mirror of https://github.com/vladmandic/human
5087 lines
1.3 MiB
5087 lines
1.3 MiB
|
|
/*
|
|
Human library
|
|
homepage: <https://github.com/vladmandic/human>
|
|
author: <https://github.com/vladmandic>'
|
|
*/
|
|
|
|
var o9=Object.defineProperty;var lr=(e,t)=>{for(var n in t)o9(e,n,{get:t[n],enumerable:!0})};var l5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)},ur=(e,t,n)=>(l5(e,t,"read from private field"),n?n.call(e):t.get(e)),us=(e,t,n,r)=>(l5(e,t,"write to private field"),r?r.call(e,n):t.set(e,n),n);function pt(e,t){let n=e.endsWith("/")?"":"/",a=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!a.toLocaleLowerCase().includes(".json"))throw new Error(`Human: ModelPath Error: ${a} Expecting JSON file`);return a}function le(...e){let t=new Date,n=`${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 Ye=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Jn(...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]=Jn(s,i):n[a]=i}),n),{})}function u5(){let e,t;if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);if(n&&n[0]){let r=n[0].match(/\(([^()]+)\)/g);e=r?r[0].replace(/\(|\)/g,""):"",t=navigator.userAgent.replace(n[0],""),e[1]&&(t=t.replace(n[1],"")),t=t.replace(/ /g," ")}}else typeof process!="undefined"&&(e=`${process.platform} ${process.arch}`,t=`NodeJS ${process.version}`);return{platform:e,agent:t}}var gu={};lr(gu,{Abs:()=>so,Acos:()=>io,Acosh:()=>oo,AdadeltaOptimizer:()=>ep,AdagradOptimizer:()=>tp,AdamOptimizer:()=>np,AdamaxOptimizer:()=>rp,Add:()=>Ca,AddN:()=>ds,All:()=>lo,Any:()=>uo,ArgMax:()=>ps,ArgMin:()=>vu,Asin:()=>co,Asinh:()=>ho,Atan:()=>po,Atan2:()=>mo,Atanh:()=>fo,AvgPool:()=>fs,AvgPool3D:()=>ku,AvgPool3DGrad:()=>Oh,AvgPoolGrad:()=>Dh,BackendWasm:()=>Mv,BatchMatMul:()=>ms,BatchToSpaceND:()=>Iu,Bincount:()=>zh,BroadcastTo:()=>tw,Callback:()=>v4,CallbackList:()=>b6,Cast:()=>ys,Ceil:()=>As,ClipByValue:()=>Ra,Complex:()=>Ph,ComplexAbs:()=>Su,Concat:()=>yo,Conv2D:()=>gs,Conv2DBackpropFilter:()=>Lh,Conv2DBackpropInput:()=>xs,Conv3D:()=>Nu,Conv3DBackpropFilterV2:()=>Wh,Conv3DBackpropInputV2:()=>Bh,Cos:()=>ws,Cosh:()=>Ao,CropAndResize:()=>go,Cumsum:()=>bs,CustomCallback:()=>v6,DataStorage:()=>Rh,DenseBincount:()=>Vh,DepthToSpace:()=>xo,DepthwiseConv2dNative:()=>_s,DepthwiseConv2dNativeBackpropFilter:()=>jh,DepthwiseConv2dNativeBackpropInput:()=>Uh,Diag:()=>Hh,Dilation2D:()=>Tu,Dilation2DBackpropFilter:()=>qh,Dilation2DBackpropInput:()=>Gh,ENV:()=>_r,EarlyStopping:()=>I4,Einsum:()=>Xh,Elu:()=>wo,EluGrad:()=>Kh,Environment:()=>Qx,Equal:()=>_o,Erf:()=>bo,Exp:()=>ks,ExpandDims:()=>vo,Expm1:()=>ko,FFT:()=>Zh,Fill:()=>Eu,FlipLeftRight:()=>Io,Floor:()=>Is,FloorDiv:()=>Ss,FromPixels:()=>hd,FusedBatchNorm:()=>Ns,FusedConv2D:()=>oi,FusedDepthwiseConv2D:()=>li,GPGPUContext:()=>wp,GatherNd:()=>No,GatherV2:()=>So,GraphModel:()=>t8,Greater:()=>To,GreaterEqual:()=>Ts,History:()=>_6,IFFT:()=>Yh,Identity:()=>Es,Imag:()=>Jh,InputSpec:()=>Ft,IsFinite:()=>Eo,IsInf:()=>Co,IsNan:()=>Ro,KernelBackend:()=>wu,LRN:()=>Mu,LRNGrad:()=>ed,LayerVariable:()=>y6,LayersModel:()=>ga,LeakyRelu:()=>Cs,Less:()=>Mo,LessEqual:()=>Fo,LinSpace:()=>Qh,Log:()=>Rs,Log1p:()=>$o,LogSoftmax:()=>nw,LogicalAnd:()=>Do,LogicalNot:()=>Cu,LogicalOr:()=>Ru,MathBackendCPU:()=>op,MathBackendWebGL:()=>Wl,Max:()=>Ms,MaxPool:()=>$s,MaxPool3D:()=>Fu,MaxPool3DGrad:()=>nd,MaxPoolGrad:()=>td,MaxPoolWithArgmax:()=>rd,Maximum:()=>Fs,Mean:()=>Ds,Min:()=>Os,Minimum:()=>zs,MirrorPad:()=>Ps,Mod:()=>Oo,MomentumOptimizer:()=>ap,Multinomial:()=>ad,Multiply:()=>Ls,Neg:()=>zo,NonMaxSuppressionV3:()=>Lo,NonMaxSuppressionV4:()=>Wo,NonMaxSuppressionV5:()=>Bo,NotEqual:()=>Po,OP_SCOPE_SUFFIX:()=>pw,OneHot:()=>Ws,OnesLike:()=>Vo,Optimizer:()=>fa,Pack:()=>jo,PadV2:()=>Bs,Pool:()=>lI,Pow:()=>Vs,Prelu:()=>js,Prod:()=>Uo,RMSPropOptimizer:()=>sp,RNN:()=>Zr,Range:()=>$u,Rank:()=>Of,Real:()=>sd,RealDiv:()=>vs,Reciprocal:()=>Ho,Reduction:()=>hn,Relu:()=>Us,Relu6:()=>Gs,Reshape:()=>Go,ResizeBilinear:()=>Hs,ResizeBilinearGrad:()=>od,ResizeNearestNeighbor:()=>Du,ResizeNearestNeighborGrad:()=>id,Reverse:()=>qs,RotateWithOffset:()=>sl,Round:()=>Xs,Rsqrt:()=>Ks,SGDOptimizer:()=>dc,ScatterNd:()=>qo,Select:()=>Xo,Selu:()=>Ko,Sequential:()=>Kl,Sigmoid:()=>Ys,Sign:()=>Jo,Sin:()=>Zs,Sinh:()=>Yo,Slice:()=>Zo,Softmax:()=>ei,Softplus:()=>Qo,SpaceToBatchND:()=>Ou,SparseToDense:()=>ld,SplitV:()=>el,Sqrt:()=>Js,Square:()=>zu,SquaredDifference:()=>ti,Step:()=>Fa,StridedSlice:()=>tl,Sub:()=>ni,Sum:()=>Qs,SymbolicTensor:()=>Mr,Tan:()=>ri,Tanh:()=>ai,Tensor:()=>Pe,TensorBuffer:()=>Dt,Tile:()=>Ma,TopK:()=>nl,Transform:()=>ud,Transpose:()=>si,Unique:()=>cd,Unpack:()=>rl,UnsortedSegmentSum:()=>Pu,Variable:()=>Hu,ZerosLike:()=>al,_FusedMatMul:()=>ii,abs:()=>Ot,acos:()=>lm,acosh:()=>um,add:()=>se,addN:()=>Pa,all:()=>vd,any:()=>Zu,argMax:()=>mi,argMin:()=>cm,asin:()=>hm,asinh:()=>dm,atan:()=>pm,atan2:()=>fm,atanh:()=>mm,avgPool:()=>Ju,avgPool3d:()=>gm,backend:()=>Xw,backend_util:()=>C,basicLSTMCell:()=>WN,batchNorm:()=>Ai,batchNorm2d:()=>Jw,batchNorm3d:()=>Qw,batchNorm4d:()=>eb,batchToSpaceND:()=>Qu,bincount:()=>tb,booleanMaskAsync:()=>GC,broadcastTo:()=>Al,browser:()=>pi,buffer:()=>Be,callbacks:()=>$ae,cast:()=>ge,ceil:()=>xm,clipByValue:()=>Tn,clone:()=>Wr,complex:()=>$a,concat:()=>rt,concat1d:()=>nb,concat2d:()=>gl,concat3d:()=>rb,concat4d:()=>ab,constraints:()=>jv,conv1d:()=>Id,conv2d:()=>ca,conv2dTranspose:()=>Sd,conv3d:()=>bm,conv3dTranspose:()=>ib,copyRegisteredKernels:()=>hI,cos:()=>ec,cosh:()=>Nd,cosineWindow:()=>Km,cumsum:()=>Td,customGrad:()=>Vr,data:()=>n8,denseBincount:()=>ob,deprecationWarn:()=>im,depthToSpace:()=>_m,depthwiseConv2d:()=>xl,deregisterOp:()=>Oae,device_util:()=>qu,diag:()=>mT,dilation2d:()=>vm,disableDeprecationWarnings:()=>eN,dispose:()=>_e,disposeVariables:()=>tN,div:()=>me,divNoNan:()=>km,dot:()=>lb,dropout:()=>Eb,einsum:()=>ub,elu:()=>wl,enableDebugMode:()=>QS,enableProdMode:()=>JS,enclosingPowerOfTwo:()=>Cb,engine:()=>ua,env:()=>J,equal:()=>Wa,erf:()=>Im,exp:()=>er,expandDims:()=>Jt,expm1:()=>Sm,eye:()=>Nm,fft:()=>cc,fill:()=>tc,findBackend:()=>om,findBackendFactory:()=>oN,floor:()=>bl,floorDiv:()=>_d,forceHalfFloat:()=>j3,fused:()=>Ua,gather:()=>gi,gatherND:()=>Tb,gather_util:()=>Qf,getBackend:()=>sN,getGradient:()=>Ff,getKernel:()=>dd,getKernelsForBackend:()=>ol,gpgpu_util:()=>p3,grad:()=>HT,grads:()=>GT,greater:()=>pr,greaterEqual:()=>Va,ifft:()=>Sl,imag:()=>Ed,image:()=>Le,inTopKAsync:()=>rR,initializers:()=>Zv,input:()=>u6,io:()=>Nn,irfft:()=>Hd,isFinite:()=>cb,isInf:()=>hb,isNaN:()=>Tm,keep:()=>jt,kernel_impls:()=>Hr,layers:()=>l6,leakyRelu:()=>nc,less:()=>Cd,lessEqual:()=>xi,linalg:()=>Vb,linspace:()=>db,loadGraphModel:()=>ct,loadLayersModel:()=>eae,localResponseNormalization:()=>Em,log:()=>zn,log1p:()=>Rd,logSigmoid:()=>fb,logSoftmax:()=>Fd,logSumExp:()=>Mm,logicalAnd:()=>fr,logicalNot:()=>rc,logicalOr:()=>$d,logicalXor:()=>gb,losses:()=>wM,matMul:()=>Ve,math:()=>Ew,max:()=>Cn,maxPool:()=>ac,maxPool3d:()=>Fm,maxPoolWithArgmax:()=>xb,maximum:()=>jr,mean:()=>It,memory:()=>bd,meshgrid:()=>fE,metrics:()=>w4,min:()=>vl,minimum:()=>kl,mirrorPad:()=>$m,mod:()=>Dm,model:()=>Jre,models:()=>b4,moments:()=>Dd,movingAverage:()=>KC,mul:()=>P,multiRNNCell:()=>_E,multinomial:()=>wb,neg:()=>kt,nextFrame:()=>ip,norm:()=>Kd,notEqual:()=>bi,oneHot:()=>dl,ones:()=>Pn,onesLike:()=>Ln,op:()=>D,outerProduct:()=>NE,pad:()=>ha,pad1d:()=>CE,pad2d:()=>ME,pad3d:()=>$E,pad4d:()=>OE,pool:()=>bb,pow:()=>da,prelu:()=>ic,print:()=>vw,prod:()=>Od,profile:()=>an,rand:()=>HE,randomGamma:()=>KE,randomNormal:()=>_b,randomUniform:()=>Il,range:()=>zd,ready:()=>aN,real:()=>oc,reciprocal:()=>Pm,registerBackend:()=>fl,registerCallbackConstructor:()=>tae,registerGradient:()=>rw,registerKernel:()=>ui,registerOp:()=>Dae,regularizers:()=>_4,relu:()=>Ur,relu6:()=>Pd,removeBackend:()=>iN,reshape:()=>H,reverse:()=>Wn,reverse1d:()=>aC,reverse2d:()=>iC,reverse3d:()=>lC,reverse4d:()=>cC,rfft:()=>hc,round:()=>Lm,rsqrt:()=>Ld,scalar:()=>xe,scatterND:()=>Nb,scatter_util:()=>em,selu:()=>Wd,separableConv2d:()=>Wm,sequential:()=>Qre,serialization:()=>re,setBackend:()=>rN,setPlatform:()=>lN,setWasmPath:()=>XJ,setWasmPaths:()=>KJ,setWebGLContext:()=>yp,setdiff1dAsync:()=>vb,shared:()=>ey,sigmoid:()=>On,sign:()=>Bm,signal:()=>xM,sin:()=>Bd,sinh:()=>Vd,slice:()=>Ce,slice1d:()=>jd,slice2d:()=>Vm,slice3d:()=>Ud,slice4d:()=>lc,slice_util:()=>un,softmax:()=>uc,softplus:()=>_l,spaceToBatchND:()=>sc,sparseToDense:()=>Xm,spectral:()=>gM,split:()=>Pt,sqrt:()=>Qt,square:()=>ot,squaredDifference:()=>Gd,squeeze:()=>ja,stack:()=>cn,step:()=>Nl,stridedSlice:()=>jm,sub:()=>ye,sum:()=>Te,sumOutType:()=>yd,tan:()=>Um,tanh:()=>yl,tensor:()=>Ir,tensor1d:()=>sn,tensor2d:()=>Rn,tensor3d:()=>xd,tensor4d:()=>OC,tensor5d:()=>zC,tensor6d:()=>PC,tensor_util:()=>vr,test_util:()=>Hw,tidy:()=>z,tile:()=>Ba,time:()=>nN,topk:()=>Hm,train:()=>vi,transpose:()=>Je,truncatedNormal:()=>qd,unique:()=>Xd,unregisterGradient:()=>cI,unregisterKernel:()=>uI,unsortedSegmentSum:()=>Gm,unstack:()=>mr,upcastType:()=>dr,util:()=>_,valueAndGrad:()=>qT,valueAndGrads:()=>XT,variable:()=>kb,variableGrads:()=>pb,version:()=>bie,version_converter:()=>Dse,version_core:()=>YS,version_cpu:()=>w_,version_layers:()=>xA,version_wasm:()=>$v,version_webgl:()=>V3,webgl:()=>eB,webgl_util:()=>B_,where:()=>En,whereAsync:()=>qm,zeros:()=>Rt,zerosLike:()=>He});var l9=Object.create,Ch=Object.defineProperty,u9=Object.getPrototypeOf,c9=Object.prototype.hasOwnProperty,h9=Object.getOwnPropertyNames,d9=Object.getOwnPropertyDescriptor,p9=e=>Ch(e,"__esModule",{value:!0}),_t=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Me=(e,t)=>{for(var n in t)Ch(e,n,{get:t[n],enumerable:!0})},f9=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of h9(t))!c9.call(e,r)&&r!=="default"&&Ch(e,r,{get:()=>t[r],enumerable:!(n=d9(t,r))||n.enumerable});return e},no=e=>f9(p9(Ch(e!=null?l9(u9(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),m9=_t(()=>{}),y9=_t((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)}),A9=_t((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)}),g9=_t((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)}),x9=_t((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)}),w9=_t((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,y,A,g=[],x=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,x=Math.max(x,d.length)),m=0,y=-32;y<x;++y)d&&(f^=d.charCodeAt((y+32)%d.length)),y===0&&(A=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,y>=0&&(A=A+1640531527|0,p=g[y&127]^=f+A,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,y=4*128;y>0;--y)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=A,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)}),b9=_t((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)}),c5=_t(()=>{}),_9=_t((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(w,b,k){var N=[];b=b==!0?{entropy:!0}:b||{};var E=g(A(b.entropy?[w,v(n)]:w==null?x():w,3),N),F=new m(N),O=function(){for(var L=F.g(i),V=c,j=0;L<u;)L=(L+j)*s,V*=s,j=F.g(1);for(;L>=h;)L/=2,V/=2,j>>>=1;return(L+j)/V};return O.int32=function(){return F.g(4)|0},O.quick=function(){return F.g(4)/4294967296},O.double=O,g(v(F.S),n),(b.pass||k||function(L,V,j,U){return U&&(U.S&&y(U,F),L.state=function(){return y(F,{})}),j?(r[l]=L,V):L})(O,E,"global"in b?b.global:this==r,b.state)}r["seed"+l]=f;function m(w){var b,k=w.length,N=this,E=0,F=N.i=N.j=0,O=N.S=[];for(k||(w=[k++]);E<s;)O[E]=E++;for(E=0;E<s;E++)O[E]=O[F=d&F+w[E%k]+(b=O[E])],O[F]=b;(N.g=function(L){for(var V,j=0,U=N.i,X=N.j,G=N.S;L--;)V=G[U=d&U+1],j=j*s+G[d&(G[U]=G[X=d&X+V])+(G[X]=V)];return N.i=U,N.j=X,j})(s)}function y(w,b){return b.i=w.i,b.j=w.j,b.S=w.S.slice(),b}function A(w,b){var k=[],N=typeof w,E;if(b&&N=="object")for(E in w)try{k.push(A(w[E],b-1))}catch(F){}return k.length?k:N=="string"?w:w+"\0"}function g(w,b){for(var k=w+"",N,E=0;E<k.length;)b[d&E]=d&(N^=b[d&E]*19)+k.charCodeAt(E++);return v(b)}function x(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),v(w)}catch(N){var b=a.navigator,k=b&&b.plugins;return[+new Date,a,k,a.screen,v(n)]}}function v(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=c5()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),h5=_t((e,t)=>{var n=y9(),r=A9(),a=g9(),s=x9(),i=w9(),o=b9(),l=_9();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),xu=_t(()=>{}),v9=_t(()=>{}),k9=_t(()=>{}),I9=_t((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&&Kt(Q.buffer),_n}function i(){return Q.buffer!=je&&Kt(Q.buffer),bt}function o(){return Q.buffer!=je&&Kt(Q.buffer),vn}function l(){return Q.buffer!=je&&Kt(Q.buffer),Zn}function c(){return Q.buffer!=je&&Kt(Q.buffer),on}var u=typeof a!="undefined"?a:{},h,d;u.ready=new Promise(function(S,T){h=S,d=T});var p={},f;for(f in u)u.hasOwnProperty(f)&&(p[f]=u[f]);var m=[],y="./this.program",A=function(S,T){throw T},g=!1,x=!1,v=!1,w=!1;g=typeof window=="object",x=typeof importScripts=="function",v=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=!g&&!v&&!x;var b=u.ENVIRONMENT_IS_PTHREAD||!1;b&&(je=u.buffer);var k="";function N(S){return u.locateFile?u.locateFile(S,k):k+S}var E,F,O,L,V,j;if(v){x?k=xu().dirname(k)+"/":k=__dirname+"/",E=function(S,T){return V||(V=require("fs")),j||(j=xu()),S=j.normalize(S),V.readFileSync(S,T?null:"utf8")},O=function(S){var T=E(S,!0);return T.buffer||(T=new Uint8Array(T)),pe(T.buffer),T},process.argv.length>1&&(y=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(S){if(!(S instanceof Au))throw S}),process.on("unhandledRejection",aa),A=function(S){process.exit(S)},u.inspect=function(){return"[Emscripten Module object]"};var U;try{U=v9()}catch(S){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),S}global.Worker=U.Worker}else w?(typeof read!="undefined"&&(E=function(S){return read(S)}),O=function(S){var T;return typeof readbuffer=="function"?new Uint8Array(readbuffer(S)):(T=read(S,"binary"),pe(typeof T=="object"),T)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(A=function(S){quit(S)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(g||x)&&(x?k=self.location.href:typeof document!="undefined"&&document.currentScript&&(k=document.currentScript.src),typeof r!="undefined"&&r&&(k=r),k.indexOf("blob:")!==0?k=k.substr(0,k.lastIndexOf("/")+1):k="",v?(E=function(S,T){return V||(V=require("fs")),j||(j=xu()),S=j.normalize(S),V.readFileSync(S,T?null:"utf8")},O=function(S){var T=E(S,!0);return T.buffer||(T=new Uint8Array(T)),pe(T.buffer),T}):(E=function(S){var T=new XMLHttpRequest;return T.open("GET",S,!1),T.send(null),T.responseText},x&&(O=function(S){var T=new XMLHttpRequest;return T.open("GET",S,!1),T.responseType="arraybuffer",T.send(null),new Uint8Array(T.response)}),F=function(S,T,W){var q=new XMLHttpRequest;q.open("GET",S,!0),q.responseType="arraybuffer",q.onload=function(){if(q.status==200||q.status==0&&q.response){T(q.response);return}W()},q.onerror=W,q.send(null)}),L=function(S){document.title=S});v&&typeof performance=="undefined"&&(global.performance=k9().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&&(y=u.thisProgram),u.quit&&(A=u.quit);var ee=Atomics.load,Y=Atomics.store,ae=Atomics.compareExchange,te;u.wasmBinary&&(te=u.wasmBinary);var ie=u.noExitRuntime||!0;typeof WebAssembly!="object"&&aa("no native wasm support detected");var Q,he,oe=!1,fe;function pe(S,T){S||aa("Assertion failed: "+T)}function ke(S){var T=u["_"+S];return pe(T,"Cannot call unknown function "+S+", make sure it is exported"),T}function Se(S,T,W,q,de){var ue={string:function(Sn){var to=0;if(Sn!=null&&Sn!==0){var o5=(Sn.length<<2)+1;to=Ji(o5),tt(Sn,to,o5)}return to},array:function(Sn){var to=Ji(Sn.length);return Ke(Sn,to),to}};function ce(Sn){return T==="string"?$e(Sn):T==="boolean"?Boolean(Sn):Sn}var be=ke(S),nt=[],nn=0;if(q)for(var Yt=0;Yt<q.length;Yt++){var Na=ue[W[Yt]];Na?(nn===0&&(nn=yu()),nt[Yt]=Na(q[Yt])):nt[Yt]=q[Yt]}var eo=be.apply(null,nt);return eo=ce(eo),nn!==0&&Yi(nn),eo}function Fe(S,T,W,q){W=W||[];var de=W.every(function(ce){return ce==="number"}),ue=T!=="string";return ue&&de&&!q?ke(S):function(){return Se(S,T,W,arguments,q)}}function Oe(S,T,W){for(var q=T+W,de="";!(T>=q);){var ue=S[T++];if(!ue)return de;if(!(ue&128)){de+=String.fromCharCode(ue);continue}var ce=S[T++]&63;if((ue&224)==192){de+=String.fromCharCode((ue&31)<<6|ce);continue}var be=S[T++]&63;if((ue&240)==224?ue=(ue&15)<<12|ce<<6|be:ue=(ue&7)<<18|ce<<12|be<<6|S[T++]&63,ue<65536)de+=String.fromCharCode(ue);else{var nt=ue-65536;de+=String.fromCharCode(55296|nt>>10,56320|nt&1023)}}return de}function $e(S,T){return S?Oe(i(),S,T):""}function et(S,T,W,q){if(!(q>0))return 0;for(var de=W,ue=W+q-1,ce=0;ce<S.length;++ce){var be=S.charCodeAt(ce);if(be>=55296&&be<=57343){var nt=S.charCodeAt(++ce);be=65536+((be&1023)<<10)|nt&1023}if(be<=127){if(W>=ue)break;T[W++]=be}else if(be<=2047){if(W+1>=ue)break;T[W++]=192|be>>6,T[W++]=128|be&63}else if(be<=65535){if(W+2>=ue)break;T[W++]=224|be>>12,T[W++]=128|be>>6&63,T[W++]=128|be&63}else{if(W+3>=ue)break;T[W++]=240|be>>18,T[W++]=128|be>>12&63,T[W++]=128|be>>6&63,T[W++]=128|be&63}}return T[W]=0,W-de}function tt(S,T,W){return et(S,i(),T,W)}function it(S){for(var T=0,W=0;W<S.length;++W){var q=S.charCodeAt(W);q>=55296&&q<=57343&&(q=65536+((q&1023)<<10)|S.charCodeAt(++W)&1023),q<=127?++T:q<=2047?T+=2:q<=65535?T+=3:T+=4}return T}function Ke(S,T){s().set(S,T)}function dt(S,T){return S%T>0&&(S+=T-S%T),S}var je,_n,bt,Kn,Xt,vn,Zn,Dn,on;function Kt(S){je=S,u.HEAP8=_n=new Int8Array(S),u.HEAP16=Kn=new Int16Array(S),u.HEAP32=vn=new Int32Array(S),u.HEAPU8=bt=new Uint8Array(S),u.HEAPU16=Xt=new Uint16Array(S),u.HEAPU32=Zn=new Uint32Array(S),u.HEAPF32=Dn=new Float32Array(S),u.HEAPF64=on=new Float64Array(S)}var Or=u.INITIAL_MEMORY||16777216;if(b)Q=u.wasmMemory,je=u.buffer;else if(u.wasmMemory)Q=u.wasmMemory;else if(Q=new WebAssembly.Memory({initial:Or/65536,maximum:2147483648/65536,shared:!0}),!(Q.buffer instanceof SharedArrayBuffer))throw G("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),v&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Q&&(je=Q.buffer),Or=je.byteLength,Kt(je);var ir,or=[],ba=[],na=[],_a=[],Hi=[],zr=!1,lh=!1;b||ba.push({func:function(){vh()}});function Y0(){if(!b){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)ch(u.preRun.shift());qi(or)}}function ou(){zr=!0,!b&&qi(ba)}function J0(){b||qi(na)}function uh(){b||(lh=!0)}function kn(){if(!b){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)Q0(u.postRun.shift());qi(Hi)}}function ch(S){or.unshift(S)}function Q0(S){Hi.unshift(S)}var ra=0,va=null,is=null;function e1(S){pe(!b,"addRunDependency cannot be used in a pthread worker"),ra++,u.monitorRunDependencies&&u.monitorRunDependencies(ra)}function t1(S){if(ra--,u.monitorRunDependencies&&u.monitorRunDependencies(ra),ra==0&&(va!==null&&(clearInterval(va),va=null),is)){var T=is;is=null,T()}}u.preloadedImages={},u.preloadedAudios={};function aa(S){u.onAbort&&u.onAbort(S),b&&console.error("Pthread aborting at "+new Error().stack),S+="",G(S),oe=!0,fe=1,S="abort("+S+"). Build with -s ASSERTIONS=1 for more info.";var T=new WebAssembly.RuntimeError(S);throw d(T),T}function hh(S,T){return String.prototype.startsWith?S.startsWith(T):S.indexOf(T)===0}var Gi="data:application/octet-stream;base64,";function dh(S){return hh(S,Gi)}var n1="file://";function ph(S){return hh(S,n1)}var In="tfjs-backend-wasm.wasm";dh(In)||(In=N(In));function fh(S){try{if(S==In&&te)return new Uint8Array(te);if(O)return O(S);throw"both async and sync fetching of the wasm failed"}catch(T){aa(T)}}function r1(){if(!te&&(g||x)){if(typeof fetch=="function"&&!ph(In))return fetch(In,{credentials:"same-origin"}).then(function(S){if(!S.ok)throw"failed to load wasm binary file at '"+In+"'";return S.arrayBuffer()}).catch(function(){return fh(In)});if(F)return new Promise(function(S,T){F(In,function(W){S(new Uint8Array(W))},T)})}return Promise.resolve().then(function(){return fh(In)})}function a1(){var S={a:G1};function T(ce,be){var nt=ce.exports;u.asm=nt,ir=u.asm.D,he=be,b||t1("wasm-instantiate")}b||e1("wasm-instantiate");function W(ce){T(ce.instance,ce.module)}function q(ce){return r1().then(function(be){return WebAssembly.instantiate(be,S)}).then(ce,function(be){G("failed to asynchronously prepare wasm: "+be),aa(be)})}function de(){return!te&&typeof WebAssembly.instantiateStreaming=="function"&&!dh(In)&&!ph(In)&&typeof fetch=="function"?fetch(In,{credentials:"same-origin"}).then(function(ce){var be=WebAssembly.instantiateStreaming(ce,S);return be.then(W,function(nt){return G("wasm streaming compile failed: "+nt),G("falling back to ArrayBuffer instantiation"),q(W)})}):q(W)}if(u.instantiateWasm)try{var ue=u.instantiateWasm(S,T);return ue}catch(ce){return G("Module.instantiateWasm callback failed with error: "+ce),!1}return de().catch(d),{}}var s1={9800:function(){throw"Canceled!"},9818:function(S,T){setTimeout(function(){t5(S,T)},0)}};function mh(){Ie.initRuntime()}function qi(S){for(;S.length>0;){var T=S.shift();if(typeof T=="function"){T(u);continue}var W=T.func;typeof W=="number"?T.arg===void 0?ir.get(W)():ir.get(W)(T.arg):W(T.arg===void 0?null:T.arg)}}function lu(S,T){if(S<=0||S>s().length||S&!0||T<0)return-28;if(T==0)return 0;T>=2147483647&&(T=Infinity);var W=Atomics.load(o(),Qi>>2),q=0;if(W==S){var de=Atomics.compareExchange(o(),Qi>>2,W,0);if(de==W&&(--T,q=1,T<=0))return 1}var ue=Atomics.notify(o(),S>>2,T);if(ue>=0)return ue+q;throw"Atomics.notify returned an unexpected value "+ue}u._emscripten_futex_wake=lu;function i1(S){if(b)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!S)throw"Internal Error! Null pthread_ptr in killThread!";o()[S+12>>2]=0;var T=Ie.pthreads[S];T.worker.terminate(),Ie.freeThreadData(T),Ie.runningWorkers.splice(Ie.runningWorkers.indexOf(T.worker),1),T.worker.pthread=void 0}function o1(S){if(b)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!S)throw"Internal Error! Null pthread_ptr in cancelThread!";var T=Ie.pthreads[S];T.worker.postMessage({cmd:"cancel"})}function l1(S){if(b)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!S)throw"Internal Error! Null pthread_ptr in cleanupThread!";var T=Ie.pthreads[S];if(T){o()[S+12>>2]=0;var W=T.worker;Ie.returnWorkerToPool(W)}}var Ie={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){},initRuntime:function(){for(var S=ls(228),T=0;T<228/4;++T)l()[S/4+T]=0;o()[S+12>>2]=S;var W=S+152;o()[W>>2]=W;for(var q=ls(512),T=0;T<128;++T)l()[q/4+T]=0;Atomics.store(l(),S+100>>2,q),Atomics.store(l(),S+40>>2,S),xf(S,!x,1),e5(S)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ie.threadExitHandlers.length>0;)Ie.threadExitHandlers.pop()();b&&Zi()&&Q2()},runExitHandlersAndDeinitThread:function(S,T){Atomics.store(l(),S+56>>2,1),Atomics.store(l(),S+60>>2,0),Ie.runExitHandlers(),Atomics.store(l(),S+4>>2,T),Atomics.store(l(),S+0>>2,1),lu(S+0,2147483647),xf(0,0,0)},threadExit:function(S){var T=Zi();T&&(Ie.runExitHandlersAndDeinitThread(T,S),b&&postMessage({cmd:"exit"}))},threadCancel:function(){Ie.runExitHandlersAndDeinitThread(Zi(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var S in Ie.pthreads){var T=Ie.pthreads[S];T&&T.worker&&Ie.returnWorkerToPool(T.worker)}Ie.pthreads={};for(var W=0;W<Ie.unusedWorkers.length;++W){var q=Ie.unusedWorkers[W];q.terminate()}Ie.unusedWorkers=[];for(var W=0;W<Ie.runningWorkers.length;++W){var q=Ie.runningWorkers[W],T=q.pthread;Ie.freeThreadData(T),q.terminate()}Ie.runningWorkers=[]},freeThreadData:function(S){if(S){if(S.threadInfoStruct){var T=o()[S.threadInfoStruct+100>>2];o()[S.threadInfoStruct+100>>2]=0,mu(T),mu(S.threadInfoStruct)}S.threadInfoStruct=0,S.allocatedOwnStack&&S.stackBase&&mu(S.stackBase),S.stackBase=0,S.worker&&(S.worker.pthread=null)}},returnWorkerToPool:function(S){Ie.runWithoutMainThreadQueuedCalls(function(){delete Ie.pthreads[S.pthread.threadInfoStruct],Ie.unusedWorkers.push(S),Ie.runningWorkers.splice(Ie.runningWorkers.indexOf(S),1),Ie.freeThreadData(S.pthread),S.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(S){o()[i5>>2]=0;try{S()}finally{o()[i5>>2]=1}},receiveObjectTransfer:function(S){},loadWasmModuleToWorker:function(S,T){S.onmessage=function(W){var q=W.data,de=q.cmd;if(S.pthread&&(Ie.currentProxiedOperationCallerThread=S.pthread.threadInfoStruct),q.targetThread&&q.targetThread!=Zi()){var ue=Ie.pthreads[q.targetThread];ue?ue.worker.postMessage(W.data,q.transferList):console.error('Internal error! Worker sent a message "'+de+'" to target pthread '+q.targetThread+", but that thread no longer exists!"),Ie.currentProxiedOperationCallerThread=void 0;return}if(de==="processQueuedMainThreadWork")Af();else if(de==="spawnThread")bh(W.data);else if(de==="cleanupThread")l1(q.thread);else if(de==="killThread")i1(q.thread);else if(de==="cancelThread")o1(q.thread);else if(de==="loaded")S.loaded=!0,T&&T(S),S.runPthread&&(S.runPthread(),delete S.runPthread);else if(de==="print")X("Thread "+q.threadId+": "+q.text);else if(de==="printErr")G("Thread "+q.threadId+": "+q.text);else if(de==="alert")alert("Thread "+q.threadId+": "+q.text);else if(de==="exit"){var ce=S.pthread&&Atomics.load(l(),S.pthread.threadInfoStruct+64>>2);ce&&Ie.returnWorkerToPool(S)}else if(de==="exitProcess")try{i9(q.returnCode)}catch(be){if(be instanceof Au)return;throw be}else de==="cancelDone"?Ie.returnWorkerToPool(S):de==="objectTransfer"?Ie.receiveObjectTransfer(W.data):W.data.target==="setimmediate"?S.postMessage(W.data):G("worker sent an unknown command "+de);Ie.currentProxiedOperationCallerThread=void 0},S.onerror=function(W){G("pthread sent an error! "+W.filename+":"+W.lineno+": "+W.message)},v&&(S.on("message",function(W){S.onmessage({data:W})}),S.on("error",function(W){S.onerror(W)}),S.on("exit",function(W){})),S.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||r,wasmMemory:Q,wasmModule:he})},allocateUnusedWorker:function(){var S=N("tfjs-backend-wasm.worker.js");Ie.unusedWorkers.push(new Worker(S))},getNewWorker:function(){return Ie.unusedWorkers.length==0&&(Ie.allocateUnusedWorker(),Ie.loadWasmModuleToWorker(Ie.unusedWorkers[0])),Ie.unusedWorkers.length>0?Ie.unusedWorkers.pop():null},busySpinWait:function(S){for(var T=performance.now()+S;performance.now()<T;);}};function u1(S,T){a5(S,T),Yi(S)}u.establishStackSpace=u1;function c1(){return ie}u.getNoExitRuntime=c1;function h1(S,T){return ir.get(S)(T)}u.invokeEntryPoint=h1;function d1(S,T,W,q){aa("Assertion failed: "+$e(S)+", at: "+[T?$e(T):"unknown filename",W,q?$e(q):"unknown function"])}var os;v?os=function(){var S=process.hrtime();return S[0]*1e3+S[1]/1e6}:b?os=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?os=dateNow:os=function(){return performance.now()};function p1(S){return o()[Y2()>>2]=S,S}function f1(S,T){if(b)return ka(1,1,S,T)}function m1(S,T){if(S==T)postMessage({cmd:"processQueuedMainThreadWork"});else if(b)postMessage({targetThread:S,cmd:"processThreadQueue"});else{var W=Ie.pthreads[S],q=W&&W.worker;if(!q)return;q.postMessage({cmd:"processThreadQueue"})}return 1}function y1(){aa()}function A1(S,T,W){var q=_1(T,W);return s1[S].apply(null,q)}function g1(S,T){}function x1(S,T,W){if(S<=0||S>s().length||S&!0)return-28;if(g){if(Atomics.load(o(),S>>2)!=T)return-6;for(var q=performance.now(),de=q+W,ue=Atomics.exchange(o(),Qi>>2,S);;){if(q=performance.now(),q>de)return ue=Atomics.exchange(o(),Qi>>2,0),-73;if(ue=Atomics.exchange(o(),Qi>>2,0),ue==0)break;if(Af(),Atomics.load(o(),S>>2)!=T)return-6;ue=Atomics.exchange(o(),Qi>>2,S)}return 0}else{var ce=Atomics.wait(o(),S>>2,T,W);if(ce==="timed-out")return-73;if(ce==="not-equal")return-6;if(ce==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ce}}function w1(S,T,W){i().copyWithin(S,T,T+W)}function b1(){return v?require("os").cpus().length:navigator.hardwareConcurrency}function ka(S,T){for(var W=arguments.length-2,q=yu(),de=W,ue=Ji(de*8),ce=ue>>3,be=0;be<W;be++){var nt=arguments[2+be];c()[ce+be]=nt}var nn=r5(S,de,ue,T);return Yi(q),nn}var uu=[],cu=[];function _1(S,T){cu.length=0;var W;for(T>>=2;W=i()[S++];){var q=W<105;q&&T&1&&T++,cu.push(q?c()[T++>>1]:o()[T]),++T}return cu}function v1(S,T,W){uu.length=T;for(var q=W>>3,de=0;de<T;de++)uu[de]=c()[q+de];var ue=S<0,ce=ue?s1[-S-1]:H1[S];return ce.apply(null,uu)}function k1(){return i().length}function I1(S){try{return Q.grow(S-je.byteLength+65535>>>16),Kt(Q.buffer),1}catch(T){}}function S1(S){var T=k1();if(S<=T)return!1;var W=2147483648;if(S>W)return!1;for(var q=1;q<=4;q*=2){var de=T*(1+.2/q);de=Math.min(de,S+100663296);var ue=Math.min(W,dt(Math.max(S,de),65536)),ce=I1(ue);if(ce)return!0}return!1}var We={inEventHandler:0,removeAllEventListeners:function(){for(var S=We.eventHandlers.length-1;S>=0;--S)We._removeHandler(S);We.eventHandlers=[],We.deferredCalls=[]},registerRemoveEventListeners:function(){We.removeEventListenersRegistered||(_a.push(We.removeAllEventListeners),We.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(S,T,W){function q(ce,be){if(ce.length!=be.length)return!1;for(var nt in ce)if(ce[nt]!=be[nt])return!1;return!0}for(var de in We.deferredCalls){var ue=We.deferredCalls[de];if(ue.targetFunction==S&&q(ue.argsList,W))return}We.deferredCalls.push({targetFunction:S,precedence:T,argsList:W}),We.deferredCalls.sort(function(ce,be){return ce.precedence<be.precedence})},removeDeferredCalls:function(S){for(var T=0;T<We.deferredCalls.length;++T)We.deferredCalls[T].targetFunction==S&&(We.deferredCalls.splice(T,1),--T)},canPerformEventHandlerRequests:function(){return We.inEventHandler&&We.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(We.canPerformEventHandlerRequests())for(var S=0;S<We.deferredCalls.length;++S){var T=We.deferredCalls[S];We.deferredCalls.splice(S,1),--S,T.targetFunction.apply(null,T.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(S,T){for(var W=0;W<We.eventHandlers.length;++W)We.eventHandlers[W].target==S&&(!T||T==We.eventHandlers[W].eventTypeString)&&We._removeHandler(W--)},_removeHandler:function(S){var T=We.eventHandlers[S];T.target.removeEventListener(T.eventTypeString,T.eventListenerFunc,T.useCapture),We.eventHandlers.splice(S,1)},registerOrRemoveHandler:function(S){var T=function(q){++We.inEventHandler,We.currentEventHandler=S,We.runDeferredCalls(),S.handlerFunc(q),We.runDeferredCalls(),--We.inEventHandler};if(S.callbackfunc)S.eventListenerFunc=T,S.target.addEventListener(S.eventTypeString,T,S.useCapture),We.eventHandlers.push(S),We.registerRemoveEventListeners();else for(var W=0;W<We.eventHandlers.length;++W)We.eventHandlers[W].target==S.target&&We.eventHandlers[W].eventTypeString==S.eventTypeString&&We._removeHandler(W--)},queueEventHandlerOnThread_iiii:function(S,T,W,q,de){var ue=yu(),ce=Ji(12);o()[ce>>2]=W,o()[ce+4>>2]=q,o()[ce+8>>2]=de,gf(0,S,637534208,T,q,ce),Yi(ue)},getTargetThreadForEventCallback:function(S){switch(S){case 1:return 0;case 2:return Ie.currentProxiedOperationCallerThread;default:return S}},getNodeNameForTarget:function(S){return S?S==window?"#window":S==screen?"#screen":S&&S.nodeName?S.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function N1(S){var T=it(S)+1,W=ls(T);return tt(S,W,T),W}function T1(S,T,W,q){var de=yu(),ue=Ji(12),ce=0;T&&(ce=N1(T)),o()[ue>>2]=ce,o()[ue+4>>2]=W,o()[ue+8>>2]=q,gf(0,S,657457152,0,ce,ue),Yi(de)}function E1(S,T,W,q){T=T?$e(T):"",T1(S,T,W,q)}function C1(S){return S>2?$e(S):S}var R1=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function M1(S){S=C1(S);var T=R1[S]||(typeof document!="undefined"?document.querySelector(S):void 0);return T}function hu(S){return M1(S)}function yh(S,T,W){var q=hu(S);if(!q)return-4;if(q.canvasSharedPtr&&(o()[q.canvasSharedPtr>>2]=T,o()[q.canvasSharedPtr+4>>2]=W),q.offscreenCanvas||!q.controlTransferredOffscreen){q.offscreenCanvas&&(q=q.offscreenCanvas);var de=!1;if(q.GLctxObject&&q.GLctxObject.GLctx){var ue=q.GLctxObject.GLctx.getParameter(2978);de=ue[0]===0&&ue[1]===0&&ue[2]===q.width&&ue[3]===q.height}q.width=T,q.height=W,de&&q.GLctxObject.GLctx.viewport(0,0,T,W)}else if(q.canvasSharedPtr){var ce=o()[q.canvasSharedPtr+8>>2];return E1(ce,S,T,W),1}else return-4;return 0}function Ah(S,T,W){return b?ka(2,1,S,T,W):yh(S,T,W)}function F1(S,T,W){var q=hu(S);return q?yh(S,T,W):Ah(S,T,W)}function $1(S){}function D1(S){var T=S.getExtension("ANGLE_instanced_arrays");if(T)return S.vertexAttribDivisor=function(W,q){T.vertexAttribDivisorANGLE(W,q)},S.drawArraysInstanced=function(W,q,de,ue){T.drawArraysInstancedANGLE(W,q,de,ue)},S.drawElementsInstanced=function(W,q,de,ue,ce){T.drawElementsInstancedANGLE(W,q,de,ue,ce)},1}function O1(S){var T=S.getExtension("OES_vertex_array_object");if(T)return S.createVertexArray=function(){return T.createVertexArrayOES()},S.deleteVertexArray=function(W){T.deleteVertexArrayOES(W)},S.bindVertexArray=function(W){T.bindVertexArrayOES(W)},S.isVertexArray=function(W){return T.isVertexArrayOES(W)},1}function z1(S){var T=S.getExtension("WEBGL_draw_buffers");if(T)return S.drawBuffers=function(W,q){T.drawBuffersWEBGL(W,q)},1}function P1(S){return!!(S.multiDrawWebgl=S.getExtension("WEBGL_multi_draw"))}var Qe={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(S){Qe.lastError||(Qe.lastError=S)},getNewId:function(S){for(var T=Qe.counter++,W=S.length;W<T;W++)S[W]=null;return T},getSource:function(S,T,W,q){for(var de="",ue=0;ue<T;++ue){var ce=q?o()[q+ue*4>>2]:-1;de+=$e(o()[W+ue*4>>2],ce<0?void 0:ce)}return de},createContext:function(S,T){var W=S.getContext("webgl",T);if(!W)return 0;var q=Qe.registerContext(W,T);return q},registerContext:function(S,T){var W=ls(8);o()[W+4>>2]=Zi();var q={handle:W,attributes:T,version:T.majorVersion,GLctx:S};return S.canvas&&(S.canvas.GLctxObject=q),Qe.contexts[W]=q,(typeof T.enableExtensionsByDefault=="undefined"||T.enableExtensionsByDefault)&&Qe.initExtensions(q),W},makeContextCurrent:function(S){return Qe.currentContext=Qe.contexts[S],u.ctx=Ia=Qe.currentContext&&Qe.currentContext.GLctx,!(S&&!Ia)},getContext:function(S){return Qe.contexts[S]},deleteContext:function(S){Qe.currentContext===Qe.contexts[S]&&(Qe.currentContext=null),typeof We=="object"&&We.removeAllHandlersOnTarget(Qe.contexts[S].GLctx.canvas),Qe.contexts[S]&&Qe.contexts[S].GLctx.canvas&&(Qe.contexts[S].GLctx.canvas.GLctxObject=void 0),mu(Qe.contexts[S].handle),Qe.contexts[S]=null},initExtensions:function(S){if(S||(S=Qe.currentContext),!S.initExtensionsDone){S.initExtensionsDone=!0;var T=S.GLctx;D1(T),O1(T),z1(T),T.disjointTimerQueryExt=T.getExtension("EXT_disjoint_timer_query"),P1(T);var W=T.getSupportedExtensions()||[];W.forEach(function(q){q.indexOf("lose_context")<0&&q.indexOf("debug")<0&&T.getExtension(q)})}},populateUniformTable:function(S){for(var T=Qe.programs[S],W=Qe.programInfos[S]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},q=W.uniforms,de=Ia.getProgramParameter(T,35718),ue=0;ue<de;++ue){var ce=Ia.getActiveUniform(T,ue),be=ce.name;W.maxUniformLength=Math.max(W.maxUniformLength,be.length+1),be.slice(-1)=="]"&&(be=be.slice(0,be.lastIndexOf("[")));var nt=Ia.getUniformLocation(T,be);if(nt){var nn=Qe.getNewId(Qe.uniforms);q[be]=[ce.size,nn],Qe.uniforms[nn]=nt;for(var Yt=1;Yt<ce.size;++Yt){var Na=be+"["+Yt+"]";nt=Ia.getUniformLocation(T,Na),nn=Qe.getNewId(Qe.uniforms),Qe.uniforms[nn]=nt}}}}},L1=["default","low-power","high-performance"];function W1(S,T){var W=T>>2,q=o()[W+(24>>2)],de={alpha:!!o()[W+(0>>2)],depth:!!o()[W+(4>>2)],stencil:!!o()[W+(8>>2)],antialias:!!o()[W+(12>>2)],premultipliedAlpha:!!o()[W+(16>>2)],preserveDrawingBuffer:!!o()[W+(20>>2)],powerPreference:L1[q],failIfMajorPerformanceCaveat:!!o()[W+(28>>2)],majorVersion:o()[W+(32>>2)],minorVersion:o()[W+(36>>2)],enableExtensionsByDefault:o()[W+(40>>2)],explicitSwapControl:o()[W+(44>>2)],proxyContextToMainThread:o()[W+(48>>2)],renderViaOffscreenBackBuffer:o()[W+(52>>2)]},ue=hu(S);if(!ue||de.explicitSwapControl)return 0;var ce=Qe.createContext(ue,de);return ce}function B1(S,T){return W1(S,T)}var Xi={mappings:{},buffers:[null,[],[]],printChar:function(S,T){var W=Xi.buffers[S];T===0||T===10?((S===1?X:G)(Oe(W,0)),W.length=0):W.push(T)},varargs:void 0,get:function(){Xi.varargs+=4;var S=o()[Xi.varargs-4>>2];return S},getStr:function(S){var T=$e(S);return T},get64:function(S,T){return S}};function gh(S){return b?ka(3,1,S):0}function xh(S,T,W,q,de){if(b)return ka(4,1,S,T,W,q,de)}function wh(S,T,W,q){if(b)return ka(5,1,S,T,W,q);for(var de=0,ue=0;ue<W;ue++){for(var ce=o()[T+ue*8>>2],be=o()[T+(ue*8+4)>>2],nt=0;nt<be;nt++)Xi.printChar(S,i()[ce+nt]);de+=be}return o()[q>>2]=de,0}function V1(S){var T=Ie.threadExitHandlers.pop();S&&T()}function j1(S,T){Ie.threadExitHandlers.push(function(){ir.get(S)(T)})}function bh(S){if(b)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var T=Ie.getNewWorker();if(T.pthread!==void 0)throw"Internal error!";if(!S.pthread_ptr)throw"Internal error, no pthread ptr!";Ie.runningWorkers.push(T);for(var W=ls(128*4),q=0;q<128;++q)o()[W+q*4>>2]=0;var de=S.stackBase+S.stackSize,ue=Ie.pthreads[S.pthread_ptr]={worker:T,stackBase:S.stackBase,stackSize:S.stackSize,allocatedOwnStack:S.allocatedOwnStack,threadInfoStruct:S.pthread_ptr},ce=ue.threadInfoStruct>>2;Atomics.store(l(),ce+(64>>2),S.detached),Atomics.store(l(),ce+(100>>2),W),Atomics.store(l(),ce+(40>>2),ue.threadInfoStruct),Atomics.store(l(),ce+(80>>2),S.stackSize),Atomics.store(l(),ce+(76>>2),de),Atomics.store(l(),ce+(104>>2),S.stackSize),Atomics.store(l(),ce+(104+8>>2),de),Atomics.store(l(),ce+(104+12>>2),S.detached);var be=J2(),nt=be+40;Atomics.store(l(),ce+(172>>2),nt),T.pthread=ue;var nn={cmd:"run",start_routine:S.startRoutine,arg:S.arg,threadInfoStruct:S.pthread_ptr,stackBase:S.stackBase,stackSize:S.stackSize};T.runPthread=function(){nn.time=performance.now(),T.postMessage(nn,S.transferList)},T.loaded&&(T.runPthread(),delete T.runPthread)}function U1(S,T,W,q){if(typeof SharedArrayBuffer=="undefined")return G("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!S)return G("pthread_create called with a null thread pointer!"),28;var de=[],ue=0;if(b&&(de.length===0||ue))return n5(687865856,S,T,W,q);if(ue)return ue;var ce=0,be=0,nt=0;T&&T!=-1?(ce=o()[T>>2],ce+=81920,be=o()[T+8>>2],nt=o()[T+12>>2]!==0):ce=2097152;var nn=be==0;nn?be=s5(16,ce):(be-=ce,pe(be>0));for(var Yt=ls(228),Na=0;Na<228>>2;++Na)l()[(Yt>>2)+Na]=0;o()[S>>2]=Yt,o()[Yt+12>>2]=Yt;var eo=Yt+152;o()[eo>>2]=eo;var Sn={stackBase:be,stackSize:ce,allocatedOwnStack:nn,detached:nt,startRoutine:W,pthread_ptr:Yt,arg:q,transferList:de};return b?(Sn.cmd="spawnThread",postMessage(Sn,de)):bh(Sn),0}function _h(S){if(b)return ka(6,1,S);switch(S){case 30:return 16384;case 85:var T=2147483648;return T/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return p1(28),-1}b||Ie.initMainThreadBlock();var Ia,H1=[null,f1,Ah,gh,xh,wh,_h],G1={e:d1,v:m1,b:y1,w:A1,j:g1,c:x1,d:lu,f:os,p:w1,x:b1,s:v1,r:S1,t:F1,i:$1,u:B1,m:gh,n:xh,g:wh,o:mh,a:Q||u.wasmMemory,k:V1,l:j1,h:U1,q:_h},Z2=a1(),vh=u.___wasm_call_ctors=function(){return(vh=u.___wasm_call_ctors=u.asm.y).apply(null,arguments)},q1=u._init=function(){return(q1=u._init=u.asm.z).apply(null,arguments)},X1=u._register_tensor=function(){return(X1=u._register_tensor=u.asm.A).apply(null,arguments)},K1=u._dispose_data=function(){return(K1=u._dispose_data=u.asm.B).apply(null,arguments)},Z1=u._dispose=function(){return(Z1=u._dispose=u.asm.C).apply(null,arguments)},Y1=u._Abs=function(){return(Y1=u._Abs=u.asm.E).apply(null,arguments)},J1=u._Add=function(){return(J1=u._Add=u.asm.F).apply(null,arguments)},Q1=u._AddN=function(){return(Q1=u._AddN=u.asm.G).apply(null,arguments)},ef=u._All=function(){return(ef=u._All=u.asm.H).apply(null,arguments)},tf=u._Any=function(){return(tf=u._Any=u.asm.I).apply(null,arguments)},nf=u._ArgMax=function(){return(nf=u._ArgMax=u.asm.J).apply(null,arguments)},rf=u._AvgPool=function(){return(rf=u._AvgPool=u.asm.K).apply(null,arguments)},af=u._BatchMatMul=function(){return(af=u._BatchMatMul=u.asm.L).apply(null,arguments)},sf=u._Ceil=function(){return(sf=u._Ceil=u.asm.M).apply(null,arguments)},of=u._ClipByValue=function(){return(of=u._ClipByValue=u.asm.N).apply(null,arguments)},lf=u._Conv2D=function(){return(lf=u._Conv2D=u.asm.O).apply(null,arguments)},uf=u._Conv2DBackpropInput=function(){return(uf=u._Conv2DBackpropInput=u.asm.P).apply(null,arguments)},cf=u._Cos=function(){return(cf=u._Cos=u.asm.Q).apply(null,arguments)},hf=u._CropAndResize=function(){return(hf=u._CropAndResize=u.asm.R).apply(null,arguments)},df=u._Cumsum=function(){return(df=u._Cumsum=u.asm.S).apply(null,arguments)},pf=u._DepthToSpace=function(){return(pf=u._DepthToSpace=u.asm.T).apply(null,arguments)},ff=u._DepthwiseConv2dNative=function(){return(ff=u._DepthwiseConv2dNative=u.asm.U).apply(null,arguments)},mf=u._Equal=function(){return(mf=u._Equal=u.asm.V).apply(null,arguments)},kh=u._Exp=function(){return(kh=u._Exp=u.asm.W).apply(null,arguments)},Ih=u._FlipLeftRight=function(){return(Ih=u._FlipLeftRight=u.asm.X).apply(null,arguments)},Sh=u._Floor=function(){return(Sh=u._Floor=u.asm.Y).apply(null,arguments)},du=u._FloorDiv=function(){return(du=u._FloorDiv=u.asm.Z).apply(null,arguments)},Ki=u._FusedBatchNorm=function(){return(Ki=u._FusedBatchNorm=u.asm._).apply(null,arguments)},yf=u._FusedConv2D=function(){return(yf=u._FusedConv2D=u.asm.$).apply(null,arguments)},pu=u._FusedDepthwiseConv2D=function(){return(pu=u._FusedDepthwiseConv2D=u.asm.aa).apply(null,arguments)},K=u._Gather=function(){return(K=u._Gather=u.asm.ba).apply(null,arguments)},ne=u._GatherNd=function(){return(ne=u._GatherNd=u.asm.ca).apply(null,arguments)},Ne=u._Greater=function(){return(Ne=u._Greater=u.asm.da).apply(null,arguments)},Ze=u._GreaterEqual=function(){return(Ze=u._GreaterEqual=u.asm.ea).apply(null,arguments)},Nt=u._LeakyRelu=function(){return(Nt=u._LeakyRelu=u.asm.fa).apply(null,arguments)},At=u._Less=function(){return(At=u._Less=u.asm.ga).apply(null,arguments)},Ue=u._LessEqual=function(){return(Ue=u._LessEqual=u.asm.ha).apply(null,arguments)},Ge=u._Log=function(){return(Ge=u._Log=u.asm.ia).apply(null,arguments)},Zt=u._LogicalAnd=function(){return(Zt=u._LogicalAnd=u.asm.ja).apply(null,arguments)},sa=u._Max=function(){return(sa=u._Max=u.asm.ka).apply(null,arguments)},ia=u._MaxPool=function(){return(ia=u._MaxPool=u.asm.la).apply(null,arguments)},Nh=u._Maximum=function(){return(Nh=u._Maximum=u.asm.ma).apply(null,arguments)},fu=u._Mean=function(){return(fu=u._Mean=u.asm.na).apply(null,arguments)},Yn=u._Min=function(){return(Yn=u._Min=u.asm.oa).apply(null,arguments)},Sa=u._Minimum=function(){return(Sa=u._Minimum=u.asm.pa).apply(null,arguments)},Th=u._MirrorPad=function(){return(Th=u._MirrorPad=u.asm.qa).apply(null,arguments)},bk=u._Multiply=function(){return(bk=u._Multiply=u.asm.ra).apply(null,arguments)},_k=u._Neg=function(){return(_k=u._Neg=u.asm.sa).apply(null,arguments)},vk=u._NonMaxSuppressionV3=function(){return(vk=u._NonMaxSuppressionV3=u.asm.ta).apply(null,arguments)},kk=u._NonMaxSuppressionV4=function(){return(kk=u._NonMaxSuppressionV4=u.asm.ua).apply(null,arguments)},Ik=u._NonMaxSuppressionV5=function(){return(Ik=u._NonMaxSuppressionV5=u.asm.va).apply(null,arguments)},Sk=u._NotEqual=function(){return(Sk=u._NotEqual=u.asm.wa).apply(null,arguments)},Nk=u._OneHot=function(){return(Nk=u._OneHot=u.asm.xa).apply(null,arguments)},Tk=u._PadV2=function(){return(Tk=u._PadV2=u.asm.ya).apply(null,arguments)},Ek=u._Pow=function(){return(Ek=u._Pow=u.asm.za).apply(null,arguments)},Ck=u._Prelu=function(){return(Ck=u._Prelu=u.asm.Aa).apply(null,arguments)},Rk=u._Prod=function(){return(Rk=u._Prod=u.asm.Ba).apply(null,arguments)},Mk=u._RealDiv=function(){return(Mk=u._RealDiv=u.asm.Ca).apply(null,arguments)},Fk=u._Relu=function(){return(Fk=u._Relu=u.asm.Da).apply(null,arguments)},$k=u._Relu6=function(){return($k=u._Relu6=u.asm.Ea).apply(null,arguments)},Dk=u._ResizeBilinear=function(){return(Dk=u._ResizeBilinear=u.asm.Fa).apply(null,arguments)},Ok=u._Reverse=function(){return(Ok=u._Reverse=u.asm.Ga).apply(null,arguments)},zk=u._RotateWithOffset=function(){return(zk=u._RotateWithOffset=u.asm.Ha).apply(null,arguments)},Pk=u._Round=function(){return(Pk=u._Round=u.asm.Ia).apply(null,arguments)},Lk=u._Rsqrt=function(){return(Lk=u._Rsqrt=u.asm.Ja).apply(null,arguments)},Wk=u._ScatterNd=function(){return(Wk=u._ScatterNd=u.asm.Ka).apply(null,arguments)},Bk=u._SelectV2=function(){return(Bk=u._SelectV2=u.asm.La).apply(null,arguments)},Vk=u._Sigmoid=function(){return(Vk=u._Sigmoid=u.asm.Ma).apply(null,arguments)},jk=u._Sin=function(){return(jk=u._Sin=u.asm.Na).apply(null,arguments)},Uk=u._Softmax=function(){return(Uk=u._Softmax=u.asm.Oa).apply(null,arguments)},Hk=u._Sqrt=function(){return(Hk=u._Sqrt=u.asm.Pa).apply(null,arguments)},Gk=u._Square=function(){return(Gk=u._Square=u.asm.Qa).apply(null,arguments)},qk=u._SquaredDifference=function(){return(qk=u._SquaredDifference=u.asm.Ra).apply(null,arguments)},Xk=u._Step=function(){return(Xk=u._Step=u.asm.Sa).apply(null,arguments)},Kk=u._StridedSlice=function(){return(Kk=u._StridedSlice=u.asm.Ta).apply(null,arguments)},Zk=u._Sub=function(){return(Zk=u._Sub=u.asm.Ua).apply(null,arguments)},Yk=u._Sum=function(){return(Yk=u._Sum=u.asm.Va).apply(null,arguments)},Jk=u._Tan=function(){return(Jk=u._Tan=u.asm.Wa).apply(null,arguments)},Qk=u._Tanh=function(){return(Qk=u._Tanh=u.asm.Xa).apply(null,arguments)},e9=u._Tile=function(){return(e9=u._Tile=u.asm.Ya).apply(null,arguments)},t9=u._TopK=function(){return(t9=u._TopK=u.asm.Za).apply(null,arguments)},n9=u._Transpose=function(){return(n9=u._Transpose=u.asm._a).apply(null,arguments)},r9=u.__FusedMatMul=function(){return(r9=u.__FusedMatMul=u.asm.$a).apply(null,arguments)},ls=u._malloc=function(){return(ls=u._malloc=u.asm.ab).apply(null,arguments)},mu=u._free=function(){return(mu=u._free=u.asm.bb).apply(null,arguments)},Y2=u.___errno_location=function(){return(Y2=u.___errno_location=u.asm.cb).apply(null,arguments)},J2=u._emscripten_get_global_libc=function(){return(J2=u._emscripten_get_global_libc=u.asm.db).apply(null,arguments)},Zi=u._pthread_self=function(){return(Zi=u._pthread_self=u.asm.eb).apply(null,arguments)},Q2=u.___pthread_tsd_run_dtors=function(){return(Q2=u.___pthread_tsd_run_dtors=u.asm.fb).apply(null,arguments)},Af=u._emscripten_main_thread_process_queued_calls=function(){return(Af=u._emscripten_main_thread_process_queued_calls=u.asm.gb).apply(null,arguments)},a9=u._emscripten_current_thread_process_queued_calls=function(){return(a9=u._emscripten_current_thread_process_queued_calls=u.asm.hb).apply(null,arguments)},e5=u._emscripten_register_main_browser_thread_id=function(){return(e5=u._emscripten_register_main_browser_thread_id=u.asm.ib).apply(null,arguments)},t5=u.__emscripten_do_dispatch_to_thread=function(){return(t5=u.__emscripten_do_dispatch_to_thread=u.asm.jb).apply(null,arguments)},n5=u._emscripten_sync_run_in_main_thread_4=function(){return(n5=u._emscripten_sync_run_in_main_thread_4=u.asm.kb).apply(null,arguments)},r5=u._emscripten_run_in_main_runtime_thread_js=function(){return(r5=u._emscripten_run_in_main_runtime_thread_js=u.asm.lb).apply(null,arguments)},gf=u.__emscripten_call_on_thread=function(){return(gf=u.__emscripten_call_on_thread=u.asm.mb).apply(null,arguments)},s9=u._emscripten_tls_init=function(){return(s9=u._emscripten_tls_init=u.asm.nb).apply(null,arguments)},xf=u.__emscripten_thread_init=function(){return(xf=u.__emscripten_thread_init=u.asm.ob).apply(null,arguments)},yu=u.stackSave=function(){return(yu=u.stackSave=u.asm.pb).apply(null,arguments)},Yi=u.stackRestore=function(){return(Yi=u.stackRestore=u.asm.qb).apply(null,arguments)},Ji=u.stackAlloc=function(){return(Ji=u.stackAlloc=u.asm.rb).apply(null,arguments)},a5=u._emscripten_stack_set_limits=function(){return(a5=u._emscripten_stack_set_limits=u.asm.sb).apply(null,arguments)},s5=u._memalign=function(){return(s5=u._memalign=u.asm.tb).apply(null,arguments)},i5=u.__emscripten_allow_main_runtime_queued_calls=9792,Qi=u.__emscripten_main_thread_futex=11408;u.cwrap=Fe,u.PThread=Ie,u.PThread=Ie,u.wasmMemory=Q,u.ExitStatus=Au;var Eh;function Au(S){this.name="ExitStatus",this.message="Program terminated with exit("+S+")",this.status=S}is=function S(){Eh||wf(),Eh||(is=S)};function wf(S){if(S=S||m,ra>0)return;if(b){h(u),ou(),postMessage({cmd:"loaded"});return}if(Y0(),ra>0)return;function T(){Eh||(Eh=!0,u.calledRun=!0,!oe&&(ou(),J0(),h(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),kn()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),T()},1)):T()}u.run=wf;function i9(S,T){if(!(T&&ie&&S===0)){if(!T&&b)throw postMessage({cmd:"exitProcess",returnCode:S}),new Au(S);ie||(Ie.terminateAllThreads(),fe=S,uh(),u.onExit&&u.onExit(S),oe=!0),A(S,new Au(S))}}if(u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return b&&(ie=!1,Ie.initWorker()),wf(),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)}),S9=_t((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,y=!1;p=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",y=!p&&!m&&!f;var A="";function g(K){return s.locateFile?s.locateFile(K,A):A+K}var x,v,w,b,k,N;m?(f?A=xu().dirname(A)+"/":A=__dirname+"/",x=function(K,ne){return k||(k=require("fs")),N||(N=xu()),K=N.normalize(K),k.readFileSync(K,ne?null:"utf8")},w=function(K){var ne=x(K,!0);return ne.buffer||(ne=new Uint8Array(ne)),X(ne.buffer),ne},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),u=process.argv.slice(2),process.on("uncaughtException",function(K){if(!(K instanceof yf))throw K}),process.on("unhandledRejection",zr),d=function(K){process.exit(K)},s.inspect=function(){return"[Emscripten Module object]"}):y?(typeof read!="undefined"&&(x=function(K){return read(K)}),w=function(K){var ne;return typeof readbuffer=="function"?new Uint8Array(readbuffer(K)):(ne=read(K,"binary"),X(typeof ne=="object"),ne)},typeof scriptArgs!="undefined"?u=scriptArgs:typeof arguments!="undefined"&&(u=arguments),typeof quit=="function"&&(d=function(K){quit(K)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(p||f)&&(f?A=self.location.href:typeof document!="undefined"&&document.currentScript&&(A=document.currentScript.src),r&&(A=r),A.indexOf("blob:")!==0?A=A.substr(0,A.lastIndexOf("/")+1):A="",x=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.send(null),ne.responseText},f&&(w=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),v=function(K,ne,Ne){var Ze=new XMLHttpRequest;Ze.open("GET",K,!0),Ze.responseType="arraybuffer",Ze.onload=function(){if(Ze.status==200||Ze.status==0&&Ze.response){ne(Ze.response);return}Ne()},Ze.onerror=Ne,Ze.send(null)},b=function(K){document.title=K});var E=s.print||console.log.bind(console),F=s.printErr||console.warn.bind(console);for(c in l)l.hasOwnProperty(c)&&(s[c]=l[c]);l=null,s.arguments&&(u=s.arguments),s.thisProgram&&(h=s.thisProgram),s.quit&&(d=s.quit);var O;s.wasmBinary&&(O=s.wasmBinary);var L=s.noExitRuntime||!0;typeof WebAssembly!="object"&&zr("no native wasm support detected");var V,j=!1,U;function X(K,ne){K||zr("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,Ne,Ze,Nt){var At={string:function(Yn){var Sa=0;if(Yn!=null&&Yn!==0){var Th=(Yn.length<<2)+1;Sa=du(Th),he(Yn,Sa,Th)}return Sa},array:function(Yn){var Sa=du(Yn.length);return oe(Yn,Sa),Sa}};function Ue(Yn){return ne==="string"?ie(Yn):ne==="boolean"?Boolean(Yn):Yn}var Ge=G(K),Zt=[],sa=0;if(Ze)for(var ia=0;ia<Ze.length;ia++){var Nh=At[Ne[ia]];Nh?(sa===0&&(sa=Ih()),Zt[ia]=Nh(Ze[ia])):Zt[ia]=Ze[ia]}var fu=Ge.apply(null,Zt);return fu=Ue(fu),sa!==0&&Sh(sa),fu}function Y(K,ne,Ne,Ze){Ne=Ne||[];var Nt=Ne.every(function(Ue){return Ue==="number"}),At=ne!=="string";return At&&Nt&&!Ze?G(K):function(){return ee(K,ne,Ne,arguments,Ze)}}var ae=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function te(K,ne,Ne){for(var Ze=ne+Ne,Nt=ne;K[Nt]&&!(Nt>=Ze);)++Nt;if(Nt-ne>16&&K.subarray&&ae)return ae.decode(K.subarray(ne,Nt));for(var At="";ne<Nt;){var Ue=K[ne++];if(!(Ue&128)){At+=String.fromCharCode(Ue);continue}var Ge=K[ne++]&63;if((Ue&224)==192){At+=String.fromCharCode((Ue&31)<<6|Ge);continue}var Zt=K[ne++]&63;if((Ue&240)==224?Ue=(Ue&15)<<12|Ge<<6|Zt:Ue=(Ue&7)<<18|Ge<<12|Zt<<6|K[ne++]&63,Ue<65536)At+=String.fromCharCode(Ue);else{var sa=Ue-65536;At+=String.fromCharCode(55296|sa>>10,56320|sa&1023)}}return At}function ie(K,ne){return K?te(Se,K,ne):""}function Q(K,ne,Ne,Ze){if(!(Ze>0))return 0;for(var Nt=Ne,At=Ne+Ze-1,Ue=0;Ue<K.length;++Ue){var Ge=K.charCodeAt(Ue);if(Ge>=55296&&Ge<=57343){var Zt=K.charCodeAt(++Ue);Ge=65536+((Ge&1023)<<10)|Zt&1023}if(Ge<=127){if(Ne>=At)break;ne[Ne++]=Ge}else if(Ge<=2047){if(Ne+1>=At)break;ne[Ne++]=192|Ge>>6,ne[Ne++]=128|Ge&63}else if(Ge<=65535){if(Ne+2>=At)break;ne[Ne++]=224|Ge>>12,ne[Ne++]=128|Ge>>6&63,ne[Ne++]=128|Ge&63}else{if(Ne+3>=At)break;ne[Ne++]=240|Ge>>18,ne[Ne++]=128|Ge>>12&63,ne[Ne++]=128|Ge>>6&63,ne[Ne++]=128|Ge&63}}return ne[Ne]=0,Ne-Nt}function he(K,ne,Ne){return Q(K,Se,ne,Ne)}function oe(K,ne){ke.set(K,ne)}function fe(K,ne){return K%ne>0&&(K+=ne-K%ne),K}var pe,ke,Se,Fe,Oe,$e,et,tt,it;function Ke(K){pe=K,s.HEAP8=ke=new Int8Array(K),s.HEAP16=Fe=new Int16Array(K),s.HEAP32=$e=new Int32Array(K),s.HEAPU8=Se=new Uint8Array(K),s.HEAPU16=Oe=new Uint16Array(K),s.HEAPU32=et=new Uint32Array(K),s.HEAPF32=tt=new Float32Array(K),s.HEAPF64=it=new Float64Array(K)}var dt=s.INITIAL_MEMORY||16777216,je,_n=[],bt=[],Kn=[],Xt=[],vn=!1;bt.push({func:function(){mh()}});function Zn(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Or(s.preRun.shift());va(_n)}function Dn(){vn=!0,va(bt)}function on(){va(Kn)}function Kt(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)ir(s.postRun.shift());va(Xt)}function Or(K){_n.unshift(K)}function ir(K){Xt.unshift(K)}var or=0,ba=null,na=null;function _a(K){or++,s.monitorRunDependencies&&s.monitorRunDependencies(or)}function Hi(K){if(or--,s.monitorRunDependencies&&s.monitorRunDependencies(or),or==0&&(ba!==null&&(clearInterval(ba),ba=null),na)){var ne=na;na=null,ne()}}s.preloadedImages={},s.preloadedAudios={};function zr(K){s.onAbort&&s.onAbort(K),K+="",F(K),j=!0,U=1,K="abort("+K+"). Build with -s ASSERTIONS=1 for more info.";var ne=new WebAssembly.RuntimeError(K);throw o(ne),ne}function lh(K,ne){return String.prototype.startsWith?K.startsWith(ne):K.indexOf(ne)===0}var Y0="data:application/octet-stream;base64,";function ou(K){return lh(K,Y0)}var J0="file://";function uh(K){return lh(K,J0)}var kn="tfjs-backend-wasm.wasm";ou(kn)||(kn=g(kn));function ch(K){try{if(K==kn&&O)return new Uint8Array(O);if(w)return w(K);throw"both async and sync fetching of the wasm failed"}catch(ne){zr(ne)}}function Q0(){if(!O&&(p||f)){if(typeof fetch=="function"&&!uh(kn))return fetch(kn,{credentials:"same-origin"}).then(function(K){if(!K.ok)throw"failed to load wasm binary file at '"+kn+"'";return K.arrayBuffer()}).catch(function(){return ch(kn)});if(v)return new Promise(function(K,ne){v(kn,function(Ne){K(new Uint8Array(Ne))},ne)})}return Promise.resolve().then(function(){return ch(kn)})}function ra(){var K={a:a1};function ne(Ue,Ge){var Zt=Ue.exports;s.asm=Zt,V=s.asm.i,Ke(V.buffer),je=s.asm.o,Hi("wasm-instantiate")}_a("wasm-instantiate");function Ne(Ue){ne(Ue.instance)}function Ze(Ue){return Q0().then(function(Ge){return WebAssembly.instantiate(Ge,K)}).then(Ue,function(Ge){F("failed to asynchronously prepare wasm: "+Ge),zr(Ge)})}function Nt(){return!O&&typeof WebAssembly.instantiateStreaming=="function"&&!ou(kn)&&!uh(kn)&&typeof fetch=="function"?fetch(kn,{credentials:"same-origin"}).then(function(Ue){var Ge=WebAssembly.instantiateStreaming(Ue,K);return Ge.then(Ne,function(Zt){return F("wasm streaming compile failed: "+Zt),F("falling back to ArrayBuffer instantiation"),Ze(Ne)})}):Ze(Ne)}if(s.instantiateWasm)try{var At=s.instantiateWasm(K,ne);return At}catch(Ue){return F("Module.instantiateWasm callback failed with error: "+Ue),!1}return Nt().catch(o),{}}function va(K){for(;K.length>0;){var ne=K.shift();if(typeof ne=="function"){ne(s);continue}var Ne=ne.func;typeof Ne=="number"?ne.arg===void 0?je.get(Ne)():je.get(Ne)(ne.arg):Ne(ne.arg===void 0?null:ne.arg)}}function is(){zr()}function e1(K,ne,Ne){Se.copyWithin(K,ne,ne+Ne)}function t1(){return Se.length}function aa(K){try{return V.grow(K-pe.byteLength+65535>>>16),Ke(V.buffer),1}catch(ne){}}function hh(K){var ne=t1(),Ne=2147483648;if(K>Ne)return!1;for(var Ze=1;Ze<=4;Ze*=2){var Nt=ne*(1+.2/Ze);Nt=Math.min(Nt,K+100663296);var At=Math.min(Ne,fe(Math.max(K,Nt),65536)),Ue=aa(At);if(Ue)return!0}return!1}var Gi={mappings:{},buffers:[null,[],[]],printChar:function(K,ne){var Ne=Gi.buffers[K];ne===0||ne===10?((K===1?E:F)(te(Ne,0)),Ne.length=0):Ne.push(ne)},varargs:void 0,get:function(){Gi.varargs+=4;var K=$e[Gi.varargs-4>>2];return K},getStr:function(K){var ne=ie(K);return ne},get64:function(K,ne){return K}};function dh(K){return 0}function n1(K,ne,Ne,Ze,Nt){}function ph(K,ne,Ne,Ze){for(var Nt=0,At=0;At<Ne;At++){for(var Ue=$e[ne+At*8>>2],Ge=$e[ne+(At*8+4)>>2],Zt=0;Zt<Ge;Zt++)Gi.printChar(K,Se[Ue+Zt]);Nt+=Ge}return $e[Ze>>2]=Nt,0}function In(){return 6}function fh(K){return $e[kh()>>2]=K,K}function r1(K){switch(K){case 30:return 16384;case 85:var ne=2147483648;return ne/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return fh(28),-1}var a1={a:is,d:e1,e:hh,f:dh,c:n1,b:ph,g:In,h:r1},s1=ra(),mh=s.___wasm_call_ctors=function(){return(mh=s.___wasm_call_ctors=s.asm.j).apply(null,arguments)},qi=s._init=function(){return(qi=s._init=s.asm.k).apply(null,arguments)},lu=s._register_tensor=function(){return(lu=s._register_tensor=s.asm.l).apply(null,arguments)},i1=s._dispose_data=function(){return(i1=s._dispose_data=s.asm.m).apply(null,arguments)},o1=s._dispose=function(){return(o1=s._dispose=s.asm.n).apply(null,arguments)},l1=s._Abs=function(){return(l1=s._Abs=s.asm.p).apply(null,arguments)},Ie=s._Add=function(){return(Ie=s._Add=s.asm.q).apply(null,arguments)},u1=s._AddN=function(){return(u1=s._AddN=s.asm.r).apply(null,arguments)},c1=s._All=function(){return(c1=s._All=s.asm.s).apply(null,arguments)},h1=s._Any=function(){return(h1=s._Any=s.asm.t).apply(null,arguments)},d1=s._ArgMax=function(){return(d1=s._ArgMax=s.asm.u).apply(null,arguments)},os=s._AvgPool=function(){return(os=s._AvgPool=s.asm.v).apply(null,arguments)},p1=s._BatchMatMul=function(){return(p1=s._BatchMatMul=s.asm.w).apply(null,arguments)},f1=s._Ceil=function(){return(f1=s._Ceil=s.asm.x).apply(null,arguments)},m1=s._ClipByValue=function(){return(m1=s._ClipByValue=s.asm.y).apply(null,arguments)},y1=s._Conv2D=function(){return(y1=s._Conv2D=s.asm.z).apply(null,arguments)},A1=s._Conv2DBackpropInput=function(){return(A1=s._Conv2DBackpropInput=s.asm.A).apply(null,arguments)},g1=s._Cos=function(){return(g1=s._Cos=s.asm.B).apply(null,arguments)},x1=s._CropAndResize=function(){return(x1=s._CropAndResize=s.asm.C).apply(null,arguments)},w1=s._Cumsum=function(){return(w1=s._Cumsum=s.asm.D).apply(null,arguments)},b1=s._DepthToSpace=function(){return(b1=s._DepthToSpace=s.asm.E).apply(null,arguments)},ka=s._DepthwiseConv2dNative=function(){return(ka=s._DepthwiseConv2dNative=s.asm.F).apply(null,arguments)},uu=s._Equal=function(){return(uu=s._Equal=s.asm.G).apply(null,arguments)},cu=s._Exp=function(){return(cu=s._Exp=s.asm.H).apply(null,arguments)},_1=s._FlipLeftRight=function(){return(_1=s._FlipLeftRight=s.asm.I).apply(null,arguments)},v1=s._Floor=function(){return(v1=s._Floor=s.asm.J).apply(null,arguments)},k1=s._FloorDiv=function(){return(k1=s._FloorDiv=s.asm.K).apply(null,arguments)},I1=s._FusedBatchNorm=function(){return(I1=s._FusedBatchNorm=s.asm.L).apply(null,arguments)},S1=s._FusedConv2D=function(){return(S1=s._FusedConv2D=s.asm.M).apply(null,arguments)},We=s._FusedDepthwiseConv2D=function(){return(We=s._FusedDepthwiseConv2D=s.asm.N).apply(null,arguments)},N1=s._Gather=function(){return(N1=s._Gather=s.asm.O).apply(null,arguments)},T1=s._GatherNd=function(){return(T1=s._GatherNd=s.asm.P).apply(null,arguments)},E1=s._Greater=function(){return(E1=s._Greater=s.asm.Q).apply(null,arguments)},C1=s._GreaterEqual=function(){return(C1=s._GreaterEqual=s.asm.R).apply(null,arguments)},R1=s._LeakyRelu=function(){return(R1=s._LeakyRelu=s.asm.S).apply(null,arguments)},M1=s._Less=function(){return(M1=s._Less=s.asm.T).apply(null,arguments)},hu=s._LessEqual=function(){return(hu=s._LessEqual=s.asm.U).apply(null,arguments)},yh=s._Log=function(){return(yh=s._Log=s.asm.V).apply(null,arguments)},Ah=s._LogicalAnd=function(){return(Ah=s._LogicalAnd=s.asm.W).apply(null,arguments)},F1=s._Max=function(){return(F1=s._Max=s.asm.X).apply(null,arguments)},$1=s._MaxPool=function(){return($1=s._MaxPool=s.asm.Y).apply(null,arguments)},D1=s._Maximum=function(){return(D1=s._Maximum=s.asm.Z).apply(null,arguments)},O1=s._Mean=function(){return(O1=s._Mean=s.asm._).apply(null,arguments)},z1=s._Min=function(){return(z1=s._Min=s.asm.$).apply(null,arguments)},P1=s._Minimum=function(){return(P1=s._Minimum=s.asm.aa).apply(null,arguments)},Qe=s._MirrorPad=function(){return(Qe=s._MirrorPad=s.asm.ba).apply(null,arguments)},L1=s._Multiply=function(){return(L1=s._Multiply=s.asm.ca).apply(null,arguments)},W1=s._Neg=function(){return(W1=s._Neg=s.asm.da).apply(null,arguments)},B1=s._NonMaxSuppressionV3=function(){return(B1=s._NonMaxSuppressionV3=s.asm.ea).apply(null,arguments)},Xi=s._NonMaxSuppressionV4=function(){return(Xi=s._NonMaxSuppressionV4=s.asm.fa).apply(null,arguments)},gh=s._NonMaxSuppressionV5=function(){return(gh=s._NonMaxSuppressionV5=s.asm.ga).apply(null,arguments)},xh=s._NotEqual=function(){return(xh=s._NotEqual=s.asm.ha).apply(null,arguments)},wh=s._OneHot=function(){return(wh=s._OneHot=s.asm.ia).apply(null,arguments)},V1=s._PadV2=function(){return(V1=s._PadV2=s.asm.ja).apply(null,arguments)},j1=s._Pow=function(){return(j1=s._Pow=s.asm.ka).apply(null,arguments)},bh=s._Prelu=function(){return(bh=s._Prelu=s.asm.la).apply(null,arguments)},U1=s._Prod=function(){return(U1=s._Prod=s.asm.ma).apply(null,arguments)},_h=s._RealDiv=function(){return(_h=s._RealDiv=s.asm.na).apply(null,arguments)},Ia=s._Relu=function(){return(Ia=s._Relu=s.asm.oa).apply(null,arguments)},H1=s._Relu6=function(){return(H1=s._Relu6=s.asm.pa).apply(null,arguments)},G1=s._ResizeBilinear=function(){return(G1=s._ResizeBilinear=s.asm.qa).apply(null,arguments)},Z2=s._Reverse=function(){return(Z2=s._Reverse=s.asm.ra).apply(null,arguments)},vh=s._RotateWithOffset=function(){return(vh=s._RotateWithOffset=s.asm.sa).apply(null,arguments)},q1=s._Round=function(){return(q1=s._Round=s.asm.ta).apply(null,arguments)},X1=s._Rsqrt=function(){return(X1=s._Rsqrt=s.asm.ua).apply(null,arguments)},K1=s._ScatterNd=function(){return(K1=s._ScatterNd=s.asm.va).apply(null,arguments)},Z1=s._SelectV2=function(){return(Z1=s._SelectV2=s.asm.wa).apply(null,arguments)},Y1=s._Sigmoid=function(){return(Y1=s._Sigmoid=s.asm.xa).apply(null,arguments)},J1=s._Sin=function(){return(J1=s._Sin=s.asm.ya).apply(null,arguments)},Q1=s._Softmax=function(){return(Q1=s._Softmax=s.asm.za).apply(null,arguments)},ef=s._Sqrt=function(){return(ef=s._Sqrt=s.asm.Aa).apply(null,arguments)},tf=s._Square=function(){return(tf=s._Square=s.asm.Ba).apply(null,arguments)},nf=s._SquaredDifference=function(){return(nf=s._SquaredDifference=s.asm.Ca).apply(null,arguments)},rf=s._Step=function(){return(rf=s._Step=s.asm.Da).apply(null,arguments)},af=s._StridedSlice=function(){return(af=s._StridedSlice=s.asm.Ea).apply(null,arguments)},sf=s._Sub=function(){return(sf=s._Sub=s.asm.Fa).apply(null,arguments)},of=s._Sum=function(){return(of=s._Sum=s.asm.Ga).apply(null,arguments)},lf=s._Tan=function(){return(lf=s._Tan=s.asm.Ha).apply(null,arguments)},uf=s._Tanh=function(){return(uf=s._Tanh=s.asm.Ia).apply(null,arguments)},cf=s._Tile=function(){return(cf=s._Tile=s.asm.Ja).apply(null,arguments)},hf=s._TopK=function(){return(hf=s._TopK=s.asm.Ka).apply(null,arguments)},df=s._Transpose=function(){return(df=s._Transpose=s.asm.La).apply(null,arguments)},pf=s.__FusedMatMul=function(){return(pf=s.__FusedMatMul=s.asm.Ma).apply(null,arguments)},ff=s._malloc=function(){return(ff=s._malloc=s.asm.Na).apply(null,arguments)},mf=s._free=function(){return(mf=s._free=s.asm.Oa).apply(null,arguments)},kh=s.___errno_location=function(){return(kh=s.___errno_location=s.asm.Pa).apply(null,arguments)},Ih=s.stackSave=function(){return(Ih=s.stackSave=s.asm.Qa).apply(null,arguments)},Sh=s.stackRestore=function(){return(Sh=s.stackRestore=s.asm.Ra).apply(null,arguments)},du=s.stackAlloc=function(){return(du=s.stackAlloc=s.asm.Sa).apply(null,arguments)};s.cwrap=Y;var Ki;function yf(K){this.name="ExitStatus",this.message="Program terminated with exit("+K+")",this.status=K}na=function K(){Ki||pu(),Ki||(na=K)};function pu(K){if(K=K||u,or>0||(Zn(),or>0))return;function ne(){Ki||(Ki=!0,s.calledRun=!0,!j&&(Dn(),on(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Kt()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ne()},1)):ne()}if(s.run=pu,s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();return pu(),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)}),N9=_t((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)}),T9=_t((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)}),E9=_t((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)}),C9=_t((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)}),R9=_t((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,y,A,g=[],x=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,x=Math.max(x,d.length)),m=0,y=-32;y<x;++y)d&&(f^=d.charCodeAt((y+32)%d.length)),y===0&&(A=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,y>=0&&(A=A+1640531527|0,p=g[y&127]^=f+A,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,y=4*128;y>0;--y)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=A,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)}),M9=_t((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)}),F9=_t((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(w,b,k){var N=[];b=b==!0?{entropy:!0}:b||{};var E=g(A(b.entropy?[w,v(r)]:w==null?x():w,3),N),F=new m(N),O=function(){for(var L=F.g(i),V=c,j=0;L<u;)L=(L+j)*s,V*=s,j=F.g(1);for(;L>=h;)L/=2,V/=2,j>>>=1;return(L+j)/V};return O.int32=function(){return F.g(4)|0},O.quick=function(){return F.g(4)/4294967296},O.double=O,g(v(F.S),r),(b.pass||k||function(L,V,j,U){return U&&(U.S&&y(U,F),L.state=function(){return y(F,{})}),j?(a[l]=L,V):L})(O,E,"global"in b?b.global:this==a,b.state)}function m(w){var b,k=w.length,N=this,E=0,F=N.i=N.j=0,O=N.S=[];for(k||(w=[k++]);E<s;)O[E]=E++;for(E=0;E<s;E++)O[E]=O[F=d&F+w[E%k]+(b=O[E])],O[F]=b;(N.g=function(L){for(var V,j=0,U=N.i,X=N.j,G=N.S;L--;)V=G[U=d&U+1],j=j*s+G[d&(G[U]=G[X=d&X+V])+(G[X]=V)];return N.i=U,N.j=X,j})(s)}function y(w,b){return b.i=w.i,b.j=w.j,b.S=w.S.slice(),b}function A(w,b){var k=[],N=typeof w,E;if(b&&N=="object")for(E in w)try{k.push(A(w[E],b-1))}catch(F){}return k.length?k:N=="string"?w:w+"\0"}function g(w,b){for(var k=w+"",N,E=0;E<k.length;)b[d&E]=d&(N^=b[d&E]*19)+k.charCodeAt(E++);return v(b)}function x(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(n.crypto||n.msCrypto).getRandomValues(w)),v(w)}catch(N){var b=n.navigator,k=b&&b.plugins;return[+new Date,n,k,n.screen,v(r)]}}function v(w){return String.fromCharCode.apply(0,w)}if(g(a.random(),r),typeof t=="object"&&t.exports){t.exports=f;try{p=c5()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return f}):a["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}),d5=_t((e,t)=>{var n=N9(),r=T9(),a=E9(),s=C9(),i=R9(),o=M9(),l=F9();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),$9=_t(()=>{}),bf={};Me(bf,{bin:()=>v5,browser:()=>E5,default:()=>D9,dependencies:()=>T5,description:()=>m5,devDependencies:()=>S5,jsdelivr:()=>x5,license:()=>I5,main:()=>A5,miniprogram:()=>_5,module:()=>g5,name:()=>p5,private:()=>y5,repository:()=>k5,scripts:()=>N5,types:()=>b5,unpkg:()=>w5,version:()=>f5});var p5="@tensorflow/tfjs",f5="3.4.0",m5="An open-source machine learning framework.",y5=!1,A5="dist/tf.node.js",g5="dist/index.js",x5="dist/tf.min.js",w5="dist/tf.min.js",b5="dist/index.d.ts",_5="dist/miniprogram",v5={"tfjs-custom-module":"dist/tools/custom_module/cli.js"},k5={type:"git",url:"https://github.com/tensorflow/tfjs.git"},I5="Apache-2.0",S5={"@babel/core":"^7.9.0","@babel/polyfill":"^7.10.4","@babel/preset-env":"^7.9.5","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@types/argparse":"^1.0.38","@types/jasmine":"2.8.7","@types/node":"~10.17.50","@types/shelljs":"^0.8.4","@types/yargs":"^15.0.7","clang-format":"~1.2.2",commander:"~2.14.1",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.2","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.5.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-babel":"^4.4.0","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~4.2.2",shelljs:"~0.8.1","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"1.0.0-pre.50"},N5={build:"tsc && yarn build-cli && yarn bundle","build-ci":"tsc && yarn build-cli && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-converter":"cd ../tfjs-converter && yarn && yarn build","build-converter-ci":"cd ../tfjs-converter && yarn && yarn build-ci","build-data":"cd ../tfjs-data && yarn && yarn build","build-data-ci":"cd ../tfjs-data && yarn && yarn build-ci","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-converter && yarn build-data && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-converter-ci && yarn build-data-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-cli":"tsc --project ./tools/custom_module/tsconfig.json && chmod +x ./dist/tools/custom_module/cli.js","run-custom-build":"ts-node -s ./tools/custom_module/cli.ts","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",test:"yarn && yarn build-deps && yarn build && karma start","test-dev":"karma start","test-tools":"ts-node --project ./tools/custom_module/tsconfig.json run_tools_tests.ts","test-ci":"./scripts/test-ci.sh"},T5={"@tensorflow/tfjs-backend-cpu":"3.4.0","@tensorflow/tfjs-backend-webgl":"3.4.0","@tensorflow/tfjs-converter":"3.4.0","@tensorflow/tfjs-core":"3.4.0","@tensorflow/tfjs-data":"3.4.0","@tensorflow/tfjs-layers":"3.4.0",argparse:"^1.0.10",chalk:"^4.1.0","core-js":"3","regenerator-runtime":"^0.13.5",yargs:"^16.0.3"},E5={"node-fetch":!1,util:!1,crypto:!1},D9={name:p5,version:f5,description:m5,private:y5,main:A5,module:g5,jsdelivr:x5,unpkg:w5,types:b5,miniprogram:_5,bin:v5,repository:k5,license:I5,devDependencies:S5,scripts:N5,dependencies:T5,browser:E5},_f={};Me(_f,{browser:()=>q5,default:()=>O9,dependencies:()=>G5,description:()=>M5,devDependencies:()=>U5,engines:()=>B5,jsdelivr:()=>D5,"jsnext:main":()=>P5,license:()=>j5,main:()=>$5,miniprogram:()=>W5,module:()=>L5,name:()=>C5,private:()=>F5,repository:()=>V5,scripts:()=>H5,sideEffects:()=>X5,types:()=>z5,unpkg:()=>O5,version:()=>R5});var C5="@tensorflow/tfjs-core",R5="3.4.0",M5="Hardware-accelerated JavaScript library for machine intelligence",F5=!1,$5="dist/tf-core.node.js",D5="dist/tf-core.min.js",O5="dist/tf-core.min.js",z5="dist/index.d.ts",P5="dist/index.js",L5="dist/index.js",W5="dist/miniprogram",B5={yarn:">= 1.3.2"},V5={type:"git",url:"https://github.com/tensorflow/tfjs-core.git"},j5="Apache-2.0",U5={"@bazel/bazelisk":"^1.3.0","@bazel/typescript":"^0.27.8","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"link:../tfjs-backend-cpu","@types/jasmine":"~3.0.0","@types/node":"~9.6.0","@types/node-fetch":"~2.1.2","clang-format":"~1.2.4",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"6.3.1","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-jasmine":"~1.1.0","karma-typescript":"~4.1.1","npm-run-all":"~4.1.3",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~5.3.0","rollup-plugin-visualizer":"~3.3.2",shelljs:"~0.8.3","ts-node":"~8.8.2",tslint:"~5.11.0","tslint-no-circular-imports":"~0.5.0",typescript:"3.5.3",yalc:"~1.0.0-pre.21",yargs:"~13.2.2"},H5={"build-ci":"./scripts/enumerate-tests.js --ci && tsc && yarn bundle-ci && yarn build-test-snippets",build:"node ./scripts/enumerate-tests.js && tsc && yarn bundle",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-npm":"./scripts/build-npm.sh","build-deps":"yarn build && yarn build-cpu-backend","build-cpu-backend":"cd ../tfjs-backend-cpu && yarn && yarn build","build-cpu-backend-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build:bazel":"bazelisk build //...","build-test-snippets":"yarn tsc --project ./scripts/test_snippets/tsconfig.json","format-all":"clang-format -i -style=Google --glob=src/**/*.ts","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build && rollup -c && yalc push","publish-npm":"npm publish",lint:"tslint -p . -t verbose",coverage:"KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-webworker":"karma start --worker","run-browserstack":"karma start --browserstack","test-bundle-size":"./scripts/test-bundle-size.js","test-node":"rimraf dist/ && yarn build-deps && yarn build && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-dev":"tsc && ts-node --transpile-only --skip-ignore -P tsconfig.test.json dist/test_node.js","test-node-ci":"ts-node --transpile-only -P tsconfig.test.json dist/test_node.js","test-async-backends":"rimraf dist/ && yarn build && ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-async-backends-ci":"ts-node --transpile-only -P tsconfig.test.json dist/test_async_backends.js","test-snippets":"yarn build && yarn build-cpu-backend && ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts","test-snippets-ci":"ts-node -P tsconfig.test.json ./scripts/test_snippets/test_snippets.ts"},G5={"@types/offscreencanvas":"~2019.3.0","@types/seedrandom":"2.4.27","@types/webgl-ext":"0.0.30","node-fetch":"~2.6.1",seedrandom:"2.4.3"},q5={"node-fetch":!1,util:!1,crypto:!1,worker_threads:!1},X5=["./dist/index.js","./dist/engine.js","./dist/tensor.js","./dist/base_side_effects.js","./dist/flags.js","./dist/platforms/*.js","./dist/register_all_gradients.js","./dist/public/chained_ops/*.js","./dist/io/*.js"],O9={name:C5,version:R5,description:M5,private:F5,main:$5,jsdelivr:D5,unpkg:O5,types:z5,"jsnext:main":P5,module:L5,miniprogram:W5,engines:B5,repository:V5,license:j5,devDependencies:U5,scripts:H5,dependencies:G5,browser:q5,sideEffects:X5},vf={};Me(vf,{browser:()=>hx,default:()=>z9,dependencies:()=>cx,description:()=>Y5,devDependencies:()=>ox,jsdelivr:()=>ex,"jsnext:main":()=>rx,license:()=>ix,main:()=>Q5,miniprogram:()=>sx,module:()=>ax,name:()=>K5,peerDependencies:()=>ux,private:()=>J5,scripts:()=>lx,types:()=>nx,unpkg:()=>tx,version:()=>Z5});var K5="@tensorflow/tfjs-data",Z5="3.4.0",Y5="TensorFlow Data API in JavaScript",J5=!1,Q5="dist/tf-data.node.js",ex="dist/tf-data.min.js",tx="dist/tf-data.min.js",nx="dist/index.d.ts",rx="dist/index.js",ax="dist/index.js",sx="dist/miniprogram",ix="Apache-2.0",ox={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.4.0","@tensorflow/tfjs-core":"3.4.0","@tensorflow/tfjs-layers":"3.4.0","@types/jasmine":"~2.5.53","@types/seedrandom":"^2.4.27","@types/utf8":"~2.1.6","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~7.0.0",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"^1.0.0-pre.50"},lx={build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-layers":"cd ../tfjs-layers && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-layers-ci":"cd ../tfjs-layers && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-layers && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-layers-ci && yarn build-backend-cpu-ci","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"rimraf dist/ && yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-dev":"tsc && ts-node --transpile-only --project tsconfig.test.json src/test_node.ts","test-browsers":"karma start --browsers='Chrome,Firefox'","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/test_node.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore --project tsconfig.test.json ./scripts/test_snippets.ts",lint:"tslint -p . -t verbose"},ux={"@tensorflow/tfjs-core":"3.4.0",seedrandom:"~2.4.3"},cx={"@types/node-fetch":"^2.1.2","node-fetch":"~2.6.1"},hx={fs:!1,"node-fetch":!1,string_decoder:!1,crypto:!1},z9={name:K5,version:Z5,description:Y5,private:J5,main:Q5,jsdelivr:ex,unpkg:tx,types:nx,"jsnext:main":rx,module:ax,miniprogram:sx,license:ix,devDependencies:ox,scripts:lx,peerDependencies:ux,dependencies:cx,browser:hx},kf={};Me(kf,{default:()=>P9,description:()=>fx,devDependencies:()=>kx,jsdelivr:()=>bx,"jsnext:main":()=>xx,license:()=>mx,main:()=>Ax,miniprogram:()=>vx,module:()=>wx,name:()=>dx,peerDependencies:()=>Sx,private:()=>yx,scripts:()=>Ix,types:()=>gx,unpkg:()=>_x,version:()=>px});var dx="@tensorflow/tfjs-layers",px="3.4.0",fx="TensorFlow layers API in JavaScript",mx="Apache-2.0 AND MIT",yx=!1,Ax="dist/tf-layers.node.js",gx="dist/index.d.ts",xx="dist/index.js",wx="dist/index.js",bx="dist/tf-layers.min.js",_x="dist/tf-layers.min.js",vx="dist/miniprogram",kx={"@babel/polyfill":"^7.8.7","@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.4.0","@tensorflow/tfjs-backend-webgl":"3.4.0","@tensorflow/tfjs-core":"3.4.0","@types/jasmine":"~2.5.53","clang-format":"~1.2.2","http-server":"~0.12.3",jasmine:"~3.1.0","jasmine-core":"~3.1.0",karma:"~6.3.1","karma-browserstack-launcher":"~1.6.0","karma-chrome-launcher":"~2.2.0","karma-firefox-launcher":"~1.1.0","karma-jasmine":"~1.1.1","karma-typescript":"~5.5.1","karma-typescript-es6-transform":"^5.0.2",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"^0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},Ix={prep:"yarn install && yarn build-ci",build:"tsc && yarn bundle","build-ci":"tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-backend-webgl":"cd ../tfjs-backend-webgl && yarn && yarn build","build-backend-webgl-ci":"cd ../tfjs-backend-webgl && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu && yarn build-backend-webgl","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu-ci && yarn build-backend-webgl-ci","build-npm":"./scripts/build-npm.sh",format:"./tools/clang_format_ts.