=N;for(;T<0;)T+=u;let E=Math.min(r.inWidth,d+N),M=Number.NEGATIVE_INFINITY,O=-1;for(let W=b;W<_;W+=c){let V=W-w;for(let U=T;U<E;U+=u){let j=U-N,X=m.get(A,W,U,y);X>M&&(M=X,a?O=s?((A*r.inHeight+W)*r.inWidth+U)*r.inChannels+y:(W*r.inWidth+U)*r.inChannels+y:O=V*d+j)}}i.set(O,A,g,x,y)}}return i}function zw(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,f=a.effectiveFilterWidth,m=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=Ve(a.outShape,n),b=w.values,_=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],x=a.outShape[2]*a.outShape[3]*a.outShape[4],N=a.outShape[3]*a.outShape[4],T=a.outShape[4];for(let E=0;E<a.batchSize;++E){let M=E*_,O=E*r[0];for(let W=0;W<a.inChannels;++W)for(let V=0;V<a.outDepth;++V){let U=V*i-m,j=U;for(;j<0;)j+=c;let X=Math.min(a.inDepth,d+U),G=M+V*x;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-A,se=Y;for(;se<0;)se+=u;let te=Math.min(a.inHeight,p+Y),le=G+ee*N;for(let Q=0;Q<a.outWidth;++Q){let pe=Q*l-y,ue=pe;for(;ue<0;)ue+=h;let ye=Math.min(a.inWidth,f+pe),me=le+Q*T,Ne=g,Ce=0,De=0;for(let Oe=j;Oe<X;Oe+=c){let nt=O+Oe*r[1];for(let rt=se;rt<te;rt+=u){let lt=nt+rt*r[2];for(let Je=ue;Je<ye;Je+=h){let pt=lt+Je*r[3],je=e[pt+W];if(s==="max"&&je>Ne?Ne=je:s==="avg"&&(Ce+=je,De++),isNaN(Ne))break}if(isNaN(Ne))break}if(isNaN(Ne))break}let Pe=me+W;b[Pe]=s==="avg"?Ce/De:Ne}}}}return w}function BF(e,t){let n=Ve(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,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-d,w=g;for(;w<0;)w+=i;let b=Math.min(t.inDepth,c+g);for(let _=0;_<t.outHeight;++_){let x=_*a-p,N=x;for(;N<0;)N+=o;let T=Math.min(t.inHeight,u+x);for(let E=0;E<t.outWidth;++E){let M=E*s-f,O=M;for(;O<0;)O+=l;let W=Math.min(t.inWidth,h+M),V=Number.NEGATIVE_INFINITY,U=-1;for(let j=w;j<b;j+=i){let X=j-g;for(let G=N;G<T;G+=o){let ee=G-x;for(let Y=O;Y<W;Y+=l){let se=Y-M,te=e.get(m,j,G,Y,A);te>=V&&(V=te,U=X*u*h+ee*u+se)}}}n.set(U,m,y,_,E,A)}}}return n}function VF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ke(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))h=jr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),f=Jm(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var UF={kernelName:ms,backendName:"cpu",kernelFunc:VF};function jF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;ke(a,"avgPool3d");let u=R.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=zw(h,a.shape,a.dtype,v.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var HF={kernelName:wu,backendName:"cpu",kernelFunc:jF};function GF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;ke([a,s],"avgPool3DGrad");let u=R.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,w=u.dilationWidth,b=u.effectiveFilterDepth,_=u.effectiveFilterHeight,x=u.effectiveFilterWidth,N=b-1-u.padInfo.front,T=x-1-u.padInfo.left,E=_-1-u.padInfo.top,M=Ve(s.shape,"float32"),O=1/(f*m*A),W=n.bufferSync(a);for(let V=0;V<u.batchSize;++V)for(let U=0;U<u.inChannels;++U)for(let j=0;j<u.inDepth;++j)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=j-N,Y=X-E,se=G-T,te=0;for(let le=0;le<b;le+=y){let Q=(ee+le)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let pe=0;pe<_;pe+=g){let ue=(Y+pe)/d;if(!(ue<0||ue>=u.outHeight||Math.floor(ue)!==ue))for(let ye=0;ye<x;ye+=w){let me=(se+ye)/p;me<0||me>=u.outWidth||Math.floor(me)!==me||(te+=W.get(V,Q,ue,me,U))}}}M.set(te*O,V,j,X,G,U)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var qF={kernelName:zh,backendName:"cpu",kernelFunc:GF};function XF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ke([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,w=g-1-u.padInfo.left,b=y-1-u.padInfo.top,_=Ve(i.shape,"float32"),x=1/(p*f),N=n.data.get(a.dataId).values,T=Ve(a.shape,"float32",N);for(let E=0;E<u.batchSize;++E)for(let M=0;M<u.inChannels;++M)for(let O=0;O<u.inHeight;++O)for(let W=0;W<u.inWidth;++W){let V=O-b,U=W-w,j=0;for(let X=0;X<y;X+=m){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=(U+ee)/d;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(j+=T.get(E,G,Y,M))}}_.set(j*x,E,O,W,M)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var KF={kernelName:Oh,backendName:"cpu",kernelFunc:XF};function ZF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ke([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]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),A=f.length,y=p.length,g=d.length,w=h.length,b=0,_=0,x=0,N=0;for(let T=0;T<u.length;++T)m[T]=f[b++]+(u[T]-h[_++])*p[x++]/Math.sqrt(d[N++]+c),b>=A&&(b=0),_>=w&&(_=0),x>=y&&(x=0),N>=g&&(N=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var YF={kernelName:Ts,backendName:"cpu",kernelFunc:ZF};function JF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ke([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=gt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=hr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=_i({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var QF={kernelName:bu,backendName:"cpu",kernelFunc:JF};function eM(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 tM={kernelName:Ph,backendName:"cpu",kernelFunc:eM},nM=ot(Ca,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),rM={kernelName:Ca,backendName:"cpu",kernelFunc:nM},aM=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(v.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")},sM={kernelName:_u,backendName:"cpu",kernelFunc:aM};function El(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 iM={kernelName:Jh,backendName:"cpu",kernelFunc:El};function Cl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>v.sizeFromShape(m.shape)>0);if(o.length===1)return jr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(R.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(b=>bi({inputs:{input:b},backend:n})),A=o.map(b=>El({inputs:{input:b},backend:n})),y=Cl({inputs:m,backend:n,attrs:{axis:s}}),g=Cl({inputs:A,backend:n,attrs:{axis:s}}),w=Pn({inputs:{real:y,imag:g},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),w}let c=o.map(m=>{let A=v.sizeFromShape(m.shape.slice(s));return gt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=R.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,d=jm(u,i,t[0].dtype,h),p=R.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var oM={kernelName:po,backendName:"cpu",kernelFunc:Cl};function Pw(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;ke([a,s],"conv2d");let h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,w=d.dataFormat==="channelsLast",b=new Pt(d.outShape,a.dtype),_=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=_[0],T=w?_[1]:_[2],E=w?_[2]:1,M=w?1:_[1],O=b.strides[0],W=w?b.strides[1]:b.strides[2],V=w?b.strides[2]:1,U=w?1:b.strides[1],j=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=b.values;for(let ee=0;ee<d.batchSize;++ee){let Y=ee*N,se=ee*O;for(let te=0;te<d.outHeight;++te){let le=se+te*W,Q=te*d.strideHeight-g;for(let pe=0;pe<p;++pe){let ue=Q+pe*m;if(ue<0||ue>=d.inHeight)continue;let ye=pe*x[0],me=Y+ue*T;for(let Ne=0;Ne<d.outWidth;++Ne){let Ce=le+Ne*V,De=Ne*d.strideWidth-y;for(let Pe=0;Pe<f;++Pe){let Oe=De+Pe*A;if(Oe<0||Oe>=d.inWidth)continue;let nt=ye+Pe*x[1],rt=me+Oe*E,lt=nt;for(let Je=0;Je<d.inChannels;++Je){let pt=j[rt+Je*M];for(let je=0;je<d.outChannels;++je)G[Ce+je*U]+=pt*X[lt+je];lt+=d.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,G)}var lM={kernelName:xs,backendName:"cpu",kernelFunc:Pw};function uM(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;ke([a,s],"conv2dBackpropFilter");let h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:A}=d,y=d.dataFormat==="channelsLast",g=new Pt(d.filterShape,"float32"),w=d.padInfo.left,b=d.padInfo.top,_=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,N=new Pt(a.shape,a.dtype,_),T=new Pt(s.shape,s.dtype,x);for(let E=0;E<m;++E){let M=Math.max(0,Math.ceil((b-E)/p)),O=Math.min(d.outHeight,(d.inHeight+b-E)/p);for(let W=0;W<A;++W){let V=Math.max(0,Math.ceil((w-W)/f)),U=Math.min(d.outWidth,(d.inWidth+w-W)/f);for(let j=0;j<d.inChannels;++j)for(let X=0;X<d.outChannels;++X){let G=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=M;Y<O;++Y){let se=E+Y*p-b;for(let te=V;te<U;++te){let le=W+te*f-w;y?G+=N.get(ee,se,le,j)*T.get(ee,Y,te,X):G+=N.get(ee,j,se,le)*T.get(ee,X,Y,te)}}g.set(G,E,W,j,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var cM={kernelName:Wh,backendName:"cpu",kernelFunc:uM};function hM(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;ke([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),d=v.computeStrides(a.shape),p=R.convertConv2DDataFormat(c),f=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),m=new Pt(f.inShape,"float32"),A=m.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[w,b,_]=h,{batchSize:x,filterHeight:N,filterWidth:T,inChannels:E,inHeight:M,inWidth:O,outChannels:W,outHeight:V,outWidth:U,strideHeight:j,strideWidth:X}=f;p=f.dataFormat;let G=N-1-f.padInfo.top,ee=T-1-f.padInfo.left,Y=p==="channelsLast",se=m.strides[0],te=Y?m.strides[1]:m.strides[2],le=Y?m.strides[2]:1,Q=Y?1:m.strides[1],pe=d[0],ue=Y?d[1]:d[2],ye=Y?d[2]:1,me=Y?1:d[1];for(let Ne=0;Ne<x;++Ne)for(let Ce=0;Ce<E;++Ce)for(let De=0;De<M;++De){let Pe=De-G,Oe=Math.max(0,Math.ceil(Pe/j)),nt=Math.min(V,(N+Pe)/j);for(let rt=0;rt<O;++rt){let lt=rt-ee,Je=Math.max(0,Math.ceil(lt/X)),pt=Math.min(U,(T+lt)/X),je=0;for(let vt=Oe;vt<nt;++vt){let Gn=vt*j-Pe;for(let Jt=Je;Jt<pt;++Jt){let gn=Jt*X-lt,qn=pe*Ne+ue*vt+ye*Jt,Mn=w*(N-1-Gn)+b*(T-1-gn)+_*Ce;for(let cn=0;cn<W;++cn){let Qt=y[qn+me*cn],Mr=g[Mn+cn];je+=Qt*Mr}}}let yn=se*Ne+te*De+le*rt+Q*Ce;A[yn]=je}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var dM={kernelName:ws,backendName:"cpu",kernelFunc:hM};function pM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;ke([a,s],"conv3d");let c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=c,y=A.front,g=A.left,w=A.top,b=new Pt(c.outShape,a.dtype),_=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,N=b.values,T=v.computeStrides(a.shape),E=v.computeStrides(s.shape);for(let M=0;M<c.batchSize;++M){let O=M*T[0],W=M*b.strides[0];for(let V=0;V<c.outDepth;++V){let U=W+V*b.strides[1],j=V*c.strideDepth-y;for(let X=0;X<u;++X){let G=j+X*p;if(G<0||G>=c.inDepth)continue;let ee=X*E[0],Y=O+G*T[1];for(let se=0;se<c.outHeight;++se){let te=U+se*b.strides[2],le=se*c.strideHeight-w;for(let Q=0;Q<h;++Q){let pe=le+Q*f;if(pe<0||pe>=c.inHeight)continue;let ue=ee+Q*E[1],ye=Y+pe*T[2];for(let me=0;me<c.outWidth;++me){let Ne=te+me*c.outChannels,Ce=me*c.strideWidth-g;for(let De=0;De<d;++De){let Pe=Ce+De*m;if(Pe<0||Pe>=c.inWidth)continue;let Oe=ue+De*E[2],nt=ye+Pe*c.inChannels,rt=Oe;for(let lt=0;lt<c.inChannels;++lt){let Je=_[nt+lt];for(let pt=0;pt<c.outChannels;++pt)N[Ne+pt]+=Je*x[rt+pt];rt+=c.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var fM={kernelName:vu,backendName:"cpu",kernelFunc:pM};function mM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;ke([a,s],"conv3dBackpropFilterV2");let c=v.computeStrides(a.shape),u=v.computeStrides(s.shape),h=R.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=new Pt(h.filterShape,"float32"),w=g.values,[b,_,x,N]=g.strides,T=n.data.get(s.dataId).values,[E,M,O,W]=u,V=n.data.get(a.dataId).values,[U,j,X,G]=c,ee=h.padInfo.front,Y=h.padInfo.left,se=h.padInfo.top;for(let te=0;te<m;++te){let le=Math.max(0,Math.ceil((ee-te)/d)),Q=Math.min(h.outDepth,(h.inDepth+ee-te)/d),pe=te*b;for(let ue=0;ue<A;++ue){let ye=Math.max(0,Math.ceil((se-ue)/p)),me=Math.min(h.outHeight,(h.inHeight+se-ue)/p),Ne=ue*_+pe;for(let Ce=0;Ce<y;++Ce){let De=Math.max(0,Math.ceil((Y-Ce)/f)),Pe=Math.min(h.outWidth,(h.inWidth+Y-Ce)/f),Oe=Ce*x+Ne;for(let nt=0;nt<h.inChannels;++nt){let rt=nt*N+Oe;for(let lt=0;lt<h.outChannels;++lt){let Je=0;for(let pt=0;pt<h.batchSize;++pt){let je=pt*U,yn=pt*E;for(let vt=le;vt<Q;++vt){let Gn=(te+vt*d-ee)*j+je,Jt=vt*M+yn;for(let gn=ye;gn<me;++gn){let qn=(ue+gn*p-se)*X+Gn,Mn=gn*O+Jt;for(let cn=De;cn<Pe;++cn){let Qt=(Ce+cn*f-Y)*G+qn,Mr=cn*W+Mn;Je+=V[Qt+nt]*T[Mr+lt]}}}}w[rt+lt]=Je}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var AM={kernelName:Bh,backendName:"cpu",kernelFunc:mM};function yM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;ke([a],"conv3dBackpropInputV2");let c=v.computeStrides(a.shape),u=v.computeStrides(s.shape),h=R.computeConv3DInfo(l,s.shape,o,1,i),d=new Pt(h.inShape,"float32"),p=d.values,[f,m,A,y]=d.strides,g=n.data.get(a.dataId).values,[w,b,_,x]=c,N=n.data.get(s.dataId).values,[T,E,M,O]=u,{batchSize:W,filterDepth:V,filterHeight:U,filterWidth:j,inChannels:X,inDepth:G,inHeight:ee,inWidth:Y,outChannels:se,outDepth:te,outHeight:le,outWidth:Q,strideDepth:pe,strideHeight:ue,strideWidth:ye}=h,me=V-1-h.padInfo.front,Ne=U-1-h.padInfo.top,Ce=j-1-h.padInfo.left;for(let De=0;De<W;++De)for(let Pe=0;Pe<X;++Pe)for(let Oe=0;Oe<G;++Oe){let nt=Oe-me,rt=Math.max(0,Math.ceil(nt/pe)),lt=Math.min(te,(V+nt)/pe);for(let Je=0;Je<ee;++Je){let pt=Je-Ne,je=Math.max(0,Math.ceil(pt/ue)),yn=Math.min(le,(U+pt)/ue);for(let vt=0;vt<Y;++vt){let Gn=vt-Ce,Jt=Math.max(0,Math.ceil(Gn/ye)),gn=Math.min(Q,(j+Gn)/ye),qn=0;for(let Mn=rt;Mn<lt;++Mn){let cn=Mn*pe-nt;for(let Qt=je;Qt<yn;++Qt){let Mr=Qt*ue-pt;for(let nr=Jt;nr<gn;++nr){let rr=nr*ye-Gn,wa=w*De+b*Mn+_*Qt+x*nr,ea=T*(V-1-cn)+E*(U-1-Mr)+M*(j-1-rr)+O*Pe;for(let ba=0;ba<se;++ba){let Vi=g[wa+ba],yr=N[ea+ba];qn+=Vi*yr}}}}p[f*De+m*Oe+A*Je+y*vt+Pe]=qn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var gM={kernelName:Vh,backendName:"cpu",kernelFunc:yM},xM=ot(bs,e=>Math.cos(e)),wM={kernelName:bs,backendName:"cpu",kernelFunc:xM},bM=ot(fo,e=>Math.cosh(e)),_M={kernelName:fo,backendName:"cpu",kernelFunc:bM};function vM(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,f=s.shape[0],[m,A]=o,y=Ve([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,w=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,_=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N<f;N++){let T=N*4,E=g[T],M=g[T+1],O=g[T+2],W=g[T+3],V=w[N];if(V>=u)continue;let U=m>1?(O-E)*(h-1)/(m-1):0,j=A>1?(W-M)*(d-1)/(A-1):0;for(let X=0;X<m;X++){let G=m>1?E*(h-1)+X*U:.5*(E+O)*(h-1);if(G<0||G>h-1){for(let ee=0;ee<A;ee++)for(let Y=0;Y<p;Y++){let se=Y+ee*x[2]+X*x[1]+N*x[0];y.values[se]=c}continue}if(l==="bilinear"){let ee=Math.floor(G),Y=Math.ceil(G),se=G-ee;for(let te=0;te<A;te++){let le=A>1?M*(d-1)+te*j:.5*(M+W)*(d-1);if(le<0||le>d-1){for(let ye=0;ye<p;ye++){let me=ye+te*x[2]+X*x[1]+N*x[0];y.values[me]=c}continue}let Q=Math.floor(le),pe=Math.ceil(le),ue=le-Q;for(let ye=0;ye<p;ye++){let me=ye+Q*_[2]+ee*_[1]+V*_[0],Ne=b[me];me=ye+pe*_[2]+ee*_[1]+V*_[0];let Ce=b[me];me=ye+Q*_[2]+Y*_[1]+V*_[0];let De=b[me];me=ye+pe*_[2]+Y*_[1]+V*_[0];let Pe=b[me],Oe=Ne+(Ce-Ne)*ue,nt=De+(Pe-De)*ue;me=ye+te*x[2]+X*x[1]+N*x[0],y.values[me]=Oe+(nt-Oe)*se}}}else for(let ee=0;ee<A;++ee){let Y=A>1?M*(d-1)+ee*j:.5*(M+W)*(d-1);if(Y<0||Y>d-1){for(let le=0;le<p;le++){let Q=le+ee*x[2]+X*x[1]+N*x[0];y.values[Q]=c}continue}let se=Math.round(Y),te=Math.round(G);for(let le=0;le<p;le++){let Q=le+se*_[2]+te*_[1]+V*_[0],pe=le+ee*x[2]+X*x[1]+N*x[0];y.values[pe]=b[Q]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var kM={kernelName:mo,backendName:"cpu",kernelFunc:vM};function IM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;ke(a,"cumsum");let l=R.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=hr({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=R.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=or(c.dtype,"int32"),d=v.makeZerosTypedArray(v.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(y,g)=>y+f-g-1:(y,g)=>y+g;for(let y=0;y<p.length;y+=f)for(let g=0;g<f;g++){let w=m(y,g);if(g===0)d[w]=i?0:p[w];else{let b=m(y,g-1);d[w]=i?p[b]+d[b]:p[w]+d[b]}}let A=n.makeTensorInfo(c.shape,h,d);if(l!=null){let y=R.getUndoAxesPermutation(l),g=hr({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(c),g}return A}var NM={kernelName:_s,backendName:"cpu",kernelFunc:IM};function SM(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=sw(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 TM={kernelName:Uh,backendName:"cpu",kernelFunc:SM};function EM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),v.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),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*d*p),A=0;for(let y=0;y<o;++y)for(let g=0;g<h;++g){let w=Math.floor(g/s),b=g%s;for(let _=0;_<d;++_){let x=Math.floor(_/s),N=_%s,T=(b*s+N)*p;for(let E=0;E<p;++E){let M=E+T+u*(x+c*(w+l*y));m[A++]=f[M]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,m)}var CM={kernelName:Ao,backendName:"cpu",kernelFunc:EM};function Lw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r;ke([a,s],"depthwiseConv2DNative");let u=v.computeStrides(a.shape),h=v.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=R.computeConv2DInfo(a.shape,s.shape,i,d,o,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:y,padInfo:g}=p,w=g.left,b=g.top,_=p.outChannels/p.inChannels,x=new Pt(p.outShape,a.dtype),N=n.data.get(a.dataId).values,T=n.data.get(s.dataId).values,E=x.values;for(let M=0;M<p.batchSize;++M){let O=M*u[0],W=M*x.strides[0];for(let V=0;V<p.outHeight;++V){let U=W+V*x.strides[1],j=V*p.strideHeight-w;for(let X=0;X<f;++X){let G=j+X*A;if(G<0||G>=p.inHeight)continue;let ee=X*h[0],Y=O+G*u[1];for(let se=0;se<p.outWidth;++se){let te=U+se*x.strides[2],le=se*p.strideWidth-b;for(let Q=0;Q<m;++Q){let pe=le+Q*y;if(pe<0||pe>=p.inWidth)continue;let ue=ee+Q*h[1],ye=Y+pe*p.inChannels,me=te,Ne=ue;for(let Ce=0;Ce<p.inChannels;++Ce){let De=N[ye+Ce];for(let Pe=0;Pe<_;++Pe)E[me+Pe]+=De*T[Ne+Pe];me+=_,Ne+=_}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var RM={kernelName:vs,backendName:"cpu",kernelFunc:Lw};function FM(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;ke([a,s],"depthwiseConv2dNativeBackpropFilter");let h=R.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,A=new Pt(h.filterShape,"float32"),y=h.padInfo.left,g=h.padInfo.top,w=h.outChannels/h.inChannels,b=n.data.get(a.dataId).values,_=new Pt(a.shape,a.dtype,b),x=n.data.get(s.dataId).values,N=new Pt(s.shape,s.dtype,x);for(let T=0;T<f;++T){let E=Math.max(0,Math.ceil((g-T)/d)),M=Math.min(h.outHeight,(h.inHeight+g-T)/d);for(let O=0;O<m;++O){let W=Math.max(0,Math.ceil((y-O)/p)),V=Math.min(h.outWidth,(h.inWidth+y-O)/p);for(let U=0;U<h.outChannels;++U){let j=Math.trunc(U/w),X=U%w,G=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=E;Y<M;++Y){let se=T+Y*d-g;for(let te=W;te<V;++te){let le=O+te*p-y;G+=_.get(ee,se,le,j)*N.get(ee,Y,te,U)}}A.set(G,T,O,j,X)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var MM={kernelName:jh,backendName:"cpu",kernelFunc:FM};function $M(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;ke([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),d=v.computeStrides(s.shape),p=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new Pt(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,w=n.data.get(a.dataId).values,[b,_,x]=h,N=n.data.get(s.dataId).values,[T,E,M]=d,{batchSize:O,filterHeight:W,filterWidth:V,inChannels:U,inHeight:j,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:se,strideWidth:te}=p,le=W-1-p.padInfo.top,Q=V-1-p.padInfo.left,pe=G/U;for(let ue=0;ue<O;++ue)for(let ye=0;ye<U;++ye)for(let me=0;me<j;++me){let Ne=me-le,Ce=Math.max(0,Math.ceil(Ne/se)),De=Math.min(ee,(W+Ne)/se);for(let Pe=0;Pe<X;++Pe){let Oe=Pe-Q,nt=Math.max(0,Math.ceil(Oe/te)),rt=Math.min(Y,(V+Oe)/te),lt=0;for(let Je=Ce;Je<De;++Je){let pt=Je*se-Ne;for(let je=nt;je<rt;++je){let yn=je*te-Oe,vt=b*ue+_*Je+x*je,Gn=T*(W-1-pt)+E*(V-1-yn)+M*ye;for(let Jt=0;Jt<pe;++Jt){let gn=ye*pe+Jt,qn=w[vt+gn],Mn=N[Gn+Jt];lt+=qn*Mn}}}m[A*ue+y*me+g*Pe+ye]=lt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var DM={kernelName:Hh,backendName:"cpu",kernelFunc:$M};function OM(e){let{inputs:t,backend:n}=e,{x:r}=t,a=v.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=Ve([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 zM={kernelName:Gh,backendName:"cpu",kernelFunc:OM},PM={kernelName:ku,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:f,inWidth:m,inChannels:A,outHeight:y,outWidth:g,padInfo:w,strideHeight:b,strideWidth:_,filterHeight:x,filterWidth:N,dilationHeight:T,dilationWidth:E,outShape:M}=R.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),O=v.sizeFromShape(M),W=M.length,V=v.getArrayFromDType(r.dtype,O);for(let U=0;U<p;++U)for(let j=0;j<y;++j){let X=j*b-w.top;for(let G=0;G<g;++G){let ee=G*_-w.left;for(let Y=0;Y<A;++Y){let se=Number.MIN_SAFE_INTEGER;for(let le=0;le<x;++le){let Q=X+le*T;if(Q>=0&&Q<f)for(let pe=0;pe<N;++pe){let ue=ee+pe*E;if(ue>=0&&ue<m){let ye=v.locToIndex([U,Q,ue,Y],u,v.computeStrides(r.shape)),me=v.locToIndex([le,pe,Y],d,v.computeStrides(a.shape)),Ne=c[ye]+h[me];Ne>se&&(se=Ne)}}}let te=v.locToIndex([U,j,G,Y],W,v.computeStrides(M));V[te]=se}}}return{dataId:l.write(v.toTypedArray(V,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},LM={kernelName:Xh,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=v.toNestedArray(r.shape,c.data.get(r.dataId).values),h=v.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:w,strideWidth:b,filterHeight:_,filterWidth:x,dilationHeight:N,dilationWidth:T,outShape:E}=R.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===E.length,()=>`Error in ${Xh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=v.toNestedArray(E,c.data.get(s.dataId).values),O=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let W=0;W<d;++W)for(let V=0;V<A;++V){let U=V*w-g.top;for(let j=0;j<y;++j){let X=j*b-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=0,se=0;for(let te=0;te<_;++te){let le=U+te*N;if(le>=0&&le<p)for(let Q=0;Q<x;++Q){let pe=X+Q*T;if(pe>=0&&pe<f){let ue=u[W][le][pe][G]+h[te][Q][G];ue>ee&&(ee=ue,Y=te,se=Q)}}}O[Y][se][G]+=M[W][V][j][G]}}}return{dataId:c.write(v.toTypedArray(O,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},WM={kernelName:qh,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=v.toNestedArray(r.shape,c.data.get(r.dataId).values),h=v.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:w,strideWidth:b,filterHeight:_,filterWidth:x,dilationHeight:N,dilationWidth:T,outShape:E}=R.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===E.length,()=>`Error in ${qh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=v.toNestedArray(E,c.data.get(s.dataId).values),O=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let W=0;W<d;++W)for(let V=0;V<A;++V){let U=V*w-g.top;for(let j=0;j<y;++j){let X=j*b-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=U<0?0:U,se=X<0?0:X;for(let te=0;te<_;++te){let le=U+te*N;if(le>=0&&le<p)for(let Q=0;Q<x;++Q){let pe=X+Q*T;if(pe>=0&&pe<f){let ue=u[W][le][pe][G]+h[te][Q][G];ue>ee&&(ee=ue,Y=le,se=pe)}}}O[W][Y][se][G]+=M[W][V][j][G]}}}return{dataId:c.write(v.toTypedArray(O,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function BM(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;ke([r,a],"eluGrad");let s=new Float32Array(v.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(a.shape,"float32",s)}var VM={kernelName:Kh,backendName:"cpu",kernelFunc:BM},UM=$t((e,t)=>e===t?1:0),Ww=Xt(xo,UM,null,"bool"),jM={kernelName:xo,backendName:"cpu",kernelFunc:Ww},HM=R.ERF_P,GM=R.ERF_A1,qM=R.ERF_A2,XM=R.ERF_A3,KM=R.ERF_A4,ZM=R.ERF_A5,YM=ot(go,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+HM*n);return t*(1-((((ZM*r+KM)*r+XM)*r+qM)*r+GM)*r*Math.exp(-n*n))}),JM={kernelName:go,backendName:"cpu",kernelFunc:YM};function cp(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&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),gt({inputs:{x:a},backend:n,attrs:{shape:o}})}var QM={kernelName:wo,backendName:"cpu",kernelFunc:cp},e$=$t((e,t)=>e/t),Qm=Xt(ks,e$),eA={kernelName:ks,backendName:"cpu",kernelFunc:Qm};function Bw(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=v.sizeFromShape(c),h=v.getTypedArrayFromDType("float32",u),d=v.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]}}),w=Pn({inputs:{real:y,imag:g},backend:n}),{real:b,imag:_}=t$(w,t,n),x=R.mergeRealAndImagArrays(b,_);for(let N=0;N<s;N++){let T=R.getComplexWithIndex(x,N);h[A*s+N]=T.real,d[A*s+N]=T.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(w)}let p=n.makeTensorInfo(c,"float32",h),f=n.makeTensorInfo(c,"float32",d),m=Pn({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function t$(e,t,n){let r=v.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(n$(r)){let o=tA(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",v.createScalarValue(r,"float32")),d=jr({inputs:{x:h},backend:n}),p=eA.kernelFunc({inputs:{a:c,b:h},backend:n}),f=eA.kernelFunc({inputs:{a:u,b:d},backend:n}),m=n.data.get(p.dataId).values,A=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:A}}return o}else{let o=R.mergeRealAndImagArrays(s,i),l=r$(o,r,t);return R.splitRealAndImagArrays(l)}}function n$(e){return(e&e-1)==0}function tA(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=R.mergeRealAndImagArrays(e,t),i=n/2,o=R.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],h=a.makeTensorInfo(u,"float32",l),d=a.makeTensorInfo(u,"float32",c),p=Pn({inputs:{real:h,imag:d},backend:a}),f=R.complexWithOddIndex(s),m=f.real,A=f.imag,y=[m.length],g=a.makeTensorInfo(y,"float32",m),w=a.makeTensorInfo(y,"float32",A),b=Pn({inputs:{real:g,imag:w},backend:a}),_=tA(l,c,i,r,a),x=_.real,N=_.imag,T=[x.length],E=a.makeTensorInfo(T,"float32",x),M=a.makeTensorInfo(T,"float32",N),O=Pn({inputs:{real:E,imag:M},backend:a}),W=tA(m,A,i,r,a),V=W.real,U=W.imag,j=[V.length],X=a.makeTensorInfo(j,"float32",V),G=a.makeTensorInfo(j,"float32",U),ee=Pn({inputs:{real:X,imag:G},backend:a}),Y=R.exponents(n,r),se=[Y.real.length],te=a.makeTensorInfo(se,"float32",Y.real),le=a.makeTensorInfo(se,"float32",Y.imag),Q=Pn({inputs:{real:te,imag:le},backend:a}),pe=Km({inputs:{a:Q,b:ee},backend:a}),ue=dc({inputs:{a:O,b:pe},backend:a}),ye=Zm({inputs:{a:O,b:pe},backend:a}),me=bi({inputs:{input:ue},backend:a}),Ne=bi({inputs:{input:ye},backend:a}),Ce=El({inputs:{input:ue},backend:a}),De=El({inputs:{input:ye},backend:a}),Pe=Cl({inputs:[me,Ne],backend:a,attrs:{axis:0}}),Oe=Cl({inputs:[Ce,De],backend:a,attrs:{axis:0}}),nt=a.data.get(Pe.dataId).values,rt=a.data.get(Oe.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(w),a.disposeIntermediateTensorInfo(b),a.disposeIntermediateTensorInfo(E),a.disposeIntermediateTensorInfo(M),a.disposeIntermediateTensorInfo(O),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(G),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(le),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(ue),a.disposeIntermediateTensorInfo(ye),a.disposeIntermediateTensorInfo(me),a.disposeIntermediateTensorInfo(Ce),a.disposeIntermediateTensorInfo(Ne),a.disposeIntermediateTensorInfo(De),a.disposeIntermediateTensorInfo(Pe),a.disposeIntermediateTensorInfo(Oe),{real:nt,imag:rt}}function r$(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=R.exponent(a*o,t,n),c=R.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),R.assignToTypedArray(r,s,i,a)}return r}function a$(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=gt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Bw(o,!1,n),c=gt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var s$={kernelName:Zh,backendName:"cpu",kernelFunc:a$};function nA(e){let{backend:t,attrs:n}=e,{shape:r,value:a,dtype:s}=n,i=s||v.inferDtype(a),o=v.getArrayFromDType(i,v.sizeFromShape(r));return i$(o,a,i),t.makeTensorInfo(r,i,o)}var o$={kernelName:Iu,backendName:"cpu",kernelFunc:nA};function i$(e,t,n){e.fill(t)}var l$={kernelName:_o,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(r.shape)),[i,o,l,c]=r.shape,u=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let d=h*l*o*c;for(let p=0;p<o;p++){let f=p*(l*c);for(let m=0;m<l;m++){let A=m*c;for(let y=0;y<c;y++){let g=[i,p,m,y][2],w=Math.round(l-g),b=d+f+A+y,_=u[b];if(w>=0&&w<l){let x=w*c,N=d+f+x+y;_=u[N]}s[b]=_}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},u$=$t((e,t)=>Math.floor(e/t)),c$=Xt(Ss,u$,null,"int32"),h$={kernelName:Ss,backendName:"cpu",kernelFunc:c$};function d$(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:f}=r,m=Pw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=dc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=Ym(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var p$={kernelName:ii,backendName:"cpu",kernelFunc:d$};function f$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=Lw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=dc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=Ym(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var m$={kernelName:oi,backendName:"cpu",kernelFunc:f$};function A$(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,c,u,h]=R.prepareAndValidate(r,a);if(c===0)return n.makeTensorInfo(l,r.dtype,[]);let d=Ve([c,u],r.dtype),p=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m<c;m++){let A=[],y=0;for(let g=0;g<o;g++){let w=p[m*o+g];y+=w*h[g],A.push(w)}if(y<0||y>=s/u)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;g<u;g++)d.values[m*u+g]=f[y*u+g]}return n.makeTensorInfo(l,d.dtype,d.values)}var y$={kernelName:ko,backendName:"cpu",kernelFunc:A$};function g$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r;ke([a,s],"gatherV2");let l=o;o==null&&(l=0);let c=v.sizeFromShape(s.shape),u=v.parseAxisParam(i,a.shape)[0],h=R.segment_util.collectGatherOpShapeInfo(a,s,u,l),d=gt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),p=gt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],m=n.bufferSync(p),A=n.bufferSync(d),y=cw(A,m,f);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var x$={kernelName:vo,backendName:"cpu",kernelFunc:g$},w$=$t((e,t)=>e>=t?1:0),b$=Xt(Es,w$,null,"bool"),_$={kernelName:Es,backendName:"cpu",kernelFunc:b$};function v$(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=gt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Bw(o,!0,n),c=gt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var k$={kernelName:Yh,backendName:"cpu",kernelFunc:v$},I$=ot(No,e=>Number.isFinite(e)?1:0,"bool"),N$={kernelName:No,backendName:"cpu",kernelFunc:I$},S$=ot(So,e=>Math.abs(e)===Infinity?1:0,"bool"),T$={kernelName:So,backendName:"cpu",kernelFunc:S$},E$=ot(To,e=>Number.isNaN(e)?1:0,"bool"),C$={kernelName:To,backendName:"cpu",kernelFunc:E$},R$=$t((e,t)=>e<=t?1:0),F$=Xt(Co,R$,null,"bool"),M$={kernelName:Co,backendName:"cpu",kernelFunc:F$};function $$(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=pw(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var D$={kernelName:Qh,backendName:"cpu",kernelFunc:$$},O$=ot(Ro,e=>Math.log1p(e)),z$={kernelName:Ro,backendName:"cpu",kernelFunc:O$},P$=$t((e,t)=>e&&t),L$=Xt(Fo,P$,null,"bool"),W$={kernelName:Fo,backendName:"cpu",kernelFunc:L$},B$=ot(Nu,e=>e?0:1,"bool"),V$={kernelName:Nu,backendName:"cpu",kernelFunc:B$},U$=$t((e,t)=>e||t),j$=Xt(Su,U$,null,"bool"),H$={kernelName:Su,backendName:"cpu",kernelFunc:j$};function G$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;ke(a,"LRN");let c=a.shape[3],u=c-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function f(m){let A=m%c,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,u),w=0;for(;y<=g;y++){let b=h[y];w+=b*b}return w}for(let m=0;m<d;m++){let A=f(m),y=h[m]*Math.pow(i+o*A,-l);p[m]=y}return n.makeTensorInfo(a.shape,a.dtype,p)}var q$={kernelName:Tu,backendName:"cpu",kernelFunc:G$};function X$(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;ke(i,"LRNGrad");let h=v.sizeFromShape(i.shape),d=i.shape[3],p=n.data.get(i.dataId).values,f=n.data.get(a.dataId).values,m=n.data.get(s.dataId).values,A=new Float32Array(h),y=h;for(let g=0;g<y;g++){let w=g%d,b=g-w+Math.max(0,w-o),_=g-w+Math.min(d,w+o+1),x=0;for(let N=b;N<_;N++)x+=Math.pow(f[N],2);x=c*x+l;for(let N=b;N<_;N++){let T=-2*c*u*f[N]*m[g]/x;g===N&&(T+=Math.pow(x,-u)),T*=p[g],A[N]+=T}}return n.makeTensorInfo(i.shape,a.dtype,A)}var K$={kernelName:ed,backendName:"cpu",kernelFunc:X$};function Vw(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=v.parseAxisParam(s,l),h=u,d=R.getAxesPermutation(h,c),p=o.data.get(a.dataId).values;if(d!=null){let b=new Array(c);for(let _=0;_<b.length;_++)b[_]=l[d[_]];p=Gm(p,l,a.dtype,d,b),h=R.getInnerMostAxes(h.length,c),l=b}ke(a,"max"),R.assertAxesAreInnerMostDims("max",h,c);let[f,m]=R.computeOutAndReduceShapes(l,h),A=v.sizeFromShape(m),y=mw(p,A,f,a.dtype),g=o.write(y,f,a.dtype),w=f;return i&&(w=R.expandShapeToKeepDim(f,u)),{dataId:g,shape:w,dtype:a.dtype}}var Z$={kernelName:Ms,backendName:"cpu",kernelFunc:Vw};function Y$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ke(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))h=jr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),f=Jm(d,a.shape,a.dtype,p,u,"max");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var J$={kernelName:Ds,backendName:"cpu",kernelFunc:Y$};function Q$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;ke(a,"maxPool3d");let u=R.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=zw(h,a.shape,a.dtype,v.computeStrides(a.shape),u,"max");return n.makeTensorInfo(d.shape,"float32",d.values)}var eD={kernelName:Eu,backendName:"cpu",kernelFunc:Q$};function tD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;ke([a,s],"maxPool3DGrad");let u=R.computePool3DInfo(s.shape,i,o,1,l,c),h=n.bufferSync(s),d=BF(h,u),p=u.strideDepth,f=u.strideHeight,m=u.strideWidth,A=u.dilationDepth,y=u.dilationHeight,g=u.dilationWidth,w=u.effectiveFilterDepth,b=u.effectiveFilterHeight,_=u.effectiveFilterWidth,x=w-1-u.padInfo.front,N=_-1-u.padInfo.left,T=b-1-u.padInfo.top,E=Ve(s.shape,"float32"),M=n.bufferSync(a);for(let O=0;O<u.batchSize;++O)for(let W=0;W<u.inChannels;++W)for(let V=0;V<u.inDepth;++V)for(let U=0;U<u.inHeight;++U)for(let j=0;j<u.inWidth;++j){let X=V-x,G=U-T,ee=j-N,Y=0;for(let se=0;se<w;se+=A){let te=(X+se)/p;if(!(te<0||te>=u.outDepth||Math.floor(te)!==te))for(let le=0;le<b;le+=y){let Q=(G+le)/f;if(!(Q<0||Q>=u.outHeight||Math.floor(Q)!==Q))for(let pe=0;pe<_;pe+=g){let ue=(ee+pe)/m;if(ue<0||ue>=u.outWidth||Math.floor(ue)!==ue)continue;let ye=w*b*_-1-d.get(O,te,Q,ue,W),me=se*b*_+le*_+pe,Ne=ye===me?1:0;Ne!==0&&(Y+=M.get(O,te,Q,ue,W)*Ne)}}}E.set(Y,O,V,U,j,W)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var nD={kernelName:nd,backendName:"cpu",kernelFunc:tD};function rD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;ke([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,c,1,u,h),p=n.data.get(o.dataId).values,f=Ve(d.outShape,o.dtype,Ow(p,o.shape,o.dtype,d).values),m=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,w=d.effectiveFilterHeight,b=d.effectiveFilterWidth,_=b-1-d.padInfo.left,x=w-1-d.padInfo.top,N=Ve(o.shape,"float32"),T=n.data.get(a.dataId).values,E=Ve(a.shape,"float32",T);for(let M=0;M<d.batchSize;++M)for(let O=0;O<d.inChannels;++O)for(let W=0;W<d.inHeight;++W)for(let V=0;V<d.inWidth;++V){let U=W-x,j=V-_,X=0;for(let G=0;G<w;G+=y){let ee=(U+G)/m;if(!(ee<0||ee>=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y<b;Y+=g){let se=(j+Y)/A;if(se<0||se>=d.outWidth||Math.floor(se)!==se)continue;let te=w*b-1-f.get(M,ee,se,O),le=G*b+Y,Q=te===le?1:0;Q!==0&&(X+=E.get(M,ee,se,O)*Q)}}N.set(X,M,W,V,O)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var aD={kernelName:td,backendName:"cpu",kernelFunc:rD};function sD(e,t,n,r,a){let s=v.computeStrides(t),i=Jm(e,t,n,s,a,"max"),o=Ow(e,t,n,a,!0,r);return[i.values,o.values]}var iD={kernelName:rd,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ke(r,"MaxPoolWithArgmax");let c=l.data.get(r.dataId).values,u=R.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=sD(c,r.shape,r.dtype,o,u),p=l.write(h,u.outShape,r.dtype),f=l.write(d,u.outShape,r.dtype);return[{dataId:p,shape:u.outShape,dtype:r.dtype},{dataId:f,shape:u.outShape,dtype:"int32"}]}};function hp(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ke(a,"sum");let o;a.dtype==="bool"?o=ja({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=jr({inputs:{x:a},backend:n});let l=o.shape.length,c=v.parseAxisParam(s,o.shape),u=R.getAxesPermutation(c,l),h=c,d=o;u!=null&&(d=hr({inputs:{x:o},backend:n,attrs:{perm:u}}),h=R.getInnerMostAxes(h.length,l)),R.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=R.computeOutAndReduceShapes(d.shape,h),m=R.upcastType(d.dtype,"int32"),A=up(n,p,m),y=v.sizeFromShape(f),g=n.data.get(A.dataId).values,w=n.data.get(d.dataId).values;for(let b=0;b<g.length;++b){let _=b*y,x=0;for(let N=0;N<y;++N)x+=w[_+N];g[b]=x}if(i){let b=R.expandShapeToKeepDim(A.shape,c),_=A;A=gt({inputs:{x:A},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(_)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),A}var oD={kernelName:Qs,backendName:"cpu",kernelFunc:hp};function lD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=v.parseAxisParam(s,a.shape),l=R.computeOutAndReduceShapes(a.shape,o)[1],c=v.sizeFromShape(l),u=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(h);let d=ja({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 f=hp({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var uD={kernelName:Os,backendName:"cpu",kernelFunc:lD};function cD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ke(a,"min");let o=v.parseAxisParam(s,a.shape),l=o,c=R.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=hr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",l,u.shape.length);let[h,d]=R.computeOutAndReduceShapes(u.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let b=0;b<p;++b){let _=m[g+b];_<w&&(w=_)}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var hD={kernelName:zs,backendName:"cpu",kernelFunc:cD};function dD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,mode:i}=r;ke(a,"mirrorPad");let o=s.map((g,w)=>g[0]+a.shape[w]+g[1]),l=s.map(g=>g[0]),c=s.map((g,w)=>g[0]+a.shape[w]),u=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=v.computeStrides(a.shape),f=v.sizeFromShape(o),m=o.length,A=v.computeStrides(o),y=v.getTypedArrayFromDType(a.dtype,f);for(let g=0;g<f;g++){let w=v.indexToLoc(g,m,A);for(let _=0;_<m;_++)w[_]<l[_]?w[_]=l[_]*2-w[_]-u:w[_]>=c[_]&&(w[_]=(c[_]-1)*2-w[_]+u);w=w.map((_,x)=>_-l[x]);let b=v.locToIndex(w,d,p);y[g]=h[b]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var pD={kernelName:Cu,backendName:"cpu",kernelFunc:dD},fD=$t((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),mD=Xt(Mo,fD),AD={kernelName:Mo,backendName:"cpu",kernelFunc:mD},yD=to(J2());function Uw(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=v.parseAxisParam([o],a.shape),c=Vw({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=R.expandShapeToKeepDim(c.shape,l),h=gt({inputs:{x:c},backend:n,attrs:{shape:u}}),d=Zm({inputs:{a,b:h},backend:n}),p=Tw({inputs:{x:d},backend:n}),f=hp({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=Qm({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var gD={kernelName:ei,backendName:"cpu",kernelFunc:Uw};function xD(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r;ke(a,"multinomial");let l=o?a:Uw({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=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let f=0;f<c;++f){let m=f*u,A=new Float32Array(u-1);A[0]=h[m];for(let w=1;w<A.length;++w)A[w]=A[w-1]+h[m+w];let y=yD.alea(i.toString()),g=f*s;for(let w=0;w<s;++w){let b=y();p[g+w]=A.length;for(let _=0;_<A.length;_++)if(b<A[_]){p[g+w]=_;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",p)}var wD={kernelName:ad,backendName:"cpu",kernelFunc:xD},bD=Ur.nonMaxSuppressionV3Impl;function _D(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r;ke(a,"NonMaxSuppression");let c=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:h}=bD(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var vD={kernelName:Oo,backendName:"cpu",kernelFunc:_D},kD=Ur.nonMaxSuppressionV4Impl;function ID(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r;ke(a,"NonMaxSuppressionPadded");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:p}=kD(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var ND={kernelName:zo,backendName:"cpu",kernelFunc:ID},SD=Ur.nonMaxSuppressionV5Impl;function TD(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r;ke(a,"NonMaxSuppressionWithScore");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=SD(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ED={kernelName:Po,backendName:"cpu",kernelFunc:TD};function CD(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;ke(a,"oneHot");let l=v.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 RD={kernelName:Ws,backendName:"cpu",kernelFunc:CD};function dp(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=dp({inputs:{x:a},backend:n}),i=El({inputs:{input:r},backend:n}),o=dp({inputs:{x:i},backend:n}),l=Pn({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 FD={kernelName:nl,backendName:"cpu",kernelFunc:dp};function jw(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=jw({inputs:{x:a},backend:n}),i=El({inputs:{input:r},backend:n}),o=dp({inputs:{x:i},backend:n}),l=Pn({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 MD={kernelName:Lo,backendName:"cpu",kernelFunc:jw};function Hw(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return cp({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=cp({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=Cl({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var $D={kernelName:Wo,backendName:"cpu",kernelFunc:Hw};function DD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r;ke(a,"pad");let o=s.map((y,g)=>y[0]+a.shape[g]+y[1]),l=s.map(y=>y[0]),c=n.data.get(a.dataId).values,u=v.sizeFromShape(a.shape),h=a.shape.length,d=v.computeStrides(a.shape),p=v.sizeFromShape(o),f=o.length,m=v.computeStrides(o),A=v.getTypedArrayFromDType(a.dtype,p);i!==0&&A.fill(i);for(let y=0;y<u;y++){let g=v.indexToLoc(y,h,d).map((b,_)=>b+l[_]),w=v.locToIndex(g,f,m);A[w]=c[y]}return{dataId:n.write(A,o,a.dtype),shape:o,dtype:a.dtype}}var Gw={kernelName:Bs,backendName:"cpu",kernelFunc:DD},OD=$t((e,t)=>Math.pow(e,t)),zD=Xt(Vs,OD),PD={kernelName:Vs,backendName:"cpu",kernelFunc:zD};function LD(e){let{backend:t,attrs:n}=e,{start:r,stop:a,dtype:s,step:i}=n,o=qm(r,a,i,s);return t.makeTensorInfo([o.length],s,o)}var WD={kernelName:Ru,backendName:"cpu",kernelFunc:LD},BD=ot(Vo,e=>1/e),VD={kernelName:Vo,backendName:"cpu",kernelFunc:BD};function UD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;ke(a,"resizeBilinear");let l=v.computeStrides(a.shape),[c,u]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(v.sizeFromShape([h,c,u,f])),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],w=0,b=y[0]/g[0],_=y[1]/g[1];for(let x=0;x<h;x++)for(let N=0;N<c;N++){let T;i?T=b*(N+.5)-.5:T=b*N;let E=Math.max(0,Math.floor(T)),M=T-E,O=Math.min(d-1,Math.ceil(T)),W=x*l[0]+E*l[1],V=x*l[0]+O*l[1];for(let U=0;U<u;U++){let j;i?j=_*(U+.5)-.5:j=_*U;let X=Math.max(0,Math.floor(j)),G=j-X,ee=Math.min(p-1,Math.ceil(j)),Y=W+X*l[2],se=V+X*l[2],te=W+ee*l[2],le=V+ee*l[2];for(let Q=0;Q<f;Q++){let pe=m[Y+Q],ue=m[se+Q],ye=m[te+Q],me=m[le+Q],Ne=pe+(ye-pe)*G,Ce=ue+(me-ue)*G,De=Ne+(Ce-Ne)*M;A[w++]=De}}}return n.makeTensorInfo([h,c,u,f],"float32",A)}var jD={kernelName:Hs,backendName:"cpu",kernelFunc:UD};function HD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;ke([s,a],"resizeBilinearGrad");let o=v.computeStrides(a.shape),[l,c,u,h]=a.shape,[,d,p]=s.shape,f=new Float32Array(l*c*u*h),m=[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=m[0]/A[0],g=m[1]/A[1],w=n.data.get(s.dataId).values,b=0;for(let _=0;_<l;_++){let x=_*o[0];for(let N=0;N<d;N++){let T=N*y,E=Math.floor(T),M=Math.min(Math.ceil(T),c-1),O=x+E*o[1],W=x+M*o[1],V=T-E,U=1-V;for(let j=0;j<p;j++){let X=j*g,G=Math.floor(X),ee=Math.min(Math.ceil(X),u-1),Y=X-G,se=1-Y,te=O+G*o[2],le=O+ee*o[2],Q=W+G*o[2],pe=W+ee*o[2],ue=U*se,ye=U*Y,me=V*se,Ne=V*Y;for(let Ce=0;Ce<h;Ce++){let De=w[b++];f[te+Ce]+=De*ue,f[le+Ce]+=De*ye,f[Q+Ce]+=De*me,f[pe+Ce]+=De*Ne}}}}return n.makeTensorInfo([l,u,c,h],"float32",f)}var GD={kernelName:od,backendName:"cpu",kernelFunc:HD};function qD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;ke(a,"resizeNearestNeighbor");let l=v.computeStrides(a.shape),[c,u]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(h*c*u*f),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],w=y[0]/g[0],b=y[1]/g[1],_=0;for(let x=0;x<h;x++){let N=x*l[0];for(let T=0;T<c;T++){let E=i?w*(T+.5):w*T,M=Math.min(d-1,s?Math.round(E):Math.floor(E));i&&(M=Math.max(0,M));let O=N+M*l[1];for(let W=0;W<u;W++){let V=i?b*(W+.5):b*W,U=Math.min(p-1,s?Math.round(V):Math.floor(V));i&&(U=Math.max(0,U));let j=O+U*l[2];for(let X=0;X<f;X++){let G=m[j+X];A[_++]=G}}}}return n.makeTensorInfo([h,c,u,f],a.dtype,A)}var XD={kernelName:Fu,backendName:"cpu",kernelFunc:qD};function KD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;ke([s,a],"resizeNearestNeighborGrad");let o=v.computeStrides(a.shape),l=v.computeStrides(s.shape),[c,u,h,d]=a.shape,[,p,f]=s.shape,m=new Float32Array(c*u*h*d),A=n.data.get(s.dataId).values,y=[i&&p>1?u-1:u,i&&f>1?h-1:h],g=[i&&p>1?p-1:p,i&&f>1?f-1:f],w=y[0]/g[0],b=y[1]/g[1],_=1/w,x=1/b,N=Math.ceil(_)*2+2,T=Math.ceil(x)*2+2;for(let E=0;E<c;E++){let M=E*o[0];for(let O=0;O<u;O++){let W=M+O*o[1],V=Math.floor(O*_),U=Math.floor(V-N/2);for(let j=0;j<h;j++){let X=W+j*o[2],G=Math.floor(j*x),ee=Math.floor(G-T/2);for(let Y=0;Y<d;Y++){let se=0;for(let te=0;te<N;te++){let le=te+U;if(le<0||le>=p)continue;let Q=M+le*l[1],pe=le*w,ue=Math.min(u-1,i?Math.round(pe):Math.floor(pe));if(O===ue)for(let ye=0;ye<T;ye++){let me=ye+ee;if(me<0||me>=f)continue;let Ne=Q+me*l[2],Ce=me*b,De=Math.min(h-1,i?Math.round(Ce):Math.floor(Ce));j===De&&(se+=A[Ne+Y])}}m[X+Y]=se}}}}return n.makeTensorInfo(a.shape,a.dtype,m)}var ZD={kernelName:id,backendName:"cpu",kernelFunc:KD};function YD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r;ke(a,"reverse");let i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return jr({inputs:{x:a},backend:n});let l=new Pt(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 JD={kernelName:qs,backendName:"cpu",kernelFunc:YD},QD={kernelName:rl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(r.shape)),[c,u,h,d]=r.shape,[p,f]=R.getImageCenter(i,u,h),m=255,A=Math.sin(a),y=Math.cos(a),g=o.data.get(r.dataId).values;for(let w=0;w<c;w++){let b=w*h*u*d;for(let _=0;_<u;_++){let x=_*(h*d);for(let N=0;N<h;N++){let T=N*d;for(let E=0;E<d;E++){let M=[c,_,N,E],O=M[2],W=M[1],V=(O-p)*y-(W-f)*A,U=(O-p)*A+(W-f)*y;V=Math.round(V+p),U=Math.round(U+f);let j=s;if(typeof s!="number"&&(E===3?j=m:j=s[E]),V>=0&&V<h&&U>=0&&U<u){let G=U*(h*d),ee=V*d,Y=b+G+ee+E;j=g[Y]}let X=b+x+T+E;l[X]=j}}}}return{dataId:o.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},eO=ot(Xs,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}),tO={kernelName:Xs,backendName:"cpu",kernelFunc:eO};function qw(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 Ve(n,t.dtype);let p=Ve(u,t.dtype);p.values.fill(l);for(let f=0;f<s;f++){let m=[],A=0;for(let y=0;y<i;y++){let g=h[f*i+y];m.push(g),A+=g*o[y]}if(A<0||A>=r/a)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<a;y++)c?p.values[A*a+y]+=d[f*a+y]:p.values[A*a+y]=t.rank===0?d[0]:d[f*a+y]}return p}function nO(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}=R.calculateShapes(s,a,i),d=!0,p=n.bufferSync(a),f=n.bufferSync(s),m=qw(p,f,i,h,c,l,o,u,0,d);return n.makeTensorInfo(i,m.dtype,m.values)}var rO={kernelName:jo,backendName:"cpu",kernelFunc:nO};function aO(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t;ke([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=or(a.dtype,s.dtype),h=v.makeZerosTypedArray(v.sizeFromShape(a.shape),u),d=0,p=i===0||i>1||a.shape.length===1?1:v.sizeFromShape(a.shape.slice(1));for(let f=0;f<o.length;f++)for(let m=0;m<p;m++)o[f]===1?h[d++]=l[f]:h[d++]=c[f];return n.makeTensorInfo(a.shape,u,h)}var sO={kernelName:Ho,backendName:"cpu",kernelFunc:aO},iO=R.SELU_SCALEALPHA,oO=R.SELU_SCALE,lO=ot(Go,e=>e>=0?oO*e:iO*(Math.exp(e)-1)),uO={kernelName:Go,backendName:"cpu",kernelFunc:lO},cO=ot(Ys,e=>1/(1+Math.exp(-e))),hO={kernelName:Ys,backendName:"cpu",kernelFunc:cO},dO=ot(Ko,e=>e<0?-1:e>0?1:0),pO={kernelName:Ko,backendName:"cpu",kernelFunc:dO},fO=ot(Zs,e=>Math.sin(e)),mO={kernelName:Zs,backendName:"cpu",kernelFunc:fO},AO=ot(Xo,e=>Math.sinh(e)),yO={kernelName:Xo,backendName:"cpu",kernelFunc:AO},gO=11920928955078125e-23,Xw=Math.log(gO)+2,xO=ot(Zo,e=>{let t=e>-Xw,n=e<Xw,r=Math.exp(e),a;return n?a=r:t?a=e:a=Math.log(1+r),a}),wO={kernelName:Zo,backendName:"cpu",kernelFunc:xO};function bO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;ke([a],"spaceToBatchND");let o=v.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=Gw.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),u=R.getReshaped(c.shape,s,o,!1),h=R.getPermuted(u.length,s.length,!1),d=R.getReshapedPermuted(c.shape,s,o,!1),p=gt({inputs:{x:c},backend:n,attrs:{shape:u}}),f=hr({inputs:{x:p},backend:n,attrs:{perm:h}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}var _O={kernelName:Mu,backendName:"cpu",kernelFunc:bO};function vO(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}=R.calculateShapes(s,a,o),p=!1,f=n.bufferSync(a),m=n.bufferSync(s),A=n.data.get(i.dataId).values[0],y=qw(f,m,o,d,u,c,l,h,A,p);return n.makeTensorInfo(o,y.dtype,y.values)}var kO={kernelName:ld,backendName:"cpu",kernelFunc:vO};function IO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.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 NO={kernelName:Yo,backendName:"cpu",kernelFunc:IO},SO=ot(Js,e=>Math.sqrt(e)),TO={kernelName:Js,backendName:"cpu",kernelFunc:SO},EO={kernelName:$u,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;ke(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}}},CO=ot(Fa,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),RO={kernelName:Fa,backendName:"cpu",kernelFunc:CO};function FO(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;ke(a,"stridedSlice");let{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=hn.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=gt({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=_i({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=gt({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else{let x=n.bufferSync(w),N=vw(g,x,m,f);b=n.makeTensorInfo(N.shape,N.dtype,N.values)}let _=gt({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var MO={kernelName:Jo,backendName:"cpu",kernelFunc:FO},$O=ot(Qo,e=>Math.tan(e)),DO={kernelName:Qo,backendName:"cpu",kernelFunc:$O},OO=ot(ri,e=>Math.tanh(e)),zO={kernelName:ri,backendName:"cpu",kernelFunc:OO};function PO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;ke(a,"tile");let i=Iw(n.bufferSync(a),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var LO={kernelName:Ra,backendName:"cpu",kernelFunc:PO};function WO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r;ke(a,"topk");let o=n.data.get(a.dataId).values,[l,c]=Nw(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 BO={kernelName:el,backendName:"cpu",kernelFunc:WO};function jO(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,[f,m]=c!=null?c:[h,d],A=[u,f,m,p],y=v.computeStrides(a.shape),g=y[0],w=y[1],b=y[2],_=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(A));_.fill(l);let x=r.data.get(a.dataId).values,N=r.data.get(s.dataId).values;for(let T=0;T<u;++T){let E=s.shape[0]===1?N:N.subarray(T*8,T*8+8);for(let M=0;M<f;++M)for(let O=0;O<m;++O)for(let W=0;W<p;++W){let V,U=E[6]*O+E[7]*M+1;if(U===0)continue;let j=(E[0]*O+E[1]*M+E[2])/U,X=(E[3]*O+E[4]*M+E[5])/U,G=Kw(j,d,o),ee=Kw(X,h,o);switch(i){case"nearest":V=VO(x,h,d,g,w,b,T,ee,G,W,l);break;case"bilinear":V=UO(x,h,d,g,w,b,T,ee,G,W,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let Y=T*g+M*w+O*b+W;_[Y]=V}return r.makeTensorInfo(A,a.dtype,_)}return{dataId:r.write(_,A,a.dtype),shape:a.shape,dtype:a.dtype}}var HO={kernelName:ud,backendName:"cpu",kernelFunc:jO};function Kw(e,t,n){switch(n){case"reflect":return GO(e,t);case"wrap":return qO(e,t);case"nearest":return KO(e,t);case"constant":default:return XO(e,t)}}function GO(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 v.clamp(0,n,t-1)}function qO(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 v.clamp(0,n,t-1)}function XO(e,t){return e}function KO(e,t){return v.clamp(0,e,t-1)}function pc(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 VO(e,t,n,r,a,s,i,o,l,c,u){let h=Math.round(o),d=Math.round(l);return pc(e,t,n,r,a,s,i,h,d,c,u)}function UO(e,t,n,r,a,s,i,o,l,c,u){let h=Math.floor(o),d=Math.floor(l),p=h+1,f=d+1,m=(f-l)*pc(e,t,n,r,a,s,i,h,d,c,u)+(l-d)*pc(e,t,n,r,a,s,i,h,f,c,u),A=(f-l)*pc(e,t,n,r,a,s,i,p,d,c,u)+(l-d)*pc(e,t,n,r,a,s,i,p,f,c,u);return(p-o)*m+(o-h)*A}function ZO(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;ke(s,"unique");let i=r.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:c}=Sw(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var YO={kernelName:cd,backendName:"cpu",kernelFunc:ZO};function JO(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 f=_i({inputs:{x:a},backend:n,attrs:{begin:u,size:h}});d[p]=gt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return d}var QO={kernelName:tl,backendName:"cpu",kernelFunc:JO};function ez(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r;ke(a,"unsortedSegmentSum");let o=a.shape.length,l=s.shape.length,c=[],u=[],h=o-l,d=s;for(let f=0;f<h;++f){let m=cp({inputs:{input:d},backend:n,attrs:{dim:f+1}});d=m,u.push(m)}for(let f=0;f<i;++f){let m=v.createScalarValue(f,"int32"),A=n.makeTensorInfo([],"int32",m),y=Ww({inputs:{a:A,b:d},backend:n}),g=ja({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),w=Km({inputs:{a:g,b:a},backend:n}),b=hp({inputs:{x:w},backend:n,attrs:{axis:0,keepDims:!1}});c.push(b),u.push(A),u.push(y),u.push(g),u.push(w),u.push(b)}let p=Hw({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var tz={kernelName:Du,backendName:"cpu",kernelFunc:ez},nz=[mF,wR,yF,xF,NR,bF,vF,IF,SF,EF,RF,MF,DF,PF,WF,UF,HF,qF,KF,pF,YF,QF,tM,kR,TR,rM,bR,sM,oM,cM,dM,lM,AM,gM,fM,wM,_M,kM,NM,TM,CM,RM,MM,DM,zM,PM,WM,LM,eA,iF,VM,jM,JM,ER,QM,RR,s$,o$,l$,MR,h$,p$,m$,y$,x$,DR,_$,_R,k$,iM,N$,T$,C$,oF,zR,M$,D$,LR,z$,W$,V$,H$,q$,K$,BR,J$,eD,nD,aD,iD,Z$,uD,hD,UR,pD,AD,wD,HR,qR,vD,ND,ED,KR,RD,MD,$D,Gw,PD,uF,JR,WD,vR,VD,cF,hF,dF,jD,GD,XD,ZD,JD,QD,tO,eF,rO,sO,uO,hO,pO,mO,yO,tF,gD,wO,_O,kO,NO,TO,EO,rF,RO,MO,sF,oD,DO,zO,LO,BO,ZR,HO,YO,QO,tz,FD];for(let e of nz)li(e);var Zw={};Le(Zw,{assertNotComplex:()=>Rl,bindCanvasToFramebuffer:()=>sz,bindColorTextureToFramebuffer:()=>fp,bindTextureToProgramUniformSampler:()=>hb,bindTextureUnit:()=>lb,bindVertexBufferToProgramAttribute:()=>rA,callAndCheck:()=>_e,canBeRepresented:()=>Yw,createFragmentShader:()=>eb,createFramebuffer:()=>ob,createProgram:()=>tb,createStaticIndexBuffer:()=>ab,createStaticVertexBuffer:()=>rb,createTexture:()=>sb,createVertexShader:()=>Qw,getBatchDim:()=>vi,getExtensionOrThrow:()=>fc,getFramebufferErrorMessage:()=>db,getMaxTexturesInShader:()=>mb,getNumChannels:()=>rz,getProgramUniformLocation:()=>cb,getProgramUniformLocationOrThrow:()=>ub,getRowsCols:()=>ki,getShapeAs3D:()=>mp,getTextureShapeFromLogicalShape:()=>pb,getWebGLDisjointQueryTimerVersion:()=>Ab,getWebGLErrorMessage:()=>Jw,getWebGLMaxTextureSize:()=>fb,hasExtension:()=>Jn,isCapableOfRenderingToFloatTexture:()=>yb,isDownloadFloatTextureEnabled:()=>gb,isReshapeFree:()=>Ac,isWebGLFenceEnabled:()=>xb,isWebGLVersionEnabled:()=>sA,linkProgram:()=>nb,resetMaxTextureSize:()=>iz,resetMaxTexturesInShader:()=>oz,unbindColorTextureFromFramebuffer:()=>aA,unbindTextureUnit:()=>az,validateFramebuffer:()=>mc,validateProgram:()=>pp,validateTextureSize:()=>ib});var Ii={},iA={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function Ap(e,t){Ii[e]=t}function Hr(e){if(!(e in Ii)){let n=lz(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],Hr(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 uz(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 lz(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=uz(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 yc;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(yc||(yc={}));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 rn;(function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(rn||(rn={}));function gc(e,t){return[t,e]}function cz(e,t){return e*t}function xc(e){let t=v.sizeFromShape(e),n=Math.ceil(t/4);return v.sizeToSquarishShape(n)}function Fl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function hz(e,t){let[n,r]=Fl(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 _e(e,t){let n=t();return J().getBool("DEBUG")&&dz(e),n}function dz(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+Jw(e,t))}var pz=596e-10,fz=65504;function Yw(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||pz<Math.abs(e)&&Math.abs(e)<fz)}function Jw(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 fc(e,t){return da(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function Qw(e,t){let n=da(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(_e(e,()=>e.shaderSource(n,t)),_e(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 eb(e,t){let n=da(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(_e(e,()=>e.shaderSource(n,t)),_e(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw mz(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var Az=/ERROR: [0-9]+:([0-9]+):/g;function mz(e,t){let n=Az.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)=>v.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 ${v.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