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && karma start","test-dev":"karma start","test-ci":"./scripts/test-ci.sh","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts","run-browserstack":"karma start --browsers='bs_chrome_mac' --singleRun --reporters='dots,karma-typescript'",lint:"tslint -p . -t verbose"},Sx={"@tensorflow/tfjs-core":"3.4.0"},P9={name:dx,version:px,description:fx,license:mx,private:yx,main:Ax,types:gx,"jsnext:main":xx,module:wx,jsdelivr:bx,unpkg:_x,miniprogram:vx,devDependencies:kx,scripts:Ix,peerDependencies:Sx},If={};Me(If,{default:()=>L9,description:()=>Ex,devDependencies:()=>Wx,jsdelivr:()=>Dx,"jsnext:main":()=>Rx,license:()=>Px,main:()=>Cx,miniprogram:()=>Ox,module:()=>Mx,name:()=>Nx,peerDependencies:()=>Lx,repository:()=>zx,scripts:()=>Bx,types:()=>Fx,unpkg:()=>$x,version:()=>Tx});var Nx="@tensorflow/tfjs-converter",Tx="3.4.0",Ex="Tensorflow model converter for javascript",Cx="dist/tf-converter.node.js",Rx="dist/index.js",Mx="dist/index.js",Fx="dist/index.d.ts",$x="dist/tf-converter.min.js",Dx="dist/tf-converter.min.js",Ox="dist/miniprogram",zx={type:"git",url:"https://github.com/tensorflow/tfjs-converter.git"},Px="Apache-2.0",Lx={"@tensorflow/tfjs-core":"3.4.0"},Wx={"@rollup/plugin-commonjs":"^11.0.2","@rollup/plugin-node-resolve":"^7.1.1","@rollup/plugin-replace":"^2.3.3","@rollup/plugin-typescript":"^3.0.0","@tensorflow/tfjs-backend-cpu":"3.4.0","@tensorflow/tfjs-core":"3.4.0","@types/argparse":"^1.0.38","@types/deep-equal":"^1.0.1","@types/jasmine":"~2.8.6","@types/long":"~3.0.32","@types/node-fetch":"1.6.9",ajv:"~6.3.0",argparse:"^1.0.10","babel-core":"~6.26.3","babel-plugin-external-helpers":"~6.22.0","babel-preset-env":"~1.7.0","clang-format":"~1.2.2",copyfiles:"~1.2.0","deep-equal":"^1.0.1","jasmine-core":"~3.5.0","node-fetch":"~2.6.1",opn:"~5.1.0",protobufjs:"~6.8.6",rimraf:"~2.6.2",rollup:"~2.3.2","rollup-plugin-terser":"~7.0.2","rollup-plugin-visualizer":"~3.3.2","ts-morph":"^7.1.3","ts-node":"~8.8.2",tslint:"~6.1.3","tslint-no-circular-imports":"~0.7.0",typescript:"3.5.3",yalc:"~1.0.0-pre.50"},Bx={build:"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle","build-ci":"yarn gen-json --test && yarn gen-kernel2ops && tsc && yarn bundle-ci",bundle:"rollup -c","bundle-ci":"rollup -c --ci","build-core":"cd ../tfjs-core && yarn && yarn build","build-backend-cpu":"cd ../tfjs-backend-cpu && yarn && yarn build","build-backend-cpu-ci":"cd ../tfjs-backend-cpu && yarn && yarn build-ci","build-core-ci":"cd ../tfjs-core && yarn && yarn build-ci","build-deps":"yarn build-core && yarn build-backend-cpu","build-deps-ci":"yarn build-core-ci && yarn build-backend-cpu","build-npm":"./scripts/build-npm.sh","link-local":"yalc link","publish-local":"yarn build-npm && yalc push","publish-npm":"npm publish",test:"yarn && yarn build-deps && yarn build && yarn gen-json --test && yarn gen-kernel2ops && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-ci":"ts-node --transpile-only --skip-ignore -P tsconfig.test.json src/run_tests.ts","test-dev":"tsc && ts-node --transpile-only -P tsconfig.test.json src/run_tests.ts","test-snippets":"yarn && yarn build-deps && yarn build && ts-node --skip-ignore -s ./scripts/test_snippets.ts","test-snippets-ci":"ts-node --skip-ignore -s ./scripts/test_snippets.ts",lint:"tslint -p . -t verbose","make-version":"sh -c ./scripts/make-version","gen-doc":"ts-node -s ./scripts/gen_doc.ts","gen-json":"ts-node -s ./scripts/gen_json.ts","model-summary":"ts-node -s ./tools/model_summary.ts",pb2json:"ts-node -s ./tools/pb2json_converter.ts","build-pip-package":"yarn gen-json --test && cd python && ./build-pip-package.sh --test /tmp/tfjs-pips","run-python-tests":"yarn gen-json --test && cd python && ./run-python-tests.sh","gen-kernel2ops":"ts-node -s scripts/kernels_to_ops.ts --out metadata/kernel2op.json"},L9={name:Nx,version:Tx,description:Ex,main:Cx,"jsnext:main":Rx,module:Mx,types:Fx,unpkg:$x,jsdelivr:Dx,miniprogram:Ox,repository:zx,license:Px,peerDependencies:Lx,devDependencies:Wx,scripts:Bx},W9=1e-7,B9=1e-4,Rh=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}},wu=class{refCount(e){return cr("refCount")}incRef(e){return cr("incRef")}timerAvailable(){return!0}time(e){return cr("time")}read(e){return cr("read")}readSync(e){return cr("readSync")}numDataIds(){return cr("numDataIds")}disposeData(e,t){return cr("disposeData")}write(e,t,n){return cr("write")}move(e,t,n,r,a){return cr("move")}memory(){return cr("memory")}floatPrecision(){return cr("floatPrecision")}epsilon(){return this.floatPrecision()===32?W9:B9}dispose(){return cr("dispose")}};function cr(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 Vx(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 V9(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 bu(e,t,n){return Math.max(e,Math.min(t,n))}function j9(e){return e%2==0?e:e+1}function U9(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function H9(e,t){let n=Math.random();return t*n+(1-n)*e}function G9(e,t){let n=0;for(let r=0;r<e.length;r++){let a=Number(e[r])-Number(t[r]);n+=a*a}return n}function M(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function ln(e,t,n=""){M(oa(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function cs(e){M(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function hs(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||rn(e)&&!n)for(let r=0;r<e.length;++r)hs(e[r],t,n);else t.push(e);return t}function Et(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 q9(e){return e.length===0}function oa(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 Vt(e){return e%1==0}function X9(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 K9(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function Z9(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return Vx(t),t}function _u(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function Y9(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 J9(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} / ${n}`);let a=e.slice();return a[r]=t/n,a}function hr(e,t){let n=t.length;return e=e==null?t.map((r,a)=>a):[].concat(e),M(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),M(e.every(r=>Vt(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function jx(e,t){let n=[],r=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:hr(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 Ux(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 Hx(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 Gx(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 qx(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function Q9(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function rn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function Sf(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function Xx(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function Ta(e){return typeof e=="string"||e instanceof String}function Kx(e){return typeof e=="boolean"}function Zx(e){return typeof e=="number"}function Mh(e){return Array.isArray(e)?Mh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Zx(e)?"float32":Ta(e)?"string":Kx(e)?"bool":"float32"}function Ea(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Fh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function ro(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function Yx(e,t,n,r=!1){let a=new Array;if(t.length===1){let s=t[0]*(r?2:1);for(let i=0;i<s;i++)a[i]=n[e+i]}else{let s=t[0],i=t.slice(1),o=i.reduce((l,c)=>l*c)*(r?2:1);for(let l=0;l<s;l++)a[l]=Yx(e+l*o,i,n,r)}return a}function ao(e,t,n=!1){if(e.length===0)return t[0];let r=e.reduce((a,s)=>a*s)*(n?2:1);if(r===0)return[];if(r!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return Yx(0,e,t,n)}function Nf(e,t){let n=$h(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function $h(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 eI(e,t){let n=e.reduce((r,a)=>r*a,1);if(t==null||t==="float32")return ao(e,new Float32Array(n));if(t==="int32")return ao(e,new Int32Array(n));if(t==="bool")return ao(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function Tf(e){e.forEach(t=>{M(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function tI(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 nI(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 Ef(e){return e&&e.then&&typeof e.then=="function"}var Jx="tfjsflags",Qx=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=rI,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(Ef(t))throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[e]=t,this.flags[e]}getNumber(e){return this.get(e)}getBool(e){return this.get(e)}getFlags(){return this.flags}get features(){return this.flags}set(e,t){if(this.flagRegistry[e]==null)throw new Error(`Cannot set flag ${e} as it has not been registered.`);this.flags[e]=t,this.flagRegistry[e].setHook!=null&&this.flagRegistry[e].setHook(t)}evaluateFlag(e){if(this.flagRegistry[e]==null)throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);return this.flagRegistry[e].evaluationFn()}setFlags(e){this.flags=Object.assign({},e)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;let e=this.getQueryParams(this.global.location.search);Jx in e&&e[Jx].split(",").forEach(t=>{let[n,r]=t.split(":");this.urlFlags[n]=aI(n,r)})}};function rI(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(sI(t,r[0],r[1]),r.join("="))),t}function sI(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function aI(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 ${e}.`)}function J(){return _r}var _r=null;function iI(e){_r=e}var Cf;function ew(){if(Cf==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");Cf=e}return Cf}function oI(){let e=ew();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function Rf(e,t){let n=oI();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var so="Abs",io="Acos",oo="Acosh",Ca="Add",ds="AddN",lo="All",uo="Any",ps="ArgMax",vu="ArgMin",co="Asin",ho="Asinh",po="Atan",fo="Atanh",mo="Atan2",fs="AvgPool",Dh="AvgPoolGrad",ku="AvgPool3D",Oh="AvgPool3DGrad",ms="BatchMatMul",Iu="BatchToSpaceND",zh="Bincount",tw="BroadcastTo",ys="Cast",As="Ceil",Ra="ClipByValue",Ph="Complex",Su="ComplexAbs",yo="Concat",gs="Conv2D",Lh="Conv2DBackpropFilter",xs="Conv2DBackpropInput",Nu="Conv3D",Wh="Conv3DBackpropFilterV2",Bh="Conv3DBackpropInputV2",ws="Cos",Ao="Cosh",bs="Cumsum",go="CropAndResize",Vh="DenseBincount",xo="DepthToSpace",_s="DepthwiseConv2dNative",jh="DepthwiseConv2dNativeBackpropFilter",Uh="DepthwiseConv2dNativeBackpropInput",Hh="Diag",Tu="Dilation2D",Gh="Dilation2DBackpropInput",qh="Dilation2DBackpropFilter",vs="RealDiv",Xh="Einsum",wo="Elu",Kh="EluGrad",bo="Erf",_o="Equal",ks="Exp",vo="ExpandDims",ko="Expm1",Zh="FFT",Eu="Fill",Io="FlipLeftRight",Is="Floor",Ss="FloorDiv",Ns="FusedBatchNorm",So="GatherV2",No="GatherNd",To="Greater",Ts="GreaterEqual",Es="Identity",Yh="IFFT",Jh="Imag",Eo="IsFinite",Co="IsInf",Ro="IsNan",Cs="LeakyRelu",Mo="Less",Fo="LessEqual",Qh="LinSpace",Rs="Log",$o="Log1p",Do="LogicalAnd",Cu="LogicalNot",Ru="LogicalOr",nw="LogSoftmax",Mu="LRN",ed="LRNGrad",Ms="Max",Fs="Maximum",$s="MaxPool",td="MaxPoolGrad",Fu="MaxPool3D",nd="MaxPool3DGrad",rd="MaxPoolWithArgmax",Ds="Mean",Os="Min",zs="Minimum",Ps="MirrorPad",Oo="Mod",ad="Multinomial",Ls="Multiply",zo="Neg",Po="NotEqual",Lo="NonMaxSuppressionV3",Wo="NonMaxSuppressionV4",Bo="NonMaxSuppressionV5",Vo="OnesLike",Ws="OneHot",jo="Pack",Bs="PadV2",lI="Pool",Vs="Pow",js="Prelu",Uo="Prod",$u="Range",sd="Real",Ho="Reciprocal",Us="Relu",Go="Reshape",Du="ResizeNearestNeighbor",id="ResizeNearestNeighborGrad",Hs="ResizeBilinear",od="ResizeBilinearGrad",Gs="Relu6",qs="Reverse",Xs="Round",Ks="Rsqrt",qo="ScatterNd",Xo="Select",Ko="Selu",Zo="Slice",Zs="Sin",Yo="Sinh",Jo="Sign",Ys="Sigmoid",Qo="Softplus",Js="Sqrt",Qs="Sum",Ou="SpaceToBatchND",el="SplitV",ei="Softmax",ti="SquaredDifference",zu="Square",ni="Sub",ld="SparseToDense",tl="StridedSlice",ri="Tan",ai="Tanh",Ma="Tile",nl="TopK",ud="Transform",si="Transpose",cd="Unique",rl="Unpack",Pu="UnsortedSegmentSum",al="ZerosLike",Fa="Step",hd="FromPixels",sl="RotateWithOffset",ii="_FusedMatMul",oi="FusedConv2D",li="FusedDepthwiseConv2D",il=Rf("kernelRegistry",()=>new Map),Lu=Rf("gradRegistry",()=>new Map);function dd(e,t){let n=Mf(e,t);return il.get(n)}function Ff(e){return Lu.get(e)}function ol(e){let t=il.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 ui(e){let{kernelName:t,backendName:n}=e,r=Mf(t,n);il.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),il.set(r,e)}function rw(e){let{kernelName:t}=e;Lu.has(t)&&J().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Lu.set(t,e)}function uI(e,t){let n=Mf(e,t);if(!il.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);il.delete(n)}function cI(e){if(!Lu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Lu.delete(e)}function hI(e,t){ol(e).forEach(n=>{let r=Object.assign({},n,{backendName:t});ui(r)})}function Mf(e,t){return`${t}_${e}`}var _={};Me(_,{arraysEqual:()=>oa,assert:()=>M,assertNonNegativeIntegerDimensions:()=>Tf,assertNonNull:()=>cs,assertShapesMatch:()=>ln,bytesFromStringArray:()=>Xx,bytesPerElement:()=>Sf,checkConversionForErrors:()=>Gx,clamp:()=>bu,computeStrides:()=>ro,createScalarValue:()=>dI,createShuffledIndices:()=>Z9,decodeString:()=>fd,distSquared:()=>G9,encodeString:()=>Bu,fetch:()=>pI,flatten:()=>hs,getArrayFromDType:()=>Hx,getTypedArrayFromDType:()=>Ux,hasEncodingLoss:()=>Q9,indexToLoc:()=>nI,inferDtype:()=>Mh,inferFromImplicitShape:()=>J9,isBoolean:()=>Kx,isFunction:()=>Ea,isInt:()=>Vt,isNumber:()=>Zx,isPromise:()=>Ef,isScalarShape:()=>q9,isString:()=>Ta,isTypedArray:()=>rn,isValidDtype:()=>qx,locToIndex:()=>tI,makeOnesTypedArray:()=>Nf,makeZerosNestedTypedArray:()=>eI,makeZerosTypedArray:()=>$h,nearestDivisor:()=>Fh,nearestLargerEven:()=>j9,now:()=>Wu,parseAxisParam:()=>hr,randUniform:()=>H9,repeatedTry:()=>Y9,rightPad:()=>_u,shuffle:()=>Vx,shuffleCombo:()=>V9,sizeFromShape:()=>Et,sizeToSquarishShape:()=>K9,squeezeShape:()=>jx,sum:()=>U9,tanh:()=>X9,toNestedArray:()=>ao,toTypedArray:()=>pd});function dI(e,t){return t==="string"?Bu(e):pd([e],t)}function fI(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=hs(e)),J().getBool("DEBUG")&&Gx(e,t),fI(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 Wu(){return J().platform.now()}function pI(e,t){return J().platform.fetch(e,t)}function Bu(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 AI=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new yI)}profileKernel(e,t,n){let r,a=()=>{r=n()},s,i=Wu();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(a);else{a();for(let o of r)o.dataSync();s=Promise.resolve({kernelMs:Wu()-i})}if(J().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<r.length;o++){let l=r[o];l.data().then(c=>{mI(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 mI(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 yI=class{logKernelProfile(e,t,n,r,a,s){let i=typeof r=="number"?_u(`${r}ms`,9):r.error,o=_u(e,25),l=t.rank,c=t.size,u=_u(t.shape.toString(),14),h="";for(let d in a){let p=a[d];if(p!=null){let f=p.shape||t.shape,m=f.length;h+=`${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 gI(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 xI(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(!oa(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 aw=20,Vu=3,$f=7;function bI(e,t,n,r){let a=ro(t),s=wI(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 wI(e,t,n,r){let a=Et(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Uu(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],ju(l[u+h],0,n).length)}return i}function ju(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed($f))} + ${parseFloat(e[1].toFixed($f))}j`:Ta(e)?r=`'${e}'`:n==="bool"?r=sw(e):r=parseFloat(e.toFixed($f)).toString(),_u(r,t)}function sw(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=Uu(e);return[ju(m[0],0,n)]}return n==="bool"?[sw(e[0])]:[e[0].toString()]}if(l===1){if(o>aw){let y=Vu*i,A=Array.from(e.slice(0,y)),g=Array.from(e.slice((o-Vu)*i,o*i));return n==="complex64"&&(A=Uu(A),g=Uu(g)),["["+A.map((x,v)=>ju(x,a[v],n)).join(", ")+", ..., "+g.map((x,v)=>ju(x,a[o-Vu+v],n)).join(", ")+"]"]}let m=n==="complex64"?Uu(e):Array.from(e);return["["+m.map((y,A)=>ju(y,a[A],n)).join(", ")+"]"]}let c=t.slice(1),u=r.slice(1),h=r[0]*i,d=[];if(o>aw){for(let m=0;m<Vu;m++){let y=m*h,A=y+h;d.push(...md(e.slice(y,A),c,n,u,a,!1))}d.push("...");for(let m=o-Vu;m<o;m++){let y=m*h,A=y+h;d.push(...md(e.slice(y,A),c,n,u,a,m===o-1))}}else for(let m=0;m<o;m++){let y=m*h,A=y+h;d.push(...md(e.slice(y,A),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 Uu(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Dt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Et(e),n!=null){let r=n.length;M(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Hx(t,this.size),this.strides=ro(e)}set(e,...t){t.length===0&&(t=[0]),M(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Pr().makeTensor(this.values,this.shape,this.dtype)}},Pr=null,ll=null,_I=null;function vI(e){Pr=e}function kI(e){ll=e}function II(e){_I=e}var Pe=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Et(e),this.strides=ro(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 ll.buffer(this.shape,this.dtype,e)}bufferSync(){return ll.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ao(this.shape,e,this.dtype==="complex64")}arraySync(){return ao(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Pr().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=Pr().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 Pr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Pr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ll.print(this,e)}clone(){return this.throwIfDisposed(),ll.clone(this)}toString(e=!1){let t=this.dataSync();return bI(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ll.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Pr().makeVariable(this,e,t,n)}};Object.defineProperty(Pe,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return Rf("Tensor",()=>Pe)}Z();var Hu=class extends Pe{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(!oa(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Pr().disposeTensor(this),this.dataId=e.dataId,Pr().incRef(this,null)}dispose(){Pr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Hu,Symbol.hasInstance,{value:e=>e instanceof Pe&&e.assign!=null&&e.assign instanceof Function});var vr={};Me(vr,{assertTypesMatch:()=>iw,getTensorsInContainer:()=>Df,isTensorInList:()=>SI,makeTypesMatch:()=>vt});var Of;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Of||(Of={}));var zf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(zf||(zf={}));var Pf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Pf||(Pf={}));var Lf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Lf||(Lf={}));var Wf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Wf||(Wf={}));var NI={float32:Lf,int32:zf,bool:Pf,complex64:Wf};function dr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return NI[e][t]}function yd(e){return dr(e,"int32")}function vt(e,t){if(e.dtype===t.dtype)return[e,t];let n=dr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function iw(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function SI(e,t){return t.some(n=>n.id===e.id)}function Df(e){let t=[],n=new Set;return ow(e,t,n),t}function ow(e,t,n){if(e==null)return;if(e instanceof Pe){t.push(e);return}if(!TI(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),ow(s,t,n))}}function TI(e){return Array.isArray(e)||typeof e=="object"}function Bf(e){return e.kernelName!=null}var lw=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Gu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new lw}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 AI(this.backendInstance),!0}setupRegisteredKernels(){ol(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){ol(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 wu)&&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 Gu.nextTensorId++}nextVariableId(){return Gu.nextVariableId++}clone(e){let t=$.runKernel(Es,{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=Bf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Bf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let y=dd(p,this.backendName);M(y!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let A=this.backend.numDataIds();o=y.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,A,g);let x=g.map(v=>{if(v.rank!=null)return v;let{dataId:w,shape:b,dtype:k}=v;return this.makeTensorFromDataId(w,b,k)});if(r){let v=this.getTensorsForGradient(p,f,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(y=>this.keep(this.clone(y))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let y=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,y),y}}let{inputs:c,attrs:u}=e,h=Bf(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=Ff(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&Ta(e[0])&&(a=e.map(o=>Bu(o)));let s=r.write(a,t,n),i=new Pe(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Xx(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Pe(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 Hu(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*Sf(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 Hu||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*Sf(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=Ff(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let h=n[u],d=$h(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=Df(e),n=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!n.has(s.id)&&s.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(M(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));M(a instanceof Pe,()=>"The result y returned by f() must be a tensor.");let s=gI(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?EI(a.shape):n,xI(i,s,l=>this.tidy(l),CI);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return M(Ea(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{M(t.every(i=>i instanceof Pe),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((i,o)=>{r[o]=i});let a=(i,o)=>(n=e(...t,o),M(n.value instanceof Pe,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),M(Ea(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),c=Array.isArray(l)?l:[l];M(c.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),M(c.every(h=>h instanceof Pe),()=>"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=Wu(),n=await this.backend.time(e);return n.wallMs=Wu()-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 lw;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}};Gu.nextTensorId=0;Gu.nextVariableId=0;function EI(e){let t=Nf(Et(e),"float32");return $.makeTensor(t,e,"float32")}function uw(){let e=ew();if(e._tfengine==null){let t=new Qx(e);e._tfengine=new Gu(t)}return iI(e._tfengine.ENV),vI(()=>e._tfengine),e._tfengine}var $=uw();function CI(e,t){let n={a:e,b:t};return $.runKernel(Ca,n)}var qu={};Me(qu,{isBrowser:()=>cw,isMobile:()=>RI});function MI(){return typeof navigator!="undefined"&&navigator!=null}function RI(e){if(e||MI()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function cw(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var kr=J();kr.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.")});kr.registerFlag("IS_BROWSER",()=>cw());kr.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");kr.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));kr.registerFlag("PROD",()=>!1);kr.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>kr.getBool("DEBUG"));kr.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);kr.registerFlag("IS_TEST",()=>!1);kr.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);kr.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Lr(e,t){let n=e;if(rn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||rn(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&hw(e,r,[]),r}function hw(e,t,n){if(n=n||[],!Array.isArray(e)&&!rn(e)){M(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}M(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),M(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let a=0;a<e.length;++a)hw(e[a],r,n.concat(a))}function dw(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function R(e,t,n,r="numeric"){if(e instanceof Pe)return dw(r,e.dtype,t,n),e;let a=Mh(e);if(a!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(a=r),dw(r,a,t,n),e==null||!rn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Lr(e,a);!rn(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?pd(e,a):hs(e,[],!0);return $.makeTensor(i,s,a)}function Xu(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>R(a,`${t}[${s}]`,n,r))}var pw="__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 ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+pw;let a=(...s)=>{$.startScope(n);try{let i=r(...s);return Ef(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 FI(e,t){let n=R(e,"real","complex"),r=R(t,"imag","complex");ln(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(Ph,a)}var $a=D({complex_:FI});function Da(e,t,n,r){if(r==null&&(r=Mh(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!rn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Tf(t);let a=Et(t),s=Et(n);M(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==Et(t.slice(i)):!0;M(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!rn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?pd(e,r):hs(e,[],!0),$.makeTensor(e,t,r)}function Ir(e,t,n){let r=Lr(e,n);return Da(e,t,r,n)}var Vf={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},Ad=4;async function DI(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}': ${l.dtype}`);let c={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let u=new Promise(async h=>{let d=await l.bytes(),p=d.reduce((y,A)=>y+A.length,0)+Ad*d.length,f=new Uint8Array(p),m=0;for(let y=0;y<d.length;y++){let A=d[y],g=new Uint8Array(new Uint32Array([A.length]).buffer);f.set(g,m),m+=Ad,f.set(A,m),m+=A.length}h(f)});r.push(u)}else r.push(l.data());t!=null&&(c.group=t),n.push(c)}let s=await Promise.all(r);return{data:$I(s),specs:n}}function fw(e,t){let n={},r,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,c=Et(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=Vf[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 y=f[m];u[m]=y*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=OI()),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 ${h.dtype} for weight type int32.`);u=new Int32Array(f.length);for(let m=0;m<f.length;m++){let y=f[m];u[m]=Math.round(y*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*d}else if(o==="string"){let h=Et(s.shape);u=[];for(let d=0;d<h;d++){let p=new Uint32Array(e.slice(a,a+Ad))[0];a+=Ad;let f=new Uint8Array(e.slice(a,a+p));u.push(f),a+=p}}else{let h=Vf[o],d=e.slice(a,a+c*h);if(o==="float32")u=new Float32Array(d);else if(o==="int32")u=new Int32Array(d);else if(o==="bool")u=new Uint8Array(d);else if(o==="complex64"){u=new Float32Array(d);let p=new Float32Array(u.length/2),f=new Float32Array(u.length/2);for(let A=0;A<p.length;A++)p[A]=u[A*2],f[A]=u[A*2+1];let m=Ir(p,l,"float32"),y=Ir(f,l,"float32");n[i]=$a(m,y),m.dispose(),y.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*h}o!=="complex64"&&(n[i]=Ir(u,l,o))}return n}function $I(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 jf=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function mw(e){return jf?Buffer.byteLength(e):new Blob([e]).size}function zI(e){if(jf)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 PI(e){if(jf){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 Uf(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 yw(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 Ku(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:mw(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:mw(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function LI(){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 WI(){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 BI(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function OI(){let e=LI(),t=WI(),n=BI();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 Tt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Tt.instance==null&&(Tt.instance=new Tt),Tt.instance}static registerSaveRouter(e){Tt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Tt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Tt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Tt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?Tt.getInstance().loadRouters:Tt.getInstance().saveRouters).forEach(a=>{let s=a(e,n);s!==null&&r.push(s)}),r}},VI=e=>Tt.registerSaveRouter(e),jI=e=>Tt.registerLoadRouter(e),UI=e=>Tt.getSaveHandlers(e),HI=(e,t)=>Tt.getLoadHandlers(e,t),Hf="tensorflowjs",Gf=1,ci="models_store",Oa="model_info_store";function Aw(){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 qf(e){let t=e.result;t.createObjectStore(ci,{keyPath:"modelPath"}),t.createObjectStore(Oa,{keyPath:"modelPath"})}var hi=class{constructor(e){if(this.indexedDB=Aw(),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(Hf,Gf);a.onupgradeneeded=()=>qf(a),a.onsuccess=()=>{let s=a.result;if(t==null){let i=s.transaction(ci,"readonly"),o=i.objectStore(ci).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=Ku(t),o=s.transaction(Oa,"readwrite"),l=o.objectStore(Oa),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),u;c.onsuccess=()=>{u=s.transaction(ci,"readwrite");let h=u.objectStore(ci).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});h.onsuccess=()=>n({modelArtifactsInfo:i}),h.onerror=d=>{l=o.objectStore(Oa);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)})}};hi.URL_SCHEME="indexeddb://";var gw=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(hi.URL_SCHEME)?GI(e.slice(hi.URL_SCHEME.length)):null;Tt.registerSaveRouter(gw);Tt.registerLoadRouter(gw);function GI(e){return new hi(e)}function qI(e){return e.startsWith(hi.URL_SCHEME)?e.slice(hi.URL_SCHEME.length):e}var XI=class{constructor(){this.indexedDB=Aw()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(Hf,Gf);n.onupgradeneeded=()=>qf(n),n.onsuccess=()=>{let r=n.result,a=r.transaction(Oa,"readonly"),s=a.objectStore(Oa).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=qI(e),new Promise((t,n)=>{let r=this.indexedDB.open(Hf,Gf);r.onupgradeneeded=()=>qf(r),r.onsuccess=()=>{let a=r.result,s=a.transaction(Oa,"readwrite"),i=s.objectStore(Oa),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(ci,"readwrite");let h=l.objectStore(ci).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)})}},la="/",ul="tensorflowjs_models",xw="info",KI="model_topology",ZI="weight_specs",YI="weight_data",JI="model_metadata";function ww(e){return{info:[ul,e,xw].join(la),topology:[ul,e,KI].join(la),weightSpecs:[ul,e,ZI].join(la),weightData:[ul,e,YI].join(la),modelMetadata:[ul,e,JI].join(la)}}function QI(e){let t=e.split(la);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(la)}function eS(e){return e.startsWith(di.URL_SCHEME)?e.slice(di.URL_SCHEME.length):e}var di=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=ww(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=Ku(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,zI(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:r}}catch(a){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=PI(s),t}};di.URL_SCHEME="localstorage://";var bw=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(di.URL_SCHEME)?tS(e.slice(di.URL_SCHEME.length)):null;Tt.registerSaveRouter(bw);Tt.registerLoadRouter(bw);function tS(e){return new di(e)}var nS=class{constructor(){M(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),M(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=ul+la,n=la+xw;for(let r=0;r<this.LS.length;++r){let a=this.LS.key(r);if(a.startsWith(t)&&a.endsWith(n)){let s=QI(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=eS(e);let t=ww(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${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}},cl="://",Qn=class{constructor(){this.managers={}}static getInstance(){return Qn.instance==null&&(Qn.instance=new Qn),Qn.instance}static registerManager(e,t){M(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(cl)&&(e=e.slice(0,e.indexOf(cl))),M(e.length>0,()=>"scheme must not be an empty string.");let n=Qn.getInstance();M(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function gd(e){if(e.indexOf(cl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Qn.getSchemes().join(",")}`);return{scheme:e.split(cl)[0],path:e.split(cl)[1]}}async function _w(e,t,n=!1){M(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=Tt.getLoadHandlers(e);M(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),M(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let a=r[0],s=Tt.getSaveHandlers(t);M(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),M(s.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let i=s[0],o=gd(e).scheme,l=gd(e).path,c=o===gd(e).scheme,u=await a.load();n&&c&&await Qn.getManager(o).removeModel(l);let h=await i.save(u);return n&&!c&&await Qn.getManager(o).removeModel(l),h.modelArtifactsInfo}async function rS(){let e=Qn.getSchemes(),t={};for(let n of e){let r=await Qn.getManager(n).listModels();for(let a in r){let s=n+cl+a;t[s]=r[a]}}return t}async function aS(e){let t=gd(e);return Qn.getManager(t.scheme).removeModel(t.path)}async function sS(e,t){return _w(e,t,!1)}async function iS(e,t){return _w(e,t,!0)}var oS=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 oS);try{Qn.registerManager(di.URL_SCHEME,new nS)}catch(e){}try{Qn.registerManager(hi.URL_SCHEME,new XI)}catch(e){}}var lS={importFetch:()=>m9()},Xf,uS=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):(Xf==null&&(Xf=lS.importFetch()),Xf(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 uS);function Be(e,t="float32",n){return t=t||"float32",Tf(e),new Dt(e,t,n)}function cS(e,t){let n=R(e,"x","cast");if(!qx(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_:cS});function hS(e){let t={x:R(e,"x","clone","string_or_numeric")};return $.runKernel(Es,t)}var Wr=D({clone_:hS});function vw(e,t=!1){console.log(e.toString(t))}uw();var dS={buffer:Be,cast:ge,clone:Wr,print:vw};kI(dS);var Nn={};Me(Nn,{browserFiles:()=>pS,browserHTTPRequest:()=>mS,concatenateArrayBuffers:()=>Uf,copyModel:()=>sS,decodeWeights:()=>fw,encodeWeights:()=>DI,fromMemory:()=>yS,getLoadHandlers:()=>HI,getModelArtifactsInfoForJSON:()=>Ku,getSaveHandlers:()=>UI,http:()=>Zf,isHTTPScheme:()=>Kf,listModels:()=>rS,loadWeights:()=>fS,moveModel:()=>iS,registerLoadRouter:()=>jI,registerSaveRouter:()=>VI,removeModel:()=>aS,weightsLoaderFactory:()=>kw,withSaveHandler:()=>AS});var gS="model",xS=".json",wS=".weights.bin";function Iw(e){return new Promise(t=>setTimeout(t)).then(e)}var hl=class{constructor(e){if(!J().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(hl.URL_SCHEME)&&(e=e.slice(hl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=gS),this.modelTopologyFileName=e+xS,this.weightDataFileName=e+wS}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 Iw(()=>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 Iw(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ku(e)}}}};hl.URL_SCHEME="downloads://";var bS=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 ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let u=[],h=[],d=[];l.forEach(p=>{p.paths.forEach(f=>{h.push(f),d.push(null)}),u.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(f=>{let m=new FileReader;m.onload=y=>{let A=y.target.result,g=h.indexOf(f);if(d[g]=A,d.indexOf(null)===-1){let x={modelTopology:o,weightSpecs:u,weightData:Uf(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(x.signature=i.signature),i.userDefinedMetadata!=null&&(x.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(x.modelInitializer=i.modelInitializer),n(x)}},m.onerror=y=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(c[f])})})},a.onerror=s=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),a.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(s=>yw(s.name)),a={};for(let s of e)s.paths.forEach(i=>{let o=yw(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${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}},vS=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(hl.URL_SCHEME)?_S(e.slice(hl.URL_SCHEME.length)):null;Tt.registerSaveRouter(vS);function _S(e="model"){return new hl(e)}function pS(e){return new bS(e)}function Sw(e,t,n,r){i(e),n=n==null?0:n,r=r==null?1:r,o(n,r);let a=0,s=l=>(l.then(c=>{let u=n+ ++a/e.length*(r-n);return t(u),c}),l);function i(l){M(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,c){M(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),M(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),M(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(s))}async function Nw(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 Sw(r,t.onProgress,a,s)).map(c=>c.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await Sw(i,t.onProgress,o,l)}async function fS(e,t="",n,r){return kw(a=>Nw(a,{requestInit:r}))(e,t,n)}function kw(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(y=>{let A="quantization"in y?y.quantization.dtype:y.dtype,g=Vf[A]*Et(y.shape),x=()=>{a[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:y,groupOffset:m,sizeBytes:g})};r!=null?r.forEach((v,w)=>{v===y.name&&(x(),i[w]=!0)}):x(),o.push(y.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 x=0;x<f;x++)m+=u[d+x].byteLength;let y=new ArrayBuffer(m),A=new Uint8Array(y),g=0;for(let x=0;x<f;x++){let v=new Uint8Array(u[d+x]);A.set(v,g),g+=v.byteLength}s[p].forEach(x=>{let v=y.slice(x.groupOffset,x.groupOffset+x.sizeBytes),w=fw(v,[x.manifestEntry]);for(let b in w)h[b]=w[b]}),d+=f}),h}}var kS="application/octet-stream",IS="application/json",Yf=class{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(M(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=J().platform.fetch,M(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&M(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:IS}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:kS}),"model.weights.bin");let a=await this.fetch(this.path,t);if(a.ok)return{modelArtifactsInfo:Ku(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]=SS(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 Nw(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,Uf(l)]}};Yf.URL_SCHEME_REGEX=/^https?:\/\//;function SS(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),a=n>t?e.substring(n):"";return[r+"/",a]}function Kf(e){return e.match(Yf.URL_SCHEME_REGEX)!=null}var Tw=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>Kf(r)):n=Kf(e),n)return Zf(e,t)}return null};Tt.registerSaveRouter(Tw);Tt.registerLoadRouter(Tw);function Zf(e,t){return new Yf(e,t)}function mS(e,t){return Zf(e,t)}var Jf=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},NS=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function yS(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new Jf(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 Jf({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 Jf({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function AS(e){return new NS(e)}var Ew={};Me(Ew,{confusionMatrix:()=>TS});function ES(e,t,n=!1,r=!1){let a=R(e,"a","matMul"),s=R(t,"b","matMul");[a,s]=vt(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return $.runKernel(ms,i,o)}var Ve=D({matMul_:ES});function CS(e,t,n=1,r=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:R(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:r};return $.runKernel(Ws,a,s)}var dl=D({oneHot_:CS});function RS(e,t){let n=R(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{M(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let r={x:n},a={perm:t};return $.runKernel(si,r,a)}var Je=D({transpose_:RS});function MS(e,t,n){let r=R(e,"labels","confusionMatrix"),a=R(t,"predictions","confusionMatrix");M(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),M(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),M(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),M(r.shape[0]===a.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${a.shape[0]}. Labels and predictions should have the same number of elements.`),M(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=dl(ge(r,"int32"),n),i=dl(ge(a,"int32"),n),o=Je(s),l=Ve(o,i);return ge(l,"int32")}var TS=D({confusionMatrix_:MS}),pi={};Me(pi,{fromPixels:()=>DS,fromPixelsAsync:()=>FS,toPixels:()=>$S});function xd(e,t,n){if(cs(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=Lr(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 Da(e,t,r,n)}var pl;function Cw(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)&&(pl==null&&(pl=document.createElement("canvas").getContext("2d")),pl.canvas.width=l,pl.canvas.height=c,pl.drawImage(e,0,0,l,c),u=pl.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 OS(e){return e!=null&&e.data instanceof Uint8Array}function zS(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function PS(e){return e!=null&&e.width!==0&&e.height!==0}function LS(e){return zS()&&!(e instanceof ImageBitmap)&&PS(e)&&!OS(e)}async function FS(e,t=3){let n=null;if(J().getBool("WRAP_TO_IMAGEBITMAP")&&LS(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 Cw(n,t)}async function $S(e,t){let n=R(e,"img","toPixels");if(!(e instanceof Pe)){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 DS=D({fromPixels_:Cw}),Qf={};Me(Qf,{prepareAndValidate:()=>Rw});function Rw(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(Et(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=[...ro(e.shape).map(h=>h/c),1].slice(0,s);return[l,i,c,u]}var em={};Me(em,{calculateShapes:()=>Mw,validateInput:()=>nm,validateUpdateShape:()=>tm});function tm(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 nm(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: ${e.shape}`)}tm(n,t,e)}function Mw(e,t,n){let r=t.shape.length,a=r>1?t.shape[r-1]:1,s=n.length,i=1;for(let h=a;h<s;++h)i*=n[h];let o=a<1?1:a,l=Et(t.shape)/o,c=[...ro(n.slice(0,a)),1],u=Et(n);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:c,outputSize:u}}var un={};Me(un,{assertParamsValid:()=>WS,computeFlatOffset:()=>VS,computeOutShape:()=>Fw,getNormalizedAxes:()=>Dw,isSliceContinous:()=>BS,maskToAxes:()=>wd,parseSliceParams:()=>Bw,sliceInfo:()=>jS,startForAxis:()=>Lw,startIndicesWithElidedDims:()=>Ow,stopForAxis:()=>Ww,stopIndicesWithElidedDims:()=>zw,stridesForAxis:()=>Pw,stridesWithElidedDims:()=>$w});function WS(e,t,n){let r=e.shape.length;M(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),M(r===n.length,()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`);for(let a=0;a<r;++a)M(t[a]+n[a]<=e.shape[a],()=>`Error in slice${r}D: begin[${a}] + size[${a}] (${t[a]+n[a]}) would overflow input.shape[${a}] (${e.shape[a]})`)}function wd(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function Fw(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 $w(e,t,n,r){let a=[...e];for(let s=a.length;s<r.length;s++)a.push(1);for(let s=0;s<n;s++)s===0?a[t]=1:(a.splice(t,0,1),a.pop());return a}function Vw(e,t,n){return n<=e?n:n-(t-1)}function jw(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function Dw(e,t,n,r,a,s,i,o,l){let c=e.length,u=new Array(c),h=new Array(c),d=new Array(c);if(t.length&&n>0){let p=t[0],f=n+1;u=Ow(i,p,f,r,e),h=zw(o,p,f,a,e),d=$w(s,p,f,e)}else for(let p=0;p<c;p++)u[p]=Lw(i,r,s,e,p,l),h[p]=Ww(o,a,s,e,p,l),d[p]=Pw(s,p,l);return{begin:u,end:h,strides:d}}function Ow(e,t,n,r,a){let s=[...a],i=jw(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=Vw(t,n,o),c=r[l];e&1<<l&&(c=0),s[o]=c}return s}function zw(e,t,n,r,a){let s=[...a],i=jw(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=Vw(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]=bu(0,s[o],a[o])}return s}function Pw(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function Lw(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=bu(0,i,l-1),i}function Ww(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=bu(0,i,l):i=bu(-1,i,l-1),i}function BS(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 VS(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 Bw(e,t,n){let r,a=e.shape.length;typeof t=="number"?r=[t,...new Array(a-1).fill(0)]:t.length<a?r=t.concat(new Array(a-t.length).fill(0)):r=t.slice(),r.forEach(i=>{M(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(a).fill(-1):typeof n=="number"?s=[n,...new Array(a-1).fill(-1)]:n.length<a?s=n.concat(new Array(a-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(M(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-r[o])),[r,s]}function jS(e,t,n,r,a,s,i,o,l){let c=t.slice(),u=n.slice(),h=r;r==null&&(h=new Array(c.length));let d=wd(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-c.length,f=wd(o),m=e.slice();f.forEach(b=>{c[b]=0,u[b]=1,m.splice(b,0,1)});let{begin:y,end:A,strides:g}=Dw(m,d,p,c,u,h,a,s,i);c=y,u=A,h=g;let x=wd(l);x.forEach(b=>{u[b]=c[b]+1,h[b]=1});let v=Fw(c,u,h),w=v.filter((b,k)=>x.indexOf(k)===-1);return{nonStrided:h.every(b=>b===1),$begin:c,$end:u,$strides:h,size:v,newShape:m,outShape:w}}var re={};Me(re,{Serializable:()=>Uw,SerializationMap:()=>fi,registerClass:()=>za});var Uw=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},fi=class{constructor(){this.classNameMap={}}static getMap(){return fi.instance==null&&(fi.instance=new fi),fi.instance}static register(e){fi.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function za(e){M(e.className!=null,()=>"Class being registered does not have the static className property defined."),M(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),M(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),fi.register(e)}var Hw={};Me(Hw,{TEST_EPSILON_FLOAT16:()=>Gw,encodeStrings:()=>qw,expectArrayBuffersEqual:()=>KS,expectArraysClose:()=>US,expectArraysEqual:()=>GS,expectNumbersClose:()=>qS,expectPromiseToFail:()=>HS,expectValuesInRange:()=>XS,testEpsilon:()=>rm});var ZS=.001,Gw=.1;function US(e,t,n){return n==null&&(n=rm()),am(e,t,(r,a)=>sm(r,a,n))}function rm(){return $.backend.floatPrecision()===32?ZS:Gw}function am(e,t,n){let r=!0;if((rn(e)||rn(t))&&(r=!1),rn(e)&&rn(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: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Lr(e),o=Lr(t);if(!oa(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let a=rn(e)?e:hs(e),s=rn(t)?t:hs(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 HS(e,t){e().then(()=>t.fail(),()=>t())}function GS(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Ta(e)||Ta(e[0])||Ta(t)||Ta(t[0])?am(e,n,(r,a)=>r==a):am(e,t,(r,a)=>sm(r,a,0))}function qS(e,t,n){if(n==null&&(n=rm()),!sm(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function sm(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function XS(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function KS(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function qw(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?qw(n):e[t]=Bu(n)}return e}var YS="3.4.0";function JS(){J().set("PROD",!0)}function QS(){J().set("DEBUG",!0)}function eN(){J().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function im(e){J().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}II(im);function tN(){$.disposeVariables()}function ua(){return $}function bd(){return $.memory()}function an(e){return $.profile(e)}function z(e,t){return $.tidy(e,t)}function _e(e){Df(e).forEach(t=>t.dispose())}function jt(e){return $.keep(e)}function nN(e){return $.time(e)}function rN(e){return $.setBackend(e)}function aN(){return $.ready()}function sN(){return $.backendName}function iN(e){$.removeBackend(e)}function om(e){return $.findBackend(e)}function oN(e){return $.findBackendFactory(e)}function fl(e,t,n=1){return $.registerBackend(e,t,n)}function Xw(){return $.backend}function lN(e,t){J().setPlatform(e,t)}function uN(e,t){let n=R(e,"a","add"),r=R(t,"b","add");[n,r]=vt(n,r);let a={a:n,b:r};return $.runKernel(Ca,a)}var se=D({add_:uN});function cN(e,t){let n=R(e,"a","floorDiv"),r=R(t,"b","floorDiv");[n,r]=vt(n,r);let a={a:n,b:r};return $.runKernel(Ss,a)}var _d=D({floorDiv_:cN});function hN(e,t){let n=R(e,"a","div"),r=R(t,"b","div");if([n,r]=vt(n,r),n.dtype==="int32"&&r.dtype==="int32")return _d(n,r);let a={a:n,b:r},s={};return $.runKernel(vs,a,s)}var me=D({div_:hN});function dN(e,t){let n=R(e,"a","mul"),r=R(t,"b","mul");[n,r]=vt(n,r);let a={a:n,b:r};return $.runKernel(Ls,a)}var P=D({mul_:dN});function pN(e){let t=R(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return $.runKernel(Su,n)}else{let n={x:t};return $.runKernel(so,n)}}var Ot=D({abs_:pN});function fN(e){let t={x:R(e,"x","acos")};return $.runKernel(io,t)}var lm=D({acos_:fN});function mN(e){let t={x:R(e,"x","acosh")};return $.runKernel(oo,t)}var um=D({acosh_:mN});function yN(e){M(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),M(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>R(a,`tensors${s}`,"addN")),n=t[0];t.forEach(a=>{if(a.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(a=>{if(!oa(a.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return $.runKernel(ds,r)}var Pa=D({addN_:yN});function AN(e,t=null,n=!1){let r={x:R(e,"x","all","bool")},a={axis:t,keepDims:n};return $.runKernel(lo,r,a)}var vd=D({all_:AN});function gN(e,t=null,n=!1){let r={x:R(e,"x","any","bool")},a={axis:t,keepDims:n};return $.runKernel(uo,r,a)}var Zu=D({any_:gN});function xN(e,t=0){let n={x:R(e,"x","argMax")},r={axis:t};return $.runKernel(ps,n,r)}var mi=D({argMax_:xN});function wN(e,t=0){let n={x:R(e,"x","argMin")},r={axis:t};return $.runKernel(vu,n,r)}var cm=D({argMin_:wN});function bN(e){let t={x:R(e,"x","asin")};return $.runKernel(co,t)}var hm=D({asin_:bN});function _N(e){let t={x:R(e,"x","asinh")};return $.runKernel(ho,t)}var dm=D({asinh_:_N});function vN(e){let t={x:R(e,"x","atan")};return $.runKernel(po,t)}var pm=D({atan_:vN});function kN(e,t){let n=R(e,"a","atan2"),r=R(t,"b","atan2");[n,r]=vt(n,r);let a={a:n,b:r};return $.runKernel(mo,a)}var fm=D({atan2_:kN});function IN(e){let t={x:R(e,"x","atanh")};return $.runKernel(fo,t)}var mm=D({atanh_:IN});function SN(e,t,n,r,a="NHWC",s){let i=e[3],o=[...t,i],l=Kw(a);return Yu(e,o,n,s,r,null,null,l)}function Zw(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 Yu(e,c,n,r,a,s,!1,i)}function NN(e,t,n,r,a,s,i="NDHWC"){let[o,l,c]=ym(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 Yw(e,u,n,r,a,!1,h,s)}function Yu(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,y]=kd(n),[A,g]=kd(r),x=ml(d,A),v=ml(p,g),{padInfo:w,outHeight:b,outWidth:k}=TN(a,c,u,m,y,x,v,s,o),N=i?f*h:f,E;return o==="channelsFirst"?E=[l,N,b,k]:o==="channelsLast"&&(E=[l,b,k,N]),{batchSize:l,dataFormat:o,inHeight:c,inWidth:u,inChannels:h,outHeight:b,outWidth:k,outChannels:N,padInfo:w,strideHeight:m,strideWidth:y,filterHeight:d,filterWidth:p,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:A,dilationWidth:g,inShape:e,outShape:E,filterShape:t}}function Yw(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,,y]=t,[A,g,x]=ym(n),[v,w,b]=ym(r),k=ml(p,v),N=ml(f,w),E=ml(m,b),{padInfo:F,outDepth:O,outHeight:L,outWidth:V}=EN(a,c,u,h,A,g,x,k,N,E,o),j=s?y*d:y,U;return i==="channelsFirst"?U=[l,j,O,L,V]:i==="channelsLast"&&(U=[l,O,L,V,j]),{batchSize:l,dataFormat:i,inDepth:c,inHeight:u,inWidth:h,inChannels:d,outDepth:O,outHeight:L,outWidth:V,outChannels:j,padInfo:F,strideDepth:A,strideHeight:g,strideWidth:x,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:k,effectiveFilterHeight:N,effectiveFilterWidth:E,dilationDepth:v,dilationHeight:w,dilationWidth:b,inShape:e,outShape:U,filterShape:t}}function CN(e,t,n,r,a){r==null&&(r=Am(e,t,n));let s=e[0],i=e[1],o=yi((s-t+2*r)/n+1,a),l=yi((i-t+2*r)/n+1,a);return[o,l]}function RN(e,t,n,r,a,s){a==null&&(a=Am(e,t,r));let i=e[0],o=e[1],l=e[2],c=yi((i-t+2*a)/r+1,s),u=yi((o-t+2*a)/r+1,s),h=yi((l-t+2*a)/r+1,s);return[c,u,h,n]}function Am(e,t,n,r=1){let a=ml(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 ym(e){return typeof e=="number"?[e,e,e]:e}function ml(e,t){return t<=1?e:e+(e-1)*(t-1)}function TN(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=CN([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,y=Math.floor(p/2),A=p-y;c={top:f,bottom:m,left:y,right:A,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=yi((t-s+d+p)/r+1,o),h=yi((n-i+f+m)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:h}}function EN(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=RN([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,y=(p-1)*s+l-n,A=(f-1)*i+c-r,g=Math.floor(m/2),x=m-g,v=Math.floor(y/2),w=y-v,b=Math.floor(A/2),k=A-b;h={top:v,bottom:w,left:b,right:k,front:g,back:x,type:"SAME"}}else if(e==="valid")h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/a),p=Math.ceil((n-l+1)/s),f=Math.ceil((r-c+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:d,outHeight:p,outWidth:f}}function yi(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 La(e){let[t,n,r]=kd(e);return t===1&&n===1&&r===1}function Br(e,t){return La(e)||La(t)}function Kw(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function MN(e,t){let n={x:R(e,"x","reshape","string_or_numeric")},r={shape:t};return $.runKernel(Go,n,r)}var H=D({reshape_:MN});function FN(e,t,n,r,a){let s=R(e,"x","avgPool","float32"),i=1;M(Br(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),M(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&M(Vt(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(fs,c,u);return h=ge(h,s.dtype),l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Ju=D({avgPool_:FN});function $N(e,t,n,r,a,s="NDHWC"){let i=R(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),M(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&M(Vt(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(ku,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 gm=D({avgPool3d_:$N});function DN(e,t=0){M(e.length>=1,()=>"Pass at least one tensor to concat");let n=Xu(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 Wr(n[0]);let r=n,a={axis:t};return $.runKernel(yo,r,a)}var rt=D({concat_:DN});function ON(e){let t={x:R(e,"x","sigmoid")};return $.runKernel(Ys,t)}var On=D({sigmoid_:ON});function zN(e,t,n){let r=R(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return $.runKernel(Zo,a,s)}var Ce=D({slice_:zN});function PN(e){let t={x:R(e,"x","tanh")};return $.runKernel(ai,t)}var yl=D({tanh_:PN});function LN(e,t,n,r,a,s){let i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(n,"lstmBias","basicLSTMCell"),c=R(r,"data","basicLSTMCell"),u=R(a,"c","basicLSTMCell"),h=R(s,"h","basicLSTMCell"),d=rt([c,h],1),p=Ve(d,o),f=se(p,l),m=f.shape[0],y=f.shape[1]/4,A=[m,y],g=Ce(f,[0,0],A),x=Ce(f,[0,y],A),v=Ce(f,[0,y*2],A),w=Ce(f,[0,y*3],A),b=se(P(On(g),yl(x)),P(u,On(se(i,v)))),k=P(yl(b),On(w));return[b,k]}var WN=D({basicLSTMCell_:LN});function BN(e,t,n){let r=R(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);M(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),M(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),M(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return $.runKernel(Iu,s,i)}var Qu=D({batchToSpaceND_:BN});function VN(e){let t;return e.rank===0||e.rank===1?t=H(e,[1,1,1,e.size]):e.rank===2?t=H(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function jN(e,t,n,r,a,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;r!=null&&(u=R(r,"offset","batchNorm")),M(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),M(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),M(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:VN(i),scale:c,offset:u,mean:o,variance:l},d={varianceEpsilon:s},p=$.runKernel(Ns,h,d);return H(p,i.shape)}var Ai=D({batchNorm_:jN});function UN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),M(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),M(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var Jw=D({batchNorm2d_:UN});function HN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),M(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var Qw=D({batchNorm3d_:HN});function GN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),M(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var eb=D({batchNorm4d_:GN});function qN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");M(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return $.runKernel(zh,s,i)}var tb=D({bincount_:qN});function XN(e,t){let n=R(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=H(n,l)}let a=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return Wr(n);let i={x:n},o={reps:s};return $.runKernel(Ma,i,o)}var Al=D({broadcastTo_:XN});function KN(e){let t={x:R(e,"x","ceil")};return $.runKernel(As,t)}var xm=D({ceil_:KN});function ZN(e,t,n){let r=R(e,"x","clipByValue");M(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Ra,a,s)}var Tn=D({clipByValue_:ZN});function YN(e){return rt(e,0)}var nb=D({concat1d_:YN});function JN(e,t){return rt(e,t)}var gl=D({concat2d_:JN});function QN(e,t){return rt(e,t)}var rb=D({concat3d_:QN});function eT(e,t){return rt(e,t)}var ab=D({concat4d_:eT});function tT(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","conv2d"),l=R(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&M(Vt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];M(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),M(Br(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(gs,d,p);return u?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ca=D({conv2d_:tT});function nT(e,t,n,r,a="NWC",s=1,i){let o=R(e,"x","conv1d"),l=R(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1]])),M(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&M(Vt(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),M(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(Br(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),M(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=ca(d,h,[1,n],r,"NHWC",[1,s],i);return u?H(p,[p.shape[2],p.shape[3]]):H(p,[p.shape[0],p.shape[2],p.shape[3]])}var Id=D({conv1d_:nT});function rT(e,t,n,r,a,s="NHWC",i){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),M(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&M(Vt(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(xs,d,p);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var wm=D({conv2DBackpropInput_:rT});function aT(e,t,n,r,a,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return wm(n,i,o,r,a,"NHWC",s)}var Sd=D({conv2dTranspose_:aT});function sT(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),M(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),M(Br(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),M(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let u={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=$.runKernel(Nu,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var bm=D({conv3d_:sT});function iT(e,t,n,r,a){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],c=i.shape[4];M(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),M(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=$.runKernel(Bh,u,h);return o?H(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var sb=D({conv3DBackpropInput_:iT});function oT(e,t,n,r,a){let s=R(e,"x","conv3dTranspose"),i=R(t,"filter","conv3dTranspose");return sb(n,s,i,r,a)}var ib=D({conv3dTranspose_:oT});function lT(e){let t={x:R(e,"x","cos")};return $.runKernel(ws,t)}var ec=D({cos_:lT});function uT(e){let t={x:R(e,"x","cosh")};return $.runKernel(Ao,t)}var Nd=D({cosh_:uT});function cT(e,t=0,n=!1,r=!1){let a={x:R(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return $.runKernel(bs,a,s)}var Td=D({cumsum_:cT});function hT(e,t,n,r=!1){let a=R(e,"x","denseBincount"),s=R(t,"weights","denseBincount");M(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),M(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:n,binaryOutput:r};return $.runKernel(Vh,i,o)}var ob=D({denseBincount_:hT});function dT(e,t,n="NHWC"){let r=R(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${a} and ${t} for depthToSpace with input shape
|
|
${r.shape}`),M(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${s} and ${t} for depthToSpace with input shape
|
|
${r.shape}`),M(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${r.shape}`);let o={x:r},l={blockSize:t,dataFormat:n};return $.runKernel(xo,o,l)}var _m=D({depthToSpace_:dT});function pT(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","depthwiseConv2d"),l=R(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),M(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&M(Vt(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(_s,h,d);return u?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var xl=D({depthwiseConv2d_:pT});function fT(e){let t={x:R(e,"x","diag")};return $.runKernel(Hh,t)}var mT=D({diag_:fT});function yT(e,t,n,r,a=[1,1],s="NHWC"){let i=R(e,"x","dilation2d"),o=R(t,"filter","dilation2d");M(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),M(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),M(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},h={strides:n,pad:r,dilations:a},d=$.runKernel(Tu,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var vm=D({dilation2d_:yT});function AT(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 zt(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 ft(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 gT(e,t){let n=R(e,"a","equal"),r=R(t,"b","equal");[n,r]=vt(n,r),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(_o,a)}var Wa=D({equal_:gT});function xT(e,t,n){let r=R(t,"a","where"),a=R(n,"b","where"),s=R(e,"condition","where","bool"),i=ft(ft(s.shape,r.shape),a.shape),o=Al(s,i),l=Al(r,i),c=Al(a,i),u={condition:o,t:l,e:c};return $.runKernel(Xo,u)}var En=D({where_:xT});function wT(e){let t={x:R(e,"x","zerosLike")};return $.runKernel(al,t)}var He=D({zerosLike_:wT});function bT(e,t){let n=R(e,"a","div"),r=R(t,"b","div");[n,r]=vt(n,r);let a=me(n,r),s=He(a),i=Wa(r,s);return En(i,s,a)}var km=D({divNoNan_:bT});function _T(e,t){let n=R(e,"t1","dot"),r=R(t,"t2","dot");M((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let a=n.rank===1?n.size:n.shape[1],s=r.rank===1?r.size:r.shape[0];if(M(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),n.rank===1&&r.rank===1){let i=H(n,[1,-1]),o=H(r,[-1,1]),l=Ve(i,o);return H(l,[])}else if(n.rank===1&&r.rank===2){let i=H(n,[1,-1]),o=H(r,[r.shape[0],r.shape[1]]),l=Ve(i,o);return H(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=H(r,[-1,1]),o=Ve(n,i);return H(o,[o.size])}else{let i=H(r,[r.shape[0],r.shape[1]]);return Ve(n,i)}}var lb=D({dot_:_T});function vT(e,...t){let n=t.map((a,s)=>R(a,`tensors${s}`,"einsum")),r={equation:e};return $.runKernel(Xh,n,r)}var ub=D({einsum_:vT});function kT(e){let t={x:R(e,"x","elu")};return $.runKernel(wo,t)}var wl=D({elu_:kT});function IT(e){let t=R(e,"x","erf");M(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=ge(t,"float32"));let n={x:t};return $.runKernel(bo,n)}var Im=D({erf_:IT});function ST(e){let t={x:R(e,"x","exp")};return $.runKernel(ks,t)}var er=D({exp_:ST});function NT(e,t=0){let n=R(e,"x","expandDims","string_or_numeric");M(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return $.runKernel(vo,r,a)}var Jt=D({expandDims_:NT});function TT(e){let t={x:R(e,"x","expm1")};return $.runKernel(ko,t)}var Sm=D({expm1_:TT});function ET(e,t){let n=R(e,"x","tile","string_or_numeric");M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return $.runKernel(Ma,r,a)}var Ba=D({tile_:ET});function CT(e,t,n,r="float32"){t==null&&(t=e);let a=Be([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 Ba(Jt(i,0),[n[0],1,1]);if(n.length===2)return Ba(Jt(Jt(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return Ba(Jt(Jt(Jt(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 Nm=D({eye_:CT});function tc(e,t,n){let r={shape:e,value:t,dtype:n};return $.runKernel(Eu,{},r)}function RT(e){let t={x:R(e,"x","floor")};return $.runKernel(Is,t)}var bl=D({floor_:RT});function MT(e,t,n=0,r=0){let a=R(e,"x","gather"),s=R(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return $.runKernel(So,i,o)}var gi=D({gather_:MT});function FT(e,t){let n=R(e,"a","greater"),r=R(t,"b","greater");[n,r]=vt(n,r),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(To,a)}var pr=D({greater_:FT});function $T(e,t){let n=R(e,"a","greaterEqual"),r=R(t,"b","greaterEqual");[n,r]=vt(n,r),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Ts,a)}var Va=D({greaterEqual_:$T});function DT(e){let t={input:R(e,"input","imag")};return $.runKernel(Jh,t)}var Ed=D({imag_:DT});function OT(e){let t={x:R(e,"x","isFinite")};return $.runKernel(Eo,t)}var cb=D({isFinite_:OT});function zT(e){let t={x:R(e,"x","isInf")};return $.runKernel(Co,t)}var hb=D({isInf_:zT});function PT(e){let t={x:R(e,"x","isNaN")};return $.runKernel(Ro,t)}var Tm=D({isNaN_:PT});function LT(e,t=.2){let n={x:R(e,"x","leakyRelu")},r={alpha:t};return $.runKernel(Cs,n,r)}var nc=D({leakyRelu_:LT});function WT(e,t){let n=R(e,"a","less"),r=R(t,"b","less");[n,r]=vt(n,r),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Mo,a)}var Cd=D({less_:WT});function BT(e,t){let n=R(e,"a","lessEqual"),r=R(t,"b","lessEqual");[n,r]=vt(n,r),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Fo,a)}var xi=D({lessEqual_:BT});function db(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 VT(e,t=5,n=1,r=1,a=.5){let s=R(e,"x","localResponseNormalization");M(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
|
|
rank ${s.rank}.`),M(Vt(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(Mu,l,c);return o?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Em=D({localResponseNormalization_:VT});function jT(e){let t={x:R(e,"x","log")};return $.runKernel(Rs,t)}var zn=D({log_:jT});function UT(e){let t={x:R(e,"x","log1p")};return $.runKernel($o,t)}var Rd=D({log1p_:UT});function HT(e){return M(Ea(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=R(t,"x","tf.grad","string_or_numeric"),a=n!=null?R(n,"dy","tf.grad"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(r),[r],a);return a!=null&&ln(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Md(i),i[0]})}}function GT(e){return M(Ea(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{M(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let r=Xu(t,"args","tf.grads","string_or_numeric"),a=n!=null?R(n,"dy","tf.grads"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(...r),r,a);return a!=null&&ln(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Md(i),i})}}function qT(e){return M(Ea(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof Pe,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof Pe,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=$.gradients(()=>e(t),[t],n);return Md(r),{grad:r[0],value:a}}}function XT(e){return M(Ea(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(a=>a instanceof Pe),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof Pe,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=$.gradients(()=>e(...t),t,n);return n!=null&&ln(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Md(r.grads),r}}function pb(e,t){M(Ea(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(c=>c instanceof Hu),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in $.registeredVariables)t.push($.registeredVariables[c])}let r=n?t.filter(c=>!c.trainable):null,a=t.length;t=t.filter(c=>c.trainable),M(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);let s=!0,{value:i,grads:o}=$.gradients(e,t,null,s);M(o.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((c,u)=>{o[u]!=null&&(l[c.name]=o[u])}),r!=null&&r.forEach(c=>l[c.name]=null),{value:i,grads:l}}function Vr(e){return $.customGrad(e)}function Md(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 KT(e){let t={x:R(e,"x","neg")};return $.runKernel(zo,t)}var kt=D({neg_:KT});function ZT(e){let t={x:R(e,"x","softplus")};return $.runKernel(Qo,t)}var _l=D({softplus_:ZT});function YT(e){let t=R(e,"x","logSigmoid");return Vr(n=>({value:kt(_l(kt(n))),gradFunc:r=>P(r,On(kt(n)))}))(t)}var fb=D({logSigmoid_:YT});function JT(e,t=null,n=!1){let r={x:R(e,"x","max")},a={reductionIndices:t,keepDims:n};return $.runKernel(Ms,r,a)}var Cn=D({max_:JT});function QT(e,t){let n=R(e,"a","sub"),r=R(t,"b","sub");[n,r]=vt(n,r);let a={a:n,b:r};return $.runKernel(ni,a)}var ye=D({sub_:QT});function eE(e,t=null,n=!1){let r=R(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 Te=D({sum_:eE});function tE(e,t=-1){let n=R(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);return Vr((r,a)=>{let s=!0,i=Cn(r,t,!0),o=ye(r,i),l=ye(ge(o,"float32"),zn(Te(er(o),t,s)));return a([l]),{value:l,gradFunc:(c,u)=>{let[h]=u,d=!0,p=er(h);return ye(c,P(Te(c,t,d),p))}}})(n)}var Fd=D({logSoftmax_:tE});function Cm(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function mb(e,t,n){let r=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<r;o++)n.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function yb(e,t){let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let a=t.map(s=>e[s]);return[n,a]}function wi(e,t){let n=t.map(r=>1);return mb(e,n,t)}function nE(e,t,n){M(Cm(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Ab(e,t){if(Cm(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 Rm(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function rE(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function aE(e,t=null,n=!1){let r=R(e,"x","logSumExp"),a=hr(t,r.shape),s=Cn(r,a,!0),i=ye(r,s),o=er(i),l=Te(o,a),c=zn(l),u=se(H(s,c.shape),c);if(n){let h=wi(u.shape,a);return H(u,h)}return u}var Mm=D({logSumExp_:aE});function sE(e,t){let n=R(e,"a","logicalAnd","bool"),r=R(t,"b","logicalAnd","bool");ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Do,a)}var fr=D({logicalAnd_:sE});function iE(e){let t={x:R(e,"x","logicalNot","bool")};return $.runKernel(Cu,t)}var rc=D({logicalNot_:iE});function oE(e,t){let n=R(e,"a","logicalOr","bool"),r=R(t,"b","logicalOr","bool");ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Ru,a)}var $d=D({logicalOr_:oE});function lE(e,t){let n=R(e,"a","logicalXor","bool"),r=R(t,"b","logicalXor","bool");return ft(n.shape,r.shape),fr($d(e,t),rc(fr(e,t)))}var gb=D({logicalXor_:lE});function uE(e,t,n,r,a){let s=R(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),M(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),M(Br(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),a!=null&&M(Vt(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($s,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var ac=D({maxPool_:uE});function cE(e,t=[1,1,1],n,r,a,s="NDHWC"){let i=R(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),M(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&M(Vt(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(Fu,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var Fm=D({maxPool3d_:cE});function hE(e,t,n,r,a=!1){let s={x:R(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 xb=D({maxPoolWithArgmax_:hE});function dE(e,t){let n=R(e,"a","maximum"),r=R(t,"b","maximum");[n,r]=vt(n,r),n.dtype==="bool"&&(n=ge(n,"int32"),r=ge(r,"int32")),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Fs,a)}var jr=D({maximum_:dE});function pE(e,t=null,n=!1){let r={x:R(e,"x","mean")},a={axis:t,keepDims:n};return $.runKernel(Ds,r,a)}var It=D({mean_:pE});function Rt(e,t="float32"){if(t==="complex64"){let r=Rt(e,"float32"),a=Rt(e,"float32");return $a(r,a)}let n=$h(Et(e),t);return $.makeTensor(n,e,t)}function Pn(e,t="float32"){if(t==="complex64"){let r=Pn(e,"float32"),a=Rt(e,"float32");return $a(r,a)}let n=Nf(Et(e),t);return $.makeTensor(n,e,t)}function fE(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let r=R(e,"x","meshgrid",e instanceof Pe?e.dtype:"float32");if(t===void 0)return[r];let a=R(t,"y","meshgrid",t instanceof Pe?t.dtype:"float32"),s=Et(r.shape),i=Et(a.shape);return n==="xy"?(r=H(r,[1,-1]),a=H(a,[-1,1]),[Ve(Pn([i,1],r.dtype),r),Ve(a,Pn([1,s],a.dtype))]):(r=H(r,[-1,1]),a=H(a,[1,-1]),[Ve(r,Pn([1,i],r.dtype)),Ve(Pn([s,1],a.dtype),a)])}function mE(e,t=null,n=!1){let r={x:R(e,"x","min")},a={axis:t,keepDims:n};return $.runKernel(Os,r,a)}var vl=D({min_:mE});function yE(e,t){let n=R(e,"a","minimum"),r=R(t,"b","minimum");[n,r]=vt(n,r),n.dtype==="bool"&&(n=ge(n,"int32"),r=ge(r,"int32")),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(zs,a)}var kl=D({minimum_:yE});function AE(e,t,n){M(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=R(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");M(t.length===r.rank,()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`);let a=n==="reflect"?1:0;for(let o=0;o<r.rank;o++)M(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),M(t[o][0]>=0&&t[o][0]<=r.shape[o]-a&&t[o][1]>=0&&t[o][1]<=r.shape[o]-a,()=>`Padding in dimension ${o} cannot be greater than or equal to ${r.shape[o]-a} or less than 0 for input of shape ${r.shape}`);let s={paddings:t,mode:n},i={x:r};return $.runKernel(Ps,i,s)}var $m=D({mirrorPad_:AE});function gE(e,t){let n=R(e,"a","mod"),r=R(t,"b","mod");[n,r]=vt(n,r);let a={a:n,b:r};return $.runKernel(Oo,a)}var Dm=D({mod_:gE});function xE(e){let t=R(e,"x","square"),n={};return $.runKernel("Square",{x:t},n)}var ot=D({square_:xE});function wE(e,t=null,n=!1){e=R(e,"x","moments");let r=hr(t,e.shape),a=It(e,r,n),s=a.shape;n||(s=wi(a.shape,r));let i=ot(ye(ge(e,"float32"),H(a,s))),o=It(i,r,n);return{mean:a,variance:o}}var Dd=D({moments_:wE});function bE(e,t,n,r){let a=R(t,"data","multiRNNCell"),s=Xu(n,"c","multiRNNCell"),i=Xu(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 _E=D({multiRNNCell_:bE});function vE(e,t,n,r=!1){let a=R(e,"logits","multinomial"),s=a.size,i=a.rank;if(s<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);if(i>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);n=n||Math.random();let o={logits:i===1?H(a,[1,-1]):a},l={numSamples:t,seed:n,normalized:r},c=$.runKernel(ad,o,l);return i===1?H(c,[c.size]):c}var wb=D({multinomial_:vE});function kE(e,t){let n=R(e,"a","notEqual"),r=R(t,"b","notEqual");[n,r]=vt(n,r),ft(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Po,a)}var bi=D({notEqual_:kE});function IE(e){let t={x:R(e,"x","onesLike")};return $.runKernel(Vo,t)}var Ln=D({onesLike_:IE});function SE(e,t){let n=R(e,"v1","outerProduct"),r=R(t,"v2","outerProduct");M(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let a=H(n,[-1,1]),s=H(r,[1,-1]);return Ve(a,s)}var NE=D({outerProduct_:SE});function TE(e,t,n=0){let r=R(e,"x","pad");if(r.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let a={paddings:t,constantValue:n},s={x:r};return $.runKernel(Bs,s,a)}var ha=D({pad_:TE});function EE(e,t,n=0){return M(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),ha(e,[t],n)}var CE=D({pad1d_:EE});function RE(e,t,n=0){return M(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ha(e,t,n)}var ME=D({pad2d_:RE});function FE(e,t,n=0){return M(t.length===3&&t[0].length===2&&t[1].length===2&&t[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ha(e,t,n)}var $E=D({pad3d_:FE});function DE(e,t,n=0){return M(t.length===4&&t[0].length===2&&t[1].length===2&&t[2].length===2&&t[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ha(e,t,n)}var OE=D({pad4d_:DE});function zE(e,t,n){let r=R(e,"x","spaceToBatchND");M(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),M(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),M(r.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]==0:i,!0),()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let a={x:r},s={blockShape:t,paddings:n};return $.runKernel(Ou,a,s)}var sc=D({spaceToBatchND_:zE});function WE(e,t,n,r,a,s){a==null&&(a=[1,1]),s==null&&(s=1),r===0&&(r="valid");let i=R(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(Br(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let c=Zw(o.shape,t,s,a,r),u=[c.dilationHeight,c.dilationWidth],h;r==="same"?h=LE([c.filterHeight,c.filterWidth],u):h=[[0,0],[0,0]];let d=u[0]===1&&u[1]===1,[p,f]=PE([c.inHeight,c.inWidth],u,h),m=d?r:"valid",y=d?o:sc(o,u,p),A=(n==="avg"?()=>Ju(y,t,s,m):()=>ac(y,t,s,m))(),g=d?A:Qu(A,u,f);return l?H(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function PE(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 LE(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 bb=D({pool_:WE});function BE(e,t){let n=R(e,"base","pow"),r=R(t,"exp","pow");[n,r]=vt(n,r);let a={a:n,b:r};return $.runKernel(Vs,a)}var da=D({pow_:BE});function VE(e,t){let n=R(e,"x","prelu"),r=R(t,"alpha","prelu"),a={x:n,alpha:r};return $.runKernel(js,a)}var ic=D({prelu_:VE});function jE(e,t=null,n=!1){let r=R(e,"x","prod");r.dtype==="bool"&&(r=ge(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Uo,a,s)}var Od=D({prod_:jE});function UE(e,t,n){let r=Et(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 HE=D({rand_:UE}),Om=no(h5()),zm=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=Om.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let r=this.nextVal;return this.nextVal=NaN,r}let e,t,n=!1;for(;!n;){let r,a,s;do r=2*this.random()-1,a=2*this.random()-1,s=r*r+a*a;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*r*i,t=this.mean+this.stdDev*a*i,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},GE=class{constructor(e,t,n,r){this.alpha=e,this.beta=1/t,this.dtype=n;let a=r||Math.random();this.randu=Om.alea(a.toString()),this.randn=new zm(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)}},qE=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=Om.alea(r)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function XE(e,t,n=1,r="float32",a){if(n==null&&(n=1),r==null&&(r="float32"),r!=="float32"&&r!