`))}function tb(e){return da(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function nb(e,t){if(_e(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function pp(e,t){if(_e(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function rb(e,t){let n=da(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return _e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),_e(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function ab(e,t){let n=da(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return _e(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),_e(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function rz(){return J().getNumber("WEBGL_VERSION")===2?1:4}function sb(e){return da(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function ib(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${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 ob(e){return da(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:(_e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),_e(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),_e(e,()=>e.enableVertexAttribArray(o)),!0)}function lb(e,t,n){wb(e,n),_e(e,()=>e.activeTexture(e.TEXTURE0+n)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function az(e,t){wb(e,t),_e(e,()=>e.activeTexture(e.TEXTURE0+t)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function ub(e,t,n){return da(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function cb(e,t,n){return e.getUniformLocation(t,n)}function hb(e,t,n,r){_e(e,()=>lb(e,t,r)),_e(e,()=>e.uniform1i(n,r))}function sz(e){_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),_e(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),_e(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function fp(e,t,n){_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),_e(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function aA(e,t){_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),_e(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function mc(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+db(e,t))}function db(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 ${t}`}}function da(e,t,n){let r=_e(e,()=>t());if(r==null)throw new Error(n);return r}function wb(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function vi(e,t=2){return v.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 mp(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[vi(e),...ki(e)]),t}function pb(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?v.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let r=v.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),v.sizeToSquarishShape(r).map(o=>o*2)}return v.sizeToSquarishShape(r)}function yp(e){return e%2==0}function Ac(e,t){if(e=e.slice(-2),t=t.slice(-2),v.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||yp(n)&&yp(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&yp(e[0])&&yp(t[0])}var gp,xp;function fb(e){if(gp==null){let t=Hr(e);gp=t.getParameter(t.MAX_TEXTURE_SIZE)}return gp}function iz(){gp=null}function oz(){xp=null}function mb(e){if(xp==null){let t=Hr(e);xp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,xp)}function Ab(e){if(e===0)return 0;let t,n=Hr(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(Hr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function yb(e){if(e===0)return!1;let t=Hr(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 gb(e){if(e===0)return!1;let t=Hr(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 yz(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 yz(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 xb(e){return e!==2?!1:Hr(e).fenceSync!=null}function Rl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var $e=J();$e.registerFlag("HAS_WEBGL",()=>$e.getNumber("WEBGL_VERSION")>0);$e.registerFlag("WEBGL_VERSION",()=>sA(2)?2:sA(1)?1:0);$e.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);$e.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>$e.get("WEBGL_VERSION")===2);$e.registerFlag("WEBGL_CPU_FORWARD",()=>!0);$e.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);$e.registerFlag("WEBGL_PACK",()=>$e.getBool("HAS_WEBGL"));$e.registerFlag("WEBGL_PACK_NORMALIZATION",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_CLIP",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);$e.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_REDUCE",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_LAZILY_UNPACK",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_CONV_IM2COL",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>fb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>mb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=$e.getNumber("WEBGL_VERSION");return e===0?0:Ab(e)});$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>$e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!ju.isMobile());$e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>yb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>$e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:$e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));$e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>gb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>xb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>$e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);$e.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 ${e}.`)});$e.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>ju.isMobile()&&$e.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 fn(){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 Ni(e,t,n="index"){let r=v.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=v.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}var bb=`
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;
}
`,gz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=yc.DENSE;let t=xc(e),n=fn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Ni(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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;
}
`}},xz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=yc.DENSE;let t=xc(e),n=fn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Ni(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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;
}
`}},wz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Qn.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
${bb}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},bz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Qn.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
${bb}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},_z=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=fn(),[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.);
}
`}},vz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=fn(),[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} = ${o};
}
`}},_b={};Le(_b,{bindVertexProgramAttributeStreams:()=>Rb,createBufferFromOutputTexture:()=>$b,createFloat16MatrixTexture:()=>Sb,createFloat16PackedMatrixTexture:()=>Cb,createFloat32MatrixTexture:()=>Nb,createIndexBuffer:()=>Ib,createPackedMatrixTexture:()=>Eb,createUnsignedBytesMatrixTexture:()=>Tb,createVertexBuffer:()=>kb,createVertexShader:()=>vb,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Ob,downloadFloat32MatrixFromBuffer:()=>Db,downloadMatrixFromPackedOutputTexture:()=>Pb,downloadPackedMatrixFromBuffer:()=>zb,getInternalFormatForFloat16MatrixTexture:()=>hA,getInternalFormatForFloat16PackedMatrixTexture:()=>fA,getInternalFormatForFloat32MatrixTexture:()=>cA,getInternalFormatForPackedMatrixTexture:()=>pA,getInternalFormatForUnsignedBytesMatrixTexture:()=>dA,uploadDenseMatrixToTexture:()=>Fb,uploadPixelDataToTexture:()=>Mb});function vb(e){let t=fn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return Qw(e,n)}function kb(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 rb(e,t)}function Ib(e){let t=new Uint16Array([0,1,2,2,1,3]);return ab(e,t)}function wc(e,t,n,r,a,s){ib(t,n);let i=sb(e),o=e.TEXTURE_2D;return _e(e,()=>e.bindTexture(o,i)),_e(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),_e(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),_e(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),_e(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),_e(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function cA(e){return e.internalFormatFloat}function Nb(e,t,n,r){let[a,s]=gc(t,n);return wc(e,a,s,cA(r),r.textureFormatFloat,e.FLOAT)}function hA(e){return e.internalFormatHalfFloat}function Sb(e,t,n,r){let[a,s]=gc(t,n);return wc(e,a,s,hA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function dA(e){return e.downloadTextureFormat}function Tb(e,t,n,r){let[a,s]=gc(t,n);return wc(e,a,s,dA(r),e.RGBA,e.UNSIGNED_BYTE)}function pA(e){return e.internalFormatPackedFloat}function Eb(e,t,n,r){let[a,s]=Fl(t,n);return wc(e,a,s,pA(r),e.RGBA,e.FLOAT)}function fA(e){return e.internalFormatPackedHalfFloat}function Cb(e,t,n,r){let[a,s]=Fl(t,n);return wc(e,a,s,fA(r),e.RGBA,r.textureTypeHalfFloat)}function Rb(e,t,n){let r=0,a=3*4,s=3*4+2*4;return _e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),rA(e,t,"clipSpacePos",n,3,s,r)&&rA(e,t,"uv",n,2,s,a)}function Fb(e,t,n,r,a,s){_e(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),_e(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Mb(e,t,n){_e(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?_e(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):_e(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function $b(e,t,n,r){let a=e.createBuffer();_e(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return _e(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),_e(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),_e(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function Db(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 Ob(e,t,n,r){let[a,s]=gc(t,n),i=4,o=new Uint8Array(cz(t*n,i));return _e(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function zb(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(hz(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 Pb(e,t,n){let r=new Float32Array(t*n*4);return _e(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var wp=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,Ap(t,e)):this.gl=Hr(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=fc(this.gl,a),Jn(this.gl,s))this.textureHalfFloatExtension=fc(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=fc(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=kb(this.gl),this.indexBuffer=Ib(this.gl),this.framebuffer=ob(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;_e(e,()=>e.finish()),_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),_e(e,()=>e.deleteFramebuffer(this.framebuffer)),_e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),_e(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),_e(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Nb(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Sb(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Tb(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Mb(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Fb(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Cb(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Eb(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(aA(this.gl,this.framebuffer),this.outputTexture=null),_e(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Ob(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return zb(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Db(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=$b(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,()=>Pb(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=eb(t,e),r=vb(t),a=tb(t);return _e(t,()=>t.attachShader(a,r)),_e(t,()=>t.attachShader(a,n)),nb(t,a),this.debug&&pp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Rb(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&_e(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pp(this.gl,this.program),_e(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?ub(this.gl,e,t):cb(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),_e(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(),hb(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=Fl(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&&pp(this.gl,this.program),mc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),_e(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),_e(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fc(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 v.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=kz(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)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),fp(this.gl,e,this.framebuffer),this.debug&&mc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(fp(this.gl,this.outputTexture,this.framebuffer),this.debug&&mc(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;fp(r,e,this.framebuffer),this.debug&&mc(r),this.outputTexture=e,_e(r,()=>r.viewport(0,0,t,n)),_e(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),_e(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 kz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Lb}=R;function Mz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
`),i=e.map(p=>Iz(p,t,r)).join(`
`),o=t.texShape,l=fn(),c=Tz(l),u,h,d=Rz(l);return t.isPacked?(u=Nz(t.logicalShape,o),h=Cz(l)):(u=Sz(t.logicalShape,o),h=Ez(l)),r&&(d+=Fz),[d,c,h,s,u,i,n].join(`
`)}function Ml(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return $z(e);case 1:return Dz(e);case 2:return Oz(e);case 3:return zz(e);case 4:return Pz(e);case 5:return Lz(e);case 6:return Wz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Wb(e){switch(e.shapeInfo.logicalShape.length){case 0:return Bz(e);case 1:return Vz(e);case 2:return Uz(e);case 3:return jz(e);default:return Hz(e)}}function Iz(e,t,n=!1){let r="";n?r+=Wb(e):r+=Ml(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Gz(e,t):r+=qz(e,t)),r}function Nz(e,t){switch(e.length){case 0:return Bb();case 1:return Xz(e,t);case 2:return Yz(e,t);case 3:return Kz(e,t);default:return Zz(e,t)}}function Sz(e,t){switch(e.length){case 0:return Bb();case 1:return Jz(e,t);case 2:return rP(e,t);case 3:return Qz(e,t);case 4:return eP(e,t);case 5:return tP(e,t);case 6:return nP(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Tz(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function Ez(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function Cz(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function Rz(e){return`${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);
}
${aP}
${sP}
${iP}
`}var aP=`
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);
}
`,sP=`
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);
}
`,iP=`
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);
}
`,Fz=`
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 Bb(){return`
int getOutputCoords() {
return 0;
}
`}function Xz(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 Jz(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 * ${t[1]} + resTexRC.y;
}
`}function Kz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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, ${r}) * 2;
return ivec3(b, r, c);
}
`}function Qz(e,t){let n=Ni(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function Zz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${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}(${o});
}
`}function eP(e,t){let n=Ni(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function tP(e,t){let n=Ni(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function nP(e,t){let n=Ni(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function Yz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${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, ${r}) * 2;
return ivec2(r, c);
}
`}function rP(e,t){return v.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
return ivec2(r, c);
}
`}function Si(e){return`offset${e}`}function Bz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=fn();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function $z(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=Si(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function Vz(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=fn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function Dz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${$l(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=Si(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 Uz(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=fn();if(a!=null&&v.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 Oz(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&&v.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}=v.squeezeShape(t),o=s;if(o.length<t.length){let h=Dl(e,o),d=["row","col"];return`
${Ml(h)}
float ${r}(int row, int col) {
return ${r}(${Ol(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${$l(e)}
}
`;let l=a[0],c=a[1],u=Si(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 jz(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=Dl(e,h),f=["b","row","col"];return`
${Wb(p)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${Ol(f,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=fn();return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${c}, ${l}, b, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function zz(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}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Dl(e,l),m=["row","col","depth"];return`
${Ml(f)}
float ${r}(int row, int col, int depth) {
return ${r}(${Ol(m,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)));
${$l(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=Si(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 Hz(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) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=fn();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 Pz(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}=v.squeezeShape(t);if(o.length<t.length){let f=Dl(e,o),m=["row","col","depth","depth2"];return`
${Ml(f)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${Ol(m,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${a}, 1)));
${$l(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=Si(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 Lz(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}=v.squeezeShape(t);if(l.length<t.length){let m=Dl(e,l),A=["row","col","depth","depth2","depth3"];return`
${Ml(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Ol(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;
${$l(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 f=Si(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 + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function Wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Dl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${Ml(A)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Ol(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)));
${$l(e)}
}
`;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===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}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(f===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]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let m=Si(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 + ${m};
vec2 uv = uvFromFlat(${p}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function $l(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function Gz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Lb(e.shapeInfo.logicalShape,t.logicalShape),l=ct(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${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;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(f&&!m)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 qz(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&&v.arraysEqual(i,s))return`
float ${a}() {
return sampleTexture(${n}, resultUV);
}
`;let c=ct(l),u=Lb(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(m=>`coords.${p[m+h]} = 0;`).join(`
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
float ${a}() {
${c} coords = getOutputCoords();
${d}
return get${r}(${f});
}
`}function ct(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 ${e} is not yet supported`)}function Dl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Ol(e,t){return t.map(n=>e[n]).join(", ")}function oP(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=Mz(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function Vb(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(!v.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(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function lP(e,t,n,r,a){Vb(t.inShapeInfos,n),Vb([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(v.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 uP(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}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:cP,bincountImpl:Ub,bincountReduceImpl:hP,ceilImpl:dP,concatImpl:pP,expImpl:fP,expm1Impl:mP,floorImpl:AP,gatherV2Impl:yP,greaterImpl:gP,lessImpl:xP,linSpaceImpl:wP,logImpl:bP,maxImpl:_P,maximumImpl:vP,minimumImpl:kP,multiplyImpl:IP,negImpl:NP,prodImpl:SP,rangeImpl:TP,rsqrtImpl:EP,simpleAbsImpl:jb,sliceImpl:CP,stridedSliceImpl:RP,subImpl:FP,tileImpl:MP,topKImpl:$P,transposeImpl:mA,uniqueImpl:DP}=Vm;function Hb(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function mn(e,t){return t===1?[e]:Hb(e,t)}function OP(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 WP=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=mn("rc",t),r=ct(t),a=zP(t,e,n),s=PP(t,e[e.length-1],e[e.length-2],n),i=LP(e,n);this.userCode=`
void main() {
${r} rc = getOutputCoords();
if(${a}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
}
}
`}}};function BP(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 zP(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 PP(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 LP(e,t){let n=e.length,r=BP(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 Gb=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=`
${VP(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 VP(e){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${Ni(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var UP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Xb(t,n),a=Kb(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=qb(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===rn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===rn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===rn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===rn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===rn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Xb(n,r),s=Kb(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=qb(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 jP(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 qb(e,t,n,r,a){let s=HP(t,r),i;if(a){let[l,c]=Fl(e[0],e[1]);i=l*c}else{let[l,c]=gc(e[0],e[1]);i=l*c}let o=jP(n,s);return i*o}function HP(e,t){switch(e){case rn.PACKED_2X2_FLOAT32:return pA(t);case rn.PACKED_2X2_FLOAT16:return fA(t);case rn.UNPACKED_FLOAT32:return cA(t);case rn.UNPACKED_FLOAT16:return hA(t);case rn.PACKED_4X1_UNSIGNED_BYTE:return dA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function GP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?rn.PACKED_2X2_FLOAT32:rn.UNPACKED_FLOAT32:e?rn.PACKED_2X2_FLOAT16:rn.UNPACKED_FLOAT16}function Xb(e,t){if(e===Qn.UPLOAD)return rn.PACKED_2X2_FLOAT32;if(e===Qn.RENDER||e==null)return GP(t);if(e===Qn.DOWNLOAD||e===Qn.PIXELS)return rn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Kb(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ha=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);
}
`}},vr="if (isnan(x)) return x;",qP="return x;",Zb="return abs(x);",XP="return (x >= 0.0) ? x : (exp(x) - 1.0);",KP=vr+`
return (x < 0.0) ? 0.0 : x;
`,ZP=vr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,bp="return x;",YP="return x;",JP=`
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;
`,QP=`
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;
`,eL=`
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;
`,zl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},tL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=mn("rc",t),r=ct(t),a=OP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 packedInput = getA(${a});
setOutput(getChannel(packedInput, ${i}));
}
`}},nL=Ur.whereImpl,rL=1e-7,aL=1e-4,AA={};function sL(e){return e in AA||(AA[e]={}),AA[e]}var iL=128,oL=600;function lL(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*oL/1024/1024}var Pl=class extends Au{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.warnedAboutCPUBackend=!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=Hr(J().getNumber("WEBGL_VERSION"));this.binaryCache=sL(J().getNumber("WEBGL_VERSION")),this.gpgpu=new wp(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 UP(this.gpgpu),this.numMBBeforeWarning=lL(),this.texData=new Ch(this,zr())}nextDataId(){return Pl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-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 zl(i,bp):h=new Ha(i,bp);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=v.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=R.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new zl(r,bp):p=new Ha(r,bp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}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,...xc(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)]),f=p[0],m=p[1];u=R.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=v.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)&&zr().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=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ve(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Yw(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...xc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?mp(t):t,o=s?new bz(i):new wz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.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)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=zr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=iL){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return nL(e.shape,t)}packedUnaryOp(e,t,n){let r=new zl(e.shape,t),a=this.compileAndRun(r,[e],n);return zr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=jb(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,Zb,e.dtype);let t=new Ha(e.shape,Zb),n=this.compileAndRun(t,[e]);return zr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.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 zr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new tL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new WP(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 Gb(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=mp(r),i;n?i=new xz(s):i=new gz(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===yc.DENSE){let m=xc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!Ac(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=uP(e,l,c),h=this.getAndSaveBinary(u,()=>oP(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),lP(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}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=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?rL:aL}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=v.now());let u=t.texShape;if(u==null&&(u=pb(n,o),t.texShape=u),a!=null){let h=mp(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=Fl(u[0],u[1]),d=new vz(h,[f,p],m)):d=new _z(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Qn.PIXELS:this.texData.get(A.dataId).usage=Qn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.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=uL(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]*v.bytesPerElement(t)}};Pl.nextDataId=0;function uL(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 Yb="3.3.0";function Jb(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}ju.isBrowser()&&pl("webgl",()=>new Pl,2);var cL={forceHalfFloat:Jb},Qb=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Ll=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},_p=`
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;
`,bc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${ct(a)} coords = getOutputCoords();
`,a===1)s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=mn("coords",a);s+=`
bool nextRowOutOfBounds =
(${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 Ln(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 hL={kernelName:Cs,backendName:"webgl",kernelFunc:Ln};function Ga(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=Ln({inputs:{x:r},backend:n}),l=Ln({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var dL={kernelName:Lh,backendName:"webgl",kernelFunc:Ga},e_="return (a < 0.) ? b * a : a;",t_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function pL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bc(t_,a.shape,i.shape):new Ll(e_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var fL={kernelName:Rs,backendName:"webgl",kernelFunc:pL},n_="return (a < 0.) ? b * a : a;",r_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function mL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bc(r_,r.shape,a.shape):new Ll(n_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var AL={kernelName:Us,backendName:"webgl",kernelFunc:mL},a_="if (isnan(x)) return x;",yL=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,gL=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype: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 zl(i.shape,t):u=new Ha(i.shape,e),o.runWebGLProgram(u,[i],l)}}function an({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 f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:c.shape},T=new Ll(e,l.shape,c.shape);return u.runWebGLProgram(T,[x,N],or(b.dtype,_.dtype))}),g=Ga({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||or(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),w=u.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new bc(t,l.shape,c.shape,n):p=new Ll(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function vp(e,t=!1){if(e==="linear")return t?YP:qP;if(e==="relu")return t?QP:KP;if(e==="elu")return t?JP:XP;if(e==="relu6")return t?eL:ZP;if(e==="prelu")return t?r_:n_;if(e==="leakyrelu")return t?t_:e_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var s_=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"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:m=`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",w="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
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 = ${w};
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]} * ${f[0]});
result += (${p[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${A}
setOutput(result);
}
`}},i_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},o_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},l_="return a * b;";function u_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new o_(i_.REAL,r.shape,a.shape),u=new o_(i_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Ga({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=IP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new bc(l_,r.shape,a.shape):i=new Ll(l_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var xL={kernelName:Ls,backendName:"webgl",kernelFunc:u_};function wL(e,t,n){let r=[vi(e.shape),...ki(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[vi(t),...ki(t)],i=new Gb(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),c=v.sizeFromShape(l);v.assert(o===c,()=>`The new shape (${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&&!Ac(a.shape,l)&&!(u.texture!==null&&Ac(u.shape,l))?wL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var bL={kernelName:Uo,backendName:"webgl",kernelFunc:xe},c_=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 * ${v.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);
${l}
}
setOutput(sumValue);
}
`}},_L=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 vL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ti(e,t,n,r){let a=vL(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 c_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new c_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new _L({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 IL=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=ct(this.rank),a=kL(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function kL(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 NL=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 ${this.rank} is not yet supported.`);let r=ct(this.rank),a=Hb("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${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] = ${l};
}
}
setOutput(result);
}
`}};function kp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new NL(e.shape,t):new IL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function SL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=kp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=xe({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=Ad(e.dtype),g=Ti(A,y,"sum",r),w=xe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function yA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return SL(a,s,i,n)}var TL={kernelName:Qs,backendName:"webgl",kernelFunc:yA};function Sn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=mA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=kp(a,s,i);return c}var EL={kernelName:ai,backendName:"webgl",kernelFunc:Sn},h_=1e3;function Ip({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(c>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=xe({inputs:{x:e},backend:a,attrs:{shape:_}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],M=Math.max(y,g),O=n?N.shape[1]:N.shape[2],W=s!=null,V=i!=null,U=l==="leakyrelu",j=l!=null?vp(l,!0):null,X=W||V||U||j!=null,G;if((p===1||f===1)&&O>h_&&X===!1){let Y=N,se=T;n&&(Y=Sn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(se=Sn({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(se));let te=f!==1,le=f===1,Q=Y;te&&(Q=xe({inputs:{x:Y},backend:a,attrs:{shape:[M,O,1]}}),E.push(Q));let pe=f===1?2:1,ue=se;le&&(ue=xe({inputs:{x:se},backend:a,attrs:{shape:[M,1,O]}}),E.push(ue));let ye=u_({inputs:{a:Q,b:ue},backend:a});G=yA({inputs:{x:ye},backend:a,attrs:{axis:pe,keepDims:!0}}),E.push(ye)}else{let Y=or(e.dtype,t.dtype),se=new s_(_,x,[M,p,f],n,r,W,j,V,U),te=[N,T];if(s!=null&&te.push(s),V&&te.push(i),U){let le=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(le),E.push(le)}G=a.runWebGLProgram(se,te,Y)}let ee=xe({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function CL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return Ip({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var RL={kernelName:si,backendName:"webgl",kernelFunc:CL},d_="return abs(x);";function FL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=jb(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new zl(r.shape,d_):a=new Ha(r.shape,d_),n.runWebGLProgram(a,[r],r.dtype)}var ML={kernelName:ao,backendName:"webgl",kernelFunc:FL},$L=vr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,DL=Ye({opSnippet:$L}),OL={kernelName:so,backendName:"webgl",kernelFunc:DL},zL=vr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,PL=Ye({opSnippet:zL}),LL={kernelName:io,backendName:"webgl",kernelFunc:PL},p_="return a + b;",WL=an({opSnippet:p_,packedOpSnippet:p_,supportsComplex:!0,cpuKernelImpl:cP}),BL={kernelName:Ea,backendName:"webgl",kernelFunc:WL},VL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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 = ${r};
setOutput(result);
}
`}},UL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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 = ${r};
setOutput(result);
}
`}};function Np(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Ln({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Np({inputs:r.slice(0,o),backend:n}),c=Np({inputs:r.slice(o),backend:n});return Np({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>or(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new UL(r[0].shape,s):new VL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var jL={kernelName:ps,backendName:"webgl",kernelFunc:Np};function HL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=Sn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("all",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ti(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var GL={kernelName:$h,backendName:"webgl",kernelFunc:HL};function qL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=Sn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("any",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ti(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var XL={kernelName:Dh,backendName:"webgl",kernelFunc:qL},KL=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 ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},ZL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${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=ct(o),c=mn("coords",o),u,h;if(s===1){h=o+1;let N=ct(h);u=`
${N} sourceLocR = ${N}(${c.join()}, 0);
++${c[o-1]};
${N} sourceLocG = ${N}(${c.join()}, 0);
++${c[o-2]};
${N} sourceLocA = ${N}(${c.join()}, 0);
--${c[o-1]};
${N} sourceLocB = ${N}(${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],f=d.map(N=>"int "+N),m=mn("sourceLocR",h-1).concat("inIdx.r"),A=mn("sourceLocG",h-1).concat("inIdx.g"),y=mn("sourceLocB",h-1).concat("inIdx.b"),g=mn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${g.join()})));`,_=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${A.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,x=r?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${x}
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 = ${_};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${_};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${w}(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 f_(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=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new KL(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=f_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function m_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new ZL(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=m_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function A_(e,t,n,r){let a=[n];if(R.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]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),c=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=f_(e,c,r);s.push(u);let h=xe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return m_(e,t,r)}function YL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=Sn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=A_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var JL={kernelName:fs,backendName:"webgl",kernelFunc:YL};function QL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=Sn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=A_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var eW={kernelName:xu,backendName:"webgl",kernelFunc:QL},tW=vr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,nW=Ye({opSnippet:tW}),rW={kernelName:oo,backendName:"webgl",kernelFunc:nW},aW=vr+"return log(x + sqrt(x * x + 1.0));",sW=Ye({opSnippet:aW}),iW={kernelName:lo,backendName:"webgl",kernelFunc:sW},oW=vr+`
return atan(x);
`,lW=Ye({opSnippet:oW}),uW={kernelName:uo,backendName:"webgl",kernelFunc:lW},cW=yL+`
return atan(a, b);
`,hW=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+gL+`
return result;
`,dW=an({opSnippet:cW,packedOpSnippet:hW}),pW={kernelName:ho,backendName:"webgl",kernelFunc:dW},fW=vr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mW=Ye({opSnippet:fW}),AW={kernelName:co,backendName:"webgl",kernelFunc:mW},_c=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 f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${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 ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let g="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,_=s%4,x=`
if (${f}) {
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 < ${b}; 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)
);
${x}
}
int xC = xCCorner + ${b};
if (${_===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${x}
} else if (${_===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${x}
} else if (${_===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${x}
}
}
setOutput(${w});
}
`}},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,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${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 < ${f};
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 ${E} 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} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,T=`
if (${g}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${A}, ${y});
const float initializationValue = ${w};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${w});
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 < ${x}; 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)
);
${T}
}
int xC = xCCorner + ${x};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${T}
} else if (${N===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
);
${T}
}
}
setOutput(${_});
}
}
`}};function yW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Rl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Ln({inputs:{x:a},backend:n});let h=new _c(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var gW={kernelName:ms,backendName:"webgl",kernelFunc:yW};function xW(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=R.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new gA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var wW={kernelName:wu,backendName:"webgl",kernelFunc:xW},bW=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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},_W=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${p}, ${f}, ${m});
const float avgMultiplier = float(${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 vW(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=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new _W(d);return n.runWebGLProgram(p,[a],i.dtype)}var kW={kernelName:zh,backendName:"webgl",kernelFunc:vW};function IW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Rl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=new bW(u);return n.runWebGLProgram(h,[a],i.dtype)}var NW={kernelName:Oh,backendName:"webgl",kernelFunc:IW};function SW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Ip({a,b:s,transposeA:i,transposeB:o,backend:n})}var TW={kernelName:As,backendName:"webgl",kernelFunc:SW},EW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.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)));
}
`}},CW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.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);
}
`}},RW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.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 CW(r.shape,a.shape,s.shape,u,h,l):new EW(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},FW={kernelName:Ts,backendName:"webgl",kernelFunc:RW},$W=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ct(this.rank),n=`uniform int start[${this.rank}];`,r=MW(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 MW(e){if(e===1)return"sourceLoc";if(e<=6)return xA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var DW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ct(this.rank),n=mn("coords",this.rank),r=mn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${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 OW(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=hn.computeFlatOffset(t,v.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 vc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=hn.parseSliceParams(a,s,i);if(hn.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=CP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=hn.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new DW(l):new $W(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),OW(a,o,l,n)}var zW={kernelName:qo,backendName:"webgl",kernelFunc:vc},PW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=[],f=xe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=Sn({inputs:{x:f},backend:n,attrs:{perm:c}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:u}}),y=vc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},LW={kernelName:bu,backendName:"webgl",kernelFunc:PW};function WW(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=Ub(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var BW={kernelName:Ph,backendName:"webgl",kernelFunc:WW},VW="return float(a != b);",y_=an({opSnippet:VW,dtype:"bool"}),UW={kernelName:Do,backendName:"webgl",kernelFunc:y_};function kc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Ln({inputs:{x:a.complexTensorInfos.real},backend:n})}var jW={kernelName:sd,backendName:"webgl",kernelFunc:kc},HW="return float(int(x));";function GW(e,t){let n=new Ha(e.shape,HW),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 Ln({inputs:{x:a},backend:n});let i=Mt(a.shape),o=wA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ga({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=kc({inputs:{input:a},backend:n}),o=wA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Ln({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return GW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=y_({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 qW={kernelName:ys,backendName:"webgl",kernelFunc:wA},g_="return ceil(x);",XW=Ye({opSnippet:g_,packedOpSnippet:g_,cpuKernelImpl:dP}),KW={kernelName:gs,backendName:"webgl",kernelFunc:XW},ZW=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)}}},YW=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 JW(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 YW(a.shape):o=new ZW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var QW={kernelName:Ca,backendName:"webgl",kernelFunc:JW},eB=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 x_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function tB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new eB(r.shape),i=[x_(r,a.complexTensorInfos.real),x_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var nB={kernelName:_u,backendName:"webgl",kernelFunc:tB},rB=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.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;
${n.join(`
`)}
}
`}},aB=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ct(r),s=mn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
return getChannel(
getT${f}(${Sp(i,l,m)}),
vec2(${Sp(c,l,m)}));
}`}let d=o.length,p=o[o.length-1];h+=`
return getChannel(
getT${d}(${Sp(i,l,p)}),
vec2(${Sp(c,l,p)}));`,this.userCode=`
float getValue(${i.map(f=>"int "+f)}) {
${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 Sp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Tp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Ln({inputs:{x:a.complexTensorInfos.imag},backend:n})}var sB={kernelName:Jh,backendName:"webgl",kernelFunc:Tp};function Wl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>kc({inputs:{input:f},backend:n})),u=e.map(f=>Tp({inputs:{input:f},backend:n})),h=Wl(c,t,n),d=Wl(u,t,n),p=Ga({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=w_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=pP(h,u,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Wl(e.slice(0,c),t,n),h=Wl(e.slice(c),t,n),d=Wl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new aB(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=w_(e,t,n),i=new rB(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function w_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>xe({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function b_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(c=>c.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>v.sizeFromShape(c.shape)>0);if(o.length===1)return Ln({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return R.assertParamsConsistent(l,s),Wl(o,s,n)}var iB={kernelName:po,backendName:"webgl",kernelFunc:b_},__=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,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?w=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:w=`
float activation(float x) {
${n}
}
`,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${w}
const ivec2 strides = ivec2(${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 < ${p}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${p}) *
getW(wR, wC, ${p}, d2);
} else {
dotProd +=
getX(batch, ${p}, xR, xC) *
getW(wR, wC, ${p}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2),
getW(wR, wC, ${p} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1),
getX(batch, xR, xC, ${p} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC),
getX(batch, ${p} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${_}
${b}
setOutput(result);
}
`}},oB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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 < ${p}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${p}) *
getW(wF, wR, wC, ${p}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1),
getX(batch, xF, xR, xC, ${p} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2),
getW(wF, wR, wC, ${p} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},lB=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,f=a*r,m=fn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
blockIndex = rc.y + ${_};
pos = rc.x + ${b};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
d0 = offsetY + ${u} * (pos / ${f});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
if(d1 < ${t[g]} && d1 >= 0) {
ch = int(mod(float(pos), ${a}.));
if (${A}) {
innerDims = vec2(d1, ch);
result[${b*2+_}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${b*2+_}] = 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;
${w}
${m.output} = result;
}
`}};function v_({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",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>h_,w=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=xe({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Ip({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=xe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(Ac(c.shape,_.shape),()=>`packed reshape ${c.shape} to ${_.shape} isn't free`);let N=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=Ip({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=x,E.shape=n.outShape,A=Ln({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function k_({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,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=xe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new lB(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),E=xe({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let M=a!=null,O=s!=null,W=o==="leakyrelu",V=o?vp(o,!0):null,U=new s_(E.shape,x.shape,[1,A,n.outChannels],g,w,M,V,O,W),j=[E,x];if(a&&j.push(a),O&&j.push(s),W){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,j,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=xe({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function uB(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=R.convertConv2DDataFormat(l),d=R.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=v_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=k_({x:a,filter:s,convInfo:d,backend:n});else{let m=new __(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=xe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var cB={kernelName:xs,backendName:"webgl",kernelFunc:uB},hB=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);
}
`}},dB=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);
}
`}},pB=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},fB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${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 mB(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=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new hB(d);return n.runWebGLProgram(p,[a,s],"float32")}var AB={kernelName:Wh,backendName:"webgl",kernelFunc:mB};function yB(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=R.convertConv2DDataFormat(c),d=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new dB(d);return n.runWebGLProgram(p,[a,s],"float32")}var gB={kernelName:ws,backendName:"webgl",kernelFunc:yB};function xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new oB(c);return n.runWebGLProgram(u,[a,s],"float32")}var wB={kernelName:vu,backendName:"webgl",kernelFunc:xB};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=R.computeConv3DInfo(a.shape,l,i,1,o),u=new pB(c);return n.runWebGLProgram(u,[a,s],"float32")}var _B={kernelName:Bh,backendName:"webgl",kernelFunc:bB};function vB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=R.computeConv3DInfo(l,s.shape,o,1,i),u=new fB(c);return n.runWebGLProgram(u,[a,s],"float32")}var kB={kernelName:Vh,backendName:"webgl",kernelFunc:vB},IB=a_+`
return cos(x);
`,NB=Ye({opSnippet:IB}),SB={kernelName:bs,backendName:"webgl",kernelFunc:NB},TB=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,EB=Ye({opSnippet:TB}),CB={kernelName:fo,backendName:"webgl",kernelFunc:EB},RB=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,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${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 = ${w};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${p} ) {
setOutput(float(${a}));
return;
}
float in_x = ${b};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${a}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},FB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new RB(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},MB={kernelName:mo,backendName:"webgl",kernelFunc:FB},S_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${I_(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() {
${ct(r)} coords = getOutputCoords();
int end = ${N_(r,"coords")};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${N_(r,"coords")} = idx;
val += getX(${I_(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 I_(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 ${e} is not yet supported`)}function N_(e,t){if(e===1)return`${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 $B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=R.getAxesPermutation([s],l),u=a;c!=null&&(u=Sn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Ln({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new S_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new S_(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=R.getUndoAxesPermutation(c),m=Sn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var DB={kernelName:_s,backendName:"webgl",kernelFunc:$B};function OB(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=Ub(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=hP(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 zB={kernelName:Uh,backendName:"webgl",kernelFunc:OB},PB=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 LB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.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),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new PB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var WB={kernelName:Ao,backendName:"webgl",kernelFunc:LB},T_=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,f=e.filterWidth,m=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 / ${m};
int q = d2 - d1 * ${m};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${h};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${f}; 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}
${y}
setOutput(result);
}
`}},E_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
vec4 xTexelR${b}C${_*2} = vec4(0.);
vec4 wR${b}C${_} = vec4(0.);
vec4 xR${b}C${_} = vec4(0.);`;for(let b=0;b<p;b++)for(let _=0;_<m;_++){let x=_*2;if(A+=`
xR = xRCorner + ${b*h};
xC = xCCorner + ${x*d};
`,u===1){if(x<f&&(l%2==1?A+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${b}C${x}.zw = vec2(0.);
}
} else {
xTexelR${b}C${x} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${b}C${x} = vec4(previous.zw, xTexelR${b}C${x}.xy);
} else {
xR${b}C${x} = vec4(0, 0, xTexelR${b}C${x}.xy);
}
`:A+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xC, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
xR${b}C${x} = xTexelR${b}C${x};
`,x+1<f)){let N=l%2==0?v.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(A+=`
xCOffset = xC + ${l%2} + ${N};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
}
`,d>1&&(A+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
`),A+=`
xR${b}C${x+1} = vec4(
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.xy);
`):A+=`
xCOffset = xC + ${N};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
}
xR${b}C${x+1} = xTexelR${b}C${x+2};
`}}else x<f&&(A+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(A+=`
xCOffset = xC + 1 - ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${b}C${x+2} = vec4(0.);
}
xR${b}C${x} = vec4(
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
`,x+1<f&&(A+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${b}C${x+1} = vec4(xTexelR${b}C${x+2}.xy, final.xy);
`)):(A+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xC, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
xCOffset = xC + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${b}C${x+2} = vec4(0.);
}
xR${b}C${x} = vec4(
xTexelR${b}C${x}.xy, xTexelR${b}C${x+2}.xy);
`,x+1<f&&(A+=`
xR${b}C${x+1} = vec4(
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
`)),A+="}");x<f&&(A+=`
vec4 wTexelR${b}C${x} = getW(${b}, ${x}, d1, q);
wR${b}C${x} = vec4(wTexelR${b}C${x}.xz, wTexelR${b}C${x}.xz);
`,x+1<f&&(A+=`
vec4 wTexelR${b}C${x+1} = getW(${b}, ${x+1}, d1, q);
wR${b}C${x+1} =
vec4(wTexelR${b}C${x+1}.xz, wTexelR${b}C${x+1}.xz);`))}for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`dotProd += xR${b}C${_} * wR${b}C${_};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`vec4 activation(vec4 x) {
${n}
}`,g="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=`
${y}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${A}
vec4 result = dotProd;
${w}
${g}
setOutput(result);
}
`}};function BB(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]),v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=R.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 E_(h):d=new T_(h),n.runWebGLProgram(d,[a,s],"float32")}var VB={kernelName:vs,backendName:"webgl",kernelFunc:BB},UB=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},jB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function HB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=R.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new UB(h);return n.runWebGLProgram(d,[a,s],"float32")}var GB={kernelName:jh,backendName:"webgl",kernelFunc:HB};function qB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new jB(h);return n.runWebGLProgram(d,[a,s],"float32")}var XB={kernelName:Hh,backendName:"webgl",kernelFunc:qB},KB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function ZB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=xe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new KB(s),l=n.runWebGLProgram(o,[i],i.dtype),c=xe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var YB={kernelName:Gh,backendName:"webgl",kernelFunc:ZB},JB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
const ivec2 strides = ivec2(${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 QB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new JB(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=xe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var eV={kernelName:ku,backendName:"webgl",kernelFunc:QB},tV="return (x >= 0.0) ? x : (exp(x) - 1.0);",nV=`
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;
`,rV=Ye({opSnippet:tV,packedOpSnippet:nV}),aV={kernelName:yo,backendName:"webgl",kernelFunc:rV},sV="return (b >= 1.0) ? a : a * (b + 1.0);",iV=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,oV=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bc(iV,r.shape,a.shape):new Ll(sV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},lV={kernelName:Kh,backendName:"webgl",kernelFunc:oV},uV=`
return vec4(equal(a, b));
`,cV="return float(a == b);",hV=an({opSnippet:cV,packedOpSnippet:uV,dtype:"bool"}),dV={kernelName:xo,backendName:"webgl",kernelFunc:hV},pV=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${R.ERF_P};
float a1 = ${R.ERF_A1};
float a2 = ${R.ERF_A2};
float a3 = ${R.ERF_A3};
float a4 = ${R.ERF_A4};
float a5 = ${R.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,fV=Ye({opSnippet:pV}),mV={kernelName:go,backendName:"webgl",kernelFunc:fV},C_="return exp(x);",R_=Ye({opSnippet:C_,packedOpSnippet:C_,cpuKernelImpl:fP}),AV={kernelName:Is,backendName:"webgl",kernelFunc:R_};function bA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),xe({inputs:{x:s},backend:r,attrs:{shape:o}})}var yV={kernelName:wo,backendName:"webgl",kernelFunc:bA},F_="return exp(x) - 1.0;",gV=Ye({opSnippet:F_,packedOpSnippet:F_,cpuKernelImpl:mP}),xV={kernelName:bo,backendName:"webgl",kernelFunc:gV},M_=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 $_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new M_("real",l,t),u=new M_("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"),f=Ga({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=xe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function wV(e){let{inputs:t,backend:n}=e,{input:r}=t;return $_(r,!1,n)}var bV={kernelName:Zh,backendName:"webgl",kernelFunc:wV},_V=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||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new _V(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var vV={kernelName:Iu,backendName:"webgl",kernelFunc:_A},kV=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},IV={kernelName:_o,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new kV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},D_="return floor(x);",NV=Ye({opSnippet:D_,packedOpSnippet:D_,cpuKernelImpl:AP}),SV={kernelName:Ns,backendName:"webgl",kernelFunc:NV},TV=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,EV=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,CV=an({opSnippet:TV,packedOpSnippet:EV,dtype:"int32"}),RV={kernelName:Ss,backendName:"webgl",kernelFunc:CV},FV=class{constructor(e){this.variableNames=["A"];let t=fn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},MV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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);
}
}
${t.output} = result;
}
`}},DV={kernelName:hd,backendName:"webgl",kernelFunc:$V},Bl;function $V(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)&&(Bl==null&&(Bl=document.createElement("canvas").getContext("2d")),Bl.canvas.width=l,Bl.canvas.height=c,Bl.drawImage(a,0,0,l,c),a=Bl.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 MV(h):new FV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function OV(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:f}=r,m=R.convertConv2DDataFormat(u),A=R.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),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=v_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=k_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?vp(p,!1):null,T=new __(A,b,N,_,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let w=xe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var zV={kernelName:ii,backendName:"webgl",kernelFunc:OV};function PV(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,f=[],m=u;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?vp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(E),f.push(E)}let N;y?N=new E_(A,b,g,_,x):N=new T_(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var LV={kernelName:oi,backendName:"webgl",kernelFunc:PV},WV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ct(t.length),a=ct(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 BV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=R.prepareAndValidate(r,a),h=xe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/c,c]}}),p=new WV(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var VV={kernelName:ko,backendName:"webgl",kernelFunc:BV},jV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ct(this.rank),r=UV(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function UV(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 HV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],c=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=v.sizeFromShape(s.shape),h=[],d=xe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=xe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=yP(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new jV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=xe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var GV={kernelName:vo,backendName:"webgl",kernelFunc:HV},qV="return float(a > b);",XV=`
return vec4(greaterThan(a, b));
`,KV=an({opSnippet:qV,packedOpSnippet:XV,cpuKernelImpl:gP,dtype:"bool"}),ZV={kernelName:Io,backendName:"webgl",kernelFunc:KV},YV="return float(a >= b);",JV=`
return vec4(greaterThanEqual(a, b));
`,QV=an({opSnippet:YV,packedOpSnippet:JV,dtype:"bool"}),eU={kernelName:Es,backendName:"webgl",kernelFunc:QV};function tU(e){let{inputs:t,backend:n}=e,{input:r}=t;return $_(r,!0,n)}var nU={kernelName:Yh,backendName:"webgl",kernelFunc:tU},rU="return float(!isnan(x) && !isinf(x));",aU=Ye({opSnippet:rU,dtype:"bool"}),sU={kernelName:No,backendName:"webgl",kernelFunc:aU},iU="return float(isinf(x));",oU=Ye({opSnippet:iU,dtype:"bool"}),lU={kernelName:So,backendName:"webgl",kernelFunc:oU},uU="return float(isnan(x));",cU=Ye({opSnippet:uU,dtype:"bool"}),hU={kernelName:To,backendName:"webgl",kernelFunc:cU},dU="return float(a < b);",pU=`
return vec4(lessThan(a, b));
`,fU=an({opSnippet:dU,packedOpSnippet:pU,cpuKernelImpl:xP,dtype:"bool"}),mU={kernelName:Eo,backendName:"webgl",kernelFunc:fU},AU="return float(a <= b);",yU=`
return vec4(lessThanEqual(a, b));
`,gU=an({opSnippet:AU,packedOpSnippet:yU,dtype:"bool"}),xU={kernelName:Co,backendName:"webgl",kernelFunc:gU};function wU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=wP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var bU={kernelName:Qh,backendName:"webgl",kernelFunc:wU},_U=`if (x < 0.0) return NAN;
return log(x);`,vU=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,kU=Ye({opSnippet:_U,packedOpSnippet:vU,cpuKernelImpl:bP}),IU={kernelName:Fs,backendName:"webgl",kernelFunc:kU},NU="return log(1.0 + x);",SU=Ye({opSnippet:NU}),TU={kernelName:Ro,backendName:"webgl",kernelFunc:SU},EU="return float(a >= 1.0 && b >= 1.0);",CU=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,RU=an({opSnippet:EU,packedOpSnippet:CU,dtype:"bool"}),FU={kernelName:Fo,backendName:"webgl",kernelFunc:RU},MU="return float(!(x >= 1.0));",$U=Ye({opSnippet:MU}),DU={kernelName:Nu,backendName:"webgl",kernelFunc:$U},OU="return float(a >= 1.0 || b >= 1.0);",zU=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,PU=an({opSnippet:OU,packedOpSnippet:zU,dtype:"bool"}),LU={kernelName:Su,backendName:"webgl",kernelFunc:PU},WU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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 * ${o};
setOutput(val);
}
`}},BU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
}
`}},VU=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 BU(a.shape,s,i,o,l):new WU(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},UU={kernelName:Tu,backendName:"webgl",kernelFunc:VU},jU=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 * ${a});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},HU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new jU(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},GU={kernelName:ed,backendName:"webgl",kernelFunc:HU};function qU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ti(i,e.dtype,"max",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function O_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[u[x]];let b=mA(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=kp(a,u,n);c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("max",c,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=_P(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=qU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var XU={kernelName:Ms,backendName:"webgl",kernelFunc:O_},KU=Qb+`
return max(a, b);
`,ZU=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+_p+`
return result;
`,YU=an({opSnippet:KU,packedOpSnippet:ZU,cpuKernelImpl:vP}),JU={kernelName:$s,backendName:"webgl",kernelFunc:YU};function QU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Rl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Ln({inputs:{x:a},backend:n});let h=new _c(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var ej={kernelName:Ds,backendName:"webgl",kernelFunc:QU};function tj(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=R.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new gA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var nj={kernelName:Eu,backendName:"webgl",kernelFunc:tj},rj=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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},aj=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${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 sj(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=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new gA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new aj(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var ij={kernelName:nd,backendName:"webgl",kernelFunc:sj};function oj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Rl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new _c(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new rj(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var lj={kernelName:td,backendName:"webgl",kernelFunc:oj};function uj(e,t,n,r){let a=new _c(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new _c(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var cj={kernelName:rd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=R.computePool2DInfo(r.shape,a,s,c,i),[h,d]=uj(r,o,u,l);return[h,d]}};function hj(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ti(i,"float32","mean",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var dj={kernelName:Os,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=v.parseAxisParam(s,r.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[u[N]];let _=mA(w,r.shape,r.dtype,u,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=kp(r,u,i);p.push(f),c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=hj(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=Sn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ti(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var fj={kernelName:zs,backendName:"webgl",kernelFunc:pj},mj=Qb+`
return min(a, b);
`,Aj=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+_p+`
return result;
`,yj=an({opSnippet:mj,packedOpSnippet:Aj,cpuKernelImpl:kP}),gj={kernelName:Ps,backendName:"webgl",kernelFunc:yj},xj=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=ct(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}));
}
`}},wj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ct(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=mn("rc",r),l=mn("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);
}
`}},bj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wj(r.shape,a,s):new xj(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},_j={kernelName:Cu,backendName:"webgl",kernelFunc:bj},vj=`if (b == 0.0) return NAN;
return mod(a, b);`,kj=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+_p+`
return result;
`,Ij=an({opSnippet:vj,packedOpSnippet:kj}),Nj={kernelName:Mo,backendName:"webgl",kernelFunc:Ij},Sj=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)}}},Tj=`
if (a == b) {
return 1.0;
};
return a / b;`,Ej=`
// 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;
`,z_=an({opSnippet:Tj,packedOpSnippet:Ej,checkOutOfBounds:!0}),Cj={kernelName:ks,backendName:"webgl",kernelFunc:z_},P_="return a - b;",L_=an({opSnippet:P_,packedOpSnippet:P_,supportsComplex:!0,cpuKernelImpl:FP}),Rj={kernelName:ni,backendName:"webgl",kernelFunc:L_};function W_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=O_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),c=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=L_({inputs:{a,b:c},backend:n}),h=R_({inputs:{x:u},backend:n}),d=yA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=z_({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),f}var Fj={kernelName:ei,backendName:"webgl",kernelFunc:W_};function Mj(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:W_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new Sj(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var $j={kernelName:ad,backendName:"webgl",kernelFunc:Mj},B_="return -x;";function Dj(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=NP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new zl(r.shape,B_):a=new Ha(r.shape,B_),n.runWebGLProgram(a,[r],r.dtype)}var Oj={kernelName:$o,backendName:"webgl",kernelFunc:Dj},zj=Ur.nonMaxSuppressionV3Impl;function Pj(e){R.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}=zj(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Lj={kernelName:Oo,backendName:"webgl",kernelFunc:Pj},Wj=Ur.nonMaxSuppressionV4Impl;function Bj(e){R.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}=Wj(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var Vj={kernelName:zo,backendName:"webgl",kernelFunc:Bj},Uj=Ur.nonMaxSuppressionV5Impl;function jj(e){R.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,f=l,m=c,{selectedIndices:A,selectedScores:y}=Uj(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Hj={kernelName:Po,backendName:"webgl",kernelFunc:jj},Gj=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)));
}
`}},qj=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),c=new Gj(l,s,i,o),u=xe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=xe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},Xj={kernelName:Ws,backendName:"webgl",kernelFunc:qj};function Ep(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=kc({inputs:{input:r},backend:n}),s=Ep({inputs:{x:a},backend:n}),i=Tp({inputs:{input:r},backend:n}),o=Ep({inputs:{x:i},backend:n}),l=Ga({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 Kj={kernelName:nl,backendName:"webgl",kernelFunc:Ep};function V_(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=kc({inputs:{input:r},backend:n}),s=V_({inputs:{x:a},backend:n}),i=Tp({inputs:{input:r},backend:n}),o=Ep({inputs:{x:i},backend:n}),l=Ga({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 Zj={kernelName:Lo,backendName:"webgl",kernelFunc:V_};function Yj(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=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.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=b_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Jj={kernelName:Wo,backendName:"webgl",kernelFunc:Yj},Qj=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=ct(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)}}},eH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ct(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=mn("rc",r),l=mn("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 f=0,m=r===1?2:4;f<m;f++)p+=`
${h[f]}
if (${d}) {
result[${f}] = float(value);
} else {
${a} source = rc - start;
result[${f}] = 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)}}},U_=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 eH(a.shape,s,i):new Qj(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},tH={kernelName:Bs,backendName:"webgl",kernelFunc:U_},nH=`
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);
`,rH=`
// 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));
`+_p+`
return result;
`,aH=an({opSnippet:nH,packedOpSnippet:rH}),sH={kernelName:Vs,backendName:"webgl",kernelFunc:aH};function iH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=v.parseAxisParam(s,a.shape),u=c,h=R.getAxesPermutation(u,o),d=a;h!=null&&(d=Sn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=R.getInnerMostAxes(u.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=SP(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,u),A=v.sizeFromShape(m),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Ad(a.dtype),w=Ti(y,g,"prod",n);p=xe({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,c);p=xe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var oH={kernelName:Bo,backendName:"webgl",kernelFunc:iH},j_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=TP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},lH={kernelName:Ru,backendName:"webgl",kernelFunc:j_},uH="return 1.0 / x;",cH=Ye({opSnippet:uH}),hH={kernelName:Vo,backendName:"webgl",kernelFunc:cH},dH=vr+`
return (x < 0.0) ? 0.0 : x;
`,pH=`
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;
`,fH=Ye({opSnippet:dH,packedOpSnippet:pH}),mH={kernelName:js,backendName:"webgl",kernelFunc:fH},AH=vr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,yH=`
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;
`,gH=Ye({opSnippet:AH,packedOpSnippet:yH}),xH={kernelName:Gs,backendName:"webgl",kernelFunc:gH},wH=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);
}
`}},bH=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 _H(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 bH(a.shape,l,c,s,i):new wH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var vH={kernelName:Hs,backendName:"webgl",kernelFunc:_H},kH=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,f=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(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${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 IH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new kH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var NH={kernelName:od,backendName:"webgl",kernelFunc:IH},SH=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 + ${h})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function TH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new SH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var EH={kernelName:Fu,backendName:"webgl",kernelFunc:TH},CH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${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(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${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),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function RH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new CH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var FH={kernelName:id,backendName:"webgl",kernelFunc:RH},MH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=ct(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},$H=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=mn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ct(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${u(r.slice())};
}
}
setOutput(result);
}
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function DH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Ln({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $H(a.shape,o):new MH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var OH={kernelName:qs,backendName:"webgl",kernelFunc:DH},zH=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 < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},PH={kernelName:rl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new zH(r.shape,s),[c,u]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},LH=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,WH=Ye({opSnippet:LH}),BH={kernelName:Xs,backendName:"webgl",kernelFunc:WH},VH="return inversesqrt(x);",UH=Ye({opSnippet:VH,cpuKernelImpl:EP}),jH={kernelName:Ks,backendName:"webgl",kernelFunc:UH},H_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ct(a.length),l=ct(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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 HH(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}=R.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=xe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=xe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new H_(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var GH={kernelName:jo,backendName:"webgl",kernelFunc:HH},qH=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=ct(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${r});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function XH(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new qH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],or(a.dtype,s.dtype))}var KH={kernelName:Ho,backendName:"webgl",kernelFunc:XH},ZH=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${R.SELU_SCALEALPHA};
float scale = ${R.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,YH=Ye({opSnippet:ZH}),JH={kernelName:Go,backendName:"webgl",kernelFunc:YH},QH="return 1.0 / (1.0 + exp(-1.0 * x));",eG=Ye({opSnippet:QH}),tG={kernelName:Ys,backendName:"webgl",kernelFunc:eG},nG=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,rG=Ye({opSnippet:nG}),aG={kernelName:Ko,backendName:"webgl",kernelFunc:rG},sG=a_+`
return sin(x);
`,iG=Ye({opSnippet:sG}),oG={kernelName:Zs,backendName:"webgl",kernelFunc:iG},lG=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,uG=Ye({opSnippet:lG}),cG={kernelName:Xo,backendName:"webgl",kernelFunc:uG},hG=`
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;
`,dG=Ye({opSnippet:hG}),pG={kernelName:Zo,backendName:"webgl",kernelFunc:dG},fG=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.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=U_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(u.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(u.shape,s,o,!1),f=xe({inputs:{x:u},backend:n,attrs:{shape:h}}),m=Sn({inputs:{x:f},backend:n,attrs:{perm:d}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},mG={kernelName:Mu,backendName:"webgl",kernelFunc:fG};function AG(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}=R.calculateShapes(s,a,o),d=!1,p=new H_(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var yG={kernelName:ld,backendName:"webgl",kernelFunc:AG};function gG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.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 f=vc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var xG={kernelName:Yo,backendName:"webgl",kernelFunc:gG},wG="return sqrt(x);",bG=Ye({opSnippet:wG}),_G={kernelName:Js,backendName:"webgl",kernelFunc:bG},vG="return x * x;",kG=Ye({opSnippet:vG}),IG={kernelName:$u,backendName:"webgl",kernelFunc:kG},G_="return (a - b) * (a - b);",NG=an({opSnippet:G_,packedOpSnippet:G_}),SG={kernelName:ti,backendName:"webgl",kernelFunc:NG};function TG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=vr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Ha(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var EG={kernelName:Fa,backendName:"webgl",kernelFunc:TG},CG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ct(n.length),s=ct(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${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 RG(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,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=hn.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=xe({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=vc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=xe({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=Ve(w.shape,w.dtype,x),T=RP(g,N,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new CG(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=xe({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var FG={kernelName:Jo,backendName:"webgl",kernelFunc:RG},MG="return tan(x);",$G=Ye({opSnippet:MG}),DG={kernelName:Qo,backendName:"webgl",kernelFunc:$G},OG=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,zG=Ye({opSnippet:OG}),PG={kernelName:ri,backendName:"webgl",kernelFunc:zG},WG=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=ct(this.rank),a=LG(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function LG(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]}, ${e[a]})`);return r.join()}function q_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>v.decodeString(u)),l=Ve(a.shape,a.dtype,o),c=MP(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new WG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var BG={kernelName:Ra,backendName:"webgl",kernelFunc:q_};function VG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=$P(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 UG={kernelName:el,backendName:"webgl",kernelFunc:VG},jG=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 HG(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,[f,m]=c!=null?c:[h,d],A=[u,f,m,p],y=new jG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var GG={kernelName:ud,backendName:"webgl",kernelFunc:HG};function qG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;Rl(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}=DP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var XG={kernelName:cd,backendName:"webgl",kernelFunc:qG};function KG(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 m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=vc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=xe({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ZG={kernelName:tl,backendName:"webgl",kernelFunc:KG},YG=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 JG(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=R.getAxesPermutation([c],o),h=a;u!=null&&(h=Sn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=R.getInnerMostAxes(1,o)[0]);let d=R.segment_util.computeOutShape(h.shape,c,i),p=v.sizeFromShape([h.shape[c]]),f=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=Ad(a.dtype),A=(b,_,x,N,T)=>{let E=b.shape[0],M=b.shape[1],O=R.segment_util.segOpComputeOptimalWindowSize(M,T),W={windowSize:O,inSize:M,batchSize:E,numSegments:T},V=new YG(W,_),U=n.compileAndRun(V,[b,x],N);if(l.push(U),U.shape[1]===T)return U;let j=j_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=q_({inputs:{x:j},backend:n,attrs:{reps:[M/O]}});return l.push(j),l.push(X),A(U,_,X,N,T)},y=A(f,"unsortedSegmentSum",s,m,i),g=xe({inputs:{x:y},backend:n,attrs:{shape:d}}),w=g;if(u!=null){l.push(g);let b=R.getUndoAxesPermutation(u);w=Sn({inputs:{x:w},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var QG={kernelName:Du,backendName:"webgl",kernelFunc:JG},eq=[UU,GU,RL,ML,OL,LL,BL,jL,GL,XL,JL,eW,rW,iW,pW,uW,AW,wW,gW,kW,NW,TW,FW,LW,BW,qW,KW,QW,nB,dL,iB,AB,gB,cB,_B,kB,wB,SB,CB,MB,DB,zB,WB,GB,XB,VB,YB,eV,aV,lV,dV,mV,AV,yV,xV,bV,vV,IV,SV,RV,DV,zV,LV,VV,GV,ZV,eU,hL,nU,sB,sU,lU,hU,fL,mU,xU,bU,TU,IU,FU,DU,LU,XU,nj,ej,ij,lj,cj,JU,dj,fj,gj,_j,Nj,$j,xL,Oj,Lj,Vj,Hj,UW,Xj,Zj,Jj,tH,sH,AL,oH,lH,jW,Cj,hH,xH,mH,bL,vH,NH,EH,FH,OH,PH,BH,jH,GH,KH,JH,tG,aG,oG,cG,zW,Fj,pG,mG,yG,xG,_G,IG,SG,EG,FG,Rj,TL,DG,PG,BG,UG,GG,EL,XG,ZG,QG,Kj];for(let e of eq)li(e);var Wn;(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"})(Wn||(Wn={}));var Ic;(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"})(Ic||(Ic={}));var X_;function tq(e){X_=e.wasm.cwrap(si,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function nq(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,f=0;if(i!=null){let T=n.dataIdMap.get(i.dataId);if(T.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${T.shape.length}.`);f=T.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=Ic[u];if(A==null)throw new Error(`${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],w=a.shape[0],b=n.makeOutput([w,y,g],a.dtype),_=n.dataIdMap.get(b.dataId).id,x=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return X_(d,x,a.shape.length,p,N,s.shape.length,l,c,A,f,m,h||0,_),b}var rq={kernelName:si,backendName:"wasm",setupFunc:tq,kernelFunc:nq};function Tn(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 v.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var aq=Tn(ao);function An(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,f=R.assertAndGetBroadcastShape(c.shape,u.shape),m=o.makeOutput(f,p);if(v.sizeFromShape(f)===0)return m;let A=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(m.dataId).id,w=()=>r(h,A,c.shape.length,d,y,u.shape.length,Wn[c.dtype],g);if(t&&c.dtype==="float32")return w(),m;let b=R.getBroadcastDims(c.shape,f),_=R.getBroadcastDims(u.shape,f),x=b.every((T,E)=>T===E),N=_.every((T,E)=>T===E);if(x&&N)return w(),m;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 sq=!0,iq=An(Ea,sq),K_;function oq(e){K_=e.wasm.cwrap(ps,null,["array","number","number","number"])}function lq(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(v.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 K_(s,a.length,Wn[r.dtype],i),r}var uq={kernelName:ps,backendName:"wasm",setupFunc:oq,kernelFunc:lq};function Cp(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 cq={kernelName:Cs,backendName:"wasm",kernelFunc:Cp},Z_;function hq(e){Z_=e.wasm.cwrap(ai,null,["number","array","number","number","number","array","number"])}function Rp(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=pq(t.x.shape,r.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=dq(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=Cp({inputs:t,backend:n});return f.shape=o,f}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 Z_(u,p,l.shape.length,Wn[l.dtype],h,d,s.length),c}function dq(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function pq(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 fq={kernelName:ai,backendName:"wasm",kernelFunc:Rp,setupFunc:hq};function Vl(e,t,n){let r=e.shape,a=e.shape.length,s=v.parseAxisParam(t,r),i=s,o=R.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=R.getInnerMostAxes(i.length,a),l=Rp({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 Y_;function mq(e){Y_=e.wasm.cwrap(fs,null,["number","number","number","number","number"])}function Aq(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}=Vl(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"),f=t.dataIdMap.get(p.dataId).id,m=v.sizeFromShape(p.shape),A=l.shape[u[0]];return Y_(o,Wn[l.dtype],m,A,f),h&&t.disposeData(c.dataId),p}var yq={kernelName:fs,backendName:"wasm",kernelFunc:Aq,setupFunc:mq},J_;function gq(e){J_=e.wasm.cwrap(ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xq(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=R.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.strideHeight,g=u.strideWidth,w=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 b=r.makeOutput(u.outShape,"float32"),_=r.dataIdMap.get(b.dataId).id;return J_(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,w,_),b}var wq={kernelName:ms,backendName:"wasm",setupFunc:gq,kernelFunc:xq};function kr(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=v.sizeFromShape(r.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:i,dtype:r.dtype}}var bq={kernelName:Uo,backendName:"wasm",kernelFunc:kr},Q_;function _q(e){Q_=e.wasm.cwrap(As,null,["number","array","number","number","array","number","number","number","number"])}function vq(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[c-2]:s.shape[c-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=v.sizeFromShape(f),y=v.sizeFromShape(m),g=A===y||A===1||y===1;v.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let w=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);v.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let b=i?[A,u,d]:[A,d,u],_=o?[y,p,h]:[y,h,p],x=kr({inputs:{x:a},backend:n,attrs:{shape:b}}),N=kr({inputs:{x:s},backend:n,attrs:{shape:_}}),T=n.dataIdMap.get(x.dataId).id,E=n.dataIdMap.get(N.dataId).id,M=i?x.shape[2]:x.shape[1],O=o?N.shape[1]:N.shape[2],W=Math.max(A,y),V=n.makeOutput([W,M,O],x.dtype),U=n.dataIdMap.get(V.dataId).id,j=new Uint8Array(new Int32Array(x.shape).buffer),X=new Uint8Array(new Int32Array(N.shape).buffer);return Q_(T,j,x.shape.length,E,X,N.shape.length,i,o,U),n.disposeData(x.dataId),n.disposeData(N.dataId),V.shape=w,V}var kq={kernelName:As,backendName:"wasm",setupFunc:_q,kernelFunc:vq};function Fp(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 Iq={kernelName:ys,backendName:"wasm",kernelFunc:Fp},Nq=Tn(gs),e3;function Sq(e){e3=e.wasm.cwrap(Ca,null,["number","number","number","number"])}function Tq(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 e3(o,s,i,c),l}var Eq={kernelName:Ca,backendName:"wasm",setupFunc:Sq,kernelFunc:Tq};function t3(e){let{inputs:t,backend:n}=e,r=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=R.computeOutShape(t.map(p=>p.shape),r),s=t.filter(p=>v.sizeFromShape(p.shape)>0);if(s.length===1)return Cp({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(v.sizeFromShape(a)===0)return i;let o=s.map(p=>p.shape);if(R.assertParamsConsistent(o,r),s[0].dtype==="string"){let p=s.map(w=>{let b=v.sizeFromShape(w.shape.slice(r));return kr({inputs:{x:w},backend:n,attrs:{shape:[-1,b]}})}),f=p.map(w=>({vals:n.readSync(w.dataId),shape:w.shape}));a=R.computeOutShape(p.map(w=>w.shape),1);let m=p[0].shape[0]===1,A=jm(f,a,t[0].dtype,m),y=R.computeOutShape(s.map(w=>w.shape),r);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=R.fromStringArrayToUint8(A),p.forEach(w=>n.disposeData(w.dataId)),i}let l=v.sizeFromShape(s[0].shape.slice(0,r)),c=0,u=s.map(p=>{let f=v.sizeFromShape(p.shape.slice(r));return c+=f,f}),h=s.map(p=>n.typedArrayFromHeap(p)),d=n.typedArrayFromHeap(i);for(let p=0;p<l;p++){let f=p*c;for(let m=0;m<h.length;m++){let A=u[m],y=p*A,g=h[m].subarray(y,y+A);d.set(g,f),f+=A}}return i}var Cq={kernelName:po,backendName:"wasm",kernelFunc:t3},n3;function Rq(e){n3=e.wasm.cwrap(xs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fq(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=R.convertConv2DDataFormat(d),f=R.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!1,p),m=f.filterHeight,A=f.filterWidth,y=f.padInfo.top,g=f.padInfo.right,w=f.padInfo.bottom,b=f.padInfo.left,_=f.dilationHeight,x=f.dilationWidth,N=f.strideHeight,T=f.strideWidth,E=f.inChannels,M=f.outChannels,O=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let W=r.makeOutput(f.outShape,"float32"),V=r.dataIdMap.get(W.dataId).id;return n3(i,a.shape[0],a.shape[1],a.shape[2],o,m,A,y,g,w,b,O,_,x,N,T,E,M,V),W}var Mq={kernelName:xs,backendName:"wasm",setupFunc:Rq,kernelFunc:Fq},r3;function $q(e){r3=e.wasm.cwrap(ws,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dq(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=r,h=1,d=R.convertConv2DDataFormat(l),p=R.computeConv2DInfo(u,s.shape,i,h,o,c,!1,d),{batchSize:f,filterHeight:m,filterWidth:A,inChannels:y,inHeight:g,inWidth:w,outChannels:b,outHeight:_,outWidth:x,strideHeight:N,strideWidth:T}=p,E=m-1-p.padInfo.top,M=A-1-p.padInfo.left,O=p.dataFormat==="channelsLast",W=v.computeStrides(p.inShape),V=v.computeStrides(a.shape),[U,j,X]=v.computeStrides(s.shape),G=W[0],ee=O?W[1]:W[2],Y=O?W[2]:1,se=O?1:W[1],te=V[0],le=O?V[1]:V[2],Q=O?V[2]:1,pe=O?1:V[1],ue=t.makeOutput(p.inShape,"float32"),ye=t.dataIdMap.get(ue.dataId).id,me=t.dataIdMap.get(a.dataId).id,Ne=t.dataIdMap.get(s.dataId).id;return r3(me,Ne,f,m,A,g,w,y,_,x,b,N,T,E,M,U,j,X,G,ee,Y,se,te,le,Q,pe,ye),ue}var Oq={kernelName:ws,backendName:"wasm",setupFunc:$q,kernelFunc:Dq},zq=Tn(bs),vA;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(vA||(vA={}));var a3;function Pq(e){a3=e.wasm.cwrap(mo,null,["number","number","number","number","array","number","number","number","number","number"])}function Lq(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]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=Fp({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let A=f.id,y=t.dataIdMap.get(l.dataId).id,g=t.dataIdMap.get(c.dataId).id,w=t.makeOutput(p,"float32"),b=t.dataIdMap.get(w.dataId).id,_=new Uint8Array(new Int32Array(o.shape).buffer);return a3(A,y,g,u,_,h,d,vA[a],s,b),m!=null&&t.disposeData(m.dataId),w}var Wq={kernelName:mo,backendName:"wasm",setupFunc:Pq,kernelFunc:Lq},s3;function Bq(e){s3=e.wasm.cwrap(_s,null,["number","number","number","number","number","number"])}function Vq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let c=R.getAxesPermutation([s],l),u=a;c!==null&&(u=Rp({inputs:{x:a},attrs:{perm:c},backend:n}));let h=R.getInnerMostAxes(1,l)[0];R.assertAxesAreInnerMostDims("cumsum",[h],l);let d=n.makeOutput(u.shape,u.dtype),p=u.shape[h],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(d.dataId).id;s3(f,i?1:0,o?1:0,p,m,Wn[a.dtype]);let A=d;if(c!==null){let y=R.getUndoAxesPermutation(c);A=Rp({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return A}var Uq={kernelName:_s,backendName:"wasm",setupFunc:Bq,kernelFunc:Vq},i3;function jq(e){i3=e.wasm.cwrap(Ao,null,["number","number","number","array","number","array","array","number","number"])}function Hq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(a.shape)).buffer),g=new Uint8Array(new Int32Array(f).buffer),w=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return i3(A,s,i==="NHWC"?1:0,y,a.shape.length-1,g,w,f.length,b),m}var Gq={kernelName:Ao,backendName:"wasm",setupFunc:jq,kernelFunc:Hq},o3;function qq(e){o3=e.wasm.cwrap(vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Xq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h}=n,d=c==null?[1,1]:c,p=R.computeConv2DInfo(a.shape,s.shape,l,d,u,h,!0),f=p.filterHeight,m=p.filterWidth,A=p.padInfo.top,y=p.padInfo.right,g=p.padInfo.bottom,w=p.padInfo.left,b=p.dilationHeight,_=p.dilationWidth,x=p.strideHeight,N=p.strideWidth,T=p.inChannels,E=p.outChannels,M=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let O=r.makeOutput(p.outShape,"float32"),W=r.dataIdMap.get(O.dataId).id;return o3(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,w,M,b,_,x,N,T,E,W),O}var Kq={kernelName:vs,backendName:"wasm",setupFunc:qq,kernelFunc:Xq},Zq=!1,Yq=An(xo,Zq,"bool"),Jq=Tn(Is);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&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),kr({inputs:{x:a},backend:r,attrs:{shape:o}})}var Qq={kernelName:wo,backendName:"wasm",kernelFunc:kA};function eX(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var tX={kernelName:Iu,backendName:"wasm",kernelFunc:eX},l3;function nX(e){l3=e.wasm.cwrap(_o,null,["number","number","number","number","number","number"])}function rX(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return l3(s,o,l,c,u,i),a}var aX={kernelName:_o,backendName:"wasm",kernelFunc:rX,setupFunc:nX},sX=Tn(Ns),iX=!1,oX=An(Ss,iX),u3;function lX(e){u3=e.wasm.cwrap(Ts,null,["number","number","number","number","number","number","number"])}function uX(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return u3(u,h,d,p,f,a,A),m}var cX={kernelName:Ts,backendName:"wasm",setupFunc:lX,kernelFunc:uX},c3;function hX(e){c3=e.wasm.cwrap(ii,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=R.computeConv2DInfo(a.shape,s.shape,l,u,c,d),A=Ic[p];if(A==null)throw new Error(`${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,w=m.outChannels,b=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 ${Q.shape.length}.`);if(Q.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${w})`);b=Q.id}let _=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,O=m.dilationHeight,W=m.dilationWidth,V=m.strideHeight,U=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let se=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(se.dataId).id,le=o==null?0:r.dataIdMap.get(o.dataId).id;return c3(y,G,ee,Y,g,_,x,b,N,T,E,M,X,O,W,V,U,j,w,A,le,f||0,te),se}var pX={kernelName:ii,backendName:"wasm",setupFunc:hX,kernelFunc:dX},h3;function fX(e){h3=e.wasm.cwrap(oi,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 mX(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:f}=n,m=R.computeConv2DInfo(a.shape,s.shape,l,u,c,d,!0),A=Ic[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,w=m.outChannels,b=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 ${Q.shape.length}.`);if(Q.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${w})`);b=Q.id}let _=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,O=m.dilationHeight,W=m.dilationWidth,V=m.strideHeight,U=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let se=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(se.dataId).id,le=o==null?0:r.dataIdMap.get(o.dataId).id;return h3(y,G,ee,Y,g,_,x,b,N,T,E,M,X,O,W,V,U,j,w,A,le,f||0,te),se}var AX={kernelName:oi,backendName:"wasm",setupFunc:fX,kernelFunc:mX},d3;function yX(e){d3=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","array","number"])}function gX(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=Uf.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,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(c.dataId).id;return d3(d,Wn[r.dtype],p,i,h,o,f,m),c}var xX={kernelName:ko,backendName:"wasm",setupFunc:yX,kernelFunc:gX},p3;function wX(e){p3=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function bX(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],c=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=kr({inputs:{x:a},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),d=kr({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),p=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],f=t.makeOutput(p,a.dtype);if(v.sizeFromShape(a.shape)===0)return f;let m=u.shape.length-1,A=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(f.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(u.shape)).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(p)).buffer);return p3(A,Wn[a.dtype],w,m,y,c.batchSize,b,g),t.disposeData(u.dataId),t.disposeData(d.dataId),f.shape=c.outputShape,f}var _X={kernelName:vo,backendName:"wasm",setupFunc:wX,kernelFunc:bX},vX=!1,kX=An(Io,vX,"bool"),IX=!1,NX=An(Es,IX,"bool"),f3;function SX(e){f3=e.wasm.cwrap(Rs,null,["number","number","number"])}function TX(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(v.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;f3(a,n,i)}return s}var EX={kernelName:Rs,backendName:"wasm",setupFunc:SX,kernelFunc:TX},CX=!1,RX=An(Eo,CX,"bool"),FX=!1,MX=An(Co,FX,"bool"),$X=Tn(Fs),DX=!1,OX=An(Fo,DX,"bool"),m3;function zX(e){m3=e.wasm.cwrap(Ms,null,["number, number, number"])}function PX(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=Vl(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;R.assertAxesAreInnerMostDims("max",u,p);let[f,m]=R.computeOutAndReduceShapes(l.shape,u),A=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;m3(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=R.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var LX={kernelName:Ms,backendName:"wasm",setupFunc:zX,kernelFunc:PX},WX=!1,BX=An($s,WX),A3;function VX(e){A3=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function UX(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=R.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.dilationHeight,g=u.dilationWidth,w=u.strideHeight,b=u.strideWidth,_=u.inChannels,x=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let N=r.makeOutput(u.outShape,"float32"),T=r.dataIdMap.get(N.dataId).id;return A3(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,w,b,_,x,T),N}var jX={kernelName:Ds,backendName:"wasm",setupFunc:VX,kernelFunc:UX},y3;function HX(e){y3=e.wasm.cwrap(Os,null,["number, number, number"])}function GX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=Vl(i,a,t),f=h;if(p){let b=t.dataIdMap.get(u.dataId).id;b!==o&&(c=u,l=b,f=R.getInnerMostAxes(f.length,c.shape.length))}R.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,A]=R.computeOutAndReduceShapes(c.shape,f),y=v.sizeFromShape(A),g=c;c.dtype!=="float32"&&(g=Fp({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let w=t.makeOutput(m,"float32");if(v.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(w.dataId).id;y3(l,y,b)}if(p&&t.disposeData(u.dataId),s){let b=R.expandShapeToKeepDim(w.shape,d);w.shape=b}return c.dtype!=="float32"&&t.disposeData(g.dataId),w}var qX={kernelName:Os,backendName:"wasm",setupFunc:HX,kernelFunc:GX},g3;function XX(e){g3=e.wasm.cwrap(zs,null,["number, number, number"])}function KX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=Vl(i,a,t);if(p){let w=t.dataIdMap.get(u.dataId).id;w!==o&&(c=u,l=w)}let f=c.shape.length;R.assertAxesAreInnerMostDims("min",h,f);let[m,A]=R.computeOutAndReduceShapes(c.shape,h),y=v.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;g3(l,y,w)}if(p&&t.disposeData(u.dataId),s){let w=R.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var ZX={kernelName:zs,backendName:"wasm",setupFunc:XX,kernelFunc:KX},YX=!1,JX=An(Ps,YX),QX=!0,eK=An(Ls,QX),tK=Tn($o);function IA(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 x3;function nK(e){x3=e.wasm.cwrap(Oo,"number",["number","number","number","number","number"])}function rK(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=x3(c,u,s,a,i),{pSelectedIndices:d,selectedSize:p,pSelectedScores:f,pValidOutputs:m}=IA(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([p],"int32",d)}var aK={kernelName:Oo,backendName:"wasm",setupFunc:nK,kernelFunc:rK},w3;function sK(e){w3=e.wasm.cwrap(zo,"number",["number","number","number","number","number","bool"])}function iK(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=w3(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=IA(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var oK={kernelName:zo,backendName:"wasm",setupFunc:sK,kernelFunc:iK},b3;function lK(e){b3=e.wasm.cwrap(Po,"number",["number","number","number","number","number","number"])}function uK(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=b3(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=IA(t,d);t.wasm._free(A);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[y,g]}var cK={kernelName:Po,backendName:"wasm",setupFunc:lK,kernelFunc:uK},hK=!1,dK=An(Do,hK,"bool"),_3;function pK(e){_3=e.wasm.cwrap(Ws,null,["number","number","number","number","number"])}function fK(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 _3(u,s,i,o,c),l}var mK={kernelName:Ws,backendName:"wasm",setupFunc:pK,kernelFunc:fK};function AK(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var yK={kernelName:Lo,backendName:"wasm",kernelFunc:AK};function gK(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=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.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=t3({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeData(u.dataId)),c}var xK={kernelName:Wo,backendName:"wasm",kernelFunc:gK},v3;function wK(e){v3=e.wasm.cwrap(Bs,null,["number","array","number","number","array","array","number","number"])}function bK(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[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(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return v3(i,c,t.shape.length,Wn[t.dtype],d,p,a,l),o}var _K={kernelName:Bs,backendName:"wasm",kernelFunc:bK,setupFunc:wK},vK=!1,kK=An(Vs,vK),k3;function IK(e){k3=e.wasm.cwrap(Us,null,["number","number","number"])}function NK(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 k3(s,i,l),o}var SK={kernelName:Us,backendName:"wasm",setupFunc:IK,kernelFunc:NK},I3;function TK(e){I3=e.wasm.cwrap(Bo,null,["number","number","number","number"])}function EK(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}=Vl(i,a,t),f=h;if(p){let w=t.dataIdMap.get(u.dataId).id;w!==o&&(c=u,l=w,f=R.getInnerMostAxes(f.length,c.shape.length))}R.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,A]=R.computeOutAndReduceShapes(c.shape,f),y=v.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;I3(l,y,Wn[g.dtype],w)}if(p&&t.disposeData(u.dataId),s){let w=R.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var CK={kernelName:Bo,backendName:"wasm",setupFunc:TK,kernelFunc:EK},RK=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=qm(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},FK={kernelName:Ru,backendName:"wasm",kernelFunc:RK},MK=!0,$K=An(ks,MK),DK=Tn(js),OK=Tn(Gs),N3;function zK(e){N3=e.wasm.cwrap(Hs,null,["number","number","number","number","number","number","number","number","number","number"])}function PK(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,f=[u,l,c,p],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=Fp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(v.sizeFromShape(a.shape)===0)return g;let w=t.dataIdMap.get(g.dataId).id;return N3(y,u,h,d,p,l,c,s?1:0,i?1:0,w),A!=null&&t.disposeData(A.dataId),g}var LK={kernelName:Hs,backendName:"wasm",setupFunc:zK,kernelFunc:PK},S3;function WK(e){S3=e.wasm.cwrap(qs,null,["number","array","number","array","number","number"])}function BK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=v.parseAxisParam(s,a.shape);if(a.shape.length===0)return Cp({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);S3(l,u,i.length,h,a.shape.length,c);let d=kr({inputs:{x:o},attrs:{shape:a.shape},backend:n});return n.disposeData(o.dataId),d}var VK={kernelName:qs,backendName:"wasm",kernelFunc:BK,setupFunc:WK},T3;function UK(e){T3=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function jK(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,f]=a.shape,[m,A]=R.getImageCenter(o,d,p),y=i===0,g=255,w=typeof i=="number"?[i,i,i,y?0:g]:[...i,g],b=new Uint8Array(new Int32Array(w).buffer);return T3(c,h,d,p,f,s,m,A,b,w.length,u),l}var HK={kernelName:rl,backendName:"wasm",kernelFunc:jK,setupFunc:UK},GK=Tn(Xs),qK=Tn(Ks),E3;function XK(e){E3=e.wasm.cwrap(jo,null,["number","number","number","number","number","number","array","number","number"])}function KK(e){let{backend:t,inputs:n,attrs:r}=e,{indices:a,updates:s}=n,{shape:i}=r,o=t.makeOutput(i,s.dtype);if(v.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,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),A=t.dataIdMap.get(o.dataId).id;return E3(p,f,Wn[s.dtype],l,c,u,m,d,A),o}var ZK={kernelName:jo,backendName:"wasm",setupFunc:XK,kernelFunc:KK},C3;function YK(e){C3=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function JK(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:v.sizeFromShape(a.shape.slice(1));return C3(i,o,l,p,u),c}var QK={kernelName:Ho,backendName:"wasm",kernelFunc:JK,setupFunc:YK},R3;function eZ(e){R3=e.wasm.cwrap(Ys,null,["number","number"])}function tZ(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 v.sizeFromShape(a.shape)===0||R3(r,s),a}var nZ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:eZ,kernelFunc:tZ},rZ=Tn(Zs);function Mp(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=hn.parseSliceParams(t,n,r),o=hn.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),c=a.makeOutput(i,t.dtype),u=v.computeStrides(t.shape),h=a.dataIdMap.get(c.dataId);if(o){let f=hn.computeFlatOffset(s,u);return t.dtype==="string"?h.stringBytes=l.slice(f,f+v.sizeFromShape(i)):a.typedArrayFromHeap(c).set(l.subarray(f,f+v.sizeFromShape(i))),c}if(t.dtype==="string"){let f=lp(l,s,i,t.shape,t.dtype);return h.stringBytes=f,c}let d=a.typedArrayFromHeap(c),p=t.shape.length;if(p===2)aZ(l,u[0],d,s,i);else if(p===3)sZ(l,u[0],u[1],d,s,i);else if(p===4)iZ(l,u[0],u[1],u[2],d,s,i);else{let f=lp(l,s,i,t.shape,t.dtype);d.set(f)}return c}function aZ(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 sZ(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 f=d*t+p*n+c;r.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function iZ(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],f=s[3];for(let m=l;m<h;m++)for(let A=c;A<d;A++)for(let y=u;y<p;y++){let g=m*t+A*n+y*r+f;a.set(e.subarray(g,g+i[3]),o),o+=i[3]}}var oZ={kernelName:qo,backendName:"wasm",kernelFunc:Mp},F3;function lZ(e){F3=e.wasm.cwrap(ei,null,["number","number","number","number"])}function uZ(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=v.sizeFromShape(n.shape)/o;return v.sizeFromShape(s.shape)===0||F3(a,i,o,l),s}var cZ={kernelName:ei,backendName:"wasm",setupFunc:lZ,kernelFunc:uZ};function hZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=R.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=Mp({inputs:{x:a},attrs:{begin:c,size:d},backend:r});return c[o]+=h,p})}var dZ={kernelName:Yo,backendName:"wasm",kernelFunc:hZ},pZ=Tn(Js),fZ=Tn($u),mZ=!0,AZ=An(ti,mZ),M3;function yZ(e){M3=e.wasm.cwrap(Fa,null,["number","number","number"])}function gZ(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 M3(i,a,l),o}var xZ={kernelName:Fa,backendName:"wasm",setupFunc:yZ,kernelFunc:gZ},$3;function wZ(e){$3=e.wasm.cwrap(Jo,null,["number","array","number","array","array","array","array","array","number","number"])}function bZ(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=R.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 f=a.shape.length-s.length,m=R.slice_util.maskToAxes(h),A=a.shape.slice();m.forEach(M=>{s[M]=0,i[M]=1,A.splice(M,0,1)});let y=kr({inputs:{x:a},attrs:{shape:A},backend:t}),{begin:g,end:w,strides:b}=R.slice_util.getNormalizedAxes(y.shape,p,f,s,i,o,l,c,u);s=g,i=w,o=b;let _=R.slice_util.maskToAxes(d);_.forEach(M=>{i[M]=s[M]+1,o[M]=1});let x=R.slice_util.computeOutShape(s,i,o),N=x.filter((M,O)=>_.indexOf(O)===-1);if(o.every(M=>M===1)){let M=Mp({inputs:{x:y},attrs:{begin:s,size:x},backend:t});t.disposeData(y.dataId);let O=kr({inputs:{x:M},attrs:{shape:N},backend:t});return t.disposeData(M.dataId),O}let T=t.makeOutput(N,"float32");if(!N.some(M=>M===0)){let M=t.dataIdMap.get(y.dataId).id,O=new Uint8Array(new Int32Array(v.computeStrides(y.shape)).buffer),W=new Uint8Array(new Int32Array(s).buffer),V=new Uint8Array(new Int32Array(i).buffer),U=new Uint8Array(new Int32Array(o).buffer),j=new Uint8Array(new Int32Array(N).buffer),X=new Uint8Array(new Int32Array(v.computeStrides(N)).buffer),G=t.dataIdMap.get(T.dataId).id;$3(M,O,y.shape.length,W,V,U,j,X,N.length,G)}t.disposeData(y.dataId);let E=kr({inputs:{x:T},attrs:{shape:N},backend:t});return t.disposeData(T.dataId),E}var _Z={kernelName:Jo,backendName:"wasm",setupFunc:wZ,kernelFunc:bZ},vZ=!0,kZ=An(ni,vZ),D3;function IZ(e){D3=e.wasm.cwrap(Qs,null,["number, number, number"])}function NZ(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}=Vl(i,a,t),f=h;if(p){let w=t.dataIdMap.get(u.dataId).id;w!