=="int32")throw new Error(`Unsupported data type ${r}`);let s=new GE(t,n,r,a),i=Be(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var KE=D({randomGamma_:XE});function ZE(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let s=new zm(t,n,r,!1,a),i=Be(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var _b=D({randomNormal_:ZE});function YE(e,t=0,n=1,r="float32",a){let s=Be(e,r),i=new qE(t,n,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var Il=D({randomUniform_:YE});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 $.runKernel($u,{},a)}function JE(e){let t={input:R(e,"input","real")};return $.runKernel(sd,t)}var oc=D({real_:JE});function QE(e){let t={x:R(e,"x","reciprocal")};return $.runKernel(Ho,t)}var Pm=D({reciprocal_:QE});function eC(e){let t={x:R(e,"x","relu")};return $.runKernel(Us,t)}var Ur=D({relu_:eC});function tC(e){let t={x:R(e,"x","relu6")};return $.runKernel(Gs,t)}var Pd=D({relu6_:tC});function nC(e,t){let n={x:R(e,"x","reverse")},r={dims:t};return $.runKernel(qs,n,r)}var Wn=D({reverse_:nC});function rC(e){let t=R(e,"x","reverse");return M(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Wn(t,0)}var aC=D({reverse1d_:rC});function sC(e,t){let n=R(e,"x","reverse");return M(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Wn(n,t)}var iC=D({reverse2d_:sC});function oC(e,t){let n=R(e,"x","reverse");return M(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Wn(n,t)}var lC=D({reverse3d_:oC});function uC(e,t){let n=R(e,"x","reverse");return M(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Wn(n,t)}var cC=D({reverse4d_:uC});function hC(e){let t={x:R(e,"x","round")};return $.runKernel(Xs,t)}var Lm=D({round_:hC});function dC(e){let t={x:R(e,"x","rsqrt")};return $.runKernel(Ks,t)}var Ld=D({rsqrt_:dC});function xe(e,t){if((rn(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"&&rn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Da(e,[],[],t)}function pC(e){let t={x:R(e,"x","selu")};return $.runKernel(Ko,t)}var Wd=D({selu_:pC});function fC(e,t,n,r,a,s=[1,1],i="NHWC"){let o=R(e,"x","separableConv2d"),l=R(t,"depthwiseFilter","separableConv2d"),c=R(n,"pointwiseFilter","separableConv2d"),u=o,h=!1;if(o.rank===3&&(h=!0,u=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");M(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),M(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),M(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),M(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${c.shape[0]}.`),M(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${c.shape[1]}.`);let d=l.shape[2],p=l.shape[3];M(c.shape[2]===d*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*p}, but got ${c.shape[2]}.`);let f=xl(u,l,r,a,i,s),m=ca(f,c,1,"valid",i);return h?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Wm=D({separableConv2d_:fC});async function mC(e,t){let n=R(e,"x","setdiff1d"),r=R(t,"y","setdiff1d");M(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),M(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),M(r.rank===1,()=>`y should be 1D tensor, but got y (${r.shape}).`);let a=await n.data(),s=await r.data(),i=new Set(s),o=0;for(let u=0;u<a.length;u++)i.has(a[u])||o++;let l=new Dt([o],n.dtype),c=new Dt([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 vb=mC;function yC(e){let t={x:R(e,"x","sign")};return $.runKernel(Jo,t)}var Bm=D({sign_:yC});function AC(e){let t={x:R(e,"x","sin")};return $.runKernel(Zs,t)}var Bd=D({sin_:AC});function gC(e){let t={x:R(e,"x","sinh")};return $.runKernel(Yo,t)}var Vd=D({sinh_:gC});function xC(e,t,n){let r=R(e,"x","slice1d");return M(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Ce(r,[t],[n])}var jd=D({slice1d_:xC});function wC(e,t,n){let r=R(e,"x","slice2d");return M(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Ce(r,t,n)}var Vm=D({slice2d_:wC});function bC(e,t,n){let r=R(e,"x","slice3d");return M(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Ce(r,t,n)}var Ud=D({slice3d_:bC});function _C(e,t,n){let r=R(e,"x","slice4d");return M(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Ce(r,t,n)}var lc=D({slice4d_:_C});function vC(e,t=-1){let n=R(e,"logits","softmax","float32");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);let r={logits:n},a={dim:t};return $.runKernel(ei,r,a)}var uc=D({softmax_:vC});function kC(e){M(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 cc=D({fft_:kC});function IC(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Yh,t)}var Sl=D({ifft_:IC});function SC(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let a=H(e,[n,t]);r=Sl(a)}else{let a=[n,2*(t-1)],s=H(oc(e),[n,t]),i=H(Ed(e),[n,t]),o=Wn(Ce(s,[0,1],[n,t-2]),1),l=P(Wn(Ce(i,[0,1],[n,t-2]),1),xe(-1)),c=rt([s,o],1),u=rt([i,l],1),h=H($a(c,u),[a[0],a[1]]);r=Sl(h)}if(r=oc(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_:SC});function NC(e,t,n=0){let r={x:R(e,"x","split")},a={numOrSizeSplits:t,axis:n};return $.runKernel(el,r,a)}var Pt=D({split_:NC});function TC(e,t){M(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,a;if(t!=null&&t<n){let f=e.shape.map(y=>0),m=e.shape.map(y=>y);m[e.shape.length-1]=t,a=Ce(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,a=rt([e,Rt(f)],e.shape.length-1),n=t}else a=e;let s=He(a),i=H($a(a,s),[r,n]),o=cc(i),l=Math.floor(n/2)+1,c=oc(o),u=Ed(o),h=Pt(c,[l,n-l],c.shape.length-1),d=Pt(u,[l,n-l],u.shape.length-1),p=a.shape.slice();return p[a.shape.length-1]=l,H($a(h[0],d[0]),p)}var hc=D({rfft_:TC});function EC(e){let t={x:R(e,"x","sqrt")};return $.runKernel(Js,t)}var Qt=D({sqrt_:EC});function CC(e,t){let n=R(e,"a","squaredDifference"),r=R(t,"b","squaredDifference");[n,r]=vt(n,r),ft(n.shape,r.shape);let a={a:n,b:r},s={};return $.runKernel(ti,a,s)}var Gd=D({squaredDifference_:CC});function RC(e,t){let n=R(e,"x","squeeze");return H(n,jx(n.shape,t).newShape)}var ja=D({squeeze_:RC});function MC(e,t=0){let n=Xu(e,"tensors","stack","string_or_numeric");M(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&M(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,a={axis:t};return $.runKernel(jo,r,a)}var cn=D({stack_:MC});function FC(e,t=0){let n={x:R(e,"x","step")},r={alpha:t};return $.runKernel(Fa,n,r)}var Nl=D({step_:FC});function $C(e,t,n,r,a=0,s=0,i=0,o=0,l=0){let c={x:R(e,"x","stridedSlice")},u={begin:t,end:n,strides:r,beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return $.runKernel(tl,c,u)}var jm=D({stridedSlice_:$C});function DC(e){let t={x:R(e,"x","tan")};return $.runKernel(ri,t)}var Um=D({tan_:DC});function sn(e,t){cs(e);let n=Lr(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Da(e,null,n,t)}function Rn(e,t,n){if(cs(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=Lr(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Da(e,t,r,n)}function OC(e,t,n){if(cs(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=Lr(e,n);if(r.length!==4&&r.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Da(e,t,r,n)}function zC(e,t,n){if(cs(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=Lr(e,n);if(r.length!==5&&r.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Da(e,t,r,n)}function PC(e,t,n){if(cs(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=Lr(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,Da(e,t,r,n)}function LC(e,t=1,n=!0){let r=R(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let a=r.shape[r.shape.length-1];if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${a}) but got ${t}`);let s={x:r},i={k:t,sorted:n},[o,l]=$.runKernel(nl,s,i);return{values:o,indices:l}}var Hm=D({topk_:LC});function WC(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new zm(t,n,r,!0,a),i=Be(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var qd=D({truncatedNormal_:WC});function BC(e,t=0){let n=R(e,"x","unique","string_or_numeric");M(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},a={axis:t},[s,i]=$.runKernel(cd,r,a);return{values:s,indices:i}}var Xd=D({unique_:BC});function VC(e,t,n){let r=R(e,"x","unsortedSegmentSum"),a=R(t,"segmentIds","unsortedSegmentSum","int32");M(Vt(n),()=>"numSegments must be of dtype int");let s={x:r,segmentIds:a},i={numSegments:n};return $.runKernel(Pu,s,i)}var Gm=D({unsortedSegmentSum_:VC});function jC(e,t=0){let n=R(e,"x","unstack","string_or_numeric");M(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},a={axis:t};return $.runKernel(rl,r,a)}var mr=D({unstack_:jC});function kb(e,t=!0,n,r){return $.makeVariable(e,t,n,r)}function Ib(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let r=Be(e,"int32"),a=Be([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 UC(e){let t=R(e,"condition","whereAsync","bool"),n=await t.data(),r=Ib(t.shape,n);return e!==t&&t.dispose(),r}var qm=UC;async function HC(e,t,n){let r=R(e,"tensor","boolMask"),a=R(t,"mask","boolMask","bool"),s=n==null?0:n,i=a.rank,o=r.shape;M(i>0,()=>"mask cannot be scalar"),ln(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 qm(h),p=ja(d,[1]),f=gi(u,p,s);return e!==r&&r.dispose(),t!==a&&a.dispose(),p.dispose(),u.dispose(),h.dispose(),d.dispose(),f}var GC=HC;function qC(e,t="euclidean",n=null,r=!1){e=R(e,"x","norm");let a=Sb(e,t,n),s=a.shape;if(r){let i=hr(n,e.shape);s=wi(a.shape,i)}return H(a,s)}function Sb(e,t,n=null){if(e.rank===0)return Ot(e);if(e.rank!==1&&n===null)return Sb(H(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Te(Ot(e),n);if(t===Infinity)return Cn(Ot(e),n);if(t===-Infinity)return vl(Ot(e),n);if(t==="euclidean"||t===2)return Qt(Te(da(Ot(e),xe(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Cn(Te(Ot(e),n[0]),n[1]-1);if(t===Infinity)return Cn(Te(Ot(e),n[1]),n[0]);if(t===-Infinity)return vl(Te(Ot(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return Qt(Te(ot(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_:qC});function XC(e,t,n,r,a=!0){let s=R(e,"v","movingAverage"),i=R(t,"x","movingAverage"),o=R(n,"decay","movingAverage");iw(s,i),M(oa(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=xe(1),c=ye(l,o),u=P(ye(i,s),c);if(a){M(r!=null,()=>"When using zeroDebias: true, step is required.");let h=R(r,"step","movingAverage");u=me(u,ye(l,da(o,h)))}return se(s,u)}var KC=D({movingAverage_:XC});function ZC(e,t,n){let r=R(e,"indices","scatterND","int32"),a=R(t,"updates","scatterND");nm(a,r,n);let s={indices:r,updates:a},i={shape:n};return $.runKernel(qo,s,i)}var Nb=D({scatterND_:ZC});function YC(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 JC(e,t,n,r=0){let a=R(e,"sparseIndices","sparseToDense","int32"),s=R(t,"sparseValues","sparseToDense"),i=R(r,"defaultValue","sparseToDense",s.dtype);YC(a,s,n,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:n};return $.runKernel(ld,o,l)}var Xm=D({sparseToDense_:JC});function QC(e,t){let n=R(t,"indices","gatherND","int32"),r={params:R(e,"x","gatherND"),indices:n};return $.runKernel(No,r)}var Tb=D({gatherND_:QC});function eR(e,t){if(t==null)return e.shape.slice();if(oa(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 tR(e,t,n,r){let a=R(e,"x","dropout");if(M(a.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${a.dtype} tensor instead.`),M(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Pe?a.clone():a;let s=eR(a,n),i=1-t,o=me(bl(se(Il(s,0,1,"float32",r),i)),i);return P(a,o)}var Eb=D({dropout_:tR});function Cb(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function Km(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 sn(a,"float32")}async function nR(e,t,n=1){let r=R(e,"predictions","inTopK"),a=R(t,"targets","inTopK");M(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),M(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),ln(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=r.shape[r.shape.length-1];M(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await r.data(),o=await a.data(),[l,c]=[i.length/s,s],u=Ux("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,y)=>y.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(),Ir(u,a.shape,"bool")}var rR=nR,Ua={};Me(Ua,{conv2d:()=>aR,depthwiseConv2d:()=>sR,matMul:()=>iR});function oR(e,t,n,r,a,s="NHWC",i){let o=e;e.rank===3&&(o=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]])),M(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),M(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),M(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let c=s==="NHWC"?o.shape[3]:o.shape[1],u=s==="NHWC"?l.shape[3]:l.shape[1];M(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),M(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),i!=null&&M(Vt(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(Lh,h,d)}var Zm=D({conv2DBackpropFilter_:oR});function Zd(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return P(e,Nl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Yd(e,t){let n=t,r=zt(e.shape,t.shape);return r.length>0&&(n=Te(n,r)),H(n,e.shape)}function Jd(e,t,n,r){if(t==="linear")return e;if(t==="relu")return Ur(e);if(t==="elu")return wl(e);if(t==="relu6")return Pd(e);if(t==="prelu")return ic(e,n);if(t==="leakyrelu")return nc(e,r);throw new Error(`Unknown fused activation ${t}.`)}var Qd=(e,t)=>!(e>0)||t==="linear";function lR({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Qd($.state.gradientDepth,l)===!1){let w=ca(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Jd(w,l,c,u)}let h=R(e,"x","conv2d"),d=R(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]])),M(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),M(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&M(Vt(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),M(p.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${d.shape[2]}.`),M(Br(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),M(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let m=Yu(p.shape,d.shape,n,s,r,i),y;o!=null&&(y=R(o,"bias","fused conv2d"),[y]=vt(y,h),ft(m.outShape,y.shape));let A;c!=null&&(A=R(c,"prelu weights","fused conv2d"));let g=(w,b)=>{let[k,N,E,F]=b,O=Zd(w,E,l);M(La(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let L=wm(N.shape,O,k,n,r),V=Zm(N,O,k.shape,n,r),j=[L,V];if(F!=null){let U=Yd(F,O);j.push(U)}return j},x={x:p,filter:d,bias:y,preluActivationWeights:A},v={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Vr((w,b,k)=>{let N=$.runKernel(oi,x,v);return k([b,w,N]),f&&(N=H(N,[N.shape[1],N.shape[2],N.shape[3]])),{value:N,gradFunc:g}})(p,d):Vr((w,b,k,N)=>{let E=$.runKernel(oi,x,v);return N([b,w,E,k]),f&&(E=H(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(p,d,y)}var aR=D({fusedConv2d_:lR});function uR(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 $.runKernel(jh,c,u)}var Rb=D({depthwiseConv2dNativeBackpropFilter_:uR});function cR(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:o,filter:n},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=$.runKernel(Uh,c,u);return l?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Mb=D({depthwiseConv2dNativeBackpropInput_:cR});function hR({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 w=xl(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Jd(w,l,c,u)}let h=R(e,"x","depthwiseConv2d"),d=R(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]])),M(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),M(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),M(p.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),M(Br(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&M(Vt(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${r}.`);let m=Yu(p.shape,d.shape,n,s,r,i,!0),y;o!=null&&(y=R(o,"bias","fused conv2d"),[y]=vt(y,h),ft(m.outShape,y.shape));let A;c!=null&&(A=R(c,"prelu weights","fused depthwiseConv2d"));let g=(w,b)=>{M(La(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[k,N,E,F]=b,O=Zd(w,E,l),L=Mb(N.shape,O,k,n,r,s,i),V=Rb(N,O,k.shape,n,r,s,i);if(F!=null){let j=Yd(y,O);return[L,V,j]}return[L,V]},x={x:p,filter:d,bias:y,preluActivationWeights:A},v={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Vr((w,b,k)=>{let N=$.runKernel(li,x,v);return k([b,w,N]),f&&(N=H(N,[N.shape[1],N.shape[2],N.shape[3]])),{value:N,gradFunc:g}})(p,d):Vr((w,b,k,N)=>{let E=$.runKernel(li,x,v);return N([b,w,E,k]),f&&(E=H(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(p,d,y)}var sR=D({fusedDepthwiseConv2d_:hR});function dR({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 F=Ve(e,t,n,r);return a!=null&&(F=se(F,a)),Jd(F,s,i,o)}let l=R(e,"a","fused matMul"),c=R(t,"b","fused matMul");[l,c]=vt(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),y=Et(f),A=Et(m);M(l.rank>=2&&c.rank>=2&&l.rank===c.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${c.rank}.`),M(oa(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),M(u===h,()=>`Error in fused matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${r} must match.`);let g=l.shape.slice(0,-2).concat([d,p]),x=n?H(l,[y,u,d]):H(l,[y,d,u]),v=r?H(c,[A,p,h]):H(c,[A,h,p]),w;a!=null&&(w=R(a,"bias","fused matMul"),[w]=vt(w,l),ft(g,w.shape));let b;i!=null&&(b=R(i,"prelu weights","fused matMul"));let k=(F,O)=>{let[L,V,j,U]=O,X=Zd(H(F,j.shape),j,s),G,ee;if(!n&&!r?(G=Ve(X,V,!1,!0),ee=Ve(L,X,!0,!1)):!n&&r?(G=Ve(X,V,!1,!1),ee=Ve(X,L,!0,!1)):n&&!r?(G=Ve(V,X,!1,!0),ee=Ve(L,X,!1,!1)):(G=Ve(V,X,!0,!0),ee=Ve(X,L,!0,!0)),a!=null){let Y=Yd(U,X);return[G,ee,Y]}else return[G,ee]},N={a:x,b:v,bias:w,preluActivationWeights:b},E={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return a==null?Vr((F,O,L)=>{let V=$.runKernel(ii,N,E);return L([F,O,V]),{value:H(V,g),gradFunc:k}})(x,v):Vr((F,O,L,V)=>{let j=$.runKernel(ii,N,E);return V([F,O,j,L]),{value:H(j,g),gradFunc:k}})(x,v,w)}var iR=D({fusedMatMul_:dR});function pR(e){return Km(e,.54,.46)}var fR=D({hammingWindow_:pR});function mR(e){return Km(e,.5,.5)}var Fb=D({hannWindow_:mR});function yR(e,t,n,r=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Ce(e,s,t)),s+=n;if(r)for(;s<e.size;){let o=s+t-e.size,l=rt([Ce(e,s,t-o),tc([o],a)]);i.push(l),s+=n}return i.length===0?Rn([],[0,t]):H(rt(i),[i.length,t])}var $b=D({frame_:yR});function AR(e,t,n,r,a=Fb){r==null&&(r=Cb(t));let s=$b(e,t,n),i=P(s,a(t));return hc(i,r)}var gR=D({stft_:AR});function xR(e,t,n,r,a="bilinear",s=0){let i=R(e,"image","cropAndResize"),o=R(t,"boxes","cropAndResize","float32"),l=R(n,"boxInd","cropAndResize","int32"),c=o.shape[0];M(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),M(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${o.shape}.`),M(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${o.shape}.`),M(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),M(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),M(a==="bilinear"||a==="nearest",()=>`method must be bilinear or nearest, but was ${a}`);let u={image:i,boxes:o,boxInd:l},h={method:a,extrapolationValue:s,cropSize:r};return $.runKernel(go,u,h)}var wR=D({cropAndResize_:xR});function bR(e){let t=R(e,"image","flipLeftRight","float32");M(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return $.runKernel(Io,n,{})}var _R=D({flipLeftRight_:bR});function vR(e,t,n=0,r=.5){let a=R(e,"image","rotateWithOffset","float32");M(a.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`);let s={image:a},i={radians:t,fillValue:n,center:r};return $.runKernel(sl,s,i)}var kR=D({rotateWithOffset_:vR});function Tl(e,t,n,r,a,s){r==null&&(r=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),M(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),M(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),M(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),M(t.rank===1,()=>"scores must be a 1D tensor"),M(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),M(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function IR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppression"),i=R(t,"scores","nonMaxSuppression"),o=Tl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:a};return $.runKernel(Lo,{boxes:s,scores:i},l)}var SR=D({nonMaxSuppression_:IR});function TR(e,t,n){let r=NR(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function NR(e,t,n){return CR(e,t,n||ER)}function ER(e,t){return e>t?1:e<t?-1:0}function CR(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 Db(e,t,n,r,a){return Ym(e,t,n,r,a,0)}function Ob(e,t,n,r,a,s){return Ym(e,t,n,r,a,0,!1,s,!0)}function zb(e,t,n,r,a,s){return Ym(e,t,n,r,a,s,!0)}function Ym(e,t,n,r,a,s,i=!1,o=!1,l=!1){let c=[];for(let y=0;y<t.length;y++)t[y]>a&&c.push({score:t[y],boxIndex:y,suppressBeginIndex:0});c.sort(Pb);let u=s>0?-.5/s:0,h=[],d=[];for(;h.length<n&&c.length>0;){let y=c.pop(),{score:A,boxIndex:g,suppressBeginIndex:x}=y;if(A<a)break;let v=!1;for(let w=h.length-1;w>=x;--w){let b=RR(e,g,h[w]);if(b>=r){v=!0;break}if(y.score=y.score*MR(r,u,b),y.score<=a)break}y.suppressBeginIndex=h.length,v||(y.score===A?(h.push(g),d.push(y.score)):y.score>a&&TR(c,y,Pb))}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 RR(e,t,n){let r=e.subarray(t*4,t*4+4),a=e.subarray(n*4,n*4+4),s=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),o=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),c=Math.min(a[0],a[2]),u=Math.min(a[1],a[3]),h=Math.max(a[0],a[2]),d=Math.max(a[1],a[3]),p=(o-s)*(l-i),f=(h-c)*(d-u);if(p<=0||f<=0)return 0;let m=Math.max(s,c),y=Math.max(i,u),A=Math.min(o,h),g=Math.min(l,d),x=Math.max(A-m,0)*Math.max(g-y,0);return x/(p+f-x)}function MR(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function Pb(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function FR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppressionAsync"),i=R(t,"scores","nonMaxSuppressionAsync"),o=Tl(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}=Db(c,u,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),sn(h,"int32")}var $R=FR;function DR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=R(e,"boxes","nonMaxSuppression"),o=R(t,"scores","nonMaxSuppression"),l=Tl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let c={boxes:i,scores:o},u={maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s},h=$.runKernel(Bo,c,u);return{selectedIndices:h[0],selectedScores:h[1]}}var OR=D({nonMaxSuppressionWithScore_:DR});async function zR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=R(e,"boxes","nonMaxSuppressionAsync"),o=R(t,"scores","nonMaxSuppressionAsync"),l=Tl(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}=zb(u,h,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:sn(d,"int32"),selectedScores:sn(p)}}var PR=zR;function LR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=R(e,"boxes","nonMaxSuppression"),o=R(t,"scores","nonMaxSuppression"),l=Tl(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(Wo,d,p);return{selectedIndices:f[0],validOutputs:f[1]}}var WR=D({nonMaxSuppressionPadded_:LR});async function BR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=R(e,"boxes","nonMaxSuppressionAsync"),o=R(t,"scores","nonMaxSuppressionAsync"),l=Tl(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}=Ob(d,p,c,u,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:sn(f,"int32"),validOutputs:xe(m,"int32")}}var VR=BR;function jR(e,t,n=!1,r=!1){let a=R(e,"images","resizeBilinear");M(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),M(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),M(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(Hs,o,l);return i?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Lb=D({resizeBilinear_:jR});function UR(e,t,n=!1,r=!1){let a=R(e,"images","resizeNearestNeighbor");M(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),M(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),M(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),M(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(Du,o,l);return i?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Wb=D({resizeNearestNeighbor_:UR});function HR(e,t,n="nearest",r="constant",a=0,s){let i=R(e,"image","transform","float32"),o=R(t,"transforms","transform","float32");M(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),M(o.rank===2&&(o.shape[0]===i.shape[0]||o.shape[0]===1)&&o.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),M(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},c={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return $.runKernel(ud,l,c)}var GR=D({transform_:HR});function qR(e,t,n){M(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),M(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=R(e,"a","bandPart");M(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let a=r.shape,[s,i]=r.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=H(zd(0,s,1,"int32"),[-1,1]),l=zd(0,i,1,"int32"),c=ye(o,l),u=fr(xi(c,xe(+t,"int32")),Va(c,xe(-n,"int32"))),h=Rt([s,i],r.dtype);return H(cn(mr(H(r,[-1,s,i])).map(d=>En(u,d,h))),a)}var XR=D({bandPart_:qR});function KR(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)M(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=Pt(e,e.shape[0],0).map(a=>ja(a,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let a=0;a<e.length;++a)n.push($.tidy(()=>{let s=r[a];if(a>0)for(let i=0;i<a;++i){let o=P(Te(P(n[i],s)),n[i]);s=ye(s,o)}return me(s,Kd(s,"euclidean"))}));return t?cn(n,0):n}var ZR=D({gramSchmidt_:KR});function YR(e,t=!1){if(M(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return Bb(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),r=mr(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(l=>{let[c,u]=Bb(l,t);a.push(c),s.push(u)});let i=H(cn(a,0),e.shape),o=H(cn(s,0),e.shape);return[i,o]}}function Bb(e,t=!1){return $.tidy(()=>{M(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],a=Nm(n),s=Wr(e),i=Rn([[1]],[1,1]),o=Wr(i),l=n>=r?r:n;for(let c=0;c<l;++c){let u=s,h=o,d=a;[o,s,a]=$.tidy(()=>{let p=Ce(s,[c,c],[n-c,1]),f=Kd(p),m=Ce(s,[c,c],[1,1]),y=En(pr(m,0),Rn([[-1]]),Rn([[1]])),A=ye(m,P(y,f)),g=me(p,A);g.shape[0]===1?o=Wr(i):o=rt([i,Ce(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let x=kt(me(Ve(y,A),f)),v=Ce(s,[c,0],[n-c,r]),w=P(x,o),b=Je(o);if(c===0)s=ye(v,Ve(w,Ve(b,v)));else{let E=ye(v,Ve(w,Ve(b,v)));s=rt([Ce(s,[0,0],[c,r]),E],0)}let k=Je(w),N=Ce(a,[0,c],[n,a.shape[1]-c]);if(c===0)a=ye(N,Ve(Ve(N,o),k));else{let E=ye(N,Ve(Ve(N,o),k));a=rt([Ce(a,[0,0],[n,c]),E],1)}return[o,s,a]}),_e([u,h,d])}return!t&&n>r&&(a=Ce(a,[0,0],[n,r]),s=Ce(s,[0,0],[r,r])),[a,s]})}var JR=D({qr_:YR}),hn;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(hn||(hn={}));function QR(e,t,n=hn.SUM_BY_NONZERO_WEIGHTS){let r=R(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=R(t,"weights","computeWeightedLoss"));let s=a==null?r:P(r,a);if(n===hn.NONE)return s;if(n===hn.SUM)return Te(s);if(n===hn.MEAN){if(a==null)return It(s);{let i=r.size/a.size,o=me(Te(s),Te(a));return i>1?me(o,xe(i)):o}}if(n===hn.SUM_BY_NONZERO_WEIGHTS){if(a==null)return me(Te(s),xe(r.size));{let i=P(a,Pn(r.shape)),o=ge(Te(bi(i,xe(0))),"float32");return me(Te(s),o)}}throw Error(`Unknown reduction: ${n}`)}var pa=D({computeWeightedLoss_:QR});function eM(e,t,n,r=hn.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","absoluteDifference"),s=R(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=R(n,"weights","absoluteDifference")),ln(a.shape,s.shape,"Error in absoluteDifference: ");let o=Ot(ye(a,s));return pa(o,i,r)}var tM=D({absoluteDifference_:eM});function nM(e,t,n,r,a=hn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","cosineDistance"),i=R(t,"predictions","cosineDistance"),o=null;r!=null&&(o=R(r,"weights","cosineDistance")),ln(s.shape,i.shape,"Error in cosineDistance: ");let l=xe(1),c=ye(l,Te(P(s,i),n,!0));return pa(c,o,a)}var rM=D({cosineDistance_:nM});function aM(e,t,n,r=hn.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","hingeLoss"),s=R(t,"predictions","hingeLoss"),i=null;n!=null&&(i=R(n,"weights","hingeLoss")),ln(a.shape,s.shape,"Error in hingeLoss: ");let o=xe(1);a=ye(P(xe(2),a),o);let l=Ur(ye(o,P(a,s)));return pa(l,i,r)}var sM=D({hingeLoss_:aM});function iM(e,t,n,r=1,a=hn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","huberLoss"),i=R(t,"predictions","huberLoss"),o=null;n!=null&&(o=R(n,"weights","huberLoss")),ln(s.shape,i.shape,"Error in huberLoss: ");let l=xe(r),c=Ot(ye(i,s)),u=kl(c,l),h=ye(c,u),d=se(P(xe(.5),ot(u)),P(l,h));return pa(d,o,a)}var oM=D({huberLoss_:iM});function lM(e,t,n,r=1e-7,a=hn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","logLoss"),i=R(t,"predictions","logLoss"),o=null;n!=null&&(o=R(n,"weights","logLoss")),ln(s.shape,i.shape,"Error in logLoss: ");let l=xe(1),c=xe(r),u=kt(P(s,zn(se(i,c)))),h=P(ye(l,s),zn(se(ye(l,i),c))),d=ye(u,h);return pa(d,o,a)}var uM=D({logLoss_:lM});function cM(e,t,n,r=hn.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","meanSquaredError"),s=R(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=R(n,"weights","meanSquaredError")),ln(a.shape,s.shape,"Error in meanSquaredError: ");let o=Gd(a,s);return pa(o,i,r)}var hM=D({meanSquaredError_:cM});function dM(e,t){let n=R(e,"labels","sigmoidCrossEntropyWithLogits"),r=R(t,"logits","sigmoidCrossEntropyWithLogits");ln(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=Ur(r),s=P(r,n),i=Rd(er(kt(Ot(r))));return se(ye(a,s),i)}function pM(e,t,n,r=0,a=hn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"multiClassLabels","sigmoidCrossEntropy"),i=R(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","sigmoidCrossEntropy")),ln(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let c=xe(r),u=xe(1),h=xe(.5);s=se(P(s,ye(u,c)),P(h,c))}let l=dM(s,i);return pa(l,o,a)}var fM=D({sigmoidCrossEntropy_:pM});function mM(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 ${n}`);return Vr((r,a,s)=>{let i=Mm(a,[n],!0),o=ye(ge(a,"float32"),i);s([r,o]);let l=kt(P(o,r));return{value:Te(l,[n]),gradFunc:(c,u)=>{let[h,d]=u,p=wi(c.shape,[n]);return[P(H(c,p),ye(ge(h,"float32"),er(d))),P(H(c,p),ye(er(d),ge(h,"float32")))]}}})(e,t)}function yM(e,t,n,r=0,a=hn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"onehotLabels","softmaxCrossEntropy"),i=R(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","softmaxCrossEntropy")),ln(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let c=xe(r),u=xe(1),h=xe(s.shape[1]);s=se(P(s,ye(u,c)),me(c,h))}let l=mM(s,i);return pa(l,o,a)}var AM=D({softmaxCrossEntropy_:yM}),gM={fft:cc,ifft:Sl,rfft:hc,irfft:Hd},xM={hammingWindow:fR,hannWindow:Fb,frame:$b,stft:gR},Le={flipLeftRight:_R,resizeNearestNeighbor:Wb,resizeBilinear:Lb,rotateWithOffset:kR,cropAndResize:wR,nonMaxSuppression:SR,nonMaxSuppressionAsync:$R,nonMaxSuppressionWithScore:OR,nonMaxSuppressionWithScoreAsync:PR,nonMaxSuppressionPadded:WR,nonMaxSuppressionPaddedAsync:VR,transform:GR},Vb={bandPart:XR,gramSchmidt:ZR,qr:JR},wM={absoluteDifference:tM,computeWeightedLoss:pa,cosineDistance:rM,hingeLoss:sM,huberLoss:oM,logLoss:uM,meanSquaredError:hM,sigmoidCrossEntropy:fM,softmaxCrossEntropy:AM},fa=class extends Uw{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 _e(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 pb(e,t)}dispose(){this.iterations_!=null&&_e(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:xe(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(fa,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var ep=class extends fa{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.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(()=>He(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:z(()=>He(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;z(()=>{let l=se(P(i,this.rho),P(ot(s),1-this.rho)),c=P(me(Qt(se(o,this.epsilon)),Qt(se(i,this.epsilon))),s),u=se(P(o,this.rho),P(ot(c),1-this.rho));i.assign(l),o.assign(u);let h=se(P(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(_e(this.accumulatedGrads.map(e=>e.variable)),_e(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ep.className="Adadelta";za(ep);var tp=class extends fa{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:z(()=>tc(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;z(()=>{let i=se(s,ot(a));s.assign(i);let o=se(P(me(a,Qt(se(i,$.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&_e(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";za(tp);var np=class extends fa{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=xe(t).variable(),this.accBeta2=xe(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);z(()=>{let n=ye(1,this.accBeta1),r=ye(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:z(()=>He(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:z(()=>He(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,h=se(P(c,this.beta1),P(l,1-this.beta1)),d=se(P(u,this.beta2),P(ot(l),1-this.beta2)),p=me(h,n),f=me(d,r);c.assign(h),u.assign(d);let m=se(P(me(p,se(Qt(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&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(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(da(this.beta1,this.iterations_+1)),this.accBeta2.assign(da(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";za(np);var rp=class extends fa{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=xe(0).variable(),this.accBeta1=xe(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);z(()=>{let n=ye(1,this.accBeta1),r=me(-this.learningRate,se(P(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:He(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:He(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,h=se(P(c,this.beta1),P(l,1-this.beta1)),d=P(u,this.beta2),p=Ot(l),f=jr(d,p);c.assign(h),u.assign(f);let m=se(P(me(r,n),me(h,se(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&_e(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";za(rp);var dc=class extends fa{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=$.registeredVariables[t];z(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};dc.className="SGD";za(dc);var ap=class extends dc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=xe(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:z(()=>He(r).variable(i))}}let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&z(()=>{let i,o=se(P(this.m,a),s);this.useNesterov?i=se(P(this.c,se(s,P(o,this.m))),r):i=se(P(this.c,o),r),a.assign(o),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&_e(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ap.className="Momentum";za(ap);var sp=class extends fa{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.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(()=>He(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:z(()=>He(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:z(()=>He(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=se(P(i,this.decay),P(ot(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=se(P(c,this.decay),P(s,1-this.decay)),h=me(P(s,this.learningRate),Qt(ye(l,se(ot(u),this.epsilon)))),d=se(P(o,this.momentum),h);i.assign(l),c.assign(u),o.assign(d);let p=ye(r,d);r.assign(p)}else{let c=se(P(i,this.decay),P(ot(s),1-this.decay)),u=se(P(o,this.momentum),me(P(s,this.learningRate),Qt(se(c,this.epsilon))));i.assign(c),o.assign(u);let h=ye(r,u);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&_e(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";za(sp);var _i=class{static sgd(e){return new dc(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)}},vi={sgd:_i.sgd,momentum:_i.momentum,adadelta:_i.adadelta,adagrad:_i.adagrad,rmsprop:_i.rmsprop,adamax:_i.adamax,adam:_i.adam},bM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function ip(){return new Promise(e=>bM(()=>e()))}var C={};Me(C,{ERF_A1:()=>MM,ERF_A2:()=>FM,ERF_A3:()=>$M,ERF_A4:()=>DM,ERF_A5:()=>OM,ERF_P:()=>RM,PARALLELIZE_THRESHOLD:()=>Jm,SELU_SCALE:()=>Ub,SELU_SCALEALPHA:()=>jb,applyActivation:()=>Jd,assertAndGetBroadcastShape:()=>ft,assertAxesAreInnerMostDims:()=>nE,assertParamsConsistent:()=>_M,assignToTypedArray:()=>UM,axesAreInnerMostDims:()=>Cm,calculateShapes:()=>Mw,checkEinsumDimSizes:()=>KM,combineLocations:()=>mb,complexWithEvenIndex:()=>BM,complexWithOddIndex:()=>VM,computeConv2DInfo:()=>Yu,computeConv3DInfo:()=>Yw,computeDefaultPad:()=>Am,computeDilation2DInfo:()=>SN,computeOptimalWindowSize:()=>kM,computeOutAndReduceShapes:()=>yb,computeOutShape:()=>vM,computePool2DInfo:()=>Zw,computePool3DInfo:()=>NN,convertConv2DDataFormat:()=>Kw,decodeEinsumEquation:()=>qM,eitherStridesOrDilationsAreOne:()=>Br,expandShapeToKeepDim:()=>wi,exponent:()=>GM,exponents:()=>HM,fromStringArrayToUint8:()=>eF,fromUint8ToStringArray:()=>QM,getAxesPermutation:()=>Ab,getBroadcastDims:()=>AT,getComplexWithIndex:()=>jM,getEinsumComputePath:()=>ZM,getEinsumPermutation:()=>XM,getFusedBiasGradient:()=>Yd,getFusedDyActivation:()=>Zd,getImageCenter:()=>IM,getInnerMostAxes:()=>rE,getPermuted:()=>NM,getReductionAxes:()=>zt,getReshaped:()=>SM,getReshapedPermuted:()=>TM,getSliceBeginCoords:()=>EM,getSliceSize:()=>CM,getUndoAxesPermutation:()=>Rm,isIdentityPermutation:()=>YM,log:()=>PM,mergeRealAndImagArrays:()=>LM,prepareAndValidate:()=>Rw,prepareSplitSize:()=>JM,segment_util:()=>Hb,shouldFuse:()=>Qd,slice_util:()=>un,splitRealAndImagArrays:()=>WM,tupleValuesAreOne:()=>La,upcastType:()=>dr,validateInput:()=>nm,validateUpdateShape:()=>tm,warn:()=>zM});function _M(e,t){let n=e[0].length;e.forEach((a,s)=>{M(a.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),M(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((a,s)=>{for(let i=0;i<n;i++)M(i===t||a[i]===r[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`)})}function vM(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var Jm=30;function kM(e){return e<=Jm?e:Fh(e,Math.floor(Math.sqrt(e)))}function IM(e,t,n){let r=n*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[r,a]}function SM(e,t,n,r=!0){let a=[];if(r)a=a.concat(t.slice(0)),a.push(e[0]/n),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function NM(e,t,n=!0){let r=[];if(n){r.push(t);for(let a=t+1;a<e;++a)a<=2*t?(r.push(a),r.push(a-(t+1))):r.push(a)}else{let a=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):a.push(i);r.push(...a),r.push(0),r.push(...s)}return r}function TM(e,t,n,r=!0){let a=[];r?a.push(e[0]/n):a.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?r?a.push(t[s-1]*e[s]):a.push(e[s]/t[s-1]):a.push(e[s]);return a}function EM(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function CM(e,t,n){let r=e.slice(0,1);for(let a=0;a<n;++a)r.push(e[a+1]-t[a][0]-t[a][1]);return r}var jb=1.7580993408473768,Ub=1.0507009873554805,RM=.3275911,MM=.254829592,FM=-.284496736,$M=1.421413741,DM=-1.453152027,OM=1.061405429;function zM(...e){J().getBool("IS_TEST")||console.warn(...e)}function PM(...e){J().getBool("IS_TEST")||console.log(...e)}function LM(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function WM(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function BM(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=0;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function VM(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=2;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function jM(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function UM(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function HM(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);n[a]=Math.cos(s),r[a]=Math.sin(s)}return{real:n,imag:r}}function GM(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),a=Math.cos(r),s=Math.sin(r);return{real:a,imag:s}}var Qm="->",tF=/->/g,Gb=",",qb="...";function qM(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(tF,"").length)/Qm.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${Qm}").`);let[r,a]=e.split(Qm);M(r.indexOf(qb)===-1,()=>`The ellipsis notation ("${qb}") is not supported yet.`);let s=r.split(Gb),i=s.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let o=[];for(let d=0;d<a.length;++d){let p=a[d];if(!s.some(f=>f.indexOf(p)!==-1))throw new Error(`Output subscripts contain the label ${p} not present in the input subscripts.`);o.indexOf(p)===-1&&o.push(p)}for(let d=0;d<r.length;++d){let p=r[d];o.indexOf(p)===-1&&p!==Gb&&o.push(p)}let l=new Array(s.length);for(let d=0;d<i;++d){if(new Set(s[d].split("")).size!==s[d].length)throw new Error(`Found duplicate axes in input component ${s[d]}. Support for duplicate axes in input is not implemented yet.`);l[d]=[];for(let p=0;p<s[d].length;++p)l[d].push(o.indexOf(s[d][p]))}let c=o.length,u=a.length,h=[];for(let d=u;d<c;++d)h.push(d);return{allDims:o,summedDims:h,idDims:l}}function XM(e,t){let n=new Array(e);n.fill(-1);for(let a=0;a<t.length;++a)n[t[a]]=a;let r=[];for(let a=0;a<e;++a)n[a]===-1&&r.push(a);return n=n.filter(a=>a!==-1),{permutationIndices:n,expandDims:r}}function KM(e,t,n){let r=new Array(e);for(let a=0;a<n.length;++a){let s=n[a].shape;for(let i=0;i<t[a].length;++i)r[t[a][i]]===void 0?r[t[a][i]]=s[i]:M(r[t[a][i]]===s[i],()=>`Expected dimension ${r[t[a][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function ZM(e,t){let n=e,r=[],a=0;e.length===0&&n.push(-1),a=e.length+1;for(let i=0;i<a;++i)r.push([]);let s=[];for(let i=0;i<n.length;++i){let o=n[i],l=nF(t,o);for(let c of l)s.indexOf(c)===-1&&(r[i].push(c),s.push(c))}return{path:n,steps:r}}function YM(e){return e.every((t,n)=>t===n)}function nF(e,t){let n=[];for(let r=0;r<e.length;++r)(e[r].length===0||e[r].indexOf(t)!==-1||t===-1)&&n.push(r);return n}function JM(e,t,n=0){let r=[];if(typeof t=="number")M(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);M(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}M(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var Hb={};Me(Hb,{collectGatherOpShapeInfo:()=>sF,computeOutShape:()=>aF,segOpComputeOptimalWindowSize:()=>rF});function rF(e,t){let n=!1,r;for(e<=Jm?(r=e,n=!0):r=Fh(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=Fh(e,r+1);return r}function aF(e,t,n){let r=[],a=e.length;for(let s=0;s<a;s++)s!==t?r.push(e[s]):r.push(n);return r}function sF(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${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 QM(e){try{return e.map(t=>fd(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function eF(e){return e.map(t=>Bu(t))}var Hr={};Me(Hr,{nonMaxSuppressionV3Impl:()=>Db,nonMaxSuppressionV4Impl:()=>Ob,nonMaxSuppressionV5Impl:()=>zb,whereImpl:()=>Ib});function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&_.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var iF=Hr.whereImpl,op=class extends wu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Rh(this,ua())}nextDataId(){return op.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&C.warn(`
|
|
============================
|
|
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
|
|
============================`));let r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&_.isString(n[0])){let a=n.map(s=>_.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return C.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>_.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return ua().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=_.now();return e(),{kernelMs:_.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){ve([e],"where");let t=this.readSync(e.dataId);return iF(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};op.nextDataId=0;var ey={};Me(ey,{addImpl:()=>Kb,bincountImpl:()=>ty,bincountReduceImpl:()=>Zb,ceilImpl:()=>Yb,concatImpl:()=>ny,expImpl:()=>Jb,expm1Impl:()=>Qb,floorImpl:()=>e_,gatherV2Impl:()=>t_,greaterImpl:()=>n_,lessImpl:()=>r_,linSpaceImpl:()=>a_,logImpl:()=>s_,maxImpl:()=>i_,maximumImpl:()=>o_,minimumImpl:()=>l_,multiplyImpl:()=>ry,negImpl:()=>u_,notEqualImpl:()=>c_,prodImpl:()=>h_,rangeImpl:()=>sy,rsqrtImpl:()=>d_,simpleAbsImpl:()=>Xb,sliceImpl:()=>lp,squaredDifferenceImpl:()=>p_,stridedSliceImpl:()=>f_,subImpl:()=>m_,tileImpl:()=>y_,topKImpl:()=>A_,transposeImpl:()=>ay,uniqueImpl:()=>g_});function Xb(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var oF=e=>{let{x:t}=e.inputs,n=e.backend;ve(t,"abs");let r=new Float32Array(_.sizeFromShape(t.shape)),a=n.data.get(t.dataId).values;return r=Xb(a),n.makeOutput(r,t.shape,"float32")},lF={kernelName:so,backendName:"cpu",kernelFunc:oF};function Mt(e){return(t,n,r,a,s)=>{let i=C.assertAndGetBroadcastShape(t,n),o=i.length,l=_.computeStrides(i),c=_.sizeFromShape(i),u=_.getTypedArrayFromDType(s,c),h=t.length,d=n.length,p=_.computeStrides(t),f=_.computeStrides(n),m=C.getBroadcastDims(t,i),y=C.getBroadcastDims(n,i);if(m.length+y.length===0)for(let A=0;A<u.length;++A)u[A]=e(r[A%r.length],a[A%a.length]);else for(let A=0;A<u.length;++A){let g=_.indexToLoc(A,o,l),x=g.slice(-h);m.forEach(k=>x[k]=0);let v=_.locToIndex(x,h,p),w=g.slice(-d);y.forEach(k=>w[k]=0);let b=_.locToIndex(w,d,f);u[A]=e(r[v],a[b])}return[u,i]}}function Bn(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 uF={kernelName:Ph,backendName:"cpu",kernelFunc:Bn};function up(e,t,n="float32"){if(n==="complex64"){let a=up(e,t,"float32"),s=up(e,t,"float32");return Bn({inputs:{real:a,imag:s},backend:e})}let r=_.makeZerosTypedArray(_.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function Gr(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 cF={kernelName:Es,backendName:"cpu",kernelFunc:Gr};function ki(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 hF={kernelName:sd,backendName:"cpu",kernelFunc:ki};function Ha(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Gr({inputs:{x:a},backend:n});let i=up(n,a.shape,a.dtype),o=Ha({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Bn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=ki({inputs:{input:a},backend:n}),o=Ha({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=Gr({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(a.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(a.shape,"int32",o)}if(s==="bool"){let i=n.data.get(a.dataId).values,o=_.toTypedArray([0],a.dtype),[l,c]=Mt((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 dF={kernelName:ys,backendName:"cpu",kernelFunc:Ha};function Ut(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;ve([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=Ha({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=Ha({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),y=l.data.get(m.dataId),A=y.complexTensorInfos.real,g=y.complexTensorInfos.imag,x=l.data.get(A.dataId).values,v=l.data.get(g.dataId).values,[w,b,k]=n(i.shape,o.shape,p,f,x,v),N=l.makeTensorInfo(k,"float32",w),E=l.makeTensorInfo(k,"float32",b),F=Bn({inputs:{real:N,imag:E},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(N),l.disposeIntermediateTensorInfo(E),F}else{let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,c,u,h);return l.makeTensorInfo(p,h,d)}}}function iy(e){return(t,n,r,a,s,i)=>{let o=C.assertAndGetBroadcastShape(t,n),l=_.sizeFromShape(o),c=o.length,u=_.computeStrides(o),h=_.getTypedArrayFromDType("float32",l),d=_.getTypedArrayFromDType("float32",l),p=C.getBroadcastDims(t,o),f=C.getBroadcastDims(n,o),m=C.mergeRealAndImagArrays(r,a),y=C.mergeRealAndImagArrays(s,i),A=t.length,g=_.computeStrides(t),x=n.length,v=_.computeStrides(n);if(p.length+f.length===0)for(let w=0;w<h.length;w++){let b=w%m.length,k=w%y.length,N=e(m[b*2],m[b*2+1],y[k*2],y[k*2+1]);h[w]=N.real,d[w]=N.imag}else for(let w=0;w<h.length;w++){let b=_.indexToLoc(w,c,u),k=b.slice(-A);p.forEach(L=>k[L]=0);let N=_.locToIndex(k,A,g),E=b.slice(-x);f.forEach(L=>E[L]=0);let F=_.locToIndex(E,x,v),O=e(m[N*2],m[N*2+1],y[F*2],y[F*2+1]);h[w]=O.real,d[w]=O.imag}return[h,d,o]}}var Kb=Mt((e,t)=>e+t),pF=iy((e,t,n,r)=>({real:e+n,imag:t+r})),pc=Ut(Ca,Kb,pF),fF={kernelName:Ca,backendName:"cpu",kernelFunc:pc};function ty(e,t,n,r,a){let s=_.sizeFromShape(r),i=_.makeZerosTypedArray(a,n);for(let o=0;o<e.length;o++){let l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=a||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function Zb(e,t,n,r=!1){let a=e.shape[0],s=e.shape[1],i=Be([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 El(e){return(t,n,r)=>{let a=_.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function at(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(ve(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,c=_.sizeFromShape(i.shape),u=n||i.dtype,h=_.getArrayFromDType(u,c);for(let d=0;d<c;++d)h[d]=t(l[d],a);return o.makeTensorInfo(i.shape,u,h)}}function Cl(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(ve(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,c=n||i.dtype,u=t(l,c,a);return o.makeTensorInfo(i.shape,c,u)}}var Yb=El(e=>Math.ceil(e)),mF=Cl(As,Yb),yF={kernelName:As,backendName:"cpu",kernelFunc:mF};function ny(e,t,n,r){let a=_.getArrayFromDType(n,_.sizeFromShape(t));if(r&&n!=="string"){let s=0;e.forEach(i=>{let o=_.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?C.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 Jb=El(e=>Math.exp(e)),x_=Cl(ks,Jb),AF={kernelName:ks,backendName:"cpu",kernelFunc:x_},Qb=El(e=>Math.expm1(e)),gF=Cl(ko,Qb),xF={kernelName:ko,backendName:"cpu",kernelFunc:gF},e_=El(e=>Math.floor(e)),wF=Cl(Is,e_),bF={kernelName:Is,backendName:"cpu",kernelFunc:wF};function t_(e,t,n){let r=Be(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 n_=Mt((e,t)=>e>t?1:0),_F=Ut(To,n_,null,"bool"),vF={kernelName:To,backendName:"cpu",kernelFunc:_F},r_=Mt((e,t)=>e<t?1:0),kF=Ut(Mo,r_,null,"bool"),IF={kernelName:Mo,backendName:"cpu",kernelFunc:kF};function a_(e,t,n){let r=(t-e)/(n-1),a=_.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}var s_=El(e=>Math.log(e)),SF=Cl(Rs,s_),NF={kernelName:Rs,backendName:"cpu",kernelFunc:SF};function i_(e,t,n,r){let a=_.getTypedArrayFromDType(r,_.sizeFromShape(n));for(let s=0;s<a.length;++s){let i=s*t,o=e[i];for(let l=0;l<t;++l){let c=e[i+l];c>o&&(o=c)}a[s]=o}return a}var o_=Mt((e,t)=>Math.max(e,t)),TF=Ut(Fs,o_),EF={kernelName:Fs,backendName:"cpu",kernelFunc:TF},l_=Mt((e,t)=>Math.min(e,t)),CF=Ut(zs,l_),RF={kernelName:zs,backendName:"cpu",kernelFunc:CF},ry=Mt((e,t)=>e*t),MF=iy((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),cp=Ut(Ls,ry,MF),FF={kernelName:Ls,backendName:"cpu",kernelFunc:cp};function u_(e,t,n){let r=_.createScalarValue(-1,n);return ry([],t,r,e,n)}function $F(e){let{inputs:t,backend:n}=e,{x:r}=t;ve(r,"neg");let a=n.data.get(r.dataId).values,[s,i]=u_(a,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,s)}var DF={kernelName:zo,backendName:"cpu",kernelFunc:$F},c_=Mt((e,t)=>e!==t?1:0),OF=Ut(Po,c_,null,"bool"),zF={kernelName:Po,backendName:"cpu",kernelFunc:OF};function ay(e,t,n,r,a){let s=t.length,i=_.sizeFromShape(t),o=_.computeStrides(t),l=_.computeStrides(a),c=_.getTypedArrayFromDType(n,_.sizeFromShape(a));for(let u=0;u<i;++u){let h=_.indexToLoc(u,s,o),d=new Array(h.length);for(let f=0;f<d.length;f++)d[f]=h[r[f]];let p=_.locToIndex(d,s,l);c[p]=e[u]}return c}function tr(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{perm:s}=n;ve(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=ay(l,a.shape,a.dtype,s,o);return{dataId:r.write(c,o,a.dtype),shape:o,dtype:a.dtype}}var PF={kernelName:si,backendName:"cpu",kernelFunc:tr};function h_(e,t,n,r){let[a,s]=C.computeOutAndReduceShapes(e,r),i=dr(t,"int32"),o=_.makeZerosTypedArray(_.sizeFromShape(a),i),l=_.sizeFromShape(s);for(let c=0;c<o.length;++c){let u=c*l,h=1;for(let d=0;d<l;++d)h*=n[u+d];o[c]=h}return{outVals:o,outShape:a,outDtype:i}}function LF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"prod");let o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=C.getAxesPermutation(l,o),u=l,h=a,d=[];c!=null&&(h=tr({inputs:{x:a},backend:n,attrs:{perm:c}}),d.push(h),u=C.getInnerMostAxes(u.length,o));let p=n.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:y}=h_(h.shape,h.dtype,p,u),A=m;return i&&(A=C.expandShapeToKeepDim(m,l)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(A,y,f)}var WF={kernelName:Uo,backendName:"cpu",kernelFunc:LF};function sy(e,t,n,r){let a=e===t,s=e<t&&n<0,i=t<e&&n>1;if(a||s||i)return _.makeZerosTypedArray(0,r);let o=Math.abs(Math.ceil((t-e)/n)),l=_.makeZerosTypedArray(o,r);t<e&&n===1&&(n=-1),l[0]=e;for(let c=1;c<l.length;c++)l[c]=l[c-1]+n;return l}var d_=El(e=>1/Math.sqrt(e)),BF=Cl(Ks,d_),VF={kernelName:Ks,backendName:"cpu",kernelFunc:BF};function lp(e,t,n,r,a){let s=un.isSliceContinous(r,t,n),i=_.sizeFromShape(n),o=_.computeStrides(r);if(s){let h=un.computeFlatOffset(t,o);return a==="string"?e.slice(h,h+i):e.subarray(h,h+i)}let l=a==="string"?C.fromUint8ToStringArray(e):e,c=Be(r,a,l),u=Be(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"?C.fromStringArrayToUint8(u.values):u.values}function Ii(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r;ve(a,"slice");let[o,l]=un.parseSliceParams(a,s,i);un.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 jF={kernelName:Zo,backendName:"cpu",kernelFunc:Ii},p_=Mt((e,t)=>{let n=e-t;return n*n}),UF=Ut(ti,p_),HF={kernelName:ti,backendName:"cpu",kernelFunc:UF};function f_(e,t,n,r){let a=Be(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 m_=Mt((e,t)=>e-t),GF=iy((e,t,n,r)=>({real:e-n,imag:t-r})),oy=Ut(ni,m_,GF),qF={kernelName:ni,backendName:"cpu",kernelFunc:oy};function y_(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=Be(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 A_(e,t,n,r,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=_.getTypedArrayFromDType(n,i*r),c=_.getTypedArrayFromDType("int32",i*r);for(let h=0;h<i;h++){let d=h*o,p=e.subarray(d,d+o),f=[];for(let g=0;g<p.length;g++)f.push({value:p[g],index:g});f.sort((g,x)=>x.value-g.value);let m=h*r,y=l.subarray(m,m+r),A=c.subarray(m,m+r);for(let g=0;g<r;g++)y[g]=f[g].value,A[g]=f[g].index}let u=t.slice();return u[u.length-1]=r,[Be(u,n,l),Be(u,"int32",c)]}function g_(e,t,n,r){let a=_.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 Dt(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 y=[];for(let A=0;A<s[0];A++)for(let g=0;g<s[2];g++)y.push(l.get(A,f,g));m=y.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let y=Object.keys(i).length;i[m]=y,o[f]=y,c.push(f)}}let h=s.slice();h[1]=Object.keys(i).length;let d=new Dt(h,r);c.forEach((f,m)=>{for(let y=0;y<s[0];y++)for(let A=0;A<s[2];A++)d.set(l.get(y,f,A),y,m,A)});let p=n.slice();return p[a]=h[1],{outputValues:d.values,outputShape:p,indices:o}}var w_="3.4.0";fl("cpu",()=>new op,1);var b_=at(wo,e=>e>=0?e:Math.exp(e)-1),XF={kernelName:wo,backendName:"cpu",kernelFunc:b_};function __(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;ve([a],"leakyRelu");let i=_.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=_.getTypedArrayFromDType("float32",i);for(let c=0;c<o.length;c++)l[c]=o[c]<0?s*o[c]:o[c];return n.makeTensorInfo(a.shape,"float32",l)}var KF={kernelName:Cs,backendName:"cpu",kernelFunc:__},ZF=Mt((e,t)=>e<0?t*e:e);function v_(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t;ve([r,a],"prelu");let s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[o,l]=ZF(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var YF={kernelName:js,backendName:"cpu",kernelFunc:v_},k_=at(Us,e=>Math.max(0,e)),JF={kernelName:Us,backendName:"cpu",kernelFunc:k_},I_=at(Gs,e=>Math.min(Math.max(0,e),6)),QF={kernelName:Gs,backendName:"cpu",kernelFunc:I_};function ly(e,t,n,r,a){if(n==="linear")return Gr({inputs:{x:t},backend:e});if(n==="relu")return k_({inputs:{x:t},backend:e});if(n==="elu")return b_({inputs:{x:t},backend:e});if(n==="relu6")return I_({inputs:{x:t},backend:e});if(n==="prelu")return v_({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return __({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function mt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=_.sizeFromShape(a.shape),o=_.inferFromImplicitShape(s,i),l=_.sizeFromShape(o);_.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(a.dataId);let c=n.data.get(a.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,h=c.complexTensorInfos.imag;u.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var e$={kernelName:Go,backendName:"cpu",kernelFunc:mt};function S_(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;ve([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),y=_.sizeFromShape(f),A=_.sizeFromShape(m),g=y===A||y===1||A===1;_.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let x=(y>A?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);_.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[y,u,d]:[y,d,u],w=o?[A,p,h]:[A,h,p],b=mt({inputs:{x:a},backend:n,attrs:{shape:v}}),k=mt({inputs:{x:s},backend:n,attrs:{shape:w}}),N=i?b.shape[1]:b.shape[2],E=i?b.shape[2]:b.shape[1],F=o?k.shape[1]:k.shape[2],O=Math.max(y,A),L=n.data.get(b.dataId).values,V=n.data.get(k.dataId).values,j=_.computeStrides(b.shape),U=_.computeStrides(k.shape),[X,G,ee]=i?[j[0],1,j[1]]:[j[0],j[1],1],[Y,ae,te]=o?[1,U[1],U[0]]:[U[1],1,U[0]],ie=E*F,Q=Be([O,E,F],b.dtype),he=Q.values,oe=n.blockSize;for(let fe=0;fe<O;fe++)for(let pe=0;pe<E;pe+=oe)for(let ke=0;ke<F;ke+=oe)for(let Se=0;Se<N;Se+=oe){let Fe=Math.min(pe+oe,E),Oe=Math.min(ke+oe,F),$e=Math.min(Se+oe,N);for(let et=pe;et<Fe;et++)for(let tt=ke;tt<Oe;tt++){let it=0;for(let Ke=Se;Ke<$e;Ke++){let dt=Math.min(fe,y-1)*X,je=Math.min(fe,A-1)*te,_n=L[dt+et*G+Ke*ee],bt=V[Ke*Y+tt*ae+je];it+=_n*bt}he[fe*ie+(et*F+tt)]+=it}}return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(k),n.makeTensorInfo(x,Q.dtype,Q.values)}var t$={kernelName:ms,backendName:"cpu",kernelFunc:S_};function n$(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,d,p,f,m=[];d=S_({inputs:{a,b:s},attrs:{transposeA:l,transposeB:c},backend:n}),i&&(p=pc({inputs:{a:d,b:i},backend:n}),m.push(d),d=p),u&&(f=ly(n,d,u,o,h),m.push(d),d=f);for(let y of m)n.disposeIntermediateTensorInfo(y);return d}var r$={kernelName:ii,backendName:"cpu",kernelFunc:n$},a$=at(io,e=>Math.acos(e)),s$={kernelName:io,backendName:"cpu",kernelFunc:a$},i$=at(oo,e=>Math.acosh(e)),o$={kernelName:oo,backendName:"cpu",kernelFunc:i$};function l$(e){let{inputs:t,backend:n}=e,r=t;ve(t,"addN");let a=r.map(o=>n.data.get(o.dataId).values),s=Be(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 u$={kernelName:ds,backendName:"cpu",kernelFunc:l$};function c$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"all");let o=_.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=tr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),f=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let g=A*p,x=m[g];for(let v=0;v<p;++v){let w=m[g+v];x=x&&w}f[A]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let y=n.makeTensorInfo(h,u.dtype,f);if(i){let A=C.expandShapeToKeepDim(h,o),g=mt({inputs:{x:y},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(y),g}return y}var h$={kernelName:lo,backendName:"cpu",kernelFunc:c$};function d$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"any");let o=_.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=tr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),f=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let g=A*p,x=m[g];for(let v=0;v<p;++v){let w=m[g+v];x=x||w}f[A]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let y=n.makeTensorInfo(h,u.dtype,f);if(i){let A=C.expandShapeToKeepDim(h,o),g=mt({inputs:{x:y},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(y),g}return y}var p$={kernelName:uo,backendName:"cpu",kernelFunc:d$};function f$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ve(a,"argMax");let i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=tr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=_.sizeFromShape(u),p=_.makeZerosTypedArray(d,"int32"),f=_.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let y=0;y<p.length;++y){let A=y*f,g=m[A],x=0;for(let v=0;v<f;++v){let w=m[A+v];w>g&&(g=w,x=v)}p[y]=x}return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(u,"int32",p)}var m$={kernelName:ps,backendName:"cpu",kernelFunc:f$};function y$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ve(a,"argMin");let i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=tr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=_.sizeFromShape(u),p=_.makeZerosTypedArray(d,"int32"),f=_.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let y=0;y<p.length;++y){let A=y*f,g=m[A],x=0;for(let v=0;v<f;++v){let w=m[A+v];w<g&&(g=w,x=v)}p[y]=x}return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(u,"int32",p)}var A$={kernelName:vu,backendName:"cpu",kernelFunc:y$},g$=at(co,e=>Math.asin(e)),x$={kernelName:co,backendName:"cpu",kernelFunc:g$},w$=at(ho,e=>Math.asinh(e)),b$={kernelName:ho,backendName:"cpu",kernelFunc:w$},_$=at(po,e=>Math.atan(e)),v$={kernelName:po,backendName:"cpu",kernelFunc:_$},k$=Mt((e,t)=>Math.atan2(e,t)),I$=Ut(mo,k$),S$={kernelName:mo,backendName:"cpu",kernelFunc:I$},N$=at(fo,e=>Math.atanh(e)),T$={kernelName:fo,backendName:"cpu",kernelFunc:N$};function uy(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=Be(a.outShape,n),y=m.values,A=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],x=a.outShape[3];for(let v=0;v<a.batchSize;++v){let w=v*A,b=v*r[0];for(let k=0;k<a.inChannels;++k)for(let N=0;N<a.outHeight;++N){let E=N*i-d,F=Math.max(0,E),O=Math.min(a.inHeight,u+E),L=w+N*g;for(let V=0;V<a.outWidth;++V){let j=V*o-p,U=Math.max(0,j),X=Math.min(a.inWidth,h+j),G=f,ee=0,Y=0;for(let te=F;te<O;te+=l){let ie=b+te*r[1];for(let Q=U;Q<X;Q+=c){let he=ie+Q*r[2],oe=e[he+k];s==="max"&&oe>G?G=oe:s==="avg"&&(ee+=oe,Y++)}if(isNaN(G))break}let ae=L+V*x+k;y[ae]=s==="avg"?ee/Y:G}}}return m}function N_(e,t,n,r,a=!1,s=!1){let i=Be(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=Be(t,n,e);for(let y=0;y<r.batchSize;++y)for(let A=0;A<r.inChannels;++A)for(let g=0;g<r.outHeight;++g){let x=g*o-p,v=x;for(;v<0;)v+=c;let w=Math.min(r.inHeight,h+x);for(let b=0;b<r.outWidth;++b){let k=b*l-f,N=k;for(;N<0;)N+=u;let E=Math.min(r.inWidth,d+k),F=Number.NEGATIVE_INFINITY,O=-1;for(let L=v;L<w;L+=c){let V=L-x;for(let j=N;j<E;j+=u){let U=j-k,X=m.get(y,L,j,A);X>F&&(F=X,a?O=s?((y*r.inHeight+L)*r.inWidth+j)*r.inChannels+A:(L*r.inWidth+j)*r.inChannels+A:O=V*d+U)}}i.set(O,y,g,b,A)}}return i}function T_(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,y=a.padInfo.top,A=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Be(a.outShape,n),v=x.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],b=a.outShape[2]*a.outShape[3]*a.outShape[4],k=a.outShape[3]*a.outShape[4],N=a.outShape[4];for(let E=0;E<a.batchSize;++E){let F=E*w,O=E*r[0];for(let L=0;L<a.inChannels;++L)for(let V=0;V<a.outDepth;++V){let j=V*i-m,U=j;for(;U<0;)U+=c;let X=Math.min(a.inDepth,d+j),G=F+V*b;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-y,ae=Y;for(;ae<0;)ae+=u;let te=Math.min(a.inHeight,p+Y),ie=G+ee*k;for(let Q=0;Q<a.outWidth;++Q){let he=Q*l-A,oe=he;for(;oe<0;)oe+=h;let fe=Math.min(a.inWidth,f+he),pe=ie+Q*N,ke=g,Se=0,Fe=0;for(let $e=U;$e<X;$e+=c){let et=O+$e*r[1];for(let tt=ae;tt<te;tt+=u){let it=et+tt*r[2];for(let Ke=oe;Ke<fe;Ke+=h){let dt=it+Ke*r[3],je=e[dt+L];if(s==="max"&&je>ke?ke=je:s==="avg"&&(Se+=je,Fe++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Oe=pe+L;v[Oe]=s==="avg"?Se/Fe:ke}}}}return x}function E$(e,t){let n=Be(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let y=0;y<t.inChannels;++y)for(let A=0;A<t.outDepth;++A){let g=A*r-d,x=g;for(;x<0;)x+=i;let v=Math.min(t.inDepth,c+g);for(let w=0;w<t.outHeight;++w){let b=w*a-p,k=b;for(;k<0;)k+=o;let N=Math.min(t.inHeight,u+b);for(let E=0;E<t.outWidth;++E){let F=E*s-f,O=F;for(;O<0;)O+=l;let L=Math.min(t.inWidth,h+F),V=Number.NEGATIVE_INFINITY,j=-1;for(let U=x;U<v;U+=i){let X=U-g;for(let G=k;G<N;G+=o){let ee=G-b;for(let Y=O;Y<L;Y+=l){let ae=Y-F,te=e.get(m,U,G,Y,y);te>=V&&(V=te,j=X*u*h+ee*u+ae)}}}n.set(j,m,A,w,E,y)}}}return n}function C$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ve(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))h=Gr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=_.computeStrides(a.shape),f=uy(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var R$={kernelName:fs,backendName:"cpu",kernelFunc:C$};function M$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;ve(a,"avgPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=T_(h,a.shape,a.dtype,_.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var F$={kernelName:ku,backendName:"cpu",kernelFunc:M$};function $$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;ve([a,s],"avgPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,y=u.filterWidth,A=u.dilationDepth,g=u.dilationHeight,x=u.dilationWidth,v=u.effectiveFilterDepth,w=u.effectiveFilterHeight,b=u.effectiveFilterWidth,k=v-1-u.padInfo.front,N=b-1-u.padInfo.left,E=w-1-u.padInfo.top,F=Be(s.shape,"float32"),O=1/(f*m*y),L=n.bufferSync(a);for(let V=0;V<u.batchSize;++V)for(let j=0;j<u.inChannels;++j)for(let U=0;U<u.inDepth;++U)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=U-k,Y=X-E,ae=G-N,te=0;for(let ie=0;ie<v;ie+=A){let Q=(ee+ie)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(Y+he)/d;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe<b;fe+=x){let pe=(ae+fe)/p;pe<0||pe>=u.outWidth||Math.floor(pe)!==pe||(te+=L.get(V,Q,oe,pe,j))}}}F.set(te*O,V,U,X,G,j)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var D$={kernelName:Oh,backendName:"cpu",kernelFunc:$$};function O$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ve([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,y=u.dilationWidth,A=u.effectiveFilterHeight,g=u.effectiveFilterWidth,x=g-1-u.padInfo.left,v=A-1-u.padInfo.top,w=Be(i.shape,"float32"),b=1/(p*f),k=n.data.get(a.dataId).values,N=Be(a.shape,"float32",k);for(let E=0;E<u.batchSize;++E)for(let F=0;F<u.inChannels;++F)for(let O=0;O<u.inHeight;++O)for(let L=0;L<u.inWidth;++L){let V=O-v,j=L-x,U=0;for(let X=0;X<A;X+=m){let G=(V+X)/h;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=y){let Y=(j+ee)/d;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(U+=N.get(E,G,Y,F))}}w.set(U*b,E,O,L,F)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var z$={kernelName:Dh,backendName:"cpu",kernelFunc:O$};function P$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;_.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ve([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),y=f.length,A=p.length,g=d.length,x=h.length,v=0,w=0,b=0,k=0;for(let N=0;N<u.length;++N)m[N]=f[v++]+(u[N]-h[w++])*p[b++]/Math.sqrt(d[k++]+c),v>=y&&(v=0),w>=x&&(w=0),b>=A&&(b=0),k>=g&&(k=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var L$={kernelName:Ns,backendName:"cpu",kernelFunc:P$};function W$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((A,g)=>A*g),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=mt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=tr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Ii({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),y}var B$={kernelName:Iu,backendName:"cpu",kernelFunc:W$};function V$(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=ty(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var j$={kernelName:zh,backendName:"cpu",kernelFunc:V$},U$=at(Ra,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),H$={kernelName:Ra,backendName:"cpu",kernelFunc:U$},G$=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(_.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},q$={kernelName:Su,backendName:"cpu",kernelFunc:G$};function Rl(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 X$={kernelName:Jh,backendName:"cpu",kernelFunc:Rl};function Ml(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(m=>m.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>_.sizeFromShape(m.shape)>0);if(o.length===1)return Gr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(v=>ki({inputs:{input:v},backend:n})),y=o.map(v=>Rl({inputs:{input:v},backend:n})),A=Ml({inputs:m,backend:n,attrs:{axis:s}}),g=Ml({inputs:y,backend:n,attrs:{axis:s}}),x=Bn({inputs:{real:A,imag:g},backend:n});return m.forEach(v=>n.disposeIntermediateTensorInfo(v)),y.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(g),x}let c=o.map(m=>{let y=_.sizeFromShape(m.shape.slice(s));return mt({inputs:{x:m},backend:n,attrs:{shape:[-1,y]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,d=ny(u,i,t[0].dtype,h),p=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var K$={kernelName:yo,backendName:"cpu",kernelFunc:Ml};function E_(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;ve([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,y=d.dilationWidth,A=d.padInfo.left,g=d.padInfo.top,x=d.dataFormat==="channelsLast",v=new Dt(d.outShape,a.dtype),w=_.computeStrides(a.shape),b=_.computeStrides(s.shape),k=w[0],N=x?w[1]:w[2],E=x?w[2]:1,F=x?1:w[1],O=v.strides[0],L=x?v.strides[1]:v.strides[2],V=x?v.strides[2]:1,j=x?1:v.strides[1],U=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=v.values;for(let ee=0;ee<d.batchSize;++ee){let Y=ee*k,ae=ee*O;for(let te=0;te<d.outHeight;++te){let ie=ae+te*L,Q=te*d.strideHeight-g;for(let he=0;he<p;++he){let oe=Q+he*m;if(oe<0||oe>=d.inHeight)continue;let fe=he*b[0],pe=Y+oe*N;for(let ke=0;ke<d.outWidth;++ke){let Se=ie+ke*V,Fe=ke*d.strideWidth-A;for(let Oe=0;Oe<f;++Oe){let $e=Fe+Oe*y;if($e<0||$e>=d.inWidth)continue;let et=fe+Oe*b[1],tt=pe+$e*E,it=et;for(let Ke=0;Ke<d.inChannels;++Ke){let dt=U[tt+Ke*F];for(let je=0;je<d.outChannels;++je)G[Se+je*j]+=dt*X[it+je];it+=d.outChannels}}}}}}return n.makeTensorInfo(v.shape,v.dtype,G)}var Z$={kernelName:gs,backendName:"cpu",kernelFunc:E_};function Y$(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;ve([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:y}=d,A=d.dataFormat==="channelsLast",g=new Dt(d.filterShape,"float32"),x=d.padInfo.left,v=d.padInfo.top,w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,k=new Dt(a.shape,a.dtype,w),N=new Dt(s.shape,s.dtype,b);for(let E=0;E<m;++E){let F=Math.max(0,Math.ceil((v-E)/p)),O=Math.min(d.outHeight,(d.inHeight+v-E)/p);for(let L=0;L<y;++L){let V=Math.max(0,Math.ceil((x-L)/f)),j=Math.min(d.outWidth,(d.inWidth+x-L)/f);for(let U=0;U<d.inChannels;++U)for(let X=0;X<d.outChannels;++X){let G=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=F;Y<O;++Y){let ae=E+Y*p-v;for(let te=V;te<j;++te){let ie=L+te*f-x;A?G+=k.get(ee,ae,ie,U)*N.get(ee,Y,te,X):G+=k.get(ee,U,ae,ie)*N.get(ee,X,Y,te)}}g.set(G,E,L,U,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var J$={kernelName:Lh,backendName:"cpu",kernelFunc:Y$};function Q$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;ve([a,s],"conv2dBackpropInput");let h=_.computeStrides(s.shape),d=_.computeStrides(a.shape),p=C.convertConv2DDataFormat(c),f=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),m=new Dt(f.inShape,"float32"),y=m.values,A=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[x,v,w]=h,{batchSize:b,filterHeight:k,filterWidth:N,inChannels:E,inHeight:F,inWidth:O,outChannels:L,outHeight:V,outWidth:j,strideHeight:U,strideWidth:X}=f;p=f.dataFormat;let G=k-1-f.padInfo.top,ee=N-1-f.padInfo.left,Y=p==="channelsLast",ae=m.strides[0],te=Y?m.strides[1]:m.strides[2],ie=Y?m.strides[2]:1,Q=Y?1:m.strides[1],he=d[0],oe=Y?d[1]:d[2],fe=Y?d[2]:1,pe=Y?1:d[1];for(let ke=0;ke<b;++ke)for(let Se=0;Se<E;++Se)for(let Fe=0;Fe<F;++Fe){let Oe=Fe-G,$e=Math.max(0,Math.ceil(Oe/U)),et=Math.min(V,(k+Oe)/U);for(let tt=0;tt<O;++tt){let it=tt-ee,Ke=Math.max(0,Math.ceil(it/X)),dt=Math.min(j,(N+it)/X),je=0;for(let bt=$e;bt<et;++bt){let Kn=bt*U-Oe;for(let Xt=Ke;Xt<dt;++Xt){let vn=Xt*X-it,Zn=he*ke+oe*bt+fe*Xt,Dn=x*(k-1-Kn)+v*(N-1-vn)+w*Se;for(let on=0;on<L;++on){let Kt=A[Zn+pe*on],Or=g[Dn+on];je+=Kt*Or}}}let _n=ae*ke+te*Fe+ie*tt+Q*Se;y[_n]=je}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var eD={kernelName:xs,backendName:"cpu",kernelFunc:Q$};function tD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;ve([a,s],"conv3d");let c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:y}=c,A=y.front,g=y.left,x=y.top,v=new Dt(c.outShape,a.dtype),w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,k=v.values,N=_.computeStrides(a.shape),E=_.computeStrides(s.shape);for(let F=0;F<c.batchSize;++F){let O=F*N[0],L=F*v.strides[0];for(let V=0;V<c.outDepth;++V){let j=L+V*v.strides[1],U=V*c.strideDepth-A;for(let X=0;X<u;++X){let G=U+X*p;if(G<0||G>=c.inDepth)continue;let ee=X*E[0],Y=O+G*N[1];for(let ae=0;ae<c.outHeight;++ae){let te=j+ae*v.strides[2],ie=ae*c.strideHeight-x;for(let Q=0;Q<h;++Q){let he=ie+Q*f;if(he<0||he>=c.inHeight)continue;let oe=ee+Q*E[1],fe=Y+he*N[2];for(let pe=0;pe<c.outWidth;++pe){let ke=te+pe*c.outChannels,Se=pe*c.strideWidth-g;for(let Fe=0;Fe<d;++Fe){let Oe=Se+Fe*m;if(Oe<0||Oe>=c.inWidth)continue;let $e=oe+Fe*E[2],et=fe+Oe*c.inChannels,tt=$e;for(let it=0;it<c.inChannels;++it){let Ke=w[et+it];for(let dt=0;dt<c.outChannels;++dt)k[ke+dt]+=Ke*b[tt+dt];tt+=c.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var nD={kernelName:Nu,backendName:"cpu",kernelFunc:tD};function rD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;ve([a,s],"conv3dBackpropFilterV2");let c=_.computeStrides(a.shape),u=_.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,y=h.filterHeight,A=h.filterWidth,g=new Dt(h.filterShape,"float32"),x=g.values,[v,w,b,k]=g.strides,N=n.data.get(s.dataId).values,[E,F,O,L]=u,V=n.data.get(a.dataId).values,[j,U,X,G]=c,ee=h.padInfo.front,Y=h.padInfo.left,ae=h.padInfo.top;for(let te=0;te<m;++te){let ie=Math.max(0,Math.ceil((ee-te)/d)),Q=Math.min(h.outDepth,(h.inDepth+ee-te)/d),he=te*v;for(let oe=0;oe<y;++oe){let fe=Math.max(0,Math.ceil((ae-oe)/p)),pe=Math.min(h.outHeight,(h.inHeight+ae-oe)/p),ke=oe*w+he;for(let Se=0;Se<A;++Se){let Fe=Math.max(0,Math.ceil((Y-Se)/f)),Oe=Math.min(h.outWidth,(h.inWidth+Y-Se)/f),$e=Se*b+ke;for(let et=0;et<h.inChannels;++et){let tt=et*k+$e;for(let it=0;it<h.outChannels;++it){let Ke=0;for(let dt=0;dt<h.batchSize;++dt){let je=dt*j,_n=dt*E;for(let bt=ie;bt<Q;++bt){let Kn=(te+bt*d-ee)*U+je,Xt=bt*F+_n;for(let vn=fe;vn<pe;++vn){let Zn=(oe+vn*p-ae)*X+Kn,Dn=vn*O+Xt;for(let on=Fe;on<Oe;++on){let Kt=(Se+on*f-Y)*G+Zn,Or=on*L+Dn;Ke+=V[Kt+et]*N[Or+it]}}}}x[tt+it]=Ke}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var aD={kernelName:Wh,backendName:"cpu",kernelFunc:rD};function sD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;ve([a],"conv3dBackpropInputV2");let c=_.computeStrides(a.shape),u=_.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),d=new Dt(h.inShape,"float32"),p=d.values,[f,m,y,A]=d.strides,g=n.data.get(a.dataId).values,[x,v,w,b]=c,k=n.data.get(s.dataId).values,[N,E,F,O]=u,{batchSize:L,filterDepth:V,filterHeight:j,filterWidth:U,inChannels:X,inDepth:G,inHeight:ee,inWidth:Y,outChannels:ae,outDepth:te,outHeight:ie,outWidth:Q,strideDepth:he,strideHeight:oe,strideWidth:fe}=h,pe=V-1-h.padInfo.front,ke=j-1-h.padInfo.top,Se=U-1-h.padInfo.left;for(let Fe=0;Fe<L;++Fe)for(let Oe=0;Oe<X;++Oe)for(let $e=0;$e<G;++$e){let et=$e-pe,tt=Math.max(0,Math.ceil(et/he)),it=Math.min(te,(V+et)/he);for(let Ke=0;Ke<ee;++Ke){let dt=Ke-ke,je=Math.max(0,Math.ceil(dt/oe)),_n=Math.min(ie,(j+dt)/oe);for(let bt=0;bt<Y;++bt){let Kn=bt-Se,Xt=Math.max(0,Math.ceil(Kn/fe)),vn=Math.min(Q,(U+Kn)/fe),Zn=0;for(let Dn=tt;Dn<it;++Dn){let on=Dn*he-et;for(let Kt=je;Kt<_n;++Kt){let Or=Kt*oe-dt;for(let ir=Xt;ir<vn;++ir){let or=ir*fe-Kn,ba=x*Fe+v*Dn+w*Kt+b*ir,na=N*(V-1-on)+E*(j-1-Or)+F*(U-1-or)+O*Oe;for(let _a=0;_a<ae;++_a){let Hi=g[ba+_a],zr=k[na+_a];Zn+=Hi*zr}}}}p[f*Fe+m*$e+y*Ke+A*bt+Oe]=Zn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var iD={kernelName:Bh,backendName:"cpu",kernelFunc:sD},oD=at(ws,e=>Math.cos(e)),lD={kernelName:ws,backendName:"cpu",kernelFunc:oD},uD=at(Ao,e=>Math.cosh(e)),cD={kernelName:Ao,backendName:"cpu",kernelFunc:uD};function hD(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,[u,h,d,p]=a.shape,f=s.shape[0],[m,y]=o,A=Be([f,m,y,p],"float32"),g=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(a.dataId).values,w=_.computeStrides(a.shape),b=_.computeStrides(A.shape);for(let k=0;k<f;k++){let N=k*4,E=g[N],F=g[N+1],O=g[N+2],L=g[N+3],V=x[k];if(V>=u)continue;let j=m>1?(O-E)*(h-1)/(m-1):0,U=y>1?(L-F)*(d-1)/(y-1):0;for(let X=0;X<m;X++){let G=m>1?E*(h-1)+X*j:.5*(E+O)*(h-1);if(G<0||G>h-1){for(let ee=0;ee<y;ee++)for(let Y=0;Y<p;Y++){let ae=Y+ee*b[2]+X*b[1]+k*b[0];A.values[ae]=c}continue}if(l==="bilinear"){let ee=Math.floor(G),Y=Math.ceil(G),ae=G-ee;for(let te=0;te<y;te++){let ie=y>1?F*(d-1)+te*U:.5*(F+L)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe<p;fe++){let pe=fe+te*b[2]+X*b[1]+k*b[0];A.values[pe]=c}continue}let Q=Math.floor(ie),he=Math.ceil(ie),oe=ie-Q;for(let fe=0;fe<p;fe++){let pe=fe+Q*w[2]+ee*w[1]+V*w[0],ke=v[pe];pe=fe+he*w[2]+ee*w[1]+V*w[0];let Se=v[pe];pe=fe+Q*w[2]+Y*w[1]+V*w[0];let Fe=v[pe];pe=fe+he*w[2]+Y*w[1]+V*w[0];let Oe=v[pe],$e=ke+(Se-ke)*oe,et=Fe+(Oe-Fe)*oe;pe=fe+te*b[2]+X*b[1]+k*b[0],A.values[pe]=$e+(et-$e)*ae}}}else for(let ee=0;ee<y;++ee){let Y=y>1?F*(d-1)+ee*U:.5*(F+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;ie<p;ie++){let Q=ie+ee*b[2]+X*b[1]+k*b[0];A.values[Q]=c}continue}let ae=Math.round(Y),te=Math.round(G);for(let ie=0;ie<p;ie++){let Q=ie+ae*w[2]+te*w[1]+V*w[0],he=ie+ee*b[2]+X*b[1]+k*b[0];A.values[he]=v[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var dD={kernelName:go,backendName:"cpu",kernelFunc:hD};function pD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;ve(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=tr({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=C.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=dr(c.dtype,"int32"),d=_.makeZerosTypedArray(_.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(A,g)=>A+f-g-1:(A,g)=>A+g;for(let A=0;A<p.length;A+=f)for(let g=0;g<f;g++){let x=m(A,g);if(g===0)d[x]=i?0:p[x];else{let v=m(A,g-1);d[x]=i?p[v]+d[v]:p[x]+d[v]}}let y=n.makeTensorInfo(c.shape,h,d);if(l!=null){let A=C.getUndoAxesPermutation(l),g=tr({inputs:{x:y},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(c),g}return y}var fD={kernelName:bs,backendName:"cpu",kernelFunc:pD};function mD(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=ty(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=Zb(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 yD={kernelName:Vh,backendName:"cpu",kernelFunc:mD};function AD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],c=a.shape[2],u=a.shape[3],h=l*s,d=c*s,p=u/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*d*p),y=0;for(let A=0;A<o;++A)for(let g=0;g<h;++g){let x=Math.floor(g/s),v=g%s;for(let w=0;w<d;++w){let b=Math.floor(w/s),k=w%s,N=(v*s+k)*p;for(let E=0;E<p;++E){let F=E+N+u*(b+c*(x+l*A));m[y++]=f[F]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,m)}var gD={kernelName:xo,backendName:"cpu",kernelFunc:AD};function C_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r;ve([a,s],"depthwiseConv2DNative");let u=_.computeStrides(a.shape),h=_.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),_.