==o&&(c=u,l=w,f=R.getInnerMostAxes(f.length,c.shape.length))}R.assertAxesAreInnerMostDims("sum",f,c.shape.length);let[m,A]=R.computeOutAndReduceShapes(c.shape,f),y=v.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;D3(l,y,w)}if(p&&t.disposeData(u.dataId),s){let w=R.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var SZ={kernelName:Qs,backendName:"wasm",setupFunc:IZ,kernelFunc:NZ},TZ=Tn(ri),O3;function EZ(e){O3=e.wasm.cwrap(Ra,null,["number","array","number","array","number","number"])}function CZ(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 O3(s,l,a.shape.length,c,o.length,Wn[u.dtype],h),u}var RZ={kernelName:Ra,backendName:"wasm",setupFunc:EZ,kernelFunc:CZ},z3;function FZ(e){z3=e.wasm.cwrap(el,null,["number","array","number","number","number","bool","number","number"])}var MZ=({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 z3(i,o,r.shape.length,Wn[r.dtype],a,s,u,d),[c,h]},$Z={kernelName:el,backendName:"wasm",setupFunc:FZ,kernelFunc:MZ};function DZ(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]=Mp({inputs:{x:a},attrs:{begin:h,size:d},backend:n});return u.map(({dataId:p,dtype:f})=>({dataId:p,dtype:f,shape:l}))}var OZ={kernelName:tl,backendName:"wasm",kernelFunc:DZ};function zZ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var PZ={kernelName:nl,backendName:"wasm",kernelFunc:zZ},LZ=[aq,iq,uq,yq,wq,kq,Iq,Nq,Eq,Cq,Mq,Oq,zq,Wq,Uq,Gq,Kq,Yq,Jq,Qq,tX,aX,sX,oX,rq,cX,pX,AX,xX,_X,kX,NX,cq,EX,RX,MX,$X,OX,LX,BX,jX,qX,ZX,JX,eK,tK,aK,oK,cK,dK,mK,yK,xK,_K,kK,SK,CK,FK,$K,DK,OK,bq,LK,VK,HK,qK,GK,ZK,QK,nZ,rZ,oZ,cZ,dZ,pZ,fZ,AZ,xZ,_Z,kZ,SZ,TZ,RZ,$Z,fq,OZ,PZ];for(let e of LZ)li(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 P3=to(H8()),WZ='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()}}}}',BZ=to(G8()),L3=class extends Au{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Ch(this,zr())}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=v.now();return e(),{kernelMs:v.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=v.sizeFromShape(n),o=i*v.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+v.sizeFromShape(r)*v.bytesPerElement(n));return VZ(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(),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=v.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=v.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 ${t}`)}}};function UZ(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{n(s.instance)})})}),{})}function W3(e,t,n){if($p!=null)return $p;let r="tfjs-backend-wasm.wasm";return e&&t?r="tfjs-backend-wasm-threaded-simd.wasm":e&&(r="tfjs-backend-wasm-simd.wasm"),Nc!=null&&Nc[r]!=null?Nc[r]:n+r}async function jZ(){let[e,t]=await Promise.all([J().getAsync("WASM_HAS_SIMD_SUPPORT"),J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,r)=>{let a={};a.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let c=WZ,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return o.endsWith(".wasm")?W3(e,t,Sc!=null?Sc:l):l+o},SA&&(a.instantiateWasm=UZ(W3(e,t,Sc!=null?Sc:"")));let s=!1;a.onAbort=()=>{s||Tc||(Tc=!0,r({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&$p==null?(a.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+P3.default.toString()],{type:"text/javascript"}),i=(0,P3.default)(a)):i=(0,BZ.default)(a),i.then(o=>{s=!0,Tc=!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 VZ(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 ${t}`)}}var HZ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],$p=null,Sc=null,Nc={},Tc=!1,SA=!1;function GZ(e,t=!1){if(Zf("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Tc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");$p=e,SA=t}function qZ(e,t=!1){if(Tc)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")Sc=e;else{Nc=e;let n=HZ.filter(r=>Nc[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.`)}SA=t}var B3="3.3.0",XZ=2;pl("wasm",async()=>{let{wasm:e}=await jZ();return new L3(e)},XZ);Z().prototype.abs=function(){return this.throwIfDisposed(),Lt(this)};Z().prototype.acos=function(){return this.throwIfDisposed(),Jf(this)};Z().prototype.acosh=function(){return this.throwIfDisposed(),Qf(this)};Z().prototype.add=function(e){return this.throwIfDisposed(),ie(this,e)};Z().prototype.all=function(e,t){return this.throwIfDisposed(),vd(this,e,t)};Z().prototype.any=function(e,t){return this.throwIfDisposed(),qu(this,e,t)};Z().prototype.argMax=function(e){return this.throwIfDisposed(),Xu(this,e)};Z().prototype.argMin=function(e){return this.throwIfDisposed(),em(this,e)};Z().prototype.asScalar=function(){return this.throwIfDisposed(),F(this.size===1,()=>"The array must have only 1 element."),H(this,[])};Z().prototype.asType=function(e){return this.throwIfDisposed(),ge(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(),tm(this)};Z().prototype.asinh=function(){return this.throwIfDisposed(),nm(this)};Z().prototype.atan=function(){return this.throwIfDisposed(),rm(this)};Z().prototype.atan2=function(e){return this.throwIfDisposed(),am(this,e)};Z().prototype.atanh=function(){return this.throwIfDisposed(),sm(this)};Z().prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),Zu(this,e,t,n,r)};Z().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Yu(this,e,t)};Z().prototype.batchNorm=function(e,t,n,r,a){return this.throwIfDisposed(),fi(this,e,t,n,r,a)};Z().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Ju(this,e)};Z().prototype.cast=function(e){return this.throwIfDisposed(),ge(this,e)};Z().prototype.ceil=function(){return this.throwIfDisposed(),um(this)};Z().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),kn(this,e,t)};Z().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof He&&(e=[e]),it([this,...e],t)};Z().prototype.conv1d=function(e,t,n,r,a,s){return this.throwIfDisposed(),Id(this,e,t,n,r,a,s)};Z().prototype.conv2dTranspose=function(e,t,n,r,a){return this.throwIfDisposed(),Nd(this,e,t,n,r,a)};Z().prototype.conv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),oa(this,e,t,n,r,a,s)};Z().prototype.cos=function(){return this.throwIfDisposed(),Qu(this)};Z().prototype.cosh=function(){return this.throwIfDisposed(),Sd(this)};Z().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),Td(this,e,t,n)};Z().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),dm(this,e,t)};Z().prototype.depthwiseConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),yl(this,e,t,n,r,a,s)};Z().prototype.dilation2d=function(e,t,n,r,a){return this.throwIfDisposed(),pm(this,e,t,n,r,a)};Z().prototype.divNoNan=function(e){return this.throwIfDisposed(),fm(this,e)};Z().prototype.div=function(e){return this.throwIfDisposed(),be(this,e)};Z().prototype.dot=function(e){return this.throwIfDisposed(),xx(this,e)};Z().prototype.elu=function(){return this.throwIfDisposed(),gl(this)};Z().prototype.equal=function(e){return this.throwIfDisposed(),La(this,e)};Z().prototype.erf=function(){return this.throwIfDisposed(),mm(this)};Z().prototype.exp=function(){return this.throwIfDisposed(),Zn(this)};Z().prototype.expandDims=function(e){return this.throwIfDisposed(),tn(this,e)};Z().prototype.expm1=function(){return this.throwIfDisposed(),Am(this)};Z().prototype.fft=function(){return this.throwIfDisposed(),uc(this)};Z().prototype.flatten=function(){return this.throwIfDisposed(),H(this,[this.size])};Z().prototype.floor=function(){return this.throwIfDisposed(),xl(this)};Z().prototype.floorDiv=function(e){return this.throwIfDisposed(),_d(this,e)};Z().prototype.gather=function(e,t){return this.throwIfDisposed(),mi(this,e,t)};Z().prototype.greaterEqual=function(e){return this.throwIfDisposed(),Ba(this,e)};Z().prototype.greater=function(e){return this.throwIfDisposed(),lr(this,e)};Z().prototype.ifft=function(){return this.throwIfDisposed(),kl(this)};Z().prototype.irfft=function(){return this.throwIfDisposed(),Hd(this)};Z().prototype.isFinite=function(){return this.throwIfDisposed(),wx(this)};Z().prototype.isInf=function(){return this.throwIfDisposed(),bx(this)};Z().prototype.isNaN=function(){return this.throwIfDisposed(),_x(this)};Z().prototype.leakyRelu=function(e){return this.throwIfDisposed(),tc(this,e)};Z().prototype.lessEqual=function(e){return this.throwIfDisposed(),Ai(this,e)};Z().prototype.less=function(e){return this.throwIfDisposed(),Cd(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(),Ix(this)};Z().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Md(this,e)};Z().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),bm(this,e,t)};Z().prototype.log=function(){return this.throwIfDisposed(),Dn(this)};Z().prototype.log1p=function(){return this.throwIfDisposed(),Rd(this)};Z().prototype.logicalAnd=function(e){return this.throwIfDisposed(),ur(this,e)};Z().prototype.logicalNot=function(){return this.throwIfDisposed(),nc(this)};Z().prototype.logicalOr=function(e){return this.throwIfDisposed(),$d(this,e)};Z().prototype.logicalXor=function(e){return this.throwIfDisposed(),Ex(this,e)};Z().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Ke(this,e,t,n)};Z().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),rc(this,e,t,n,r)};Z().prototype.max=function(e,t){return this.throwIfDisposed(),Yn(this,e,t)};Z().prototype.maximum=function(e){return this.throwIfDisposed(),Wr(this,e)};Z().prototype.mean=function(e,t){return this.throwIfDisposed(),St(this,e,t)};Z().prototype.min=function(e,t){return this.throwIfDisposed(),bl(this,e,t)};Z().prototype.minimum=function(e){return this.throwIfDisposed(),_l(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(),P(this,e)};Z().prototype.neg=function(){return this.throwIfDisposed(),Nt(this)};Z().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Kd(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(),cl(this,e,t,n)};Z().prototype.onesLike=function(){return this.throwIfDisposed(),On(this)};Z().prototype.pad=function(e,t){return this.throwIfDisposed(),la(this,e,t)};Z().prototype.pool=function(e,t,n,r,a){return this.throwIfDisposed(),Fx(this,e,t,n,r,a)};Z().prototype.pow=function(e){return this.throwIfDisposed(),ua(this,e)};Z().prototype.prelu=function(e){return this.throwIfDisposed(),sc(this,e)};Z().prototype.prod=function(e,t){return this.throwIfDisposed(),Od(this,e,t)};Z().prototype.reciprocal=function(){return this.throwIfDisposed(),Sm(this)};Z().prototype.relu=function(){return this.throwIfDisposed(),Vr(this)};Z().prototype.relu6=function(){return this.throwIfDisposed(),Pd(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(),Zx(this,e,t,n)};Z().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),Yx(this,e,t,n)};Z().prototype.reverse=function(e){return this.throwIfDisposed(),zn(this,e)};Z().prototype.rfft=function(){return this.throwIfDisposed(),cc(this)};Z().prototype.round=function(){return this.throwIfDisposed(),Tm(this)};Z().prototype.rsqrt=function(){return this.throwIfDisposed(),Ld(this)};Z().prototype.selu=function(){return this.throwIfDisposed(),Wd(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(),$n(this)};Z().prototype.sign=function(){return this.throwIfDisposed(),Cm(this)};Z().prototype.sin=function(){return this.throwIfDisposed(),Bd(this)};Z().prototype.sinh=function(){return this.throwIfDisposed(),Vd(this)};Z().prototype.slice=function(e,t){return this.throwIfDisposed(),Me(this,e,t)};Z().prototype.softmax=function(e){return this.throwIfDisposed(),lc(this,e)};Z().prototype.softplus=function(){return this.throwIfDisposed(),wl(this)};Z().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),ac(this,e,t)};Z().prototype.split=function(e,t){return this.throwIfDisposed(),Bt(this,e,t)};Z().prototype.sqrt=function(){return this.throwIfDisposed(),nn(this)};Z().prototype.square=function(){return this.throwIfDisposed(),ut(this)};Z().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Gd(this,e)};Z().prototype.squeeze=function(e){return this.throwIfDisposed(),Va(this,e)};Z().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof He?[this,e]:[this,...e];return dn(n,t)};Z().prototype.step=function(e){return this.throwIfDisposed(),Il(this,e)};Z().prototype.stridedSlice=function(e,t,n,r,a,s,i,o){return this.throwIfDisposed(),Fm(this,e,t,n,r,a,s,i,o)};Z().prototype.sub=function(e){return this.throwIfDisposed(),we(this,e)};Z().prototype.sum=function(e,t){return this.throwIfDisposed(),Fe(this,e,t)};Z().prototype.tan=function(){return this.throwIfDisposed(),Mm(this)};Z().prototype.tanh=function(){return this.throwIfDisposed(),ml(this)};Z().prototype.tile=function(e){return this.throwIfDisposed(),Wa(this,e)};Z().prototype.toBool=function(){return this.throwIfDisposed(),ge(this,"bool")};Z().prototype.toFloat=function(){return this.throwIfDisposed(),ge(this,"float32")};Z().prototype.toInt=function(){return this.throwIfDisposed(),ge(this,"int32")};Z().prototype.topk=function(e,t){return this.throwIfDisposed(),$m(this,e,t)};Z().prototype.transpose=function(e){return this.throwIfDisposed(),st(this,e)};Z().prototype.unique=function(e){return this.throwIfDisposed(),Xd(this,e)};Z().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),Dm(this,e,t)};Z().prototype.unstack=function(e){return this.throwIfDisposed(),cr(this,e)};Z().prototype.where=function(e,t){return this.throwIfDisposed(),In(e,this,t)};Z().prototype.zerosLike=function(){return this.throwIfDisposed(),qe(this)};var V3={kernelName:ao,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,Il(ge(n,"float32"),-1))}}},KZ={kernelName:so,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=ut(ge(n,"float32")),a=nn(we(Ie(1),r));return Nt(be(e,a))}}}},ZZ={kernelName:io,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=nn(we(ut(ge(n,"float32")),1));return be(e,r)}}}},YZ={kernelName:Ea,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=e,i=Wt(n.shape,a);return i.length>0&&(s=Fe(s,i)),H(s,n.shape)},b:()=>{let s=e,i=Wt(r.shape,a);return i.length>0&&(s=Fe(s,i)),H(s,r.shape)}}}},JZ={kernelName:ps,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,a)=>{n[a]=()=>e.clone()}),n}},QZ={kernelName:fs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>qe(n)}}},eY={kernelName:xu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>qe(n)}}},tY={kernelName:oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,nn(we(Ie(1),ut(ge(n,"float32")))))}}},nY={kernelName:lo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=nn(ie(Ie(1),ut(ge(n,"float32"))));return be(e,r)}}}},rY={kernelName:ho,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=ie(ut(n),ut(r)),i=P(e,be(r,s)),o=Wt(n.shape,a);return o.length>0&&(i=Fe(i,o)),H(i,n.shape)},b:()=>{let s=ie(ut(n),ut(r)),i=Nt(P(e,be(n,s))),o=Wt(r.shape,a);return o.length>0&&(i=Fe(i,o)),H(i,r.shape)}}}},aY={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,ie(ut(ge(n,"float32")),1))}}},sY={kernelName:co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,we(Ie(1),ut(ge(n,"float32"))))}}};function iY(e,t,n,r,a,s){let i=C(e,"dy","avgPool3dGrad"),o=C(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]])),F(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),F(c.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${c.rank}.`),s!=null&&F(Gt(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(zh,h,d);return u?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var oY=D({avgPool3dGrad_:iY}),lY={kernelName:wu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>oY(e,r,a,s,i,o)}}};function uY(e,t,n,r,a){let s=C(e,"dy","avgPoolGrad"),i=C(t,"input","avgPoolGrad");F(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,c=!1;i.rank===3&&(c=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),F(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let u={dy:l,input:o},h={filterSize:n,strides:r,pad:a},d=$.runKernel(Oh,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var cY=D({avgPoolGrad_:uY}),hY={kernelName:ms,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i}=n;return{x:()=>cY(e,r,a,s,i)}}},dY={kernelName:As,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[r,a]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>Ke(e,a,!1,!0),b:()=>Ke(r,e,!0,!1)}:!s&&i?{a:()=>Ke(e,a,!1,!1),b:()=>Ke(e,r,!0,!1)}:s&&!i?{a:()=>Ke(a,e,!1,!0),b:()=>Ke(r,e,!1,!1)}:{a:()=>Ke(a,e,!0,!0),b:()=>Ke(e,r,!0,!0)}}},pY={kernelName:bu,gradFunc:(e,t,n)=>{let{blockShape:r,crops:a}=n;return{x:()=>ac(e,r,a)}}},fY={kernelName:p5,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:()=>Fe(e,o,!0)}}},mY={kernelName:ys,gradFunc:e=>({x:()=>e.clone()})},AY={kernelName:gs,gradFunc:e=>({x:()=>qe(e)})},yY={kernelName:Ca,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:a,clipValueMax:s}=n;return{x:()=>In(ur(Ba(r,a),Ai(r,s)),e,qe(e))}}},gY={kernelName:_u,inputsToSave:["x"],gradFunc:V3.gradFunc},xY={kernelName:po,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(o=>o.shape),{axis:a}=n,s=ir(a,t[0].shape)[0],i=r.map(o=>o[s]);return Bt(e,i,s).map(o=>()=>o)}},wY={kernelName:xs,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return F(Pa(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>cm(r.shape,e,a,i,o,l),filter:()=>Lm(r,e,a.shape,i,o,l)}}},bY={kernelName:ws,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>oa(e,a,s,i,o,1,l),filter:()=>Lm(e,r,a.shape,s,i,o,l)}}};function _Y(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]])),F(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),F(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),F(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),F(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),F(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:r,pad:a,filterShape:n};return $.runKernel(Bh,o,l)}var vY=D({conv3DBackpropFilter_:_Y}),kY={kernelName:vu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s}=n;F(Pa(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[i,o]=t;return{x:()=>yx(i.shape,e,o,a,s),filter:()=>vY(i,e,o.shape,a,s)}}},IY={kernelName:bs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Nt(Bd(ge(n,"float32"))),e)}}},NY={kernelName:fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Vd(ge(n,"float32")),e)}}},SY={kernelName:_s,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a,exclusive:s,reverse:i}=n;return{x:()=>{let o=Tx([a],r.rank),l=Td(e,a,s,!i);return o!=null&&(l=st(l,o)),l}}}},TY={kernelName:vs,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s,dimRoundingMode:i}=n,o=r==null?[1,1]:r;F(Pa(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,c]=t;return F(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),F(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),F(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),F(Pr(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${o}'.`),i!=null&&F(Gt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>Ux(l.shape,e,c,a,s,r,i),filter:()=>Vx(l,e,c.shape,a,s,r,i)}}},EY={kernelName:ku,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,s={x:r,filter:a,dy:e},i={x:r,filter:a,dy:e};return{x:()=>$.runKernel(qh,s,n),filter:()=>$.runKernel(Xh,i,n)}}},CY={kernelName:yo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>$.runKernel(Kh,r)}}},RY={kernelName:go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(Zn(Nt(ut(n))),2/Math.sqrt(Math.PI));return{x:()=>P(e,r)}}},FY={kernelName:Is,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,n)}}},MY={kernelName:wo,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>H(e,n.shape)}}},$Y={kernelName:bo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,Zn(n))}}},DY={kernelName:Ns,gradFunc:e=>({x:()=>qe(e)})},OY={kernelName:Ss,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=be(e,ge(r,"float32")),i=Wt(n.shape,a);return i.length>0?H(Fe(s,i),n.shape):s},b:()=>{let s=P(e,ge(n,"float32")),i=Wt(r.shape,a);i.length>0&&(s=H(Fe(s,i),r.shape));let o=ut(r);return Nt(be(s,ge(o,"float32")))}}}},zY={kernelName:Ts,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:r}=n,[a,s,i,o]=t,l=o==null?Ie(1):o,c=Wt(s.shape,a.shape),u=[];if(s.rank===1){for(let m=0;m<a.shape.length-1;++m)u.push(a.shape[m]);u.push(1)}let h=we(a,s),d=P(e,l),p=Ld(ie(i,Ie(r))),f=P(P(P(p,p),p),Ie(-.5));return{x:()=>s.rank===1?H(P(P(e,Wa(H(p,[1,1,1,s.shape[0]]),u)),l),a.shape):H(P(P(e,p),l),a.shape),mean:()=>{let m=P(P(p,Ie(-1)),d);return s.rank===1&&(m=Fe(m,c)),H(m,s.shape)},variance:()=>{let m=P(P(f,h),d);return s.rank===1&&(m=Fe(m,c)),H(m,s.shape)},scale:()=>{let m=P(h,p),A=P(e,m);return s.rank===1&&(A=Fe(A,c)),H(A,s.shape)},offset:()=>{let m=e;return s.rank===1&&(m=Fe(m,c)),H(m,s.shape)}}}},PY={kernelName:vo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,a]=t,{axis:s}=n,i=ir(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=U3(0,u),f=U3(u+1,u+1+d),m=j3([c,[l],h]),A=H(e,m),y=H(a,[l]),g=j3([[u],p,f]),w=st(A,g),b=Dm(w,y,r.shape[i]),_=wm(g);return b=st(b,_),b},indices:()=>a}}};function U3(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function j3(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 LY={kernelName:Es,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>qe(n),b:()=>qe(r)}}},WY={kernelName:Cs,gradFunc:e=>({x:()=>ge(e,"float32")})},BY={kernelName:No,gradFunc:e=>({x:()=>qe(e)})},VY={kernelName:So,gradFunc:e=>({x:()=>qe(e)})},UY={kernelName:To,gradFunc:e=>({x:()=>qe(e)})},jY={kernelName:Rs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:a}=n,s=lr(r,0);return{x:()=>In(s,e,P(e,a))}}},HY={kernelName:Ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,ie(n,1))}}},GY={kernelName:Fs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,ge(n,"float32"))}}},qY={kernelName:f5,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n;return{logits:()=>{let s=!0,i=Zn(r);return we(e,P(Fe(e,a,s),i))}}}};function XY(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 $.runKernel(ed,o,l)}var KY=D({localResponseNormalizationBackprop_:XY}),ZY={kernelName:Tu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>KY(r,a,e,s,i,o,l)}}};function H3(e,t,n,r){return t.rank<n.rank&&(t=H(t,yi(t.shape,r))),e.rank<n.rank&&(e=H(e,yi(e.shape,r))),{x:()=>P(e,ge(La(n,t),e.dtype))}}var G3={kernelName:Ms,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{reductionIndices:a}=r,s=t[0],i=t[1],o=ir(a,s.shape),l=H3(e,i,s,o);return{x:()=>l.x()}}},YY={kernelName:$s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,ge(Ba(n,r),"float32")),b:()=>P(e,ge(Cd(n,r),"float32"))}}};function JY(e,t,n,r,a,s,i){let o=C(e,"dy","maxPool3dGrad"),l=C(t,"input","maxPool3dGrad"),c=C(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]])),F(u.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),F(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),F(d.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`),i!=null&&F(Gt(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let f={dy:u,input:h,output:d},m={filterSize:r,strides:a,pad:s,dimRoundingMode:i},A=$.runKernel(nd,f,m);return p?H(A,[A.shape[1],A.shape[2],A.shape[3],A.shape[4]]):A}var QY=D({maxPool3dGrad_:JY}),eJ={kernelName:Eu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>QY(e,r,a,s,i,o,l)}}};function tJ(e,t,n,r,a,s,i){let o=C(e,"dy","maxPoolGrad"),l=C(t,"input","maxPoolGrad"),c=C(n,"output","maxPoolGrad");F(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),F(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),F(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&F(Gt(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 $.runKernel(td,u,h)}var nJ=D({maxPoolGrad_:tJ}),rJ={kernelName:Ds,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>nJ(e,r,a,s,i,o)}}},aJ={kernelName:Os,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n,s=ir(a,r.shape),i=Sx(r.shape,s)[1],o=zt(i);return{x:()=>{let l=r.shape.slice();s.forEach(u=>{l[u]=1});let c=H(e,l);return be(P(c,Br(r.shape,"float32")),o)}}}},sJ={kernelName:zs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:a}=r,[s,i]=t,o=ir(a,s.shape),l=H3(e,i,s,o);return{x:()=>l.x()}}},iJ={kernelName:Ps,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,ge(Ai(n,r),"float32")),b:()=>P(e,ge(lr(n,r),"float32"))}}},oJ={kernelName:Cu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Me(e,s,r.shape)}}},lJ={kernelName:Mo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=Wt(n.shape,a);return s.length>0?H(Fe(e,s),n.shape):e},b:()=>{let s=P(e,Nt(xl(be(n,r)))),i=Wt(r.shape,a);return i.length>0?H(Fe(s,i),r.shape):s}}}},uJ={kernelName:Ls,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=P(e,ge(r,"float32")),i=Wt(n.shape,a);return i.length>0?H(Fe(s,i),n.shape):s},b:()=>{let s=P(e,ge(n,"float32")),i=Wt(r.shape,a);return i.length>0?H(Fe(s,i),r.shape):s}}}},cJ={kernelName:$o,gradFunc:e=>({x:()=>Nt(e)})},hJ={kernelName:Ws,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Mt(n.shape,"float32")}}},dJ={kernelName:Lo,gradFunc:e=>({x:()=>qe(e)})},pJ={kernelName:Wo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return cr(e,r).map(a=>()=>a)}},q3={kernelName:Bs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Me(e,s,r.shape)}}},fJ={kernelName:Vs,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,a]=t,s=n,i=r,o=yt(s.shape,i.shape);return{a:()=>{let l=ge(i,"float32"),c=P(e,P(l,ua(s,we(l,Ie(1))))),u=Wt(s.shape,o);return u.length>0&&(c=Fe(c,u)),H(c,s.shape)},b:()=>{let l=lr(s,0),c=In(l,Dn(s),qe(s)),u=P(e,P(a,c)),h=Wt(i.shape,o);return h.length>0&&(u=Fe(u,h)),H(u,i.shape)}}}},mJ={kernelName:Us,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,a=lr(n,0);return{x:()=>In(a,e,P(e,r)),alpha:()=>{let s=In(a,qe(e),P(e,n)),i=Wt(r.shape,e.shape);return i.length>0&&(s=Fe(s,i)),H(s,r.shape)}}}},AJ={kernelName:ks,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=be(e,ge(r,"float32")),i=Wt(n.shape,a);return i.length>0?H(Fe(s,i),n.shape):s},b:()=>{let s=P(e,ge(n,"float32")),i=Wt(r.shape,a);i.length>0&&(s=H(Fe(s,i),r.shape));let o=ut(r);return Nt(be(s,ge(o,"float32")))}}}},yJ={kernelName:Vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,Nt(ut(n)))}}},gJ={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(Ai(n,6),Il(n));return{x:()=>P(e,ge(r,"float32"))}}},xJ={kernelName:js,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,ge(Il(n),"float32"))}}},wJ={kernelName:Uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>H(e,n.shape)}}},bJ={kernelName:Hs,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(od,a,n)}}},_J={kernelName:Fu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(id,a,n)}}},vJ={kernelName:qs,gradFunc:(e,t,n)=>{let{dims:r}=n,a=ir(r,e.shape);return{x:()=>zn(e,a)}}},kJ={kernelName:Xs,gradFunc:e=>({x:()=>qe(e)})},IJ={kernelName:Ks,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Nt(be(e,P(ua(n,1.5),2)))}}},NJ={kernelName:Ho,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>ge(qe(n),"float32"),t:()=>P(e,ge(n,e.dtype)),e:()=>P(e,ge(nc(n),e.dtype))}}},SJ={kernelName:Go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=lr(n,Ie(0)),a=Ie(ew),s=Ie(tw),i=P(e,s),o=P(P(e,a),Zn(ge(n,"float32")));return In(r,i,o)}}}},TJ={kernelName:Ys,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(n,we(Ie(1),n)))}}},EJ={kernelName:Ko,gradFunc:e=>({x:()=>qe(e)})},CJ={kernelName:Zs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Qu(ge(n,"float32")),e)}}},RJ={kernelName:Xo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Sd(ge(n,"float32")),e)}}},FJ={kernelName:qo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:a,size:s}=n,i=r.shape,[o,l]=Q5(r,a,s),c=[];for(let u=0;u<e.rank;u++)c.push([o[u],i[u]-o[u]-l[u]]);return{x:()=>la(e,c)}}},MJ={kernelName:ei,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:a}=n,s=!0,i=P(e,r);return{logits:()=>we(i,P(Fe(i,[a],s),r))}}},$J={kernelName:Zo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,$n(n))}}},X3={kernelName:Mu,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:a}=n;return{x:()=>Yu(e,r,a)}}},K3={kernelName:Yo,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>it(e,r)}}},DJ={kernelName:Js,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,P(nn(ge(n,"float32")),2))}}},OJ={kernelName:$u,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(ge(n,"float32"),2))}}},zJ={kernelName:ti,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=Ie(2);return{a:()=>P(e,P(a,we(n,r))),b:()=>P(e,P(a,we(r,n)))}}},PJ={kernelName:Fa,gradFunc:e=>({x:()=>qe(e)})},LJ={kernelName:ni,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=e,i=Wt(n.shape,a);return i.length>0&&(s=Fe(s,i)),H(s,n.shape)},b:()=>{let s=e,i=Wt(r.shape,a);return i.length>0&&(s=Fe(s,i)),H(Nt(s),r.shape)}}}},WJ={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,a=r.shape.slice(),{axis:s}=n;ir(s,r.shape).forEach(l=>{a[l]=1});let i=H(e,a),o=P(i,Br(r.shape,"float32"));return{x:()=>o}}},BJ={kernelName:Qo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,ut(Qu(n)))}}},VJ={kernelName:ri,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(we(Ie(1),ut(n)),e)}}},UJ={kernelName:Ra,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{reps:a}=n;return{x:()=>{let s=qe(r);if(r.rank===1)for(let i=0;i<a[0];++i)s=ie(s,Me(e,[i*r.shape[0]],[r.shape[0]]));else if(r.rank===2)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)s=ie(s,Me(e,[i*r.shape[0],o*r.shape[1]],[r.shape[0],r.shape[1]]));else if(r.rank===3)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)s=ie(s,Me(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2]],[r.shape[0],r.shape[1],r.shape[2]]));else if(r.rank===4)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)for(let c=0;c<a[3];++c)s=ie(s,Me(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2],c*r.shape[3]],[r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${r.rank} tensors yet.`);return s}}}},jJ={kernelName:ai,gradFunc:(e,t,n)=>{let r=n,{perm:a}=r,s=wm(a);return{x:()=>st(e,s)}}},HJ={kernelName:tl,gradFunc:(e,t,n)=>{let r=n,{axis:a}=r;return{value:()=>dn(e,a)}}},qJ={kernelName:Du,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>GJ(e,n)}}};function GJ(e,t){let n=Wr(t,qe(t)),r=mi(e,n),a=Ba(t,Ie(0,"int32")),s=r.rank-a.rank;for(let o=0;o<s;++o)a=tn(a,o+1);a=ur(a,Br(r.shape,"bool"));let i=qe(r);return In(a,r,i)}var XJ={kernelName:nl,gradFunc:e=>({x:()=>qe(e)})},KJ=[V3,KZ,ZZ,YZ,JZ,QZ,eY,tY,nY,rY,aY,sY,lY,hY,dY,pY,fY,mY,AY,yY,gY,xY,bY,wY,kY,IY,NY,SY,TY,EY,AJ,CY,RY,FY,MY,$Y,OY,DY,zY,PY,LY,WY,BY,VY,UY,jY,HY,GY,qY,ZY,G3,G3,YY,eJ,rJ,aJ,sJ,iJ,oJ,lJ,uJ,cJ,hJ,dJ,pJ,q3,q3,fJ,mJ,yJ,gJ,xJ,wJ,bJ,_J,vJ,kJ,IJ,NJ,SJ,TJ,EJ,CJ,RJ,FJ,MJ,$J,X3,X3,K3,K3,DJ,zJ,OJ,PJ,LJ,WJ,BJ,VJ,UJ,jJ,HJ,qJ,XJ];for(let e of KJ)m5(e);var Z3={};Le(Z3,{maxNorm:()=>ZJ,minMaxNorm:()=>QJ,nonNeg:()=>JJ,unitNorm:()=>YJ});var TA;function Vt(){return TA==null&&(TA=ix().epsilon()),TA}function Ir(){return"channelsLast"}var pa=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,pa.prototype)}},Nr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Nr.prototype)}},B=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,B.prototype)}},ze=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,ze.prototype)}},Y3=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Y3.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 Gr(e,t){if(!e)throw new Y3(t)}function J3(e,t){let n=0;for(let r of e)r===t&&n++;return n}function En(e){return e.length===1?e[0]:e}function ft(e){return Array.isArray(e)?e:[e]}function fa(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 dr={};function EA(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function CA(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>CA(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:CA(r))}}}function Ec(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 dr)i=dr[s];else if(i=t[s],i==null)throw new B(`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 ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new B(`${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 dr?[o,l]=dr.className:i in t&&([o,l]=t[i]),o==null)throw new B(`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(dr))c[p]=dr[p];for(let p of Object.keys(n))c[p]=n[p];let u=s.config;u.customObjects=c;let h=Object.assign({},dr);for(let p of Object.keys(n))dr[p]=n[p];CA(s.config);let d=l(o,s.config,n,a);return dr=Object.assign({},h),d}else{let c=Object.assign({},dr);for(let h of Object.keys(n))dr[h]=n[h];let u=new o(s.config);return dr=Object.assign({},c),u}}}function eQ(e,t){return e<t?-1:e>t?1:0}function Dp(e,t){return-1*eQ(e,t)}function qa(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function tQ(e){if(e==null)throw new B(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Ri(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new B(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function RA(e,t,n=0,r=Infinity){return Gr(n>=0),Gr(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(a=>typeof a===t)}function Kt(e,t){Array.isArray(e)?(v.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>Kt(n,`element ${r+1} of ${t}`))):v.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${Q3(e)}.`)}function Q3(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>Q3(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function nQ(e,t){let n=v.now(),r;return(...a)=>{let s=v.now();return s-n<t||(n=s,r=e(...a)),r}}function e7(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function FA(e,t){return z(()=>nn(Fe(P(e,e),t,!0)))}var Cc=class extends ae.Serializable{getConfig(){return{}}},MA=class extends Cc{constructor(e){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return z(()=>{let t=FA(e,this.axis),n=kn(t,0,this.maxValue);return P(e,be(n,ie(Vt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MA.className="MaxNorm";ae.registerClass(MA);var $A=class extends Cc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return z(()=>be(e,ie(Vt(),FA(e,this.axis))))}getConfig(){return{axis:this.axis}}};$A.className="UnitNorm";ae.registerClass($A);var DA=class extends Cc{apply(e){return Vr(e)}};DA.className="NonNeg";ae.registerClass(DA);var OA=class extends Cc{constructor(e){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=e.minValue!=null?e.minValue:this.defaultMinValue,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.rate=e.rate!=null?e.rate:this.defaultRate,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return z(()=>{let t=FA(e,this.axis),n=ie(P(this.rate,kn(t,this.minValue,this.maxValue)),P(1-this.rate,t));return P(e,be(n,ie(Vt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};OA.className="MinMaxNorm";ae.registerClass(OA);var t7={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ut(e){return EA(e)}function n7(e,t={}){return Ec(e,ae.SerializationMap.getMap().classNameMap,t,"constraint")}function jt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in t7?t7[e]:e,config:{}};return n7(t)}else return e instanceof Cc?e:n7(e)}function ZJ(e){return new MA(e)}function YJ(e){return new $A(e)}function JJ(){return new DA}function QJ(e){return new OA(e)}var r7={};Le(r7,{constant:()=>sQ,glorotNormal:()=>dQ,glorotUniform:()=>hQ,heNormal:()=>pQ,heUniform:()=>fQ,identity:()=>uQ,leCunNormal:()=>mQ,leCunUniform:()=>AQ,ones:()=>aQ,orthogonal:()=>yQ,randomNormal:()=>oQ,randomUniform:()=>iQ,truncatedNormal:()=>lQ,varianceScaling:()=>cQ,zeros:()=>rQ});var gQ=["channelsFirst","channelsLast"],xQ=["nearest","bilinear"],wQ=["valid","same","causal"],bQ=["max","avg"],_Q=["sum","mul","concat","ave"],Ul=new Map;function Rt(e){Ri(gQ,"DataFormat",e)}function vQ(e){Ri(xQ,"InterpolationFormat",e)}function er(e){Ri(wQ,"PaddingMode",e)}function a7(e){Ri(bQ,"PoolMode",e)}var Rc=[],s7="/";function Fi(e,t){Rc.push(e);try{let n=t();return Rc.pop(),n}catch(n){throw Rc.pop(),n}}function kQ(){return Rc.length===0?"":Rc.join(s7)+s7}function o7(e){if(!i7(e))throw new Error("Not a valid tensor name: '"+e+"'");return kQ()+e}function l7(e){if(!i7(e))throw new Error("Not a valid tensor name: '"+e+"'");Ul.has(e)||Ul.set(e,0);let t=Ul.get(e);if(Ul.set(e,Ul.get(e)+1),t>0){let n=`${e}_${t}`;return Ul.set(n,1),n}else return e}var IQ=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function i7(e){return!!e.match(IQ)}function NQ(e){return e===parseInt(e.toString(),10)}function Xa(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 u7(e){return e=Array.isArray(e)?new Float32Array(e):e,un(e)}function jl(e){return bl(u7(e)).dataSync()[0]}function Ka(e){return Yn(u7(e)).dataSync()[0]}function Sr(e,t){if(t<e)throw new B(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let r=e;r<t;++r)n.push(r);return n}function Fc(e,t){return e.asType(t)}function Mc(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 SQ(e,t){return z(()=>{if(e.shape.length!==2)throw new B(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Mc(e,1);return zA(n,[1,t,1])})}function TQ(e){let t=[Xa(e.shape)];return e.reshape(t)}function EQ(e){if(e.rank<=1)throw new B(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Xa(e.shape,1)];return e.reshape(t)}function Mi(e,t,n){return z(()=>{switch(e.rank){case 1:return Ud(e,t,n);case 2:return Rm(e,[t,0],[n,e.shape[1]]);case 3:return jd(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return oc(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Me(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Me(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new B(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function PA(e,t,n){return z(()=>{switch(e.rank){case 1:return Ud(e,t,n);case 2:return Rm(e,[0,t],[e.shape[0],n]);case 3:return jd(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return oc(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new B(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Op(e,t,n,r){return z(()=>{switch(e.rank){case 1:return Ud(e,t,n);case 2:switch(r){case 1:return Mi(e,t,n);case 2:return PA(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${r}`)}case 3:switch(r){case 1:return Mi(e,t,n);case 2:return jd(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return PA(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${r}`)}case 4:switch(r){case 1:return Mi(e,t,n);case 2:return oc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return oc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return PA(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${r}`)}default:throw new B(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function LA(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),it(e,t)}function c7(e,t){switch(e.rank){case 1:return fx([e,t]);case 2:return Al([e,t],0);case 3:return mx([e,t],0);case 4:return Ax([e,t],0);default:throw new B(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function zA(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new B(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Wa(e,t)}function zp(e,t=0,n=1,r,a){return Mx(e,t,n,r,a)}function qr(e,t,n,r){if(e.rank<2||t.rank<2)throw new ze(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let a=e.shape.slice(-1)[0],s=t.shape.slice(-2)[0];if(a!==s)throw new ze(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let a=!1,s=!1;return Ua.matMul({a:e,b:t,transposeA:a,transposeB:s,bias:r?WA(e.rank,r,Ir()):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},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=t.transpose(u).reshape([l,-1]);let h=[...a,...c],d=!1,p=!1;return Ua.matMul({a:e,b:t,transposeA:d,transposeB:p,bias:r?WA(e.rank,r,Ir()):null,activation:n}).reshape(h)}}function h7(e,t,n){return z(()=>(Array.isArray(t)?t=un(t,"int32"):t=t.toInt(),mi(e,t,n)))}function $c(e){return P(e,e)}function WA(e,t,n){let r=t.shape;if(t.rank!==1&&t.rank!==e)throw new B(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return 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 B(`Unsupported input rank by biasAdd: ${t.rank}`)}function Xr(e,t,n){return z(()=>(n==null&&(n=Ir()),Rt(n),e.add(WA(e.rank,t,n))))}function CQ(e,t=1){if(t!==1)throw new ze(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return gl(e)}function RQ(e){return z(()=>be(e,Lt(e).add(1)))}function d7(e,t,n,r){return z(()=>Wx(e,t,n,r))}function FQ(e){return z(()=>{let t=ie(.5,P(.2,e));return kn(t,0,1)})}function Dc(e,t,n=!1){return n?e():t()}var MQ=["fanIn","fanOut","fanAvg"],$Q=["normal","uniform","truncatedNormal"];function DQ(e){Ri(MQ,"FanMode",e)}function OQ(e){Ri($Q,"Distribution",e)}var pr=class extends ae.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},BA=class extends pr{apply(e,t){return Mt(e,t)}};BA.className="Zeros";ae.registerClass(BA);var Pp=class extends pr{apply(e,t){return Br(e,t)}};Pp.className="Ones";ae.registerClass(Pp);var VA=class extends pr{constructor(e){super();if(typeof e!="object")throw new B(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new B(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return z(()=>P(Ie(this.value),Br(e,t)))}getConfig(){return{value:this.value}}};VA.className="Constant";ae.registerClass(VA);var UA=class extends pr{constructor(e){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=e.minval||this.DEFAULT_MINVAL,this.maxval=e.maxval||this.DEFAULT_MAXVAL,this.seed=e.seed}apply(e,t){return vl(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};UA.className="RandomUniform";ae.registerClass(UA);var jA=class extends pr{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ze(`randomNormal does not support dType ${t}.`);return zp(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};jA.className="RandomNormal";ae.registerClass(jA);var HA=class extends pr{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ze(`truncatedNormal does not support dType ${t}.`);return qd(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};HA.className="TruncatedNormal";ae.registerClass(HA);var GA=class extends pr{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return z(()=>{if(e.length!==2||e[0]!==e[1])throw new B("Identity matrix initializer can only be used for 2D square matrices.");return P(this.gain,ym(e[0]))})}getConfig(){return{gain:this.gain}}};GA.className="Identity";ae.registerClass(GA);function zQ(e,t="channelsLast"){let n,r;if(Rt(t),e.length===2)n=e[0],r=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let a=Xa(e,2);n=e[1]*a,r=e[0]*a}else if(t==="channelsLast"){let a=Xa(e,0,e.length-2);n=e[e.length-2]*a,r=e[e.length-1]*a}}else{let a=Xa(e);n=Math.sqrt(a),r=Math.sqrt(a)}return[n,r]}var Cn=class extends pr{constructor(e){super();if(e.scale<0)throw new B(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,DQ(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,OQ(this.distribution),this.seed=e.seed}apply(e,t){let n=zQ(e),r=n[0],a=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,r):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new ze(`${this.getClassName()} does not support dType ${t}.`);return qd(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return vl(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Cn.className="VarianceScaling";ae.registerClass(Cn);var Lp=class extends Cn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Lp.className="GlorotUniform";ae.registerClass(Lp);var Wp=class extends Cn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Wp.className="GlorotNormal";ae.registerClass(Wp);var Bp=class extends Cn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Bp.className="HeNormal";ae.registerClass(Bp);var Vp=class extends Cn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Vp.className="HeUniform";ae.registerClass(Vp);var Up=class extends Cn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Up.className="LeCunNormal";ae.registerClass(Up);var jp=class extends Cn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};jp.className="LeCunNormal";ae.registerClass(jp);var qA=class extends pr{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new ze("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return z(()=>{if(e.length<2)throw new ze("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=zp(n,0,1,"float32"),a=Qx.gramSchmidt(r);return e[0]>e[1]&&(a=a.transpose()),P(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};qA.className="Orthogonal";ae.registerClass(qA);var p7={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 f7(e,t={}){return Ec(e,ae.SerializationMap.getMap().classNameMap,t,"initializer")}function Tt(e){return EA(e)}function xt(e){if(typeof e=="string"){let t=e in p7?p7[e]:e;if(t==="GlorotNormal")return new Wp;if(t==="GlorotUniform")return new Lp;if(t==="HeNormal")return new Bp;if(t==="HeUniform")return new Vp;if(t==="LeCunNormal")return new Up;if(t==="LeCunUniform")return new jp;{let n={};return n.className=t,n.config={},f7(n)}}else return e instanceof pr?e:f7(e)}function rQ(){return new BA}function aQ(){return new Pp}function sQ(e){return new VA(e)}function iQ(e){return new UA(e)}function oQ(e){return new jA(e)}function lQ(e){return new HA(e)}function uQ(e){return new GA(e)}function cQ(e){return new Cn(e)}function hQ(e){return new Lp(e)}function dQ(e){return new Wp(e)}function pQ(e){return new Bp(e)}function fQ(e){return new Vp(e)}function mQ(e){return new Up(e)}function AQ(e){return new jp(e)}function yQ(e){return new qA(e)}var m7={};Le(m7,{Layer:()=>Ze,RNN:()=>Kr,RNNCell:()=>Oc,activation:()=>QQ,add:()=>lee,alphaDropout:()=>Hee,average:()=>uee,averagePooling1d:()=>XA,averagePooling2d:()=>KA,averagePooling3d:()=>ZA,avgPool1d:()=>gee,avgPool2d:()=>wee,avgPool3d:()=>_ee,avgPooling1d:()=>xee,avgPooling2d:()=>bee,avgPooling3d:()=>vee,batchNormalization:()=>mee,bidirectional:()=>zee,concatenate:()=>cee,conv1d:()=>HQ,conv2d:()=>GQ,conv2dTranspose:()=>qQ,conv3d:()=>XQ,convLstm2d:()=>Mee,convLstm2dCell:()=>$ee,cropping2D:()=>ZQ,dense:()=>eee,depthwiseConv2d:()=>JQ,dot:()=>fee,dropout:()=>tee,elu:()=>LQ,embedding:()=>oee,flatten:()=>ree,gaussianDropout:()=>jee,gaussianNoise:()=>Uee,globalAveragePooling1d:()=>kee,globalAveragePooling2d:()=>Iee,globalMaxPool1d:()=>Lee,globalMaxPool2d:()=>Wee,globalMaxPooling1d:()=>y7,globalMaxPooling2d:()=>g7,gru:()=>See,gruCell:()=>Tee,input:()=>A7,inputLayer:()=>PQ,layerNormalization:()=>Aee,leakyReLU:()=>BQ,lstm:()=>Eee,lstmCell:()=>Cee,masking:()=>Gee,maxPool1d:()=>Bee,maxPool2d:()=>Vee,maxPooling1d:()=>x7,maxPooling2d:()=>w7,maxPooling3d:()=>Nee,maximum:()=>hee,minimum:()=>dee,multiply:()=>pee,permute:()=>iee,prelu:()=>VQ,reLU:()=>WQ,repeatVector:()=>aee,reshape:()=>see,rnn:()=>Dee,separableConv2d:()=>KQ,simpleRNN:()=>Ree,simpleRNNCell:()=>Fee,softmax:()=>UQ,spatialDropout1d:()=>nee,stackedRNNCells:()=>Oee,thresholdedReLU:()=>jQ,timeDistributed:()=>Pee,upSampling2d:()=>YQ,zeroPadding2d:()=>yee});var qee=0;function b7(){return qee++}var Hp={};function Gp(e=""){return e in Hp||(Hp[e]=0),Hp[e]+=1,e+Hp[e].toString()}function YA(e){return Array.isArray(e)&&Array.isArray(e[0])}function qp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function We(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new B(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function ht(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new B(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Xp(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 _7="Variable",v7=class{constructor(e,t="float32",n=_7,r=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=b7(),n=n==null?_7:n,this.originalName=o7(n),this.name=l7(this.originalName),this.trainable_=r,this.constraint=a,this.val=Dx(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Xee(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 Xee(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function JA(e){return e.map(t=>t.read())}function QA(e){e.forEach(t=>{t[0].write(t[1])})}var Zt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Tr=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=b7(),s!=null&&(this.originalName=o7(s),this.name=l7(this.originalName)),this.rank=t.length}},Kee=0,Kp=class{constructor(e,t){this.callArgs=t,this.id=Kee++,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}}},Zee=0,Ze=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Zee++,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=fa(n)+"_"+Gp(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 Nr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new B(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return En(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return En(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new pa(`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 pa(`Layer ${this.name} is not connected, no input to return.`);return En(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new pa(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new pa(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return En(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 B(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],a=t[n];if(a==null)continue;let s=r.rank;if(a.ndim!=null&&s!==a.ndim)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new B(`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 B(`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 B(`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 B(`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 B(`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 Tr)){r=!1;break}let a=!0;for(let s of n)if(s instanceof Tr){a=!1;break}if(r===a)throw new B("Arguments to apply() must be all SymbolicTensors or all Tensors");return Fi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of ft(e))s.push(i.shape);this.build(En(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=En(o),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=Yee(e),i=this.computeOutputShape(s),o,l=Jee(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 Tr(l,c,this,ft(e),t,this.name,u)):o=new Tr(l,i,this,ft(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 pa(`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 pa(`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 Nr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Xp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return JA(e?this.trainableWeights:this.weights)}setWeights(e){z(()=>{let t=this.weights;if(t.length!==e.length)throw new B(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],r=JA(t);for(let a=0;a<r.length;++a){let s=r[a],i=t[a],o=e[a];if(!v.arraysEqual(s.shape,o.shape))throw new B(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}QA(n)})}addWeight(e,t,n,r,a,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new B(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(r=xt("zeros"));let o=r.apply(t,n),l=new v7(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,a,s,i=null){let o=ft(e);t=ft(t),n=ft(n),r=ft(r),a=qp(a),s=qp(s);let l=[],c=[],u=[];for(let h of o)l.push(h.sourceLayer),c.push(h.nodeIndex),u.push(h.tensorIndex);new Kp({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 Yee(e){e=ft(e);let t=[];for(let n of e)t.push(n.shape);return En(t)}function Jee(e){return"float32"}function k7(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=k7(i,o,l);for(let u of c)a.indexOf(u)===-1&&a.push(u)}return a}}}var Hl=class extends Ze{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Gp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new B("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new B("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new B("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let r=new Tr(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new Kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new B(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};Hl.className="InputLayer";ae.registerClass(Hl);function I7(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new B("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new Hl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Za(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 N7(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var S7;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(S7||(S7={}));var Qee=125,Gl=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){}},T7=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)}},ete=class extends Gl{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=z(()=>ie(this.totals[r],P(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:z(()=>{let r=P(be(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),qt(t[n])}))}},E7=class extends Gl{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]}},C7=class extends Gl{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Qee),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");v.isNumber(this.yieldEvery)&&(this.maybeWait=nQ(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 Za(n),r.push(this.yield(e,t,n))),r.push(ip()),await Promise.all(r)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Za(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Za(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(ip()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Za(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Za(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(ip()):v.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Za(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Za(e),await this.trainEnd(e))}};function R7(e,t){return e==null&&(e={}),e instanceof Gl?[e]:Array.isArray(e)&&e[0]instanceof Gl?e:ft(e).map(n=>new C7(n,t))}var fr=class{constructor(){}static registerCallbackConstructor(e,t){v.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),fr.checkForDuplicate(t),fr.constructors[e]==null&&(fr.constructors[e]=[]),fr.constructors[e].push(t)}static checkForDuplicate(e){for(let t in fr.constructors)fr.constructors[+t].forEach(n=>{if(n===e)throw new B("Duplicate callback constructor.")})}static clear(){fr.constructors={}}static createCallbacks(e){let t=[];for(let n in fr.constructors){let r=+n;e>=r&&t.push(...fr.constructors[r])}return t.map(n=>new n)}};fr.constructors={};function F7(e,t,n,r,a,s,i,o,l){let c=new E7,u=[new ete,...fr.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let h=new T7(u);return h.setParams({epochs:n,initialEpoch:r,samples:a,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:c}}function Er(e,t={},n=!1){return Ec(e,ae.SerializationMap.getMap().classNameMap,t,"layer",n)}function Zp(e,t){return z(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Fe($c(e),t,!0),r=ec(n.shape,Vt()),a=nn(Wr(n,r));return be(e,a)})}function $i(e,t){return z(()=>St($c(we(t,e)),-1))}function Yp(e,t){return z(()=>St(Lt(we(t,e)),-1))}function ql(e,t){return z(()=>{let n=we(e,t),r=kn(Lt(e),Vt(),Number.MAX_VALUE),a=Lt(be(n,r));return P(100,St(a,-1))})}function tte(e,t){return z(()=>{let n=kn(t,Vt(),Number.MAX_VALUE),r=Dn(ie(1,n)),a=kn(e,Vt(),Number.MAX_VALUE),s=Dn(ie(1,a));return St($c(we(r,s)),-1)})}function nte(e,t){return z(()=>{let n=Wr(0,we(1,P(e,t)));return St($c(n),-1)})}function rte(e,t){return z(()=>{let n=Wr(0,we(1,P(e,t)));return St(n,-1)})}function ate(e,t){return z(()=>{let n=Fe(P(e,t),-1),r=Yn(P(we(1,e),t),-1);return Wr(0,ie(1,we(r,n)))})}function ste(e,t){return z(()=>{let n=Math.log(2),r=we(t,e),a=we(ie(r,wl(P(-2,r))),n);return St(a,-1)})}function zc(e,t,n=!1){return z(()=>{if(n)t=lc(t);else{let r=Fe(t,t.shape.length-1,!0);t=be(t,r)}return t=kn(t,Vt(),1-Vt()),Nt(Fe(P(e.toFloat(),Dn(t)),t.shape.length-1))})}function Jp(e,t,n=!1){return z(()=>{let r=xl(TQ(e)).toInt();t=kn(t,Vt(),1-Vt());let a=t.shape,s=cl(r,a[a.length-1]).reshape(a);return zc(s,t,n)})}function ite(e,t){if(!v.arraysEqual(e.shape,t.shape))throw new B(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return z(()=>{let n=t.relu(),r=t.abs().neg();return n.sub(t.mul(e)).add(r.exp().log1p())})}function Qp(e,t){return z(()=>{let n;return n=kn(t,Vt(),1-Vt()),n=Dn(be(n,we(1,n))),St(ite(e,n),-1)})}function ote(e,t){return z(()=>{let n=kn(e,Vt(),1),r=kn(t,Vt(),1);return Fe(P(e,Dn(be(n,r))),-1)})}function lte(e,t){return z(()=>{let n=Dn(ie(Vt(),t));return St(we(t,P(e,n)),-1)})}function ey(e,t){return z(()=>{let n=Zp(e,-1),r=Zp(t,-1),a=P(n,r);return Nt(Fe(a,-1))})}var e0={meanSquaredError:$i,meanAbsoluteError:Yp,meanAbsolutePercentageError:ql,meanSquaredLogarithmicError:tte,squaredHinge:nte,hinge:rte,categoricalHinge:ate,logcosh:ste,categoricalCrossentropy:zc,sparseCategoricalCrossentropy:Jp,binaryCrossentropy:Qp,kullbackLeiblerDivergence:ote,poisson:lte,cosineProximity:ey};function ty(e){if(typeof e=="string"){if(e in e0)return e0[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new B(t)}else return e}function ny(e,t){return z(()=>{let n=P(.5,On(t)),r=Fc(lr(t,n),e.dtype);return St(La(e,r),-1)})}function ry(e,t){return z(()=>Fc(La(Xu(e,-1),Xu(t,-1)),"float32"))}function M7(e,t){return z(()=>ur(e.equal(1),t.equal(1)).sum().cast("float32"))}function ute(e,t){return z(()=>ur(e.equal(1),t.equal(0)).sum().cast("float32"))}function cte(e,t){return z(()=>ur(e.equal(0),t.equal(1)).sum().cast("float32"))}function $7(e,t){return z(()=>{let n=M7(e,t),r=cte(e,t),a=n.add(r);return In(lr(a,0),n.div(a),0).cast("float32")})}function hte(e,t){return z(()=>{let n=M7(e,t),r=ute(e,t),a=n.add(r);return In(lr(a,0),n.div(a),0).cast("float32")})}function D7(e,t){return Qp(e,t)}function O7(e,t){return e.rank===t.rank&&(e=e.squeeze([e.rank-1])),t=t.argMax(-1),t.dtype!