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=C.computeConv2DInfo(a.shape,s.shape,i,d,o,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:y,dilationWidth:A,padInfo:g}=p,x=g.left,v=g.top,w=p.outChannels/p.inChannels,b=new Dt(p.outShape,a.dtype),k=n.data.get(a.dataId).values,N=n.data.get(s.dataId).values,E=b.values;for(let F=0;F<p.batchSize;++F){let O=F*u[0],L=F*b.strides[0];for(let V=0;V<p.outHeight;++V){let j=L+V*b.strides[1],U=V*p.strideHeight-x;for(let X=0;X<f;++X){let G=U+X*y;if(G<0||G>=p.inHeight)continue;let ee=X*h[0],Y=O+G*u[1];for(let ae=0;ae<p.outWidth;++ae){let te=j+ae*b.strides[2],ie=ae*p.strideWidth-v;for(let Q=0;Q<m;++Q){let he=ie+Q*A;if(he<0||he>=p.inWidth)continue;let oe=ee+Q*h[1],fe=Y+he*p.inChannels,pe=te,ke=oe;for(let Se=0;Se<p.inChannels;++Se){let Fe=k[fe+Se];for(let Oe=0;Oe<w;++Oe)E[pe+Oe]+=Fe*N[ke+Oe];pe+=w,ke+=w}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var xD={kernelName:_s,backendName:"cpu",kernelFunc:C_};function wD(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;ve([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,y=new Dt(h.filterShape,"float32"),A=h.padInfo.left,g=h.padInfo.top,x=h.outChannels/h.inChannels,v=n.data.get(a.dataId).values,w=new Dt(a.shape,a.dtype,v),b=n.data.get(s.dataId).values,k=new Dt(s.shape,s.dtype,b);for(let N=0;N<f;++N){let E=Math.max(0,Math.ceil((g-N)/d)),F=Math.min(h.outHeight,(h.inHeight+g-N)/d);for(let O=0;O<m;++O){let L=Math.max(0,Math.ceil((A-O)/p)),V=Math.min(h.outWidth,(h.inWidth+A-O)/p);for(let j=0;j<h.outChannels;++j){let U=Math.trunc(j/x),X=j%x,G=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=E;Y<F;++Y){let ae=N+Y*d-g;for(let te=L;te<V;++te){let ie=O+te*p-A;G+=w.get(ee,ae,ie,U)*k.get(ee,Y,te,j)}}y.set(G,N,O,U,X)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var bD={kernelName:jh,backendName:"cpu",kernelFunc:wD};function _D(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;ve([a,s],"depthwiseConv2DNativeBackpropInput");let h=_.computeStrides(a.shape),d=_.computeStrides(s.shape),p=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new Dt(p.inShape,"float32"),m=f.values,[y,A,g]=f.strides,x=n.data.get(a.dataId).values,[v,w,b]=h,k=n.data.get(s.dataId).values,[N,E,F]=d,{batchSize:O,filterHeight:L,filterWidth:V,inChannels:j,inHeight:U,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,ie=L-1-p.padInfo.top,Q=V-1-p.padInfo.left,he=G/j;for(let oe=0;oe<O;++oe)for(let fe=0;fe<j;++fe)for(let pe=0;pe<U;++pe){let ke=pe-ie,Se=Math.max(0,Math.ceil(ke/ae)),Fe=Math.min(ee,(L+ke)/ae);for(let Oe=0;Oe<X;++Oe){let $e=Oe-Q,et=Math.max(0,Math.ceil($e/te)),tt=Math.min(Y,(V+$e)/te),it=0;for(let Ke=Se;Ke<Fe;++Ke){let dt=Ke*ae-ke;for(let je=et;je<tt;++je){let _n=je*te-$e,bt=v*oe+w*Ke+b*je,Kn=N*(L-1-dt)+E*(V-1-_n)+F*fe;for(let Xt=0;Xt<he;++Xt){let vn=fe*he+Xt,Zn=x[bt+vn],Dn=k[Kn+Xt];it+=Zn*Dn}}}m[y*oe+A*pe+g*Oe+fe]=it}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var vD={kernelName:Uh,backendName:"cpu",kernelFunc:_D};function kD(e){let{inputs:t,backend:n}=e,{x:r}=t,a=_.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=Be([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 ID={kernelName:Hh,backendName:"cpu",kernelFunc:kD},SD={kernelName:Tu,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:y,outHeight:A,outWidth:g,padInfo:x,strideHeight:v,strideWidth:w,filterHeight:b,filterWidth:k,dilationHeight:N,dilationWidth:E,outShape:F}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),O=_.sizeFromShape(F),L=F.length,V=_.getArrayFromDType(r.dtype,O);for(let j=0;j<p;++j)for(let U=0;U<A;++U){let X=U*v-x.top;for(let G=0;G<g;++G){let ee=G*w-x.left;for(let Y=0;Y<y;++Y){let ae=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<b;++ie){let Q=X+ie*N;if(Q>=0&&Q<f)for(let he=0;he<k;++he){let oe=ee+he*E;if(oe>=0&&oe<m){let fe=_.locToIndex([j,Q,oe,Y],u,_.computeStrides(r.shape)),pe=_.locToIndex([ie,he,Y],d,_.computeStrides(a.shape)),ke=c[fe]+h[pe];ke>ae&&(ae=ke)}}}let te=_.locToIndex([j,U,G,Y],L,_.computeStrides(F));V[te]=ae}}}return{dataId:l.write(_.toTypedArray(V,r.dtype),F,r.dtype),shape:F,dtype:r.dtype}}},ND={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=_.toNestedArray(r.shape,c.data.get(r.dataId).values),h=_.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:y,outWidth:A,padInfo:g,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:b,dilationHeight:k,dilationWidth:N,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);_.assert(s.rank===E.length,()=>`Error in ${qh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let F=_.toNestedArray(E,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(a.shape,a.dtype);for(let L=0;L<d;++L)for(let V=0;V<y;++V){let j=V*x-g.top;for(let U=0;U<A;++U){let X=U*v-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=0,ae=0;for(let te=0;te<w;++te){let ie=j+te*k;if(ie>=0&&ie<p)for(let Q=0;Q<b;++Q){let he=X+Q*N;if(he>=0&&he<f){let oe=u[L][ie][he][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=te,ae=Q)}}}O[Y][ae][G]+=F[L][V][U][G]}}}return{dataId:c.write(_.toTypedArray(O,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},TD={kernelName:Gh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=_.toNestedArray(r.shape,c.data.get(r.dataId).values),h=_.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:y,outWidth:A,padInfo:g,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:b,dilationHeight:k,dilationWidth:N,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);_.assert(s.rank===E.length,()=>`Error in ${Gh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let F=_.toNestedArray(E,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(r.shape,r.dtype);for(let L=0;L<d;++L)for(let V=0;V<y;++V){let j=V*x-g.top;for(let U=0;U<A;++U){let X=U*v-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=j<0?0:j,ae=X<0?0:X;for(let te=0;te<w;++te){let ie=j+te*k;if(ie>=0&&ie<p)for(let Q=0;Q<b;++Q){let he=X+Q*N;if(he>=0&&he<f){let oe=u[L][ie][he][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=ie,ae=he)}}}O[L][Y][ae][G]+=F[L][V][U][G]}}}return{dataId:c.write(_.toTypedArray(O,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function fc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"sum");let o;a.dtype==="bool"?o=Ha({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Gr({inputs:{x:a},backend:n});let l=o.shape.length,c=_.parseAxisParam(s,o.shape),u=C.getAxesPermutation(c,l),h=c,d=o;u!=null&&(d=tr({inputs:{x:o},backend:n,attrs:{perm:u}}),h=C.getInnerMostAxes(h.length,l)),C.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=C.computeOutAndReduceShapes(d.shape,h),m=C.upcastType(d.dtype,"int32"),y=up(n,p,m),A=_.sizeFromShape(f),g=n.data.get(y.dataId).values,x=n.data.get(d.dataId).values;for(let v=0;v<g.length;++v){let w=v*A,b=0;for(let k=0;k<A;++k)b+=x[w+k];g[v]=b}if(i){let v=C.expandShapeToKeepDim(y.shape,c),w=y;y=mt({inputs:{x:y},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),y}var ED={kernelName:Qs,backendName:"cpu",kernelFunc:fc};function CD(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=C.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,f=[];for(let m=0;m<h;++m){for(let y of u[m]){let{permutationIndices:A,expandDims:g}=C.getEinsumPermutation(p,l[y]),x;C.isIdentityPermutation(A)?x=s[y]:(x=tr({inputs:{x:s[y]},backend:n,attrs:{perm:A}}),f.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=mt({inputs:{x},backend:n,attrs:{shape:v}}),f.push(x)),d===null?d=x:(d=cp({inputs:{a:x,b:d},backend:n}),f.push(d))}m<h-1&&(c[m]>=0&&(d=fc({inputs:{x:d},backend:n,attrs:{axis:c[m]-(i.length-p),keepDims:!1}}),f.push(d)),p--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var RD={kernelName:Xh,backendName:"cpu",kernelFunc:CD};function MD(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;ve([r,a],"eluGrad");let s=new Float32Array(_.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(a.shape,"float32",s)}var FD={kernelName:Kh,backendName:"cpu",kernelFunc:MD},$D=Mt((e,t)=>e===t?1:0),R_=Ut(_o,$D,null,"bool"),DD={kernelName:_o,backendName:"cpu",kernelFunc:R_},OD=C.ERF_P,zD=C.ERF_A1,PD=C.ERF_A2,LD=C.ERF_A3,WD=C.ERF_A4,BD=C.ERF_A5,VD=at(bo,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+OD*n);return t*(1-((((BD*r+WD)*r+LD)*r+PD)*r+zD)*r*Math.exp(-n*n))}),jD={kernelName:bo,backendName:"cpu",kernelFunc:VD};function hp(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),mt({inputs:{x:a},backend:n,attrs:{shape:o}})}var UD={kernelName:vo,backendName:"cpu",kernelFunc:hp},HD=Mt((e,t)=>e/t),cy=Ut(vs,HD),hy={kernelName:vs,backendName:"cpu",kernelFunc:cy};function M_(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,c=[a,s],u=_.sizeFromShape(c),h=_.getTypedArrayFromDType("float32",u),d=_.getTypedArrayFromDType("float32",u);for(let y=0;y<a;y++){let A=Ii({inputs:{x:o},backend:n,attrs:{begin:[y,0],size:[1,s]}}),g=Ii({inputs:{x:l},backend:n,attrs:{begin:[y,0],size:[1,s]}}),x=Bn({inputs:{real:A,imag:g},backend:n}),{real:v,imag:w}=GD(x,t,n),b=C.mergeRealAndImagArrays(v,w);for(let k=0;k<s;k++){let N=C.getComplexWithIndex(b,k);h[y*s+k]=N.real,d[y*s+k]=N.imag}n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x)}let p=n.makeTensorInfo(c,"float32",h),f=n.makeTensorInfo(c,"float32",d),m=Bn({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function GD(e,t,n){let r=_.sizeFromShape(e.shape),a=n.data.get(e.dataId),s=n.data.get(a.complexTensorInfos.real.dataId).values,i=n.data.get(a.complexTensorInfos.imag.dataId).values;if(qD(r)){let o=dy(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",_.createScalarValue(r,"float32")),d=Gr({inputs:{x:h},backend:n}),p=hy.kernelFunc({inputs:{a:c,b:h},backend:n}),f=hy.kernelFunc({inputs:{a:u,b:d},backend:n}),m=n.data.get(p.dataId).values,y=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:y}}return o}else{let o=C.mergeRealAndImagArrays(s,i),l=XD(o,r,t);return C.splitRealAndImagArrays(l)}}function qD(e){return(e&e-1)==0}function dy(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=C.mergeRealAndImagArrays(e,t),i=n/2,o=C.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],h=a.makeTensorInfo(u,"float32",l),d=a.makeTensorInfo(u,"float32",c),p=Bn({inputs:{real:h,imag:d},backend:a}),f=C.complexWithOddIndex(s),m=f.real,y=f.imag,A=[m.length],g=a.makeTensorInfo(A,"float32",m),x=a.makeTensorInfo(A,"float32",y),v=Bn({inputs:{real:g,imag:x},backend:a}),w=dy(l,c,i,r,a),b=w.real,k=w.imag,N=[b.length],E=a.makeTensorInfo(N,"float32",b),F=a.makeTensorInfo(N,"float32",k),O=Bn({inputs:{real:E,imag:F},backend:a}),L=dy(m,y,i,r,a),V=L.real,j=L.imag,U=[V.length],X=a.makeTensorInfo(U,"float32",V),G=a.makeTensorInfo(U,"float32",j),ee=Bn({inputs:{real:X,imag:G},backend:a}),Y=C.exponents(n,r),ae=[Y.real.length],te=a.makeTensorInfo(ae,"float32",Y.real),ie=a.makeTensorInfo(ae,"float32",Y.imag),Q=Bn({inputs:{real:te,imag:ie},backend:a}),he=cp({inputs:{a:Q,b:ee},backend:a}),oe=pc({inputs:{a:O,b:he},backend:a}),fe=oy({inputs:{a:O,b:he},backend:a}),pe=ki({inputs:{input:oe},backend:a}),ke=ki({inputs:{input:fe},backend:a}),Se=Rl({inputs:{input:oe},backend:a}),Fe=Rl({inputs:{input:fe},backend:a}),Oe=Ml({inputs:[pe,ke],backend:a,attrs:{axis:0}}),$e=Ml({inputs:[Se,Fe],backend:a,attrs:{axis:0}}),et=a.data.get(Oe.dataId).values,tt=a.data.get($e.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(v),a.disposeIntermediateTensorInfo(E),a.disposeIntermediateTensorInfo(F),a.disposeIntermediateTensorInfo(O),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(G),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(ie),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(he),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(fe),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(Se),a.disposeIntermediateTensorInfo(ke),a.disposeIntermediateTensorInfo(Fe),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo($e),{real:et,imag:tt}}function XD(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=C.exponent(a*o,t,n),c=C.getComplexWithIndex(e,o);s+=c.real*l.real-c.imag*l.imag,i+=c.real*l.imag+c.imag*l.real}n&&(s/=t,i/=t),C.assignToTypedArray(r,s,i,a)}return r}function KD(e){let{inputs:t,backend:n}=e,{input:r}=t,a=_.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=mt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=M_(o,!1,n),c=mt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var ZD={kernelName:Zh,backendName:"cpu",kernelFunc:KD};function py(e){let{backend:t,attrs:n}=e,{shape:r,value:a,dtype:s}=n,i=s||_.inferDtype(a),o=_.getArrayFromDType(i,_.sizeFromShape(r));return YD(o,a,i),t.makeTensorInfo(r,i,o)}var JD={kernelName:Eu,backendName:"cpu",kernelFunc:py};function YD(e,t,n){e.fill(t)}var QD={kernelName:Io,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=_.getTypedArrayFromDType(r.dtype,_.sizeFromShape(r.shape)),[i,o,l,c]=r.shape,u=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let d=h*l*o*c;for(let p=0;p<o;p++){let f=p*(l*c);for(let m=0;m<l;m++){let y=m*c;for(let A=0;A<c;A++){let g=[i,p,m,A][2],x=Math.round(l-g),v=d+f+y+A,w=u[v];if(x>=0&&x<l){let b=x*c,k=d+f+b+A;w=u[k]}s[v]=w}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},eO=Mt((e,t)=>Math.floor(e/t)),tO=Ut(Ss,eO,null,"int32"),nO={kernelName:Ss,backendName:"cpu",kernelFunc:tO};function rO(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=E_({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let y=m;m=pc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(y)}if(p){let y=m;m=ly(n,m,p,o,f),n.disposeIntermediateTensorInfo(y)}return m}var aO={kernelName:oi,backendName:"cpu",kernelFunc:rO};function sO(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=C_({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let y=m;m=pc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(y)}if(p){let y=m;m=ly(n,m,p,o,f),n.disposeIntermediateTensorInfo(y)}return m}var iO={kernelName:li,backendName:"cpu",kernelFunc:sO};function oO(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=_.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,c,u,h]=C.prepareAndValidate(r,a);if(c===0)return n.makeTensorInfo(l,r.dtype,[]);let d=Be([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 y=[],A=0;for(let g=0;g<o;g++){let x=p[m*o+g];A+=x*h[g],y.push(x)}if(A<0||A>=s/u)throw new Error(`Invalid indices: ${y} does not index into ${r.shape}`);for(let g=0;g<u;g++)d.values[m*u+g]=f[A*u+g]}return n.makeTensorInfo(l,d.dtype,d.values)}var lO={kernelName:No,backendName:"cpu",kernelFunc:oO};function uO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r;ve([a,s],"gatherV2");let l=o;o==null&&(l=0);let c=_.sizeFromShape(s.shape),u=_.parseAxisParam(i,a.shape)[0],h=C.segment_util.collectGatherOpShapeInfo(a,s,u,l),d=mt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),p=mt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],m=n.bufferSync(p),y=n.bufferSync(d),A=t_(y,m,f);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(h.outputShape,A.dtype,A.values)}var cO={kernelName:So,backendName:"cpu",kernelFunc:uO},hO=Mt((e,t)=>e>=t?1:0),dO=Ut(Ts,hO,null,"bool"),pO={kernelName:Ts,backendName:"cpu",kernelFunc:dO};function fO(e){let{inputs:t,backend:n}=e,{input:r}=t,a=_.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=mt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=M_(o,!0,n),c=mt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var mO={kernelName:Yh,backendName:"cpu",kernelFunc:fO},yO=at(Eo,e=>Number.isFinite(e)?1:0,"bool"),AO={kernelName:Eo,backendName:"cpu",kernelFunc:yO},gO=at(Co,e=>Math.abs(e)===Infinity?1:0,"bool"),xO={kernelName:Co,backendName:"cpu",kernelFunc:gO},wO=at(Ro,e=>Number.isNaN(e)?1:0,"bool"),bO={kernelName:Ro,backendName:"cpu",kernelFunc:wO},_O=Mt((e,t)=>e<=t?1:0),vO=Ut(Fo,_O,null,"bool"),kO={kernelName:Fo,backendName:"cpu",kernelFunc:vO};function IO(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=a_(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var SO={kernelName:Qh,backendName:"cpu",kernelFunc:IO},NO=at($o,e=>Math.log1p(e)),TO={kernelName:$o,backendName:"cpu",kernelFunc:NO},EO=Mt((e,t)=>e&&t),CO=Ut(Do,EO,null,"bool"),RO={kernelName:Do,backendName:"cpu",kernelFunc:CO},MO=at(Cu,e=>e?0:1,"bool"),FO={kernelName:Cu,backendName:"cpu",kernelFunc:MO},$O=Mt((e,t)=>e||t),DO=Ut(Ru,$O,null,"bool"),OO={kernelName:Ru,backendName:"cpu",kernelFunc:DO};function zO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;ve(a,"LRN");let c=a.shape[3],u=c-1,h=n.data.get(a.dataId).values,d=_.sizeFromShape(a.shape),p=new Float32Array(d);function f(m){let y=m%c,A=m-y+Math.max(0,y-s),g=m-y+Math.min(y+s,u),x=0;for(;A<=g;A++){let v=h[A];x+=v*v}return x}for(let m=0;m<d;m++){let y=f(m),A=h[m]*Math.pow(i+o*y,-l);p[m]=A}return n.makeTensorInfo(a.shape,a.dtype,p)}var PO={kernelName:Mu,backendName:"cpu",kernelFunc:zO};function LO(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;ve(i,"LRNGrad");let h=_.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,y=new Float32Array(h),A=h;for(let g=0;g<A;g++){let x=g%d,v=g-x+Math.max(0,x-o),w=g-x+Math.min(d,x+o+1),b=0;for(let k=v;k<w;k++)b+=Math.pow(f[k],2);b=c*b+l;for(let k=v;k<w;k++){let N=-2*c*u*f[k]*m[g]/b;g===k&&(N+=Math.pow(b,-u)),N*=p[g],y[k]+=N}}return n.makeTensorInfo(i.shape,a.dtype,y)}var WO={kernelName:ed,backendName:"cpu",kernelFunc:LO};function F_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=n,l=a.shape,c=l.length,u=_.parseAxisParam(s,l),h=u,d=C.getAxesPermutation(h,c),p=o.data.get(a.dataId).values;if(d!=null){let v=new Array(c);for(let w=0;w<v.length;w++)v[w]=l[d[w]];p=ay(p,l,a.dtype,d,v),h=C.getInnerMostAxes(h.length,c),l=v}ve(a,"max"),C.assertAxesAreInnerMostDims("max",h,c);let[f,m]=C.computeOutAndReduceShapes(l,h),y=_.sizeFromShape(m),A=i_(p,y,f,a.dtype),g=o.write(A,f,a.dtype),x=f;return i&&(x=C.expandShapeToKeepDim(f,u)),{dataId:g,shape:x,dtype:a.dtype}}var BO={kernelName:Ms,backendName:"cpu",kernelFunc:F_};function VO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ve(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))h=Gr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=_.computeStrides(a.shape),f=uy(d,a.shape,a.dtype,p,u,"max");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var jO={kernelName:$s,backendName:"cpu",kernelFunc:VO};function UO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;ve(a,"maxPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=T_(h,a.shape,a.dtype,_.computeStrides(a.shape),u,"max");return n.makeTensorInfo(d.shape,"float32",d.values)}var HO={kernelName:Fu,backendName:"cpu",kernelFunc:UO};function GO(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;ve([a,s],"maxPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=n.bufferSync(s),d=E$(h,u),p=u.strideDepth,f=u.strideHeight,m=u.strideWidth,y=u.dilationDepth,A=u.dilationHeight,g=u.dilationWidth,x=u.effectiveFilterDepth,v=u.effectiveFilterHeight,w=u.effectiveFilterWidth,b=x-1-u.padInfo.front,k=w-1-u.padInfo.left,N=v-1-u.padInfo.top,E=Be(s.shape,"float32"),F=n.bufferSync(a);for(let O=0;O<u.batchSize;++O)for(let L=0;L<u.inChannels;++L)for(let V=0;V<u.inDepth;++V)for(let j=0;j<u.inHeight;++j)for(let U=0;U<u.inWidth;++U){let X=V-b,G=j-N,ee=U-k,Y=0;for(let ae=0;ae<x;ae+=y){let te=(X+ae)/p;if(!(te<0||te>=u.outDepth||Math.floor(te)!==te))for(let ie=0;ie<v;ie+=A){let Q=(G+ie)/f;if(!(Q<0||Q>=u.outHeight||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(ee+he)/m;if(oe<0||oe>=u.outWidth||Math.floor(oe)!==oe)continue;let fe=x*v*w-1-d.get(O,te,Q,oe,L),pe=ae*v*w+ie*w+he,ke=fe===pe?1:0;ke!==0&&(Y+=F.get(O,te,Q,oe,L)*ke)}}}E.set(Y,O,V,j,U,L)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var qO={kernelName:nd,backendName:"cpu",kernelFunc:GO};function XO(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;ve([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,c,1,u,h),p=n.data.get(o.dataId).values,f=Be(d.outShape,o.dtype,N_(p,o.shape,o.dtype,d).values),m=d.strideHeight,y=d.strideWidth,A=d.dilationHeight,g=d.dilationWidth,x=d.effectiveFilterHeight,v=d.effectiveFilterWidth,w=v-1-d.padInfo.left,b=x-1-d.padInfo.top,k=Be(o.shape,"float32"),N=n.data.get(a.dataId).values,E=Be(a.shape,"float32",N);for(let F=0;F<d.batchSize;++F)for(let O=0;O<d.inChannels;++O)for(let L=0;L<d.inHeight;++L)for(let V=0;V<d.inWidth;++V){let j=L-b,U=V-w,X=0;for(let G=0;G<x;G+=A){let ee=(j+G)/m;if(!(ee<0||ee>=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y<v;Y+=g){let ae=(U+Y)/y;if(ae<0||ae>=d.outWidth||Math.floor(ae)!==ae)continue;let te=x*v-1-f.get(F,ee,ae,O),ie=G*v+Y,Q=te===ie?1:0;Q!==0&&(X+=E.get(F,ee,ae,O)*Q)}}k.set(X,F,L,V,O)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var KO={kernelName:td,backendName:"cpu",kernelFunc:XO};function ZO(e,t,n,r,a){let s=_.computeStrides(t),i=uy(e,t,n,s,a,"max"),o=N_(e,t,n,a,!0,r);return[i.values,o.values]}var YO={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;ve(r,"MaxPoolWithArgmax");let c=l.data.get(r.dataId).values,u=C.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=ZO(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 JO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=_.parseAxisParam(s,a.shape),l=C.computeOutAndReduceShapes(a.shape,o)[1],c=_.sizeFromShape(l),u=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(h);let d=Ha({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});u.push(d);let p=cy({inputs:{a:d,b:h},backend:n});u.push(p);let f=fc({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var QO={kernelName:Ds,backendName:"cpu",kernelFunc:JO};function ez(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"min");let o=_.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=tr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),f=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let g=A*p,x=m[g];for(let v=0;v<p;++v){let w=m[g+v];w<x&&(x=w)}f[A]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let y=n.makeTensorInfo(h,u.dtype,f);if(i){let A=C.expandShapeToKeepDim(h,o),g=mt({inputs:{x:y},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(y),g}return y}var tz={kernelName:Os,backendName:"cpu",kernelFunc:ez};function nz(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,mode:i}=r;ve(a,"mirrorPad");let o=s.map((g,x)=>g[0]+a.shape[x]+g[1]),l=s.map(g=>g[0]),c=s.map((g,x)=>g[0]+a.shape[x]),u=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=_.computeStrides(a.shape),f=_.sizeFromShape(o),m=o.length,y=_.computeStrides(o),A=_.getTypedArrayFromDType(a.dtype,f);for(let g=0;g<f;g++){let x=_.indexToLoc(g,m,y);for(let w=0;w<m;w++)x[w]<l[w]?x[w]=l[w]*2-x[w]-u:x[w]>=c[w]&&(x[w]=(c[w]-1)*2-x[w]+u);x=x.map((w,b)=>w-l[b]);let v=_.locToIndex(x,d,p);A[g]=h[v]}return{dataId:n.write(A,o,a.dtype),shape:o,dtype:a.dtype}}var rz={kernelName:Ps,backendName:"cpu",kernelFunc:nz},az=Mt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),sz=Ut(Oo,az),iz={kernelName:Oo,backendName:"cpu",kernelFunc:sz},oz=no(h5());function $_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=a.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=_.parseAxisParam([o],a.shape),c=F_({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=C.expandShapeToKeepDim(c.shape,l),h=mt({inputs:{x:c},backend:n,attrs:{shape:u}}),d=oy({inputs:{a,b:h},backend:n}),p=x_({inputs:{x:d},backend:n}),f=fc({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:u}}),y=cy({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),y}var lz={kernelName:ei,backendName:"cpu",kernelFunc:$_};function uz(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r;ve(a,"multinomial");let l=o?a:$_({inputs:{logits:a},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],h=n.data.get(l.dataId).values,d=[c,s],p=_.makeZerosTypedArray(_.sizeFromShape(d),"int32");for(let f=0;f<c;++f){let m=f*u,y=new Float32Array(u-1);y[0]=h[m];for(let x=1;x<y.length;++x)y[x]=y[x-1]+h[m+x];let A=oz.alea(i.toString()),g=f*s;for(let x=0;x<s;++x){let v=A();p[g+x]=y.length;for(let w=0;w<y.length;w++)if(v<y[w]){p[g+x]=w;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",p)}var cz={kernelName:ad,backendName:"cpu",kernelFunc:uz},hz=Hr.nonMaxSuppressionV3Impl;function dz(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r;ve(a,"NonMaxSuppression");let c=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:h}=hz(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var pz={kernelName:Lo,backendName:"cpu",kernelFunc:dz},fz=Hr.nonMaxSuppressionV4Impl;function mz(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r;ve(a,"NonMaxSuppressionPadded");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:p}=fz(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var yz={kernelName:Wo,backendName:"cpu",kernelFunc:mz},Az=Hr.nonMaxSuppressionV5Impl;function gz(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r;ve(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:y,selectedScores:A}=Az(u,h,d,p,f,m);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var xz={kernelName:Bo,backendName:"cpu",kernelFunc:gz};function wz(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;ve(a,"oneHot");let l=_.sizeFromShape(a.shape),c=new Float32Array(l*s);c.fill(o);let u=n.data.get(a.dataId).values;for(let h=0;h<l;++h)u[h]>=0&&u[h]<s&&(c[h*s+u[h]]=i);return n.makeTensorInfo([...a.shape,s],"int32",c)}var bz={kernelName:Ws,backendName:"cpu",kernelFunc:wz};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=ki({inputs:{input:r},backend:n}),s=dp({inputs:{x:a},backend:n}),i=Rl({inputs:{input:r},backend:n}),o=dp({inputs:{x:i},backend:n}),l=Bn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return py({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var _z={kernelName:al,backendName:"cpu",kernelFunc:dp};function D_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(r.dtype==="complex64"){let a=ki({inputs:{input:r},backend:n}),s=D_({inputs:{x:a},backend:n}),i=Rl({inputs:{input:r},backend:n}),o=dp({inputs:{x:i},backend:n}),l=Bn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return py({backend:n,attrs:{shape:r.shape,value:1,dtype:r.dtype}})}var vz={kernelName:Vo,backendName:"cpu",kernelFunc:D_};function O_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return hp({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=hp({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=Ml({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var kz={kernelName:jo,backendName:"cpu",kernelFunc:O_};function Iz(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r;ve(a,"pad");let o=s.map((A,g)=>A[0]+a.shape[g]+A[1]),l=s.map(A=>A[0]),c=n.data.get(a.dataId).values,u=_.sizeFromShape(a.shape),h=a.shape.length,d=_.computeStrides(a.shape),p=_.sizeFromShape(o),f=o.length,m=_.computeStrides(o),y=_.getTypedArrayFromDType(a.dtype,p);i!==0&&y.fill(i);for(let A=0;A<u;A++){let g=_.indexToLoc(A,h,d).map((v,w)=>v+l[w]),x=_.locToIndex(g,f,m);y[x]=c[A]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var z_={kernelName:Bs,backendName:"cpu",kernelFunc:Iz},Sz=Mt((e,t)=>Math.pow(e,t)),Nz=Ut(Vs,Sz),Tz={kernelName:Vs,backendName:"cpu",kernelFunc:Nz};function Ez(e){let{backend:t,attrs:n}=e,{start:r,stop:a,dtype:s,step:i}=n,o=sy(r,a,i,s);return t.makeTensorInfo([o.length],s,o)}var Cz={kernelName:$u,backendName:"cpu",kernelFunc:Ez},Rz=at(Ho,e=>1/e),Mz={kernelName:Ho,backendName:"cpu",kernelFunc:Rz};function Fz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;ve(a,"resizeBilinear");let l=_.computeStrides(a.shape),[c,u]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,y=new Float32Array(_.sizeFromShape([h,c,u,f])),A=[s&&c>1?d-1:d,s&&u>1?p-1:p],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],x=0,v=A[0]/g[0],w=A[1]/g[1];for(let b=0;b<h;b++)for(let k=0;k<c;k++){let N;i?N=v*(k+.5)-.5:N=v*k;let E=Math.max(0,Math.floor(N)),F=N-E,O=Math.min(d-1,Math.ceil(N)),L=b*l[0]+E*l[1],V=b*l[0]+O*l[1];for(let j=0;j<u;j++){let U;i?U=w*(j+.5)-.5:U=w*j;let X=Math.max(0,Math.floor(U)),G=U-X,ee=Math.min(p-1,Math.ceil(U)),Y=L+X*l[2],ae=V+X*l[2],te=L+ee*l[2],ie=V+ee*l[2];for(let Q=0;Q<f;Q++){let he=m[Y+Q],oe=m[ae+Q],fe=m[te+Q],pe=m[ie+Q],ke=he+(fe-he)*G,Se=oe+(pe-oe)*G,Fe=ke+(Se-ke)*F;y[x++]=Fe}}}return n.makeTensorInfo([h,c,u,f],"float32",y)}var $z={kernelName:Hs,backendName:"cpu",kernelFunc:Fz};function Dz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;ve([s,a],"resizeBilinearGrad");let o=_.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],y=[i&&d>1?d-1:d,i&&p>1?p-1:p],A=m[0]/y[0],g=m[1]/y[1],x=n.data.get(s.dataId).values,v=0;for(let w=0;w<l;w++){let b=w*o[0];for(let k=0;k<d;k++){let N=k*A,E=Math.floor(N),F=Math.min(Math.ceil(N),c-1),O=b+E*o[1],L=b+F*o[1],V=N-E,j=1-V;for(let U=0;U<p;U++){let X=U*g,G=Math.floor(X),ee=Math.min(Math.ceil(X),u-1),Y=X-G,ae=1-Y,te=O+G*o[2],ie=O+ee*o[2],Q=L+G*o[2],he=L+ee*o[2],oe=j*ae,fe=j*Y,pe=V*ae,ke=V*Y;for(let Se=0;Se<h;Se++){let Fe=x[v++];f[te+Se]+=Fe*oe,f[ie+Se]+=Fe*fe,f[Q+Se]+=Fe*pe,f[he+Se]+=Fe*ke}}}}return n.makeTensorInfo([l,u,c,h],"float32",f)}var Oz={kernelName:od,backendName:"cpu",kernelFunc:Dz};function zz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;ve(a,"resizeNearestNeighbor");let l=_.computeStrides(a.shape),[c,u]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,y=new Float32Array(h*c*u*f),A=[s&&c>1?d-1:d,s&&u>1?p-1:p],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],x=A[0]/g[0],v=A[1]/g[1],w=0;for(let b=0;b<h;b++){let k=b*l[0];for(let N=0;N<c;N++){let E=i?x*(N+.5):x*N,F=Math.min(d-1,s?Math.round(E):Math.floor(E));i&&(F=Math.max(0,F));let O=k+F*l[1];for(let L=0;L<u;L++){let V=i?v*(L+.5):v*L,j=Math.min(p-1,s?Math.round(V):Math.floor(V));i&&(j=Math.max(0,j));let U=O+j*l[2];for(let X=0;X<f;X++){let G=m[U+X];y[w++]=G}}}}return n.makeTensorInfo([h,c,u,f],a.dtype,y)}var Pz={kernelName:Du,backendName:"cpu",kernelFunc:zz};function Lz(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;ve([s,a],"resizeNearestNeighborGrad");let o=_.computeStrides(a.shape),l=_.computeStrides(s.shape),[c,u,h,d]=a.shape,[,p,f]=s.shape,m=new Float32Array(c*u*h*d),y=n.data.get(s.dataId).values,A=[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],x=A[0]/g[0],v=A[1]/g[1],w=1/x,b=1/v,k=Math.ceil(w)*2+2,N=Math.ceil(b)*2+2;for(let E=0;E<c;E++){let F=E*o[0];for(let O=0;O<u;O++){let L=F+O*o[1],V=Math.floor(O*w),j=Math.floor(V-k/2);for(let U=0;U<h;U++){let X=L+U*o[2],G=Math.floor(U*b),ee=Math.floor(G-N/2);for(let Y=0;Y<d;Y++){let ae=0;for(let te=0;te<k;te++){let ie=te+j;if(ie<0||ie>=p)continue;let Q=F+ie*l[1],he=ie*x,oe=Math.min(u-1,i?Math.round(he):Math.floor(he));if(O===oe)for(let fe=0;fe<N;fe++){let pe=fe+ee;if(pe<0||pe>=f)continue;let ke=Q+pe*l[2],Se=pe*v,Fe=Math.min(h-1,i?Math.round(Se):Math.floor(Se));U===Fe&&(ae+=y[ke+Y])}}m[X+Y]=ae}}}}return n.makeTensorInfo(a.shape,a.dtype,m)}var Wz={kernelName:id,backendName:"cpu",kernelFunc:Lz};function Bz(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r;ve(a,"reverse");let i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return Gr({inputs:{x:a},backend:n});let l=new Dt(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 Vz={kernelName:qs,backendName:"cpu",kernelFunc:Bz},jz={kernelName:sl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=_.getTypedArrayFromDType(r.dtype,_.sizeFromShape(r.shape)),[c,u,h,d]=r.shape,[p,f]=C.getImageCenter(i,u,h),m=255,y=Math.sin(a),A=Math.cos(a),g=o.data.get(r.dataId).values;for(let x=0;x<c;x++){let v=x*h*u*d;for(let w=0;w<u;w++){let b=w*(h*d);for(let k=0;k<h;k++){let N=k*d;for(let E=0;E<d;E++){let F=[c,w,k,E],O=F[2],L=F[1],V=(O-p)*A-(L-f)*y,j=(O-p)*y+(L-f)*A;V=Math.round(V+p),j=Math.round(j+f);let U=s;if(typeof s!="number"&&(E===3?U=m:U=s[E]),V>=0&&V<h&&j>=0&&j<u){let G=j*(h*d),ee=V*d,Y=v+G+ee+E;U=g[Y]}let X=v+b+N+E;l[X]=U}}}}return{dataId:o.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Uz=at(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}),Hz={kernelName:Xs,backendName:"cpu",kernelFunc:Uz};function P_(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 Be(n,t.dtype);let p=Be(u,t.dtype);p.values.fill(l);for(let f=0;f<s;f++){let m=[],y=0;for(let A=0;A<i;A++){let g=h[f*i+A];m.push(g),y+=g*o[A]}if(y<0||y>=r/a)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let A=0;A<a;A++)c?p.values[y*a+A]+=d[f*a+A]:p.values[y*a+A]=t.rank===0?d[0]:d[f*a+A]}return p}function Gz(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}=C.calculateShapes(s,a,i),d=!0,p=n.bufferSync(a),f=n.bufferSync(s),m=P_(p,f,i,h,c,l,o,u,0,d);return n.makeTensorInfo(i,m.dtype,m.values)}var qz={kernelName:qo,backendName:"cpu",kernelFunc:Gz};function Xz(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t;ve([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=dr(a.dtype,s.dtype),h=_.makeZerosTypedArray(_.sizeFromShape(a.shape),u),d=0,p=i===0||i>1||a.shape.length===1?1:_.sizeFromShape(a.shape.slice(1));for(let 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 Kz={kernelName:Xo,backendName:"cpu",kernelFunc:Xz},Zz=C.SELU_SCALEALPHA,Yz=C.SELU_SCALE,Jz=at(Ko,e=>e>=0?Yz*e:Zz*(Math.exp(e)-1)),Qz={kernelName:Ko,backendName:"cpu",kernelFunc:Jz},eP=at(Ys,e=>1/(1+Math.exp(-e))),tP={kernelName:Ys,backendName:"cpu",kernelFunc:eP},nP=at(Jo,e=>e<0?-1:e>0?1:0),rP={kernelName:Jo,backendName:"cpu",kernelFunc:nP},aP=at(Zs,e=>Math.sin(e)),sP={kernelName:Zs,backendName:"cpu",kernelFunc:aP},iP=at(Yo,e=>Math.sinh(e)),oP={kernelName:Yo,backendName:"cpu",kernelFunc:iP},lP=11920928955078125e-23,L_=Math.log(lP)+2,uP=at(Qo,e=>{let t=e>-L_,n=e<L_,r=Math.exp(e),a;return n?a=r:t?a=e:a=Math.log(1+r),a}),cP={kernelName:Qo,backendName:"cpu",kernelFunc:uP};function hP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;ve([a],"spaceToBatchND");let o=_.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=z_.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),u=C.getReshaped(c.shape,s,o,!1),h=C.getPermuted(u.length,s.length,!1),d=C.getReshapedPermuted(c.shape,s,o,!1),p=mt({inputs:{x:c},backend:n,attrs:{shape:u}}),f=tr({inputs:{x:p},backend:n,attrs:{perm:h}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}var dP={kernelName:Ou,backendName:"cpu",kernelFunc:hP};function pP(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}=C.calculateShapes(s,a,o),p=!1,f=n.bufferSync(a),m=n.bufferSync(s),y=n.data.get(i.dataId).values[0],A=P_(f,m,o,d,u,c,l,h,y,p);return n.makeTensorInfo(o,A.dtype,A.values)}var fP={kernelName:ld,backendName:"cpu",kernelFunc:pP};function mP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=C.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=Ii({inputs:{x:a},backend:n,attrs:{begin:c,size:d}});return c[o]+=h,p})}var yP={kernelName:el,backendName:"cpu",kernelFunc:mP},AP=at(Js,e=>Math.sqrt(e)),gP={kernelName:Js,backendName:"cpu",kernelFunc:AP},xP={kernelName:zu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;ve(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}}},wP=at(Fa,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),bP={kernelName:Fa,backendName:"cpu",kernelFunc:wP};function _P(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;ve(a,"stridedSlice");let{nonStrided:p,$begin:f,$strides:m,size:y,newShape:A,outShape:g}=un.sliceInfo(a.shape,s,i,o,l,c,u,h,d),x=mt({inputs:{x:a},backend:n,attrs:{shape:A}}),v;if(p){let b=Ii({inputs:{x},backend:n,attrs:{begin:f,size:y}});v=mt({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))v=n.makeTensorInfo(g,a.dtype,[]);else{let b=n.bufferSync(x),k=f_(g,b,m,f);v=n.makeTensorInfo(k.shape,k.dtype,k.values)}let w=mt({inputs:{x:v},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),w}var vP={kernelName:tl,backendName:"cpu",kernelFunc:_P},kP=at(ri,e=>Math.tan(e)),IP={kernelName:ri,backendName:"cpu",kernelFunc:kP},SP=at(ai,e=>Math.tanh(e)),NP={kernelName:ai,backendName:"cpu",kernelFunc:SP};function TP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;ve(a,"tile");let i=y_(n.bufferSync(a),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var EP={kernelName:Ma,backendName:"cpu",kernelFunc:TP};function CP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r;ve(a,"topk");let o=n.data.get(a.dataId).values,[l,c]=A_(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 RP={kernelName:nl,backendName:"cpu",kernelFunc:CP};function $P(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],y=[u,f,m,p],A=_.computeStrides(a.shape),g=A[0],x=A[1],v=A[2],w=_.getTypedArrayFromDType(a.dtype,_.sizeFromShape(y));w.fill(l);let b=r.data.get(a.dataId).values,k=r.data.get(s.dataId).values;for(let N=0;N<u;++N){let E=s.shape[0]===1?k:k.subarray(N*8,N*8+8);for(let F=0;F<f;++F)for(let O=0;O<m;++O)for(let L=0;L<p;++L){let V,j=E[6]*O+E[7]*F+1;if(j===0)continue;let U=(E[0]*O+E[1]*F+E[2])/j,X=(E[3]*O+E[4]*F+E[5])/j,G=W_(U,d,o),ee=W_(X,h,o);switch(i){case"nearest":V=MP(b,h,d,g,x,v,N,ee,G,L,l);break;case"bilinear":V=FP(b,h,d,g,x,v,N,ee,G,L,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let Y=N*g+F*x+O*v+L;w[Y]=V}return r.makeTensorInfo(y,a.dtype,w)}return{dataId:r.write(w,y,a.dtype),shape:a.shape,dtype:a.dtype}}var DP={kernelName:ud,backendName:"cpu",kernelFunc:$P};function W_(e,t,n){switch(n){case"reflect":return OP(e,t);case"wrap":return zP(e,t);case"nearest":return LP(e,t);case"constant":default:return PP(e,t)}}function OP(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=2*t;n<r&&(n=r*Math.trunc(-n/r)+n),n=n<-t?n+r:-n-1}else if(n>t-1)if(t<=1)n=0;else{let r=2*t;n-=r*Math.trunc(n/r),n>=t&&(n=r-n-1)}return _.clamp(0,n,t-1)}function zP(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=t-1;n+=t*(Math.trunc(-n/r)+1)}else if(n>t-1)if(t<=1)n=0;else{let r=t-1;n-=t*Math.trunc(n/r)}return _.clamp(0,n,t-1)}function PP(e,t){return e}function LP(e,t){return _.clamp(0,e,t-1)}function mc(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 MP(e,t,n,r,a,s,i,o,l,c,u){let h=Math.round(o),d=Math.round(l);return mc(e,t,n,r,a,s,i,h,d,c,u)}function FP(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)*mc(e,t,n,r,a,s,i,h,d,c,u)+(l-d)*mc(e,t,n,r,a,s,i,h,f,c,u),y=(f-l)*mc(e,t,n,r,a,s,i,p,d,c,u)+(l-d)*mc(e,t,n,r,a,s,i,p,f,c,u);return(p-o)*m+(o-h)*y}function WP(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;ve(s,"unique");let i=r.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:c}=g_(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var BP={kernelName:cd,backendName:"cpu",kernelFunc:WP};function VP(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=Ii({inputs:{x:a},backend:n,attrs:{begin:u,size:h}});d[p]=mt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return d}var jP={kernelName:rl,backendName:"cpu",kernelFunc:VP};function UP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r;ve(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=hp({inputs:{input:d},backend:n,attrs:{dim:f+1}});d=m,u.push(m)}for(let f=0;f<i;++f){let m=_.createScalarValue(f,"int32"),y=n.makeTensorInfo([],"int32",m),A=R_({inputs:{a:y,b:d},backend:n}),g=Ha({inputs:{x:A},backend:n,attrs:{dtype:"float32"}}),x=cp({inputs:{a:g,b:a},backend:n}),v=fc({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});c.push(v),u.push(y),u.push(A),u.push(g),u.push(x),u.push(v)}let p=O_({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var HP={kernelName:Pu,backendName:"cpu",kernelFunc:UP},GP=[r$,lF,s$,o$,fF,u$,h$,p$,m$,A$,x$,b$,v$,S$,T$,R$,F$,D$,z$,t$,L$,B$,j$,dF,yF,H$,uF,q$,K$,J$,eD,Z$,aD,iD,nD,lD,cD,dD,fD,yD,gD,xD,bD,vD,ID,SD,TD,ND,hy,RD,XF,FD,DD,jD,AF,UD,xF,ZD,JD,QD,bF,nO,aO,iO,lO,cO,vF,pO,cF,mO,X$,AO,xO,bO,KF,IF,kO,SO,NF,TO,RO,FO,OO,PO,WO,EF,jO,HO,qO,KO,YO,BO,QO,tz,RF,rz,iz,cz,FF,DF,pz,yz,xz,zF,bz,vz,kz,z_,Tz,YF,WF,Cz,hF,Mz,JF,QF,e$,$z,Oz,Pz,Wz,Vz,jz,Hz,VF,qz,Kz,Qz,tP,rP,sP,oP,jF,lz,cP,dP,fP,yP,gP,xP,HF,bP,vP,qF,ED,IP,NP,EP,RP,PF,DP,BP,jP,HP,_z];for(let e of GP)ui(e);var B_={};Me(B_,{assertNotComplex:()=>Fl,bindCanvasToFramebuffer:()=>KP,bindColorTextureToFramebuffer:()=>fp,bindTextureToProgramUniformSampler:()=>n3,bindTextureUnit:()=>Q_,bindVertexBufferToProgramAttribute:()=>fy,callAndCheck:()=>we,canBeRepresented:()=>V_,createFragmentShader:()=>H_,createFramebuffer:()=>J_,createProgram:()=>G_,createStaticIndexBuffer:()=>K_,createStaticVertexBuffer:()=>X_,createTexture:()=>Z_,createVertexShader:()=>U_,getBatchDim:()=>Si,getExtensionOrThrow:()=>yc,getFramebufferErrorMessage:()=>r3,getMaxTexturesInShader:()=>i3,getNumChannels:()=>qP,getProgramUniformLocation:()=>t3,getProgramUniformLocationOrThrow:()=>e3,getRowsCols:()=>Ni,getShapeAs3D:()=>mp,getTextureShapeFromLogicalShape:()=>a3,getWebGLDisjointQueryTimerVersion:()=>o3,getWebGLErrorMessage:()=>j_,getWebGLMaxTextureSize:()=>s3,hasExtension:()=>nr,isCapableOfRenderingToFloatTexture:()=>l3,isDownloadFloatTextureEnabled:()=>u3,isReshapeFree:()=>gc,isWebGLFenceEnabled:()=>c3,isWebGLVersionEnabled:()=>yy,linkProgram:()=>q_,resetMaxTextureSize:()=>ZP,resetMaxTexturesInShader:()=>YP,unbindColorTextureFromFramebuffer:()=>my,unbindTextureUnit:()=>XP,validateFramebuffer:()=>Ac,validateProgram:()=>pp,validateTextureSize:()=>Y_});var Ti={},Ay={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function yp(e,t){Ti[e]=t}function qr(e){if(!(e in Ti)){let n=JP(e);if(n!==null)Ti[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Ti[e];return t.isContextLost()?(delete Ti[e],qr(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),Ti[e])}function QP(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 JP(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=QP(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Ti[e]},!1),e===1?t.getContext("webgl",Ay)||t.getContext("experimental-webgl",Ay):t.getContext("webgl2",Ay)}var xc;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(xc||(xc={}));var rr;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(rr||(rr={}));var en;(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"})(en||(en={}));function wc(e,t){return[t,e]}function eL(e,t){return e*t}function bc(e){let t=_.sizeFromShape(e),n=Math.ceil(t/4);return _.sizeToSquarishShape(n)}function $l(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function tL(e,t){let[n,r]=$l(e,t);return n*r*4}function gy(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 we(e,t){let n=t();return J().getBool("DEBUG")&&nL(e),n}function nL(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+j_(e,t))}var rL=596e-10,aL=65504;function V_(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||rL<Math.abs(e)&&Math.abs(e)<aL)}function j_(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 yc(e,t){return ma(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function U_(e,t){let n=ma(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(we(e,()=>e.shaderSource(n,t)),we(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 H_(e,t){let n=ma(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(we(e,()=>e.shaderSource(n,t)),we(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw sL(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var iL=/ERROR: [0-9]+:([0-9]+):/g;function sL(e,t){let n=iL.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let r=+n[1],a=e.split(`
|
|
`),s=a.length.toString().length+2,i=a.map((h,d)=>_.rightPad((d+1).toString(),s)+h),o=0;for(let h=0;h<i.length;h++)o=Math.max(i[h].length,o);let l=i.slice(0,r-1),c=i.slice(r-1,r),u=i.slice(r);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${_.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
|
|
`))}function G_(e){return ma(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function q_(e,t){if(we(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function pp(e,t){if(we(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function X_(e,t){let n=ma(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),we(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function K_(e,t){let n=ma(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),we(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function qP(){return J().getNumber("WEBGL_VERSION")===2?1:4}function Z_(e){return ma(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Y_(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${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 J_(e){return ma(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function fy(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),we(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),we(e,()=>e.enableVertexAttribArray(o)),!0)}function Q_(e,t,n){h3(e,n),we(e,()=>e.activeTexture(e.TEXTURE0+n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function XP(e,t){h3(e,t),we(e,()=>e.activeTexture(e.TEXTURE0+t)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function e3(e,t,n){return ma(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function t3(e,t,n){return e.getUniformLocation(t,n)}function n3(e,t,n,r){we(e,()=>Q_(e,t,r)),we(e,()=>e.uniform1i(n,r))}function KP(e){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),we(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function fp(e,t,n){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function my(e,t){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Ac(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+r3(e,t))}function r3(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 ${t}`}}function ma(e,t,n){let r=we(e,()=>t());if(r==null)throw new Error(n);return r}function h3(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function Si(e,t=2){return _.sizeFromShape(e.slice(0,e.length-t))}function Ni(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=[Si(e),...Ni(e)]),t}function a3(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?_.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=_.squeezeShape(e).newShape);let r=_.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=Si(e),s=2,i=2;return e.length&&([s,i]=Ni(e)),r=a*(s/2)*(i/2),_.sizeToSquarishShape(r).map(o=>o*2)}return _.sizeToSquarishShape(r)}function Ap(e){return e%2==0}function gc(e,t){if(e=e.slice(-2),t=t.slice(-2),_.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||Ap(n)&&Ap(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Ap(e[0])&&Ap(t[0])}var gp,xp;function s3(e){if(gp==null){let t=qr(e);gp=t.getParameter(t.MAX_TEXTURE_SIZE)}return gp}function ZP(){gp=null}function YP(){xp=null}function i3(e){if(xp==null){let t=qr(e);xp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,xp)}function o3(e){if(e===0)return 0;let t,n=qr(e);return nr(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:nr(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function nr(e,t){return e.getExtension(t)!=null}function yy(e){try{if(qr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function l3(e){if(e===0)return!1;let t=qr(e);if(e===1){if(!nr(t,"OES_texture_float"))return!1}else if(!nr(t,"EXT_color_buffer_float"))return!1;return xy(t)}function u3(e){if(e===0)return!1;let t=qr(e);if(e===1){if(!nr(t,"OES_texture_float")||!nr(t,"WEBGL_color_buffer_float"))return!1}else{if(nr(t,"EXT_color_buffer_float"))return xy(t);let n="EXT_color_buffer_half_float";if(nr(t,n)){let r=t.getExtension(n);return oL(t,r)}return!1}return xy(t)}function xy(e){let t=gy(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 oL(e,t){let n=gy(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 c3(e){return e!==2?!1:qr(e).fenceSync!=null}function Fl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&_.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ee=J();Ee.registerFlag("HAS_WEBGL",()=>Ee.getNumber("WEBGL_VERSION")>0);Ee.registerFlag("WEBGL_VERSION",()=>yy(2)?2:yy(1)?1:0);Ee.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ee.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ee.get("WEBGL_VERSION")===2);Ee.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ee.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ee.registerFlag("WEBGL_PACK",()=>Ee.getBool("HAS_WEBGL"));Ee.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_CLIP",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!0);Ee.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_REDUCE",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_CONV_IM2COL",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>s3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>i3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ee.getNumber("WEBGL_VERSION");return e===0?0:o3(e)});Ee.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ee.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!qu.isMobile());Ee.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>l3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ee.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ee.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ee.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>u3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_FENCE_API_ENABLED",()=>c3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ee.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ee.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}.`)});Ee.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>qu.isMobile()&&Ee.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 dn(){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 Ei(e,t,n="index"){let r=_.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function wy(e){let t=_.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}var d3=`
|
|
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;
|
|
}
|
|
`,lL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xc.DENSE;let t=bc(e),n=dn();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Ei(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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;
|
|
}
|
|
`}},uL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xc.DENSE;let t=bc(e),n=dn();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Ei(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}},cL=class{constructor(e){this.variableNames=["A"],this.outTexUsage=rr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
|
|
${d3}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},hL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=rr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
|
|
${d3}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},dL=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=dn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${wy(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.);
|
|
}
|
|
`}},pL=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=dn(),[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=`
|
|
${wy(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${r.output} = ${o};
|
|
}
|
|
`}},p3={};Me(p3,{bindVertexProgramAttributeStreams:()=>_3,createBufferFromOutputTexture:()=>I3,createFloat16MatrixTexture:()=>g3,createFloat16PackedMatrixTexture:()=>b3,createFloat32MatrixTexture:()=>A3,createIndexBuffer:()=>y3,createPackedMatrixTexture:()=>w3,createUnsignedBytesMatrixTexture:()=>x3,createVertexBuffer:()=>m3,createVertexShader:()=>f3,downloadByteEncodedFloatMatrixFromOutputTexture:()=>N3,downloadFloat32MatrixFromBuffer:()=>S3,downloadMatrixFromPackedOutputTexture:()=>E3,downloadPackedMatrixFromBuffer:()=>T3,getInternalFormatForFloat16MatrixTexture:()=>_y,getInternalFormatForFloat16PackedMatrixTexture:()=>Iy,getInternalFormatForFloat32MatrixTexture:()=>by,getInternalFormatForPackedMatrixTexture:()=>ky,getInternalFormatForUnsignedBytesMatrixTexture:()=>vy,uploadDenseMatrixToTexture:()=>v3,uploadPixelDataToTexture:()=>k3});function f3(e){let t=dn(),n=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return U_(e,n)}function m3(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return X_(e,t)}function y3(e){let t=new Uint16Array([0,1,2,2,1,3]);return K_(e,t)}function _c(e,t,n,r,a,s){Y_(t,n);let i=Z_(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function by(e){return e.internalFormatFloat}function A3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,by(r),r.textureFormatFloat,e.FLOAT)}function _y(e){return e.internalFormatHalfFloat}function g3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,_y(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function vy(e){return e.downloadTextureFormat}function x3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,vy(r),e.RGBA,e.UNSIGNED_BYTE)}function ky(e){return e.internalFormatPackedFloat}function w3(e,t,n,r){let[a,s]=$l(t,n);return _c(e,a,s,ky(r),e.RGBA,e.FLOAT)}function Iy(e){return e.internalFormatPackedHalfFloat}function b3(e,t,n,r){let[a,s]=$l(t,n);return _c(e,a,s,Iy(r),e.RGBA,r.textureTypeHalfFloat)}function _3(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),fy(e,t,"clipSpacePos",n,3,s,r)&&fy(e,t,"uv",n,2,s,a)}function v3(e,t,n,r,a,s){we(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),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function k3(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function I3(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function S3(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 N3(e,t,n,r){let[a,s]=wc(t,n),i=4,o=new Uint8Array(eL(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function T3(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(tL(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 E3(e,t,n){let r=new Float32Array(t*n*4);return we(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,yp(t,e)):this.gl=qr(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=yc(this.gl,a),nr(this.gl,s))this.textureHalfFloatExtension=yc(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),nr(this.gl,r))this.colorBufferHalfFloatExtension=yc(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",nr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(nr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=m3(this.gl),this.indexBuffer=y3(this.gl),this.framebuffer=J_(this.gl),this.textureConfig=gy(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;we(e,()=>e.finish()),we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.deleteFramebuffer(this.framebuffer)),we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),we(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),A3(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),g3(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),x3(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),k3(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),v3(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),b3(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),w3(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(my(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>N3(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return T3(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return S3(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=I3(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,()=>E3(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=H_(t,e),r=f3(t),a=G_(t);return we(t,()=>t.attachShader(a,r)),we(t,()=>t.attachShader(a,n)),q_(t,a),this.debug&&pp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=_3(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pp(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?e3(this.gl,e,t):t3(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(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(),n3(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=$l(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),Ac(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=yc(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await _.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=fL(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&_.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),fp(this.gl,e,this.framebuffer),this.debug&&Ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(fp(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ac(this.gl)):my(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&&Ac(r),this.outputTexture=e,we(r,()=>r.viewport(0,0,t,n)),we(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),we(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 fL(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:C3}=C;function vL(e,t,n,r){let a=[];e.forEach(p=>{let f=_.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=>mL(p,t,r)).join(`
|
|
`),o=t.texShape,l=dn(),c=gL(l),u,h,d=bL(l);return t.isPacked?(u=yL(t.logicalShape,o),h=wL(l)):(u=AL(t.logicalShape,o),h=xL(l)),r&&(d+=_L),[d,c,h,s,u,i,n].join(`
|
|
`)}function Dl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return kL(e);case 1:return IL(e);case 2:return SL(e);case 3:return NL(e);case 4:return TL(e);case 5:return EL(e);case 6:return CL(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function R3(e){switch(e.shapeInfo.logicalShape.length){case 0:return RL(e);case 1:return ML(e);case 2:return FL(e);case 3:return $L(e);default:return DL(e)}}function mL(e,t,n=!1){let r="";n?r+=R3(e):r+=Dl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=OL(e,t):r+=zL(e,t)),r}function yL(e,t){switch(e.length){case 0:return M3();case 1:return PL(e,t);case 2:return BL(e,t);case 3:return LL(e,t);default:return WL(e,t)}}function AL(e,t){switch(e.length){case 0:return M3();case 1:return VL(e,t);case 2:return qL(e,t);case 3:return jL(e,t);case 4:return UL(e,t);case 5:return HL(e,t);case 6:return GL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function gL(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function xL(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function wL(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function bL(e){return`${e.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${e.varyingFs} vec2 resultUV;
|
|
${e.defineOutput}
|
|
const vec2 halfCR = vec2(0.5, 0.5);
|
|
|
|
struct ivec5
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
};
|
|
|
|
struct ivec6
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
int v;
|
|
};
|
|
|
|
uniform float NAN;
|
|
${e.defineSpecialNaN}
|
|
${e.defineSpecialInf}
|
|
${e.defineRound}
|
|
|
|
int imod(int x, int y) {
|
|
return x - y * (x / y);
|
|
}
|
|
|
|
int idiv(int a, int b, float sign) {
|
|
int res = a / b;
|
|
int mod = imod(a, b);
|
|
if (sign < 0. && mod != 0) {
|
|
res -= 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//Based on the work of Dave Hoskins
|
|
//https://www.shadertoy.com/view/4djSRW
|
|
#define HASHSCALE1 443.8975
|
|
float random(float seed){
|
|
vec2 p = resultUV * seed;
|
|
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
|
|
p3 += dot(p3, p3.yzx + 19.19);
|
|
return fract((p3.x + p3.y) * p3.z);
|
|
}
|
|
|
|
${XL}
|
|
${KL}
|
|
${ZL}
|
|
`}var XL=`
|
|
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
|
|
int texelIndex = index / 2;
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,KL=`
|
|
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
|
|
int texNumC, int row, int col) {
|
|
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,ZL=`
|
|
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);
|
|
}
|
|
`,_L=`
|
|
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 M3(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function PL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${n[1]}.0);
|
|
}
|
|
`:n[1]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${n[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
|
|
}
|
|
`}function VL(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function LL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function jL(e,t){let n=Ei(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function WL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
|
|
int b${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}(${o});
|
|
}
|
|
`}function UL(e,t){let n=Ei(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function HL(e,t){let n=Ei(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function GL(e,t){let n=Ei(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function BL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(_.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function qL(e,t){return _.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Ci(e){return`offset${e}`}function RL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=dn();return`
|
|
vec4 ${n}() {
|
|
return ${r.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function kL(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=Ci(t);return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function ML(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=dn();return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${a[0]}, ${a[1]}, index);
|
|
return ${s.texture2D}(${t}, uv);
|
|
}
|
|
`}function IL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${Ol(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=Ci(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 FL(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=dn();if(a!=null&&_.arraysEqual(t,a))return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
|
|
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function SL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&_.arraysEqual(t,a)){let h=a[0],d=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:i}=_.squeezeShape(t),o=s;if(o.length<t.length){let h=zl(e,o),d=["row","col"];return`
|
|
${Dl(h)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${Pl(d,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
|
|
${Ol(e)}
|
|
}
|
|
`;let l=a[0],c=a[1],u=Ci(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 $L(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=zl(e,h),f=["b","row","col"];return`
|
|
${R3(p)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${Pl(f,d)});
|
|
}
|
|
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=dn();return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${c}, ${l}, b, row, col);
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`}function NL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=_.squeezeShape(t),l=i;if(l.length<t.length){let f=zl(e,l),m=["row","col","depth"];return`
|
|
${Dl(f)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${Pl(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)));
|
|
${Ol(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=Ci(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 DL(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=dn();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 TL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=_.squeezeShape(t);if(o.length<t.length){let f=zl(e,o),m=["row","col","depth","depth2"];return`
|
|
${Dl(f)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${Pl(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)));
|
|
${Ol(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=Ci(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 EL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=_.squeezeShape(t);if(l.length<t.length){let m=zl(e,l),y=["row","col","depth","depth2","depth3"];return`
|
|
${Dl(m)}
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${r}(${Pl(y,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;
|
|
${Ol(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=Ci(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 CL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=_.squeezeShape(t);if(a.length<t.length){let y=zl(e,a),A=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Dl(y)}
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${r}(${Pl(A,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)));
|
|
${Ol(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=Ci(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 Ol(e){let t=e.name,n=_.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function OL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=C3(e.shapeInfo.logicalShape,t.logicalShape),l=lt(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(y=>`coords.${h[y+c]} = 0;`).join(`
|
|
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((y,A)=>`coords.${h[A+c]}`).join(", ");let p="return outputValue;",f=_.sizeFromShape(e.shapeInfo.logicalShape)===1,m=_.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 y=s-2,A=s-1;o.indexOf(y)>-1&&o.indexOf(A)>-1?p="return vec4(outputValue.x);":o.indexOf(y)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(A)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${a}() {
|
|
${l} coords = getOutputCoords();
|
|
${u}
|
|
vec4 outputValue = get${r}(${d});
|
|
${p}
|
|
}
|
|
`}function zL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&_.arraysEqual(i,s))return`
|
|
float ${a}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let c=lt(l),u=C3(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.${p[m+h]} = 0;`).join(`
|
|
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,y)=>`coords.${p[y+h]}`).join(", "),`
|
|
float ${a}() {
|
|
${c} coords = getOutputCoords();
|
|
${d}
|
|
return get${r}(${f});
|
|
}
|
|
`}function lt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function zl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Pl(e,t){return t.map(n=>e[n]).join(", ")}function YL(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=vL(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${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 F3(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!_.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!_.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function JL(e,t,n,r,a){F3(t.inShapeInfos,n),F3([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(_.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function QL(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:eW,bincountImpl:$3,bincountReduceImpl:tW,ceilImpl:nW,concatImpl:rW,expImpl:aW,expm1Impl:sW,floorImpl:iW,gatherV2Impl:oW,greaterImpl:lW,lessImpl:uW,linSpaceImpl:cW,logImpl:hW,maxImpl:dW,maximumImpl:pW,minimumImpl:fW,multiplyImpl:mW,negImpl:yW,prodImpl:AW,rangeImpl:gW,rsqrtImpl:xW,simpleAbsImpl:D3,sliceImpl:wW,stridedSliceImpl:bW,subImpl:_W,tileImpl:vW,topKImpl:kW,transposeImpl:Sy,uniqueImpl:IW}=ey;function O3(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function pn(e,t){return t===1?[e]:O3(e,t)}function SW(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 CW=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=pn("rc",t),r=lt(t),a=NW(t,e,n),s=TW(t,e[e.length-1],e[e.length-2],n),i=EW(e,n);this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
|
|
if(${a}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function RW(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 NW(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 TW(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 EW(e,t){let n=e.length,r=RW(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 z3=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=`
|
|
${MW(t)}
|
|
${wy(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function MW(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${Ei(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var FW=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=L3(t,n),a=W3(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=P3(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===en.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===en.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===en.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=L3(n,r),s=W3(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=P3(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 $W(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 ${t}`)}function P3(e,t,n,r,a){let s=DW(t,r),i;if(a){let[l,c]=$l(e[0],e[1]);i=l*c}else{let[l,c]=wc(e[0],e[1]);i=l*c}let o=$W(n,s);return i*o}function DW(e,t){switch(e){case en.PACKED_2X2_FLOAT32:return ky(t);case en.PACKED_2X2_FLOAT16:return Iy(t);case en.UNPACKED_FLOAT32:return by(t);case en.UNPACKED_FLOAT16:return _y(t);case en.PACKED_4X1_UNSIGNED_BYTE:return vy(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function OW(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?en.PACKED_2X2_FLOAT32:en.UNPACKED_FLOAT32:e?en.PACKED_2X2_FLOAT16:en.UNPACKED_FLOAT16}function L3(e,t){if(e===rr.UPLOAD)return en.PACKED_2X2_FLOAT32;if(e===rr.RENDER||e==null)return OW(t);if(e===rr.DOWNLOAD||e===rr.PIXELS)return en.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function W3(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ga=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);
|
|
}
|
|
`}},Sr="if (isnan(x)) return x;",zW="return x;",B3="return abs(x);",PW="return (x >= 0.0) ? x : (exp(x) - 1.0);",LW=Sr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,WW=Sr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,bp="return x;",BW="return x;",VW=`
|
|
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;
|
|
`,jW=`
|
|
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;
|
|
`,UW=`
|
|
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;
|
|
`,Ll=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},HW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=pn("rc",t),r=lt(t),a=SW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${a});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},GW=Hr.whereImpl,qW=1e-7,XW=1e-4,Ny={};function KW(e){return e in Ny||(Ny[e]={}),Ny[e]}var ZW=128,YW=600;function JW(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*YW/1024/1024}var Wl=class extends wu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=qr(J().getNumber("WEBGL_VERSION"));this.binaryCache=KW(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 FW(this.gpgpu),this.numMBBeforeWarning=JW(),this.texData=new Rh(this,ua())}nextDataId(){return Wl.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:rr.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:rr.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 Ll(i,bp):h=new Ga(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=_.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=C.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=_.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(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 Ll(r,bp):p=new Ga(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,...bc(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=C.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=_.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ua().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>_.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!V_(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=_.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...bc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?mp(t):t,o=s?new hL(i):new cL(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=_.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=_.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=_.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:_.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=_.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=ZW){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&_.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return GW(e.shape,t)}packedUnaryOp(e,t,n){let r=new Ll(e.shape,t),a=this.compileAndRun(r,[e],n);return ua().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=D3(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,B3,e.dtype);let t=new Ga(e.shape,B3),n=this.compileAndRun(t,[e]);return ua().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&_.isString(n[0])){let a=n.map(s=>_.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return ua().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new HW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new CW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Si(e.shape),...Ni(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Si(t),...Ni(t)],s=new z3(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 uL(s):i=new lL(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===xc.DENSE){let m=bc(e.outputShape);i.texShape=m.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),_.sizeFromShape(s.shape)===0)return i.values=_.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(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 y=this.texData.get(m.dataId);if(y.texture==null){if(!e.packedInputs&&_.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=m.shape)}else if(!!y.isPacked!=!!e.packedInputs)m=y.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),y=this.texData.get(m.dataId);else if(y.isPacked&&!gc(y.shape,m.shape)){let A=m,g=m.shape;m.shape=y.shape,m=this.packedReshape(m,g),o.push(m),y=this.texData.get(m.dataId),A.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:y,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=QL(e,l,c),h=this.getAndSaveBinary(u,()=>YL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),JL(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=_.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(xe(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?qW:XW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=_.now());let u=t.texShape;if(u==null&&(u=a3(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]=$l(u[0],u[1]),d=new pL(h,[f,p],m)):d=new dL(h,[f,p],m);let y=this.makeTensorInfo([f,p],r);m?this.texData.get(y.dataId).usage=rr.PIXELS:this.texData.get(y.dataId).usage=rr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),p,f,a);let A=!0,g=this.runWebGLProgram(d,[y],r,null,A),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=_.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=QW(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*_.bytesPerElement(t)}};Wl.nextDataId=0;function QW(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 V3="3.4.0";function j3(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}qu.isBrowser()&&fl("webgl",()=>new Wl,2);var eB={forceHalfFloat:j3},U3=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Bl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.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;
|
|
`,vc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||_.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${lt(a)} coords = getOutputCoords();
|
|
`,a===1)s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=pn("coords",a);s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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 Vn(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 tB={kernelName:Es,backendName:"webgl",kernelFunc:Vn};function qa(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=Vn({inputs:{x:r},backend:n}),l=Vn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var nB={kernelName:Ph,backendName:"webgl",kernelFunc:qa},H3="return (a < 0.) ? b * a : a;",G3=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function rB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",_.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(G3,a.shape,i.shape):new Bl(H3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var aB={kernelName:Cs,backendName:"webgl",kernelFunc:rB},q3="return (a < 0.) ? b * a : a;",X3=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function sB(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(X3,r.shape,a.shape):new Bl(q3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var iB={kernelName:js,backendName:"webgl",kernelFunc:sB},K3="if (isnan(x)) return x;",oB=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,lB=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype: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 Ll(i.shape,t):u=new Ga(i.shape,e),o.runWebGLProgram(u,[i],l)}}function tn({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),[y,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[v,w]=x,b={dataId:v.dataId,dtype:v.dtype,shape:l.shape},k={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new Bl(e,l.shape,c.shape);return u.runWebGLProgram(N,[b,k],dr(v.dtype,w.dtype))}),g=qa({inputs:{real:y,imag:A},backend:u});return u.disposeIntermediateTensorInfo(y),u.disposeIntermediateTensorInfo(A),g}let h=s||dr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[y,A]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(A,h),x=u.texData.get(g.dataId);return x.values=y,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new vc(t,l.shape,c.shape,n):p=new Bl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function vp(e,t=!1){if(e==="linear")return t?BW:zW;if(e==="relu")return t?jW:LW;if(e==="elu")return t?VW:PW;if(e==="relu6")return t?UW:WW;if(e==="prelu")return t?X3:q3;if(e==="leakyrelu")return t?G3:H3;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Z3=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="",y="";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}
|
|
}`,y="result = activation(result);");let A=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${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 = ${x};
|
|
vec4 a = getMatrixA(batchA, ${h});
|
|
vec4 b = getMatrixB(batchB, ${d});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${p[0]} * ${f[0]});
|
|
result += (${p[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${A}
|
|
|
|
${y}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},Y3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},J3=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},Q3="return a * b;";function Ty(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new J3(Y3.REAL,r.shape,a.shape),u=new J3(Y3.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=mW(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new vc(Q3,r.shape,a.shape):i=new Bl(Q3,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var uB={kernelName:Ls,backendName:"webgl",kernelFunc:Ty};function cB(e,t,n){let r=[Si(e.shape),...Ni(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[Si(t),...Ni(t)],i=new z3(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=_.sizeFromShape(a.shape),l=_.inferFromImplicitShape(s,o),c=_.sizeFromShape(l);_.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!gc(a.shape,l)&&!(u.texture!==null&&gc(u.shape,l))?cB(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var hB={kernelName:Go,backendName:"webgl",kernelFunc:Ae},e7=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${_.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},dB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 pB(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=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ri(e,t,n,r){let a=pB(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 e7({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new e7({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new dB({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 mB=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=lt(this.rank),a=fB(t);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function fB(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 yB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=lt(this.rank),a=O3("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${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] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function kp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yB(e.shape,t):new mB(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function AB(e,t,n,r){let a=t,s=e.shape.length,i=_.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=kp(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let f=_.sizeFromShape(d),m=_.sizeFromShape(e.shape)/f,y=Ae({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),A=yd(e.dtype),g=Ri(y,A,"sum",r),x=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),x}function Ip(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return AB(a,s,i,n)}var gB={kernelName:Qs,backendName:"webgl",kernelFunc:Ip};function fn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=Sy(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=kp(a,s,i);return c}var xB={kernelName:si,backendName:"webgl",kernelFunc:fn},t7=1e3;function Sp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),y=t.shape.slice(0,-2),A=_.sizeFromShape(m),g=_.sizeFromShape(y),x=A===g||A===1||g===1;_.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${y}).`);let v=(A>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);_.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[A,h,p]:[A,p,h],b=r?[g,f,d]:[g,d,f],k=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),N=Ae({inputs:{x:t},backend:a,attrs:{shape:b}}),E=[k,N],F=Math.max(A,g),O=n?k.shape[1]:k.shape[2],L=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?vp(l,!0):null,X=L||V||j||U!=null,G;if((p===1||f===1)&&O>t7&&X===!1){let Y=k,ae=N;n&&(Y=fn({inputs:{x:k},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=fn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[F,O,1]}}),E.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[F,1,O]}}),E.push(oe));let fe=Ty({inputs:{a:Q,b:oe},backend:a});G=Ip({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),E.push(fe)}else{let Y=dr(e.dtype,t.dtype),ae=new Z3(w,b,[F,p,f],n,r,L,U,V,j),te=[k,N];if(s!=null&&te.push(s),V&&te.push(i),j){let ie=a.makeTensorInfo([],"float32",_.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:v}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function wB(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 Sp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var bB={kernelName:ii,backendName:"webgl",kernelFunc:wB},n7="return abs(x);";function _B(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=D3(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,n7):a=new Ga(r.shape,n7),n.runWebGLProgram(a,[r],r.dtype)}var vB={kernelName:so,backendName:"webgl",kernelFunc:_B},kB=Sr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,IB=Xe({opSnippet:kB}),SB={kernelName:io,backendName:"webgl",kernelFunc:IB},NB=Sr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,TB=Xe({opSnippet:NB}),EB={kernelName:oo,backendName:"webgl",kernelFunc:TB},r7="return a + b;",CB=tn({opSnippet:r7,packedOpSnippet:r7,supportsComplex:!0,cpuKernelImpl:eW}),RB={kernelName:Ca,backendName:"webgl",kernelFunc:CB},MB=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 = ${r};
|
|
setOutput(result);
|
|
}
|
|
`}},FB=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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 Vn({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)=>dr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new FB(r[0].shape,s):new MB(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var $B={kernelName:ds,backendName:"webgl",kernelFunc:Np};function DB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("all",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"all",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var OB={kernelName:lo,backendName:"webgl",kernelFunc:DB};function zB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("any",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"any",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var PB={kernelName:uo,backendName:"webgl",kernelFunc:zB},LB=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 ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},WB=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=lt(o),c=pn("coords",o),u,h;if(s===1){h=o+1;let k=lt(h);u=`
|
|
${k} sourceLocR = ${k}(${c.join()}, 0);
|
|
++${c[o-1]};
|
|
${k} sourceLocG = ${k}(${c.join()}, 0);
|
|
++${c[o-2]};
|
|
${k} sourceLocA = ${k}(${c.join()}, 0);
|
|
--${c[o-1]};
|
|
${k} sourceLocB = ${k}(${c.join()}, 0);
|
|
--${c[o-2]};`}else h=o,u=`
|
|
${l} sourceLocR = coords;
|
|
++${c[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${c[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${c[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(k=>"int "+k),m=pn("sourceLocR",h-1).concat("inIdx.r"),y=pn("sourceLocG",h-1).concat("inIdx.g"),A=pn("sourceLocB",h-1).concat("inIdx.b"),g=pn("sourceLocA",h-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=r?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${g.join()})));`,w=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,b=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()}));
|
|
}
|
|
${b}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
|
|
${u}
|
|
ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
|
|
sourceLocB${p}, sourceLocA${p}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${w};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${v}
|
|
vec4 candidate = ${w};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
|
|
|
|
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
|
|
replace.y ? candidate.y : bestValue.y,
|
|
replace.z ? candidate.z : bestValue.z,
|
|
replace.w ? candidate.w : bestValue.w);
|
|
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
|
|
srcIdx++;
|
|
}
|
|
setOutput(bestIndex);
|
|
}
|
|
`}};function a7(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=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new LB(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=a7(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function s7(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new WB(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=s7(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function i7(e,t,n,r){let a=[n];if(C.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]=C.computeOutAndReduceShapes(t.shape,a),l=_.sizeFromShape(o),c=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=a7(e,c,r);s.push(u);let h=Ae({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return s7(e,t,r)}function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=i7(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var VB={kernelName:ps,backendName:"webgl",kernelFunc:BB};function jB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=i7(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var UB={kernelName:vu,backendName:"webgl",kernelFunc:jB},HB=Sr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,GB=Xe({opSnippet:HB}),qB={kernelName:co,backendName:"webgl",kernelFunc:GB},XB=Sr+"return log(x + sqrt(x * x + 1.0));",KB=Xe({opSnippet:XB}),ZB={kernelName:ho,backendName:"webgl",kernelFunc:KB},YB=Sr+`
|
|
return atan(x);
|
|
`,JB=Xe({opSnippet:YB}),QB={kernelName:po,backendName:"webgl",kernelFunc:JB},eV=oB+`
|
|
return atan(a, b);
|
|
`,tV=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+lB+`
|
|
return result;
|
|
`,nV=tn({opSnippet:eV,packedOpSnippet:tV}),rV={kernelName:mo,backendName:"webgl",kernelFunc:nV},aV=Sr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sV=Xe({opSnippet:aV}),iV={kernelName:fo,backendName:"webgl",kernelFunc:sV},kc=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`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let k=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${d}, ${p});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h};
|
|
wC += ${c}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${k} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${r?a?m:y:`wR * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,w=s%4,b=`
|
|
if (${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 = ${A};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${A});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${v}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
getValue(batch, xR, xC + 3 * ${c}, d)
|
|
);
|
|
|
|
${b}
|
|
}
|
|
|
|
int xC = xCCorner + ${v};
|
|
if (${w===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${b}
|
|
} else if (${w===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${b}
|
|
} else if (${w===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${b}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},Ey=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,y=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${y}, ${A});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${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 * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let v="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,k=s%4,N=`
|
|
if (${g}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${v}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${y}, ${A});
|
|
const float initializationValue = ${x};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${x});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${d};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${h};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
|
|
);
|
|
|
|
${N}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${k===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${k===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${k===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
}
|
|
}
|
|
setOutput(${w});
|
|
}
|
|
}
|
|
`}};function oV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new kc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var lV={kernelName:fs,backendName:"webgl",kernelFunc:oV};function uV(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=C.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new Ey(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var cV={kernelName:ku,backendName:"webgl",kernelFunc:uV},hV=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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},dV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,y=1/(t*n*r);this.userCode=`
|
|
const ivec3 pads = ivec3(${p}, ${f}, ${m});
|
|
const float avgMultiplier = float(${y});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${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 pV(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=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new dV(d);return n.runWebGLProgram(p,[a],i.dtype)}var fV={kernelName:Oh,backendName:"webgl",kernelFunc:pV};function mV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Fl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=new hV(u);return n.runWebGLProgram(h,[a],i.dtype)}var yV={kernelName:Dh,backendName:"webgl",kernelFunc:mV};function AV(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Sp({a,b:s,transposeA:i,transposeB:o,backend:n})}var gV={kernelName:ms,backendName:"webgl",kernelFunc:AV},xV=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.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)));
|
|
}
|
|
`}},wV=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.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);
|
|
}
|
|
`}},bV=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;_.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new wV(r.shape,a.shape,s.shape,u,h,l):new xV(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},_V={kernelName:Ns,backendName:"webgl",kernelFunc:bV},kV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=vV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Cy[o]} = start[${o}] + coords.${Cy[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)}}},Cy=["x","y","z","w","u","v"];function vV(e){if(e===1)return"sourceLoc";if(e<=6)return Cy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var IV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=pn("coords",this.rank),r=pn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${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 SV(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=un.computeFlatOffset(t,_.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Ic(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=un.parseSliceParams(a,s,i);if(un.assertParamsValid(a,o,l),_.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=wW(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=un.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IV(l):new kV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),SV(a,o,l,n)}var NV={kernelName:Zo,backendName:"webgl",kernelFunc:Ic},TV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=[],f=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:c}}),y=Ae({inputs:{x:m},backend:n,attrs:{shape:u}}),A=Ic({inputs:{x:y},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(y),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),A},EV={kernelName:Iu,backendName:"webgl",kernelFunc:TV};function CV(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=$3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var RV={kernelName:zh,backendName:"webgl",kernelFunc:CV},MV="return float(a != b);",o7=tn({opSnippet:MV,dtype:"bool"}),FV={kernelName:Po,backendName:"webgl",kernelFunc:o7};function Sc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.real},backend:n})}var $V={kernelName:sd,backendName:"webgl",kernelFunc:Sc},DV="return float(int(x));";function OV(e,t){let n=new Ga(e.shape,DV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Ry(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Vn({inputs:{x:a},backend:n});let i=Rt(a.shape),o=Ry({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=qa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Sc({inputs:{input:a},backend:n}),o=Ry({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=Vn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return OV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",_.getTypedArrayFromDType("bool",1)),o=o7({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 zV={kernelName:ys,backendName:"webgl",kernelFunc:Ry},l7="return ceil(x);",PV=Xe({opSnippet:l7,packedOpSnippet:l7,cpuKernelImpl:nW}),LV={kernelName:As,backendName:"webgl",kernelFunc:PV},WV=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)}}},BV=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 VV(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 BV(a.shape):o=new WV(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var jV={kernelName:Ra,backendName:"webgl",kernelFunc:VV},UV=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 u7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function HV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new UV(r.shape),i=[u7(r,a.complexTensorInfos.real),u7(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var GV={kernelName:Su,backendName:"webgl",kernelFunc:HV},qV=class{constructor(e){this.outputShape=[],this.outputShape=C.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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},XV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=lt(r),s=pn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${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}(${Tp(i,l,m)}),
|
|
vec2(${Tp(c,l,m)}));
|
|
}`}let d=o.length,p=o[o.length-1];h+=`
|
|
return getChannel(
|
|
getT${d}(${Tp(i,l,p)}),
|
|
vec2(${Tp(c,l,p)}));`,this.userCode=`
|
|
float getValue(${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 Tp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Ep(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var KV={kernelName:Jh,backendName:"webgl",kernelFunc:Ep};function Vl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>Sc({inputs:{input:f},backend:n})),u=e.map(f=>Ep({inputs:{input:f},backend:n})),h=Vl(c,t,n),d=Vl(u,t,n),p=qa({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}=c7(e,t,n),h=c.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),d=c[0].shape[0]===1,p=rW(h,u,r,d),f=C.computeOutShape(e.map(y=>y.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Vl(e.slice(0,c),t,n),h=Vl(e.slice(c),t,n),d=Vl([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 XV(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=c7(e,t,n),i=new qV(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function c7(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,_.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function h7(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(c=>c.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>_.sizeFromShape(c.shape)>0);if(o.length===1)return Vn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return C.assertParamsConsistent(l,s),Vl(o,s,n)}var ZV={kernelName:yo,backendName:"webgl",kernelFunc:h7},d7=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",y=m?1:2,A=m?2:3,g=m?3:1,x="",v="";n&&(r?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,v="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${o}, ${l});
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${g}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${y}], coords[${A}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${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;
|
|
${w}
|
|
${v}
|
|
setOutput(result);
|
|
}
|
|
`}},YV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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);
|
|
}
|
|
`}},JV=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=dn(),y=h==="channelsLast",A=y?0:1,g=y?1:2,x="";for(let v=0;v<=1;v++)for(let w=0;w<=1;w++)x+=`
|
|
blockIndex = rc.y + ${w};
|
|
pos = rc.x + ${v};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
|
|
d0 = offsetY + ${u} * (pos / ${f});
|
|
|
|
if(d0 < ${t[A]} && 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), ${a}.));
|
|
|
|
if (${y}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${v*2+w}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${v*2+w}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${x}
|
|
|
|
${m.output} = result;
|
|
}
|
|
`}};function p7({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,y,A=[],g=(h===1||d===1)&&u>t7,x=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,v,n.inChannels]}}),b=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),k=Sp({a:w,b,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});y=Ae({inputs:{x:k},backend:r,attrs:{shape:n.outShape}}),A.push(w),A.push(b),A.push(k)}else{let v=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,_.assert(gc(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let k=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(k);let N=Sp({a:w,b:k,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(N.dataId);_.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,E.shape=n.outShape,y=Vn({inputs:{x:N},backend:r}),y.shape=n.outShape,A.push(N)}for(let v of A)r.disposeIntermediateTensorInfo(v);return y}function f7({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,y=d*h,A=[m,y],g=!0,x=!1,v=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,m,_.sizeFromShape(t.shape)/m]}});v.push(w),v.push(b);let k=new JV(A,w.shape,n),N=r.runWebGLProgram(k,[w],"float32"),E=Ae({inputs:{x:N},backend:r,attrs:{shape:[1,A[0],A[1]]}});v.push(N),v.push(E);let F=a!=null,O=s!=null,L=o==="leakyrelu",V=o?vp(o,!0):null,j=new Z3(E.shape,b.shape,[1,y,n.outChannels],g,x,F,V,O,L),U=[E,b];if(a&&U.push(a),O&&U.push(s),L){let Y=r.makeTensorInfo([],"float32",_.createScalarValue(i,"float32"));U.push(Y),v.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});v.push(X);for(let Y of v)r.disposeIntermediateTensorInfo(Y);return ee}function QV(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=C.convertConv2DDataFormat(l),d=C.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=p7({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=f7({x:a,filter:s,convInfo:d,backend:n});else{let m=new d7(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var ej={kernelName:gs,backendName:"webgl",kernelFunc:QV},tj=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);
|
|
}
|
|
`}},nj=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);
|
|
}
|
|
`}},rj=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},aj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 sj(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=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new tj(d);return n.runWebGLProgram(p,[a,s],"float32")}var ij={kernelName:Lh,backendName:"webgl",kernelFunc:sj};function oj(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=C.convertConv2DDataFormat(c),d=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new nj(d);return n.runWebGLProgram(p,[a,s],"float32")}var lj={kernelName:xs,backendName:"webgl",kernelFunc:oj};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new YV(c);return n.runWebGLProgram(u,[a,s],"float32")}var cj={kernelName:Nu,backendName:"webgl",kernelFunc:uj};function hj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=C.computeConv3DInfo(a.shape,l,i,1,o),u=new rj(c);return n.runWebGLProgram(u,[a,s],"float32")}var dj={kernelName:Wh,backendName:"webgl",kernelFunc:hj};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=C.computeConv3DInfo(l,s.shape,o,1,i),u=new aj(c);return n.runWebGLProgram(u,[a,s],"float32")}var fj={kernelName:Bh,backendName:"webgl",kernelFunc:pj},mj=K3+`
|
|
return cos(x);
|
|
`,yj=Xe({opSnippet:mj}),Aj={kernelName:ws,backendName:"webgl",kernelFunc:yj},gj=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,xj=Xe({opSnippet:gj}),wj={kernelName:Ao,backendName:"webgl",kernelFunc:xj},bj=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`,`${o-1}.0`],[m,y,A]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,x,v]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${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 = ${y};
|
|
float width_scale = ${x};
|
|
|
|
float in_y = ${A};
|
|
if( in_y < 0.0 || in_y > ${p} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
float in_x = ${v};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${d} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},_j=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new bj(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},vj={kernelName:go,backendName:"webgl",kernelFunc:_j},A7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${m7(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${lt(r)} coords = getOutputCoords();
|
|
int end = ${y7(r,"coords")};
|
|
float val = ${a};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${y7(r,"coords")} = idx;
|
|
val += getX(${m7(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 m7(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 ${e} is not yet supported`)}function y7(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function kj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=C.getAxesPermutation([s],l),u=a;c!=null&&(u=fn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Vn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new A7(u.shape,!1,o),y=m.getCustomSetupFunc(f),A=p;p=n.runWebGLProgram(m,[p],p.dtype,y),n.disposeIntermediateTensorInfo(A)}if(i){let f=new A7(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=C.getUndoAxesPermutation(c),m=fn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var Ij={kernelName:bs,backendName:"webgl",kernelFunc:kj};function Sj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=$3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=tW(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Nj={kernelName:Vh,backendName:"webgl",kernelFunc:Sj},Tj=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 Ej(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new Tj(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var Cj={kernelName:xo,backendName:"webgl",kernelFunc:Ej},g7=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,y="",A="";n&&(r?y=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?y=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:y=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,A="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),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 / ${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}
|
|
${A}
|
|
setOutput(result);
|
|
}
|
|
`}},x7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,h=e.strideWidth,d=e.dilationHeight,p=e.dilationWidth,f=e.filterHeight,m=e.filterWidth,y=m,A=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let w=0;w<m;w++)A+=`
|
|
vec4 xTexelC${w*2};
|
|
vec4 xC${w};`;for(let w=0;w<f;w++){for(let b=0;b<m;b++)A+=`
|
|
xTexelC${b*2} = vec4(0.0);
|
|
xC${b} = vec4(0.0);`;A+=`
|
|
xR = xRCorner + ${w*d};
|
|
if (xR >=0 && xR < ${i}) {
|
|
`;for(let b=0;b<y/2+1;b++){let k=b*2;if(A+=`
|
|
xC = xCCorner + ${k*p};
|
|
`,h===1){if(k<m&&(c%2==1?(A+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
xTexelC${k} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${k}.zw = vec2(0.0);
|
|
}
|
|
}
|
|
`,p===1&&k>0?A+=`
|
|
xC${k} = vec4(xTexelC${k-2}.zw, xTexelC${k}.xy);
|
|
`:A+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${k} = vec4(previous.zw, xTexelC${k}.xy);
|
|
} else {
|
|
xC${k} = vec4(0.0, 0.0, xTexelC${k}.xy);
|
|
}
|
|
`):A+=`
|
|
if (xC >= 0 && xC < ${o}) {
|
|
xTexelC${k} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${k}.zw = vec2(0.0);
|
|
}
|
|
}
|
|
|
|
xC${k} = xTexelC${k};
|
|
`,k+1<m)){let N=c%2==0?_.nearestLargerEven(p):p;p%2==0&&c%2==1||p%2!=0&&c%2!=1?(A+=`
|
|
xCOffset = xC + ${c%2} + ${N};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${k+2}.zw = vec2(0.0);
|
|
}
|
|
}
|
|
`,p>1&&(A+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
xTexelC${k} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
`),A+=`
|
|
xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.xy);
|
|
`):N===1?A+=`
|
|
xC${k+1} = xTexelC${k};
|
|
`:A+=`
|
|
xCOffset = xC + ${N};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${k+2}.zw = vec2(0.0);
|
|
}
|
|
}
|
|
|
|
xC${k+1} = xTexelC${k+2};
|
|
`}}else k<m&&(c%2==1?(A+=`
|
|
xCOffset = xC + 1 - ${h};
|
|
if(xCOffset >= 0 && xCOffset < ${o}) {
|
|
xTexelC${k} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${k}.zw = vec2(0.0);
|
|
}
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${o}) {
|
|
xTexelC${k+2} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= ${o}) {
|
|
xTexelC${k+2}.zw = vec2(0.0);
|
|
}
|
|
}
|
|
|
|
xC${k} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
|
|
`,k+1<m&&(A+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + ${h};
|
|
if(xCOffset >= 0 && xCOffset < ${o}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${k+1} = vec4(xTexelC${k+2}.xy, final.xy);
|
|
`)):(A+=`
|
|
if(xC >= 0 && xC < ${o}) {
|
|
xTexelC${k} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${k}.zw = vec2(0.0);
|
|
}
|
|
}
|
|
|
|
xCOffset = xC + ${h};
|
|
if(xCOffset >= 0 && xCOffset < ${o}) {
|
|
xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${k+2}.zw = vec2(0.);
|
|
}
|
|
}
|
|
|
|
xC${k} = vec4(
|
|
xTexelC${k}.xy, xTexelC${k+2}.xy);
|
|
`,k+1<m&&(A+=`
|
|
xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
|
|
`)));k<m&&(A+=`
|
|
wTexel = getW(${w}, ${k}, d1, q);
|
|
dotProd += xC${k} * vec4(wTexel.xz, wTexel.xz);
|
|
`,k+1<m&&(A+=`
|
|
wTexel = getW(${w}, ${k+1}, d1, q);
|
|
dotProd += xC${k+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}A+=`
|
|
}
|
|
`}let g="",x="";n&&(r?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:g=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,x="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${g}
|
|
|
|
const ivec2 strides = ivec2(${u}, ${h});
|
|
const ivec2 pads = ivec2(${l}, ${c});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${s};
|
|
int q = d2 - d1 * ${s};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${A}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${v}
|
|
${x}
|
|
setOutput(result);
|
|
}
|
|
`}};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),_.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=C.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 x7(h):d=new g7(h),n.runWebGLProgram(d,[a,s],"float32")}var Mj={kernelName:_s,backendName:"webgl",kernelFunc:Rj},Fj=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},$j=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 Dj(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=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new Fj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Oj={kernelName:jh,backendName:"webgl",kernelFunc:Dj};function zj(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=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new $j(h);return n.runWebGLProgram(d,[a,s],"float32")}var Pj={kernelName:Uh,backendName:"webgl",kernelFunc:zj},Lj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function Wj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=_.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new Lj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var Bj={kernelName:Hh,backendName:"webgl",kernelFunc:Wj},Vj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 jj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new Vj(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var Uj={kernelName:Tu,backendName:"webgl",kernelFunc:jj};function Hj(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=C.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,f=[];for(let m=0;m<h;++m){for(let y of u[m]){let{permutationIndices:A,expandDims:g}=C.getEinsumPermutation(p,l[y]),x;C.isIdentityPermutation(A)?x=s[y]:(x=fn({inputs:{x:s[y]},backend:n,attrs:{perm:A}}),f.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=Ae({inputs:{x},backend:n,attrs:{shape:v}}),f.push(x)),d===null?d=x:(d=Ty({inputs:{a:x,b:d},backend:n}),f.push(d))}m<h-1&&(c[m]>=0&&(d=Ip({inputs:{x:d},backend:n,attrs:{axis:c[m]-(i.length-p),keepDims:!1}}),f.push(d)),p--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var Gj={kernelName:Xh,backendName:"webgl",kernelFunc:Hj},qj="return (x >= 0.0) ? x : (exp(x) - 1.0);",Xj=`
|
|
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;
|
|
`,Kj=Xe({opSnippet:qj,packedOpSnippet:Xj}),Zj={kernelName:wo,backendName:"webgl",kernelFunc:Kj},Yj="return (b >= 1.0) ? a : a * (b + 1.0);",Jj=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,Qj=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(Jj,r.shape,a.shape):new Bl(Yj,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},eU={kernelName:Kh,backendName:"webgl",kernelFunc:Qj},tU=`
|
|
return vec4(equal(a, b));
|
|
`,nU="return float(a == b);",rU=tn({opSnippet:nU,packedOpSnippet:tU,dtype:"bool"}),aU={kernelName:_o,backendName:"webgl",kernelFunc:rU},sU=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${C.ERF_P};
|
|
float a1 = ${C.ERF_A1};
|
|
float a2 = ${C.ERF_A2};
|
|
float a3 = ${C.ERF_A3};
|
|
float a4 = ${C.ERF_A4};
|
|
float a5 = ${C.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,iU=Xe({opSnippet:sU}),oU={kernelName:bo,backendName:"webgl",kernelFunc:iU},w7="return exp(x);",b7=Xe({opSnippet:w7,packedOpSnippet:w7,cpuKernelImpl:aW}),lU={kernelName:ks,backendName:"webgl",kernelFunc:b7};function My(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(_.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var uU={kernelName:vo,backendName:"webgl",kernelFunc:My},_7="return exp(x) - 1.0;",cU=Xe({opSnippet:_7,packedOpSnippet:_7,cpuKernelImpl:sW}),hU={kernelName:ko,backendName:"webgl",kernelFunc:cU},v7=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 k7(e,t,n){let r=n.texData.get(e.dataId),a=_.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new v7("real",l,t),u=new v7("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=qa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=Ae({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function dU(e){let{inputs:t,backend:n}=e,{input:r}=t;return k7(r,!1,n)}var pU={kernelName:Zh,backendName:"webgl",kernelFunc:dU},fU=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 Fy(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||_.inferDtype(a),s==="string"){let i=_.getArrayFromDType(s,_.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new fU(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var mU={kernelName:Eu,backendName:"webgl",kernelFunc:Fy},yU=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},AU={kernelName:Io,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new yU(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},I7="return floor(x);",gU=Xe({opSnippet:I7,packedOpSnippet:I7,cpuKernelImpl:iW}),xU={kernelName:Is,backendName:"webgl",kernelFunc:gU},wU=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,bU=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,_U=tn({opSnippet:wU,packedOpSnippet:bU,dtype:"int32"}),vU={kernelName:Ss,backendName:"webgl",kernelFunc:_U},kU=class{constructor(e){this.variableNames=["A"];let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},IU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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;
|
|
}
|
|
`}},NU={kernelName:hd,backendName:"webgl",kernelFunc:SU},jl;function SU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(jl==null&&(jl=document.createElement("canvas").getContext("2d")),jl.canvas.width=l,jl.canvas.height=c,jl.drawImage(a,0,0,l,c),a=jl.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=rr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new IU(h):new kU(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function TU(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=C.convertConv2DDataFormat(u),y=C.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),A,g=[];if(y.filterHeight===1&&y.filterWidth===1&&y.dilationHeight===1&&y.dilationWidth===1&&y.strideHeight===1&&y.strideWidth===1&&(y.padInfo.type==="SAME"||y.padInfo.type==="VALID"))A=p7({x:a,filter:s,convInfo:y,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)A=f7({x:a,filter:s,convInfo:y,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let v=i!=null,w=o!=null,b=p==="leakyrelu",k=p?vp(p,!1):null,N=new d7(y,v,k,w,b),E=[a,s];if(i&&E.push(i),o&&E.push(o),b){let F=n.makeTensorInfo([],"float32",_.createScalarValue(f,"float32"));E.push(F),g.push(F)}A=n.runWebGLProgram(N,E,"float32")}let x=Ae({inputs:{x:A},backend:n,attrs:{shape:y.outShape}});return g.push(A),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var EU={kernelName:oi,backendName:"webgl",kernelFunc:TU};function CU(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]),_.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let y=C.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),A=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,g=d?vp(d,A):null,x=[a,s],v=i!=null,w=o!=null,b=d==="leakyrelu";if(v&&x.push(i),w&&x.push(o),b){let E=n.makeTensorInfo([],"float32",_.createScalarValue(p,"float32"));x.push(E),f.push(E)}let k;A?k=new x7(y,v,g,w,b):k=new g7(y,v,g,w,b);let N=n.runWebGLProgram(k,x,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),N}var RU={kernelName:li,backendName:"webgl",kernelFunc:CU},MU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${r} strides = ${r}(${this.strides});
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${s};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function FU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[_.sizeFromShape(r.shape)/c,c]}}),p=new MU(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var $U={kernelName:No,backendName:"webgl",kernelFunc:FU},OU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=DU(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function DU(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 zU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=_.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=_.sizeFromShape(s.shape),h=[],d=Ae({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=Ae({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),x=n.bufferSync(d),v=oW(x,g,f);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let m=new OU(d.shape,f),y=n.runWebGLProgram(m,[d,p],d.dtype);h.push(y);let A=Ae({inputs:{x:y},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),A}var PU={kernelName:So,backendName:"webgl",kernelFunc:zU},LU="return float(a > b);",WU=`
|
|
return vec4(greaterThan(a, b));
|
|
`,BU=tn({opSnippet:LU,packedOpSnippet:WU,cpuKernelImpl:lW,dtype:"bool"}),VU={kernelName:To,backendName:"webgl",kernelFunc:BU},jU="return float(a >= b);",UU=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,HU=tn({opSnippet:jU,packedOpSnippet:UU,dtype:"bool"}),GU={kernelName:Ts,backendName:"webgl",kernelFunc:HU};function qU(e){let{inputs:t,backend:n}=e,{input:r}=t;return k7(r,!0,n)}var XU={kernelName:Yh,backendName:"webgl",kernelFunc:qU},KU="return float(!isnan(x) && !isinf(x));",ZU=Xe({opSnippet:KU,dtype:"bool"}),YU={kernelName:Eo,backendName:"webgl",kernelFunc:ZU},JU="return float(isinf(x));",QU=Xe({opSnippet:JU,dtype:"bool"}),eH={kernelName:Co,backendName:"webgl",kernelFunc:QU},tH="return float(isnan(x));",nH=Xe({opSnippet:tH,dtype:"bool"}),rH={kernelName:Ro,backendName:"webgl",kernelFunc:nH},aH="return float(a < b);",sH=`
|
|
return vec4(lessThan(a, b));
|
|
`,iH=tn({opSnippet:aH,packedOpSnippet:sH,cpuKernelImpl:uW,dtype:"bool"}),oH={kernelName:Mo,backendName:"webgl",kernelFunc:iH},lH="return float(a <= b);",uH=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,cH=tn({opSnippet:lH,packedOpSnippet:uH,dtype:"bool"}),hH={kernelName:Fo,backendName:"webgl",kernelFunc:cH};function dH(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=cW(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var pH={kernelName:Qh,backendName:"webgl",kernelFunc:dH},fH=`if (x < 0.0) return NAN;
|
|
return log(x);`,mH=`
|
|
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;
|
|
`,yH=Xe({opSnippet:fH,packedOpSnippet:mH,cpuKernelImpl:hW}),AH={kernelName:Rs,backendName:"webgl",kernelFunc:yH},gH="return log(1.0 + x);",xH=Xe({opSnippet:gH}),wH={kernelName:$o,backendName:"webgl",kernelFunc:xH},bH="return float(a >= 1.0 && b >= 1.0);",_H=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,vH=tn({opSnippet:bH,packedOpSnippet:_H,dtype:"bool"}),kH={kernelName:Do,backendName:"webgl",kernelFunc:vH},IH="return float(!(x >= 1.0));",SH=Xe({opSnippet:IH}),NH={kernelName:Cu,backendName:"webgl",kernelFunc:SH},TH="return float(a >= 1.0 || b >= 1.0);",EH=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,CH=tn({opSnippet:TH,packedOpSnippet:EH,dtype:"bool"}),RH={kernelName:Ru,backendName:"webgl",kernelFunc:CH},MH=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 * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},FH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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 * ${o};
|
|
setOutput(result);
|
|
}
|
|
`}},$H=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 FH(a.shape,s,i,o,l):new MH(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},DH={kernelName:Mu,backendName:"webgl",kernelFunc:$H},OH=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);
|
|
}
|
|
`}},zH=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 OH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},PH={kernelName:ed,backendName:"webgl",kernelFunc:zH};function LH(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ri(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function S7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,x=new Array(o);for(let b=0;b<x.length;b++)x[b]=a.shape[u[b]];let v=Sy(g,a.shape,a.dtype,u,x);p=n.makeTensorInfo(x,a.dtype);let w=n.texData.get(p.dataId);w.values=v}else p=kp(a,u,n);c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("max",c,o);let[f,m]=C.computeOutAndReduceShapes(p.shape,c),y=f;i&&(y=C.expandShapeToKeepDim(f,l));let A;if(d){let g=n.texData.get(p.dataId).values,x=dW(g,_.sizeFromShape(m),y,a.dtype);A=n.makeTensorInfo(y,a.dtype);let v=n.texData.get(A.dataId);v.values=x}else A=LH(p,m,y,n);return h&&n.disposeIntermediateTensorInfo(p),A}var WH={kernelName:Ms,backendName:"webgl",kernelFunc:S7},BH=U3+`
|
|
return max(a, b);
|
|
`,VH=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+_p+`
|
|
return result;
|
|
`,jH=tn({opSnippet:BH,packedOpSnippet:VH,cpuKernelImpl:pW}),UH={kernelName:Fs,backendName:"webgl",kernelFunc:jH};function HH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new kc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var GH={kernelName:$s,backendName:"webgl",kernelFunc:HH};function qH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new Ey(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var XH={kernelName:Fu,backendName:"webgl",kernelFunc:qH},KH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},ZH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 YH(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=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new Ey(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new ZH(d),y=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),y}var JH={kernelName:nd,backendName:"webgl",kernelFunc:YH};function QH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Fl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new kc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),y=new KH(d),A=n.runWebGLProgram(y,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),A}var eG={kernelName:td,backendName:"webgl",kernelFunc:QH};function tG(e,t,n,r){let a=new kc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new kc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var nG={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;_.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];_.assert(C.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=C.computePool2DInfo(r.shape,a,s,c,i),[h,d]=tG(r,o,u,l);return[h,d]}};function rG(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ri(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var aG={kernelName:Ds,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=_.parseAxisParam(s,r.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let x=i.texData.get(f.dataId).values,v=new Array(o);for(let k=0;k<v.length;k++)v[k]=r.shape[u[k]];let w=Sy(x,r.shape,r.dtype,u,v);f=i.makeTensorInfo(v,r.dtype);let b=i.texData.get(f.dataId);b.values=w}else f=kp(r,u,i);p.push(f),c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("sum",c,o);let[m,y]=C.computeOutAndReduceShapes(f.shape,c),A=m;a&&(A=C.expandShapeToKeepDim(m,l));let g=rG(f,y,A,i);for(let x of p)i.disposeIntermediateTensorInfo(x);return g}};function sG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"min",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var iG={kernelName:Os,backendName:"webgl",kernelFunc:sG},oG=U3+`
|
|
return min(a, b);
|
|
`,lG=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+_p+`
|
|
return result;
|
|
`,uG=tn({opSnippet:oG,packedOpSnippet:lG,cpuKernelImpl:fW}),cG={kernelName:zs,backendName:"webgl",kernelFunc:uG},hG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=lt(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${a} start = ${a}(${s});
|
|
${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outC = getOutputCoords();
|
|
for (int i = 0; i < ${r}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${a} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},dG=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=lt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=pn("rc",r),l=pn("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);
|
|
}
|
|
`}},pG=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dG(r.shape,a,s):new hG(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},fG={kernelName:Ps,backendName:"webgl",kernelFunc:pG},mG=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,yG=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+_p+`
|
|
return result;
|
|
`,AG=tn({opSnippet:mG,packedOpSnippet:yG}),gG={kernelName:Oo,backendName:"webgl",kernelFunc:AG},xG=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)}}},wG=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,bG=`
|
|
// 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;
|
|
`,N7=tn({opSnippet:wG,packedOpSnippet:bG,checkOutOfBounds:!0}),_G={kernelName:vs,backendName:"webgl",kernelFunc:N7},T7="return a - b;",E7=tn({opSnippet:T7,packedOpSnippet:T7,supportsComplex:!0,cpuKernelImpl:_W}),vG={kernelName:ni,backendName:"webgl",kernelFunc:E7};function C7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=_.parseAxisParam([s],a.shape),o=S7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),c=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),u=E7({inputs:{a,b:c},backend:n}),h=b7({inputs:{x:u},backend:n}),d=Ip({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),f=N7({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 kG={kernelName:ei,backendName:"webgl",kernelFunc:C7};function IG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:C7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new xG(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var SG={kernelName:ad,backendName:"webgl",kernelFunc:IG},R7="return -x;";function NG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=yW(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,R7):a=new Ga(r.shape,R7),n.runWebGLProgram(a,[r],r.dtype)}var TG={kernelName:zo,backendName:"webgl",kernelFunc:NG},EG=Hr.nonMaxSuppressionV3Impl;function CG(e){C.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}=EG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var RG={kernelName:Lo,backendName:"webgl",kernelFunc:CG},MG=Hr.nonMaxSuppressionV4Impl;function FG(e){C.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}=MG(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var $G={kernelName:Wo,backendName:"webgl",kernelFunc:FG},DG=Hr.nonMaxSuppressionV5Impl;function OG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:y,selectedScores:A}=DG(u,h,d,p,f,m);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var zG={kernelName:Bo,backendName:"webgl",kernelFunc:OG},PG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${r}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},LG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=_.sizeFromShape(a.shape),c=new PG(l,s,i,o),u=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},WG={kernelName:Ws,backendName:"webgl",kernelFunc:LG};function Cp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Sc({inputs:{input:r},backend:n}),s=Cp({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Fy({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var BG={kernelName:al,backendName:"webgl",kernelFunc:Cp};function M7(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=Sc({inputs:{input:r},backend:n}),s=M7({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Fy({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var VG={kernelName:Vo,backendName:"webgl",kernelFunc:M7};function jG(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return My({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=My({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=h7({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var UG={kernelName:jo,backendName:"webgl",kernelFunc:jG},HG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
uniform float value;
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${a} start = ${a}(${s});
|
|
${a} end = ${a}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${a} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${a} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},GG=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=lt(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=pn("rc",r),l=pn("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)}}},F7=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 GG(a.shape,s,i):new HG(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},qG={kernelName:Bs,backendName:"webgl",kernelFunc:F7},XG=`
|
|
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);
|
|
`,KG=`
|
|
// 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;
|
|
`,ZG=tn({opSnippet:XG,packedOpSnippet:KG}),YG={kernelName:Vs,backendName:"webgl",kernelFunc:ZG};function JG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=_.parseAxisParam(s,a.shape),u=c,h=C.getAxesPermutation(u,o),d=a;h!=null&&(d=fn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=C.getInnerMostAxes(u.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:y,outDtype:A}=AW(d.shape,d.dtype,f,u);p=n.makeTensorInfo(y,A,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,u),y=_.sizeFromShape(m),A=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,y]}}),g=yd(a.dtype),x=Ri(A,g,"prod",n);p=Ae({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(i){l.push(p);let f=C.expandShapeToKeepDim(p.shape,c);p=Ae({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var QG={kernelName:Uo,backendName:"webgl",kernelFunc:JG},$7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=gW(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},eq={kernelName:$u,backendName:"webgl",kernelFunc:$7},tq="return 1.0 / x;",nq=Xe({opSnippet:tq}),rq={kernelName:Ho,backendName:"webgl",kernelFunc:nq},aq=Sr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,sq=`
|
|
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;
|
|
`,iq=Xe({opSnippet:aq,packedOpSnippet:sq}),oq={kernelName:Us,backendName:"webgl",kernelFunc:iq},lq=Sr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,uq=`
|
|
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;
|
|
`,cq=Xe({opSnippet:lq,packedOpSnippet:uq}),hq={kernelName:Gs,backendName:"webgl",kernelFunc:cq},dq=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);
|
|
}
|
|
`}},pq=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 fq(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 pq(a.shape,l,c,s,i):new dq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var mq={kernelName:Hs,backendName:"webgl",kernelFunc:fq},yq=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 Aq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new yq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var gq={kernelName:od,backendName:"webgl",kernelFunc:Aq},xq=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 + ${h})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function wq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new xq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var bq={kernelName:Du,backendName:"webgl",kernelFunc:wq},_q=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function vq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new _q(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var kq={kernelName:id,backendName:"webgl",kernelFunc:vq},Iq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=lt(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${a}));
|
|
}
|
|
`}},Sq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=pn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${a}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${o(r.slice())};
|
|
if(${a}){
|
|
result.g = ${l(r.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${c(r.slice())};
|
|
if(${a}) {
|
|
result.a = ${u(r.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((A,g)=>d(g,p)),m=f.join(","),y=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${y}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Nq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return Vn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Sq(a.shape,o):new Iq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Tq={kernelName:qs,backendName:"webgl",kernelFunc:Nq},Eq=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},Cq={kernelName:sl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Eq(r.shape,s),[c,u]=C.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},Rq=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Mq=Xe({opSnippet:Rq}),Fq={kernelName:Xs,backendName:"webgl",kernelFunc:Mq},$q="return inversesqrt(x);",Dq=Xe({opSnippet:$q,cpuKernelImpl:xW}),Oq={kernelName:Ks,backendName:"webgl",kernelFunc:Dq},D7=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${a});
|
|
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${u});
|
|
flattenedIndex += index * ${p};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${d};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function zq(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}=C.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),y=new D7(l,o,p.shape.length,f.shape.length,u,d),A=n.runWebGLProgram(y,[f,p,m],f.dtype),g=Ae({inputs:{x:A},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),g}var Pq={kernelName:qo,backendName:"webgl",kernelFunc:zq},Lq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=lt(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${r});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${a}));
|
|
} else {
|
|
setOutput(getB(${a}));
|
|
}
|
|
}
|
|
`}};function Wq(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new Lq(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],dr(a.dtype,s.dtype))}var Bq={kernelName:Xo,backendName:"webgl",kernelFunc:Wq},Vq=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${C.SELU_SCALEALPHA};
|
|
float scale = ${C.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,jq=Xe({opSnippet:Vq}),Uq={kernelName:Ko,backendName:"webgl",kernelFunc:jq},Hq="return 1.0 / (1.0 + exp(-1.0 * x));",Gq=Xe({opSnippet:Hq}),qq={kernelName:Ys,backendName:"webgl",kernelFunc:Gq},Xq=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,Kq=Xe({opSnippet:Xq}),Zq={kernelName:Jo,backendName:"webgl",kernelFunc:Kq},Yq=K3+`
|
|
return sin(x);
|
|
`,Jq=Xe({opSnippet:Yq}),Qq={kernelName:Zs,backendName:"webgl",kernelFunc:Jq},eX=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,tX=Xe({opSnippet:eX}),nX={kernelName:Yo,backendName:"webgl",kernelFunc:tX},rX=`
|
|
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;
|
|
`,aX=Xe({opSnippet:rX}),sX={kernelName:Qo,backendName:"webgl",kernelFunc:aX},iX=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,g)=>A*g),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let c=[],u=F7({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(u.shape,s,o,!1),d=C.getPermuted(h.length,s.length,!1),p=C.getReshapedPermuted(u.shape,s,o,!1),f=Ae({inputs:{x:u},backend:n,attrs:{shape:h}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:d}}),y=Ae({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},oX={kernelName:Ou,backendName:"webgl",kernelFunc:iX};function lX(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}=C.calculateShapes(s,a,o),d=!1,p=new D7(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var uX={kernelName:ld,backendName:"webgl",kernelFunc:lX};function cX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=C.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=Ic({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var hX={kernelName:el,backendName:"webgl",kernelFunc:cX},dX="return sqrt(x);",pX=Xe({opSnippet:dX}),fX={kernelName:Js,backendName:"webgl",kernelFunc:pX},mX="return x * x;",yX=Xe({opSnippet:mX}),AX={kernelName:zu,backendName:"webgl",kernelFunc:yX},O7="return (a - b) * (a - b);",gX=tn({opSnippet:O7,packedOpSnippet:O7}),xX={kernelName:ti,backendName:"webgl",kernelFunc:gX};function wX({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Sr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Ga(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var bX={kernelName:Fa,backendName:"webgl",kernelFunc:wX},_X=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=lt(n.length),s=lt(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${a} begin = ${a}(${e});
|
|
${a} strides = ${a}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function vX(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:y,newShape:A,outShape:g}=un.sliceInfo(a.shape,s,i,o,l,c,u,h,d),x=Ae({inputs:{x:a},backend:n,attrs:{shape:A}}),v;if(p){let b=Ic({inputs:{x},backend:n,attrs:{begin:f,size:y}});v=Ae({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))v=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let b=n.texData.get(x.dataId).values,k=Be(x.shape,x.dtype,b),N=bW(g,k,m,f);v=n.makeTensorInfo(g,x.dtype,N.values)}else{let b=new _X(f,m,g);v=n.runWebGLProgram(b,[x],x.dtype)}let w=Ae({inputs:{x:v},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),w}var kX={kernelName:tl,backendName:"webgl",kernelFunc:vX},IX="return tan(x);",SX=Xe({opSnippet:IX}),NX={kernelName:ri,backendName:"webgl",kernelFunc:SX},TX=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,EX=Xe({opSnippet:TX}),CX={kernelName:ai,backendName:"webgl",kernelFunc:EX},MX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=lt(this.rank),a=RX(e);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function RX(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 z7(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=>_.decodeString(u)),l=Be(a.shape,a.dtype,o),c=vW(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new MX(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var FX={kernelName:Ma,backendName:"webgl",kernelFunc:z7};function $X(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=kW(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var DX={kernelName:nl,backendName:"webgl",kernelFunc:$X},OX=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 zX(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],y=[u,f,m,p],A=new OX(h,d,i,o,l,y);return n.runWebGLProgram(A,[a,s],"float32")}var PX={kernelName:ud,backendName:"webgl",kernelFunc:zX};function LX(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;Fl(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}=IW(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var WX={kernelName:cd,backendName:"webgl",kernelFunc:LX};function BX(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 y=Ic({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}});f[m]=A,h.push(y)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var VX={kernelName:rl,backendName:"webgl",kernelFunc:BX},jX=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 UX(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=C.getAxesPermutation([c],o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(h.shape,c,i),p=_.sizeFromShape([h.shape[c]]),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=yd(a.dtype),y=(v,w,b,k,N)=>{let E=v.shape[0],F=v.shape[1],O=C.segment_util.segOpComputeOptimalWindowSize(F,N),L={windowSize:O,inSize:F,batchSize:E,numSegments:N},V=new jX(L,w),j=n.compileAndRun(V,[v,b],k);if(l.push(j),j.shape[1]===N)return j;let U=$7({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),X=z7({inputs:{x:U},backend:n,attrs:{reps:[F/O]}});return l.push(U),l.push(X),y(j,w,X,k,N)},A=y(f,"unsortedSegmentSum",s,m,i),g=Ae({inputs:{x:A},backend:n,attrs:{shape:d}}),x=g;if(u!=null){l.push(g);let v=C.getUndoAxesPermutation(u);x=fn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var HX={kernelName:Pu,backendName:"webgl",kernelFunc:UX},GX=[DH,PH,bB,vB,SB,EB,RB,$B,OB,PB,VB,UB,qB,ZB,rV,QB,iV,cV,lV,fV,yV,gV,_V,EV,RV,zV,LV,jV,GV,nB,ZV,ij,lj,ej,dj,fj,cj,Aj,wj,vj,Ij,Nj,Cj,Oj,Pj,Mj,Bj,Uj,Gj,Zj,eU,aU,oU,lU,uU,hU,pU,mU,AU,xU,vU,NU,EU,RU,$U,PU,VU,GU,tB,XU,KV,YU,eH,rH,aB,oH,hH,pH,wH,AH,kH,NH,RH,WH,XH,GH,JH,eG,nG,UH,aG,iG,cG,fG,gG,SG,uB,TG,RG,$G,zG,FV,WG,VG,UG,qG,YG,iB,QG,eq,$V,_G,rq,hq,oq,hB,mq,gq,bq,kq,Tq,Cq,Fq,Oq,Pq,Bq,Uq,qq,Zq,Qq,nX,NV,kG,sX,oX,uX,hX,fX,AX,xX,bX,kX,vG,gB,NX,CX,FX,DX,PX,xB,WX,VX,HX,BG];for(let e of GX)ui(e);var Mn;(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"})(Mn||(Mn={}));var Nc;(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"})(Nc||(Nc={}));var P7;function qX(e){P7=e.wasm.cwrap(ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function XX(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 N=n.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,y=Nc[u];if(y==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],x=a.shape[0],v=n.makeOutput([x,A,g],a.dtype),w=n.dataIdMap.get(v.dataId).id,b=new Uint8Array(new Int32Array(a.shape).buffer),k=new Uint8Array(new Int32Array(s.shape).buffer);return P7(d,b,a.shape.length,p,k,s.shape.length,l,c,y,f,m,h||0,w),v}var KX={kernelName:ii,backendName:"wasm",setupFunc:qX,kernelFunc:XX};function mn(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),c=s.dataIdMap.get(l.dataId).id;return _.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var ZX=mn(so);function yn(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=C.assertAndGetBroadcastShape(c.shape,u.shape),m=o.makeOutput(f,p);if(_.sizeFromShape(f)===0)return m;let y=new Uint8Array(new Int32Array(c.shape).buffer),A=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(m.dataId).id,x=()=>r(h,y,c.shape.length,d,A,u.shape.length,Mn[c.dtype],g);if(t&&c.dtype==="float32")return x(),m;let v=C.getBroadcastDims(c.shape,f),w=C.getBroadcastDims(u.shape,f),b=v.every((N,E)=>N===E),k=w.every((N,E)=>N===E);if(b&&k)return x(),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 YX=!0,JX=yn(Ca,YX),L7;function QX(e){L7=e.wasm.cwrap(ds,null,["array","number","number","number"])}function eK(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(_.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return L7(s,a.length,Mn[r.dtype],i),r}var tK={kernelName:ds,backendName:"wasm",setupFunc:QX,kernelFunc:eK};function Rp(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 nK={kernelName:Es,backendName:"wasm",kernelFunc:Rp},W7;function rK(e){W7=e.wasm.cwrap(si,null,["number","array","number","number","number","array","number"])}function Mp(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=sK(t.x.shape,r.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=aK(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=Rp({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 W7(u,p,l.shape.length,Mn[l.dtype],h,d,s.length),c}function aK(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function sK(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 iK={kernelName:si,backendName:"wasm",kernelFunc:Mp,setupFunc:rK};function Xa(e,t,n){let r=e.shape,a=e.shape.length,s=_.parseAxisParam(t,r),i=s,o=C.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=C.getInnerMostAxes(i.length,a),l=Mp({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 B7;function oK(e){B7=e.wasm.cwrap(lo,null,["number, number, number"])}function lK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=Xa(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("all",u,p);let[f,m]=C.computeOutAndReduceShapes(l.shape,u),y=_.sizeFromShape(m),A=t.makeOutput(f,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(A.dataId).id;B7(o,y,g)}if(d&&t.disposeData(c.dataId),s){let g=C.expandShapeToKeepDim(A.shape,h);A.shape=g}return A}var uK={kernelName:lo,backendName:"wasm",setupFunc:oK,kernelFunc:lK},V7;function cK(e){V7=e.wasm.cwrap(uo,null,["number, number, number"])}function hK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=Xa(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("any",u,p);let[f,m]=C.computeOutAndReduceShapes(l.shape,u),y=_.sizeFromShape(m),A=t.makeOutput(f,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(A.dataId).id;V7(o,y,g)}if(d&&t.disposeData(c.dataId),s){let g=C.expandShapeToKeepDim(A.shape,h);A.shape=g}return A}var dK={kernelName:uo,backendName:"wasm",setupFunc:cK,kernelFunc:hK},j7;function pK(e){j7=e.wasm.cwrap(ps,null,["number","number","number","number","number"])}function fK(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}=Xa(s,a,t);if(h){let A=t.dataIdMap.get(c.dataId).id;A!==i&&(l=c,o=A)}let d=l.shape.slice(0,-1),p=t.makeOutput(d,"int32"),f=t.dataIdMap.get(p.dataId).id,m=_.sizeFromShape(p.shape),y=l.shape[u[0]];return j7(o,Mn[l.dtype],m,y,f),h&&t.disposeData(c.dataId),p}var mK={kernelName:ps,backendName:"wasm",kernelFunc:fK,setupFunc:pK},U7;function yK(e){U7=e.wasm.cwrap(fs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function AK(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=C.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,y=u.padInfo.left,A=u.strideHeight,g=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let v=r.makeOutput(u.outShape,"float32"),w=r.dataIdMap.get(v.dataId).id;return U7(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,y,A,g,x,w),v}var gK={kernelName:fs,backendName:"wasm",setupFunc:yK,kernelFunc:AK};function Nr(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=_.sizeFromShape(r.shape),i=_.inferFromImplicitShape(a,s);return _.assert(s===_.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:i,dtype:r.dtype}}var xK={kernelName:Go,backendName:"wasm",kernelFunc:Nr},H7;function wK(e){H7=e.wasm.cwrap(ms,null,["number","array","number","number","array","number","number","number","number"])}function bK(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[c-2]:s.shape[c-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),y=_.sizeFromShape(f),A=_.sizeFromShape(m),g=y===A||y===1||A===1;_.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let x=(y>A?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);_.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[y,u,d]:[y,d,u],w=o?[A,p,h]:[A,h,p],b=Nr({inputs:{x:a},backend:n,attrs:{shape:v}}),k=Nr({inputs:{x:s},backend:n,attrs:{shape:w}}),N=n.dataIdMap.get(b.dataId).id,E=n.dataIdMap.get(k.dataId).id,F=i?b.shape[2]:b.shape[1],O=o?k.shape[1]:k.shape[2],L=Math.max(y,A),V=n.makeOutput([L,F,O],b.dtype),j=n.dataIdMap.get(V.dataId).id,U=new Uint8Array(new Int32Array(b.shape).buffer),X=new Uint8Array(new Int32Array(k.shape).buffer);return H7(N,U,b.shape.length,E,X,k.shape.length,i,o,j),n.disposeData(b.dataId),n.disposeData(k.dataId),V.shape=x,V}var _K={kernelName:ms,backendName:"wasm",setupFunc:wK,kernelFunc:bK};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 vK={kernelName:ys,backendName:"wasm",kernelFunc:Fp},kK=mn(As),G7;function IK(e){G7=e.wasm.cwrap(Ra,null,["number","number","number","number"])}function SK(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 G7(o,s,i,c),l}var NK={kernelName:Ra,backendName:"wasm",setupFunc:IK,kernelFunc:SK};function q7(e){let{inputs:t,backend:n}=e,r=_.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=C.computeOutShape(t.map(p=>p.shape),r),s=t.filter(p=>_.sizeFromShape(p.shape)>0);if(s.length===1)return Rp({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(_.sizeFromShape(a)===0)return i;let o=s.map(p=>p.shape);if(C.assertParamsConsistent(o,r),s[0].dtype==="string"){let p=s.map(x=>{let v=_.sizeFromShape(x.shape.slice(r));return Nr({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),f=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));a=C.computeOutShape(p.map(x=>x.shape),1);let m=p[0].shape[0]===1,y=ny(f,a,t[0].dtype,m),A=C.computeOutShape(s.map(x=>x.shape),r);i.shape=A;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=C.fromStringArrayToUint8(y),p.forEach(x=>n.disposeData(x.dataId)),i}let l=_.sizeFromShape(s[0].shape.slice(0,r)),c=0,u=s.map(p=>{let f=_.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 y=u[m],A=p*y,g=h[m].subarray(A,A+y);d.set(g,f),f+=y}}return i}var TK={kernelName:yo,backendName:"wasm",kernelFunc:q7},X7;function EK(e){X7=e.wasm.cwrap(gs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function CK(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=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!1,p),m=f.filterHeight,y=f.filterWidth,A=f.padInfo.top,g=f.padInfo.right,x=f.padInfo.bottom,v=f.padInfo.left,w=f.dilationHeight,b=f.dilationWidth,k=f.strideHeight,N=f.strideWidth,E=f.inChannels,F=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 L=r.makeOutput(f.outShape,"float32"),V=r.dataIdMap.get(L.dataId).id;return X7(i,a.shape[0],a.shape[1],a.shape[2],o,m,y,A,g,x,v,O,w,b,k,N,E,F,V),L}var RK={kernelName:gs,backendName:"wasm",setupFunc:EK,kernelFunc:CK},K7;function MK(e){K7=e.wasm.cwrap(xs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function FK(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=r,h=1,d=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(u,s.shape,i,h,o,c,!1,d),{batchSize:f,filterHeight:m,filterWidth:y,inChannels:A,inHeight:g,inWidth:x,outChannels:v,outHeight:w,outWidth:b,strideHeight:k,strideWidth:N}=p,E=m-1-p.padInfo.top,F=y-1-p.padInfo.left,O=p.dataFormat==="channelsLast",L=_.computeStrides(p.inShape),V=_.computeStrides(a.shape),[j,U,X]=_.computeStrides(s.shape),G=L[0],ee=O?L[1]:L[2],Y=O?L[2]:1,ae=O?1:L[1],te=V[0],ie=O?V[1]:V[2],Q=O?V[2]:1,he=O?1:V[1],oe=t.makeOutput(p.inShape,"float32"),fe=t.dataIdMap.get(oe.dataId).id,pe=t.dataIdMap.get(a.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return K7(pe,ke,f,m,y,g,x,A,w,b,v,k,N,E,F,j,U,X,G,ee,Y,ae,te,ie,Q,he,fe),oe}var $K={kernelName:xs,backendName:"wasm",setupFunc:MK,kernelFunc:FK},DK=mn(ws),$y;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})($y||($y={}));var Z7;function OK(e){Z7=e.wasm.cwrap(go,null,["number","number","number","number","array","number","number","number","number","number"])}function zK(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 y=f.id,A=t.dataIdMap.get(l.dataId).id,g=t.dataIdMap.get(c.dataId).id,x=t.makeOutput(p,"float32"),v=t.dataIdMap.get(x.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return Z7(y,A,g,u,w,h,d,$y[a],s,v),m!=null&&t.disposeData(m.dataId),x}var PK={kernelName:go,backendName:"wasm",setupFunc:OK,kernelFunc:zK},Y7;function LK(e){Y7=e.wasm.cwrap(bs,null,["number","number","number","number","number","number"])}function WK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;_.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let c=C.getAxesPermutation([s],l),u=a;c!==null&&(u=Mp({inputs:{x:a},attrs:{perm:c},backend:n}));let h=C.getInnerMostAxes(1,l)[0];C.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;Y7(f,i?1:0,o?1:0,p,m,Mn[a.dtype]);let y=d;if(c!==null){let A=C.getUndoAxesPermutation(c);y=Mp({inputs:{x:d},attrs:{perm:A},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return y}var BK={kernelName:bs,backendName:"wasm",setupFunc:LK,kernelFunc:WK},J7;function VK(e){J7=e.wasm.cwrap(xo,null,["number","number","number","array","number","array","array","number","number"])}function jK(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r;_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(a.dataId).id,A=new Uint8Array(new Int32Array(_.computeStrides(a.shape)).buffer),g=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(_.computeStrides(f)).buffer),v=t.dataIdMap.get(m.dataId).id;return J7(y,s,i==="NHWC"?1:0,A,a.shape.length-1,g,x,f.length,v),m}var UK={kernelName:xo,backendName:"wasm",setupFunc:VK,kernelFunc:jK},Q7;function HK(e){Q7=e.wasm.cwrap(_s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function GK(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h}=n,d=c==null?[1,1]:c,p=C.computeConv2DInfo(a.shape,s.shape,l,d,u,h,!0),f=p.filterHeight,m=p.filterWidth,y=p.padInfo.top,A=p.padInfo.right,g=p.padInfo.bottom,x=p.padInfo.left,v=p.dilationHeight,w=p.dilationWidth,b=p.strideHeight,k=p.strideWidth,N=p.inChannels,E=p.outChannels,F=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let O=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(O.dataId).id;return Q7(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,y,A,g,x,F,v,w,b,k,N,E,L),O}var qK={kernelName:_s,backendName:"wasm",setupFunc:HK,kernelFunc:GK},XK=!1,KK=yn(_o,XK,"bool"),ZK=mn(ks);function Dy(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Nr({inputs:{x:a},backend:r,attrs:{shape:o}})}var YK={kernelName:vo,backendName:"wasm",kernelFunc:Dy};function JK(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var QK={kernelName:Eu,backendName:"wasm",kernelFunc:JK},ev;function eZ(e){ev=e.wasm.cwrap(Io,null,["number","number","number","number","number","number"])}function tZ(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return ev(s,o,l,c,u,i),a}var nZ={kernelName:Io,backendName:"wasm",kernelFunc:tZ,setupFunc:eZ},rZ=mn(Is),aZ=!1,sZ=yn(Ss,aZ),tv;function iZ(e){tv=e.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number"])}function oZ(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(_.sizeFromShape(s.shape)===0)return m;let y=t.dataIdMap.get(m.dataId).id;return tv(u,h,d,p,f,a,y),m}var lZ={kernelName:Ns,backendName:"wasm",setupFunc:iZ,kernelFunc:oZ},nv;function uZ(e){nv=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d),y=Nc[p];if(y==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let A=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=m.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=m.filterHeight,b=m.filterWidth,k=m.padInfo.top,N=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,O=m.dilationHeight,L=m.dilationWidth,V=m.strideHeight,j=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return nv(A,G,ee,Y,g,w,b,v,k,N,E,F,X,O,L,V,j,U,x,y,ie,f||0,te),ae}var hZ={kernelName:oi,backendName:"wasm",setupFunc:uZ,kernelFunc:cZ},rv;function dZ(e){rv=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pZ(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=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d,!0),y=Nc[p];if(y==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=m.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=m.filterHeight,b=m.filterWidth,k=m.padInfo.top,N=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,O=m.dilationHeight,L=m.dilationWidth,V=m.strideHeight,j=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return rv(A,G,ee,Y,g,w,b,v,k,N,E,F,X,O,L,V,j,U,x,y,ie,f||0,te),ae}var fZ={kernelName:li,backendName:"wasm",setupFunc:dZ,kernelFunc:pZ},av;function mZ(e){av=e.wasm.cwrap(No,null,["number","number","number","number","number","number","array","number"])}function yZ(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=Qf.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 av(d,Mn[r.dtype],p,i,h,o,f,m),c}var AZ={kernelName:No,backendName:"wasm",setupFunc:mZ,kernelFunc:yZ},sv;function gZ(e){sv=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function xZ(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=_.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=Nr({inputs:{x:a},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=_.sizeFromShape(s.shape),d=Nr({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(_.sizeFromShape(a.shape)===0)return f;let m=u.shape.length-1,y=t.dataIdMap.get(u.dataId).id,A=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(f.dataId).id,x=new Uint8Array(new Int32Array(_.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(_.computeStrides(p)).buffer);return sv(y,Mn[a.dtype],x,m,A,c.batchSize,v,g),t.disposeData(u.dataId),t.disposeData(d.dataId),f.shape=c.outputShape,f}var wZ={kernelName:So,backendName:"wasm",setupFunc:gZ,kernelFunc:xZ},bZ=!1,_Z=yn(To,bZ,"bool"),vZ=!1,kZ=yn(Ts,vZ,"bool"),iv;function IZ(e){iv=e.wasm.cwrap(Cs,null,["number","number","number"])}function SZ(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(_.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;iv(a,n,i)}return s}var NZ={kernelName:Cs,backendName:"wasm",setupFunc:IZ,kernelFunc:SZ},TZ=!1,EZ=yn(Mo,TZ,"bool"),CZ=!1,RZ=yn(Fo,CZ,"bool"),MZ=mn(Rs),FZ=!1,$Z=yn(Do,FZ,"bool"),ov;function DZ(e){ov=e.wasm.cwrap(Ms,null,["number, number, number"])}function OZ(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=Xa(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("max",u,p);let[f,m]=C.computeOutAndReduceShapes(l.shape,u),y=_.sizeFromShape(m),A=t.makeOutput(f,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(A.dataId).id;ov(o,y,g)}if(d&&t.disposeData(c.dataId),s){let g=C.expandShapeToKeepDim(A.shape,h);A.shape=g}return A}var zZ={kernelName:Ms,backendName:"wasm",setupFunc:DZ,kernelFunc:OZ},PZ=!1,LZ=yn(Fs,PZ),lv;function WZ(e){lv=e.wasm.cwrap($s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function BZ(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=C.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,y=u.padInfo.left,A=u.dilationHeight,g=u.dilationWidth,x=u.strideHeight,v=u.strideWidth,w=u.inChannels,b=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let k=r.makeOutput(u.outShape,"float32"),N=r.dataIdMap.get(k.dataId).id;return lv(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,y,A,g,x,v,w,b,N),k}var VZ={kernelName:$s,backendName:"wasm",setupFunc:WZ,kernelFunc:BZ},uv;function jZ(e){uv=e.wasm.cwrap(Ds,null,["number, number, number"])}function UZ(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}=Xa(i,a,t),f=h;if(p){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,y]=C.computeOutAndReduceShapes(c.shape,f),A=_.sizeFromShape(y),g=c;c.dtype!=="float32"&&(g=Fp({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let x=t.makeOutput(m,"float32");if(_.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;uv(l,A,v)}if(p&&t.disposeData(u.dataId),s){let v=C.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(g.dataId),x}var HZ={kernelName:Ds,backendName:"wasm",setupFunc:jZ,kernelFunc:UZ},cv;function GZ(e){cv=e.wasm.cwrap(Os,null,["number, number, number"])}function qZ(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}=Xa(i,a,t);if(p){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let f=c.shape.length;C.assertAxesAreInnerMostDims("min",h,f);let[m,y]=C.computeOutAndReduceShapes(c.shape,h),A=_.sizeFromShape(y),g=t.makeOutput(m,c.dtype);if(_.