==e.dtype&&(t=t.asType(e.dtype)),La(e,t).asType("float32")}var dte=$i,pte=$i,fte=Yp,mte=Yp,Ate=ql,yte=ql,ay=zc,gte=ey,z7=Jp,t0={binaryAccuracy:ny,categoricalAccuracy:ry,precision:$7,categoricalCrossentropy:ay,sparseCategoricalCrossentropy:z7,mse:dte,MSE:pte,mae:fte,MAE:mte,mape:Ate,MAPE:yte,cosine:gte};function xte(e){if(typeof e=="string"&&e in t0)return t0[e];if(typeof e!="string"&&e!=null)return e;throw new B(`Unknown metric ${e}`)}function n0(e){if(Gr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(e0))if(e0[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(t0))if(t0[n]===e){t=n;break}return t!==void 0?t:e.name}}function wte(e){let t={Adagrad:()=>wi.adagrad(.01),Adadelta:()=>wi.adadelta(1,.95,Vt()),Adam:()=>wi.adam(.001,.9,.999,Vt()),Adamax:()=>wi.adamax(.002,.9,.999,Vt(),0),RMSProp:()=>wi.rmsprop(.001,.9,0,Vt()),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 B(`Unknown Optimizer ${e}`)}var P7=1*1024*1024;function L7(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!sy(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>P7&&console.warn(`User-defined metadata of model "${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 <= ${P7}.`)}}function sy(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"||!sy(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!sy(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function Ite(e,t,n,r=console.log){let a=_te(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)),r0(s,n,r),r("=".repeat(t));let o=e.layers;for(let u=0;u<o.length;++u)a?vte(o[u],n,r):kte(o[u],n,i,r),r((u===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=bte(e),c=Xp(e.nonTrainableWeights);r(`Total params: ${l+c}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${c}`),r("_".repeat(t))}function bte(e){let t;return e.collectedTrainableWeights!=null?t=Xp(e.collectedTrainableWeights):t=Xp(e.trainableWeights),t}function _te(e){let t=!0,n=[],r=[];for(let a in e.nodesByDepth)n.push(e.nodesByDepth[a]);for(let a of n){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}r.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(r.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function r0(e,t,n=console.log){let r="";for(let a=0;a<e.length;++a)a>0&&(r=r.slice(0,r.length-1)+" "),r+=e[a],r=r.slice(0,t[a]),r+=" ".repeat(t[a]-r.length);n(r)}function vte(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(o){r="multiple"}let a=e.name,s=e.getClassName(),i=[`${a} (${s})`,r,e.countParams().toString()];r0(i,t,n)}function kte(e,t,n,r){let a;try{a=JSON.stringify(e.outputShape)}catch(u){a="multiple"}let s=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let h=0;h<u.inboundLayers.length;++h){let d=u.inboundLayers[h].name,p=u.nodeIndices[h],f=u.tensorIndices[h];s.push(`${d}[${p}][${f}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],c=[`${i} (${o})`,a,e.countParams().toString(),l];r0(c,t,r);for(let u=1;u<s.length;++u)r0(["","","",s[u]],t,r)}function W7(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Pc(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];W7(t,a,s)?n.push(s):n.push(Pc(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]=Pc(a,s)}}return n}}function iy(e,t){if(e==null)return null;if(typeof e=="string")return fa(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];W7(t,a,s)?n.push(s):n.push(iy(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r],s=fa(r);(r==="name"||r==="className")&&typeof a=="string"?n[s]=a:n[s]=iy(a,r)}return n}}var oy="3.3.0";function Nte(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return ge(t,e.dtype)}catch(n){throw new B(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Di=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Di)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=Nte(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new B(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof Tr){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Tr){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Te(this.id2Mask)}},ly={},B7={};function Lc(e,t,n,r){let a=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(f=>f.name),l=[],c=t.names();for(let f of o)c.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let u=o.join(",")+"|"+t.names().join(","),h,d;if(ly[u]==null){let f=Ste(i,t);h=f.sorted,d=f.recipientCounts,ly[u]=h,B7[u]=d}h=ly[u],d={},a||Object.assign(d,B7[u]);let p=new Di(t);for(let f=0;f<h.length;++f){if(r!=null){let E=bd().numTensors;E>r.maxNumTensors&&(r.maxNumTensors=E),E<r.minNumTensors&&(r.minNumTensors=E)}let m=h[f],A=m.sourceLayer;if(A instanceof Hl)continue;let y=[],g=[],w=[],b=!1;for(let E of m.inputs){let M=p.getValue(E),O=p.getMask(E);y.push(M),g.push(O),O!=null&&(b=!0),a||(d[E.name]--,d[E.name]===0&&!t.hasKey(E)&&o.indexOf(E.name)===-1&&!M.isDisposed&&E.sourceLayer.stateful!==!0&&w.push(M))}b&&(n=n||{},n.mask=g[0]);let _=ft(A.apply(y,n)),x=null;A.supportsMasking&&(x=A.computeMask(y,g));let N=Tte(m),T=Array.isArray(N)?N:[N];for(let E=0;E<T.length;++E){p.hasKey(T[E])||p.add(T[E],_[E],Array.isArray(x)?x[0]:x);let M=o.indexOf(T[E].name);M!==-1&&(l[M]=_[E])}a||Te(w)}return p.disposeMasks(),s?l:l[0]}function Ste(e,t){v.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let a=V7(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=V7(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:Ete(r)}}function Ete(e){let t={};for(let n in e)t[n]=e[n].size;return t}function V7(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 Tte(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 Zr=class extends Ze{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Gp(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],qa(this.inputs).length!==this.inputs.length)throw new B(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);qa(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,w=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let g=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex;Gr(w===0,"input layer has >1 nodes"),Gr(b===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(b)}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 Hl))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,w,b,_,x)=>{(b==null||_==null||x==null)&&(b=y.sourceLayer,_=y.nodeIndex,x=y.tensorIndex);let N=b.inboundNodes[_];if(w.indexOf(N)!==-1)throw new Nr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(g.indexOf(N)!==-1)return;this.containerNodes.add(Zr.nodeKey(b,_)),b.id in s||(s[b.id]=Object.keys(s).length),w.indexOf(N)===-1&&w.push(N);let T=N.inboundLayers.length;for(let E=0;E<T;E++){let M=N.inputTensors[E],O=N.inboundLayers[E],W=N.nodeIndices[E],V=N.tensorIndices[E];o(M,g,w,O,W,V)}for(g.push(N);w.indexOf(N)>=0;)w.splice(w.indexOf(N),1);i.push(N)},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],w=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,w),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let b=0;b<y.inboundLayers.length;b++){let _=y.inboundLayers[b],x=y.nodeIndices[b],N=_.inboundNodes[x],T=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(g+1,T),n[N.id]=N}}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(Dp);this.layers=[];for(let y of p){let g=d[y];g.sort((w,b)=>{let _=s[w.id],x=s[b.id];return _<x?-1:_>x?1:0});for(let w of g)w instanceof Zr&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(Dp);let f=this.inputs.slice(),m=[];for(let y of p)for(let g of h[y]){let w=g.outboundLayer;if(w!=null){for(let b of g.inputTensors)if(f.indexOf(b)===-1)throw new Nr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${w.name}". The following previous layers were accessed without issue: ${m}`);for(let b of g.outputTensors)f.push(b);m.push(w.name)}}this.nodesByDepth=h;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(w=>w===y).length;if(g!==1)throw new Nr(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new B("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,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 B(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new B(`${s.length} of ${r} weights are not set: ${s}`)}QA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${oy}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=iy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return z(()=>{e=ft(e);let n=new Di;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Lc(this.outputs,n,t)})}computeMask(e,t){return z(()=>{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=qp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Dp);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],y=l.tensorIndices[f],g=`${m.name}_${A}_${y}`,w=n[g];u.push(w)}let h=c.computeOutputShape(En(u)),d=qp(h),p=c.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${c.name}_${p}_${f}`;n[m]=d[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Gr(o in n),a.push(n[o])}return En(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(Dp);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 f of h)f.id in n&&p.push(n[f.id]);if(p.length===h.length){let f={},m,A,y,g;if(c.callArgs!=null&&(f=c.callArgs),p.length===1){let[w,b]=p[0];f.mask==null&&(f.mask=b),y=ft(u.call(w,f)),g=ft(u.computeMask(w,b)),m=[w],A=[b]}else m=p.map(w=>w[0]),A=p.map(w=>w[1]),f.mask==null&&(f.mask=A),y=ft(u.call(m,f)),g=ft(u.computeMask(m,A));if(u.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let w=0;w<d.length;++w){let b=d[w],_=y[w],x=g[w];n[b.id]=[_,x]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Gr(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 Zr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=Zr.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 B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return z(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=Zr.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=Zr.nodeKey(s,u),p={};if(this.containerNodes.has(d)){if(h.callArgs)try{JSON.stringify(h.callArgs),p=h.callArgs}catch(f){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 f=[];for(let m=0;m<h.inboundLayers.length;m++){let A=h.inboundLayers[m],y=h.nodeIndices[m],g=h.tensorIndices[m],w=Zr.nodeKey(A,y),b=t[w];b==null&&(b=0),f.push([A.name,b,g,p])}l.push(f)}}}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=Zr.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=Zr.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(m,A){m.name in s?s[m.name].push(A):s[m.name]=[A]}function o(m,A){let y=[],g;for(let w of A){let b=w[0],_=w[1],x=w[2];if(g=w[3]==null?{}:w[3],!(b in a)){i(m,A);return}let N=a[b];if(N.inboundNodes.length<=_){i(m,A);return}let T=N.inboundNodes[_];y.push(T.outputTensors[x])}y.length>0&&m.apply(En(y),g)}function l(m){let A=m.name,y=Er(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!tQ(s);)for(let m of u){let A=a[m.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 m of p){let A=m[0],y=m[1],g=m[2];Gr(A in a);let w=a[A].inboundNodes[y].outputTensors;h.push(w[g])}let f=t.outputLayers;for(let m of f){let A=m[0],y=m[1],g=m[2];Gr(A in a);let w=a[A].inboundNodes[y].outputTensors;d.push(w[g])}return new e({inputs:h,outputs:d,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){z(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function Cte(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: ${JSON.stringify(e)}`)}function U7(e,t){return Cte(e,t,"classWeight")}async function j7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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])}),un(i,"float32")}else return null}function Rte(e,t){return P(e,t)}var Fte=32;function G7(e,t){let n,r,a=t;n=a.xs,r=a.ys,v.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=H7("input",e.inputNames,n),i=H7("output",e.outputNames,r),o=s[0].shape[0];v.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)})`),v.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++)v.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++)v.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 H7(e,t,n){if(n instanceof He)return[n];if(Array.isArray(n))return v.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 B(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Mte(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Dte(e,t,n){let r=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.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}`),v.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}`),v.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(q7(n.validationData))v.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 ${n.validationBatches}`);else{let A=Mte(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(A=>"val_"+A)):c=l.slice();let u=R7(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=F7(u,h,n.epochs,null,null,$te(t,n),null,a,c);d.setModel(e),e.history=p,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let A={};await d.onEpochBegin(f);let y=0,g=0;for(r||(m=await t.iterator());r?y<n.batchesPerEpoch:!0;){let w=await m.next();if(r&&w.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, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(w.value!=null){let{xs:b,ys:_}=G7(e,w.value),x={};x.batch=g,x.size=b[0].shape[0],await d.onBatchBegin(g,x);let N=[];if(n.classWeight!=null){let M=U7(n.classWeight,e.outputNames);for(let O=0;O<M.length;++O)N.push(await j7(_[O],null,M[O]))}let T=b.concat(_).concat(N),E=o(T);Te(T);for(let M=0;M<l.length;++M){let O=l[M],W=E[M];x[O]=W,qt(W)}await d.onBatchEnd(g,x),N7(x),g++,y++}if(r?y>=n.batchesPerEpoch:w.done){if(a){let b;q7(n.validationData)?b=ft(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=ft(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?Fte:n.validationBatchSize,verbose:0}));for(let _=0;_<e.metricsNames.length;++_)A[`val_${e.metricsNames[_]}`]=b[_]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(f,A),f++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function $te(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function q7(e){return typeof e.iterator=="function"}function Ote(e){return typeof e.next=="function"}async function zte(e,t,n){n=n||{};let r=n.batches!=null,a=e.testFunction,s=[];if(n.verbose>0)throw new ze("Verbose mode is not implemented yet.");v.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=Ote(t)?t:await t.iterator(),o=0,l=0;for(;r?l<n.batches:!0;){let c=await i.next();if(s=z(()=>{if(c.value){let{xs:u,ys:h}=G7(e,c.value),d=u.concat(h),p=z(()=>a(d));if(Te(d),l===0)for(let m=0;m<p.length;++m)s.push(Ie(0));let f=d[0].shape[0];for(let m=0;m<p.length;++m){let A=p[m],y=s[m];s[m]=z(()=>ie(s[m],P(f,A))),l>0&&Te(y)}Te(p),o+=f,++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]=be(s[c],o),Te(u)}return En(s)}function uy(e){v.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Wc(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(r=>Mi(r,t,n-t)):Mi(e,t,n-t)}function cy(e,t){return z(()=>e==null?null:Array.isArray(e)?e.map(n=>cy(n,t)):h7(e,t.dtype==="int32"?t:t.toInt()))}function hy(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 Pte(e,t,n,r,a,s,i,o,l,c,u,h,d,p,f){a==null&&(a=32),s==null&&(s=1),u==null&&(u=!0),d==null&&(d=0);let m=!1;if(l!=null&&c!=null&&(m=!0),f!=null&&(m=!0,p==null))throw new B("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=Sr(0,A)),i==null&&(i=1);let{callbackList:g,history:w}=F7(o,i,s,d,A,p,a,m,h);g.setModel(e),e.history=w,await g.onTrainBegin(),e.stopTraining_=!1;for(let b=d;b<s;++b){await g.onEpochBegin(b);let _={};if(p!=null)throw new ze("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new ze("batch shuffling is not implemneted yet");u&&v.shuffle(y);let x=un(y),N=hy(A,a);for(let T=0;T<N.length;++T){let E={};if(await g.onBatchBegin(T,E),z(()=>{let M=N[T][0],O=N[T][1],W=Mi(x,M,O-M);E.batch=T,E.size=O-M;let V=cy(n,W),U=t(V);for(let j=0;j<r.length;++j){let X=r[j],G=U[j];E[X]=G,qt(G)}if(T===N.length-1&&m){let j=e.testLoop(l,c,a);for(let X=0;X<r.length;++X){let G=r[X],ee=j[X];qt(ee),_["val_"+G]=ee}}}),await g.onBatchEnd(T,E),N7(E),e.stopTraining_)break}x.dispose()}if(await g.onEpochEnd(b,_),e.stopTraining_)break}return await g.onTrainEnd(),await e.history.syncData(),e.history}async function Lte(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;uy(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 f=!1,m;if(r.validationData!=null&&r.validationData.length>0){if(f=!0,r.validationData.length===2)i=r.validationData[0],o=r.validationData[1];else throw r.validationData.length===3?new ze("validationData including sample weights is not supported yet."):new B(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);let x=!0,N=await e.standardizeUserData(i,o,null,null,x,h);l=N[0],c=N[1],m=l.concat(c)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){f=!0;let x=Math.floor(a[0].shape[0]*(1-r.validationSplit)),N=a[0].shape[0];l=Wc(a,x,N),a=Wc(a,0,x),c=Wc(s,x,N),s=Wc(s,0,x),m=l.concat(c)}else r.validationSteps!=null&&(f=!0);let A=a.concat(s).concat(u);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),w,b;f?(e.makeTestFunction(),w=e.testFunction,b=g.slice().concat(g.map(x=>"val_"+x))):(w=null,m=[],b=g.slice());let _=R7(r.callbacks,r.yieldEvery);return await Pte(e,y,A,g,h,r.epochs,r.verbose,_,w,m,r.shuffle,b,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 X7(e){let t=[];e instanceof He&&(e=[e]);for(let n=0;n<e.length;++n){let r=e[n];if(r.rank===1)t.push(Mc(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 He)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 He)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 Wte(e){return e instanceof He}function dy(e){return Array.isArray(e)}function K7(e){return!Wte(e)&&!dy(e)}function Z7(e,t,n,r=!0,a=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(dy(e)&&e.length>0)i=!0;else if(K7(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new B(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(K7(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new B(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(dy(e)){if(e=e,e.length!==t.length)throw new B(`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 B(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=X7(s),n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new B(`Error when checking ${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 B(`Error when checking ${a}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function Bte(e,t,n){let r=qa(e.map(s=>s.shape[0]));r.sort();let a=qa(t.map(s=>s.shape[0]));if(a.sort(),r.length>1)throw new B(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(a.length>1)throw new B(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(r.length>0&&a.length>0&&!v.arraysEqual(r,a))throw new B(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${a[0]} target sample(s).`)}function Vte(e,t,n){let r=[$i,Qp,zc];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=n[a];if(i!=null){if(i===zc&&s.shape[s.shape.length-1]===1)throw new B(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(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 B(`A target Tensor with shape ${s.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function Y7(e,t,n,r=!0,a=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new B(`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 B(`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 B(`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 B(`Error when checking ${a}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function Ute(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: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let a of t){let s=n.hasOwnProperty(a)?n[a]:[];Array.isArray(s)||(s=[s]),r.push(s)}return r}}var jte="layers-model",ma=class extends Zr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new B("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");Ite(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=wte(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof ha))throw new B("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new B(`Unknown entry in loss dictionary: "${s}". Only expected the following keys: ${this.outputNames}`);for(let s of this.outputNames)e.loss[s]==null&&console.warn(`Output "${s}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${s} during training`),t.push(ty(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new B(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(s=>ty(s))}else{let s=ty(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=[],Fi("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=Ute(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])};Fi("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 f=this.internalOutputShapes[s];f[f.length-1]===1||this.lossFunctions[s]===Qp?["accuracy","acc"].indexOf(d)!==-1?u=ny:["crossentropy","ce"].indexOf(d)!==-1&&(u=D7):this.lossFunctions[s]===Jp?["accuracy","acc"].indexOf(d)!==-1?u=O7:["crossentropy","ce"].indexOf(d)!==-1&&(u=z7):["accuracy","acc"].indexOf(d)!==-1?u=ry:["crossentropy","ce"].indexOf(d)!==-1&&(u=ay);let m;["accuracy","acc"].indexOf(d)!==-1?m="acc":["crossentropy","ce"].indexOf(d)!==-1&&(m="ce"),h=u,c=l+m}else h=xte(d),c=l+n0(d);let p;Fi(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;uy(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 En(l)}finally{Oi(s[0],e),Oi(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),zte(this,e,t)}checkNumSamples(e,t,n,r="steps"){let a;if(n!=null){if(a=null,t!=null)throw new B(`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 B(`Either the input data should have a defined shape, or ${r} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new B("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Di;if(e instanceof He&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new B(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;o<this.inputs.length;++o)s.add(this.inputs[o],e[o])}else for(let o of this.inputs){let l=e[o.name];if(l==null)throw new B(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=Lc(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 B(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(r)}`)}return t}predictLoop(e,t=32,n=!1){return z(()=>{let r=this.checkNumSamples(e);if(n)throw new ze("Verbose predictLoop() is not implemented yet.");let a=hy(r,t),s=this.outputs.map(i=>[]);for(let i=0;i<a.length;++i)z(()=>{let o=a[i][0],l=a[i][1],c=Wc(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 Lc(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return En(s.map(i=>it(i,0)))})}predict(e,t={}){let n=X7(e);Y7(n,this.inputNames,this.feedInputShapes,!1);try{let r=t.batchSize==null?32:t.batchSize;return uy(r),this.predictLoop(n,r)}finally{Oi(n,e)}}predictOnBatch(e){Y7(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 Nr("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]===Jp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=Z7(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Z7(t,this.feedOutputNames,a,!1,"target"),Bte(e,t,null),Vte(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new B(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let c=U7(r,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await j7(o[u],null,c[u]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return z(()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new ze("Verbose mode is not implemented yet.");if(a!=null)throw new ze("steps mode in testLoop() is not implemented yet");{let o=hy(s,n),l=un(Sr(0,s));for(let c=0;c<o.length;++c){let u=o[c][0],h=o[c][1],d=Mi(l,u,h-u),p=cy(t,d),f=e(p);if(c===0)for(let m=0;m<f.length;++m)i.push(Ie(0));for(let m=0;m<f.length;++m){let A=f[m];i[m]=ie(i[m],P(h-u,A))}}for(let c=0;c<i.length;++c)i[c]=be(i[c],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;J3(e,r)>1&&(a+=`_${J3(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=Lc(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Rte(f,a[p]));let m=St(f);t.push(m),p===0?d=f:d=ie(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=St(m(r[A],h[A]))}qt(f),s.push(f)}return d=St(d),this.calculateLosses().forEach(p=>{d=ie(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=>z(()=>{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=Lc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=St(c(a[l],o[l]));l===0?n=u:n=ie(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=St(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return Lte(this,e,t,n)}async fitDataset(e,t){return Dte(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),En(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=bd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-bd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=fa(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=>fa(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]=fa(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[fa(n0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>fa(n0(e)));{let e={};for(let t in this.metrics)e[t]=fa(n0(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=Pc(e.optimizer_config),n=Er(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=_n.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await _n.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:jte,generatedBy:`TensorFlow.js tfjs-layers v${oy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await _n.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=_n.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;L7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){L7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ma.className="Model";ae.registerClass(ma);var J7=class extends ma{};J7.className="Functional";ae.registerClass(J7);async function Hte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Pc(n),a=Er(r,t);if(e.weightsManifest!=null){let s=await _n.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 qte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=_n.getLoadHandlers(e,t);if(n.length===0)n.push(_n.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Gte(e,void 0,t)}async function Gte(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let 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=Er(Pc(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 B("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=Xte(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 Xte(e,t){let n=_n.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 Xl=class extends ma{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Gp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new B(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Xl||e instanceof ma,n;if(t){if(n=e,n.outputs.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new B("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new B("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=I7({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 B(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=k7(this.outputs[0])}this.inboundNodes=[],new Kp({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(ht(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 ma({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 Nr("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 Nr("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 Nr("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 Nr("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 B("Legacy serialization format not supported yet.");a=t}else v.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 Xl))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Er(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Xl.className="Sequential";ae.registerClass(Xl);function Kte(e){return new ma(e)}function Zte(e){return new Xl(e)}function Yte(e,t){return t==null&&(t={}),qte(e,t)}function A7(e){return I7(e)}function Jte(e,t){fr.registerCallbackConstructor(e,t)}var Bn=class extends ae.Serializable{getConfig(){return{}}},Q7=class extends Bn{apply(e,t=1){return CQ(e,t)}};Q7.className="elu";ae.registerClass(Q7);var ev=class extends Bn{apply(e){return Wd(e)}};ev.className="selu";ae.registerClass(ev);var tv=class extends Bn{apply(e){return Vr(e)}};tv.className="relu";ae.registerClass(tv);var nv=class extends Bn{apply(e){return z(()=>_l(6,Vr(e)))}};nv.className="relu6";ae.registerClass(nv);var rv=class extends Bn{apply(e){return e}};rv.className="linear";ae.registerClass(rv);var av=class extends Bn{apply(e){return $n(e)}};av.className="sigmoid";ae.registerClass(av);var sv=class extends Bn{apply(e){return FQ(e)}};sv.className="hardSigmoid";ae.registerClass(sv);var iv=class extends Bn{apply(e){return wl(e)}};iv.className="softplus";ae.registerClass(iv);var ov=class extends Bn{apply(e){return RQ(e)}};ov.className="softsign";ae.registerClass(ov);var lv=class extends Bn{apply(e){return ml(e)}};lv.className="tanh";ae.registerClass(lv);var py=class extends Bn{apply(e,t=-1){return lc(e,t)}};py.className="softmax";ae.registerClass(py);var uv=class extends Bn{apply(e,t=-1){return Md(e,t)}};uv.className="logSoftmax";ae.registerClass(uv);var cv=class extends Bn{apply(e,t=1){return z(()=>$n(e.mul(t)).mul(e))}};cv.className="swish";ae.registerClass(cv);function Ya(e){return e.getClassName()}function fy(e,t={}){return Ec(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function Ja(e){if(e==null){let t={};return t.className="linear",t.config={},fy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},fy(t)}else return e instanceof Bn?e:fy(e)}function my(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var hv=class extends ae.Serializable{},Bc=class extends hv{constructor(e){super();my(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return z(()=>{let t=Mt([1]);return this.hasL1&&(t=ie(t,Fe(P(this.l1,Lt(e))))),this.hasL2&&(t=ie(t,Fe(P(this.l2,$c(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Bc.className="L1L2";ae.registerClass(Bc);function Qte(e){return my(e),new Bc({l1:e!=null?e.l1:null,l2:0})}function ene(e){return my(e),new Bc({l2:e!=null?e.l2:null,l1:0})}var dv={l1l2:"L1L2"};function dt(e){return EA(e)}function pv(e,t={}){return Ec(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function wt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in dv?dv[e]:e,config:{}};return pv(t)}else return e instanceof hv?e:pv(e)}var Ay=class extends Ze{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=We(e);let n=Vr(e);return this.maxValue!=null&&(n=kn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="ReLU";ae.registerClass(Ay);var yy=class extends Ze{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=We(e);return tc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};yy.className="LeakyReLU";ae.registerClass(yy);var gy=class extends Ze{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=xt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=wt(e.alphaRegularizer),this.alphaConstraint=jt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ht(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=We(e),sc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Tt(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:Ut(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};gy.className="PReLU";ae.registerClass(gy);var xy=class extends Ze{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${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=We(e);return gl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};xy.className="ELU";ae.registerClass(xy);var wy=class extends Ze{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=We(e);return n.mul(Fc(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};wy.className="ThresholdedReLU";ae.registerClass(wy);var by=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new py().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=We(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}};by.className="Softmax";ae.registerClass(by);function Kl(e,t,n){if(typeof e=="number")return Ei(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!NQ(a))throw new B(`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 Cr(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 a0(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ka([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function _y(e,t){return z(()=>(Rt(t),t==="channelsFirst"?st(e,[0,2,3,1]):e))}function fv(e,t){return z(()=>(Rt(t),t==="channelsFirst"?st(e,[0,2,3,4,1]):e))}function tne(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=Ir()),Rt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=st(e,[0,2,1])),a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Id(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Xr(o,n)),o})}function mv(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=Ir()),Rt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=_y(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ua.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=st(l,[0,3,1,2])),l})}function nne(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=Ir()),Rt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=fv(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=hm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Xr(o,n)),s==="channelsFirst"&&(o=st(o,[0,4,1,2,3])),o})}var vy=class extends Ze{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",vy.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Kl(t.kernelSize,e,"kernelSize"),this.strides=Kl(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,Rt(this.dataFormat),this.activation=Ja(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=xt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=jt(t.biasConstraint),this.biasRegularizer=wt(t.biasRegularizer),this.activityRegularizer=wt(t.activityRegularizer),this.dilationRate=Kl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Gr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!RA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ya(this.activation),useBias:this.useBias,biasInitializer:Tt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Vc=class extends vy{constructor(e,t){super(e,t);this.kernel=null,Vc.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=xt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=jt(t.kernelConstraint),this.kernelRegularizer=wt(t.kernelRegularizer)}build(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=We(e);let n,r=this.bias==null?null:this.bias.read(),a=e7(this.activation.getClassName());if(a!=null&&this.rank===2)n=mv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=tne(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=mv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=nne(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ht(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=Cr(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:Tt(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Ut(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Uc=class extends Vc{constructor(e){super(2,e);Uc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!RA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Uc.className="Conv2D";ae.registerClass(Uc);var s0=class extends Vc{constructor(e){super(3,e);s0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};s0.className="Conv3D";ae.registerClass(s0);var ky=class extends Uc{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=We(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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=a0(o,h,c,this.padding),f=a0(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=st(n,[0,2,3,1]));let A=Nd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=st(A,[0,3,1,2])),this.bias!=null&&(A=Xr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ht(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]=a0(t[r],o,s,this.padding),t[a]=a0(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ky.className="Conv2DTranspose";ae.registerClass(ky);var Av=class extends Vc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=wt(t.depthwiseRegularizer),this.depthwiseConstraint=jt(t.depthwiseConstraint),this.pointwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=wt(t.pointwiseRegularizer),this.pointwiseConstraint=jt(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],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 Zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=We(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=st(e,[0,2,3,1])),n=Em(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Xr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=st(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=Tt(this.depthwiseInitializer),e.pointwiseInitializer=Tt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};Av.className="SeparableConv";var Iy=class extends Av{constructor(e){super(2,e)}};Iy.className="SeparableConv2D";ae.registerClass(Iy);var i0=class extends Vc{constructor(e){super(1,e);i0.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"&&!RA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};i0.className="Conv1D";ae.registerClass(i0);var Ny=class extends Ze{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=We(e),this.dataFormat==="channelsLast"){let n=Op(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Op(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Op(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Op(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}};Ny.className="Cropping2D";ae.registerClass(Ny);var Sy=class extends Ze{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,vQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=We(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=st(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 st(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}};Sy.className="UpSampling2D";ae.registerClass(Sy);function rne(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Ir()),Rt(a);let i=_y(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=yl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=st(i,[0,3,1,2])),i})}var Ty=class extends vy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=jt(e.depthwiseConstraint),this.depthwiseRegularizer=wt(e.depthwiseRegularizer)}build(e){if(e=ht(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=We(e);let n=rne(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Xr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ht(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=Cr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Cr(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=Tt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};Ty.className="DepthwiseConv2D";ae.registerClass(Ty);function yv(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("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 gv(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Sr(2,l));if(t=st(t,c),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=tn(a,-1)),a=st(a,c)),r&&(t=zn(t,0),a!=null&&(a=zn(a,0)));let u=[],h,d=n,p=t.shape[0],f=cr(t),m;a!=null&&(m=cr(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=On(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,M)=>w[1][M].mul(_).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&u.push(h)}let A;return o&&(A=dn(u,1)),[h,A,d]})}var Kr=class extends Ze{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new o0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Sr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){YA(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 z(()=>{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 ze("Constants support is not implemented in RNN yet.");YA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Zt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("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(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new pa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Mt([n,r])):this.states_=[Mt([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=>Mt([n,r])):this.states_[0]=Mt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):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(!v.arraysEqual(a.shape,i))throw new B(`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=>qt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=yv(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 Zt({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 Tr){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 z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=We(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 B(`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=gv((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.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 z(()=>{let t=Mt(e.shape);return t=Fe(t,[1,2]),t=Mc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?zA(t,[1,n]):t):this.cell.stateSize>1?[zA(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()===Kr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Er(r,n);return new e(Object.assign(t,{cell:a}))}};Kr.className="RNN";ae.registerClass(Kr);var Oc=class extends Ze{},l0=class extends Oc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Ja(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=jl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ht(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`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=Qa({ones:()=>On(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>On(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=qr(P(e,s),this.kernel.read()):a=qr(e,this.kernel.read()),this.bias!=null&&(a=Xr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=ie(a,qr(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:Ya(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};l0.className="SimpleRNNCell";ae.registerClass(l0);var Ey=class extends Kr{constructor(e){e.cell=new l0(e),super(e)}call(e,t){return z(()=>{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)}};Ey.className="SimpleRNN";ae.registerClass(Ey);var u0=class extends Oc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=Ja(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ja(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=jl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ht(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`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=Qa({ones:()=>On(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>On(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=P(e,a[0]));let c=qr(e,this.kernel.read());this.useBias&&(c=Xr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Bt(u,[2*this.units,this.units],u.rank-1),p=qr(r,h),[f,m,A]=Bt(c,3,c.rank-1),[y,g]=Bt(p,2,p.rank-1);i=this.recurrentActivation.apply(ie(f,y)),o=this.recurrentActivation.apply(ie(m,g));let w=qr(P(o,r),d);l=this.activation.apply(ie(A,w));let b=ie(P(i,r),P(ie(1,Nt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),recurrentActivation:Ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};u0.className="GRUCell";ae.registerClass(u0);var Cy=class extends Kr{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 u0(e),super(e)}call(e,t){return z(()=>{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)}};Cy.className="GRU";ae.registerClass(Cy);var jc=class extends Oc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Ja(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ja(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=jl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,Ka([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=ht(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 pr{apply(i,o){let l=a.apply([s]),c=new Pp().apply([s]),u=a.apply([s*2]);return c7(c7(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 z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>On(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>On(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=P(e,s[0]));let h=qr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=ie(h,qr(r,this.recurrentKernel.read())),this.useBias&&(h=Xr(h,this.bias.read()));let[d,p,f,m]=Bt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=ie(P(l,a),P(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=P(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),recurrentActivation:Ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};jc.className="LSTMCell";ae.registerClass(jc);var Ry=class extends Kr{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 jc(e),super(e)}call(e,t){return z(()=>{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)}};Ry.className="LSTM";ae.registerClass(Ry);var o0=class extends Oc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let 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){YA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Fi(`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(Er(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 JA(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]])}QA(t)}};o0.className="StackedRNNCells";ae.registerClass(o0);function Qa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>d7(t(),n),i=()=>Dc(s,t,r);return!a||a<=1?qt(i().clone()):Array(a).fill(void 0).map(i).map(o=>qt(o.clone()))}var ane=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},xv=class extends Kr{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Zt({ndim:5})]}call(e,t){return z(()=>{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 B("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 z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Mt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new pa("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 B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Mt(a)):this.states_=[Mt(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(()=>Mt(a)):this.states_[0]=Mt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>qt(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=Cr(l,r[0],a,s[0],i[0]),h=Cr(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};xv.className="ConvRNN2D";var c0=class extends jc{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,Kt(this.filters,"filters"),this.kernelSize=Kl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Kl(r||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=a||"valid",er(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Kl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Kt(o,"dilationRate"))}build(e){var t;e=ht(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let 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 pr{apply(u,h){let d=l.apply([c]),p=Br([c]),f=l.apply([c*2]);return LA([d,p,f])}},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 z(()=>{if(e.length!==3)throw new B(`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=Qa({ones:()=>On(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,se,te)=>!se||!se[te]?Y:P(se[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=Qa({ones:()=>On(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=Bt(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Bt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,N,this.padding),u=this.inputConv(u,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[O,W,V,U]=Bt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,O),m=this.recurrentConv(m,W),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(ie(c,f)),X=this.recurrentActivation.apply(ie(u,m)),G=ie(P(X,s),P(j,this.activation.apply(ie(h,A)))),ee=P(this.recurrentActivation.apply(ie(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ane(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=oa(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Xr(a,n,this.dataFormat):a}recurrentConv(e,t){return oa(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};c0.className="ConvLSTM2DCell";ae.registerClass(c0);var Fy=class extends xv{constructor(e){let t=new c0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Fy.className="ConvLSTM2D";ae.registerClass(Fy);var h0=class extends Ze{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Dc(()=>d7(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()}};h0.className="Dropout";ae.registerClass(h0);var My=class extends h0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};My.className="SpatialDropout1D";ae.registerClass(My);var $y=class extends Ze{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=Ja(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=jt(e.kernelConstraint),this.biasConstraint=jt(e.biasConstraint),this.kernelRegularizer=wt(e.kernelRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ht(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=ht(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(e),r=e7(this.activation.getClassName()),a;return r!=null?a=qr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=qr(n,this.kernel.read()),this.bias!=null&&(a=Xr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ya(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Dense";ae.registerClass($y);var Dy=class extends Ze{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ht(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Xa(e,1)]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(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 EQ(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Dy.className="Flatten";ae.registerClass(Dy);var Oy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ja(e.activation)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ya(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="Activation";ae.registerClass(Oy);var zy=class extends Ze{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return z(()=>(e=We(e),SQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};zy.className="RepeatVector";ae.registerClass(zy);var Py=class extends Ze{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 B("Can only specifiy one unknown dimension.");else a*=l}let i=Xa(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(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}};Py.className="Reshape";ae.registerClass(Py);var Ly=class extends Ze{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=Sr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ht(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return st(We(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="Permute";ae.registerClass(Ly);var Wy=class extends Ze{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=We(e),r=-1;return qu(gi(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(e),r=-1,a=!0,s=qu(gi(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Wy.className="Masking";ae.registerClass(Wy);var By=class extends Ze{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ft(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=xt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=wt(e.embeddingsRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.embeddingsConstraint=jt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return z(()=>this.maskZero?(e=We(e),gi(e,qe(e))):null)}computeOutputShape(e){if(e=ht(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"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 z(()=>{this.invokeCallHook(e,t);let n=We(e);return n.dtype!=="int32"&&(n=Fc(n,"int32")),h7(this.embeddings.read(),n.as1D()).reshape(ht(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Tt(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:Ut(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};By.className="Embedding";ae.registerClass(By);var zi=class extends Ze{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}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 B("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=[ht(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=qa(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&qa(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return z(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Ka(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Mc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Xa(c.slice(1))));d=st(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=Sr(1,l).concat([0]);n.push(st(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=st(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Sr(0,i-1));s=st(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=qa(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return z(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:tn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=ur(n,t[r]);return n})}},Vy=class extends zi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};Vy.className="Add";ae.registerClass(Vy);var Uy=class extends zi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=P(t,e[n]);return t})}};Uy.className="Multiply";ae.registerClass(Uy);var jy=class extends zi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return P(1/e.length,t)})}};jy.className="Average";ae.registerClass(jy);var Hy=class extends zi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Wr(t,e[n]);return t})}};Hy.className="Maximum";ae.registerClass(Hy);var Gy=class extends zi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_l(t,e[n]);return t})}};Gy.className="Minimum";ae.registerClass(Gy);var qy=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 B("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(v.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return z(()=>LA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),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 B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return z(()=>{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(On(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(tn(t[s],-1)):r.push(t[s]);let a=it(r,this.axis);return vd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};qy.className="Concatenate";ae.registerClass(qy);function Hc(e,t){for(;e<0;)e+=t;return e}function sne(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{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 Xy=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){v.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 ze("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 B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>Hc(a,e[s].shape.length)):r=[Hc(this.axes,t.shape.length),Hc(this.axes,n.shape.length)],this.normalize&&(t=Zp(t,r[0]),n=Zp(n,r[1])),sne(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Hc(this.axes,e.length),Hc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let 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}};Xy.className="Dot";ae.registerClass(Xy);var Ky=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(e);return Dc(()=>zp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Ky.className="GaussianNoise";ae.registerClass(Ky);var Zy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=We(e);return this.rate>0&&this.rate<1?Dc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(zp(n.shape,1,r))},()=>n,t.training||!1):n})}};Zy.className="GaussianDropout";ae.registerClass(Zy);var Yy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||We(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Dc(()=>{let r=We(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ba(vl(n),this.rate);o=Fc(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)},()=>We(e),t.training||!1)}return e})}};Yy.className="AlphaDropout";ae.registerClass(Yy);function Gc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=cx(e,t,n,r,a,s);else if(e.rank===3)i=hx(e,t,n,r,a,s);else if(e.rank===4)i=dx(e,t,n,r,a,s);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function ine(e,t,n,r,a=.001){return z(()=>{let s=Dd(e,r),i=s.mean,o=s.variance;return[Gc(e,i,o,n,t,a),i,o]})}function one(e,t,n,r,a=.001){return z(()=>{let s=Dd(e,r),i=s.mean,o=s.variance,l=[];for(let p of Sr(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[Gc(e,c,u,d,h,a),i,o]})}function lne(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),Sr(0,e.rank-1))?ine(e,t,n,r,a):one(e,t,n,r,a)}var Jy=class extends Ze{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=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.movingMeanInitializer=xt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=xt(e.movingVarianceInitializer||"ones"),this.betaConstraint=jt(e.betaConstraint),this.gammaConstraint=jt(e.gammaConstraint),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer)}build(e){e=ht(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Zt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=We(e),a=r.shape,s=a.length,i=Sr(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=!v.arraysEqual(c,Sr(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,w=this.scale?this.gamma.read().reshape(l):null;return Gc(r,A,y,g,w,this.epsilon)}else return Gc(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,f]=lne(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{z(()=>{let w=1-g,b=A.read(),_=b.sub(y).mul(w);A.write(b.sub(_))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),movingMeanInitializer:Tt(this.movingMeanInitializer),movingVarianceInitializer:Tt(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:Ut(this.betaConstraint),gammaConstraint:Ut(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Jy.className="BatchNormalization";ae.registerClass(Jy);var Qy=class extends Ze{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=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ht(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!