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;cv(l,A,x)}if(p&&t.disposeData(u.dataId),s){let x=C.expandShapeToKeepDim(g.shape,d);g.shape=x}return g}var XZ={kernelName:Os,backendName:"wasm",setupFunc:GZ,kernelFunc:qZ},KZ=!1,ZZ=yn(zs,KZ),Oy;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(Oy||(Oy={}));var hv;function YZ(e){hv=e.wasm.cwrap(Ps,null,["number","array","number","number","array","array","number","number"])}function JZ(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode: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 hv(i,c,t.shape.length,Mn[t.dtype],d,p,Oy[a],l),o}var QZ={kernelName:Ps,backendName:"wasm",kernelFunc:JZ,setupFunc:YZ},eY=!0,tY=yn(Ls,eY),nY=mn(zo);function zy(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 dv;function rY(e){dv=e.wasm.cwrap(Lo,"number",["number","number","number","number","number"])}function aY(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=dv(c,u,s,a,i),{pSelectedIndices:d,selectedSize:p,pSelectedScores:f,pValidOutputs:m}=zy(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([p],"int32",d)}var sY={kernelName:Lo,backendName:"wasm",setupFunc:rY,kernelFunc:aY},pv;function iY(e){pv=e.wasm.cwrap(Wo,"number",["number","number","number","number","number","bool"])}function oY(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=pv(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:y}=zy(t,d);t.wasm._free(m);let A=t.makeOutput([f],"int32",p),g=t.makeOutput([],"int32",y);return[A,g]}var lY={kernelName:Wo,backendName:"wasm",setupFunc:iY,kernelFunc:oY},fv;function uY(e){fv=e.wasm.cwrap(Bo,"number",["number","number","number","number","number","number"])}function cY(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=fv(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:y}=zy(t,d);t.wasm._free(y);let A=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[A,g]}var hY={kernelName:Bo,backendName:"wasm",setupFunc:uY,kernelFunc:cY},dY=!1,pY=yn(Po,dY,"bool"),mv;function fY(e){mv=e.wasm.cwrap(Ws,null,["number","number","number","number","number"])}function mY(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 mv(u,s,i,o,c),l}var yY={kernelName:Ws,backendName:"wasm",setupFunc:fY,kernelFunc:mY};function AY(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var gY={kernelName:Vo,backendName:"wasm",kernelFunc:AY};function xY(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Dy({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=Dy({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=q7({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeData(u.dataId)),c}var wY={kernelName:jo,backendName:"wasm",kernelFunc:xY},yv;function bY(e){yv=e.wasm.cwrap(Bs,null,["number","array","number","number","array","array","number","number"])}function _Y(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 yv(i,c,t.shape.length,Mn[t.dtype],d,p,a,l),o}var vY={kernelName:Bs,backendName:"wasm",kernelFunc:_Y,setupFunc:bY},kY=!1,IY=yn(Vs,kY),Av;function SY(e){Av=e.wasm.cwrap(js,null,["number","number","number"])}function NY(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 Av(s,i,l),o}var TY={kernelName:js,backendName:"wasm",setupFunc:SY,kernelFunc:NY},gv;function EY(e){gv=e.wasm.cwrap(Uo,null,["number","number","number","number"])}function CY(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}=Xa(i,a,t),f=h;if(p){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,y]=C.computeOutAndReduceShapes(c.shape,f),A=_.sizeFromShape(y),g=t.makeOutput(m,c.dtype);if(_.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;gv(l,A,Mn[g.dtype],x)}if(p&&t.disposeData(u.dataId),s){let x=C.expandShapeToKeepDim(g.shape,d);g.shape=x}return g}var RY={kernelName:Uo,backendName:"wasm",setupFunc:EY,kernelFunc:CY},MY=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=sy(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},FY={kernelName:$u,backendName:"wasm",kernelFunc:MY},$Y=!0,DY=yn(vs,$Y),OY=mn(Us),zY=mn(Gs),xv;function PY(e){xv=e.wasm.cwrap(Hs,null,["number","number","number","number","number","number","number","number","number","number"])}function LY(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),y;m.dtype!=="float32"&&(y=Fp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(y.dataId));let A=m.id,g=t.makeOutput(f,"float32");if(_.sizeFromShape(a.shape)===0)return g;let x=t.dataIdMap.get(g.dataId).id;return xv(A,u,h,d,p,l,c,s?1:0,i?1:0,x),y!=null&&t.disposeData(y.dataId),g}var WY={kernelName:Hs,backendName:"wasm",setupFunc:PY,kernelFunc:LY},wv;function BY(e){wv=e.wasm.cwrap(qs,null,["number","array","number","array","number","number"])}function VY(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=_.parseAxisParam(s,a.shape);if(a.shape.length===0)return Rp({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);wv(l,u,i.length,h,a.shape.length,c);let d=Nr({inputs:{x:o},attrs:{shape:a.shape},backend:n});return n.disposeData(o.dataId),d}var jY={kernelName:qs,backendName:"wasm",kernelFunc:VY,setupFunc:BY},bv;function UY(e){bv=e.wasm.cwrap(sl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function HY(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,y]=C.getImageCenter(o,d,p),A=i===0,g=255,x=typeof i=="number"?[i,i,i,A?0:g]:[...i,g],v=new Uint8Array(new Int32Array(x).buffer);return bv(c,h,d,p,f,s,m,y,v,x.length,u),l}var GY={kernelName:sl,backendName:"wasm",kernelFunc:HY,setupFunc:UY},qY=mn(Xs),XY=mn(Ks),_v;function KY(e){_v=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number","number"])}function ZY(e){let{backend:t,inputs:n,attrs:r}=e,{indices:a,updates:s}=n,{shape:i}=r,o=t.makeOutput(i,s.dtype);if(_.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:h,outputSize:d}=em.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),y=t.dataIdMap.get(o.dataId).id;return _v(p,f,Mn[s.dtype],l,c,u,m,d,y),o}var YY={kernelName:qo,backendName:"wasm",setupFunc:KY,kernelFunc:ZY},vv;function JY(e){vv=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function QY(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(a.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(c.dataId).id,h=r.shape.length,d=a.shape.length,p=h===0||h>1||d===1?1:_.sizeFromShape(a.shape.slice(1));return vv(i,o,l,p,u),c}var eJ={kernelName:Xo,backendName:"wasm",kernelFunc:QY,setupFunc:JY},kv;function tJ(e){kv=e.wasm.cwrap(Ys,null,["number","number"])}function nJ(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(a.dataId).id;return _.sizeFromShape(a.shape)===0||kv(r,s),a}var rJ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:tJ,kernelFunc:nJ},aJ=mn(Zs);function $p(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=un.parseSliceParams(t,n,r),o=un.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),c=a.makeOutput(i,t.dtype),u=_.computeStrides(t.shape),h=a.dataIdMap.get(c.dataId);if(o){let f=un.computeFlatOffset(s,u);return t.dtype==="string"?h.stringBytes=l.slice(f,f+_.sizeFromShape(i)):a.typedArrayFromHeap(c).set(l.subarray(f,f+_.sizeFromShape(i))),c}if(t.dtype==="string"){let f=lp(l,s,i,t.shape,t.dtype);return h.stringBytes=f,c}let d=a.typedArrayFromHeap(c),p=t.shape.length;if(p===2)sJ(l,u[0],d,s,i);else if(p===3)iJ(l,u[0],u[1],d,s,i);else if(p===4)oJ(l,u[0],u[1],u[2],d,s,i);else{let f=lp(l,s,i,t.shape,t.dtype);d.set(f)}return c}function sJ(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+a[1]),s),s+=a[1]}}function iJ(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],c=a[2],u=o+s[0],h=l+s[1];for(let d=o;d<u;d++)for(let p=l;p<h;p++){let f=d*t+p*n+c;r.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function oJ(e,t,n,r,a,s,i){let o=0,l=s[0],c=s[1],u=s[2],h=l+i[0],d=c+i[1],p=u+i[2],f=s[3];for(let m=l;m<h;m++)for(let y=c;y<d;y++)for(let A=u;A<p;A++){let g=m*t+y*n+A*r+f;a.set(e.subarray(g,g+i[3]),o),o+=i[3]}}var lJ={kernelName:Zo,backendName:"wasm",kernelFunc:$p},Iv;function uJ(e){Iv=e.wasm.cwrap(ei,null,["number","number","number","number"])}function cJ(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,a=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[r],l=_.sizeFromShape(n.shape)/o;return _.sizeFromShape(s.shape)===0||Iv(a,i,o,l),s}var hJ={kernelName:ei,backendName:"wasm",setupFunc:uJ,kernelFunc:cJ};function dJ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=_.parseAxisParam(i,a.shape)[0],l=C.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=$p({inputs:{x:a},attrs:{begin:c,size:d},backend:r});return c[o]+=h,p})}var pJ={kernelName:el,backendName:"wasm",kernelFunc:dJ},fJ=mn(Js),mJ=mn(zu),yJ=!0,AJ=yn(ti,yJ),Sv;function gJ(e){Sv=e.wasm.cwrap(Fa,null,["number","number","number"])}function xJ(e){let{backend:t,inputs:n,attrs:r}=e,{alpha:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return Sv(i,a,l),o}var wJ={kernelName:Fa,backendName:"wasm",setupFunc:gJ,kernelFunc:xJ},Nv;function bJ(e){Nv=e.wasm.cwrap(tl,null,["number","array","number","array","array","array","array","array","number","number"])}function _J(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{begin:s,end:i,strides:o}=r;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,p=C.slice_util.maskToAxes(u);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&h!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=a.shape.length-s.length,m=C.slice_util.maskToAxes(h),y=a.shape.slice();m.forEach(F=>{s[F]=0,i[F]=1,y.splice(F,0,1)});let A=Nr({inputs:{x:a},attrs:{shape:y},backend:t}),{begin:g,end:x,strides:v}=C.slice_util.getNormalizedAxes(A.shape,p,f,s,i,o,l,c,u);s=g,i=x,o=v;let w=C.slice_util.maskToAxes(d);w.forEach(F=>{i[F]=s[F]+1,o[F]=1});let b=C.slice_util.computeOutShape(s,i,o),k=b.filter((F,O)=>w.indexOf(O)===-1);if(o.every(F=>F===1)){let F=$p({inputs:{x:A},attrs:{begin:s,size:b},backend:t});t.disposeData(A.dataId);let O=Nr({inputs:{x:F},attrs:{shape:k},backend:t});return t.disposeData(F.dataId),O}let N=t.makeOutput(k,"float32");if(!k.some(F=>F===0)){let F=t.dataIdMap.get(A.dataId).id,O=new Uint8Array(new Int32Array(_.computeStrides(A.shape)).buffer),L=new Uint8Array(new Int32Array(s).buffer),V=new Uint8Array(new Int32Array(i).buffer),j=new Uint8Array(new Int32Array(o).buffer),U=new Uint8Array(new Int32Array(k).buffer),X=new Uint8Array(new Int32Array(_.computeStrides(k)).buffer),G=t.dataIdMap.get(N.dataId).id;Nv(F,O,A.shape.length,L,V,j,U,X,k.length,G)}t.disposeData(A.dataId);let E=Nr({inputs:{x:N},attrs:{shape:k},backend:t});return t.disposeData(N.dataId),E}var vJ={kernelName:tl,backendName:"wasm",setupFunc:bJ,kernelFunc:_J},kJ=!0,IJ=yn(ni,kJ),Tv;function SJ(e){Tv=e.wasm.cwrap(Qs,null,["number, number, number"])}function NJ(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}=Xa(i,a,t),f=h;if(p){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("sum",f,c.shape.length);let[m,y]=C.computeOutAndReduceShapes(c.shape,f),A=_.sizeFromShape(y),g=t.makeOutput(m,c.dtype);if(_.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;Tv(l,A,x)}if(p&&t.disposeData(u.dataId),s){let x=C.expandShapeToKeepDim(g.shape,d);g.shape=x}return g}var TJ={kernelName:Qs,backendName:"wasm",setupFunc:SJ,kernelFunc:NJ},EJ=mn(ri),CJ=mn(ai),Ev;function RJ(e){Ev=e.wasm.cwrap(Ma,null,["number","array","number","array","number","number"])}function MJ(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 Ev(s,l,a.shape.length,c,o.length,Mn[u.dtype],h),u}var FJ={kernelName:Ma,backendName:"wasm",setupFunc:RJ,kernelFunc:MJ},Cv;function $J(e){Cv=e.wasm.cwrap(nl,null,["number","array","number","number","number","bool","number","number"])}var DJ=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{k:a,sorted:s}=n,i=t.dataIdMap.get(r.dataId).id,o=new Uint8Array(new Int32Array(r.shape).buffer),l=r.shape.slice();l[l.length-1]=a;let c=t.makeOutput(l,r.dtype),u=t.dataIdMap.get(c.dataId).id,h=t.makeOutput(l,"int32"),d=t.dataIdMap.get(h.dataId).id;return Cv(i,o,r.shape.length,Mn[r.dtype],a,s,u,d),[c,h]},OJ={kernelName:nl,backendName:"wasm",setupFunc:$J,kernelFunc:DJ};function zJ(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]=$p({inputs:{x:a},attrs:{begin:h,size:d},backend:n});return u.map(({dataId:p,dtype:f})=>({dataId:p,dtype:f,shape:l}))}var PJ={kernelName:rl,backendName:"wasm",kernelFunc:zJ};function LJ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var WJ={kernelName:al,backendName:"wasm",kernelFunc:LJ},BJ=[ZX,JX,tK,uK,dK,mK,gK,_K,vK,kK,NK,TK,RK,$K,DK,PK,BK,UK,qK,KK,ZK,YK,QK,nZ,rZ,sZ,KX,lZ,hZ,fZ,AZ,wZ,_Z,kZ,nK,NZ,EZ,RZ,MZ,$Z,zZ,LZ,VZ,HZ,XZ,ZZ,QZ,tY,nY,sY,lY,hY,pY,yY,gY,wY,vY,IY,TY,RY,FY,DY,OY,zY,xK,WY,jY,GY,XY,qY,YY,eJ,rJ,aJ,lJ,hJ,pJ,fJ,mJ,AJ,wJ,vJ,IJ,TJ,EJ,CJ,FJ,OJ,iK,PJ,WJ];for(let e of BJ)ui(e);var Py=J();Py.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])));Py.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Py.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 Rv=no(I9()),VJ='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModule(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()}}}}',jJ=no(S9()),Mv=class extends wu{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Rh(this,ua())}write(e,t,n){let r={id:this.dataIdNextNumber++};return this.move(r,e,t,n,1),r}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=_.now();return e(),{kernelMs:_.now()-t}}move(e,t,n,r,a){let s=this.dataIdNextNumber++;if(r==="string"){let c=t;this.dataIdMap.set(e,{id:s,stringBytes:c,shape:n,dtype:r,memoryOffset:null,refCount:a});return}let i=_.sizeFromShape(n),o=i*_.bytesPerElement(r),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:r,refCount:a}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:r,stringBytes:a}=this.dataIdMap.get(e);if(n==="string")return a;let s=this.wasm.HEAPU8.slice(t,t+_.sizeFromShape(r)*_.bytesPerElement(n));return UJ(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=_.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,s,n)}return{dataId:r,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let r=this.wasm.HEAPU8.buffer,{memoryOffset:a}=this.dataIdMap.get(n),s=_.sizeFromShape(e);switch(t){case"float32":return new Float32Array(r,a,s);case"int32":return new Int32Array(r,a,s);case"bool":return new Uint8Array(r,a,s);default:throw new Error(`Unknown dtype ${t}`)}}};function HJ(e){return(t,n)=>(_.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{n(s.instance)})})}),{})}function Fv(e,t,n){if(Dp!=null)return Dp;let r="tfjs-backend-wasm.wasm";return e&&t?r="tfjs-backend-wasm-threaded-simd.wasm":e&&(r="tfjs-backend-wasm-simd.wasm"),Tc!=null&&Tc[r]!=null?Tc[r]:n+r}async function GJ(){let[e,t]=await Promise.all([J().getAsync("WASM_HAS_SIMD_SUPPORT"),J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,r)=>{let a={};a.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let c=VJ,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return o.endsWith(".wasm")?Fv(e,t,Ec!=null?Ec:l):l+o},Ly&&(a.instantiateWasm=HJ(Fv(e,t,Ec!=null?Ec:"")));let s=!1;a.onAbort=()=>{s||Cc||(Cc=!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&&Dp==null?(a.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+Rv.default.toString()],{type:"text/javascript"}),i=(0,Rv.default)(a)):i=(0,jJ.default)(a),i.then(o=>{s=!0,Cc=!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 UJ(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 qJ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Dp=null,Ec=null,Tc={},Cc=!1,Ly=!1;function XJ(e,t=!1){if(im("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Cc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Dp=e,Ly=t}function KJ(e,t=!1){if(Cc)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")Ec=e;else{Tc=e;let n=qJ.filter(r=>Tc[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.`)}Ly=t}var $v="3.4.0",ZJ=2;fl("wasm",async()=>{let{wasm:e}=await GJ();return new Mv(e)},ZJ);Z().prototype.abs=function(){return this.throwIfDisposed(),Ot(this)};Z().prototype.acos=function(){return this.throwIfDisposed(),lm(this)};Z().prototype.acosh=function(){return this.throwIfDisposed(),um(this)};Z().prototype.add=function(e){return this.throwIfDisposed(),se(this,e)};Z().prototype.all=function(e,t){return this.throwIfDisposed(),vd(this,e,t)};Z().prototype.any=function(e,t){return this.throwIfDisposed(),Zu(this,e,t)};Z().prototype.argMax=function(e){return this.throwIfDisposed(),mi(this,e)};Z().prototype.argMin=function(e){return this.throwIfDisposed(),cm(this,e)};Z().prototype.asScalar=function(){return this.throwIfDisposed(),M(this.size===1,()=>"The array must have only 1 element."),H(this,[])};Z().prototype.asType=function(e){return this.throwIfDisposed(),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(),hm(this)};Z().prototype.asinh=function(){return this.throwIfDisposed(),dm(this)};Z().prototype.atan=function(){return this.throwIfDisposed(),pm(this)};Z().prototype.atan2=function(e){return this.throwIfDisposed(),fm(this,e)};Z().prototype.atanh=function(){return this.throwIfDisposed(),mm(this)};Z().prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),Ju(this,e,t,n,r)};Z().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Qu(this,e,t)};Z().prototype.batchNorm=function(e,t,n,r,a){return this.throwIfDisposed(),Ai(this,e,t,n,r,a)};Z().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Al(this,e)};Z().prototype.cast=function(e){return this.throwIfDisposed(),ge(this,e)};Z().prototype.ceil=function(){return this.throwIfDisposed(),xm(this)};Z().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),Tn(this,e,t)};Z().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Pe&&(e=[e]),rt([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(),Sd(this,e,t,n,r,a)};Z().prototype.conv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),ca(this,e,t,n,r,a,s)};Z().prototype.cos=function(){return this.throwIfDisposed(),ec(this)};Z().prototype.cosh=function(){return this.throwIfDisposed(),Nd(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(),_m(this,e,t)};Z().prototype.depthwiseConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),xl(this,e,t,n,r,a,s)};Z().prototype.dilation2d=function(e,t,n,r,a){return this.throwIfDisposed(),vm(this,e,t,n,r,a)};Z().prototype.divNoNan=function(e){return this.throwIfDisposed(),km(this,e)};Z().prototype.div=function(e){return this.throwIfDisposed(),me(this,e)};Z().prototype.dot=function(e){return this.throwIfDisposed(),lb(this,e)};Z().prototype.elu=function(){return this.throwIfDisposed(),wl(this)};Z().prototype.equal=function(e){return this.throwIfDisposed(),Wa(this,e)};Z().prototype.erf=function(){return this.throwIfDisposed(),Im(this)};Z().prototype.exp=function(){return this.throwIfDisposed(),er(this)};Z().prototype.expandDims=function(e){return this.throwIfDisposed(),Jt(this,e)};Z().prototype.expm1=function(){return this.throwIfDisposed(),Sm(this)};Z().prototype.fft=function(){return this.throwIfDisposed(),cc(this)};Z().prototype.flatten=function(){return this.throwIfDisposed(),H(this,[this.size])};Z().prototype.floor=function(){return this.throwIfDisposed(),bl(this)};Z().prototype.floorDiv=function(e){return this.throwIfDisposed(),_d(this,e)};Z().prototype.gather=function(e,t){return this.throwIfDisposed(),gi(this,e,t)};Z().prototype.greaterEqual=function(e){return this.throwIfDisposed(),Va(this,e)};Z().prototype.greater=function(e){return this.throwIfDisposed(),pr(this,e)};Z().prototype.ifft=function(){return this.throwIfDisposed(),Sl(this)};Z().prototype.irfft=function(){return this.throwIfDisposed(),Hd(this)};Z().prototype.isFinite=function(){return this.throwIfDisposed(),cb(this)};Z().prototype.isInf=function(){return this.throwIfDisposed(),hb(this)};Z().prototype.isNaN=function(){return this.throwIfDisposed(),Tm(this)};Z().prototype.leakyRelu=function(e){return this.throwIfDisposed(),nc(this,e)};Z().prototype.lessEqual=function(e){return this.throwIfDisposed(),xi(this,e)};Z().prototype.less=function(e){return this.throwIfDisposed(),Cd(this,e)};Z().prototype.localResponseNormalization=function(e,t,n,r){return this.throwIfDisposed(),Em(this,e,t,n,r)};Z().prototype.logSigmoid=function(){return this.throwIfDisposed(),fb(this)};Z().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Fd(this,e)};Z().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),Mm(this,e,t)};Z().prototype.log=function(){return this.throwIfDisposed(),zn(this)};Z().prototype.log1p=function(){return this.throwIfDisposed(),Rd(this)};Z().prototype.logicalAnd=function(e){return this.throwIfDisposed(),fr(this,e)};Z().prototype.logicalNot=function(){return this.throwIfDisposed(),rc(this)};Z().prototype.logicalOr=function(e){return this.throwIfDisposed(),$d(this,e)};Z().prototype.logicalXor=function(e){return this.throwIfDisposed(),gb(this,e)};Z().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Ve(this,e,t,n)};Z().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),ac(this,e,t,n,r)};Z().prototype.max=function(e,t){return this.throwIfDisposed(),Cn(this,e,t)};Z().prototype.maximum=function(e){return this.throwIfDisposed(),jr(this,e)};Z().prototype.mean=function(e,t){return this.throwIfDisposed(),It(this,e,t)};Z().prototype.min=function(e,t){return this.throwIfDisposed(),vl(this,e,t)};Z().prototype.minimum=function(e){return this.throwIfDisposed(),kl(this,e)};Z().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),$m(this,e,t)};Z().prototype.mod=function(e){return this.throwIfDisposed(),Dm(this,e)};Z().prototype.mul=function(e){return this.throwIfDisposed(),P(this,e)};Z().prototype.neg=function(){return this.throwIfDisposed(),kt(this)};Z().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Kd(this,e,t,n)};Z().prototype.notEqual=function(e){return this.throwIfDisposed(),bi(this,e)};Z().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),dl(this,e,t,n)};Z().prototype.onesLike=function(){return this.throwIfDisposed(),Ln(this)};Z().prototype.pad=function(e,t){return this.throwIfDisposed(),ha(this,e,t)};Z().prototype.pool=function(e,t,n,r,a){return this.throwIfDisposed(),bb(this,e,t,n,r,a)};Z().prototype.pow=function(e){return this.throwIfDisposed(),da(this,e)};Z().prototype.prelu=function(e){return this.throwIfDisposed(),ic(this,e)};Z().prototype.prod=function(e,t){return this.throwIfDisposed(),Od(this,e,t)};Z().prototype.reciprocal=function(){return this.throwIfDisposed(),Pm(this)};Z().prototype.relu=function(){return this.throwIfDisposed(),Ur(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(),Lb(this,e,t,n)};Z().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),Wb(this,e,t,n)};Z().prototype.reverse=function(e){return this.throwIfDisposed(),Wn(this,e)};Z().prototype.rfft=function(){return this.throwIfDisposed(),hc(this)};Z().prototype.round=function(){return this.throwIfDisposed(),Lm(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(),Wm(this,e,t,n,r,a,s)};Z().prototype.sigmoid=function(){return this.throwIfDisposed(),On(this)};Z().prototype.sign=function(){return this.throwIfDisposed(),Bm(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(),Ce(this,e,t)};Z().prototype.softmax=function(e){return this.throwIfDisposed(),uc(this,e)};Z().prototype.softplus=function(){return this.throwIfDisposed(),_l(this)};Z().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),sc(this,e,t)};Z().prototype.split=function(e,t){return this.throwIfDisposed(),Pt(this,e,t)};Z().prototype.sqrt=function(){return this.throwIfDisposed(),Qt(this)};Z().prototype.square=function(){return this.throwIfDisposed(),ot(this)};Z().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Gd(this,e)};Z().prototype.squeeze=function(e){return this.throwIfDisposed(),ja(this,e)};Z().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Pe?[this,e]:[this,...e];return cn(n,t)};Z().prototype.step=function(e){return this.throwIfDisposed(),Nl(this,e)};Z().prototype.stridedSlice=function(e,t,n,r,a,s,i,o){return this.throwIfDisposed(),jm(this,e,t,n,r,a,s,i,o)};Z().prototype.sub=function(e){return this.throwIfDisposed(),ye(this,e)};Z().prototype.sum=function(e,t){return this.throwIfDisposed(),Te(this,e,t)};Z().prototype.tan=function(){return this.throwIfDisposed(),Um(this)};Z().prototype.tanh=function(){return this.throwIfDisposed(),yl(this)};Z().prototype.tile=function(e){return this.throwIfDisposed(),Ba(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(),Hm(this,e,t)};Z().prototype.transpose=function(e){return this.throwIfDisposed(),Je(this,e)};Z().prototype.unique=function(e){return this.throwIfDisposed(),Xd(this,e)};Z().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),Gm(this,e,t)};Z().prototype.unstack=function(e){return this.throwIfDisposed(),mr(this,e)};Z().prototype.where=function(e,t){return this.throwIfDisposed(),En(e,this,t)};Z().prototype.zerosLike=function(){return this.throwIfDisposed(),He(this)};var Dv={kernelName:so,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,Nl(ge(n,"float32"),-1))}}},YJ={kernelName:io,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=ot(ge(n,"float32")),a=Qt(ye(xe(1),r));return kt(me(e,a))}}}},JJ={kernelName:oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Qt(ye(ot(ge(n,"float32")),1));return me(e,r)}}}},QJ={kernelName:Ca,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ft(n.shape,r.shape);return{a:()=>{let s=e,i=zt(n.shape,a);return i.length>0&&(s=Te(s,i)),H(s,n.shape)},b:()=>{let s=e,i=zt(r.shape,a);return i.length>0&&(s=Te(s,i)),H(s,r.shape)}}}},eQ={kernelName:ds,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,a)=>{n[a]=()=>e.clone()}),n}},tQ={kernelName:ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},nQ={kernelName:vu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},rQ={kernelName:co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,Qt(ye(xe(1),ot(ge(n,"float32")))))}}},aQ={kernelName:ho,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Qt(se(xe(1),ot(ge(n,"float32"))));return me(e,r)}}}},sQ={kernelName:mo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ft(n.shape,r.shape);return{a:()=>{let s=se(ot(n),ot(r)),i=P(e,me(r,s)),o=zt(n.shape,a);return o.length>0&&(i=Te(i,o)),H(i,n.shape)},b:()=>{let s=se(ot(n),ot(r)),i=kt(P(e,me(n,s))),o=zt(r.shape,a);return o.length>0&&(i=Te(i,o)),H(i,r.shape)}}}},iQ={kernelName:po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,se(ot(ge(n,"float32")),1))}}},oQ={kernelName:fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ye(xe(1),ot(ge(n,"float32"))))}}};function lQ(e,t,n,r,a,s){let i=R(e,"dy","avgPool3dGrad"),o=R(t,"input","avgPool3dGrad"),l=i,c=o,u=!1;o.rank===4&&(u=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),c=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),M(c.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${c.rank}.`),s!=null&&M(Vt(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(Oh,h,d);return u?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var uQ=D({avgPool3dGrad_:lQ}),cQ={kernelName:ku,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>uQ(e,r,a,s,i,o)}}};function hQ(e,t,n,r,a){let s=R(e,"dy","avgPoolGrad"),i=R(t,"input","avgPoolGrad");M(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,c=!1;i.rank===3&&(c=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),M(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),M(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let u={dy:l,input:o},h={filterSize:n,strides:r,pad:a},d=$.runKernel(Dh,u,h);return c?H(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var dQ=D({avgPoolGrad_:hQ}),pQ={kernelName:fs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i}=n;return{x:()=>dQ(e,r,a,s,i)}}},fQ={kernelName:ms,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[r,a]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>Ve(e,a,!1,!0),b:()=>Ve(r,e,!0,!1)}:!s&&i?{a:()=>Ve(e,a,!1,!1),b:()=>Ve(e,r,!0,!1)}:s&&!i?{a:()=>Ve(a,e,!1,!0),b:()=>Ve(r,e,!1,!1)}:{a:()=>Ve(a,e,!0,!0),b:()=>Ve(e,r,!0,!0)}}},mQ={kernelName:Iu,gradFunc:(e,t,n)=>{let{blockShape:r,crops:a}=n;return{x:()=>sc(e,r,a)}}},yQ={kernelName:tw,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:()=>Te(e,o,!0)}}},AQ={kernelName:ys,gradFunc:e=>({x:()=>e.clone()})},gQ={kernelName:As,gradFunc:e=>({x:()=>He(e)})},xQ={kernelName:Ra,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:a,clipValueMax:s}=n;return{x:()=>En(fr(Va(r,a),xi(r,s)),e,He(e))}}},wQ={kernelName:Su,inputsToSave:["x"],gradFunc:Dv.gradFunc},bQ={kernelName:yo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(o=>o.shape),{axis:a}=n,s=hr(a,t[0].shape)[0],i=r.map(o=>o[s]);return Pt(e,i,s).map(o=>()=>o)}},_Q={kernelName:gs,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return M(La(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>wm(r.shape,e,a,i,o,l),filter:()=>Zm(r,e,a.shape,i,o,l)}}},vQ={kernelName:xs,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>ca(e,a,s,i,o,1,l),filter:()=>Zm(e,r,a.shape,s,i,o,l)}}};function kQ(e,t,n,r,a){let s=e;e.rank===4&&(s=H(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),M(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),M(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),M(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),M(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),M(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:r,pad:a,filterShape:n};return $.runKernel(Wh,o,l)}var IQ=D({conv3DBackpropFilter_:kQ}),SQ={kernelName:Nu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s}=n;M(La(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[i,o]=t;return{x:()=>sb(i.shape,e,o,a,s),filter:()=>IQ(i,e,o.shape,a,s)}}},NQ={kernelName:ws,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(kt(Bd(ge(n,"float32"))),e)}}},TQ={kernelName:Ao,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Vd(ge(n,"float32")),e)}}},EQ={kernelName:bs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a,exclusive:s,reverse:i}=n;return{x:()=>{let o=Ab([a],r.rank),l=Td(e,a,s,!i);return o!=null&&(l=Je(l,o)),l}}}},CQ={kernelName:_s,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s,dimRoundingMode:i}=n,o=r==null?[1,1]:r;M(La(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,c]=t;return M(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),M(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),M(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),M(Br(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${o}'.`),i!=null&&M(Vt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>Mb(l.shape,e,c,a,s,r,i),filter:()=>Rb(l,e,c.shape,a,s,r,i)}}},RQ={kernelName:Tu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,s={x:r,filter:a,dy:e},i={x:r,filter:a,dy:e};return{x:()=>$.runKernel(Gh,s,n),filter:()=>$.runKernel(qh,i,n)}}},MQ={kernelName:wo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>$.runKernel(Kh,r)}}},FQ={kernelName:bo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(er(kt(ot(n))),2/Math.sqrt(Math.PI));return{x:()=>P(e,r)}}},$Q={kernelName:ks,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,n)}}},DQ={kernelName:vo,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>H(e,n.shape)}}},OQ={kernelName:ko,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,er(n))}}},zQ={kernelName:Is,gradFunc:e=>({x:()=>He(e)})},PQ={kernelName:Ss,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ft(n.shape,r.shape);return{a:()=>{let s=me(e,ge(r,"float32")),i=zt(n.shape,a);return i.length>0?H(Te(s,i),n.shape):s},b:()=>{let s=P(e,ge(n,"float32")),i=zt(r.shape,a);i.length>0&&(s=H(Te(s,i),r.shape));let o=ot(r);return kt(me(s,ge(o,"float32")))}}}},LQ={kernelName:Ns,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:r}=n,[a,s,i,o]=t,l=o==null?xe(1):o,c=zt(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=ye(a,s),d=P(e,l),p=Ld(se(i,xe(r))),f=P(P(P(p,p),p),xe(-.5));return{x:()=>s.rank===1?H(P(P(e,Ba(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,xe(-1)),d);return s.rank===1&&(m=Te(m,c)),H(m,s.shape)},variance:()=>{let m=P(P(f,h),d);return s.rank===1&&(m=Te(m,c)),H(m,s.shape)},scale:()=>{let m=P(h,p),y=P(e,m);return s.rank===1&&(y=Te(y,c)),H(y,s.shape)},offset:()=>{let m=e;return s.rank===1&&(m=Te(m,c)),H(m,s.shape)}}}},WQ={kernelName:So,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,a]=t,{axis:s}=n,i=hr(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=Ov(0,u),f=Ov(u+1,u+1+d),m=zv([c,[l],h]),y=H(e,m),A=H(a,[l]),g=zv([[u],p,f]),x=Je(y,g),v=Gm(x,A,r.shape[i]),w=Rm(g);return v=Je(v,w),v},indices:()=>a}}};function Ov(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function zv(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 BQ={kernelName:Ts,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>He(n),b:()=>He(r)}}},VQ={kernelName:Es,gradFunc:e=>({x:()=>ge(e,"float32")})},jQ={kernelName:Eo,gradFunc:e=>({x:()=>He(e)})},UQ={kernelName:Co,gradFunc:e=>({x:()=>He(e)})},HQ={kernelName:Ro,gradFunc:e=>({x:()=>He(e)})},GQ={kernelName:Cs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:a}=n,s=pr(r,0);return{x:()=>En(s,e,P(e,a))}}},qQ={kernelName:$o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,se(n,1))}}},XQ={kernelName:Rs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ge(n,"float32"))}}},KQ={kernelName:nw,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n;return{logits:()=>{let s=!0,i=er(r);return ye(e,P(Te(e,a,s),i))}}}};function ZQ(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 YQ=D({localResponseNormalizationBackprop_:ZQ}),JQ={kernelName:Mu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>YQ(r,a,e,s,i,o,l)}}};function Pv(e,t,n,r){return t.rank<n.rank&&(t=H(t,wi(t.shape,r))),e.rank<n.rank&&(e=H(e,wi(e.shape,r))),{x:()=>P(e,ge(Wa(n,t),e.dtype))}}var Lv={kernelName:Ms,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{reductionIndices:a}=r,s=t[0],i=t[1],o=hr(a,s.shape),l=Pv(e,i,s,o);return{x:()=>l.x()}}},QQ={kernelName:Fs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,ge(Va(n,r),"float32")),b:()=>P(e,ge(Cd(n,r),"float32"))}}};function eee(e,t,n,r,a,s,i){let o=R(e,"dy","maxPool3dGrad"),l=R(t,"input","maxPool3dGrad"),c=R(n,"output","maxPool3dGrad"),u=o,h=l,d=c,p=!1;l.rank===4&&(p=!0,u=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),h=H(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),d=H(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]])),M(u.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),M(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),M(d.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`),i!=null&&M(Vt(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let f={dy:u,input:h,output:d},m={filterSize:r,strides:a,pad:s,dimRoundingMode:i},y=$.runKernel(nd,f,m);return p?H(y,[y.shape[1],y.shape[2],y.shape[3],y.shape[4]]):y}var tee=D({maxPool3dGrad_:eee}),nee={kernelName:Fu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>tee(e,r,a,s,i,o,l)}}};function ree(e,t,n,r,a,s,i){let o=R(e,"dy","maxPoolGrad"),l=R(t,"input","maxPoolGrad"),c=R(n,"output","maxPoolGrad");M(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),M(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),M(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&M(Vt(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 $.runKernel(td,u,h)}var aee=D({maxPoolGrad_:ree}),see={kernelName:$s,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>aee(e,r,a,s,i,o)}}},iee={kernelName:Ds,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n,s=hr(a,r.shape),i=yb(r.shape,s)[1],o=Et(i);return{x:()=>{let l=r.shape.slice();s.forEach(u=>{l[u]=1});let c=H(e,l);return me(P(c,Pn(r.shape,"float32")),o)}}}},oee={kernelName:Os,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:a}=r,[s,i]=t,o=hr(a,s.shape),l=Pv(e,i,s,o);return{x:()=>l.x()}}},lee={kernelName:zs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,ge(xi(n,r),"float32")),b:()=>P(e,ge(pr(n,r),"float32"))}}},uee={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Ce(e,s,r.shape)}}},cee={kernelName:Oo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ft(n.shape,r.shape);return{a:()=>{let s=zt(n.shape,a);return s.length>0?H(Te(e,s),n.shape):e},b:()=>{let s=P(e,kt(bl(me(n,r)))),i=zt(r.shape,a);return i.length>0?H(Te(s,i),r.shape):s}}}},hee={kernelName:Ls,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ft(n.shape,r.shape);return{a:()=>{let s=P(e,ge(r,"float32")),i=zt(n.shape,a);return i.length>0?H(Te(s,i),n.shape):s},b:()=>{let s=P(e,ge(n,"float32")),i=zt(r.shape,a);return i.length>0?H(Te(s,i),r.shape):s}}}},dee={kernelName:zo,gradFunc:e=>({x:()=>kt(e)})},pee={kernelName:Ws,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Rt(n.shape,"float32")}}},fee={kernelName:Vo,gradFunc:e=>({x:()=>He(e)})},mee={kernelName:jo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return mr(e,r).map(a=>()=>a)}},Wv={kernelName:Bs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Ce(e,s,r.shape)}}},yee={kernelName:Vs,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,a]=t,s=n,i=r,o=ft(s.shape,i.shape);return{a:()=>{let l=ge(i,"float32"),c=P(e,P(l,da(s,ye(l,xe(1))))),u=zt(s.shape,o);return u.length>0&&(c=Te(c,u)),H(c,s.shape)},b:()=>{let l=pr(s,0),c=En(l,zn(s),He(s)),u=P(e,P(a,c)),h=zt(i.shape,o);return h.length>0&&(u=Te(u,h)),H(u,i.shape)}}}},Aee={kernelName:js,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,a=pr(n,0);return{x:()=>En(a,e,P(e,r)),alpha:()=>{let s=En(a,He(e),P(e,n)),i=zt(r.shape,e.shape);return i.length>0&&(s=Te(s,i)),H(s,r.shape)}}}},gee={kernelName:vs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ft(n.shape,r.shape);return{a:()=>{let s=me(e,ge(r,"float32")),i=zt(n.shape,a);return i.length>0?H(Te(s,i),n.shape):s},b:()=>{let s=P(e,ge(n,"float32")),i=zt(r.shape,a);i.length>0&&(s=H(Te(s,i),r.shape));let o=ot(r);return kt(me(s,ge(o,"float32")))}}}},xee={kernelName:Ho,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,kt(ot(n)))}}},wee={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(xi(n,6),Nl(n));return{x:()=>P(e,ge(r,"float32"))}}},bee={kernelName:Us,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,ge(Nl(n),"float32"))}}},_ee={kernelName:Go,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>H(e,n.shape)}}},vee={kernelName:Hs,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(od,a,n)}}},kee={kernelName:Du,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(id,a,n)}}},Iee={kernelName:qs,gradFunc:(e,t,n)=>{let{dims:r}=n,a=hr(r,e.shape);return{x:()=>Wn(e,a)}}},See={kernelName:Xs,gradFunc:e=>({x:()=>He(e)})},Nee={kernelName:Ks,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>kt(me(e,P(da(n,1.5),2)))}}},Tee={kernelName:Xo,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>ge(He(n),"float32"),t:()=>P(e,ge(n,e.dtype)),e:()=>P(e,ge(rc(n),e.dtype))}}},Eee={kernelName:Ko,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=pr(n,xe(0)),a=xe(jb),s=xe(Ub),i=P(e,s),o=P(P(e,a),er(ge(n,"float32")));return En(r,i,o)}}}},Cee={kernelName:Ys,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(n,ye(xe(1),n)))}}},Ree={kernelName:Jo,gradFunc:e=>({x:()=>He(e)})},Mee={kernelName:Zs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(ec(ge(n,"float32")),e)}}},Fee={kernelName:Yo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Nd(ge(n,"float32")),e)}}},$ee={kernelName:Zo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:a,size:s}=n,i=r.shape,[o,l]=Bw(r,a,s),c=[];for(let u=0;u<e.rank;u++)c.push([o[u],i[u]-o[u]-l[u]]);return{x:()=>ha(e,c)}}},Dee={kernelName:ei,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:a}=n,s=!0,i=P(e,r);return{logits:()=>ye(i,P(Te(i,[a],s),r))}}},Oee={kernelName:Qo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,On(n))}}},Bv={kernelName:Ou,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:a}=n;return{x:()=>Qu(e,r,a)}}},Vv={kernelName:el,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>rt(e,r)}}},zee={kernelName:Js,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,P(Qt(ge(n,"float32")),2))}}},Pee={kernelName:zu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(ge(n,"float32"),2))}}},Lee={kernelName:ti,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=xe(2);return{a:()=>P(e,P(a,ye(n,r))),b:()=>P(e,P(a,ye(r,n)))}}},Wee={kernelName:Fa,gradFunc:e=>({x:()=>He(e)})},Bee={kernelName:ni,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ft(n.shape,r.shape);return{a:()=>{let s=e,i=zt(n.shape,a);return i.length>0&&(s=Te(s,i)),H(s,n.shape)},b:()=>{let s=e,i=zt(r.shape,a);return i.length>0&&(s=Te(s,i)),H(kt(s),r.shape)}}}},Vee={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,a=r.shape.slice(),{axis:s}=n;hr(s,r.shape).forEach(l=>{a[l]=1});let i=H(e,a),o=P(i,Pn(r.shape,"float32"));return{x:()=>o}}},jee={kernelName:ri,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>me(e,ot(ec(n)))}}},Uee={kernelName:ai,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(ye(xe(1),ot(n)),e)}}},Hee={kernelName:Ma,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{reps:a}=n;return{x:()=>{let s=He(r);if(r.rank===1)for(let i=0;i<a[0];++i)s=se(s,Ce(e,[i*r.shape[0]],[r.shape[0]]));else if(r.rank===2)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)s=se(s,Ce(e,[i*r.shape[0],o*r.shape[1]],[r.shape[0],r.shape[1]]));else if(r.rank===3)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)s=se(s,Ce(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2]],[r.shape[0],r.shape[1],r.shape[2]]));else if(r.rank===4)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)for(let c=0;c<a[3];++c)s=se(s,Ce(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}}}},Gee={kernelName:si,gradFunc:(e,t,n)=>{let r=n,{perm:a}=r,s=Rm(a);return{x:()=>Je(e,s)}}},qee={kernelName:rl,gradFunc:(e,t,n)=>{let r=n,{axis:a}=r;return{value:()=>cn(e,a)}}},Kee={kernelName:Pu,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Xee(e,n)}}};function Xee(e,t){let n=jr(t,He(t)),r=gi(e,n),a=Va(t,xe(0,"int32")),s=r.rank-a.rank;for(let o=0;o<s;++o)a=Jt(a,o+1);a=fr(a,Pn(r.shape,"bool"));let i=He(r);return En(a,r,i)}var Zee={kernelName:al,gradFunc:e=>({x:()=>He(e)})},Yee=[Dv,YJ,JJ,QJ,eQ,tQ,nQ,rQ,aQ,sQ,iQ,oQ,cQ,pQ,fQ,mQ,yQ,AQ,gQ,xQ,wQ,bQ,vQ,_Q,SQ,NQ,TQ,EQ,CQ,RQ,gee,MQ,FQ,$Q,DQ,OQ,PQ,zQ,LQ,WQ,BQ,VQ,jQ,UQ,HQ,GQ,qQ,XQ,KQ,JQ,Lv,Lv,QQ,nee,see,iee,oee,lee,uee,cee,hee,dee,pee,fee,mee,Wv,Wv,yee,Aee,xee,wee,bee,_ee,vee,kee,Iee,See,Nee,Tee,Eee,Cee,Ree,Mee,Fee,$ee,Dee,Oee,Bv,Bv,Vv,Vv,zee,Lee,Pee,Wee,Bee,Vee,jee,Uee,Hee,Gee,qee,Kee,Zee];for(let e of Yee)rw(e);var jv={};Me(jv,{maxNorm:()=>Jee,minMaxNorm:()=>tte,nonNeg:()=>ete,unitNorm:()=>Qee});var Wy;function Lt(){return Wy==null&&(Wy=Xw().epsilon()),Wy}function Tr(){return"channelsLast"}var ya=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,ya.prototype)}},Er=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Er.prototype)}},B=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,B.prototype)}},De=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,De.prototype)}},Uv=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Uv.prototype)}};function Mi(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 Xr(e,t){if(!e)throw new Uv(t)}function Hv(e,t){let n=0;for(let r of e)r===t&&n++;return n}function Fn(e){return e.length===1?e[0]:e}function yt(e){return Array.isArray(e)?e:[e]}function Aa(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function Fi(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var yr={};function By(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function Vy(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>Vy(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:Vy(r))}}}function Rc(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 yr)i=yr[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 yr?[o,l]=yr.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(yr))c[p]=yr[p];for(let p of Object.keys(n))c[p]=n[p];let u=s.config;u.customObjects=c;let h=Object.assign({},yr);for(let p of Object.keys(n))yr[p]=n[p];Vy(s.config);let d=l(o,s.config,n,a);return yr=Object.assign({},h),d}else{let c=Object.assign({},yr);for(let h of Object.keys(n))yr[h]=n[h];let u=new o(s.config);return yr=Object.assign({},c),u}}}function nte(e,t){return e<t?-1:e>t?1:0}function Op(e,t){return-1*nte(e,t)}function Ka(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function rte(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 $i(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new B(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function jy(e,t,n=0,r=Infinity){return Xr(n>=0),Xr(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(a=>typeof a===t)}function Ht(e,t){Array.isArray(e)?(_.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>Ht(n,`element ${r+1} of ${t}`))):_.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${Gv(e)}.`)}function Gv(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>Gv(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function ate(e,t){let n=_.now(),r;return(...a)=>{let s=_.now();return s-n<t||(n=s,r=e(...a)),r}}function qv(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function Uy(e,t){return z(()=>Qt(Te(P(e,e),t,!0)))}var Mc=class extends re.Serializable{getConfig(){return{}}},Hy=class extends Mc{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=Uy(e,this.axis),n=Tn(t,0,this.maxValue);return P(e,me(n,se(Lt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};Hy.className="MaxNorm";re.registerClass(Hy);var Gy=class extends Mc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return z(()=>me(e,se(Lt(),Uy(e,this.axis))))}getConfig(){return{axis:this.axis}}};Gy.className="UnitNorm";re.registerClass(Gy);var qy=class extends Mc{apply(e){return Ur(e)}};qy.className="NonNeg";re.registerClass(qy);var Xy=class extends Mc{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=Uy(e,this.axis),n=se(P(this.rate,Tn(t,this.minValue,this.maxValue)),P(1-this.rate,t));return P(e,me(n,se(Lt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};Xy.className="MinMaxNorm";re.registerClass(Xy);var Xv={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Wt(e){return By(e)}function Kv(e,t={}){return Rc(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function Bt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in Xv?Xv[e]:e,config:{}};return Kv(t)}else return e instanceof Mc?e:Kv(e)}function Jee(e){return new Hy(e)}function Qee(e){return new Gy(e)}function ete(){return new qy}function tte(e){return new Xy(e)}var Zv={};Me(Zv,{constant:()=>ote,glorotNormal:()=>fte,glorotUniform:()=>pte,heNormal:()=>mte,heUniform:()=>yte,identity:()=>hte,leCunNormal:()=>Ate,leCunUniform:()=>gte,ones:()=>ite,orthogonal:()=>xte,randomNormal:()=>ute,randomUniform:()=>lte,truncatedNormal:()=>cte,varianceScaling:()=>dte,zeros:()=>ste});var wte=["channelsFirst","channelsLast"],bte=["nearest","bilinear"],_te=["valid","same","causal"],vte=["max","avg"],kte=["sum","mul","concat","ave"],Ul=new Map;function Ct(e){$i(wte,"DataFormat",e)}function Ite(e){$i(bte,"InterpolationFormat",e)}function ar(e){$i(_te,"PaddingMode",e)}function Yv(e){$i(vte,"PoolMode",e)}var Fc=[],Jv="/";function Di(e,t){Fc.push(e);try{let n=t();return Fc.pop(),n}catch(n){throw Fc.pop(),n}}function Ste(){return Fc.length===0?"":Fc.join(Jv)+Jv}function e6(e){if(!Qv(e))throw new Error("Not a valid tensor name: '"+e+"'");return Ste()+e}function t6(e){if(!Qv(e))throw new Error("Not a valid tensor name: '"+e+"'");Ul.has(e)||Ul.set(e,0);let t=Ul.get(e);if(Ul.set(e,Ul.get(e)+1),t>0){let n=`${e}_${t}`;return Ul.set(n,1),n}else return e}var Nte=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Qv(e){return!!e.match(Nte)}function Tte(e){return e===parseInt(e.toString(),10)}function Za(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 n6(e){return e=Array.isArray(e)?new Float32Array(e):e,sn(e)}function Hl(e){return vl(n6(e)).dataSync()[0]}function Ya(e){return Cn(n6(e)).dataSync()[0]}function Cr(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 $c(e,t){return e.asType(t)}function Dc(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function Ete(e,t){return z(()=>{if(e.shape.length!==2)throw new B(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Dc(e,1);return Ky(n,[1,t,1])})}function Cte(e){let t=[Za(e.shape)];return e.reshape(t)}function Rte(e){if(e.rank<=1)throw new B(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Za(e.shape,1)];return e.reshape(t)}function Oi(e,t,n){return z(()=>{switch(e.rank){case 1:return jd(e,t,n);case 2:return Vm(e,[t,0],[n,e.shape[1]]);case 3:return Ud(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return lc(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Ce(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Ce(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 Zy(e,t,n){return z(()=>{switch(e.rank){case 1:return jd(e,t,n);case 2:return Vm(e,[0,t],[e.shape[0],n]);case 3:return Ud(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return lc(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 zp(e,t,n,r){return z(()=>{switch(e.rank){case 1:return jd(e,t,n);case 2:switch(r){case 1:return Oi(e,t,n);case 2:return Zy(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 Oi(e,t,n);case 2:return Ud(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return Zy(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 Oi(e,t,n);case 2:return lc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return lc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return Zy(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 Yy(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),rt(e,t)}function r6(e,t){switch(e.rank){case 1:return nb([e,t]);case 2:return gl([e,t],0);case 3:return rb([e,t],0);case 4:return ab([e,t],0);default:throw new B(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function Ky(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new B(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Ba(e,t)}function Pp(e,t=0,n=1,r,a){return _b(e,t,n,r,a)}function Kr(e,t,n,r){if(e.rank<2||t.rank<2)throw new De(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let a=e.shape.slice(-1)[0],s=t.shape.slice(-2)[0];if(a!==s)throw new De(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let a=!1,s=!1;return Ua.matMul({a:e,b:t,transposeA:a,transposeB:s,bias:r?Jy(e.rank,r,Tr()):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?Jy(e.rank,r,Tr()):null,activation:n}).reshape(h)}}function a6(e,t,n){return z(()=>(Array.isArray(t)?t=sn(t,"int32"):t=t.toInt(),gi(e,t,n)))}function Oc(e){return P(e,e)}function Jy(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: ${t.rank}`)}function Rr(e,t,n){return z(()=>(n==null&&(n=Tr()),Ct(n),e.add(Jy(e.rank,t,n))))}function Mte(e,t=1){if(t!==1)throw new De(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return wl(e)}function Fte(e){return z(()=>me(e,Ot(e).add(1)))}function s6(e,t,n,r){return z(()=>Eb(e,t,n,r))}function $te(e){return z(()=>{let t=se(.5,P(.2,e));return Tn(t,0,1)})}function zc(e,t,n=!1){return n?e():t()}var Dte=["fanIn","fanOut","fanAvg"],Ote=["normal","uniform","truncatedNormal"];function zte(e){$i(Dte,"FanMode",e)}function Pte(e){$i(Ote,"Distribution",e)}var Ar=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Qy=class extends Ar{apply(e,t){return Rt(e,t)}};Qy.className="Zeros";re.registerClass(Qy);var Lp=class extends Ar{apply(e,t){return Pn(e,t)}};Lp.className="Ones";re.registerClass(Lp);var eA=class extends Ar{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(xe(this.value),Pn(e,t)))}getConfig(){return{value:this.value}}};eA.className="Constant";re.registerClass(eA);var tA=class extends Ar{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 Il(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};tA.className="RandomUniform";re.registerClass(tA);var nA=class extends Ar{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new De(`randomNormal does not support dType ${t}.`);return Pp(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};nA.className="RandomNormal";re.registerClass(nA);var rA=class extends Ar{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new De(`truncatedNormal does not support dType ${t}.`);return qd(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};rA.className="TruncatedNormal";re.registerClass(rA);var aA=class extends Ar{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,Nm(e[0]))})}getConfig(){return{gain:this.gain}}};aA.className="Identity";re.registerClass(aA);function Lte(e,t="channelsLast"){let n,r;if(Ct(t),e.length===2)n=e[0],r=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let a=Za(e,2);n=e[1]*a,r=e[0]*a}else if(t==="channelsLast"){let a=Za(e,0,e.length-2);n=e[e.length-2]*a,r=e[e.length-1]*a}}else{let a=Za(e);n=Math.sqrt(a),r=Math.sqrt(a)}return[n,r]}var $n=class extends Ar{constructor(e){super();if(e.scale<0)throw new B(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,zte(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,Pte(this.distribution),this.seed=e.seed}apply(e,t){let n=Lte(e),r=n[0],a=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,r):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new De(`${this.getClassName()} does not support dType ${t}.`);return qd(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return Il(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};$n.className="VarianceScaling";re.registerClass($n);var Wp=class extends $n{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return $n.className}};Wp.className="GlorotUniform";re.registerClass(Wp);var Bp=class extends $n{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return $n.className}};Bp.className="GlorotNormal";re.registerClass(Bp);var Vp=class extends $n{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return $n.className}};Vp.className="HeNormal";re.registerClass(Vp);var jp=class extends $n{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return $n.className}};jp.className="HeUniform";re.registerClass(jp);var Up=class extends $n{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return $n.className}};Up.className="LeCunNormal";re.registerClass(Up);var Hp=class extends $n{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return $n.className}};Hp.className="LeCunNormal";re.registerClass(Hp);var sA=class extends Ar{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new De("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return z(()=>{if(e.length<2)throw new De("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=Pp(n,0,1,"float32"),a=Vb.gramSchmidt(r);return e[0]>e[1]&&(a=a.transpose()),P(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};sA.className="Orthogonal";re.registerClass(sA);var i6={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 o6(e,t={}){return Rc(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function St(e){return By(e)}function gt(e){if(typeof e=="string"){let t=e in i6?i6[e]:e;if(t==="GlorotNormal")return new Bp;if(t==="GlorotUniform")return new Wp;if(t==="HeNormal")return new Vp;if(t==="HeUniform")return new jp;if(t==="LeCunNormal")return new Up;if(t==="LeCunUniform")return new Hp;{let n={};return n.className=t,n.config={},o6(n)}}else return e instanceof Ar?e:o6(e)}function ste(){return new Qy}function ite(){return new Lp}function ote(e){return new eA(e)}function lte(e){return new tA(e)}function ute(e){return new nA(e)}function cte(e){return new rA(e)}function hte(e){return new aA(e)}function dte(e){return new $n(e)}function pte(e){return new Wp(e)}function fte(e){return new Bp(e)}function mte(e){return new Vp(e)}function yte(e){return new jp(e)}function Ate(e){return new Up(e)}function gte(e){return new Hp(e)}function xte(e){return new sA(e)}var l6={};Me(l6,{Layer:()=>qe,RNN:()=>Zr,RNNCell:()=>Pc,activation:()=>nne,add:()=>hne,alphaDropout:()=>Xne,average:()=>dne,averagePooling1d:()=>iA,averagePooling2d:()=>oA,averagePooling3d:()=>lA,avgPool1d:()=>bne,avgPool2d:()=>vne,avgPool3d:()=>Ine,avgPooling1d:()=>_ne,avgPooling2d:()=>kne,avgPooling3d:()=>Sne,batchNormalization:()=>gne,bidirectional:()=>Wne,concatenate:()=>pne,conv1d:()=>qte,conv2d:()=>Xte,conv2dTranspose:()=>Kte,conv3d:()=>Zte,conv3dTranspose:()=>Yte,convLstm2d:()=>One,convLstm2dCell:()=>zne,cropping2D:()=>Qte,dense:()=>rne,depthwiseConv2d:()=>tne,dot:()=>Ane,dropout:()=>ane,elu:()=>Bte,embedding:()=>cne,flatten:()=>ine,gaussianDropout:()=>qne,gaussianNoise:()=>Gne,globalAveragePooling1d:()=>Nne,globalAveragePooling2d:()=>Tne,globalMaxPool1d:()=>Vne,globalMaxPool2d:()=>jne,globalMaxPooling1d:()=>c6,globalMaxPooling2d:()=>h6,gru:()=>Cne,gruCell:()=>Rne,input:()=>u6,inputLayer:()=>Wte,layerNormalization:()=>xne,leakyReLU:()=>jte,lstm:()=>Mne,lstmCell:()=>Fne,masking:()=>Kne,maxPool1d:()=>Une,maxPool2d:()=>Hne,maxPooling1d:()=>d6,maxPooling2d:()=>p6,maxPooling3d:()=>Ene,maximum:()=>fne,minimum:()=>mne,multiply:()=>yne,permute:()=>une,prelu:()=>Ute,reLU:()=>Vte,repeatVector:()=>one,reshape:()=>lne,rnn:()=>Pne,separableConv2d:()=>Jte,simpleRNN:()=>$ne,simpleRNNCell:()=>Dne,softmax:()=>Hte,spatialDropout1d:()=>sne,stackedRNNCells:()=>Lne,thresholdedReLU:()=>Gte,timeDistributed:()=>Bne,upSampling2d:()=>ene,zeroPadding2d:()=>wne});var Zne=0;function f6(){return Zne++}var Gp={};function qp(e=""){return e in Gp||(Gp[e]=0),Gp[e]+=1,e+Gp[e].toString()}function uA(e){return Array.isArray(e)&&Array.isArray(e[0])}function Xp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function ze(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new B(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function st(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new B(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Kp(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 m6="Variable",y6=class{constructor(e,t="float32",n=m6,r=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=f6(),n=n==null?m6:n,this.originalName=e6(n),this.name=t6(this.originalName),this.trainable_=r,this.constraint=a,this.val=kb(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Yne(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 Yne(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function cA(e){return e.map(t=>t.read())}function hA(e){e.forEach(t=>{t[0].write(t[1])})}var Ft=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||{}}},Mr=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=f6(),s!=null&&(this.originalName=e6(s),this.name=t6(this.originalName)),this.rank=t.length}},Jne=0,Zp=class{constructor(e,t){this.callArgs=t,this.id=Jne++,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}}},Qne=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Qne++,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=Aa(n)+"_"+qp(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 Er(`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 Fn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Fn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ya(`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 ya(`Layer ${this.name} is not connected, no input to return.`);return Fn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new ya(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new ya(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Fn(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=yt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=yt(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=yt(e),r=!0;for(let s of n)if(!(s instanceof Mr)){r=!1;break}let a=!0;for(let s of n)if(s instanceof Mr){a=!1;break}if(r===a)throw new B("Arguments to apply() must be all SymbolicTensors or all Tensors");return Di(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of yt(e))s.push(i.shape);this.build(Fn(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=yt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Fn(o),this.activityRegularizer!=null)throw new De("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=ere(e),i=this.computeOutputShape(s),o,l=tre(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 Mr(l,c,this,yt(e),t,this.name,u)):o=new Mr(l,i,this,yt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new De("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ya(`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 ya(`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 Er(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Kp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return cA(e?this.trainableWeights:this.weights)}setWeights(e){z(()=>{let t=this.weights;if(t.length!==e.length)throw new B(`You called setWeights(weights) on layer "${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=cA(t);for(let a=0;a<r.length;++a){let s=r[a],i=t[a],o=e[a];if(!_.arraysEqual(s.shape,o.shape))throw new B(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}hA(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=gt("zeros"));let o=r.apply(t,n),l=new y6(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=yt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,a,s,i=null){let o=yt(e);t=yt(t),n=yt(n),r=yt(r),a=Xp(a),s=Xp(s);let l=[],c=[],u=[];for(let h of o)l.push(h.sourceLayer),c.push(h.nodeIndex),u.push(h.tensorIndex);new Zp({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 ere(e){e=yt(e);let t=[];for(let n of e)t.push(n.shape);return Fn(t)}function tre(e){return"float32"}function A6(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=A6(i,o,l);for(let u of c)a.indexOf(u)===-1&&a.push(u)}return a}}}var Gl=class extends qe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:qp("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 Mr(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new Zp({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}}};Gl.className="InputLayer";re.registerClass(Gl);function g6(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 Gl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Ja(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];_e(r)}}function x6(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var w6;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(w6||(w6={}));var nre=125,ql=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){}},b6=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)}},rre=class extends ql{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(()=>se(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(me(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),jt(t[n])}))}},_6=class extends ql{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]}},v6=class extends ql{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=nre),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");_.isNumber(this.yieldEvery)&&(this.maybeWait=ate(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 Ja(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 Ja(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Ja(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 Ja(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Ja(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(ip()):_.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Ja(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Ja(e),await this.trainEnd(e))}};function k6(e,t){return e==null&&(e={}),e instanceof ql?[e]:Array.isArray(e)&&e[0]instanceof ql?e:yt(e).map(n=>new v6(n,t))}var gr=class{constructor(){}static registerCallbackConstructor(e,t){_.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),gr.checkForDuplicate(t),gr.constructors[e]==null&&(gr.constructors[e]=[]),gr.constructors[e].push(t)}static checkForDuplicate(e){for(let t in gr.constructors)gr.constructors[+t].forEach(n=>{if(n===e)throw new B("Duplicate callback constructor.")})}static clear(){gr.constructors={}}static createCallbacks(e){let t=[];for(let n in gr.constructors){let r=+n;e>=r&&t.push(...gr.constructors[r])}return t.map(n=>new n)}};gr.constructors={};function I6(e,t,n,r,a,s,i,o,l){let c=new _6,u=[new rre,...gr.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let h=new b6(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 Fr(e,t={},n=!1){return Rc(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function Yp(e,t){return z(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Te(Oc(e),t,!0),r=tc(n.shape,Lt()),a=Qt(jr(n,r));return me(e,a)})}function zi(e,t){return z(()=>It(Oc(ye(t,e)),-1))}function Jp(e,t){return z(()=>It(Ot(ye(t,e)),-1))}function Xl(e,t){return z(()=>{let n=ye(e,t),r=Tn(Ot(e),Lt(),Number.MAX_VALUE),a=Ot(me(n,r));return P(100,It(a,-1))})}function are(e,t){return z(()=>{let n=Tn(t,Lt(),Number.MAX_VALUE),r=zn(se(1,n)),a=Tn(e,Lt(),Number.MAX_VALUE),s=zn(se(1,a));return It(Oc(ye(r,s)),-1)})}function sre(e,t){return z(()=>{let n=jr(0,ye(1,P(e,t)));return It(Oc(n),-1)})}function ire(e,t){return z(()=>{let n=jr(0,ye(1,P(e,t)));return It(n,-1)})}function ore(e,t){return z(()=>{let n=Te(P(e,t),-1),r=Cn(P(ye(1,e),t),-1);return jr(0,se(1,ye(r,n)))})}function lre(e,t){return z(()=>{let n=Math.log(2),r=ye(t,e),a=ye(se(r,_l(P(-2,r))),n);return It(a,-1)})}function Lc(e,t,n=!1){return z(()=>{if(n)t=uc(t);else{let r=Te(t,t.shape.length-1,!0);t=me(t,r)}return t=Tn(t,Lt(),1-Lt()),kt(Te(P(e.toFloat(),zn(t)),t.shape.length-1))})}function Qp(e,t,n=!1){return z(()=>{let r=bl(Cte(e)).toInt();t=Tn(t,Lt(),1-Lt());let a=t.shape,s=dl(r,a[a.length-1]).reshape(a);return Lc(s,t,n)})}function ure(e,t){if(!_.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 e0(e,t){return z(()=>{let n;return n=Tn(t,Lt(),1-Lt()),n=zn(me(n,ye(1,n))),It(ure(e,n),-1)})}function cre(e,t){return z(()=>{let n=Tn(e,Lt(),1),r=Tn(t,Lt(),1);return Te(P(e,zn(me(n,r))),-1)})}function hre(e,t){return z(()=>{let n=zn(se(Lt(),t));return It(ye(t,P(e,n)),-1)})}function dA(e,t){return z(()=>{let n=Yp(e,-1),r=Yp(t,-1),a=P(n,r);return kt(Te(a,-1))})}var t0={meanSquaredError:zi,meanAbsoluteError:Jp,meanAbsolutePercentageError:Xl,meanSquaredLogarithmicError:are,squaredHinge:sre,hinge:ire,categoricalHinge:ore,logcosh:lre,categoricalCrossentropy:Lc,sparseCategoricalCrossentropy:Qp,binaryCrossentropy:e0,kullbackLeiblerDivergence:cre,poisson:hre,cosineProximity:dA};function pA(e){if(typeof e=="string"){if(e in t0)return t0[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new B(t)}else return e}function fA(e,t){return z(()=>{let n=P(.5,Ln(t)),r=$c(pr(t,n),e.dtype);return It(Wa(e,r),-1)})}function mA(e,t){return z(()=>$c(Wa(mi(e,-1),mi(t,-1)),"float32"))}function S6(e,t){return z(()=>fr(e.equal(1),t.equal(1)).sum().cast("float32"))}function dre(e,t){return z(()=>fr(e.equal(1),t.equal(0)).sum().cast("float32"))}function pre(e,t){return z(()=>fr(e.equal(0),t.equal(1)).sum().cast("float32"))}function N6(e,t){return z(()=>{let n=S6(e,t),r=pre(e,t),a=n.add(r);return En(pr(a,0),n.div(a),0).cast("float32")})}function fre(e,t){return z(()=>{let n=S6(e,t),r=dre(e,t),a=n.add(r);return En(pr(a,0),n.div(a),0).cast("float32")})}function T6(e,t){return e0(e,t)}function E6(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)),Wa(e,t).asType("float32")}var mre=zi,yre=zi,Are=Jp,gre=Jp,xre=Xl,wre=Xl,yA=Lc,bre=dA,C6=Qp,n0={binaryAccuracy:fA,categoricalAccuracy:mA,precision:N6,categoricalCrossentropy:yA,sparseCategoricalCrossentropy:C6,mse:mre,MSE:yre,mae:Are,MAE:gre,mape:xre,MAPE:wre,cosine:bre};function _re(e){if(typeof e=="string"&&e in n0)return n0[e];if(typeof e!="string"&&e!=null)return e;throw new B(`Unknown metric ${e}`)}function r0(e){if(Xr(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(t0))if(t0[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(n0))if(n0[n]===e){t=n;break}return t!==void 0?t:e.name}}function vre(e){let t={Adagrad:()=>vi.adagrad(.01),Adadelta:()=>vi.adadelta(1,.95,Lt()),Adam:()=>vi.adam(.001,.9,.999,Lt()),Adamax:()=>vi.adamax(.002,.9,.999,Lt(),0),RMSProp:()=>vi.rmsprop(.001,.9,0,Lt()),SGD:()=>vi.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 ${e}`)}var R6=1*1024*1024;function M6(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!AA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>R6&&console.warn(`User-defined metadata of model "${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 <= ${R6}.`)}}function AA(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"||!AA(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!AA(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function Tre(e,t,n,r=console.log){let a=Ire(e),s=["Layer (type)","Output shape","Param #"];a?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(u=>Math.floor(t*u)));let i;if(!a){s.push("Receives inputs"),i=[];for(let u in e.nodesByDepth)i.push(...e.nodesByDepth[u])}r("_".repeat(t)),a0(s,n,r),r("=".repeat(t));let o=e.layers;for(let u=0;u<o.length;++u)a?Sre(o[u],n,r):Nre(o[u],n,i,r),r((u===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=kre(e),c=Kp(e.nonTrainableWeights);r(`Total params: ${l+c}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${c}`),r("_".repeat(t))}function kre(e){let t;return e.collectedTrainableWeights!=null?t=Kp(e.collectedTrainableWeights):t=Kp(e.trainableWeights),t}function Ire(e){let t=!0,n=[],r=[];for(let a in e.nodesByDepth)n.push(e.nodesByDepth[a]);for(let a of n){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}r.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(r.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function a0(e,t,n=console.log){let r="";for(let a=0;a<e.length;++a)a>0&&(r=r.slice(0,r.length-1)+" "),r+=e[a],r=r.slice(0,t[a]),r+=" ".repeat(t[a]-r.length);n(r)}function Sre(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(o){r="multiple"}let a=e.name,s=e.getClassName(),i=[`${a} (${s})`,r,e.countParams().toString()];a0(i,t,n)}function Nre(e,t,n,r){let a;try{a=JSON.stringify(e.outputShape)}catch(u){a="multiple"}let s=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let h=0;h<u.inboundLayers.length;++h){let d=u.inboundLayers[h].name,p=u.nodeIndices[h],f=u.tensorIndices[h];s.push(`${d}[${p}][${f}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],c=[`${i} (${o})`,a,e.countParams().toString(),l];a0(c,t,r);for(let u=1;u<s.length;++u)a0(["","","",s[u]],t,r)}function F6(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Wc(e,t){if(e===null)return null;if(typeof e=="string")return Fi(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];F6(t,a,s)?n.push(s):n.push(Wc(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=Fi(r);n[s]=Wc(a,s)}}return n}}function gA(e,t){if(e==null)return null;if(typeof e=="string")return Aa(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];F6(t,a,s)?n.push(s):n.push(gA(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r],s=Aa(r);(r==="name"||r==="className")&&typeof a=="string"?n[s]=a:n[s]=gA(a,r)}return n}}var xA="3.4.0";function Ere(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}' (${e.dtype}).`)}}var Pi=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Pi)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=Ere(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new B(`Duplicate key: name=${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 Mr){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 Mr){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&&_e(this.id2Mask)}},wA={},$6={};function Bc(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(wA[u]==null){let f=Cre(i,t);h=f.sorted,d=f.recipientCounts,wA[u]=h,$6[u]=d}h=wA[u],d={},a||Object.assign(d,$6[u]);let p=new Pi(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],y=m.sourceLayer;if(y instanceof Gl)continue;let A=[],g=[],x=[],v=!1;for(let E of m.inputs){let F=p.getValue(E),O=p.getMask(E);A.push(F),g.push(O),O!=null&&(v=!0),a||(d[E.name]--,d[E.name]===0&&!t.hasKey(E)&&o.indexOf(E.name)===-1&&!F.isDisposed&&E.sourceLayer.stateful!==!0&&x.push(F))}v&&(n=n||{},n.mask=g[0]);let w=yt(y.apply(A,n)),b=null;y.supportsMasking&&(b=y.computeMask(A,g));let k=Rre(m),N=Array.isArray(k)?k:[k];for(let E=0;E<N.length;++E){p.hasKey(N[E])||p.add(N[E],w[E],Array.isArray(b)?b[0]:b);let F=o.indexOf(N[E].name);F!==-1&&(l[F]=w[E])}a||_e(x)}return p.disposeMasks(),s?l:l[0]}function Cre(e,t){_.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let a=D6(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=D6(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:Mre(r)}}function Mre(e){let t={};for(let n in e)t[n]=e[n].size;return t}function D6(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 Rre(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 Yr=class extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=qp(A)}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],Ka(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(A=>A.name)}`);Ka(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let g=A.sourceLayer,x=A.nodeIndex,v=A.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let A of this.inputs){let g=A.sourceLayer,x=A.nodeIndex,v=A.tensorIndex;Xr(x===0,"input layer has >1 nodes"),Xr(v===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let g=this.inputLayers[A];if(!(g instanceof Gl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (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 A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},r={},a={},s={},i=[],o=(A,g,x,v,w,b)=>{(v==null||w==null||b==null)&&(v=A.sourceLayer,w=A.nodeIndex,b=A.tensorIndex);let k=v.inboundNodes[w];if(x.indexOf(k)!==-1)throw new Er(`The tensor ${A.name} at layer "${v.name}" is part of a cycle.`);if(g.indexOf(k)!==-1)return;this.containerNodes.add(Yr.nodeKey(v,w)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(k)===-1&&x.push(k);let N=k.inboundLayers.length;for(let E=0;E<N;E++){let F=k.inputTensors[E],O=k.inboundLayers[E],L=k.nodeIndices[E],V=k.tensorIndices[E];o(F,g,x,O,L,V)}for(g.push(k);x.indexOf(k)>=0;)x.splice(x.indexOf(k),1);i.push(k)},l=[],c=[];for(let A of this.outputs)o(A,l,c);let u=i.slice().reverse();for(let A of u){n[A.id]=A,A.id in t||(t[A.id]=0);let g=t[A.id],x=r[A.outboundLayer.id]==null?0:r[A.outboundLayer.id];g=Math.max(g,x),r[A.outboundLayer.id]=g,a[A.outboundLayer.id]=A.outboundLayer,t[A.id]=g;for(let v=0;v<A.inboundLayers.length;v++){let w=A.inboundLayers[v],b=A.nodeIndices[v],k=w.inboundNodes[b],N=t[k.id]==null?0:t[k.id];t[k.id]=Math.max(g+1,N),n[k.id]=k}}let h={};for(let A in t){let g=t[A];g in h||(h[g]=[]),h[g].push(n[A])}let d={};for(let A in r){let g=r[A];g in d||(d[g]=[]),d[g].push(a[A])}let p=Object.keys(d).map(A=>parseInt(A,10)).sort(Op);this.layers=[];for(let A of p){let g=d[A];g.sort((x,v)=>{let w=s[x.id],b=s[v.id];return w<b?-1:w>b?1:0});for(let x of g)x instanceof Yr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=d,p=Object.keys(h).map(A=>parseInt(A,10)).sort(Op);let f=this.inputs.slice(),m=[];for(let A of p)for(let g of h[A]){let x=g.outboundLayer;if(x!=null){for(let v of g.inputTensors)if(f.indexOf(v)===-1)throw new Er(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let v of g.outputTensors)f.push(v);m.push(x.name)}}this.nodesByDepth=h;let y=this.layers.map(A=>A.name);for(let A of y){let g=y.filter(x=>x===A).length;if(g!==1)throw new Er(`The name "${A}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(y))}this.outboundNodes=[],this.inboundNodes=[],new Zp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new 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}`)}hA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${xA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=gA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return z(()=>{e=yt(e);let n=new Pi;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Bc(this.outputs,n,t)})}computeMask(e,t){return z(()=>{e=yt(e);let n;return t==null?n=Mi(null,e.length):n=yt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Xp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Op);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],y=l.nodeIndices[f],A=l.tensorIndices[f],g=`${m.name}_${y}_${A}`,x=n[g];u.push(x)}let h=c.computeOutputShape(Fn(u)),d=Xp(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];Xr(o in n),a.push(n[o])}return Fn(a)}runInternalGraph(e,t){t==null&&(t=Mi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Op);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,y,A,g;if(c.callArgs!=null&&(f=c.callArgs),p.length===1){let[x,v]=p[0];f.mask==null&&(f.mask=v),A=yt(u.call(x,f)),g=yt(u.computeMask(x,v)),m=[x],y=[v]}else m=p.map(x=>x[0]),y=p.map(x=>x[1]),f.mask==null&&(f.mask=y),A=yt(u.call(m,f)),g=yt(u.computeMask(m,y));if(u.activityRegularizer)throw new De("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<d.length;++x){let v=d[x],w=A[x],b=g[x];n[v.id]=[w,b]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Xr(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 Yr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=Yr.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=Yr.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=Yr.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 y=h.inboundLayers[m],A=h.nodeIndices[m],g=h.tensorIndices[m],x=Yr.nodeKey(y,A),v=t[x];v==null&&(v=0),f.push([y.name,v,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=Yr.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=Yr.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,y){m.name in s?s[m.name].push(y):s[m.name]=[y]}function o(m,y){let A=[],g;for(let x of y){let v=x[0],w=x[1],b=x[2];if(g=x[3]==null?{}:x[3],!(v in a)){i(m,y);return}let k=a[v];if(k.inboundNodes.length<=w){i(m,y);return}let N=k.inboundNodes[w];A.push(N.outputTensors[b])}A.length>0&&m.apply(Fn(A),g)}function l(m){let y=m.name,A=Fr(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(r),a[y]=A,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${g}`);i(A,g)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!rte(s);)for(let m of u){let y=a[m.name];if(y.name in s){let A=s[y.name];delete s[y.name];for(let g of A)o(y,g)}}let h=[],d=[],p=t.inputLayers;for(let m of p){let y=m[0],A=m[1],g=m[2];Xr(y in a);let x=a[y].inboundNodes[A].outputTensors;h.push(x[g])}let f=t.outputLayers;for(let m of f){let y=m[0],A=m[1],g=m[2];Xr(y in a);let x=a[y].inboundNodes[A].outputTensors;d.push(x[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 Fre(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function O6(e,t){return Fre(e,t,"classWeight")}async function z6(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());_e(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])}),sn(i,"float32")}else return null}function $re(e,t){return P(e,t)}var Dre=32;function L6(e,t){let n,r,a=t;n=a.xs,r=a.ys,_.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=P6("input",e.inputNames,n),i=P6("output",e.outputNames,r),o=s[0].shape[0];_.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),_.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)_.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)_.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function P6(e,t,n){if(n instanceof Pe)return[n];if(Array.isArray(n))return _.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Ore(e){if(e.length===3)throw new De("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Pre(e,t,n){let r=n.batchesPerEpoch!=null;if(_.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),_.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),_.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),_.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),_.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=n.validationData!=null,s,i;if(a)if(W6(n.validationData))_.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let y=Ore(n.validationData);s=y.xs,i=y.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(y=>"val_"+y)):c=l.slice();let u=k6(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=I6(u,h,n.epochs,null,null,zre(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 y={};await d.onEpochBegin(f);let A=0,g=0;for(r||(m=await t.iterator());r?A<n.batchesPerEpoch:!0;){let x=await m.next();if(r&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${A} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:v,ys:w}=L6(e,x.value),b={};b.batch=g,b.size=v[0].shape[0],await d.onBatchBegin(g,b);let k=[];if(n.classWeight!