==qa(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=We(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=Dd(n,this.axis,s),l=Ei(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),Gc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Qy.className="LayerNormalization";ae.registerClass(Qy);function une(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ir()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],la(e,r)})}var eg=class extends Ze{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Ir():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=ht(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return z(()=>une(We(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};eg.className="ZeroPadding2D";ae.registerClass(eg);function d0(e,t,n,r,a,s){return z(()=>{Rt(a),a7(s),er(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=Ir()),s==null&&(s="max"),e=_y(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=rc(e,t,n,o):i=Zu(e,t,n,o),a==="channelsFirst"&&(i=st(i,[0,3,1,2])),i})}function wv(e,t,n,r,a,s){return z(()=>{Rt(a),a7(s),er(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Ir()),s==null&&(s="max"),e=fv(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=_m(e,t,n,o):i=lm(e,t,n,o),a==="channelsFirst"&&(i=st(i,[0,4,1,2,3])),i})}var bv=class extends Ze{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,er(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=ht(e);let t=Cr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=Mc(We(e),2);let n=this.poolingFunction(We(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Va(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},tg=class extends bv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),d0(e,t,n,r,a,"max")}};tg.className="MaxPooling1D";ae.registerClass(tg);var ng=class extends bv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),d0(e,t,n,r,a,"avg")}};ng.className="AveragePooling1D";ae.registerClass(ng);var _v=class extends Ze{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),er(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Cr(t,this.poolSize[0],this.padding,this.strides[0]),n=Cr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(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}},rg=class extends _v{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),d0(e,t,n,r,a,"max")}};rg.className="MaxPooling2D";ae.registerClass(rg);var ag=class extends _v{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),d0(e,t,n,r,a,"avg")}};ag.className="AveragePooling2D";ae.registerClass(ag);var vv=class extends Ze{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),er(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=ht(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=Cr(t,this.poolSize[0],this.padding,this.strides[0]),n=Cr(n,this.poolSize[1],this.padding,this.strides[1]),r=Cr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(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}},sg=class extends vv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),wv(e,t,n,r,a,"max")}};sg.className="MaxPooling3D";ae.registerClass(sg);var ig=class extends vv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),wv(e,t,n,r,a,"avg")}};ig.className="AveragePooling3D";ae.registerClass(ig);var kv=class extends Ze{constructor(e){super(e);this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},og=class extends kv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=We(e);return St(n,1)})}};og.className="GlobalAveragePooling1D";ae.registerClass(og);var lg=class extends kv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=We(e);return Yn(n,1)})}};lg.className="GlobalMaxPooling1D";ae.registerClass(lg);var Iv=class extends Ze{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ug=class extends Iv{call(e,t){return z(()=>{let n=We(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};ug.className="GlobalAveragePooling2D";ae.registerClass(ug);var cg=class extends Iv{call(e,t){return z(()=>{let n=We(e);return this.dataFormat==="channelsLast"?Yn(n,[1,2]):Yn(n,[2,3])})}};cg.className="GlobalMaxPooling2D";ae.registerClass(cg);var Nv=class extends Ze{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=Er(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},hg=class extends Nv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ht(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ht(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=We(e),gv((n,r)=>[We(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};hg.className="TimeDistributed";ae.registerClass(hg);function cne(e){Ri(_Q,"BidirectionalMergeMode",e)}var hne="concat",dg=class extends Nv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Er(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Er(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?hne:e.mergeMode,cne(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,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()):En(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=yv(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 B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new Zt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(r!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Tr;for(let l of s)if(l instanceof Tr!==o)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let h=super.apply(l,t);return this.inputSpec=u,h}else return super.apply(e,t)}call(e,t){return z(()=>{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=zn(a,1));let i;return this.mergeMode==="concat"?i=LA([r,a]):this.mergeMode==="sum"?i=ie(r,a):this.mergeMode==="ave"?i=P(.5,ie(r,a)):this.mergeMode==="mul"?i=P(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){Fi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Fi(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=Er(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};dg.className="Bidirectional";ae.registerClass(dg);function PQ(e){return new Hl(e)}function LQ(e){return new xy(e)}function WQ(e){return new Ay(e)}function BQ(e){return new yy(e)}function VQ(e){return new gy(e)}function UQ(e){return new by(e)}function jQ(e){return new wy(e)}function HQ(e){return new i0(e)}function GQ(e){return new Uc(e)}function qQ(e){return new ky(e)}function XQ(e){return new s0(e)}function KQ(e){return new Iy(e)}function ZQ(e){return new Ny(e)}function YQ(e){return new Sy(e)}function JQ(e){return new Ty(e)}function QQ(e){return new Oy(e)}function eee(e){return new $y(e)}function tee(e){return new h0(e)}function nee(e){return new My(e)}function ree(e){return new Dy(e)}function aee(e){return new zy(e)}function see(e){return new Py(e)}function iee(e){return new Ly(e)}function oee(e){return new By(e)}function lee(e){return new Vy(e)}function uee(e){return new jy(e)}function cee(e){return new qy(e)}function hee(e){return new Hy(e)}function dee(e){return new Gy(e)}function pee(e){return new Uy(e)}function fee(e){return new Xy(e)}function mee(e){return new Jy(e)}function Aee(e){return new Qy(e)}function yee(e){return new eg(e)}function XA(e){return new ng(e)}function gee(e){return XA(e)}function xee(e){return XA(e)}function KA(e){return new ag(e)}function wee(e){return KA(e)}function bee(e){return KA(e)}function ZA(e){return new ig(e)}function _ee(e){return ZA(e)}function vee(e){return ZA(e)}function kee(e){return new og(e)}function Iee(e){return new ug(e)}function y7(e){return new lg(e)}function g7(e){return new cg(e)}function x7(e){return new tg(e)}function w7(e){return new rg(e)}function Nee(e){return new sg(e)}function See(e){return new Cy(e)}function Tee(e){return new u0(e)}function Eee(e){return new Ry(e)}function Cee(e){return new jc(e)}function Ree(e){return new Ey(e)}function Fee(e){return new l0(e)}function Mee(e){return new Fy(e)}function $ee(e){return new c0(e)}function Dee(e){return new Kr(e)}function Oee(e){return new o0(e)}function zee(e){return new dg(e)}function Pee(e){return new hg(e)}var Lee=y7,Wee=g7,Bee=x7,Vee=w7;function Uee(e){return new Ky(e)}function jee(e){return new Zy(e)}function Hee(e){return new Yy(e)}function Gee(e){return new Wy(e)}var Sv={};Le(Sv,{MAPE:()=>_ne,MSE:()=>Ine,binaryAccuracy:()=>dne,binaryCrossentropy:()=>pne,categoricalAccuracy:()=>mne,categoricalCrossentropy:()=>Ane,cosineProximity:()=>xne,mape:()=>vne,meanAbsoluteError:()=>wne,meanAbsolutePercentageError:()=>bne,meanSquaredError:()=>kne,mse:()=>Nne,precision:()=>yne,recall:()=>gne,sparseCategoricalAccuracy:()=>fne});function dne(e,t){return ny(e,t)}function pne(e,t){return D7(e,t)}function fne(e,t){return O7(e,t)}function mne(e,t){return ry(e,t)}function Ane(e,t){return ay(e,t)}function yne(e,t){return $7(e,t)}function gne(e,t){return hte(e,t)}function xne(e,t){return ey(e,t)}function wne(e,t){return Yp(e,t)}function bne(e,t){return ql(e,t)}function _ne(e,t){return ql(e,t)}function vne(e,t){return ql(e,t)}function kne(e,t){return $i(e,t)}function Ine(e,t){return $i(e,t)}function Nne(e,t){return $i(e,t)}var Tv={};Le(Tv,{modelFromJSON:()=>Hte});var Ev={};Le(Ev,{l1:()=>Tne,l1l2:()=>Sne,l2:()=>Ene});function Sne(e){return new Bc(e)}function Tne(e){return Qte(e)}function Ene(e){return ene(e)}var Cv=class extends Gl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof ma))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function p0(e,t){return e<t}function Rv(e,t){return e>t}var Fv=class extends Cv{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new ze("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=p0:this.mode==="max"?this.monitorFunc=Rv:this.monitor.indexOf("acc")!==-1?this.monitorFunc=Rv:this.monitorFunc=p0,this.monitorFunc===p0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===p0?Infinity:-Infinity}async onEpochEnd(e,t){await Za(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 Cne(e){return new Fv(e)}var Rne={earlyStopping:Cne},Rr;(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"})(Rr||(Rr={}));var Mv;(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={}))})(Mv||(Mv={}));var pg={};function Fne(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};pg[e]=n}function $v(e){return pg[e]}function Mne(e){delete pg[e]}function k(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 Rn(t.inputNames[s.inputIndexStart],n,r,a);if(s.type==="tensors")return t.inputNames.slice(o,l).map(h=>Rn(h,n,r,a));let c=Rn(t.inputNames.slice(o)[0],n,r,a),u=c.dataSync();return s.type==="number"?u[0]:v.toNestedArray(c.shape,u)}let i=t.attrParams[e];return i&&i.value}function Rn(e,t,n,r){let[a,s]=Vn(e);if(r!=null){let o=r.getHashTableHandleByName(a);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[f0(a,o)]);return i!==void 0?t[f0(a,i)][s]:void 0}function $ne(e,t,n){return t[f0(e,n.currentContextId)]}function Aa(e,t){let[n,r]=Vn(e);return[f0(n,t&&t.currentContextId),r]}function f0(e,t){return t?`${e}-${t}`:e}function Vn(e){let t=e.split(":");return t.length===1?[e,0]:[t[0],Number(t[t.length-1])]}function m0(e,t,n){let r=k("pad",e,t,n);if(r==="explicit"){r=k("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 ya(e){return e.kept?e:Or(e)}var Dv={};Le(Dv,{json:()=>Dne});var Dne=[{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}]}],Ov={};Le(Ov,{json:()=>One});var One=[{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}]}],zv={};Le(zv,{json:()=>zne});var zne=[{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"}]}],Pv={};Le(Pv,{json:()=>Pne});var Pne=[{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"}]}],Lv={};Le(Lv,{json:()=>Lne});var Lne=[{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"}]}],Wv={};Le(Wv,{json:()=>Wne});var Wne=[{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}]}],Bv={};Le(Bv,{json:()=>Bne});var Bne=[{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"}]}],Vv={};Le(Vv,{json:()=>Vne});var Vne=[{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"}]}],Uv={};Le(Uv,{json:()=>Une});var Une=[{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"}]}],jv={};Le(jv,{json:()=>jne});var jne=[{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"}]}],Hv={};Le(Hv,{json:()=>Hne});var Hne=[{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}]}],Gv={};Le(Gv,{json:()=>Gne});var Gne=[{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}]}],qv={};Le(qv,{json:()=>qne});var qne=[{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}]}],Xv={};Le(Xv,{json:()=>Xne});var Xne=[{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"}]}],Kv={};Le(Kv,{json:()=>Kne});var Kne=[{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}]}],Zv={};Le(Zv,{json:()=>Zne});var Zne=[{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}]}],Yv={};Le(Yv,{json:()=>Yne});var Yne=[{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:[]}],Qv=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[Dv,Ov,zv,Pv,Lv,Wv,Bv,Hv,jv,Vv,Gv,qv,Xv,Kv,Zv,Yv,Uv],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((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?r.push(f[m.name]):m.op==="Const"?a.push(f[m.name]):(m.input==null||m.input.length===0)&&s.push(f[m.name]),f),{}),o=[],l=[],c={},u={};t!=null&&(c=this.mapSignatureEntries(t.inputs),u=this.mapSignatureEntries(t.outputs));let h=Object.keys(i);h.forEach(f=>{let m=i[f];m.inputNames.forEach(A=>{let[y]=Aa(A);m.inputs.push(i[y]),i[y].children.push(m)})}),Object.keys(u).length===0?h.forEach(f=>{let m=i[f];m.children.length===0&&l.push(m)}):Object.keys(u).forEach(f=>{let[m]=Aa(f),A=i[m];A!=null&&(A.signatureKey=u[f],l.push(A))}),Object.keys(c).length>0?Object.keys(c).forEach(f=>{let[m]=Aa(f),A=i[m];A&&(A.signatureKey=c[f],o.push(A))}):o=r;let d={};e.library!=null&&e.library.function!=null&&(d=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));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=$v(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=fg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=fg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"string[]":i=_g(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=_g(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number":i=Ag(e.attr,a.tfName,a.defaultValue||0),i===void 0&&!!a.tfDeprecatedName&&(i=Ag(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number[]":i=bg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=bg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool":i=mg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=mg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool[]":i=kg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=kg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape":i=wg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=wg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape[]":i=vg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=vg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype":i=gg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=gg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype[]":i=xg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=xg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"func":i=Jv(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Jv(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]=Aa(c.name),h={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:yg(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]=Aa(h);u.inputs.push(a[d]),a[d].children.push(u)})});let o=e.ret;e.signature.outputArg.forEach(c=>{let[u,h]=Aa(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 Jne(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 e6(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Jne(e);return t?n:n.toLowerCase()}function fg(e,t,n,r=!1){let a=e[t];return a!=null?e6(a.s,r):n}function mg(e,t,n){let r=e[t];return r?r.b:n}function Ag(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 yg(e){switch(typeof e=="string"&&(e=Rr[e]),e){case Rr.DT_FLOAT:return"float32";case Rr.DT_INT32:case Rr.DT_INT64:case Rr.DT_INT8:case Rr.DT_UINT8:return"int32";case Rr.DT_BOOL:return"bool";case Rr.DT_DOUBLE:return"float32";case Rr.DT_STRING:return"string";default:return null}}function Jv(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function gg(e,t,n){let r=e[t];return r&&r.type?yg(r.type):n}function xg(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>yg(a)):n}function t6(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function wg(e,t,n){let r=e[t];return r&&r.shape?t6(r.shape):n}function bg(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 _g(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>e6(s,r)):n}function vg(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>t6(a)):n}function kg(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var Qne=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 Rn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Rn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Ag(this.node.rawAttrs,e,t);if(n.s!=null)return fg(this.node.rawAttrs,e,t);if(n.b!=null)return mg(this.node.rawAttrs,e,t);if(n.shape!=null)return wg(this.node.rawAttrs,e,t);if(n.type!=null)return gg(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return bg(this.node.rawAttrs,e,t);if(n.list.s!=null)return _g(this.node.rawAttrs,e,t);if(n.list.shape!=null)return vg(this.node.rawAttrs,e,t);if(n.list.b!=null)return kg(this.node.rawAttrs,e,t);if(n.list.type!=null)return xg(this.node.rawAttrs,e,t)}return t}},ere=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[ie(k("a",e,t,n),k("b",e,t,n))];case"AddN":return[za(k("tensors",e,t,n))];case"FloorMod":case"Mod":return[km(k("a",e,t,n),k("b",e,t,n))];case"Mul":return[P(k("a",e,t,n),k("b",e,t,n))];case"RealDiv":case"Div":return[be(k("a",e,t,n),k("b",e,t,n))];case"DivNoNan":return[fm(k("a",e,t,n),k("b",e,t,n))];case"FloorDiv":return[_d(k("a",e,t,n),k("b",e,t,n))];case"Sub":return[we(k("a",e,t,n),k("b",e,t,n))];case"Minimum":return[_l(k("a",e,t,n),k("b",e,t,n))];case"Maximum":return[Wr(k("a",e,t,n),k("b",e,t,n))];case"Pow":return[ua(k("a",e,t,n),k("b",e,t,n))];case"SquaredDifference":return[Gd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tre=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Lt(k("x",e,t,n))];case"Acos":return[Jf(k("x",e,t,n))];case"Acosh":return[Qf(k("x",e,t,n))];case"Asin":return[tm(k("x",e,t,n))];case"Asinh":return[nm(k("x",e,t,n))];case"Atan":return[rm(k("x",e,t,n))];case"Atan2":return[am(k("x",e,t,n),k("y",e,t,n))];case"Atanh":return[sm(k("x",e,t,n))];case"Ceil":return[um(k("x",e,t,n))];case"Complex":return[Ma(k("real",e,t,n),k("imag",e,t,n))];case"Cos":return[Qu(k("x",e,t,n))];case"Cosh":return[Sd(k("x",e,t,n))];case"Elu":return[gl(k("x",e,t,n))];case"Erf":return[mm(k("x",e,t,n))];case"Exp":return[Zn(k("x",e,t,n))];case"Expm1":return[Am(k("x",e,t,n))];case"Floor":return[xl(k("x",e,t,n))];case"Log":return[Dn(k("x",e,t,n))];case"Log1p":return[Rd(k("x",e,t,n))];case"Imag":return[Ed(k("x",e,t,n))];case"Neg":return[Nt(k("x",e,t,n))];case"Reciprocal":return[Sm(k("x",e,t,n))];case"Real":return[ic(k("x",e,t,n))];case"Relu":return[Vr(k("x",e,t,n))];case"Round":return[Tm(k("x",e,t,n))];case"Selu":return[Wd(k("x",e,t,n))];case"Sigmoid":return[$n(k("x",e,t,n))];case"Sin":return[Bd(k("x",e,t,n))];case"Sign":return[Cm(k("x",e,t,n))];case"Sinh":return[Vd(k("x",e,t,n))];case"Softplus":return[wl(k("x",e,t,n))];case"Sqrt":return[nn(k("x",e,t,n))];case"Square":return[ut(k("x",e,t,n))];case"Tanh":return[ml(k("x",e,t,n))];case"Tan":return[Mm(k("x",e,t,n))];case"ClipByValue":return[kn(k("x",e,t,n),k("clipValueMin",e,t,n),k("clipValueMax",e,t,n))];case"Relu6":return[Pd(k("x",e,t,n))];case"Rsqrt":return[Ld(Rn(e.inputNames[0],t,n))];case"Prod":return[Od(k("x",e,t,n),k("axes",e,t,n))];case"LeakyRelu":return[tc(k("x",e,t,n),k("alpha",e,t,n))];case"Prelu":return[sc(k("x",e,t,n),k("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function mr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.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];v.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function n6(e){return!(typeof e=="number"||e.some(t=>t<0))}function qc(e,t,n){let r=Ig(e,n),a=!n6(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=Ig(s.shape,r)}),!n6(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function Ig(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 nre=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=Ie(0),qt(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),mr(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,qt(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 _r([],[0].concat(this.elementShape));let n=this.readMany(e);return mr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),dn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return _r([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return mr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),it(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,cr(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=[];z(()=>{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(Me(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},Xc=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}`);mr(t,a.shape,"TensorList shape mismatch: "),qt(a)}),this.idTensor=Ie(0),this.maxNumElements=r,qt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Xc([...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.`);mr(e,this.elementShape,"TensorList shape mismatch: ");let r=qc(this.elementShape,this.tensors,e);return z(()=>{let a=this.tensors.map(s=>H(s,r));return dn(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=qc(this.elementShape,this.tensors,e),r=this.tensors.pop();return mr(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(mr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");qt(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.`);mr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=qc(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.`);mr(this.elementShape,t.shape,"TensorList shape mismatch: "),qt(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}`);mr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=qc(this.elementShape,this.tensors,n);return e.length===0?_r([],[0].concat(r)):z(()=>{let a=e.map(s=>H(this.tensors[s],r));return dn(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);mr(this.elementShape,t,"TensorList shape mismatch: ");let n=qc(this.elementShape,this.tensors,t);return this.size()===0?_r([],[0].concat(n)):z(()=>{let r=this.tensors.map(a=>H(a,n));return it(r,0)})}};function rre(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);mr(a,t,"TensorList shape mismatch: ");let s=cr(e);return new Xc(s,t,r)}function are(e,t,n){return new Xc([],e,t,n)}function sre(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 Xc([],n,e.dtype,r),i=cr(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function ire(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=Ig(s,n),o=r===0?0:e.size/r,l=z(()=>{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],f=[1,t[h],o];u[h]=H(Me(e,p,f),i)}return e.dispose(),u}),c=new Xc([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var ore=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=k("thenBranch",e,t,n),a=k("elseBranch",e,t,n),s=k("cond",e,t,n),i=k("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=k("body",e,t,n),a=k("cond",e,t,n),s=k("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=k("pred",e,t,n);return[ya(r)]}case"Switch":{let r=k("pred",e,t,n),a=k("data",e,t,n);return a.kept||(a=ya(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>Rn(a,t,n)!==void 0);if(r){let a=Rn(r,t,n);return[ya(a)]}return}case"Enter":{let r=k("frameName",e,t,n),a=k("tensor",e,t,n);return n.enterFrame(r),[ya(a)]}case"Exit":{let r=k("tensor",e,t,n);return n.exitFrame(),[ya(r)]}case"NextIteration":{let r=k("tensor",e,t,n);return n.nextIteration(),[ya(r)]}case"TensorArrayV3":{let r=k("size",e,t,n),a=k("dtype",e,t,n),s=k("elementShape",e,t,n),i=k("dynamicSize",e,t,n),o=k("clearAfterRead",e,t,n),l=k("identicalElementShapes",e,t,n),c=k("name",e,t,n),u=new nre(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,Ie(1)]}case"TensorArrayWriteV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=k("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=k("tensorArrayId",e,t,n),a=k("tensor",e,t,n),s=k("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[Ie(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=k("indices",e,t,n),a=k("tensor",e,t,n),s=k("elementShape",e,t,n),i=k("numElements",e,t,n),o=sre(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,n),o=are(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=k("tensorListId",e,t,n),a=k("indices",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=k("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=rre(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=k("tensorListId",e,t,n),a=n.getTensorList(r.id),s=k("dtype",e,t,n),i=k("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=k("tensorListId",e,t,n),a=k("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("lengths",e,t,n),i=ire(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function r6(e,t,n){let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="fusedbatchnorm",l=k("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=k("strides",e,t,n),u=m0(e,t,n),h=k("dataFormat",e,t,n).toUpperCase(),d=k("dilations",e,t,n),[p,f]=k("args",e,t,n),m=k("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:h,dilations:d,biasArg:p,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var lre=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=k("stride",e,t,n),a=k("pad",e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilation",e,t,n);return[Id(k("x",e,t,n),k("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=k("strides",e,t,n),a=m0(e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilations",e,t,n);return[oa(k("x",e,t,n),k("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}=r6(e,t,n);return[Ua.conv2d({x:k("x",e,t,n),filter:k("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}=r6(e,t,n);return[Ua.depthwiseConv2d({x:k("x",e,t,n),filter:k("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=k("outputShape",e,t,n),a=k("strides",e,t,n),s=m0(e,t,n);return[Nd(k("x",e,t,n),k("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=k("strides",e,t,n),a=m0(e,t,n),s=k("dilations",e,t,n),i=k("dataFormat",e,t,n).toUpperCase();return[yl(k("input",e,t,n),k("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilations",e,t,n);return[hm(k("x",e,t,n),k("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[Zu(k("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[rc(k("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n),i=k("includeBatchInIndex",e,t,n),{result:o,indexes:l}=Cx(k("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[lm(k("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[_m(k("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("dilations",e,t,n),i=r[1],o=r[2],l=s[1],c=s[2];return[pm(k("x",e,t,n),k("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ure=(e,t,n)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),a=k("dtype",e,t,n),s=k("value",e,t,n);return[ec(r,s,a)]}case"LinSpace":{let r=k("start",e,t,n),a=k("stop",e,t,n),s=k("num",e,t,n);return[vx(r,a,s)]}case"Multinomial":{let r=k("logits",e,t,n),a=k("numSamples",e,t,n),s=k("seed",e,t,n);return[Rx(r,a,s)]}case"OneHot":{let r=k("indices",e,t,n),a=k("depth",e,t,n),s=k("onValue",e,t,n),i=k("offValue",e,t,n);return[cl(r,a,s,i)]}case"Ones":return[Br(k("shape",e,t,n),k("dtype",e,t,n))];case"OnesLike":return[On(k("x",e,t,n))];case"RandomUniform":return[vl(k("shape",e,t,n),k("minval",e,t,n),k("maxval",e,t,n),k("dtype",e,t,n))];case"Range":{let r=k("start",e,t,n),a=k("stop",e,t,n),s=k("step",e,t,n);return[zd(r,a,s,k("dtype",e,t,n))]}case"TruncatedNormal":{let r=k("shape",e,t,n),a=k("mean",e,t,n),s=k("stdDev",e,t,n),i=k("seed",e,t,n);return[qd(r,a,s,k("dtype",e,t,n),i)]}case"Zeros":return[Mt(k("shape",e,t,n),k("dtype",e,t,n))];case"ZerosLike":return[qe(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ng(e,t,n){let r=k("boxes",e,t,n),a=k("scores",e,t,n),s=k("maxOutputSize",e,t,n),i=k("iouThreshold",e,t,n),o=k("scoreThreshold",e,t,n),l=k("softNmsSigma",e,t,n);return{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var cre=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=Ng(e,t,n),c=await Ue.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}=Ng(e,t,n),l=k("padToMaxOutputSize",e,t,n),c=await Ue.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}=Ng(e,t,n);return[await Ue.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=ge(k("condition",e,t,n),"bool"),a=[await Om(r)];return r.dispose(),a}case"ListDiff":return $x(k("x",e,t,n),k("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},hre=(e,t,n)=>{switch(e.op){case"TopKV2":{let r=k("x",e,t,n),a=k("k",e,t,n),s=k("sorted",e,t,n),i=$m(r,a,s);return[i.values,i.indices]}case"Unique":{let r=k("x",e,t,n),a=Xd(r);return[a.values,a.indices]}case"UniqueV2":{let r=k("x",e,t,n),a=k("axis",e,t,n),s=Xd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},dre=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=k("default",e,t,n);return[Rn(e.name,t,n)||r];case"Placeholder":return[Rn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=k("x",e,t,n);return[ya(c)]}case"IdentityN":return k("x",e,t,n).map(c=>ya(c));case"Snapshot":let a=k("x",e,t,n);return[ya(a)];case"Shape":return[un(k("x",e,t,n).shape,"int32")];case"ShapeN":return k("x",e,t,n).map(c=>un(c.shape));case"Size":return[Ie(k("x",e,t,n).size,"int32")];case"Rank":return[Ie(k("x",e,t,n).rank,"int32")];case"NoOp":return[Ie(1)];case"Print":let s=k("x",e,t,n),i=k("data",e,t,n),o=k("message",e,t,n),l=k("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`)}},pre=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ie(0),this.tensorMap=new Map,qt(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 Ie(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),z(()=>{let r=cr(t),a=n.length,s=r.length;v.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];qt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return z(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return dn(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}`)}},fre=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=k("keyDType",e,t,n),s=k("valueDType",e,t,n),i=new pre(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=k("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},mre=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[Ue.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[Ue.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=k("image",e,t,n),a=k("boxes",e,t,n),s=k("boxInd",e,t,n),i=k("cropSize",e,t,n),o=k("method",e,t,n),l=k("extrapolationValue",e,t,n);return[Ue.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Are=(e,t,n)=>{switch(e.op){case"Equal":return[La(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[gi(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[lr(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[Ba(k("a",e,t,n),k("b",e,t,n))];case"Less":return[Cd(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[Ai(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[ur(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[nc(k("a",e,t,n))];case"LogicalOr":return[$d(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[In(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yre=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ke(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Transpose":return[st(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=k("numArgs",e,t,n),l=k("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]=k("args",e,t,n);return[Ua.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:c,activation:a,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gre=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[fi(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[fi(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[gm(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[lc(k("x",e,t,n))];case"LogSoftmax":return[Md(k("x",e,t,n))];case"SparseToDense":return[zm(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},xre=(e,t,n)=>{switch(e.op){case"Max":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Yn(k("x",e,t,n),i,o)]}case"Mean":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[St(k("x",e,t,n),i,o)]}case"Min":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[bl(k("x",e,t,n),i,o)]}case"Sum":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Fe(k("x",e,t,n),i,o)]}case"All":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[vd(k("x",e,t,n),i,o)]}case"Any":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[qu(k("x",e,t,n),i,o)]}case"ArgMax":{let i=k("axis",e,t,n);return[Xu(k("x",e,t,n),i)]}case"ArgMin":{let i=k("axis",e,t,n);return[em(k("x",e,t,n),i)]}case"Prod":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Od(k("x",e,t,n),i,o)]}case"Cumsum":{let i=k("axis",e,t,n),o=k("exclusive",e,t,n),l=k("reverse",e,t,n);return[Td(k("x",e,t,n),i,o,l)]}case"Bincount":let r=k("x",e,t,n),a=k("weights",e,t,n),s=k("size",e,t,n);return[px(r,a,s)];case"DenseBincount":{let i=k("x",e,t,n),o=k("weights",e,t,n),l=k("size",e,t,n),c=k("binaryOutput",e,t,n);return[gx(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},wre=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[it(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[mi(r,ge(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[mi(s,ge(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let s=k("x",e,t,n);return[zn(s,a)]}case"ReverseV2":{let r=k("axis",e,t,n),a=k("x",e,t,n);return[zn(a,r)]}case"Slice":{let r=k("begin",e,t,n),a=k("size",e,t,n);return[Me(k("x",e,t,n),r,a)]}case"StridedSlice":{let r=k("begin",e,t,n),a=k("end",e,t,n),s=k("strides",e,t,n),i=k("beginMask",e,t,n),o=k("endMask",e,t,n),l=k("ellipsisMask",e,t,n),c=k("newAxisMask",e,t,n),u=k("shrinkAxisMask",e,t,n),h=k("x",e,t,n);return[Fm(h,r,a,s,i,o,l,c,u)]}case"Pack":return z(()=>{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=Va(a[0]).shape,o=a.map(l=>{let c=v.arraysEqual(l.shape,s);if(!c&&!v.arraysEqual(Va(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:H(l,s)});return[dn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return cr(a,r)}case"Tile":{let r=k("reps",e,t,n);return[Wa(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),a=k("numOrSizeSplits",e,t,n),s=k("x",e,t,n);return Bt(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[Px(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[Lx(r,a)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),a=k("outputShape",e,t,n),s=k("sparseValues",e,t,n),i=k("defaultValue",e,t,n);return[zm(r,s,a,s.dtype===i.dtype?i:ge(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},bre=(e,t,n)=>{switch(e.op){case"FFT":return[uc(k("x",e,t,n))];case"IFFT":return[kl(k("x",e,t,n))];case"RFFT":return[cc(k("x",e,t,n))];case"IRFFT":return[Hd(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_re=(e,t,n)=>{switch(e.op){case"Cast":return[ge(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[tn(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[Va(k("x",e,t,n),r)]}case"Reshape":return[H(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[vm(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[la(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),a=k("paddings",e,t,n);return[ac(k("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),a=k("crops",e,t,n);return[Yu(k("x",e,t,n),r,a)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),a=k("dataFormat",e,t,n).toUpperCase();return[dm(k("x",e,t,n),r,a)]}case"BroadcastTo":return[Ju(k("x",e,t,n),k("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function a6(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return z(()=>ere(s,i,o));case"basic_math":return z(()=>tre(s,i,o));case"control":return ore(s,i,o);case"convolution":return z(()=>lre(s,i,o));case"creation":return z(()=>ure(s,i,o));case"dynamic":return cre(s,i,o);case"evaluation":return z(()=>hre(s,i,o));case"image":return z(()=>mre(s,i,o));case"graph":return z(()=>dre(s,i,o));case"logical":return z(()=>Are(s,i,o));case"matrices":return z(()=>yre(s,i,o));case"normalization":return z(()=>gre(s,i,o));case"reduction":return z(()=>xre(s,i,o));case"slice_join":return z(()=>wre(s,i,o));case"spectral":return z(()=>bre(s,i,o));case"transformation":return z(()=>_re(s,i,o));case"hash_table":return fre(s,i,o,r);case"custom":let l=$v(s.op);if(l&&l.customExecutor)return l.customExecutor(new Qne(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 v.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var s6=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 o6(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Vn(d)[0]),u=[];r!=null&&(u=r.map(d=>Vn(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((i6(d)||vre(d)||kre(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 Ire(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Vn(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 Nre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Sre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Tre=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function i6(e){return Nre.indexOf(e.op)>=0}function vre(e){return Sre.indexOf(e.op)>=0}function kre(e){return Tre.indexOf(e.op)>=0}var Sg=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 Sg(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=o6(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 Ire(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[Vn(u)[0]]),a=t.map(u=>Vn(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 z(()=>{let u=new s6(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=Vn(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let A=a6(m,h,u,this._resourceManager);if(v.isPromise(A))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=A,this.checkTensorForDisposal(m.name,m,h,u,d,a,p)}}return this.parent==null&&u.dispose(d),t.map(f=>Rn(f,h,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=$ne(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!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 s6(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>Rn(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.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[Vn(g)[0]]),i=n.map(g=>Vn(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}=o6(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[w,b]=Vn(g),_=[];_[b]=e[g],p[w]=_});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,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=>!i6(g)&&!Rn(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}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&k("isConstant",u.node,r,n)&&([h]=Aa(u.node.name,n)),r[u.node.name]==null){let d=a6(u.node,r,n,this._resourceManager);h||([h]=Aa(u.node.name,n));let p=n.currentContext;v.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=Aa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Rn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Rn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Vn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Vn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Vn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ere=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Cre="?tfjs-format=file",Rre="model.json",l6=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ere}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=_n.browserHTTPRequest(e,this.loadOptions);else{let t=_n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(_n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=_n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Sg(Qv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Qv.Instance.transformGraph(e.modelInitializer);this.initializer=new Sg(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=_n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 He)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function bt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Rre}${Cre}`);let n=new l6(e,t);return await n.load(),n}var Fre="3.3.0",u6={};Le(u6,{CSVDataset:()=>h6,Dataset:()=>Zl,FileDataSource:()=>d6,TextLineDataset:()=>c6,URLDataSource:()=>p6,array:()=>Mre,csv:()=>Dre,func:()=>Ore,generator:()=>zre,microphone:()=>Lre,version_data:()=>Wre,webcam:()=>Pre,zip:()=>$re});var Bre=to(Q2()),Vre=to(Q2());function Ure(e,t){return A0(e,t)}function A0(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(Yl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=A0(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 jre(e,t=m6){return f6(e,t)}function f6(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(Yl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=f6(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 m6(e){return e===null?null:Yl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function A6(e,t){let n=new Map;A0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return A0(e,t,n)}function Yl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof He))}function Gre(e){return e==null||Hre(e)||Array.isArray(e)||typeof e=="object"&&e instanceof He||v.isTypedArray(e)}function Hre(e){return e===null||typeof e!="object"&&typeof e!="function"}function Xre(e){return Ure(e,qre)}function qre(e){return e instanceof He?{value:e.clone(),recurse:!1}:Yl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var y6=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}},Tg=class extends y6{constructor(){super(Tg.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}};Tg.INITIAL_CAPACITY=32;function g6(e){return new Kre(e)}function Eg(e){return new Zre(e)}function Yre(e,t){return new x6(e,t)}function Qre(e,t=es.FAIL){return new Jre(e,t)}var Yt=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 iae(this,e)}filter(e){return new aae(this,e)}map(e){return new sae(this,e)}mapAsync(e){return new w6(this,e)}serialMapAsync(e){return new w6(this,e).serial()}flatmap(e){return new oae(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 rae(this,e,t)}columnMajorBatch(e,t=!0,n=m6){return this.rowMajorBatch(e,t).map(r=>jre(r,n))}concatenate(e,t){return new x6(g6([this,e]),t)}take(e){return e<0||e==null?this:new nae(this,e)}skip(e){return e<0||e==null?this:new tae(this,e)}prefetch(e){return new b6(this,e)}shuffle(e,t){return new lae(this,e,t)}serial(){return new eae(this)}},Kre=class extends Yt{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:Xre(e),done:!1}}},Zre=class extends Yt{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: ${e.message}`,e}}},eae=class extends Yt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},tae=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Te(e.value)}return this.upstream.next()}},nae=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},rae=class extends Yt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},aae=class extends Yt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},sae=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=wr.getTensorsInContainer(e.value),n=this.transform(e.value),r=wr.getTensorsInContainer(n);for(let a of t)wr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},iae=class extends Yt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},w6=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=wr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=wr.getTensorsInContainer(n);for(let a of t)wr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Cg=class extends Yt{constructor(){super();this.outputQueue=new Tg,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},oae=class extends Cg{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=wr.getTensorsInContainer(e.value),n=this.transform(e.value),r=wr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)wr.isTensorInList(a,r)||a.dispose();return!0}},x6=class extends Yt{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}},es;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(es||(es={}));var Jre=class extends Yt{constructor(e,t=es.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 Yt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await A6(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case es.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case es.SHORTEST:return{value:null,done:!0};case es.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},b6=class extends Yt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new y6(t)}summary(){return`${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()}},lae=class extends b6{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Vre.alea(n||v.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}}},Zl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Un(async()=>(await n.iterator()).columnMajorBatch(e,t,uae),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Un(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Un(async()=>(await t.iterator()).filter(r=>z(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Un(async()=>(await t.iterator()).map(n=>z(()=>e(n))),this.size)}mapAsync(e){let t=this;return Un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Un(async()=>{let r=Eg(async()=>({value:await t.iterator(),done:!1}));return Yre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Un(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Bre.alea(t||v.now().toString());return Un(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,Un(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Zl.MAX_BUFFER_SIZE=1e4;function Un(e,t=null){return new class extends Zl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Mre(e){return Un(async()=>g6(e),e.length)}function $re(e){if(!Yl(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 Un(async()=>{let n=await A6(e,r=>{if(r instanceof Zl)return{value:r.iterator(),recurse:!1};if(Yl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Qre(n,es.SHORTEST)},t)}function uae(e){if(e===null)return null;let t=e[0];return Gre(t)?{value:cae(e),recurse:!1}:{value:null,recurse:!0}}function cae(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof He?dn(e):_r(e)}var c6=class extends Zl{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))}},y0='"',Kc=Symbol("out"),_6=Symbol("field"),g0=Symbol("quote"),Rg=Symbol("quoteafterquote"),v6=Symbol("quoteinquote"),h6=class extends Zl{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 c6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.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&&v.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(v.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=Kc;for(let i=0;i<a;i++)switch(s){case Kc:switch(e.charAt(i)){case y0:r=i+1,s=g0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Kc;break;default:s=_6,r=i;break}break;case _6:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Kc,r=i+1;break;default:}break;case g0:switch(e.charAt(i)){case y0:s=Rg;break;default:}break;case Rg:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Kc,r=i+1;break;case y0:s=g0;break;default:s=v6;break}break;case v6:switch(e.charAt(i)){case y0:s=g0;break;default:}break;default:}if(s===Rg?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}},k6=class extends Yt{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 k6(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(v.sizeFromShape(t));return n.set(e,n.length-e.length),_r(n,t)}},I6=class extends Yt{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=un([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=Nn([s,a,o,i],[1,4])}else this.cropBox=Nn([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 I6(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.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=hl.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=tn(ge(e,"float32"),0),n;n=Ue.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},N6=class{},S6=class extends Yt{split(e){return new hae(this,e)}},hae=class extends S6{constructor(e,t){super();this.upstream=e,this.impl=new dae(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dae=class extends Cg{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},fae=class extends Yt{decodeUTF8(){return new pae(this)}},pae=class extends S6{constructor(e){super();this.upstream=e,this.impl=new mae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mae=class extends Cg{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ek();this.decoder=new t("utf8")}}summary(){return`${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}},T6=class extends fae{constructor(e,t={}){super();this.file=e,this.options=t,v.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 yae(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=Aae(e));let a=await v.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new T6(s,t)}else throw new Error(a.statusText)}var Aae=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function E6(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var d6=class extends N6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(E6(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new T6(this.input,this.options)}},p6=class extends N6{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return E6(this.url)?new d6(this.url,this.fileOptions).iterator():yae(this.url,this.fileOptions)}};function Dre(e,t={}){return new h6(new p6(e),t)}function Ore(e){let t=Eg(e);return Un(async()=>t)}function zre(e){return Un(async()=>{let t=await e();return Eg(()=>t.next())})}async function Pre(e,t){return I6.create(e,t)}async function Lre(e){return k6.create(e)}var Wre="3.3.0",gae={tfjs:tk,"tfjs-core":nk,"tfjs-data":rk,"tfjs-layers":ak,"tfjs-converter":sk,"tfjs-backend-cpu":Ew,"tfjs-backend-webgl":Yb,"tfjs-backend-wasm":B3};var jn={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 C6(){if(!Yf(jn.name)){Ee("backend registration:",jn.name);try{jn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(jn.width,jn.height):document.createElement("canvas")}catch(e){Ee("error: cannot create canvas:",e);return}try{jn.gl=jn.canvas.getContext("webgl2",jn.webGLattr)}catch(e){Ee("error: cannot get WebGL2 context:",e);return}try{Ap(2,jn.gl)}catch(e){Ee("error: cannot set WebGL2 context:",e);return}try{let e=new wp(jn.gl);pl(jn.name,()=>new Pl(e),jn.priority)}catch(e){Ee("error: cannot register WebGL backend:",e);return}try{sl("webgl").forEach(t=>{let n={...t,backendName:jn.name};li(n)})}catch(e){Ee("error: cannot update WebGL backend registration:",e);return}try{xr.set("WEBGL_VERSION",2)}catch(e){Ee("error: cannot set WebGL backend flags:",e);return}Ee("backend registered:",jn.name)}}var Mg={};gr(Mg,{load:()=>$g,predict:()=>b0});var Fg={};function Hn(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};Fg[e]=i,Ee("Human profiler",e,i)}var ts,x0={age:0},w0=Number.MAX_SAFE_INTEGER;async function $g(e){return ts||(ts=await bt(e.face.age.modelPath),e.debug&&Ee(`load model: ${e.face.age.modelPath.match(/\/(.*)\./)[1]}`)),ts}async function b0(e,t){return ts?w0<t.face.age.skipFrames&&t.videoOptimized&&x0.age&&x0.age>0?(w0++,x0):(t.videoOptimized?w0=0:w0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Ue.resizeBilinear(e,[ts.inputs[0].shape[2],ts.inputs[0].shape[1]],!1),a=P(r,[255]);Te(r);let s,i={age:0};if(!t.profile)t.face.age.enabled&&(s=await ts.predict(a));else{let o=t.face.age.enabled?await vn(()=>ts.predict(a)):{};s=o.result.clone(),o.result.dispose(),Hn("age",o)}if(a.dispose(),s){let o=s.dataSync();i.age=Math.trunc(10*o[0])/10}s.dispose(),x0=i,n(i)})):null}var Dg={};gr(Dg,{load:()=>Lg,predict:()=>v0});var ga,Og={gender:""},_0=Number.MAX_SAFE_INTEGER,zg=!1,Pg=[.2989,.587,.114];async function Lg(e){return ga||(ga=await bt(e.face.gender.modelPath),zg=ga.inputs[0].shape[3]===1,e.debug&&Ee(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),ga}async function v0(e,t){return ga?_0<t.face.gender.skipFrames&&t.videoOptimized&&Og.gender!==""?(_0++,Og):(t.videoOptimized?_0=0:_0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Ue.resizeBilinear(e,[ga.inputs[0].shape[2],ga.inputs[0].shape[1]],!1),a;zg?a=z(()=>{let[o,l,c]=Bt(r,3,3),u=P(o,Pg[0]),h=P(l,Pg[1]),d=P(c,Pg[2]);return za([u,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Te(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await ga.predict(a));else{let o=t.face.gender.enabled?await vn(()=>ga.predict(a)):{};s=o.result.clone(),o.result.dispose(),Hn("gender",o)}if(a.dispose(),s)if(Array.isArray(s)){let o=s[0].dataSync(),l=Math.trunc(200*Math.abs(o[0]-.5))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]<=.5?"female":"male",i.confidence=Math.min(.99,l)),s.forEach(c=>Te(c))}else{let o=s.dataSync();if(zg)(o[0]>t.face.gender.minConfidence||o[1]>t.face.gender.minConfidence)&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=o[0]>o[1]?Math.trunc(100*o[0])/100:Math.trunc(100*o[1])/100);else{let l=Math.trunc(200*Math.abs(o[0]-.5))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]<=.5?"female":"male",i.confidence=Math.min(.99,l))}s.dispose()}Og=i,n(i)})):null}var Wg={};gr(Wg,{load:()=>Ug,predict:()=>I0});var xae=["angry","disgust","fear","happy","sad","surprise","neutral"],ns,Bg=[],k0=Number.MAX_SAFE_INTEGER,Vg=[.2989,.587,.114];async function Ug(e){return ns||(ns=await bt(e.face.emotion.modelPath),e.debug&&Ee(`load model: ${e.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),ns}async function I0(e,t){return ns?k0<t.face.emotion.skipFrames&&t.videoOptimized&&Bg.length>0?(k0++,Bg):(t.videoOptimized?k0=0:k0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Ue.resizeBilinear(e,[ns.inputs[0].shape[2],ns.inputs[0].shape[1]],!1),[a,s,i]=Bt(r,3,3);r.dispose();let o=P(a,Vg[0]),l=P(s,Vg[1]),c=P(i,Vg[2]);a.dispose(),s.dispose(),i.dispose();let u=za([o,l,c]);o.dispose(),l.dispose(),c.dispose();let h=z(()=>u.sub(.5).mul(2));u.dispose();let d=[];if(t.face.emotion.enabled){let p;if(t.profile){let f=await vn(()=>ns.predict(h));p=f.result.dataSync(),f.result.dispose(),Hn("emotion",f)}else{let f=await ns.predict(h);p=f.dataSync(),Te(f)}for(let f=0;f<p.length;f++)p[f]>t.face.emotion.minConfidence&&d.push({score:Math.min(.99,Math.trunc(100*p[f])/100),emotion:xae[f]});d.sort((f,m)=>m.score-f.score)}h.dispose(),Bg=d,n(d)})):null}var Yr;async function jg(e){return Yr||(Yr=await bt(e.face.embedding.modelPath),e.debug&&Ee(`load model: ${e.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),Yr}function R6(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=e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(Math.trunc(1e3*(1-r))/1e3,0)}function wae(e){return z(()=>{let n=[[.05,.15,.85,.85]],r=e.image||e.tensor;if(!(r instanceof He))return null;let a=r.shape.length===3?Ue.cropAndResize(tn(r,0),n,[0],[Yr.inputs[0].shape[2],Yr.inputs[0].shape[1]]):Ue.cropAndResize(r,n,[0],[Yr.inputs[0].shape[2],Yr.inputs[0].shape[1]]),s=[.2989,.587,.114],[i,o,l]=Bt(a,3,3),c=P(i,s[0]),u=P(o,s[1]),h=P(l,s[2]),d=za([c,u,h]),p=dn([d,d,d],3).squeeze(4),f=p.sub(p.min());return f.div(f.max())})}async function Hg(e,t){return Yr?new Promise(async n=>{let r=[];if(t.face.embedding.enabled){let a=wae(e);if(!t.profile)r=z(()=>[...Yr.predict(a).reshape([128,2]).logSumExp(1).dataSync()]);else{let s=await vn(()=>Yr.predict({img_inputs:a}));r=[...s.result.dataSync()],s.result.dispose(),Hn("emotion",s)}Te(a)}n(r)}):[]}var Gg={};gr(Gg,{enhance:()=>Kg,load:()=>qg,match:()=>F6,predict:()=>T0,similarity:()=>Xg});var Jr,N0={age:0},S0=Number.MAX_SAFE_INTEGER;async function qg(e){return Jr||(Jr=await bt(e.face.description.modelPath),e.debug&&Ee(`load model: ${e.face.description.modelPath.match(/\/(.*)\./)[1]}`)),Jr}function Xg(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=4*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 F6(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=Xg(e,a.embedding);s>n&&s>r.similarity&&(r={...a,similarity:s})}return r}function Kg(e){return z(()=>{let n=e.image||e.tensor||e;if(!(n instanceof He))return null;let r=[[.05,.15,.85,.85]];return(n.shape.length===3?Ue.cropAndResize(tn(n,0),r,[0],[Jr.inputs[0].shape[2],Jr.inputs[0].shape[1]]):Ue.cropAndResize(n,r,[0],[Jr.inputs[0].shape[2],Jr.inputs[0].shape[1]])).mul(255)})}async function T0(e,t){return Jr?S0<t.face.description.skipFrames&&t.videoOptimized&&N0.age&&N0.age>0?