=null){let F=O6(n.classWeight,e.outputNames);for(let O=0;O<F.length;++O)k.push(await z6(w[O],null,F[O]))}let N=v.concat(w).concat(k),E=o(N);_e(N);for(let F=0;F<l.length;++F){let O=l[F],L=E[F];b[O]=L,jt(L)}await d.onBatchEnd(g,b),x6(b),g++,A++}if(r?A>=n.batchesPerEpoch:x.done){if(a){let v;W6(n.validationData)?v=yt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):v=yt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?Dre:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)y[`val_${e.metricsNames[w]}`]=v[w]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(f,y),f++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function zre(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function W6(e){return typeof e.iterator=="function"}function Lre(e){return typeof e.next=="function"}async function Wre(e,t,n){n=n||{};let r=n.batches!=null,a=e.testFunction,s=[];if(n.verbose>0)throw new De("Verbose mode is not implemented yet.");_.assert(!r||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=Lre(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}=L6(e,c.value),d=u.concat(h),p=z(()=>a(d));if(_e(d),l===0)for(let m=0;m<p.length;++m)s.push(xe(0));let f=d[0].shape[0];for(let m=0;m<p.length;++m){let y=p[m],A=s[m];s[m]=z(()=>se(s[m],P(f,y))),l>0&&_e(A)}_e(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]=me(s[c],o),_e(u)}return Fn(s)}function bA(e){_.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Vc(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(r=>Oi(r,t,n-t)):Oi(e,t,n-t)}function _A(e,t){return z(()=>e==null?null:Array.isArray(e)?e.map(n=>_A(n,t)):a6(e,t.dtype==="int32"?t:t.toInt()))}function vA(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 Bre(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 y=e.checkNumSamples(n,a,p,"steps_per_epoch"),A;y!=null&&(A=Cr(0,y)),i==null&&(i=1);let{callbackList:g,history:x}=I6(o,i,s,d,y,p,a,m,h);g.setModel(e),e.history=x,await g.onTrainBegin(),e.stopTraining_=!1;for(let v=d;v<s;++v){await g.onEpochBegin(v);let w={};if(p!=null)throw new De("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new De("batch shuffling is not implemneted yet");u&&_.shuffle(A);let b=sn(A),k=vA(y,a);for(let N=0;N<k.length;++N){let E={};if(await g.onBatchBegin(N,E),z(()=>{let F=k[N][0],O=k[N][1],L=Oi(b,F,O-F);E.batch=N,E.size=O-F;let V=_A(n,L),j=t(V);for(let U=0;U<r.length;++U){let X=r[U],G=j[U];E[X]=G,jt(G)}if(N===k.length-1&&m){let U=e.testLoop(l,c,a);for(let X=0;X<r.length;++X){let G=r[X],ee=U[X];jt(ee),w["val_"+G]=ee}}}),await g.onBatchEnd(N,E),x6(E),e.stopTraining_)break}b.dispose()}if(await g.onEpochEnd(v,w),e.stopTraining_)break}return await g.onTrainEnd(),await e.history.syncData(),e.history}async function Vre(e,t,n,r={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let a,s,i,o,l,c,u;try{let h=r.batchSize==null?32:r.batchSize;bA(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 De("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 b=!0,k=await e.standardizeUserData(i,o,null,null,b,h);l=k[0],c=k[1],m=l.concat(c)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){f=!0;let b=Math.floor(a[0].shape[0]*(1-r.validationSplit)),k=a[0].shape[0];l=Vc(a,b,k),a=Vc(a,0,b),c=Vc(s,b,k),s=Vc(s,0,b),m=l.concat(c)}else r.validationSteps!=null&&(f=!0);let y=a.concat(s).concat(u);e.checkTrainableWeightsConsistency();let A=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),x,v;f?(e.makeTestFunction(),x=e.testFunction,v=g.slice().concat(g.map(b=>"val_"+b))):(x=null,m=[],v=g.slice());let w=k6(r.callbacks,r.yieldEvery);return await Bre(e,A,y,g,h,r.epochs,r.verbose,w,x,m,r.shuffle,v,r.initialEpoch,null,null)}finally{e.isTraining=!1,Li(a,t),Li(s,n),Li(l,i),Li(c,o),u!=null&&_e(u)}}function B6(e){let t=[];e instanceof Pe&&(e=[e]);for(let n=0;n<e.length;++n){let r=e[n];if(r.rank===1)t.push(Dc(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 Li(e,t){if(e==null)return;let n=[];if(t instanceof Pe)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 Pe)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 jre(e){return e instanceof Pe}function kA(e){return Array.isArray(e)}function V6(e){return!jre(e)&&!kA(e)}function j6(e,t,n,r=!0,a=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(kA(e)&&e.length>0)i=!0;else if(V6(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 ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(V6(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new B(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(kA(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=B6(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 Ure(e,t,n){let r=Ka(e.map(s=>s.shape[0]));r.sort();let a=Ka(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&&!_.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 Hre(e,t,n){let r=[zi,e0,Lc];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=n[a];if(i!=null){if(i===Lc&&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 U6(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 Gre(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: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let a of t){let s=n.hasOwnProperty(a)?n[a]:[];Array.isArray(s)||(s=[s]),r.push(s)}return r}}var qre="layers-model",ga=class extends Yr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new B("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");Tre(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=vre(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof fa))throw new B("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new B(`Unknown entry in loss dictionary: "${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(pA(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=>pA(s))}else{let s=pA(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=[],Di("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=Gre(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])};Di("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]===e0?["accuracy","acc"].indexOf(d)!==-1?u=fA:["crossentropy","ce"].indexOf(d)!==-1&&(u=T6):this.lossFunctions[s]===Qp?["accuracy","acc"].indexOf(d)!==-1?u=E6:["crossentropy","ce"].indexOf(d)!==-1&&(u=C6):["accuracy","acc"].indexOf(d)!==-1?u=mA:["crossentropy","ce"].indexOf(d)!==-1&&(u=yA);let m;["accuracy","acc"].indexOf(d)!==-1?m="acc":["crossentropy","ce"].indexOf(d)!==-1&&(m="ce"),h=u,c=l+m}else h=_re(d),c=l+r0(d);let p;Di(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;bA(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 Fn(l)}finally{Li(s[0],e),Li(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),Wre(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 ${r} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new B("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Pi;if(e instanceof Pe&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new B(`The number of inputs provided (${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=Bc(a,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=Mi(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 De("Verbose predictLoop() is not implemented yet.");let a=vA(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=Vc(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 Pi(u);return Bc(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return Fn(s.map(i=>rt(i,0)))})}predict(e,t={}){let n=B6(e);U6(n,this.inputNames,this.feedInputShapes,!1);try{let r=t.batchSize==null?32:t.batchSize;return bA(r),this.predictLoop(n,r)}finally{Li(n,e)}}predictOnBatch(e){U6(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 Er("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]===Qp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=j6(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=j6(t,this.feedOutputNames,a,!1,"target"),Ure(e,t,null),Hre(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: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let c=O6(r,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await z6(o[u],null,c[u]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return z(()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new De("Verbose mode is not implemented yet.");if(a!=null)throw new De("steps mode in testLoop() is not implemented yet");{let o=vA(s,n),l=sn(Cr(0,s));for(let c=0;c<o.length;++c){let u=o[c][0],h=o[c][1],d=Oi(l,u,h-u),p=_A(t,d),f=e(p);if(c===0)for(let m=0;m<f.length;++m)i.push(xe(0));for(let m=0;m<f.length;++m){let y=f[m];i[m]=se(i[m],P(h-u,y))}}for(let c=0;c<i.length;++c)i[c]=me(i[c],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;Hv(e,r)>1&&(a+=`_${Hv(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 Pi(c),h=Bc(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=$re(f,a[p]));let m=It(f);t.push(m),p===0?d=f:d=se(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],y=this.metricsTensors[p][1];f=It(m(r[y],h[y]))}jt(f),s.push(f)}return d=It(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>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 Pi(s),o=Bc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=It(c(a[l],o[l]));l===0?n=u:n=se(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=It(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return Vre(this,e,t,n)}async fitDataset(e,t){return Pre(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 _e(s),Fn(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=Aa(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=>Aa(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]=Aa(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[Aa(r0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Aa(r0(e)));{let e={};for(let t in this.metrics)e[t]=Aa(r0(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=Wc(e.optimizer_config),n=Fr(t),r;if(typeof e.loss=="string")r=Fi(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>Fi(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=Fi(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>Fi(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=Fi(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Nn.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 Nn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:qre,generatedBy:`TensorFlow.js tfjs-layers v${xA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Nn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Nn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;M6(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){M6(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ga.className="Model";re.registerClass(ga);var H6=class extends ga{};H6.className="Functional";re.registerClass(H6);async function Xre(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Wc(n),a=Fr(r,t);if(e.weightsManifest!=null){let s=await Nn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),_e(s)}return a}async function Zre(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Nn.getLoadHandlers(e,t);if(n.length===0)n.push(Nn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Kre(e,void 0,t)}async function Kre(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=Fr(Wc(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}=Yre(r.weightData,r.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),_e(c),_e(u.map(h=>h.tensor))}return o}function Yre(e,t){let n=Nn.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 Kl=class extends ga{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:qp("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 Kl||e instanceof ga,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=g6({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=A6(this.outputs[0])}this.inboundNodes=[],new Zp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Mi(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(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ga({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 Er("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 Er("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 Er("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 Er("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 _.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 Kl))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Fr(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}}};Kl.className="Sequential";re.registerClass(Kl);function Jre(e){return new ga(e)}function Qre(e){return new Kl(e)}function eae(e,t){return t==null&&(t={}),Zre(e,t)}function u6(e){return g6(e)}function tae(e,t){gr.registerCallbackConstructor(e,t)}var jn=class extends re.Serializable{getConfig(){return{}}},G6=class extends jn{apply(e,t=1){return Mte(e,t)}};G6.className="elu";re.registerClass(G6);var q6=class extends jn{apply(e){return Wd(e)}};q6.className="selu";re.registerClass(q6);var X6=class extends jn{apply(e){return Ur(e)}};X6.className="relu";re.registerClass(X6);var K6=class extends jn{apply(e){return z(()=>kl(6,Ur(e)))}};K6.className="relu6";re.registerClass(K6);var Z6=class extends jn{apply(e){return e}};Z6.className="linear";re.registerClass(Z6);var Y6=class extends jn{apply(e){return On(e)}};Y6.className="sigmoid";re.registerClass(Y6);var J6=class extends jn{apply(e){return $te(e)}};J6.className="hardSigmoid";re.registerClass(J6);var Q6=class extends jn{apply(e){return _l(e)}};Q6.className="softplus";re.registerClass(Q6);var e4=class extends jn{apply(e){return Fte(e)}};e4.className="softsign";re.registerClass(e4);var t4=class extends jn{apply(e){return yl(e)}};t4.className="tanh";re.registerClass(t4);var IA=class extends jn{apply(e,t=-1){return uc(e,t)}};IA.className="softmax";re.registerClass(IA);var n4=class extends jn{apply(e,t=-1){return Fd(e,t)}};n4.className="logSoftmax";re.registerClass(n4);var r4=class extends jn{apply(e,t=1){return z(()=>On(e.mul(t)).mul(e))}};r4.className="swish";re.registerClass(r4);function Qa(e){return e.getClassName()}function SA(e,t={}){return Rc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function es(e){if(e==null){let t={};return t.className="linear",t.config={},SA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},SA(t)}else return e instanceof jn?e:SA(e)}function NA(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 a4=class extends re.Serializable{},jc=class extends a4{constructor(e){super();NA(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=Rt([1]);return this.hasL1&&(t=se(t,Te(P(this.l1,Ot(e))))),this.hasL2&&(t=se(t,Te(P(this.l2,Oc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};jc.className="L1L2";re.registerClass(jc);function nae(e){return NA(e),new jc({l1:e!=null?e.l1:null,l2:0})}function rae(e){return NA(e),new jc({l2:e!=null?e.l2:null,l1:0})}var s4={l1l2:"L1L2"};function ut(e){return By(e)}function i4(e,t={}){return Rc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in s4?s4[e]:e,config:{}};return i4(t)}else return e instanceof a4?e:i4(e)}var TA=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=Ur(e);return this.maxValue!=null&&(n=Tn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};TA.className="ReLU";re.registerClass(TA);var EA=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return nc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};EA.className="LeakyReLU";re.registerClass(EA);var CA=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=gt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=xt(e.alphaRegularizer),this.alphaConstraint=Bt(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 ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Ft({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=ze(e),ic(e,this.alpha.read())}getConfig(){let e={alphaInitializer:St(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:Wt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};CA.className="PReLU";re.registerClass(CA);var RA=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return wl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};RA.className="ELU";re.registerClass(RA);var MA=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=ze(e);return n.mul($c(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};MA.className="ThresholdedReLU";re.registerClass(MA);var FA=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new IA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=ze(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};FA.className="Softmax";re.registerClass(FA);function Zl(e,t,n){if(typeof e=="number")return Mi(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!Tte(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 $r(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 Jr(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ya([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function $A(e,t){return z(()=>(Ct(t),t==="channelsFirst"?Je(e,[0,2,3,1]):e))}function o4(e,t){return z(()=>(Ct(t),t==="channelsFirst"?Je(e,[0,2,3,4,1]):e))}function aae(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=Tr()),Ct(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=Je(e,[0,2,1])),a==="causal")throw new De("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=Rr(o,n)),o})}function l4(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=Tr()),Ct(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 ${e.rank}.`);let l=$A(e,s);if(a==="causal")throw new De("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=Je(l,[0,3,1,2])),l})}function sae(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=Tr()),Ct(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 ${e.rank}.`);let o=o4(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=bm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Rr(o,n)),s==="channelsFirst"&&(o=Je(o,[0,4,1,2,3])),o})}var DA=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DA.verifyArgs(t),this.rank=e,Ht(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ar(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Xr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!jy(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:Qa(this.activation),useBias:this.useBias,biasInitializer:St(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Uc=class extends DA{constructor(e,t){super(e,t);this.kernel=null,Uc.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=st(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 ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=ze(e);let n,r=this.bias==null?null:this.bias.read(),a=qv(this.activation.getClassName());if(a!=null&&this.rank===2)n=l4(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=aae(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=l4(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=sae(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=$r(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:St(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Wt(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)}`)}},Hc=class extends Uc{constructor(e){super(2,e);Hc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!jy(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)}.`)}};Hc.className="Conv2D";re.registerClass(Hc);var Gc=class extends Uc{constructor(e){super(3,e);Gc.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)}.`)}};Gc.className="Conv3D";re.registerClass(Gc);var OA=class extends Hc{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=ze(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Jr(o,h,c,this.padding),f=Jr(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Je(n,[0,2,3,1]));let y=Sd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=Je(y,[0,3,1,2])),this.bias!=null&&(y=Rr(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=st(e);let t=e.slice(),n,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]=Jr(t[r],o,s,this.padding),t[a]=Jr(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};OA.className="Conv2DTranspose";re.registerClass(OA);var zA=class extends Gc{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new B("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=ze(e);if(n.shape.length!==5)throw new B(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=r[o],c=r[s],u=r[i],h=this.kernelSize[0],d=this.kernelSize[1],p=this.kernelSize[2],f=this.strides[0],m=this.strides[1],y=this.strides[2],A=Jr(l,f,h,this.padding),g=Jr(c,m,d,this.padding),x=Jr(u,y,p,this.padding),v=[a,A,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Je(n,[0,2,3,4,1]));let w=ib(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Je(w,[0,4,1,2,3])),this.bias!==null&&(w=Rr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=st(e);let t=e.slice(),n,r,a,s;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3,s=4):(n=4,r=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],h=this.strides[2];return t[n]=this.filters,t[r]=Jr(t[r],c,i,this.padding),t[a]=Jr(t[a],u,o,this.padding),t[s]=Jr(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};zA.className="Conv3DTranspose";re.registerClass(zA);var u4=class extends Uc{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${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 Ft({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=ze(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Je(e,[0,2,3,1])),n=Wm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Je(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=St(this.depthwiseInitializer),e.pointwiseInitializer=St(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};u4.className="SeparableConv";var PA=class extends u4{constructor(e){super(2,e)}};PA.className="SeparableConv2D";re.registerClass(PA);var s0=class extends Uc{constructor(e){super(1,e);s0.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"&&!jy(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)}.`)}};s0.className="Conv1D";re.registerClass(s0);var LA=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=zp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return zp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=zp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return zp(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}};LA.className="Cropping2D";re.registerClass(LA);var WA=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Ite(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=ze(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Je(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 Je(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}};WA.className="UpSampling2D";re.registerClass(WA);function iae(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Tr()),Ct(a);let i=$A(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=xl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}var BA=class extends DA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=st(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=ze(e);let n=iae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=$r(t,this.kernelSize[0],this.padding,this.strides[0]),s=$r(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=St(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};BA.className="DepthwiseConv2D";re.registerClass(BA);function c4(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 h4(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(Cr(2,l));if(t=Je(t,c),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Jt(a,-1)),a=Je(a,c)),r&&(t=Wn(t,0),a!=null&&(a=Wn(a,0)));let u=[],h,d=n,p=t.shape[0],f=mr(t),m;a!=null&&(m=mr(a));for(let A=0;A<p;++A){let g=f[A],x=z(()=>e(g,d));if(a==null)h=x[0],d=x[1];else{let v=z(()=>{let w=m[A],b=Ln(w).sub(w),k=x[0].mul(w).add(d[0].mul(b)),N=d.map((E,F)=>x[1][F].mul(w).add(E.mul(b)));return{output:k,newStates:N}});h=v.output,d=v.newStates}o&&u.push(h)}let y;return o&&(y=cn(u,1)),[h,y,d]})}var Zr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new i0({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 Ft({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 Cr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){uA(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 De("Constants support is not implemented in RNN yet.");uA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Ft({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!_.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new 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 Ft({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ya("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=>Rt([n,r])):this.states_=[Rt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_[0]=Rt([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()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!_.arraysEqual(a.shape,i))throw new 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=>jt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=c4(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 Ft({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 Mr){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=ze(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new 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=h4((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=Rt(e.shape);return t=Te(t,[1,2]),t=Dc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ky(t,[1,n]):t):this.cell.stateSize>1?[Ky(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()===Zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Fr(r,n);return new e(Object.assign(t,{cell:a}))}};Zr.className="RNN";re.registerClass(Zr);var Pc=class extends qe{},o0=class extends Pc{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,Ht(this.units,"units"),this.activation=es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return 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=ts({ones:()=>Ln(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Kr(P(e,s),this.kernel.read()):a=Kr(e,this.kernel.read()),this.bias!=null&&(a=Rr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Kr(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:Qa(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};o0.className="SimpleRNNCell";re.registerClass(o0);var VA=class extends Zr{constructor(e){e.cell=new o0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(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)}};VA.className="SimpleRNN";re.registerClass(VA);var l0=class extends Pc{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,Ht(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return 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=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(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=Kr(e,this.kernel.read());this.useBias&&(c=Rr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Pt(u,[2*this.units,this.units],u.rank-1),p=Kr(r,h),[f,m,y]=Pt(c,3,c.rank-1),[A,g]=Pt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,A)),o=this.recurrentActivation.apply(se(m,g));let x=Kr(P(o,r),d);l=this.activation.apply(se(y,x));let v=se(P(i,r),P(se(1,kt(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),recurrentActivation:Qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};l0.className="GRUCell";re.registerClass(l0);var jA=class extends Zr{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 l0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(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)}};jA.className="GRU";re.registerClass(jA);var qc=class extends Pc{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,Ht(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends Ar{apply(i,o){let l=a.apply([s]),c=new Lp().apply([s]),u=a.apply([s*2]);return r6(r6(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=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(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=Kr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=se(h,Kr(r,this.recurrentKernel.read())),this.useBias&&(h=Rr(h,this.bias.read()));let[d,p,f,m]=Pt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(P(l,a),P(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let y=P(u,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),recurrentActivation:Qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};qc.className="LSTMCell";re.registerClass(qc);var UA=class extends Zr{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 qc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(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)}};UA.className="LSTM";re.registerClass(UA);var i0=class extends Pc{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){uA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Di(`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(Fr(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 cA(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]])}hA(t)}};i0.className="StackedRNNCells";re.registerClass(i0);function ts(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>s6(t(),n),i=()=>zc(s,t,r);return!a||a<=1?jt(i().clone()):Array(a).fill(void 0).map(i).map(o=>jt(o.clone()))}var oae=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},d4=class extends Zr{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ft({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(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=Rt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ya("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(()=>Rt(a)):this.states_=[Rt(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_[0]=Rt(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()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!_.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=$r(l,r[0],a,s[0],i[0]),h=$r(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};d4.className="ConvRNN2D";var u0=class extends qc{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,Ht(this.filters,"filters"),this.kernelSize=Zl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ht(o,"kernelSize")),this.strides=Zl(r||1,2,"strides"),this.strides.forEach(o=>Ht(o,"strides")),this.padding=a||"valid",ar(this.padding),this.dataFormat=s||"channelsLast",Ct(this.dataFormat),this.dilationRate=Zl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ht(o,"dilationRate"))}build(e){var t;e=st(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 Ar{apply(u,h){let d=l.apply([c]),p=Pn([c]),f=l.apply([c*2]);return Yy([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=ts({ones:()=>Ln(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),y=l(a,p,2),A=l(a,p,3),g=3,[x,v,w,b]=Pt(this.kernel.read(),i,g),[k,N,E,F]=this.useBias?Pt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,k,this.padding),u=this.inputConv(u,v,N,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,b,F,this.padding);let[O,L,V,j]=Pt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,O),m=this.recurrentConv(m,L),y=this.recurrentConv(y,V),A=this.recurrentConv(A,j);let U=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(P(X,s),P(U,this.activation.apply(se(h,y)))),ee=P(this.recurrentActivation.apply(se(d,A)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=oae(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=ca(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Rr(a,n,this.dataFormat):a}recurrentConv(e,t){return ca(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};u0.className="ConvLSTM2DCell";re.registerClass(u0);var HA=class extends d4{constructor(e){let t=new u0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};HA.className="ConvLSTM2D";re.registerClass(HA);var c0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let 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=ze(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return zc(()=>s6(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()}};c0.className="Dropout";re.registerClass(c0);var GA=class extends c0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};GA.className="SpatialDropout1D";re.registerClass(GA);var qA=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e),r=qv(this.activation.getClassName()),a;return r!=null?a=Kr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Kr(n,this.kernel.read()),this.bias!=null&&(a=Rr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Qa(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Dense";re.registerClass(qA);var XA=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new 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],Za(e,1)]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return Rte(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};XA.className="Flatten";re.registerClass(XA);var KA=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=es(e.activation)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.activation.apply(n)})}getConfig(){let e={activation:Qa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Activation";re.registerClass(KA);var ZA=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return z(()=>(e=ze(e),Ete(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};ZA.className="RepeatVector";re.registerClass(ZA);var YA=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",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=Za(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=ze(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};YA.className="Reshape";re.registerClass(YA);var JA=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Cr(1,e.dims.length+1);if(!_.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ft({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Je(ze(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};JA.className="Permute";re.registerClass(JA);var QA=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=ze(e),r=-1;return Zu(bi(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e),r=-1,a=!0,s=Zu(bi(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};QA.className="Masking";re.registerClass(QA);var eg=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,Ht(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ht(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=Bt(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=ze(e),bi(e,He(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(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 ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e);return n.dtype!=="int32"&&(n=$c(n,"int32")),a6(this.embeddings.read(),n.as1D()).reshape(st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:St(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Wt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};eg.className="Embedding";re.registerClass(eg);var Wi=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new 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=[st(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=Ka(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ka(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return z(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Ya(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Dc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Za(c.slice(1))));d=Je(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=Cr(1,l).concat([0]);n.push(Je(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Je(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Cr(0,i-1));s=Je(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ka(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return z(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Jt(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=fr(n,t[r]);return n})}},tg=class extends Wi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};tg.className="Add";re.registerClass(tg);var ng=class extends Wi{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})}};ng.className="Multiply";re.registerClass(ng);var rg=class extends Wi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return P(1/e.length,t)})}};rg.className="Average";re.registerClass(rg);var ag=class extends Wi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=jr(t,e[n]);return t})}};ag.className="Maximum";re.registerClass(ag);var sg=class extends Wi{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=kl(t,e[n]);return t})}};sg.className="Minimum";re.registerClass(sg);var ig=class extends Wi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(_.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return z(()=>Yy(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(Ln(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Jt(t[s],-1)):r.push(t[s]);let a=rt(r,this.axis);return vd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ig.className="Concatenate";re.registerClass(ig);function Xc(e,t){for(;e<0;)e+=t;return e}function lae(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(_.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),_.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return 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 og=class extends Wi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){_.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new 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)=>Xc(a,e[s].shape.length)):r=[Xc(this.axes,t.shape.length),Xc(this.axes,n.shape.length)],this.normalize&&(t=Yp(t,r[0]),n=Yp(n,r[1])),lae(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Xc(this.axes,e.length),Xc(this.axes,t.length)],n}computeOutputShape(e){_.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};og.className="Dot";re.registerClass(og);var lg=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e);return zc(()=>Pp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};lg.className="GaussianNoise";re.registerClass(lg);var ug=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?zc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Pp(n.shape,1,r))},()=>n,t.training||!1):n})}};ug.className="GaussianDropout";re.registerClass(ug);var cg=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return zc(()=>{let r=ze(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Va(Il(n),this.rate);o=$c(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>ze(e),t.training||!1)}return e})}};cg.className="AlphaDropout";re.registerClass(cg);function Kc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=Jw(e,t,n,r,a,s);else if(e.rank===3)i=Qw(e,t,n,r,a,s);else if(e.rank===4)i=eb(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function uae(e,t,n,r,a=.001){return z(()=>{let s=Dd(e,r),i=s.mean,o=s.variance;return[Kc(e,i,o,n,t,a),i,o]})}function cae(e,t,n,r,a=.001){return z(()=>{let s=Dd(e,r),i=s.mean,o=s.variance,l=[];for(let p of Cr(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[Kc(e,c,u,d,h,a),i,o]})}function hae(e,t,n,r,a=.001){return _.arraysEqual(r.slice().sort(),Cr(0,e.rank-1))?uae(e,t,n,r,a):cae(e,t,n,r,a)}var hg=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Bt(e.betaConstraint),this.gammaConstraint=Bt(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new 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 Ft({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=ze(e),a=r.shape,s=a.length,i=Cr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Mi(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!_.arraysEqual(c,Cr(0,s).slice(0,s-1)),h=()=>{if(u){let y=this.movingMean.read().reshape(l),A=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return Kc(r,y,A,g,x,this.epsilon)}else return Kc(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]=hae(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(y,A,g)=>{z(()=>{let x=1-g,v=y.read(),w=v.sub(A).mul(x);y.write(v.sub(w))})};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:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),movingMeanInitializer:St(this.movingMeanInitializer),movingVarianceInitializer:St(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Wt(this.betaConstraint),gammaConstraint:Wt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};hg.className="BatchNormalization";re.registerClass(hg);var dg=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(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!==Ka(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=ze(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=Dd(n,this.axis,s),l=Mi(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),Kc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};dg.className="LayerNormalization";re.registerClass(dg);function dae(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=Tr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],ha(e,r)})}var pg=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Tr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 Ft({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return z(()=>dae(ze(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};pg.className="ZeroPadding2D";re.registerClass(pg);function h0(e,t,n,r,a,s){return z(()=>{Ct(a),Yv(s),ar(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=Tr()),s==null&&(s="max"),e=$A(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=ac(e,t,n,o):i=Ju(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}function p4(e,t,n,r,a,s){return z(()=>{Ct(a),Yv(s),ar(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Tr()),s==null&&(s="max"),e=o4(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Fm(e,t,n,o):i=gm(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,4,1,2,3])),i})}var f4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ht(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 ${JSON.stringify(e.strides)}`);Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ar(this.padding),this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){e=st(e);let t=$r(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=Dc(ze(e),2);let n=this.poolingFunction(ze(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ja(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},fg=class extends f4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),h0(e,t,n,r,a,"max")}};fg.className="MaxPooling1D";re.registerClass(fg);var mg=class extends f4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),h0(e,t,n,r,a,"avg")}};mg.className="AveragePooling1D";re.registerClass(mg);var m4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),ar(this.padding),this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=$r(t,this.poolSize[0],this.padding,this.strides[0]),n=$r(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(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},yg=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),h0(e,t,n,r,a,"max")}};yg.className="MaxPooling2D";re.registerClass(yg);var Ag=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),h0(e,t,n,r,a,"avg")}};Ag.className="AveragePooling2D";re.registerClass(Ag);var y4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),ar(this.padding),this.inputSpec=[new Ft({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=$r(t,this.poolSize[0],this.padding,this.strides[0]),n=$r(n,this.poolSize[1],this.padding,this.strides[1]),r=$r(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(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},gg=class extends y4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),p4(e,t,n,r,a,"max")}};gg.className="MaxPooling3D";re.registerClass(gg);var xg=class extends y4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),p4(e,t,n,r,a,"avg")}};xg.className="AveragePooling3D";re.registerClass(xg);var A4=class extends qe{constructor(e){super(e);this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},wg=class extends A4{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=ze(e);return It(n,1)})}};wg.className="GlobalAveragePooling1D";re.registerClass(wg);var bg=class extends A4{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=ze(e);return Cn(n,1)})}};bg.className="GlobalMaxPooling1D";re.registerClass(bg);var g4=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_g=class extends g4{call(e,t){return z(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};_g.className="GlobalAveragePooling2D";re.registerClass(_g);var vg=class extends g4{call(e,t){return z(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?Cn(n,[1,2]):Cn(n,[2,3])})}};vg.className="GlobalMaxPooling2D";re.registerClass(vg);var x4=class extends qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=Fr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},kg=class extends x4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=ze(e),h4((n,r)=>[ze(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};kg.className="TimeDistributed";re.registerClass(kg);function pae(e){$i(kte,"BidirectionalMergeMode",e)}var fae="concat",Ig=class extends x4{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Fr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Fr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?fae:e.mergeMode,pae(this.mergeMode),e.weights)throw new De("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Fn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=c4(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 Ft({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(r!=null)throw new De("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Mr;for(let l of s)if(l instanceof Mr!==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=Wn(a,1));let i;return this.mergeMode==="concat"?i=Yy([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=P(.5,se(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){Di(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Di(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=Fr(t.layer);if(delete t.layer,t.numConstants!=null)throw new De("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};Ig.className="Bidirectional";re.registerClass(Ig);function Wte(e){return new Gl(e)}function Bte(e){return new RA(e)}function Vte(e){return new TA(e)}function jte(e){return new EA(e)}function Ute(e){return new CA(e)}function Hte(e){return new FA(e)}function Gte(e){return new MA(e)}function qte(e){return new s0(e)}function Xte(e){return new Hc(e)}function Kte(e){return new OA(e)}function Zte(e){return new Gc(e)}function Yte(e){return new zA(e)}function Jte(e){return new PA(e)}function Qte(e){return new LA(e)}function ene(e){return new WA(e)}function tne(e){return new BA(e)}function nne(e){return new KA(e)}function rne(e){return new qA(e)}function ane(e){return new c0(e)}function sne(e){return new GA(e)}function ine(e){return new XA(e)}function one(e){return new ZA(e)}function lne(e){return new YA(e)}function une(e){return new JA(e)}function cne(e){return new eg(e)}function hne(e){return new tg(e)}function dne(e){return new rg(e)}function pne(e){return new ig(e)}function fne(e){return new ag(e)}function mne(e){return new sg(e)}function yne(e){return new ng(e)}function Ane(e){return new og(e)}function gne(e){return new hg(e)}function xne(e){return new dg(e)}function wne(e){return new pg(e)}function iA(e){return new mg(e)}function bne(e){return iA(e)}function _ne(e){return iA(e)}function oA(e){return new Ag(e)}function vne(e){return oA(e)}function kne(e){return oA(e)}function lA(e){return new xg(e)}function Ine(e){return lA(e)}function Sne(e){return lA(e)}function Nne(e){return new wg(e)}function Tne(e){return new _g(e)}function c6(e){return new bg(e)}function h6(e){return new vg(e)}function d6(e){return new fg(e)}function p6(e){return new yg(e)}function Ene(e){return new gg(e)}function Cne(e){return new jA(e)}function Rne(e){return new l0(e)}function Mne(e){return new UA(e)}function Fne(e){return new qc(e)}function $ne(e){return new VA(e)}function Dne(e){return new o0(e)}function One(e){return new HA(e)}function zne(e){return new u0(e)}function Pne(e){return new Zr(e)}function Lne(e){return new i0(e)}function Wne(e){return new Ig(e)}function Bne(e){return new kg(e)}var Vne=c6,jne=h6,Une=d6,Hne=p6;function Gne(e){return new lg(e)}function qne(e){return new ug(e)}function Xne(e){return new cg(e)}function Kne(e){return new QA(e)}var w4={};Me(w4,{MAPE:()=>Iae,MSE:()=>Tae,binaryAccuracy:()=>mae,binaryCrossentropy:()=>yae,categoricalAccuracy:()=>gae,categoricalCrossentropy:()=>xae,cosineProximity:()=>_ae,mape:()=>Sae,meanAbsoluteError:()=>vae,meanAbsolutePercentageError:()=>kae,meanSquaredError:()=>Nae,mse:()=>Eae,precision:()=>wae,recall:()=>bae,sparseCategoricalAccuracy:()=>Aae});function mae(e,t){return fA(e,t)}function yae(e,t){return T6(e,t)}function Aae(e,t){return E6(e,t)}function gae(e,t){return mA(e,t)}function xae(e,t){return yA(e,t)}function wae(e,t){return N6(e,t)}function bae(e,t){return fre(e,t)}function _ae(e,t){return dA(e,t)}function vae(e,t){return Jp(e,t)}function kae(e,t){return Xl(e,t)}function Iae(e,t){return Xl(e,t)}function Sae(e,t){return Xl(e,t)}function Nae(e,t){return zi(e,t)}function Tae(e,t){return zi(e,t)}function Eae(e,t){return zi(e,t)}var b4={};Me(b4,{modelFromJSON:()=>Xre});var _4={};Me(_4,{l1:()=>Rae,l1l2:()=>Cae,l2:()=>Mae});function Cae(e){return new jc(e)}function Rae(e){return nae(e)}function Mae(e){return rae(e)}var v4=class extends ql{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof ga))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function d0(e,t){return e<t}function k4(e,t){return e>t}var I4=class extends v4{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new De("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=d0:this.mode==="max"?this.monitorFunc=k4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=k4:this.monitorFunc=d0,this.monitorFunc===d0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===d0?Infinity:-Infinity}async onEpochEnd(e,t){await Ja(t);let n=this.getMonitorValue(t);n!=null&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=e,this.model.stopTraining=!0)))}async onTrainEnd(e){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(e){e==null&&(e={});let t=e[this.monitor];return t==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`),t}};function Fae(e){return new I4(e)}var $ae={earlyStopping:Fae},Dr;(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"})(Dr||(Dr={}));var S4;(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={}))})(S4||(S4={}));var Sg={};function Dae(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Sg[e]=n}function N4(e){return Sg[e]}function Oae(e){delete Sg[e]}function I(e,t,n,r,a){let s=t.inputParams[e];if(s&&s.inputIndexStart!==void 0){let o=s.inputIndexStart,l=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?o+1:s.inputIndexEnd;if(s.type==="tensor")return An(t.inputNames[s.inputIndexStart],n,r,a);if(s.type==="tensors")return t.inputNames.slice(o,l).map(h=>An(h,n,r,a));let c=An(t.inputNames.slice(o)[0],n,r,a),u=c.dataSync();return s.type==="number"?u[0]:_.toNestedArray(c.shape,u)}let i=t.attrParams[e];return i&&i.value}function An(e,t,n,r){let[a,s]=Un(e);if(r!=null){let o=r.getHashTableHandleByName(a);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[p0(a,o)]);return i!==void 0?t[p0(a,i)][s]:void 0}function zae(e,t,n){return t[p0(e,n.currentContextId)]}function xa(e,t){let[n,r]=Un(e);return[p0(n,t&&t.currentContextId),r]}function p0(e,t){return t?`${e}-${t}`:e}function Un(e){let t=e.split(":");return t.length===1?[e,0]:[t[0],Number(t[t.length-1])]}function f0(e,t,n){let r=I("pad",e,t,n);if(r==="explicit"){r=I("explicitPaddings",e,t,n);let a=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)a[s][0]=r[s*2],a[s][1]=r[s*2+1];return a}return r}function wa(e){return e.kept?e:Wr(e)}var T4={};Me(T4,{json:()=>Pae});var Pae=[{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}]}],E4={};Me(E4,{json:()=>Lae});var Lae=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"IsNan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],C4={};Me(C4,{json:()=>Wae});var Wae=[{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"}]}],R4={};Me(R4,{json:()=>Bae});var Bae=[{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"}]}],M4={};Me(M4,{json:()=>Vae});var Vae=[{tfOpName:"Fill",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"},{start:1,name:"value",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"LinSpace",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"num",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"OneHot",category:"creation",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"depth",type:"number"},{start:2,name:"onValue",type:"number",defaultValue:1},{start:3,name:"offValue",type:"number",defaultValue:0}],attrs:[{tfName:"axis",name:"axis",type:"number",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ones",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"OnesLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"RandomUniform",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number",defaultValue:0},{tfName:"maxval",name:"maxval",type:"number",defaultValue:1},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Range",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"step",type:"number",defaultValue:0}],attrs:[{tfName:"Tidx",name:"dtype",type:"dtype"}]},{tfOpName:"TruncatedNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"means",name:"mean",type:"number",defaultValue:0},{tfName:"stddev",name:"stdDev",type:"number",defaultValue:1},{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Zeros",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"ZerosLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"Multinomial",category:"creation",inputs:[{start:0,name:"logits",type:"tensor"},{start:1,name:"numSamples",type:"number"}],attrs:[{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number"},{tfName:"T",name:"dtype",type:"dtype"},{tfName:"output_dtype",name:"output_dtype",type:"dtype"}]}],F4={};Me(F4,{json:()=>jae});var jae=[{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}]}],$4={};Me($4,{json:()=>Uae});var Uae=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}],D4={};Me(D4,{json:()=>Hae});var Hae=[{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"}]}],O4={};Me(O4,{json:()=>Gae});var Gae=[{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"}]}],z4={};Me(z4,{json:()=>qae});var qae=[{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"}]}],P4={};Me(P4,{json:()=>Xae});var Xae=[{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}]}],L4={};Me(L4,{json:()=>Kae});var Kae=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Einsum",category:"matrices",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"equation",name:"equation",type:"string"},{tfName:"N",name:"n",type:"number",defaultValue:2},{tfName:"T",name:"dtype",type:"dtype"}]}],W4={};Me(W4,{json:()=>Zae});var Zae=[{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}]}],B4={};Me(B4,{json:()=>Yae});var Yae=[{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"}]}],V4={};Me(V4,{json:()=>Jae});var Jae=[{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}]}],j4={};Me(j4,{json:()=>Qae});var Qae=[{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}]}],U4={};Me(U4,{json:()=>ese});var ese=[{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:[]}],G4=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[T4,E4,C4,R4,M4,F4,$4,P4,z4,D4,L4,W4,B4,V4,j4,U4,O4],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(y=>{let[A]=xa(y);m.inputs.push(i[A]),i[A].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]=xa(f),y=i[m];y!=null&&(y.signatureKey=u[f],l.push(y))}),Object.keys(c).length>0?Object.keys(c).forEach(f=>{let[m]=xa(f),y=i[m];y&&(y.signatureKey=c[f],o.push(y))}):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=N4(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=Ng(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Ng(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"string[]":i=Dg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Dg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number":i=Eg(e.attr,a.tfName,a.defaultValue||0),i===void 0&&!!a.tfDeprecatedName&&(i=Eg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number[]":i=$g(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=$g(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool":i=Tg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Tg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool[]":i=zg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=zg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape":i=Fg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Fg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape[]":i=Og(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Og(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype":i=Rg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Rg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype[]":i=Mg(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Mg(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"func":i=H4(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=H4(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]=xa(c.name),h={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Cg(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]=xa(h);u.inputs.push(a[d]),a[d].children.push(u)})});let o=e.ret;e.signature.outputArg.forEach(c=>{let[u,h]=xa(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 tse(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 q4(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):tse(e);return t?n:n.toLowerCase()}function Ng(e,t,n,r=!1){let a=e[t];return a!=null?q4(a.s,r):n}function Tg(e,t,n){let r=e[t];return r?r.b:n}function Eg(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 Cg(e){switch(typeof e=="string"&&(e=Dr[e]),e){case Dr.DT_FLOAT:return"float32";case Dr.DT_INT32:case Dr.DT_INT64:case Dr.DT_INT8:case Dr.DT_UINT8:return"int32";case Dr.DT_BOOL:return"bool";case Dr.DT_DOUBLE:return"float32";case Dr.DT_STRING:return"string";default:return null}}function H4(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function Rg(e,t,n){let r=e[t];return r&&r.type?Cg(r.type):n}function Mg(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>Cg(a)):n}function X4(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function Fg(e,t,n){let r=e[t];return r&&r.shape?X4(r.shape):n}function $g(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 Dg(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>q4(s,r)):n}function Og(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>X4(a)):n}function zg(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var nse=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 An(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return An(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Eg(this.node.rawAttrs,e,t);if(n.s!=null)return Ng(this.node.rawAttrs,e,t);if(n.b!=null)return Tg(this.node.rawAttrs,e,t);if(n.shape!=null)return Fg(this.node.rawAttrs,e,t);if(n.type!=null)return Rg(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return $g(this.node.rawAttrs,e,t);if(n.list.s!=null)return Dg(this.node.rawAttrs,e,t);if(n.list.shape!=null)return Og(this.node.rawAttrs,e,t);if(n.list.b!=null)return zg(this.node.rawAttrs,e,t);if(n.list.type!=null)return Mg(this.node.rawAttrs,e,t)}return t}},rse=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[se(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[Pa(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[Dm(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[P(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[me(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[km(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[_d(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[ye(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[kl(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[jr(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[da(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[Gd(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ase=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Ot(I("x",e,t,n))];case"Acos":return[lm(I("x",e,t,n))];case"Acosh":return[um(I("x",e,t,n))];case"Asin":return[hm(I("x",e,t,n))];case"Asinh":return[dm(I("x",e,t,n))];case"Atan":return[pm(I("x",e,t,n))];case"Atan2":return[fm(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[mm(I("x",e,t,n))];case"Ceil":return[xm(I("x",e,t,n))];case"Complex":return[$a(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[ec(I("x",e,t,n))];case"Cosh":return[Nd(I("x",e,t,n))];case"Elu":return[wl(I("x",e,t,n))];case"Erf":return[Im(I("x",e,t,n))];case"Exp":return[er(I("x",e,t,n))];case"Expm1":return[Sm(I("x",e,t,n))];case"Floor":return[bl(I("x",e,t,n))];case"Log":return[zn(I("x",e,t,n))];case"Log1p":return[Rd(I("x",e,t,n))];case"Imag":return[Ed(I("x",e,t,n))];case"Neg":return[kt(I("x",e,t,n))];case"Reciprocal":return[Pm(I("x",e,t,n))];case"Real":return[oc(I("x",e,t,n))];case"Relu":return[Ur(I("x",e,t,n))];case"Round":return[Lm(I("x",e,t,n))];case"Selu":return[Wd(I("x",e,t,n))];case"Sigmoid":return[On(I("x",e,t,n))];case"Sin":return[Bd(I("x",e,t,n))];case"Sign":return[Bm(I("x",e,t,n))];case"Sinh":return[Vd(I("x",e,t,n))];case"Softplus":return[_l(I("x",e,t,n))];case"Sqrt":return[Qt(I("x",e,t,n))];case"Square":return[ot(I("x",e,t,n))];case"Tanh":return[yl(I("x",e,t,n))];case"Tan":return[Um(I("x",e,t,n))];case"ClipByValue":return[Tn(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Pd(I("x",e,t,n))];case"Rsqrt":return[Ld(An(e.inputNames[0],t,n))];case"Prod":return[Od(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[nc(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[ic(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[Tm(An(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function xr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){_.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let r=0;r<e.length;r++){let a=e[r],s=t[r];_.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function K4(e){return!(typeof e=="number"||e.some(t=>t<0))}function Zc(e,t,n){let r=Pg(e,n),a=!K4(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=Pg(s.shape,r)}),!K4(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function Pg(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 sse=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=xe(0),jt(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),xr(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,jt(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 Ir([],[0].concat(this.elementShape));let n=this.readMany(e);return xr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),cn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Ir([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return xr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),rt(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,mr(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(Ce(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},Yc=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}`);xr(t,a.shape,"TensorList shape mismatch: "),jt(a)}),this.idTensor=xe(0),this.maxNumElements=r,jt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Yc([...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.`);xr(e,this.elementShape,"TensorList shape mismatch: ");let r=Zc(this.elementShape,this.tensors,e);return z(()=>{let a=this.tensors.map(s=>H(s,r));return cn(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=Zc(this.elementShape,this.tensors,e),r=this.tensors.pop();return xr(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(xr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");jt(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.`);xr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=Zc(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.`);xr(this.elementShape,t.shape,"TensorList shape mismatch: "),jt(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}`);xr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=Zc(this.elementShape,this.tensors,n);return e.length===0?Ir([],[0].concat(r)):z(()=>{let a=e.map(s=>H(this.tensors[s],r));return cn(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);xr(this.elementShape,t,"TensorList shape mismatch: ");let n=Zc(this.elementShape,this.tensors,t);return this.size()===0?Ir([],[0].concat(n)):z(()=>{let r=this.tensors.map(a=>H(a,n));return rt(r,0)})}};function ise(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);xr(a,t,"TensorList shape mismatch: ");let s=mr(e);return new Yc(s,t,r)}function ose(e,t,n){return new Yc([],e,t,n)}function lse(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 Yc([],n,e.dtype,r),i=mr(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function use(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=Pg(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(Ce(e,p,f),i)}return e.dispose(),u}),c=new Yc([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var cse=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=I("thenBranch",e,t,n),a=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=I("body",e,t,n),a=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let h=c.map(p=>p.id);u.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()});let d=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()})}return c}case"LoopCond":{let r=I("pred",e,t,n);return[wa(r)]}case"Switch":{let r=I("pred",e,t,n),a=I("data",e,t,n);return a.kept||(a=wa(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>An(a,t,n)!==void 0);if(r){let a=An(r,t,n);return[wa(a)]}return}case"Enter":{let r=I("frameName",e,t,n),a=I("tensor",e,t,n);return n.enterFrame(r),[wa(a)]}case"Exit":{let r=I("tensor",e,t,n);return n.exitFrame(),[wa(r)]}case"NextIteration":{let r=I("tensor",e,t,n);return n.nextIteration(),[wa(r)]}case"TensorArrayV3":{let r=I("size",e,t,n),a=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new sse(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,xe(1)]}case"TensorArrayWriteV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=I("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=I("tensorArrayId",e,t,n),a=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[xe(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=I("indices",e,t,n),a=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=lse(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=ose(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),a=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=ise(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),a=n.getTensorList(r.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),a=I("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=use(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Z4(e,t,n){let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",e,t,n),u=f0(e,t,n),h=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[p,f]=I("args",e,t,n),m=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:h,dilations:d,biasArg:p,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var hse=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=I("stride",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Id(I("x",e,t,n),I("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=I("strides",e,t,n),a=f0(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[ca(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=Z4(e,t,n);return[Ua.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=Z4(e,t,n);return[Ua.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=I("outputShape",e,t,n),a=I("strides",e,t,n),s=f0(e,t,n);return[Sd(I("x",e,t,n),I("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=I("strides",e,t,n),a=f0(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[xl(I("input",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[bm(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Ju(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[ac(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=xb(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[gm(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Fm(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dilations",e,t,n),i=r[1],o=r[2],l=s[1],c=s[2];return[vm(I("x",e,t,n),I("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},dse=(e,t,n)=>{switch(e.op){case"Fill":{let r=I("shape",e,t,n),a=I("dtype",e,t,n),s=I("value",e,t,n);return[tc(r,s,a)]}case"LinSpace":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("num",e,t,n);return[db(r,a,s)]}case"Multinomial":{let r=I("logits",e,t,n),a=I("numSamples",e,t,n),s=I("seed",e,t,n);return[wb(r,a,s)]}case"OneHot":{let r=I("indices",e,t,n),a=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[dl(r,a,s,i)]}case"Ones":return[Pn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Ln(I("x",e,t,n))];case"RandomUniform":return[Il(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("step",e,t,n);return[zd(r,a,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let r=I("shape",e,t,n),a=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[qd(r,a,s,I("dtype",e,t,n),i)]}case"Zeros":return[Rt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[He(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Lg(e,t,n){let r=I("boxes",e,t,n),a=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var pse=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=Lg(e,t,n),c=await Le.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}=Lg(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Le.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}=Lg(e,t,n);return[await Le.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=ge(I("condition",e,t,n),"bool"),a=[await qm(r)];return r.dispose(),a}case"ListDiff":return vb(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},fse=(e,t,n)=>{switch(e.op){case"TopKV2":{let r=I("x",e,t,n),a=I("k",e,t,n),s=I("sorted",e,t,n),i=Hm(r,a,s);return[i.values,i.indices]}case"Unique":{let r=I("x",e,t,n),a=Xd(r);return[a.values,a.indices]}case"UniqueV2":{let r=I("x",e,t,n),a=I("axis",e,t,n),s=Xd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},mse=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=I("default",e,t,n);return[An(e.name,t,n)||r];case"Placeholder":return[An(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",e,t,n);return[wa(c)]}case"IdentityN":return I("x",e,t,n).map(c=>wa(c));case"Snapshot":let a=I("x",e,t,n);return[wa(a)];case"Shape":return[sn(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(c=>sn(c.shape));case"Size":return[xe(I("x",e,t,n).size,"int32")];case"Rank":return[xe(I("x",e,t,n).rank,"int32")];case"NoOp":return[xe(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yse=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=xe(0),this.tensorMap=new Map,jt(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 xe(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=mr(t),a=n.length,s=r.length;_.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=n[i],l=r[i];jt(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 cn(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}`)}},Ase=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new yse(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=I("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},gse=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Le.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Le.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=I("image",e,t,n),a=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[Le.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},xse=(e,t,n)=>{switch(e.op){case"Equal":return[Wa(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[bi(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[pr(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[Va(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Cd(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[xi(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[fr(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[rc(I("a",e,t,n))];case"LogicalOr":return[$d(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[En(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},wse=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ve(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[ub(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Je(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,u]=I("args",e,t,n);return[Ua.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:c,activation:a,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bse=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ai(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[Ai(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[Em(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[uc(I("x",e,t,n))];case"LogSoftmax":return[Fd(I("x",e,t,n))];case"SparseToDense":return[Xm(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_se=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Cn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[It(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[vl(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Te(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[vd(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Zu(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[mi(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[cm(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Od(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[Td(I("x",e,t,n),i,o,l)]}case"Bincount":let r=I("x",e,t,n),a=I("weights",e,t,n),s=I("size",e,t,n);return[tb(r,a,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),l=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[ob(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},vse=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=I("n",e,t,n),a=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,r),[rt(s,a)]}case"Gather":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[gi(r,ge(a,"int32"),0)]}case"GatherV2":{let r=I("axis",e,t,n),a=I("batchDims",e,t,n),s=I("x",e,t,n),i=I("indices",e,t,n);return[gi(s,ge(i,"int32"),r,a)]}case"Reverse":{let r=I("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let s=I("x",e,t,n);return[Wn(s,a)]}case"ReverseV2":{let r=I("axis",e,t,n),a=I("x",e,t,n);return[Wn(a,r)]}case"Slice":{let r=I("begin",e,t,n),a=I("size",e,t,n);return[Ce(I("x",e,t,n),r,a)]}case"StridedSlice":{let r=I("begin",e,t,n),a=I("end",e,t,n),s=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),c=I("newAxisMask",e,t,n),u=I("shrinkAxisMask",e,t,n),h=I("x",e,t,n);return[jm(h,r,a,s,i,o,l,c,u)]}case"Pack":return z(()=>{let r=I("axis",e,t,n),a=I("tensors",e,t,n),s=a[0].shape,i=ja(a[0]).shape,o=a.map(l=>{let c=_.arraysEqual(l.shape,s);if(!c&&!_.arraysEqual(ja(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:H(l,s)});return[cn(o,r)]});case"Unpack":{let r=I("axis",e,t,n),a=I("tensor",e,t,n);return mr(a,r)}case"Tile":{let r=I("reps",e,t,n);return[Ba(I("x",e,t,n),r)]}case"Split":case"SplitV":{let r=I("axis",e,t,n),a=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return Pt(s,a,r)}case"ScatterNd":{let r=I("indices",e,t,n),a=I("values",e,t,n),s=I("shape",e,t,n);return[Nb(r,a,s)]}case"GatherNd":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[Tb(r,a)]}case"SparseToDense":{let r=I("sparseIndices",e,t,n),a=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[Xm(r,s,a,s.dtype===i.dtype?i:ge(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},kse=(e,t,n)=>{switch(e.op){case"FFT":return[cc(I("x",e,t,n))];case"IFFT":return[Sl(I("x",e,t,n))];case"RFFT":return[hc(I("x",e,t,n))];case"IRFFT":return[Hd(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ise=(e,t,n)=>{switch(e.op){case"Cast":return[ge(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[Jt(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[ja(I("x",e,t,n),r)]}case"Reshape":return[H(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[$m(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ha(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let r=I("blockShape",e,t,n),a=I("paddings",e,t,n);return[sc(I("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=I("blockShape",e,t,n),a=I("crops",e,t,n);return[Qu(I("x",e,t,n),r,a)]}case"DepthToSpace":{let r=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[_m(I("x",e,t,n),r,a)]}case"BroadcastTo":return[Al(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Y4(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return z(()=>rse(s,i,o));case"basic_math":return z(()=>ase(s,i,o));case"control":return cse(s,i,o);case"convolution":return z(()=>hse(s,i,o));case"creation":return z(()=>dse(s,i,o));case"dynamic":return pse(s,i,o);case"evaluation":return z(()=>fse(s,i,o));case"image":return z(()=>gse(s,i,o));case"graph":return z(()=>mse(s,i,o));case"logical":return z(()=>xse(s,i,o));case"matrices":return z(()=>wse(s,i,o));case"normalization":return z(()=>bse(s,i,o));case"reduction":return z(()=>_se(s,i,o));case"slice_join":return z(()=>vse(s,i,o));case"spectral":return z(()=>kse(s,i,o));case"transformation":return z(()=>Ise(s,i,o));case"hash_table":return Ase(s,i,o,r);case"custom":let l=N4(s.op);if(l&&l.customExecutor)return l.customExecutor(new nse(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return _.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var J4=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 e8(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Un(d)[0]),u=[];r!=null&&(u=r.map(d=>Un(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((Q4(d)||Sse(d)||Nse(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 Tse(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Un(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 Ese=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Cse=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Rse=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Q4(e){return Ese.indexOf(e.op)>=0}function Sse(e){return Cse.indexOf(e.op)>=0}function Nse(e){return Rse.indexOf(e.op)>=0}var Wg=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 Wg(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=e8(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 Tse(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[Un(u)[0]]),a=t.map(u=>Un(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 J4(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,y]=Un(f),A=[];A[y]=e[f],h[m]=A});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let y=Y4(m,h,u,this._resourceManager);if(_.isPromise(y))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=y,this.checkTensorForDisposal(m.name,m,h,u,d,a,p)}}return this.parent==null&&u.dispose(d),t.map(f=>An(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=zae(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!a.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new J4(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>An(h,i,s)),l=o.map(h=>h.id),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.kept&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[Un(g)[0]]),i=n.map(g=>Un(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}=e8(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[x,v]=Un(g),w=[];w[v]=e[g],p[x]=w});let f={},m=this.getFrozenTensorIds(p),y={};for(;d.length>0;){let g=this.processStack(s,d,t,p,y,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 A=o.filter(g=>!Q4(g)&&!An(g.name,p,t)).map(g=>g.name);if(A.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 [${A}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=xa(u.node.name,n)),r[u.node.name]==null){let d=Y4(u.node,r,n,this._resourceManager);h||([h]=xa(u.node.name,n));let p=n.currentContext;_.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=xa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!An(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!An(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Un(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);_.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&_.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Un(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Un(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Mse=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Fse="?tfjs-format=file",$se="model.json",t8=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Mse}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=Nn.browserHTTPRequest(e,this.loadOptions);else{let t=Nn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Nn.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=Nn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Wg(G4.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=G4.Instance.transformGraph(e.modelInitializer);this.initializer=new Wg(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=Nn.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 Pe)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${$se}${Fse}`);let n=new t8(e,t);return await n.load(),n}var Dse="3.4.0",n8={};Me(n8,{CSVDataset:()=>a8,Dataset:()=>Yl,FileDataSource:()=>s8,TextLineDataset:()=>r8,URLDataSource:()=>i8,array:()=>Ose,csv:()=>Pse,func:()=>Lse,generator:()=>Wse,microphone:()=>Vse,version_data:()=>jse,webcam:()=>Bse,zip:()=>zse});var Use=no(d5()),Hse=no(d5());function Gse(e,t){return m0(e,t)}function m0(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(Jl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=m0(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 qse(e,t=l8){return o8(e,t)}function o8(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(Jl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=o8(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 l8(e){return e===null?null:Jl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function u8(e,t){let n=new Map;m0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(_.isPromise(a)){let s=await a;n.set(r,s)}}return m0(e,t,n)}function Jl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Pe))}function Kse(e){return e==null||Xse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Pe||_.isTypedArray(e)}function Xse(e){return e===null||typeof e!="object"&&typeof e!="function"}function Yse(e){return Gse(e,Zse)}function Zse(e){return e instanceof Pe?{value:e.clone(),recurse:!1}:Jl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var c8=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}},Bg=class extends c8{constructor(){super(Bg.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}};Bg.INITIAL_CAPACITY=32;function h8(e){return new Jse(e)}function Vg(e){return new Qse(e)}function eie(e,t){return new d8(e,t)}function nie(e,t=ns.FAIL){return new tie(e,t)}var Gt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new uie(this,e)}filter(e){return new oie(this,e)}map(e){return new lie(this,e)}mapAsync(e){return new p8(this,e)}serialMapAsync(e){return new p8(this,e).serial()}flatmap(e){return new cie(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 iie(this,e,t)}columnMajorBatch(e,t=!0,n=l8){return this.rowMajorBatch(e,t).map(r=>qse(r,n))}concatenate(e,t){return new d8(h8([this,e]),t)}take(e){return e<0||e==null?this:new sie(this,e)}skip(e){return e<0||e==null?this:new aie(this,e)}prefetch(e){return new f8(this,e)}shuffle(e,t){return new hie(this,e,t)}serial(){return new rie(this)}},Jse=class extends Gt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Yse(e),done:!1}}},Qse=class extends Gt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},rie=class extends Gt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},aie=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;_e(e.value)}return this.upstream.next()}},sie=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},iie=class extends Gt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},oie=class extends Gt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},lie=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},uie=class extends Gt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},p8=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},jg=class extends Gt{constructor(){super();this.outputQueue=new Bg,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},cie=class extends jg{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return!0}},d8=class extends Gt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ns;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ns||(ns={}));var tie=class extends Gt{constructor(e,t=ns.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Gt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await u8(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ns.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ns.SHORTEST:return{value:null,done:!0};case ns.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},f8=class extends Gt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new c8(t)}summary(){return`${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()}},hie=class extends f8{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Hse.alea(n||_.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Yl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;_.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Hn(async()=>(await n.iterator()).columnMajorBatch(e,t,die),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Hn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Hn(async()=>(await t.iterator()).filter(r=>z(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Hn(async()=>(await t.iterator()).map(n=>z(()=>e(n))),this.size)}mapAsync(e){let t=this;return Hn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Hn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Hn(async()=>{let r=Vg(async()=>({value:await t.iterator(),done:!1}));return eie(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Hn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Use.alea(t||_.now().toString());return Hn(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,Hn(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()}};Yl.MAX_BUFFER_SIZE=1e4;function Hn(e,t=null){return new class extends Yl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Ose(e){return Hn(async()=>h8(e),e.length)}function zse(e){if(!Jl(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 Hn(async()=>{let n=await u8(e,r=>{if(r instanceof Yl)return{value:r.iterator(),recurse:!1};if(Jl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return nie(n,ns.SHORTEST)},t)}function die(e){if(e===null)return null;let t=e[0];return Kse(t)?{value:pie(e),recurse:!1}:{value:null,recurse:!0}}function pie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Pe?cn(e):Ir(e)}var r8=class extends Yl{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='"',Jc=Symbol("out"),m8=Symbol("field"),A0=Symbol("quote"),Ug=Symbol("quoteafterquote"),y8=Symbol("quoteinquote"),a8=class extends Yl{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 r8(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(_.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&_.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(_.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Jc;for(let i=0;i<a;i++)switch(s){case Jc:switch(e.charAt(i)){case y0:r=i+1,s=A0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Jc;break;default:s=m8,r=i;break}break;case m8:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Jc,r=i+1;break;default:}break;case A0:switch(e.charAt(i)){case y0:s=Ug;break;default:}break;case Ug:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Jc,r=i+1;break;case y0:s=A0;break;default:s=y8;break}break;case y8:switch(e.charAt(i)){case y0:s=A0;break;default:}break;default:}if(s===Ug?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}},A8=class extends Gt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new A8(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(_.sizeFromShape(t));return n.set(e,n.length-e.length),Ir(n,t)}},g8=class extends Gt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=sn([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=Rn([s,a,o,i],[1,4])}else this.cropBox=Rn([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 g8(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&_.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=pi.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=Jt(ge(e,"float32"),0),n;n=Le.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},x8=class{},w8=class extends Gt{split(e){return new fie(this,e)}},fie=class extends w8{constructor(e,t){super();this.upstream=e,this.impl=new mie(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mie=class extends jg{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${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}},Aie=class extends Gt{decodeUTF8(){return new yie(this)}},yie=class extends w8{constructor(e){super();this.upstream=e,this.impl=new gie(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},gie=class extends jg{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=$9();this.decoder=new t("utf8")}}summary(){return`${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}},b8=class extends Aie{constructor(e,t={}){super();this.file=e,this.options=t,_.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function wie(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=xie(e));let a=await _.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new b8(s,t)}else throw new Error(a.statusText)}var xie=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 _8(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var s8=class extends x8{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(_8(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new b8(this.input,this.options)}},i8=class extends x8{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return _8(this.url)?new s8(this.url,this.fileOptions).iterator():wie(this.url,this.fileOptions)}};function Pse(e,t={}){return new a8(new i8(e),t)}function Lse(e){let t=Vg(e);return Hn(async()=>t)}function Wse(e){return Hn(async()=>{let t=await e();return Vg(()=>t.next())})}async function Bse(e,t){return g8.create(e,t)}async function Vse(e){return A8.create(e)}var jse="3.4.0",bie={tfjs:(bf==null?void 0:bf.version)||void 0,"tfjs-core":(_f==null?void 0:_f.version)||void 0,"tfjs-data":(vf==null?void 0:vf.version)||void 0,"tfjs-layers":(kf==null?void 0:kf.version)||void 0,"tfjs-converter":(If==null?void 0:If.version)||void 0,"tfjs-backend-cpu":w_||void 0,"tfjs-backend-webgl":V3||void 0,"tfjs-backend-wasm":$v||void 0};var Gn={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 v8(){if(!om(Gn.name)){le("backend registration:",Gn.name);try{Gn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Gn.width,Gn.height):document.createElement("canvas")}catch(e){le("error: cannot create canvas:",e);return}try{Gn.gl=Gn.canvas.getContext("webgl2",Gn.webGLattr)}catch(e){le("error: cannot get WebGL2 context:",e);return}try{yp(2,Gn.gl)}catch(e){le("error: cannot set WebGL2 context:",e);return}try{let e=new wp(Gn.gl);fl(Gn.name,()=>new Wl(e),Gn.priority)}catch(e){le("error: cannot register WebGL backend:",e);return}try{ol("webgl").forEach(t=>{let n={...t,backendName:Gn.name};ui(n)})}catch(e){le("error: cannot update WebGL backend registration:",e);return}try{_r.set("WEBGL_VERSION",2)}catch(e){le("error: cannot set WebGL backend flags:",e);return}le("backend registered:",Gn.name)}}var Hg={};lr(Hg,{load:()=>Gg,predict:()=>b0});var g0={};function gn(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(i=>i.kernelTimeMs>0).reduce((i,o)=>i+=o.kernelTimeMs,0),a=t.kernels.map((i,o)=>(i.id=o,i)).filter(i=>i.kernelTimeMs>0).sort((i,o)=>o.kernelTimeMs-i.kernelTimeMs),s=t.kernels.map((i,o)=>(i.id=o,i)).filter(i=>i.totalBytesSnapshot>0).sort((i,o)=>o.totalBytesSnapshot-i.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n),g0[e]={model:e,newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s},le("profiler",e,g0[e])}var wr,x0={age:0},w0=Number.MAX_SAFE_INTEGER;async function Gg(e){return wr?e.debug&&le("cached model:",wr.modelUrl):(wr=await ct(pt(e.modelBasePath,e.face.age.modelPath)),!wr||!wr.modelUrl?le("load model failed:",e.face.age.modelPath):e.debug&&le("load model:",wr.modelUrl)),wr}async function b0(e,t){return wr?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=Le.resizeBilinear(e,[wr.inputs[0].shape[2],wr.inputs[0].shape[1]],!1),a=P(r,[255]);_e(r);let s,i={age:0};if(!t.profile)t.face.age.enabled&&(s=await wr.predict(a));else{let o=t.face.age.enabled?await an(()=>wr.predict(a)):{};s=o.result.clone(),o.result.dispose(),gn("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 qg={};lr(qg,{load:()=>Yg,predict:()=>v0});var sr,Xg={gender:""},_0=Number.MAX_SAFE_INTEGER,Kg=!1,Zg=[.2989,.587,.114];async function Yg(e){return sr?e.debug&&le("cached model:",sr.modelUrl):(sr=await ct(pt(e.modelBasePath,e.face.gender.modelPath)),Kg=sr.inputs[0].shape[3]===1,!sr||!sr.modelUrl?le("load model failed:",e.face.gender.modelPath):e.debug&&le("load model:",sr.modelUrl)),sr}async function v0(e,t){return sr?_0<t.face.gender.skipFrames&&t.videoOptimized&&Xg.gender!==""?(_0++,Xg):(t.videoOptimized?_0=0:_0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Le.resizeBilinear(e,[sr.inputs[0].shape[2],sr.inputs[0].shape[1]],!1),a;Kg?a=z(()=>{let[o,l,c]=Pt(r,3,3),u=P(o,Zg[0]),h=P(l,Zg[1]),d=P(c,Zg[2]);return Pa([u,h,d]).sub(.5).mul(2)}):a=P(r,[255]),_e(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await sr.predict(a));else{let o=t.face.gender.enabled?await an(()=>sr.predict(a)):{};s=o.result.clone(),o.result.dispose(),gn("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=>_e(c))}else{let o=s.dataSync();if(Kg)(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()}Xg=i,n(i)})):null}var Jg={};lr(Jg,{load:()=>t2,predict:()=>I0});var _ie=["angry","disgust","fear","happy","sad","surprise","neutral"],br,Qg=[],k0=Number.MAX_SAFE_INTEGER,e2=[.2989,.587,.114];async function t2(e){return br?e.debug&&le("cached model:",br.modelUrl):(br=await ct(pt(e.modelBasePath,e.face.emotion.modelPath)),!br||!br.modelUrl?le("load model failed:",e.face.emotion.modelPath):e.debug&&le("load model:",br.modelUrl)),br}async function I0(e,t){return br?k0<t.face.emotion.skipFrames&&t.videoOptimized&&Qg.length>0?(k0++,Qg):(t.videoOptimized?k0=0:k0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Le.resizeBilinear(e,[br.inputs[0].shape[2],br.inputs[0].shape[1]],!1),[a,s,i]=Pt(r,3,3);r.dispose();let o=P(a,e2[0]),l=P(s,e2[1]),c=P(i,e2[2]);a.dispose(),s.dispose(),i.dispose();let u=Pa([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 an(()=>br.predict(h));p=f.result.dataSync(),f.result.dispose(),gn("emotion",f)}else{let f=await br.predict(h);p=f.dataSync(),_e(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:_ie[f]});d.sort((f,m)=>m.score-f.score)}h.dispose(),Qg=d,n(d)})):null}var qn;async function n2(e){return qn?e.debug&&le("cached model:",qn.modelUrl):(qn=await ct(pt(e.modelBasePath,e.face.embedding.modelPath)),!qn||!qn.modelUrl?le("load model failed:",e.face.embedding.modelPath):e.debug&&le("load model:",qn.modelUrl)),qn}function k8(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 vie(e){return z(()=>{let n=[[.05,.15,.85,.85]],r=e.image||e.tensor;if(!(r instanceof Pe))return null;let a=r.shape.length===3?Le.cropAndResize(Jt(r,0),n,[0],[qn.inputs[0].shape[2],qn.inputs[0].shape[1]]):Le.cropAndResize(r,n,[0],[qn.inputs[0].shape[2],qn.inputs[0].shape[1]]),s=[.2989,.587,.114],[i,o,l]=Pt(a,3,3),c=P(i,s[0]),u=P(o,s[1]),h=P(l,s[2]),d=Pa([c,u,h]),p=cn([d,d,d],3).squeeze(4),f=p.sub(p.min());return f.div(f.max())})}async function r2(e,t){return qn?new Promise(async n=>{let r=[];if(t.face.embedding.enabled){let a=vie(e);if(!t.profile)r=z(()=>[...qn.predict(a).reshape([128,2]).logSumExp(1).dataSync()]);else{let s=await an(()=>qn.predict({img_inputs:a}));r=[...s.result.dataSync()],s.result.dispose(),gn("emotion",s)}_e(a)}n(r)}):[]}var a2={};lr(a2,{enhance:()=>o2,load:()=>s2,match:()=>I8,predict:()=>T0,similarity:()=>i2});var Xn,S0={age:0},N0=Number.MAX_SAFE_INTEGER;async function s2(e){return Xn?e.debug&&le("cached model:",Xn.modelUrl):(Xn=await ct(pt(e.modelBasePath,e.face.description.modelPath)),!Xn||!Xn.modelUrl?le("load model failed:",e.face.description.modelPath):e.debug&&le("load model:",Xn.modelUrl)),Xn}function i2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let r=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-r)/100}function I8(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=i2(e,a.embedding);s>n&&s>r.similarity&&(r={...a,similarity:s})}return r}function o2(e){return z(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Pe))return null;let r=[[.05,.15,.85,.85]];return(n.shape.length===3?Le.cropAndResize(Jt(n,0),r,[0],[Xn.inputs[0].shape[2],Xn.inputs[0].shape[1]]):Le.cropAndResize(n,r,[0],[Xn.inputs[0].shape[2],Xn.inputs[0].shape[1]])).mul(255)})}async function T0(e,t){return Xn?N0<t.face.description.skipFrames&&t.videoOptimized&&S0.age&&S0.age>0?(N0++,S0):(t.videoOptimized?N0=0:N0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=o2(e),a,s={age:0,gender:"unknown",genderConfidence:0,descriptor:[]};if(!t.profile)t.face.description.enabled&&(a=await Xn.predict(r));else{let i=t.face.description.enabled?await an(()=>Xn.predict(r)):{};a=i.result,gn("faceres",i)}_e(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=>_e(i))),S0=s,n(s)})):null}var kie=(e,t)=>{let n=y=>y*180/Math.PI,r=y=>{let A=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=A,y[1]/=A,y[2]/=A,y},a=(y,A)=>{let g=y[0]-A[0],x=y[1]-A[1],v=y[2]-A[2];return[g,x,v]},s=(y,A)=>{let g=y[1]*A[2]-y[2]*A[1],x=y[2]*A[0]-y[0]*A[2],v=y[0]*A[1]-y[1]*A[0];return[g,x,v]},i=y=>{let[A,g,x,v,w,b,k,N,E]=y,F,O,L;return v<1?v>-1?(L=Math.asin(v),O=Math.atan2(-k,A),F=Math.atan2(-b,w)):(L=-Math.PI/2,O=-Math.atan2(N,E),F=0):(L=Math.PI/2,O=Math.atan2(N,E),F=0),{pitch:2*-F,yaw:2*-O,roll:2*-L}},o=y=>{let A=(x,v,w,b)=>Math.atan2(b-v,w-x);return{pitch:A(y[10][1],y[10][2],y[152][1],y[152][2]),yaw:A(y[33][0],y[33][2],y[263][0],y[263][2]),roll:A(y[33][0],y[33][1],y[263][0],y[263][1])}},l=e.meshRaw;if(!l||l.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1]};let c=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[l[10],l[152],l[234],l[454]].map(y=>[y[0]*t[0]/c,y[1]*t[1]/c,y[2]]),h=r(a(u[1],u[0])),d=r(a(u[3],u[2])),p=r(s(d,h));d=s(h,p);let f=[d[0],d[1],d[2],h[0],h[1],h[2],p[0],p[1],p[2]];return{angle:i(f),matrix:f}},l2=async(e,t)=>{var u,h,d,p,f,m,y;let n,r,a,s,i,o,l=[];e.state="run:face",n=Ye();let c=await((u=e.models.face)==null?void 0:u.estimateFaces(t,e.config));if(e.perf.face=Math.trunc(Ye()-n),!c)return[];for(let A of c){if(e.analyze("Get Face"),!A.image||A.image.isDisposedInternal){le("Face object is disposed:",A.image);continue}let g=kie(A,[t.shape[2],t.shape[1]]);e.analyze("Start Age:"),e.config.async?r=e.config.face.age.enabled?b0(A.image,e.config):{}:(e.state="run:age",n=Ye(),r=e.config.face.age.enabled?await b0(A.image,e.config):{},e.perf.age=Math.trunc(Ye()-n)),e.analyze("Start Gender:"),e.config.async?a=e.config.face.gender.enabled?v0(A.image,e.config):{}:(e.state="run:gender",n=Ye(),a=e.config.face.gender.enabled?await v0(A.image,e.config):{},e.perf.gender=Math.trunc(Ye()-n)),e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?I0(A.image,e.config):{}:(e.state="run:emotion",n=Ye(),s=e.config.face.emotion.enabled?await I0(A.image,e.config):{},e.perf.emotion=Math.trunc(Ye()-n)),e.analyze("End Emotion:"),e.analyze("Start Embedding:"),e.config.async?i=e.config.face.embedding.enabled?r2(A,e.config):[]:(e.state="run:embedding",n=Ye(),i=e.config.face.embedding.enabled?await r2(A,e.config):[],e.perf.embedding=Math.trunc(Ye()-n)),e.analyze("End Embedding:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?T0(A,e.config):[]:(e.state="run:description",n=Ye(),o=e.config.face.description.enabled?await T0(A.image,e.config):[],e.perf.embedding=Math.trunc(Ye()-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=A==null?void 0:A.annotations)==null?void 0:h.leftEyeIris)&&((d=A==null?void 0:A.annotations)==null?void 0:d.rightEyeIris)&&(delete A.annotations.leftEyeIris,delete A.annotations.rightEyeIris);let x=((p=A.annotations)==null?void 0:p.leftEyeIris)&&((f=A.annotations)==null?void 0:f.rightEyeIris)?11.7*Math.max(Math.abs(A.annotations.leftEyeIris[3][0]-A.annotations.leftEyeIris[1][0]),Math.abs(A.annotations.rightEyeIris[4][1]-A.annotations.rightEyeIris[2][1])):0;l.push({...A,age:o.age||r.age,gender:o.gender||a.gender,genderConfidence:o.genderConfidence||a.confidence,embedding:o.descriptor||i,emotion:s,iris:x!==0?Math.trunc(x)/100:0,rotation:g,tensor:e.config.face.detector.return?(m=A.image)==null?void 0:m.squeeze():null}),(y=A.image)==null||y.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 f2={};lr(f2,{MediaPipeFaceMesh:()=>m2,load:()=>y2,triangulation:()=>D8,uvmap:()=>O8});var S8=6;function Iie(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r<t.strides.length;r++){let a=t.strides[r],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[r];for(let l=0;l<s;l++){let c=a*(l+.5);for(let u=0;u<i;u++){let h=a*(u+.5);for(let d=0;d<o;d++)n.push([h,c])}}}return n}var Sie=e=>({startEndTensor:e,startPoint:Ce(e,[0,0],[-1,2]),endPoint:Ce(e,[0,2],[-1,2])});function Nie(e,t,n){let r=Ce(e,[0,1],[-1,2]),a=se(r,t),s=Ce(e,[0,3],[-1,2]),i=me(s,n),o=me(a,n),l=me(i,2),c=ye(o,l),u=se(o,l),h=P(c,n),d=P(u,n);return gl([h,d],1)}var N8=class{constructor(t,n){this.model=t,this.anchorsData=Iie(t.inputs[0].shape[1]),this.anchors=Rn(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((b,k)=>b.size-k.size),x=rt([g[0],g[2]],2),v=rt([g[1],g[3]],2);f=rt([v,x],1).squeeze(0)}else f=p.squeeze();let m=Nie(f,this.anchors,[this.inputSize,this.inputSize]),y=Ce(f,[0,0],[-1,1]),A=On(y).squeeze();return[f,m,A]}),s=await Le.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=>Ce(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=Sie(l[h]),m=this.anchorsData[d],y=z(()=>Ce(n,[d,S8-1],[1,-1]).squeeze().reshape([S8,-1]));u.push({box:f,landmarks:y,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 T8(e){let t=await ct(pt(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new N8(t,e);return!t||!t.modelUrl?le("load model failed:",e.face.detector.modelPath):e.debug&&le("load model:",t.modelUrl),n}function E8(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 Qc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ql(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function eu(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 Le.cropAndResize(t,s,[0],n)}function E0(e,t=1.5){let n=Ql(e),r=Qc(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=Ql(e),n=Qc(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 Tie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function u2(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Tie(n)}function C8(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 Eie(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function R8(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],Eie(t,s)))}return n}function M0(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=C8(t[0],t[1]),i=R8(s,a),o=C8(-t[0],-t[1]);return R8(i,o)}function M8(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 F8(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]},c2=[{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]}],eh=[[.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]],Bi=[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 Cie=[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],Rie=[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],Mie=[33,133,362,263,1,78,308],joe=Cie.map(e=>eh[e]),Uoe=Rie.map(e=>eh[e]),Hoe=Mie.map(e=>eh[e]);var h2=Qr.leftEyeLower0,d2=Qr.rightEyeLower0,tu={leftBounds:[h2[0],h2[h2.length-1]],rightBounds:[d2[0],d2[d2.length-1]]},F0={count:468,mouth:13,symmetryLine:[13,Qr.midwayBetweenEyes[0]]},$8={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},nu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function $0(e,t,n,r){for(let a=0;a<c2.length;a++){let{key:s,indices:i}=c2[a],o=Qr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var p2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Qc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?M0(r,[0,0]):R0,l=r!==0?i.map(h=>[...F8(h,o),h[2]]):i,c=r!==0?M8(a):R0,u=[...Ql({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+rs(u,c[0]),h[1]+rs(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[tu.leftBounds[0]][2],r=t[tu.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=C0(E0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Qc(i),l=Le.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&_r.flags.IS_BROWSER&&(l=Le.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<nu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(nu.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Qr[`${r}EyeUpper0`][nu.upperCenter]][2],s=t[Qr[`${r}EyeLower0`][nu.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=E8({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&&_r.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=F0.count?F0.symmetryLine:$8.symmetryLine;u=u2(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],E=Le.rotateWithOffset(t,u,0,N);h=M0(-u,k),n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=R0;let w=t.clone();n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let y=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:b,crop:k}=this.getEyeBox(y,c,tu.leftBounds[0],tu.leftBounds[1],!0),{box:N,boxSize:E,crop:F}=this.getEyeBox(y,c,tu.rightBounds[0],tu.rightBounds[1]),L=this.irisModel.predict(rt([k,F])).dataSync(),V=L.slice(0,nu.numCoordinates*3),{rawCoords:j,iris:U}=this.getEyeCoords(V,w,b,!0),X=L.slice(nu.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,N,E),Y=this.getLeftToRightEyeDepthDifference(y);Math.abs(Y)<30?($0(y,j,"left",null),$0(y,G,"right",null)):Y<1?$0(y,j,"left",["EyeUpper0","EyeLower0"]):$0(y,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(y,U,"left"),te=this.getAdjustedIrisCoords(y,ee,"right");y=y.concat(ae).concat(te)}let A=this.transformRawCoords(y,i,u,h);i=E0(this.calculateLandmarksBoundingBox(A),1.5);let g=Rn(A);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&_r.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=F0.count?F0.symmetryLine:$8.symmetryLine;u=u2(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],E=Le.rotateWithOffset(t.toFloat(),u,0,N);h=M0(-u,k),c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let x={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:y},v=C0(i);return this.storedBoxes[o]={...v,landmarks:A,confidence:i.confidence,faceConfidence:f},x}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var m2=class{constructor(t,n,r,a){this.facePipeline=new p2(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[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},qt=[null,null,null];async function y2(e){return!qt[0]&&e.face.enabled||!qt[1]&&e.face.mesh.enabled||!qt[2]&&e.face.iris.enabled?(qt=await Promise.all([!qt[0]&&e.face.enabled?T8(e):null,!qt[1]&&e.face.mesh.enabled?ct(pt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!qt[2]&&e.face.iris.enabled?ct(pt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!qt[1]||!qt[1].modelUrl?le("load model failed:",e.face.mesh.modelPath):e.debug&&le("load model:",qt[1].modelUrl)),e.face.iris.enabled&&(!qt[2]||!qt[1].modelUrl?le("load model failed:",e.face.iris.modelPath):e.debug&&le("load model:",qt[2].modelUrl))):e.debug&&(le("cached model:",qt[0].model.modelUrl),le("cached model:",qt[1].modelUrl),le("cached model:",qt[2].modelUrl)),new m2(qt[0],qt[1],qt[2],e)}var D8=Bi,O8=eh;var S2={};lr(S2,{PoseNet:()=>N2,load:()=>T2});function Fie(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var A2=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=Fie(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function g2(e){return Math.floor(e/2)}var x2=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(g2(t),t);)this.exchange(t,g2(t)),t=g2(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 $ie(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function z8(e,t,n){let[r,a,s]=n.shape,i=new x2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||$ie(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var ru=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],au=ru.length,th=ru.reduce((e,t,n)=>(e[t]=n,e),{}),Die=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],lle=Die.map(([e,t])=>[th[e],th[t]]),P8=[["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 w2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+au)}}function D0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=w2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function b2(e,t,n){return e<t?t:e>n?n:e}function L8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function _2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function W8(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 Oie(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+au)}}function zie(e,t){let n=[];for(let r=0;r<au;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=Oie(a,s,r,t);n.push(o),n.push(i)}return Rn(n,[au,2])}function B8(e,t,n){return z(()=>e.toTensor().mul(xe(t,"int32")).toFloat().add(zie(e,n)))}function Pie(e,t){return z(()=>{let n=e.div(xe(t,"int32"));return e.sub(n.mul(xe(t,"int32")))})}function V8(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(xe(n,"int32")).expandDims(1),o=Pie(s,n).expandDims(1);return rt([i,o],1)})}var j8=P8.map(([e,t])=>[th[e],th[t]]),v2=j8.map(([,e])=>e),U8=j8.map(([e])=>e),Lie=16;function Wie(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 k2(e,t,n,r){return{y:b2(Math.round(e.y/t),0,n-1),x:b2(Math.round(e.x/t),0,r-1)}}function H8(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=k2(t.position,s,l,c),h=Wie(e,u,i),p=_2(t.position,h);for(let y=0;y<o;y++){let A=k2(p,s,l,c),g=w2(A.y,A.x,n,a);p=_2({x:A.x*s,y:A.y*s},{x:g.x,y:g.y})}let f=k2(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:ru[n],score:m}}function G8(e,t,n,r,a,s){let i=t.shape[2],o=v2.length,l=new Array(i),{part:c,score:u}=e,h=D0(c,r,n);l[c.id]={score:u,part:ru[c.id],position:h};for(let d=o-1;d>=0;--d){let p=v2[d],f=U8[d];l[p]&&!l[f]&&(l[f]=H8(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=U8[d],f=v2[d];l[p]&&!l[f]&&(l[f]=H8(d,l[p],f,t,n,r,a))}return l}async function q8(e,t,n){let r=0,a=V8(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=B8(l,Lie,o),u=await c.buffer(),d=Array.from(W8(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:ru[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var Bie=1,X8=16;function K8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return L8(r,n,i.y,i.x)<=t})}function Vie(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(K8(e,t,s,o)||(a+=i),a),0)/n.length}function Z8(e,t,n,r,a,s,i){let o=[],l=z8(i,Bie,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=D0(u.part,X8,t);if(K8(o,c,h,u.part.id))continue;let d=G8(u,e,t,X8,n,r),p=Vie(o,c,d);p>i&&o.push({keypoints:d,score:Math.round(100*p)/100})}return o}async function Y8(e){return Promise.all(e.map(t=>t.buffer()))}function jie(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 J8(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function I2(e,[t,n],[r,a]){return e.map(i=>jie(i,t/r,n/a))}var Vi;async function Uie(e,t,n,r){return new Promise(async a=>{let s=await Y8([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),i=s[0],o=s[1],l=s[2],c=s[3],u=await Z8(i,o,l,c,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=I2(u,[e.shape[1],e.shape[2]],[r,r]);a(h)})}async function Hie(e,t,n,r){return new Promise(async a=>{let s=await q8(t.heatmapScores,t.offsets,n.body.scoreThreshold),i=I2([s],[e.shape[1],e.shape[2]],[r,r]);a(i)})}var N2=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=J8(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await Hie(t,a,n,this.inputSize):await Uie(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 T2(e){Vi?e.debug&&le("cached model:",Vi.modelUrl):(Vi=await ct(pt(e.modelBasePath,e.body.modelPath)),!Vi||!Vi.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",Vi.modelUrl));let t=new A2(Vi);return new N2(t)}var F2={};lr(F2,{HandPose:()=>D2,load:()=>O2});function O0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function nh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Q8(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 Le.cropAndResize(t,s,[0],n)}function ek(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=nh(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=nh(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 E2=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=Rn(this.anchors),this.inputSize=n,this.inputSizeTensor=sn([n,n]),this.doubleInputSizeTensor=sn([n*2,n*2])}normalizeBoxes(t){return z(()=>{let n=Ce(t,[0,0],[-1,2]),r=Ce(t,[0,2],[-1,2]),a=se(me(n,this.inputSizeTensor),this.anchorsTensor),s=me(r,this.doubleInputSizeTensor),i=P(ye(a,s),this.inputSizeTensor),o=P(se(a,s),this.inputSizeTensor);return gl([i,o],1)})}normalizeLandmarks(t,n){return z(()=>{let r=se(me(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(()=>On(Ce(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Ce(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let c=await Le.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=Ce(l,[d,0],[1,-1]),f=Ce(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(ek({startPoint:u,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function Gie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function tk(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Gie(n)}var nk=(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 qie(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function rk(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],qie(t,s)))}return n}function C2(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=nk(t[0],t[1]),i=rk(s,a),o=nk(-t[0],-t[1]);return rk(i,o)}function ak(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 R2(e,t){return[as(e,t[0]),as(e,t[1])]}var Xie=5,sk=1.65,ik=[0,5,9,13,17,1,2],Kie=0,Zie=2,M2=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=>R2([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return z0(P0(a),Xie)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=z0(P0(n),sk);r.palmLandmarks=[];for(let a=0;a<ik.length;a++)r.palmLandmarks.push(t[ik[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=C2(r,[0,0]),c=o.map(p=>[...R2(p,l),p[2]]),u=ak(a),h=[...nh(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?tk(o.palmLandmarks[Kie],o.palmLandmarks[Zie]):0,c=nh(o),u=[c[0]/t.shape[2],c[1]/t.shape[1]],h=n.hand.rotation?Le.rotateWithOffset(t,l,0,u):t.clone(),d=C2(-l,c),p=r?this.getBoxForPalmLandmarks(o.palmLandmarks,d):o,f=Q8(p,h,[this.inputSize,this.inputSize]),m=f.div(255);f.dispose(),h.dispose();let[y,A]=await this.landmarkDetector.predict(m);m.dispose();let g=y.dataSync()[0];if(y.dispose(),g>=n.hand.minConfidence){let x=H(A,[-1,3]),v=x.arraySync();A.dispose(),x.dispose();let w=this.transformRawCoords(v,p,l,d),b=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=b;let k={landmarks:w,confidence:g,box:{topLeft:b.startPoint,bottomRight:b.endPoint}};s.push(k)}else this.storedBoxes[i]=null;A.dispose()}else{let l=z0(P0(o),sk),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 ok=[{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 $2={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]},D2=class{constructor(t){this.handPipeline=t}static getAnnotations(){return $2}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($2))i[c]=$2[c].map(u=>s.landmarks[u]);let o=s.box?[Math.max(0,s.box.topLeft[0]),Math.max(0,s.box.topLeft[1]),Math.min(t.shape[2],s.box.bottomRight[0])-Math.max(0,s.box.topLeft[0]),Math.min(t.shape[1],s.box.bottomRight[1])-Math.max(0,s.box.topLeft[1])]:[],l=[s.box.topLeft[0]/t.shape[2],s.box.topLeft[1]/t.shape[1],(s.box.bottomRight[0]-s.box.topLeft[0])/t.shape[2],(s.box.bottomRight[1]-s.box.topLeft[1])/t.shape[1]];a.push({confidence:Math.round(100*s.confidence)/100,box:o,boxRaw:l,landmarks:s.landmarks,annotations:i})}return a}},ea,ta;async function O2(e){!ea||!ta?([ea,ta]=await Promise.all([e.hand.enabled?ct(pt(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?ct(pt(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!ea||!ea.modelUrl?le("load model failed:",e.hand.detector.modelPath):e.debug&&le("load model:",ea.modelUrl),!ta||!ta.modelUrl?le("load model failed:",e.hand.skeleton.modelPath):e.debug&&le("load model:",ta.modelUrl))):(e.debug&&le("cached model:",ea.modelUrl),e.debug&&le("cached model:",ta.modelUrl));let t=new E2(ea,ea==null?void 0:ea.inputs[0].shape[2],ok),n=new M2(t,ta,ta==null?void 0:ta.inputs[0].shape[2]);return new D2(n)}var z2={};lr(z2,{load:()=>P2,predict:()=>L2});var lk=["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"],uk=["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 xn;async function P2(e){return xn?e.debug&&le("cached model:",xn.modelUrl):(xn=await ct(pt(e.modelBasePath,e.body.modelPath)),xn.width=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[2].size),xn.height=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!xn||!xn.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",xn.modelUrl)),xn}async function L2(e,t){if(!xn||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Le.resizeBilinear(e,[xn.width,xn.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let u=await an(()=>xn.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),gn("blazepose",u)}else{let u=await xn.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?lk:uk,l=5;for(let u=0;u<s.length/l;u++)i.push({id:u,part:o[u],position:{x:Math.trunc(n.width*s[l*u+0]/255),y:Math.trunc(n.height*s[l*u+1]/255),z:Math.trunc(s[l*u+2])+0},score:(100-Math.trunc(100/(1+Math.exp(s[l*u+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(s[l*u+4]))))/100});return[{score:i.reduce((u,h)=>h.score>u?h.score:u,0),keypoints:i}]}var wn,rh=[],L0=Number.MAX_SAFE_INTEGER,Yie=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function W2(e){return wn?e.debug&&le("cached model:",wn.modelUrl):(wn=await ct(pt(e.modelBasePath,e.body.modelPath)),!wn||!wn.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",wn.modelUrl)),wn}function Jie(e,t){let[n,r]=e.shape;return z(()=>{let a=(o,l)=>ye(o,P(me(o,xe(l,"int32")),xe(l,"int32"))),s=H(e,[r*n]),i=Cn(s,0).dataSync()[0];if(i>t){let o=mi(s,0),l=a(o,n).dataSync()[0],c=me(o,xe(n,"int32")).dataSync()[0];return[l,c,i]}return[0,0,i]})}async function B2(e,t){return wn?L0<t.body.skipFrames&&t.videoOptimized&&Object.keys(rh).length>0?(L0++,rh):(t.videoOptimized?L0=0:L0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=z(()=>{let i=Le.resizeBilinear(e,[wn.inputs[0].shape[2],wn.inputs[0].shape[1]],!1);return P(i,2).sub(1)}),a;if(!t.profile)t.body.enabled&&(a=await wn.predict(r));else{let i=t.body.enabled?await an(()=>wn.predict(r)):{};a=i.result.clone(),i.result.dispose(),gn("body",i)}if(r.dispose(),a){let i=[],o=a.squeeze();_e(a);let l=o.unstack(2);_e(o);for(let c=0;c<l.length;c++){let[u,h,d]=Jie(l[c],t.body.scoreThreshold);d>t.body.scoreThreshold&&i.push({id:c,score:Math.round(100*d)/100,part:Yie[c],positionRaw:{xRaw:u/wn.inputs[0].shape[2],yRaw:h/wn.inputs[0].shape[1]},position:{x:Math.round(e.shape[2]*u/wn.inputs[0].shape[2]),y:Math.round(e.shape[1]*h/wn.inputs[0].shape[1])}})}l.forEach(c=>_e(c)),rh=i}let s=rh.reduce((i,o)=>o.score>i?o.score:i,0);n([{score:s,keypoints:rh}])})):null}var V2={};lr(V2,{load:()=>U2,predict:()=>H2});var W0=[{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 bn,j2=[],B0=Number.MAX_SAFE_INTEGER,V0=2.5;async function U2(e){if(bn)e.debug&&le("cached model:",bn.modelUrl);else{bn=await ct(pt(e.modelBasePath,e.object.modelPath));let t=Object.values(bn.modelSignature.inputs);if(bn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!bn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!bn||!bn.modelUrl?le("load model failed:",e.object.modelPath):e.debug&&le("load model:",bn.modelUrl)}return bn}async function Qie(e,t,n,r){let a=0,s=[];for(let c of[1,2,4])z(()=>{var y,A;let u=c*13,h=(y=e.find(g=>g.shape[1]===u**2&&g.shape[2]===W0.length))==null?void 0:y.squeeze(),d=(A=e.find(g=>g.shape[1]===u**2&&g.shape[2]<W0.length))==null?void 0:A.squeeze(),f=d.reshape([-1,4,d.shape[1]/4]).argMax(2).arraySync(),m=h.arraySync();for(let g=0;g<h.shape[0];g++)for(let x=0;x<h.shape[1];x++){let v=m[g][x];if(v>r.object.minConfidence&&x!==61){let w=(.5+Math.trunc(g%u))/u,b=(.5+Math.trunc(g/u))/u,k=f[g].map(U=>U*(u/c/t)),[N,E]=[w-V0/c*k[0],b-V0/c*k[1]],[F,O]=[w+V0/c*k[2]-N,b+V0/c*k[3]-E],L=[N,E,F,O];L=L.map(U=>Math.max(0,Math.min(U,1)));let V=[L[0]*n[0],L[1]*n[1],L[2]*n[0],L[3]*n[1]],j={id:a++,strideSize:c,score:Math.round(100*v)/100,class:x+1,label:W0[x].label,center:[Math.trunc(n[0]*w),Math.trunc(n[1]*b)],centerRaw:[w,b],box:V.map(U=>Math.trunc(U)),boxRaw:L};s.push(j)}}});e.forEach(c=>_e(c));let i=s.map(c=>c.boxRaw),o=s.map(c=>c.score),l=[];if(i&&i.length>0){let c=await Le.nonMaxSuppressionAsync(i,o,r.object.maxResults,r.object.iouThreshold,r.object.minConfidence);l=c.dataSync(),_e(c)}return s=s.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),s}async function H2(e,t){return bn?B0<t.object.skipFrames&&t.videoOptimized&&j2.length>0?(B0++,j2):(t.videoOptimized?B0=0:B0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=[e.shape[2],e.shape[1]],a=Le.resizeBilinear(e,[bn.inputSize,bn.inputSize],!1),s=a.div(255),i=s.transpose([0,3,1,2]);s.dispose(),a.dispose();let o;if(!t.profile)t.object.enabled&&(o=await bn.predict(i));else{let c=t.object.enabled?await an(()=>bn.predict(i)):{};o=c.result,gn("object",c)}i.dispose();let l=await Qie(o,bn.inputSize,r,t);j2=l,n(l)})):null}var ck=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},hk=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let r=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${r<0?"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 ${o<0?"up":"down"}`})}return t},dk=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],a=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(r*a),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o),c=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let h=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].annotations.rightEyeIris[0][0],d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].annotations.leftEyeIris[0][0];(d>.033||h>.033)&&(c=!1),d>.033&&t.push({iris:n,gesture:"looking right"}),h>.033&&t.push({iris:n,gesture:"looking left"});let p=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].annotations.rightEyeIris[0][1],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].annotations.leftEyeIris[0][1];(f<.015||p<.015||f>.03||p>.03)&&(c=!1),(f<.015||p<.015)&&t.push({iris:n,gesture:"looking down"}),(f>.03||p>.03)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},pk=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[a,s]of Object.entries(e[n].annotations))a!=="palmBase"&&Array.isArray(s)&&r.push({name:a.toLowerCase(),position:s[0]});if(r&&r.length>0){let a=r.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=r.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${a.name} forward ${s.name} up`})}}return t};var G2={};lr(G2,{process:()=>q2});function eoe(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 fk(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(w){let b=Array.prototype.slice.call(arguments,1),k=h[w];i.push({func:k,args:b})},this.reset=function(){i=[]};let y=function(w,b){if(!(w===o&&b===l)){if(d.width=w,o=w,d.height=b,l=b,!c){let k=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);c=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,c),m.bufferData(m.ARRAY_BUFFER,k,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,o,l),s=[null,null]}},A=function(w,b){let k=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,k);let N=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,N);let E=m.createTexture();return m.bindTexture(m.TEXTURE_2D,E),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,w,b,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:k,texture:E}},g=function(w){return s[w]=s[w]||A(o,l),s[w]},x=function(w=null){var E,F;let b=null,k=null,N=!1;t===0?b=n:b=(E=g(a))==null?void 0:E.texture,t++,r&&!(w&f.INTERMEDIATE)?(k=null,N=t%2==0):(a=(a+1)%2,k=(F=g(a))==null?void 0:F.fbo),m.bindTexture(m.TEXTURE_2D,b),m.bindFramebuffer(m.FRAMEBUFFER,k),m.uniform1f(u.uniform.flipY,N?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(w){if(y(w.width,w.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,w),i.length===0)return x(),d;for(let b=0;b<i.length;b++){r=b===i.length-1;let k=i[b];k.func.apply(this,k.args||[])}return d};let v=function(w){if(p[w])return u=p[w],m.useProgram(u.id),u;let b={};b.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
|
|
`),b.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
|
|
`),u=new eoe(m,b.VERTEX_IDENTITY,w);let k=Float32Array.BYTES_PER_ELEMENT,N=4*k;return m.enableVertexAttribArray(u.attribute.pos),m.vertexAttribPointer(u.attribute.pos,2,m.FLOAT,!1,N,0*k),m.enableVertexAttribArray(u.attribute.uv),m.vertexAttribPointer(u.attribute.uv,2,m.FLOAT,!1,N,2*k),p[w]=u,u};h.colorMatrix=function(w){let b=new Float32Array(w);b[4]/=255,b[9]/=255,b[14]/=255,b[19]/=255;let k=b[18]===1&&b[3]===0&&b[8]===0&&b[13]===0&&b[15]===0&&b[16]===0&&b[17]===0&&b[19]===0?h.colorMatrix.SHADER.WITHOUT_ALPHA:h.colorMatrix.SHADER.WITH_ALPHA,N=v(k);m.uniform1fv(N.uniform.m,b),x()},h.colorMatrix.SHADER={},h.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
|
|
`),h.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
|
|
`),h.brightness=function(w){let b=(w||0)+1;h.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},h.saturation=function(w){let b=(w||0)*2/3+1,k=(b-1)*-.5;h.colorMatrix([b,k,k,0,0,k,b,k,0,0,k,k,b,0,0,0,0,0,1,0])},h.desaturate=function(){h.saturation(-1)},h.contrast=function(w){let b=(w||0)+1,k=-128*(b-1);h.colorMatrix([b,0,0,0,k,0,b,0,0,k,0,0,b,0,k,0,0,0,1,0])},h.negative=function(){h.contrast(-2)},h.hue=function(w){w=(w||0)/180*Math.PI;let b=Math.cos(w),k=Math.sin(w),N=.213,E=.715,F=.072;h.colorMatrix([N+b*(1-N)+k*-N,E+b*-E+k*-E,F+b*-F+k*(1-F),0,0,N+b*-N+k*.143,E+b*(1-E)+k*.14,F+b*-F+k*-.283,0,0,N+b*-N+k*-(1-N),E+b*-E+k*E,F+b*(1-F)+k*F,0,0,0,0,0,1,0])},h.desaturateLuminance=function(){h.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},h.sepia=function(){h.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},h.brownie=function(){h.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},h.vintagePinhole=function(){h.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},h.kodachrome=function(){h.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},h.technicolor=function(){h.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},h.polaroid=function(){h.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},h.shiftToBGR=function(){h.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},h.convolution=function(w){let b=new Float32Array(w),k=1/o,N=1/l,E=v(h.convolution.SHADER);m.uniform1fv(E.uniform.m,b),m.uniform2f(E.uniform.px,k,N),x()},h.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
|
|
`),h.detectEdges=function(){h.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},h.sobelX=function(){h.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},h.sobelY=function(){h.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},h.sharpen=function(w){let b=w||1;h.convolution.call(this,[0,-1*b,0,-1*b,1+4*b,-1*b,0,-1*b,0])},h.emboss=function(w){let b=w||1;h.convolution.call(this,[-2*b,-1*b,0,-1*b,1,1*b,0,1*b,2*b])},h.blur=function(w){let b=w/7/o,k=w/7/l,N=v(h.blur.SHADER);m.uniform2f(N.uniform.px,0,k),x(f.INTERMEDIATE),m.uniform2f(N.uniform.px,b,0),x()},h.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
|
|
`),h.pixelate=function(w){let b=w/o,k=w/l,N=v(h.pixelate.SHADER);m.uniform2f(N.uniform.size,b,k),x()},h.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
|
|
`)}var j0=2048,Re,wt,$t;function q2(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof Pe)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof Pe)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Wr(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>j0&&(i=j0,o=i*s/a),o>j0&&(o=j0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Re||(Re==null?void 0:Re.width)!==i||(Re==null?void 0:Re.height)!==o)&&(Re=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Re==null?void 0:Re.width)!==i&&(Re.width=i),(Re==null?void 0:Re.height)!==o&&(Re.height=o));let l=Re.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Re==null?void 0:Re.width,Re==null?void 0:Re.height),t.filter.enabled){if((!$t||!wt||Re.width!==wt.width||(Re==null?void 0:Re.height)!==(wt==null?void 0:wt.height))&&(wt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Re==null?void 0:Re.width,Re==null?void 0:Re.height):document.createElement("canvas"),(wt==null?void 0:wt.width)!==(Re==null?void 0:Re.width)&&(wt.width=Re==null?void 0:Re.width),(wt==null?void 0:wt.height)!==(Re==null?void 0:Re.height)&&(wt.height=Re==null?void 0:Re.height),$t=_r.flags.IS_BROWSER?new fk({canvas:wt}):null),!$t)return{tensor:null,canvas:Re};$t.reset(),$t.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&$t.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&$t.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&$t.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&$t.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&$t.addFilter("hue",t.filter.hue),t.filter.negative&&$t.addFilter("negative"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.vintage&&$t.addFilter("brownie"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.kodachrome&&$t.addFilter("kodachrome"),t.filter.technicolor&&$t.addFilter("technicolor"),t.filter.polaroid&&$t.addFilter("polaroid"),t.filter.pixelate!==0&&$t.addFilter("pixelate",t.filter.pixelate),$t.apply(Re)}else wt=Re,$t&&($t=null);let c;if(wt.data){let h=[wt.height,wt.width,3];c=xd(wt.data,h,"int32")}else if(wt instanceof ImageData)c=pi.fromPixels(wt);else if(t.backend==="webgl"||t.backend==="humangl"){let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(wt,0,0),c=pi.fromPixels(h)}else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(wt,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=pi.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?wt:null;return{tensor:n,canvas:r}}var X2={};lr(X2,{all:()=>noe,body:()=>Ak,canvas:()=>toe,face:()=>yk,gesture:()=>mk,hand:()=>gk,object:()=>xk,options:()=>ji});var ht={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"},age:{enabled:!1,modelPath:"age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"mobileface.json"}},body:{enabled:!0,modelPath:"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:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var ji={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1,calculateHandBox:!0};function U0(e,t,n,r=0,a){e.fillStyle=a.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,n,a.pointSize,0,2*Math.PI),e.fill()}function su(e,t,n,r,a,s){if(e.beginPath(),s.useCurves){let i=(t+t+r)/2,o=(n+n+a)/2;e.ellipse(i,o,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+r-s.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+s.roundRect),e.lineTo(t+r,n+a-s.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-s.roundRect,n+a),e.lineTo(t+s.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function K2(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let r of t)e.strokeStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.lineTo(r[0],parseInt(r[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function ah(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){K2(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let r=0;r<t.length-2;r++){let a=(t[r][0]+t[r+1][0])/2,s=(t[r][1]+t[r+1][1])/2;e.quadraticCurveTo(t[r][0],t[r][1],a,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function mk(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!a)return;a.font=r.font,a.fillStyle=r.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let c=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${c}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(u,8,2+s*r.lineHeight)),a.fillStyle=r.labelColor,a.fillText(u,6,0+s*r.lineHeight),s+=1}}}async function yk(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a)for(let s of t){a.font=r.font,a.strokeStyle=r.color,a.fillStyle=r.color,r.drawBoxes&&(r.useRawBoxes?su(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):su(a,s.box[0],s.box[1],s.box[2],s.box[3],r));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}s.rotation&&s.rotation.angle&&s.rotation.angle.roll&&i.push(`roll: ${Math.trunc(100*s.rotation.angle.roll)/100} yaw:${Math.trunc(100*s.rotation.angle.yaw)/100} pitch:${Math.trunc(100*s.rotation.angle.pitch)/100}`),i.length===0&&i.push("face"),a.fillStyle=r.color;for(let o=i.length-1;o>=0;o--){let l=Math.max(s.box[0],0),c=o*r.lineHeight+s.box[1];r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i[o],l+5,c+16)),a.fillStyle=r.labelColor,a.fillText(i[o],l+4,c+15)}if(a.lineWidth=1,s.mesh&&s.mesh.length>0){if(r.drawPoints)for(let o of s.mesh)U0(a,o[0],o[1],o[2],r);if(r.drawPolygons){a.lineWidth=1;for(let o=0;o<Bi.length/3;o++){let l=[Bi[o*3+0],Bi[o*3+1],Bi[o*3+2]].map(c=>s.mesh[c]);K2(a,l,r)}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}}}}}var ss=[];async function Ak(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round";for(let s=0;s<t.length;s++){if(!ss[s]&&r.bufferedOutput&&(ss[s]={...t[s]}),a.strokeStyle=r.color,a.lineWidth=r.lineWidth,r.drawPoints)for(let i=0;i<t[s].keypoints.length;i++)a.fillStyle=r.useDepth&&t[s].keypoints[i].position.z?`rgba(${127.5+2*t[s].keypoints[i].position.z}, ${127.5-2*t[s].keypoints[i].position.z}, 255, 0.5)`:r.color,r.bufferedOutput?(ss[s].keypoints[i][0]=(ss[s].keypoints[i][0]+t[s].keypoints[i].position.x)/2,ss[s].keypoints[i][1]=(ss[s].keypoints[i][1]+t[s].keypoints[i].position.y)/2,U0(a,ss[s].keypoints[i][0],ss[s].keypoints[i][1],0,r)):U0(a,t[s].keypoints[i].position.x,t[s].keypoints[i].position.y,0,r);if(r.drawLabels&&(a.font=r.font,t[s].keypoints))for(let i of t[s].keypoints)a.fillStyle=r.useDepth&&i.position.z?`rgba(${127.5+2*i.position.z}, ${127.5-2*i.position.z}, 255, 0.5)`:r.color,a.fillText(`${i.part}`,i.position.x+4,i.position.y+4);if(r.drawPolygons&&t[s].keypoints){let i,o=[];o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),ah(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),o.length===4&&K2(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftKnee"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftAnkle"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHeel"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftFoot"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),ah(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightKnee"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightAnkle"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHeel"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightFoot"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),ah(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftElbow"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftWrist"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftPalm"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),ah(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightElbow"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightWrist"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightPalm"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),ah(a,o,r)}}}}async function gk(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t){if(r.drawBoxes){a.strokeStyle=r.color,a.fillStyle=r.color;let i;if(!r.calculateHandBox)i=r.useRawBoxes?s.boxRaw:s.box;else if(i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],s.landmarks&&s.landmarks.length>0){for(let o of s.landmarks)o[0]<i[0]&&(i[0]=o[0]),o[1]<i[1]&&(i[1]=o[1]),o[0]>i[2]&&(i[2]=o[0]),o[1]>i[3]&&(i[3]=o[1]);i[2]-=i[0],i[3]-=i[1]}r.useRawBoxes?su(a,e.width*i[0],e.height*i[1],e.width*i[2],e.height*i[3],r):su(a,i[0],i[1],i[2],i[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText("hand",i[0]+3,1+i[1]+r.lineHeight,i[2])),a.fillStyle=r.labelColor,a.fillText("hand",i[0]+2,0+i[1]+r.lineHeight,i[2])),a.stroke()}if(r.drawPoints&&s.landmarks&&s.landmarks.length>0)for(let i of s.landmarks)a.fillStyle=r.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:r.color,U0(a,i[0],i[1],0,r);if(r.drawPolygons){let i=o=>{if(!!o)for(let l=0;l<o.length;l++)a.lineWidth=r.lineWidth,a.beginPath(),a.strokeStyle=r.useDepth?`rgba(${127.5+2*o[l][2]}, ${127.5-2*o[l][2]}, 255, 0.5)`:r.color,a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()};i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function xk(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t)if(r.drawBoxes){if(a.strokeStyle=r.color,a.fillStyle=r.color,r.useRawBoxes?su(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):su(a,s.box[0],s.box[1],s.box[2],s.box[3],r),r.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+r.lineHeight,s.box[2])),a.fillStyle=r.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+r.lineHeight,s.box[2])}a.stroke()}}}async function toe(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 noe(e,t,n){let r=Jn(ji,n);!t||!e||e instanceof HTMLCanvasElement&&(yk(e,t.face,r),Ak(e,t.body,r),gk(e,t.hand,r),mk(e,t.gesture,r),xk(e,t.object,r))}var H0=`
|
|
/9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA
|
|
AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
|
|
bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob
|
|
IxwWFiAsICMmJykqKRkfLTAtKDAlKCko/9sAQwEHBwcKCAoTCgoTKBoWGigoKCgoKCgoKCgoKCgo
|
|
KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgo/8AAEQgBAAEAAwEhAAIRAQMRAf/E
|
|
AB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAE
|
|
EQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZH
|
|
SElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1
|
|
tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEB
|
|
AQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXET
|
|
IjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFla
|
|
Y2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG
|
|
x8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A+qaKACigApGOKAML
|
|
Xp8xlF5A7V4X8RtYs7PzfNImnx8sa8Kp9z3q2tEgp6angWs62ZZ5CTGoJ6DArGNz5p+UrID6EUrF
|
|
PUlW1EuN0XNW7PQ2L5j3JnoKXN0KijqNP0eYoqXBdgPuuo+ZPeupisWn2Jd4+0r924XgsQOCff3/
|
|
AJ1FzRKxDqGii6m3siiQ8F1XGfXI6YNWLfRbiRQMkcZI9fpTDluT2/h6Qy8gDPbtmtG38JeY480Z
|
|
5zSLUTZg8M28YwYxjAArXtdPt402qgHbpSaLWhma3o0Uqk7Nx9DWLaaVblgPs6qRyds2M/gRSQp9
|
|
zZOni2iWS2hlQ+kjYz9OMGrdjq89vIPPVhj+8M/lQyDq9P1WOYBlMZz1AOD+VdDaTiReOKulK0jO
|
|
tHmi0WDTlr0TyxRVhT8tJjIX+9SUxHXUV553BRQAVBcPhSBTSuxPY86+IGti0s5I7dsORy9fM3i6
|
|
8e8mfDO5P90ZrWWiJicNPpZZtxV/xrW0jQt4DOv6Vk2dEEdTY6BHuB25rpbPSo0QARjP0qTRI17W
|
|
wA/hFaMWmoQMgflQXYsDS142rU9tpqqenfNA7GgtihxkdKuRW6qMY/GkDZY8sY4Ap4hXbyB+VArk
|
|
EtuH4wPyrk/EGkOm+a3jw3suRQLc5i38SX9hJ9nnY+XnBUdPyNdFY6pa3KkkAE9l6f8AfJ/pSJT6
|
|
GhDmI+Zb4ZRycdv6ium0nUhKFydrelTsNnS2829RnrVgV6NKXNG55lWPLIM81Op+WrZkRMfmNNzT
|
|
A7GivPO4KKAEY4XNYWt3vkwPg4OK0giJdjw/xrqhm87Zs8tc7pX5A+leSajf6aHYJ50kn4AZpTep
|
|
rBWRm2Vobm4BXfyehPFdnpmnBFUY5rI2SN63tlToK0YI+KZpFF+3QdavwoKTLtoW0Toaswpk5pCb
|
|
LCxipAhoIuP2dKevHXoaYDylRyxhlwRQI4nxVoCXWZI1GfpXGtbSWjYPGP73+NIGupt6TqMsLruZ
|
|
ih4xnP5V09mQ+JLd8gn0xSYJnVaVdkook69K34zuUGunDS3Rx4qOzHVIp4rrOMY3NJQI7GivPO8K
|
|
KAILt9kZrz3xlebYiu8KCCWb0XvW0NFch6ysfO3jLVjfXLIn+pQkKorl7WxNxIPl71g2dUUdpo+l
|
|
pBGvHPet23iC8ihFosrxirkHQUFo0IF4FXI1O726CpKLacCrMJoJLYHAPpTwucHpSRJJ5e4AZI9x
|
|
UqpxzVpCuOC8cUpQUMRnXttuB4rjNdsYyeVwfXpmpGmcvcQyafMCFJjPY10eg34BUg4DcZP8jUO4
|
|
HaRq3lLNF+IHet7R7jz7c56rwa2wz9+xhiVeFy/T1PFegeaNPWigDsc0ZrzzvDNIaAM7VpNqdegr
|
|
xL4l6kywyRhseZ19lrdfAZL4jxYg3Fw20d63tJsdrDI5rm3Z3R0R0Mce1eKnQYAplIkWrMJ45oZS
|
|
NO3PHbNXIyfpSGWowSOasxLUiZdjFSqtNEMkUemKlAGKsRJjAppFAiORMjmsTVrNZEO4cfSoZSOD
|
|
1eJ7WXBUzQZ+7nkfSo7e2Ei+ZaMzxntjBX2NSU1Y6/wxqojiEFzkA8KTXYaUoWRyv3W5rSjpNHPX
|
|
+BmpSg8V6J5gUUAdhRXnneFFAGHrTfu5PpXzj8S70/aZtxzztXFbv4DKHxHI+H4GZiz9zxXXW8G3
|
|
GBXMjvLRXAx0oPGPSmMVeOnWrMTYpFI0bcg1fh54xmgovRcD3qxETSIZcRvzp+/BpEkqsBUqsM9K
|
|
q4Em4Gkxk0yRGXrVW6i8yFhkg+tJjRxGsWrxllkUMh9eK5uMz6bcebbnfG33kPcVkay2OntPKuo0
|
|
nhXI67c8qa7Lw3c+adjcEDGK1paSRhVV4s6A0or0jyRRQ1AHX0V553hRQBz+vNtt5z3xXzX8Qbdm
|
|
uic5YnOMdK3l8JnTXvlbwpYl+WySOgrp5YfLOOB9O1c62O7qQkc+9RsKChFPWp4DluOlSykaNruH
|
|
ArUgHShFNF2NT1qxGO3NBmyxGcE1N2560CFzjrUysO9JAPDDjFOVuKoQuSRTWouBkazbCa3cd8cV
|
|
wF7IISQccHBzUSWpV9C3o1x5b5GAjdQD1rs9DjC3kckbEhqKfxIzn8LOupRXqnkPccBSkUAzraK8
|
|
87wooA5rxMSI3HqK8B8bQl9Q8sffY5b/AAraXwkUviNrw9pH2W1ViMMRTdRjw4HpWNtDti9TPc4P
|
|
FQs2M5qdyyMHLcfjV63HTAoBGtap0wK0YxigpsuRDtVhVYd6GQydVwwIqdRnqKCR23I5pCMUW6gD
|
|
YNKuetAEise9KTxQBWuFyhrznxNZkXjFeN3I+tTIZg2OqmzmxNF0PO3vXp/g2+hukVl4zyPanTXv
|
|
JmVR+60dpThXpnlPceopWFAbnV0V553hSGgRynjC5FujOey14Ssp1HxNmTnc+a3kvcIpv37HoEYQ
|
|
QmMdVHSsnVbYJF5jVk0dsNzlruVIsl2wKxbjWrVHILjg1CRbZJb+ILHPzyhfStODWLQgFJFYd+el
|
|
UJM27HUIXxhga1Y5lLVLKLkMnoauxnPPrSEx7ShF+Y/n2qrc6xBbhizDAqkK1zJuvG9nbg8ZA681
|
|
ly/Ei052RO3uKAsZlx8QGd8xxvt9Aa1NH8dK7AXMcip64zigdkdrZX8F7EJLdwwNXMkrz1qRMRly
|
|
CK4TxmpidWI49felPYSOMmi80NIoOV6qRzXYeA5SskYPfirpfEjGr8LPWVHyD6U4CvQPL3ZItOYc
|
|
UDOoNFeed4Uhpks4H4iE/Z5MeleMeGULeLgjds10S+BGdL+Jc9OSBU2Huc5Nc74yvUtrcDBrJnZF
|
|
63PJdXvLy/lKWw46bvQVz82jXhkLO5Y+9ZlsYthcRnbIjY9R3q3awTRkEM3WmJI6C0ea3dGRsr1x
|
|
XY6TqW9FLHnjrUs0izpLK5DDjofSta3ckH09KRUkZuuTvFGdvPauE1Y3U6Mqbssf/rUxHPTaJPK2
|
|
ZmJPbBqzY6DCZh5xJC9s9aBJHU6dpemJjfEmfetJtI0+VPkUr/unFOxdiextHs33W07YHQHk11mk
|
|
Xb3KbZ1xIvcd6LEyWho4Nct41sTPYb16ipexCPPZN+wYGCvH1rrPAEJmvkPoc1VL4kZVvgZ6yFwK
|
|
cBXoHkkqinFaVyzo80GuE7WJRQSziPiGdthK5HQV4x4J/wBI8WPIewNdEvgRNL42emO/yj1UHNef
|
|
eNpRczbC+I17DvWT2OqJxc0sMK4TCisy41q0hfEkqj8aixdwTXNOlwvmqD9anS9tXH7uVG+hosO4
|
|
/wC0oOhrR0+6G4YNIEzsNEuCxAPNdjZruA4xxUmjINSjURksOlcbqFykbnjFA1sYGoassaknCqO5
|
|
rl7rxhGm7yBnBxuJq0rkSlYpw+NLlsfd5P8AerVsvHEqSBHwPVgcgVpyMyVXU3rXxcHYETAk+hru
|
|
/DWti6ZSTyOKzZqndHaxvvUGq2rQ+dYyqR24qWI8dvbr7LqDxyDAzXpvw6FvIxePGSM06Xxoyr/A
|
|
zviKFHNegeX1J41zUhXioGbuaSuM6wpCaBHG/EcA6HN/exxXjXw2jL67cv8A3Qa6H8CFR+NnoWpO
|
|
I4XI44rxLxrqjQzSEsQM1gdSPM9U1uR1YbmWIdXHf2rmpIb67YS28UrRlsLI3c/jW0VZGUpO5pW1
|
|
jfLNOjahawzwReYI5cjzMkDavHJ5/SrVv9uhtPtVxCPLBwzxnlT9KGghLU3tKvvPjHzbl7EGuisJ
|
|
GRxWLOg7nRXJEbDjmvSNK+aFSfSoZr0KutRkphc4NcRrdkVjL9aVio7Hk3iqS8ubhrWzUlsZY9kG
|
|
cZNc5D4aee5MclzJIFTzHAO0MfatqSOWu7bFS1srDUZEis0vIZoUxPvfcC+4/dx2xjr712XiTwXb
|
|
WmlQ6hol3cRhoFd4rlg3zY5wR0GelavQwjq7GD4etdVvSnk2wAB+9v8A8mvcfA2kXiRo0/UdcDis
|
|
ZnTTulqeoWqbUAJqWUb42X1FZlnjfjSwlGrr5S/eNdD4RkvLAAQ4yRyaUZcruVKl7TQ9I0G+mnzH
|
|
ckFwM8VuIK7ac3KF2eXiKapz5UWYxipNtMyNejNch0jSar3cjR27uoyQCRVRWom9DxTx54gu5fMi
|
|
lbKdMVjfCZPNlv5v9rFbVHpYqjGzbOn8SzFI9o715L4u0r7arYzk+lYdTqSujy7U/C0u4vHk+WwO
|
|
xuh9q3J9dgvbdVukMV1EwbDDgn04rZMwlHoZ+orZ6hfQ3RWVnQYCgZAq+8U0ln5NtBsV2yxYcfgK
|
|
JtW0CnB31LlroVwJ1nQLGDjeP7w+lb0dsFxjrWB0tHS6NuWPJ6A16ToUm63T3Gallr4S7cxiTjrX
|
|
PaxaF7dlVeSMUhxZ5jd+H7qCa4eF3DSE5x3zXN3Wk6jbyeaiFWUY6ZyPStYS5SalPmVipFbX0E4c
|
|
W0alvmPHJrag0rVvEE6LdljGpG2NRtQD+tW5XMI0uU9M8NeFo9PiQhecDIIrtrOMIoG3H4VlJm9t
|
|
C6CB06VPGM1IHLeItGS6uw+ORT7e3jsbQvj7gzUNam0JaWE+HN7NqOqX80n3FO1RXo8YzXdS+BHk
|
|
4z+KyzGPapcU2YIv7qQtiuaxvcaWqG4O6FwfSrS1JbPnrxoxkv7qIfejcitj4V2f2exumI+8+aKn
|
|
xHTT+G5d8Txlm4rjLxMsQwzWT3OiK0Mm6sEkVsAcjFc1d+FEmlGwEDPQVopaEuOpr6f4ZWNAu3tW
|
|
vHpAj5ZQcUFIWaDjGMVUMQ3cVDBmvbhY7QAV2nh+T/R1yeKhlrY31+b61FcQK6nIoJMi401WblRi
|
|
qr6PCw5UYq9y+YgOgWzNkRrx3xWjp+nx2v3FQcelAbmko9anQ4GBUNisPHWr1qMrQhS2K11HvmYV
|
|
hamcxSRZ5xRIqluS/DKAQQXZxyXrvo2FdlL4EeZjH+/ZbjNSZpswLNBrE1Gt7VE4ODVIlnh/j61F
|
|
j4lmeTGyUbq6LwdEqWbeX0YbhSqfEddP4Bddj4JIrhL5d8h7VjI6oLQqKNzelWre3yc4/ClFjaL6
|
|
wqBxxUUxwCKu5BmXRA6c+9ZjP83FSBoQuPs4BrsNBlUW659KmRrDY6G1lyQtW3Hy0lqQ1qVJnAbm
|
|
oy3b9KYJCqRj3o4zRctIlhjLHmpSuOBRbQOpLGpPFaES7UqkZzKN1KsEc87/AHUUmvPLTVGv72aQ
|
|
k7WJwKmRrQ3ud74Ltilgz4++2a6iNDXdS0gjyMU71my7GpqTbxSbMki3SViajTTHqkSeR/GeyZmg
|
|
nQHkEE1S+F+oPPavBL96I4/Cia1udVF+4dVrkW+Fq8+v4tjMDWUkdVJ6WM0cNV+F+MVmjUcZgqnP
|
|
1qpNNnkcVRLiZtxIS1UzzIF7mghlxUZpVQdq6nTVdAoAOKzkbQWhvwM6gMM1twOJYx3NOJE11Kt1
|
|
H1/pVVlwBkk+9NocXoOQ45FPj+fkUJFF2NSB700v/hTEty5ZpkjvVyUgcCq6GM9zC14/8Se6GcZQ
|
|
1574Xs5WkI2HBPHFQ1dm1KSSZ7Rotn9l0+KPHIHNacae1dy0Vjxaj5ptlhVp+2s2CJ9ppCKzuWNx
|
|
zSFc1SYrHNeNdIGpaYw25ZeRXmvheyk0jVpEdcLJ0q3ZxNKTa0O3vQHg/DNcHrsJDmsmjspnNzNt
|
|
fFIJ24GazOhC+azDmgZIOOKBsp3J2qSaZodubq58yQ4QAnmhGT3NO18pb7BORmu205LfYpyKVkWp
|
|
Oxr5gKYWoIZWgfGfloFq1qTPLubnGO1RPtxg4P0oBAkY/hBz6VNDDkZ6AU0W2WSdqkdKr9ZOaGSj
|
|
VtcLHmnOcgmmYvcz7mBLy3MbdD1q9ouiRK6bUAVeelOC1InPlidSsWMDFOCEdq3uefykqrinYqGy
|
|
rFvApMVka2DAowKAsMkRXQqwyDXn/iWyitNQ3qPl6itIvRoF8RXinW4tQ6HI6GuW8SIVBPalc6qe
|
|
5x9x97r3qruwTjrWZ0ksZ9TUmcDNAmZ9/wAoao63rR0+w22MLPtAzt6mghmfofiB76LdJBJBIp5D
|
|
d/oa7bSdWLIPnpDi9TM8TeKdas51XTbIyxd3J/pXS+E/EFxqNoFu7do5OmD60maHWrnZyDRkn/69
|
|
MlEyOR0xntVoNx+FUgYjPxg4FLCuWDZyKQr2RoRnP0qO+nEFpJITgAUzLqZnhu6+0rknOTXpOmwJ
|
|
Fbrt5yMmnHYyr6Oxb2ijaKLnPYMClwKQWK3n0hn+lachHOJ9pNNN0apQFzsY10a4v4hXQh0xpieQ
|
|
MA1XLZNjhK80cT8OdV+3Wl3A7ZZJCw+hrR1qLcjZ/CsbnfHRnFXseHJArOYYbrUs1uPhYbuatqFP
|
|
ByfSkMq3UIINYkto+87Tx6GkSxfsDbflGD7CtTw/pk4nzITtPIFMFudsukh4Rxz71paTpKwP5jcn
|
|
0qTRy0NORMDgVCqewoJTJgAoxjntTiTu7fWmFxAcnn1q3EPl+X8KZMi4gKqB1Peob/Tv7Us5bfeU
|
|
yOoq4R5nYxqT5I8xieH9J1DTbvyJELRg8ODwa9Ms5mSFV9BWiptbnNVrKdmif7Q1KLg96XIZc5Is
|
|
pNL5pqeUrmMtZs0jzV08phchaY00zH1p2ZNxjS1g+LdJOt6U9ssmxjyGp2urDjLlaZzng/wUPDqz
|
|
TSTmWeTrjpVjVk3Rvjr2rnqQ5dDvo1XUd2cTqSNk9OKxXGCeKxZ1DAxHTr2q5C/y8GokUhsz54qu
|
|
uCxzSQjQ0+FZblR2ro4bZYiMVQ0dBb7Qi5x0qzuG5QOh71LYErDufpSeWrHnimIXbjkUjLkH1Hem
|
|
gGxryc+tXI19KYmWegq9YLiLJ7mtqS945cS7QsWehqxA9dEjz4krPSxyZqbFFhGxUm6smjRM55Lk
|
|
HvSvNxXTY57kLT+9MNwKdhXGm5FIbkU7Bca1wMEVhaiuQcVhXWiZ14R6tHGanGBI2OtYkqEHjgVy
|
|
s9ErEeo6UBsHipKEZs5qpPdRxcbhx70NCSuybTNWihc5brW9Fq6vjMnFSdEIdDRi8RRKygZbHFbu
|
|
m6nb3RA3gMegNJhOm0jbXGOoxTuCc1Rz3FyoGKawz9KaAVcZqeMgCmIkB4FaUTbYwB6V00Fuzixb
|
|
0SFMuDU8Mlbs4UPeXHeiOXkUrDuXYnyKk3cVk0ap6HMxxketSMhrcwRC0dMMZFMQ3yzSeVQAeUaz
|
|
9Vj8uPd271nVV4m+GdpnHX67pCeKyLtBtNcR6xlk9RVeWTb3qRnO6trgttyIfm71z7ai8j7/AJmN
|
|
DNqUVa5Yi1AnjynHuBV+11YJhWWXcP8AZNSzqgmaEerSsf3NtIQP4mGKtRavdRgMIpVI9KjU0a7n
|
|
R6T43uYQI7qN2Tpkqciu503VVuQGAYZHQjFVc4alPlZrpKGAznpTwxOc9+lWjIlUACnM4XApiLNk
|
|
nmvnsK0NvpXZRVonmYqV52GsmanhXitTmFkSiJTSAvwrxUxXIrJ7miOfjf1pzNWxkRlqYWpgJupu
|
|
6gQbuahvIxPA6eo4pNXVioS5WmefakGhndH4INZs5DJXA10PaTurmLO21uKpSZqGMoXGnRzBiyjd
|
|
9Kx5rcQS428fSkjanLoaOliHGZFB56VswW+mtPufcBsGOAfmxz+tFkd8HpoaUx09FAtFY8DO71qb
|
|
Sms/Nb7RbecG6AEjFLS5c78t+p0djpVs9wsyQiJAdyr1rW+zqjErzSe559Sbk9S3C+MA1bjbgE1S
|
|
MSXzMVG0vNUI2tPKrAuCMnrVzNd0PhR49W/O2xrHmp4TxVMzQshpIzzQBehqesnuaI5VGzT2bitz
|
|
FEbNTC1ADS1JupgG6l3UAc14s04yR/aYRll+8BXCtLncDXFWjys9TCz5oW7GddH5qqNzWDOgQnC8
|
|
VSuo1kHzAGkPYopEY2+RWxV23Vzj5G/Kg3jWaNazhZuqNXS6TaKhB2c0jR1nJWOlhOxRxU4YkCgx
|
|
Y0OQatQyDbyaaFYe8uF4NY3iC9ltbVGj43NTIL3h7WzMihjzXVQXYYDdW9Cf2WcOJpfaRZ3g9KsQ
|
|
mupnCLIabGeaAL0LcVY3cVmzRHIxtUhetzEjZqjLUAIWpN1ArhupwagAfDKQ3Q1594v0c2bm6tx+
|
|
5Y8j+6ayrR5onThp8s7dzkZjuqAAmuBnqC7c0iwgtzSA0rWzjfGRW3ZadDu4AoNYo2rfS4v7orSh
|
|
05UA2r0pDbsTm29KRottBNyJ0wpJ9KhD7f6U0ikNWffIFBz60zVUW52ow4UcUN6EPcx44WsbgOmd
|
|
ua7TT5Bd24KHnFKnLlZFSN4koluLdueRWvp14swweG9DXoxldHlTjYtzGoo25qzEvwtUxas2jRPQ
|
|
5CNqkLVsYoYzUzdQA3dSFqBBmnqaBhuqhriCXTpVIzxUz+Fl03aSPI9QTypW2/dz0qKNw3SvOPZR
|
|
Mqin8VLKRcs3O4Cuk0w/MDjt1NBtHY6O2IIHY1pxgFaETIRwMkjtVSUEk4570MlFW5bap6dKzWm8
|
|
1tqH8aY+hp2FvGoGayNevVt7/ap4xzUvYjqTLtvLPcvJxSaVcyWsxTnFZlnT2t15xHmCtOBYwQy4
|
|
B9q7cPO+jPPxFO2qLEj5HWo42+aus4HpoX4W4FTF+KlotbHII9SFuK0MUNZqiLUDE3UbqBBupwag
|
|
Bc1DefPbyD/ZND2KjujyPWlKzuPesRZjHJXms9lMuw3StjnmphKDSLTJ7OfE3JrpbO4GQc9qlnRA
|
|
3LO82k5NbFvdADkjBoCSHyXIIIzgVQvdRigT7wzjgUzO1jHknlvG7qnp61etYFQDIpCZoqVijzXn
|
|
3iC8EmsOuaCGb/heR/s0ijkVv6fbxy3QMg5xmsnuX0Ldzut3+UYTPWk+2GJSe+M1pFtamcldalmx
|
|
1eO4XaThhWnC+TXqR2PHqL3maUJ4qRjxSEjj42qXdxVmaGs1MJoATfSbqBAG5p6mgAzTJTmNvpQU
|
|
tzzHXY83D/U1zF5FhjgV5r3Pa6FMsV5HWnLe7RhqBRdmTwagN2d2K2rPU1C5LAnPrUs6Iysbdrq6
|
|
f3gK0BrUKj/WClY05iM6xLOcQAj3NT29uznfKSzHuadzNu7NSBFjHNSm5VO9IRnajqoWMhTzXFtA
|
|
bvUfMduSeg702Qz0rS7FbTToQFwzjJqaGTFyfK5PQViyzUuFmuIdgGABya5u/vTaN5cnUHFUmLoZ
|
|
zyskwlgJweSK6zQdUEwVJeGr0aUrxPLxEfe0OrhPAqVjxWhznGRtUwatDK4jNxURbmkAm6jNABup
|
|
6tQAFqhupNtu59qUnZFwV5JHnWsHdIx96w5lz15rzT2uhRmt85xWbcxMnUGmZlB0bdxmrNvFIcfM
|
|
350mWjbs7YkDJY/jW5ZWW4jikWkdNp9mqYJFaJdEHHakUULu/VB1rLn1Ld/FgetMGYd/qWSQmSa0
|
|
/AemS32pfa7piLeLkg9z6UmQtz0W7uQ2cZx0A9BVzR7cAea6j2rPqX0L99KRat5A6Dk1wOoKZ52a
|
|
YfMORTYRLujiGWEq6/NWza2yKQVHNdOHerRy4laJo6TTnbbtb8KuM3Fdh5z3OJjbmpt3FaMxAtUZ
|
|
agBN1GaQBzTwaAAms3VbjERUGsa07RsdeFpuUuY4jUjljWTKK4j02RE4IpJYFk6imQkVl0xWarsO
|
|
mAEcUi0bNnZBR0rWtoguMCkUi21wI161mXuocEKaYXMS4u+pY/hVCSWSY4HT0pEmlouiSahdpEBl
|
|
mOceleiwWcNjClvHgJH97Hc1EmVFFi3Czy7mwIl/WtJbjP7uLgd/apQ2VNVvtsBhiPzdK5S4nAuR
|
|
nqOCaTGi9pcytPlU+XpmumtWII44rah8ZjiNIXRuWeNvvViQ/LXpJWPJbu7nCRvVkNxVsxBmqJmo
|
|
EPiXca0YLMuOlJsuKuPlsSi5IrNuG8s4HWs5VEkbwoOTKsk+FJY4rC1K53k1xTk5O7PSpwVNWRzt
|
|
4cms+WpKICtSLTETQj5q0YeBSGiys23pUguGxQMq3E59ayrm4x3yaAKiRtO2WPHcmhruKFxFajzZ
|
|
ScA44qRHoXhuMaLpxaUg6hcDLMf4F9KlhuDeXGASIl+8azZslYma68y48m1+7nFW5rtbRNhb5z1p
|
|
iMKbUg0zuW4A4rPgb7VdKXOMmpA7HRbMS7nUYiUda0lkQOBngVrS+JGdbWLRt2bAx5BqeQ/LXpnj
|
|
PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l
|
|
c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1
|
|
8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3
|
|
ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY
|
|
euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,G0=`
|
|
/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 wk="1.6.0";var iu,sh,ih,Ui,q0,oh,X0,K0,Z0,aoe=class{constructor(t={}){iu.set(this,void 0);sh.set(this,void 0);ih.set(this,void 0);Ui.set(this,void 0);this.analyze=(...t)=>{if(!ur(this,sh))return;let n=this.tf.engine().state.numTensors,r=ur(this,iu);us(this,iu,n);let a=n-r;a!==0&&le(...t,a)};q0.set(this,t=>{if(!ur(this,ih))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Pe))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});oh.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){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==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&le("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&le("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&le(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!a&&le("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&v8();try{await this.tf.setBackend(this.config.backend)}catch(a){le("error: cannot set backend:",this.config.backend,a)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(le("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let a=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&le(`gl version:${a.getParameter(a.VERSION)} renderer:${a.getParameter(a.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-r)}});X0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(H0);break;case"full":n=await t(G0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});K0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+H0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+G0;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)}));Z0.set(this,async()=>{let t=a=>Buffer.from(a,"base64"),n;if(this.config.warmup==="face"&&(n=t(H0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(G0)),!n)return null;let r;if(typeof void 0!="undefined"){let a=(void 0).decodeJpeg(n),s=a.expandDims(0);this.tf.dispose(a),r=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&le("Warmup tfjs-node not loaded");return r});this.tf=gu,this.draw=X2,this.version=wk,this.config=Jn(ht,t),this.state="idle",us(this,iu,0),us(this,sh,!1),us(this,ih,!1),us(this,Ui,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>q2(n,this.config),this.classes={facemesh:f2,age:Hg,gender:qg,emotion:Jg,faceres:a2,body:this.config.body.modelPath.includes("posenet")?S2:z2,hand:F2,nanodet:V2},this.faceTriangulation=D8,this.faceUVMap=O8,this.sysinfo=u5()}profileData(){return this.config.profile?g0:{}}similarity(t,n){return this.config.face.description.enabled?i2(t,n):this.config.face.embedding.enabled?k8(t,n):0}enhance(t){return o2(t)}match(t,n,r=0){return I8(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=Jn(this.config,t)),ur(this,Ui)&&(this.config.debug&&le(`version: ${this.version}`),this.config.debug&&le(`tfjs version: ${this.tf.version_core}`),this.config.debug&&le("platform:",this.sysinfo.platform),this.config.debug&&le("agent:",this.sysinfo.agent),await ur(this,oh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&le("configuration:",this.config),this.config.debug&&le("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.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?y2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Gg(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Yg(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?t2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?n2(this.config):null),this.models.handpose||(this.config.hand.enabled?O2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?T2(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?P2(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?W2(this.config):null),this.models.nanodet||(this.config.object.enabled?U2(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?s2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await y2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Gg(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Yg(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await t2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await n2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await O2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await T2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await P2(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await W2(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await U2(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await s2(this.config))),ur(this,Ui)&&(this.config.debug&&le("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),us(this,Ui,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var y,A,g,x;this.state="config";let a;this.config=Jn(this.config,n),this.state="check";let s=ur(this,q0).call(this,t);s&&(le(s,t),r({error:s}));let i=Ye();await ur(this,oh).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(typeof HTMLImageElement!="undefined"&&t instanceof HTMLImageElement||typeof Image!="undefined"&&t instanceof Image||typeof ImageData!="undefined"&&t instanceof ImageData||typeof ImageBitmap!="undefined"&&G2 instanceof ImageBitmap)&&(le("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=Ye();let l=q2(t,this.config);if(!l||!l.tensor){le("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?l2(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),h=this.config.face.enabled?await l2(this,l.tensor):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?(y=this.models.posenet)==null?void 0:y.estimatePoses(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?L2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?B2(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(l.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await L2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?await B2(l.tensor,this.config):[]),p=Math.trunc(Ye()-a),p>0&&(this.perf.body=p)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(g=this.models.handpose)==null?void 0:g.estimateHands(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((x=this.models.handpose)==null?void 0:x.estimateHands(l.tensor,this.config)):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.hand=p)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(d=this.config.object.enabled?H2(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),d=this.config.object.enabled?await H2(l.tensor,this.config):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.object=p)),this.analyze("End Object:"),this.config.async&&([h,c,u,d]=await Promise.all([h,c,u,d])),_e(l.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(a=Ye(),f=[...hk(h),...ck(c),...pk(u),...dk(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let m={face:h,body:c,hand:u,gesture:f,object:d,performance:this.perf,canvas:l.canvas};r(m)})}async warmup(t={}){let n=Ye();if(t&&(this.config=Jn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ur(this,X0).call(this):typeof Image!="undefined"?a=await ur(this,K0).call(this):a=await ur(this,Z0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&le("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};iu=new WeakMap,sh=new WeakMap,ih=new WeakMap,Ui=new WeakMap,q0=new WeakMap,oh=new WeakMap,X0=new WeakMap,K0=new WeakMap,Z0=new WeakMap;export{aoe as Human,aoe as default};
|
|
/**
|
|
* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|
|
//# sourceMappingURL=human.esm.js.map
|