(S0++,N0):(t.videoOptimized?S0=0:S0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Kg(e),a,s={age:0,gender:"unknown",genderConfidence:0,descriptor:[]};if(!t.profile)t.face.description.enabled&&(a=await Jr.predict(r));else{let i=t.face.description.enabled?await vn(()=>Jr.predict(r)):{};a=i.result.clone(),i.result.dispose(),Hn("age",i)}Te(r),a&&(z(()=>{let i=a.find(h=>h.shape[1]===1).dataSync(),o=Math.trunc(200*Math.abs(i[0]-.5))/100;o>t.face.gender.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))),N0=s,n(s)})):null}var bae=e=>{if(!e||e.length<300)return{roll:null,yaw:null,pitch:null};let t=(a,s,i,o)=>Math.atan2(o-s,i-a),n=a=>Math.abs(a*180/Math.PI%360);return{roll:t(e[33][0],e[33][1],e[263][0],e[263][1]),yaw:t(e[33][0],e[33][2],e[263][0],e[263][2]),pitch:t(e[10][1],e[10][2],e[152][1],e[152][2])}},Zg=async(e,t)=>{var u,h,d,p,f,m,A;let n,r,a,s,i,o,l=[];e.state="run:face",n=et();let c=await((u=e.models.face)==null?void 0:u.estimateFaces(t,e.config));if(e.perf.face=Math.trunc(et()-n),!c)return[];for(let y of c){if(e.analyze("Get Face"),!y.image||y.image.isDisposedInternal){Ee("Face object is disposed:",y.image);continue}let g=bae(y.mesh);e.analyze("Start Age:"),e.config.async?r=e.config.face.age.enabled?b0(y.image,e.config):{}:(e.state="run:age",n=et(),r=e.config.face.age.enabled?await b0(y.image,e.config):{},e.perf.age=Math.trunc(et()-n)),e.analyze("Start Gender:"),e.config.async?a=e.config.face.gender.enabled?v0(y.image,e.config):{}:(e.state="run:gender",n=et(),a=e.config.face.gender.enabled?await v0(y.image,e.config):{},e.perf.gender=Math.trunc(et()-n)),e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?I0(y.image,e.config):{}:(e.state="run:emotion",n=et(),s=e.config.face.emotion.enabled?await I0(y.image,e.config):{},e.perf.emotion=Math.trunc(et()-n)),e.analyze("End Emotion:"),e.analyze("Start Embedding:"),e.config.async?i=e.config.face.embedding.enabled?Hg(y,e.config):[]:(e.state="run:embedding",n=et(),i=e.config.face.embedding.enabled?await Hg(y,e.config):[],e.perf.embedding=Math.trunc(et()-n)),e.analyze("End Embedding:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?T0(y,e.config):[]:(e.state="run:description",n=et(),o=e.config.face.description.enabled?await T0(y.image,e.config):[],e.perf.embedding=Math.trunc(et()-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 w=((p=y.annotations)==null?void 0:p.leftEyeIris)&&((f=y.annotations)==null?void 0:f.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||r.age,gender:o.gender||a.gender,genderConfidence:o.genderConfidence||a.confidence,embedding:o.descriptor||i,emotion:s,iris:w!==0?Math.trunc(w)/100:0,angle:g,tensor:e.config.face.detector.return?(m=y.image)==null?void 0:m.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 r2={};gr(r2,{MediaPipeFaceMesh:()=>a2,load:()=>s2,triangulation:()=>Cae});var M6=6;function _ae(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 vae=e=>({startEndTensor:e,startPoint:Me(e,[0,0],[-1,2]),endPoint:Me(e,[0,2],[-1,2])});function kae(e,t,n){let r=Me(e,[0,1],[-1,2]),a=ie(r,t),s=Me(e,[0,3],[-1,2]),i=be(s,n),o=be(a,n),l=be(i,2),c=we(o,l),u=ie(o,l),h=P(c,n),d=P(u,n);return Al([h,d],1)}var $6=class{constructor(t,n){this.model=t,this.anchorsData=_ae(t.inputs[0].shape[1]),this.anchors=Nn(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=z(()=>{let d=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),p=this.model.predict(d),f;if(Array.isArray(p)){let g=p.sort((x,N)=>x.size-N.size),w=it([g[0],g[2]],2),b=it([g[1],g[3]],2);f=it([b,w],1).squeeze(0)}else f=p.squeeze();let m=kae(f,this.anchors,[this.inputSize,this.inputSize]),A=Me(f,[0,0],[-1,1]),y=$n(A).squeeze();return[f,m,y]}),s=await Ue.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=>Me(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 f=vae(l[h]),m=this.anchorsData[d],A=z(()=>Me(n,[d,M6-1],[1,-1]).squeeze().reshape([M6,-1]));u.push({box:f,landmarks:A,anchor:m,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),{boxes:u,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function D6(e){let t=await bt(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new $6(t,e);return e.debug&&Ee(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function O6(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 Zc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Jl(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Ql(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 Ue.cropAndResize(t,s,[0],n)}function E0(e,t=1.5){let n=Jl(e),r=Zc(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 C0(e){let t=Jl(e),n=Zc(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 R0=[[1,0,0],[0,1,0],[0,0,1]];function Iae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Yg(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Iae(n)}function z6(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function rs(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Nae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function P6(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(rs(e[a],Nae(t,s)))}return n}function F0(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=z6(t[0],t[1]),i=P6(s,a),o=z6(-t[0],-t[1]);return P6(i,o)}function L6(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-rs(t[0],n),-rs(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function W6(e,t){return[rs(e,t[0]),rs(e,t[1])]}var Qr={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]},Jg=[{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]}],Qg=[[.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 Sae=[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],Tae=[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],Eae=[33,133,362,263,1,78,308],The=Sae.map(e=>Qg[e]),Ehe=Tae.map(e=>Qg[e]),Che=Eae.map(e=>Qg[e]);var e2=Qr.leftEyeLower0,t2=Qr.rightEyeLower0,eu={leftBounds:[e2[0],e2[e2.length-1]],rightBounds:[t2[0],t2[t2.length-1]]},M0={count:468,mouth:13,symmetryLine:[13,Qr.midwayBetweenEyes[0]]},B6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},tu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function $0(e,t,n,r){for(let a=0;a<Jg.length;a++){let{key:s,indices:i}=Jg[a],o=Qr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var n2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Zc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?F0(r,[0,0]):R0,l=r!==0?i.map(h=>[...W6(h,o),h[2]]):i,c=r!==0?L6(a):R0,u=[...Jl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+rs(u,c[0]),h[1]+rs(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[eu.leftBounds[0]][2],r=t[eu.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=C0(E0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Zc(i),l=Ue.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&xr.flags.IS_BROWSER&&(l=Ue.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<tu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(tu.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Qr[`${r}EyeUpper0`][tu.upperCenter]][2],s=t[Qr[`${r}EyeLower0`][tu.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=O6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=E0(o),c=C0(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=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&xr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=M0.count?M0.symmetryLine:B6.symmetryLine;u=Yg(i.landmarks[_],i.landmarks[x]);let N=Jl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=Ue.rotateWithOffset(t,u,0,T);h=F0(-u,N),n.face.mesh.enabled?c=Ql({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):c=Ql({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=R0;let _=t.clone();n.face.mesh.enabled?c=Ql({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):c=Ql({startPoint:i.startPoint,endPoint:i.endPoint},_,[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),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:N}=this.getEyeBox(A,c,eu.leftBounds[0],eu.leftBounds[1],!0),{box:T,boxSize:E,crop:M}=this.getEyeBox(A,c,eu.rightBounds[0],eu.rightBounds[1]),W=this.irisModel.predict(it([N,M])).dataSync(),V=W.slice(0,tu.numCoordinates*3),{rawCoords:U,iris:j}=this.getEyeCoords(V,_,x,!0),X=W.slice(tu.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,E),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?($0(A,U,"left",null),$0(A,G,"right",null)):Y<1?$0(A,U,"left",["EyeUpper0","EyeLower0"]):$0(A,G,"right",["EyeUpper0","EyeLower0"]);let se=this.getAdjustedIrisCoords(A,j,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(se).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=E0(this.calculateLandmarksBoundingBox(y),1.5);let g=Nn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&xr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=M0.count?M0.symmetryLine:B6.symmetryLine;u=Yg(i.landmarks[_],i.landmarks[x]);let N=Jl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=Ue.rotateWithOffset(t,u,0,T);h=F0(-u,N),c=Ql({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},b=C0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));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 a2=class{constructor(t,n,r,a){this.facePipeline=new n2(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(Qr))l[h]=Qr[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[1],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[2],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:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,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}},Li=[null,null,null];async function s2(e){Li=await Promise.all([!Li[0]&&e.face.enabled?D6(e):null,!Li[1]&&e.face.mesh.enabled?bt(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Li[2]&&e.face.iris.enabled?bt(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new a2(Li[0],Li[1],Li[2],e);return e.face.mesh.enabled&&e.debug&&Ee(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ee(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var Cae=Pi;var m2={};gr(m2,{PoseNet:()=>A2,load:()=>y2});function Rae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var i2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Rae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function o2(e){return Math.floor(e/2)}var l2=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(o2(t),t);)this.exchange(t,o2(t)),t=o2(t)}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 Fae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function V6(e,t,n){let[r,a,s]=n.shape,i=new l2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||Fae(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var nu=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ru=nu.length,Yc=nu.reduce((e,t,n)=>(e[t]=n,e),{}),Mae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],qhe=Mae.map(([e,t])=>[Yc[e],Yc[t]]),U6=[["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 u2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+ru)}}function D0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=u2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function c2(e,t,n){return e<t?t:e>n?n:e}function j6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function h2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function H6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function $ae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+ru)}}function Dae(e,t){let n=[];for(let r=0;r<ru;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=$ae(a,s,r,t);n.push(o),n.push(i)}return Nn(n,[ru,2])}function G6(e,t,n){return z(()=>e.toTensor().mul(Ie(t,"int32")).toFloat().add(Dae(e,n)))}function Oae(e,t){return z(()=>{let n=e.div(Ie(t,"int32"));return e.sub(n.mul(Ie(t,"int32")))})}function q6(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(Ie(n,"int32")).expandDims(1),o=Oae(s,n).expandDims(1);return it([i,o],1)})}var X6=U6.map(([e,t])=>[Yc[e],Yc[t]]),d2=X6.map(([,e])=>e),K6=X6.map(([e])=>e),zae=16;function Pae(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function p2(e,t,n,r){return{y:c2(Math.round(e.y/t),0,n-1),x:c2(Math.round(e.x/t),0,r-1)}}function Z6(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=p2(t.position,s,l,c),h=Pae(e,u,i),p=h2(t.position,h);for(let A=0;A<o;A++){let y=p2(p,s,l,c),g=u2(y.y,y.x,n,a);p=h2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=p2(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:nu[n],score:m}}function Y6(e,t,n,r,a,s){let i=t.shape[2],o=d2.length,l=new Array(i),{part:c,score:u}=e,h=D0(c,r,n);l[c.id]={score:u,part:nu[c.id],position:h};for(let d=o-1;d>=0;--d){let p=d2[d],f=K6[d];l[p]&&!l[f]&&(l[f]=Z6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=K6[d],f=d2[d];l[p]&&!l[f]&&(l[f]=Z6(d,l[p],f,t,n,r,a))}return l}async function J6(e,t,n){let r=0,a=q6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=G6(l,zae,o),u=await c.buffer(),d=Array.from(H6(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:nu[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var Lae=1,Q6=16;function e4(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return j6(r,n,i.y,i.x)<=t})}function Wae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(e4(e,t,s,o)||(a+=i),a),0)/n.length}function t4(e,t,n,r,a,s,i){let o=[],l=V6(i,Lae,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=D0(u.part,Q6,t);if(e4(o,c,h,u.part.id))continue;let d=Y6(u,e,t,Q6,n,r),p=Wae(o,c,d);p>i&&o.push({keypoints:d,score:p})}return o}async function n4(e){return Promise.all(e.map(t=>t.buffer()))}function Bae(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:r,part:a,position:s})=>({score:r,part:a,position:{x:Math.trunc(s.x*n),y:Math.trunc(s.y*t)}}))}}function r4(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function f2(e,[t,n],[r,a]){return e.map(i=>Bae(i,t/r,n/a))}async function Vae(e,t,n,r){return new Promise(async a=>{let s=await n4([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),i=s[0],o=s[1],l=s[2],c=s[3],u=await t4(i,o,l,c,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=f2(u,[e.shape[1],e.shape[2]],[r,r]);a(h)})}async function Uae(e,t,n,r){return new Promise(async a=>{let s=await J6(t.heatmapScores,t.offsets,n.body.scoreThreshold),i=f2([s],[e.shape[1],e.shape[2]],[r,r]);a(i)})}var A2=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let r=r4(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await Uae(t,a,n,this.inputSize):await Vae(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function y2(e){let t=await bt(e.body.modelPath),n=new i2(t);return e.debug&&Ee(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new A2(n)}var _2={};gr(_2,{HandPose:()=>k2,load:()=>I2});function O0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Jc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function a4(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Ue.cropAndResize(t,s,[0],n)}function s4(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function z0(e,t=1.5){let n=Jc(e),r=O0(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function P0(e){let t=Jc(e),n=O0(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var g2=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=Nn(this.anchors),this.inputSize=n,this.inputSizeTensor=un([n,n]),this.doubleInputSizeTensor=un([n*2,n*2])}normalizeBoxes(t){return z(()=>{let n=Me(t,[0,0],[-1,2]),r=Me(t,[0,2],[-1,2]),a=ie(be(n,this.inputSizeTensor),this.anchorsTensor),s=be(r,this.doubleInputSizeTensor),i=P(we(a,s),this.inputSizeTensor),o=P(ie(a,s),this.inputSizeTensor);return Al([i,o],1)})}normalizeLandmarks(t,n){return z(()=>{let r=ie(be(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return P(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=z(()=>$n(Me(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Me(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let c=await Ue.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=Me(l,[d,0],[1,-1]),f=Me(a,[d,5],[1,14]),m=z(()=>this.normalizeLandmarks(f,d).reshape([-1,2]));f.dispose(),h.push({box:p,palmLandmarks:m,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=z(()=>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(s4({startPoint:u,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function jae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function i4(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return jae(n)}var o4=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function as(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Hae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function l4(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(as(e[a],Hae(t,s)))}return n}function x2(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=o4(t[0],t[1]),i=l4(s,a),o=o4(-t[0],-t[1]);return l4(i,o)}function u4(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-as(t[0],n),-as(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function w2(e,t){return[as(e,t[0]),as(e,t[1])]}var Gae=5,c4=1.65,h4=[0,5,9,13,17,1,2],qae=0,Xae=2,b2=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=>w2([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return z0(P0(a),Gae)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=z0(P0(n),c4);r.palmLandmarks=[];for(let a=0;a<h4.length;a++)r.palmLandmarks.push(t[h4[a]].slice(0,2));return r}transformRawCoords(t,n,r,a){let s=O0(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=x2(r,[0,0]),c=o.map(p=>[...w2(p,l),p[2]]),u=u4(a),h=[...Jc(n),1],d=[as(h,u[0]),as(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?i4(o.palmLandmarks[qae],o.palmLandmarks[Xae]):0,c=Jc(o),u=[c[0]/t.shape[2],c[1]/t.shape[1]],h=n.hand.rotation?Ue.rotateWithOffset(t,l,0,u):t.clone(),d=x2(-l,c),p=r?this.getBoxForPalmLandmarks(o.palmLandmarks,d):o,f=a4(p,h,[this.inputSize,this.inputSize]),m=f.div(255);f.dispose(),h.dispose();let[A,y]=await this.landmarkDetector.predict(m);m.dispose();let g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let w=H(y,[-1,3]),b=w.arraySync();y.dispose(),w.dispose();let _=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(_);this.storedBoxes[i]=x;let N={landmarks:_,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=z0(P0(o),c4),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 d4=[{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 v2={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]},k2=class{constructor(t){this.handPipeline=t}static getAnnotations(){return v2}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(v2))i[c]=v2[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:s.confidence,box:o,boxRaw:l,landmarks:s.landmarks,annotations:i})}return a}};async function I2(e){let[t,n]=await Promise.all([e.hand.enabled?bt(e.hand.detector.modelPath,{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?bt(e.hand.skeleton.modelPath,{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),r=new g2(t,t==null?void 0:t.inputs[0].shape[2],d4),a=new b2(r,n,n==null?void 0:n.inputs[0].shape[2]),s=new k2(a);return e.hand.enabled&&e.debug&&Ee(`load model: ${e.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),e.hand.landmarks&&e.debug&&Ee(`load model: ${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}var N2={};gr(N2,{load:()=>S2,predict:()=>T2});var p4=["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"],f4=["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 Ar;async function S2(e){return Ar||(Ar=await bt(e.body.modelPath),Ar.width=parseInt(Ar.signature.inputs["input_1:0"].tensorShape.dim[2].size),Ar.height=parseInt(Ar.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ee(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),Ar}async function T2(e,t){if(!Ar||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Ue.resizeBilinear(e,[Ar.width,Ar.height],!1),a=be(r,[255]);r.dispose();let s;if(t.profile){let c=await vn(()=>Ar.predict(a));s=c.result.find(u=>u.size===195||u.size===155).dataSync(),c.result.forEach(u=>u.dispose()),Hn("blazepose",c)}else{let c=await Ar.predict(a);s=c.find(u=>u.size===195||u.size===155).dataSync(),c.forEach(u=>u.dispose())}a.dispose();let i=[],o=s.length===195?p4:f4,l=5;for(let c=0;c<s.length/l;c++)i.push({id:c,part:o[c],position:{x:Math.trunc(n.width*s[l*c+0]/255),y:Math.trunc(n.height*s[l*c+1]/255),z:Math.trunc(s[l*c+2])+0},score:(100-Math.trunc(100/(1+Math.exp(s[l*c+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(s[l*c+4]))))/100});return[{keypoints:i}]}var E2={};gr(E2,{load:()=>R2,predict:()=>F2});var m4=[{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 Fr,C2=[],L0=Number.MAX_SAFE_INTEGER,W0=2.5,A4=!1;async function R2(e){return Fr||(Fr=await bt(e.object.modelPath),Fr.inputSize=parseInt(Object.values(Fr.modelSignature.inputs)[0].tensorShape.dim[2].size),e.debug&&Ee(`load model: ${e.object.modelPath.match(/\/(.*)\./)[1]}`)),Fr}async function Kae(e,t,n,r){let a=0,s=[];for(let u of[1,2,4])z(()=>{var y,g;let h=u*13,d=(y=e.find(w=>w.shape[1]===h**2&&w.shape[2]===80))==null?void 0:y.squeeze(),p=(g=e.find(w=>w.shape[1]===h**2&&w.shape[2]<80))==null?void 0:g.squeeze(),m=p.reshape([-1,4,p.shape[1]/4]).argMax(2).arraySync(),A=A4?d.exp(1).arraySync():d.arraySync();for(let w=0;w<d.shape[0];w++)for(let b=0;b<d.shape[1];b++){let _=A[w][b]-(A4?1:0);if(_>r.object.minConfidence){let x=(.5+Math.trunc(w%h))/h,N=(.5+Math.trunc(w/h))/h,T=m[w].map(W=>W*(h/u/t)),E=[x-W0/u*T[0],N-W0/u*T[1],x+W0/u*T[2],N+W0/u*T[3]];E=E.map(W=>Math.max(0,Math.min(W,1)));let M=[E[0]*n[0],E[1]*n[1],E[2]*n[0],E[3]*n[1]],O={id:a++,strideSize:u,score:_,class:b+1,label:m4[b].label,center:[Math.trunc(n[0]*x),Math.trunc(n[1]*N)],centerRaw:[x,N],box:M.map(W=>Math.trunc(W)),boxRaw:E};s.push(O)}}});e.forEach(u=>Te(u));let i=s.map(u=>u.boxRaw),o=s.map(u=>u.score),l=await Ue.nonMaxSuppressionAsync(i,o,r.object.maxResults,r.object.iouThreshold,r.object.minConfidence),c=l.dataSync();return Te(l),s=s.filter((u,h)=>c.includes(h)).sort((u,h)=>h.score-u.score),s}async function F2(e,t){return Fr?L0<t.object.skipFrames&&t.videoOptimized&&C2.length>0?(L0++,C2):(t.videoOptimized?L0=0:L0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=[e.shape[2],e.shape[1]],a=Ue.resizeBilinear(e,[Fr.inputSize,Fr.inputSize],!1),s=a.div(255);a.dispose();let i=s.transpose([0,3,1,2]);s.dispose();let o;if(!t.profile)t.object.enabled&&(o=await Fr.predict(i));else{let c=t.object.enabled?await vn(()=>Fr.predict(i)):{};o=c.result.clone(),c.result.dispose(),Hn("object",c)}i.dispose();let l=await Kae(o,Fr.inputSize,r,t);C2=l,n(l)})):null}var y4=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},g4=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 camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),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 ${o<0?"up":"down"}`})}return t},x4=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],a=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(r*a),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o);Math.abs(s-l)/Math.max(s,l)<.25&&t.push({iris:n,gesture:"looking at camera"})}return t},w4=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[a,s]of Object.entries(e[n].annotations))a!=="palmBase"&&r.push({name:a.toLowerCase(),position:s[0]});if(r&&r.length>0){let a=r.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=r.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${a.name} forward ${s.name} up`})}}return t};function Zae(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 b4(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={},f={INTERMEDIATE:1},m=d.getContext("webgl");if(!m)throw new Error("Filter: getContext() failed");this.addFilter=function(_){let x=Array.prototype.slice.call(arguments,1),N=h[_];i.push({func:N,args:x})},this.reset=function(){i=[]};let A=function(_,x){if(!(_===o&&x===l)){if(d.width=_,o=_,d.height=x,l=x,!c){let N=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=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,c),m.bufferData(m.ARRAY_BUFFER,N,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,o,l),s=[null,null]}},y=function(_,x){let N=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,N);let T=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,T);let E=m.createTexture();return m.bindTexture(m.TEXTURE_2D,E),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,_,x,0,m.RGBA,m.UNSIGNED_BYTE,null),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.framebufferTexture2D(m.FRAMEBUFFER,m.COLOR_ATTACHMENT0,m.TEXTURE_2D,E,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:N,texture:E}},g=function(_){return s[_]=s[_]||y(o,l),s[_]},w=function(_=null){var E,M;let x=null,N=null,T=!1;t===0?x=n:x=(E=g(a))==null?void 0:E.texture,t++,r&&!(_&f.INTERMEDIATE)?(N=null,T=t%2==0):(a=(a+1)%2,N=(M=g(a))==null?void 0:M.fbo),m.bindTexture(m.TEXTURE_2D,x),m.bindFramebuffer(m.FRAMEBUFFER,N),m.uniform1f(u.uniform.flipY,T?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(_){if(A(_.width,_.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,_),i.length===0)return w(),d;for(let x=0;x<i.length;x++){r=x===i.length-1;let N=i[x];N.func.apply(this,N.args||[])}return d};let b=function(_){if(p[_])return u=p[_],m.useProgram(u.id),u;let x={};x.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(`
`),x.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`),u=new Zae(m,x.VERTEX_IDENTITY,_);let N=Float32Array.BYTES_PER_ELEMENT,T=4*N;return m.enableVertexAttribArray(u.attribute.pos),m.vertexAttribPointer(u.attribute.pos,2,m.FLOAT,!1,T,0*N),m.enableVertexAttribArray(u.attribute.uv),m.vertexAttribPointer(u.attribute.uv,2,m.FLOAT,!1,T,2*N),p[_]=u,u};h.colorMatrix=function(_){let x=new Float32Array(_);x[4]/=255,x[9]/=255,x[14]/=255,x[19]/=255;let N=x[18]===1&&x[3]===0&&x[8]===0&&x[13]===0&&x[15]===0&&x[16]===0&&x[17]===0&&x[19]===0?h.colorMatrix.SHADER.WITHOUT_ALPHA:h.colorMatrix.SHADER.WITH_ALPHA,T=b(N);m.uniform1fv(T.uniform.m,x),w()},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(_){let x=(_||0)+1;h.colorMatrix([x,0,0,0,0,0,x,0,0,0,0,0,x,0,0,0,0,0,1,0])},h.saturation=function(_){let x=(_||0)*2/3+1,N=(x-1)*-.5;h.colorMatrix([x,N,N,0,0,N,x,N,0,0,N,N,x,0,0,0,0,0,1,0])},h.desaturate=function(){h.saturation(-1)},h.contrast=function(_){let x=(_||0)+1,N=-128*(x-1);h.colorMatrix([x,0,0,0,N,0,x,0,0,N,0,0,x,0,N,0,0,0,1,0])},h.negative=function(){h.contrast(-2)},h.hue=function(_){_=(_||0)/180*Math.PI;let x=Math.cos(_),N=Math.sin(_),T=.213,E=.715,M=.072;h.colorMatrix([T+x*(1-T)+N*-T,E+x*-E+N*-E,M+x*-M+N*(1-M),0,0,T+x*-T+N*.143,E+x*(1-E)+N*.14,M+x*-M+N*-.283,0,0,T+x*-T+N*-(1-T),E+x*-E+N*E,M+x*(1-M)+N*M,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(_){let x=new Float32Array(_),N=1/o,T=1/l,E=b(h.convolution.SHADER);m.uniform1fv(E.uniform.m,x),m.uniform2f(E.uniform.px,N,T),w()},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(_){let x=_||1;h.convolution.call(this,[0,-1*x,0,-1*x,1+4*x,-1*x,0,-1*x,0])},h.emboss=function(_){let x=_||1;h.convolution.call(this,[-2*x,-1*x,0,-1*x,1,1*x,0,1*x,2*x])},h.blur=function(_){let x=_/7/o,N=_/7/l,T=b(h.blur.SHADER);m.uniform2f(T.uniform.px,0,N),w(f.INTERMEDIATE),m.uniform2f(T.uniform.px,x,0),w()},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(_){let x=_/o,N=_/l,T=b(h.pixelate.SHADER);m.uniform2f(T.uniform.size,x,N),w()},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 B0=2048,Ft=null,sn=null,Dt=null;function M2(e,t){let n;if(e instanceof He)n=Or(e);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>B0&&(i=B0,o=i*s/a),o>B0&&(o=B0,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)return Ee("Human: invalid input",e),{tensor:null,canvas:null};(!Ft||Ft.width!==i||Ft.height!==o)&&(Ft=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),Ft.width!==i&&(Ft.width=i),Ft.height!==o&&(Ft.height=o));let l=Ft.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Ft.width,Ft.height),t.filter.enabled){if((!Dt||!sn||Ft.width!==sn.width||Ft.height!==sn.height)&&(sn=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ft.width,Ft.height):document.createElement("canvas"),sn.width!==Ft.width&&(sn.width=Ft.width),sn.height!==Ft.height&&(sn.height=Ft.height),Dt=xr.flags.IS_BROWSER?new b4({canvas:sn}):null),!Dt)return{tensor:null,canvas:Ft};Dt.reset(),Dt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Dt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Dt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Dt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Dt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Dt.addFilter("hue",t.filter.hue),t.filter.negative&&Dt.addFilter("negative"),t.filter.sepia&&Dt.addFilter("sepia"),t.filter.vintage&&Dt.addFilter("brownie"),t.filter.sepia&&Dt.addFilter("sepia"),t.filter.kodachrome&&Dt.addFilter("kodachrome"),t.filter.technicolor&&Dt.addFilter("technicolor"),t.filter.polaroid&&Dt.addFilter("polaroid"),t.filter.pixelate!==0&&Dt.addFilter("pixelate",t.filter.pixelate),Dt.apply(Ft)}else sn=Ft,Dt&&(Dt=null);let c;if(sn.data){let h=[sn.height,sn.width,3];c=xd(sn.data,h,"int32")}else if(t.backend==="webgl"||sn instanceof ImageData)c=hl.fromPixels(sn);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(sn,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=hl.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?sn:null;return{tensor:n,canvas:r}}var $2={};gr($2,{all:()=>Jae,body:()=>k4,canvas:()=>Yae,drawOptions:()=>oe,face:()=>v4,gesture:()=>_4,hand:()=>I4,object:()=>N4});var mt={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},description:{enabled:!0,modelPath:"../models/faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"../models/emotion.json"},age:{enabled:!1,modelPath:"../models/age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}},object:{enabled:!1,modelPath:"../models/nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var oe={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function V0(e,t,n,r=null){e.fillStyle=oe.useDepth&&r?`rgba(${127.5+2*(r||0)}, ${127.5-2*(r||0)}, 255, 0.3)`:oe.color,e.beginPath(),e.arc(t,n,oe.pointSize,0,2*Math.PI),e.fill()}function au(e,t,n,r,a){if(e.beginPath(),oe.useCurves){let s=(t+t+r)/2,i=(n+n+a)/2;e.ellipse(s,i,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=oe.lineWidth,e.moveTo(t+oe.roundRect,n),e.lineTo(t+r-oe.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+oe.roundRect),e.lineTo(t+r,n+a-oe.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-oe.roundRect,n+a),e.lineTo(t+oe.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-oe.roundRect),e.lineTo(t,n+oe.roundRect),e.quadraticCurveTo(t,n,t+oe.roundRect,n),e.closePath();e.stroke()}function D2(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=oe.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:oe.color,e.fillStyle=oe.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:oe.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),oe.fillPolygons&&(e.closePath(),e.fill())}}function U0(e,t=[]){if(!(t===void 0||t.length===0)){if(!oe.useCurves||t.length<=2){D2(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let r=(t[n][0]+t[n+1][0])/2,a=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),oe.fillPolygons&&(e.closePath(),e.fill())}}async function _4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=oe.font,n.fillStyle=oe.color;let r=1;for(let a=0;a<t.length;a++){let s=[],i=[];if([s,i]=Object.entries(t[a]),i.length>1&&i[1].length>0){let o=s[1]>0?`#${s[1]}`:"",l=`${s[0]} ${o}: ${i[1]}`;oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(l,8,2+r*oe.lineHeight)),n.fillStyle=oe.labelColor,n.fillText(l,6,0+r*oe.lineHeight),r+=1}}}async function v4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let r of t){n.font=oe.font,n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.drawBoxes&&(oe.useRawBoxes?au(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):au(n,r.box[0],r.box[1],r.box[2],r.box[3]));let a=[];if(a.push(`face confidence: ${Math.trunc(100*r.confidence)}%`),r.genderConfidence&&a.push(`${r.gender||""} ${Math.trunc(100*r.genderConfidence)}% confident`),r.age&&a.push(`age: ${r.age||""}`),r.iris&&a.push(`iris distance: ${r.iris}`),r.emotion&&r.emotion.length>0){let s=r.emotion.map(i=>`${Math.trunc(100*i.score)}% ${i.emotion}`);a.push(s.join(" "))}r.angle&&r.angle.roll&&a.push(`roll: ${Math.trunc(100*r.angle.roll)/100} yaw:${Math.trunc(100*r.angle.yaw)/100} pitch:${Math.trunc(100*r.angle.pitch)/100}`),a.length===0&&a.push("face"),n.fillStyle=oe.color;for(let s=a.length-1;s>=0;s--){let i=Math.max(r.box[0],0),o=s*oe.lineHeight+r.box[1];oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=oe.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh&&r.mesh.length>0){if(oe.drawPoints)for(let s of r.mesh)V0(n,s[0],s[1],s[2]);if(oe.drawPolygons){n.lineWidth=1;for(let s=0;s<Pi.length/3;s++){let i=[Pi[s*3+0],Pi[s*3+1],Pi[s*3+2]].map(o=>r.mesh[o]);D2(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=oe.useDepth?"rgba(255, 200, 255, 0.3)":oe.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),oe.fillPolygons&&(n.fillStyle=oe.useDepth?"rgba(255, 255, 200, 0.3)":oe.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=oe.useDepth?"rgba(255, 200, 255, 0.3)":oe.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),oe.fillPolygons&&(n.fillStyle=oe.useDepth?"rgba(255, 255, 200, 0.3)":oe.color,n.fill())}}}}}var ss=[];async function k4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!ss[r]&&oe.bufferedOutput&&(ss[r]={...t[r]}),n.strokeStyle=oe.color,n.lineWidth=oe.lineWidth,oe.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=oe.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:oe.color,oe.bufferedOutput?(ss[r].keypoints[a][0]=(ss[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,ss[r].keypoints[a][1]=(ss[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,V0(n,ss[r].keypoints[a][0],ss[r].keypoints[a][1])):V0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(oe.drawLabels){n.font=oe.font;for(let a of t[r].keypoints)n.fillStyle=oe.useDepth&&a.position.z?`rgba(${127.5+2*a.position.z}, ${127.5-2*a.position.z}, 255, 0.5)`:oe.color,n.fillText(`${a.part}`,a.position.x+4,a.position.y+4)}if(oe.drawPolygons){let a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&D2(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),U0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),U0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),U0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),U0(n,s)}}}}async function I4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=oe.font;for(let r of t){if(oe.drawBoxes&&(n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.useRawBoxes?au(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):au(n,r.box[0],r.box[1],r.box[2],r.box[3]),oe.drawLabels&&(oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+oe.lineHeight,r.box[2])),n.fillStyle=oe.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+oe.lineHeight,r.box[2])),n.stroke()),oe.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=oe.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:oe.color,V0(n,a[0],a[1]);if(oe.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=oe.lineWidth,n.beginPath(),n.strokeStyle=oe.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:oe.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function N4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=oe.font;for(let r of t)if(oe.drawBoxes){if(n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.useRawBoxes?au(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):au(n,r.box[0],r.box[1],r.box[2],r.box[3]),oe.drawLabels){let a=`${Math.round(100*r.score)}% ${r.label}`;oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(a,r.box[0]+3,1+r.box[1]+oe.lineHeight,r.box[2])),n.fillStyle=oe.labelColor,n.fillText(a,r.box[0]+2,0+r.box[1]+oe.lineHeight,r.box[2])}n.stroke()}}}async function Yae(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 Jae(e,t){!t||!e||e instanceof HTMLCanvasElement&&(v4(e,t.face),k4(e,t.body),I4(e,t.hand),_4(e,t.gesture),N4(e,t.object))}var j0=`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==`,H0=`
/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
AhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAEDAgQFBgf/xABDEAEAAgECBAMECQIDBgUFAQAA
AQIDBBEFEiExE0FRBiJhcRQjMkJSgZGhsWLBJDNyFSVTY3OSNEPR4fAHFjWCokT/xAAYAQEAAwEA
AAAAAAAAAAAAAAAAAQIDBP/EACARAQEBAQADAQEBAQEBAAAAAAABAhEDITFBEjJRIhP/2gAMAwEA
AhEDEQA/APqYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAKNTq8OkxzfNkisQC8eb1XtRNbzXT4q7eU2nu0MntRq/D8StMccvW29ZmdvgjsTyvZjxOLj
+s8WLxn8TFPXs6Oj9oct7c14rkxz22nrB2I49KOdTjelmszfmpMeUxv/AA28OqwZ4icWWtt/SUi4
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAmdo3nsPNe0Pt
Fh09Z0+DNWL7+9O/7A3eJcZppsV5raI27esvH6jX5ddM25p79Ilo59VbUZOe2Tm/PeGvfPfT2iKR
PLv1+DO678XmW/a97U6TtOyzTbTF538/T9WjTNecm9a7126tqk3rSYxY5ta1plRZqZNXGjyZcPXl
mZmsx+qjBrsuO16xM7eXRt04JrdTltk5OWJnfaWf0a2lty5MdZnfzSn+WOHiOutFpjHa9e8bQ2fp
+alYy462pk7zXbuxjPesbRS0f6ZZV1ET1tErzXFLHo+A+1ddZf6NrI8PJHa1vN6iJi0bxMTHwfOa
zhzd61v1846utwniM6DUdb3nBaNrVmd9vjC/ZVePYirBqMWppz4rxaPgtEAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAItaK1m09ojcHnvarjM8P0vh49+a/eY8ng9D
h1fGM1rxjtGPfvbzdbjuTJxHX48cTPNltM/KsS9Dw7S49Jp6UpHaGe2vjz1y9J7LYK13vHWe7bj2
ex1tvM80ekuxW3RnW3Vm6P5jRx8H0+OYmMcb+bapo8GKPdpC6bQwtdHU8JpWkdJ/JweL6e23iU67
d4dubSqyVi9Zi0bwIs68XGp36TtEq7ZJmZmevzdbifCKWtbJinkt6eTgZPFw32t+sRurbWVzxs1y
Rv6T8V1NZNPtfq0seTm+Kevr+SZuxXjvaPiV8N4viycto9HseG6+uu08W6Rkj7UPmFck1tE1nlmP
Ld3eA8V8HVVi1pjq6Ma/pnqce/ERMTETHaUrKgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAADW19+TQ5p/p2bLS4v04Zmt5VjeQeJ4bjnLqsupv+Ka1+ERLv4reTmcNxcuC
vy3l0qdI2hlr66sT02ot0ZV7qqrInruzrVZLGSZ37JjqgYTG0K5lbaFVhDT1Ub456RPweY4hixWi
eSdpjvD1eWejz3FNHWYtkpvFo9EIseb3tS3SerOms22rfpPqZKzvvHSYUz70TExG6Gdbs2rljeJ/
Mx5L0vEzPaelnOi98c9J2bFNTFpit47+a+PVUvx9T9nOIfT+GV5p3yY/ds67wvsXqpxau+G09Lx+
r3TqrEAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADV4ljnLw3U0jvO
O0fs2lWqyUw6XLkyfYrWZkHldBEV09eveG3Fq1mI3jd4vPrOIaid8G9MP3Y38k6fNrt/rMk9Ou8s
tfXXn49rGWInuy8SO/k5Gl1E3rG/fzbOe94wTy99mbRvTrMOOvNfJWsesywniukrG/jU6fF43WYN
TmtEeJtEQ06aSmK2+bNtEd+qfSO17unF9Hmvy1y13XWyVmN4tExLxVK8PmNq5NrT58zawam+m/yc
0Xj8NpRYSvQZ7xEOdqI3rPozxayNRXe0ct/ON03jmrKB5nV4q1yTO20Obmv4c+cx8HoeI6WZpNoj
q83niYmYscU0r8aJ6T1n49zeJ+Meqm1drb9J+Kd5p136StGVem9l9TbHxLDFp7W7+sS+q1nesT6w
+PcAzVjiGHftzQ+v4f8AJpv6On8jH9ZgIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAABp8VrW/C9TW0ztOO3b5Nxp8VmI4bn37TWYB8f1HFtTfUfR9FWJmsdZ9I7MtJxDX5s
d8ta1y0xzteaR2277rcuhycP12SceLxMeWNpjttHwlu8I0mfQ1y+D7k5YmJmY36T36Ka43z/AF1t
cI1ds+qxVj7/AEej19PCw9HJ4NoK4OIU5Y35YmZdzVTGebVZabx5jJS+Tmns81rNLm1Wrzc9rVw4
Yibbem72mXTTS0w0M3BvEta1bWrM95ie5EanY87wXgNOL6XPfxraXLhra/W28bR/dzYzarBqJxRe
bzE7Rt5vWU9n8mPHOGmS0Ypnea1naJb+k9ncNLR7u2y/WcxXO4TOoyUrN6zD0FaW5Y3hu49FiwUi
KxCvLMR0hlW0jn6ukWw3iXjOJzbDlneOj3GaN6zDzfFOH+LE7SRGo83XNSZ2lbG2/WfdlvaT2cy6
rNFInlrv1mfJ37cK4PwTTxOoidRm2+/2/KFuyMp47XB4LivXiunrH2b2iH2qn2K/J8x4fGDNxTSZ
9Nh8OviRvTyfT6xtWI+DeXs9MNZubypASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAOZx6/LoOWPvWiHTcf2hiZ0e8fc2mf1E5+vP/AEeuSd7RC2uKtI6QjHfeINTfwtPf
Jvty9WPfbt/lucP03gxfJf7d/wBoReYpm97zaNeLb4Ims9Nt94auDjem1Wo5PFi1onylS+1o7l8V
bxvtupjDMdNkYtXS1+Stt+m63xImEJ4xjHER2ZxMUjeUTO3VRmydBbjLJqPi08mbeVOXJPq1sl5Q
Vbkz9+rRy35rxHqzmZlVEe/Ez5LRlW5iyfR6zffaIjq1OSNZps2a21rZInafSPJhxGMl9LStLRWM
lorM/A4dkrWbYfLZC2W/7K6eubX6b4RzT+W76K8b7G6X62cu3Sten59nsm3j+OXz3/0ANGIAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0OIYfpOHPijvNNo+fdvtXJO18k/
/OwPFYbz2ls3jx8VqW6xMdWPEdP9D4lkx/dt79flLLHbkxTPwY6nt2512ORTRzE2x4/dpE7cvkme
E4IrW3hRMxO8THRtU1FKWtvtvK2upx22rzRCtXkqzh2jtF7ZbT122b01ndnpuWuP3Z3+Ky20qDVv
fauzVy3mejZzNK8dVjqi87KLRLYtXruqvXzkQp7Qoid88R6rcl+WGlW0/Sa22mfhCZOq2x082ix6
jkm822pO8VrPdr4dNObVeDo8XW3uzMbzK+mvxT7szE27cvnu9j7PcNjSaXx8mOIzZevbrEeic5tN
+SZnpt8J4fHD9HXHO3PPW0x/DeBtJxx29vaAJQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAKNRim9Z5e89Nl4DzXtVh5babURHrSf7f3ec1+qnDorWrvvt5Pccb0n0zhmWk
Rvevv1+cPE2rGTFNZU26PFfxwa5dVkjelI2772nZnX6bbrEUq3o0d678u8wmuDL2ittvVjXdneeK
cGv4jpJ6U56+kS7+j118+GLXpakzHaWlp9NNY3tv+bbiYiNoQy1y30uyZJlrWmZnuym6q1iIJnop
yW2Te8bdWnnypQqzZOadokiIpSZntWN5lrxki19vNRxrUeBwnNNd+fJEY6/OejXLn3Xe/wDp9wyn
E8uo4lqqxblv7lJ26T6vpD5X7G8QycKzeBMbzMRM1/FH/wA/h9QwZ6ajDXLitvWzRgsAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeL45w+dDrZvWv1OWd4+E+j2jX
12jx67TWw5Y6T2nzifU+rZ1y9eHwzDYxxEy18+DJodXfT5o96vafWPVbjyxDn1OOzHudbM0rt2UW
iI69mVtRXZq5tREb9VUoy2iIlRbJ0UX1VZ6btTLrI7V6yk62M2oisT1c7JmtkttVMUyZp6x0beDS
RWOvdKijDimvWd3G9pNRMfRcNfvZOb9Hpb0itJeP47k/3hgjaZnbaP1XxWW3T0movbNS0W645nbf
0nrMPpXs3xamoxdJiLbe/X1n8Uf3fKsOTw4jbaXo+EarJhtGTHMxeJ6xH7Sti9Zaj6x3HM4NxXFx
DS1mtoi8dJrv2l011QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AGjxLhODieOIye7kr9m8d4eM4to9RwjPXFa0ZIvG9bR0fQXmPbDFvTTZPOJmEWS/V8bs9R43NxLL
G8eFbePg1bajU5/s0l1ceKLx1hbjwRE9mOpx0y2uRTSZsm3PMw2aaKtIjo6kYo9EXpET0hVLXxYK
xC6MZvyx1lFs0RHfaPiCnU12pLyHGNDbUajBekWma2npWN3p8+opa20e9LSyZLxExTlpM+vdOdcZ
a9tPS8MyUvFrzWlI6727u1pYxYrbVmb7x+TQx6au3Nqcl7/0rcmW9axGnwZJj1novmxnZXV0fFp4
ZxLBPgTGK8xzXr5fOH0bFlpmxVyY7Rato3iYfNuG2x56Wrqa8s2jz+7Lu8O12bS6jkwzN6THNNI6
tvrN68Y4rxlx1vHa0bskAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAA4XtTTm0OKfTJ/aXdcL2pyRGjwU362yb7fkJz9eTxxyZJjyltRXzUZK7TFtl9Lbwy06YzrHwa+
fJFd/wCVt8m0bQ0eS2qzcm+1K/an+zNZFL5M1pjFXeI72ky48eGnPkvNp27+TPU6nHpMfLXaIjpE
erk5dRMxOfN1mPeisfshW1ne1a1577Y6x5R3U0zze31FOWI6ze0byU098kRlzbxM9qrMlPDpyRMR
Md5Vt/Ihp5898mWZm1pjftE91uCt7fCI7dWeHDEW3t723l6rslqxWZnasR+SYhFbzhnfxJ2jyeq9
lcGXWZcmW0zWKxHLaI7794eJx5fpfEKabT8t8l5isddo3l9S4VjrwrRUwzSJt3tav3pdOL6Y6dXD
j8HFWm+/KsU4NRXPvtWazHquWVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAa+fXYNP9u8b+kdZBsDkZOO135cWOZn4y5Wu4xqctbe9y19Kp4njt6vi+PDm8DFMWybbzPlV
5PiGtz67UxbNbeKTtWIjaIXYpnwuaftT5tXJT3vmi1pMsrU5qIrG1V1a+5DCa7b9GFbRr5J6Wnbt
Cu+Wmk0m8956z8ZWZNorbfzcbX5rZslazPux3hUt41NTntktObJ13+zX1bek01r4/HzVm0bxPXy/
+bNfDgjVa2uOY92kdfg6ufJOKvLXtttVVSqbcta2vM7zXtHpLQy5ZtMd+vWd+7Zy3mdJHXra3f0c
vUarw7zFY5rT2hH1Lavnrgx81p3U49Pk4nE5L35MO/StfNRXR5tXnrS8W67WvfyiPSPi7uLHFK1p
jrtSsbR5Lc4RzsXBaYreP4l45esRD2HD9fnw6evvWvO3Tfr0aGk0U55ra0TFInv6uzgrXFXlx0i0
77RPlC83Yj+JW7oddqr6vHzTTw9/f6dod+L1t9m0T8pcbFSmPHER3892W0zPuz+jSbVvidkcqmfP
Sel7bekrI4n4dZnPWIrHeYnZee2Wpy8dEaml4npNZblw5qzb8M9JbYgAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAABEzFYmZnaI7yCXL1XGa0jJXT0571nbee27DiXEprp8nhbxG20W8
5cbD0ikfnKO+urTPvjoZdXqctdsmTaPSvRpWmsdZ6yztfaGplvv3lWW1tyRlz1x0vkn7Vo5atTNe
Y0+1o79V2KsZsvX7Ne5mwxnyTNvsx2iGneM/rCdRSuOsTasTt5kRFtpjqmOH4t4nk7estiMNa97R
Hwhna0iuKTEdmGWa4672nZtRele1N59Zlq6vLOSsYorEc07qcW65euzRvtXvPZy52naZ7ujr6fXV
rWdukREK8+njHgmZmPc67bq6ivVWhxxgxZLztNrT1mZ/SP4VZs0zaOvfp84WUtNsXLvtv3699+rU
z7+Jtt5qURqMnPpctaR1rMSw4ZoK57eNk6xHaJRh97Ltt7lo5Z+L1HAPZvVauZ2nFTSzMTzeJEz8
to6xPfvsZntPZ9rXxabmxzefdrv0j1dXh/BcmstW1qxTHHasR3+b0GPhGl+kWmd64dNEVjf73T7X
y8vy+Ddx6O3iRakxTH5RXrMw1/lX+3Itw2MFIraN48qRHdZi0cUjmmPen9noox1iO0fNzdXEYrTt
stcmd9aX0bJ+HePmiKTitO8TMLZ1cVjrMfqpz6ys4pjfrPRWZ9rXXptUit6zO+23VyaRHEc05L1/
w9J9ys/en1ljqdVbwYw452tlnl3jyjzbmmiMeKtYjpEbLeTXPUU8ee/+qjJpsV5rbkrFqzE1tEbT
DpYNbW21Mnu29fKWna0KbqTdjXXjld0cvQ63ltGHNPSfs2n+HUbS9c2s2UASqAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAOVxPWe99HpP8ArmP4b+r1EabT3yT3iOkesvMVtN7za07zad5l
XV5GmM9vVfEstvDx0jtaVVMlq+UJ18b5cMRvPeSuK87bUt+i2Z3PtG7zXpjkzXt6R+TXyTMzvM7t
ydHqZ+zhv1+Cv/ZuqvPTHMfOYaTMil1a1K2vHSLTELq2v+KWzThGo84rH5rq8JzedqR+ZeI7WnOS
34pYTafWXR/2Pln/AMyrKOCWnvmiPyR6O1y9585lhWJvl557Q6eo4T4dYiMvW3b3UanhldHpJtGX
e09unmjsT7eb1l4trI2t0hsZfrdNO0bzy+nzU20/+NmkzO9esz+TZxWis9dttvPv+Tn21jjaW8zn
26bTG3mp1M/Wzv3t0jyWXiKZJmsTERaZhXXDbNl8WaztWenxZLstPp5pau8frDtVrNMM5cfTfpMf
3aunxxbes9d/R09Dp8ebJi09ptFr3jtt2WyrW9wy1Jx132mK+Xq9PotT0iIU19ntLtExa3T47T+q
6nBaYvsZstZ+cT/LeMnUi0TXffo1s2m8Ws2/OIMWk5Jib5L328rS2t94Sh5TV4ppklpW6PT6rh+P
NbebTHyas8E081mZy5P2W6OFhjxNTE/hr/LoRO0Kvo9dPqctKzMxEx1la5t3tdnjnMs4noievcrO
yZjeFF1OSnNV0OG62cn1GWffj7Mz5w05joovzY7xes7TE7w0xrjPeex6Ua+j1UarBFu1o6Wj0lsN
3JfQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrU5o0+nvlt92P3BxuM6nxNRGCs+7Tv8
2hToxm1r3m9utrTvMsonqyt7XTmcja0u3O6FMfi5t/u0/lzdJM81p9O3zdvHTwsUR5+bfPqOfX1h
dqV+3O7bs1+T31oqmI3TEM4rvCdkDGIIhlFd2daboS0NXG2bD6bufxXU1vlmu/u4us/N0+L1tTSx
kr9qk7w89j1FNZMV3jxLzvaJ8mer+LSOZqK2xZotbvljfr/89U453rXt9lse081xZtNjx7TGKu0t
DHlrevSevaN5Y6+tJ8c7VRNMt63n3ub+6/R54rERMztDYy4a5omclYmfxKcenrjtHLvtPrCnVmdb
eFe3JXmjy6eS/DrMuLVYsta9Mdt++6qLxO+0dEc8UmInr18iUfReHcXrqccb9Z27Q61Lb13eJ9nc
1Z35rTvE9avY4bTkpG8xEfB05vYxqybc07R281naGMREdoT5JQqy9mply7Q3bV3iXG1eXw7TWSka
c258t7+tpT5/BjT7MfHqndz12Z+M4lMMKyziUJJiN1WSu9fku23RaOgKNJqbaTU1t9yelo+D0cTE
xEx1iXmM1Nt3W4PqvFweDaffx9vjDbGvxz+TP66QDRiAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAOJxzU73rp6z296zsZMkYsdr2naKxvLyObNOfNfJbvad1dXkaeOdpvsc2yuZVzfbfqybutwu
s5s8R92J3dvJb3tnO4HSMegtmt3nfZvYp8SZl0z45NfSK7onH1bNcfRFqnUKJr0Y7dVtq7prjEsK
0XVpEM6028mW20IHK41aPo3J6zs4ODhdcvPnvExFevNXpMOrxi/PlrTee7PLX6Pwa09uaNlKtHg9
dM3z5d7ReOu02nu0JzZMfblrv5R5uvrcdImZ26T1mYhxs1Os7RH93PZ7axuafNfLitvbaYU3yZYt
PXs9NwHhui1HBa5LVicsb81onrEuVqNNSuS8Y67dZ6xPZa59Il9uX41vEitImZme3q2Kxbxora0T
Md/ROSa4Ztkj7c9OafL5LuGYubmyX3iu/TfbdSfVnpvZLT/XZK233+Mbbva1xRXyiPk8pwbH4N6T
adq5a71n0tD1WDL4tPe6Xr0tDpz8YVnJHWEXYxbqlBedoef4tW0XraO09HdyztSZcbUz43C+ee9b
SVMaeOfqq7+jGckQ1Yz7+7v2RN/WXPXZPjci2+2yyJaVMuy+uSJlA2d+pNoVRbeDcSxyTE+TDDlt
pdRXLTynrHrDOyiyZeVFnY9TjvXJjres71tG8MnJ4Nqt4tp7T1jrV1nRL1x2cvABKAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAHJ49qfD09cNZ97JPX5PPw2uI6j6Vrsl/ux7tfk1mWr7dOM8iLdm
vfebREefRsWldw7SxqNbWbR7lPesrn3Vteo7dYjDpMGCvfbeXQ0uLlxRLRxROfUc34p6fCHYrXlr
EejqrjY8uzCYW7MZjdVKqK9VlaxCYrsnYExBMRMJRPZA8/xPHtmpP9W2xx76vhWOInvt/C7ike7N
vwzE9kcapGfhlevTaFbFo8RqJ5vy8/RoW09ek0msxHfp3dzNoLzp4zUmZpMbT8HJyYJi20X2n0lh
ZY1li/RaidBF4w2mK3jrHaFGp1lN+tptPp5IjBkid5mIp16TKu0abBPv33vPlM7z+iPdFNcWXU5I
tkrNce/b1W5db1nTaf3ax9q0fxDW1ebNk2phty1mOu09VOm8W19orEz23j1TwfSeERFuEYMddptW
d43dvBn21eKJ75KbW+cf/JcTgMxXTb3nbljz+TpcPmc2uyZO1KRtVtGVdi0bx07qJnllsRO6rNTe
N4XVamsy8mnvPwc3R2jPwe8TPbdlxXNOPSZfhWWpwO85OFzv57qrODkzeHntSe8Sn6Rv0a3EZ218
8nXekfr1a0ZLVnqx19dWb6demXybOO7lYMvNMdW9S/VVLo0us7tPHdtUtEwJiZU3jq2Jhham8CVG
PNODNTJXvWd3qcWSubFXJWd4tG8PK3pPd1OB6veLaa89Y61/u2xfxh5c/rsgNHOAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAANLimq+i6O0xPv392rdeZ4rq/pOqnlnelOkIt5F8Z7Wj27I2I6sb25YY
V1ImY3dbQ08LRc23vZp2j5OJG+XJWle9p2h6HHtbJXFT7OOIpX+7TxT31j5rycdTh+Dpz+XaG/sw
w18PHWseULN2trBE9UcrJKBhFU7JAQi0dEomegNDUYovM7x3jb5tO1ZvpbaTLtzRExWfWPJ08kbT
Ex5NXWYYyV5omYtHWJieyeDzuizfRs19Jn6TM7Ru1uMcJxZqTkw+5f4ebqa7SV1MR4tdrx2vEfy1
axqsNOTLjnLXytVXi3Xj8+nmsxTLM16d5npPyUzpekTtSK+U7vS6vQ/SYmK1vWPS1HOn2dvvvvE/
tDO5XlcO+LbfHSd/W3o6/BdDOXPTnj3Kz38rS6Wm4FNrRyRzTH3p6RH/AKvR8L4dXSzE3jmtHn5I
mbfqLV+m4dbLSsZInHjr3iI6zLpYaxS01rHuxHRHiT9mv6s67Vj1aqL6326MrWiYa+/Q54BxPaGe
XRZpj8MquB4+Xg8zPnB7SX30to379GxpK1xcHiKz5IS8xr8PLPixH2bftLTy05o6dHYyVjLhy0t1
izjZa3pMVv3iO/qz1G2L+NbSajbNyW7xLsY8kTDz+fJXFqKZN4iZnafi6WHL0iYlStI7OO+7axW2
crFl7dW9jvE9ULN+J3ZbdFGOy+AYWpEqN7afNXLj+1Wd23KrJVMvCzseh0+auow1yU7WhY4fCdV4
OadPefcvPuz6S7jol649Tl4AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAV581NPhtkvO0R+4NPi2
r8DB4dJ9+/7Q83Po2NTqLanNbLfvPaPSFDHV66sZ5ET0hRknyW2lTtMyouz0c8usx2n7s7vScKwx
zc1vu/y85p+maJh6Th+SOWeveXR4/wDLm8v+nX5mUWa9bbrInolmu5jdTNkxYFk2Isr3TuCzeGMz
+THdEyDDJO9Ja823rt2XWnya946pGvktDXta0ztWu/ybvLE9dkcoOf4GbJPWK1j49VmLh9JtE33v
Mevb9G7WsW8l1ccREISophiJ2jpDYpijbaOjOuOJ8ujOdqxsgVcsUjaETYvbaFFrgu5lVsm0yUtu
ryg43H5m+GIj1XcJzePoL4pnrWGtxmfchr8JvfHS1622if3QljzTTLes+qrNjrkiYtCzPMxnm095
YZJ6boS5teB49Tqscza97VtvWvlv8V/FOF34RrIxTM2xXjelp/eHoeA6XnzReY3ivX/0dfivDcfE
9HbDbaLx1pb0lOs+jO7K8Lis3cN+0NKcd9PmthzV5clJ2mF9J9GHHVL108dm1SznYr/Ft0tuhLb8
mNohFbMhLWy0mJ3rPXvDvcO1karBG8/WV6Wj+7kWrvDDBlvpdRGSnbzj1hpjX4z8mOx6UYYstc2O
uSk71tG7Ns5AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeXneJ62dVl5KT9VTt8Z9W9xbWclPo+O
fft9qfSHEU1pv48ftYST23ZTDC/p0YtlVuvVjMbM5+LCZjYGWGdrTPxiHY4ffaf3cjTxz1v6xMS6
Olty2iXVj/Dk8n+ndrkhnGRo1v8AFdW3RCrZ5uiYsqrboncSu508yjmZRYQt50TfowYTbYGVrKrT
uTZjvukQnYhMIGVY2ZxPVWyrHVCWzXpVXkt3TE7Va+W4K7X3jv1auTNy3jdba0RZpamfroQN7Hk3
6wr1GTaN2OOJiu6Mu98NvgDi8Wy74d/yZ8PiPAiO2zU4nb6qIn1bugjfFE/ASp1ke9u15mbbRDZ1
Mb823kx0Ontn1OOkedoJCvT8I03gaKsz9q/WW+isRWsVjtHRKyrhe0XCfpWL6Vgr9fjjrEfeh5fF
feH0V5Dj3DPoOo+k4a/U5J6xH3ZZ7z3228evytOk7NvFbo0cdols47bSybt7HbddHVqUs2aW3Qnq
xVeu8LILR3SlZw3V/R8nhXn6u0/pLuPMXjeHT4Zruf6jLPvR9mZ8/g1xrvpz+TH7HUAaMAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAABRq9VXSYJyW79qx6yvmdo3l5viGs+maqYrO+OnSvx+KLeLZz2te1rZL2v
ed7WneZYWnZl5K72YV1xEyxmeqJljzIEWlVkszvbZp5soN3h2SJz3pP3odCnuWmPRxuERfJrZmtZ
mtY96fR28kbX3dXj/wAuTyf6bmK+9YX1s0cNtm3Sd4LFY2K23W1s16StiUJW7bp22RW3RluBuruz
mWEgrmCGWyNkoExKE1QlPmsqRDKeyBjaejWy2W3ttDUyz1QKslvehVqKTNosyyTvELabXptIJpaP
B39Ia2mz+JGpr51jdZefDx2hzuHZObNq58poJaGtjxJ2+LoaKP8ADRPo5+T3skx5OhpOmC0fBNQ0
5yTbn+bt8A0u9raiY6RHLVwY62mI6zMvaaHBGn0mPHt1iN5+aYVsACBXqMFNTgviyxvW0bSsAeE1
mkvw7V2w5Ote9besJx2er4rw2nEdNNekZa9aW9JeQjnxZLYskTW9Z2mJY7zz26fHrrdpbZsY7NGt
mxjvso1b9NmUwpx33XRO4K7VUTE1nmrvEx1bVo2VWiJE/XY4frY1WPlt0y17x6/FuPM0m+HJGTHO
1qu9pNVXVYt46Xj7VfRtnXXL5MfzexsALsgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHM4jxOMFJphmJv529Dq
ZLfjDjPEIx450+K3v2+1MeUOHSOWFc3nJkmZnf4yujpVlqunOeFpV2nctLCZUXRM7MJtsWlRkv3Q
ky5NmpWt9RnrixVm17TtEQnJabXisRMzPSIew9n+CRoccajURvqLx5/chfOest642OGcIpoOG2w7
ROW9d72+LQvXevyejcPUU5M+SvpLeOataraw2a0dLbLqTtK1G3Es4lVWWUSoldFtmcXUbpidgXzK
GEW3TuCUSncnsDFMMLSms9EC6J6FpVzbZE5ALy0809ZbFr9GtfrEoFMzuuwz0Ueey3HbaBLDXe7i
tMOfwWnP9I+NZbuttvhs1uBRtXPb4SDm3iIvf57N7Dbl0VrS5+XrltEd+Z1Jx7cNms9N4TURRw3T
+PrcO3WszEvZOD7P6aYiMlvu16S7y1QAIAABxOPcLnUY/pWCv1tI96I+9DtgmXl68Biy7/NtUu3+
O8HnFa2s0tfd75KR5fFyMWTdhrPHVnX9R0cd21S3Rzsdm1iuqs256wrmGcT0RYSx5d047X02SMmO
esd49YRE9WcdSXhZ2O1p89NRji9J+cei1xMc3wXi+KZj1j1dTTaqmor06WjvWW+ddcu8XK8BZmAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAMMmWmKu952UZ9XFZmuP3revlDTtzWnmvO8q3XGmfHb9ZanV3yxtWeWn7y4es
vPNtDqZJ6Ts5mppvdl/XXRMyfGvSNlu/RVvtOzLfoipLT1VTKbSpvfogRkvtDVyZOhkyvQcA4Dzz
XV6yvTvTHMfvK+c9U3rkW+zvA/D21urr789cdZ8vi9KDb45rejl8Rry6iJ/FV1HP4vXbBTJEfYt1
+UpiHM295bXsqrO9l8QkZ0lZEqqLeyBZHZLGvZkhIndADKJ3TMoqWQMZ6pjsxll2jsCLSrmU2lFY
36gieyu0LJk3jbsga0wdqzK20QpyztQGprL/AFMrOE05NLkt6qdVWZxNrSe5o9vWBLiUjnzXn0vL
q555dHt8HOwV928/1z/LpzXxbYccRvzTB+jucOwxh0dI22mY3ltIrHLWIjyjZKyoAAAAACJiJjaY
3iXleM8InR5J1GniZw2n3oj7s/8Ao9Wi9a3rNbRE1mNpifNFnVs65XhcWTdt47bnFuF24dm8TFEz
p7T0/pn0a+HJux1OOrOux08d1ndqY7tillVkzExLOk7yd4YxGwluViJhE45raL0na0dtlWO0+bZr
1TKi+2zptZGTamT3b/tLacvJjiY3XaTWdYxZZ6/dtPm1zrv1z78fPcbwC7EAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABhkyV
xUm152iAZWtFazNp2iGhm1Vss8uP3aevnKrNntqLdelI7VRHRnrX/HRjx/tZREVjZXeybW6KbWZt
pCZ6S08tN7Nmbb7zCrJtyoS5145bSx5mWafelr3tsKmS/o08uXyhlly7RPV2+AcBnPNdZrK+53pS
fP4ytnPVda4y4BwHxOXV6uvu96Unz+MvVxG0bQRG0bR2G0nHLb2gCUDX12LxtFmpHeazt82wT1gH
mMN4tWs+rcr2aEV8DU5sM/cvO3yb+O0csLUTSdrLphRE8tlkZI7Atr2ZMazDJVKTYSCawi7Ksq7z
1QERvLK3ZGPrKbyCrbdnMcsbeaa18/RhvvM7oGEwTG0JmYYTIML22a2e28xELM19oURPNO4lOem+
n3ZY5+prVnMc2GYU4/L4A0a15cNf6rz/AC6fC6+NxCPOuOu/5tHJTbHj+F5/l1+BYumXJMd9o3/d
MRXYASgAAAAAAABhlxUz4rY8lYtS0bTEvH8R4ffhmo6bzhtPu29Pg9mq1Gnx6rDbFmrzVsizq2df
zXkMWTeIbNL7tbXaHLwzUctvexWn3bmPL8WFnHVL326VZ91MfFVjvvVlz79kLrcf2m7j7bNHH3bl
J2SirLQoy4t1++7G0dBC/RanxI8PJPv18/WG241+alovSdrV6w6mDNGfFF4/OPSW2b1zeTPL1aAs
zAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAVZ9RXBTe3WZ7R6iZOpzZq4ac1p+UermZMl89+a/byj0Ra9815ted59PQ32hlrXXRjH
DpCLX6ML5NlNsm/ZRqstfdXzbsZt06sLZNvNB1Za8RDWyZdo7q8udq5Mu/mIMt4md2lmy7JzZuWJ
dHgfBL8RvGo1MTXTxPSPx/8AstJ1XWpIs4BwSdbeNVqq/URPu0n73/s9hEREbRG0QUpWlYrWIisR
tER5JbSccur2gCUAAAAPM8Sry8Uyz67fwuxbzVPGsE49XGbvF42V4M0TEL33ERnktsxpk3sumK2j
admFdPFZ33VS2Mdui2J3UU6LYlFSsN2O5NkCyJ6K7T1TEsbAsxdpReerKkTFGMxvYEz0rsqtbbpC
b2VT1QEzuwtbaGUxspuJU3neWdKoiu8rq12gCI92YatLcublnzbEz1aOptyZqTuDHLfxN6R0+t5X
qdJhjBp6UiPLeXl9NSMnEKxHa1+bb8nrlvxUAAAAAAAAAAABTqtNj1eC2LLXeto/R43VabJw/VTh
ydY+7b1h7ho8V4dXiGlmvbJXrS3xRZ1fGv5rzeHN02bEW3cys3xZJx5ImtqztMS3MeTeGFjqlb2O
8btql3NpbZtYsnSBLeiWfdTjtutid+ghherHS5p0+f3vsX6T8Fkw181d4lMvEWdnHaGnw/UeNh5L
T7+PpPxbjdyWcvAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAo1Oprgr63ntAmTqdRqK4K9etp7Q5d7Wy2m953lNrWyWm953mVd77R0
Za1104xxlN9lV8qnJl2a9s3xUXX2ybsJyRDWtl3YWydEC+2VRkzeW6q+T4tbJm+KRdfK1cmWZnlr
vNp7RC/R6HU8SycmCk7ed57Q9ZwvgOn4fEXtHi5/O9o7fJaZ6z1uRyOEezVstq6jiEbV71xevzer
rWtKxWsRFY6REeSRrJxz22gCUAAAAAANbX6aNVpL0npMRvWfSXlKamsRMVvXm+EvZXjmpaPWHzfL
oNRjzXicfWJ8phfPxFejx72x7xMzK+sXiNoiXlq+Pi6fWV/VfTNqfLJl/WTg9Pji8R70LqvMV1Gq
j/zcv6yz+lanzzZP1lWpelTET6S81Gp1P/Gyf90s412rjtnyfqql6asREdWM9+jz9eJ6yP8Az7uh
odZqMt458tpB1JvEViI3/RhzRt13/R1MNaziiZiJn5K9ZNceKZiIiQcu/WekT+iYrWI3lzdTrs+8
8uW0fJzcur1Np/zsn6g79phVaIeetqNR/wAXJ/3SwnUaj/i5P+6UD0ldonum161h5mNRqP8Ai5P1
lNtRqJjacuT9Qd22WN5aGeZyZd/KHJy59RHbLf8AVq31Gp/4uT9ZEvS8Lr/vSs2npzRtL1z53wK+
oza/HW2XJNd99pmX0Rb8VAAAAAAAAAAAAAAcHj/C5yV+l4I9+v24jzj1cLFk8nu5jeNpeW41wmdL
knU6ev1Vp96sfdn/ANFdTrXG+eq1q5F2LLtbZoY8m8d11bbSydErsYsm+zZrO/zcnBm226uhiyRK
EtrvCrJDOJTeu8A1MWX6Lqq5N/dnpb5O5ExMbx2cPNTeJb/DM/iYPDtPvY+nzhri/jDy5/W6AuwA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAa2p1UYo5adbz+xbxMlvqJ1OqjDHLXree0ejmzNrWm953tPmTPWbWneZ7yoy5YhjrXXTjH8s75N
mtkyxt0VZM2/m175N1V03yTKubMLXVXybeYLLX2VXy7eam+b0bOg4VquJW+rry4/O9uyZOq3UjVm
9r25axMzPaIdvhns1kzbZddM0p5Y47z8/R2+HcF03Doi1a8+Xzvbv+TotJnjDXkt+K8ODHp8cY8N
IpSO0RCwF2YAAAAAAAAACvUZYw6fJkntWN3k8dfHz2vLucdz8mkjFE9bz1+UOZosX1UzPm0nqI/W
MYo9FlcPNklfFGeH/NshLGun+Cz6PtHZtVZWlRLS+jxPkRpIn7rdoupHTdA5s6SI+7H6Mfo+32Y2
+To3neSIiZ7A0IjPXpXLePlMotGW3272t85datKzHZjbTVnsDj+FG/2Y/RlGP4R+jo20u7H6N1Ql
o+H8I/REY957R+jpfReiK6eOYHLtj2tttH6KrY/6Y/R2c+kjeJiFVtLG24hxpw7/AHY/RRkw9O37
O99Hrt1YX0tfOBLjcGp4XF8c+u8fs9c4dcVcGemSI61nd3IneN1orQAAAAAAAAAAAAABFqxes1tE
TE9JiUgPKcX4RbRXnNgiZwWnrH4XPi28PdXpW9JraImsxtMS8pxXhF9DecuGJtgmf+1TWW2N/la1
L7N7T5e3Vy6W3hsYcvLbqzbO9jvvCzvDR0+XeO7crO6FmGSvRThy/RtVXJ92elvk2rRvDUzU7pl4
izsd2J3jeBpcNz+Lg5LT7+Pp+Xk3W7js5eAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADs0NTrN96Yp6edkW8Wzm6+LNTq4pvTHO9vOfRoWtt
1mes95YWvs1s2fZldddOczLPLn2ju0MmebT3YZc2/mpm3qqllN1drsbZIhr3yzvtHf4AsvlYYseb
V5Yx4KTe0+UQ6nDvZ3UazbJqd8OKeu33peq0eh0+hxcmnxxWPOfOfm0mP+steT/ji8N9mKY9suum
L37+HHaPm9DSlaVitKxWsdohI0Y22gAgAAAAAAAAAABXnyRhw3yT92Nwef4xm8bVzET0rPJH5d12
CvLhho3rN9RWs9Z23n5y6O21YhrVYbdGOCfrrLPJRpv863zVS6FS09SvZj3lVZZRdPSqmnSWdrIE
ebOkK4ldTsgW1WKqd1oMZhEVZyRAImOjGI6rJ7IiATNd46qL02bHkiaxaoNGY2n4ImPgtyV2n0Vo
Gvlx7x2beiyTk08RPevSVUxux00+Fn2n7N+n5rRFb4AAAAAAAAAAAAAAACLVres1tETWekxKQHlu
L8InR2nPp43wz3j8P/s5dLveWrFqzW0bxPeJeV4xwmdFec+CJnDM9Y/CrY1xv8qvTZ+WYdbDk5oh
5zHk283U0eo3jaZZ2N5XYjrCnLSJhOK+8d1kxvCqzSwZvousrb7k9LfJ3nB1OLeJdLhufx9LEWn3
6e7LXN9Ofy5/W4AuxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAETaKxMzO0Qi9646Ta07RDmZ9VbPbaOlI7Qi3i+c3TPUaqcu9adKfy0722ZXvFa9
XO1OrjrESxt66ZJmcjPUanlidmhkzTZVfLN5VWvsC2b7R3U3yqrZZtO1esz2h2+F+zWTUcuXXTNM
feKR3n5+iZLVbqRzNJo9TxHLyaekz62ntD1fDOA6fQbZL7Zc/wCKY6R8odLBgxabFGPDSKUjyiFj
SZkYa3aALKAAAAAAAAAAAAAADQ4pl2pTFH3p3n5Q33E12Tn1eSfKscsLZ+orS00eJqbW+Lfnu1tF
XaJnZsz3WpCfsyp00fWSvmPdVYOmSUDd8kR3InoQosy7JmUX7MdwZ17ro7KKT1XRPRAsrO0rYndr
79V1ZBaQiJ6JgCSIJASwrO07MpV2nqBlrv1a1o2bf2qtfLXaQUTO0sb05o3jv3ZXhjS20xEphW5h
yeJjjf7UdJWNKLziyRePsz0lux1SgAQAAAAAAAAAAAAAADG9K5KTS8Rato2mJZAPIcU4ZbQZuekT
OC3afT4NXFkmlntc2GmoxWx5K71tG0vHa/RX0GpmlutJ61t6wrY2xr8dXS5uesN+tt4ef0eaa223
2dnHk3juyreM81OaFGiy/RtZET9jJ7s/2bdutd2jqKeic3iNTsd8a2h1H0jTVtP2o6W+bZbOO+gA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABje9cdJt
adohGTLXFTmvO0fy52bJfU23t0pHaqLeL5xdK9Rnvqb+cUjtCi94xxvK3JetKuHrdZvaa1ljb10y
cnIs1Wt3naJc++TmVWvMz1YWybfMGdsm3eWek0mo4jm8PT0mfW3lDf4V7P5tdMZdRviwfvZ6/TaX
DpMMYsFIpWPTzXmf+steT8jn8L4Dp+HxF77Zc/4pjpHydYGjC3oAAAAAAAAAAAAAAAAADG9opS1p
7RG7zszN6WtPe0zLua+3Joss/wBOzhzG2OsL5+IrY09dsSyYRijbHEMvOChb7KjF0yS2LQ169Mso
S24noyrPVXWejNVKbTuw3T3REdQWU6LYlVvsyiUDPfqupPRr79VuOQX1lZEqoZxIMksd0gT2VT0l
bPZVbuCaW8i8bwr32WxbcGnkjaZa9p2ndv5qbw5+aNugLItF6TEtvTX5sMb969HMpfazc0d9stqe
vVZDdAQAAAAAAAAAAAAAAAADV1+iprtPOO/2u9bektoB4TJTJpNRbHkja1Z6uto8viVht+0HDvpG
H6Tjj6zHHvbecONw7Ltfkmeqmo6Ma69DXbbZTkr1mGWO3RneOaGbZRoM30fVzSelMnT83aef1FZ7
x3h1tBqfpGnjmn369LNc3sc3kzy9bQCzIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAa+q1dNNXr7157VhGp1Xh70x+9f9ocy283m1p5rz3mVbrjXHjt91lz
5c9+fJ1nyjyhdM8lZlOOIiqrUXikd+kMreunnI5XEdX4dZiZcG+XmtNl/F83PeeWWHDOGanieSKY
q+5H2rz2hMzWd1Iqx1yajJXHhrNrW6REeb1nCPZumn2z62Ivl7xTyr/6uhwzhGn4Zj2xxzZJ+1kn
vLoNJnjHW7TbbsAszAAAAAAAAAAAAAAAAAAAAaPFrbaSK/itEOXt0rDf4xb/ACa/GZacRvaF58Q2
IjasQnzPIhCU92tMbZGzHmotG10C6nZkwpPRmipIllEbMIZIE7solgmJBnCyk9VMM6z1BtVllEqK
z0WRILYlluriWcSDJVbusV27gwInaSWM9ECyZ3hqamnSWxFmOSOaqRx725bNnSZNs9J+OynVY+WZ
YYr7TE+nVaIr0Ais81Yn1hKAAAAAAAAAAAAAAAAAABExvG09peU4nov9n66L0j6q/WPg9Y1OJaON
ZpL0+9HWs/EWzeVz9PbmrEtnyc3h9reHy26TWdnSr2YX6657ijLXpLX0+onSamL/AHJ6W+Tbv2aW
ekTv16JzeI1Ox6KJiYiY7Slz+E6jxdN4dp3vj6fl5Og2clnKACAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeQRMxEbzO0Q08uqtkma4ulfO3r8lefUePMxWf
cjy9WvlzVxV6T1Z61/x0Y8f7Wc7Ur1lqVy+LqOWJ2hp6rXddon5rOF1tfmz5OkT0qzb8dWbxjp1c
biuuilJ5Z6r+IcQrixzEy8zl1E6rNt1tMztFY81sztU1eRucN4ffi2p5esRM72n0h7rS6XFo8FcO
CkVpX082nwXh3+z9FWLxHi36328vg6TZyW9ABAAAAAAAAAAAAAAAAAAAAAADj8Unm1tK/hqppHvw
y1k8/EMk+m0GOPeafiFpCZYwolnXspvHvLa9mF46gmnZmwozRUiUCBKYYsoBLOFbKAX0llEqqyzi
QXRLOJVRLOOwLIljZMEgrlhKyYYTAK5nZPN0RZjugUanHzVlz6xtLq361c+9eXItPpXX0dubTU+E
bL2lw2++O1fSW6m/VYAISAAAAAAAAAAAAAAAAAp1GbwcfTreelYEydcuMcRrM/L9nnlsV6wqpi2r
tv133mfWVkRyRtEdGFva7MzkYZNoamWN4bV4mYa9qztKIujhVppxGI8r1mJegeZpknBqKZY+7L0t
LRekWrO8TG8Ns/HJ5ZypAWZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAADS12fp4VJ6z9qVuq1HgUiI+3bpDl589cOKZmevqprXPTbx477rDJlrhr1nq4+s182tMRP
RqaziXiZJrWekNG17ZbxWJ336M5LXRbI3dLTJrs07RMY6fan1dHLrowY+X7MVjt6N3R6Kul0EbWm
s7bz8Z+LnabQX43r7Y53php/mXj+Dnv0f1JO1x/8ZxbUzj02O15mfLtD13AvZqnDds+pmMmo26el
XX0Wh0/D8EYtNjilY7+s/NstpOOTW7QBKgAAAAAAAAAAAAAAAAAAAAAADG88tLW9I3BwJtz6nNf1
vK/DHVqYJ3pzT5y3MPZeojOWMQylEKpTVjZnDCwkqzYQyRRICATCITAJZQxhMAshnEq4ZQC2srKq
qrIBZCWNZZgwswmFloVyCu0dFcx1WyrtCBhv5NTPHXds2U5o3hIz4ffbPt+KHUcTSW5c9Jme0u2v
VYAKpAAAAAAAAAAAAAAAAYZctcVOa35R6tLrltN795/YvknNqrfhpPLH92V5isd9mWq6fHjk6rn0
ZxG8KK5Jm/wbVZiYZtqrmkqL023bkxvCiY3lJHNyRG81mHS4Rn5sNsNp64+3yaWaNrzOzHBl+i6q
mT7s9J+S+ay8mex6EIneN47SNXKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAImYiJme0JafEs3h6fkidrZOn5eaLeJk7eOdm1Hi2vmtPTry/CHmOJcUvmvOPF1n09Pm
6HF9ZGm01qxO3R5vSY7XwzmzTy47zzTEd7en5Mfvt2/PURWdo3tvPrPlKymbktFqTtMTvHzbOLDG
f63JXbFX7FdnoODcDprZpq9TjiMMTvSn4vj8l5fxnrk91saPSa7i2hpOfbTVt5x1m0fLydzR6PDo
dPGHBXasd585n1lsRERG0dIF5OOe6tAEqgAAAAAAAAAAAAAAAAAAAAAAADX11+TRZrf0y2Gjxe22
gtH4piP3TPpXKwxtjhuYo9xq442iIblI2pC1RET2ILd9kxCqRjZmwlCSEohIJAQAAJZISDKGUd2M
MoBnVbVVCyAWVWeSuqyOwIlXZZKue4MJV2WWYT2QKbKL9YlfdRdIo35b7/Hd3KTzUrPrDh27uxpb
c2mpPwX/ABX9XAKpAAAAAAAAAAAAAACekTIp1eTwtJmv+GkyJn1oafeazbfpMzLR4jq/o8b823zX
6XNF8ERCvTcNpxLV5LauvPhx9Irv3lhztdtv8TtaWLicXrt03jzjzb2k1nid56ty3s/w+a7Uwzjn
1raejlarhmbhl/FpbxMO/fzj5p/ixSeXOvTtRfeI280ZI26tfDm3pWe63LaZx7qtGvniJ6tPLvOK
fOa9WzbJvTbza02jl3n5SSljscK1MajSxWZ96nSW88xw/VfQ9XMT9nfa3yemid43jtLeXsce88qQ
EqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADia3UTm1l4j7OP3Y/u
7Vp2rM+kPJW1PhYcmS0+9MzKm/jbwz31weMzbV8UppazPL9q0/BF4rk1GLDSNqxPWPhCnHmnNrtT
qPKteWPm6U6OdHaZvO+SaRNvhv12Ub/q3FhtrNVj0uKOt56z6R5y9zix1w4qY6RtWsREOJ7L6OKa
S2rvX6zNM7T6Vh3mmZyOfya7eACzIAAAAAAAAAAAAAAAAAAAAAAAAAAczjVvqMVfW/8AZ03I41bf
Lp6/OVs/UVrY47NyOzUxd4bUJpEbb3Z7IiOrKIVSjZhMLJYyhKIgmGUQSDESIEbJEgQmCITEAmGU
IiGUAyhZVhDOoM4Wx2VQtqBKuyyWEgqlhKyyuyBVaGtkbNmvk7A15l1eH2300R6TMORPSXT4ZO+O
8fFefEX63gEAAAAAAAAAAAAAAAq1WPxdLlp+Kkx+y1Fvsz8gjhaDauGK8sx07y3OE3m1tT6RaP4c
vU6yMNKUx73zT0ilY3l2eF6a+m0kRl/zbzz3+Ez5M8z26fJruW6wzYq5sV8d43raNpZjRzPPaTmx
5b6bJ9rHO3zb2WJ8GWPEscY9bgzxH2t62n19GWW0eHOzHU5XbjXZ1x8WTnz2iZ7S2M1IjH2+LX0V
KTqs8zO9ot0j8nUthi1J3UaOFMTfLFo6xMbS9BwHWTqdHOO8+/hnln5eTjYMFo1WTH5VnePzXcIm
2k4zlpPSmXy/hfF5eMfJns69OA2cgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAADG/2LfJ874rW845mubliY7bPoto5qzHrDz0+yePNF41OotaJ7RWNtpV1OtfHqZ715fhu
j8adNpcVfeyzE2/vLuanhOu1nEctIxTTFa/+ZPbZ3eHcF0vDbTfFE2yzG03t32+DokynXl9+leDB
TTYKYccbUpWIhYCzEAAAAAAAAAAAAAAAAAAAAAAAAAAAAcXjE/4zDH9M/wAu04XF5/3jj/0f3Wz9
RUYmzDWxS2I7FSyjuzY1ZKpRKEygEwiWUIkGIk2QJNhKQhMIhkCYZQxhlAMoZwwZwgWQshVCyATL
CWc9ldpBhZXLOVdpQK7NfJPRdaWvknoDVvPvOnwuel4+TlXn3nS4VPvXj4QtEV0wAAAAAAAAAAAA
AAAAAVV02CmTxK4qRf8AFFeq0AAAanEsfPpZmO9Ji0NDLfkwdOsulrumiyzHlVzJrz4Ovoy26vB8
cTBa9NffLtMY77Rv8Yegx5ImkKdJoY1HC81Y+3OSbVn0mGGkmbY45u6tnrrTOu2xGO0RxCd+nNVj
qKxTV1vH2pjaGtnyzXXYdo96ZmGXEMk15b7/AGZiVerWPTYckZcNbx5wzc7hGbnxXxzPWk7x8pdF
0S9jh1OXgAlUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAcPjEf4/FP9H93ccXjMf4vDP9Mx+62fqKrx+S+GvibEFSsqyYwlVK
ZYsmIMoRKYJQIPIEiQ2ATCUQygCGUIhMAyhnDCGUIFkLIV1ZxIMpVWWSrsCuyqyyyq09ECq8tfJK
66jJ2Bp5J6upwn7dv9Lk5J951uE/av8AJaIrqAAAAAAAAAAAAAAAAAAAAAAq1Mc2myxPnWf4cmtu
XT9fR0tffk0WSe28bfq5Wbamm3326MtunwfK6PCv/AxPraZ/dz9PO97/AOqf5dHhdZrw7Dv3mOb9
XOxRFM+avpe38mvkPHf/AFWlrKba7Tzt99ZxKkfR7euyNXMTrtPHfa0z+zPiM/UR8Zj+Wbdu8HpN
M2bfzrV13M4dO2pyR61dNvj44/J/oAWZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADj8bj63BPzdhyeNx0wz8ZWz9RWri7Nmv
VrYu0NmqaRZHZlDGGSiwxZSgCEkCBCQSCQBMJRCYgEsoYx3Z17AlMIhlCBnDOGEM4AlhZZKq4KrK
7LLKrIFN2vdfZReAaObu6/CO9vk5OePR1uEd7fJeIrqAIAAAAAAAAAAAAAAAAAAAAGtxCk5NFliI
3mI32+XVyNTyZOHTee946PQKPoeDffw4777eW/yVs60xv+ZxOnr4Okx1t05KRv8Ao41Z5q3yed5m
XY1szXRZ5jvFJ/hxItP0aOSN9q7yrtr4f2tHFM5+KT16Yq/vK/iGSbXw4vO14UcPx5MGfNbPG18m
1oj4THRsTw7VanPXVYpi3gzMcnrvCnG11JOupwuN8+a3pEQ6jT4divjxWnJExa09pbjbM5HHu90A
JUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAHM41H1GOf6nTc/jEf4Ws+lls/UX45uGekNujTwdm5RNIthKIZKLDFlsiQIShIC
EgCUJ7AmGTGO7IDzZQhMSDJMMYZQgZwzhhDOATuqssmVdgVWVWWyqtCBTeVF19lF+wNLNG7q8I+9
8nLyupwnt+S8RXUAQAAAAAAAAAAAAAAAAAAAAAAItWL1mto3iY2lyrcLyUxzix2ia2nvPeK+jrCL
OrTVnxpanhuPPemSs8l6RtE7dJj0ldpNP9GwRSZ3neZmV4cR/Vs4AJQAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANHi1d9H
M+kt5ra+vPoskfDdOfqK4mn7Q3aNHBPZu0W0RdDOGFWcKLCJZeTGQQlCQSgASBsCYZQxhlAJTAmA
TsmAgGcM4YQyjsgRLC3VnaVcgwsrt3Z2V2QK7tbJ1bN5a9waeWO7p8Knt8nNyebpcK8vkvlFdQBA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAK9RXmwZI+ErEWjesx6wQeZwejeo0cccuW8
elpblJaaRGxVnCuss4ZrMvJEgCAASISCQIBlCYYpieoM0wx8k7gzIRueYM4Z79FcSy3QEsLJmWFp
BjaVVpZWlXMoGNmvkXXlr3kGtknu6XCf7OXkl1OEdl8orqgIAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAHmskcmtzV/rls0U62OXiWX4zErcc9GmkRfWVkSqqziWayxCPIANwBIhIJSxS
CRG6dwZwlhEs4BluMdzfqgZxLLdXuy3AmVdpZTKuZBjaVVpWWV2QlhZRdfZRcGpl7urwfrzfJy8r
rcH61vPyWitdMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHA4nHLxKZ9awnH2ZcY
jbW459aq8fZpfiI2IZwrqzhmsz3Ebm4JN0AMhCQSIASndiAziWUSriWcAyRujc80DM3RCfIETLCW
UsZEsJYSslXZAwlTddPZTkBp5e7r8Gj6rJPxhx8k9Xa4PG2C8/FaK10QAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAcfjcbZMFvnDWx9m5x2PqcNvS+zSxT7sNPxH62YZQwqzhRZO6UCB
KUAJTux3SDIRuAncQAmJZRLBMSgZ7iIAZRKd2DICUSlAljLCYWMLIFVukNfI2bNbIDTyT7zu8Ijb
Sz/qcG/2nf4T/wCE/wD2WnxWt4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHL9oL
+Hw2cm28VvEuPptfgyVj6yIn0no7/FtJfW8NzYMe3PaPd39d3iMug1WktNc2C9dvPbeP1aZ9xF+v
T471tHu2iflK2HkqWmvaZj5Surqc9Ps5bx+alTHqYHm68S1Vf/NmfnC2vGNTXvyT84Ql6A3cSvHM
sfaxVn5Ssrxyv3sM/lKB1xza8bwT3pePyWV4tpZ+/MfOEjfGrXiGlt2zV/PotrqcN/s5aT/+wLRj
FontMSlAlKEgndO6IAZQljDIEgeQljLCzOVdkCu/SGrkbF56NPNeKxMzMRHxENe0+89DwuNtHHzl
5PJr8NcnLW3Pbf7r1nCZm2gpae8zMrz4i/W6AgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAETETG0xukB4HVaeMHEtRi26RedvkyjBSfX9W77QYvC4xz7dMlYlrU7M929dWJLFc6aPK0q
7YLxPS0S22FlP6q38Zac0yR92s/KVc3tHfFf8tpbcsLRvB/dR/8ALLVnU0r9uL1+dZI1mnmdvGpv
6TOy6ym+Oto2tWJ+cJ/tW+KLK5KW+zes/KU7tG+h01p64qx8Y6NXNo6Y+uPJlp8rLf0rfG7MXtHa
0x8pZxqs9e2a8f8A7Oj7HaTHn0+f6RWM23LETfr6vRW4PoL99NT8ui7F4+vEdXXtnt+fVbXjGsr/
AOZE/OsPS29nuH27YrV+VpeV9pdPXhOtw49NG9Mld55+vXcTPd42I47qo7xSfyWV9oM8d8VJ/VxM
d8l46xWF9cV7en6o/qLfxp2I9ob+eCv/AHMo9op89P8A/wBORGmyT5R+qfo2X8P7n9Q/jTsx7RR5
6ef+4/8AuHftg/8A6cWcOSO9J/WEbWr3pY7Efzp2Lcfv5YK/9zWy8d1E/ZpSv5Oba1/+Hb9lc+LP
bFt87I7E/wAabWbiurvEx4nL/pjZzc2bJkn372t85ZXx55/BX85lucC0vPxnTxlnnjm32mOiZqUu
LJ2p4TwnVavNWaYbRTfre0bQ99pcH0bT0xb78vmtiIiNojaErMwAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAHnfarF7umzRHaZrLjYrdIen9ocPi8JyTt1xzF4eUw23rCm3R4r6bMy
wt6kdTaWLdjswmNoZontsCm0K5XWjopnuDC0dGpqG5bs08/daKV672MjbSaif6oh6Z5f2LtvptRX
0tEvUN3Jfo8f7cYve0eX4zV7B5z20xc/C8eSPuZIRficfXlcPaG7ino08HWIbePpLF2NuiyOyrHK
3fZFSwuovHVfaVF4QK5YWTM9UT0EKry6Ps1Tn4zjn8NZn9nOtLseydObiWW34cf918fWfk+PYANn
KAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAq1WKM+ly4p+/WYeBxTNd6zG0xO0
vobw3FcP0bi2em20Tbmj5Srr418V9sa2Z7qKyzi07MXUylhaU7yjqhLCeiq3ddaFNxFYW7NLNG8t
zya+WO6Va9J7FW66mvwidnrXiPY3Ny8RyUn71Jj9Ht3RPjk19HK9pMHj8D1ER3rHN+jqqtTjjNps
uOe16zAifXzfTz7kNyndpYazS9qT0mszDdoxrsi6m8LazMq6zDOsq1ZEyrt1WWlXaUCqyq0rbKbi
Fdp6PReyFd8uqv8ACsfy83aXrPZHHto89/xX2/SP/dpj6y8vx6EBq5gAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAB5n2q03LfDqqx39y39npmlxbS/TOG5se29tuavzgWzeV4mtui2
O3RRSY2hdVhqO2MvI36iu9lUsrSrvDHn6spnmSiq5jooyV6tq1VV69RC32byTh43h8otMx+r6I+Z
aK/g8TwX7bXh9Mid4iW+fjl8n1ICWb57xLBOm4zqse20Tbmj8+qKdnS9q8PhcTw5tumSm0/OHMxz
0Za+uzx3sX1t0Zxurr1ZxvspWiZYWZbsbT0QK7KLrZVZJFaqt5vbezNOTg9J/FaZeJns93wCvLwb
T/GJn92uGHldIBowAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADuAPA67F9H4l
qMW20VvO3yRWW97T4fC4rXJHSMtI/WGhVlue3b473K2KzMML4+62tujG9pnozXaOSOVFMnVbmq1t
trJRW5E7wwvUxTvCyY6CHOt7moxz6Wh9PxTzYaT61h8x1MbZK/OH0zTf+Fxf6I/htj45vL9WgLMn
mvbPFvocGWO9L7fq85p5maw9d7VYvE4JkmPu2if3eW0+PasdFNOnxfF1Y2hlykRsmY+LJ0MZjZXa
eq2eyi8oQTO0KLdZWzPRjWu6VaqtHR73g0bcI0sf0Q8Nkq93wqNuFaWP+XDTDDytwBowAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAef9q8HNpcGaI60vtPyl56k9Iew49j8ThGe
PwxFv0l4zH2U26fDfTYiyJljvsjf4sm6vJ1hrXjq2MkqLdZEVbgbMx0auGdmzNt6iHN1Ub5af6of
TdPG2nxx6Vj+HzaaTm1+nx/iyVj930ysbViPRrj45vL9SAuyc7j1efguqj+jd4/T33rD3HEcPj8O
1GP8WOY/Z4TTT7sKadHhbcsZnaCJ3TPZk6VdrKbTutmP0U2nqgrGOsr8deiuI2X09EqKM1dt3uuG
f/jdN/06/wAPE546S9rwud+Gaaf+XH8NMMPK2wGjAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAABrcRp4nDtRWPPHP8PCYusPoWSvNjtX1iYfPuWaXtX8MzCuvjfw32siu8ptXoxi
0wy5t4YulReqmazu2skbquURWFInddM7VYRGyL291KFnCcfj8e0le/Lbmn8n0N4b2Ur4nHLWmPsY
5e5a5+OXyXugBZmiY3iY9Xz7NjnTa3Ph/BeYj5PoTxftFg8Hjk2iOmWkW/Psrr418V5WrWd2faFc
V2jdnEMXWxntupmN7NiYU27iWML6dVMVnddjgVqMsdHr+CW5uE6f4Rt+7yuSsTDv+zWXn0WTHP3L
/tK+GHl+O0A1c4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA8Dn93W56/wDM
t/L3z59qp24jn+OS38lnpr4r7ZxHQ2TEstt3PXUrt27K57rr1VT0BjKnJPRbMqMs7QlV2fYvHvrd
VknyrEfu9m8f7FZI8fVU85iJewbT45NfQBKo817W4eulzxHaZrL0rje09ItwqbfhtBVs3leai8RD
KLw1sduesL606dWFdsZT1jdhNeq6K9DlhCVUU6s4jZnt1YzAhnM71dH2bycmszY/K1d/0c6OzY4R
fwuK4p8rTstn6z8k7HrwGzkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHz3
Vxvr80/8y38voTwGpj/F5/8AqT/JfjTx/WVeyY6FPspc9dZPVXaOq2WEwIUTVRmjo2rNfLHRI3vZ
DJycXtX8dZh7t879nsnhcbwz23tt+r6I2nxyb+gCVBzuPY/E4PqI9K7ui19fTxNBnp60n+Aj5/pJ
3jZu1aOnnltMNussdfXbm+l3ZM9URHREdZVXTuT1Nk7boQiOkJw28PU47/htEp5eivJPLMTCZ9Vv
x7mJ3iJ9UqNHk8XR4b+tIXuhxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD
weqjbWZ4/wCZP8vePCaz/wDIaiP+Zb+UX408f0r9lOxWOifJhXWjfyYWllPRXYQxnrCrJHRd3YZI
6A1NJecHEsN/S0T+76bE7xE+r5dk93LW3pL6ZpMni6PDf8VIn9m2fjm8s9rgFmQxvHNS0esbMiew
PnHLyai9fS0w2aNfUTtrs3+uf5bGPqy068fF227KtSsdFlKqNGMV6myyY6sbdIQI8tlOWOi6Jhhk
j3RD0vA8nicMx9etZmHRcT2Zyb6XNT8N9/2dt0T449T2AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAHhdfG3E9TH9cvdPEcXjk4zqI/q3L8aeP6xr2TsxpLOekMK6mFo6qpXSrm
OqBixvHSVmzC4OfqK7S9/wAByeLwbTW9K7fo8Fqo6Paeyl+fglI/Da0NcMPK7QC7AAB8313TiOf/
AKk/y2MHWrX4jG3E9R/1Lfyv0/aFNOrHxuU7LI7MMayGTVlHWUXhNe6Z6wIUsb9d1m20q7dkDpez
N9tRqKT5xEvRvKez9+Xis1/FSYerb5+OTyf6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAB43j9eXjN/jWJ/Z7J5L2mry8Upb8VIF8f6aGOey2eynHvOy7bowrrYSxZSwQJ2YXZ
92N4BoanrEvVexmTm4blr+HJ/aHltRHSXofYm/1Wrp5RaJaYY+X49WA0c4AD51xONuKan/qW/lbp
+0MOLRtxbU/9SU4J7KadWPjep2WQrr2WRPRk1TvsndXMpiRCb9FNu0rbTuqvKBscCjfi9PhWZeue
V9n434rafTHL1TfPxy+T/QAszAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHmv
avHtfTZfnV6VxPajHzcNrf8ABeJFs/XnMcr4no18c+6vr2YadkY2YM57sEDLyY37Mo7MMnYGlqO0
vQ+xNfqNVb1tEfs87qZ2rL0/sVX/AHdnt65P7Q0wx8vx6UBo5wAHz/jUbcX1PT78qtO2vaCnJxjP
8Zif2amnnspp04+OjWejKJ6MKdmcMmyJn4m5ZHzEVPMwtJv0VZLbQDqezcb8RzT6Y/7vUPM+ytZt
n1OTyiIh6Ztn45N/6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABocbxeLw
nUR5xXm/Rvq8+OMuDJjntaswEeBxT0bNZ6NatZpNqz3rO0rqsdO3PxlaWEMpY+aqWXkryT0ZT2V3
7A0dVPuy9f7G124NM/iyT/Z4zWT7sw957MYfB4Fp4/FE2/WWmGHldcBowAAeM9qKcvFeb8VIly9P
0nq7ntbTbVYL+tJj93CwT76unR4/jo0nozhhTsy3Y1sWljM9Ce7HyQIm3RRlttVbaWrnt0Sh6n2U
x8vD8mSfv3/h3XN4Bi8Lg2nj8Uc36y6TeOPXugCUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAPD8RxeBxXUU26Tbmj8+quro+02Lw+I4ssdslNvzhzazvDPbq8d7GW7Dfqz2VzG
0s2qd+iu/Zn5Ksk9BVztX1mI8930zh2LwOHabH+HHWP2fNYp4+vwYvxXiP3fUqxtWIjyjZtj45/L
faQFmQADzftfj3w6fJ6WmHmsP23rvaqnNwqLfhvEvIYZ+sV038bo0noy36MK9oZQxrdMyrlnMbMZ
QKrS1M07zEestq/RRjr4utwY/wAV4j91p9V18fQdJj8LR4ccfdpEfsuREbREJbuMAAAAAAAAAAAA
BAJAAAAEAJEAJQAJQAJEAJQAJQAJEACUJAQlAJEAJQAJQJAAAEAJEAJBAAAJAABAJEJAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABwvanDzaPFmjvjv8A
tLztJ3h7HjGHx+FainnFeaPnHV4vFbeIU038VbHeGF+kso7Mb9mTdhKnLK3dRm7SIrHhGPxeP6Sv
9cT/AHfSnz72Zx+J7Q45/BWZ/Z9BbZ+OXyfQBZQABzeP4/E4NqI9Ii36S8Ng/wAx9C4jTxOH6ivr
jn+Hz3B/mQi/GvjdCnWNlsdI2V07LIlg6USrt2ZzZXMoFV+zPhGLxeOaavpbm/RVltEN72Yx+Jxm
b7dKUmf7L5+s9/HtRA2cqRACRACRACRACUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAACQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQCQQCRACRACRCQBCQBCQB
ACRACRACRACRACL1i9LVntMbPATTwdRkxT3pea/u+gPE8Xx+DxrPHlaYt+qNfGvjvtXXsi0dOrKk
dEXjZg6VMtbP2bMtXUdpEV0/Y2nNxbNf8OP+727xvsXH+N1U/wBEfy9k3nxyb+gCVQAGOWvNivX1
rMPnGGOXNNfOJ2fSZ6w+dZKeHxDPX8N7R+6L8a+L63KdoZ7q6zvEMpnowdKJ6ywmWUyqvIKM0vQ+
x+D6rU55+9aKx+TzWa36vbezmDwODYenW+95/Nphj5L6dQBo5wAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAEiAAAEoA
AAAAAAAAAAAAAEAkEAkRuAkQbgkQAkQAkQAkQAl5T2nx8nEMOT8dNv0l6pwfarHvpcGWPu32/WCr
YvK4mOem6b9mGKd4Z3idmFdka0y1c892zfpMtLPaNpEV6D2Kj/Eauf6YeweQ9ieuTVz8K/3evbT4
5NfQBKoAA8FxCvJxrUx/XMvevD8Zry8fz/Haf2RfjTx/6RSOnRMyypHu9kXjowrqVSrvPRnZVl6V
kK0775MsUjvadn0nT4ow6bFijtSsVfPuFYvpPGtNTy54mfy6vorXDm8l9pEC7JIgBIgBIgBIgBIg
BIgBIhIAgBIhIAgBIgBIIBIAAhIAhIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJAAAAAAAAAAAAAAA
AAAAAAAAABAJQkAEAAAAAAAAAAjc3BIjdG4Mkbo5kcwMjdhzHMDPc3V8xzAs3N1fMjmBZubq+Y5g
Wbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmTzAz3N2HMnmBlu5ftFTx
OEZJ/DMW/d0t2rxKni8N1FPWkiZ9eS08e7Cy8dGGn6UhZaJljXZGnmc3UT3dPP2cnUT78xCIV6j2
H/8A9c/6f7vXPI+w8bU1U+vL/d63du5NfUiDcVSIAS8b7RV5eOb/AIqRL2TyXtNX/e2KfXH/AHlF
+NPH/pr4+2xcxx0hFpY11K7R16KM32ZWz3UaidqSgrc9kcPicWyZJjfw6T+727y3sXh2xarN+K0V
h6lvPjj3e0ASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJQAAAAAkQAkQAkAAAAAAAAAAAAAAA
EgAAAAAAAAAAAAAAAAAAAAAgAAABKDcAN0bgkY8xzAyRux5kcwM9zdXNkTcFm6OZXzMeYFvMibKu
ZHMC2bo51U2RuC2bom6rc3BZzom6sBZzI52ADPnOdggFnMc6skFnMc6rc3BbznOp3RzAv50c6nml
HMC/nOf4qOY5wX85zqOc5wbHOc7X5znBsc6edr85zg2ec52vzpi4NjmY5bROG+/bllVzsNTk5dLl
n0pP8BHmMHWNmzt0aum8obm08vVjfrtnxztR0mXHzTvaZdjVRMTLkZo6yiFen9iZ2pqY/wBP93rN
3kPY+/LfPX1rE/u9XzN3HfqzdO6vmTuIZ7m7Hc3Bnu8t7TR/vHBP9E/y9Pu837SV31umn+if5Rfi
/j/01MMb1hjkrtKzBG0bMsmOZY11tOYamr6Und0LUc7XT7u3rJPqL8er9lcPhcFpbzyWm39v7O00
+FYvA4Zpsc94xxu227jv1IAgAAAAAAAAABKAAAASgASgBIgBIgBIgBIhIAAAAAAAAAAAAAAAAAAC
UACUJAAAAAAAAAAAABIAAAAAAAAAAAAAAAAAAAAg3AEbomQZbo3YzLGbAz3RNlc3YzcFs2YzdVN2
M2Bdzom6nmNwW86JurTAMuY3REJ2BB1ZRVMVBhsbSsiqeUFXLucq3lTygp5TlXcpygp5TlXcpygp
5TlXcqOUFXKjlXcrGYBXysdlswiYBVMdUTCyY6sZBWxlnMMZgGLGZZSwkDdHMiWO4MuY5mEyjcFn
N1OdVzHMC3nTzqeY5gX85zqOZPMC+Lqdbk20eb/RKOZr8QybaK/XvtH7iZ9aGlp2luzT3fg19NHS
OjbmPcYX67XH1XSZ9XIzRvMuzrK7zLkZYmYnciunb9lZ5dTk+OP+71cXeP8AZnJ/ip2nf3J/l6iL
/Fu5L9bMWZczXi6YuIbEWTzKIuyiwLt3nuO25uI4a/hx7/rLuczg8TicvFLbfdpEK6+NPH/phhjo
stLGkctUWnoxrrU3j1cnWTzZq1jzl1clo5Zcu8c+txR63iP3Tn6pv4+g4o5cVI9IiGe7CJ2iE7t3
GyN2O6dwSINwSISAlAAlACRAAlAAlACRACRCQAAAAAAAAAASgASISAAAAAAAAAAAAACQAAAAAAAA
AAAAAASAAAAAAAAAAAAAAAAIAAAQCAJljuljsCJlhMs9mOwMJYys5TkBVsjZdyHICrZPKt5E8oK4
qmKrOVOwMIqyirPY2Bjyp2ZbAI2NmSARsbMgEbI2ZAMdjZICNkbMkSCNmOzJEgx2YyzljMAwlhKy
WEwCuWErJhhMArlhLOWEgxljMpljIImWMyTKJA3N0IBO5vux3NwZbnMx3NwZczT4jf3MdPW27a3a
fJOq1XNP2KdIRfi+J2trSYfcjeF+Wm1OicVeWIiN9kai8xjY12ORqultnI1Ecsujq79XP1FovWYI
rTgeq+j8QrWZ+3Mx+r2UXeC0WG2Ti2kiN5mL807eUREvbzbaejefHJv62Iv8WUXa0WTFhVtRdlF2
rz9WUXBtc7jR9dqc2T1ttHyhvZMvJitb0jdq6XHNcNenWVN3028U99WRj6Kb02be3Tq18/SN2Lpc
3UdN9nOmZrqKX/DaJ/d0svvTLRzV3jomK6+Pd1vvWJj0ZczT0mXxNJht60hfFnQ4qu3N1cWTEgs3
Tur5k7gz3N2O5uDM3Y7m4MtxBuCQASIASIASAAAAAAACRCQAAAAAAAAEoSAAAAAAAAAAAlAAlCQA
AAAAAAAAAAASAAAAAAAAAAAAIASgAAAEJAQJQCNkbMgGOyOVnsAw5TlZ7GwMOVPKy2NgY7GzIBGx
skA2AAAAAAAAAAQkBAEghEskAxYzDPZGwK5hjMLJhjMAqmGEwumrCagomFcw2JqqtUFEsLLrV82F
o7gqljKyYYTGwMZRKUSCAQAboJnaN5Bjkneu0d5W4ccViIiOzHFWbTzNumP1Zarr8eeRMbxDW1Mx
NO67NbkhzNVnmInqzaOZrL93JyZeV0M1++7S02jvxDWxhxx033tPpC8Z6rrezWjmZyazJG2/u03h
2vFibTHoqvamiwVwY+nLGzV0+SZ1Mx8G0/45tOhzJ5lXMc3UVXRdlF1HP+iYsDPLPPy49/tz1+Te
pSIr0ho6ak5Ms5J8o2q6NImOrHV7XX488ypzTtHXo0s9t6zG7c1G1qz6ubeZiZ3UatXJG3yauSO7
cvMTEx5tPLb3prPRMVr0HB8vicNxf0+7+kt+LOJwTJyY/Bnz3tH93X36N58cWvq6LSyiyndMSlC7
mZcymLJiwLosmJVRLKLAtiU7q4lMSCzc3YxJuDMRuAlKAEgAAAlAkAAAAAABKAEgAAAAAJAAAAAA
AAAAAAAEgAAAAAAAAAAAAAkAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAhIAAACAAAASgAAAAAAEAAAA
hGzJAImGMwzQDDZjNVuyNgUTVhNGxysZqDVmiu1G5NN2M4waM0+DCaN2cbGcQNGaMZq3JxMJxA1J
qx2bU4kU09slorWNwa20z02RXHbJbl26QvtFovbHWkxEdJt5y2MOHlr2U1W3jx+1hiw8vSO63lmI
XRTaEWmtY6snRHO1VpmJ+DjavpSZl2s8b7y4HFcnh0n0gha5ebJN55KRM2mdoiPN6fh+kpwXh0Wy
RHj5Otp/s5Ps1p62y31+em9aTMYt/OfVfxTiPjZ52naI7fBrI5t66xz5+a1rW7yx0eSL6iZjtEOX
qNbSletom3lENjh2fbHzbbWt3iVozruc+5ztWubf4M4ybpQ2Oboyrva0Vjza8WdDR4OkXt3n9ldX
kaePP9VtYqctYhdvt5oivTeCZ2YOxXk6ubqMfV0b9mrljfqlFcq88k7z2U5axeItDa1OPessuC8P
ya7XRWYnwqdbT/ZMilvIu4dpslNdixXja8Y5tt85djZdbDWnGOesRtXFtuw6T27No5Kx2OrKYQlC
ExKJgBnEpiyvdlEgsizKLKollFgWxLKJVRLKJBbEp3VxLKJBnuMWQJEbpBIAAAJAAAABIAAAAAAA
lAJAAAAAAAAAAAAAASAAAAAAAAAAAAAJAAAABAJABAlAAAAAAAAAAAAAAAAAAAAAAAAIAAAAAAAA
AAABAJQAAAAgAABAAI2EoBGyJhkgGPKxmqxAKpownHC+YRMdN5BrTj67R3bOn01o7p01Iv71u89o
b9a7LfBTfS1vWI2jf12VfQPSW8KX2mas+NC2iv6xMNfJpMnLtEbuuxtMRCtzF55NR5rPps1N/ctP
y6uHreE6nXZ4pak48X3rT06fB7fNeI33cbX6mI32R/MWu7XF116aDSRhxbRERs8f499bkyZeeKae
kzE2mdon81/tfxDLGOunwbzlzbx08oaHBvZHJlx48mrvaa94pu04y617576rNGLRRM0397JEd/lu
9Dw/S3x4qxffo6mm4NjwUiKY4iI9Ib1dHFY6QIaNabbrYrLfrpJtaK1rMzPZb/s+05IpP59OyLeJ
k7eNfRaOc1ue32I7fGXYpi5Y77M8OGMeOKxHSFsU3Y29deZMzirl6dlVvhLatCjJHeYQv1rXnps1
8k9/VsW6qLVmZIi1rzitlvFKRvaZ2h6TSaenC9FFY+3brM+sqeG8Prp4+kZ+lvuxPkr1mqm95nfp
DXM459676a2q1dsV7XietvNno78+CJn1cjX6mOeIm0bR33dfRU5NJjidt9t5afjG/V6JZ7I2QMNh
nyo2BhsMuVG3wAhMSbbQRAMolnE+iuGUSCyJZRKuGUSCyJZK4llEgyZMYTuCUsYSCQASISAAAlCQ
AAAAAAEoASCASAAAAAAAAAAAAlACRACQAAAAAAAAAEgCEoASCAAAAAAAAAAAAAAAAAAAAAAABAAA
AAAAAAAISAIAAAAAAQAAACASgAAAQJAQAAhIDHZhln3do7z0WS18mWsajHjmes7pg3dNi5aRMNqO
yvDHTpPRaigHZhN4hHRlaVN59JY3zRENLUavaO+yq0iNVlitJ6vNcR1MVi0zO0era1/Ea0rPvbz5
PM5MWp45qvo2GZrhmfrsnpHpHzTCseEcM/2vrr8Q1Eb4qzy44nziPN63HpYiIiI7LNHoqabBTFii
IpSNohuVxrKtWMEejPwY9G1FFmHB4mWJn7MdfnIM9JpIx15to5pbUaas/a6rqViI7MxPxqX0UT1r
O3wVzpbR2hviP5i03Y5s6a879FNtHljydhExCv8AMTPJXBnRZbz0iG5ptFjwe/l96zctMVamTJtE
yTMibu1VrdTzRMR0j0ed4lr64MVpm0RERvMz5NvX62uOJ69XhOKX1HH9bHDtFvNYnfJeOy0Z2ojX
6jjnEq6fRUmccTvN/J9H0eKcOnx45neaxEbubwHgOHg+milI3vP2resu3Wu0JQmITsmISDHZHKz2
JgFc1RMLJhGwK9iIZ7MZgEdgmAEwyiWCdwWRLKJVxKYsC2JTuriWUSDNlEsIlMAySx3SCRCQSIAS
AAACRACQAAAAAAASIASAAAAAAAAAAAAAAACRACRACQASIAAAAAAAAAAAAAAAAAAAAAAAAQCUAAAA
AAAAAAIAAAAAAAAQAAAAAACBICBICAAEJAQJQCJcLjuS2ny6fPG/LWdpd1o8T0X07SXx/e7wCdJx
Wa0jmneHQpxPDMdZmJfNtZm49weZrh0/j4o7VtSZ2+Uw0/8A7o49k92vBLc/ntFohFW9PqGXimOI
6Tu1L8T3eCx6r2t1O3JwvHjifO99v7t/Bwf2l1PXU6rS6eJ8qUm8x+so5TsekzcSjbvs4mt4rzW5
K2mbT0itesy2cHsvbvqtbmyz5xERWP2jd1tJwrTaONsOKtZ8585+cnDrzmn4Rq+IZObUROHD32n7
Vv8A0ej0uhxaXFGPFSK1j0bkY4jyZRVZVXFGUVWbGwKsk8mObekNrSW3pWf1a2aYjHbm7bNnQ1id
PW0TvuDdhJEbQABMsLW2R0ZTMQrvfbz2YWzVhpanUxEd0dWkW5c8R5uXxDX1w4pnfr5Q19XxKuOJ
2neXltVqtVxbV/RdJ715+1bypANfiOu1HENV9C0MTfNeesx2rD1PAeBYuE6aKx72W3W9/WVnBuB4
eF4dqRzZbdb5J72l160WVK02ZxCYhOwI23TsnY2BGxsnYBjsiYZsZBjMMZZSgGEolMsQDdG6NwZ7
piVe6YkFsSziVMWZRILolMSriWUSCyJTuwhMSDMRCQSI3SAlACRCQAAEoAEoASAAAAAAAAACUACR
ACQAAAAAAAAAAAAASAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAABAAAAAAAAAAAAACBKAAAAAAAQ
JQAAAhICEbJAYTWJ7wx8KvpC0BV4ceieWGewDHlNmWwCNjZICNhIDmcZredBecdpiY69FXCOLW+i
UiZidukulmxxlx2paN4mNng+K4+I8Hy2yaTfl37TXetoCPfRxfp1qi3F48ofKMvtvxak8s6LDv61
rZji9rPaLUf5PC+bfttS0q8q3p9W/wBrRMdpUZuKdN99nzvFqPbTVz7nD8OKs+do2/mW3h4D7Xaq
ZnPrtNpqz35aRaYOHY9Zk4pNt9rR+rl6zi+OnS+WN57Rv1lXp/YrNaYtruL6zNPnGO3hxP6O5w/2
f0HDuun09Yv55Le9afznqcOvO4tBreMTHu30unnva0bWt8on+70nDuE4OHYYx4Kbesz3tPrMuhGO
IjpDOKrK9YVpsyiGUQnYGOyUgI2SlAIEmwMWMs9kTAMJYzDOYRMArmGErZhhMArlHmzmGMwDE3Ts
bAbs4swj5pgFkSziVcM4BZEsolXDKAZwyhjCYBkACQhIAAAAAAAJAAAAAAAAAAAAAAAAAAAShIAA
AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
2Q==`;var S4="1.2.4";var su,Qc,eh,Wi,G0,th,q0,X0,K0,O2=class{constructor(t={}){su.set(this,void 0);Qc.set(this,void 0);eh.set(this,void 0);Wi.set(this,void 0);this.analyze=(...t)=>{if(!ar(this,Qc))return;let n=this.tf.engine().state.numTensors,r=ar(this,su);cs(this,su,n);let a=n-r;a!==0&&Ee(...t,a)};G0.set(this,t=>{if(!ar(this,eh))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof He))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});th.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=et();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(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==="wasm")&&(this.config.backend="tensorflow"),this.config.debug&&Ee("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ee("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ee(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ee("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&C6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ee("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ee("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ee(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(et()-n)}});q0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(j0);break;case"full":n=await t(H0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});X0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+j0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+H0;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)}));K0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(j0):t(H0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=Th,this.draw=$2,this.version=S4,this.config=eo(mt,t),this.state="idle",cs(this,su,0),cs(this,Qc,!1),cs(this,eh,!1),cs(this,Wi,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>M2(n,this.config),this.classes={facemesh:r2,age:Mg,gender:Dg,emotion:Wg,faceres:Gg,body:this.config.body.modelPath.includes("posenet")?m2:N2,hand:_2,nanodet:E2},this.sysinfo=Z2()}profileData(){return this.config.profile?Fg:{}}similarity(t,n){return this.config.face.description.enabled?Xg(t,n):this.config.face.embedding.enabled?R6(t,n):0}enhance(t){return Kg(t)}match(t,n,r=0){return F6(t,n,r)}async load(t={}){this.state="load";let n=et();t&&(this.config=eo(this.config,t)),ar(this,Wi)&&(this.config.debug&&Ee(`version: ${this.version}`),this.config.debug&&Ee(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Ee("platform:",this.sysinfo.platform),this.config.debug&&Ee("agent:",this.sysinfo.agent),await ar(this,th).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ee("configuration:",this.config),this.config.debug&&Ee("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?s2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?$g(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Lg(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Ug(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?jg(this.config):null),this.models.handpose||(this.config.hand.enabled?I2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?y2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?S2(this.config):null),this.models.nanodet||(this.config.object.enabled?R2(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?qg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await s2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await $g(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Lg(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Ug(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await jg(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await I2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await y2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await S2(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await R2(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await qg(this.config))),ar(this,Wi)&&(this.config.debug&&Ee("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),cs(this,Wi,!1));let r=Math.trunc(et()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var m,A,y,g;this.state="config";let a;this.config=eo(this.config,n),this.state="check";let s=ar(this,G0).call(this,t);s&&(Ee(s,t),r({error:s}));let i=et();await ar(this,th).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=et();let o=M2(t,this.config);if(!o||!o.tensor){Ee("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(et()-a),this.analyze("Get Image:");let l,c,u,h,d;this.config.async?(u=this.config.face.enabled?Zg(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=et(),u=this.config.face.enabled?await Zg(this,o.tensor):[],d=Math.trunc(et()-a),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(m=this.models.posenet)==null?void 0:m.estimatePoses(o.tensor,this.config):[]:l=this.config.body.enabled?T2(o.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=et(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(o.tensor,this.config)):[]:l=this.config.body.enabled?await T2(o.tensor,this.config):[],d=Math.trunc(et()-a),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(c=this.config.hand.enabled?(y=this.models.handpose)==null?void 0:y.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=et(),c=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],d=Math.trunc(et()-a),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?F2(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=et(),h=this.config.object.enabled?await F2(o.tensor,this.config):[],d=Math.trunc(et()-a),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([u,l,c,h]=await Promise.all([u,l,c,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(a=et(),p=[...g4(u),...y4(l),...w4(c),...x4(u)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(et()-a)),this.perf.total=Math.trunc(et()-i),this.state="idle";let f={face:u,body:l,hand:c,gesture:p,object:h,performance:this.perf,canvas:o.canvas};r(f)})}async warmup(t={}){let n=et();t&&(this.config=eo(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ar(this,q0).call(this):typeof Image!="undefined"?a=await ar(this,X0).call(this):a=await ar(this,K0).call(this),this.config.videoOptimized=r;let s=et();return this.config.debug&&Ee("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};su=new WeakMap,Qc=new WeakMap,eh=new WeakMap,Wi=new WeakMap,G0=new WeakMap,th=new WeakMap,q0=new WeakMap,X0=new WeakMap,K0=new WeakMap;var nh=0,T4=!1,_t={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function Qae(){if(T4)return;let e=`
:root { --rounded: 0.1rem; }
.menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;
box-shadow: 0 0 8px dimgrey; background: ${_t.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }
.menu:hover { box-shadow: 0 0 8px ${_t.hover}; }
.menu-container { display: block; max-height: 100vh; }
.menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }
.menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }
.menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }
.menu-title { cursor: pointer; }
.menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }
.menu-label { padding: 0; font-weight: 800; }
.menu-list { margin-right: 0.8rem; }
select:focus { outline: none; }
.menu-list-item { background: ${_t.itemBackground}; color: ${_t.itemColor}; border: none; padding: 0.2rem; font-family: inherit;
font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }
.menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}
.menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }
.menu-button { border: 0; background: ${_t.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;
border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }
.menu-button:hover { background: ${_t.buttonHover}; box-shadow: 4px 4px 4px 0 black; }
.menu-button:focus { outline: none; }
.menu-checkbox { width: 2.8rem; height: 1rem; background: ${_t.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }
.menu-checkbox:after { content: 'OFF'; color: ${_t.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
.menu-checkbox:before { content: 'ON'; color: ${_t.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
.menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${_t.checkboxOff};
border-radius: var(--rounded); transition: left 0.6s ease; }
input[type=checkbox] { visibility: hidden; }
input[type=checkbox]:checked + label { left: 1.4rem; background: ${_t.checkboxOn}; }
.menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${_t.rangeBackground}; }
.menu-range:before { color: ${_t.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }
input[type=range] { -webkit-appearance: none; }
input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${_t.itemBackground}; border-radius: var(--rounded); border: 1px; }
input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${_t.itemBackground}; border-radius: var(--rounded); border: 1px; }
input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${_t.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${_t.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
.svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }
.svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }
`,t=document.createElement("style");t.innerHTML=e,document.getElementsByTagName("head")[0].appendChild(t),T4=!0}var E4=class{constructor(t,n,r,a){a&&(_t={..._t,...a}),Qae(),this.createMenu(t,n,r),this.id=0,this.instance=nh,nh++,this._maxFPS=0,this.hidden=0}createMenu(t,n="",r={top:null,left:null,bottom:null,right:null}){this.menu=document.createElement("div"),this.menu.id=`menu-${nh}`,this.menu.className="menu",r&&(r.top&&(this.menu.style.top=r.top),r.bottom&&(this.menu.style.bottom=r.bottom),r.left&&(this.menu.style.left=r.left),r.right&&(this.menu.style.right=r.right)),this.container=document.createElement("div"),this.container.id=`menu-container-${nh}`,this.container.className="menu-container menu-container-fadein";let a=document.createElement("div");a.className="menu-title",a.id=`menu-title-${nh}`;let s=`<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" style="width: 2rem; height: 2rem; vertical-align: top;">
<path d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h352a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48zm-51.37 182.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z" class="svg-background"/>
<path d="M348.63 214.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z" class="svg-foreground"/>
</svg>`;n&&(a.innerHTML=`${n}${s}`),this.menu.appendChild(a),a.addEventListener("click",()=>{this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.menu.style.borderStyle=this.container.classList.contains("menu-container-fadeout")?"none":"solid"}),this.menu.appendChild(this.container),typeof t=="object"?t.appendChild(this.menu):document.getElementById(t).appendChild(this.menu)}get newID(){return this.id++,`menu-${this.instance}-${this.id}`}get ID(){return`menu-${this.instance}-${this.id}`}get width(){return this.menu.offsetWidth}get height(){return this.menu.offsetHeight}hide(){this.container.classList.contains("menu-container-fadein")&&(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"))}visible(){return this.container.classList.contains("menu-container-fadein")}toggle(t){if(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.container.classList.contains("menu-container-fadein")&&t){let n=t.x||(t.touches&&t.touches[0]?t.touches[0].pageX:null);n&&(this.menu.style.left=`${n-this.menu.offsetWidth/2}px`),this.menu.offsetLeft<0&&(this.menu.style.left=0),this.menu.offsetLeft+this.menu.offsetWidth>window.innerWidth&&(this.menu.style.left=null,this.menu.style.right=0),this.menu.style.borderStyle="solid"}else this.menu.style.borderStyle="none"}addTitle(t){let n=document.createElement("div");return n.className="menu-title",n.id=this.newID,n.innerHTML=t,this.menu.appendChild(n),n.addEventListener("click",()=>{this.hidden=!this.hidden;let r=document.getElementsByClassName("menu");for(let a of r)a.style.display=this.hidden?"none":"block"}),n}addLabel(t){let n=document.createElement("div");return n.className="menu-item menu-label",n.id=this.newID,n.innerHTML=t,this.container.appendChild(n),n}addBool(t,n,r,a){let s=document.createElement("div");return s.className="menu-item",s.innerHTML=`<div class="menu-checkbox"><input class="menu-checkbox" type="checkbox" id="${this.newID}" ${n[r]?"checked":""}/><label class="menu-checkbox-label" for="${this.ID}"></label></div>${t}`,this.container.appendChild(s),s.addEventListener("change",i=>{n[r]=i.target.checked,a&&a(i.target.checked)}),s}async addList(t,n,r,a){let s=document.createElement("div");s.className="menu-item";let i="";for(let o of n)i+=`<option value="${o}" ${o===r?"selected":""}>${o}</option>`;return s.innerHTML=`<div class="menu-list"><select name="${this.ID}" class="menu-list-item">${i}</select><label for="${this.ID}"></label></div>${t}`,s.style.fontFamily=document.body.style.fontFamily,s.style.fontSize=document.body.style.fontSize,s.style.fontVariant=document.body.style.fontVariant,this.container.appendChild(s),s.addEventListener("change",o=>{a&&a(n[o.target.selectedIndex])}),s}addRange(t,n,r,a,s,i,o){let l=document.createElement("div");return l.className="menu-item",l.innerHTML=`<input class="menu-range" type="range" id="${this.newID}" min="${a}" max="${s}" step="${i}" value="${n[r]}">${t}`,this.container.appendChild(l),l.addEventListener("change",c=>{n[r]=parseInt(c.target.value)===parseFloat(c.target.value)?parseInt(c.target.value):parseFloat(c.target.value),c.target.setAttribute("value",c.target.value),o&&o(c.target.value)}),l.input=l.children[0],l}addHTML(t){let n=document.createElement("div");return n.className="menu-item",n.id=this.newID,t&&(n.innerHTML=t),this.container.appendChild(n),n}addButton(t,n,r){let a=document.createElement("button");return a.className="menu-item menu-button",a.style.fontFamily=document.body.style.fontFamily,a.style.fontSize=document.body.style.fontSize,a.style.fontVariant=document.body.style.fontVariant,a.type="button",a.id=this.newID,a.innerText=t,this.container.appendChild(a),a.addEventListener("click",()=>{a.innerText===t?a.innerText=n:a.innerText=t,r&&r(a.innerText!==t)}),a}addValue(t,n,r=""){let a=document.createElement("div");return a.className="menu-item",a.id=`menu-val-${t}`,a.innerText=`${t}: ${n}${r}`,this.container.appendChild(a),a}updateValue(t,n,r=""){let a=document.getElementById(`menu-val-${t}`);a?a.innerText=`${t}: ${n}${r}`:this.addValue(t,n)}addChart(t,n,r=150,a=40,s){s&&(_t.chartColor=s);let i=document.createElement("div");return i.className="menu-item menu-chart-title",i.id=this.newID,i.innerHTML=`<font color=${_t.chartColor}>${t}</font><canvas id="menu-canvas-${n}" class="menu-chart-canvas" width="${r}px" height="${a}px"></canvas>`,this.container.appendChild(i),i}async updateChart(t,n){if(!n||n.length===0)return;let r=document.getElementById(`menu-canvas-${t}`);if(!r)return;let a=r.getContext("2d");a.fillStyle=_t.background,a.fillRect(0,0,r.width,r.height);let s=r.width/n.length,i=1+Math.max(...n),o=r.height/i;for(let l=0;l<n.length;l++){let c=a.createLinearGradient(0,(i-n[l])*o,0,0);c.addColorStop(.1,_t.chartColor),c.addColorStop(.4,_t.background),a.fillStyle=c,a.fillRect(l*s,0,s-4,r.height),a.fillStyle=_t.background,a.font=`${s/1.5}px "Segoe UI"`,a.fillText(Math.round(n[l]),l*s+1,r.height-1,s-1)}}},rh=E4;var ese=`
#gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }
#gl-bench div { position: relative; display: block; margin: 4px; padding: 0 2px 0 2px; background: darkslategray; border-radius: 0.1rem; cursor: pointer; opacity: 0.9; }
#gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }
#gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }
#gl-bench .gl-mem { font-size: 12px; fill: white; }
#gl-bench .gl-fps { font-size: 13px; fill: white; }
#gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }
#gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }
#gl-bench rect { fill: black; }
#gl-bench .opacity { stroke: black; }
`,tse=`
<div class="gl-box">
<svg viewBox="0 0 60 60">
<text x="27" y="56" class="gl-fps">00 FPS</text>
<text x="30" y="8" class="gl-mem"></text>
<rect x="0" y="14" rx="4" ry="4" width="60" height="32"></rect>
<polyline class="gl-chart"></polyline>
</svg>
<svg viewBox="0 0 14 60" class="gl-cpu-svg">
<line x1="7" y1="38" x2="7" y2="11" class="opacity"/>
<line x1="7" y1="38" x2="7" y2="11" class="gl-cpu" stroke-dasharray="0 27"/>
<path d="M5.35 43c-.464 0-.812.377-.812.812v1.16c-.783.1972-1.421.812-1.595 1.624h-1.16c-.435 0-.812.348-.812.812s.348.812.812.812h1.102v1.653H1.812c-.464 0-.812.377-.812.812 0 .464.377.812.812.812h1.131c.1943.783.812 1.392 1.595 1.595v1.131c0 .464.377.812.812.812.464 0 .812-.377.812-.812V53.15h1.653v1.073c0 .464.377.812.812.812.464 0 .812-.377.812-.812v-1.131c.783-.1943 1.392-.812 1.595-1.595h1.131c.464 0 .812-.377.812-.812 0-.464-.377-.812-.812-.812h-1.073V48.22h1.102c.435 0 .812-.348.812-.812s-.348-.812-.812-.812h-1.16c-.1885-.783-.812-1.421-1.595-1.624v-1.131c0-.464-.377-.812-.812-.812-.464 0-.812.377-.812.812v1.073H6.162v-1.073c0-.464-.377-.812-.812-.812zm.58 3.48h2.088c.754 0 1.363.609 1.363 1.363v2.088c0 .754-.609 1.363-1.363 1.363H5.93c-.754 0-1.363-.609-1.363-1.363v-2.088c0-.754.609-1.363 1.363-1.363z" style="fill: grey"></path>
</svg>
<svg viewBox="0 0 14 60" class="gl-gpu-svg">
<line x1="7" y1="38" x2="7" y2="11" class="opacity"/>
<line x1="7" y1="38" x2="7" y2="11" class="gl-gpu" stroke-dasharray="0 27"/>
<path d="M1.94775 43.3772a.736.736 0 10-.00416 1.472c.58535.00231.56465.1288.6348.3197.07015.18975.04933.43585.04933.43585l-.00653.05405v8.671a.736.736 0 101.472 0v-1.4145c.253.09522.52785.1495.81765.1495h5.267c1.2535 0 2.254-.9752 2.254-2.185v-3.105c0-1.2075-1.00625-2.185-2.254-2.185h-5.267c-.28865 0-.5635.05405-.8165.1495.01806-.16445.04209-.598-.1357-1.0787-.22425-.6072-.9499-1.2765-2.0125-1.2765zm2.9095 3.6455c.42435 0 .7659.36225.7659.8119v2.9785c0 .44965-.34155.8119-.7659.8119s-.7659-.36225-.7659-.8119v-2.9785c0-.44965.34155-.8119.7659-.8119zm4.117 0a2.3 2.3 0 012.3 2.3 2.3 2.3 0 01-2.3 2.3 2.3 2.3 0 01-2.3-2.3 2.3 2.3 0 012.3-2.3z" style="fill: grey"></path>
</svg>
</div>
`,C4=class{constructor(t,n={}){this.css=ese,this.svg=tse,this.paramLogger=()=>{},this.chartLogger=()=>{},this.chartLen=20,this.chartHz=20,this.names=[],this.cpuAccums=[],this.gpuAccums=[],this.activeAccums=[],this.chart=new Array(this.chartLen),this.now=()=>performance&&performance.now?performance.now():Date.now(),this.updateUI=()=>{[].forEach.call(this.nodes["gl-gpu-svg"],o=>o.style.display=this.trackGPU?"inline":"none")},Object.assign(this,n),this.detected=0,this.finished=[],this.isFramebuffer=0,this.frameId=0;let r,a=0,s,i=o=>{++a<20?r=requestAnimationFrame(i):(this.detected=Math.ceil(1e3*a/(o-s)/70),cancelAnimationFrame(r)),s||(s=o)};if(requestAnimationFrame(i),t){let o=async(u,h)=>Promise.resolve(setTimeout(()=>{t.getError();let d=this.now()-u;h.forEach((p,f)=>{p&&(this.gpuAccums[f]+=d)})},0)),l=(u,h,d)=>{let p=h.now();u.apply(d,arguments),h.trackGPU&&h.finished.push(o(p,h.activeAccums.slice(0)))},c="drawElements";t[c]?t[c]=l(t[c],this,t):console.log("bench: cannot attach to webgl function")}if(!this.withoutUI){this.dom||(this.dom=document.body);let o=document.createElement("div");o.id="gl-bench",this.dom.appendChild(o),this.dom.insertAdjacentHTML("afterbegin",'<style id="gl-bench-style">'+this.css+"</style>"),this.dom=o,this.dom.addEventListener("click",()=>{this.trackGPU=!this.trackGPU,this.updateUI()}),this.paramLogger=((l,c,u)=>{let h=["gl-cpu","gl-gpu","gl-mem","gl-fps","gl-gpu-svg","gl-chart"],d={...h};return h.forEach(p=>d[p]=c.getElementsByClassName(p)),this.nodes=d,(p,f,m,A,y,g,w)=>{d["gl-cpu"][p].style.strokeDasharray=(f*.27).toFixed(0)+" 100",d["gl-gpu"][p].style.strokeDasharray=(m*.27).toFixed(0)+" 100",d["gl-mem"][p].innerHTML=u[p]?u[p]:A?"mem: "+A.toFixed(0)+"mb":"",d["gl-fps"][p].innerHTML="FPS: "+y.toFixed(1),l(u[p],f,m,A,y,g,w)}})(this.paramLogger,this.dom,this.names),this.chartLogger=((l,c)=>{let u={"gl-chart":c.getElementsByClassName("gl-chart")};return(h,d,p)=>{let f="",m=d.length;for(let A=0;A<m;A++){let y=(p+A+1)%m;d[y]!==void 0&&(f=f+" "+(60*A/(m-1)).toFixed(1)+","+(45-d[y]*.5/this.detected).toFixed(1))}u["gl-chart"][h].setAttribute("points",f),l(this.names[h],d,p)}})(this.chartLogger,this.dom)}}addUI(t){this.names.indexOf(t)===-1&&(this.names.push(t),this.dom&&(this.dom.insertAdjacentHTML("beforeend",this.svg),this.updateUI()),this.cpuAccums.push(0),this.gpuAccums.push(0),this.activeAccums.push(!1))}nextFrame(t){this.frameId++;let n=t||this.now();if(this.frameId<=1)this.paramFrame=this.frameId,this.paramTime=n;else{let r=n-this.paramTime;if(r>=1e3){let a=this.frameId-this.paramFrame,s=a/r*1e3;for(let i=0;i<this.names.length;i++){let o=this.cpuAccums[i]/r*100,l=this.gpuAccums[i]/r*100,c=performance&&performance.memory?performance.memory.usedJSHeapSize/(1<<20):0;this.paramLogger(i,o,l,c,s,r,a),this.cpuAccums[i]=0,Promise.all(this.finished).then(()=>{this.gpuAccums[i]=0,this.finished=[]})}this.paramFrame=this.frameId,this.paramTime=n}}if(!this.detected||!this.chartFrame)this.chartFrame=this.frameId,this.chartTime=n,this.circularId=0;else{let r=n-this.chartTime,a=this.chartHz*r/1e3;for(;--a>0&&this.detected;){let i=(this.frameId-this.chartFrame)/r*1e3;this.chart[this.circularId%this.chartLen]=i;for(let o=0;o<this.names.length;o++)this.chartLogger(o,this.chart,this.circularId);this.circularId++,this.chartFrame=this.frameId,this.chartTime=n}}}begin(t){this.updateAccums(t)}end(t){this.updateAccums(t)}updateAccums(t){let n=this.names.indexOf(t);n===-1&&(n=this.names.length,this.addUI(t));let r=this.now(),a=r-this.t0;for(let s=0;s<n+1;s++)this.activeAccums[s]&&(this.cpuAccums[s]+=a);this.activeAccums[n]=!this.activeAccums[n],this.t0=r}},R4=C4;var is={backend:"webgl"},re=new O2(is),he={baseBackground:"rgba(50, 50, 50, 1)",crop:!0,columns:2,facing:!0,useWorker:!1,worker:"worker.js",samples:["../assets/sample6.jpg","../assets/sample1.jpg","../assets/sample4.jpg","../assets/sample5.jpg","../assets/sample3.jpg","../assets/sample2.jpg"],compare:"../assets/sample-me.jpg",console:!0,maxFPSframes:10,modelsPreload:!0,busy:!1,menuWidth:0,menuHeight:0,camera:{},detectFPS:[],drawFPS:[],buffered:!1,drawWarmup:!1,drawThread:null,detectThread:null,framesDraw:0,framesDetect:0,bench:!0,lastFrame:0},Ae={},Z0,Bi,Y0={};function nse(...e){if(!Array.isArray(e))return e;let t="";for(let n of e)typeof n=="object"?t+=JSON.stringify(n).replace(/{|}|"|\[|\]/g,"").replace(/,/g,", "):t+=n;return t}function Fn(...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")}`;he.console&&console.log(n,...e)}function tr(e){let t=document.getElementById("status");t&&(t.innerText=e)}var xa={enabled:!1,original:null};async function rse(e){var n,r,a,s,i,o;if(document.getElementById("compare-container").style.display=xa.enabled?"block":"none",!xa.enabled||!(((n=e==null?void 0:e.face)==null?void 0:n.length)>0)||((a=(r=e==null?void 0:e.face[0])==null?void 0:r.embedding)==null?void 0:a.length)<=64)return;if(!xa.original)if(xa.original=e,Fn("setting face compare baseline:",e.face[0]),e.face[0].tensor){let l=re.enhance(e.face[0]);if(l){let c=document.getElementById("orig"),u=l.squeeze(),h=u.div(255);re.tf.browser.toPixels(h,c),l.dispose(),u.dispose(),h.dispose()}}else document.getElementById("compare-canvas").getContext("2d").drawImage(xa.original.canvas,0,0,200,200);let t=re.similarity((i=(s=xa.original)==null?void 0:s.face[0])==null?void 0:i.embedding,(o=e==null?void 0:e.face[0])==null?void 0:o.embedding);document.getElementById("similarity").innerText=`similarity: ${Math.trunc(1e3*t)/10}%`}var F4=performance.now();async function J0(e){let t=Y0,n=document.getElementById("canvas");if(he.drawFPS.push(1e3/(performance.now()-F4)),he.drawFPS.length>he.maxFPSframes&&he.drawFPS.shift(),F4=performance.now(),await Ae.process.updateChart("FPS",he.detectFPS),he.buffered||!t.canvas){let h=await re.image(e);t.canvas=h.canvas,re.tf.dispose(h.tensor)}let r=n.getContext("2d");r.fillStyle=he.baseBackground,r.fillRect(0,0,n.width,n.height),t.canvas?(t.canvas.width!==n.width&&(n.width=t.canvas.width),t.canvas.height!==n.height&&(n.height=t.canvas.height),r.drawImage(t.canvas,0,0,t.canvas.width,t.canvas.height,0,0,t.canvas.width,t.canvas.height)):r.drawImage(e,0,0,e.width,e.height,0,0,n.width,n.height),re.draw.face(n,t.face),re.draw.body(n,t.body),re.draw.hand(n,t.hand),re.draw.object(n,t.object),re.draw.gesture(n,t.gesture),await rse(t);let a=re.tf.engine(),s=a.backendInstance?`gpu: ${(a.backendInstance.numBytesInGPU?a.backendInstance.numBytesInGPU:0).toLocaleString()} bytes`:"",i=`system: ${a.state.numBytes.toLocaleString()} bytes ${s} | tensors: ${a.state.numTensors.toLocaleString()}`,o=t.canvas?`processing: ${t.canvas.width} x ${t.canvas.height}`:"",l=Math.trunc(10*he.detectFPS.reduce((h,d)=>h+d,0)/he.detectFPS.length)/10,c=Math.trunc(10*he.drawFPS.reduce((h,d)=>h+d,0)/he.drawFPS.length)/10,u=he.detectFPS.length>5&&l<5?'<font color="lightcoral">warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models</font>':"";document.getElementById("log").innerHTML=`
video: ${he.camera.name} | facing: ${he.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${he.camera.width} x ${he.camera.height} ${o}<br>
backend: ${re.tf.getBackend()} | ${i}<br>
performance: ${nse(t.performance)}ms FPS process:${l} refresh:${c}<br>
${u}<br>
`,he.framesDraw++,he.lastFrame=performance.now(),he.buffered?he.drawThread=requestAnimationFrame(()=>J0(e,n)):!he.buffered&&he.drawThread&&(Fn("stopping buffered refresh"),cancelAnimationFrame(he.drawThread),he.drawThread=null)}async function Q0(){var c;if(he.busy)return null;he.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(tr("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=`
${a}`,Fn(a),tr(a),he.busy=!1,a;let s,i={audio:!1,video:{facingMode:he.facing?"user":"environment",resizeMode:he.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(u){return u.name==="PermissionDeniedError"||u.name==="NotAllowedError"?a="camera permission denied":u.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${u.message||u}`,n.innerText+=`
${a}`,tr(a),Fn("camera error:",u),he.busy=!1,a}if(s)e.srcObject=s;else return he.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return he.camera={name:(c=o.label)==null?void 0:c.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(u=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",he.menuWidth.input.setAttribute("value",e.width),he.menuHeight.input.setAttribute("value",e.height),r&&e.play(),r&&!he.detectThread&&ah(e,t),he.busy=!1,tr(""),u()}})}function M4(){if(!Bi){let e=null;Bi=new R4(e,{trackGPU:!1,chartHz:20,chartLen:20}),Bi.begin()}}function ase(e,t,n,r){Z0||(Fn("creating worker thread"),Z0=new Worker(he.worker,{type:"module"}),Z0.addEventListener("message",a=>{a.data.result.performance&&a.data.result.performance.total&&he.detectFPS.push(1e3/a.data.result.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Bi||M4(),Bi.nextFrame(r)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),Y0=a.data.result,he.framesDetect++,he.drawThread||J0(e),he.detectThread=requestAnimationFrame(s=>ah(e,n,s))})),Z0.postMessage({image:t.data.buffer,width:n.width,height:n.height,userConfig:is},[t.data.buffer])}function ah(e,t,n){var a;if(!(e.srcObject&&e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused)&&e.srcObject){he.drawThread&&cancelAnimationFrame(he.drawThread),he.detectThread&&cancelAnimationFrame(he.detectThread),he.drawThread=null,he.detectThread=null,e.paused?Fn("camera paused"):e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState<=2?setTimeout(()=>ah(e,t),500):Fn(`camera not ready: track state: ${(a=e.srcObject)==null?void 0:a.getVideoTracks()[0].readyState} stream state: ${e.readyState}`),clearTimeout(he.drawThread),he.drawThread=null,Fn("frame statistics: process:",he.framesDetect,"refresh:",he.framesDraw),Fn("memory",re.tf.engine().memory());return}if(tr(""),he.useWorker){let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t.width,t.height):document.createElement("canvas");s.width=t.width,s.height=t.height;let i=s.getContext("2d");i.drawImage(e,0,0,e.width,e.height,0,0,t.width,t.height);let o=i.getImageData(0,0,t.width,t.height);ase(e,o,t,is,n)}else re.detect(e,is).then(s=>{s.performance&&s.performance.total&&he.detectFPS.push(1e3/s.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Bi||M4(),Bi.nextFrame(n)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),s.error?(Fn(s.error),document.getElementById("log").innerText+=`
Human error: ${s.error}`):(Y0=s,he.drawThread||J0(e),he.framesDetect++,he.detectThread=requestAnimationFrame(i=>ah(e,t,i)))})}async function sse(e){return new Promise(t=>{let n=new Image;n.onload=async()=>{Fn("processing image:",encodeURI(n.src));let r=document.getElementById("canvas");n.width=n.naturalWidth,n.height=n.naturalHeight,r.width=re.config.filter.width&&re.config.filter.width>0?re.config.filter.width:n.naturalWidth,r.height=re.config.filter.height&&re.config.filter.height>0?re.config.filter.height:n.naturalHeight;let a=await re.detect(n,is);Y0=a,await J0(n);let s=document.createElement("canvas");s.className="thumbnail",s.width=window.innerWidth/(he.columns+.1),s.height=s.width*r.height/r.width,a.face&&a.face.length>0?s.title=a.face.map((o,l)=>`#${l} face: ${Math.trunc(100*o.faceConfidence)}% box: ${Math.trunc(100*o.boxConfidence)}% age: ${Math.trunc(o.age)} gender: ${Math.trunc(100*o.genderConfidence)}% ${o.gender}`).join(" | "):s.title="no face detected",s.getContext("2d").drawImage(r,0,0,r.width,r.height,0,0,s.width,s.height),document.getElementById("samples-container").appendChild(s),n.src="",t(!0)},n.src=e})}async function $4(){document.getElementById("samples-container").style.display="none",document.getElementById("canvas").style.display="block";let e=document.getElementById("video"),t=document.getElementById("canvas");if(e.srcObject!==null&&!e.paused)document.getElementById("play").style.display="block",document.getElementById("btnStart").className="button button-start",document.getElementById("btnStart").innerHTML="start<br>video",tr("paused"),e.pause();else{let n=await Q0();if(n)tr(n);else{document.getElementById("play").style.display="none";for(let r of Object.values(Ae))r.hide();tr(""),document.getElementById("btnStart").className="button button-stop",document.getElementById("btnStart").innerHTML="pause<br>video",await e.play(),he.detectThread||ah(e,t)}}}async function ise(){is.videoOptimized=!1,document.getElementById("play").style.display="none",document.getElementById("canvas").style.display="none",document.getElementById("samples-container").style.display="block",Fn("running detection of sample images"),tr("processing images"),document.getElementById("samples-container").innerHTML="";for(let e of Object.values(Ae))e.hide();for(let e of he.samples)await sse(e);tr("")}function ose(){let e=[];window.innerWidth>800?e=[`${document.getElementById("btnDisplay").offsetLeft-50}px`,`${document.getElementById("btnImage").offsetLeft-50}px`,`${document.getElementById("btnProcess").offsetLeft-50}px`,`${document.getElementById("btnModel").offsetLeft-50}px`]:e=["0rem","11rem","21.1rem","33rem"],Ae.display=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[0]}),Ae.display.addBool("perf monitor",he,"bench",t=>he.bench=t),Ae.display.addBool("buffered output",he,"buffered",t=>he.buffered=t),Ae.display.addBool("crop & scale",he,"crop",t=>{he.crop=t,Q0()}),Ae.display.addBool("camera facing",he,"facing",t=>{he.facing=t,Q0()}),Ae.display.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.display.addBool("use 3D depth",re.draw.drawOptions,"useDepth"),Ae.display.addBool("draw with curves",re.draw.drawOptions,"useCurves"),Ae.display.addBool("print labels",re.draw.drawOptions,"drawLabels"),Ae.display.addBool("draw points",re.draw.drawOptions,"drawPoints"),Ae.display.addBool("draw boxes",re.draw.drawOptions,"drawBoxes"),Ae.display.addBool("draw polygons",re.draw.drawOptions,"drawPolygons"),Ae.display.addBool("fill polygons",re.draw.drawOptions,"fillPolygons"),Ae.image=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[1]}),Ae.image.addBool("enabled",re.config.filter,"enabled",t=>re.config.filter.enabled=t),he.menuWidth=Ae.image.addRange("image width",re.config.filter,"width",0,3840,10,t=>re.config.filter.width=parseInt(t)),he.menuHeight=Ae.image.addRange("image height",re.config.filter,"height",0,2160,10,t=>re.config.filter.height=parseInt(t)),Ae.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.image.addRange("brightness",re.config.filter,"brightness",-1,1,.05,t=>re.config.filter.brightness=parseFloat(t)),Ae.image.addRange("contrast",re.config.filter,"contrast",-1,1,.05,t=>re.config.filter.contrast=parseFloat(t)),Ae.image.addRange("sharpness",re.config.filter,"sharpness",0,1,.05,t=>re.config.filter.sharpness=parseFloat(t)),Ae.image.addRange("blur",re.config.filter,"blur",0,20,1,t=>re.config.filter.blur=parseInt(t)),Ae.image.addRange("saturation",re.config.filter,"saturation",-1,1,.05,t=>re.config.filter.saturation=parseFloat(t)),Ae.image.addRange("hue",re.config.filter,"hue",0,360,5,t=>re.config.filter.hue=parseInt(t)),Ae.image.addRange("pixelate",re.config.filter,"pixelate",0,32,1,t=>re.config.filter.pixelate=parseInt(t)),Ae.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.image.addBool("negative",re.config.filter,"negative",t=>re.config.filter.negative=t),Ae.image.addBool("sepia",re.config.filter,"sepia",t=>re.config.filter.sepia=t),Ae.image.addBool("vintage",re.config.filter,"vintage",t=>re.config.filter.vintage=t),Ae.image.addBool("kodachrome",re.config.filter,"kodachrome",t=>re.config.filter.kodachrome=t),Ae.image.addBool("technicolor",re.config.filter,"technicolor",t=>re.config.filter.technicolor=t),Ae.image.addBool("polaroid",re.config.filter,"polaroid",t=>re.config.filter.polaroid=t),Ae.process=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[2]}),Ae.process.addList("backend",["cpu","webgl","wasm","humangl"],re.config.backend,t=>re.config.backend=t),Ae.process.addBool("async operations",re.config,"async",t=>re.config.async=t),Ae.process.addBool("use web worker",he,"useWorker"),Ae.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.process.addLabel("model parameters"),Ae.process.addRange("max objects",re.config.face.detector,"maxFaces",1,50,1,t=>{re.config.face.detector.maxFaces=parseInt(t),re.config.body.maxDetections=parseInt(t),re.config.hand.maxHands=parseInt(t)}),Ae.process.addRange("skip frames",re.config.face.detector,"skipFrames",0,50,1,t=>{re.config.face.detector.skipFrames=parseInt(t),re.config.face.emotion.skipFrames=parseInt(t),re.config.face.age.skipFrames=parseInt(t),re.config.hand.skipFrames=parseInt(t)}),Ae.process.addRange("min confidence",re.config.face.detector,"minConfidence",0,1,.05,t=>{re.config.face.detector.minConfidence=parseFloat(t),re.config.face.gender.minConfidence=parseFloat(t),re.config.face.emotion.minConfidence=parseFloat(t),re.config.hand.minConfidence=parseFloat(t)}),Ae.process.addRange("score threshold",re.config.face.detector,"scoreThreshold",.1,1,.05,t=>{re.config.face.detector.scoreThreshold=parseFloat(t),re.config.hand.scoreThreshold=parseFloat(t),re.config.body.scoreThreshold=parseFloat(t)}),Ae.process.addRange("overlap",re.config.face.detector,"iouThreshold",.1,1,.05,t=>{re.config.face.detector.iouThreshold=parseFloat(t),re.config.hand.iouThreshold=parseFloat(t)}),Ae.process.addBool("detection rotation",re.config.face.detector,"rotation",t=>{re.config.face.detector.rotation=t,re.config.hand.rotation=t}),Ae.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.process.addButton("process sample images","process images",()=>ise()),Ae.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.process.addChart("FPS","FPS"),Ae.models=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[3]}),Ae.models.addBool("face detect",re.config.face,"enabled",t=>re.config.face.enabled=t),Ae.models.addBool("face mesh",re.config.face.mesh,"enabled",t=>re.config.face.mesh.enabled=t),Ae.models.addBool("face iris",re.config.face.iris,"enabled",t=>re.config.face.iris.enabled=t),Ae.models.addBool("face description",re.config.face.description,"enabled",t=>re.config.face.age.description=t),Ae.models.addBool("face emotion",re.config.face.emotion,"enabled",t=>re.config.face.emotion.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("body pose",re.config.body,"enabled",t=>re.config.body.enabled=t),Ae.models.addBool("hand pose",re.config.hand,"enabled",t=>re.config.hand.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("gestures",re.config.gesture,"enabled",t=>re.config.gesture.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("object detection",re.config.object,"enabled",t=>re.config.object.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("face compare",xa,"enabled",t=>{xa.enabled=t,xa.original=null}),document.getElementById("btnDisplay").addEventListener("click",t=>Ae.display.toggle(t)),document.getElementById("btnImage").addEventListener("click",t=>Ae.image.toggle(t)),document.getElementById("btnProcess").addEventListener("click",t=>Ae.process.toggle(t)),document.getElementById("btnModel").addEventListener("click",t=>Ae.models.toggle(t)),document.getElementById("btnStart").addEventListener("click",()=>$4()),document.getElementById("play").addEventListener("click",()=>$4())}async function lse(e){let t=document.getElementById("canvas");t.width=e.canvas.width,t.height=e.canvas.height,t.getContext("2d").drawImage(e.canvas,0,0,e.canvas.width,e.canvas.height,0,0,t.width,t.height),await re.draw.all(t,e)}async function use(){if(Fn("demo starting ..."),ose(),document.getElementById("log").innerText=`Human: version ${re.version}`,he.modelsPreload&&!he.useWorker){tr("loading"),await re.load(is);let e=Object.keys(re.models).filter(t=>re.models[t]);Fn("demo loaded models:",e)}if(!he.useWorker){tr("initializing");let e=await re.warmup(is);e&&e.canvas&&he.drawWarmup&&await lse(e)}tr("human: ready"),document.getElementById("loader").style.display="none",document.getElementById("play").style.display="block",Fn("demo ready...")}window.onload=use;window.onresize=Q0;
/**
* @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=demo-browser-index.js.map
Reference in New Issue View Git Blame Copy Permalink